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Sex-linked barring is a fascinating plumage pattern in chickens recently shown to be associated with two non-coding and two missense mutations affecting the ARF transcript at the CDKN2A tumor suppressor locus . It however remained a mystery whether all four mutations are indeed causative and how they contribute to the barring phenotype . Here , we show that Sex-linked barring is genetically heterogeneous , and that the mutations form three functionally different variant alleles . The B0 allele carries only the two non-coding changes and is associated with the most dilute barring pattern , whereas the B1 and B2 alleles carry both the two non-coding changes and one each of the two missense mutations causing the Sex-linked barring and Sex-linked dilution phenotypes , respectively . The data are consistent with evolution of alleles where the non-coding changes occurred first followed by the two missense mutations that resulted in a phenotype more appealing to humans . We show that one or both of the non-coding changes are cis-regulatory mutations causing a higher CDKN2A expression , whereas the missense mutations reduce the ability of ARF to interact with MDM2 . Caspase assays for all genotypes revealed no apoptotic events and our results are consistent with a recent study indicating that the loss of melanocyte progenitors in Sex-linked barring in chicken is caused by premature differentiation and not apoptosis . Our results show that CDKN2A is a major locus driving the differentiation of avian melanocytes in a temporal and spatial manner .
Birds show an astonishing variety of plumage coloration and pattern , both across the body as well as on individual feathers . The phenotypic diversity in plumage color is due to the distribution of melanin ( both eu- and pheomelanin ) , deposition of carotenoids ( yellow and red colors ) , and structural colors caused by reflection , refraction and scattering of light in the feathers . The domestic chicken is a prime model species for exploring the underlying genetic mechanisms for variation in avian pigmentation due to the extensive plumage diversity that has accumulated since domestication . As it is more challenging to understand how color patterns are generated than to explain reductions or absence of pigmentation , barring is one of the most interesting feather patterns in chickens yet to be understood . Furthermore , barring in chicken resemble barring patterns that are common in wild birds . There are two different barring patterns in chicken , Autosomal and Sex-linked barring . Both barring patterns are characterized by alternating bars of two different colors on individual feathers . However , whereas chickens said to carry Autosomal barring , exhibit a black bar on a white or red background , feathers of Sex-linked barred chickens are characterized by a fully white bar on a red or black background ( Fig 1A ) [1] . Other characteristics of Sex-linked barring are the dilution of dermal pigment in the shanks and beak as well as a white spot on the head present at hatch ( S1 Fig ) , which can be utilized for sex determination [2] . We have previously demonstrated that Sex-linked barring is controlled by dominant alleles at the CDKN2A locus , which encodes the alternate reading frame protein ( ARF ) [3] . We identified four SNPs located within a 12 kb region including CDKN2A exon 1 ( Fig 1B ) . Two of the SNPs are present in non-coding regions , SNP1 in the promoter and SNP2 in intron 1 . The other two SNPs are missense mutations; SNP3 causes a Valine to Aspartic acid ( V9D ) substitution while SNP4 causes an Arginine to Cysteine ( R10C ) substitution . These two neighboring residues are located in the binding site for the MDM2 ( Mouse double minute 2 homolog ) protein . The four mutations form three different alleles ( Fig 1B ) , B*B0 ( from now on referred to as B0 ) , B*B1 ( from now on referred to as B1 ) ( Fig 1A ) , and B*B2 ( from now on referred to as B2 ) ( Fig 1C ) and the wild-type allele at this locus is denoted B*N ( N ) . As CDKN2A is located on the Z chromosome , male chickens can be either hetero- or homozygous for variant alleles , whereas females can only be hemizygous ( e . g . B1/W ) . All three variant alleles carry the two non-coding mutations whereas V9D and R10C are associated with the B1 and B2 allele , respectively ( Fig 1B ) . The B1 allele determines the classical Sex-linked barring phenotype with sharp white and pigmented stripes also in homozygous birds as observed in Barred Plymouth Rock and Coucou de Rennes ( Fig 1A ) . The B2 allele corresponds to the Sex-linked dilution allele previously defined based on phenotype data [4 , 5] . B2/N heterozygotes and B2/W hemizygotes show a clear barring phenotype whereas the B2/B2 homozygotes show strong dilution of pigmentation to various degrees depending on the body region ( Fig 1C and S2 Fig ) . The marked phenotypic difference between B2/W hemizygous females and B2/B2 homozygous males illustrates the incomplete dosage compensation for sex-linked genes in birds . This allele also occurs in White Leghorn lines [3] and most likely contributes to pure white plumage in this breed . All Sex-linked barred chickens studied so far carry either the B1 or B2 alleles , whereas the phenotype associated with the B0 allele remained unknown . In our previous study the B0 allele was only found in White Leghorn chickens where the Dominant white allele ( I ) , a strong dilutor of black pigment , prevents the observation of any patterning and is thus epistatic to Sex-linked barring [3] . However , the fact that the variant alleles at SNP1 and SNP2 were not found in any wild-type haplotype despite an extensive screening , suggested that they might be functionally important . The finding that mutations in CDKN2A cause Sex-linked barring in chickens was unexpected as it is an important tumor suppressor gene that had not previously been associated with pigmentation phenotypes in any species . However , there is a strong link between CDKN2A and melanocyte biology as mutations inactivating the ARF protein are a major risk factor for familial forms of melanoma in humans [6–8] . In mammals , CDKN2A encodes two proteins ( ARF and INK4A ) via exon sharing [9] . Both proteins exhibit anti-proliferative properties , although mediated through different mechanisms by either activating p53 or interacting with the retinoblastoma protein [10] . ARF associates with a number of proteins promoting their posttranslational modification such as sumoylation and phosphorylation to activate or deactivate their function [11 , 12] . Among those pathways , the one most frequently studied and best known , is the involvement of ARF in protecting the transcription factor p53 from degradation by binding to MDM2 [13 , 14] . With only 60 amino acids ( aa ) the chicken ARF is substantially shorter than the human protein which comprises 132 aa [11] and there is only about a 35% overall sequence identity between chicken and mammalian ARF for the 60 shared residues [15] . Although studies do indicate that individual amino acid residues throughout the ARF protein can play an important role [16] , the most N-terminal region ( first 14 residues ) seems to be functionally most important across species . In comparison to the rest of the protein , this region shows a relative high degree of sequence conservation between mammals and chicken and is implicated in nuclear localization , MDM2 binding , and the well-known role of ARF in inducing cell cycle arrest [11 , 17] . Chicken ARF has been shown to interact with MDM2 and is able to protect the transcription factor p53 from degradation [15] . Melanocytes in both mammals and birds are derived from the neural crest and migrate during embryonic development to their biological destinations , mainly hair and feather follicles as well as the epidermal layer of the skin [18] . In the hair follicle , melanocyte progenitor cells that maintain their ability to divide are present in the hair bulge and give rise to fully functional melanocytes [19] . Similarly , in resting feathers quiescent melanocyte progenitor cells are present in a 3D ring at the base of the follicle and become activated in regenerating feathers [20] . The melanocyte progenitor cells start migrating up from the follicle base into the feather shaft and along the way become positive for a number of pigmentation markers as well as bigger in size and dendricity , indicating the differentiation of the pigment cell . Upon reaching the barbs , the progenitor cells become fully functional and pigment-producing melanocytes [20] . In contrast to avian melanocyte stem cells , mammalian melanocyte progenitor cells retained BrdU labeling almost 10 times longer , a finding that indicates that avian melanocyte stem cells cycle much more actively than the corresponding mammalian cells . The aim of this study was ( i ) to determine if the B0 allele , involving the non-coding SNP1 and SNP2 but none of the missense mutations , has a phenotypic effect and ( ii ) to explore the molecular mechanism causing Sex-linked barring . We show that B0 has a more drastic effect on reducing pigmentation than B1 and B2 , and that the Sex-linked barring phenotype is caused by the combined effect of regulatory and coding mutations .
We crossed heterozygous B0/B2 White Leghorn males , homozygous for the Dominant white allele I/I , with Red Junglefowl females ( B*N/W , I*N/N ) . Doubly heterozygous males ( either B0/N or B2/N and I/N ) were then backcrossed to Red Junglefowl females . A total of 17 progeny carried the Dominant white allele ( I/N ) and were not informative . The 14 birds that were homozygous N/N or hemizygous N/W at the B locus were all non-barred as expected ( Table 1 ) . Three male progeny were heterozygous B2/N and exhibited Sex-linked barring . Eight males and four females were heterozygous or hemizygous for the B0 allele and they showed a barring pattern that was markedly lighter than the more typical Sex-linked barring presented by B2/N birds ( Fig 1D ) . No obvious difference in pigment intensity or bar spacing was observed between B0/W females and B0/N males . The phenotypic differences between B0/- ( B0/N males or B0/W females ) and B2/- ( B2/N or B2/W ) were already visible at hatch . Chicks carrying the B2 allele were dark , almost black colored with a small light spot on the top of the head whereas B0/- chicks were much lighter with extended light spots both on the head as well as on the back ( S1 Fig ) . The pedigree data , in combination with our previous genetic analysis [3] , provided evidence that SNP1 and/or SNP2 are causing the strong phenotypic effects observed in B0/N and B0/W birds . This implies a regulatory change since both SNPs are non-coding ( Fig 1B ) . We therefore analyzed the relative expression of CDKN2A in growing feathers in 7 B0/- , 23 B2/- and 17 non-barred ( N/- ) chickens . In both sets of barred feathers , CDKN2A was on average 2 . 5 times ( B0/- ) or 3 . 3 ( B2/- ) times higher expressed as compared to the control samples ( Fig 2A; Student’s t-test , P = 3 . 6x10-4 and P = 3 . 2x10-5 , respectively ) . There was no statistically significant difference in CDKN2A expression between the two barring genotypes ( Student’s t-test , P = 0 . 4 ) . To verify that the observed differential expression is caused by altered cis-regulation , we evaluated the relative expression of the B2 and wild-type alleles within heterozygous B2/N chickens . As the B2 allele harbors both the two non-coding mutations as well as one of the missense mutations in the first exon of CDKN2A , this coding SNP can be used to distinguish the relative expression of the two alleles . For this purpose we reverse transcribed RNA obtained from different tissues ( feather , skin and liver ) of B2/N chickens and sequenced them using pyro-sequencing . The pyrograms obtained from four heterozygous chickens consistently showed a higher peak for the B2 allele in feathers ( x = 79 . 4±6 . 8%; Fig 2B ) and was statistically different from the one observed in liver ( x = 42 . 6±8 . 3% ) and skin ( x = 36±10 . 2%; Student’s t-test , P = 0 . 03 and P = 0 . 003 , respectively ) . In liver and skin the two alleles did not show a significant allelic imbalance since the B2/N ratio was very similar to the one found in genomic control DNA for B2/N heterozygotes where the copy number should be 50:50 . We did not successfully amplify any CDKN2A transcripts from muscle tissue . Previous work has shown that ARF is involved in protecting the transcription factor p53 from ubiquitination and degradation by binding to MDM2 [13 , 14] . An altered expression of ARF could therefore affect expression of p53 downstream targets . We therefore evaluated the expression of four genes involved in cell cycle regulation and apoptosis: Bcl2 associated X protein ( BAX ) , Cyclin-dependent kinase inhibitor 1 A ( alias p21 ) ( CDKN1A ) , damage-regulated autophagy modulator 1 ( DRAM1 ) as well as Pleckstrin homology-like domain , family A , member 3 ( PHDLA3 ) . We were able to detect expression of all genes under the described conditions except for BAX , but only PHLDA3 showed a marginally significant higher expression in B0/- feathers ( Student’s t-test , P = 0 . 03; S3 Fig ) . Since ARF is interacting with a number of proteins other than p53 that could directly or indirectly be involved in cell cycle regulation , we also examined the relative RNA expression levels of four members of the 14-3-3 gene family involved in cell cycle arrest regulation using the same sample set as used for p53 downstream targets: Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein beta ( YWHAB ) , Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein epsilon ( YWHAE ) , Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta ( YWHAZ ) and Stratifin ( SFN ) . Each target gene was normalized with two housekeeping genes stably expressed in the respective tissue . YWHAB showed lower relative expression in mutant feathers compared to non-barred feathers ( Student’s t-test , P = 0 . 03; S3 Fig ) but no other member of this gene family showed a statistically significant differential expression . This suggestive downstream effect on YWHAB expression needs to be further corroborated in new samples where the specific stage of feather development is better defined than was available in the current study . To summarize , CDKN2A shows higher expression both in B0/- and B2/- barred feathers than in wild-type feathers , which might affect downstream targets of p53 like PHLDA3 or cell cycle regulation genes such as YWHAB . A possible explanation for our failure to detect stronger significant changes in CDKN2A downstream targets could very well be that only a small proportion of cells in the feather follicle show up-regulated expression of CDKN2A ( see below ) . Furthermore , we observed allelic imbalance in favor of the mutant allele in B2/N birds suggesting that one or both non-coding changes act cis-regulatory . As we did not observe statistically significant differences in CDKN2A expression between the B0/- and B2/- genotypes , we conclude that the coding changes do not have any major effect on gene expression . During active growth of a feather , a 3-D ring of melanocyte progenitor cells residing on the bottom of the feather follicle becomes activated and migrates upwards through the bulge region , the ramogenic zone and eventually reaches the barbs where they become fully differentiated and start producing pigment . ARF might play a critical role in this developmental process as it affects the stability of the transcription factor p53 [13 , 14] , which activates a number of downstream targets leading to either cell cycle arrest or apoptosis . In order to distinguish these two processes we used IHC to assess the presence of cleaved Caspase-3 proteins , which are indicative of apoptosis . In all growing feather follicles irrespective of genotype ( B2/N n = 4 , B0/W n = 4 , and N/N n = 4 ) , many Caspase-3 positive cells were observed in the pulp region of the feathers all the way across from the papilla ectoderm to the barb region ( S4 Fig ) . No positive cells were observed in the barb region for any genotype . We therefore conclude that melanocytes were not in a pre-apoptotic state in any feather part . Using the melanocyte marker Microphthalmia-associated transcription factor ( MITF ) , IHC revealed that MITF+ cells first appeared in the lower bulge ( LB ) region in wild-type ( N/N ) feathers ( although they were more commonly observed in the middle bulge ( MB ) region ) , in the upper bulge ( UB ) region in B2/N chicken , whereas in feathers from all four B0/W chicken MITF+ cells were not observed until the ramogenic zone ( RGZ ) just below the barbs ( Fig 3 , S1 Table ) . The number of MITF+ cells increased towards the barbs ( BA ) in all genotypes . Statistical analysis revealed significant differences in the numbers of MITF+ cells in the barbs among the three genotypes ( Fig 3C; One-way ANOVA , Tukey’s multi-comparison post-hoc test , P<0 . 01 and P<0 . 05 for N/N vs . B0/W and N/N vs . B2/N , respectively ) . Whereas the average number of cells expressing MITF+ protein/mm2 reached almost 3470±454 in N/N chickens , the corresponding numbers for B0/W and B2/N birds were only 1070±419 and 2090±648 , respectively ( S1 Table ) . The trend for Melanoma antigen recognized by T-cells 1 ( MART1; Fig 3 , S1 Table ) was quite similar to MITF , but there was a tendency that MART1+ cells appeared further down in the feather follicle in B0/N or B2/W chicken compared to wild-type . Starting from UB to the barbs the number of MART1+ cells increased steadily for all genotypes , reaching on average 1970±207 cells/mm2 in N/N , 1740±109 in B2/N and less than 790±209 in B0/W chicken , a difference which again reach statistical significance ( Fig 3C; One-way ANOVA , Tukey’s multi-comparison post-hoc test , P<0 . 001 and P<0 . 01 for N/N vs . B0/W and B0/W vs . B2/N , respectively ) . Fully pigmented wild-type cells were first observed in the RGZ , which corresponded to the results of the ISH analysis of Tyrosinase gene ( TYR ) , encoding the rate-limiting enzyme in pigment production ( Fig 3 , S1 Table ) . In B0/W chickens , few TYR+ cells were present in the UB region of the feather and the number of these cells just slightly increased towards the barbs and reached on average 481±113 cells/mm2 whereas in wild-type chickens more than 2130±323 TYR+ cells/mm2 were counted . The number of TYR+ cells in the barbs of B2/N birds was almost three-fold higher than in B0/W birds but still remained at about 50% of those seen in wild-type chickens ( Fig 3C; One-way ANOVA , Tukey’s multi-comparison post-hoc test , P<0 . 001 for N/N vs . B0/W ) . Whereas we could observe an increase of cells expressing any of the melanocyte-specific markers from UB to the barbs , the opposite trend was true in CDKN2A+ cells ( Fig 3 , S1 Table ) . In the RGZ , CDKN2A+ cells were only present in the mutant genotypes and in the barb region the number of CDKN2A+ positive cells in B0/W and B2/N birds were significantly higher than in wild-type birds ( Fig 3C; One-way ANOVA , Tukey’s multi-comparison post-hoc test , P<0 . 05 ) . The signal intensity for the CDKN2A in situ probe was quite weak and we measured the signal intensity per cell to test the possibility that mutant birds showed higher expression and therefore a larger number of CDKN2A+ cells were called in the mutant genotypes . However , this analysis did not reveal any significant difference in signal intensity among the three CDKN2A genotypes ( S2 Table ) . In summary , we observed a reduction in total pigment cell numbers in the barbs of Sex-linked barred chickens compared to the wild-type . The reduction was already visible in lower parts of the feather shaft and was most prominent in B0/- feathers . The opposite trend is true for CDKN2A , which is expressed earlier in the melanocyte migration process in feathers from B0/- and B2/- birds . Furthermore , compared to pigment cells in the same region of wild-type chickens , B0/W and B2/N pigment cells appeared more dendritic-like and more closely resembled mature melanocytes already below the barbs . The striking phenotypic differences associated with the B0 , B1 , and B2 alleles imply that the two missense mutations must affect ARF function since all three alleles share the two non-coding changes associated with Sex-linked barring ( Fig 1 ) . To learn more about the specific effects of the coding ARF mutations we performed biophysical studies on peptides corresponding to the N-terminus of wild-type and mutant chicken ARF ( ARF1-14WT , ARF1-14V9D , ARF1-14R10C ) and purified chicken MDM2204-298 . Previously , using a combination of ultracentrifugation , far-UV circular dichroism ( CD ) and NMR experiments , it was shown that the mammalian ARF N-terminus and MDM2210-304 ( human MDM2 NCBI accession number XP_005268929; corresponding to chicken MDM2204-298 ) are both intrinsically disordered in their free states and interact by forming an oligomeric β-structure [21 , 22] . While the structure of the ARF/MDM2 complex was not determined , its formation could easily be detected by far-UV CD , which is sensitive to optically active chiral molecules and thus can monitor protein secondary structure . We therefore performed CD experiments with different combinations of chicken MDM2204-298 and the ARF1-14 peptides . MDM2204-298 in buffer yielded a CD spectrum consistent with an intrinsically disordered protein , and so did the ARF1-14 peptides ( Figs 4A and S5 ) . However , when MDM2204-298 was mixed with wild-type ARF1-14WT peptide the spectrum adopted a shape strikingly similar to that of the mammalian ARF/MDM2 complex , and thus consistent with formation of a β-structure ( Figs 4A and S5A ) . Moreover , the ARF1-14R10C peptide produced a similar spectrum as ARF1-14WT upon mixing with MDM2204-298 , but it required a higher peptide concentration to fully recapitulate the ARF1-14WT spectrum ( Figs 4A and S5C ) . On the other hand , the spectrum with the ARF1-14V9D peptide showed no evidence of secondary structure formation suggesting it did not bind to MDM2204-298 at the concentrations used in the CD experiment ( Figs 4A and S5B ) . To corroborate the results from the CD experiments we pursued isothermal titration calorimetry ( ITC ) experiments with MDM2204-298 and ARF1-14 peptides . ITC measures the change in enthalpy ( heat ) for any reaction and is therefore perfectly suited for studying protein-ligand interactions . For each injection of ligand into the protein solution the change in heat is recorded and subsequently analyzed with a suitable model , which in the simplest case is a 1:1 binding . The data clearly show that two of the interactions , ARF1-14WT/MDM2204-298 and ARF1-14R10C/MDM2204-298 , respectively , are qualitatively similar ( Figs 4B , S6A and S6C ) . Moreover , the non-sigmoidal shape of the integrated peaks for these two interactions suggest that they do not follow a simple 1:1 binding mechanism and were therefore not quantitatively analyzed . The complex binding isotherm is consistent with the β-structure model proposed for mammalian ARF/MDM2 [21 , 22] . In contrast to CD , the ITC experiment of ARF1-14V9D/MDM2204-298 suggested an interaction , but the titration profile was distinct from those of ARF1-14WT and ARF1-14R10C and reflected a binding event of lower affinity ( Figs 4B and S6B ) . The detection of an interaction between ARF1-14V9D/MDM2204-298 with ITC but not CD can be attributed to the greater total concentrations of MDM2204-298 and ARF1-14 peptide in the ITC experiment as well as higher sensitivity of ITC in comparison with CD . Thus , the data from ITC and CD agree well . As our data show that the mutations in chicken ARF affect its direct interaction with MDM2 , we decided to explore if this effect is translated further downstream on the ability of ARF to protect p53 from degradation . We utilized a previously described luciferase assay involving a p53 binding promoter element and transient transfection of U2OS cells ( which do not express INK4a and ARF proteins ) [16] . If the cells expressed the chicken ARF version containing the mutation V9D as harbored in the B1 allele , the ability of p53 to activate the luciferase promoter was decreased to on average 58% compared to the wild-type ( Fig 4C , P = 0 . 001 ) . A similar picture was observed for R10C ( as in the B2 allele ) , although with a slightly less obvious trend ( on average 77% , P = 0 . 03 ) . The activity of V9D- and R10C- was significantly different ( P = 0 . 04 ) . In conclusion , our biophysical data showed that chicken ARF1-14WT and MDM2204-298 interact , likely by forming a β-sheet structure similar to that of mammalian ARF/MDM2 [21 , 22] . Furthermore , both the luciferase as well as the ITC/CD experiments showed a similar trend consistent with a more severe effect of the V9D mutation , which appears to be more disruptive resulting in much lower affinity for MDM2 , whereas the R10C mutant of ARF shows a similar behavior as ARF1-14WT but with slightly lower apparent affinity . This trend was corroborated with the results by the luciferase assay .
Our previous study provided conclusive evidence that Sex-linked barring is controlled by mutations in CDKN2A and that two different non-synonymous substitutions ( V9D and R10C ) are associated with two different alleles ( B1 and B2 , respectively ) at this locus [3] . However , this previous study left unanswered the enigma of why both missense mutations were associated with two non-coding changes that were not detected in any sequenced wild-type haplotype . Thus , the two missense mutations must have occurred on the very rare haplotype containing the two non-coding variants . We have now resolved this enigma by showing that the B0 allele , carrying only the two non-coding changes ( Fig 1B ) , causes a more extreme reduction of pigmentation than the B1 and B2 alleles ( Fig 1D ) . This implies that at least one or both of the non-coding changes constitute cis-acting regulatory mutation ( s ) ; if only one is causal , the other has hitchhiked with the causal mutation on this haplotype . This hypothesis was supported by the observed up-regulated expression of CDKN2A in Sex-linked barred feathers as well as the observed allelic imbalance with higher expression of the B2 allele in B2/N heterozygotes in growing feathers ( Fig 2 ) . We also show that this up-regulated expression is highly tissue-specific since it was observed in feather follicles but not in skin and liver . We propose that the Sex-linked barring locus is composed of four alleles with distinct phenotypic effects: N , wild-type; B0 , Sex-linked extreme dilution; B1 , Sex-linked barring; and B2 , Sex-linked dilution . The B0 allele was first defined in our previous study based on sequence data [3] and we now document that it in fact has the strongest effect on pigmentation ( Fig 1D ) . Therefore we propose the name Sex-linked extreme dilution . This allele has so far only been found in White Leghorn chickens and we have not yet observed the phenotype of B0/B0 homozygotes in the absence of the epistatic Dominant white allele , but we assume that these birds have very little pigmentation . Available phenotypic data indicate a ranking of the three variant alleles regarding pigment reduction , as follows: Sex-linked extreme dilution > Sex-linked dilution > Sex-linked barring . Our functional data are fully consistent with the proposed ranking . Firstly , expression analysis shows that one or both of the non-coding changes cause an up-regulation of CDKN2A expression in feather follicles during feather growth ( Fig 5A ) . A higher expression of ARF , encoded by CDKN2A , is expected to lead to a reduction of pigment cells due to apoptosis , cell cycle arrest or premature differentiation of melanocytes . The Sex-linked extreme dilution ( B0 ) allele carries only these non-coding changes and is associated with a drastic reduction in pigmentation . In contrast , our three functional assays ( CD , ITC , and luciferase reporter assay ) all indicate that the two missense mutations ( V9D and R10C ) result in hypomorphic ARF alleles . Thus , these mutations are expected to counteract the effect of up-regulated ARF expression , most likely by impairing the ARF-MDM2 interaction and thereby lead to a less severe reduction in pigmentation ( Fig 5B ) . Furthermore , the three functional assays all indicate that the V9D substitution ( B1 ) is expected to limit the effect of the non-coding mutations ( B0 ) on pigment dilution to a larger extent than does R10C ( B2 ) , which is consistent with the observation that Sex-linked dilution ( B2 ) shows a stronger reduction in pigmentation than does Sex-linked barring ( B1 ) , at least in the homozygous condition ( Fig 1A and 1C ) . Our results imply an evolutionary scenario where the non-coding change ( s ) occurred first and resulted in Sex-linked extreme dilution ( B0 ) . This was followed by the independent occurrence of the two missense mutations resulting in the appearance of Sex-linked barring ( B1 ) and Sex-linked dilution ( B2 ) alleles . The latter two alleles are much more widespread among chicken breeds [3] and it is likely that the missense mutations have been under strong positive selection simply because they generated phenotypes more appealing to humans as illustrated by the iconic Barred Plymouth Rock chicken or the French breed Coucou de Rennes ( Fig 1A ) . The Sex-linked barring locus is another striking example of the ‘evolution of alleles’ that has occurred in domestic animals by the accumulation of multiple causal mutations affecting the same gene [23] . Other examples include dominant white color in pigs [24] , dominant white/smoky plumage color in chicken [25] , rose-comb in chicken [26] and white spotting in dogs [27] . It is very likely that allelic variants differing by multiple causal changes are common in natural populations . An excellent candidate for this scenario is the ALX1 haplotype associated with blunt beaks in Darwin’s finches [28] . This haplotype is associated with derived changes at two highly conserved amino acid residues as well as changes at highly conserved non-coding sites . The beauty with studying the ‘evolution of alleles’ in domestic animals , as illustrated in our study , is that the phenotypic consequences of the intermediate steps in such a process can be revealed due to the relatively short evolutionary history of domestic animals . A previous study on Sex-linked barring in chickens suggested that the alternate barring pattern might be caused by premature apoptosis as a result of the gain-of-function mutations in CDNK2A [3] . This was based on the finding that no pigment cells are present in the feather during white band formations [29 , 30] as well as the observation that melanocytes from Barred Plymouth Rock chickens in cell culture die five times earlier than wild-type melanocytes [31] . More recently , Lin et al . [20] proposed , based on negative TUNEL staining in growing feathers from Sex-linked barred chickens , that the lack of melanocytes in the white bar is not attributed to apoptosis but to premature differentiation of melanocytes . Our result is fully consistent with this hypothesis since our Caspase-3 assay did not reveal any apoptotic melanocytes or precursors in any feather region of any mutant phenotype tested . The only cells that were expressing Caspase-3 proteins , were located in the middle of the feather shaft , the pulp region , and represent a population of keratinocytes which will disappear after the feather has finished growing , leaving behind a hollow skin structure [32] . When we examined the presence of melanocyte progenitor and melanocyte cells in the feather follicles of both mutant and wild-type chickens , it became clear that fewer cells expressed MART1 and Tyrosinase ( TYR ) in feathers from mutant birds ( B2/N and B0/W ) . The cells of mutant birds also appeared more dendritic and more closely resembled mature melanocytes at a much earlier stage of development . This suggests that feather melanocytes from mutant birds ( B0 , B1 , B2 ) reach a more mature state at an earlier point in migration as compared to the wild-type . Although we were not able to directly follow individual cell maturation through their migration in the feather follicle , we did observe expression of CDKN2A mRNA in mutant birds already in the ramogenic zone of the feather follicle . This result is consistent with the recently proposed model that loss of melanocyte progenitors in Sex-linked barred chicken is caused by premature differentiation and not apoptosis [20] . This mechanism implies that the melanocyte progenitor pool in Sex-linked barred birds becomes smaller while the cells are migrating up the feather shaft , leading to fewer pigment-producing cells towards the end of the formation of the pigmented bar . Eventually , the progenitor pool becomes exhausted causing a complete lack of pigment-producing cells and the formation of the white bar ( Fig 5C ) . The melanocyte progenitor cells are later replenished by a new pool of undifferentiated cells , migrating up from the feather base and leading to pigment production as well as the formation of the next pigmented bar . As the feather continues to grow , the cyclic behavior of the melanocyte progenitor cells leads to a periodic striping pattern . Despite mutant birds had a reduced number of cells of the melanocyte lineage in the upper feather shaft region , they had more CDKN2A+ cells as compared to the wild-type . This result suggested that the increased CDKN2A expression detected by RT-qPCR ( Fig 2A ) could be due to an increased number of cells expressing CDKN2A rather than higher expression per cell . In fact , measurement of the in situ hybridization signal intensity for CDKN2A revealed no significant difference in expression level per cell among genotypes . How is this result compatible with the highly significant allelic imbalance test showing that the mutant allele is expressed at a higher level than the wild-type allele in feather follicles ( Fig 2B ) ? One possible explanation is that the wild-type allele is down-regulated in the presence of a highly expressed mutant CDKN2A allele . However , this appears unlikely since that would indicate that there should be no phenotypic effects in birds carrying the B0 allele , which express an ARF wild-type form at the protein level . The striking phenotypic effects associated with this allele as regards the increased number of CDKN2A-positive cells , fewer pigment cells and reduced pigmentation must be mediated through altered gene regulation . Therefore , a more plausible explanation is that the CDKN2A-positive cells detected in the upper feather shaft region are a mixed population . In mutant birds a fraction of these solely or predominantly expresses the mutant CDKN2A allele possibly explaining the observed allelic imbalance . This can be addressed in future studies by double labeling experiments or single cell transcriptomics . Although CDKN2A is expressed in most tissues and has an important role in cell cycle regulation , there is a fascinating tissue-specificity of the phenotypic effects of both the human loss-of-function mutations and the chicken mutations , suggesting that melanocytes are particularly sensitive for changes in ARF function . CDKN2A loss-of-function mutations are associated with a high risk of melanoma in humans but not for other tumor forms [6–8] . Similarly , the chicken CDKN2A alleles have a striking effect on pigmentation , but no or only minor pleiotropic effects on other tissues . Our expression data indicate that the regulatory mutations have a very tissue-specific effect in feather follicles whereas the missense mutations will affect ARF function in all cells that express this protein . It is remarkable that a good proportion of the world-wide production of animal protein is based on chickens carrying CDKN2A alleles involving the V9D or R10C missense mutations , resulting in hypomorphic ARF proteins . These include white-egg layers , i . e . White Leghorn , and many commercial broiler lines used for meat production , most of them having a white plumage . We assume that the overall protein interaction ability of the mutated chicken ARF is still sufficient to maintain its function in other tissues . Alternatively , the missense mutations could only affect a pathway specific to the feather follicle or the ARF-dependent activation of p53-dependent cell cycle arrest may be mediated by other structural parts of ARF or even by a completely different protein in other cell types . However , since domestication is an evolutionary process , it is possible that these mutant CDKN2A alleles initially had slightly deleterious effects but that genetic modifiers counteracting the effects of the ARF mutations have accumulated over time .
Tissue samples and phenotype data were obtained from either an F8 generation of a pedigree originally used to map Sex-linked barring [3] as well as a separate backcross which was set up involving four White Leghorn L13 males heterozygous B0/B2 ( for breed pedigree details see [33] ) . These birds were first crossed with four Red Junglefowl females hemizygous N/W . Four F1 males were heterozygous B0/N and three were heterozygous B2/N . All F1 males were also heterozygous for the Dominant White mutation , I/N , ( fixed in the WL L13 population , and absent from the Red Junglefowl population ) . Each F1 male was then back-crossed to a Red Junglefowl female , with a total of 48 back-cross offspring . These birds were genotyped for B and I and phenotyped at hatch as well as soon as the adult plumage was apparent ( at 49 days ) . Approval from the Ethical Committee for animal experiments in Uppsala , Sweden was obtained for all experiments involving the F8–generation chickens ( 2011-11-25 , C307/11 ) . The experiment involving the F2 barring intercross was approved by the Regional Committee for Ethical Approval of Animal Experiments ( Swedish Board of Agriculture DNR# 122–10 ) . All animals were handled by trained personnel and reared according to the guidelines of the Swedish Board of Agriculture for chickens . For RNA extraction , growing feathers of different sizes were either collected from different parts of the body where they occurred naturally and which showed a Sex-linked barring phenotype or from regions , which were plucked seven to ten days before ( i . e . in the neck ) . Skin and muscle samples were obtained from the back and the breast respectively and , just like the liver samples , collected right after euthanasia of the animal using 1 ml of 100 mg/ml Thiopental Inresa/kg body weight ( Inresa Arzeneimittel GmbH ) or cervical vertebra dislocation . The feather shafts and tissue samples were shock-frozen on dry ice or liquid nitrogen immediately after collection and stored at -80°C until further processing . Skin tissue samples including feathers used for in situ hybridization ( ISH ) or immunohistochemistry ( IHC ) were first fixed in 4% paraformaldehyde in phosphate-buffered saline for 1 h ( ISH ) or 15 min ( IHC ) at 4°C . The tissue samples were then incubated over night ( ISH ) or for 3 h ( IHC ) in 30% phosphate-buffered sucrose at 4°C , embedded in Neg-50 frozen section medium ( Thermo Fisher Scientific ) , frozen and sectioned in a cryostat . Ten μm thick sections were collected on glass slides ( Super Frost Plus; Menzel-Gläser , Menzel GmbH & Co KG ) . One or two frozen feather shafts or small pieces of liver or skin tissue were removed from the storage tube and transferred to an RNase free tube containing Zirconia beads ( Biospec Products ) and 1 ml of TRIzol ( LifeTechnologies ) . Feather , liver and muscle samples were homogenized immediately using a Mini-Beadbeater ( Biospec Products ) at highest speed for 20 s up to 1 min depending on the softness of the tissue . For skin , a Precellys24 Tissue homogenizer ( Bertin Technologies ) was used at 6800 rpm/min for 30 s intervals until the samples were properly homogenized . The homogenate was centrifuged 5 min at 2 , 000 x g and 4°C and the supernatant transferred to a new RNase free 1 . 5 ml Eppendorf tube . Skin samples were subjected to an additional isolation step . Following homogenization , the samples were centrifuged at 12 , 000 x g for 10 min at 4°C . The cleared supernatant was transferred to a new tube . The TRIzol volume was adjusted to 1 ml , 0 . 2 ml of Chloroform ( Sigma-Aldrich ) were added and the sample tube properly mixed using a Vortexer ( Scientific Industries , Inc ) . The mixture was incubated at room temperature for 2 min to allow for phase separation . To obtain the aqueous phase containing the RNA , the samples were centrifuged at 12 , 000 x g for 15 min ( 4°C ) and the upper phase was transferred to a new tube . An equal volume of 70% ethanol was added to the upper-phase solution and 0 . 7 ml of the mixture applied to a PureLink RNA Spin Cartridge ( PureLinkRNA Mini Kit , LifeTechnologies ) and centrifuged at 12 , 000 x g for 15 s . The purification procedure followed the manufacturer’s recommendation ( PureLinkRNA Mini Kit , LifeTechnologies ) , including DNase treatment of each sample . The muscle tissue samples were homogenized using Zirconia beads as described above in 300 μl lysis buffer containing 1% β-Mercaptoethanol ( Sigma-Aldrich ) . The samples were either kept at -80°C until further use or processed immediately using the RNeasy Fibrous Tissue Mini Kit ( Qiagen ) according to the manufacturer’s protocol with the following modification: samples were incubated with 10 μl proteinase K solution for 7 min at 55°C , then another 10 μl proteinase K aliquot was added and incubated for additional 7 min . The eluted RNA was evaluated for its quantity and quality using a NanoDrop- 1000 Spectrophotometer ( Thermo Scientific ) and stored at -80°C until further use . RNA samples were treated again with DNase using the DNAfree Kit ( Life Technologies ) according to the manufacturer’s suggestions with the following modifications: 1 μl of DNase was used before the 30 min incubation and after 15 min at 37°C an additional 1 μl of DNase was added to each tube . DNA-free RNA samples were reversed transcribed using the RT-PCR protocol and reagents for the Maxima H Minus First Strand cDNA Synthesis Kit ( Thermo Scientific ) . In step one , 1 μl of 1:1 Oligo ( dT ) 18 primer/random hexamer mixture was used . The samples were incubated as follows: 10 min at 25°C , 30 min at 50°C and finally 5 min at 85°C to inactivate the enzyme . The obtained cDNA was used right away for qPCR or pyro-sequencing or temporary stored at -20°C . DNA samples for genotyping were either obtained from blood routinely collected as a part of monitoring and documenting information on the chicken crosses or from muscle samples after euthanasia of the animal . DNA from blood was extracted using standard salting-out methods [34] whereas DNA from muscle tissue was obtained using the DNeasy Blood and Tissue Kit ( Qiagen ) . Primers were designed using Primer3Plus software [35] and the PCR products were checked for possible formations of secondary structures using the online tool mFold ( http://mfold . rna . albany . edu/ ? q=mfold ) . Before usage in the actual assay , the primers were tested for their PCR efficiency in a four point standard curve and their specificity was evaluated on a gel as well as by performing a melting curve analysis . Primer sequences are provided in S3 Table . In order to determine relative gene expression , 2 μl of 1:1 diluted cDNA was used with 1 μM of each primer in SYBRgreen Real-Time PCR Master Mix ( LifeTechnologies ) on a 7900 HT Fast Real-Time PCR System machine ( LifeTechnologies ) with standard PCR conditions . The obtained Ct values were analyzed using the ΔΔCt method . Relative expression levels of the target genes CDKN2A , BAX , CDKN1A , DRAM1 , PHLDA1 SFN , YWHAB , YWHAE and YWHAZ were normalized with up to three different housekeeping genes depending on the tissues analyzed—feathers: Eukaryotic translation elongation factor 2 ( EEF2; [36] ) , Glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) and Ubiquitin ( UB; [37] ) ; skin: EEF2 , UB and β-actin ( [37]; liver: EEF2 and β-actin; muscle: EEF2 and β-actin ) . All samples were run in quadruplicates with target and housekeeping genes simultaneously . Unpaired Student’s t-test was used to assess the significance of the average expression values . As we were lacking a positive , apoptotic control sample for BAX qPCR experiments , we cannot formally exclude that the absence of BAX transcripts in our samples is due to technical errors . However , the primers were successfully used under similar conditions [38] and alternative methods did not suggest any ongoing apoptosis event in melanocytes . Four different pyro-sequencing assays were designed to properly determine the genotype at any of the four SNP positions associated with the Sex-linked barring phenotype . The primers were designed using PyroMark Assay Design 2 . 0 ( Qiagen ) . cDNA or DNA samples were amplified as outlined in the Supplementary section . The obtained PCR products were purified and transferred to a PyroMark Q96 MD pyro-sequencing machine ( Qiagen ) . In short , 20 μl of PCR product were hybridized to Streptavidin Sepharose High performance beads ( GE Healthcare ) and washed using 70% ethanol ( Solveco ) , denatured in 0 . 2 M NaOH and washed again in wash buffer ( composition as described in the pyro-sequencing manual ) followed by a hybridization for 2 min incubation at 82°C . For the immunohistochemistry analysis , primary antibodies were incubated overnight at 4°C , and secondary antibodies for 2 h at room temperature . Primary antibodies were against MITF ( ab12039 , Abcam ) , MART1 ( ab731 , Abcam ) and Caspase-3 ( Cleaved Caspase-3 ( Asp175 ) ; Cell Signaling Technology ) . Alexa Fluor conjugated secondary antibodies were obtained from Invitrogen . Images were captured using a Zeiss Axioplan2 microscope equipped with Axiovision software ( Carl Zeiss Vision GmbH ) . The statistical analysis was performed as One-way ANOVA; Tukey’s multiple comparison post-hoc test ( n = 4 ) . Probes for in-situ hybridization detection of the TYR and CDKN2A transcripts ( nucleotides 561–1168; NM_204160 and nucleotides 140–781; NM_204434 respectively ) were generated by PCR using gene-specific primers ( S3 Table ) and cDNA obtained from chicken feathers in a reaction composed as followed: 1x KAPA2G GC buffer with 1 . 5 mM MgCl2 ( KAPA Biosystems ) , 200 nM dNTPs , 200 pmol forward and reverse primer each with 1 U of KAPA2G Robust HotStart DNA Polymerase ( KAPA Biosystems ) . The very same cycling program as described above was used with the only adjustment of 30 s for elongation time . PCR products were analyzed on an ethidium bromide stained low-melting-agarose gel and purified using the QIAquick Gel Extraction Kit ( Qiagen ) . Quantity and quality of the obtained amplicons were measured using NanoDrop- 1000 Spectrophotometer ( Thermo Scientific ) . Three μl of the purified PCR product were used for cloning into a pcDNA3 . 1/V5-His TOPO TA cloning vector ( LifeTechnologies ) . The vector DNA from the clones was isolated using the QIAprep Miniprep Kit ( Qiagen ) . To determine whether the insert was successfully cloned into the vector , the isolated plasmids were used directly in a PCR reaction with specific primers binding just outside the cloning site ( T7 forward and BGH reverse provided with the cloning kit ) . As the procedure allows for insertion of the insert in both directions , the clones were sequenced and only antisense inserts were utilized to produce an in-situ probe . In-situ hybridization analysis was performed as previously described [39] . In short , complementary RNA ( cRNA ) probes for ARF and TYR were made using the DIG RNA Labeling Kit ( Roche Diagnostic GmbH ) . Probes were hybridized to untreated tissue sections overnight at 68°C under conditions containing 50% formamide and 5X Saline sodium citrate buffer in a humidified chamber . The DIG-labeled probes were detected using an alkaline phosphatase-conjugated anti-DIG antibody ( Roche ) , followed by incubation with BCIP/NBT developing solution ( Roche ) for 2–5 h at 37°C . The intensity of the ISH-signal was analyzed using ImageJ ( Rasband , W . S . , ImageJ , NIH , Bethesda , Maryland , USA , http://imagej . nih . gov/ij/ , 1997–2016 ) . Bright field micrograph images were transformed to 8-bit grayscale and the overall intensity of the whole images was used for normalization and the mean intensity was analyzed after outlining each individual cell . Ten cells from each genotype were analyzed . One-way ANOVA with Tukey’s post hoc test , was used for statistical testing . For both IHC and ISH four chickens/genotype were analyzed using four to six adjacent sections of two feathers shafts from the same individual . A truncated version of chicken MDM2 ( NCBI accession number NP_001186313 ) containing amino acid residues 204–298 ( MDM2204-298 ) was synthesized ( GenScript ) and cloned into the expression vector pSY5 , a modified pET-21d ( + ) plasmid ( Novagen ) , encoding an 8-histidine tag ahead of the N-terminus of the protein [40] . The protein was expressed in Escherichia coli strain BL21 ( DE3 ) cells ( Invitrogen ) by inducing the cells at OD600 = 0 . 6 with 0 . 4 mM IPTG overnight at 16°C . All cells were grown in media containing 100 μg/ml ampicillin . Cells were harvested by centrifugation at 3 , 700 x g for 30 min . The pellet was subsequently resuspended in 100 ml binding buffer ( 50 mM Tris-HCl , pH 7 . 5 , 500 mM NaCl , 20 mM imidazole ) , and disrupted by 2 mg/ml lysozyme treatment for 1 h at 4°C followed by sonication . Insoluble cell debris was removed by centrifugation at 32 , 000 x g for 45 min at 4°C . The supernatant was subjected to a multistep purification scheme using an ÄKTAxpress system ( GE Healthcare ) , including ( i ) Ni2+ affinity chromatography ( His-Trap FF 1 ml ) , in which the protein was eluted by an increasing concentration of imidazole; ( ii ) desalting ( HiPrep 26/10 ) and ion exchange chromatography ( Resource Q 1 ml ) by using a buffer containing 50 mM Tris-HCl , pH 7 . 5 and eluting the protein with an increasing concentration of NaCl; and ( iii ) gel filtration chromatography ( HiLoad 16/600 Superdex 200 ) equilibrated with 50 mM Tris-HCl , pH 7 . 5 , 200 mM NaCl ) . The purified protein was concentrated to about 500 μM by using a centrifugal concentrator ( Vivaspin 20 , MWCO 3 kDa , Sartorius ) . Purity of the protein was checked by SDS-PAGE , identity by MALDI-TOF mass spectrometry ( Bruker ultraflex TOF/TOF ) and concentration estimated by a bicinchoninic acid assay ( BCA; Thermo Scientific ) . Peptides corresponding to the N-terminus of wild-type and mutant chicken ARF ( residues 1–14; NCBI accession number AAN38848 ) were purchased from GL Biochem and denoted ARF1-14WT ARF1-14V9D and ARF1-14R10C , respectively . The concentrations of the peptides were estimated by measuring the free thiol groups in the peptides by mixing with 5 , 5-dithiobis- ( 2-nitrobenzoic acid ) ( DTNB , Ellman's reagent ) in PBS and measuring the absorbance at 412 nm ( extinction coefficient = 13 . 6 mM-1cm-1 ) . All biophysical experiments were performed in PBS buffer , pH 7 . 3 . Far-UV circular dichroism ( CD ) spectra between 200–260 nm were recorded on a Jasco J-810 spectropolarimeter ( Jasco , Easton , MD ) at 20°C using a cuvette with 1 mm path length . Four spectra were taken and averaged for 10 μM MDM2204-298 in presence or absence of 25 and 64 μM ARF1-14 peptide , respectively . The spectrum of buffer was subtracted from the protein/peptide spectra and the raw CD signal reported in mDeg . Isothermal titration calorimetry ( ITC ) experiments were performed on a MicroCal iTC200 instrument ( Malvern Instruments ) . The temperature during all experiments was 25°C . Before each ITC measurement , proteins were dialyzed using a dialysis cassette ( Slide-A-Lyzer , MWCO 3 . 5 kDa , Thermo Scientific ) against the experimental PBS buffer . The peptides were dissolved in the same dialyzing PBS buffer to reduce buffer mismatch in the ITC experiments . The background resulting from buffer to buffer titration was subtracted from the protein/peptide titration curve . The ARF1-14WT peptide , ARF1-14V9D peptide or ARF1-14R10C peptide ( 1 . 27 mM in the syringe ) was titrated into MDM2204-298 ( 100 μM initial concentration in the cell ) , respectively . A titration usually consisted of one 0 . 5 μl injection followed by 19 injections of 2 . 0 μl . Because of the unknown but likely complicated mechanism of the ARF/MDM2 interaction [22] , the experimental ITC data were not fit to a particular model but evaluated qualitatively . U2 Osteosarcoma ( U2OS ) cells were obtained from Cell Line Service , Germany and cultured in Minimum Essential Media ( LifeTechnologies ) enriched with 10% Fetal Bovine Serum ( LifeTechnologies ) as well as 2 mM L-Glutamine ( Sigma-Aldrich ) , 2 , 500 U of penicillin and 2 . 5 mg streptomycin ( Sigma-Aldrich ) . The cells were kept at 37°C in a humidified atmosphere with 5% CO2 . Media was changed every 2–3 days . Cells were sub-cultured at 90% confluence using 0 . 05% Trypsin-EDTA ( LifeTechnologies ) . Cells were regularly tested for presence of Mycoplasma . The ability of the missense mutations to affect p53 transcriptional activity through MDM2 interaction was tested in an assay as described [41] . A p53 reporter construct , p53-luc ( Stratagene ) , containing several p53 responsive promoter elements fused to firefly luciferase , was used to assess p53 transcriptional activity following transfection with wild-type chicken ARF and mutant forms carrying the two different Sex-linked barring ARF missense mutations ( V9D and R10C ) . The coding regions of the wild-type chicken ARF , V9D ARF and R10C ARF were cloned into pcDNA3 . 1 vector ( Invitrogen , Life Technologies ) . U2OS cells were co-transfected at approximately 90% confluency with 1 μg of ARF plasmid , 1 μg p53-luc and 150 ng of phRG-Basic control Renilla plasmid ( Promega ) in 6-well plates utilizing 4 μl of Lipofectamine 2000 reagent ( Invitrogen ) in Opti-MEM medium ( Gibco , Life Technologies , ThermoFisher Scientific ) . Five replicates for each construct were performed in three independent experiments . The cells were lysed 48 h post-transfection and firefly and Renilla luciferase activities were measured using the Dual Luciferase Reporter Assay System ( Promega ) with an Infinite M200 Luminometer ( Tecan Munich GmbH ) . Firefly values were divided by Renilla values to normalize for fluctuations in plated cells and transfection efficiency . Relative luciferase units were then calculated by dividing the value of the different ARF forms by the value of the empty vector . Differences between the ARF versions were analyzed by Student’s t-test . In brief 1 ml of re-suspended cell suspension was used for DNA extraction using the culture cell protocol for the DNeasy Blood and Tissue Kit ( Qiagen ) . The suspension was centrifuged for 3 min at 1 , 000 rpm to pellet the cells and re-suspended in 200 μl DPBS containing with 20 μl proteinase K . The protocol was then followed as described by Qiagen . The obtained DNA was tested for the presence of Mycoplasma DNA by using specific primers as well as the internal control in the Venor Germ Mycoplasma Detection Kit ( Minerva Biolabs ) with slight modifications . Only 0 . 5 U of Platinum Taq Polymerase ( LifeTechnologies ) were used per 25 μl reaction with 20–200 ng of DNA . The samples were amplified in an initial denaturing stage at 95°C for 5 min followed by 41 cycles at 95°C for 20 s , 55°C for 20 s , 72°C for 30 s and a final elongation time for 2 min at 72°C . Five μl PCR product were checked on an 3% agarose gel stained with ethidium bromide . | Barring patterns on individual feathers are widespread phenomena in a number of wild bird species . Still , the genetic background and molecular mechanisms that give rise to barring remains poorly understood . Sex-linked barring is a striping pattern present on individual feathers in domestic chickens , which can be utilized as a model species to gain an understanding of the underlying ‘mode of action’ of biological pattern formation . Our findings suggest that regulatory mutations in the tumor suppressor gene CDKN2A first resulted in a primitive barring pattern and that two missense mutations in the same gene occurred later and independently , causing the more distinct barring pattern of extant chicken breeds . A plausible mechanism is that the altered expression of CDKN2A causes melanocyte progenitor cells to prematurely stop dividing and instead differentiate into pigment-producing cells . The temporary lack of melanocytes expresses itself as a white bar until the progenitor cells are replenished and pigment is produced to form a pigmented bar . It is remarkable that a good proportion of the world-wide production of animal protein is based on chicken that are carrying a functionally important missense mutation in the CDKN2A tumor suppressor gene . | [
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] | 2017 | The evolution of Sex-linked barring alleles in chickens involves both regulatory and coding changes in CDKN2A |
Khon Kaen Province in northeast Thailand is known as a hot spot for opisthorchiasis in Southeast Asia . Preliminary allozyme and mitochondrial DNA haplotype data from within one endemic district in this Province ( Ban Phai ) , indicated substantial genetic variability within Opisthorchis viverrini . Here , we used microsatellite DNA analyses to examine the genetic diversity and population structure of O . viverrini from four geographically close localities in Khon Kaen Province . Genotyping based on 12 microsatellite loci yielded a mean number of alleles per locus that ranged from 2 . 83 to 3 . 7 with an expected heterozygosity in Hardy–Weinberg equilibrium of 0 . 44–0 . 56 . Assessment of population structure by pairwise FST analysis showed inter-population differentiation ( P<0 . 05 ) which indicates population substructuring between these localities . Unique alleles were found in three of four localities with the highest number observed per locality being three . Our results highlight the existence of genetic diversity and population substructuring in O . viverrini over a small spatial scale which is similar to that found at a larger scale . This provides the basis for the investigation of the role of parasite genetic diversity and differentiation in transmission dynamics and control of O . viverrini .
The liver fluke , Opisthorchis viverrini is a food-borne trematode endemic in Southeast Asia , including Thailand , Lao PDR , Vietnam and Cambodia with more than 10 million people infected [1] , [2] , [3] . Infection occurs by eating raw or uncooked cyprinid fish containing metacercariae [4] , [5] . O . viverrini infection is a significant medical problem because of its involvement as a major risk factor causing bile duct cancer ( cholangiocarcinoma , CCA ) [6] . Liver cancer , predominantly CCA , is the fourth and fifth cause of mortality in males and females , respectively in Thailand [7] . Globally , Khon Kaen Province , Thailand is one of the hot spots of CCA with incidence levels ( per 100 , 000 ) of 78 . 4 in males and 33 . 3 in females [8] . Recently , we reported that O . viverrini does not represent a single species but consists of at least two morphologically similar but genetically distinct ( i . e . cryptic ) species from Thailand and Lao PDR [9] . We also showed that there were at least six genetically distinct groups that are associated with different major wetlands . Additionally , biological variation between populations of O . viverrini from different wetlands in Thailand and Lao PDR has been detected . For instance , worm recovery as well as the fecundity of O . viverrini from the Songkram River in Thailand was significantly different from other wetland systems ( Chi , Mun and Wang Rivers ) in Thailand and Lao PDR ( Nam Ngum River ) [10] . Furthermore , worms belonging to this population were significantly different in body size from populations from the Chi and Nam Ngum River wetlands [10] . The fine scale population genetics of O . viverrini has to date only been studied from a single locality ( Ban Phai in Khon Kaen , Thailand ) , but the results indicated considerable genetic diversity and heterozygote deficiency occurring within a small geographical area [11] . More detailed information on the population genetic structure of O . viverrini is , however , needed to fully determine whether population substructuring and/or differential genetic diversity are associated with geographical differences in distinct wetlands , river systems and flooding patterns [12] . Recently , we characterized , optimized and demonstrated the utility of microsatellite DNA markers for O . viverrini and provided evidence of population subdivision over a large spatial scale with the maximum distant apart of up to 770 km [13] . However , whether such a population pattern occurs over a small spatial scale or not is unknown . In this study , we examined the genetic diversity and population structure of O . viverrini populations occurring within and between four geographically close localities ( small geographical scale population comparisons ) less than 60 km apart in Khon Kaen Province , northeast Thailand . Comparisons were also made with data previously reported for populations separated by much greater distances ( widely spaced/large scale population comparisons ) . Finally , potential population mechanism ( s ) related to the host and environmental factors that may contribute to the current population structure were discussed .
Khon Kaen Province is geographically centrally located in northeast Thailand and by political division it currently consists of 26 districts . Samples of O . viverrini for this study were obtained from four reservoirs located within three adjacent districts namely Khon Kaen , Ban Phai and Phu Wiang in Khon Kaen Province , Thailand as shown in Figure 1 . Sampling localities from Khon Kaen district were from Ban Sa-ard ( KBs ) and Ban Lerngpleuy ( KLp ) and the other two were from Ban Phai district ( KBp ) and Phu Wiang district ( KPv ) . The Ban Phai , Lerngpleuy and Ban Sa-ard localities are connected to the Chi River , whereas the Phu Wiang locality is close to the Nam Phong River upstream from Ubonratana Dam . The Nam Phong River feeds into the Chi River downstream from Ban Sa-ard ( Figure 1 ) . The maximum and minimum geographical distance between localities is between Ban Phai and Phu Wiang ( 60 km ) and Ban Sa-ard and Ban Lerngpluey ( 10 km ) , respectively . For comparisons of widely spaced populations , the distance between localities ranged from 225–771 km [13] . Samples of adult O . viverrini were recovered from hamsters experimentally infected with metacercariae obtained from pools of naturally infected cyprinid fish ( Cyclocheilichthys armatus ) . In each sampling locality , approximately 500 fish weighing at least five kilograms were processed by a pepsin digestion method to isolate metacercariae [14] . Of these a random selection of a maximum of 50 metacercariae were fed orally to each hamster and four months post infection the adult worms were recovered from the biliary system of infected hamsters . Five hamsters were used per locality for worm recovery and previous studies have shown that a dose of up to 50 metacercariae per animal for a period of four months infection does not harm the hamsters and causes no morbidity compared with uninfected control hamsters [15] . The worms were identified based on standard morphological methods and washed several times with 0 . 85% NaCl . A minimum number of hamsters was used to provide a sufficient number of worms for different experiments that we are undertaking . For microsatellite analyses a random sample of 30 individual worms from a mean of 26–34 worms per hamster per locality were selected . Individual worms were homogenized on ice in a microcentrifuge tube using a handmade glass pestle . Genomic DNA was extracted by GenomicPrep Cells and a Tissue DNA Isolation kit following manufacturer recommendations ( GE Healthcare , NJ , USA ) . DNA concentration and purity were determined by spectrophotometry ( Pharmacia Biotech , Cambridge , UK ) . Previously isolated and characterized O . viverrini microsatellite loci ( 12 ) were used in this study [13] . The forward primer of each pair was modified with fluorescent dye ( 6-FAM or HEX or NED; PE Applied Biosystems , CA , USA ) . Microsatellite analyses were performed using a Polymerase Chain Reaction ( PCR ) containing 1 ng of template DNA , 2 mM Tris-HCl , 10 mM KCl , 2 mM Mg2+ , 0 . 2 mM of each nucleotide , 0 . 2 pmol of each primer , and 0 . 05 units Taq polymerase ( Takara Biomedicals , Tokyo , JP ) . PCR amplifications were carried out in a BIORAD thermocycler ( BIORAD , CA , USA ) in a total volume of 25 µl . The cycling conditions included 30 cycles of 1 min at 94°C , 1 min at the optimized annealing temperature , and 3 min at 72°C . PCR products were diluted in HIDI formamide with internal GeneScan size standard , ROX-400HD ( PE Applied Biosystems ) then loaded on the ABI 3100 DNA sequencer ( PE Applied Biosystems ) . Before analysis , the PCR products were denatured in the thermocycler at 95°C and rapidly cooled on ice . Allele sizes were determined using ABI Prism GeneScan Analysis 3 . 1 and Genotyper 2 . 5 ( PE Applied Biosystems ) . PCR reactions were redone in all cases where samples could not be amplified and in each case non amplification was confirmed . To avoid scoring artificial bands resulting in scoring errors , all PCR of samples with electropherograms with many peaks or non-specific products were repeated until unambiguous results were obtained . Furthermore , only clear electropherograms with one or two peaks of the expected size were considered in the analysis . For each locus , the number of alleles , allelic frequencies , and linkage disequilibrium among polymorphic loci using the Markov chain approach [16] , and observed and expected heterozygosity were calculated [17] . To avoid genotyping errors ( i . e . the presence of null alleles , large allele dropout and scoring errors due to stuttering peaks ) , the program Micro-Checker version 2 . 2 . 3 was used [18] to correct allele frequencies as described by Brookfield [19] . Hardy–Weinberg Equilibrium ( HWE ) for each locus was examined using the exact test [20] . The fixation index within subpopulations ( FIS ) and genetic differentiation between populations ( FST ) based on Weir and Cockerham [21] was determined using F-statistics [22] . The significance of pairwise FST values was evaluated [21] . The relationship between genetic isolation among localities was assessed by testing for independence between FST and geographical distances by a Mantel test . All the calculations described above were conducted using GENEPOP Version 3 . 4 software [20] . Allelic richness and overall estimated FIS of the parasite populations were calculated by using FSTAT [23] . Data analyses were done by comparing small scale geographically defined populations in Khon Kaen Province ( the Chi River wetland distinct genetic groups defined by Saijuntha et al . [9] ) . These closely associated populations were compared with more widely distributed populations ( from other wetlands containing distinct genetic groups and/or cryptic species ) occurring in Thailand and Lao PDR [13] . By using analysis of multilocus genotypes , genetic ancestry can be inferred regardless of the sampling location of individuals . This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Research Council of Thailand . The protocol of animal experimentation was approved by the Institutional Animal Ethics Committee , Khon Kaen University ( AEKKU20/2551 ) . All surgery and necropsy was performed under sodium pentobarbital anesthesia , and every effort was made to minimize pain and suffering to the animals .
Allele distribution patterns at 12 microsatellite loci varied greatly among samples of O . viverrini ( Table 1 ) . For the populations in Khon Kaen Province , 52 alleles were recorded across all individuals ( 120 worms ) at 12 microsatellite loci . The total number of alleles per locus ranged from 2–12 . The locality with highest total number of alleles was KBp ( 44 ) and the lowest was KBs ( 34 ) . Localities KLp ( 40 ) and KPv ( 37 ) had intermediate numbers . Genotypic disequilibrium analysis indicated that genotypes of most loci were associated randomly ( P>0 . 05 ) ( data not shown ) . However , linkage disequilibrium was found for Ovms14 and 15 , but this was not significant after sequential Bonferroni's adjustment ( P>0 . 01 ) [24] . Within O . viverrini populations unique alleles are of low frequency ( <6% ) . Three of the four Khon Kaen populations ( KPv , KBp and KLp ) exhibited unique alleles ( 3 , 2 and 2 respectively ) across five loci ( Table 1 ) . Widely spaced populations used in the earlier study [13] exhibited a total of 16 unique alleles across eight loci ( Table 1 ) . However , three alleles occurring in two Khon Kaen populations were also “unique” in three populations from reference 13 . With respect to genetic diversity among Khon Kaen O . viverrini populations as shown in Table 2 , the average number of alleles per locus for KPv , KBp and KLp ( range 3 . 33–3 . 7 ) were higher than that for KBs ( 2 . 83 ) but the difference was not significant ( P>0 . 05 ) . Expected heterozygosity per locus ( HE ) for the four localities ranged from 0 . 44–0 . 56 . The mean allelic richness which is a measure of allelic diversity based on the number of alleles per locus for KPv was 3 . 56 , significantly higher than that for KBp and KLp ( 1 . 88 ) and KBs ( 1 . 83 ) ( F = 5 . 2 , P<0 . 05 ) . To determine whether Khon Kaen populations deviate from HWE , FIS values were calculated ( Table 2 ) . Significant departures from HWE due to homozygote excess were seen in populations KPv , ( at loci Ovms1 , 2 & 6 ) , KBp ( loci Ovms2 , 14 & 16 ) , KLp ( loci Ovms1 & 2 ) and KBs ( loci Ovms1 , 2 , 13 & 16 ) ( Table 2 ) . Significant departures due to heterozygote excess were seen only in populations KBs and KLp at locus Ovms10 . Overall estimates of FIS for each population showed significant homozygote excess with FIS of 0 . 318 , 0 . 249 , 0 . 106 and 0 . 258 for KBs , KLp , KBp and KPv , respectively which indicates a tendency to inbreeding . To compare genetic differentiation between localities , FST statistics were calculated and revealed significant ( P<0 . 05 ) genetic differentiation between all pairs of localities at all geographic scales ( Table 3 ) . Qualitative guidelines suggested by Wright ( 1978 ) [22] were adopted , namely , FST genetic differentiation: 0–0 . 05 ‘little’; 0 . 05–0 . 15 ‘moderate’; 0 . 15–0 . 25 ‘great’; and >0 . 25 indicate ‘very great’ . The level of genetic differentiation detected here ranged from 0 . 0002–0 . 0776 which suggests that O . viverrini in Khon Kaen Province is not panmictic but has low to moderate genetic differentiation among populations . Data obtained from the present study concerning genetic diversity and population differentiation were compared with a previous report which analyzed five widely spaced populations of O . viverrini in Thailand and Lao PDR [13] . This was possible because the same 12 microsatellite loci were used in the analysis . As shown in Tables 2 and 3 , the measurements of allelic diversity , expected heterozygosity , allelic richness and FST from this study were not different from widely spaced populations . The overall FST for the populations in Khon Kaen Province was 0 . 038 [95% confidence interval ( CI ) 0 . 002–0 . 105] , and that of the more widely spaced populations in the previous study was 0 . 043 ( CI = 0 . 016–0 . 075 ) [13] . Within Khon Kaen Province , ( Chi River wetland genetic group ) in this study , 67% and 33% of the populations had ‘little’ and ‘moderate’ genetic differentiation , respectively ( Table 3 ) . Of the five widely spaced populations studied [13] , 60% had ‘little’ and 30% had ‘moderate’ genetic differentiation . ‘Great’ genetic differentiation ( FST = 0 . 165 ) occurred in only a single comparison , between LP and NP . Comparisons between Khon Kaen and widely spaced populations yielded 60% and 40% of populations with little and moderate differentiation , respectively . A Mantel regression test was done to determine the correlation between the FST and geographical distance between populations . No correlation between genetic and geographic distance was found among populations from Khon Kaen as well as between Khon Kaen and widely spaced populations , whether distances were calculated in straight lines or along river courses ( Table 3 ) . At the broader geographic scale , no correlation between genetic and geographic distance was found when distances were measured along river courses , but was found when distances were measured in straight lines ( Table 3 ) . That is , isolation-by-distance was only indicated at the broader geographic scale and only when straight-line distances between populations were used .
The population genetic data on O . viverrini from the four Khon Kaen localities considered in this study showed that there was considerable variation in allelic diversity , heterozygosity and allelic richness . Particularly , allelic richness for worms from KPv was significantly higher than for worms from the other three localities . The cause of this genetic diversity may be due to the transmission dynamics of the parasite's life cycle as a consequence of selection against specific genotypes of parasite by different species of host ( snails , fish or humans ) . Of these hosts , snails in particular show high levels of genetic diversity [25] and the potential for co-evolution between parasite and host species , i . e . the Bithynia snail intermediate host , which in turn may play a role in the observed genetic , biological and/or morphologically variation in this parasite . An alternate explanation of relatively low genetic diversity in three localities ( KBs , KBp and KLp ) , as opposed to high diversity in KPv , may be due to the history of the parasite control program by chemotherapy . All of these areas are endemic for opisthorchiasis and there are records of praziquantel treatments [26] , [27] , [28] , although no details on the frequency and coverage are available . It is possible , as for Schistosoma mansoni , that parasite genetic diversity is reduced after praziquantel treatment [29] . In this study the mean number of alleles per locus ranged from 2 . 83 to 3 . 7 which is lower than that found in other trematode parasites such as schistosomes [30] , [31] . O . viverrini sensu lato contains at least three species each genetically distinct with two morphologically similar ( hence cryptic species ) in river wetlands in Thailand and Lao PDR and one morphologically and biologically distinct isolate in Sakon Nakhon and Nakhon Phanom in Songkram River wetland , Thailand [9] , [10] . Additionally there are five distinct genetic groups of isolates that correspond to different wetlands which may in turn be different species within the complex as they have fixed genetic differences to an extent that define such species in many other parasite taxa [32] . Interestingly , even with the sample size of 30 adult worms per Khon Kaen locality analyzed in this study , a private or unique allele was observed in five of the 12 microsatellite loci we examined , albeit at low frequency ( <6% ) . It is possible that with a larger sample size more unique alleles can be detected and could be used as markers to differentiate between populations of O . viverrini . In the case of other parasites , such as S . mansoni , a similar range of frequency of unique ( private ) alleles ( 1 . 1–4 . 1% ) was observed [30] . Only a single locus ( Ovms10 ) showed significant negative FIS values for all samples . This could be the result of possible extensive migration rates of the second intermediate host causing cross fertilization between distinct populations of O . viverrini . Most pairs of populations presented in Table 3 are “significantly” differentiated from each other , indicating lack of panmixia . Actual values of FST suggest mostly low to moderate differentiation between populations . Gene flow between the closely spaced populations analysed in Khon Kaen Province and the populations of O . viverrini from other wetlands in Thailand and Lao PDR is unlikely via river flow and flooding patterns of snails and fish hosts as each wetland is distinct . Additionally , the Nam Ngum River , which contains a genetically very distinct O . viverrini cryptic species , enters the Mekong River 578 km upstream from where the Mun River meets the Mekong River . Whether , human and/or food ( in this case fish ) cross border movement may provide an avenue for parasite gene flow in Thailand and Lao PDR requires further investigations . The observed levels of genetic differentiation between the four spatially close populations of O . viverrini ( 10 to 60 km separation ) within the Chi River wetland in Khon Kaen and more distant populations ( up to 770 km separation ) including Thailand and Lao PDR river wetland indicates the existence of intra-specific population structuring [13] . A significant correlation between genetic differentiation and geographical distance observed only for the distant populations ( although only when distances were measured in straight lines ) suggests that geographical separation is an important factor for population structure . However , connectivity along a river course between localities , and other factors such as drug treatment and the level of elevation , may influence the variation in genetic differentiation as hypothesized in schistosomes [29] , [31] . Thus , data from this and the previous study [13] suggest that O . viverrini is not a panmictic population but rather is differentiated genetically into different gene pools , indicating the existence of intraspecific population structures that may be associated with different cryptic species within defined wetlands that make up the O . viverrini species complex . Although the average pairwise FST within a single species for the Khon Kaen populations in this study was not different from that between cryptic species in the widely spaced populations [13] , the FST estimated from the overall loci of the cryptic species populations ( 0 . 043 ) was higher than that for the closely spaced Khon Kaen populations ( 0 . 038 ) . Substantial genetic differentiation ( FST = 0 . 165 ) was observed herein only between the cryptic species at a large geographical scale . This provides further independent evidence to support the hypothesis of the existence of cryptic species of O . viverrini within Thailand and Lao PDR [9] and is similar to the situation found for S . japonicum [31] . Several factors such as the intensity of O . viverrini infection , the life style and behavior , as well as genetic polymorphism in human genes and the frequency of praziquantel treatment may influence the transmission dynamics of O . viverrini and thus the incidence of CCA [4] , [33] , [34] , [35] . Khon Kaen Province is known to have a generally high prevalence of opisthorchiasis and incidence of CCA [36] , which includes the same small geographical area examined here . Different levels of genetic diversity of O . viverrini have been found between localities in close proximity and even within the Lawa Lake , Ban Bhai district [11] , [37] . These results suggest that O . viverrini from different reservoirs and streams , and hence different ecological and geographical environments , have different levels of gene/allele frequency and/or number of genotypes , as has been shown for schistosomes [38] , [39] , [40] , [41] , [42] . Although expulsion chemotherapy to collect adult worms from humans is possible for O . viverrini [43] , [44] , it is challenging under field conditions and has its limitations . For instance , worm recovery is unpredictable , most infected individuals have light infections and hence a low worm burden . In this study , caution in interpretation of the result is needed since only adult worms from experimental animals were used and these may not fully reflect the situation in the field due to potential laboratory host selection . Further study on patterns of genetic diversity and population genetic structure of O . viverrini using eggs from feces , cercariae from snails and metacercariae from fish should be compared to the adult worms to determine whether there is any affect of host selection or not as has been shown for schistosomes [45] . In conclusion we have shown that substantial genetic diversity and population genetic differentiation exists between four geographically close localities in the northeast of Thailand . Significantly higher allelic richness was found in worms from the KPv locality compared to worms from the other three localities . The overall genetic diversity and population structure observed at a relatively small spatial scale with a maximum population separation of 60 km was largely similar to that found at a much larger scale where the populations analyzed were separated by a distance of up to 770 km . The level of genetic differentiation was , however , significantly correlated with the distance between populations . | Infection with the liver fluke ( Opisthorchis viverrini ) is a risk factor for cholangiocarcinoma ( CCA ) , which is highly prevalent in Khon Kaen Province , Thailand . Within this Province , there is considerable variation in the epidemiology of opisthorchiasis among districts . Preliminary allozyme and mitochondrial DNA data indicate that genetic variation in O . viverrini occurs even over a small endemic area within the province . Here , we used microsatellite DNA analyses to examine the population genetic structure of O . viverrini from four geographically close localities . Analyses of adult worms based on 12 microsatellite loci revealed varying levels of genetic diversity and population substructuring between the four localities . Worms originating from one locality ( Phu Wiang ) had significantly higher genetic diversity than the other three localities . Data on the population genetic structure observed in these localities are similar to those found at a larger geographic scale . This provides background data to further investigate the biological and epidemiological significance of these genetic variants . | [
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] | 2012 | Population Genetic Structuring in Opisthorchis viverrini over Various Spatial Scales in Thailand and Lao PDR |
Photorhabdus asymbiotica is one of the three recognized species of the Photorhabdus genus , which consists of gram-negative bioluminescent bacteria belonging to the family Morganellaceae . These bacteria live in a symbiotic relationship with nematodes from the genus Heterorhabditis , together forming a complex that is highly pathogenic for insects . Unlike other Photorhabdus species , which are strictly entomopathogenic , P . asymbiotica is unique in its ability to act as an emerging human pathogen . Analysis of the P . asymbiotica genome identified a novel fucose-binding lectin designated PHL with a strong sequence similarity to the recently described P . luminescens lectin PLL . Recombinant PHL exhibited high affinity for fucosylated carbohydrates and the unusual disaccharide 3 , 6-O-Me2-Glcβ1–4 ( 2 , 3-O-Me2 ) Rhaα-O- ( p-C6H4 ) -OCH2CH2NH2 from Mycobacterium leprae . Based on its crystal structure , PHL forms a seven-bladed β-propeller assembling into a homo-dimer with an inter-subunit disulfide bridge . Investigating complexes with different ligands revealed the existence of two sets of binding sites per monomer—the first type prefers l-fucose and its derivatives , whereas the second type can bind d-galactose . Based on the sequence analysis , PHL could contain up to twelve binding sites per monomer . PHL was shown to interact with all types of red blood cells and insect haemocytes . Interestingly , PHL inhibited the production of reactive oxygen species induced by zymosan A in human blood and antimicrobial activity both in human blood , serum and insect haemolymph . Concurrently , PHL increased the constitutive level of oxidants in the blood and induced melanisation in haemolymph . Our results suggest that PHL might play a crucial role in the interaction of P . asymbiotica with both human and insect hosts .
Photorhabdus is a genus of three species belonging to the gram-negative entomopathogenic bacteria of the family Morganellaceae . Unlike the other two species of the genus , P . asymbiotica is not only an insect pathogen . Using a still poorly understood mechanism , P . asymbiotica can infect humans and cause both locally invasive soft tissue infection and disseminated bacteraemic disease characterised by multifocal skin and soft tissue abscesses [1–4] . While other members of the genus are not able to replicate and survive above 32–34°C , P . asymbiotica has the ability to grow at temperatures above 37°C [4–6] . P . asymbiotica can be further subdivided into two apparent subspecies—American and Australian isolates according to genotypic criteria and the occurrence of human infection . In general , it was found that Australian strains are more virulent than American ones [3 , 7 , 8] . The life cycle of Photorhabdus as an insect pathogen is well-characterized [2 , 9] . Photorhabdus does not exist in a free-living form in the soil , but engages in a specific mutualistic association with entomopathogenic nematodes ( EPN ) of the genus Heterorhabditis . This nematobacterial complex is highly pathogenic for a broad range of insects . Using EPN as a vector , Photorhabdus bacteria cells are delivered into the haemocoel of insect larvae , where they are regurgitated by the nematodes and kill the host within 48 h by a combination of the toxins’ action and septicaemia [3 , 10] . The cadaver serves as a nutrient source for both the bacterial pathogen and developing nematodes . Subsequently , the bacteria and new infective juvenile nematodes re-associate and search for a new host . In humans , P . asymbiotica employs a so-called “nutritional virulence” strategy—it aggressively acquires amino acids , peptides and other nutrients from the host [5] . Previous studies revealed that many of the cytotoxins and virulence factors produced by Photorhabdus are equally effective against both insect and mammalian immune defence mechanisms [5 , 11] . The treatment of reported cases has required extensive antibiotic intervention with relapses in many cases [1 , 4] . It is interesting to note that P . asymbiotica is not the only bacterial symbiont of nematodes associated with human diseases . Wolbachia , an endosymbiont of the nematodes Onchocera volvulus and Brugia malayia that cause river blindness and lymphatic filariasis , was reported to stimulate human immune system via production of endo-toxins that are released from nematodes upon death or damage [1 , 12–14] . Compared with P . asymbiotica , Wolbachia is not capable of active reproduction in the human host , which makes P . asymbiotica a potentially more dangerous pathogen [15] . There is also a close phylogenetic relation to Yersinia pestis , a cause of plague , and the parallel between their behaviour is noteworthy [3 , 5] . Pathogenic bacteria often use lectins , i . e . protein receptors with a high specificity for glycoconjugates , to recognize and adhere to human tissues [16 , 17] . In general , lectins are ubiquitous carbohydrate-binding proteins , which play a crucial role in many physiological and pathophysiological processes [18] . Different affinity towards various ligands enables lectins to “read” the information stored in carbohydrate molecules , thus making them a powerful tool in cell-cell recognition , immunity , cancer , pathogen adhesion , etc . [19 , 20] . Recently , a new fucose-binding lectin PLL was identified in P . luninescens and structurally characterized [21] . This article describes the identification , cloning and production of a novel recombinant l-fucose/d-galactose-binding lectin from P . asymbiotica ( designated PHL ) . The interaction of PHL with carbohydrate ligands was analysed through biophysical methods , and the structures of PHL and its complexes with saccharides were solved . The ability of PHL to act as a host-cell recognizing agent was investigated through interaction with the haemolymph of Galleria mellonella ( order Lepidoptera , family Pyralidae ) and human blood components .
PHL was identified as a PLL-like protein in the translated genome of Photorhabdus asymbiotica ( strain ATCC 43949 ) . The protein consists of 369 amino acids with 72% similarity to PLL ( 63% identity ) ( Fig 1 ) . The synthetic phl gene was cloned and overexpressed in E . coli . The recombinant protein was isocratically purified using single-step affinity chromatography on a mannose-agarose column , and its purity was verified by SDS-PAGE . The protein was confirmed to be PHL ( 40 . 18 kDa monomer ) by MALDI-MS/MS . A screening of PHL binding to various glycans was performed using glycan array microchips containing over 600 different mammalian glycans , bacterial polysaccharides , glycosylated peptides and proteins ( S1 and S2 Tables ) . PHL recognized 12 saccharides with at least a 5-fold higher response than that with trehalose ( standard blank , sugar ID 629 ) . The lectin was found to be specific mainly towards fucose and oligosaccharides containing terminal fucose residues as shown in Fig 2 . PHL displays a considerable preference for α-fucoside , but β-fucoside is also strongly recognized . The most preferred complex saccharide was 3 , 6-O-Me2-Glcβ1-4 ( 2 , 3-O-Me2 ) Rhaα-O- ( p-C6H4 ) -OCH2CH2NH2 , an unusual disaccharide present in the Mycobacterium leprae glycolipid PGL-I [22 , 23] . PHL displayed a preference for short fucosylated glycans , as is demonstrated by its preference towards Fucα1-4GlcNAc and Fucα1-3GlcNAc disaccharides over the complex saccharides , e . g . whole Lewis antigens . The strongest binding to human-related oligosaccharides is observed towards blood group B trisaccharide . The Surface Plasmon Resonance ( SPR ) technique was employed to further analyze the specificity/affinity of PHL towards various sugars . It was revealed that PHL interacts with the immobilized α-l-fucoside , giving an apparent KD of 1 . 4±0 . 21 μM , whereas no visible binding to d-mannoside and d-galactoside was observed . Its competitive inhibition with seven monosaccharides ( l-Fuc , Me-α-l-Fuc , Me-β-l-Fuc , d-Man , d-Gal , d-Glc , d-GlcNAc ) and five oligosaccharides ( Fucα1-3GlcNAc , Fucα1-4GlcNAc , blood group H , A and B trisaccharides ) were also tested ( Table 1 , Fig 3A and 3B ) . The lowest IC50 was determined for Me-α-l-Fuc and blood group B trisaccharide , being about 10 and 5 times stronger inhibitors than free l-fucose , respectively . Its binding to a range of saccharides was further characterized by isothermal titration calorimetry ( ITC ) , enabling determination of the complete thermodynamic profile of the molecular interaction ( Fig 3C ) . The calculated dissociation constants ( Table 2 ) are in the millimolar range , indicating a low affinity to the chosen carbohydrate ligands , as is usually observed for lectin/saccharide interactions . Me-α-l-Fuc , blood group B trisaccharide and d-Gal were revealed to be stronger binders than l-Fuc ( KD is in submillimolar values ) . The highest affinity was determined towards Me-α-l-Fuc , with a KD 5 times lower than l-Fuc . With Me-α-l-Fuc and the blood group B trisaccharide , the equilibrium dissociation constants are 0 . 27 mM and 0 . 49 mM , with binding stoichiometry of approx . 2 . 9 and 4 . 3 , respectively . However , the stoichiometry value n cannot be properly calculated , and especially for other ligands , due to the low affinity . Therefore , the stoichiometry was fixed during the fitting procedure to 3 and 4 , respectively , which enabled a comparison of individual sugars ( Table 2 ) . The determination of the oligomeric state of the PHL lectin was carried out via both techniques of analytical ultracentrifugation ( AUC ) –sedimentation velocity and sedimentation equilibrium . The continuous size distribution of sedimentation profiles for PHL resulted in a peak with a sedimentation coefficient of 4 . 98 S suggesting that a PHL exists as a dimer ( Fig 4A and 4B ) . This result was also supported by sedimentation equilibrium experiment ( Fig 4C ) . A global analysis of multi-speed experiments gave a molecular weight of 78 . 6 kDa what corresponds well with the theoretical molecular weight of the dimer ( 80 . 1 kDa ) . In solution , PHL forms a homo-dimer in contrast to the homologous PLL from P . luminescens , which exists as a tetramer [21] . PHL forms a single domain structure , which exhibits a seven-bladed β-propeller fold organized around a seven-fold pseudoaxis of symmetry ( Fig 5A ) . Each repeat of the PHL lectin ( W-motif ) consists of a four-stranded antiparallel β-sheet connected by relatively long loops . Superposition of the seven β-blades for the PHL lectin gives an overall RMSD value no larger than 0 . 63 Å . The shape of the PHL lectin monomer is a short torus , with a diameter of 45 Å and a height of 30 Å . The tunnel in the center broadens from 13 Å at the C- and N- termini side to a diameter of 18 Å on the opposite side . The data-collection and refinement statistics are given in Table 3 . The overall structure of the PHL monomer is very similar to the recently determined structure of a homologous PLL lectin , with a backbone RMSD of 0 . 65 Å [21] . The dimeric state of PHL shown by AUC was also seen in the crystal ( Fig 5B ) . A pseudo-2-fold axis of symmetry generates the dimer associated in a “tunnel to tunnel” manner , so that the C- and N-termini are exposed to the solvent . The main dimer-stabilizing element is a disulfide bridge formed between the Cys279 residues of both monomers in the loop interconnecting β-strands in repeat W6 ( Fig 5B ) . Additional hydrogen bonds are formed by residues in the loops of all repeats except W5 . The analysis of PHL/Me-α-l-Fuc and PHL/BGH complexes revealed fucose-binding sites located between individual blades of the β-propeller in the upper half of the monomer , where the N- and C- termini are located ( Fig 5A ) . Based on the sequence alignment of individual repetitions , there may be up to seven potential fucose-binding sites per PHL monomer forming a circle ( Fig 5 ) . They are referred to as site 1F ( between blades W1 and W2 ) , site 2F ( between W2 and W3 ) and so on . In the crystal structure of PHL/Me-α-l-Fuc , which was solved after soaking ligand-free PHL crystals with Me-α-l-Fuc , only sites 3F and 6F were occupied by the ligand . At site 3F ( Fig 6A ) Fuc-O3 , Fuc-O4 and Fuc-O5 atoms are coordinated by the Thr194 side chain and backbone atoms of Thr194 and Val172 . The side chains of Trp199 and Trp214 form a hydrophobic pocket which adopts the C6 methyl group of Fuc . Trp199 also forms CH-π interactions with the hydrophobic surface of Fuc C3 , C4 and C5 . Analogously , hydrogen bonds are formed by Val316 and Thr338 , while Trp343 and Trp355 stabilize the hydrophobic part of the saccharide in site 6F ( Fig 6A ) . Soaking with blood group H trisaccharide resulted in three saccharides coordinated in sites 1F , 3F and 6F ( Fig 6B ) . In all cases , the Fuc moiety of the trisaccharide is recognized in the same way as with Me-α-l-Fuc . The oligosaccharide conformation is identical in all binding sites . In addition to interactions of the Fuc part with the protein , the trisaccharide in site 1F is further stabilized by the interaction of Gal-O4 with the Gly76 backbone and interaction of GlcNAc-N2 and GlcNAc-O3 with the Trp118 backbone . Analogously , in site 3F the saccharide interacts with the Gly171 and Trp214 backbone , in site 6F with the Gly315 and Trp355 backbone . The conformation of the trisaccharide is further stabilized by a water molecule bridging Gal-O4 , Gal-O5 and GlcNAc-O3 to backbone of Trp118 , Trp214 and Trp355 , respectively . An additional water molecule was detected in sites 1F and 3F , bridging Gal-O3 with Gly76 and Gln77 ( site 1F ) and Gal-O3 with Gly171 ( site 3F ) . The crystal structure of the PHL/d-Gal complex revealed three d-Gal monosaccharides coordinated in binding pockets other than the Fuc-binding sites . These Gal-binding sites are located in between blades in the opposite half to the N- and C- termini of the protein monomer ( Fig 7 ) . Similarly to Fuc-binding sites , potential Gal-binding sites are further reffered to as 1G ( between blades W1 and W2 ) , 2G and so on . The binding sites consist of several polar amino acids forming a complex hydrogen bond net with Gal oxygen atoms . For site 2G , Gal-O1 , O2 , O3 , O4 and O5 atoms are coordinated by Arg91 , Glu93 , Gln155 , Ser160 and Trp163 side chains and by the Gly161 main chain ( Fig 6C ) . The Trp107 side chain stabilizes the hydrophobic surface of C1 , C3 and C5 atoms of the coordinated Gal molecule through CH-π interactions . The saccharide is further stabilized by water molecules bridging the sugar hydroxyls to His105 , Trp107 , Gln119 and Ser160 side chains and the His159 main chain . The ligand at site 4G is coordinated in the same manner by Arg186 , Glu188 , Gln251 and Trp259 side chains , the Gly256 main chain and through a stacking interaction with a Trp202 side chain . Water bridges to Arg186 and Ser256 side chains and His255 main chain were also detected . In site 6G the hydrogen bonds with the Arg282 , Glu284 , Gln347 and Trp352 side chains are established , while the Trp298 side chain stabilizes the hydrophobic part . An additional water molecule forms a bridge to the Ser351 main chain . In all binding sites , only the beta anomer of d-Gal was identified . It is oriented in such a way , that only a Gal monomer could be bound . In sites 2G and 4G two conformations of Gal-C6 hydroxyl were identified as not being directly stabilized by the protein residues . Based on sequence alignment of the repetitions ( Fig 5C ) , only 5 Gal-binding sites contain the sugar binding motif . The conserved Trp residue is missing in sites 2G and 7G . Interestingly , site 2F is occupied by the Met60 side chain of the symmetry-related molecule in all structures . For site 7F in PHL/ Me-α-l-Fuc and PHL/d-Gal complexes , electron density corresponding to another Met side chain was found ( Fig 6A ) . This residue was identified as the initial Met1 , since no other unassigned Met residue is present in the crystal . In addition , the electron density was also clear enough to assign Gln2 and Pro3 residues in the PHL/d-Gal complex . An overview of site occupancy of PHL protein by sugars is given in Table 4 . Several methods were used to prove the biological influence of PHL on human blood and insect haemolymph . Using human blood samples , its interaction with red blood cells ( RBCs ) , effect on production of cytotoxic factors and serum antibacterial activity were determined . The potential influence of PHL on insects was tested on haemolymph: its antimicrobial and phenoloxidase activity were measured . The carbohydrate specificity of PHL lectin was observed through its interaction with the surface oligosaccharides of human RBCs under microscope . PHL displays considerable haemagglutination activity with RBCs of blood group O , but not with blood groups A and B . Experiments with FITC-labeled PHL under fluorescence microscope revealed that PHL was able to bind all RBC types , but only O RBCs were agglutinated ( Fig 8 ) . A set of biologically relevant mono- and oligosaccharides was used to determine their inhibition potency on haemagglutination by PHL . Interestingly , the best inhibitor proved to be the blood group B trisaccharide ( its minimal inhibitory concentration ( MIC ) was 0 . 313 mM ) followed by Me-α-l-Fuc ( MIC 0 . 390 mM ) that are 10 times and 8 times more effective than l-fucose ( MIC 3 . 125 mM ) , respectively ( Table 5 ) . This indicates that blood group oligosaccharides are well recognized in their free form ( terminal trisaccharides ) , while in bound form the least sterically demanding one ( BGH ) is preferred . Except for fucosylated sugars , no other carbohydrates inhibited the haemagglutination reaction at any of the concentrations tested ( up to 250 mM ) . PHL was shown to bind to host cells , but interestingly we also observed a modulation of the host immune response when the lectin was present . Reactive oxygen species ( ROS ) are highly effective oxidants which are produced by immune cells after pathogen recognition and are responsible for the primary antimicrobial response . The level of ROS production was very low in whole human blood A/B/O in the absence of immune activators both in the blood mixed with PBS ( integral of bioluminescence 45 . 7±11 . 2 x 103 counts per second , CPS , during 2 hours of reaction ) and control protein BSA ( 26 . 9±16 . 9 x 103 CPS*min ) . In contrast , the constitutive production of reactive oxidants was significantly increased in blood which was pre-treated with PHL ( 926 . 2±650 . 4 x 103 CPS*min ) , confirming the interaction of the lectin with immune cells and its recognition by the immune system ( Fig 9A ) . Interestingly , in the blood challenged with the activator zymosan A , the production of ROS was significantly lower after pre-treatment with PHL ( 2 . 8±1 . 0 x 106 CPS*min ) compared to PBS and BSA ( 6 . 5±3 . 6 x 106 and 5 . 3±1 . 3 x 106 CPS*min , respectively ) . Mixing blood with PBS and BSA neither affected the oxidative burst in phagocytes positively nor negatively . It is worth noting that the level of ROS produced in blood with the activator was more than 140-fold higher than their constitutive level ( without the activator ) ; this is not apparent from the normalised results shown at Fig 9A . In the presented results , the integral of ROS production was normalized to the PBS control for each particular sample of blood to exclude blood donor variability . The effect of PHL was apparent both at room temperature and 37°C; the temperature influenced only course of the reaction which was faster in human optimum at 37°C . Inhibition of antimicrobial response was also observed upon the challenge of immune effectors with live bacteria . An antimicrobial assay using the Gram-negative bacteria E . coli K12 found a weaker antimicrobial activity in human serum and insect haemolymph treated with PHL ( Fig 9B ) . The viability of the bacteria used is not influenced in the absence of human serum and haemolymph , but decreases in the presence of body fluids containing antimicrobial factors . Unlike PBS or BSA that are not able to block the effect of antimicrobial factors present in body fluids , PHL interacts both with human serum and haemolymph , resulting in a delay of the observed antimicrobial effect . PHL-dependent inhibition of antimicrobial activity was also observed in whole human blood , yet the measured luminescence and variability of results were negatively influenced by the presence of erythrocytes and absorption of haemoglobin , therefore these data are not presented . Although , the ROS production is still disputable in some insect species [25 , 26] , even they possess immune mechanisms that are able to respond quickly to pathogen recognition . The phenoloxidase ( PO ) cascade is well described in insects , activated upon the detection of pathogen structures and accompanied by the production of cytotoxic factors [27] . The effect of PHL on PO activity was examined in G . mellonella haemolymph where the PO activity is manifested in rapid melanisation . A significant increase in melanisation was observed in haemolymph treated with micrograms of PHL ( Fig 10A ) . This activation was inhibited in the presence of both 0 . 2M l-Fuc and 0 . 2M Me-α-l-Fuc ( Fig 10B ) . Unlike PHL , the high doses of homologous protein PLL and control protein BSA did not have any effect on PO activity .
A functionally unique lectin PHL from the insect and human pathogen Photorhabdus asymbiotica was identified as a homologue of a recently published lectin from the strictly entomopathogenic bacterium Photorhabdus luminescens [21] . Individual steps of P . asymbiotica human infection have not been determined , however lectins produced by this pathogen can play an important role in its pathogenicity and/or the process of infection . The aim of this work was to prepare a novel PHL lectin and describe its structure-functional properties . Its biological relevance was tested on human blood and insect haemolymph , with an emphasis on early immune response and lectin interaction with circulating cells . PHL , a homo-dimer lectin ( ~ 80 . 4 kDa ) with subunits linked by a disulfide bridge , was characterized as a dual-specific l-fucose/d-galactose binding protein with an unusually high number of potential binding sites . The results of biological activity assays suggest that PHL significantly affects host defense mechanisms , specifically antimicrobial effectors such as reactive oxygen species and their production . The structure of PHL was solved using the structure of the lectin PLL from Photorhabdus luminescens [21] . Similarly to PLL , the PHL monomer consists of a seven-bladed β-propeller that has not been observed in any other lectin [21] . The seven-bladed propeller of PVL from Psathyrella velutina differs in the formation of its blades , the insertion of its N-terminus into the central cavity , and also in its larger propeller diameter . Propellers of the AAL lectin family are designed in a similar way to PHL , but contain only six blades . In contrast to tetrameric PLL , PHL exists as a dimer stabilized through a disulfide bridge . This is remarkable , as the protein is produced in an E . coli strain not expected to support disulfide formation , however this uncommon feature was already reported for PLL [21] . Unlike all lectins studied so far , PHL is unique in the presence of two sets of binding sites in one domain . So-called superlectins consist of two different domains , each with a different specificity ( e . g . BC2L-C from Burkholderia cenocepacia or CRLL lectin from Cycas revoluta ) [28 , 29] . Also , the ABL from Agaricus bisporus , the SRL from Sclerotium rolfsii or XCL from Xerocomus chrysenteron possess the ability to bind two different saccharides within one unit [30 , 31] . However , two sets of well-defined binding sites specific for two unrelated saccharides has never been reported before . Hence this is a unique bangle lectin arrangement . Remarkably , due to architecture of PHL , all potential binding sites are exposed at its surface and therefore accessible to ligands in solution . The PHL fucose-binding site exhibits a high similarity to the recently characterized binding site of homologous PLL [21] . It shares the characteristic employment of two Trp residues for ligand binding via CH-π interactions rather than creating a complex structure of H-bonds , as is common for fucose-specific AAL family lectins [32–35] . In the structure of the PLL complex with l-Fuc , the saccharide was observed in three binding sites . Similarly , three fucose sites were occupied in the PHL/BGH complex , however , the distribution of confirmed binding sites is not equal , only two of them sharing the same position and the third one being different . Moreover , the crucial amino acids in the fucose sites of PHL are more conserved than in PLL ( Fig 11 ) , where at least one site lacks a conserved Trp residue . The uniqueness of PHL resides in the presence of the second set of binding sites within the same monomer . These sites form a ring around the PHL monomer below the fucose-binding sites ( Fig 7 ) and , in contrast to them , prefer d-galactose . Galactose-binding sites exhibit a common binding scheme , with the hydrophobic part of the saccharide being stabilized by the Trp residue , while the rest of the binding pocket coordinates the saccharide hydroxyl groups . Even though PHL exhibits submillimolar affinity towards d-Gal , it is unlikely to be its natural ligand . Lectins are mostly considered to be oligosaccharide-recognizing proteins , especially if the saccharides are linked to a surface ( such as to the cell , another protein or intercellular matrix ) . In the PHL/d-Gal complex , the d-Gal anomeric oxygen is oriented towards the bottom of the binding pocket , so that recognition of the more complex saccharide is sterically hindered . This corresponds well with the inability of PHL to recognize a d-Gal-modified surface in SPR measurements . Hence , we can assume that the natural ligand is different from d-Gal , e . g . d-Man , which is utilized for PHL purification and exhibits low overall affinity . Glycan microarray experiments demonstrated that the binding of PHL is highly selective for fucosylated compounds out of more than 600 tested glycans , and therefore PHL was defined as a fucose-specific lectin . The highest relative binding was observed with α-l-fucoside . PHL displayed significant preferences for short glycans , as is demonstrated by its preference for Fucα1-4GlcNAc and Fucα1-3GlcNAc disaccharides over complex fucosylated saccharides , such as Lewis antigens or blood group ABH determinants . Unexpectedly , among the highest responding epitopes unusual O-methylated saccharides were found– 3 , 6-O-Me2-Glcβ1–4 ( 2 , 3-O-Me2 ) Rhaα-O-phenol ( the terminal disaccharide from Mycobacterium leprae glycolipid I ) and monosaccharide 3 , 6-O-Me2-Glcpβ [22 , 23] . It is interesting to know that O-methyl glycans are frequently present in some species of bacteria , fungi , algae , plants , worms ( e . g . nematodes ) and molluscs , but not in mammals [36 , 37] . The results published in [37] also suggest that O-methylated glycans constitute a conserved target of the fungal and animal innate immune system . Therefore , the specificity of PHL to O-methylated glycans can point to its role in interactions with both nematodes and insects . It also nicely corresponds with specificity of PLL [21] . The ITC measurement revealed a different behavior in solution , where more complex oligosaccharides such as blood group A/B trisaccharides were determined to have a higher affinity , comparable to the above-mentioned disaccharides . Blood group B trisaccharide and d-galactose turned out to be more strongly bound ligands than l-fucose . This apparent discrepancy follows from differences between the surface assay and solution assay . This is further supported by the fact that d-galactose can probably be only bound in free form , as was demonstrated by the X-ray structure of the PHL/d-Gal complex . In comparison with PLL , PHL has a higher affinity to both l-Fuc and Me-α-l-Fuc . During ITC measurements , PHL interacted with both saccharides with an approx . 4-fold higher affinity . IC50 based on SPR inhibition experiments found a 15-fold better inhibition effect of l-Fuc for PHL than for PLL and even a 42-fold stronger effect with Me-α-l-Fuc . There are also differences in their haemagglutination assays . PHL binds all types of RBCs , but agglutination was observed only for O RBCs . This might be caused by several factors including PHL binding site non-equivalency , partial sterical hindrance of cell-bound saccharide or local interference with other epitopes on the cell [38] . However , it is interesting that homologous PLL agglutinates only A RBCs , showing different overall behaviour to RBCs [21] . P . asymbiotica is known as a facultative intracellular pathogen which is engulfed by human macrophages and insect haemocytes , but is able to avoid destruction and later disseminate from phagocytic cells [3] . Therefore , P . asymbiotica must possess efficient defense mechanisms that enable survival of the detrimental effects of antimicrobial compounds constitutively present or induced in the host organism upon its recognition . We focused on early immune response and its effectors , which are responsible for the primary response to pathogens such as P . asymbiotica . PHL interacts with host cells and was also shown to interfere with the production of ROS in human blood . The observed increase in constitutive ROS level in all blood types , including A and B that are not agglutinated by PHL , suggests the possible recognition of this lectin by the immune system . P . asymbiotica as well as P . luminescens and P . temperata are able to kill insect haemocytes and vertebrate macrophages [3 , 39] and higher concentrations of ROS can certainly contribute to this by causing oxidative damage in host tissues . However , it is worth noting that the apoptosis of host cells occurs later , and the level of ROS produced after immune activation was significantly higher than the amount of oxidants produced in reaction to PHL itself . Overall , in our experiments we did not observe any detrimental effect of PHL on host cells , which could be attributed to increased ROS concentration . Moreover , PHL was able to impair the ability of human blood cells to produce ROS after immune activation by zymosan A , which indicates the major role of PHL is to help in overcoming host defenses instead of evading their activation . The observed inhibitory effect of PHL on oxidant production could further contribute to the reported survival of P . asymbiotica in mammals [3] , which is essential for its clinical manifestation . The PHL-dependent inhibition of antimicrobial response was further confirmed using live bacteria E . coli; the lectin was able to impair the antimicrobial effect of both whole blood and haemolymph and thus provide bacteria with more time for their growth , which is accompanied by the reported depletion of host nutritional resources [5] . Surprisingly , we also observed inhibitory activity in human serum , suggesting that the PHL-mediated immune suppression is not only limited to the inhibition of cellular immunity . Cell-free body fluids of vertebrates and invertebrates contains potent antimicrobials , such as antimicrobial peptides , complement proteins or complement-like molecules with bacteriostatic or bactericidal effect [40–42] . In particular , the complement cascade is known for its activation by lectins . The mechanism of PHL interaction with the humoral immunity of the host is not known , but their competition in binding to bacterial surfaces can be assumed . An important fact , which must be taken into account when considering the ecological relevance of PHL , is the amount of the lectin produced by P . asymbiotica during infection . To inhibit the ROS production in human blood and weaken the antimicrobial activity of human serum , the dose of 100 μg was needed in our experiments ( S1 and S2 Figs ) . It is supposed that PHL is produced in significantly lower amounts within natural conditions , however , the activity of PHL during infection could be promoted by cooperation with other factors produced by bacteria and possibly even their nematode vector which might result in increased effectivity of lower lectin concentrations . Taken together , PHL might act as inhibitor of antimicrobial response , although it seems it is recognized by host defenses . Interestingly , more pronounced effect of PHL was observed on phenoloxidase activity in G . mellonella haemolymph , in which it induced melanisation , the reaction activated by pathogen-associated molecular patterns , aberrant tissues or artificial particles and mediated by enzyme phenoloxidase [27 , 43] . It is of note that two proteins , BSA and PLL , are not recognized in the same way and do not activate haemolymph melanisation . Similar to PHL , the metalloprotease PrtS from P . luminescens TTO1 described previously was associated with the induction of melanisation in insects [44] . Although the precise function of PrtS in the infection process was not specified , a role in the depletion of melanisation response is assumed . The fact that PHL activates constitutive immune mechanisms and at same time is able to restrict antimicrobial activity after immune challenge suggests a possible role of this lectin in depleting the host immune response accompanied by limiting its harmful effects on P . asymbiotica itself . The characterization of the precise mechanism of the PHL-mediated inhibition of antimicrobial response is of interest for further studies , since it could reveal novel approaches to control P . asymbiotica and related pathogens . In summary , the lectin from Photorhabdus asymbiotica was revealed as the first example of a protein with up to twelve potential binding sites in one domain with dual specificity . PHL recognizes fucose and its derivates with micromolar affinity and also an unusual terminal O-tetramethylated disaccharide from Mycobacterium leprae . Fucosylated carbohydrates are widely found on human cells , O-methylated sugars on the cells of bacteria , fungi , algae , plants , worms ( e . g . nematodes ) and molluscs . In addition , interaction with human blood cells and haemocytes revealed not only binding to cell surfaces , but also modulation of the immune response . Taken together with the observed inhibition of antimicrobial activity in human serum , our results indicate that PHL plays an important role in insect and human infections .
Methyl-α-l-fucopyranoside , methyl-β-l-fucopyranoside , d-glucose and d-galactose were purchased from Carbosynth , Compton , United Kingdom; blood group A/B/O trisaccharides , and Fucα1-GlcNAc and Fucα1-GlcNAc were purchased from Carbohydrate Synthesis , Oxford , United Kingdom; l-fucose was purchased from Applichem , Darmstadt , Germany . N-acetyl-d-glucosamine , d-mannose , d-mannose-agarose , biotin and streptavidin , bovine serum albumin , 3 , 4-dihydroxy-dl-phenylalanine , zymosan A from Saccharomyces cerevisiae and luminol were purchased from Sigma-Aldrich , St Louis , USA . Biotinylated saccharides ( biotinylated α/β-d-mannoside , α/β-d-galactoside and α-l-fucoside ) were purchased from Synthaur LLC , Moscow , Russia . Fluorescein isothiocyanate ( FITC ) and DyLight 488 were purchased from ThermoScientific , Rockford , USA . Basic chemicals were purchased from Sigma-Aldrich , St Louis , USA; Duchefa , Haarlem , Netherlands; ForMedium , Hunstanton , United Kingdom and Applichem , Darmstadt , Germany . The sequence of P . asymbiotica lectin PHL was identified in the genome of ATCC43949 strain [7] with the NCBI Blast tool using the sequence of PLL lectin from P . luminescens ( UniProt ID: Q7N8J0 ) as a probe . The nucleotide sequence coding for the peptide sequence of PHL was synthetized by Life Technologies with optimization for expression in E . coli and flanking with the NdeI and HindIII restriction endonucleases . The DNA sequence of the whole gene was inserted into the cloning site of expression vector pET25b ( Novagen ) using NdeI and HindIII restriction sites , resulting in the plasmid pET25b_phl . The construct does not introduce any tags at the C- and N-terminus of the recombinant protein . The vector of interest was transformed into E . coli XL1 using ampicillin for plasmid propagation . For the protein production , the vector pET25b_phl was transformed into E . coli Tuner ( DE3 ) cells ( Novagen ) . The sequence of the plasmid pET25b_phl and its presence in transformed E . coli cells were confirmed by restriction cleavage of the re-isolated plasmid and its sequencing . E . coli Tuner ( DE3 ) /pET25b_phl cells were grown in standard LB broth low-salt medium ( ForMedium , UK ) containing 100 μM ampicillin at 37°C until the OD600 reached ~ 0 . 5 . After induction with 0 . 2 mM isopropyl ß-d-1-thiogalactopyranoside ( ForMedium , UK ) , cells were cultured for an additional 20 hours at 18°C , harvested by centrifugation at 12 , 000 g for 10 min and resuspended in buffer A ( 300 mM NaCl , 20 mM Tris/HCl , pH 7 . 5 ) . Harvested cells were stored at -20°C prior to protein purification . Cells were disrupted by sonication ( VCX 500 , Sonics & Materials , Inc . , USA ) and the soluble fraction was collected by centrifugation at 21 , 000 g at 4°C for 1 hour and filtrated through a 0 . 45 μm pore size filter ( Carl Roth , Germany ) . Recombinant protein PHL was purified by affinity chromatography on mannose-agarose resin ( Sigma-Aldrich , USA ) equilibrated with buffer A using an ÄKTA FPLC system ( GE Healthcare , UK ) . The protein was eluted isocratically . Protein purity was assessed by SDS-PAGE ( 12% gel ) stained with Coomassie Brilliant Blue R-250/G-250 ( Sigma-Aldrich , USA ) . The fractions containing pure PHL were extensively dialyzed against an appropriate buffer and used for further studies . If desired , PHL was concentrated using an ultrafiltration unit with a 10-kDa cut-off membrane ( Vivaspin 20 , Sartorius , Germany ) . Purified PHL lectin samples were labelled with DyLight 488 NHS Ester ( Thermo Scientific ) according to the manufacturer’s instructions and dialysed against PBS buffer ( 137 mM NaCl , 2 . 7 mM KCl , 8 mM Na2HPO4 , 1 . 47 mM KH2PO4 , pH 7 . 4 ) . The labelled protein was used for glycan array screening following the manufacturer’s standard procedure ( Semiotik , Moscow , Russia ) . To determine the lectin specificity , the screening of the printed glycan microarray chip ( slide number 10085636 , with ~400 mammalian glycans and ~200 bacterial polysaccharides , all in 6 replicates ) was performed with a PHL concentration of 200 μg/ml in PBS buffer . The relative binding of PHL was calculated as an average fluorescence from six replicates of each saccharide present in the array . SPR experiments were performed in a BIAcore T200 instrument ( GE Healthcare ) at 25°C . The carboxymethyldextran surface of a CM5 ( GE Healthcare , UK ) sensor chip was activated with N-ethyl-N- ( 3-dimethylaminopropyl ) carbodiimide/N-hydroxysuccinimide solution according to the manufacturer’s standard protocol using HBS buffer ( 10 mM HEPES , 150 mM NaCl , 0 . 05% Tween 20 , pH 7 . 5 ) . Streptavidin was immobilized into all four channels to a final response of 6 , 300–7 , 500 RU . Subsequently , the sensor surface was blocked with 1 M ethanolamine . Biotinylated α/β-d-mannoside , α/β-d-galactoside and α-l-fucoside were injected into three measuring channels ( final response ~ 140 RU ) and pure biotin in the blank channel at a flow rate of 5 μl/min . In the experimental setup , measurements were carried out simultaneously in all four measuring channels using buffer A ( 300 mM NaCl , 20 mM Tris/HCl , pH 7 . 5 ) supplemented with 0 . 05% Tween20 at a flow rate of 20 μl/min . The interaction of PHL with immobilized sugars was measured over the concentration range 200–0 . 16 μg/ml . SPR inhibition experiments were performed using the same conditions described above . The PHL lectin ( 0 . 25 μM final concentration ) was mixed with various concentrations of inhibitors ( 1M – 0 . 01 mM ) and injected onto the sensor chip . Pure PHL lectin was used as a control ( 0% inhibition ) . The response of the lectin bound to the sugar surface at equilibrium was plotted against the concentration of inhibitor in order to determine IC50 . ITC experiments were performed using an ITC200 calorimeter ( Malvern , England ) . Experiments were performed at 25°C in buffer A ( 300 mM NaCl , 20 mM Tris/HCl , pH 7 . 5 ) . Carbohydrate ligands were dissolved in the same buffer . Protein in the cell ( 50 μM ) was titrated by consecutive addition ( 2 μl ) of the ligand in the syringe ( 1 . 5–50 mM ) while stirring at 1000 rpm . Control experiments performed with injections of buffer in the protein solution yielded insignificant signals . Integrated heat effects were analyzed by nonlinear regression using a single-site binding model in Origin 7 ( Microcal ) [45] . The experimental data fitted to a theoretical titration curve brought up stoichiometry ( n ) , association constant Ka , and the enthalpy of binding ΔH . The other thermodynamic parameters such as free energy ( ΔG ) and enthalpy ( ΔS ) were calculated from the equation ΔG = ΔH-TΔS = -RTlnKa , where T is the absolute temperature and R is the molar gas constant ( 8 . 314 J . mol-1 . K-1 ) . At least two independent titrations were carried out for each tested ligand . AUC experiments were performed using a ProteomeLab XL-A analytical ultracentrifuge ( Beckman Coulter , California , USA ) equipped with an An-60 Ti rotor . Before analysis , purified PHL was brought into the experimental buffer ( 20 mM Tris/HCl , 100 mM NaCl , pH 7 . 5 ) by dialysis and the dialysate was used as an optical reference . Sedimentation velocity experiments were conducted in a standard double-sector centerpiece cell loaded with 420 μl of protein sample ( 0 . 05–0 . 21 mg/ml ) and 430 μl of reference solution . Data were collected using absorbance optics at 20°C at a rotor speed of 42 , 000 rpm . Scans were performed at 280 nm at 6-min intervals and 0 . 003 cm spatial resolution in continuous scan mode . The partial specific volume of protein together with solvent density and viscosity were calculated from the amino acid sequence and buffer composition , respectively , using the software Sednterp ( http://bitcwiki . sr . unh . edu ) . The sedimentation profiles were analyzed with the program Sedfit 14 . 3 [46] . A continuous size-distribution model for non-interacting discrete species was used to provide a distribution of apparent sedimentation coefficients . Sedimentation equilibrium experiments were performed at 20°C in a six-channel centerpiece cell loaded with 110 μl of PHL ( 0 . 02 , 0 . 05 and 0 . 09 mg/ml ) and 120 μl of reference solution . The sample was gradually spun at rotor speeds of 9 , 500 rpm , 11 , 400 rpm , and 20 , 000 rpm , respectively . After equilibrium was achieved , data were collected at 280 nm by averaging 20 replicates with 0 . 001 cm spatial resolution in step mode . Data from the multi-speed experiment were globally analyzed with SEDPHAT 10 . 58 [47] using a non-interacting discrete species model with mass conservation constraints . The PHL protein was concentrated to 13 mg/ml using an ultrafiltration unit with a 10-kDa cut-off membrane ( Vivaspin 20 , Sartorius , Germany ) . Initial crystallization conditions were screened with the commercial screening kits Classic , Classic II , Classics Lite , PACT ( Qiagen , Hilden , Germany ) , and Structure I+II suites ( Molecular Dimension , UK ) using the Mosquito crystallization robot ( TTP LabTech , UK ) . Using the sitting drop vapour diffusion method , 0 . 2 μl drops of protein/precipitant solution were spotted on the crystallization plate and incubated at 17°C . PHL formed crystals within two weeks under several suitable conditions . After optimization , the final crystals were obtained under the following conditions: 4 μl sitting drop , protein solution mixed with precipitant ( 3 . 7–4 . 3 M NaCl , 100 mM HEPES pH 7 . 5 ) in ratios 1:1 , 3:5 , 1:3 and 1:7 . The drops were set against 0 . 5–1 ml of the same equilibration precipitant . To determine the PHL structure complexed with ligands , the PHL was co-crystallized with 3 mM methyl-α-l-fucoside or the crystals of PHL were soaked in a 4 mM solution of BGH trisaccharide for 1 . 5 hour or 200 mM solution of d-galactose for 70 minutes , respectively . The crystals were cryo-protected using 40% PEG 400 and frozen in liquid nitrogen . The diffraction data of free PHL were collected with an in-house Rigaku HighFlux HomeLab ( Rigaku , Tokyo , Japan ) robotized macromolecular diffraction system with ACTOR sample changer at the Cu-Kα wavelength . The diffraction data of PHL complexed with saccharides were collected at the BESSY II electron storage ring ( Berlin-Adlershof , Germany ) [48] . Images were processed using XDSAPP [49] and converted to structure factors using the program package CCP4 v . 6 . 5 [50] , with 5% of the data reserved for Rfree calculation . The initial structure of PHL was solved using the molecular replacement method with a homology model based on the structure of PLL [21] generated by a Phyre2 „one to one” approach [51] . The structures of PHL complexes were solved by molecular replacement with MOLREP [52] using the monomeric coordinates of the initial PHL structure . Refinement of the molecule was performed using REFMAC5 [53] alternated with manual model building in Coot v . 0 . 8 [54] . Sugar residues and other compounds that were present were placed manually using Coot . Water molecules were added by Coot and checked manually . The addition of alternative conformations , where necessary , resulted in final structures that were validated using the ADIT ( http://rcsb . org ) and MolProbity [55]; http://molprobity . biochem . duke . edu] validation servers and were deposited in the PDB as entries 5MXE , 5MXF , 5MXG , 5MXH . Molecular drawings were prepared using Pymol ( Schrödinger , Inc . ) . Red blood cells ( RBCs ) were washed four times with PBS buffer ( 137 mM NaCl , 2 . 7 mM KCl , 8 mM Na2HPO4 , 1 . 47 mM KH2PO4 , pH 7 . 4 ) , diluted to 50% with PBS with 0 . 005% ( w/w ) sodium azide and treated with 0 . 1% papain . Haemagglutination assay with the prepared RBCs was performed using microscopy [56] . PHL ( 2 mg/ml ) in PBS buffer and 10% RBCs were gently mixed in a 1:1 ( v/v ) ratio . The reaction mixture was incubated for 10 minutes and subsequently observed on microscope slides using a Levenhuk 2L NG microscope with a Levenhuk D2L digital camera ( Levenhuk , USA ) . A haemagglutination inhibition assay was performed to determine the specificity and semi-quantitative affinity of PHL interaction with the saccharides . A wide range of monosaccharides at concentrations from 50 mM to 0 . 5 mM were used for determining the lowest inhibiting concentration . A 20 μl mixture composed of 5 μl PHL ( 2mg/ml ) , 5 μl saccharide , and 10 μl 10% RBCs was prepared ( all components of reaction were in PBS buffer ) . After 10 minutes , the reaction was observed using a Levenhuk microscope . The PHL protein was labelled with FITC according to the manufacturer’s instructions and dialysed against PBS buffer . 1 μl of labelled PHL protein ( 2 mg/ml ) was incubated with 100 μl of cells ( RBCs and haemocytes ) . As a control individual types of RBCs were used and mixed with 1 μl FITC prepared for labelling . The mixtures were incubated at 17°C for 30 minutes , the cells washed three times with PBS buffer and observed under a fluorescence microscope ( OLYMPUS IX81 Microscope IX81F-3 with IX2-UCB-2 Controller and X-Cite 120PC Q; Olympus and Excelitas Technologies , Japan , resp . USA ) . Samples of human blood were collected from healthy donors to tubes without anticoagulant and anonymized prior to experiments . The blood was allowed to clot for at least 10 min , centrifuged at 2 , 000 g , 10 min to remove the clot and the collected serum was used immediately for subsequent analysis . The serum was analysed within two hours after blood collection in all experiments . Larvae of the greater wax moth , Galleria mellonella , were reared on an artificial diet [57] at 30 ± 1°C in constant darkness . Haemolymph was collected from the seventh instar larvae by cutting a proleg and pooling the haemolymph into a tube containing phenylthiourea ( 1 mg/ml in PBS; haemolymph mixed with phenylthiourea in the ratio 9:1 [v/v] ) or used directly for the determination of phenoloxidase activity . ROS production was measured according to previously published work [58] in whole human blood within 15 min of sampling . The fresh human blood ( 2 μl diluted 40x in HBSS; 0 . 137 M NaCl , 5 . 4 mM KCl , 0 . 44 mM KH2PO4 , 0 . 25 mM Na2HPO4 , 4 . 2 mM NaHCO3 , 1 . 0 mM MgSO4 , 1 . 3 mM CaCl2 , 5 . 55 mM glucose; pH 7 . 4 ) was mixed with 25 μl of PHL ( 125 μg in PBS for all experiments except dose-dependence assay where different dilutions of PHL were used ) , BSA ( 125 μg in PBS ) or PBS ( pH 7 . 4 ) and incubated for 10 min at 37°C or room temperature . After incubation , 25 μl of the luminophore ( 10 mM luminol ) and 25 μl of activator zymosan A ( 2 . 5 mg/ml in HBSS ) was added to activate ROS production in phagocytes . To detect the constitutive production of ROS , zymosan was replaced with the same volume of HBSS . Luminescence was recorded with a Chameleon V luminometer ( Hidex , Finland ) for two hours at 37°C or room temperature , and the integrals of reactions were compared . Results from experimental treatments ( incubation of blood with PHL and BSA ) were normalised to the integral of the oxidative response in the respective blood sample with PBS . The antimicrobial activity of human sera and insect haemolymph was measured luminometrically using the bioluminescent Gram-negative bacteria Escherichia coli K12 [59] . We have modified previously published protocol to measure the effect of PHL [60] . Briefly , 16 μl of serum or haemolymph was incubated with 64 μl PHL ( 320 μg in PBS for all experiments except dose-dependence assay where different dilutions of PHL were used ) , BSA ( 320 μg in PBS ) or PBS ( treatment control ) at room temperature for 10 min . After treatment , 120 μl of bacteria working solution containing 100 , 000 E . coli K12 cells in PBS ( pH 7 . 0 ) was added to the reaction well and the luminescence signal was recorded with a Chameleon V luminometer ( Hidex , Finland ) in counts per second ( CPS ) . Reaction wells with PHL , BSA or PBS in an 80 μl volume of the same concentrations and pH as above were measured with E . coli K12 in each experiment as viability controls , not affected by human serum nor haemolymph . The luminescence produced by E . coli K12 corresponds to the bacterial viability . The antimicrobial effect of human sera and haemolymph was determined as time elapsed until the luminescence signal decreased under 1000 or 5000 CPS , respectively . The determined time was subtracted from the total time of the assay , 90 min for human sera and 120 min for haemolymph , so that higher values would correspond to higher antimicrobial activity . PO activity was measured according to previously published studies [61 , 62] . Haemolymph of G . mellonella ( 5 μl ) was collected directly to 95 μl of PHL ( 475 μg in PBS for all experiments except dose-dependence assay where different dilutions of PHL were used ) , PLL ( 475 μg in PBS ) , BSA ( 475 μg in PBS ) or PBS ( pH 7 . 4 ) and incubated for 10 min at room temperature . After incubation , 40 μl of reaction mixture was transported to the microplate well and 160 μl of 3 , 4-dihydroxy-dl-phenylalanine ( 3 mg/ml in PBS ) was added as substrate . The reaction was allowed to proceed for 30 min , and absorbance was measured in 2-min intervals at 492 nm with a Sunrise reader ( Tecan , Switzerland ) . PO activity was expressed as linear increase in absorbance per minute . For the inhibition assay , PHL was pre-incubated with 0 . 2M l-Fuc or Me-α-l-Fuc for 10 min at room temperature prior to mixing with haemolymph . Data were analysed in Statistica 12 ( StatSoft , USA ) . To test the effect of protein treatment on antimicrobial activity and oxidative burst , the results from related samples were compared using the Wilcoxon test . Normally distributed data of phenoloxidase activity were analysed using ANOVA with post-hoc Tukey's HSD test . In dose-response experiments , effect of each particular dose of PHL was compared to PBS control using Dunnett's multiple comparisons test . Differences were considered statistically significant for p values < 0 . 05 . Anonymised human blood of blood groups A , B , O treated with natrium citrate was purchased from Transfusion and Tissue Department , The University Hospital Brno , Czech Republic . IRB approval for use of these samples is not requested . It was also confirmed by the university body . | Photorhabdus asymbiotica , originally isolated in 1989 from a patient in the USA , was revealed to be not only effective insect pathogen but also bacteria causing serious and difficultly treatable diseases in humans . The genome of P . asymbiotica is being intensively studied but little is known about the details of the host-pathogen interaction at the molecular level . In this paper , we focused on the lectin identified in P . asymbiotica genome that may be crucial for the early stage of infection . We designated this lectin as PHL and prepared it in fully functional recombinant form . We identified its specificity and investigated the structural details of ligand binding . We found the PHL structure unique on the basis of number and arrangement of binding sites present in each monomer . Furthermore , we investigated influence of PHL on immune system of human and insect to propose the biological role for this lectin . The antimicrobial activity as well as phenoloxidase activity and reactive oxygen species production which contribute to antimicrobial effect was modulated after exposition to PHL in both blood and haemolymph supporting the idea that PHL might play important role in the host-pathogen interaction . | [
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] | 2017 | Characterization of novel bangle lectin from Photorhabdus asymbiotica with dual sugar-binding specificity and its effect on host immunity |
The mechanisms governing telomere replication in humans are still poorly understood . To fill this gap , we investigated the timing of replication of single telomeres in human cells . Using in situ hybridization techniques , we have found that specific telomeres have preferential time windows for replication during the S-phase and that these intervals do not depend upon telomere length and are largely conserved between homologous chromosomes and between individuals , even in the presence of large subtelomeric segmental polymorphisms . Importantly , we show that one copy of the 3 . 3 kb macrosatellite repeat D4Z4 , present in the subtelomeric region of the late replicating 4q35 telomere , is sufficient to confer both a more peripheral localization and a later-replicating property to a de novo formed telomere . Also , the presence of β-satellite repeats next to a newly created telomere is sufficient to delay its replication timing . Remarkably , several native , non-D4Z4–associated , late-replicating telomeres show a preferential localization toward the nuclear periphery , while several early-replicating telomeres are associated with the inner nuclear volume . We propose that , in humans , chromosome arm–specific subtelomeric sequences may influence both the spatial distribution of telomeres in the nucleus and their replication timing .
Cell proliferation potential is a critical attribute that directly influences embryogenesis , development and growth . For instance , insufficient proliferation capacity compromises organogenesis , tissue regeneration and repair , while unrestrained cell proliferation promotes cancer progression [1] . The human chromosome structures that have been most directly linked to cell proliferation control are telomeres [2] . Telomeres are specialized nucleoprotein complexes found at the ends of linear chromosomes . In vertebrates , they consist primarily of thousands of double stranded hexameric repeats ( 5′-T2AG3-3′ ) that end in a 3′ G-rich protruding single strand . The double strand region is directly bound by specific telomeric factors ( TRF1 and TRF2 ) , while the 3′ overhang is bound by POT1 . Interactions of these proteins with three other telomeric proteins ( TIN2 , TPP1 and RAP1 ) constitute the shelterin/telosome complex , which is required for telomere function [3] , [4] . Telomeres protect chromosome ends from degradation and fusion . They ensure the complete replication of chromosomes by creating a buffer of expendable sequences . Because of both the end replication problem , following which conventional DNA polymerases cannot completely replicate the ends of linear molecules [5] and the post-replication processing required to form a new functional telomere [6] , telomeres shorten with every genome replication cycle . In the absence of a mechanism to add telomere repeats to the 3′ end , telomeres shorten with cell division until they reach a critical length , incompatible with proper telomere function [2] . A checkpoint signal is then triggered and cells enter senescence . Cell proliferation capacity is thus determined by initial telomere length and telomere shortening kinetics [2] . The latter is highly variable among human cell lines and ranges from 30 to 300 bp/cell division [7]–[9] . In vivo , telomere shortening in haematopoietic tissues has been estimated about 25–35 bp/year , although this pace is accelerated during the first years of life and also under stress or pathological conditions [10]–[16] . Telomerase , the dedicated reverse transcriptase that adds telomeric repeats de novo to the 3′ end , is highly active during development and its activity persists in stem cell compartments , where it ensures the cell replication potential of highly proliferative tissues [17]–[19] . However , as suggested by the telomere shortening that occurs with aging , the levels of telomerase are limiting [18] , [20] . Also , mutations that prevent full telomerase activity accelerate telomere shortening and cause the premature appearance of aging phenotypes [21]–[23] . Accelerated telomere shortening might also result from difficulties during telomere replication . For instance , it has been shown that mutations in the gene coding for the WRN exonuclease/helicase compromises the replication and integrity of the telomere G-rich strand [24] , [25] . Telomere replication defects may thus contribute to the aging phenotypes observed in Werner syndrome patients [26] , [27] . Very little is known regarding the control of telomere replication in human cells [28] . The bulk of human telomere sequences replicate all through S-phase [29] , [30] . This is seemingly different from what is observed in budding yeast where telomeres replicate in concert late in S phase [31] , [32] , although it is not known whether replication timing for individual human telomeres is spatially or temporally controlled . In a recent study , it was shown that telomeres in the muntjac deer display defined timings of replication and that telomeres on long and short arms replicate asynchronously [33] . This finding suggests that the firing of subtelomeric origins of replication in this species is subjected to chromosome arm-specific control mechanisms . We have used the ReDFISH-based approach , described previously by Zou et al for the muntjac [33] , to determine the timing of replication of individual telomeres in human cells . Our observations indicate that both chromosome arm-specific subtelomeric composition and nuclear localization influence the timing of telomere replication in humans .
We used human primary fetal lung fibroblasts ( IMR90 ) and the ReDFISH approach , which is a modified version of the CO-FISH technique [34] ( Figure 1A–1C ) , to characterize the timing of replication of individual telomeres . At least 30 metaphases were analyzed for each hour of a BrdU/C pulse to determine the percentage of individual telomeres being replicated and to calculate the mean replication timing ( mrt ) for each chromosome arm . Global analysis showed that telomere replication takes place during the whole S-phase with a peak ( about ¼ of all telomeres ) during the fourth hour after S-phase initiation ( mrt: 3 . 27; Figure 1D ) . This kinetics of bulk telomere replication has already been observed using density-labeling methods [35] or BrdU-based detection [30] , [36] . However , our results indicate that single telomeres replicate in less than one hour since 1 hour BrdU pulses are sufficient to reveal perfectly detargeted sister telomeres ( i . e . telomeres on homologous sister chromatids that are exclusively recognized by either G-rich or C-rich specific probes , Figure 1C ) . Moreover , telomeres located at specific chromosome ends tend to preferentially replicate during a defined window of the S-phase , with some telomeres replicating rather early and others replicating late ( Figure 1E ) . For instance , 50% of telomeres on the 19q arm replicate during the first two hours ( mrt: 2 . 33 , Figure 1F ) , whereas around 70% of telomeres on the 4q arm replicate during the last two hours ( mrt: 4 . 45 , Figure 1F ) . Both mean replication timings are significantly different from the mean replication timing of 6p , a mid-S replicating telomere ( mrt: 3 . 33 , Fisher exact tests: 19q vs 6p , p = 6 . 9×10−8 , 4q vs 6p , p = 1 . 2×10−9 and 4q vs 19q , p = 2 . 2×10−16 . Significance threshold: p<0 . 0025 ) . As observed in muntjac cells [33] , telomeres on short and long arms of the same chromosomes show no coordinated replication . There is no obvious correlation between the reported replication pattern of the last R/G ( R = reverse , G = giemsa ) cytogenetic bands on each chromosome arm , revealed also by incorporation of BrdU [37]–[39] , and the pattern of replication for single telomeres observed here . There is a weak , albeit not significant , correlation of single telomere replication timings and the mean replication timings reported for the most distal chromosome-specific regions included in BAC ( bacterial artificial chromosome ) arrays ( Figure S1 ) [40] suggesting some synchronicity between telomeres and distal subtelomeric regions ( Spearman's rank correlation test: p = 0 . 093 , significance threshold p<0 . 015 ) . To understand which factors regulate the replication timing of individual telomeres , we examined the impact of telomere length and telomerase expression . Indeed , recent work in budding yeast suggests that telomere length could influence the timing of replication . Particularly , a shortened telomere tends to replicate earlier [41] while the bulk of telomeres replicate rather late [42] . Using telomere Q-FISH followed by subtelomeric FISH as described previously [9] , we measured relative telomere fluorescence intensities ( which indicate telomere length relative to the mean telomere length of the cell ) specifically associated with polymorphic chromosome arms . We used the same subtelomeric FISH approach after ReDFISH to determine the replication timing of telomeres of the same chromosome pairs ( Figure 2A ) . Although significant telomere length differences exist between some alleles , their telomeres replicated with similar timings ( 7p , 8p or 16p ) ( Figure 2B ) . Also , some allelic telomeres , like those on 9q , showed differences in mean replication timing although no difference in telomere length was observed ( Figure 2B ) . A global comparison of telomere lengths and mean replication timings through a correlation analysis confirmed that no relationship exists between both variables ( Figure 2C ) . To further corroborate this observation , we examined the replication profile in IMR90 cells expressing the catalytic subunit of human telomerase ( hTERT ) . hTERT is limiting for telomerase activity in most human fibroblasts and is often sufficient to increase their replication potential [43] . However , some cells spontaneously increase the expression of p16INK4a by mechanisms that are unknown and such cells senesce even in the presence of telomerase activity . IMR90+hTERT cells fall into this category [44] , preventing us from obtaining enough analyzable material for our studies . We therefore expressed in these cells TIN2 , another telomeric factor [45] . TIN2 , through its interaction with TRF1 , exerts a negative control on telomere length . In our cells , however , telomeres were stabilized above 10 kb with individual telomere lengths being largely homogenized , as expected for a cell line expressing hTERT alone ( Figure 3A ) [9] . IMR90+hTERT+TIN2 cells grew vigorously , allowing us to perform the same study in cells that had longer and much more homogeneous telomeres than primary cells . The length of S-phase , as indicated by FACS analysis , is somewhat shorter in telomerized IMR90 cells , since it lasts 5 . 5 hours instead of a little more than 6 hours in the parental cell line ( not shown ) . Concurrently , telomere replication peaks earlier ( mrt: 2 . 61 ) and very few telomeres are seen replicating during the last pulse ( Figure 3B ) . Remarkably , however , the relative timings of replication for single telomeres in this cell line were very similar to the one observed in unmodified cells ( Figure 3C ) . A statistical analysis ( Figure 3D ) showed a highly significant positive correlation ( Spearman's rank correlation test: p<0 . 0001 , significance threshold p<0 . 01 ) , indicating little variation in the relative timings of telomere replication between both cell lines . These results strengthen the conclusion that telomere length has no visible impact on telomere replication timing in humans . Interestingly , the mean replication timings for single telomeres in telomerized IMR90 cells appear to be significantly correlated to the mean replication timings of the most distal chromosome-specific sequences reported by Woodfine et al [40] ( Spearman's rank correlation test: p = 0 . 0062 ) ( Figure S1 ) . Subtelomeric regions show extensive segmental polymorphisms , which can reach several hundreds of kilobases [46]–[48] and could directly impact replication origin firing and/or replication fork speed . In the experiment to determine the relative lengths of allelic telomeres , we used subtelomeric probes recognizing segments ( around 30–40 kilobases long ) that are present or absent at chromosome extremities and are located very close to the telomere tract ( 10 to 20 kb ) [46] , [48] , [49] . As shown in Figure 2B , we found that extremities corresponding to allelic locations but carrying segmental polymorphisms ( alleles labeled A and B on 1q , 7p , 8p , 9p and 16q in Figure 2A ) tend to replicate during the same time window , like do homologous sequences elsewhere in the genome [50] . On the other hand , telomeres on different chromosome pairs but associated with subtelomeric regions of similar segmental composition ( compare for instance 1pA and 7pA to 8pA and 9qB ) may have different timings of replication . This indicates that segmental polymorphisms do not account for the differential replication timing of individual telomeres . To determine whether telomeres with identical chromosome positions in the genome tend to replicate at similar times in different individuals , we studied the telomere replication pattern in the foreskin fibroblast cell line HCA2 , which expresses an exogenous copy of hTERT and therefore replicates indefinitely , like the IMR90+hTERT+TIN2 cells . In fact , both the length of the S-phase and the global timing of telomere replication are indistinguishable between both cell lines ( Figure 4A ) . Even more remarkably , the ranking of mean replication timings for individual telomeres was very similar ( Figure 4B ) , as indicated again by a statistically significant positive correlation coefficient ( Figure 4C ) . Also striking is the observation that chromosome extremities potentially carrying extended subtelomeric segmental variations in both cell lines harbor similar replication timings ( Figure 4D ) , strengthening the idea that these genetic polymorphisms do not have a major effect on telomere replication timing . Again , the mean replication timings for single telomeres in telomerized HACA2 cells appear also to be correlated to the mean replication timings of the most distal chromosome-specific sequences ( Figure S1 ) . Interestingly , the most conspicuous , albeit limited , variations in replication timing between IMR90 and HCA2 cells concern telomeres that replicate in the first two thirds of the S-phase , while less variation is apparent amongst telomeres that replicate later ( Figure 4C ) . Since the incidence of subtelomeric polymorphisms is equally distributed among early and late replicating telomeres , this observation suggests that differences in replication timing because of genetic variations might be more easily superseded by factors causing telomeres to replicate late in the S-phase . We therefore conducted a closer examination of late replicating telomeres . It has been suggested that transcriptionally inactive heterochromatic regions tend to replicate during the second part of the S-phase [51] . Also , in females , both X chromosomes display a different replication pattern according to their heterochromatic state . Active X chromosomes behave like autosomal chromosomes , bearing early and late replicating bands , while inactive X ( Xi ) shows a pattern of late replication that generally encompasses the entire chromosome [38] . Our analysis of telomere replication by ReDFISH revealed that , on the X chromosomes of IMR90 cells , telomeres on the short arm replicate during the middle of S-phase , rather synchronously as expected for homologous chromosomes . Replication of telomeres on the long arm presented a bimodal distribution with one peak of replication in the middle of S-phase and another peak at the end of that phase ( Figure S2 ) . The late profile of BrdU incorporation observed all along the chromosome that also presented a late replicating telomere suggested that this chromosome is Xi ( not shown ) . However , since both features depend on the same phenomenon ( BrdU incorporation during replication ) , confirmation that this is a bona fide Xi requires a replication-independent criterion . Unfortunately , detection of Xi-specific heterochromatic marks ( such as particular histone modifications ) was precluded by the type of chromosome fixation ( ethanol/acetic acid ) used in the ReDFISH approach . In the male HCA2+T cells , the Yp telomere replicated early in S-phase while the Yq telomere displayed a much later replication pattern ( Figure S2 ) ( mrt: 2 . 49 and 3 . 50 , respectively , Fisher exact test: p = 9 . 9×10−8 ) , suggesting that the replication timing of this telomere might be influenced by the constitutive heterochromatic region found on the Yq arm . On the other hand , although the Xq telomere shows a peak of replication coincident with the Xp telomere , their calculated mrts are significantly different ( 2 . 61 and 3 . 12 , respectively , Fisher exact test: p = 8 . 7×10−6 ) ( Figure S2 ) indicating that Xq replicates later than Xp in this male fibroblast cell line . On the other hand , the comparison between the mean replication timings of Xq and Yq telomeres fails to show a significant difference confirming that both telomeres have a late replicating pattern . Amongst the telomeres that consistently replicated late during S-phase in all cells examined are those located on the short arms of acrocentric chromosomes ( Figure 1E , Figure 3C , Figure 4B , and Figure 5A ) . Together with rDNA clusters , acrocentric regions carry both β-satellite sequences and D4Z4 repeats [52] . These two last kinds of repeats are also found at two other extremities , 4qter and 10qter . As shown in Figure 5B , both 4q and 10 replicate late in IMR90 , a behavior also observed in IMR90+TT and HCA2+T cells ( Figure 3C and Figure 4B ) . These observations suggest that the presence of satellite-like repeats at subtelomeric positions may influence the timing of telomere replication . However , while telomeres on acrocentric short arms have been detected as associated with the nucleolus [53] , both 4qter and , although much less consistently , 10qter have been reported as being associated with the nuclear periphery [54]–[56] , suggesting that nuclear localization could also influence the timing of telomere replication . We therefore tested both the replication timing and nuclear localization of newly created telomeres carrying a defined subtelomeric composition . To address the specific contributions of subtelomeric elements with regard to nuclear localization and telomere replication , we artificially tagged telomeres in C33A human cells with DNA molecules that carry either multiple D4Z4 repeats , a single D4Z4 repeat , 4 β-satellite repeats or both ( Figure 6A ) . Upon chromosome integration , such constructs lead to non-targeted ( random ) de novo telomere formation [57] . We have previously shown that in this cell line , polyclonal populations of stably transfected cells are representative of pools of independent clones of tagged telomeres allowing us to perform analyses on populations [56] , [58] , [59] . Also , the presence of particular subtelomeric sequences does not bias the chromosome integration sites of the seeding constructs [56] , [58] . As shown in a previous study [56] , [58] , a single D4Z4 repeat , alone or inserted together with β-satellite repeats , confers to a chromosome extremity a more peripheral position within the nucleus while multiple copies of D4Z4 repeats ( Figure 6B and 6C ) , or several β-satellite repeats ( Figure 6C ) alone , do not . We then determined the replication timing of all types of telomere seeded extremities and found that those carrying only one D4Z4 repeat ( and bearing a more peripheral localization in the nucleus ) replicate later than the others , suggesting that nuclear localization influences telomere replication timing ( Figure 6D and 6E ) . On the other hand , the mean replication timing of telomeres connected to β-satellite sequences alone is significantly higher than the mean replication timing of control telomeres ( p = 0 . 0006 , significance threshold p<0 . 003 ) . This effect is independent of nuclear localization , thus allowing the conclusion that β-satellite sequences by themselves cause a delay in telomere replication . Given the above results , we examined by immuno-FISH and 3D imaging the nuclear localization of native chromosome ends carrying telomeres that replicated either early or late in IMR90 primary cells . Our results , illustrated in Figure 7 , indicate that the late replicating extremities 2pter , 3pter , 4qter , 6qter and 12qter have a clear tendency to localize at or near the nuclear periphery , whereas the early replicating extremities 1p , 5p , 12p and 17q are found in the inner part of the nuclear volume ( Figure 7 ) . Furthermore , a correlation analysis ( p<0 . 0002 ) clearly indicates that there is a direct relationship between the mean replication timing of a telomere and its mean volume ratio determined by immuno-3D ( Figure 7E ) . Together , our data suggest , for the first time , a strong association between telomere replication timing and nuclear localization .
We characterized the replication timing of single telomeres in normal diploid human cells , either primary or immortalized by ectopic expression of telomerase . In agreement with previous studies [29] , [30] , we found that bulk telomeres replicate throughout the S-phase . Our results further indicate that single telomeres on specific chromosome ends tend to replicate during defined times in the S-phase and that this timing is conserved between homologs and among individuals . Contrary to findings in the budding yeast [31] , [32] , telomere length does not have a major impact on telomere replication timing . However , given the length of the S-phase and the inherent imprecision of the methodology used , it remains possible that subtle influences introduced by the length of telomeres and/or the presence of telomerase activity may have been overlooked . Occasionally , small differences were detected , both between homologs and among individuals , which could be explained by variations either in the DNA sequence or the epigenetic status of these extremities . Nevertheless , our study also shows that the segmental polymorphisms ( which may span up to hundreds of kilobases ) occurring very close to telomeres [47] , [48] , [60] , [61] do not exert a major influence in the replication timing of allelic telomeres . The subtelomeric duplications f7501 and ICRF10 revealed in these experiments are present in about 15 chromosome extremities , a dozen of which are potentially polymorphic . These sequences are located quite close to the telomere tract and their presence or absence indirectly indicate the presence or absence of other subtelomeric segments with which they are commonly associated . For instance , in the cell line we examined ( IMR90 ) , the presence or absence of ICRF10 on chromosome 8p ( Figure 2A ) implies the presence or absence , respectively , of at least three other ( more proximal ) segments in that extremity ( see [48] ) . This signifies that both alleles differ from each other in their subtelomeric region by at least 120kb [46] . Whether or not this distance is sufficient to introduce a difference in the replication timing for both telomeres ( either by delaying the arrival of the replication fork to the telomere or by introducing a new origin of replication ) remains to be explored . Nevertheless our experiments do suggest that such polymorphisms may occur without inducing major differences in telomere replication timing . On the other hand , some of the observable differences affect chromosome extremities without ( known ) segmental variation at subtelomeres , suggesting that other factors are at play . Previous studies on the replication timing of specific subtelomeric regions ( for instance 22q [62] and 16p [63] ) suggested that particular telomeres replicate late . The present study did not detect such trend for these particular ends in the cell lines examined . Moreover , these two telomeres are among the earliest to replicate in S phase . The aforementioned studies used subtelomeric probes and interphase nuclei FISH to follow the duplication of signals during S phase progression . However , duplication of signals depends not only on replication of that particular segment but also on the resolution of sister chromatids . This step seemingly follows a different pathway at telomeres [64] , which might explain why telomeres placed nearby other sequences may influence ( i . e . : delay ) the appearance of distinct FISH foci in interphase nuclei after replication . This interpretation is supported by the observation that duplication of telomeric signals in interphase nuclei only occurs during the second half of the S-phase [65] , while by this time , as shown here , almost half of telomeres have already replicated . It is clear that the ReD-FISH approach , although laborious and time consuming , has allowed to define in a more precise way the timings of replication of single telomeres in human cells . One striking feature of the telomere replication pattern in human cells is the late replication timing of telomeres associated with satellite-like repeats , i . e . the short arm of the acrocentric chromosomes as well as 4qter and 10qter extremities . Our experiments using newly created tagged telomeres indicate that the presence of β-satellite sequences , which have high heterochromatinization potential and are late replicated when in their natural context [29] , caused the nearby telomere to replicate significantly later than control telomeres . Strikingly , the presence of a single D4Z4 repeat , which is sufficient to increase the association of a telomere with the nuclear periphery , caused the nearby telomeres to replicate much later in the S-phase than the control telomeres and as late as the acrocentric telomeres in the same cell line . Both effects , peripheral nuclear localization and late replication , are no longer detected when multiple D4Z4 repeats are inserted next to the telomere , further supporting the connection between subnuclear localization and telomere replication timing . The reason why a single D4Z4 is able to mediate the association of a chromosome extremity to the periphery , while multiple copies of this repeat are not , remains mysterious . However , this observation is in agreement with the fact that the presence of multiple copies of D4Z4 at other locations , such as 10q and acrocentric telomeres , is not sufficient to increase the association of these extremities with the nuclear periphery [54]–[56] . As discussed in a previous work [56] , the explanation for this apparent paradox may rely on the function of a putative region centromeric to the D4Z4 repeats and only present on 4q extremities . Independently of the mechanism involved in this perinuclear association , our experiments clearly point to a tight relationship between the peripheral localization of a telomere and its late replication behavior . On the other hand , the effect of β-satellite sequences appeared to be independent of nuclear localization . It is theoretically possible that a biased genomic integration of such constructs could have placed the newly created telomeres in a context where replication is intrinsically delayed . However , as shown previously within the limit of resolution of multi-FISH analyses [56] , the telomere seeding strategy used here does not lead to a biased distribution of telomere seeds in C33A cells , supporting the contention that the observed effects are directly connected to the presence of particular juxtatelomeric elements carried by the constructions . In yeast , the well-documented association of telomeres with the nuclear envelope [66] appears to play important roles in telomere metabolism , including length regulation [67] , silencing [68] and repair [69] , [70] . In humans , telomeres are supposed to be randomly distributed within the nucleus [71] , but there have been reported exceptions , such as the nuclear peripheral localization of 4q [54] , [55] and the perinucleolar localization of telomeres on the short arms of acrocentrics [53] . Strikingly , we found that other non-D4Z4 associated chromosome extremities are also naturally localized at the nuclear periphery in unperturbed IMR90 cells , adding four more exceptions ( 2p , 3p , 6q and 12q ) to the list of telomeres with preferential nuclear localizations . Remarkably , all these telomeres replicate late in the diploid fibroblasts examined . Thus , our observations point to a relationship between telomere nuclear localization and telomere replication timing . Nuclear localization has been suggested to affect replication timing of other regions of the genome [51] , [72] , [73] . Also , recent studies have demonstrated that genome-wide interactions with the nuclear lamina implicate late replicated sequences [74] . Close examination of the subtelomeric chromosome specific sequences available for the extremities examined here ( http://genome . ucsc . edu/cgi-bin/hgGateway ) revealed that only 12q present a particular enrichment in LADs ( lamina-associated domains ) [74] . However , actual subtelomeric regions are most often not included in human genome sequence assemblies , either because they are poorly characterized or because their duplicated nature makes their chromosome assignment quite difficult . It is worth noting here that at least the last 120 kilobases of the subtelomeric region of the 6q chromosome are duplicated on other extremities , including 1p [48] , whose telomere , contrary to that one on 6q , replicates early and is not associated with the nuclear periphery . Our results also indicate that single telomere replication timing in human diploid fibroblasts is mostly determined by chromosome-specific features , perhaps at the level of large chromosome domains , as suggested recently [72] . Although telomere replication timings do not appear to be correlated with the replication timings of large cytogenetic bands , we did find a correlation between the mean replication timings for single telomeres and the timing of replication reported for the most distal chromosome-specific sequences present in a BAC-array [40] . This correlation , albeit weak ( and only statistically significant when data from telomerized diploid fibroblasts were used , perhaps reflecting the fact that the BAC study was conducted in an EBV-transformed lymphocyte cell line [40] ) , suggests that telomere replication may be , at least partially , synchronized with chromosome-specific subtelomeric sequences . Finally , our data conclusively show that telomere replication timing may also be influenced by the presence of relatively small telomere-associated sequences , such as β-satellite sequences or one repeat of the macrosatellite D4Z4 , which also confers a peripheral nuclear localization to the chromosome end . It has been recently demonstrated that this D4Z4 sequence behaves both as an A-type lamin- , CTCF-dependent peripheral tethering element and as an insulator [56] , [58] . It remains to be determined which of these two properties confer late replication . Together , our study allowed an original and certainly informative glimpse into the mechanisms regulating telomere replication timing in human cells . Our results suggest that the links between replication timing and high-order genome organization , also observed in other organisms , may have been conserved throughout evolution [28] , [41] , [75] , [76] .
To precisely determine the length of the S-phase , cells were analyzed by FACs . Every hour after release from the aphidicolin block , cells were trypsinized , centrifuged , resuspended in 0 . 5 ml of PBS , fixed by drop-wise addition of 1 . 5ml ice-cold 100% ethanol and stored at 4°C . Subsequently , cells were centrifuged and resuspended in a staining solution containing 30 µg of propidium iodide and 200 µg of RNaseA per ml . Flow cytometry was performed using a Becton Dickinson FACSort flow cytometer . The data was analyzed using FlowJo software . Replicative Detargeting ( ReD ) FISH is a modification of the CO-FISH procedure [34] and was performed as described previously [33] with some modifications . Briefly , after release from the aphidicolin block , 6 to 8 pulse-chase additions of BrdU/BrdC were made depending on the length of the S-phase . For each pulse , cells were incubated for 1 hour in the presence of 10 µM BrdU and 3 . 3 µM BrdC , then washed 3 times with pre-warmed PBS before new media was added . 7 to 10 hours after release from the aphidicolin block , cells were arrested in mitosis with 1 . 5 hour incubation in colcemid ( 0 . 1 µg/ml ) before 40 min hypotonic shock in 0 . 8 g/L sodium citrate at 37°C and fixed in ethanol/acetic acid . Metaphase spreads were obtained by dropping suspensions of fixed cells onto clean glass slides and were rapidly used for hybridization . Spreads were denatured at 80°C for 4 min in the presence of a Cy3- ( CCCTAA ) 3 PNA probe ( Applied Biosystems , 50 nM in 70% formamide , 25 mM Tris pH 7 . 4 ) and incubated at room temperature for 2 hours . After washes and ethanol dehydration , the slides were put in contact for 2h with a second LNA probe 5′- ( 6-Fam ) GGGtTAGGGttAgGGTTAGGgttAgGgttTAGGgTTA ( 6-Fam ) -3′ - where small letters correspond to positions with locked nucleic acids - ( Proligo-France , 10 mM in 50% formamide , 2xSSC ) followed again by washes and ethanol dehydration . Preparations were mounted in Vectashield ( Vector ) with DAPI ( 1 µg/ml ) and visualized with a Zeiss UV microscope equipped with appropriate excitation/emission filters for each color . Images were captured with a HQ-Coolsnap camera ( Photometrics ) using the IPlab software . When required , coordinates for all metaphases were recorded in order to retrieve them after a second ( subtelomeric ) FISH . The Q-FISH procedure was carried out exactly as described , using a Cy3- ( CCCTAA ) 3 PNA probe [9] . Metaphase spreads , prepared the day before , were fixed with formaldehyde ( Sigma , 3 . 7% ) and digested with pepsin ( Sigma , 1 mg/ml ) . Spreads were denatured at 80°C for 4 min in the presence of a Cy3- ( CCCTAA ) 3 PNA probe ( Applied Biosystems , 50 nM in 70% formamide , 25 mM Tris pH 7 . 4 ) and incubated at room temperature for 2 hours . After washes and ethanol dehydration , preparations were mounted in Vectashield ( Vector ) with DAPI ( 1 µg/ml ) and visualized with a Zeiss UV microscope equipped with appropriate excitation/emission filters . Images were captured with a HQ-Coolsnap camera ( Photometrics ) using the IPlab software . When required , coordinates for all metaphases were recorded in order to retrieve them after a second ( subtelomeric ) FISH . After CO-FISH or Q-FISH hybridizations , slides where washed 3 times in SSC 2X and dehydrated for subsequent subtelomeric FISH to distinguish homologues . Cosmids carrying subtelomeric regions , f7501 and ICRF10 , were obtained from Barbara Trask ( Human Genome Center , Lawrence Livermore National Laboratory ) and Gilles Vergnaud ( IGM , Orsay , France ) , respectively . Cosmid f7501 contains 36-kb portion of chromosome 19 including three members of the olfactory receptor ( OR ) family [49] . Cosmid ICRF10 carries minisatellite DNF92 ( GenBank accession number Y13543 ) [46] , [48] . Subtelomeric probes f7501 and ICRF10 correspond to two different segments of around 35 kb located close to the telomere tract on around 15 different chromosome extremities . The presence of one segment is exclusive of the other and both segments are typically associated with particular arrangements of other , more centromeric , segments . These probes were used for hybridization on metaphase preparations that had already been analyzed in either Q-FISH or ReDFISH experiments , thus allowing to distinguish between allelic chromosome extremities and to conduct allele-specific telomere fluorescence measurements ( as described in [9] ) or replication timing scorings ( this paper ) . One g of cosmid DNA was labeled with biotin-16-dUTP ( Roche ) using the Nick translation kit ( Vysis ) following manufacturer's instructions . For hybridization , 50 ng probe per slide was precipitated in the presence of 100 µg single-strand salmon sperm DNA and 20 µg COT-1 ( Invitrogen ) , dissolved in 25 l hybridization mix ( 50% formamide , 10% dextran sulfate , SSC 2X ) and pre-hybridized for 1 h at 37°C . Slides with metaphase spreads were treated with 0 . 1 g/ml RNase A in SSC 2X for 1 h at 37°C and washed three times in SSC 2X , 5 min each , prior to denaturation in 70% formamide/SSC 2X at 70°C for 2 min . Denaturated slides were dropped in ice-cold SSC 2X , dehydrated in a series of ice-cold ethanol baths , treated with Proteinase K ( 100 ng/ml in 20 mM Tris pH 7 . 4 and 2 mM CaCl2 ) for 8 min at 37°C , dehydrated again and hybridized over night at 37°C . The next day slides were washed three times , 3 min each in 50% formamide/SSC 2X , five times , 2 min each in SSC 2X , and once in BN ( 0 , 1 M sodium bicharbonate , 0 , 05% NP-40 ) all at 45°C . Slides were blocked with 5% milk in BN for 15 min . Biotinylated probes were detected with three layers of antibodies , each 30 min at 37°C , as follows: fluorescein avidin D ( Vector A-2001 , 1/400 ) , biotinylated anti-avidin ( Vector BA-0300 , 1/100 ) and fluorescein avidin D , all diluted in blocking buffer . After each antibody layer three 2-min washes in BN at 42°C were done . Slides were mounted in Vectashield ( Vector ) with 0 . 2 g/ml DAPI . To detect newly seeded telomeres in the C33A cell derivatives after ReD-FISH , a labeled pCMV vector was used as a probe and labeled with the DIG-Nick Translation Kit ( Roche Diagnostics ) . All probes were denatured at 80±1°C for 5 minutes before hybridization . Conditions for slide preparation , hybridization and immunodetection have been described [56] . For detection , we used mouse anti-DIG antibodies ( Roche Diagnostics ) , diluted 1/200 , followed by incubation with secondary donkey antibodies coupled with ALEXA 488 fluorochrome , directed against this epitope and diluted 1/500 ( Molecular Probes ) . Chromosomes were counterstained with DAPI antifade ( 0 . 125 µg/ml ) ( Cytocell ) . Metaphases were retrieved thanks to the recorded coordinates and original images were annotated . For each pulse , 30–50 metaphases were captured and analyzed . For each metaphase , a karyotype was carried out and chromosome ends with detargeted telomeres were identified . For each BrdU/C pulse , the average percentage of detargeted telomeres in the population of metaphases was calculated for each pair of chromosome ends . The addition of these percentages from all 6 BrdU pulses spanning the entire S phase was adjusted to 100% . The mean replication timing of single telomeres was calculated as follows: mrt = [Y]/f , where Y corresponds to the pulse ( 0 . 5 , 1 . 5 , … ) and f is the percentage of telomeres seen replicating during that pulse . For the graphic representation of replication timings of individual telomeres , percentages of replication were grouped in early S ( pulses 1+2 ) , mid-S ( pulses 3+4 ) and late S ( pulses 5+6 ) for each telomere and telomeres were ordered according to their mrt . Ten µg of genomic DNA was digested overnight with HinfI and RsaI enzymes , 50U each , and restriction fragments were separated through a pulsed-field electrophoresis agarose gel ( 1% in TBE 0 . 5X ) in a CHEF apparatus ( BioRad ) set to 200 V for 15 h with a pulse ramp between 0 . 2 and 13 s . After staining with ethidium bromide , DNA was nicked by a UV-crosslinker ( Stratagene ) at 180 , 000 J/cm2 , denatured , and transferred by capillary alkaline transfer onto Biodyne B Nylon membrane ( Pall ) for hybridization with a radioactively labeled TAA ( CCCTAA ) 4 oligonucleotide . Signals were detected in a phosphorimager apparatus . To determine the localization of telomeres in the nucleus , PAC clones recognizing the terminal region of chromosome arms 1p , 2p , 3p , 4q , 5p , 6q , 12p , 12q , 1 and 17q [77] were labeled with the DIG-Nick Translation Kit ( Roche Diagnostics ) . All probes were denatured at 80±1°C for 5 minutes before hybridization . Conditions for slides preparation , hybridization and immunodetection have been described [56] . For detection , we used mouse anti-DIG antibodies ( Roche Diagnostics ) and goat anti-Lamin B antibodies ( M-20 , Santa-Cruz ) , diluted 1/50 , followed by incubation with secondary donkey antibodies coupled with different ALEXA fluorochromes , directed against these epitopes and diluted 1/300 ( Molecular Probes ) . Nuclei were counterstained with DAPI antifade ( 0 . 125 µg/ml ) ( Cytocell ) . Images were acquired with the confocal scanning laser system , LSM510 , from Zeiss ( Germany ) . A 63× Plan-APOCHROMAT , oil immersion , NA 1 . 40 objective ( Zeiss ) was used to record optical sections at intervals of 0 . 48µm . The pinhole was set the closest to 1 Airy with optical slices in all wavelengths with identical thickness ( 0 . 8µm ) . Images were averaged 4 times to improve the signal to noise ratio . Generated . lsm files had a voxel size of 0 . 1µm×0 . 1µm×0 . 48µm and were processed through the Imaris software ( Bitplane AG ) . After 3D analysis , where at least 50 nuclei were examined , data sets are presented as the distribution of FISH signals between three concentric zones of equal volume or as the mean ratio between two volumes . The R package was used for comparisons using Fisher exact tests and Spearman's rank correlation coefficient calculations . For multiple comparisons , corrections for significance thresholds were applied depending on the number of comparisons actually carried out ( p<0 . 05/k; Bonferroni ) : k = 20 for Fisher exact test comparisons using diploid fibroblast data and k = 15 for comparisons using C33A data . | Functional telomeres are essential for genome stability . While replication of telomeres has been extensively studied in model organisms such as the baker's yeast , little is known about the mechanisms that govern the replication of human telomeres . In this study , we have determined the timing of replication of telomeres of individual human chromosomes and its association with potential modulating factors such as particular subtelomeric sequences , the presence of heterochromatic regions , and nuclear localization . We have found that native telomeres associated with D4Z4 sequences—a macrosatellite naturally located in the subtelomeric regions of 4q , 10q , and acrocentric chromosome extremities—replicate later than others . We also present descriptive and experimental evidence indicating that nuclear localization influences the timing of telomere replication . These results contribute to our understanding of telomere metabolism in humans . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/nuclear",
"structure",
"and",
"function",
"molecular",
"biology/chromosome",
"structure",
"cell",
"biology/nuclear",
"structure",
"and",
"function"
] | 2010 | Replication Timing of Human Telomeres Is Chromosome Arm–Specific, Influenced by Subtelomeric Structures and Connected to Nuclear Localization |
Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases , providing avenues for new treatments and diagnostics . However , inherent challenges have limited the successful translation of candidate biomarkers into clinical use , thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation . We have developed an end-to-end computational proteomic pipeline for biomarkers studies . At the discovery stage , the pipeline emphasizes different aspects of experimental design , appropriate statistical methodologies , and quality assessment of results . At the validation stage , the pipeline focuses on the migration of the results to a platform appropriate for external validation , and the development of a classifier score based on corroborated protein biomarkers . At the last stage towards clinical implementation , the main aims are to develop and validate an assay suitable for clinical deployment , and to calibrate the biomarker classifier using the developed assay . The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection . Starting with an untargeted screening of the human plasma proteome , five candidate biomarker proteins were identified . Rejection-regulated proteins reflect cellular and humoral immune responses , acute phase inflammatory pathways , and lipid metabolism biological processes . A multiplex multiple reaction monitoring mass-spectrometry ( MRM-MS ) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent ( ELISA ) and immunonephelometric assays ( INA ) . A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation , which is still in progress . Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care . The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies .
After the first successful human-to-human heart transplant in 1967 , cardiac transplantation became the primary therapy for patients with end-stage heart failure due to dilated cardiomyopathy or ischemic heart disease . Improvements in immunosuppressive drug therapies have significantly increased the number of successful transplants , yet episodes of acute rejection and progression of chronic rejection remain major factors that negatively impact long term graft survival . Acute rejection is predominantly considered to be an immunological reaction in response to the major and minor histocompatibility antigens recognized as ‘foreign’ by the graft recipient . This process triggers the subsequent activation , migration and infiltration of immune cells such as T- and B-lymphocytes , which can ultimately lead to cellular- and antibody-mediated destruction of the heart allograft tissue [1] . Endomyocardial biopsy ( EMB ) , through which histological features such as cellular infiltration and myocyte damage can be observed , is currently considered to be the only reliable gold standard for diagnosis and monitoring of acute cardiac allograft rejection [2] . However , the invasive and qualitative nature , risk of complications , associated cost and lack of timeliness of the results render the EMB a suboptimal procedure for routine monitoring [3] . A more reliable , minimally invasive , inexpensive , and early diagnostic tool to monitor graft survival remains a significant clinical unmet need . Since proteins may serve as molecular indicators ( i . e . , biomarkers ) of cardiac allograft rejection , plasma proteomics offers an attractive and promising avenue for the development of diagnosis tools for cardiac transplantation [4] . Technical advances in the field of quantitative proteomics in the last decade have enabled the identification and quantitation of thousands of proteins and have stimulated a large body of research focused on the discovery of new biomarkers . However , the translation of candidate biomarkers from discovery research into proteomic tests for clinical use has faced significant challenges , due mostly to a lack of an adequate analytical pipeline [5] , [6] , [7] , [8] . In a significant step forward , technological proteomic pipelines have recently been proposed , optimizing the design of the discovery , validation , and clinical implementation stages of biomarker studies [8] , [9] , [10] , [11] . Nevertheless , the development of new clinical proteomic tests hinges on a tailored computational pipeline to distill the information contained in thousands of proteins into an accurate classifier score with demonstrable clinical utility . Computational proteomics is a new and expanding field of research which primarily focuses on data management and mass-spectra analysis for the discovery phase of biomarker studies [12] , [13] , [14] . Although previous work has acknowledged the need of a tailored computational pipeline in proteomics ( e . g . , [15] ) , a systematic and complete process that specifically addresses the challenges emerging from proteomic studies has not been proposed or demonstrated to date . Using unsuitable methodological tools to explore and analyze the data may result in the selection of biomarkers that ultimately fail in the final stages of validation , or may fail to select relevant biomarkers . For example , identifying a panel of candidate markers based only on the comparison of relative abundance between case and control samples , or the use of classical statistical tests when the sample size of the study is too small , may result in the identification of many false candidate markers . We complement previous work by proposing a computation pipeline powered by extensive statistical analysis for all stages of quantitative proteomics biomarker studies ( Figure 1 ) . At the discovery stage , the pipeline focuses on selecting an appropriate experimental design and statistical methodologies to identify and assess a panel of candidate biomarkers . At the validation stage , the pipeline emphasizes on the migration of discovery results to the validation platform , and the development and validation of a biomarker classifier . At the clinical implementation stage , the main aims are to develop an assay suitable for clinical deployment , and to calibrate the biomarker classifier using the developed assay . We demonstrate the power of our methodology in a proteomic biomarker study in the context of cardiac transplantation , with a goal towards the development of a more accurate and less invasive blood test for monitoring graft survival . Our work identified a panel of five candidate plasma proteins that clearly discriminates acute cardiac allograft rejection from non-rejection . These biomarker proteins distribute broadly among three relevant biological processes: cellular and humoral immune responses , acute phase inflammatory pathways and lipid metabolism . Of the five candidate biomarkers , we corroborated four using two independent platforms . A classifier score based on these four corroborated proteins measured by multiple reaction monitoring mass-spectrometry ( MRM-MS ) demonstrated that plasma protein biomarkers have significant potential in serving as a reliable , minimally-invasive , inexpensive , and timely diagnostic tool for acute cardiac allograft rejection . Our results advance the approaches to diagnosis with respect to cardiac transplantation biomarker , as well as the computational methodologies tailored for a wide range of proteomic biomarker studies .
Recent technological advances in quantitative proteomics have enabled the untargeted quantitation and identification of hundreds to thousands of proteins simultaneously from complex samples such as human plasma . The aim of the discovery pipeline is to create a list of candidate markers from an extensive set of proteins identified and measured within each sample . As widely discussed in the literature , any list of candidate markers identified in a discovery stage must be validated in a large and independent cohort of patients before its clinical utility assessment . To bridge the gap between discovery and clinical technologies , the validation stage is usually performed in an independent platform which provides a timely and cost-effective approach to measure all samples . To overcome the dependence on antibody availability , we developed an MRM-MS assay to complete the validation stage . However , similar analytical steps would have been taken if another independent platform was used . The final translation of proteomic results from the validation to the clinical implementation stage requires careful examination of many factors , including the development of assays suitable for clinical laboratories , considerations from health economics , as well as approval of regulatory agencies ( e . g . , Food and Drug Administration , Conformité Européenne mark ) [8] . From a methodological point of view , the following steps are crucial to complete this last stage . A brief summary of the materials and methods used in the proteomic biomarker study of cardiac transplantation are outlined here and further details are given as supporting material in Text S1 .
In the discovery stage , multiplexed iTRAQ-LC-MALDI-TOF/TOF mass spectrometry was used to identify and quantitate proteins from 108 depleted plasma samples representing a time course of 20 weeks from the first 26 patients enrolled ( Figure S2 ) . These samples were processed in 50 independent iTRAQ runs , including other samples from the heart cohort . In addition , each iTRAQ run included a normal pooled control plasma sample to provide a common reference across multiple runs . A total of 924 protein groups ( PGCs ) was cumulatively identified from the 50 runs with an average of 273 PGCs within each run . Following the selection criteria and the power calculation described in the supporting material ( Text S1 ) , the first AR samples from 6 ( out of 8 ) AR patients were selected as cases , and samples from 14 ( out of 18 ) NR patients at matching time points were selected as controls ( Figure S2A and Table 1 ) . The remaining 88 longitudinally collected iTRAQ samples were used as test samples to initially validate the results at the Discovery stage . Although samples in this test set are part of BiT cohort , none of them were previously used in the training set . As described in Step 3 of the Discovery stage , only those PGCs identified in at least 2/3 of the AR and the NR groups were considered for further analysis . The resulting data consisted of 127 PGCs measured in at least 4 ( out of 6 ) AR patients and 10 ( out of 14 ) NR patients . Of these 127 PGCs , 51 PGCs contained 133 missing values out of a total of 1020 values ( i . e . , 51 PGCs×20 patients ) . A panel of 5 PGCs was identified with significant differential relative concentrations ( robust eBayes p value<0 . 01 ) between AR and NR samples ( Tables 2 and 3 ) . This panel consisted of 3 PGCs that were more abundant in AR versus NR samples: B2M , F10 , and CP , and 2 PGCs that were less abundant: PLTP , and ADIPOQ ( Wilcoxon tests are shown in the Table S3 ) . The quality assessment of the proteomics data demonstrated a strong confidence regarding identified protein identities , wherein 98% of the 127 analyzed PGCs and all 5 PGCs candidate biomarkers were identified based on two or more peptides ( Figure S5 ) . Similarly , results showed an overall good coverage and quantitative levels for the analyzed proteins ( Table S4 ) . The potential confounding of the results was examined using all available clinical data close to the event time , including daily dose of immunosuppressants , weight , and blood pressure . The GlobalAncova p values ( Table 4 and Table S5 ) demonstrate that the simultaneous relative concentrations of the 5 candidate PGCs remained significantly different in the AR group versus the NR group ( p value<0 . 05 ) after adjusting for potential confounders . The correlation values in Table 4 show that none of the clinical variables were highly correlated with the LDA classifier score ( r<0 . 5 ) . Overall , the results demonstrated that the identification of the biomarker panel was not confounded by other clinical variables available for this study cohort . To illustrate the joint performance of all candidate markers to discriminate AR from NR samples , the average LDA score was calculated for all the AR samples ( n = 10 ) and the NR samples from NR patients ( n = 40 ) available at each time point ( Figure 2 ) . Based on these initial results , the identified candidate markers together discriminated the two groups regardless of which week the rejection occurred after transplantation . Despite this differentiation , the two AR samples in week 2 were still classified as NR ( negative score ) by the classifier . Although the LDA classifier score was trained to discriminate AR from NR samples , Figure S7 also includes the score of 47 1R mild , non-treatable rejection samples . Average scores of 1R samples from NR patients were in general similar to those of NR samples , while those from AR patients were closer to the average scores of AR samples . Figure 3 illustrates the temporal correlation of the score with the diagnosis of rejection . The classifier score for AR patients was at baseline before the rejection episode ( pre-rejection point ) with a similar average value to that of NR patients at matched time point ( s ) ( no statistical evidence of differentiation ) . The score for AR patients was differentially elevated at the time point ( s ) of rejection ( as determined by biopsy ) compared to that of NR patients ( alpha level = 0 . 05 , two-sided t test , p value<0 . 001 ) at matched time point ( s ) . The score for the AR patients returned to baseline following treatment and resolution of the rejection episode ( post-rejection point , non-rejection determined by biopsy ) with a similar average value to that of NR patients . In addition , the evaluation of the score across time shows that the biomarker signature is specific to the rejection episodes , rather than reflecting confounded differences or potential bias between the groups ( e . g . , different medication regimens ) . Further results from an initial validation performed in this stage based on 88 test iTRAQ samples not included in the discovery are shown in Figure S6 . A total of 3 out the 4 AR and 29 out of the 37 NR samples tested were correctly classified ( non-highlighted cells ) . Similar results were obtained only if a single test sample per patient was randomly selected . The results from the iTRAQ discovery analysis were corroborated and initially validated by two independent assays: ELISA/INA ( available for ADIPOQ , F10 , B2M , and CP ) , and MRM-MS ( developed for ADIPOQ , F10 , B2M , CP , and PLTP ) . Following the results of the power calculation illustrated in Figure S2B , a total of 43 patients were selected and plasma and serum samples were processed by ELISA/INA for an initial validation cohort that extends the discovery cohort . A subset of 25 of these 43 samples , 7 AR , 6 1R and 12 NR , were also part of the iTRAQ discovery cohort . Of these 25 samples , 23 were also processed by MRM-MS ( Table 1 and Figures S2A and S2B ) . Samples measured by the three assays were used to perform the correlation analysis . Results showed good levels of correlations for B2M , ADIPOQ , and CP ( r>0 . 6 , Figure 4-A ) . F10 measurements from both ELISA/INA and MRM-MS and PLTP measurements from MRM-MS did not show a similar degree of correspondence with iTRAQ as seen for other proteins . However , a good correlation was observed between ELISA/INA and MRM-MS for F10 measurements ( r = 0 . 69 , Figure 4A ) . The differential protein levels between AR and NR samples observed in the discovery stage were successfully translated for 3 of 4 proteins measured by ELISA/INA ( B2M , ADIPOQ , and CP , p value<0 . 05 ) , and 4 of 5 proteins measured by MRM-MS ( B2M , ADIPOQ , CP , and PLTP ) ( Figure 4A ) . Results from the ELISA/INA data provided additional validation in 12 new patients using a platform other than iTRAQ ( Table 1 , and 0R ( E ) and 2R ( E ) samples in Figure S2A ) . Taken together , with the exception of F10 , the results showed that measurements from the three platforms were strongly correlated and corroborated most of the results from the iTRAQ discovery stage . Figure 4B demonstrates the gain in classification performance by a panel of markers combined together into a multivariate classifier score . Although estimated on a small cohort , the sensitivity estimates improved from 17% for the classifier based only on B2M to 100% for the classifier based on the 4 corroborated protein panel ( B2M&ADIPOQ&CP&PLTP ) , the specificity improved from 91% to 100% , and the AUC improved from 0 . 25 to the maximum of 1 . Based on the classification performance of the evaluated MRM-MS classifier scores , a panel of 4 proteins ( B2M , ADIPOQ , CP , and PLTP ) was selected to complete the validation stage . Figure 4C illustrates the resulting classifier score based on the 4-protein panel for the test samples resulting from a 6-fold cross-validation . Samples with a positive proteomic classifier score were classified as “rejection” , and those with a negative score were classified as “non-rejection” . In this initial validation , all test samples were correctly classified by the proteomic classifier score . However , because the test samples in the cross-validation were still part of the discovery analysis , these performance measures cannot be used to characterize the identified classifier . Although similar results were obtained using the ELISA/INA measurements on an extended cohort of patients ( Figure S8B ) , a larger validation in an external cohort of patients is still required to complete this phase . A prospective clinical assessment of the value of these proteomic markers of acute rejection is currently underway using MRM-MS measurements of over 200 samples from six Canadian sites .
Transplantation elicits a host immune response that encompasses both cellular and humoral immunity , which together lead to graft tissue damage , and episodes of acute and chronic rejection . B2M is a protein associated with MHC Class I histocompatibility antigens , with increased levels reflecting allograft rejection , autoimmune or lymphoproliferative diseases as a result of increased immune activation [45] . Several studies have reported higher circulating levels of B2M in cardiac or renal allograft rejection [46] , [47] , [48] , consistent with our observations . Importantly , our data demonstrates improved classification performance when additional markers are used with B2M . Acute rejection resulting from cellular infiltration of the graft leads to severe local inflammation , which has systemic consequences with a concomitant increase in circulating inflammatory markers . The acute phase response to inflammatory stimuli involves the production and release of numerous plasma proteins by the liver . CP , significantly up-regulated in AR relative to NR samples in this study , is a positive acute phase reactant . It is elevated in acute and chronic inflammatory states and elevated plasma CP is also associated with increased cardiovascular disease risk [49] . CP is a player in inflammation , coagulation , angiogenesis , and vasculopathy , but its role in the pathogenesis of acute rejection is unknown . Current evidence supports a relationship between inflammation and coagulation [50] . FX , a key mediator in the conversion of prothrombin to thrombin , is up-regulated in our acute rejection cohort , and this finding may reflect an intersection between inflammatory and coagulation responses in acute rejection . However , this protein was not validated in our study . C reactive protein ( CRP ) , an acute phase reactant protein previously studied in the context of acute cardiac allograft rejection , was not identified in our study . Consistent with this finding , previous work has demonstrated conflicting evidence regarding the informative value of CRP in monitoring acute cardiac allograft rejection [51] . Dyslipidemia as a consequence of immunosuppressive therapy has been reported in cardiac allograft recipients , and is a risk factor for chronic rejection [52] . Lipid metabolism is represented by two proteins in our panel: ADIPOQ and PLTP . ADIPOQ is a circulating plasma protein involved in metabolic processes shown to play a role in atherosclerotic cardiovascular diseases [53] . Work by Nakano and others described elevated ADIPOQ as reflective of tolerance following a rat model of orthotopic liver transplantation , suggesting a mechanistic role for this protein and corresponding with the observed decrease in ADIPOQ levels during acute rejection episodes [54] . Further , recent work by Okamoto and colleagues [55] has demonstrated that ADIPOQ inhibits allograft rejection in a murine model of cardiac transplantation . PLTP plays a role in HDL remodeling and cholesterol metabolism but its involvement in acute rejection is unknown . A comparison between the current panel identified for the diagnosis of cardiac allograft rejection , and that of renal allograft rejection [37] , reveals that the biological roles of identified proteins are shared in the setting of both transplantation situations . Moreover , the relative regulation of proteins involved in these biological processes is likewise shared . Our current data reveals a differentiation of particular molecules involved in the pathogenesis of cardiac versus renal allograft rejection . The plasma protein markers identified in this study have the potential to be further assessed in combinatorial analyses with Biomarkers in Transplantation ( BiT ) genomic and metabolomic data . Notably , numerous research groups , including the BiT group , have identified potential gene expression markers of cardiac allograft rejection using microarray and qPCR analyses of peripheral and whole blood [56] , [57] , [58] , [59] . These studies provide an opportunity for a systems biology approach to understanding allograft rejection . Taken together , the panel of protein markers identified and initially validated in this study offers a fresh approach to the diagnosis of acute cardiac rejection , providing novel avenues of investigation and potential new targets for therapeutic intervention . The computational pipeline proposed and applied in this biomarker is highly applicable to a wide range of biomarker proteomic studies . | Novel proteomic technology has led to the generation of vast amounts of biological data and the identification of numerous potential biomarkers . However , computational approaches to translate this information into knowledge capable of impacting clinical care have been lagging . We propose a computational proteomic pipeline for biomarker studies that is founded on the combination of advanced statistical methodologies . We demonstrate our approach through the analysis of data obtained from heart transplant patients . Heart transplantation is the gold standard treatment for patients with end-stage heart failure , but is complicated by episodes of immune rejection that can adversely impact patient outcomes . Current rejection monitoring approaches are highly invasive , requiring a biopsy of the heart . This work aims to reduce the need for biopsies , and demonstrate the power and utility of computational approaches in proteomic biomarker discovery . Our work utilizes novel high-throughput proteomic technology combined with advanced statistical techniques to identify blood markers that guide the decision as to whether a biopsy is warranted , reduce the number of unnecessary biopsies , and ultimately diagnose the presence of rejection in heart transplant patients . Additionally , the proposed computational methodologies can be applied to a range of proteomic biomarker studies of various diseases and conditions . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"mathematics",
"statistics",
"proteomics",
"biology",
"cardiovascular"
] | 2013 | Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation |
In recent decades , sporadic cases and outbreaks in humans of West Nile virus ( WNV ) infection have increased . Serological diagnosis of WNV infection can be performed by enzyme-linked immunosorbent assay ( ELISA ) , immunofluorescence assay ( IFA ) neutralization test ( NT ) and by hemagglutination-inhibition assay . The aim of this study is to collect updated information regarding the performance accuracy of WNV serological diagnostics . In 2011 , the European Network for the Diagnostics of Imported Viral Diseases-Collaborative Laboratory Response Network ( ENIVD-CLRN ) organized the second external quality assurance ( EQA ) study for the serological diagnosis of WNV infection . A serum panel of 13 samples ( included sera reactive against WNV , plus specificity and negative controls ) was sent to 48 laboratories involved in WNV diagnostics . Forty-seven of 48 laboratories from 30 countries participated in the study . Eight laboratories achieved 100% of concurrent and correct results . The main obstacle in other laboratories to achieving similar performances was the cross-reactivity of antibodies amongst heterologous flaviviruses . No differences were observed in performances of in-house and commercial test used by the laboratories . IFA was significantly more specific compared to ELISA in detecting IgG antibodies . The overall analytical sensitivity and specificity of diagnostic tests for IgM detection were 50% and 95% , respectively . In comparison , the overall sensitivity and specificity of diagnostic tests for IgG detection were 86% and 69% , respectively . This EQA study demonstrates that there is still need to improve serological tests for WNV diagnosis . The low sensitivity of IgM detection suggests that there is a risk of overlooking WNV acute infections , whereas the low specificity for IgG detection demonstrates a high level of cross-reactivity with heterologous flaviviruses .
West Nile virus ( WNV ) is a mosquito-transmitted flavivirus of the family Flaviviridae [1] . It is maintained in a cycle between birds and mosquitoes mostly belonging to the Culex genus [2] . Ochlerotatus , Culiseta , and Aedes mosquitoes are also competent vectors [2] . Besides horses and humans several other mammals are dead-end hosts of WNV [1] , [2] , [3] . About 80% of humans infected with WNV develop no or only very mild symptoms . In about 20% of the cases patients develop more severe symptoms such as fever , myalgia and lymphadenopathy . Furthermore , in small proportion of cases the infection progresses to life-threatening neuroinvasive forms characterized by meningitis , encephalitis and/or flaccid paralysis [1] , [4] . The risk of developing lethal forms is increased in the elderly or in immunocompromised patients [1] , [4] . WNV is the most widely spread flavivirus in temperate areas: it has been isolated in parts of Europe , Middle East , Africa , Asia , America and Australia , and migratory birds are responsible for the dispersal of the virus [5] , [6] , [7] . WNV is also capable of causing outbreaks of neuroinvasive infections , as demonstrated during outbreaks in Romania in 1996 ( about 800 cases ) , in Greece in 2010–2012 ( more than 500 cases , still ongoing ) , several outbreaks in the USA from 1999 to 2012 , with over 15000 cases of neuroinvasive infections and about 1500 deaths and the recently confirmed WNV cases in Tunisia , in the Balkans and in Italy [8] , [9] , [10] , [11] , [12] . Both serological and nucleic acid-based tests are available for the diagnosis of WNV infections , but due to the short period of low viremia in humans , serological tests that detect virus-specific antibodies are more reliable [1] , [13] , [14] . Following infection with WNV , IgM antibodies are produced and can be detected within 4–7 days after exposure and may persist for about one year , while IgG antibodies can be reliably detected from day 8 after infection [15] , [16] . There are several types of serological tests routinely used for WNV diagnostics: enzyme-linked immunosorbent assay ( ELISA ) , immunofluorescence assay ( IFA ) , neutralization test ( NT ) and the hemagglutination-inhibition assay . Commercial kits are available , but several laboratories have also developed their own in-house tests [1] , [13] , [17] . A major issue in WNV diagnostics is cross-reactivity with antibodies against heterologous flaviviruses , e . g . dengue virus ( DENV ) , Japanese encephalitis virus ( JEV ) , tick-borne encephalitis virus ( TBEV ) or yellow fever virus , which is especially true for IgG antibodies [18] , [19] . NT is considered the most specific technique , but it is laborious , time-consuming and it can be performed only in BSL-3 laboratories , while ELISA is rapid , reproducible and cost-effective [1] , [16] . In 2005 , the European Network for the Diagnostics of Imported Viral Diseases-Collaborative Laboratory Response Network ( ENIVD-CLRN ) organized the first external quality assurance ( EQA ) study for WNV serological diagnostics to assess the performances of laboratories involved in WNV diagnostics [18] . The study revealed that the performance of diagnostic tests varies amongst laboratories and that there is need to improve them . The aim of our study was therefore to update information on performance accuracy of WNV serological diagnostic tests used by expert laboratories through the organization of a second EQA study .
Forty-eight laboratories involved in WNV serological diagnostics were invited to participate in this second EQA study . The study was organized by the ENIVD-CLRN network . The selection of the invited laboratories was based on the register of ENIVD-CLRN members as well as on their contributions to the literature relevant to this topic . The participation in the study was open and free of charge and included publication of the results in a comparative and anonymous manner . The following 47 laboratories participated in the study: Albania: Institute Of Public Health , Tirana . Argentina: Instituto de Virología “ . J . M . Vanella” , Córdoba . Austria: Universität Wien , Wien . Belgium: Institute of Tropical Medicine , Antwerpen . Brazil: Instituto Evandro Chagas , Ananindeua . Bulgaria: National Center of Infectious and Parasitic Diseases , Sofia . Costa Rica: CNRV Inciensa , Cartago . Cuba: Institute for Tropical Medicine “Pedro Kourí” , Havana City . France 1: IRBA-IMTSSA Unité de Virologie , Marseille . France 2: LNR/LCR West Nile UMR1161 , Maisons-Alfort . Germany 1: EUROIMMUN AG , Lübeck . Germany 2: Friedrich-Loeffler-Institut , Greifswald - Insel Riems . Germany 3: Institut für Mikrobiologie der Bundeswehr Zentralbereich Diagnostik , München . Germany 4: Institut für virologie , Hannover . Germany 5: Niedersächsisches Landesuntersuchungsamt , Hannover . Greece: Aristotle University of Thessaloniki , Thessaloniki . Iran: Pasteur Institute of Iran , Tehran . Italy 1: Amedeo Hospital , Torino . Italy 2: Istituto Superiore di Sanità , Rome . Italy 3: Lazzaro Spallanzani , Rome . Italy 4: Molecular Biology Section Army Medical and Veterinary Research Center , Rome . Italy 5: Istituto G . Caporale , Teramo . Latvia: Infectology Center of Latvia , Riga . Mexico: Lab . de Virus Hemorrágicos . Depto . Enfermedades Emergentes y Urgencias . Norway: Norwegian Institute of Public Health , Oslo . Portugal: National Institute of Health , Águas de Moura . Republic of Macedonia: Institute of Public Health , Skopje . Romania: Cantacuzino Institute , Bucharest . Russia: Central Research Institute of Epidemiology , Moscow . Saudi Arabia 1: Jeddah Regional Laboratory , Jeddah . Saudi Arabia 2: King Abdulaziz University Hospital , Jeddah . Senegal: Institut Pasteur , Dakar . Slovenia: University of Ljubljana , Ljubljana . South Africa 1: NHLS , Bloemfontein . South Africa 2: National Institute for Communicable Diseases , Johannesburg . Spain 1: Instituto de Salud Carlos III , Majadahonda . Spain 2: Clinic i Provincial de Barcelona , Barcelona . Spain 3: Investigadora Campus de Bellaterra , Barcelona . Sweden: Swedish Institute for Infectious disease control , Solna . Switzerland 1 : Spiez Laboratory , Spiez . Switzerland 2: Zentrum für Labormedizin , St Gallen . Switzerland 3: University Hospital of Geneva , Geneva . Turkey: National Public Health Agency , Ankara . The Netherlands 1: Erasmus MC , Rotterdam . The Netherlands 2: National Institute of Public Health and the Environment ( RIVM ) , Bilthoven . U . K . 1: Health Protection Agency , London . U . K . 2: Animal Health and Veterinary Laboratories Agency , New Haw Surrey . The preparation and distribution of the panels were carried out as previously described for the first EQA study on WNV diagnostics [18] . The instructions provided to the participants were also the same as for the previous EQA [18] . The test panel consisted of 13 different sera , including sera reactive against WNV as positive controls , sera reactive against heterologous flaviviruses as specificity controls and negative control sera . The exact composition of the test panel was: The DENV and the WNV plasma sera were obtained from plasmapherese centres from US and Costa Rica and were purchased from SeraCare Life Sciences , Milford , MA , USA . For TBEV and JEV the sera came from vaccinees , while for USUV the sample was provided by reference laboratories of ENIVD-CLRN network routine diagnostics . All subjects provided informed oral consent . All samples taken from the collections were anonymized . Two criteria were selected to evaluate the proficiency of each laboratory: 1 ) laboratories had to identify the seven WNV-positive serum samples irrespective of differentiation between IgM and IgG , i . e . if at least one of the test gave a positive result 1 point was assigned , and 2 ) the four serum samples containing cross-reactive antibodies to the heterologous flaviviruses ( DENV , JEV , TBEV , USUV ) and the two negative controls should not give a positive result and/or should be recognized as being unspecific . Equivocal or borderline results with the six non-WNV serum samples were interpreted as negative . False positive and false negative results were evaluated as incorrect and attributed a score of 0 points . The maximum score for each laboratory is 13 points ( indicated as 100% ) , indicating that all diagnostic results were correct . For each of the 13 serum samples , the score was assigned using identical criteria , allowing the percentage of laboratories giving correct results for each specific serum to be determined . In order to be consistent and to make the results comparable , scoring criteria identical to those used during the first EQA study for the serological diagnosis of WNV infection were used [18] . The performances of the diagnostic tests with regard to IgM and IgG results were considered separately in order to give additional information concerning the quality of the laboratory diagnostics . Data were collected using Microsoft Excel ( Microsoft Corp . , Bellingham , WA , USA ) and analysed using SPSS 14 . 0 for Windows . Results with respect to categorized variables were analysed by the chi-square test . A p-value<0 . 05 was considered to be statistically significant .
Forty seven of 48 invited laboratories participated in the study ( 98% response rate ) . A total of 30 countries were involved , including 21 from Europe , 5 from America , 2 from Asia and 2 from Africa ( see materials and methods section ) . A total of 51 tested panels were received for IgM and/or IgG detection because four laboratories sent two tested panels using both ELISA and IFA or NT test . Tables 1 , 2 and 3 summarize the results obtained using ELISA , IFA and NT as detection method , respectively , and are sorted by percentage of correct results for each laboratory . The most widely used serological diagnostic test was ELISA , performed in 35 of 51 tested panels ( 69% ) , followed by IFA ( in 11 of 51 tested panels , 22% ) and NT ( in 5 of 51 tested panels , 9% ) . Four laboratories using ELISA detected only IgM antibodies and no IgG antibodies . In 37 of the 51 tested panels ( 73% ) a commercial test was used , whereas an in-house test was used in the remaining 14 tested panes ( 27% ) . According to the criteria given , heterogeneous scores were observed among the in-house and commercial tests used by the laboratories . Nevertheless , the scoring for in-house tests was the same as for commercial tests , ranging overall from 54 to 100% . In accordance with the first WNV EQA and as well as other EQA studies for the serological detection of DENV and hantavirus infections , there were no statistically significant differences in scores between laboratories using commercial or in-house assays [18] , [19] , [20] , [21] . Interestingly , several laboratories using the same commercial kit but obtained different results ( e . g . the panels 19 and 29 used ELISA kit “F” and gained 100% scores , and panels 4 , 18 , 26 and 30 also used ELISA kit “F” but gained 62% scores ) as observed in the first EQA study [18] . This may highlight the need for some laboratories to perform correctly the test . However , in the panels 19 and 29 additional tests were performed with the negative controls which permitted to identify the false positive as heterologous flaviviruses ( marked as +* in the tables ) . This indicates that the performance of additional tests for flaviviruses may help in interpreting the results , especially if not so high or borderline antibody-titres have been obtained . No significant differences have been observed in performances among the different continents and among WNV-free and WNV-endemic countries . In countries reporting several panels ( Spain , Germany and Italy ) some slight differences exists , due also to the different methods used . The best scores were obtained in eight tested panels ( 32 , 19 , 31 , 29b , 38 , 17 , 26b and 42 ) where 100% of the diagnostic results were correct ( 13 out of 13 points ) ( Tables 1 , 2 and 3 ) . Of these eight tested panels , ELISA was performed in five , IFA in one and NT in two . In the other tested panels , the percentage of correct results varied from 54 to 92% ( from 7 to 12 of 13 points ) . The major impediment preventing other laboratories from reaching the maximum level of performance was the cross-reactions with antibodies specific for heterologous flaviviruses , giving a high proportion of false-positives , especially for IgG detection . This is particularly true for cross-reactivity with DENV antibodies ( serum sample #9 ) where only 21 of the 47 tested panels for IgG ( 44% ) reported a correct result . Regarding the heterologous flaviviruses , ( JEV , TBEV and USUV ) , correct results were reported in 37 , 34 and 31 of the 47 tested panels for IgG respectively ( equating to 79% , 72% and 66% ) . A statistically significant difference exists between the proportion of correct results for DENV and for the other three flaviviruses ( p<0 . 05 ) . For the serum sample #2 ( negative control ) a correct result was reported in 36 of the 51 tested panels ( 71% ) . The high number of incorrect results with this negative control could be due to the presence of auto-antibodies that were reactive in the WNV serological tests . Serum sample #8 represents the genetic lineage II strain of WNV and was correctly detected in 40 of the 51 panels ( 78% ) . The WNV lineages I and II have about 30% of nucleotide divergence and showed antigenic variability , as observed in in cross neutralization analyses and monoclonal antibody binding assays [22] , [23] , [24] , [25] . The antibody titre of the lineage II reactive serum was 1∶100 for IgM , 1∶1000 for IgG . Serum samples #3 and #1 ( two WNV genetic lineage I strains ) were correctly detected in 100% and 98% of the panels , respectively , giving the highest rate of correct results . Serum samples #12 , #5 , #7 and #6 were 4 serial dilutions of a WNV genetic lineage I strain , and results for these serum samples showed a decrease in sensitivity with increasing dilution ( Tables 1 , 2 and 3 ) . Considering only ELISA , 29 of 35 ( 83% ) tested panels were obtained using a commercial test whereas 6 of 35 ( 17% ) tested panels were obtained using an in-house test . For IFA , 8 of 11 tested panels were obtained using a commercial test ( 73% ) whereas 3 of 11 tested panels ( 27% ) were obtained using an in-house test . All NT were in-house tests . Scores ranged from 54 to 100% in tested panels using ELISA , from 62 to 100% for tested panels using IFA and from 62 to 100% for tested panels using NT . No statistically significant difference was observed in the scores of the three different serological tests . Considering the scores related to each serum sample , it is possible to draw conclusions about the sensitivity and specificity of the different tests , particularly when comparing ELISA and IFA results , as there were only 5 laboratories performing NT . The evaluation of sensitivity ( capacity to detect true positives ) involves the serum samples positive for WNV ( serum samples #12 , #5 , #7 , #6 , #1 , #3 , #8 in tables 1 , 2 and 3 ) . For ELISA the sensitivity was 54% and 87% with respect to IgM and IgG detection , while for IFA the sensitivity was 45% and 86% with respect to IgM and IgG detection . One difference in sensitivity between ELISA and IFA is observed for the detection of the WNV lineage II . IFA seems to be more sensitive than ELISA for the detection of WNV lineage II ( 91 and 77% respectively ) , although , as no statistically significant difference was observed , this is only a tendency . The evaluation of specificity ( capacity to detect the true negatives ) involves the serum samples negative for WNV ( serum samples #9 , #4 , #14 , #11 , #2 , #13 in tables 1 , 2 and 3 ) . For ELISA the specificity was 94% and 64% with respect to IgM and IgG detection , while for IFA the specificity was 99% and 85% with respect to IgM and IgG detection The IFA was more specific than ELISA in detecting IgG antibodies ( p-value<0 . 05 ) . Although only 5 laboratories performed NT , it is interesting that low sensitivity was observed even for the highest concentrations of the WNV serum ( Table 3 ) . As the test cannot distinguish between IgM and IgG antibodies , NT is excluded from this analysis . A result for IgM detection was reported for 46 tested panels . The percentage of IgM antibodies correctly detected by the serological tests was 71% , with a sensitivity of 50% and a specificity of 95% . The low sensitivity for IgM detection can be explained mainly by the low detection of IgM antibodies of the WNV lineage II ( serum sample #8 ) : correct results were reported only in 5 of 46 tested panels . As previously described in other EQA studies , such a low sensitivity for IgM detection leads to a risk that acute WNV infections can be overlooked [18] , [21] , and this can be especially dangerous for infections with the lineage II , which has been increasingly isolated and involved in outbreaks in recent years [26] . A result for IgG detection was reported for 42 tested panels . The percentage of IgG correctly detected by the serological tests was 78% , with an overall sensitivity of 86% and a specificity of 69% . The low specificity for IgG detection can be explained mainly by the cross-reactivity of the test with antibodies of heterologous flaviviruses , especially DENV ( serum sample #9 ) : correct results were reported only in 8 of 42 tested panels . As previously reported , cross-reactivity is a well-known problem for serological assays especially among flaviviruses [17] , [18] , [19] , [20] . In this second study , the number of participating laboratories was almost double that of the first study ( 47 and 27 , respectively ) [18] . In addition , coverage has increased , with 30 countries being involved in this second study compared to 20 in the first [18] . The number of serum samples included in the panel for the second study was increased from 10 to 13 . The serum sample positive for WNV strain belonging to the genetic lineage II , the four serum samples in serial dilution and serum sample positive for JEV and USUV were included in this second study . There was no improvement in the number of laboratories that achived the 100% score in this second study compared to the first [18] . This could be due to difficulties in detecting the WNV lineage II and/or in detecting the higher dilutions of the WNV serum and/or the high level of cross-reactivity with DENV antibodies . The percentage of IgM antibodies correctly detected in this study increased from 62% to 71% while the percentage of IgG correctly detected decreased from 88% to 78% . A total of 16 laboratories participated in both studies . Among these , five laboratories increased their score , ten laboratories decreased their score and one laboratory had the same score . However , as the number and the nature of serum samples were different in these two studies , comparisons of performances between the two studies need to be considered carefully . Finally , as previously described , uneven performances and results have been obtained among laboratories using the same test [18] . The results of this second EQA study on WNV serological diagnostics demonstrate that there is still need to improve tests ( either in-house or commercial ) , and to improve the test procedures used by laboratories . Contrasting test performances were observed with respect to IgM detection ( low sensitivity ) , or IgG detection ( low specificity ) . Reliable assays for IgM detection are crucial for the diagnosis of acute or recent infections in humans and therefore their development is of first priority . Increasing of specificity for IgG detection is the second objective improving the diagnostics of WNV infection . The results of an EQA study allow all participant laboratories to identify problems and to improve their performances , as well as to receive feedback via a final anonymized report and guidance upon request from the ENIVD-CLRN network . To improve diagnostic tests performances , for any positive results identified by ELISA or IFA , a second confirmatory more specific test should be applied , e . g . NT . Of remarks , in our study only five laboratories performed a NT test for WNV diagnosis . Moreover , due to the persistence of IgM antibodies [14] , [15] , a pair of samples should be tested for all suspected cases combined with IgG avidity test to distinguish among recent and past WNV infections [27] . Due to the cross-reactivity with heterologous flaviviruses , other diagnostic tests with heterologous flaviviruses should be performed to better identify false-positive results . The broadening of the number of participants for this second EQA study compared to the first gave us a better overview of the strengths and weaknesses regarding the serological diagnosis of WNV infection . The increasing spread of WNV lineage II in Europe should be taken into account when establishing new diagnostic assays and evaluating performance in the future . | West Nile virus ( WNV ) is mantained in the environment in a cycle between mosquitoes and birds . The virus has been isolated on almost all the continents , and several migratory bird species are primarily responsible for virus spread and dispersal . Humans acquire the infection through WNV-infected mosquito bites . Although most infected humans remain symptoms-free , in a minority of cases ( especially in the elderly or immunocompromised patients ) the infection can develop into a neuroinvasive form causing life-threatening encephalitis and threatening meningitis . Diagnosis of WNV is based primarily on serological tests , i . e . the detection of the virus-specific antibodies in human serum . Our aim was to collect updated information regarding the performance accuracy of WNV serological diagnostic tests used by laboratories involved in WNV diagnostics , in order to identify the strengths and weaknesses of diagnostic techniques in each laboratory . The performance of diagnostic tests varied among the laboratories , indicating that there is still a need to improve test procedures and to harmonize protocols . | [
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] | 2013 | Second International Diagnostic Accuracy Study for the Serological Detection of West Nile Virus Infection |
Across organisms , manipulation of biosynthetic capacity arrests development early in life , but can increase health- and lifespan post-developmentally . Here we demonstrate that this developmental arrest is not sickness but rather a regulated survival program responding to reduced cellular performance . We inhibited protein synthesis by reducing ribosome biogenesis ( rps-11/RPS11 RNAi ) , translation initiation ( ifg-1/EIF3G mutation and egl-45/EIF3A RNAi ) , or ribosome progression ( cycloheximide treatment ) , all of which result in a specific arrest at larval stage 2 of C . elegans development . This quiescent state can last for weeks—beyond the normal C . elegans adult lifespan—and is reversible , as animals can resume reproduction and live a normal lifespan once released from the source of protein synthesis inhibition . The arrest state affords resistance to thermal , oxidative , and heavy metal stress exposure . In addition to cell-autonomous responses , reducing biosynthetic capacity only in the hypodermis was sufficient to drive organism-level developmental arrest and stress resistance phenotypes . Among the cell non-autonomous responses to protein synthesis inhibition is reduced pharyngeal pumping that is dependent upon AMPK-mediated signaling . The reduced pharyngeal pumping in response to protein synthesis inhibition is recapitulated by exposure to microbes that generate protein synthesis-inhibiting xenobiotics , which may mechanistically reduce ingestion of pathogen and toxin . These data define the existence of a transient arrest-survival state in response to protein synthesis inhibition and provide an evolutionary foundation for the conserved enhancement of healthy aging observed in post-developmental animals with reduced biosynthetic capacity .
The differing phenotypes stemming from the loss of essential cellular functions , such as protein synthesis , are specific to the time in life ( development or adulthood ) when the deficit occurs . Under such deficits , arresting development is an established strategy at the disposal of animals to ensure future reproductive success . During its four larval stages , the nematode C . elegans has several possible arrested states that trigger in response to different stressors , including dauer [1 , 2] , starvation-induced arrest [3] , and adult reproductive diapause [4 , 5] , among others . Dauer diapause occurs under lack of food , high temperature , or high population density , inducing an alternative larval stage 3 [2]; this dauer state carries both metabolic and behavioral changes , including increased stress resistance [6 , 7] . This stress resistant and pre-reproductive arrest state is thought to have evolved to allow the worm to conserve its resources , and it affords protection from the environment until a more favorable environment is encountered . Starvation-induced arrest can occur at larval stage 1 ( L1 ) , induced from starvation occurring immediately after hatching , and this state similarly results in stress resistance[3] . Two other arrest states are adult reproductive diapause , which is induced by L4 starvation and results in an early-adult arrest state capable of surviving long periods of nutrient deprivation with the ability to later resume reproduction , and impaired mitochondria arrest , induced by deficiency in mitochondrial respiration and resulting in L3 arrest [4 , 8]; however , these two states have not yet been directly shown to have stress resistance phenotypes . These examples suggest the existence of cellular programs that function as checkpoints throughout development that stall reproduction to promote fitness [9] . Intriguingly , the same triggers that induce these genetically regulated arrest states during development , when initiated post-developmentally , lead to increased life and healthspan ( e . g . daf-2/Insulin IGFI signaling mutants [10–12] , mitochondrial deficiency [13 , 14] ) . Moreover , the loss of essential cellular functions was shown to alter animal behavior [15] , presumably to avoid further exposure to the environment causal for the perceived loss of cellular homeostasis [16–18] . Protein synthesis inhibition is another trigger of developmental arrest early in life and increased lifespan in adults [16 , 19–21] , although the underlying mechanisms are not well understood . Similar to inhibiting the insulin-signaling pathway in adults , inhibiting protein synthesis provides several resistances from stress—starvation , thermal , and oxidative [20 , 22] . Activation of the energy sensor AMP-activated protein kinase ( AMPK ) is linked to a reduction in protein synthesis [23–25] , and AMPK can be activated by reducing growth via starvation in C . elegans [26] or via inhibiting S6 kinase in isolated mouse cells [27 , 28]; this activation includes increased lifespan that is dependent on activation of AMPK in C . elegans [28] . Here we provide new characterization of a C . elegans survival arrest state brought on by reducing protein synthesis , which confers stress resistance and is reversible . Enacting protein synthesis inhibition in the hypodermis alone was partially sufficient for both the arrest and stress resistance phenotypes . Arrested animals had very high expression of a metallothionein and were found to have higher levels of calcium , which may be linked to an observed reduction in pharyngeal pumping . All of these survival phenotypes , save the arrest , were dependent on functional AMPK . Finally , these phenotypes could be recapitulated from exposure to xenobiotics , implying a potential evolutionary context for this fitness-promoting arrest state .
To elucidate the possible connection between the developmental arrest and longevity-promoting effects of protein synthesis inhibition [16 , 19–21 , 29] , we first defined the nature of the developmental arrest in C . elegans . We analyzed the effects of protein synthesis inhibition by targeting distinct and conserved aspects of the protein biosynthesis machinery ( S1A Fig ) . We measured the synthesis of two GFP reporters; a heat shock inducible promoter ( S1B Fig ) and a mlt-10p driven construct ( S1C Fig ) that is only expressed between developmental molts as a surrogate assessment for general protein biosynthesis [30] . Because GFP from these reporters is limited to temporally distinct periods , we can robustly measure differences in GFP levels between protein synthesis inhibition conditions . We targeted the translation initiation factor , egl-45/EIF3A , or the small ribosomal protein , rps-11/RPS11 , by RNA interference ( RNAi ) , so that we could control the strength and duration of inhibition , thereby avoiding the constitutive arrest that can occur when protein synthesis is inhibited by genetic mutation [31] . While there are many genes involved in protein synthesis that can induce arrest when inhibited [16 , 19 , 20] , egl-45 and rps-11 were selected as RNAi of these genes results in a fully penetrant larval arrest phenotype ( S1D Fig ) . There is a threshold effect to this arrest , as diluting the RNAi to 10% of total food allowed more escaping animals ( S1E Fig ) , while still impairing development . In all RNAi conditions tested at 100% of total food , we observed a potent developmental arrest that could persist beyond 10 days ( S1D Fig ) . To define the developmental arrest state more precisely , we made use of the molting reporter ( mlt-10p::gfp-pest ) that marks each of the four developmental molts in C . elegans [32] . This revealed a potent arrest after the first molt at larval stage 2 ( L2 ) ( Fig 1A–1C ) . In addition , these animals are morphologically different than other arrest states like dauer and L1 arrested animals ( S1F Fig ) and are smaller in length than wild type L2s; unlike arrested L2d animals [33] ( S1G Fig ) . Together , these data support the existence of a potent developmental arrest point in response to diminished biosynthetic capacity . To address the hypothesis that the induced developmental arrest in response to protein synthesis inhibition is beneficial , we challenged L2 arrested animals and non-arrested L2 control animals to oxidative ( 20mM H2O2 , Fig 1D and S2A Fig ) or thermal ( 36°C , Fig 1E and S2B Fig ) stress and found the arrested animals were more resistant to all tested environmental insults . Animals that remained in the arrested state for longer periods of time ( 2 or 10 days ) were markedly more protected against oxidative stress and extended exposures to thermal stress ( S2C–S2F Fig ) . Thus , the durability of the response and the capacity to further enhance resistance to perceived deficiencies is enhanced so long as it is needed . Collectively , these data show that loss of protein biosynthetic capacity during development does not induce a decrepit state , but rather a beneficial health-promoting state of impeded development . The amplification of stress resistance that correlated with time in the arrested state predicted that arrested animals could persist in the L2 stage for much longer than wild type animals . Given this , we examined the lifespan of animals in the arrested state and discovered that egl-45 RNAi and rps-11 RNAi animals had a mean survival in the arrested state of 24 and 12 days , respectively ( Fig 1F ) , compared to a normal eight hour L2 stage ( Fig 1A ) . As such , the developmental arrest resulting from reduction of protein biosynthetic capacity results in health-promoting state of extended diapause . One hypothesis is that pausing development in the L2 stage alone confers survival benefits . To test this , we screened all annotated RNAi clones that induce early and fully penetrant L2 arrest ( S2G and S2H Fig ) and measured their ability to resist the same exposure to stress . Despite sharing an L2 arrest phenotype , none of these RNAi treatments resulted in the same decrease in protein synthesis ( S2I Fig ) or afforded increased survival during stress ( S2J and S2K Fig ) . As such , arrest at the L2 stage does not require a loss in biosynthetic capacity and is not inherently stress resistance-promoting . In addition , the phenotypes observed are not tied to RNAi responses , as ifg-1 ( ok1211 ) mutant animals that arrest at the L2 state [31] are more resistant to oxidative stress as compared to wild type controls ( S2L Fig ) . We also tested the long-term survival of acn-1 , let-767 , and pan-1; while only acn-1 maintained long-term L2 arrest ( S2H Fig ) , the survival of acn-1 RNAi treated animals was significantly shorter than rps-11 and egl-45 RNAi treated animals ( S2M Fig ) . Finally , we tested the necessity of daf-16/FOXO , a transcription factor that is required for dauer arrest [9] , in these survival phenotypes . Reducing protein synthesis in daf-16 ( mgDf47 ) mutants still causes developmental arrest ( S3A Fig ) and results in increased resistance to oxidative ( S3B Fig ) and thermal ( S3C Fig ) stress . We further note that these animals are not dauers , morphologically ( S1F Fig ) and are not resistant to treatment with 1% SDS—a phenotype of animals that successfully enter dauer diapause . Moreover , reducing protein synthesis in daf-2 ( e1368 ) mutants , which form constitutive dauers at the restrictive temperature of 25C , enter this L2 arrest stage instead of developing into dauers . These findings support the protein synthesis inhibition arrest state at the L2 larval stage and prior to dauer formation , which is an alternative L3 stage ( S3D Fig ) . Considering the need for every cell to sense and respond to changes in biosynthetic capacity , but also the benefit of coordinating a systemic physiological response to a perceived organism-level deficit in any tissue , we hypothesized that the response to protein synthesis inhibition would be both cell autonomous and non-autonomous . The germline is a facile model for cell division in early larval development in C . elegans [34] . Similar to the developmental arrest observed at the organism level , tissue-general protein synthesis inhibition resulted in the clear arrest of the reproductive tissue at a stage typical for L2 animal development ( Fig 2A ) . We next sought to determine which tissues were capable of initiating the L2 arrest . Using tissue-specific RNAi , we systematically reduced the expression of egl-45/EIF3 or rps-11/RPS11 in the intestine , germline , or hypodermis ( S4A Fig ) . Similar to tissue-general RNAi , hypodermal-specific protein synthesis inhibition induced potent developmental arrest ( Fig 2B–2D ) and halted germline proliferation ( Fig 2E ) . In contrast , while still slowing development , intestinal or germline-specific RNAi was unable to induce developmental arrest ( S4B–S4J Fig ) . Germline-specific protein synthesis inhibition results in sterility ( S4H–S4K Fig ) , which differentiates the cell autonomous effects of protein synthesis inhibition from the cell non-autonomous impact on the entire organism when diminished biosynthetic capacity is restricted to the hypodermis . Hypodermal-specific protein synthesis inhibition was the most effective at enhancing resistance to oxidative ( Fig 2F ) and thermal ( Fig 2G ) stress , as compared to germline- and intestine-specific RNAi ( S4M–S4P Fig ) , which had modest or no effect on stress resistance . Moreover , hypodermal-specific protein synthesis inhibition initiated post-developmentally was capable of increasing lifespan and , in the case of egl-45 RNAi , was at least equally potent as tissue-general protein synthesis inhibition ( S4Q Fig ) . As predicted by their essential roles in protein synthesis , egl-45/EIF3 and rps-11/RPS11 expression is detectable in several tissues ( S4R–S4U Fig ) , but the differences in the expression level and location could explain the variance in the strengths of phenotypes observed in egl-45 RNAi versus rps-11 RNAi . Nevertheless , these data identify the hypodermis as an important mediator of organismal regulation of growth and development in response to diminished biosynthetic capacity . We examined the transcript levels of a panel of genes with established roles in stress adaptation ( see Methods ) under both 24 hours and 120 hours exposure to protein synthesis inhibition ( collected after 24 and 120 hour exposure to RNAi ) [35] . Despite the enhanced stress resistance observed in protein synthesis inhibition-induced L2 arrested animals , the expression of most genes tested—including several heat shock proteins , redox homeostasis pathway components , and isoforms of superoxide dismutase—was significantly repressed ( S5A–S5J Fig ) . The notable exception in this panel was the expression of mtl-1 , a metallothionine involved in metal homeostasis , which after 24 hours of either egl-45/EIF3 or rps-11/RPS11 RNAi was increased >10-fold ( Fig 3A and S5E Fig ) ; in animals arrested for 5 days , mtl-1 was increased >100-fold ( Fig 3B and S5F Fig ) . This temporal enhancement was not observed for other genes involved in stress adaptation ( S5G–S5J Fig ) . Moreover , hypodermal-specific protein synthesis inhibition also induced mtl-1 expression ( Fig 3A and S5E Fig ) , consistent with the notion that the hypodermis is a potent sensor for organismal biosynthetic capacity . As mtl-1 is activated in response to heavy metals , we challenged protein synthesis inhibition-arrested animals to toxic levels of Cd2+ ( 50mM ) and discovered this arrest state also enhanced resistance to heavy metal stress ( Fig 3C ) . Because heavy metal resistance was not previously annotated in adults with protein synthesis inhibition [19–21] , we initiated protein synthesis inhibition post-developmentally by egl-45/EIF3A or rps-11/RPS11 RNAi , which also resulted in resistance to Cd2+ exposure ( S5K Fig ) . Similar to oxidative and thermal stress , hypodermal-specific RNAi of egl-45/EIF3 or rps-11/RPS11 could recapitulate the whole animal RNAi phenotype ( S5L Fig ) . We next tested whether the increase in mtl-1 was causative for the resistance , so we created a double mutant of mtl-1 ( tm1770 ) and mtl-2 ( gk125 ) ( mtl-2 is a related metallothionine also activated in response to heavy metals ) , which greatly attenuated the ability to survive Cd2+ exposure when protein synthesis is inhibited ( S5M Fig ) . Based on these heavy metal responses , we wanted to further test if hypodermal RNAi could increase mtl-1 to the same degree as observed in wild type animals exposed to protein synthesis inhibition for extended periods . Correlating with the rate of developmental arrest , mtl-1 levels increase out to 48 and 120hrs of exposure to hypodermal specific RNAi of egl-45 or rps-11 ( S5N Fig ) . However , animals with longer exposure to rps-11 RNAi have mtl-1 transcript levels that return to near wild type levels , which correlates with the escape from developmental arrest under hypodermal specific rps-11 RNAi ( Fig 2D ) . Although heavy metals are not abundant in standard growth media , these findings led us to examine the total metal content of animals in protein synthesis inhibition arrest by inductively coupled plasma-atomic emission spectroscopy ( ICP-AES ) . The metal profiles revealed a significant reduction in Mg2+ and Mn2+ and a marked increase in Ca2+ ( Fig 3D and S6A Fig ) . These steady-state concentrations of metals were maintained in animals trapped in the arrested state for 5 days ( Fig 3D and S6A Fig ) . mtl-1;mtl-2 double mutant animals reduced multiple metal species by 10–20% , but did not affect Ca2+ levels ( S6B–S6D Fig ) ; protein synthesis inhibition treatment in this mutant was still able to induce many of the same Mg2+ , Mn2+ , and Ca2+ changes as seen in wild type , consistent with the transcriptional induction of mtl-1 acting as a stress response rather than as the upstream effector . Moreover , animals acutely exposed to CaCl2 treatment as larvae have an mtl-1 transcriptional profile that mirrors animals with protein synthesis inhibition ( Fig 3E ) , suggesting that the increase in Ca2+ could be physiologically significant and promote the increased mtl-1 expression . Animals have adopted several strategies , ranging from molecular adaptation to changes in behavior , in order to cope with less than ideal growth conditions [36] , and calcium plays several critical functions in these physiological responses . As such , we examined the behaviors of animals arrested from protein synthesis inhibition and noted a marked decrease in pharyngeal pumping ( Fig 4A and S7A Fig ) , a rhythmic behavior influenced by calcium transients [37 , 38] . The reduction in pharyngeal pumping was significant after 24-hours of protein synthesis inhibition and was more pronounced the more time animals were in the arrested state ( S7B Fig ) ; despite this reduction , a basal level of pumping continues even after 15 days in the arrested state ( S7B Fig ) . Similar to the developmental arrest and enhanced stress resistance observed in daf-16 ( mgDf47 ) animals , daf-16 is not required for the reduction in pharyngeal pumping rates when protein synthesis is inhibited ( S7C Fig ) . In line with previous cell non-autonomous effects , hypodermal-specific protein synthesis inhibition effectively reduced pharyngeal pumping ( Fig 4B ) , while protein synthesis inhibition in other somatic tissues could not evoke the same magnitude of responses ( S7D and S7E Fig ) . This reduction of pharyngeal pumping is intriguing as this behavior is correlated with food intake [39] , and caloric-restriction ( CR ) is an established means of enhancing organismal health- and lifespan [40 , 41] . With this in mind , we measured pharyngeal pumping in adult worms fed egl-45 or rps-11 RNAi to induce protein synthesis inhibition , which are long-lived [16] , and also discovered a significant reduction in pharyngeal pumping ( S7F Fig ) . Taken together , these data define reduced pharyngeal pumping as a physiological response of protein synthesis inhibition during development and adulthood . Protein synthesis is energetically expensive , and it is possible that protein synthesis inhibition leads to a state of excess ATP , which could be redirected to other cytoprotective pathways that drive stress resistance [42] . However , we found that animals exposed to protein synthesis inhibition during development have 50% less cellular ATP ( Fig 4C ) . AAK-2/AMPK is a conserved sensor of energy homeostasis that responds to changes in cellular AMP/ATP levels [43] . Indeed , animals with protein synthesis inhibition have significantly higher AMP/ATP and ADP/ATP ratios ( Fig 4D ) . As such , we tested aak-2 mutants for the protein synthesis inhibition survival and arrest phenotypes . aak-2 ( ok524 ) mutants exposed to protein synthesis inhibition were still arrested as L2 animals with reduced germ cell counts ( S8A–S8C Fig ) , but failed to dampen pharyngeal pumping rates ( Fig 4E and S8D Fig ) , which importantly uncouples these two protein synthesis inhibition responses and suggests that the developmental phenotypes are not a result of diminished food intake . Additionally , aak-2 mutant animals failed to evoke protein synthesis inhibition responses observed in wild type animals ( Fig 4F ) . Specifically , aak-2 mutants have minimal , often undetectable , changes in the expression of mtl-1 during protein synthesis inhibition ( S8F Fig ) —a phenotype similar to daf-16 mutant animals ( S8G Fig ) , which is a known regulator of the mtl-1 locus ( S8H Fig ) . aak-2 mutants are also as sensitive to Cd2+ as wild type animals ( S8I Fig ) , which further supports the connection between mtl-1 expression with resistance to environmental metal exposure . Furthermore , aak-2 mutants with protein synthesis inhibition are as sensitive to oxidative and thermal stress as wild type animals ( S8J , S8K , S8M and S8N Fig ) , indicating the essentiality of AMPK signaling in protein synthesis inhibition-induced stress resistance . We then tested mutant animals harboring a truncated and constitutively active ( CA ) form of AAK-2 [44] , which slowed development [44] and afforded resistance to oxidative stress while restoring thermal stress resistance under reduced protein synthesis , relative to aak-2 mutants ( S8A , S8C , S8J , S8L , S8M and S8O Fig ) . Intriguingly , expression of a constitutively activated version of AMPK ( CA-AMPK [44] ) restored the reduction of pharyngeal pumping phenotype when protein synthesis was reduced ( S8D and S8E Fig ) . Taken together with the AMP/ATP and ADP/ATP levels ( Fig 4G ) , these data define an AAK-2/AMPK molecular pathway that initiates organismal-level physiological responses to cellular deficiencies in protein synthesis . Importantly , our studies reveal a clear role for AMPK signaling in mediating the survival responses to protein synthesis inhibition beyond developmental arrest . In the context of a worm’s natural environment , we postulated that the ability to pause development in response to a perceived cellular deficiency would be advantageous—and perhaps evolved—as a response mechanism to deal with environmental hazards . In the wild , C . elegans consume diets that are far more complex than the simple and homogenous E . coli lawn provided to them in the laboratory [1] . These wild diets include heterogeneous populations of microorganisms , some of which can produce xenobiotic compounds that can target and disable essential biological pathways . Recently , the soil and intestinal microbiome of C . elegans has been characterized [45–47] . While only appearing at rates ranging from 0 . 001–0 . 1% in soil samples found in these studies , we chose to focus on the genus Streptomyces , as it is soil-dwelling , readily accessible with the lowest biosafety level , and has several members that produce commonly utilized molecules that can potently inhibit eukaryotic protein synthesis [48] . If wild C . elegans came upon a microcosm of Streptomyces species , or any other organism capable of producing xenobiotics that reduce protein synthesis , it would be important to have defenses available against these molecules . We exposed worms to S . griseus , S . griseolus , or S . alboniger , that produce cycloheximide ( CHX ) , anisomycin , and puromycin , respectively ( S9A Fig ) . Exposure to these Streptomyces species grown under stationary conditions for five days , in order to initiate secondary metabolism and the creation of these protein synthesis inhibition molecules [49] , resulted in delayed reproduction ( S9B Fig ) and significant reduction of their pharyngeal pumping in two species ( Fig 5A ) . This is in contrast to exposure with microbes in exponential phase growth which attenuates secondary metabolism [49] ( Fig 5A ) . Exposure to pathogens can alter several physiological parameters in the host , and of all the pathogens tested , exposure to S . griseus exerted the strongest influence on pharyngeal pumping . The remarkably similar impact that exposure to S . griseus had on C . elegans development and physiology , as compared to RNAi-induced protein synthesis inhibition , drove a further examination of how exposure to cycloheximide ( CHX ) , the bioactive secondary metabolite produced by S . griseus , affected C . elegans survival during development . CHX is a potent inhibitor of ribosome processivity and has recently been shown to exert health-promoting effects in adult C . elegans by an unknown mechanism [50] . Satisfyingly , CHX exposure upon hatching , which inhibits new protein synthesis ( S9C Fig ) , also resulted in arrested animal development ( S9D Fig and Fig 5B ) , and can arrest in a dose-dependent manner ( S9D Fig ) . Although animals arrested by RNAi-mediated protein synthesis inhibition can continue development upon removal from the RNAi state , not all animals in the population mature into fertile adults ( S9E–S9G Fig ) —likely a result of the persistence of RNAi [51–54] . However , initiating protein synthesis inhibition via exposure to 0 . 05mg/ml CHX rather than RNAi of essential protein synthesis factors ( S1A Fig ) enabled studies of recovery from the arrest state without the complications of RNAi . Once removed from the xenobiotic , developmentally arrested animals resume development—indicating the arrest state is truly transient ( Fig 5B , S2 Table ) . The CHX-induced arrest state caused reduced pharyngeal pumping ( Fig 5C ) , arrested germ cell proliferation ( Fig 5D ) , increased organismal [AMP]/[ATP] ratio ( Fig 5E ) . Importantly , this arrest state phenocopied all RNAi-based protein synthesis inhibition survival responses ( Fig 5F ) including: enhanced resistance to oxidative ( S9H Fig ) and thermal stress ( S9I Fig ) , induced the expression of mtl-1 ( S9J Fig ) decreased cellular ATP ( S9K Fig ) , and resulted in metal profiles similar to animals fed RNAi targeting egl-45/EIF3 and rps-11/RPS11 ( S9L and S9M Fig ) . 0 . 05mg/ml CHX exposure may not fully arrest all animals , as some daf-2 ( e1368 ) animals at the restrictive temperature did become dauers ( S2Q Fig ) . Animals that are released from CHX arrest have minimal ( if any ) changes in reproductive output ( S9N Fig ) , have a small but significant increase in resistance of oxidative stress ( S9O Fig ) , are delayed ~16-20hrs to reproduction ( S9P Fig ) , and have normal pumping rates at physiological day 3 of adulthood ( S9Q Fig ) . Thus , this transient arrest state is survival promoting when the deficiency in protein synthesis is present and is not afforded once homeostasis is reestablished , similar to animals released from dauer [36] . Intriguingly , the ability of Streptomyces griseus to reduce pharyngeal muscle pumping required the presence of live bacteria co-culture ( S9R Fig ) . In addition , increasing doses of CHX , similar to the threshold effects seen with RNAi targeting genes involved in protein synthesis ( S1E Fig ) , could further reduce the pumping rate of the arrested animal ( S9S Fig ) . Thus , the complexity of the environment and drug dosage are important for balancing the induction of this survival state .
In response to impaired organismal protein synthesis , animals are capable of entering an arrest state , reaping survival benefits , and exiting to become reproductive adults ( Fig 5B ) . In our studies , we are forcing continual exposure of animals to protein synthesis inhibiting RNAi or xenobiotics , which is likely "unnatural" , as previous studies of lethal RNAi treatment and xenobiotic treatments leads to aversion behaviors [15 , 17] . With this in mind , we predict that in the wild the perceived loss of translation would evoke a similar aversion response—allowing animals to escape to new pathogen-free environments . This model is supported by our studies with cycloheximide exposure , which drives a rapid induction of arrest and stress resistance , from which animals can quickly recover . In this regard , we believe that the use of cycloheximide as a transient inducer of protein synthesis inhibition in the worm will be of great use in studying protein synthesis inhibition going forward in order to circumvent the complications of RNAi expansion over the worm lifespan and subsequent generations . Given that there is a dose response to CHX exposure , higher doses can be utilized to prolong the arrest state and enhance arrest phenotypes although prolonged exposure to higher concentration reduces the rate of escape ( S2 Table ) . The lack of necessity of DAF-16 for the developmental arrest in response to protein synthesis inhibition indicates that the reduced protein synthesis pathway functions independently from the dauer development pathway . Yet , while most dauer constitutive daf-2 mutants that are arrested from CHX do not form dauers , intriguingly ~20–25% will develop into dauers instead of undergoing protein synthesis arrest ( S3D Fig ) . This finding suggests that animals can either alternatively arrest in the L2d stage [33 , 55 , 56] , or that the CHX dose requires a higher threshold for complete arrest of animals ( especially given the 100% non-dauer RNAi-treated animals ) . Of note , reduced protein synthesis arrested animals are distinct from the L2d stage as they are of smaller length than wild type L2s ( S1G Fig ) ( unlike 50% longer L2d animals [33] ) , functional AMPK is not necessary for the reduction of germ cell numbers ( S8B Fig ) as it is in L2d/dauer animals [57] , and we have never observed them becoming dauers after exiting the arrest state . Future characterization of any phenotypic parallels between L2d and reduced protein synthesis arrest , especially in the context of the differing role of AMPK in controlling germ cell proliferation , will be of interest for future studies . A persistent question in biology asks how cellular status is communicated across the organism and , more importantly , how an appropriate homeostatic response is engaged . Protein synthesis inhibition in the hypodermis alone was sufficient for all arrest and healthspan phenotypes . In addition to its important role in the molting process during larval development , the hypodermis has recently been implicated as being important in dietary checkpoints in larval arrest [9 , 58] . Although it is known that C . elegans tissues have differential capacity for RNAi , our work bolsters the hypodermis as a key tissue in larval development , and identifies a new cell non-autonomous communication pathway to initiate systemic responses . Given that the hypodermis is the first barrier to its external environment that covers the entire organism , it is reasonable that C . elegans might evolve sensing mechanisms for hypodermal cellular changes to influence whole-body cellular signaling . It is also possible that the high demand for protein synthesis during growth of the developing hypodermis amplifies the tissue-general effects of protein synthesis inhibition in this tissue , with or without specifically evolved signaling pathways . However , proliferation alone is not the only factor that influences responses to protein synthesis inhibition . The germline is a highly proliferative tissue in C . elegans , and while protein synthesis inhibition in the germline did not result in the same L2 arrest state as tissue-general or hypodermis-specific reduction , it did result in pre-reproductive adult animals with mild stress resistance ( S4 Fig ) . It remains to be seen if this germline arrest is also reversible , similar to starvation-induced adult reproductive diapause [4] . It is important to note the differences in stress resistance when protein synthesis is reduced in specific tissues . While hypodermis-specific RNAi of protein synthesis components results in increased stress resistance that is consistent when RNAi is initiated in all tissues , intestine-specific RNAi resulted in no change to stress resistance capacity except for a few instances of increased resistance only observed for rps-11 RNAi . The more tissue-general expression of rps-11/RPS11 ( S4 Fig ) , may explain these minor phenotypic differences as compared to egl-45/EIF3 RNAi . Taken together , these data support the idea that the systemic stress responses that stem from the loss of rps-11 are mediated by effects across multiple tissues . In contrast to the hypodermis and intestine , germline-specific loss of protein synthesis resulted in modest or no changes in oxidative stress resistance and surprisingly lead to reduced thermal tolerance . This suggests that the oxidative and thermal stress resistance responses , at least in the germline , may be uncoupled or , alternatively , that reducing protein synthesis in the germline activates a separate pathway that negatively affects thermal stress resistance . Finally , it is also worth noting that there is considerable variation in stress resistance among these tissue-specific RNAi strains . We attribute much of this both to the use of RNAi variance , as well as the ever-present "leakiness" of these tissue specific strains that can sometimes spread RNAi effects to other tissues [59 , 60] . The metallothionein , mtl-1 , is highly ( >100-fold ) upregulated under reduced protein synthesis . The increased expression of mtl-1 was required for heavy metal resistance in animals with protein synthesis inhibition , which is notable since hypersensitivity to cadmium has not been reported in adult C . elegans lacking MTL-1 or MTL-2 [61] . This finding further advocates for the importance of uncoupling developmental and adult specific responses . Transcription of MT1 , the mammalian homolog of mtl-1 , is also upregulated by oxidative stress agents in cell lines and mice [62 , 63] , so it is possible that protein synthesis inhibition causes an increase in ROS that triggers mtl-1 transcription; however , then we would also expect to see increased transcription of SKN-1 target genes ( e . g . gst-4 ) , which we do not observe . Moreover , mtl-1 expression was not necessary for the arrest , oxidative or thermal stress resistance , or reduced pumping , as daf-16 mutants ( which lack mtl-1 expression , S8 Fig ) still display both phenotypes . Thus , given the very specific transcription of mtl-1 , the changes in expression are likely due to the presence of its most well-defined binding partners , metal cations . Traditional targets of MTL-1 are Zn2+ , Cd2+ , and Cu2+ , but mammalian homologs can bind to Mg2+ , Mn2+ , and Ca2+ [64–66] . The increase in Ca2+ ions could be the cause of this high transcriptional response , especially given that Ca2+ treatment could induce mtl-1 in worms ( Fig 3E ) . However , it is also possible that higher levels of other heavy metals , such as Cd2+ , which never reached our detection limits , are responsible . Given that mtl-1 expression was disposable for the arrest , stress resistance , and reduced pumping rate , the increased expression change is a "biomarker" for the reduction of protein synthesis , rather than a central player in this developmental state . Given the ability for calcium to upregulate this mtl-1 response ( Fig 3 ) , we expect the protein synthesis loss triggers calcium abundance and daf-16 activation [16] , that both go on to increase mtl-1 levels . It is possible that the reduction of cellular ATP we observe reflects the use of ATP to “power” survival processes [42] . However , a ~50% reduction in ATP after 24hrs of protein synthesis inhibition is a remarkable loss , and it would not explain how this energy usage would be sustained to continue stress resistance over extended time periods , especially when accompanied by a reduction in pharyngeal pumping ( thereby reducing food/energy intake even further ) . Our data support an alternative model where increases in the [AMP]/[ATP] and [ADP]/[ATP] ratios activate AMPK pathways that signal for downstream survival pathways ( Fig 4C and 4F ) . The underlying mechanism driving the imbalance to cellular adenylate pools will be of future interest . We found that AMPK was necessary for all of our protein synthesis inhibition survival phenotypes , except for arrest . AMPK activation has been implicated in survival phenotypes before , including glucose restriction pathways [67] and oxidative stress resistance [68] in C . elegans . Juxtaposed to our work , activating AMPK ( such as via AICA ribonucleotide ) causes a decrease in protein synthesis [23–25] . While our work focuses directly on protein synthesis alone , AMPK is also increased in rsks-1/S6K mutants [27 , 28] and under starvation conditions [26] . This suggests that AMPK and protein synthesis may work together in a circular pathway or that they affect each other by cell non-autonomous signaling . In addition , an upstream activator of AMPK , ARGK-1 , is both important for rsks-1/S6K mutant longevity , and its overexpression caused reduced pumping rates in worms [69]; further study into the role of ARGK-1 in this protein synthesis inhibition survival state will be of interest in future studies . As a final note , C . elegans lacking the elongation factor efk-1 , which is activated by AMPK , fare worse under nutrient starvation conditions [70]; thus , there are multiple connections between starvation , protein synthesis , and energy homeostasis , and understanding them in context of survival states is important to consider . Previous studies suggest that the effects of protein synthesis inhibition on adult lifespan are distinct from caloric restriction ( CR ) [19] and that the CR state can drive a reduction in protein synthesis[20] . Our data suggest that during development the opposite is also true: that protein synthesis inhibition can reduce pharyngeal pumping leading to a CR-like state . CR across most organisms has both life- and healthspan promoting effects; however , the evolutionary basis of the CR response is unknown . One hypothesis generated from this study is that the physiological response to CR might stem from an ancient program to promote stress resistance when the presence of diminished biosynthetic capacity is perceived . Microorganisms such as Streptomyces provide a potential evolutionary explanation to a mechanism of a pathogen-derived CR pathway by engaging behavioral avoidance phenotypes toward toxin-producing pathogens [15] . It is important to note that Streptomyces was found at very low levels in recent studies looking at C . elegans soil samples [45–47] . Our xenobiotic experiments are not meant to emulate the wild environment , but to capture the interaction between the worm and a harmful species in the environment . It is altogether possible that there are areas ( or times in history ) where Streptomyces , or other species capable of inhibiting host protein synthesis , are a more common occurrence , demanding the need for such an arrest survival response documented here . There are connections between immune function and the regulation of protein synthesis—both to exposure to protein synthesis-impairing xenobiotics ( ExoA , Hygromycin ) as well as potential surveillance mechanisms for reduced protein translation as a surrogate for infection [71–73] . Pathogen response pathways can also be closely linked to promoting proteostasis [74] . In addition , a recent study found that C . elegans can enter a diapause to avoid pathogens ( unlike our study , this is reliant on the formation of dauers [75] ) . Nevertheless , our findings support the idea that the loss of protein synthesis might be perceived as "an attack" by a pathogen , which initiates a reduction in pharyngeal pumping , that could minimize ingestion of toxin-producing microbes . Given the remarkable overlap in phenotypes resulting from protein synthesis inhibition by pathogen-derived xenobiotics and our genetic and RNAi-mediated protein synthesis inhibition , it is suggestive that this survival-arrest state could have evolved as a stress response to the presence of pathogens ( Fig 6 ) . This idea parallels models of adult longevity pathways , which may have connections to xenobiotics targeting other essential pathways besides protein synthesis [35] . Unlike previous models that suggest the developmental arrest resulting from early loss of protein synthesis is a detrimental state [42] , these studies provide an alternative way of thinking about these developmental responses . The induction of protective responses to reduced protein synthesis is survival-promoting , and we predict that the capacity to engage these pathways would enable future opportunities for reproduction once the inhibition is alleviated . Lastly , our results provide an example of how the evolution and selection of developmental pro-fitness pathways may be utilized effectively later in life under the right conditions . Just as dauer diapause from reduced insulin/IGF-1 signaling ( IIS ) has mechanistic similarities with adult longevity responses when IIS is reduced post-developmentally , our studies establish a similar fitness-driven developmental program as the underlying mechanism of the enhanced healthy aging observed in adults with compromised protein biosynthetic capacity . The exceptional degree of conservation of these cellular pathways across organisms is suggestive that the pre- and post-developmental responses to protein synthesis inhibition observed in C . elegans could be similarly shared , even among humans .
Worm strains were grown at 20°C for all experiments except dauer studies that were conducted at 25°C . All strains were unstarved for at least 3 generations ( except for L1 synchronization ) before being used in any experiments . List of strains used: N2 Bristol ( wild type ) , DR1572 daf-2 ( e1368 ) , GR1329 daf-16 ( mgDf47 ) , MGH171 ( sid-1 ( qt9 ) ; Is[vha-6::sid-1::SL2::gfp] , JM43 ( rde-1 ( ne219 ) ; Is[wrt-2p::rde-1] , myo-2p::rfp] ) , NL2098 ( rrf-1 ( pk1417 ) ) , GR1395 ( mgIs49[mlt-10p::gfp-pest , ttx-3::gfp]IV] ) , SPC365 mtl-1 ( tm1770 ) ; mtl-2 ( gk125 ) , RB754 ( aak-2 ( ok524 ) ) , SPC366 ( aak-2 ( ok524 ) ; uthIs248[aak-2p::aak-2 ( genomic aa1-321 ) ::GFP::unc-54 3'UTR ( gain of function allele ) ; myo-2p::tdTOMATO] ) , SPC363 ( Ex[egl-45p::rfp; rol-6 ( su1006 ) ] ) , SPC364 ( Ex[rps-11p::gfp; rol-6 ( su1006 ) ] ) , CL2070 ( dvIs70[hsp-16 . 2p::GFP; rol-6 ( su1006 ) ] ) , KX38 ( ifg-1 ( ok1211 ) /mIn1 [mIs14 dpy-10 ( e128 ) ] ) . Some strains were provided by the CGC , which is funded by NIH Office of Research Infrastructure Programs ( P40 OD010440 ) . E . coli strain HT115 ( DE3 ) containing empty vector L4440 ( hereafter referred to as Control RNAi ) , or plasmid against a gene of interest , was grown overnight ( 16-18hrs ) at 37°C and seeded on NGM plates containing 5mM isopropyl-β-D-thiogalactoside ( IPTG ) and 50ug/ml carbenicillin . The bacteria were allowed to generated dsRNA overnight before being used within the next 1–3 days ( stored at 20°C for this period if not used immediately ) . Dose response curves were established by feeding HT115 bacteria expressing the indicated RNAi clone diluted with HT115 bacteria harboring the control RNAi plasmid L4440 . 0 . 05mg/ml Cycloheximide ( CHX ) or water ( vehicle control ) was added on top of bacteria and allowed to dry and rest for at least 1 hour before placing worms on treated bacterial lawns; this was the concentration of CHX throughout this paper , unless otherwise noted . Loss of protein synthesis was determined via measurements of de novo synthesis of GFP through both an internal ( via natural development ) and external ( via high temperature ) induction method . External: plated animals expressing hsp-16 . 2p::GFP were maintained at 20°C and fed RNAi since hatching . After 24hrs , one set of worms was shifted to 36°C for 3hr , while the other was mounted for the baseline 0hr time point . The baseline plate was also checked after 3 hours at room temperature as a control for any room temperature-induced GFP expression . Internal: plated animals expressing mlt-10p::gfp-pest , treated with RNAi or drug since hatching , were imaged via the same methods for GFP expression at 12 , 14 , and 16 hours post-feeding . Worms were imaged at 20x magnification with bright field and GFP filter ( Zeiss Axio Imager ) . Plated animals , treated with drug or RNAi since hatching , were counted in 24 hours intervals via a compound microscope as larval stage 1–3 ( size ) , larval stage 4 ( vulval invagination ) , adult ( size ) , or reproductive ( presence of internal eggs ) . In food switching assays , worms were moved to rde-1 RNAi after 24hrs on the listed RNAi . rde-1 RNAi was used to inhibit the RNAi machinery because RNAi effects can persist even after moving animals off of food containing double stranded RNA for multiple generations . Plated animals , treated for 24 or 48 hours on drug or RNAi since hatching , were placed at 36°C for up to 12 hours . Every 3 hours , one set of plates was removed to room temperature . Worms were allowed to recover for at least 10 minutes , and then counted for survival immediately by checking for touch response to prodding with a platinum wire . Plated animals , treated for 24 or 48 hours on drug or RNAi since hatching , were washed with M9 buffer twice in microcentrifuge tubes , then treated with 20mM H2O2 for up to 1 hour while rocking at room temperature . Every 20 minutes , one set of worms was removed from rocking , washed 3 times in M9 buffer , and plated back onto new plates containing their previous treatment ( drug or RNAi ) . Worms were checked 1 hour after plating to count any acute deaths ( "straight line" bodies or ruptured vulvas ) only by eye , and 24 hours after plating to count final survival as done in thermotolerance assay . Plated animals , treated for 24 hours on RNAi since hatching or at L4/YA stage , were washed with K-medium ( 32mM KCl , 51mM NaCl in dH2O ) twice in microcentrifuge tubes , then treated with 5 or 50mM CdCl2 in K-medium ( hatched or YAs , respectively ) for 30 minutes while rocking at room temperature . After 30 minutes , worms were washed 3 times in K-medium , and plated back onto new plates containing their previous treatment ( RNAi ) . Worms were checked 1 hour after plating to count any acute deaths ( "straight line" bodies or ruptured vulvas ) only by eye , and 24 hours after plating to count final survival as done in thermotolerance assay . Wild type and daf-2 ( e1368 ) were placed as synchronized L1s onto the listed RNAi clone or drug at 25°C for 48hrs . Worms were then washed in M9 , pelleted , and treated with 1% for 30min while rocked at room temperature . Treated animals were then plated onto plates with HT115 bacteria and counted for survival . Drug- or RNAi-treated animals were washed with M9 buffer twice in microcentrifuge tubes , then frozen at -80°C in TRI-Reagent® ( Zymo Research , R2050-1-200 ) . After at least 24 hours at -80°C , RNA was extracted from samples using the Direct-zol™ RNA MiniPrep kit ( R2052 ) . Quantitative reverse transcription PCR ( qRT-PCR ) was performed on the RNA samples with gene specific primers ( Table 1 ) . For evaluation of mtl-1 induced by calcium , wild type animals , grown for 24 hours on Control RNAi , were washed with K-medium twice in microcentrifuge tubes and then treated with 500mM CaCl2 ( in K-medium ) for 30 minutes at room temperature . Animals were then washed three times with K-medium , frozen at -80°C in TRI-Reagent® as above , and the same protocol as above was utilized . Two 24-well plates , each containing a single GR1395 worm on RNAi or Control RNAi , were visualized by fluorescence microscopy every hour for 72 hours . Worms were marked as green or non-green to indicate molting or non-molting , respectively . Worms that crawled off the side of the plate or burst were censored . Plated animals , treated for 24 hours on RNAi or drug since hatching , were imaged at 20x magnification ( Zeiss Axio Imager ) , and individual germ cells were counted with the Cell Counter plugin on Fiji software [76] . Plated animals , treated for the indicated time on drug or RNAi since hatching , were imaged via the Movie Recorder at 8ms exposure using the ZEN 2 software at 10x magnification ( Zeiss Axio Imager ) . Animals with zero pumping were excluded . 1000 or 500 plated animals , treated for 24 or 48 hours on drug or RNAi since hatching respectively , were washed 3 times in M9 buffer ( keeping ~100μl of supernatant at final wash ) , snap frozen in a dry ice/ethanol bath , and placed at -80°C until use . Frozen pellets were boiled for 15 minutes and spun down at 14 , 800g at 4°C . The supernatant was then diluted in dH2O ( 1/10 ) ( Adapted from[77] ) . Samples were tested for protein content via Bradford analysis ( Amresco M173-KIT ) , and ATP was assessed via the ENLITEN® ATP Assay System ( Promega ) . To determine relative levels of ATP/ADP/AMP , we followed the same method as above , but did not dilute the supernatant . Protein supernatant was directly assayed via the ATP/ADP/AMP Assay Kit ( University at Buffalo , Cat . # A-125 ) to determine total ATP/ADP/AMP in each sample; these values were then directly compared to determine relative ratios . 8 , 000–10 , 000 ( L4 stage ) or 20 , 000–25 , 000 ( L2 stage ) animals , treated for the listed time on the listed RNAi clone , were collected into microcentrifuge tubes ( tubes weighed beforehand ) using isotonic buffer ( 150mM Choline Chloride , 1mM HEPES , pH 7 . 4 with NaOH , filter sterilized ) . Worms were washed 3 times over 30 minutes ( pelleting at 1 , 000g/30s each time ) to clear gut content and then finally pelleted at 12 , 000g/2min . Worm pellets were then dried at 60°C for 48 hours using a heat block . Worm pellets were weighed after drying , and ICP analysis of the samples was conducted by Dr . David Kililea , Children's Hospital Oakland Research Institute . Before ICP analysis , dried pellets were acid digested with Omnitrace 70% HNO3 at 60°C overnight . Samples were diluted with Omnitrace water for a final concentration of 5% HNO3 . Derived metal content was normalized to dried worm pellet weights . Each animal is compared back to 24hr Control RNAi treated animals . 48hr Control RNAi animals are given as a reference for what the metal content of a chronologically matched animal would be; albeit animals that are L4-YA stage and thus 2–3 larger with higher food intake . Streptomyces Alboniger ( ATCC 12461 ) , Griseus ( ATCC 23345 ) , or Griseolus ( ATCC 3325 ) were grown at 26°C , shaking , in Tryptone-Yeast Extract Broth ( 5g Tryptone , 3g Yeast Extract in 1L dH2O , pH 7; taken from ATCC® Medium 1877: ISP Medium 1 ) for 5 days before plating unless otherwise noted . Strains were plated on Yeast Malt Agar plates ( HiMedia Laboratories , M424 ) , and mixed 1 part to 3 parts 25x HT115 when used with worms . For the egg laying comparisons , 100ul Saccharomyces cerevisiae was also added to induce competition; to compare total number of eggs , worms were mounted at ~52hrs after dropping to food source , and imaged at 20x magnification with DIC ( Zeiss Axio Imager ) . For testing dead HT115 , 75ml/L of 2 . 5% Streptomycin was added to 25x HT115 and the mix was rocked for 24hrs at room temperature . This mixture was then used in place of the 25x HT115 above . For survival in the arrested state , worms were dropped on the listed RNAi and counted each day ( for the majority ) for survival . Survival was assessed by touch response to prodding with a platinum wire . The Control RNAi wild type control strain used in this experiment was moved each day starting at adult day 1 as necessary until reproduction ceased . For tissue-specific lifespan analysis , worms were grown on Control RNAi until L4/young adult age , and then transferred to the listed RNAi plates treated with 50μM FUdR . Survival was assessed every other day as above . For all assays , animals were only censored ( bursting , vulval protrusion , etc . ) after the first counted death . Worm morphological comparisons were imaged at 20x magnification with DIC filter ( Zeiss Axio Imager ) . Worm length comparisons were made in ImageJ using the segmented line tool down the midline of each animal from head to tail . For GFP and RFP reporter strains , worms were mounted in M9 with 10mM Sodium Azide , and imaged at 40x magnification with DIC and GFP/RFP filters ( Zeiss Axio Imager ) . Fluorescence is measured via corrected total cell fluorescence ( CTCF ) via ImageJ and Microsoft Excel . CTCF = Integrated Density– ( Area of selected cell X Mean fluorescence of background readings ) . For imaging of heat-induced GFP expression via strain CL2070 , plated animals were maintained at 20°C and fed RNAi since hatching . After 24hrs , one set of worms was shifted to 36°C for 3hr , while the other was mounted ( as above ) for the baseline 0hr time point . The baseline plate was also checked after 3 hours at room temperature as a control for any room temperature-induced GFP expression . Worms were imaged at 20x magnification with bright field and GFP filter ( Zeiss Axio Imager ) . Thermotolerance , oxidative stress , and heavy metal stress were all compared using Fisher's Exact Test using the statistical software R [78]; specifically , the bars in each graph represent a unique set of biological replicates ( 2–6 independent biological replicates , see S1 Table ) relative to its own independent control cohort ( and the significance level relative to this control is indicated by the # of stars above each bar ) ; this test is employed as we are comparing the categorical variables of Alive vs Dead , and data is presented as changes in survival . Comparison of all RNAi clones and CHX for protein synthesis rates under the mlt-10p::GFP promoter was performed using one-way ANOVA . Lifespan curves were compared and analyzed via Log-Rank using JMP Pro 12 . qPCR , worm fluorescence , metal content , ATP/ADP/AMP levels , and pharyngeal pumping comparisons were made with Student's t test using Microsoft Excel . When comparing groups of three or more , Bonferroni multiple comparison post-correction was employed on Fisher's test , ANOVA , and t tests . | Protein synthesis is an essential cellular process , but post-developmental reduction of protein synthesis across multiple species leads to improved health- and lifespan . To better understand the physiological responses to impaired protein synthesis , we characterize a novel developmental arrest state that occurs when reducing protein synthesis during C . elegans development . Arrested animals have multiple survival-promoting phenotypes that are all dependent on the cellular energy sensor , AMP kinase . This survival response acts through the hypodermis and causes a reduction in pharyngeal pumping , indicating that the animal is responding to a perceived external threat , even in adults . Furthermore , exposing animals to pathogens , or xenobiotics they produce , can recapitulate these phenotypes , providing a potential evolutionary explanation for how a beneficial response in adults could evolve through the inhibition of an essential biological process such as protein synthesis . | [
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] | 2018 | Hypodermal responses to protein synthesis inhibition induce systemic developmental arrest and AMPK-dependent survival in Caenorhabditis elegans |
Circadian KaiC phosphorylation in cyanobacteria reconstituted in vitro recently initiates a series of studies experimentally and theoretically to explore its mechanism . In this paper , we report a dynamic diversity in hexameric KaiC phosphoforms using a multi-layer reaction network based on the nonequivalence of the dual phosphorylation sites ( S431 and T432 ) in each KaiC subunit . These diverse oscillatory profiles can generate a kaleidoscopic phase modulation pattern probably responsible for the genome-wide transcription rhythms directly and/or indirectly in cyanobacteria . Particularly , our model reveals that a single KaiC hexamer is an energy-based , phosphorylation-dependent and self-regulated circadian oscillator modulated by KaiA and KaiB . We suggest that T432 is the main regulator for the oscillation amplitude , while S431 is the major phase regulator . S431 and T432 coordinately control the phosphorylation period . Robustness of the Kai network was examined by mixing samples in different phases , and varying protein concentrations and temperature . Similar results were obtained regardless of the deterministic or stochastic method employed . Therefore , the dynamic diversities and robustness of Kai oscillator make it a qualified core pacemaker that controls the cellular processes in cyanobacteria pervasively and accurately .
Cyanobacteria are the simplest organisms known that exhibit circadian rhythms . The endogenous timing system coordinates a wide range of cellular processes in cyanobacteria to the day-night cycle , including genome-wide expression [1] . For individual cyanobacterial cell , the circadian clock is autonomous with weak intercellular coupling [2] , [3] . Remarkably , the core circadian clock of S . elongatus PCC 7942 can be reconstituted in vitro only with three clock proteins and ATP [4] , [5] . KaiC , the central clock protein , forms a ring shaped hexamer in the presence of ATP [6] , [7] . The N terminal of KaiC is essential for hexamerization , while the C terminal is responsible for catalyzing both phosphorylation and dephosphorylation [8] . Each KaiC subunit has two nonequivalent phosphorylation sites , S431 and T432 [9]–[12] . Phosphorylation or dephosphorylation occurs at the interface of two neighboring subunits [10] . The positive and negative regulations by KaiA ( activator ) [13] and KaiB ( attenuator ) [14] finally maintain a sustained circadian KaiC phosphorylation cycle in vitro . An ordered ( or sequential ) program has been proposed more recently in the rhythmic KaiC phosphorylation [11] , [12] . In addition , KaiC hexamer possesses a stable ATPase activity , which may unveil the mystery pathway of energy essential for the stabilization of KaiC hexamer and the robust oscillation of KaiC phosphorylation [15] . Full understanding of the central pacemaker can provide us the insights into the mechanism of the whole cyanobacterial circadian system , e . g . the core's interaction patterns with input and output pathways . Indeed , this in vitro Kai oscillator has recently stimulated ever increasing theoretical elucidations and predictions . The pioneer viewpoint by Emberly and Wingreen suggests a KaiC cluster formation and monomer shuffling mechanism [16] , in which the monomer shuffling is confirmed by experiments [5] , [17] . A model by van Zon et al . employed the allosteric transition method to KaiC phosphorylation oscillation [18] . Many other theoretical works have been performed to mimic the KaiC phosphorylation dynamics in vitro or in vivo [12] , [17] , [19]–[24] . Most of the models have simplified one extremely important fact that two sites , S431 and T432 are nonequivalent in KaiC phosphorylation cycle . In this work , we propose a step-by-step KaiC phosphorylation network based on the nonequivalence between S431 and T432 sites [9]–[12] . The phosphorylation or dephosphorylation of KaiC hexamer is designed to exhibit kinetic cooperativity , i . e . the reaction rate varies nonlinearly with KaiC own phosphorylation level ( numbers of phosphorylated S431 and T432 sites ) . We further assume that KaiA and KaiB can strengthen the cooperativity and increase the topological complexity in the network , which culminates in an accurate and robust circadian oscillation in the Kai system . Particularly , the deterministic model shows dynamic diversities in KaiC phosphorylation cycle , e . g . a variety of phase relationships , wave forms and amplitudes . It is the reminiscence of a wide range of temporal phasing patterns exhibited in the circadian orchestration of cyanobacterial gene expression [25] . Thus , a stochastic simulation is needed to mimic the dynamic features of Kai oscillator in vivo . To explore the significance of the dynamic diversities , we suggest a kaleidoscopic mode that single KaiC phosphoform ( hexameric ) or combinations of different KaiC phosphoforms ( hexameric ) can act as independent oscillatory output ( s ) responsible for cyanobacterial gene expression in different phases . The resilience of the Kai reaction network has been well examined by varying protein concentrations , mixing samples in different phases and changing temperatures . Further examination of the model suggests that the subtle changes in KaiB-KaiC association may play a key role in explaining the slight difference between two independent studies on the KaiC dynamics with concerted increase in Kai proteins' concentrations [5] , [12] . Additionally , we explore the phase response curves obtained by transient variations in KaiA concentration , and hope they could be helpful to probe the entrainment mechanism of Kai oscillator both in vitro and in vivo .
Both S431 and T432 sites are indispensable for the generation of KaiC phosphorylation cycle , because mutation at either site results in total abolish of circadian phosphorylation [9] , [11] , [12] . As shown in Fig . 1A , a step-by-step reaction network is constructed for hexameric KaiC phosphorylation cycle ( detailed reaction flows see Table S1 ) . Each node in the network , , stands for a KaiC hexamer with s S431 and t T432 site ( s ) phosphorylated . We refer to this way of identifying the phosphorylation state of KaiC hexamer as ST representation . ST representation is convenient to outline a clear topological structure of KaiC network , although it does not explicitly contain the information of the combinations of KaiC subunits in one hexamer . Generally , each node ( or phosphoform ) in the network undergoes four-directional reactions except those on the boundaries . In fact , a degenerated single site pathway or both sites considered equivalently will dramatically reduce the topological complexity of the network . Numerous theoretical works in protein dynamics and enzymology have provided us the insights into how the conformational changes are induced , propagated , regulated and functioned [26]–[31] . In most cases , the protein conformational fluctuations are essential for the functional regulation of molecular mechanism [32] , the protein-protein interactions [33] , and the chemomechanical coupling in motor proteins [34] . Definitely , information on conformational changes can be transferred to a distant site in a protein or even far in a protein cluster [35] . For Kai system , the sophisticated methods in protein dynamics enlighten us to find an approximate way on mesoscopic scale to link the changes in free energy at transition state with the rate constant for each phosphorylation or dephosphorylation step . The Kai system is indeed driven far from equilibrium because of the continuous consumption of ATP , and most importantly , an extremely large part of ATP consumption is not attributed to KaiC kinase but to KaiC ATPase [15] . Consequently , we assume that a large portion of free energy released from ATP hydrolysis maintains the integrity and the stability of KaiC hexamer , and a relative small part of free energy is responsible for the coordination in the global structural information exchange . In Fig . 1B , we have a thermodynamic box [36] . On the left side of Fig . 1B , an isolated phosphorylation site in an isolated KaiC monomer has a quite high intrinsic free energy of activation for phosphorylation ( or dephosphorylation ) ΔG≠* . Lack of interactions with other parts of the KaiC hexamer , the reaction at this site is difficult to take place and to be self-regulated . On the other hand , taking the advantage of the ATP hydrolysis , isolated KaiC monomers can form stable and coordinated hexamer at both ground state and transition state ( right side in Fig . 1B ) . ΔUχ is the free energy difference between the isolated KaiC monomers and the stable well-coordinated hexamer at ground state , ΔUτ for the difference at transition state . Then , we have ( 1 ) Thus , ΔG≠ the apparent free energy of activation for a KaiC hexamer contains , in principle , all the structural information ( mostly with respect to the phosphorylation states ) of the six subunits . The value of ΔUτ−ΔUχ is mainly attributed to the ATP-powered intra- and inter-subunit interactions in a KaiC hexamer , and it can reduce the intrinsic free energy of activation to increase the reaction rate provided that ΔUτ−ΔUχ<0 . Furthermore , if ΔUτ−ΔUχ is sufficiently negative , the apparent free energy of activation ΔG≠ is small enough so that the reaction rate is insensible to temperature fluctuations ( according to Arrhenius equation k = Aexp ( -ΔG≠/kBT ) ) . Hence , the sufficient condition for temperature compensation must be ΔUτ−ΔUχ<0 . In such case ( ΔUχ>ΔUτ ) , within the time span of each reaction step ( phosphorylation or dephosphorylation ) , it further suggests that more energy is required from ATP hydrolysis to maintain the stability and the coordination for ground state KaiC hexamer than that for transition state . In other words , once in a stable and well coordinated ground state , KaiC hexamer can easily overcome the energy barrier ( ΔG≠ ) required for phosphorylation ( or dephosphorylation ) , because the ground state KaiC hexamer has already stood at a sufficiently high free energy level rising from ATP hydrolysis catalyzed by its own ATPase . Therefore , we postulate that the robustness against temperature fluctuation is structurally inherent in KaiC provided that the stability and the coordination of its hexamer are maintained . In our viewpoint , the intra- and inter-subunit interactions can be generated , propagated and functioned within a KaiC hexamer , which is mostly maintained and amplified by ATP hydrolysis catalyzed by KaiC ATPase . Therefore , the phosphorylation ( or dephosphorylation ) rate of a KaiC hexamer is dependent on not only the local state of the phosphorylation site ( or interface ) , but more importantly the states of adjacent subunits and even the whole hexamer . Enlightened by the sophisticated method used in protein-protein interactions [26]–[31] , we roughly decompose the interaction free energy ΔUτ−ΔUχ into three hierarchical levels: local , quasi-local and global ( with respect to the structure of one KaiC hexamer ) . To elaborate the analysis of the free energy , we use another method to describe the phosphorylation state of KaiC hexamer , the subunit representation Cα , β , γ , δ . The four subscript of Cα , β , γ , δ are the numbers of m00 ( subunit with dual non-phosphorylated S431 and T432 ) , m01 ( phosphorylated S431 only ) , m10 ( phosphorylated T432 only ) and m11 ( dual phosphorylated S431 and T432 ) in one hexamer , respectively , and α+β+γ+δ = 6 . KaiC phosphorylation ( or dephosphorylation ) occurs at the interface of two adjacent subunits , so subunit representation is actually an “interface representation” , and mij ( i , j = 0 , 1 ) also represents one interface in certain phosphorylation state . The rate constants and dissociation constants can be obtained by transforming the free energies from subunit representation into ST representation . All the details can be found in section 1 . 2 of Text S1 , Table S2 and Table S3 . It should be noticed that two methods are used to describe the phosphorylation states of KaiC hexamers , i . e . ST representation ( with 49 elements ) and subunit representation ( with 84 elements ) . The simplest reaction scheme for the phosphorylation ( dephosphorylation ) of KaiC hexamer with nonequivalent S431 and T432 sites is a network expanded by the 49 KaiC phosphoforms in ST representation ( ) , as shown in Fig . 1A . The reaction network shows a lucid dynamic relationship between S431 and T432 on KaiC hexamer . The 84 KaiC phosphorylation states in subunit representation , on the other hand , constitute a much more complex network , and this subunit representation involves so many details that hinder the analysis of dynamic reaction pathways among 84 hexameric KaiC phosphoforms . However , the subunit representation is more intuitive and suitable when we investigate the free energies of the intra- and inter-subunit interactions in each KaiC hexamer , whereas the ST representation fails in this thermo-statistical analysis , because it lacks the explicit information of phosphate distribution among six subunits . In brief , both ST representation and subunit representation have advantages and disadvantages to describe KaiC oscillator in the reaction dynamics and the estimation of statistical free energy . In this work , we take the benefits of these two representations and carefully perform the inter-transformation . In principle , the full Kai reaction network contains four layers , namely . For simplicity , the four-layer network is topologically treated as two-layer network by using rapid equilibrium approximation for KaiA-KaiC and KaiA-KaiBC binding ( refer to section 1 . 1 in Text S1 ) . We have [LC] = [C] + [AC] and [LBC] = [BC] + [ABC] . In LC layer , KaiA binds to KaiC hexamer in a hopping fashion to stimulate the phosphorylation of KaiC . According to previous data [5] , we assume that one KaiC hexamer can bind one KaiA dimer forming , and the dissociation constant Kat s ( KaiA dissociating from KaiC ) varies mildly with KaiC phosphorylation status . Autophosphorylation of free KaiC is negligible due to its relative low rate ( comparing to its dephosphorylation ) . All the reactions and interactions in LC layer are shown in Eq . 2 . ( 2 ) Phosphorylation state of KaiC hexamer greatly affects KaiB-KaiC association . The high-ordered association of KaiA and KaiB to one KaiC hexamer is well observed [21] , [37] , [38] . Here , we assign that two KaiB dimers bind to one forming . KaiB binds preferably to highly phosphorylated KaiC hexamers , especially to those with S431 phosphorylated [11] . Thus , , the rate constant of KaiB-KaiC association , is set to vary greatly with s while stay the same with t . Particularly , considering the weak effect of phosphorylated T432 on KaiB-KaiC association , we put , which means KaiB does not bind in LC layer . This assumption avoids the over early attenuation effect of KaiB on KaiA-stimulated KaiC phosphorylation , which is essential to the robustness of KaiC phosphorylation cycle against fluctuation of Kai protein concentrations . We predict that the patterns of KaiBC dephosphorylation slightly differ from those of KaiC dephosphorylation in the network . Thus , we assume S431 in KaiBC stimulates T432 dephosphorylation and attenuates its own phosphorylation . Reactions for KaiB-KaiC are represented in Eq . 3 . ( 3 ) Formation of KaiABC complex is crucial to strengthen and accurately tune the interactions among the phosphorylation sites . Binary interactions such as KaiA-KaiC and KaiB-KaiC show little signs of oscillatory KaiC phosphorylation , but when three Kai proteins are mixed with ATP , self-sustained oscillation readily appears . Thus , the circadian cycling of KaiC phosphorylation attributes to the emergent property of the Kai system , and this emergent behavior definitely develops from the formation of KaiABC complex . The formation of KaiABC can be via two pathways: KaiAC + KaiB and KaiBC + KaiA . Any structural and dynamical differences between these two types of KaiABC remain to be identified . The association and dissociation of KaiA-KaiC are 15-fold and 4-fold faster than those of KaiB-KaiC , respectively [5] . Additionally , the amount of KaiBC is larger than that of KaiAC . Thus , the pathway KaiBC + KaiA is favored in this work , in which KaiABC is formed by KaiBC sequentially binding three KaiA dimers . The binding of KaiA to KaiBC is strongly dependent on KaiC phosphorylation state in contrast to the binding of KaiA to free KaiC because direct or indirect interactions may exist between KaiA and KaiB on KaiBC complex . We also assume that the interactions among these three proteins in KaiABC complex directly facilitate its own intra- and inter-subunit interactions , which generates stronger kinetic cooperativity in phosphorylation or dephosphorylation than those of other KaiC forms . Reactions for KaiABC are shown in Eq . 4 , where n = 1 , 2 , 3 . ( 4 ) The dynamics of the full network is described by 100 equations . 98 ( 2×49 ) of them are ordinary differential equations for phosphorylation and dephosphorylation in LC and LBC layers . Other two algebraic equations represent the mass conservation law for KaiA and KaiB in the full network . Correspondingly , the total number of kinetic constants is 616 for the full Kai network ( ) . The free energy estimation provides a way of analyzing the intrinsic correlations among those kinetic constants so that the dimension of the parameter space could be greatly reduced . Technically , the 616 kinetic constants can be automatically generated with only 88 basic parameters ( Table S5 ) . The 88 parameters are not fully independent , and constraints among which are carefully deduced by qualitative and semi-quantitative analyses of structure-based interaction free energies in one KaiC hexamer ( see details in section 1 . 2 in Text S1 ) .
Except where otherwise noted , results based on the deterministic method are used in this manuscript . The simulation results of circadian KaiC phosphorylation in vitro are shown in Fig . 2A . Here , borrowing from the concept used in multi-site substrate-enzyme binding , we define YS , YT and Y as the fractional saturation of phosphates for S431 , T432 and overall , respectively: , and , where [C]T is the total amount of KaiC hexamers . YT is always ahead of YS regardless of in phosphorylation or dephosphorylation phase , and its duration is almost symmetrical between these two phases . S431 phosphorylation ( YS ) , on the other hand , shows an asymmetrical phase distribution and is difficult to be fully phosphorylated ( the maximum number of phosphates ) . Slight changes in the strengths of interaction between S431 and T432 shows a functional differentiation of the two sites ( refer to section 2 . 1 in Text S1 ) . It suggests that high phosphorylation level at T432 is the main stream to determinate the amplitude in circadian KaiC phosphorylation in vitro , and the branch stream , S431 phosphorylation probably regulates the phase of KaiC phosphorylation cycle . T432 and S431 phosphorylation processes coordinately dominate the length of period . It is more convenient to directly compare our results with the experiment measurements by transforming the results obtained in ST representation into the subunit representation of KaiC hexamer ( refer to section 1 . 2 . 2 in Text S1 ) . The circadian dynamics of all three monomeric KaiC phosphoforms , i . e . m01 ( S431 phosphorylated only ) , m10 ( T432 phosphorylated only ) and m11 ( dual phosphorylated ) , can be illustrated , as shown in Fig . 2B . The phase distribution of the three phosphoforms is m10 → m11 → m01 , which is in well agreement with experimental results [11] , [12] . The profile labeled “total” represents the sum of the three phosphoforms ( m01+m10+m11 ) , and it is the most usual variable characterizing the phosphorylation state of KaiC in experiments . Actually , extra variables are essential to demonstrate the mechanism of the Kai oscillator , such as mij ( i , j = 0 , 1 ) , YS , YT and Y . Fig . 2C shows the circadian oscillatory profiles of free KaiC and its complexes . KaiC has a high average level , and its minimum still remains ∼27% of total KaiC . KaiBC is almost anti-phase with free KaiC . The maximum amount of KaiAC only occupies ∼10 . 0% of total KaiC . The phases of three Kai complexes show: KaiAC the first , then KaiBC , and KaiABC the last , which have been well documented in previous experiments [5] . Further , we performed a stochastic simulation of KaiC phosphorylation with low protein numbers using Gillespie algorithm [39] ( see section 2 . 2 in Text S1 ) . The molecule numbers of KaiA dimers , KaiB dimers and KaiC hexamers are maintained by a fixed ratio of ∼1∶3∶1 ( a standard ratio of Kai proteins' concentrations in Ref . [5] ) . We also fixed the ratio of the Kai protein number to the cell volume . Particularly , we examined the phosphorylation cycle when there is only one KaiC hexamer ( together with one KaiA dimer and three KaiB dimers ) , shown in Fig . 3A . It suggests that each KaiC ( in the presence of KaiA and KaiB ) hexamer is actually an independent oscillator with period comparable to 24 h . However , both the period and the amplitude are highly variable . Such variation can be reduced effectively by increasing the molecule numbers of Kai proteins even to a low level , shown in Fig . 3B–D . Essentially , a single KaiC hexamer is a well coordinated subsystem with positive ( KaiA ) and negative ( KaiB ) regulators . The regulatory mechanism functions via intra- and inter-subunit interactions among the whole KaiC hexamer . Yet , the interactions within the hexamer and those from the collisions among Kai proteins ( the way of the formation of Kai complexes ) are accompanied with large fluctuations for one KaiC hexamer . This is why the period and amplitude are not stable for a single oscillator . However , the noise of this quasi-stable oscillator can be well reduced when we slightly increase the protein numbers . The snapshots of node-to-node mass evolution at different circadian times are shown in Fig . 4 . The time for minimum Y is taken as the starting point of KaiC phosphorylation cycle . Interestingly , in both layers LC and LBC , no single clear-cut pathway can be identified in the whole phosphorylation cycle; instead , the reactions proceed in dispersed pathways . In effect , node-to-node mass distribution ( NNMD ) is found not to be synchronized between LC and LBC throughout the phosphorylation cycle . Dynamic node-to-node mass evolution in one complete cycle is shown in Video S1 . In LC layer , at initial time , the relative quantity of is ∼30 . 3% of the total amount of KaiC hexamers , while the corresponding in LBC layer is merely 0 . 5% ( Fig . 4A and 4E ) . From zero to ∼7 . 1 h , a dispersed NNMD among the low phosphorylated KaiC phosphoforms at S431 ( s≤3 ) is observed in LC layer ( Fig . 4B ) . NNMD becomes much more dispersed at the peak time ( ∼10 . 1 h , in Fig . 4C ) . Additionally , in LC layer , phosphorylation process hardly reaches the pathways and . Interestingly , only attain their maxima even at a later time after 16 h ( Fig . 4D ) . LBC layer shows much different dynamic features from LC ( Fig . 4E–H ) . The pattern of NNMD concentrates around the nodes which take up ∼38 . 9% of the total KaiC at the initial time ( Fig . 4E ) . Roughly , phosphorylation proceeds mainly along the pathways and from time zero to ∼7 . 1 h ( Fig . 4F ) . Then , NNMD rapidly becomes more dispersed , and spreads almost all over the whole LBC layer at the phosphorylation peak . Notably , several nodes such as become detectable in LBC layer at the peak time , whereas their counterparts in LC layer are hardly reachable ( Fig . 4G ) . There exists a fascinating pattern: when LC layer is absolutely in phosphorylation phase , phosphorylation and dephosphorylation are coexisted in LBC layer . Two critical nodes and exclusively evolve into the convergent points of KaiC phosphorylation at ∼13 h and ∼16 h , respectively ( Fig . 4H ) . By analyzing the oscillation patterns of all the hexameric KaiC phosphoforms in the network , we found that these rhythms exhibit a variety of waveforms and phase relationships . Interestingly , it is the reminiscence of the diverse temporal phasing patterns in the genome-wide expression in cyanobacteria [25] . To examine whether the Kai oscillator has the same ( or similar ) dynamic diversity in vivo , we performed the stochastic simulation where the number of KaiC hexamers is 2000 , an approximate quantity measured by experiments [14] . The stochastic simulation confirms our results , and makes a better illustration for the various waveforms . The waveforms found in the KaiC phosphoforms are actually quite variable ( refer to section 2 . 3 in Text S1 ) , yet for convenience , we can categorize them into 4 groups . Fig . 5 shows the stereotypes of each group in both deterministic ( Fig . 5A–D ) and stochastic ( Fig . 5E–H ) simulations . Group 1 ( Fig . 5A and Fig . 5E ) exhibits a smooth asymmetric sinusoidal-like curve . The waveform in group 2 ( Fig . 5B and Fig . 5F ) shows a greatly asymmetrical “sawtooth” shape , especially in the stochastic result ( Fig . 5F ) . Most remarkably , we found several rhythms with dual peaks ( Fig . 5C and Fig . 5G ) which constitute group 3 . The profile in Fig . 5D appears to be dual-peak , but the sub-trough between the two peaks is not as low as that in Fig . 5C . Interestingly , the stochastic result ( Fig . 5H ) for the same KaiC phosphoform ( ) exhibits a plateau pattern . We categorize this kind of rhythms into group 4 . Similar waveforms as in group 1–3 have long been observed in the oscillation profiles of bioluminescence that report the circadian gene expression patterns in cyanobacteria [25] . No pattern similar to group 4 has been found in vivo . Furthermore , we analyzed the phase distribution of hexameric KaiC phosphoforms in one circadian cycle . For simplicity , we examined the phases of the 49 hexameric phosphoforms regardless of whether KaiA or/and KaiB are bound . Accordingly , the samples are , where s , t = 0 , … , 6 . We take the trough time of Y ( the overall fractional saturation of phosphates on KaiC ) as zero time point of one circadian period . We define the phase of each KaiC phosphoform using two methods: one is to define the peak time ( the highest peak for dual-peak profiles ) as its phase point , while the other the time at the trough . The 49 phase points in Fig . 6A are defined by the peak time of each . Each point is identified by both its color and the radius of its circle orbit . The color represents the number of phosphorylated S431 in one KaiC phosphoform , while the radius that of phosphorylated T432 . As in Fig . 6A , the phases of KaiC phosphoforms are mainly distributed from ∼4 h to ∼16 h , during which there is a phase burst in the window between 10 h and 12 h . Cyanobacteria are a group of photoautotrophic prokaryotes , and the gene activity is much higher during the day time than at night . The phase distribution pattern of KaiC phosphoforms in Fig . 6A is well consistent with this natural habit of cyanobacteria . Interestingly , the phase points also constitute several spiral-like curves in the phase diagram ( same color with variable radius ) . Some of them are mainly counterclockwise spiral-like curves ( starting from the center ) , i . e . s = 0 , 1 . These points are mostly in the phosphorylation phase . When s = 3 , 4 , 5 , the curves become clockwise , which correspond to the dephosphorylation phase . The curves appears to change from the counterclockwise to clockwise as s = 2 ( the dark yellow points in Fig . 6A ) . It suggests that the transition from phosphorylation to dephosphorylation in the Kai system probably occurs when s≈2 , which is consistent with our previous conclusion that S431 is the main phase regulator due to its low phase transition threshold . Fig . 6B shows the phase distribution in which the phase of each KaiC phosphoform is defined by its own trough time . The identification of each point in the diagram is the same as in Fig . 6A ( color for S431 , radius for T432 ) . Here , the phase distribution is far more converge than that in Fig . 6A . The region around 0 or 24 h is strongly favored , while that around 12 h weakly favored . Similar pattern in phase distribution has been well observed experimentally [25] . In summary , the dynamic diversity of the in vitro Kai system actually shares some similarities in waveforms and phase distributions with the circadian gene expression patterns in a living cyanobacterial cell . There comes a natural question: does the dynamic diversity of the central Kai oscillator contribute to the genome-wide gene expression in cyanobacteria ? We do not believe the 49 KaiC phosphoforms so far contain sufficient complexity and diversity to control the ∼3000 cyanobacterial genes . One possibility is via even more complicated regulation network ( s ) downstream to amplify the original diversity embedded in Kai pacemaker . However , we guess there might be an alternative and more economical way for the job . On account of the analysis above , we propose a kaleidoscopic mechanism with stochastic fluctuation to explain the global genome regulation by Kai clock proteins . Φ the output of KaiC phosphorylation signals can be characterized as: ( 5 ) A variety of combinations ( random or deterministic ) of phosphorylation signals of hexameric KaiC phosphoforms would produce extremely complicated dynamic behaviors like oceans of patterns formed in a kaleidoscope . When n = 0 to 3 , XnCt s denotes free KaiC , KaiAC , KaiBC and KaiABC complexes , respectively . ψst features the transferring process of KaiC phosphorylation signal to downstream targets , and it could be a constant , a linear or a nonlinear function . f ( Z ) stands for the manners of Z , the intermediate regulators or the targets , interacting with KaiC hexamers ( or Kai complexes ) . ξ ( t ) reflects Gaussian noise with zero mean , . η represents the magnitude of the noise which is directly determined by the features of stochastic fluctuation in corresponding . The stochastic effect might be useful in vivo . Particularly , NNMD of the stochastic model shows the changes in the range and the strength of stochastic fluctuation during the phosphorylation cycle ( data not shown ) , which may explain that two separated time points with identical average level of gene expression exhibit different fluctuations [40] . Furthermore , low molecule number in certain KaiC phosphoforms may weaken or lose their deterministic targeting to specific genes; instead , these KaiC molecules might control wider or different sets of genes due to large stochastic effect . We could hypothesize that each KaiC phosphoform may adopt one of the following modes for its phosphorylation signaling process with the time evolution in one circadian cycle: deterministic→stochastic→deterministic; pseudo-stochastic→stochastic→pseudo-stochastic; stochastic→stochastic→stochastic . Bearing null phosphorylation information , is approximately a deterministic signal with the largest amplitude and the smallest relative fluctuation ( Fig . 5E ) . Then the whole set of KaiC phosphoforms except exhibits various stochastic phosphorylation signals . To the simplest case , two different output signals can be viewed in the dynamic system , one for only , the other for . If Φ contains a random combination of certain types of , it will be a stochastic output . Different sets of random combinations result in a series of outputs , Φi ( i = 1 , 2 … ) . Simply , because LC and LBC layers have never been synchronized in circadian cycle , two oscillators will then be monitored probably responsible for downstream regulation signals on gene expressions . One oscillator consists of the combination of all nodes in LC except , and the other in LBC . A third one is the oscillator which may correlate with rhythmic chromosome compaction . There is no unique way to determine how many oscillators participate and interlock each other microscopically in the whole KaiC phosphorylation network . Hopefully , such diversified signals based on the kaleidoscopic mode and stochastic effect could be useful to harmonize the multiple oscillators [41] with the single oscillator [42] in cyanobacterial genome-wide regulations . The synchronization of different phased Kai protein samples reveals the resilience of the Kai oscillator [43] . Mixing two samples ( equal amount ) with opposite phases ( peak and trough ) results in the dephosphorylation of the mixed KaiC system ( Fig . 7A ) , which agrees well with recent observation [43] . Furthermore , we quantitatively analyzed how the phosphorylation at S431 and T432 contribute to the phase resetting behavior . Fig . 7B is a contour map of the new initial reaction rate of Y ( after equal amount mixing ) versus the interior region of the limit cycle ( the boundary of the phase portrait ) formed by YS and YT in the phase plane . The initial values of YS and YT in the new mixture can be represented by a point in the phase plane of YS and YT . Geometrically , this point is the center of the line segment connecting to the two sample points on the limit cycle , because the two samples are mixed with equal amount . If the initial reaction rate of Y at the new point in the phase plane is positive , the mixture goes into the phosphorylation phase , while it enters the dephosphorylation phase when the initial rate is negative . As shown in Fig . 7B , three apparent regions are displayed within the limit cycle . One fully favors the dephosphorylation phase ( blue ) , while another the phosphorylation phase ( red ) . The third region is the transition zone where both phosphorylation and dephosphorylation are possible . Note that approximately around the center of the phase portrait , the phosphorylation and dephosphorylation points are tangled together . A tiny perturbation can result in the system wandering between phosphorylation and dephosphorylation for a while and finally randomly entering a stable phase . All the three cases are well represented in the experiments by Ito et al . [43] . Interestingly , we found no point at which the initial rate of Y is zero . It suggests that the Kai oscillator is highly robust that mixing different phased samples is unable to abolish the oscillation . The transition region can be approximately seen as a regular stripe , and the slope of which may qualitatively suggest how S431 and T432 phosphorylation contribute to the synchrony of KaiC hexamers . If the slope is parallel to the x-axis ( YT ) , the phase resetting is totally controlled by S431 . No matter how T432 phosphorylation level varies , the mixture hardly changes its reaction direction . Likewise , if the slope of the transition region is parallel to the y-axis ( YS ) , the synchronization process is then fully regulated by T432 ( YT ) . One might imagine that when the slope's angle versus x-axis is 45° , S431 and T432 make equal contributions . Yet in our case , this angle is ∼26 . 5° ( more likely to be attracted to YT ) , thus , it suggests that S431 ( YS ) is the major phase regulator for the synchrony of an ensemble of different phased KaiC hexamers . In addition , the angle of ∼26 . 5° corresponds to the ratio of YS to YT ∼1:2 , which suggests that T432 prefers to be the main amplitude regulator . In addition , we performed a simulation in which two equal-amount Kai samples with non-standard concentrations are mixed , keeping the final mixture to be standard . The analysis of the results may suggest a mechanism giving rise to new rhythms with periods longer than 24 hours ( details can be found in section 2 . 8 in Text S1 ) . The bifurcation analysis ( using XPPAUT software package [44] ) as in Fig . 7C–D indicates that the oscillation is sustained even by lowering the total protein concentration to ∼0 . 34-fold of the standard mixture used in ref . [5] . The period changes from 29 . 6 h to 20 . 9 h as the ratio of total Kai protein goes from 0 . 34 to 6 ( Fig . 7C ) , while the amplitude shows slight variation when the ratio is larger than 1∶1 ( Fig . 7D ) . In contrast , a more recent work by Rust et al . [12] showed an opposite tendency in which the period is shortened as the protein concentrations decrease . This slight difference between the works by Kageyama et al . [5] and Rust et al . [12] needs further experimental elucidation . Furthermore , by adjusting our model's parameters ( details see Table S4 and S5 ) , we are able to obtain the same tendency of period variation ( with protein concentrations ) as the results by Rust et al . In the original model , we set , i . e . the forward reactions of KaiB binding to free and ( KaiC hexamer with one T432 phosphorylated ) are not allowed , whereas we let free be able to bind KaiB and only leave in the newly adjusted model . Based on this modification , we tune the other parameters as slightly as possible so that it can mostly reproduce the results made by Rust et al . , and meanwhile keep all our earlier simulation results valid ( refer to section 2 . 6 in Text S1 ) . Using the new parameter set , our simulation results are consistent with the experiment of KaiC dynamics under concerted variations in Kai proteins' concentrations by Rust et al . [12] . We also reproduce some other important results given by Rust et al . ( i ) The phase of KaiC phosphorylation is uniquely determined by the distribution of KaiC phosphoforms . Particularly , the phosphorylation state of S431 plays a more significant role in phase modulation . As shown in Fig . 8A , when the initial value of m01 ( monomer with only S431 phosphorylated ) reaches 7% of total KaiC , KaiC immediately enters the phosphorylation phase . As m01 takes up 24% of the total KaiC at the starting point , dephosphorylation phase is favored . The results are mostly consistent with the experimental data shown by Rust et al . Both experimental and theoretical studies suggest that S431 is more likely a phase regulator with a threshold of phosphate number approximately between 0 . 4 and 1 . 4 . Below the threshold KaiC enters the phosphorylation phase , and above which KaiC prefers dephosphorylation . ( ii ) Transient response curves of KaiB introduction into KaiA-KaiC reaction can be mimicked with our model , in which the explicit KaiB-KaiC and KaiA-KaiB-KaiC interactions are considered . In Fig . 8B , standard amount of KaiB is added at different time points in the binary KaiA-KaiC reaction mixture . At the early stage of KaiC phosphorylation in the presence of KaiA , KaiB has little effect on KaiC phosphorylation dynamics , whereas the attenuation of KaiB emerges significantly at a certain KaiC phosphorylation level . The attenuation effect of KaiB on KaiA mainly depends on the S431 phosphorylation level . Our model design agrees with the experimental fact that S431 is crucial for KaiB-KaiC interaction . Moreover , KaiB-KaiC formation induces competition between KaiA and KaiB binding to KaiC , and meanwhile the formation of KaiABC complex facilitates KaiB's attenuation on KaiA . The phase of S431 phosphorylation is always delayed compared with that of T432 phosphorylation ( due to the differentiation of their reactivity , Fig . 2A and B ) . Therefore , KaiB takes effect ( as an attenuator ) in the late phase of KaiC phosphorylation . ( iii ) The activity of KaiA is carefully examined according to the methods applied by Rust et al . , i . e . to perturb the reaction system by adding small amount ( 10% standard KaiC ) of purely unphosphorylated KaiC and introducing overdose of free KaiA at different circadian time points . Both results agree well with the corresponding experiments by Rust et al . , as shown in Fig . 8C and D . During the whole phosphorylation phase , the extra 10% unphosphorylated KaiC introduced can also undergo phosphorylation stimulated by KaiA , while the activity of KaiA is greatly reduced in the dephosphorylation phase ( Fig . 8C ) . In Fig . 8D , KaiC immediately enters into steady state phosphorylation upon the introduction of 5-fold standard KaiA at different time points . The final phosphorylation levels of KaiC are the same for the different perturbation time points . Introducing KaiA at the trough time stimulates the largest transient fluctuation ( the maximum phosphorylation level ) , suggesting that KaiA reaches its maximum activity at this time point . Therefore , the apparent activation-inactivation of KaiA does exist , as proposed by Rust et al . Our relatively complex model may show an explicit mechanism of KaiA activity variation . In effect , the reduction of KaiA activity is mainly attributed to the competition with KaiB ( on KaiC binding ) and inhibition ( or trapping ) within KaiABC complex . And all the processes are controlled by KaiC phosphorylation levels , especially those of S431 . To make our model more predictive , we further explore the phase response curve ( PRC ) [45] of the in vitro Kai oscillator , using the transient KaiA concentration variation as the stimulus pulse . Specifically , 4-hour pulses are applied to various time points in the circadian cycle as Kai oscillator is free running . At the beginning of the pulse , KaiA concentration changes to a non-standard amount , lasting for four hours , and returns to its standard quantity at the end of the pulse . In this work , four concentration values of KaiA pulses are used: 1/3× , 2/3× , 1 . 5× and 3× standard amount of KaiA . The phase shifts under these pulses are shown in Fig . 9 as the function of circadian time , and the raw dynamic phase shift profiles can be found in section 2 . 10 in Text S1 . The zero circadian time , as we defined previously , is the trough time of total KaiC phosphorylation . Under the low-KaiA stimuli ( 1/3× and 2/3× ) , the phases of KaiC dynamics delay during most of the phosphorylation course ( 0 h∼10 h ) , while the phases advance throughout dephosphorylation . In both 1/3× and 2/3× KaiA pulses , the delayed phase shifts change rapidly and reach the maximum ( −10 h ) at the circadian time 10 h , whereas the advanced shifts stay around 2 h during most of the dephosphorylation phase . There is a discontinuous transition from delays to advances ( at 10 h∼12 h ) for 1/3× and 2/3× KaiA stimuli , respectively , which mainly attributes to the choice of zero circadian time . Since the in vitro Kai oscillator has no cue of real day and night , the zero circadian time could be chosen arbitrarily . On the contrary to the 1/3× and 2/3× pulses , the high-KaiA stimuli ( 1 . 5× and 3× ) cause phase advances mostly throughout phosphorylation and delays during dephosphorylation . The two PRCs by high-KaiA stimuli ( 1 . 5× and 3× ) are similar during the phosphorylation phase ( 0∼10 h ) , while they become quite different as the stimuli are applied in the dephosphorylation phase ( 10∼22 h ) . During most of the time in dephosphorylation phase , there are only some slight shifts in the delayed phases by 1 . 5× KaiA pulses , whereas the 3× KaiA pulses cause continuous increase in phase delays . It suggests that the sensitivity of the phase delay under the high-KaiA pulses depends on the very dose of KaiA applied . Light , in most cases , is the primary Zeitgeber for the entrainment of circadian rhythms , especially for cyanobacteria , a group of photoautotrophic prokaryotes . Three proteins , CikA , LdpA and Pex are identified to be responsible for the external light-dark stimuli [1] . In vivo , they coordinately regulate the transcription of kaiA gene to facilitate the day-night entrainment . Therefore , one could assume that the transient change in KaiA concentration in vitro may , to some extent , mimic the light or dark pulse applied to the living cell . For instance , increase in KaiA amount could correspond to a light stimulus , while decrease in KaiA concentration mimics a dark pulse . In our results , the dark pulse mimicking PRCs ( 1/3× and 2/3× KaiA ) may differ considerably from those obtained by the real dark pulse in vivo . In fact , the condition of free running oscillator in vivo is continuous light ( LL ) in which the concentrations of the Kai proteins fluctuate , especially for KaiC and KaiB , whereas the concentrations of Kai proteins stay mostly unchanged in the in vitro Kai pacemaker . Nevertheless , the in vitro Kai oscillator could be analogous to the free running in vivo pacemaker in continuous dark ( DD ) . Thus , the 1 . 5× and 3× KaiA-pulse PRCs may accord with those by light-pulse stimuli in vivo . We hope the quantitative analysis of PRCs could be helpful for probing the working and entrainment mechanism of the Kai oscillator both in vitro and in vivo .
In this work , we propose a multi-layer reaction network to mimic the circadian phosphorylation of KaiC . There are two major features in our model . First , we suggest that one KaiC hexamer can be viewed as a single oscillator . In essence , one hexamer is actually a mesoscopic dissipative subsystem which can exhibit phosphorylation oscillation ( even if noisy ) in the presence of KaiA and KaiB . Macroscopically , a population of KaiC hexamers constitutes an oscillatory dissipative system which is much more robust than the single KaiC oscillator . Therefore , we can find the self-organization process in a single KaiC oscillator as well as in a bulk phase containing a number of single oscillators . This is probably the most important reason why the rhythm of Kai system is highly robust , especially against fluctuations in temperature and protein concentrations . Second , we found a dynamic diversity of temporal patterns in KaiC phosphorylation network . The oscillation patterns of the hexameric KaiC phosphoforms in the network exhibit a variety of phases , waveforms and amplitudes . Thus , we deduce that the combination of these diverse phases may produce a kaleidoscopic mode which is helpful to explain the circadian genome-wide gene expression in cyanobacteria . According to the kaleidoscopic mode , combinations of different KaiC phosphoforms in various proportions can produce numerous diverse-phased phosphorylation signals . Meanwhile , due to different random fluctuations in the absolute molecule numbers of KaiC phosphoforms , regulation of these hexamers in gene expressions can swing among deterministic , pseudo-stochastic or stochastic modes , which may be significant to the stochastic gene expressions in the out-of-steady-state system [40] . Additionally , we suppose that can be considered as a primary-master oscillator ( strictly as an oscillatory output ) . The other KaiC phosphoforms are probably the secondary-master oscillators . is the major component of KaiC phosphorylation cycle , yet it can not meet the need of the regulation of basal metabolism because it contains null phosphorylation information . The maintenance of physiological functions thus requires the participation of the other KaiC phosphoforms . If so , we deduce that S . elongates master phase should approximately be anti-phase with . Based on the stochastic kaleidoscopic regulation , it is postulated that Kai oscillator may control the global circadian transcription in coupling with some known mechanisms such as topological changes in chromosome [46] , [47] , binding to forked DNA [6] , interacting with SasA-RpaA and LabA [48] , [49] or signaling with sigma factors [1] . Based on our estimation of free energy of activation , temperature compensation can be qualitatively explained as an inherent structural property of a well-coordinated KaiC hexamer ( simulation results can be found in section 2 . 4 in Text S1 ) . Further analysis of the coupling of the three hierarchical energy modes , i . e . local , quasi-local and global , reveals subtle relationships between temperature compensation and the kinetic cooperativity . In wide-type KaiC , the local mode energy may be the coarse-tune component of temperature compensation in circadian KaiC phosphorylation , while the global mode is probably to fine-tune the kinetic cooperativity . The quasi-local mode most likely fine-tunes the temperature compensation and regulates the kinetic cooperativity as the coarse-tune component . Accordingly , we may predict several extreme dynamic behaviors in mutant KaiC phosphorylation . Elimination of global mode ( or significant change in quasi-local mode ) will result in the loss of kinetic cooperativity , and finally abolishes the oscillation of KaiC phosphorylation , yet the robustness of temperature compensation ( considering the steady-state reaction rates ) may be still maintained in this case . If a moderate change in interaction energy in local mode occurs , the temperature compensation is no longer robust but an extreme low amplitude , long period and probably noisy oscillation of KaiC phosphorylation may be observable or even the oscillation of KaiC phosphorylation is totally abolished . The deterministic dynamic simulations in a reduced model confirm that the monomer-shuffling favors the synchronization of KaiC phosphorylation level , but it results in longer period and lower amplitude ( refer to section 2 . 9 in 1 ) . Without introducing an explicit monomer-shuffling process , simulations in the full model obtain very consistent results with a recent experimental study on phase synchronization [43] . We suggest that the phosphorylation level at S431 is mainly responsible for the phase coherence for a population of KaiC hexamers . Due to the limited information about the monomer-shuffling mechanism , it definitely hinders the detailed analyses of this process . Verification of the predictions proposed in the present work and comprehensive elucidation of the KaiC circadian phosphorylation in vitro and in vivo absolutely depend on the new findings achieved experimentally and theoretically . | Circadian clocks are endogenous timing mechanisms that allow living organisms to coordinate their activities with daily environmental fluctuations . In cyanobacteria , almost all the genes are rhythmically expressed with the same ∼24 h period yet exhibit a variety of phase relationships and waveforms . Remarkably , the core pacemaker ticks robustly via simple biochemical reactions carried out by three Kai proteins: KaiC undergoes circadian phosphorylation in the presence of KaiA , KaiB and ATP . In this work , we propose a reaction network modeling the Kai oscillator based on the differentiation of dual phosphorylation sites . We found a dynamic diversity in KaiC phosphorylation which may serve as a potential regulatory mechanism related to the diverse-phased genome-wide expressions in cyanobacteria . In addition , we deduce that each KaiC hexamer is a single oscillator in regulating its own phosphorylation and interactions with KaiA or/and KaiB . In complex organisms , a number of key clock components possess similar activities ( e . g . , phosphorylation ) with multiple nonequivalent active sites , and they may also show some unusual dynamic features that are embedded in the proteins' own reaction networks . We hope our work could be helpful to study the correlations between gene expressions and circadian rhythm in prokaryotic cells , even in eukaryotic cells . | [
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Iron homeostasis is important for growth , reproduction and other metabolic processes in all eukaryotes . However , the functions of ATP-binding cassette ( ABC ) transporters in iron homeostasis are largely unknown . Here , we found that one ABC transporter ( named FgAtm1 ) is involved in regulating iron homeostasis , by screening sensitivity to iron stress for 60 ABC transporter mutants of Fusarium graminearum , a devastating fungal pathogen of small grain cereal crops worldwide . The lack of FgAtm1 reduces the activity of cytosolic Fe-S proteins nitrite reductase and xanthine dehydrogenase , which causes high expression of FgHapX via activating transcription factor FgAreA . FgHapX represses transcription of genes for iron-consuming proteins directly but activates genes for iron acquisition proteins by suppressing another iron regulator FgSreA . In addition , the transcriptional activity of FgHapX is regulated by the monothiol glutaredoxin FgGrx4 . Furthermore , the phosphorylation of FgHapX , mediated by the Ser/Thr kinase FgYak1 , is required for its functions in iron homeostasis . Taken together , this study uncovers a novel regulatory mechanism of iron homeostasis mediated by an ABC transporter in an important pathogenic fungus .
Iron is an essential element for growth , and can be present in various forms such as iron ions , heme and iron sulfur clusters that play critical roles in respiration , DNA synthesis and repair , ribosome biogenesis , metabolism and other cellular processes in all organisms [1–3] . In mammals , iron deficiency anemia is the most extended and common nutritional disorder in the world [4 , 5] . In pathogenic fungi , the defects in iron uptake lead to decreased virulence [6 , 7] . However , excess iron has the ability to generate toxic reactive oxygen species ( ROS ) through Fenton's reaction resulting in damage to cellular components [8] . Iron overload in liver and other organs from hepcidin regulation disorder is associated with hereditary hemochromatosis [9 , 10] . Consequently , all organisms have developed tightly homeostatic regulatory mechanisms to balance uptake , consumption and storage of iron . ATP-binding cassette ( ABC ) transporters that contain transmembrane domains ( TMDs ) and structurally conserved nucleotide-binding domains ( NBDs ) actively transport a wide variety of compounds across biological membranes [11] . ABC transporters play important roles in transporting compounds and regulating various physiological processes , including fatty acid metabolism , ribosome biogenesis , and mRNA translation [12 , 13] . Recently , ABC transporters have been implicated in endocytosis and hyphal formation in Candida albicans [14] , autophagy in human [15 , 16] , lifespan regulation in Drosophila [17] , and the establishment of terrestrial lifestyle in plants [18] . However , our understanding of ABC transporters involved in iron homeostasis is limited . In S . cerevisiae , two ABC transporters Atm1 and Mdl1 have been found to be associated with iron homeostasis . Atm1 regulates the assembly of cytoplasmic and nucleic iron-sulfur ( Fe-S ) proteins might via transporting glutathione ( GSH ) -linked [2Fe-2S] clusters ( ( GS ) 4-[2Fe-2S] ) from mitochondria to cytosol [19–20] . Mdl1 exports the proteolytic products generated by the m-AAA protease , and the over-expression of Mdl1 partially restores the defects in Atm1 mutant [21 , 22] . In addition , an ABC transporter in Mycosphaerella graminicola ( MgAtr7 ) harboring a dityrosine/pyoverdine biosynthetic domain is required for siderophore production and subsequently modulates iron homeostasis [23] . To date , Atm1 homologs have been found to control assembly of cytoplasmic and nuclear Fe-S proteins in S . cerevisiae , Arabidopsis thaliana and Homo sapiens [24–26] . But the regulatory mechanism of iron homeostasis modulated by Atm1 is only characterized in S . cerevisiae [27–30] . In the budding yeast , the Fe-S proteins monothiol glutaredoxins Grx3/4 sense GSH-linked [2Fe-2S] clusters exported by Atm1 from mitochondria to cytoplasm [31] . Depletion of ATM1 impairs the loading of GSH-linked [2Fe-2S] clusters onto monothiol glutaredoxins Grx3/4 , thus hindering the formation of the complex containing Grx3/4 and the cytosolic proteins Fra1/2 . This subsequently enhances retention of the transcription factor Aft1/2 at the promoter of iron acquisition genes , therefore leading to constitutive gene activation [27–31] . Except for the budding yeast , the functions and regulatory mechanisms of Atm1 orthologs in iron homeostasis have not been documented in other organisms . F . graminearum is an economically important plant pathogen that causes cereal scab disease worldwide [32] . In addition to yield reduction , mycotoxins such as deoxynivalenol ( DON ) and zearalenone ( ZEA ) produced by the causal agent constitute a serious threat to food security and human health [33] . F . graminearum contains many more ABC transporters than most other representative fungi from major evolutionary lineages within the fungal kingdom [34 , 35] . After the screening of 60 ABC knockout mutants for sensitivity to iron stress , we found that only the FgAtm1 ( Atm1 ortholog ) mutant was highly sensitive , whereas the mutants of Mdl1 ( FGSG_01885 ) and MgAtr7 ( FGSG_03735 ) orthologs were not involved in iron regulation in F . graminearum . We therefore focused on exploring the functions of FgAtm1 in regulating iron homeostasis in F . graminearum . In this study , we revealed that the deletion of FgATM1 impedes the activity of cytosolic Fe-S proteins nitrite reductase and xanthine dehydrogenase , which in turn induces transcription factor FgAreA , and subsequently activates the transcription factor FgHapX . The phosphorylation of FgHapX is mediated by the Ser/Thr kinase FgYak1 and is further required for the transcriptional regulation of iron-related genes . It is worth to note that this interaction between FgHapX and the monothiol glutaredoxin FgGrx4 is also required for the transcriptional activity of FgHapX , which is dramatically different from what is known in the budding yeast . Overall , results from this study reveal a regulatory mechanism of iron homeostasis mediated by FgAtm1 and the transcription factor cascade FgAreA-HapX in F . graminearum , which will help us improve the understanding of iron-homeostatic regulation in eukaryotes .
F . graminearum contains 62 putative ABC transporters . In order to explore functions of ABC transporters , we deleted each of them using a homology recombination strategy . Among 62 ABC transporter genes , 60 were deleted successfully , and two genes FGSG_07101 and FGSG_04181 are essential for F . graminearum growth [35] . To explore functions of ABC transporters in iron homeostasis , we screened these 60 deletion mutants for the sensitivity to iron stress and found that the mutant of FGSG_10911 was supersensitive to iron stress ( S1 Fig ) , indicating that this ABC transporter may play important roles in iron homeostasis regulation . The BLAST analysis showed that FGSG_10911 is homologous to S . cerevisiae Atm1 ( Fig 1A ) , and thus we named the gene FgAtm1 . We complemented ΔFgAtm1 with FgAtm1-GFP and N-terminal mitochondrion-targeting sequence of FgAtm1 ( FgAtm1N1-111 ) -GFP , respectively . Subcellular localization observation revealed that FgAtm1-GFP co-localized with the mitochondrial dye MitoTracker , and the N-terminal mitochondrion-targeting sequence ( http://www . cbs . dtu . dk/services/TargetP/ ) is thought to be responsible for its mitochondrial localization ( Fig 1B and S2 Fig ) . Phenotypic characterization showed that ΔFgAtm1 displayed hypersensitivity to 0 . 5 mM Fe2+ and 2 mM Fe2+ supplemented into minimal medium ( MM ) and potato dextrose agar ( PDA ) , respectively ( Fig 1C ) . In contrast , after treatment with iron-specific chelating agent bathophenanthroline disulfonate ( BPS ) at 0 . 3 mM , ΔFgAtm1 grew better than untreated cultures on MM and PDA ( Fig 1C ) . Determination of intracellular iron by using fluorescent iron-binding dye FeRhoNox-1 and a colorimetric ferrozine-based assay revealed a high level of iron accumulation in both mitochondria and the whole cell of ΔFgAtm1 ( Fig 1D ) . The iron content in mitochondria was clearly higher than that in whole cell ( Fig 1D ) . In addition , the effect of iron stress on conidial germination was also determined . As shown in Fig 1E , 98% of the wild type conidia germinated after incubation at 28°C for 24 h in the trichothecene biosynthesis induction ( TBI ) medium that contains a trace amount of Fe2+ [36] , while , ΔFgAtm1 conidia did not germinate even after 48 h under the same conditions . When 0 . 3 mM BPS was added into TBI to chelate Fe2+ , 44% and 72% of ΔFgAtm1 conidia germinated after incubation for 24 and 48 h , respectively . Conidial germination in the wild type was not affected by BPS treatment ( Fig 1E ) . To further confirm that the supersensitivity of ΔFgAtm1 to iron stress is due to the deletion of the FgATM1 gene , the mutant was complemented with a full-length wild-type FgATM1 gene amplified with the primers listed in S1 Table . The complemented strain ΔFgAtm1-C contained a single copy of FgATM1 , which was inserted into the genome of ΔFgAtm1 ( S3 Fig ) . The defects of mycelial growth , conidial germination and accumulation of iron in ΔFgAtm1 were restored to the wild-type phenotypes in ΔFgAtm1-C ( Fig 1C–1E ) . These results strongly indicate that the lack of FgAtm1 leads to accumulation of intracellular iron in F . graminearum . To determine whether FgAtm1 regulates the assembly of cytosolic Fe-S proteins , we studied the activity of Fe-S proteins isopropyl malate isomerase ( FgLeu1 , FGSG_ 09589 ) , aconitase ( FgAco1 , FGSG_07953 ) , fumarase ( FgFum1 , FGSG_08712 ) . S . cerevisiae Leu1 is a cytosolic Fe-S protein and catalyzes the second step in leucine biosynthesis [26] . Cytosolic and mitochondrial Fe-S proteins Aco1 and Fum1 both participate in glyoxylate shunt in cytosol and TCA cycle in mitochondria in the budding yeast , respectively [37 , 38] . Enzyme activity assays showed that the activities of FgLeu1 , FgAco1 and FgFum1 in the cytosol of ΔFgAtm1 were attenuated by 37 , 44 and 28% respectively , when compared to those in the wild type . In contrast , the activities of FgAco1 and FgFum1 in ΔFgAtm1 mitochondria were not significantly changed ( Fig 2A ) . Further , feeding ΔFgAtm1 with leucine ( the final catalytic product of FgLeu1 ) or the final catalytic product of other cytosolic Fe-S proteins nitrite reductase , glutamate dehydrogenase or xanthine dehydrogenase [39 , 40] also accelerated the growth of ΔFgAtm1 on MM ( Fig 2B ) . In S . cerevisiae , GSH-linked [2Fe-2S] clusters were reported to be the substrate of Atm1 , and deletion of ATM1 caused increased GSH content in the whole cell [19 , 20 , 41] . We therefore tested the content of GSH , and found that it was increased by 59% and 50% in mitochondria and the whole cell of ΔFgAtm1 , respectively ( Fig 2C ) . Similar to these reported in the budding yeast [19–20] , the results of this study indicate that FgAtm1 also modulates the assembly of cytosolic Fe-S proteins likely via transporting GSH-linked [2Fe-2S] clusters from mitochondria into F . graminearum cytoplasm . To further explore the regulatory mechanism of FgAtm1 in iron homeostasis , we determined iron stress sensitivity for nine cytoplasmic Fe-S protein mutants constructed in our laboratory and found that nitrite reductase ( FgNiiA , FGSG_08402 ) and xanthine dehydrogenase ( FgXdh , FGSG_01561 ) mutants showed increased sensitivity to Fe2+ ( Fig 3A and S5 Fig ) . Similar to what were reported in the yeasts [37 , 38 , 42 , 43] , the remaining proteins that we tested were not involved in iron stress responses . Previous studies have shown that nitrite reductase and xanthine dehydrogenase are key enzymes for non-preferred nitrogen source utilization , and are regulated by nitrogen metabolism regulator AreA in Aspergillus nidulans , F . oxysporum and F . graminearum [40 , 44–47] . Nitrite reductase is responsible for nitrate utilization [40] and xanthine dehydrogenase is required for oxidizing hypoxanthine to xanthine [48] . Quantitative reverse transcription PCR ( qRT-PCR ) assays showed that transcription of FgAREA was induced by the deletion of FgATM1 , FgNIIA ( encoding nitrite reductase ) or FgXDH ( encoding xanthine dehydrogenase ) , as well as by the non-preferred nitrogen sources , NaNO3 or hypoxanthine ( Fig 3B ) . Surprisingly , we found that ΔFgAreA also exhibited elevated sensitivity to Fe2+ ( Fig 3A ) . Therefore , we hypothesized that the deletion of FgAtm1 leads to reduced activities of FgNiiA and FgXdh , which induces overexpression of FgAreA . To explore the role of FgAreA in regulating iron homeostasis , we first performed serial analysis of gene expression ( SAGE ) assay for the mutant ΔFgAtm1 , and found that 56 iron-related genes were differentially expressed ( >2-fold ) in ΔFgAtm1 ( S2 Table ) . Further , qRT-PCR assay confirmed that the transcription level of the transcription factor FgHAPX was dramatically increased in ΔFgAtm1 as compared to that of the wild type ( S6 Fig ) . To understand the mechanism by which FgAreA regulates FgHAPX expression , we studied the binding ability of FgAreA to the promoter of FgHAPX in the wild type bearing FgAreA-GFP ( PH-1::FgAreA-GFP ) and in ΔFgAtm1 bearing FgAreA-GFP ( ΔFgAtm1::FgAreA-GFP ) using chromatin immunoprecipitation and quantitative PCR ( ChIP-qPCR ) assay . A strain transformed with GFP alone was used as a negative control . ChIP-qPCR analyses showed that enrichment of FgAreA at the FgHAPX promoter was induced by the deletion of FgATM1 as well as the treatment by NaNO3 or hypoxanthine ( Fig 3C ) . GFP enrichment at the FgHAPX promoter was undetectable in the negative control strain ( Fig 3C ) . Additionally , qRT-PCR assays revealed that FgHAPX transcription was also induced with NaNO3 or hypoxanthine treatment . Moreover the induced expression of FgHAPX upon non-preferred nitrogen source treatment was dependent on FgAreA ( Fig 3D ) . These results indicated that FgAreA binds to the promoter of FgHAPX and regulates its transcription . To explore the function of FgHapX in iron homeostasis , we first constructed a FgHAPX deletion mutant ΔFgHapX , and tested the sensitivity of ΔFgHapX to iron stress . As shown in Fig 4A and 4B , ΔFgHapX became more sensitive to iron stress in comparison with the wild type , although ΔFgHapX did not show an obvious change in total iron content . Furthermore , we determined the content of extra- and intracellular siderophores secreted by ΔFgHapX with a chrome azurol S ( CAS ) assay , and found that the lack of FgHAPX caused reduced extracellular siderophore but not intracellular siderophore ( Fig 4C ) . Similarly , qRT-PCR assays revealed that iron acquisition genes were down-regulated and iron-consuming genes were up-regulated in ΔFgHapX ( Fig 4D ) . We knocked out FgHAPX in ΔFgAtm1 , and checked whether the defects of ΔFgAtm1 were partially recovered by deletion of FgHAPX . As we expected , the double mutant ΔFgAtm1-HapX grew better and displayed decreased sensitivity to iron stress than ΔFgAtm1 ( Fig 4A ) . Determination of iron and siderophore revealed that lack of FgHAPX in ΔFgAtm1 led to a reduced iron concentration , and decreased extra- and intracellular siderophores in comparison with those in ΔFgAtm1 ( Fig 4B and 4C ) . Expression levels of iron acquisition genes in ΔFgAtm1-HapX were reduced and the transcription of iron-consuming genes were elevated compared to those in ΔFgAtm1 ( Fig 4D ) . HapX homologs in A . nidulans and A . fumigatus have been found to repress the transcription of iron-consuming genes by binding to CCAAT motif [49 , 50] . The multiple EM for motif elicitation ( MEME ) analyses showed that the genes FgCYCA , FgHEMA , FgLYSF and FgACOA involved in the iron-consuming have the CCAAT motif ( Fig 5A ) . Electrophoretic mobility shift assay ( EMSA ) further confirmed that FgHapX bound the promoters of iron-consuming genes ( Fig 5B ) . Iron acquisition genes contained the GATA , but not CCAAT , motif in their promoters ( Fig 5A ) , indicating that other regulator ( s ) modulates the transcription of iron uptake genes directly in F . graminearum . In A . fumigatus , HapX activates the expression of siderophore-mediated iron uptake genes via transcriptional repression of SreA that suppresses the transcription of iron acquisition genes via binding to the GATA motif in their promoter [50 , 51] . The MEME analysis and EMSA assay showed that FgHapX could bind the FgSREA ( SreA homolog ) promoter ( Fig 5A and 5B ) . Moreover , the qRT-PCR assay revealed that deletion of FgHAPX led to elevated transcription of FgSREA ( Fig 5C ) . We further obtained a FgSREA deletion mutant ΔFgSreA , and found that ΔFgSreA displayed increased sensitivity to iron stress ( Fig 5D ) . The deletion of FgSREA caused iron accumulation , and increased extra- and intracellular siderophores ( Fig 5E and 5F ) , and qRT-PCR assays showed that deletion of FgSREA caused elevated expression of iron acquisition genes ( Fig 5G ) . These results indicated that FgHapX represses the transcription of FgSreA , and subsequently activates transcription of iron acquisition genes . To explore the regulatory mechanism of FgHapX in iron homeostasis , we performed a yeast two-hybrid ( Y2H ) screen of F . graminearum cDNA library , and found 50 potential FgHapX-interacting proteins ( S3 Table ) , including the monothiol glutaredoxin FgGrx4 that is homologous to S . cerevisiae Grx3/4 . Furthermore , Y2H , Co-IP and BiFC assays revealed that FgGrx4 interacted with FgHapX in the nucleus and the interaction was independent of FgAtm1 ( Fig 6A–6D ) . Moreover , Y2H and BiFC assays showed that FgGrx4 interacted with FgHapX through its GRX domain but not its TRX domain ( Fig 6B and 6D ) . In S . cerevisiae , lack of Grx3/4 leads to constitutive expression of iron acquisition genes , which contributes to iron accumulation in cells [52 , 53] . We therefore generated a FgGRX4 deletion mutant , ΔFgGrx4 , and found that ΔFgGrx4 displayed increased sensitivity to iron stress although it did not exhibit an obvious alteration in the total iron content ( Fig 6E and S7A Fig ) . To explore the effect of FgGrx4 on FgHapX functions , we determined the quantity and localization of FgHapX in ΔFgGrx4 , and found that FgGRX4 deletion did not cause an obvious change in the localization and quantity of FgHapX ( S7B and S7C Fig ) . However , similar to the FgHapX deletion , the deletion of FgGRX4 led to significantly decreased expression of iron acquisition genes , and increased transcription of iron-consuming genes ( Fig 6G ) . These results indicated that FgGrx4 is required for the transcriptional activity of FgHapX in F . graminearum . In eukaryotes , phosphorylation of transcription factors frequently has been found to regulate their activities . Phosphoproteome assay showed that FgHapX contains two predicted Ser residues at 245 and 338 sites that may be subject to phosphorylation ( S8 Fig ) . To confirm the function of these two residues , we constructed a strain containing a constitutive dephosphorylated FgHapX isoform . Briefly , the two phosphorylated Ser residues were replaced by alanine , the mutated FgHapXS245A/S338A was transformed into ΔFgHapX and the resulting strain was designated as ΔFgHapX-CS245A/S338A . Next , we performed a phos-tag assay to detect the phosphorylation level of FgHapX in ΔFgHapX-C and in ΔFgHapX-CS245A/S338A . As shown in Fig 7A , the dephosphorylated level of FgHapX in ΔFgHapX-CS245A/S338A was significantly higher than that in ΔFgHapX-C . To further explore the function of FgHapX phosphorylation , we determined the sensitivity of ΔFgHapX-CS245A/S338A to iron stress . As shown in Fig 7B and 7C , similar to ΔFgHapX , ΔFgHapX-CS245A/S338A still remained highly sensitivity to iron stress . Consistently , the qRT-PCR assays showed the expression levels of iron acquisition genes were reduced and the transcription of iron-consuming genes was elevated in ΔFgHapX-CS245A/S338A ( Fig 7D ) . In addition , these mutations did not change the quantity and localization of FgHapX ( S9A and S9B Fig ) . Collectively , these results indicate that phosphorylation of FgHapX is required for regulating expression of iron-related genes . To identify the potential kinase that phosphorylates FgHapX , we screened 96 kinase mutants and found that the mutant of Ser/Thr protein kinase FgYak1 ( FGSG_05418 ) showed dramatically increased sensitivity to iron stress ( Fig 7B and S10 Fig ) . Furthermore , co-immunoprecipitation ( Co-IP ) confirmed that FgYak1 interacted with FgHapX ( Fig 7E ) . Immunofluorescence assay also revealed that FgYak1 interacted with FgHapX in the nucleus ( Fig 7F ) . The qRT-PCR assays showed that , similar to those in ΔFgHapX and ΔFgHapX-CS245A/S338A , the transcription levels of iron acquisition genes were reduced and those of iron-consuming genes were elevated in ΔFgYak1 ( Fig 7D ) . Importantly , the phos-tag assay revealed that the dephosphorylated level of FgHapX in ΔFgYak1 was higher than that in ΔFgHapX-C ( Fig 7A ) . Meanwhile , the phos-tag assays showed that the phosphorylated levels of FgHapX in ΔFGSG_13318 , ΔFGSG_00408 , ΔFGSG_10381 , ΔFGSG_06832 , ΔFGSG_05734 or ΔFGSG_11812 were similar with that in ΔFgHapX-C , although these kinase mutants also showed elevated sensitivity to iron stress ( S10A–S10D Fig ) . In addition , deletion of FgYak1 did not alter the quantity and localization of FgHapX ( S9A and S9B Fig ) . Collectively , these results indicated that FgYak1 phosphorylates FgHapX in F . graminearum .
In S . cerevisiae , the Atm1-mediated iron regulation has been well characterized . The depletion of Atm1 impedes the loading of GSH-linked [2Fe-2S] clusters onto monothiol glutaredoxins , subsequently disrupting formation of the Grx3/4-Fra1/2 complex , which results in the failure of Aft1/2 dissociation from the promoters of iron acquisition genes [27–30] . The iron acquisition genes are therefore activated constitutively in the Atm1-depleted cells . In this study , we found that lack of FgAtm1 also leads to an overload of intracellular iron . However , the regulation mechanism of iron homeostasis mediated by FgAtm1 in F . graminearum is different from what is known in S . cerevisiae . We uncovered that deletion of FgAtm1 impedes the activity of cytosolic Fe-S proteins nitrite reductase and xanthine dehydrogenase , which conversely activates the nitrogen metabolism regulator FgAreA ( Fig 8 ) . Subsequently , FgAreA activates the transcription of repressor FgHapX via binding the FgHAPX promoter ( Fig 8 ) . Moreover , we found that FgHapX directly represses the transcription of iron-consuming genes , but also activates the expression of iron acquisition genes indirectly via suppressing the transcription of another repressor FgSREA ( Fig 8 ) . It is worthy to note that S . cerevisiae does not contain a HapX ortholog , and F . graminearum and other filamentous fungi do not have the yeast Aft1/2 orthologs . These results indicate that the regulatory networks of iron homeostasis can be distinct in different fungi . AreA belongs to the GATA nitrogen regulator and is required for the transcription of genes responsible for the utilization of non-preferred nitrogen sources in several fungi [54] . Previous studies have found that the transcription of AREA is induced by nitrogen starvation or the treatment with nitrate in A . nidulans [55] , F . graminearum [56 , 57] and Fusarium fujikuroi [58] . In this study , the lack of FgAtm1 compromised the activity of nitrite reductase FgNiiA and xanthine dehydrogenase FgXdh resulting in utilization defects of the non-preferred nitrogen sources nitrite and hypoxanthine , and subsequently induced the transcription of FgAREA . This finding indicates that iron metabolism is able to affect nitrogen utilization via the Fe-S proteins in the filamentous fungus F . graminearum . HapX is an important transcriptional repressor of intracellular iron homeostasis in filamentous fungi [59] . In A . fumigatus , HapX not only represses genes involved in iron-consuming pathways to spare iron , but also activates iron acquisition genes to acquire iron via suppressing another repressor SreA during iron starvation [50] , which is consistent with our finding . However deletion of HapX homologs does not change the transcription of iron acquisition genes in A . nidulans , F . oxysporum and C . albicans [49 , 60 , 61] . Previous studies have found that the transcription factor SreA may also represses the expression of HapX during iron overload [49 , 62] . In Cryptococcus neoformans , carbon metabolism regulator Mig1 promotes the transcription of HAPX under low-iron conditions [63] . In this study , we found however that FgHapX is regulated by the GATA transcription factor FgAreA . To our knowledge , this is the first observation that the iron regulator HapX is regulated by a nitrogen metabolism regulator AreA in filamentous fungi . In the current study , we also found that the functions of Grx4 orthologs vary dramatically in F . graminearum and yeasts . First , monothiol glutaredoxin FgGrx4 is required for the transcriptional activity of FgHapX via its interaction with FgHapX in F . graminearum . In yeasts , however , Grx4 homologs combine with the GSH-linked [2Fe-2S] clusters and then interact with transcription factors S . cerevisiae Aft1/Aft2 or Schizosaccharomyces pombe Php4/ Fep1 to disassociate these factors from the promoters of iron regulation genes [28–30 , 64–66] . Second , the interaction of FgGrx4 and FgHapX is independent of the presence of FgAtm1 , and the TRX domain of FgGrx4 doesn’t interact with FgHapX . In S . cerevisiae , the interaction of Grx4 and Aft1/2 is dependent on Atm1 [30] . In S . pombe , the interaction of GRX domain of Grx4 with Php4 or Fep1 is dependent on iron conditions , whereas the TRX domain continuously binds to Php4 or Fep1 [65 , 66] . Third , similar to FgHAPX deletion , deletion of FgGRX4 did not change intracellular iron content ( S7A Fig ) . However , lack of Grx3/4 leads to iron accumulation in S . cerevisiae cells [52] . Fourth , deletion of FgGRX4 caused the down-regulation of iron acquisition genes in F . graminearum . However , deletion of GRX3 or GRX4 causes constitutive expression of iron acquisition genes in S . cerevisiae [52 , 53] . In S . pombe , GRX4 disruption causes constitutive transcription of iron acquisition genes regulated by Fep1 , or constitutive transcription of iron-consuming genes regulated by Php4 [64 , 66] . Taken together , these results indicate that the function of FgGrx4 in F . graminearum is dramatically different from that of Grx4 in the yeasts . Phosphorylation of transcription factors mediated by various kinases has been found to modulate their localization , protein accumulation and DNA binding ability in eukaryotic organisms [67] . In mammals , phosphorylation of the organismal lifespan-related transcription factor FOXO by serum and glucocorticoid-induced kinase ( SGK ) results in the exclusion from the nucleus and repression of transcriptional activity [68] . In A . thaliana , multisite light-induced phosphorylation of phy-interacting basic Helix Loop Helix ( bHLH ) transcription factor PIF3 causes its degradation [69] . In the fission yeast , phosphorylation of sterol biosynthesis regulator SpSre1 mediated by casein kinase Hhp2 reduces its protein quantity by accelerating its degradation [70] . Whereas in Lotus japonicus , phosphorylation of a root nodule development-associated transcriptional activator CYCLOPS by calcium- and calmodulin-dependent kinase ( CCaMK ) increases its DNA binding activity at the target gene promoters [71] . In this study , we discovered that phosphorylation of FgHapX is required for its transcription activity , but not for its quantity and localization ( Fig 6D , S9A and S9B Fig ) . Furthermore , we identified that FgHapX is subject to phosphorylation mediated by the kinase FgYak1 . Previous studies have reported that the Ser/Thr protein kinase Yak1 controls cell growth in response to glucose depletion by negatively regulating the cAMP-PKA pathway in S . cerevisiae , and regulates the emergence and maintenance of hyphal growth of C . albicans [72 , 73] . To our knowledge , it is the first report on involvement of a kinase ( Yak1 ) in regulating iron homeostasis by phosphorylation of a transcription factor in fungi . In A . thaliana , Atm1 ortholog Atm3 was found to regulate the assembly of cytoplasmic molybdenum cofactor ( Moco ) proteins , besides Fe-S proteins [Bernard et al . , 2009] . The precursor of Moco cyclic pyranopterin monophosphate ( cPMP ) that is synthesized in mitochondria was reported as another substrate of Atm3 [25 , 74] . In the current study , we also found that F . graminearum FgAtm1 modulates the assembly of Moco protein nitrate reductase ( FgNiaD ) ( S11 Fig ) . Further , we also found that F . graminearum FgAtm1 modulates mitochondrial function and redox balance besides iron homeostasis , which are in agreement with the studies in these reports of S . cerevisiae and C . neoformans [41 , 75 , 76] . The ΔFgAtm1 mutant displayed decreased sensitivity to the mitochondrial respiratory inhibitors diphenylene iodonium ( DPI ) and rotenone ( complex I ) and antimycin A ( complex III ) ( S12A Fig ) . The mutant also exhibited increased reactive oxygen species ( ROS ) content and elevated sensitivity to hydrogen peroxide ( H2O2 ) ( S12B–S12D Fig ) , since mitochondrial respiratory complexes I and III are known to be major generators of ROS in eukaryotic cells [77] . In addition , phenotypic determination showed that deletion of FgATM1 led to the defects in asexual and sexual development , virulence and secondary metabolite production ( S13 Fig ) . The phenotypic defects might result from the imbalance of nutrient , iron and redox and impaired mitochondrial functions in ΔFgAtm1 .
F . graminearum wild-type strain PH-1 was used as a parental strain for transformation experiments in this study . Mycelial growth of the wild type and the resulting transformants were assayed on potato dextrose agar ( PDA ) or minimal medium ( MM ) as described previously [78 , 79] . To determine sensitivity to iron stress , 5-mm mycelial plugs of each strain taken from a 3-day-old colony edge were inoculated on PDA or MM supplemented without/with Fe2+ , H2O2 , or catalytic product of each Fe-S protein , and then incubated at 25°C for 3 days in the dark . Three biological replicates were used for each strain and each experiment was repeated three times independently . The double-joint PCR approach [80] was used to generate the gene replacement construct for each target gene . Briefly , for each gene , 5’ and 3’ flanking regions were amplified with the primer pairs listed in S1 Table and the resulting amplified sequences were then fused with the hygromycin resistance gene cassette ( HPH ) driven by the constitutive trpC promoter which was amplified from the pBS-HPH1 vector [81] . Protoplast transformation of F . graminearum was carried out using the protocol described previously [82] . Putative gene deletion mutants were identified by PCR assays with relevant primers ( S1 Table ) and the FgATM1 deletion mutation was further confirmed by Southern hybridization assays ( S3 Fig ) . To construct the FgAtm1-GFP cassette , FgATM1 containing the promoter region and open-reading frame ( without the stop codon ) was amplified with the relevant primers ( S1 Table ) . The resulting PCR products were co-transformed with XhoI-digested pYF11 containing a geneticin resistance gene ( NEO ) [83] into the yeast strain XK1-25 [84] using the Alkali-Cation Yeast Transformation Kit ( MP Biomedicals , Solon , USA ) to generate the recombined FgAtm1-GFP fusion vector . Subsequently , the FgAtm1-GFP fusion vector was recovered from the yeast transformant using the Yeast Plasmid Kit ( Solarbio , Beijing , China ) and then transferred into Escherichia coli strain DH5α for amplification . Using the similar strategy , FgYak1 ( FGSG_05418 ) —and FgTri1 ( FGSG_00071 ) -GFP fusion cassettes were constructed . Using the similar strategy , FgGrx4 ( FGSG_01317 ) —and FgYak1-Flag fusion cassettes were constructed by co-transformation with XhoI-digested PHZ126 vector . Similarly , FgHapX-CYFP and FgGrx4-NYFP fusion cassettes were constructed by co-transformation with XhoI-digested PHZ68 and PHZ65 vectors , respectively . The double-joint PCR approach [80] was also used to construct FgHapX- , FgHapXS245A/S338A- and FgLeu1 ( FGSG_09589 ) -mCherry cassettes . Briefly , the target gene containing the promoter region and open-reading fragment and the geneticin resistance gene ( NEO ) were amplified , and then fused with mCherry fragment . Before protoplast transformation , each fusion construct was verified by DNA sequencing . The transformation of F . graminearum was carried out using the previously described protocol [82] . All the mutants generated in this study were preserved in 15% glycerol at −80°C . For determination of mitochondrial and total iron content , About 50 mg of fresh mycelia were lysed by 2% cellulase ( Ryon Biological Technology CO , Ltd , Shanghai , China ) , 2% lysozyme ( Ryon Biological Technology CO , Ltd , Shanghai , China ) and 0 . 2% driselase from Basidiomycetes sp . ( Sigma , St . Louis , MO , USA ) for 4–6 hours . After filtration with funnel and filter paper , the filtrate was centrifuged at 5000 g at 4°C for 10 min . The protoplast was used for total iron determination and mitochondrial extraction with a Cell Mitochondria Isolation Kit ( Beyotime Industrial Co . , Ltd . , Shanghai , China ) . Iron content was determined using the colorimetric ferrozine-based assay previously reported , with ferrozine as chelator and ferric chloride as a standard [28 , 29 , 85] . Briefly , aliquots ( 100 μl ) of cell lysates were mixed with 100 μl of 10 mM HCl ( the solvent of the iron standard FeCl3 ) , and 100 μl of the iron-releasing reagent ( a freshly mixed solution of equal volumes of 1 . 4 M HCl and 4 . 5% ( w/v ) KMnO4 in H2O ) . The mixtures were incubated within a fume hood at 60°C for 2 h . After the mixtures had cooled to room temperature , 30 μl of the iron-detection reagent ( 6 . 5 mM ferrozine , 6 . 5 mM neocuproine , and 2 . 5 M ammonium acetate and 1 M ascorbic acid ) was added to each tube . After 30 min , 200 μl of the solution in each tube was transferred into a well of a 96-well plate and the absorbance was measured at 550 nm on a microplate reader . The linear range of the ferrozine assay is from 0 . 2 to 30 nmol . To determine the content of extra- and intracellular siderophores , each strain was cultured in CM for 36 hours , then transferred to MM lack of FeSO4 and amended 0 . 3 mM iron-specific chelating agent bathophenanthroline disulfonate ( BPS ) at 25°C for 8 hours . After filtration with funnel and filter paper , the supernatant was used for determining the content of extracellular siderophore , and the mycelia was used for determining the content of intracellular siderophore . Finely ground mycelia ( 50 mg ) were resuspended in 50 mM potassium phosphate buffer . After vigorous vortexing , the cellular debris was pelleted , and the supernatant was determined for the content of intracellular siderophore . An aliquot of supernatant ( 1 ml ) was mixed with 1 ml chrome azurol S ( CAS ) assay solution that was prepared . After incubation in the dark for 1 hour at room temperature , the absorbance of each sample was measured at 630 nm and the relative content of siderophore was calculated according to previous study [86] . For the conidiation assay , fresh mycelia ( 50 mg ) of each strain were inoculated in a 50-ml flask containing 20 ml of carboxymethyl cellulose ( CMC ) liquid medium . The flasks were incubated at 25°C for 4 days in a shaker ( 180 rpm ) . Subsequently , the number of conidia in each flask was determined using a hemacytometer . Three biological replicates were used for each strain and each experiment was repeated three times independently . Virulence assays on wheat spikelets , corn silks , and wheat seedling leaves were conducted as described previously [35] . For virulence on wheat spikelets , a 10 μl aliquot of conidial suspension ( 105 conidia/ml ) was injected into a floret in the central section spikelet of a single flowering wheat head of susceptible cultivar Jimai 22 . Fifteen days after inoculation , the infected spikelets in each inoculated wheat head were recorded . For virulence on corn silks and wheat seedling leaves , a 5-mm mycelial plug of each strain was inoculated on the middle of corn silks and wheat seedling leaves , and then cultured for 4 days . Ten replicates were used for each strain and each experiment was repeated three times independently . To determine DON production , each strain was grown in liquid trichothecene biosynthesis induction ( TBI ) medium at 28°C for 3 d in a shaker ( 150 rpm ) in the dark . A DON Quantification Kit ( Wise Science , Zhenjiang , China ) was used to quantify the DON production for each sample . The experiment was repeated three times . The colony edge of each strain cultured on PDA plates at 25°C for 7 days was examined with a Leica TCS SP5 imaging system ( Leica Microsystems , Wetzlar , Germany ) . Morphology of conidia cultured in CMC liquid medium for 4 days was observed with a Leica TCS SP5 imaging system after staining with the cell wall-damaging agent calcofluor white ( CFW ) at 10 μg/ml . Germinating conidia in TBI liquid medium or iron-depleted TBI liquid medium were observed with a Leica TCS SP5 imaging system . Fluorescence signals were examined with a Zeiss LSM780 confocal microscope ( Gottingen , Niedersachsen , Germany ) . For the observation of proteins tagged with GFP , mCherry or YFP , each strain was cultured in CM at 25°C for 24 h in a shaker ( 180 rpm ) before staining with MitoTracker or 4’ , 6-diamidino-2-phenylindole ( DAPI ) [87] . For examination of iron content , each strain was stained with 5 μM FeRhoNox-1 ( Goryo Chemical , Inc . , Sapporo , Japan ) [88–91] for 1 hours after culture in complete medium ( CM ) for 36 hours . For the observation of FgTri1-GFP , each strain was cultured on wheat seedling leaves at 25°C with 100% humidity for two days . The wild type , ΔFgAtm1 and the ΔFgAtm1-C were grown in CM for 36 hours , and then were harvested after rinsing 3 times with sterile water . To determine FgLeu1 activity in cytosol , 50 mg of finely ground mycelia was resuspended in 1 ml of lysis buffer ( 1 M Tris-HCl pH 7 . 4 , 1 M NaCl , 0 . 5 M EDTA , 1% Triton 100 ) for 10 min . The lysate was centrifuged at 14 , 000 g for 10 min at 4°C . Each protein sample ( 50 μg ) was used for FgLeu1 activity determination . DL-threo-3-isopropylmalic acid ( Wako Pure Chemical Industries , Ltd , Japan ) was used as the substrate and β-isopropylmalate formation was measured by monitoring absorbance at 235 nm for 10 minutes [92 , 93] . To determine FgAco1 and FgFum1 activity in cytosol and mitochondrion , the separation of mitochondrion and cytosol ( without nuclei ) was conducted according to the above method described for iron content determination . Each protein sample ( 50 μg ) was used for FgAco1 activity determination . Cis-aconitic acid ( Sigma-Aldrich , St . Louis , MO , USA ) was used as the substrate and monitor absorbance at 340 nm for 2–4 minutes [92] . FgFum1 activity of each protein sample ( 50 μg ) was determined with a Fumarase Specific Activity Assay Kit ab110043 ( Abcam , Cambridge , UK ) according to the manufacturer’s instruction . These experiments were repeated three times independently . To examine ROS content , fresh mycelia of each strain grown in CM liquid medium for 36 hours were harvested after rinsing 3 times with sterile water . 50 mg of finely ground mycelia were resuspended in 1 ml of extraction buffer . After homogenization with a vortex shaker , the lysate was centrifuged at 14 , 000 g in a microcentrifuge for 10 min at 4°C . An aliquot of 10 μl supernatant was used for ROS determination with ROS ELISA Kit ( Tong Wei Industrial Co . , Ltd . , Shanghai , China ) . Meantime , to examine ROS , each strain cultured on PDA plates at 25°C for 3 days was stained with 0 . 05% ( wt/vol ) nitroblue tetrazolium ( NBT ) for 2 h . These experiments were all repeated three times . Total RNA isolation from mycelia of each sample with the TaKaRa RNAiso Reagent ( TaKaRa Biotechnology , Dalian , China ) , and reverse transcription was performed with a HiScript II 1st Strand cDNA Synthesis Kit ( Vazyme Biotech , Nanjing , China ) . The expression level of each gene was determined by qRT-PCR with HiScript II Q RT SuperMix ( Vazyme Biotech , Nanjing , China ) . The expression of the FgACTIN gene was performed as a reference . The experiment was repeated three times . ChIP was performed as previously described [94 , 95] with additional modifications . Briefly , fresh mycelia were cross-linked with 1% formaldehyde for 15 min and then stopped with 125 mM glycine . The cultures were ground with liquid nitrogen and re-suspended in the lysis buffer ( 250 mM , HEPES pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% Triton , 0 . 1% DeoxyCholate , 10 mM DTT ) and protease inhibitor ( Sangon Co . , Shanghai , China ) . The DNA was sheared into ~300 bp fragments with twenty pulses of 10 s and 20 s of resting at 35% amplitude ( Qsonica*sonicator , Q125 , Branson , USA ) . After centrifugation , the supernatant was diluted with 10×ChIP dilution buffer ( 1 . 1% Triton X-100 , 1 . 2 mM EDTA , 16 . 7 mM Tris–HCl , pH 8 . 0 and 167 mM NaCl ) . Immunoprecipitation was performed using monoclonal anti-GFP ab290 ( Abcam , Cambridge , UK ) antibody together with the protein A agarose ( Santa Cruz , CA , USA ) respectively . DNA was immunoprecipitated by ethanol after washing , eluting , reversing the cross-linking and digesting with proteinase K . Further , ChIP-enriched DNA was used for quantitative PCR analysis using SYBR green I fluorescent dye detection with the relative primers ( S1 Table ) . Relative enrichment of each gene was determined by quantitative PCR and calculated first by normalizing the amount of a target cDNA fragment against that of FgACTIN as an internal control , and then by normalizing the value for the immunoprecipitated sample against that for the input . The ChIP-qPCR was independently repeated three times . The cDNA encoding the N terminal 1–230 amino acids of FgHapX ( FgHapXN1–230 ) was amplified and cloned into pGEX-4T-3 vector to generate GST-tagged protein . The resulting construct was transformed into the E . coli strain BL21 ( DE3 ) after verifying the cDNA sequence . The recombinant GST-FgHapXN1–230 was purified by Ni sepharose beads and eluted by reduced glutathione . Promter DNAs were amplified using the primers in Table S1 . For EMSA , Reaction mixtures containing purified GST -FgHapXN1–230 , promter DNAs and 10×Binding buffer ( 100 mM Tris-HCl ( PH 7 . 5 ) , 0 . 5 M NaCl , 10 mM DTT , 10 mM EDTA , 50% glycerol ) were incubated for 20 min at 25°C . The purified GST was used as negative controls . The reactions were electrophoresed on 1 . 2% agarose gel in 0 . 5×TAE for 45 min in 80 V under low temperature . Signals were detected in J3-3000 imaging system after dying DNA dye ethidium bromide ( EB ) for 15 min . The experiment was conducted independently three times . To construct plasmids for Y2H analyses , the coding sequence of each tested gene was amplified from the cDNA of PH-1 with primer pairs indicated in S1 Table . The cDNA of each gene was inserted into the yeast GAL4-binding domain vector pGBKT7 ( Clontech , Mountain View , CA , USA ) and GAL4 activation domain vector pGADT7 ( Clontech , Mountain View , CA , USA ) , respectively . The pairs of Y2H plasmids were cotransformed into S . cerevisiae strain Y2H Gold lithium acetate/single-stranded DNA/polyethylene glycol transformation protocol . In addition , a pair of plasmids , pGBKT7-53 and pGADT7 and another pair of plasmids , pGBKT7-Lam and pGADT7 , served as a positive control and negative controls , respectively . Transformants were grown at 30°C for 3 d on synthetic medium ( SD ) lacking Leu and Trp , and then transferred to SD stripped of Ade , His , Trp and Leu to assess binding activity . Three independent experiments were performed to confirm yeast two-hybrid assay results . To search for FgHapX-interacting proteins , we performed Y2H screens . FgHapX was cloned into the yeast vector pGBKT7 . A F . graminearum cDNA library was constructed in the Y2H vector pGADT7 using total RNA extracted from mycelia and conidia . The Y2HGold that was co-transformed with the cDNA library as well as FgHapX-pGBKT7 were directly selected using SD-Trp-Leu-His . Approximately 150 potential yeast transformants containing cDNA clones interacting with FgHapX were further confirmed in selection medium SD-Trp-Leu-His-Ade . For western blotting assay , protein samples of strains were prepared and extracted as described previously [96] . Proteins separated on the SDS-PAGE gel were transferred onto a polyvinylidene fluoride membrane with a Bio-Rad electroblotting apparatus . The polyclonal anti-Flag A9044 ( Sigma , St . Louis , MO , USA ) , monoclonal anti-GFP ab32146 ( Abcam , Cambridge , MA , USA ) and monoclonal anti-mCherry ab125096 ( Abcam , Cambridge , UK ) antibodies were used at a 1:2000 to 1:10000 dilution for immunoblot assays . The samples were also detected with the monoclonal anti-GAPDH antibody EM1101 ( Hangzhou HuaAn Biotechnology co . , Ltd . ) as a reference . Incubation with a secondary antibody and chemiluminescent detection were performed as described previously [97] . The experiment was repeated three times independently . The mCherry and Flag fusion constructs were verified by DNA sequencing and transformed in pairs into PH-1 . Transformants expressing the fusion constructs were confirmed by western blot analysis . In addition , the transformants bearing a single fusion construct were used as references . For Co-IP assays , total proteins were extracted and incubated with the anti-Flag ( Abmart , Shanghai , China ) agarose overnight at 4°C as described previously [97] . Proteins eluted from agarose were analyzed by western blotting detection with the monoclonal anti-mCherry ab125096 ( Abcam , Cambridge , UK ) antibody . The protein samples were also detected with monoclonal anti-GAPDH antibody EM1101 ( Hangzhou HuaAn Biotechnology co . , Ltd . ) as a reference . The FgHapX-CYFP and FgGrx4-NYFP fusion constructs were generated by cloning the related fragments into pHZ68 and pHZ65 vectors , respectively . FgHapX-CYFP and FgGrx4-NYFP constructs were co-transformed into PH-1 and ΔFgAtm1 . In addition , a pair of constructs , FgHapX-CYFP and NYFP and another pair of constructs , FgGrx4-NYFP and CYFP were used as negative controls . Transformants resistant to both hygromycin and zeocin were isolated and confirmed by PCR . YFP signals were examined with a Zeiss LSM780 confocal microscope ( Gottingen , Niedersachsen , Germany ) . Proteins of the wild-type PH-1 were extracted in the lysis buffer ( 8 M urea , 50 mM Tris 8 . 0 , 1% NP40 , 1% sodium deoxycholate , 5 mM dithiothreitol , 2 mM EDTA , 30 mM nicotinamide , 3 μm trichostatin A , 1% protease inhibitor Cocktail and 1% phosphatase inhibitor cocktail ) . For trypsin digestion , the protein sample was diluted in 0 . 1 M TEAB ( triethylammonium bicarbonate ) , and digested with 1/25 trypsin ( Promega , Madison , USA ) for 12 h at 37°C . The digestion was terminated with 1% TFA ( trifluoroacetic acid ) , and the resulting peptides were cleaned with a Strata X C18 SPE column ( Phenomenex , Torrance , USA ) and vacuum-dried in a scanvac maxi-beta ( Labogene , Lynge , Denmark ) . Then , the resulting peptides ( 2 mg per sample ) were reconstituted in 120 μl 0 . 5 M TEAB , and treated with a TMTsixplex label reagent kit ( Pierce , Idaho , USA ) . Both fractionations were performed with an XBridge Shield C18 RP column ( Waters , Milford , USA ) in a LC20AD HPLC system ( Shimadzu , Kyoto , Japan ) . For phosphorylation enrichment , peptides were dissolved in 80% ACN/6% TFA and then incubated with IMAC-Ti4+ beads ( Sachtopore , Sachtleben Chemie , Germany ) at room temperature . The beads were washed once with 50% ACN/0 . 5%TFA/200 mM NaCl and once with 50% ACN/0 . 1% TFA . The bound peptides were then eluted with 10% NH4OH and 80% ACN/2% FA ( formic acid ) . All of the eluted fractions were combined , vacuum-dried and cleaned with C18 ZipTips ( Millipore , Billerica , USA ) according to the manufacturer’s instructions , followed by LC-MS/MS analysis according to a previous study [98] . The database search and bioinformatics analyses were performed as described previously [99] . For Phos-tag assay , the FgHapX-mCherry fusion construct was transferred into the ΔFgHapX and ΔFgYak1 mutants and the FgHapXS245A/S338A-mCherry fusion construct was transferred into the ΔFgHapX mutant . Protein samples were prepared and extracted as described above . Each resulting protein sample was loaded on 8% SDS-polyacrylamide gels prepared with 25 μM Phos binding reagent acrylamide ( APE×BIO , F4002 ) and 100 μM ZnCl2 . Gels were electrophoresed at 20 mA/gel for 3–5 h . Prior to protein transfer , gels were first equilibrated three times in transfer buffer containing 5 mM EDTA for 5 min and further equilibrated in transfer buffer without EDTA for 5 min for two times . Protein transfer from the Zn2+-phos-tag acrylamide gel to the PVDF membrane was performed for 4–5 h at 100 V on ice , and finally the membrane was analyzed by western blotting with the monoclonal anti-mCherry ab125096 ( Abcam , Cambridge , UK ) antibody . FgYak1-Flag and FgHapX-mCherry were co-transformed into the wild type . The nuclei of corresponding strains were extracted as described previously [100] and then fixed on the polylysine slides . Slides were incubated with the polyclonal anti-Flag antibody A9044 ( Sigma , St . Louis , MO ) and monoclonal anti-mCherry ab125096 ( Abcam , Cambridge , UK ) antibody at 1:500 dilution , respectively , followed by the secondary antibodies Andy Fluor 594 goat anti-mouse L119A ( red fluorescence ) ( GeneCopoeia , Maryland , US ) and Andy Fluor 488 goat anti-rabbit L110A ( green fluorescence ) ( GeneCopoeia , Maryland , US ) at 1:300 dilution . Nuclei were stained with DAPI for 15 minutes before fluorescence observation . | Essential element iron plays important roles in many cellular processes in all organisms . The function of an ATP-binding cassette ( ABC ) transporter Atm1 in iron homeostasis has been characterized in Saccharomyces cerevisiae . Our study found that FgAtm1 regulates iron homeostasis via the transcription factor cascade FgAreA-HapX in F . graminearum and the function of FgHapX is dependent on its interaction with FgGrx4 and phosphorylation by the Ser/Thr kinase FgYak1 . This study reveals a novel regulatory mechanism of iron homeostasis in an important plant pathogenic fungus , and advances our understanding in iron homeostasis and functions of ABC transporters in eukaryotes . | [
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] | 2019 | A fungal ABC transporter FgAtm1 regulates iron homeostasis via the transcription factor cascade FgAreA-HapX |
Growth factor independent 1 ( Gfi1 ) is a transcriptional repressor originally identified as a gene activated in T-cell leukemias induced by Moloney-murine-leukemia virus infection . Notch1 is a transmembrane receptor that is frequently mutated in human T-cell acute lymphoblastic leukemia ( T-ALL ) . Gfi1 is an important factor in the initiation and maintenance of lymphoid leukemias and its deficiency significantly impedes Notch dependent initiation of T-ALL in animal models . Here , we show that immature hematopoietic cells require Gfi1 to competently integrate Notch-activated signaling . Notch1 activation coupled with Gfi1 deficiency early in T-lineage specification leads to a dramatic loss of T-cells , whereas activation in later stages leaves development unaffected . In Gfi1 deficient multipotent precursors , Notch activation induces lethality and is cell autonomous . Further , without Gfi1 , multipotent progenitors do not maintain Notch1-activated global expression profiles typical for T-lineage precursors . In agreement with this , we find that both lymphoid-primed multipotent progenitors ( LMPP ) and early T lineage progenitors ( ETP ) do not properly form or function in Gfi1−/− mice . These defects correlate with an inability of Gfi1−/− progenitors to activate lymphoid genes , including IL7R , Rag1 , Flt3 and Notch1 . Our data indicate that Gfi1 is required for hematopoietic precursors to withstand Notch1 activation and to maintain Notch1 dependent transcriptional programming to determine early T-lymphoid lineage identity .
Growth factor independent-1 ( Gfi1 ) is a transcriptional repressor originally identified as a common proviral insertion site of the murine Moloney leukemia virus ( MMLV ) that conferred IL-2 independent growth to IL-2 dependent T-cell lymphomas [1] . Subsequently , Gfi1 was identified as the most commonly activated gene in MMLV-induced lymphoid malignancies [2] . Gfi1 contains an N-terminal “SNAG” domain that is required for transcriptional repression and nuclear localization [3] and six zinc fingers of which , three , four and five are required for specific DNA-binding [4] , [5] . Gfi1−/− mice display decreased HSC fitness , an accumulation of myeloid progenitors , and a lack of mature neutrophils [6] , [7] , [8] . Furthermore , germline deletion of Gfi1 results in a 4-fold decrease in thymic cellularity and modest increases in apoptotic cells [9]; whereas , mice with a CD4-promoter-driven Cre and floxed Gfi1 alleles ( Gfi1f/f ) demonstrate no defects in absolute thymocytes numbers[10] . Taken together , these data have been interpreted to mean that Gfi1−/− thymic phenotypes are largely due to Gfi1 anti-apoptotic functions during early thymopoiesis . Notch1 is a transmembrane receptor that is critical throughout metazoan development acting as a molecular switch to determine cell fate . Similarly , during hematopoiesis , activation of Notch1 is required for proper T cell development [11] , [12] , [13] , [14] , [15] . T cells arise from circulating bone marrow progenitors that enter the thymus and encounter Notch1 ligands of the Delta-like and Jagged family [16] , [17] , [18] . Ligand-engagement of Notch receptors results in a conformational change exposing internal cleavage sites . A disintegrin and metalloprotease ( ADAM ) - and γ-secretase complex-mediated cleavage results in intracellular Notch ( ICN ) release from the membrane , nuclear translocation [19] , [20] , [21] , and subsequent binding to CBF1/Suppressor of Hairless/Lag1 ( CSL/Rbpj-κ ) ultimately leading to Notch target gene activation . As Notch1 signal strength increases in early T lineage progenitors ( ETP ) through double negative ( DN ) 3 pro-T cells , transcriptional programs are upregulated which enforce T lymphoid identity at the expense of other lineages [22] . Notch1 signaling strength is highest leading up to TCRβ-selection , however , early progenitors in the BM may also require low level Notch signals as one component of the stimulus to proliferate and differentiate into lymphoid progenitors . Although Notch1 signaling may not be required for the maintenance of adult hematopoietic stem cells [23] , [24] , it functions as a tumor suppressor during myeloid development [25] , and inhibition of Notch1 in progenitors dramatically reduces the formation of ETPs disrupting downstream stages of T-cell development in the thymus [26] . T cell acute lymphoblastic leukemia ( T-ALL ) is a subset of acute lymphoblastic leukemia , the most prevalent pediatric malignancy comprising nearly 25% of all childhood cancers [27] . Translocations placing NOTCH1 under control of the TCRb locus , t ( 7;9 ) ( q34;q34 . 3 ) first implicated NOTCH1 in T-ALL [28] . Yet additional activating NOTCH1 mutations were found in more than 50% of T-ALL patients [29] . Moreover , mutations in NOTCH1 [30] and NOTCH1 regulatory proteins [31] have also been identified in T-ALL [32] . All of these mutations are thought to create constitutively active forms of ICN through ligand-independent activation and ICN nuclear translocation [33] . Mutations in GFI1 have not been detected in human T-ALL [34] [32]; however , transgenic overexpression of Gfi1 can accelerate oncogene-driven murine models of T-ALL [35] , [36] . Recently , we identified Gfi1 as an important factor in the initiation and maintenance of lymphoid leukemias [37] . Interestingly , in human T-ALL patients with NOTCH1 mutations , or a transcriptional signature indicative of activated NOTCH1 , GFI1 was highly expressed; while in mice , Gfi1 loss of function profoundly blocked Notch-initiated leukemia . To further investigate this unique relationship , we used genetic mouse models , which constitutively and inducibly delete Gfi1 , to demonstrate that Gfi1 is required in a cell autonomous manner for early thymocytes and lymphoid progenitors in the bone marrow to competently receive Notch signals . Furthermore , we show that Gfi1−/− lymphoid progenitors cannot respond to endogenous levels of Notch1 , potentially explaining the dramatic reduction in Gfi1−/− ETP and LMPP numbers . Thus , our findings identify Gfi1 as a critical factor in the response of immature hematopoietic cells to Notch1 signaling .
To further elucidate the mechanisms that protect Gfi1 deficient T cells from T-ALL transformation , we investigated the requirement for Gfi1 in developing T cells exposed to Notch1 activation . To do so , we bred mice in which Cre recombinase expression is driven by the T-cell-specific proximal-Lck promoter [38] with both Gfi1fex4–5 ( Gfi1f ) mice and germline Gfi1Δex2–3 ( Gfi1− ) or Gfi1 Δex4–5 deficient mice ( Gfi1 Δ ) resulting in LckCre+Gfi1f/− ( or LckCre+Gfi1f/Δ ) animals . Notably , we observed a similar 3–4-fold reduction in total thymocytes as previously published in Gfi1 germline deleted mice [9] ( Figure S1 ) . Next , we bred the LckCre+Gfi1f/Δ model with a Rosa26-driven intracellular-Notch1 ( ICN ) transgene , in which ICN-IRES-eGFP expression is prevented by a floxed “stop” cassette ( ROSAlslICN ) [39] . In the LckCre+Gfi1f/Δ ROSAlslICN mice , Cre expression should activate ICN and eGFP expression while simultaneously deleting Gfi1 ( Figure 1A ) . As previously reported [40] , we find that ICN activation , in the presence of Gfi1 , leads to an accumulation of DP and CD8+ T cells at the expense of CD4+ cells ( Figure 1B , GFP Positive LckCre+Gfi1+/+ROSAlslICN ) . In contrast , when activation of ICN is coupled with Gfi1 deletion , the majority of GFP+ cells are CD4 or CD8 single positive cells ( Figure 1B , GFP positive , LckCre+Gfi1f/Δ ROSAlslICN ) . Moreover , ICN expression coupled with Gfi1 deletion led to a dramatic reduction in thymus size ( Figure 1C ) . Further analysis of total thymocyte numbers revealed a 17-fold decrease in total cellularity when activation of ICN was combined with loss of Gfi1 ( Figure 1D , p<0 . 05 ) . Notably , this phenotype was not observed in control LckCre+ROSAlslICN or in LckCre+Gfi1f/Δ thymocytes where activation of ICN or deletion of Gfi1 occurs separately ( Figure 1D and Figure S1 ) . The few remaining thymocytes present in the LckCre+Gfi1f/Δ ROSAlslICN mice either lacked equivalent ICN transgene activation , as measured by eGFP ( Figure 1E , p<0 . 01 , ) or failed to delete the floxed allele of Gfi1 ( Figure 1F ) . Moreover , the significant decrease in the percentage of GFP+ cells in LckCre+Gfi1f/Δ ROSAlslICN mice ( Figure 1E ) is underrepresented by the flow cytometric plots shown . For example , the absolute number of GFP+ thymocytes in LckCre+ROSAlslICN mice is 49 . 8×106 versus 0 . 35×106 GFP+ thymocytes in LckCre+Gfi1f/Δ ROSAlslICN mice , a 142-fold decrease in the total number of GFP+ thymocytes between ICN-signaled Gfi1-sufficient versus ICN-signaled Gfi1-deficient cells . Taken together , these data demonstrate that Gfi1 is required to withstand chronic ICN signaling during the stages of development in which T cell malignant transformation occurs [41] , [42] . To determine whether this apparent synthetic lethal relationship was dependent upon the stage of transgene activation or whether Notch-signaled pre-leukemic T cells generally require Gfi1 , we utilized CD4Cre transgenic mice and repeated the above experiments . Notably , CD4Cre is expressed in DP thymocytes , and deletion of floxed Gfi1 , Notch1 , or Rbpj-κ by CD4Cre does not result in a reduction of thymocytes [10] , [43] , [44] . Therefore , any lethality caused by deleting Gfi1 and activating Notch should not be due to a specific developmental requirement for these factors alone , but instead would reflect a synergistic phenotype . Thus , we bred CD4Cre transgenic mice to Gfi1f/− ROSAlslICN mice ( Figure 1G ) and examined the effects on thymocyte development . Similar to LckCre-mediated activation , CD4Cre activation of ICN lead to an accumulation of DP and CD8 SP T cells at the expense of other populations ( Figure 1H , CD4Cre+ROSAlslICN , GFP Positive ) . Comparatively , deletion of Gfi1 led solely to the development of DP T cells ( Figure 1H , CD4Cre+ Gfi1f/− ROSAlslICN , GFP Positive ) . However , in contrast to the published CD4CreGfi1fex4–5/fex4–5 mice [10] or CD4Cre+ROSAlslICN mice , the CD4Cre+Gfi1f/− ROSAlslICN mice displayed a dramatic decrease in total cellularity similar to LckCre+Gfi1f/−ROSAlslICN mice ( Figure 1I ) . Despite the decrease in total number of thymocytes , the percentage of CD4CreGfi1f/−ROSAlslICN thymocytes able to activate the ICN transgene was equivalent in CD4CreROSAlslICN signaled cells with or without Gfi1 as measured by eGFP ( Figure 1J ) . Furthermore , CD4CreGfi1f/− ROSAlslICN thymocytes were able to efficiently delete the floxed allele of Gfi1 in thymocytes where Cre is active , but not in control Cre inactive tail tissue ( Figure 1K ) . Thus , the presence of eGFP-expressing Gfi1Δ/− cells in this model suggests that the DP and SP T-cells do not absolutely require Gfi1 to express activated ICN , even though this combination results in dramatically decreased thymic cellularity . To more precisely define the developmental stages susceptible to ICN activation and Gfi1 deletion , we mated the ROSAlslICN or Gfi1f/− ROSAlslICN transgenic mice to transgenic mice that activate Cre expression after TCR positive selection ( distal-LckCre = DLC ) [45] . Similar to published reports , we found that less than 5% of the thymocytes in DLC+ROSAlslICN or DLC+Gfi1f/− ROSAlslICN expressed eGFP , and only at very late stages of T cell development ( Figures 2A–C , S2 ) . As such , we examined peripheral splenic T cells and found no statistical differences in total cellularity ( Figure 2D ) or in the percentages of GFP+ T cells between DLC+ROSAlslICN and DLC+Gfi1f/−ROSAlslICN mice ( Figure 2E ) . Furthermore , FACS sorted GFP+ T cells displayed complete excision of the floxed allele of Gfi1 ( Gfi1fex4–5 ) and had detectable levels of the deleted allele of Gfi1 ( Gfi1Δ ex4–5 , Figure 2F ) . These cells still demonstrated a partial phenocopy of Gfi1 deficiency in that they have an increase in the frequency of the CD8+ population ( Figure 2G ) ; however , no differences were observed in the immunophenotype of ICN-activated T cells , with or without Gfi1 . These data provide strong evidence to suggest that the ICN+Gfi1Δ/Δ -induced hypocellularity phenotype is limited to a window during development in which T cells are susceptible to transformation ( i . e . after TCRβ-selection ) . However , as that window closes and developmental transcriptional programs turn off , they are no longer susceptible to phenotypes caused by ICN activation and Gfi1 deletion . Having established that deletion of Gfi1 early in T cell development mimics the phenotype of Gfi1 germline deletion , ( LckCre+Gfi1f/Δ , Figure S1A–E ) and that overexpression of intracellular Notch1 does not rescue this defect ( LckCre+Gfi1f/Δ ROSAlslICN , Figure 1A–F ) we further observed a direct relationship between the stage of lymphoid developmental and the synthetic lethal combination of deleting Gfi1 and activating ICN . This combination was most profound in early stages of T cell development ( LckCre ) in that GFP+ Gfi1Δ/Δ cells were not detectable . In contrast , at later developmental stages ( CD4Cre ) the absolute requirement for Gfi1 was lost ( albeit with hypocellularity ) and GFP+ Gfi1 Δ/Δ T cells could be detected . However , at very late stages of T cell development ( DLCre ) GFP+ Gfi1Δ/Δ T cells could be detected with no obvious defect in the numbers of peripheral or thymic T cells . Thus , we hypothesized that Gfi1 must be most critical during the earliest stages of lymphoid development where progenitors first experience lymphoid transcriptional programming , which includes Notch1 signaling . However , these data do not delineate between a selective event in which cells without Gfi1 die , versus an instructive event in which cells without Gfi1 fail to undergo proper lineage commitment and lymphoid gene expression changes . To clarify this , we next performed a series of in vitro assays to concisely test the cell autonomous requirement for Gfi1 in lymphoid priming by inducibly deleting Gfi1 in the context of chronic ICN expression . First , we isolated Lin− BM from RosaCreERT2Gfi1fex4–5 and control Gfi1fex4–5 mice , and retrovirally transduced the stem and progenitor cells with GFP-marked ICN . GFP+ cells were FACS-sorted and plated in methylcellulose as previously described [46] , [47] in the presence of 4-hydroxy tamoxifen ( 4-OHT , to induce Cre activity and delete Gfi1fex4–5 ) or vehicle control ( EtOH ) . After one week in culture , CFU were enumerated , methylcellulose was disrupted and CFU were re-plated into 4-OHT or control-containing methylcellulose . This process was repeated for three weeks of plating ( Figure 3A , diagram left ) . Untransformed progenitor cells generally produce 100–200 CFU within the first week , but fail to produce robust CFU in subsequent replatings [5] , [46] , [47] . In the absence of Cre expression , 4-OHT had no effect on CFU number or replating ability ( Figure 3A , middle , Cre Neg: EtOH vs . OHT ) . However , in the presence of Cre , 4-OHT treatment dramatically reduced the number of CFU ( Fig . 3A middle , Week 1 , Cre Pos: EtOH vs . OHT: 363 to 156 , p<0 . 01 ) . Replating of Gfi1f/f , or RosaCre+Gfi1f/f vehicle-treated CFU led to similar numbers of CFU seven days later , whereas replating of RosaCre+Gfi1Δ/Δ resulted in an additional three-fold reduction in total CFU ( Figure 3A middle , Week 1 vs . 2: 156 to 57 , p<0 . 01 ) . Moreover , the CFU that did form in the absence of Gfi1 displayed substantially fewer cells per CFU demonstrating their inability to respond to ICN overexpression in the same manner as Gfi1f/f controls ( Figure 3A , right ) . In the absence of ICN overexpression , interruption of Gfi1 function promotes monocytic over granulocytic CFU formation [5] . Activation of ICN in myeloid lineages has recently been suggested to be lethal [25] . To avoid potential confounding factors of ICN activation in Gfi1-deficient myeloid progenitors , we next repeated the above assay ( Figure 3A ) , but after FACS-sorting GFP+ ICN-transduced Lin− cells , we plated them for one week in the absence of 4-OHT in order to promote lymphoid priming and differentiation by ICN overexpression . After seven days in culture , CFU were enumerated , disrupted and plated in 4-OHT containing methylcellulose for an additional seven days for two rounds of replating ( Figure 3B , left ) . Lymphoid-primed Gfi1f/f CFU were again unaffected by addition of 4-OHT through subsequent replatings . Although cells from RosaCre+Gfi1f/f generated equivalent CFU to cells from Gfi1f/f mice while cultured without 4-OHT , upon addition of 4-OHT , these cells again demonstrated a significant reduction in total CFU and cells per CFU compared to Gfi1f/f controls ( Figure 3B right , Week 2: 300 to 174 , p<0 . 001 ) . These data suggest that lymphoid-primed CFU also require Gfi1 to competently respond to ICN signaling . To verify that this in vitro model truly reflects the characteristics of lymphoid progenitors , we repeated the experiment and examined global gene expression patterns . ICN-transduced Gfi1f/f and RosaCre+Gfi1Δ/Δ lineage-negative bone marrow cells were cultured for seven days without 4-OHT ( to induce lymphoid-priming , ) and then an additional seven days in 4-OHT ( to induce deletion of Gfi1f/f alleles ) before RNA was isolated and microarray expression analysis was performed ( Figure 3C , left ) . Recently , global RNA-seq and ChIP-seq analyses defined a subset of genes that definitively distinguish FACS-sorted early lymphoid populations based upon their transcriptional networks [48] . Restricting our analysis to these genes , we first questioned whether they demonstrated statistically significant gene expression differences with or without Gfi1 . Of the 378 tested , 125 genes displayed p-values <0 . 05 and were then used to cluster the expression signatures from both ICN-transduced CFU as well as normal FACS sorted lymphoid progenitors ( Table S1 ) [49] . Principal component analysis ( PCA ) clustered Gfi1f/f CFU closest to LMPP populations demonstrating that the CFU partially mimic important transcriptional programs of in vivo lymphoid progenitors ( Figure 3C , right ) . However , upon loss of Gfi1 , PCA revealed that Gfi1Δ/Δ CFU no longer cluster with LMPP ( Figure 3C–D ) , demonstrating a global inability to maintain lymphoid progenitor priming . We next used an unbiased approach and applied gene set enrichment analysis ( GSEA ) [50] to our entire dataset . GSEA showed enrichment of published lymphoid progenitor signatures in Gfi1f/f CFU , whereas Gfi1Δ/Δ CFU showed no such enrichment ( Figure 3E , “Lee_differentiating T_lymphocyte” ) . The same enrichment pattern was observed using more recently published LMPP-like and T-lineage commitment gene lists not yet curated in the MSigDB ( Figure 3E “LMPP-like Genes” & “T-Lineage Commitment” ) . Indeed , further analysis [51] of gene expression differences between Gfi1f/f and Gfi1Δ/Δ ICN CFU , demonstrated significant changes in cell surface markers ( Table S2 , GO Cellular Component GO:0009986 , p<2 . 49×10−16 ) and CD antigen genes ( Table S3 , HUGO Genenames . org , p<3 . 40×10−30 ) . These data suggest that much ( but not all ) of the ICN-instructed lymphoid progenitor programs are dependent upon Gfi1 . Taken together , we conclude that i ) ICN-transduced Gfi1+/+ CFU share critical transcriptional programs with lymphoid bone marrow progenitors; ii ) loss of Gfi1 results in a subsequent loss of key elements of those ICN-regulated transcriptional networks necessary for proper lymphoid lineage identity; and iii ) Gfi1 is required in ICN-signaled ( FACS sorted ) cells in a cell autonomous fashion . Given the similarity of gene signatures between ICN-CFU and LMPP and the reliance of these cells in vitro on Gfi1 , we questioned whether endogenous levels of Notch1 signaling experienced in vivo by lymphoid progenitor cells of Gfi1−/− mice may engender the same phenotypes identified in the transgenic and retroviral overexpression systems . To answer this question , we examined the LMPP ( Flt3 high , Lin− , cKit+ , Sca1+ ) in the BM reasoning that: i ) LMPP are the first lymphoid progenitors to respond to Notch1 signaling [52] , ii ) ICN+Gfi1f/f CFU clustered closest to FACS-sorted LMPP , and iii ) differences in the expression of Flt3 have been reported in Gfi1−/− LSK [7] , [8] . Similar to previous reports [7] , [53] , we observed a decrease in the percentage and total number of LMPPs in Gfi1−/− mice ( Figure 3F ) . To determine whether Gfi1−/− phenotypic LMPP are functionally similar to wild type LMPP , we FACS sorted LSK and LMPP from Gfi1+/+ and Gfi1−/− mice and tested for the induction of lymphoid signature genes coincident with Flt3 expression in LMPP . Whereas Gfi1+/+ progenitors upregulated the expression of Flt3 , IL7R , Rag1 and Notch1 3–10 fold during the transition from Flt3−LSK to LMPP , Gfi1−/− progenitors did not induce these genes to the same degree ( Figure 3G ) . Furthermore , we found lower expression of each of these genes in Gfi1−/− Flt3−LSK , suggesting that phenotypically normal Gfi1−/− progenitors have a functional defect in their ability to prime lymphoid transcriptional programs . Taken together , these data indicate that loss of Gfi1 in lymphoid-primed progenitors results in a cell-autonomous inability to maintain a lymphoid specific transcriptional program . ETPs are thought to be the progeny of lymphoid-primed progenitors , which reside within the BM and have significant overlap in transcriptional signatures with LMPP [49] . Therefore , we hypothesized that the role of Gfi1 during lymphoid priming may be most pronounced in ETPs , in particular since these cells experience a dramatic increase in Notch1 signaling . Gfi1+/− lymphoid progenitors express less Gfi1 protein than wild type cells [54] , therefore we examined the effect of Gfi1 deletion and haploinsufficiency upon ETP numbers . We found a Gfi1 dose-dependent reduction in ETP percentages and absolute numbers in Gfi1+/− and Gfi1−/− thymi compared to Gfi1+/+ controls ( Figure 4A–B; 6 . 4×104 , 1 . 7×104 , 0 . 06×104 between Gfi1+/+ , Gfi1+/− and Gfi1−/− respectively; p<0 . 05 and p<0 . 01 ) . Thus , we conclude that Gfi1−/− mice have few phenotypically normal ETPs . Next , we asked whether ETPs normally express Gfi1 and whether Gfi1−/− ETPs can respond to Notch1 signaling . To address the first question , we used Gfi1-GFP knock-in mice ( Gfi1GFP/+ ) [55] in which eGFP replaces Gfi1 coding exons , and the expression of eGFP mirrors that of endogenous Gfi1 . We found that Gfi1GFP/+ ETP are clearly eGFP+ , demonstrating that Gfi1 is highly expressed in ETPs ( Figure 4C ) . To address the latter question , we FACS-sorted Gfi1+/+ and Gfi1−/− ETP cells and exposed them to the Notch ligand , Delta-like 1 ( DL1 ) , by culturing the cells on OP9-DL1 stroma . Gfi1−/− ETP failed to respond and did not progress through T cell development whereas their Gfi1+/+ ETP controls began to express both CD4 and CD8 after 15 days in culture ( Figure 4D ) . Thus , these data demonstrate that phenotypically defined Gfi1 deficient ETPs do not properly function in response to Notch ligands in vitro . We next sought to genetically rescue Gfi1 expression both before and after lymphoid progenitors experience increases in basal Notch1 signaling . To examine whether endogenous levels of Notch1 signaling were correctly interpreted , we examined total thymocyte and ETP numbers , both of which are critically dependent on Notch1 [11] , [26] . First , we mated Vav-Gfi1 transgenic mice , which express Gfi1 in all hematopoietic stem/progenitors and mature lineages [56] , to germline Gfi1−/− mice . Gfi1 expression in this model occurs before endogenous increases in Notch1 signals [56] . We then analyzed the total number of thymocytes and the formation of ETPs by flow cytometry . Vav-mediated expression of Gfi1 rescued both the total thymocyte numbers ( Figure 4E ) and the total numbers of ETPs ( Figure 4F ) to the levels of Gfi1+/+ controls . Next , we mated germline Gfi1−/− mice to Lck-Gfi1 transgenic mice [57] to re-express Gfi1 at the height of Notch1 target gene expression in the thymus [57] , [58] , [59] . Transgenic Lck-Gfi1 expression failed to rescue germline Gfi1−/− defects in total thymocyte ( Figure 4E ) or ETP numbers ( Figure 4F ) . These data corroborate that Gfi1 is required early during lymphoid progenitor development and further suggest that Gfi1 is required to properly respond to endogenous levels of Notch1 signaling . Gfi1−/− lymphoid progenitors are reduced in number , but also fail to induce genes normally downstream of Notch signals . To delineate a requirement for Gfi1 to integrate Notch signaling versus to survive an apoptotic selection event , we attempted to rescue the loss of ETPs and total thymocytes in Gfi1−/− mice by crossing them with Bcl2-transgenic mice ( H2K-Bcl2 ) , which would block apoptosis . Although Bcl2 overexpression was able to rescue most of the Gfi1 loss-of-function phenotypes in T-ALL [37] , neither total thymocyte numbers or ETP numbers returned to Gfi1+/+ levels in Bcl2 transgenic Gfi1−/− mice ( Figure 4E–F ) . Thus , forced expression of an anti-apoptotic molecule is insufficient to rescue Gfi1−/− T cell development defects .
Notch1 is a central mediator of both T cell leukemogenesis and T cell development . ICN-target genes such as Myc [60] , [61] , [62] , Hes1 [47] , [63] , Notch3 [64] , [65] and IGF1R [66] are critical to T cell development and T-ALL , and Notch signaling directly controls expression of T-cell-lineage specific identity genes such as Tcf7 [67] , [68] and Bcl11b [69] . Not surprisingly , interfering with the expression of Notch1 target genes disrupts Notch1 programing of developing T- or T-ALL cells . In contrast , we previously showed that Notch signaling does not directly regulate Gfi1 expression [37] . However , in this study we demonstrate that Gfi1 is still required to execute Notch1-driven developmental and pre-leukemic programs even though it is unlikely to be an ICN-downstream-target gene . Previously , regulation of apoptosis was considered the dominant function of Gfi1 in developing T cells [9] , [70] . In transformed lymphoid cells , loss of Gfi1 leads to induction of apoptosis through the exaggeration of p53-dependent target gene activation . Overexpression of Bcl2 or knockdown of p53 rescues Gfi1 loss of function phenotypes in T-ALL [37] . However , neither loss of p53 or overexpression of Bcl2 alters Gfi1−/− total thymocyte numbers ( Figure 4 and data not shown ) . This may be due a lower threshold of DNA damage present in untransformed lymphoid precursors that is increased in T-ALL ( due to oncogenic stress ) resulting in hyperactivation of p53 . Thus , a lack of Notch1-regulated gene expression observed in Gfi1−/− lymphoid precursors might previously have been ascribed to a selective event causing those cells that express lymphoid genes to die . Because loss of Gfi1 debilitates ICN-mediated lymphoid priming in a cell autonomous manner , we now conclude that repression of pro-apoptotic genes is only one of many biological functions that are integrated by Gfi1 during lymphoid priming and T lymphopoiesis . As expression of an anti-apoptotic effector was insufficient to rescue all of the defects associated with Gfi1 deficiency , we further conclude that Gfi1 is an obligate instructive factor that is critical to effectively maintain Notch1-dependent transcriptional programs necessary for lymphoid lineage commitment . Previous studies have implicated Gfi1 at multiple stages of lymphoid development . For example , Gfi1 overexpression has been shown to partially rescue Lyl1 deficiency in LMPP [71] . Moreover , Gfi1 acts downstream of Ikaros in MPPs to mediate the differentiation choice between B cells and myeloid cells [72] by antagonizing Pu . 1 . Given previous data demonstrating that Pu . 1 can restrain Notch1 signaling in pre-T cells [73] and that Pu . 1 is a bona fide Gfi1 target gene [72] , it is attractive to hypothesize that loss of Gfi1 leads to derepression of Pu . 1 which in turn opposes Notch1 . However , Notch1-signaled cells appear to have alternative mechanisms to antagonize Pu . 1-responsive transcriptional circuits based on the observation that upregulation of Pu . 1 or Nab2 ( as seen in Gfi1−/− MPP [72] , [74] ) in Notch-activated Gfi1 deficient cells ( GSE41162 ) was not observed . Instead , we find that Gfi1 is required to maintain critical lymphoid transcriptional programs activated by Notch1 such as Rag1 , Dtx1 , and Tcf7 . It remains unclear how Gfi1 might maintain the activation of these genes; whether Gfi1 represses other transcriptional repressors , microRNAs , or whether loss of Gfi1 leads to alternative differentiation pathways for Notch-signaled cells has yet to be elucidated . We discovered that the phenotypes associated with Notch1 activation and Gfi1 loss of function were most severe in early lymphoid precursors , while immature SP and peripheral T cells showed modest effects . This stage-specific phenomenon could be due to the fact that Notch1 and Gfi1 are both endogenously expressed and required for the normal development of T cells from lymphoid progenitors up to TCRβ selection [9] , [15] , [57] . Alternatively , once T cells have completed critical development checkpoints they may no longer be susceptible to manipulation of developmental transcriptional networks . For instance , during stages of development where activation of lymphoid-associated genes is critical to establishing a T-lineage identity , Gfi1 appears to be required to maintain the activation of Notch-driven lymphoid-restricted genes such as Tcf1 . However , in a mature T cell , either the expression of these genes is maintained by other transcription networks , or an inability to maintain their expression does not result in phenotypic consequences because the cell's developmental potential has already been achieved . In either case , our work has uncovered an epistatic relationship between Notch1 and Gfi1 that is essential for proper lymphoid development . Loss of Gfi1 phenocopies the loss of Notch1 and Tcf7 ( Tcf1 ) with regard to the formation of ETPs , but unlike Notch1 and Tcf7 , Gfi1 is also required for the survival or formation of lymphoid-primed progenitors upstream of the ETP [23] , [67] . This suggests a unique role for Gfi1 in bridging lymphoid transcriptional programs from the earliest lymphoid-primed bone marrow progenitor to the thymic ETP before Notch1-regulated transcriptional programs become the dominant mechanism through which T lineage fate is enforced . Although our data do not exclude the possibility that Gfi1 participates in a shared , undiscovered , transcriptional network with other key “T cell-specific” transcription factors , it appears more likely that the phenocopy of Gfi1−/− ETP is due to the inability of Gfi1 deficient cells to integrate lymphoid progenitor transcriptional circuits , in particular those initiated by Notch1 . We have recently shown that Gfi1 deficient mice are protected from Notch1 mediated malignant transformation [37] . Here , we have uncovered a requirement for Gfi1 in Notch1 activated cells with implications for both normal lymphopoiesis as well as T cell transformation . Specifically , Gfi1 is required to maintain cellularity in Notch-signaled cells in a temporally regulated manner . These data help to clarify the almost absolute requirement for Gfi1 in Notch-mediated transformation . Gfi1 is required to maintain the pool of premalignant cells available for transformation , and to maintain Notch target genes essential for leukemogenesis . Thus , our data provide additional insight into the multiple mechanisms by which transcriptional networks may have evolved to protect developing lymphoid cells from transformation .
LckCre , CD4Cre [38] , distal LckCre [45] , Rosa26-lox-stop-lox-NotchIC [39] , Lck-Gfi1 [75] , Vav-Gfi1 [56] , Gfi1fex4–5 [10] , Gfi1Δex2–3 [76] , Gfi1 Δex2–5 [6] , RosaCreERT2 Gfi1fex4–5 [46] transgenic mice have all previously been described . Gfi1fex4–5 were bred to Gfi1Δex2–3 mice to generate Gfi1fex4–5 , Δex2–3 mice to allow for more efficient deletion of the remaining floxed allele by LckCre , CD4Cre or distal LckCre transgenic mice . All mice were bred and housed in a specific-pathogen-free barrier facility at Cincinnati Children's Hospital Medical Center ( CCHMC ) Veterinary Services or at the Institut de recherches cliniques de Montréal ( IRCM ) . The Institutional Animal Care and Use Committee at CCHMC and the Animal Care Committee at the IRCM reviewed and approved all animal experimentation protocols , certified animal technicians , regularly observed mice in all studies and took steps to maintain animal welfare and prevent undue suffering under protocol numbers 1D09075 and 2009-12 respectively . Thymi were harvested in Medium 199 ( Invitrogen ) and single cell suspensions were created . Bone marrow was flushed from femurs and tibias using Medium 199 , spun down and RBC were lysed using ACK lysis buffer ( Gibco ) . Cell counts were determined using a Coulter Counter ( Beckman ) and cells were then stained with various cocktails of monoclonal antibodies to the following antigens: Fc-block ( 2 . 4G2 ) , CD4 ( RM4–5 ) , CD8a ( 53-6 . 7 ) , CD44 ( IM7 ) , CD25 ( PC61 ) , cKit ( 2B8 ) , Sca1 ( D7 ) , Flt3 ( A2F10 ) . Lineage cocktails for T cell development contained B220 ( RA3-6B2 ) , CD11b ( M1/70 ) , CD11c ( N418 ) , NK1 . 1 , TCRγδ ( UC7-13D5 ) and Ter119 . Lineage cocktails for ETPs FACS plots contained B220 ( RA3-6B2 ) , CD3ε ( 145-2C11 ) , CD8 ( 53-6 . 7 ) , CD11b ( M1/70 ) , CD11c ( N418 ) , DX5 , Gr1 ( RB6-8C5 ) , NK1 . 1 , TCR γδ ( UC7-13D5 ) and Ter119 . Cells were stained at 4°C for 30 minutes before being washed and resuspended in PBS containing 2% FBS and 1 mM EDTA . Data was acquired on the BD LSRII , LSRFortessa or FACSCanto . Cells were FACS sorted on the BD FACSAriaII and recovered in PBS with 50% FBS . Polymerase chain reaction ( PCR ) detection of the Gfi1fex4–5 allele was performed with primers 5′-CAGTCCGTGACCCTCCAGCAT-3′ and 5′-CTGGGAGTGCACTGCCTTGTGTT-3′ , whereas detection of the Gfi1Δex4–5 allele was performed with primers 5′-CAGTCCGTGACCCTCCAGCAT-3′ and 5′-CCATCTCTCCTTGTGCTTAAGAT-3′ . Gene expression analysis was performed on RNA isolated from TRI Reagent ( Sigma ) by phenol-chloroform extraction or by the RNeasy kit ( QIAGEN ) . cDNA was synthesized from purified RNA using the cDNA High Capacity Archive Kit ( Applied Biosystems ) according to the manufacturer's instructions . Gene expression was assessed using Taqman probes ( Applied Biosystems ) or primers for cMyc ( Mm03053277_s1 , Mm00487803_m1 ) , Dtx1 ( Mm00492297_m1 ) , Hes1 ( Mm00468601_m1 , Mm01342805_m1 ) , Hey1 ( Mm00468865_m1 ) , Ptcra ( Mn00478361_m1 ) , Ccnd1 ( Mn00432359_m1 ) , Notch1 ( Mm00435245_m1 , Mm00435249_m1 ) , Notch3 ( Mm01345646_m1 ) on an ABI Prism 7900 . Threshold values were calculated and normalized to the endogenous control , Gapdh ( Mm99999915_g1 ) ; then , the ΔΔCT method was used to calculate the fold change compared to Gfi1+/+ controls . Gene array data ( GSE20282 or GSE41162 ) was analyzed using GeneSpring ( version 12 . 0 Agilent Technologies ) or the R software package . OP9-DL1 cells were cultured in 24-well plates at a concentration of 2×104cells/ml in α-MEM media supplemented with 20% FBS ( charcoal stripped ) , β-mercapto ethanol , sodium pyruvate , and non-essential amino acids . OP9-DL1 cells were seeded 24 h before FACS sorted ETP were directly sorted onto the monolayer . Fresh IL7 ( 1 ng/mL ) and Flt3L ( 5 ng/mL ) were then added . Media was changed every 4–5 days and developing T cells were transferred onto a new monolayer of OP9-DL1 cells with fresh media and cytokines . Lineage negative cells were isolated from total BM using magnetic separation ( Miltenyi ) and then placed into StemSpan SF media ( StemCell Technologies ) containing IL-3 , IL-6 , IL-7 , SCF , Flt3L and human IL-11 ( Miltenyi ) with 1% Glutamine and 1% Pen/Strep ( Gibco ) . Cells were expanded for two days before being placed on Retronectin ( Takara ) coated plates preloaded with viral supernatants harvested from MigR1-ICN-ires-eGFP transfected 293T cells . Viral supernatants were spinfected at 1000 g at 4°C for 30 minutes . The process was repeated twice and the cells were expanded for 48 hours before FACS-sorting . eGFP+ cells were resuspended in MethoCult semi-solid media ( StemCell Technologies ) and allowed to grow for one week . CFU were enumerated , cells were then dissociated , counted and replated . 4-OHT was added at a final concentration of 1 µM to induce Cre activity . Gfi1 deletion was confirmed by PCR; any CFU demonstrating incomplete excision of floxed Gfi1 was excluded from gene expression array analysis . | Understanding the mechanisms that protect lymphoid cells from transformation is a critical first step in developing therapies against blood cancers . Recently , we demonstrated that the Growth factor independent-1 transcriptional repressor protein is required for cancer development driven by activation of Notch1 signaling . Here , we investigated the mechanisms by which Gfi1 protects lymphoid transformation . Using complex genetic mouse models to delete Gfi1 and activate Notch1 , we demonstrate that Gfi1 is required to maintain both the homeostatic levels of Notch1 target genes in normal lymphoid precursors in the bone marrow , as well as to maintain the supraphysiologic levels of Notch1 signaling present in pre-malignant lymphoid progenitors . Consequently , without Gfi1 the pool of premalignant cells available for transformation is depleted . Our data provide additional insight into the multiple mechanisms by which developmental networks may have evolved to protect lymphoid cells from transformation . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2013 | Growth factor independent-1 Maintains Notch1-Dependent Transcriptional Programming of Lymphoid Precursors |
Human protection policies require favorable risk–benefit judgments prior to launch of clinical trials . For phase I and II trials , evidence for such judgment often stems from preclinical efficacy studies ( PCESs ) . We undertook a systematic investigation of application materials ( investigator brochures [IBs] ) presented for ethics review for phase I and II trials to assess the content and properties of PCESs contained in them . Using a sample of 109 IBs most recently approved at 3 institutional review boards based at German Medical Faculties between the years 2010–2016 , we identified 708 unique PCESs . We then rated all identified PCESs for their reporting on study elements that help to address validity threats , whether they referenced published reports , and the direction of their results . Altogether , the 109 IBs reported on 708 PCESs . Less than 5% of all PCESs described elements essential for reducing validity threats such as randomization , sample size calculation , and blinded outcome assessment . For most PCESs ( 89% ) , no reference to a published report was provided . Only 6% of all PCESs reported an outcome demonstrating no effect . For the majority of IBs ( 82% ) , all PCESs were described as reporting positive findings . Our results show that most IBs for phase I/II studies did not allow evaluators to systematically appraise the strength of the supporting preclinical findings . The very rare reporting of PCESs that demonstrated no effect raises concerns about potential design or reporting biases . Poor PCES design and reporting thwart risk–benefit evaluation during ethical review of phase I/II studies .
Early phase human studies ( phase I and II trials ) aim to establish the safety , rationale , and conditions for testing new drugs in rigorous , randomized controlled phase III trials . Because early phase human studies expose human research subjects to unproven—and in some cases previously untested—interventions , they present major human protection challenges [1] . Key to meeting these challenges is establishing a favorable risk–benefit ratio in prospective ethical review . In early phase trials , assessment of risks and benefits depends heavily on evidence gathered in preclinical animal studies . Over the past 10 years , many commentators have raised concerns about the design and reporting of preclinical reports [2–9] . These concerns have been mainly informed by cross-sectional studies of peer-reviewed publications [2 , 10] and study protocols [11] for preclinical studies . These analyses consistently show infrequent reporting of measures aimed at reducing bias , including a priori sample size calculation , blinding of outcome assessment , and randomization . Further analyses suggest that publication bias frequently leads to inflated estimation effect sizes [2] . However , many such analyses reflect preclinical studies that have been submitted for animal care committee review or that are described in publications . Many such studies are not necessarily embedded within drug development programs and may have been pursued after a drug had already shown efficacy in trials . In contrast , little is known about the extent , quality , and accessibility of preclinical evidence used to justify and review the launch of early phase clinical trials . As a result , it is unclear whether preclinical studies submitted to institutional review boards ( IRBs ) or regulatory agencies are described in ways that enable the respective evaluators to perform a critical assessment about the strength of evidence supporting a new trial . Nor is it clear whether such materials adhere to various standards and guidelines on the design of preclinical studies [12–14] . Clinical investigators , IRBs , data safety and monitoring boards , and regulatory agencies ( e . g . , the European Medicines Agency [EMA] and the Food and Drug Administration [FDA] ) are all charged with risk–benefit assessment . Their main source of information is the investigator brochure ( IB ) . According to the ICH ( International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use ) Guideline for Good Clinical Practice E6 [15] , the information in IBs “should be presented in a concise , simple , objective , balanced , and non-promotional form that enables a clinician , or potential investigator , to understand it and make his/her own unbiased risk–benefit assessment of the appropriateness of the proposed trial” . In general , the preclinical safety studies ( mainly pharmacokinetics and toxicology experiments ) inform judgments about risk in early phase trials . Judgments about clinical promise rely heavily on preclinical efficacy studies ( PCESs , often described as “preclinical pharmacodynamic” studies in regulatory documents ) , which aim at providing a readout of disease response in animal models . The primary objective of this study was to determine the extent , quality , and accessibility of PCESs that are contained within IBs submitted for ethical review of early-phase clinical trials .
Altogether , we obtained 109 IBs for phase I ( n = 15 ) , phase I/II ( n = 10 ) , and phase II ( n = 84 ) clinical trials that were submitted to 1 of 3 German IRBs ( see the “Materials and methods” section ) . The majority of these IBs ( n = 97 , 83% ) reflected the full sample of IBs for phase I/II trials submitted to 1 of the 3 IRBs between 2010 and 2016 . The IBs covered 8 out of 12 therapeutic areas as distinguished by the European Medicine Agency ( Table 1 ) . Seven studies ( 6% ) were “first in human , ” whereas all other IBs ( 94% ) mentioned at least some clinical evidence for the investigational product . All trials were privately funded ( 1 IRB did not allow recording of the funders of the 6 IBs they shared , so we have this information for only 103 IBs ) . These included 48 IBs ( 47% ) from the top 25 pharma companies by global sales [16] . A total of 708 PCES were identified from all 109 IBs . The median number of PCESs per IB was 5 , with 18 IBs ( 17% ) including 0 PCESs and 10 IBs ( 9% ) including more than 15 PCESs ( max = 32 PCESs ) . See Fig 1 . Table 2 presents the extent to which the 109 IBs described the implementation of practices to reduce validity threats for the 708 PCESs contained in them . A sample size was reported in 26% of PCESs ( n = 184 ) , with a median group size of 8 animals . Sample size calculation was never explained . None of the 708 PCESs were described as using blinded treatment allocation and/or outcome assessment . Only 4% of PCESs ( n = 26 ) were described as using randomization , and 5% ( n = 38 studies ) reported the exclusion of animal data . Baseline characterization of animals was described for 18% of all PCESs ( n = 127 studies ) . The animal species was reported for 88% of all PCESs ( n = 622 studies ) ; see Table 3 for further details on animal species . The animal model used in the experiment was reported in general terms ( e . g . , “xenograft” or “T-cell tolerance model” ) for 97% of all PCESs ( n = 684 studies ) , but its specifications ( e . g . , transplanted cell line for xenograft or tumor size before treatment ) were reported for only 27% of all PCESs ( n = 193 studies ) . Outcome choice was reported for 81% of all PCESs ( n = 575 studies ) . As described in the “Materials and methods” section , some validity issues pertain less to individual PCESs than to a package of several PCESs within 1 IB . These were thus rated at the IB level only . The following percentages refer to the 91 IBs that included at least 1 PCES . At the IB level , 9% ( n = 8 ) reported for at least 1 PCES whether the age of animals matched the patient group proposed in the clinical trial . Most IBs ( 74% , n = 67 ) described a preclinical dose response for at least 1 studied outcome . At least 1 PCES described mechanistic evidence of efficacy in 70% of all IBs ( n = 64 ) . At least 1 replication of an efficacy experiment was described in 82% of IBs ( n = 75 ) . These included 70 replications in different models ( 77% ) and 25 replications in a different species ( 27% ) . A reference to published , peer-reviewed reports of preclinical efficacy was provided for 80 PCESs ( 11% of all PCESs ) stemming from 20 IBs ( 18% of all IBs ) . These journal publications provided additional information on sample size ( for 91% of PCESs reported in journals versus for 26% of PCESs reported in IBs ) , baseline characterization ( 76% versus 18% ) , control groups ( 98% versus 46% ) , randomization ( 28% versus 4% ) , and blinding of outcome assessment ( 5% versus 0% ) . However , no or even less additional information in journal publications was found , for example , for sample size calculations ( 0% for PCES in journals and 0% for PCES in IBs ) and for age matching of animals and patient group ( 5% versus 9% ) . For further information , see Table 2 . Less than half of all PCESs ( 44% ) stemming from 68 IBs ( 75% ) reported results in quantitative terms that allowed scoring for the results as “demonstrating an effect” or “demonstrating no effect . ” Altogether , 30% of all PCESs ( n = 211 ) reported an effect size , and 23% of all PCES ( n = 161 ) reported a p-value . Another 53% of all PCES ( n = 372 ) provided narrative descriptions of results . With regard to outcomes , the results for 636 PCESs ( 90% ) demonstrated an effect , and the results for 43 PCESs ( 6% ) demonstrated no effect . For 29 PCESs ( 4% ) , the direction of results was unclear . The 43 PCESs demonstrating no effect came from 16 IBs ( 18% ) . S1 Table gives examples for PCESs demonstrating no effect .
Our analysis of 109 IBs for phase I/II trials uncovered 3 striking features of the 708 PCESs the IBs presented to IRBs and regulatory agencies to support early-phase trials . First , 89% of all PCESs present data without a reference to a published , peer-reviewed report . While it is possible that sponsors maintain internal review mechanisms for PCESs , members of IRBs or regulatory agencies reviewing IBs have no way of knowing whether preclinical efficacy data have been subject to critical and independent evaluation . Further , IRBs and regulatory sponsors have no way of directly accessing preclinical reports if they are not published . Such commonplace nonpublication of preclinical evidence is potentially inconsistent with numerous scientific and ethical guidelines on early-phase trial launch [13 , 17] . The second finding is that much of the information needed for a favorable appraisal of the PCES’s validity is not provided in IBs . On the positive side of the ledger , IBs often contain PCESs that characterize the mechanism of action for new drugs or a dose response . Many also contain more than 1 study testing similar hypotheses , thus establishing some level of reproducibility for efficacy claims . On the negative side , IBs contain very little information that would enable reviewers to evaluate the risk of bias in these individual studies . For example , less than 20% of PCESs reported baseline characterization , exclusion of data from analysis , or randomization . Sample size calculation and blinding for outcome assessment were never reported . Many have previously argued that internal validity is the sina qua non of a valid experimental claim [18] . The dose response , mechanism , and replication studies described in IBs are difficult to interpret without knowing how well they implemented measures to limit bias and the effects of random variation . A potential reporting bias for studies demonstrating the intended mechanism or dose response would further aggravate this difficulty . This leads us to the third striking finding: the scarcity of PCESs in IBs that do not demonstrate an effect ( n = 43 , 6% ) . Several nonexclusive explanations for this imbalance of outcomes in PCESs can be envisioned . One is biased study design . It is possible that in the absence of prespecifying end points or the limited use of techniques like blinded outcome assessment , PCESs consistently show large effects . A second explanation is biased inclusion of PCESs in IBs . With a median group size of 8 , the PCESs in our sample had a limited ability to measure treatment effects precisely . This might have resulted in studies that showed unusually large effects . Attrition of animals in small experiments might aggravate the tendency for studies to occasionally produce large effects [19] . A recent investigation from the British Medical Journal ( BMJ ) supports the notion that animal data are sometimes reported selectively in IBs [20] . A third explanation is that only those treatments that show consistently positive effects in PCES were selected for early-phase trials . Though our study does not allow us to discriminate between these explanations , we think the latter explanation is improbable . Studies demonstrating no effect are crucial for demarcating the boundaries of dosing , diagnostic eligibility , or treatment timing for a new treatment [21] . The evidentiary basis for such boundaries would be important to include in an IB . Indeed , some PCESs we found ( see S1 Table ) demonstrated such efforts at “demarcation . ” Our study has several limitations . First , because of difficulties accessing IBs [20 , 22] , our analysis did not utilize a random sample . Nevertheless , we believe our findings are likely to be generalizable to other IBs used for early-phase trials . For example , IBs used in our study covered a broad spectrum of different funders and addressed many different therapeutic areas . It is also important to note that the same IBs that funders submit to local IRBs are also submitted to the national regulatory agency ( Bundesinstitut fuer Arzneimittel und Medizinprodukte [BfArM] ) . If the studies in our sample deviate from norms that are used elsewhere , these deviations fall within the window of acceptability for drug regulators . A second limitation might be seen in the fact that only a small minority of included IBs were “first in human” studies ( 7 IBs , comprising 30 PCESs ) . However , many phase I trials that are not first in human involve new disease indications or drug combinations; PCESs are critical for justifying such studies . Moreover , phase II trials represent the first attempt to test a drug’s efficacy in human beings . Their justification ultimately rests on the evidence of clinical promise established in PCESs . In general , if information on preclinical efficacy is considered important to include in an IB , then validity reporting for the respective PCESs should be important as well . Last , there are limitations to the way we measured the degree to which validity threats were described as being addressed in IBs . For instance , our 14-item matrix did not weight any practices based on their potential impact on bias . Also , that measures aimed at strengthening the validity of PCES findings are reported so infrequently in IBs does not necessarily mean that such measures were not implemented . Nevertheless , our matrix was based on systematic review evidence and thus provided a reasonable starting point for describing how study designs were reported in IBs . Stakeholders tasked with risk–benefit assessment , such as investigators , IRBs , regulatory agencies , and data safety and monitoring boards , have no way of knowing how studies were performed if the relevant study design information is not provided in IBs—this is especially the case if the studies themselves have not been published . To improve the effective use of preclinical information for risk–benefit assessment in phase I/II trials , we offer the following recommendations . First , IBs should describe measures taken in PCESs to support clinical generalizability . Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) recommendations , established in 2010 , provide one suggestion for how to do so [23] . To facilitate efficient evaluation of IBs , it might be helpful to present relevant practices , like use of randomization or choice of endpoints , in tabular form . Furthermore , explicit remarks on the “level of evidence” for each preclinical study might be presented in such tables , with studies designated as “confirmatory” when they had prespecified hypotheses and protocols or “exploratory” when their hypotheses and protocols were not established prospectively . “Confirmatory” studies should also undertake the expense of employing methods that would enhance internal and construct validity−namely , a priori sample size calculation , concealed allocation , or blinded outcome assessment and use of clinically relevant endpoints [12] . Information on the reproducibility of confirmatory preclinical studies and meta-analysis of sufficiently similar studies might further improve the level of evidence [24] . A more stepwise presentation of preclinical evidence could further help evaluators to navigate through the most important questions to assess clinical promise and safety [9 , 25]: Have effects been reproduced in different models and/or in independent laboratories ? Do the conditions of the experiment ( for instance , age of animal models , timing of treatments , and outcomes ) match clinical scenarios ? Second , IBs should state whether they are presenting the totality of preclinical evidence , and if not , how data were selected for inclusion in the IB . One option would be to only present preclinical studies that have been preregistered [26 , 27] . This increased transparency might help with preventing selective outcome reporting , and it allows evaluators to check whether other relevant preclinical studies exist . Future studies need to evaluate how improved reporting for preclinical data presented in IBs influences risk–benefit analysis during ethical review . However , better reporting alone is unlikely to solve problems related to risk of bias in preclinical evidence . Regulatory bodies like the FDA and the EMA offer specific recommendations for the design of preclinical safety studies [28] . To our knowledge , there are no regulatory guidelines offering standards for the design and reporting of PCESs . As the IBs investigated in this study inform ethical as well as regulatory review , we recommend that regulators develop standards for the design and reporting of PCESs to be included in IBs .
IBs and study protocols submitted to IRBs in support of trials are difficult to access , because academic medical centers maintain IBs in strict confidence . Indeed , the difficulty of protocol access has been addressed as a major challenge for metaresearch and quality assurance of ethics review [22] . A recent BMJ investigation illustrated the challenges of accessing study protocols and IBs [20] . Because our inquiry was sensitive and protocol access is so restricted , we deemed it unlikely that randomly identifying centers for inquiry would be productive and produce a sample that was anything approaching random . We therefore approached 6 chairs of German IRBs personally to outline the rationale for our interest in analyzing the reporting of preclinical evidence in IBs . Three chairs were willing to grant access under the data protection conditions described below . One IRB responsible for reviewing all clinical trials to be conducted at one of the leading German university hospitals gave us access to their full sample of all 97 phase I/II trials that they approved between 2010 and 2016 . The IRBs at 2 other German university hospitals allowed us to access the 6most recently reviewed IBs for phase I/II trials . All IBs were analyzed on-site at the 3 universities . All members of the research team signed confidentiality agreements . Results are reported in an aggregated manner and do not allow the identification of investigational products , sponsors , investigators , or other commercially sensitive information . To select all PCESs from an IB for coding , we applied the following inclusion criteria: ( A ) studies were conducted in nonhuman animals and ( B ) relevant to interpreting the efficacy of the investigational product ( e . g . , molecular , behavioral , or physiological readouts that were described as correlating with clinical activity ) . We excluded preclinical studies if they were ( A ) pharmacokinetic studies only , ( B ) safety and toxicology studies only , or ( C ) in vitro/ex vivo studies . To rate the degree to which the included PCESs addressed threats to valid clinical inference , a matrix was employed based on results from a systematic review of 26 guidelines for designing and conducting PCESs [29] . This matrix contains 14 items for research practices grouped under 3 types of validity threats that the practices are designed to address: ( 1 ) threats to internal validity , ( 2 ) threats to construct validity , and ( 3 ) threats to external validity ( S2 Table ) . For items like randomization or sample size , practices pertain to individual studies and can be scored relatively easily at the PCES level ( Table 2 ) . Other items , such as whether mechanistic or replication studies were performed , pertain less to individual studies than to a package of evidence and were thus rated at the IB level ( Table 2 ) . The 14 items and their clarifying questions were to be rated as reported , not reported , or not applicable . The scoring criteria are presented in more detail in S2 Table . To score whether each PCES demonstrated an effect or not , we extracted inferential statistics ( effect sizes and significance values ) or narrative wording for results . When inferential tests were performed , we defined “demonstration of effect” based on whether the 95% confidence interval excluded the null or whether p-values were reported as being less than or equal to 0 . 05 . All rating and scoring of PCESs and IBs was piloted independently by 3 authors ( WWLC , SW , and CF ) in an initial sample of 10 IBs; these IBs contained 77 PCESs in total . Unclear ratings and initial disagreements were discussed with JK and DS , and the scoring criteria were slightly modified . Once the final scoring sheet was agreed upon , SW and WWLC selected and scored PCESs independently from a second random sample of 10 IBs including 117 PCESs . For this independent rating , we found a discordance between 0% and 16% per item , resulting in a mean inter-rater reliability of 94% ( S3 Table ) . Thereafter , WWLC selected and rated the remaining 59 IBs and SW 12 IBs . The 18 IBs that lacked any PCESs were not further analyzed . All unclear cases from this third round of analysis were discussed with all other authors and resolved . Some IBs cite peer-reviewed publications including further information on the conduct and results of their PCESs . We identified these publications ( n = 80 ) and applied the same matrix of 14 items to extract practices addressing validity threats from full-text publications ( SW rated 56 publications , and SS rated 24 publications ) . Again , all unclear ratings were discussed with all other authors and could be resolved . Descriptive statistics were applied . | To make a clinical trial ethical , regulatory agencies and institutional review boards have to judge whether the trial-related benefits ( the knowledge gain ) outweigh the trial-inherent risks . For early-phase human research , these risk–benefit assessments are often based on evidence from preclinical animal studies reported in so-called “investigator brochures . ” However , our analysis shows that the vast majority of such investigator brochures lack sufficient information to systematically appraise the strength of the supporting preclinical findings . Furthermore , the very rare reporting of preclinical efficacy studies that demonstrated no effect raises concerns about potential design and/or reporting biases . The poor preclinical study design and reporting thwarts risk–benefit evaluation during ethical review of early human research . Regulators should develop standards for the design and reporting of preclinical efficacy studies in order to support the conduct of ethical clinical trials . | [
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] | 2018 | Preclinical efficacy studies in investigator brochures: Do they enable risk–benefit assessment? |
Cases reported in the period of 2001–2011 from 14/18 CL endemic countries were included in this study by using two spreadsheet to collect the data . Two indicators were analyzed: CL cases and incidence rate . The local regression method was used to analyze case trends and incidence rates for all the studied period , and for 2011 the spatial distribution of each indicator was analyzed by quartile and stratified into four groups . From 2001–2011 , 636 , 683 CL cases were reported by 14 countries and with an increase of 30% of the reported cases . The average incidence rate in the Americas was 15 . 89/100 , 000 inhabitants . In 2011 , 15 countries reported cases in 180 from a total of 292 units of first subnational level . The global incidence rate for all countries was 17 . 42 cases per 100 , 000 inhabitants; while in 180 administrative units at the first subnational level , the average incidence rate was 57 . 52/100 , 000 inhabitants . Nicaragua and Panama had the highest incidence but more cases occurred in Brazil and Colombia . Spatial distribution was heterogeneous for each indicator , and when analyzed in different administrative level . The results showed different distribution patterns , illustrating the limitation of the use of individual indicators and the need to classify higher-risk areas in order to prioritize the actions . This study shows the epidemiological patterns using secondary data and the importance of using multiple indicators to define and characterize smaller territorial units for surveillance and control of leishmaniasis .
Leishmaniasis is an important health problem for several countries in the Americas . It is caused by different species of protozoans of the genus Leishmania and transmitted to humans and animals by insect vectors of the Psycodidae family infected during blood feeding on vertebrate reservoirs and hosts [1–3] . Various factors including climate , economic , environmental , social , and others , have an influence in the leishmaniasis transmission cycle and thus determining the epidemiological patterns of the disease [3–5] . The disease occurs in different clinical forms ( cutaneous , mucosal , and visceral ) , according to the parasite . Aspects such as the immunogenetic profile of the affected population , malnutrition , and access to health systems , among others , have an influence in the prognosis of the disease , which can progress from mild to severe , causing deformities , mutilation , and even death [3 , 5] . A recent study shows that leishmaniasis had an increase of 8 . 8% ( -6 . 4 to 25 . 3% ) in the disability-adjusted life year ( DALYs ) for all ages when comparing 2013 to 2005 , and a significantly higher increment for the cutaneous and mucocutaneous forms ( 35 . 9%–23 . 7 to 49 . 0% ) [6] . Globally , Cutaneous leishmaniasis ( CL ) is broadly distributed occurring in 83 countries , with an estimated of 0 . 7–1 . 2 million new cases per year [7] . Currently , 70–75% of all estimated cases of CL worldwide occur in ten countries: four in the Americas ( Brazil , Colombia , Nicaragua , and Peru ) and six in Africa and Eastern Mediterranean ( Afghanistan , Algeria , Iran , Syria , Ethiopia , and Sudan ) [3 , 7] . In the Americas , CL cases have been notified across a wide geographic area extending from southern United States to northern Argentina [3] . In the past decades it has become apparent that CL is more prevalent in many areas of the Latin America than previously thought . This uncertainty about its occurrence is partially because the notification in many endemic countries was often underreported and the collected information remained limited [7 , 8] . A comprehensive understanding of the transmission of leishmaniasis , incorporating the knowledge of specific spatial and temporal distribution of the disease , is essential to make decisions in order to direct and prioritize actions for surveillance and control strategies for cutaneous leishmaniasis [9] . At this juncture , the Pan American Health Organization/World Health Organization ( PAHO/WHO ) Regional Leishmaniasis Program , brought together ( in Colombia 2008 ) , the coordinators of the National Leishmaniasis Control Programs of the American Region to discuss the status of the leishmaniasis reporting system and agreed upon the development of a regional information and surveillance systems region-wide coordinated by PAHO/WHO . The meeting helped jumpstart the debate and definition of criteria for an improved epidemiological surveillance system to fully understand the epidemiological status of leishmaniasis in the Americas . The objective of this study is to describe the temporal and spatial distribution of CL cases between 2001 and 2011 utilizing the data reported to PAHO/WHO by the endemic countries of the Americas , as a first step to determine the time and spatial distribution of the disease in the subregions and region .
Over 2011 and 2012 , the Regional Leishmaniasis Program of PAHO/WHO coordinated the data collection among endemic countries , requesting the annual data of CL from 2001 to 2011 through two spreadsheet templates . One spreadsheet to collect national data standardized by country , year , and clinical form of the disease for the whole study period; and a second spreadsheet to collect only data from 2011 disaggregated at the first subnational administrative level ( department , state , or province , depending on the country ) . Additionally , consolidated , aggregated , and analyzed all the information provided by the endemic countries using the regional database [10] . There were many differences between countries in their leishmaniasis surveillance systems , including the process of reporting . Thus , while case notification for leishmaniasis is mandatory region-wide , reporting can be done on individual or aggregate basis , depending on the specific criteria established by the national epidemiological surveillance system . The definition of cases , notification process , and variables for each data collected were also different among the countries due to the different clinical forms , internal system and information flow established in each country . This scenario of uneven data structure defined the limitations of the potential analyses to be carried out in this study . The data collection included cases reported from 2001–2011 from 14 of the 18 endemic American countries ( Argentina , Brazil , Bolivia , Colombia , Costa Rica , El Salvador , Ecuador , Guatemala , Guyana , Honduras , Nicaragua , Panama , Paraguay , and Peru ) . French Guiana , Mexico , Suriname , and Venezuela , did not complete the spreadsheets templates provided by PAHO/WHO to report data in the time requested . For the 2011 spatial analysis , 15 countries participated ( the 14 countries listed above plus Mexico ) and provided information on the following variables: total number of CL cases , total population of the administrative unit ( national or first subnational administrative department , state , or province ) where the cases occurred . In this study the legal principles and ethical aspects were considered and respected . These data refer to the routine of a control program of leishmaniasis and surveillance services of the Health Ministries of endemic countries , which maintained confidential the identification of patients .
In the period of 2001–2011 , 14 countries reported a total of 636 , 683 CL cases , with an annual average of 57 , 923 cases ( ranging from 47 , 286 to 67 , 949 ) . A total of 270 , 572 of the reported cases ( 42 . 5% ) were from Brazil; 256 , 261 ( 40 . 2% ) were from countries of the Andean subregion; 100 , 475 ( 15 . 8% ) were from Central American countries; and 9 , 375 ( 1 . 5% ) were reported by Argentina , Guyana , and Paraguay , Table 1 . In the first half of the studied period ( 2001–2005 ) there was a continuous increase of the total number of CL cases reported per year , which increased 44% in 2005 when compared to 2001 . In the second half of the period ( 2006–2011 ) there was a decline of 11% of the CL in this region , but resulting in an overall increase of 30% for all study period , Table 1 , Fig 1 . The number of cases reported per year by subregion for the period of 2001–2011 , showed an overall increase of 68% and 39% in the Andean countries and in Central America , respectively . For the same period , in both Brazil and the Southern Cone there was a decline of the total number of cases . However , in the Southern Cone the number of CL cases reported tripled between 2001 and 2002 , Fig 1 . Two countries in the Andean subregion ( Colombia and Peru ) , reported an increase of cases during the overall studied period . In Colombia , the reported cases increased 4 . 36-fold in 2005 versus 2001 . In Peru , there was a continuous increase of cases over the period , with the number of cases increasing 2 . 13-fold ( Table 1 ) . Of the total cases reported between 2001 and 2011 from the Andean subregion , Colombia accounted for 50% of the cases , followed by Peru with 33% , except for 2011 , when Peru reported 47 . 7% of the total CL of the subregions . In the Central America subregion , the countries that contributed to the overall increase in the occurrence of CL for this period were: Costa Rica ( 4 . 7 times higher ) ; Honduras ( 1 . 8 times higher ) ; Panama ( 1 . 3 times higher ) and Nicaragua ( 2 . 0 times higher ) ( Table 1 ) . In the cumulative total of cases for the studied period , Nicaragua reported 36 . 74% , followed by Panama , which reported 26 . 82% . This situation was similar every year except in 2006 , when Panama reported more cases than Nicaragua , Table 1 . The annual CL incidence for the 14 selected endemic countries collectively and by subregion is shown in Table 1 . The average incidence of CL was 15 . 89 cases per 100 , 000 inhabitants . The incidence increased 35% in the first period ( 2001 to 2005 ) , but in the second half ( 2006 to 2011 ) there was a 21% reduction ( Fig 2 ) . In the Andean and Central America subregions , the average incidence rates were higher than the average for all 14 countries , with an incidence of 24 . 50 and 23 . 70 cases per 100 , 000 inhabitants respectively . Colombia and Peru showed the highest incidence rates for the Andean subregion ( Table 1 ) . On the other hand , the incidence rate in the Southern Cone declined ( in both Argentina and Paraguay ) , Table 1 . Central America countries presented the highest CL incidence rates during the period , and among these Panama had the highest number , followed by Nicaragua and Costa Rica , which had higher incidence in 2009 ( Table 1 ) . Within the 15 analyzed countries , 180 ( 61 . 6% ) out of 292 administrative units at the first subnational level ( departments , states , or provinces ) reported cases of CL , with an average of 309 . 64 cases ( ranging from 1 to 3 , 667 ) . Eight countries ( Bolivia , Brazil , Colombia , Costa Rica , Nicaragua , Panama , Paraguay , and Peru ) reported cases in more than 75% of their first-subnational-level units . The overall incidence rate for all 15 countries in 2011 was 17 . 42 cases per 100 , 000 inhabitants , while for the 180 affected administrative units at the first subnational level , the average incidence rate was 57 . 52 per 100 , 000 inhabitants ( ranging from 0 . 03 to 1 , 229 . 01 ) .
For the first time the temporal and geographical distribution of CL cases is described in the American Region . The study find that from 2001 to 2011 a total of 636 , 683 CL cases were reported by the passive surveillance system from 14 endemic countries representing a 30% increase in the period with an average incidence rate of 15 . 89/100 , 000 inhabitants . In the Americas , most of the CL endemic countries use passive surveillance as a routine reporting system; while active surveillance is mainly conducted during outbreaks or to assist specific inhabitants living in risk areas and of difficult access [15–18] . This study shows the importance of maintenance of adequate strategies for passive surveillance of CL and the need of a system that combines the information from different countries of the Americas , in order to investigate epidemiological features such as time and geographical distribution . Our findings indicate a 44% increase in the number of CL cases , during the first five years of investigation , also reflected in the incidence rate . This growth could be attributed to an increase of reported cases from the Andean and Central America countries . The reasons for this increase are unknown , but could be explained by a variety of factors , including a true change in incidence or the improvement of clinical and laboratory diagnosis , and enhancement of the CL surveillance system in the endemic countries [15–19] ( e . g . Argentina , Brazil , Colombia , Honduras , Paraguay , and Peru [20–23] ) . Then again , this increase might be even greater if we consider underreporting , which could arise due to lack of access to health services by patients , lack of awareness of the leishmaniasis reporting system by the staff , and the absence of clinical diagnosis , since subclinical or benign forms that cure spontaneously can occur [2 , 3 , 7 , 8 , 17 , 19] . Furthermore , during the period of investigation , the Region faced several risk factors that contributed to environment and demographic changes ( e . g . , deforestation , modifications in the land use , increased migration , urbanization , climate change , etc . ) that might have led to an increase of contact of human with vectors , therefore affecting the CL case trend [4 , 20–29] . Studies conducted in Argentina , Bolivia , Brazil , Colombia , Mexico , and Peru have shown that frequency of leishmaniasis cases are often associated to climate changes and their impacts on the environment and economic activities , including agriculture [4 , 19–30] . In Colombia , the Chaparral County reported an outbreak that lasted 5 years ( 2003–2008 ) and was considered the largest CL outbreak ever recorded in the country [21] . Also , Argentina recorded outbreaks in the Bella Vista , Formosa , and Salta Counties [31 , 32] . In addition , the El Niño-Southern Oscillation ( ENSO ) has caused a growth of deforestation and of the Phlebotomine population , which has led to the rise of occurrence of CL [33–36] . Studies conducted in Colombia showed an association between increase of CL incidence rates during the El Niño and decline during the La Niña [4 , 23] . On the contrary , in Bolivia the CL incidence rates increased during the La Niña and declined during the El Niño [37] . Therefore , climate forecast might be used to predict the leishmaniasis transmission risk in endemic countries affected by both phenomena [38] . The study also shows a different geographical distribution when applied to different countries and subnational levels , illustrating how limiting the use of each indicator can be when higher-risk areas need to be identified to prioritize actions . For example , Brazil has the highest number of cases reported , and at the same time one of the lowest incidence rates due to the size of the population , which emphasizes the need of new analysis strategies , including composite indicators such as the one used in this study . Moreover , it is important to use , when available , complementary information on the vectors , parasites , environment and social characteristic in order to understand the dynamic and traits of this disease [39] . The exploratory analytical tools used in this study to investigate the spatial and temporal distribution of CL in the Americas helped enhance knowledge of the epidemiology of CL and added value to the data reported by the countries . These exploratory methods and their graphic representations are an essential part for the understanding of the complexity of the data correlated over time [40] . This is particularly true in this study where our “sample” is the whole population under surveillance , and for more practical purposes it could be assumed that cases represented approximately the occurrence of CL in the population; in which statistical inference is not as useful as the descriptive method [41] . This study demonstrates that it is possible to provide , retrospectively , an overall picture of the epidemiological patterns of the endemic countries using secondary data , underlining the importance of passive surveillance as a key tool for management of the national control program and the use of the data analysis results for evidence-based programmatic actions and policy formulation . Our results should be interpreted in light of potential limitations posed by the use of data collected under passive surveillance , as showed and discussed in others studies [7 , 8 , 15–18] . The differences between reporting systems of different countries might affect the comparability of data . The countries included in this study have different types of denominator to estimate the CL incidence rate . Some countries apply the total population , others only the population within rural and urban areas , or only rural counties with CL transmission . The fact that this study uses a standard population as the denominator allowed the assessment and comparison of the incidence rate of CL between the 14 countries included . The results , however , may differ from data available in the countries or from other sources . The results presented here have shown important advances in the implementation of leishmaniasis surveillance system in the Region , globally demonstrating the epidemiological status of CL in the Americas , but also supporting managers and international organizations in defining , directing and strengthening the actions of surveillance and control of leishmaniasis , in order to reduce cases , deformities and deaths caused by the disease . Improvement of the surveillance system will allow a better understanding of the ongoing and changing dynamics of the CL transmission . Further operational research is required to design and implement preventive and control strategies [39] . For instance , operational research to determine the advantages in using multiple combined indicators , such as the two indicators used in this study , and to asses complementary indicators ( e . g . biological , environmental , social , and other indicators for each subnational administrative entity ) in order to generate a more detailed picture of the occurrence of leishmaniasis in the Americas [39 , 42] . In addition , a more detailed and integrated analysis of the indicators along with geographical data , disaggregated at the second ( or lower ) subnational administrative level ( counties ) , would also help to further clarify the epidemiological status of CL at the regional and country level . Overall , surveillance activities should also be strengthened to provide disaggregated leishmaniasis data to the countries , ideally at the local level . It is also recommended the characterization of smaller territorial units according to their own unique human and physiographic traits ( climatological , geomorphological , hydrographical , etc . ) , which can be highly complex and dynamic , and have the capacity to establish and/or influence an epidemiological situation . Generally , the characteristics of the area ( concerning CL occurrence ) are the result of interaction among different social groups that share the same space but may have different lifestyles , economies and environment [9 , 20 , 42] . In conclusion , this investigation indicates changes in the epidemiological patterns of CL in the American Region for unknown reasons , which could be due to changes in the risk factors as well as improvement of the diagnosis and notification . Further operational research and improvement of the surveillance system is needed , combined with activities to strengthen the capacity of the program . The joint use of these actions would lead to a culture of evidence–based in the leishmaniasis program management . | In the Americas , cutaneous leishmaniasis ( CL ) cases are notified across a wide geographic area , extending from southern United States to northern Argentina . Currently , 70–75% of all estimated cases of CL worldwide occur in ten countries , including four in the Americas ( Brazil , Colombia , Nicaragua , and Peru ) and six countries from Africa and the Eastern Mediterranean . This study shows the epidemiological situation and geographical distribution of CL cases reported by passive surveillance system of endemic countries of the American Region from 2001 to 2011 , collected and consolidated by the Pan American Health Organization ( PAHO ) . Furthermore , it represents a joint effort of the National Programs of leishmaniases . Despite some limitations , the data were analyzed and discussed and this study represents the first step to understand the global epidemiological situation in this Region and it contributes to the improvement of the surveillance of leishmaniases in the Americas . | [
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] | 2016 | Exploring Spatial and Temporal Distribution of Cutaneous Leishmaniasis in the Americas, 2001–2011 |
Cystic echinococcosis ( CE ) is classified by the WHO as a neglected disease inflicting economic losses on the health systems of many countries worldwide . The aim of this case-series study was to investigate the burden of human CE in Palestine during the period between 2010 and 2015 . Records of surgically confirmed CE patients from 13 public and private hospitals in the West Bank and Gaza Strip were reviewed . Patients’ cysts were collected from surgical wards and formalin-fixed paraffin-embedded ( FFPE ) blocks were collected from histopathology departments . Molecular identification of CE species /genotypes was conducted by targeting a repeat DNA sequence ( EgG1 Hae III ) within Echinococcus nuclear genome and a fragment within the mitochondrial cytochrome c oxidase subunit 1 , ( CO1 ) . Confirmation of CE species/genotypes was carried out using sequencing followed by BLAST analysis and the construction of maximum likelihood consensus dendrogram . CE cases were map-spotted and statistically significant foci identified by spatial analysis . A total of 353 CE patients were identified in 108 localities from the West Bank and Gaza Strip . The average surgical incidence in the West Bank was 2 . 1 per 100 , 000 . Spot-mapping and purely spatial analysis showed 13 out of 16 Palestinian districts had cases of CE , of which 9 were in the West Bank and 4 in Gaza Strip . Al-Khalil and Bethlehem were statistically significant foci of CE in Palestine with a six-year average incidence of 4 . 2 and 3 . 7 per 100 , 000 , respectively . To the best of our knowledge , this is the first confirmation of human CE causative agent in Palestine . This study revealed that E . granulosus sensu stricto ( s . s . ) was the predominating species responsible for CE in humans with 11 samples identified as G1 genotype and 2 as G3 genotype . This study emphasizes the need for a stringent surveillance system and risk assessment studies in the rural areas of high incidence as a prerequisite for control measures .
Cystic Echinococcosis ( CE ) is a zoonotic parasitic infection caused by the metacestode larval stage of species within the genus Echinococcus . CE as the most frequently encountered disease is caused by Echinococcus granulosus sensu lato ( s . l . ) with the dog as the main definitive host and ungulates , mainly sheep as the intermediate host . Humans are accidental hosts infected following the ingestion of eggs shed in dog faeces . Although the classification and taxonomy of the genus Echinococcus is still controversial , however recent classification recognizes nine species , five of which belong to E . granulosus sensu lato ( s . l . ) namely E . granulosus sensu stricto ( s . s . ) ( genotypes G1-G3 ) , E . felidis ( lion strain ) , E . equinus ( genotype G4 ) , E . ortleppi ( genotype G5 ) , and E . canadensis ( genotypes G6/7-G8 and G10 ) [1 , 2] . CE is considered by WHO as an important food-borne parasitic disease with estimated 1–3 million Disability adjusted life years ( DALYs ) for cystic echinococcosis ( accounting for underreporting ) , [3] . CE is common worldwide with hyperendemic areas exceeding 50 cases per 100 , 000 in some countries like Argentina , Peru , East Africa , Central Asia , and China and causing annual loss of approximately $3 billion , yet still listed as one of the 18 neglected diseases in the world [4–6] . In the Mediterranean region where CE is endemic , Tunisia and Morocco reported the highest incidence of 12 . 5 and 5 . 1 surgical cases per 100 , 000 , respectively [7–12] . In Turkey , an ultrasonography-based survey among children revealed a prevalence of 0 . 2% with E . granulsus sensu stricto ( s . s . ) ( G1/G3 ) as the main cause [13 , 14] . Historical records in Palestine put the disease incidence at 1 and 5/100 , 000 during 1922–1935 and 1959 , respectively [15 , 16] . More recently , the incidence of human CE in 2015 was officially reported to be 1 . 6 per 100 , 000 [17] . This rate depends exclusively on surgical incidence reported by government hospitals following surgery . Pilot studies showed that the incidence rate in dogs as definitive hosts using copro-PCR was 18% [18] . The most common genotype in the definitive ( dog ) and intermediate hosts ( sheep ) from Palestine is E . granulosus G1 genotype [18 , 19] . In adjacent areas like the city of Rahat and Bir-Al Saba’ , CE was predominant among Bedouin community compared to Jewish residents with an incidence of 2 . 7 and 0 . 4 per 100 , 000 , respectively [20] . In this study we aimed to investigate the clinical burden of human cystic echinococcosis in Palestine ( West Bank and Gaza Strip ) using retrospective hospital records during the period between 2010 and 2015 supported by molecular methods through amplification of DNA from human cysts and formalin-fixed paraffin embedded ( FFPE ) blocks . In this study surgical incidence was defined as the frequency of operated CE cases per 100 , 000 inhabitants per year .
This study was ethically approved by the Ministry of Health ( MoH ) in Palestine ( 162/2044/2015 ) . All patients’ data were securely archived and anonymized . A case series observational study on surgically-confirmed human CE cases in Palestine ( The West Bank and Gaza Strip ) was carried out during a six year period between 2010 and 2015 . CE patients’ records were retrieved from public ( government ) and private hospitals in the West Bank and Gaza Strip . All reviewed CE cases had been diagnosed using ultrasonography or computed tomography ( CT ) and histopathologically confirmed following surgery . Thirteen hospitals were included in the study namely Jenin Government Hospital , Al-Amal Hospital ( Jenin ) , Zakat Hospital ( Jenin ) , Rafidia Government Hospital in ( Nablus ) , Al-Khalil Government Hospital , Al-Ahli Hospital in Al-Khalil , Al-Mezan Hospital in Al-Khalil , Beit-Jala Government Hospital in Bethlehem , Jericho ( Ariha ) Government Hospital , Ramallah Government Hospital , Al-Makassed Hospital in East Jerusalem ( Al-Quds ) , Al-Shifa Government hospital in Gaza , and Gaza European Hospital in Rafah . This included all government hospitals and major private hospitals that serve a population of ca . 4 . 6 million Palestinians in the West Bank and Gaza Strip . Retrieved data included patients’ demography such as names , age , address , date of birth , sex , site of infection , and diagnosis based on histopathology reports . Missing demographic and clinical data were obtained by conducting phone interviews with patients facilitated by the Ministry of Interior in Palestine . Human CE material removed from surgically-confirmed patients was collected from the histopathology departments in the form of FFPE pathology blocks . In addition , CE cysts surgically-removed during the duration of this study were provided by the relevant wards and stored at -20°C . Kulldorff’s SaTScan programme v9 . 4 . 3 was used to assign spatial and space-time distribution of cases in Palestine based on number of cases per locality , year of diagnosis , population size of locality at time of diagnosis , and the exact latitude-longitude coordinates of each location . Data were analyzed based on a discrete Poisson model with the level of statistical significance considered at P-value ≤ 0 . 05 [24] . Spot mapping of cases and frequencies were analyzed using Epi Info statistical package ( CDC free-software ) . The level of statistical significance was considered at P-value ≤ 0 . 05 . Evolutionary analysis , genetic relationship , and multiple alignments were conducted in MEGA 7 [25] .
During 2010–2015 a total of 353 CE patients were identified in 13 hospitals in the West Bank and Gaza Strip , the vast majority of whom ( n = 282 ) were diagnosed during this study period ( Fig 1 ) . Cystic echinococcosis was reported in 108 localities in the West Bank ( 94% , 319/338 ) and Gaza Strip ( 6% , 19/338 ) ( Fig 2a ) . The average surgical incidence for the disease in the West Bank , Gaza Strip , and Palestine as a whole was approximately 2 . 1 , 0 . 13 , and 1 . 1 per 100 , 000 , respectively . Demographic habitats of CE cases were shown to include villagers ( 78% ) , city-dwellers ( 20% ) , refugee camp residents ( 2% ) and Bedouins living in encampments ( 0 . 3% ) . The female-to-male ratio was 1 . 13 ( 187:166 ) which was not statistically significant ( Chi square = 1 . 2 , P = 0 . 26 ) . The age of CE patients ranged from 2 to 86 years old , with the two age groups 10–19 and 20–29 having significantly high number of reported hydatid cyst cases than expected ( Chi square = 145 . 5 , P = 0 . 0001 ) . After this , CE cases decreased gradually until reaching 4 in the age group 80–89 years ( Table 1 ) . Human cases of CE started to appear systematically on the Palestinian Ministry of Health ( MoH ) annual report from 2006 with a peak in 2012 . The MoH annual report also reported Gaza Strip as CE-free area except for 1 case in 2011 , while this study revealed 19 cases in the same period . Of the 353 CE cases , the residential premises of 338 were known and were therefore used for spot-mapping of CE cases . CE was found to be present in thirteen out of 16 Palestinian districts , 9 regions of which were in the West Bank and 4 in the Gaza Strip ( Fig 2a ) . District-wise , Al-Khalil and Bethlehem were the main foci of CE in Palestine with a six-year average incidence of 4 . 2 and 3 . 7 per 100 , 000 , respectively . Purely spatial analysis identified villages of Yatta in Al-Khalil District and Z’atara , and Ash-shawawra in Bethlehem District to be statistically significant foci for the disease and the most prevalent urban areas in Palestine with an annual CE incidence of 9 . 6 , 13 . 9 , and 23 . 3 per 100 , 000 , respectively ( Fig 2b ) . On the other hand , in the space-time analysis two foci were spotted in certain years over the study period , one in Al-Khalil and another in Bethlehem ( Fig 2c ) . Of the 261 CE patients , information regarding localization of CE infection was known for 271 cysts . Liver cysts were exclusively found in 158 ( 58% ) of CE cases whereas pulmonary infection was recorded in 27% ( 74/271 ) of cases . Approximately 3 . 4% ( 9/261 ) of CE cases had multiple site infection with two or more organs being involved ( Table 2 ) . Of the 299 cases for which symptoms were known , the most frequent symptoms were abdominal pain ( 42% ) and dyspnea ( 13% ) ( Table 3 ) . In this study , 14 cysts were collected from patients immediately following surgery and 68 additional FFPE CE samples were collected from histopathology departments . Of these , 86% ( 12/14 ) and 82% ( 56/68 ) respectively were positive for Echinococcus species as identified through the amplification of the diagnostic tandem repeat product ( 269bp ) . In addition , a partial fragment of cox 1 gene was successfully sequenced for 11 of the 14 cyst samples , which were identified using BLAST as E . granulosus s . s . ( 9 samples as G1 and 2 as G3 genotype ) . Nucleotide sequences generated here were deposited in the GenBank under the accession numbers depicted in Fig 3 showing the genetic clustering . The dendrogram showed that all 11 isolates from Palestine clustered in one group . Seven of the 353 cases were classified at hospital level as originated by Echinococcus multilocularis based solely on clinical picture without molecular confirmation . These 7 samples were not available in this study , thus was not possible to confirm or rule out the presence of this parasite .
In Palestine , despite the fact that CE is a notifiable disease , information on the magnitude of infection is usually generated by surgical wards from public government hospitals . Using this approach , an average surgical incidence of CE in the West Bank during 2010–2015 was reported to be 1 . 6 per 100 , 000 compared to the 2 . 1 per 100 , 000 generated in this study . An earlier study in which researchers scanned hospitals in the West Bank between 1990 and 1997 reported a relatively high average incidence of 3 . 1 per 100 , 000 [26] . In addition , the number of CE cases with known year of surgery included in this study was greater than that reported by the MOH in five of the six-year study period ( Fig 1 ) . A potential explanation for this discrepancy may be related to the inaccuracy in the surveillance system since the reporting of CE in official records only began in 2006 , as CE was not reportable disease by law before then ( Fig 1 ) [17] . At the same time , the Israeli health authorities reported 38 cases between 1991 and 1995 [27] . In Jordan , the surgical incidence rate between 1985 and 1993 was 2 . 9 per 100 , 000 . [26 , 28 , 29] . Surgical incidence studies appear to underestimate the actual burden of CE . CE data collated from surgical procedures reflects the tip of the iceberg as the majority of CE cases are asymptomatic , and thus do not come to the attention of clinicians . Surgery is normally performed on symptomatic patients with complicated CE or on individuals who are inadvertently diagnosed as having the disease [30] . Furthermore , it is normal practice for patients to be referred for CE surgery to hospitals outside Palestine , such as Jordan . Those cases were not included in Palestinian annual health report . Other explanations might be due to incorrect diagnosis or underreporting by physicians or hospitals . The distribution of CE cases by sex with slight predominance in females , but not statistically significant , is in agreement with other studies [26 , 28] . Cystic echinococcosis surgical incidence was higher in the younger age groups ( 10–29 years ) . This is in congruence with other studies using surgical incidence that reported the highest number of CE cases in young patients ( 10–13 years ) [20 , 26 , 28] . In rural and Bedouin areas , herds of sheep are commonly accompanied by several dogs which are in strict contact with children , increasing their exposure to this parasite . Furthermore , it’s a habit by villagers , young and adults , to eat leafy plants such as mallow ( Malva parviflora ) or those growing in the yard such as lattice , spinach and onions , thus increasing the possibility of contracting this infection . CE has a long latency period and subsequently can be detected years after infection , which is often at an older age [31] . However , the appearance of disease among adolescents ( 10–20 years ) Palestinian patients would suggest an infection sustained at a very early stage in life . Most of CE cases were reported in rural areas , which is in agreement with studies worldwide [29 , 32 , 33] and is one of the potential risk factors for acquiring CE identified in a recent systematic review for acquiring CE [34] . In contrast , it should be stressed that surgical incidence may introduce bias in the engagement of patients in this study since young age groups , especially children , could be more likely to seek medical attention compared to older age groups . In Palestine , a study that identified high incidence of E . granulosus infection among dogs was recently published [18] . The proximity of humans to free-roaming dogs which have access to infected offal is a known potential risk factor for CE infection [34] . This study identified seventeen sites of CE infection; however 85% had a predilection for the liver and the lungs ( Table 2 ) . The liver is widely known to be the most infected organ for cystic echinococcosis in Palestine and elsewhere as a result of the portal blood flow [26 , 28 , 35–37] . Double-site infection was rare with mostly the liver involved , and a multiple-site infection was reported only in one case . Abdominal pain as a result of bile duct compression and dyspnea resulting from irritated lung membranes were the main symptoms reported by patients , which in turn reflects the predominance of liver and lung infections [37] . Of the 14 human CE cysts , 86% were positive for the Hae III E . granulosus repetitive gene sequence and two were negative with one having been preserved for over a month in 10% formalin , a potent DNA degrader . Similarly , the infection rate of FFPE samples was 85% . Nine ( 81 . 8% ) and 2 ( 18 . 2% ) of the Palestinian patients’ included in this study were molecularly confirmed as having been infected with E . granulosus G1 and G3 genotype respectively . To the best of our knowledge , this is the first molecular identification of the human CE causative agent in Palestine . E . granulosus sensu stricto ( s . s . ) had been previously confirmed from sheep [14] and dogs [13] and the findings of the current study point to the free circulation of this species within Palestine and demonstrate the active involvement of these hosts in the perpetuation and transmission of this parasite [18 , 26] . Results obtained through the construction of the maximum likelihood tree showed E . granulosus sensu stricto ( s . s . ) ( G1/G3 ) nucleotide sequences generated in this study to group within a single cluster along with E . granulosus s . s . ( G1/G3 ) from the Genbank demonstrating genetic uniformity . This is consistent with other studies from Italy , Jordan , Iran , India , China and Peru that investigated nucleotide sequence variation of DNA extracted from CE material derived from humans , livestock and dogs confirming the low nucleotide-diverse nature of E . granulosus sensu stricto ( s . s . ) worldwide [18 , 19 , 38–41] . CE is widely spread in Palestine with the majority ( 94% ) of cases in the West Bank and only 6% in Gaza . The low incidence in Gaza Strip may be a reflection of the total share of livestock in Palestine which is 20 . 2% for Gaza Strip and 79 . 8% for the West Bank [42] . Spatial and space-time distribution showed Al-Khalil district to be the main focus of the disease in Palestine . Al-Khalil district is the most populous district and holds the largest share of livestock animals in Palestine ( 21 . 1% ) including sheep ( 25 . 2% ) and goats ( 21% ) [42] . Rural areas within Al-Khalil such as Yatta , Idhna and Dura villages appear to be the hot spots for CE; as this has been the case for the last 3 decades with an incidence of 16 . 8 per 100 , 000 in Yatta village between 1990 and 1997 ( Fig 2 ) [26] . The seven cases of E . multilocularis identified at hospital level based only on clinical picture are the first reports in Palestine . However , in the absence of molecular confirmation , multiple cysts of E . granulosus s . s . , such as CE2 according to WHO-IWGE ( Informal Working Group on Echinococcosis ) classification , may be erroneously identified as those of E . multilocularis [43] . In conclusion , this study has shown that E . granulosus sensu stricto ( s . s . ) , is the most prevalent species causing human CE in the West Bank and Gaza Strip and identified Al-Khalil district as the main focus for CE infection . Risk assessment studies in the rural areas such as Yatta are a prerequisite for control measures . For this reason , we would encourage the Ministry of Health , Ministry of Agriculture , and local health authorities to implement control measures aiming at decreasing the burden of CE in humans in Palestine and interpolating a stringent surveillance system . In addition , sensitizing the Palestinian citizens by community health awareness campaigns and upgrading the level of health service by training the medical team on CE epidemiology and detection are prerequisites for effective surveillance and control of this neglected disease . | Cystic echinococcosis ( CE ) is a neglected disease caused by a parasite called Echinococcus granulosus . The dog as a definitive host plays a major role in transmitting the infection to human . The study aimed to investigate the clinical burden of human CE in Palestine during the period between 2010 and 2015 . Thirteen hospitals in the West Bank and Gaza Strip were targeted . CE patients’ records were reviewed . Patients’ samples were collected including cysts following surgery and formalin-fixed paraffin-embedded ( FFPE ) blocks from histopathology departments . Molecular identification of CE species /genotypes was conducted by targeting nuclear and mitochondrial DNA and confirmed by identifying the DNA sequence and comparing it with those in the Genebank . CE cases were spot-mapped and statistically significant foci identified . A total of 353 CE patients were identified in 108 localities in Palestine from the West Bank and Gaza Strip . The average surgical incidence in the West Bank was 2 . 1 per 100 , 000 with Al-Khalil district reporting the highest incidence in Palestine . This study revealed that E . granulosus sensu stricto ( s . s . ) , G1 genotype was the main species responsible for CE in humans . There is need for a surveillance system and risk assessment studies in the rural areas as a prerequisite for control measures . | [
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] | 2017 | The clinical burden of human cystic echinococcosis in Palestine, 2010-2015 |
The global diversity of Bacteria and Archaea , the most ancient and most widespread forms of life on Earth , is a subject of intense controversy . This controversy stems largely from the fact that existing estimates are entirely based on theoretical models or extrapolations from small and biased data sets . Here , in an attempt to census the bulk of Earth's bacterial and archaeal ( "prokaryotic" ) clades and to estimate their overall global richness , we analyzed over 1 . 7 billion 16S ribosomal RNA amplicon sequences in the V4 hypervariable region obtained from 492 studies worldwide , covering a multitude of environments and using multiple alternative primers . From this data set , we recovered 739 , 880 prokaryotic operational taxonomic units ( OTUs , 16S-V4 gene clusters at 97% similarity ) , a commonly used measure of microbial richness . Using several statistical approaches , we estimate that there exist globally about 0 . 8–1 . 6 million prokaryotic OTUs , of which we recovered somewhere between 47%–96% , representing >99 . 98% of prokaryotic cells . Consistent with this conclusion , our data set independently "recaptured" 91%–93% of 16S sequences from multiple previous global surveys , including PCR-independent metagenomic surveys . The distribution of relative OTU abundances is consistent with a log-normal model commonly observed in larger organisms; the total number of OTUs predicted by this model is also consistent with our global richness estimates . By combining our estimates with the ratio of full-length versus partial-length ( V4 ) sequence diversity in the SILVA sequence database , we further estimate that there exist about 2 . 2–4 . 3 million full-length OTUs worldwide . When restricting our analysis to the Americas , while controlling for the number of studies , we obtain similar richness estimates as for the global data set , suggesting that most OTUs are globally distributed . Qualitatively similar results are also obtained for other 16S similarity thresholds ( 90% , 95% , and 99% ) . Our estimates constrain the extent of a poorly quantified rare microbial biosphere and refute recent predictions that there exist trillions of prokaryotic OTUs .
Microorganisms are the most ancient and the most widespread form of life on Earth , inhabiting virtually every ecosystem and driving the bulk of global biogeochemical cycles . Culture-independent methods such as amplicon sequencing of 16S ribosomal RNA genes revealed the existence of a potentially vast undescribed microbial diversity , the full extent of which , however , remains highly controversial [1–9] . Determining the extent of this diversity remains an important but challenging task in our overall understanding of life , with major implications for ecological and evolutionary theory , environmental sciences and industry . Notably , a global census of microbial phylogenetic diversity , or at least knowledge of its full extent , is essential for reconstructing microbial evolution over geological time [10] . Estimates of global microbial diversity are also needed for scrutinizing proposed biodiversity scaling laws and macroecological theories [2 , 6 , 11] . Finally , undiscovered microorganisms may exhibit a large breadth of metabolic capabilities of particular interest to industry and medicine . An efficient exploration of this potential and realistic assessment of the feasibility of such an endeavor requires knowledge of the gaps in existing diversity databases [12–14] . The extent of global microbial diversity remains subject to intense controversy and widely diverging speculations [1–9] . This controversy stems largely from the fact that existing estimates are either based on extrapolations of empirical scaling laws [6] , on theoretical biodiversity models [2] , on data sets covering only a small fraction of global diversity [1 , 9] , or on taxonomically biased databases , including mostly organisms that have been cultured or are of particular medical/industrial interest [3–5] . For example , Mora and colleagues [3] used the subset of currently named prokaryotic species to estimate that there exist approximately 10 , 000 bacterial species worldwide; this is clearly a strong underestimate , given that the SILVA sequence database [14] alone now contains hundreds of thousands of bacterial operational taxonomic units ( OTUs ) , i . e . , clusters of the 16S gene at 97% similarity—a traditional microbial species analog . Yarza and colleagues [4] and Schloss and colleagues [5] estimated that there exist a few million bacterial and archaeal ( "prokaryotic" ) OTUs based on sequence discovery statistics in SILVA; however , environmental and taxonomic biases in SILVA [15] compromise the reliability of these estimates [16] . Larsen and colleagues [9] estimated that there exist billions of host-associated bacterial OTUs based on a heuristic and mathematically flawed extrapolation of bacterial OTU counts in typical insect species to all animal species ( see the "Implications" section below for a detailed discussion ) . Locey and colleagues [6] even predicted that there exist trillions of microbial OTUs ( at 97% similarity ) based on an extrapolation of empirical scaling laws of local diversity in individual communities to global scales . Locey's estimate has fueled discussions about a potentially immense undiscovered microbial diversity and its uncertain ecological roles [16–20] . Locey's extrapolation of empirical scaling laws from local to global scales and across several orders of magnitude has been criticized and remains controversial [8 , 21] . Here , to address the above shortcomings , we attempted to explicitly census a large fraction of extant prokaryotic clades and used our census to estimate and chart total global prokaryotic OTU richness . For this census , we compiled massive publicly available raw Illumina 16S amplicon sequencing data from 34 , 368 samples across 492 studies , covering a wide range of environments from over 2 , 800 distinct geographical locations worldwide ( S1 Fig ) . Environments covered include the surface and deep ocean , oxygen minimum zones , freshwater and hypersaline lakes , rivers , groundwater , marine surface and deep subsurface sediments , agricultural and forest soils , peats , permafrost , deserts , animal hosts and feces , plant leafs and rhizospheres , salt marshes , bioreactors , processed food , methane seeps , mine drainages , sewages , hydrothermal vents , and hot springs ( overview in S1 Data ) . Particular effort was put into representing soils ( 14 , 242 samples across 100 studies ) , sediments ( 3 , 198 samples across 37 studies ) , and animal guts ( 8 , 646 samples across 52 studies ) , which likely harbor a large fraction of Earth's prokaryotic diversity [22] . Sequences in this composite data set cover at least 200 basepairs in the V4 hypervariable region of the 16S gene , a commonly targeted region in microbial ecology [22–24] . By clustering the pooled sequences at 97% similarity , a commonly used threshold in microbial ecology [2 , 5 , 6 , 9] , we recovered hundreds of thousands of OTUs . Based on the recovered OTUs , henceforth referred to as Global Prokaryotic Census ( GPC ) , and through comparisons to previous surveys and existing databases , we estimate global prokaryotic OTU richness and highlight major implications for microbial ecology and evolution . We emphasize that our main objective was to estimate global prokaryotic richness using as deep of a census and covering as many environments and geographic locations as possible; as a trade off , our data set does not offer the same level of experimental standardization across samples nor the amount of metadata included in projects such as the Earth Microbiome Project ( EMP ) [22] . Here , we focus on OTUs clustered using the conventional 97% similarity threshold so as to facilitate comparison with existing prokaryotic richness estimates [2 , 5 , 6 , 9] . Recent work , however , suggests that a greater similarity threshold ( approximately 99% ) is often required for distinguishing ecologically differentiated organisms [25–28] . We thus also repeated our analyses using a 99% clustering threshold , which yielded qualitatively comparable results . That said , we point out that clusters of the 16S gene—regardless of similarity threshold and even if completely free of sequencing errors—only provide an approximate "species" analog to sexually reproducing organisms . Indeed , even strains with identical 16S sequences may exhibit different genomic content and ecological strategies; hence , the 16S gene is not always sufficient for distinguishing ecologically differentiated organisms , even when considering exact sequence variants [29–30] . Whether and how prokaryotic "species" can—or even need to—ever be reasonably defined remains highly debated [30–33] . To date , the 16S gene remains an important and the most popular marker for cataloguing prokaryotic diversity and for describing evolutionary relationships in a well-defined and reproducible manner [4 , 27] . We stress that prokaryotic 16S diversity detected and estimated based on amplicon sequences , as in this and most previous studies , is limited to clades detectable by the PCR primers used . As discussed below , the GPC partly resolves the issue of limited primer scope by using multiple alternative primers; however , it is in principle still possible that some clades are completely missed .
To ensure maximal phylogenetic coverage , the raw sequencing data from each study was considered as input to our analyses . After stringent quality- and chimera-filtering , the data set comprised 1 , 734 , 042 , 763 high-quality reads , which were pooled and clustered into OTUs at 97% similarity . To avoid spurious ( i . e . , nonbiological ) OTUs generated by sequencing errors or PCR chimeras , only OTUs found in at least two samples of the same study were kept . While this additional quality filter may also remove some biological OTUs , aggressive filtering is necessary for eliminating spurious OTUs , a common and serious problem in amplicon sequencing studies [34–37] . The resulting GPC comprises 739 , 880 prokaryotic OTUs ( 690 , 474 bacterial and 49 , 406 archaeal ) , accounting for 1 , 349 , 766 , 275 reads . Accumulation curves of bacterial and archaeal OTUs discovered by the GPC , as a function of studies included , clearly show a deceleration with increasing number of studies ( Fig 1A and 1B ) and provide an estimate of how many novel OTUs would be discovered in subsequent studies . Specifically , on average , about 93% of bacterial OTUs and 83% of archaeal OTUs found in any additional study are expected to be already included in the GPC . As we show below , this estimate is consistent with the fractions of other independent data sets and databases covered ( rediscovered ) by the GPC . Most OTUs were matched by at least three reads ( 88% ) and most were found in at least three samples ( 81% , S2 Fig ) . Based on the fraction of reads matched to the rarest OTUs ( i . e . , with only two reads ) , we estimate that any new random 16S amplicon sequence ( i . e . , from a randomly chosen prokaryotic cell ) would hit an OTU in the GPC at 97% similarity with a probability ≥99 . 98% ( using the Good–Turing frequency formula [38]; see Methods for details ) . This probability is sometimes referred to as "Good's coverage" and corresponds to the proportion of living or recently deceased prokaryotic cells , detectable by current 16S amplicon sequencing techniques , which is represented by OTUs in the GPC . We emphasize that Good's coverage should not be interpreted as the fraction of global OTU richness represented by the GPC; indeed , estimation of the latter requires additional statistical reasoning , as presented below . To estimate the total number of extant prokaryotic OTUs globally ( discovered plus undiscovered ) , we used statistical approaches based on the number of OTUs that have been discovered in exactly one study ( Q1 ) , the number of OTUs discovered in exactly two studies ( Q2 ) , and so on . Indeed , the recommended ( and only statistically admissible ) way to estimate OTU richness is by modeling the incidence frequency counts Qi in order to predict the number of unobserved OTUs Q0 [21 , 39–41] . These methods date back to mathematical theorems for cryptographic analyses during World War II and have been used for microbial as well as macrobial richness estimates [40 , 42–44] . Intuitively , widely distributed and abundant OTUs—which are almost certain to be detected—contain very little information about undetected OTUs , while rarely detected OTUs ( e . g . , detected only once or twice ) carry the most information about undetected OTUs; hence , estimators typically rely on the low-frequency counts Q1 , Q2 , etc . [40] . To ensure the robustness of our estimations , we considered several alternative estimation methods , each of which is based on a different frequency model and relies on different assumptions: the improved-Chao2 ( "iChao2" ) richness estimator [45] , based on the frequency counts Q1–Q4; the incidence coverage-based estimator ( ICE ) [41] , based on the frequency counts Q1–Q10; the CatchAll estimator [46] , based on frequency counts Q1–Qτ , in which τ is chosen adaptively based on internal quality criteria; the transformed weighted linear regression model ( tWLRM ) , which uses a linear regression model for the ratios of consecutive log-transformed frequency counts to predict Q0 [46 , 47]; and the breakaway estimator [48] , based on a nonlinear regression model for the ratios of consecutive frequency counts . All of the above estimators have been designed to account for heterogeneities in detection frequencies among OTUs ( i . e . , the presence of rare and frequent OTUs ) , and breakaway is particularly optimized for efficiently dealing with high fractions of undiscovered diversity . We note that the majority of existing richness estimators , including the ones described above , are based on models in which individual sampling units are assumed to be equivalent ( e . g . , of the same "effort" ) ; however , studies included in the GPC differ in terms of the environment sampled and the techniques used . To check whether our estimates are sensitive to this caveat , we also deployed an estimation approach whereby we randomly assigned studies to four complementary and equally sized groups ( representing four statistically equivalent global "sampling units" ) and used the iChao2 estimator based on the number of OTUs found in exactly one , two , three of four sampling units ( "iChao2split , " illustration in Fig 1E ) . All of the above methods yielded comparable estimates for global prokaryotic OTU richness , with the lowest estimate obtained using tWLRM ( 901 , 902 OTUs ) , and the highest estimate obtained using breakaway ( 1 , 588 , 567 OTUs ) . The majority of prokaryotic OTUs are estimated to be bacterial , with bacterial richness ( Fig 1C ) being roughly 10 times greater than archaeal richness ( Fig 1D ) . Importantly , all of the above estimates suggest that there only exist in the order of approximately 1–2 million prokaryotic OTUs , a substantial portion of which is represented by the GPC ( 47%–82% ) . We point out that even at a finer phylogenetic resolution ( 99% clustering similarity ) , we estimate that there exist only approximately 3–9 million prokaryotic clusters worldwide ( S4 Fig and S1 Table ) , which is six orders of magnitude lower than estimated previously via extrapolation of empirical scaling laws [6] . To further scrutinize our estimates of global OTU richness and to verify whether a substantial fraction of that richness is indeed covered by the GPC , we determined the fraction of 16S sequences from previous global surveys or existing databases that was rediscovered ( "recaptured" ) by the GPC . We found that at 97% similarity , the GPC recaptured 96% of prokaryotic sequences in the SILVA database ( nonredundant set , release 132 ) [14] , 89% of prokaryotic sequences in the Ribosomal Database Project ( RDP release 11 ) [12] , and 93% of prokaryotic sequences in the Genome Taxonomic Database ( GTDB release 86 . 1 ) [49] ( domain-specific coverages in S2 and S3 Tables ) . Using these coverages as a proxy for the fraction of global OTU richness covered by the GPC and combining this coverage fraction with the total number of OTUs in the GPC yields additional independent estimates of global prokaryotic OTU richness ( 771 , 234–832 , 420 OTUs , Fig 1F ) , roughly consistent with our previous estimates . We also found that at 97% similarity , the GPC recaptured 92% of unique noise-filtered ( "deblurred" ) 16S amplicon sequences from another recent independent massive global survey , the EMP [22] . The high fraction of EMP sequences recaptured by the GPC further supports our conclusion that the GPC covers a substantial portion of extant prokaryotic OTUs . While our statistical richness estimators ( Fig 1C and 1D ) were designed to account for variable detection probabilities among OTUs , the potential risk of neglecting a large number of extremely rare OTUs cannot be overemphasized . To further assess this risk , we also explicitly investigated the global distribution of relative OTU abundances . Specifically , for each OTU , we estimated its relative abundance in each sample ( using the Good–Turing formula ) [38] and then took the average across all samples to obtain its mean relative abundance ( MRA ) . We then created a frequency histogram of MRAs by grouping OTUs into equally sized MRA intervals on a logarithmic axis . We note that this empirical histogram only includes OTUs discovered by the GPC and may thus be skewed toward more abundant OTUs . We therefore reconstructed the total number of extant OTUs in each MRA interval ( blue continuous curve in Fig 2A ) using a probabilistic model of OTU discovery . This model accounted for our quality filtering and finite sequencing depths and was calibrated by comparing OTU discovery rates in the GPC with those in a rarefied variant of the GPC ( i . e . , using only half of the original sequences ) . Following recommendations by Shoemaker and colleagues [11] , we then fitted a log-normal model to the reconstructed distribution of MRAs of extant OTUs . We found that the latter was well described by the log-normal model ( blue dashed curve in Fig 2A ) , resembling analogous observations commonly made for larger organisms . We point out that the log-normal model is largely phenomenological , although it is sometimes derived from certain stochastic population models [50] . Hence , we make no assertion as to which mechanisms could possibly lead to the observed log-normal–like distribution of MRAs and as to whether other ( potentially yet to be discovered ) models may be even more suitable . Based on the reconstructed distribution of MRAs , as well as based on the fitted log-normal model , we estimate that the majority of extant OTUs exhibit an MRA across samples between 5 ×10-10 and 5 ×10-8 ( mode approximately 5 ×10-9 ) . For lower MRAs , the number of OTUs declines rapidly toward zero . The rapid decline of the number of OTUs for lower MRAs suggests that the number of much more rare OTUs ( specifically , with an MRA lower than the OTUs detected by the GPC ) is relatively small and that the GPC did not miss vast numbers of extremely rare OTUs . This conclusion contrasts previous speculations that there exists a vast number of extremely rare and largely undetected OTUs , sometimes referred to as "rare microbial biosphere" [6 , 17 , 51] . According to the fitted log-normal model , there exist only approximately 886 , 291 prokaryotic OTUs across the entire range of MRAs , further supporting our other estimates . Since OTUs are inevitably taxonomically identified through comparison with reference databases ( here , SILVA was used to identify OTUs at the kingdom level ) , censuses such as the GPC may in principle miss clades lacking a close relative in the databases . To investigate this potential caveat , we calculated the phylogenetic distance of each OTU to its closest match in SILVA in terms of 16S sequence divergence and created a frequency histogram of these distances that shows the overall distribution of OTUs in comparison to SILVA ( Fig 2B ) . We found that the vast majority of OTUs in the GPC has a distance to SILVA that is far below the threshold allowed for taxonomic identification ( maximum 40% ) and that the frequency of OTUs drops rapidly toward that threshold . This suggests that our taxonomic identification algorithm did not miss a substantial number of biological sequences at larger phylogenetic distances ( omitted sequences at greater distances are likely spurious , see Methods for details ) . Primer "blind spots , " i . e . , clades not captured by PCR primers , could in principle lead to an underestimation or a phylogenetically biased assessment of prokaryotic diversity by the GPC . For example , recent studies suggest that roughly 10% of prokaryotic 16S sequences may be missed by any given existing primer pair [52–54] . To investigate this caveat and to check whether a large fraction of diversity may have been missed by the GPC due to primer blind spots , we calculated the fraction of 16S sequences recovered from a multitude of environments using primer-independent ( metagenomics-based ) methods that were rediscovered by the GPC . We found that , at 97% similarity , the GPC recaptured 91% of 16S sequences in prokaryotic genomes previously assembled from metagenomes ( Uncultivated Bacteria or Archaea [UBA] ) [55] and 93% of bacterial 16S sequences extracted from thousands of public metagenomes [56] . These recapture fractions are comparable to the fraction recovered from the EMP , suggesting that the fraction of OTUs missed by the GPC due to primer blind spots is small . One reason may be that the GPC comprises sequences obtained using a multitude of alternative primers optimized for different clades , therefore partly alleviating the problem of primer nonuniversality . In particular , 16S sequences currently not detectable by any primers may only represent a minority of prokaryotic diversity , even if any given primer set has limited sensitivity scope . It is thus improbable that primer-independent methods will reveal a prokaryotic richness much ( i . e . , orders of magnitude ) higher than composite multiprimer-based surveys such as the GPC . When we repeated our analyses using only studies from the Americas or near American coasts ( 165 studies across 14 countries , see map in S1 Fig ) instead of the full GPC , OTU discovery rates for any given number of studies remained almost unchanged ( Fig 1A and 1B ) . Hence , for the same "sampling effort , " the same OTU richness is recovered from the Americas as from the full GPC , and importantly , the restriction to the Americas does not cause a stronger deceleration of OTU discovery rates . This suggests that the majority of global prokaryotic OTUs could have been censused from a single hemisphere , if sufficient samples had been available . Consistent with this conclusion , when controlling for the number of studies included and using the same methods as above , we found that prokaryotic OTU richness estimated for the Americas was very similar to estimates based on an equal number of studies randomly chosen from across the world ( 0 . 7–1 . 3 million OTUs , S5A Fig ) . Similar results were also obtained at a higher 16S similarity threshold of 99% ( S4A and S4B and S5B Figs ) . Our findings extend previous observations that for any given number of samples , similar prokaryotic OTU richness is recovered from soil in New York Central Park as from distinct soil samples worldwide [57] . Most prokaryotic OTUs thus appear to exhibit low geographic endemism and global dispersal ranges at geological time scales , i . e . , at time scales needed for 16S to diverge by more than 1% [58 , 59] . A global distribution of prokaryotic OTUs has long been a central but controversial hypothesis [60 , 61] . Our finding provides strong support for this hypothesis and is also consistent with previous findings that most marine bacterial OTUs can be recovered from a single location in the ocean with sufficiently deep sequencing [62 , 63] and with findings that salt-marsh Nitrosomonadales OTUs are globally distributed [64] . That said , we point out that a global distribution of OTUs does not rule out geographic endemism at finer phylogenetic resolutions since younger clades , e . g . , recently differentiated ecotypes with identical 16S , may not have had time to overcome dispersal barriers at global scales [65 , 66] . Our census allows an unprecedentedly precise assessment of the diversity covered by existing 16S databases such as SILVA [14] or the RDP [12] . Based on the fraction of GPC OTUs matched to entries in SILVA ( release 132 , nonredundant set ) at 97% similarity , we estimate that SILVA represents about 29% of bacterial and 14% of archaeal OTUs globally . Similarly , we find that the RDP ( release 11 ) represents about 42% of bacterial and 20% of archaeal OTUs . Our findings confirm recent estimates that SILVA covers about 30%–40% of global prokaryotic OTU richness [5 , 67] . We point out that Bacteria are currently overrepresented in SILVA and the RDP relative to Archaea . The uneven representation of various taxonomic groups is generally more pronounced at lower taxonomic levels , with some phyla being strongly overrepresented compared to others ( Fig 3B ) . In addition , about 7% of prokaryotic OTUs in the GPC could not be reliably assigned to any phylum listed in SILVA . This indicates that some phyla are not represented in SILVA at all , consistent with conclusions from metagenomic studies [56 , 68] . Our estimates also highlight strong differences in the OTU richness specific to different phyla , with Proteobacteria ( mostly Gammaproteobacteria and Deltaproteobacteria ) clearly dominating global richness , followed by the Firmicutes ( mostly Clostridia ) , Bacteroidetes ( mostly Bacteroidia ) , Nanoarchaeota ( mostly Woesearchaeia ) , Patescibacteria , and Planctomycetes ( mostly Planctomycetacia ) ( Figs 3A and S11A ) . Hence , the large representation of Proteobacteria in reference databases and among cultured species [69] is not just the result of a biased discovery rate ( e . g . , due to ease of culturing ) but also partly reflects their general ability to expand and persist in a multitude of ecological niches [70] . Similarly , the large richness of Firmicutes may be explained by their ability to colonize a wide range of animal hosts [56] . Interestingly , the Nanoarchaeota are known as a deeply branching and poorly characterized ancient clade [71] , which has been suggested to comprise a largely underestimated diversity [72] . The few isolated Nanoarchaeota indicate that they share a common history of adaptation to ectosymbiosis [73] , and this may have contributed to the difficulty of isolating representatives . In contrast , while the Actinobacteria phylum contains the second largest number of cultured strains [69 , 74] , it only ranks eigth in terms of estimated total OTU richness ( Fig 3A ) , suggesting a strong culturing bias for this phylum , consistent with previous findings [69] . We point out that extant prokaryotic diversity is the result of diversification and extinction processes operating over billions of years and throughout geological transitions [15] . It is thus possible that the relative richness of various taxa varied strongly over time . Our work suggests that global prokaryotic OTU richness is about six orders of magnitude lower than previously predicted via extrapolation of diversity scaling laws and OTU abundance distributions fitted to individual microbial communities [6 , 8] . While we find support for a log-normal distribution of mean relative OTU abundances consistent with assumptions made by Locey and colleagues [6] , at least two aspects differentiate our approach from Locey and colleagues . First , we fitted the log-normal model to a global data set comprising thousands of samples across hundreds of environments rather than to individual local communities , thus obtaining a description of relative abundances that is more suitable for global richness estimates . Second , we did not assume or extrapolate any phenomenological scaling relationships between different parameters of the model , thus relying on fewer questionable assumptions . The discrepancy between our estimates and those by Locey and colleagues [6] suggests that phenomenological scaling relationships of microbial diversity cannot be extrapolated to global scales when these relationships were fitted solely to individual communities . This conclusion also supports arguments by [21] that the extrapolations performed by Locey and colleagues [6] have no predictive power and are statistically unsound . Our estimates also contrast extrapolations by Larsen and colleagues [9] , who argued that there exist billions of animal-associated bacterial OTUs based on the number of OTUs typically found in individual insect species and the estimated total number of animal species . One reason for this discrepancy may be that Larsen's extrapolation did not properly account for the overlap of microbiomes between animal taxa ( detailed discussion in S1 Text ) . Our much lower bacterial richness estimates suggest that many symbiotic OTUs are found in multiple host species that may or may not be closely related , potentially due to host trait convergences , consistent with recent observations [75–77] . Since the microbiome of only a minuscule fraction of animal species has been examined so far , it is quite possible that many allegedly "host-specific" bacteria are shared by a broader spectrum of host species than currently known . This could explain why overall bacterial richness ( at the OTU level ) appears to have been largely unaffected by past mass animal extinctions , as recently suggested based on phylogenetic analyses [15] . Given the long evolutionary history and ubiquity of prokaryotes , a richness of only approximately 0 . 8–1 . 6 million OTUs may seem surprisingly low . To put this finding into perspective , we considered a steady state null model , in which global prokaryotic cell counts ( N ) are constant over time , in which cells are replaced randomly and regardless of phylogenetic relationships via births and deaths , and in which the 16S-V4 region evolves neutrally [59] at some constant drift rate ( r , measured in mutations per site per generation ) and independently at each site . Note that one important and potentially wrong assumption of this model is that cell turnover is statistically independent of phylogeny . A similar model was recently proposed by Straub and colleagues [78] as a null model for 16S phylogenies . Based on our model , we predict that there should exist about 2rN/0 . 03∼1022-1023 OTUs ( assuming N = 1030 [79] and r=4×10-9-5×10-10 [80 , 81] , details in S2 Text ) . This extreme discrepancy between the model and our global richness estimates persists regardless of the similarity threshold used ( 97% or 99% ) . The discrepancy also persists even if currently estimated 16S mutation rates ( r ) or global cell counts ( N ) were off by 10 orders of magnitude or even if global cell counts varied drastically ( e . g . , by 1–10 orders of magnitude ) over recent time . One explanation for this discrepancy could be that the evolution of the 16S-V4 region along a lineage is subject to strong constraints that favor some mutations or sequence variants more than others , thus effectively reducing the "permissible" sequence space [82–84] . This would suggest that only about 10−14% of the theoretically possible 16S variants are actually biologically viable and attainable over the course of approximately 4 billion years [85] . Alternatively , some processes not captured by the model may eliminate all but just a small fraction of 16S sequence variants emerging over time . Phylogenetically correlated turnover , i . e . , closely related organisms experiencing birth or death simultaneously more frequently than expected by chance ( e . g . , due to their greater ecological similarity ) , would lead to increased removal of sequence variants from the pool compared to the above null model and may also be an explanation for the relatively sparse filling of 16S sequence space found here . This would imply that extinction plays a central role in prokaryotic diversification , as recently suggested by [15] and contrasting common speculations that prokaryotic OTUs are unlikely to go extinct [1 , 86–88] . We emphasize that our results are specific to the similarity threshold used ( 97% similarity in 16S ) and the gene region targeted ( V4 ) , although these choices are a popular combination in microbial ecology [22] . For example , at coarser phylogenetic resolutions ( e . g . , 95% and 90% similarity , roughly corresponding to genera and families [89 , 90] ) , we estimate that there exist substantially fewer 16S clusters and that the GPC covers a greater fraction of those clusters ( 50%–98% and 51%–99 . 5% of extant clusters , respectively , S1 Table ) . Consistent with these estimates , we found that at these coarser resolutions the GPC recaptured 95%–96% and 98% , respectively , of previous global 16S surveys ( S3B , S3C , S8 and S9 Figs and S4 and S5 Tables ) . Reciprocally , when we analyzed a subset of our data ( approximately 0 . 2 billion reads across 111 studies ) at the finest possible phylogenetic resolution ( 100% identity ) using the recent Divisive Amplicon Denoising Algorithm ( DADA2 ) software [91] , we obtained about 3 . 4 times as many exact amplicon sequence variants ( ASVs ) as 97%-OTUs and about 1 . 5 times as many ASVs as 99%-OTUs ( S13 Fig ) . This suggests that the global richness of exact sequence variants is at most an order of magnitude larger than the number of OTUs . The sequence length considered may also affect global richness measures . For example , full-length 16S diversity ( currently much harder to census ) is expected to be greater than partial-length ( V4 ) 16S diversity [4] because short gene regions may cluster as one OTU due to the stochasticity of mutations even if the full genes differ by more than 3% . For example , when restricted to the V4 region or when considering the full 16S gene , at 97% similarity , 16S sequences in SILVA cluster into 102 , 416 or 270 , 788 OTUs , respectively , suggesting that the number of extant full-length OTUs may exceed the number of V4 OTUs by a factor of approximately 2 . 7 . When combined with our V4-based richness estimates , this suggests that there exist 2 . 2–4 . 3 million full-length OTUs worldwide . A similar ratio between full-length and partial-length clusters is also obtained at 99% similarity ( S6 Table ) . Unfortunately , while full-length sequencing undoubtedly improves phylogenetic resolution , technical complications and a higher cost currently prevent the wide adoption of full-length 16S sequencing in microbial community surveys . Finally , we stress that 16S diversity only provides a coarse surrogate for prokaryotic genomic and phenotypic diversity [29 , 30] , and it is probable that the global number of prokaryote ecotypes greatly exceeds the number of OTUs . Cataloguing the phenotypic and genomic diversity of prokaryotes will undoubtedly be an important but much more challenging future task .
In 2002 , Curtis and colleagues [2] hypothesized that experimental approaches to directly enumerating extant prokaryotic diversity will remain fruitless due to logistical challenges . Almost two decades later , we demonstrated that publicly available sequencing data from 492 studies around the world are sufficient to recover a substantial fraction ( 47%–96% ) of global prokaryotic diversity in the 16S-V4 region , the very extent of which has long been a topic of speculation [1 , 2 , 4–7] . Our composite data set , covering a multitude of environments worldwide , enabled us to strongly constrain global prokaryotic OTU richness . Indeed , our global richness estimates are similar across a multitude of statistical estimators ( Fig 1C and 1D ) , all of which are based on different models of OTU detection probabilities and , in most cases , use a different set of OTU incidence frequency counts . The high fraction of 16S sequences from other amplicon- and metagenomic-sequencing surveys ( e . g . , the EMP [22] or UBA [55] ) and large databases ( e . g . , SILVA [14] and RDP [12] ) , recaptured independently by the GPC ( 91%–93% ) , further supports our global prokaryotic richness estimates and our assessment that the GPC covers a substantial portion of that richness . While no particular 16S similarity threshold provides an ideal species analog , OTUs provide an operational and clearly defined measure of richness that can be compared across studies , environments , and geological time [15] . For example , our work revealed that global prokaryotic OTU richness is orders of magnitude lower than often predicted [1 , 6 , 9] , regardless of the considered similarity threshold ( 97% and 99% ) . Further , the fact that our global richness estimates are approximately 16–17 orders of magnitude lower than predicted by a null model for neutral OTU emergence , regardless of the similarity threshold used , suggests that extinction played a major role in prokaryotic evolution [15] and/or that the attainable 16S-V4 sequence space is extremely constrained . Our work also showed that at the phylogenetic resolutions considered here ( ≥1% divergence in 16S ) , most prokaryotic OTUs are globally distributed , yielding insight into the time scales involved in global-scale microbial dispersal . We reiterate that the goal of the GPC was to enable a more robust estimate of total extant prokaryotic richness than previous studies . Indeed , our estimates are based on an unprecedentedly large and environmentally broad composite sequencing data set , assembled from hundreds of studies utilizing alternative primers and alternative sampling techniques , and using a wide array of alternative statistical estimation methods for increased robustness . The GPC can thus facilitate future efforts to catalogue and phenotypically describe Earth's extant prokaryotes . The GPC also opens up new avenues for reconstructing prokaryotic evolution over geological time using massive phylogenetic trees and for refining macroecological theories . While long considered an unseen majority [79] , thanks to ongoing technological revolutions , prokaryotes could one day become one of the most exhaustively characterized and best understood forms of life .
Publicly available 16S rRNA amplicon sequences ( V4 region ) from various environmental and clinical studies were downloaded from the European Nucleotide Archive ( https://www . ebi . ac . uk/ena ) . Only Illumina sequences were downloaded to ensure sequence qualities en par with current standards and because Illumina-based studies typically achieve much deeper sequencing than studies using previous-generation ( e . g . , 454 ) technology . We only considered sequences covering the V4 hypervariable region for three reasons . First , use of the same gene region in all samples is necessary for clustering sequences into nonredundant OTUs . Second , the V4 region is one of the most popular regions targeted in microbial surveys , including the EMP [22] , making it easier to find publicly available data sets and allowing for comparison with the EMP . Third , the V4 region was shown to be the most suitable single hypervariable region for reconstructing bacterial phylogenetic relationships [24] . Studies were chosen to represent as wide of an environmental spectrum as possible . A total of 34 , 368 samples from 492 studies were downloaded ( description and accession numbers in S1 Data ) . Geographical sample locations ( where available ) are shown in S1 Fig . We mention that sequencing data from the EMP [22] were omitted from the GPC because this allowed us to use the EMP as an independent reference data set for assessing the fraction of OTUs rediscovered by the GPC and because the much shorter read lengths in the EMP ( 122 bp on average ) compared to the GPC ( 246 bp on average ) would reduce the available phylogenetic resolution [92–96] . Indeed , as we expected the EMP to be less phylogenetically biased than reference databases such as SILVA and RDP , the EMP provided a valuable means to further evaluate the overall coverage of extant prokaryotic diversity by the GPC ( see main text and Methods below ) . Paired-end reads with sufficient overlap were merged using flash v1 . 2 . 11 [97] ( options—min-overlap = 10—max-mismatch-density 0 . 01—phred-offset 33—allow-outies ) . Of the nonsufficiently overlapping pairs , forward reads were kept and reverse reads discarded . Single-end reads , merged paired-end reads , and nonmerged forward reads were subsequently processed in the same way , as follows . Reads were trimmed and quality filtered using vsearch v2 . 6 . 2 [98] , keeping only reads that were at least 200 bp long after trimming ( options—fastq_ascii 33—fastq_minlen 200—fastq_qmin 0—fastq_maxee 0 . 5—fastq_truncee 0 . 5—fastq_maxee_rate 0 . 002—fastq_stripleft 7—fastq_trunclen_keep 250—fastq_qmax 64 ) . Any samples with more than 106 quality-filtered reads were subsampled down to 106 randomly chosen reads to reduce computational requirements; samples with fewer quality-filtered reads were not subsampled . The 1 , 988 , 445 , 238 kept reads were then chimera-filtered de novo using vsearch ( options—abskew 1 . 9 –mindiv 0 . 5 –minh 0 . 1 ) separately for each sample . About 10% of reads were identified as chimeric ( on average , 8 . 6% of reads per sample ) , yielding in total 1 , 734 , 042 , 763 quality-filtered and chimera-filtered reads with a mean length of 246 bp . Reads from all samples were pooled and subsequently clustered de novo at 97% similarity using cd-hit-otu v0 . 0 . 1 [99] . We chose cd-hit-otu because—in contrast to most other OTU-clustering algorithms—it scales relatively well to massive data sets such as ours . For a comparison between cd-hit-otu and other clustering algorithms , we refer to [99–102] . For consistency with our own downstream error filters ( removal of spurious OTUs ) , we set the minimum size for a cluster of duplicates in the cd-hit-otu algorithm to 2 ( step clstr_sort_trim_rep ) and the primary cluster size cutoff to 1 ( disabling cd-hit-otu's noise removal algorithm ) . De novo clustering yielded 1 , 545 , 602 clusters . Because primers of the various studies included did not all cover exactly the same regions and due to the clustering algorithm implemented by cd-hit-otu , a small number of clusters was redundant , i . e . , the representative sequences of some clusters were slightly shifted versions of others . To remove this redundancy , we further clustered representative sequences using vsearch ( command—cluster_fast—usersort–id 1 . 0—iddef 2—strand plus ) , thereby obtaining 1 , 386 , 686 nonredundant OTUs . To further avoid spurious ( i . e . , nonbiological ) OTUs , we only kept OTUs that were found in at least two samples of the same study ( 944 , 863 OTUs ) . While we cannot completely rule out the inclusion of some spurious OTUs in the GPC , we point out that a hypothetical removal of these OTUs would only further decrease our estimates of global prokaryotic OTU richness . Representative sequences for the final set of prokaryotic GPC OTUs ( at 97% and 99% clustering threshold ) and OTU tables are available online at www . loucalab . com/archive/GPC . The taxonomic identity of each OTU was determined based on its similarity to entries in the SILVA database [14] and by using a consensus approach , as follows . Each OTU was mapped to SILVA's nonredundant ( NR99 ) SSU sequences using vsearch [98] , at a similarity threshold of 60% and keeping only the top 10 hits ( options "—id 0 . 6—strand both—iddef 2—maxaccepts 20—maxhits 10" ) . If at least one hit had a similarity 100% , then all hits with similarity 100% were used to form a consensus taxonomy . Otherwise , if the best hit had a similarity s≥60% , then all hits with similarity ≥ ( s-5% ) were used to form a consensus taxonomy . In either case , the consensus taxonomy of a set of hits was defined as the taxon at the lowest taxonomic possible level ( e . g . , domain , phylum , etc . ) containing all of the hits . If an OTU did not have any hit in SILVA at or above a threshold of 60% similarity or did not have a consensus taxonomy even at the domain level , it was considered unidentified and was subsequently omitted ( see justification in the next paragraph ) . The overwhelming majority ( 87% ) of OTUs had at least one hit in SILVA at similarity ≥60% , and almost all of these OTUs ( >99 . 9% ) could be identified at some taxonomic level . Any OTUs identified as eukaryotes , chloroplasts , and mitochondria were omitted from subsequent analyses . We note that our imposed similarity threshold of 60% to SILVA is much lower than the thresholds commonly suggested for delineating phyla ( e . g . , 75% similarity according to [4] ) , thus the bulk of biological ( i . e . , nonspurious ) sequences is expected to pass this threshold . While the 75% similarity threshold by [4] referred to the full-length 16S gene , the same study also showed that partial gene regions ( e . g . , "R3" in that paper , roughly corresponding to V4 ) exhibit less richness than the full-length gene for any given clustering threshold . Hence , organisms that are >75% similar to a SILVA entry in the full 16S are even more likely to be >75% similar in the V4 region; consequently , a similarity threshold of 60% in V4 is probably more permissive than a similarity threshold of 75% in the full gene . OTUs with a similarity to SILVA below 60% ( or equivalently , a distance above 40% ) are likely largely spurious . To confirm this expectation and to further investigate the nature of these omitted OTUs , we calculated the distribution of distances of OTUs to SILVA as well as the fraction of OTUs that could be matched to SILVA , as the similarity threshold decreased below 60% all the way to zero ( S14A and S14B Fig ) . We found that as one approaches the 60% similarity threshold , the fraction of OTUs matched to SILVA levels off; that is , very few OTUs lie in the 60%–65% range , while the majority of OTUs lies in the 80%–100% range ( as discussed in the main article ) . Strikingly , for slightly lower similarity thresholds , there exists a sharp peak of OTUs within the 50%–60% similarity range and virtually no OTUs below that range . This agglomeration of a small fraction of OTUs in the 50%–60% range is likely mostly spurious , specifically consisting of bichimeras ( the most common type of chimeras ) . Indeed , bichimeras inevitably include a biological segment that makes up at least 50% of their length , and that biological segment will likely match SILVA at considerable similarity . Thus , most bichimeras are expected to aggregate within the 50%–60% similarity interval , as observed in our case . When we repeated the above analysis for clusters at 99% identity , we observed that the peak within the 50%–60% similarity range decreased substantially ( S14C and S14D Fig ) . This is consistent with the expectation that chimeric sequences clustered at 99% identity are easier to detect than when clustered at 97% identity , since the variance around representative sequences hinders a reliable identification of parent sequences by chimera detectors . In fact , when we considered exact ASVs generated and chimera-filtered with DADA2 [91] for a subset of our data ( subset "AG , " see below ) , the peak in the 50%–60% similarity interval disappeared nearly completely ( S15C Fig ) . In other words , almost all ASVs had a similarity to SILVA above 60% . This is consistent with the expectation that chimeric ASVs are easier to detect than chimeric OTUs [91] and further supports our conclusion that most of the removed OTUs ( all falling within the 50%–60% similarity interval ) are likely bichimeras that have escaped our previous chimera filters . The alternative explanation that this agglomerate at 50%–60% similarity represents biological sequences is much less probable , since this would beg the question as to why these sequences aggregate within the similarity interval 50%–60% and why they disappear at higher clustering identities . To calculate the fraction of prokaryotic 16S diversity recovered by the EMP [22] that was recaptured by the GPC , we proceeded as follows . We dowloaded the EMP's set of unique quality- and chimera-filtered 16S sequences ( 202 , 540 "deblurred" sequences , covering 150 bp of the V4 region ) from the EMP FTP repository ( ftp://ftp . microbio . me/emp/release1/otu_info/deblur/emp . 150 . min25 . deblur . seq . fa ) . EMP sequences were taxonomically identified using the same methods as for the GPC , and any sequences identified as eukaryotes , chloroplasts , or mitochondria were omitted . EMP sequences were then mapped to GPC OTUs using vsearch at a similarity threshold of 97% whenever possible ( options "—id 0 . 97—iddef 2—strand both" ) . For any given taxon , the fraction of recaptured EMP sequences was calculated as Nm/NEMP , in which NEMP is the number of EMP sequences identified to be within the focal taxon and Nm is the number of EMP sequences in the focal taxon matched to a GPC OTU . An overview of recapture fractions is provided in S2 Table . To calculate the fraction of prokaryotic 16S diversity in the RDP ( release 11 ) [12] that was recaptured by the GPC , we proceeded as follows . Nonaligned bacterial and archaeal 16S sequences were downloaded as fasta files from the RDP website ( https://rdp . cme . msu . edu/misc/resources . jsp ) . The RDP's original taxonomic annotations were assumed for each RDP sequence . The fraction of RDP sequences recaptured by the GPC was calculated for various taxa , as described above for the EMP ( overview in S2 Table ) . To calculate the fraction of prokaryotic 16S diversity in the GTDB ( release 86 . 1 ) [49] that was recaptured by the GPC , we proceeded as follows . Bacterial and archaeal 16S sequences , extracted from the GTDB genomes , were downloaded as fasta files from the GTDB website ( http://gtdb . ecogenomic . org/downloads ) . Only sequences at least 1 , 000 bp long were kept . The fraction of GTDB sequences recaptured by the GPC was calculated for various taxa , as described above for the EMP ( overview in S2 Table ) . To calculate the fraction of 16S sequences from metagenome-assembled UBA genomes [55] that was recaptured by our GPC data set , we proceeded as follows . Fully or partly assembled 16S sequences for 2 , 853 metagenome-assembled genomes were downloaded from https://data . ace . uq . edu . au/public/misc_downloads/uba_genomes/ on October 25 , 2017 . Only UBA sequences longer than 1 , 000 bp were considered to increase the probability of adequate overlap with the V4 region , leaving us with 620 sequences . UBA sequences were taxonomically identified using the same methods as for the GPC , and any sequences identified as eukaryotes , chloroplasts , or mitochondria were omitted . The fraction of UBA sequences recaptured by the GPC was calculated for various taxa , as described above for the EMP ( overview in S2 Table ) . To calculate the fraction of bacterial 16S sequences previously extracted from metagenomes in the Integrated Microbial Genomes and Microbiomes ( IMG/M ) database [56] that was recaptured by the GPC , we proceeded as follows . Aligned SSU sequences ( ≥1 , 200 bp long ) extracted from IMG/M were downloaded as a fasta file from https://bitbucket . org/berkeleylab/bacterialdiversity/downloads on February 13 , 2018 ( file IMGG_SSU1200 . fasta ) . Only sequences obtained from metagenomes were kept ( tag "MTGBAC , " 63 , 367 sequences ) . Aligned sequences were dealigned ( gaps removed ) ; taxonomically identified , as described above for the GPC; and any sequences identified as eukaryotes , chloroplasts , or mitochondria were omitted . The fraction of IMG/M sequences recaptured by the GPC was calculated for various taxa , as described above for the EMP ( overview in S2 Table ) . Unless otherwise mentioned , sequences in SILVA classified as eukaryotes , mitochondria , or chloroplasts were omitted from all analyses . To calculate the fraction of 16S diversity in the SILVA database [14] that was covered ( "recaptured" ) by the GPC ( Figs 3C and S11C ) , we proceeded as follows . Nonredundant ( NR99 ) SSU alignments in SILVA release 132 were downloaded from the SILVA website ( https://www . arb-silva . de/fileadmin/silva_databases/release_132/Exports/SILVA_132_SSURef_Nr99_tax_silva_full_align_trunc . fasta . gz ) and subsequently dealigned ( gap characters removed ) . Dealigned SILVA NR99 sequences were then mapped to GPC OTUs via global alignment using vsearch , at a similarity threshold of 97% ( options "—id 0 . 97—iddef 2—strand both" ) . For any given taxon ( domain , phylum , or class ) , we calculated the coverage by the GPC ( Figs 3C and S11C ) as the ratio ( ρ ) of mapped SILVA sequences in that taxon divided by the total number of SILVA sequences in that taxon . The total number of extant OTUs within the taxon ( Figs 3A and S11A ) was estimated as NGPC/ρ , in which NGPC is the number of GPC OTUs in the taxon . To estimate the coverage of various prokaryotic taxa ( domains , phyla , or classes ) by SILVA ( Figs 3B and S11B ) , we proceeded as follows . For any given taxon , we mapped GPC OTUs within that taxon to the dealigned SILVA NR99 sequences via global alignment using vsearch at a similarity threshold of 97% ( options "—id 0 . 97—iddef 2—strand both" ) . The fraction of OTU richness covered by SILVA was estimated as the ratio of mapped GPC OTUs within that taxon divided by the total number of GPC OTUs in that taxon . To calculate the 16S diversity in SILVA , in terms of OTUs comparable to the GPC ( clusters at 97% identity in the V4 region ) , we proceeded as follows . We downloaded the full set of SSU alignments from the SILVA website ( https://www . arb-silva . de/fileadmin/silva_databases/release_132/Exports/SILVA_132_SSURef_tax_silva_full_align_trunc . fasta . gz ) . We then aligned GPC OTUs to SILVA using the QIIME script parallel_align_seqs_pynast . py [103] and using a random subset ( 1% ) of the SILVA alignments as a template . We identified the first nucleotide position in the GPC alignments that had a gap fraction below 0 . 9 ( Escherichia coli position 516 ) and extracted the part starting at that nucleotide position and extending 200 bp in the 5'→3' direction ( excluding gaps ) from the SILVA alignments . Extracted partial SILVA alignments were then dealigned ( gaps removed ) and clustered at 97% similarity using uclust v1 . 2 . 22 [104] , yielding 102 , 416 prokaryotic OTUs ( "SILVA V4-OTUs" ) . To calculate the 16S diversity in SILVA in terms of full-length OTUs , we also clustered the full-length dealigned SILVA sequences using uclust at 97% similarity , obtaining 270 , 788 prokaryotic OTUs . To calculate the distances between GPC's OTUs and SILVA ( Fig 2B ) , we proceeded as follows . OTUs were globally aligned against SILVA NR99 sequences using vsearch , keeping only the top hit ( options "—id 0 . 6—iddef 2—strand both—maxaccepts 1000—maxhits 1—top_hits_only" ) . For any OTU , its distance to SILVA was defined as 100−I , in which I is the percentage identity to the top hit . The histogram in Fig 2B was obtained after binning distances into intervals of 2% . In order to obtain a rough estimate of the global richness expected in terms of ASVs ( which , in the absence of errors , are equivalent to sequence clusters at 100% similarity ) when compared to OTU richness—the standard richness measure considered in previous studies—we investigated the density of exact ASVs in the GPC . ASVs were determined using DADA2 v1 . 10 . 0 [91] , a tool that fits a stochastic error model to the available sequencing data in order to then distinguish between likely sequencing errors and true biological sequence variants . To limit computational requirements , we only considered a pseudo-randomly chosen subset of GPC studies ( 111 studies with paired-end reads and whose names started with the letter "A" through "G" ) , henceforth referred to as "AG" subset . ( This subset was chosen for convenience of file handling , and an alphabetical choice of projects is practically random for our purposes . ) Any samples with more than 106 raw reads were subsampled down to 106 randomly chosen reads to reduce computational requirements . Reads were quality-filtered and trimmed using the DADA2 function filterAndTrim , with options maxEE = 0 . 5 , minLen = 160 , truncQ = 0 , trimLeft = 7 , truncLen = 167 for forward reads and options maxEE = 1 , minLen = 140 , truncQ = 0 , trimLeft = 7 , truncLen = 147 for reverse reads . This yielded 357 , 738 , 981 quality-filtered nonmerged paired-end reads . Error rate models were fitted using the DADA2 function learnErrors , separately for each study and separately for forward and reverse reads . ASVs were then inferred for each sample using the DADA2 functions derepFastq and dada ( with options pool = FALSE , selfConsist = FALSE ) , and paired-end denoised reads were subsequently merged using the DADA2 function mergePairs ( with options minOverlap = 10 , maxMismatch = 0 ) . A preliminary ASV table was created using the DADA2 function makeSequenceTable , yielding an ASV table comprising 258 , 448 , 458 reads across 2 , 319 , 542 ASVs . Chimeric sequences ( specifically , bichimeras ) were subsequently removed using the DADA2 function removeBimeraDenovo ( with options method = "concensus" ) , separately for each study . The resulting chimera-filtered ASV table comprised 206 , 982 , 673 reads across 725 , 682 ASVs . Only ASVs matched by at least two reads ( across all samples ) were kept for downstream analyses in order to eliminate spurious sequences . Because we were mainly interested to check if the number of detected ASVs would be substantially ( i . e . , orders of magnitude ) higher than the number of detected OTUs and because the DADA2 pipeline includes an algorithm for removing sequencing errors , we did not filter out ASVs found only in a single sample so as not risk underestimating the number of exact sequence variants . ASVs were taxonomically identified using SILVA and a consensus approach , as described above for OTUs , resulting in 580 , 965 prokaryotic ASVs , accounting for 181 , 673 , 137 reads across 5 , 584 samples . ( Note that some samples did not pass the various filtering/merging steps . ) A summary of AG samples , including sequence accession numbers , is provided as S3 Data . To compare the number of ASVs and OTUs detected , we also analyzed the same set of quality-filtered reads as used for the above DADA2 analysis using our OTU-clustering approach utilized for the full GPC . Specifically , quality-filtered nonmerged paired-end reads , produced by the first step in the DADA2 pipeline , were used as input to the GPC clustering pipeline described above . This yielded 390 , 893 prokaryotic sequence clusters at 99% similarity accounting for 190 , 247 , 727 reads or 173 , 166 prokaryotic sequence clusters at 97% similarity accounting for 192 , 718 , 873 reads . For a comparison of ASVs and sequence clusters obtained for various numbers of studies included , see S13 Fig . Accumulation curves of OTUs discovered , as a function of studies included , were calculated as follows . For any given number of studies N , we randomly chose N studies in the GPC and counted the number of OTUs detected in at least one of the chosen studies . We repeated this step 100 independent times and averaged the number of OTUs counted each time . By performing this process for various N ( from 1 to 492 ) , we obtained the accumulation curves shown in Fig 1A and 1B . To estimate the total number of OTUs globally using the statistical estimators described in the main text ( iChao2 , ICE , CatchAll , breakaway , tWLRM ) , we considered each study as an independent sampling unit and counted the number of OTUs found in exactly one sampling unit ( Q1 ) , in exactly two sampling units ( Q2 ) , and so on . Note that since our last quality filter , by which we only kept OTUs found in at least two samples of the same study , was applied separately for each study , every study can indeed be considered as an independent sampling unit . Estimates and standard errors were either calculated using the R package breakaway ( breakaway and tWLRM [48] ) , the R package SpadeR ( iChao2 and ICE [105] ) , or the CatchAll software ( CatchAll with 3-mixed exponential model [46] ) . The assumption of the above estimators that sampling units are equivalent ( e . g . , of similar effort ) is potentially violated by the GPC , since each included study was performed in a different environment and by using different techniques . To check whether our estimates are affected by this caveat , we also used a variant of iChao2 ( "iChao2split" ) , whereby we randomly assigned studies to four complementary and equally sized groups and considered each group as a single independent global sampling unit . Hence , iChao2split considered the number of OTUs found in only one study group ( Q1 ) , in exactly two study groups ( Q2 ) , in three study groups ( Q3 ) , and in all four study groups ( Q4 ) . The splitting was randomly repeated 100 times , and the obtained estimates were averaged ( Fig 1E ) ; the standard error was set to the standard deviation of estimates across repeated splittings . We mention that analogous estimators exist ( e . g . , "iChao1" ) for estimating richness in a community based on the observed OTU abundances ( such as sequencing read counts ) in a single reference sample [41] . Such abundance-based estimators are not suited for our data set for two reasons: first , to obtain a single globally ranging reference sample , we would need to pool all GPC samples so as to obtain a measure of abundance for the various OTUs . However , read counts from separate amplicon-sequencing samples cannot be combined to obtain a measure of global OTU abundances since the total number of cells that was present in each sample is unknown and sequencing depths varied between samples . Second , typical abundance-based estimators such as iChao1 rely on knowing the number of singleton OTUs ( i . e . , comprising only one read ) ; however , singleton OTUs have a high probability of being spurious and can thus not be reliably used to estimate OTU richness [37] . In fact , singleton OTUs , as well as OTUs found in at most one sample , were omitted from the GPC to minimize spurious OTUs . Note that this filter corresponds to increasing the OTU detection threshold in each study , just as sequencing depth affects detection thresholds . Since the incidence-based richness estimators used in this study all account for finite ( a priori unknown and potentially variable ) detection probabilities , their applicability is not expected to be substantially compromised by a systematic application of this filter . This is roughly analogous to performing a mark-recapture–based assessment of wildlife population size; a systematic decrease of capturing effort may increase the variance of the resulting estimate , but it will not affect the expected value of that estimate . To estimate the fraction of prokaryotic cells currently detectable by 16S amplicon sequencing that is represented by GPC OTUs ( i . e . , at 97% similarity in 16S ) , we calculated the probability ( P ) that a single additional read would hit a GPC OTU , as follows . Based on the number of OTUs with exactly two reads ( N2 = 87 , 940 ) as well as the total number of reads ( N = 1 , 734 , 042 , 763 ) and using the Good–Turing frequency formula [38] , we estimate the total probability of hitting an OTU with one read in the GPC to be P1=2N2/N=0 . 000101 . ( Note that OTUs with one hit were omitted from the final GPC . ) Using the fact that the total estimated probability of hitting an OTU with zero reads in the GPC ( P0 ) is not greater than P1 ( it is more probable to rehit some OTU with one read than to hit some OTU with zero reads ) and the fact that P≈1− ( P0+P1 ) , we obtain the lower bound P≥1-2P1=99 . 98% . Hence , the probability of a single additional amplicon sequence hitting an OTU with ≥2 reads in the GPC is estimated to be P≥99 . 98% . An overview of computed probabilities for various clustering thresholds is given in S7 Table . To estimate the distribution of relative OTU abundances in the GPC , we proceeded as follows . First , for each OTU in the GPC , we estimated its relative abundance ( α ) in each sample based on the number of assigned reads and using the Good–Turing frequency estimator [38 , 106]: α= ( r+1 ) NNr+1Nr , ( 1 ) in which r is the number of reads assigned to the OTU , Nr is the number of OTUs in the sample with exactly r reads , and N is the total number of reads in the sample . We note that the Good–Turing frequency estimator is widely used in biological statistics and has been repeatedly shown to be more robust than simply using the fraction of assigned reads [106 , 107] . Next , we averaged the relative abundances of each OTU across all samples to obtain its MRA in the GPC . We emphasize that we calculated MRAs separately for each sample , even though MRAs from shallower sequenced samples may be less accurate . This approach was preferred over the alternative of simply calculating the fraction of reads assigned to an OTU when all samples are pooled because samples differ drastically in sequencing depth; thus , OTUs that happen to occur in deeply sequenced samples would appear to be more abundant than OTUs in shallowly sequenced samples . Similarly , pooling within studies was also avoided because sequencing depth varied widely even among samples of the same study , and samples were usually not technical replicates; hence , MRAs calculated for a given study ( after pooling ) would be biased toward organisms that happened to be present in deeply sequenced samples . By calculating MRAs separately for each sample prior to averaging , we avoid biases toward OTUs in more deeply sequenced samples . Next , we grouped OTUs into small , equally sized MRA intervals ( on a logarithmic scale ) to calculate a frequency histogram of MRAs in the GPC . We note that the resulting frequency histogram should not be interpreted as a true OTU abundance distribution because it only includes OTUs discovered by the GPC and may thus be artificially positively skewed [108] . To estimate the probability that an extant OTU in an MRA interval was included in the GPC ( P ( α ) , in which α is the center of the MRA interval ) and , from that , the total number of extant OTUs in each MRA interval , we proceeded as follows . We randomly removed half of the quality- and chimera-filtered reads and repeated the OTU clustering and analyses described above , thus obtaining a rarefied variant of the GPC ( rGPC ) . A total of 514 , 432 high-fidelity prokaryotic OTUs were retrieved from the rGPC . We then calculated the frequency histogram of MRAs for the rGPC and compared it to the one obtained from the GPC to estimate P ( α ) for each MRA interval . Specifically , we assumed that the number of reads assigned to an OTU in any given MRA interval was Poisson-distributed and that the probability of being discovered was given by the probability of being matched by at least two reads , i . e . , P ( α ) =1−e−λ ( α ) −λ ( α ) e−λ ( α ) , ( 2 ) in which λ ( α ) is the unknown rate of the Poisson distribution for that MRA interval . Since the rGPC includes half the reads of the GPC , the probability of OTU discovery by the rGPC is Pr ( α ) =1−e−λr ( α ) −λr ( α ) e−λr ( α ) , in which λr = λ/2 . For each MRA interval , we estimated λ ( α ) by numerically solving the equation f ( α ) fr ( α ) =1−e−λ ( α ) −λ ( α ) e−λ ( α ) 1−e−12λ ( α ) −12λ ( α ) e−12λ ( α ) , ( 3 ) in which f ( α ) and fr ( α ) is the number of OTUs in the focal MRA interval , discovered by the GPC and the rGPC , respectively . From the estimated λ ( α ) , we thus obtained P ( α ) via Eq 2 and the total number of extant OTUs in the MRA interval as F ( α ) =f ( α ) /P ( α ) . Following suggestions by Shoemaker and colleagues [11] , who concluded that microbial communities are often well described by log-normal species abundance distributions , a log-normal model was fitted to the reconstructed OTU MRA distribution F: F ( α ) ∼S2πσ2exp[− ( log ( α ) −μ ) 22σ2] , ( 4 ) in which μ , σ , and S are fitted parameters . Fitting was performed via least-squares . The fitted log-normal model was integrated over the entire real axis to obtain an estimate for the total number of extant prokaryotic OTUs . | The global diversity of Bacteria and Archaea ( "prokaryotes" ) , the most ancient and most widespread forms of life on Earth , is subject to high uncertainty . Here , to estimate the global diversity of prokaryotes , we analyzed a large number of 16S ribosomal RNA gene sequences , found in all prokaryotes and commonly used to catalogue prokaryotic diversity . Sequences were obtained from a multitude of environments across thousands of geographic locations worldwide . From this data set , we recovered 739 , 880 prokaryotic operational taxonomic units ( OTUs ) , i . e . , 16S gene clusters sharing 97% similarity , roughly corresponding to prokaryotic species . Using several statistical approaches and through comparison with existing databases and previous independent surveys , we estimate that there exist globally between 0 . 8 and 1 . 6 million prokaryotic OTUs . When restricting our analysis to the Americas , while controlling for the number of studies , we obtain similar estimates as for the global data set , suggesting that most OTUs are not restricted to a single continent but are instead globally distributed . Our estimates constrain the extent of a commonly hypothesized but poorly quantified rare prokaryotic biosphere and refute recent predictions that there exists trillions of prokaryotic OTUs . Our findings also indicate that , contrary to common speculation , extinctions may strongly influence global prokaryotic diversity . | [
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] | 2019 | A census-based estimate of Earth's bacterial and archaeal diversity |
Fusogenic reoviruses encode fusion-associated small transmembrane ( FAST ) protein , which induces cell–cell fusion . FAST protein is the only known fusogenic protein in non-enveloped viruses , and its role in virus replication is not yet known . We generated replication-competent , FAST protein-deficient pteropine orthoreovirus and demonstrated that FAST protein was not essential for viral replication , but enhanced viral replication in the early phase of infection . Addition of recombinant FAST protein enhanced replication of FAST-deficient virus and other non-fusogenic viruses in a fusion-dependent and FAST-species-independent manner . In a mouse model , replication and pathogenicity of FAST-deficient virus were severely impaired relative to wild-type virus , indicating that FAST protein is a major determinant of the high pathogenicity of fusogenic reovirus . FAST-deficient virus also conferred effective protection against challenge with lethal homologous virus strains in mice . Our results demonstrate a novel role of a viral fusogenic protein and the existence of a cell–cell fusion-dependent replication system in non-enveloped viruses .
Proteins of the fusion-associated small transmembrane ( FAST ) family , which are encoded by some members of the Reoviridae family , are the only viral fusogenic proteins known in non-enveloped viruses , which do not require fusion to enter the host cell [1] . FAST proteins are small ( 95–198 amino acids ) and are expressed as non-structural proteins during the viral replication cycle [2] . FAST proteins induce syncytium formation by fusion of host cells , such as epithelial cells and fibroblasts [1 , 3 , 4] . By contrast , fusogenic peptides and proteins of enveloped viruses are essential components of virion structure that are required for fusion between the viral membrane ( envelope ) and the cellular membrane , which is required for viral entry into the cell . The Reoviridae family is composed of 15 genera , including rotaviruses and orthoreoviruses , both of which include common human pathogens . Among the members of the Reoviridae family , several types of FAST protein are known . In the genus Orthoreovirus , avian orthoreovirus ( ARV ) and pteropine orthoreovirus ( PRV ) encode FAST-p10 , whereas baboon orthoreovirus ( BRV ) , Broome virus , and reptilian orthoreovirus ( RRV ) encode FAST-p15 , FAST-p13 , and FAST-p14 , respectively [1 , 3–5] . In the genus Aquareovirus , FAST-p22 is encoded by group A aquareovirus , and FAST-p16 is encoded by group C and G aquareoviruses [6–8] . Those fusogenic reoviruses coding FAST proteins have important implications for public health and the poultry industry . A common human pathogen , mammalian orthoreovirus ( MRV ) , which causes asymptomatic infection in respiratory and intestinal organs [9] , is the only known non-fusogenic virus in the genus Orthoreovirus . FAST proteins are composed of an N-terminal ectodomain , a transmembrane domain , and a C-terminal cytoplasmic domain [10] . The results of mutagenesis of recombinant FAST proteins indicate that each domain is required for fusion activity [3 , 11–18] . Because of their small size and simple structure , the non-structural FAST proteins are not thought to be related to the structural enveloped virus fusion proteins [1] . Viral fusion proteins are structurally divided into four classes: class I , with a characteristic α-helix trimer ( as in human immunodeficiency virus ( HIV ) gp41 ) ; class II , with a β-sheet-based elongated ectodomain ( as in dengue virus glycoprotein ) ; class III , composed of an α-helix and β-sheet combined ectodomain ( as in rabies virus G glycoprotein ) ; and class IV , which is the FAST protein family [19] . ARV is a common avian pathogen that has been studied extensively as a prototype of fusogenic reoviruses , and syncytium formation is known to occur after infection by ARV [20] . Results with natural variants of ARV demonstrated that the level of fusion activity is associated with the degree of pathogenicity in chicken embryos , but does not affect viral replication in vitro [21] . The use of protein-transport inhibitors ( including brefeldin A and tunicamycin ) reduces syncytium formation in ARV-infected cells , and inhibits but does not prevent egress of synthesized virion [22] . Recombinant vesicular stomatitis virus ( VSV ) expressing RRV FAST-p14 has unaltered viral replication in vitro , but enhanced replication in mouse brains , with significant increase of pathogenicity compared with VSV expressing green fluorescent protein [23] . Further evidence of the association between FAST proteins and viral pathogenicity has come from epidemiological and clinical studies of ARV in chickens with arthritis [24] , BRV in baboons with encephalitis [25] , and RRV in snakes with interstitial pneumonia [26] . Addition of FAST protein dramatically enhances production of non-fusogenic MRV and group A rotavirus ( RVA ) , enabling the improvement of plasmid-based reverse genetics systems for these viruses [27] . Even with the results from these studies , no direct evidence has previously been produced for the association of cell–cell fusion by FAST with viral replication and spread . The lack of reverse genetics systems for fusogenic reoviruses has been the main obstacle to study of the biological functions of FAST proteins . We have developed a plasmid-based reverse genetics system for PRV , a fusogenic bat-borne reovirus [28] . PRV has a double-stranded ( ds ) RNA genome with 10 segments and was isolated from fruit bats in Nelson Bay , Australia , in 1968 [29] . In the past decade , PRV was isolated from patients with high fever , sore throat , cough , and/or diarrhea in south-eastern Asian countries , in addition to detection in bats [30] , suggesting that PRV is a bat-borne zoonotic virus . In this study , we examined the properties of various mutant PRV strains and recombinant FAST proteins . The results demonstrated that cell–cell fusion mediated by FAST protein increases viral replication . In a lethal mouse-infection model , FAST protein was the critical determinant of viral pathogenesis . Introduction of amino acid substitutions in PRV FAST protein produced an attenuated vaccine strain that completely protected animals from lethal infection .
We used a reverse genetics system to generate recombinant PRV strain Miyazaki-Bali/2007 with ( rsMB ) or without ( rsMB-ΔFAST ) FAST-p10 expression [28] . The rsMB-ΔFAST virus was recovered from cells transfected with rescue plasmids of nine intact PRV-MB gene segments and one segment ( S1 ) with disruption of the FAST-p10 translational start codon and insertion of a stop codon ( Fig 1A ) . Syncytium formation was detected in cells infected with rsMB , but not in those infected with rsMB-ΔFAST ( Fig 1B ) . Staining of the viral attachment protein sigmaC identified infection of individual cells by rsMB-ΔFAST ( Fig 1B ) . Immunofluorescence with FAST-p10-specific antiserum detected FAST-p10 protein in cells infected with rsMB , but not in those infected with rsMB-ΔFAST ( Fig 1C ) . To confirm this result , we also generated another FAST-p10-deficient virus ( rsMB-ΔFAST181 ) by deletion of S1 nucleotides 85–265 , corresponding to the FAST-p10 open reading frame ( ORF ) ( S1A Fig ) . Electropherotype analysis confirmed this deletion ( S1B Fig ) . Although sigmaC was detected in individual cells following infection with rsMB-ΔFAST181 , syncytium formation was not detected ( S1C Fig ) . The PRV S1 gene segment contains three ORFs , encoding FAST-p10 , p17 , and sigmaC . Atypical ribosome shunting and leaky scanning have been speculated to underlie S1 tri-cistronic expression [31] , suggesting that deficiency of the FAST-p10 ORF might disturb expression of p17 and sigmaC . However , immunoblotting demonstrated that p17 and sigmaC were both expressed in cells infected with rsMB-ΔFAST ( Fig 1D ) . The rescue of replication-competent FAST-p10-deficient viruses demonstrated that cell–cell fusion activity of FAST-p10 was not essential for viral replication . We next investigated the replication kinetics of rsMB and rsMB-ΔFAST . The growth rate of rsMB-ΔFAST was significantly lower than that of rsMB in Vero cells ( Fig 2A and 2B ) and in human cell lines ( S2A Fig ) , demonstrating that FAST-p10 has a role in enhancement of viral replication . We examined the replication and cell–cell fusion kinetics of rsMB and rsMB-ΔFAST at immediate–early time points . In rsMB-infected cells , cell–cell fusion appeared as early as 4 h after viral infection , and syncytial size increased during viral replication ( Fig 2C and S2B Fig ) . The amounts of positive-stranded viral RNA in rsMB-infected cells increased suddenly at 5 h post infection , whereas those in rsMB-ΔFAST-infected cells were lower , with only a gradual increase ( Fig 2D ) . The rsMB infectious virus titer increased at 7 h post infection and continued to increase until 10 h post infection , whereas the rsMB-ΔFAST infectious virus titer remained low ( Fig 2E ) . With Vero cells infected with rsMB at a multiplicity-of-infection ( MOI ) of 0 . 01 plaque-forming units ( PFU ) /cell , infectious viruses were identified in the culture supernatant at 15 h post infection ( S2C Fig ) . Transfection of cells with a FAST-p10 expression plasmid did not affect cell viability up to 30 h post transfection ( S2D Fig ) , suggesting that increased levels of infectious viruses in rsMB-infected cells did not result from newly synthesized virion released from disrupted cells . When cells transfected with FAST-p10 expression plasmid ( or mock-transfected ) were subsequently transfected with purified mRNA encoding secretory NanoLuc luciferase ( NLuc ) , expression of FAST-p10 did not affect NLuc activity , indicating that FAST-p10 did not affect cap-dependent translation ( S2E Fig ) . To examine whether cell–cell fusion promotes the spread of progeny virions , single-step replication kinetics at a high MOI were investigated . At a MOI of 10 PFU/cell ( as well as 0 . 01 PFU/cell ) , the titer of rsMB increased markedly at 8 h post infection , while the titer of rsMB-ΔFAST only increased gradually ( Fig 2F and 2G ) . To examine the time courses of the spread of rsMB and rsMB-ΔFAST , a monolayer of Vero cells was infected with each virus and overlaid with agarose gel . Immunostaining with specific antibodies revealed that viral antigens in rsMB-infected cells spread efficiently and syncytium formed . By contrast , the spread of rsMB-ΔFAST was restricted ( Fig 2H ) . Even at 72 h post infection , only tiny foci were observed in rsMB-ΔFAST-infected cells . These results suggest that cell–cell fusion promoted release of rsMB virions in the late stage of infection , while release of rsMB-ΔFAST virions was restricted due to the lack of FAST protein . To examine whether rsMB-ΔFAST produced deficient virus particles , we examined the ratio between the viral genome copy number and the infectious virus titer for rsMB and rsMB-ΔFAST . In whole-cell lysates and purified virus particles , this ratio was similar for rsMB and rsMB-ΔFAST ( Fig 2I ) , indicating that rsMB-ΔFAST produced intact virions as efficiently as rsMB . PRV FAST-p10 protein has a simple structure including an N-terminal extracellular domain , a transmembrane domain , and a C-terminal intracellular domain ( Fig 3A ) [2] . To investigate the relationship between fusion activity of FAST-p10 and viral replication , a series of mutant viruses encoding FAST-p10 variants with variable fusion activities were generated by single amino acid substitutions or truncation of the C-terminal region . Mutations in the S1 gene segment of the FAST-p10 ORF were confirmed by sequencing analysis of cDNAs generated from viral genomic RNA of FAST-p10 mutant viruses . Most single amino acid substitutions within the N-terminal ectodomain , including substitutions within the hydrophobic patch , abolished cell–cell fusion activity , whereas truncations of the C-terminal region affected , but did not abolish , fusion activity ( Fig 3B and S3A Fig ) . With these mutants , virus propagation and cell–cell fusion activities showed positive linear correlation in Vero cells ( Fig 3C and 3D ) and human embryonic kidney 293T cells ( S3B Fig ) . Lysophosphatidylcholine ( LPC ) is a minor phospholipid component of cell plasma membranes and inhibits membrane fusion induced by enveloped viruses and FAST proteins [32 , 33] . Syncytium formation induced by PRV infection was inhibited by addition of LPC in a dose-dependent manner , and was completely abolished with 100 μM LPC ( Fig 3E and S3C Fig ) . Replication of rsMB decreased with increasing LPC concentration ( Fig 3F ) , whereas replication of rsMB-ΔFAST was not affected by the presence of LPC ( Fig 3G ) . At 100 μM LPC , replication of rsMB was comparable to that of rsMB-ΔFAST . The time course of protein expression for rsMB and rsMB-ΔFAST was similar in the presence of 100 μM LPC ( Fig 3H ) . These results indicate that viral replication and protein expression was enhanced by cell–cell fusion activity of FAST protein . We confirmed these results using the lymphoid-like murine sarcoma S180-Meiji cell line ( S3D Fig ) . S180-Meiji cells did not exhibit cell–cell fusion upon rsMB infection ( S3E Fig ) . The growth kinetics of rsMB and rsMB-ΔFAST revealed by plaque assay and western blotting were similar in S180-Meiji cells ( S3F and S3G Fig ) , indicating that cell–cell fusion was required for increased viral protein expression and infectious virus production . To develop a complementation assay for studies of FAST protein-dependent replication , Vero cells were transfected with FAST-p10 expression plasmid before or after infection with rsMB-ΔFAST . Provision of FAST-p10 in trans rescued a substantial level ( 15–64-fold ) of rsMB-ΔFAST growth ( Fig 4A and 4B ) . Co-expression of FAST-p10 in rsMB-ΔFAST-infected cells resulted in the formation of syncytium with similar expression of PRV antigens to that found in rsMB-infected cells ( S4A Fig ) . FAST-p10 expression enhanced viral replication in a dose-dependent manner ( Fig 4C ) . Furthermore , a complementation assay was conducted in which cells were transfected with the FAST-p10 expression vector and infected with rsMB-ΔFAST at different MOIs ( 10 , 1 , 0 . 1 , and 0 . 01 ) . Overexpression of FAST-p10 enhanced virus production at all MOIs tested ( S4B Fig ) . The result obtained at a high MOI suggests that enhancement of virus production by FAST-p10 was not due to spread of progeny virions via cell–cell fusion , consistent with the replication kinetics of rsMB and rsMB-ΔFAST at low and high MOIs . FAST proteins from different Orthoreovirus and Aquareovirus species have common topologies ( ecto- , transmembrane- , and endo-domains ) , but varying levels of amino acid sequence similarity ( S5 Fig ) [2] . To determine whether FAST proteins have evolved with species-specific functions , we examined the effects of exogenous FAST protein expression in rsMB-ΔFAST propagation . Expression of recombinant RRV FAST-p14 produced the strongest fusion activity , followed by BRV FAST-p15 , PRV FAST-p10 , and ARV FAST-p10 , in transfected Vero cells ( Fig 4D ) . Expression of all recombinant FAST proteins enhanced replication of rsMB-ΔFAST , suggesting that FAST proteins can enhance PRV replication by species-independent mechanisms ( Fig 4E ) . The level of viral propagation reflected the fusion activities of the FAST proteins , with the highest level of viral replication in cells expressing RRV FAST-p14 , and the lowest level associated with ARV FAST-p10 . The fusion-inducing activity of ARV FAST-p10 was lower than that of other FAST proteins; therefore , we repeated the complementation assay using a higher amount of the ARV FAST-p10 expression plasmid . Transfection of 2 . 0 or 4 . 0 μg of the ARV FAST-p10 expression plasmid at 2 h before infection of rsMB-ΔFAST enhanced viral replication , while transfection of 1 . 0 μg of this plasmid did not ( S4C Fig ) . Syncytium formation induced by ARV FAST-p10 was slower than that induced by other FAST proteins; therefore , the incubation time between transfection and infection was varied . Transfection of 1 . 0 μg of ARV FAST-p10 expression plasmid at 8 h before infection of rsMB-ΔFAST significantly enhanced viral replication ( S4D Fig ) . These results indicate that ARV FAST-p10 also enhanced replication of rsMB-ΔFAST , although optimized experimental conditions were required due to the lower fusion-inducing activity of this protein . Notably , the replication of ARV was also enhanced by expression of PRV FAST-p10 , indicating that , in addition to PRV , other fusogenic reoviruses are sensitive to FAST protein enhancement ( Fig 4F ) . These results suggest that there is no specificity between FAST proteins and their source viruses , and that fusion activity alone is important for enhancement of viral replication . To better understand the enhancement of viral replication by cell–cell fusion , we examined the effect of fusogenic F glycoprotein , a class I viral fusion protein derived from Sendai virus ( SeV ) of the Paramyxoviridae family [19 , 34] . In SeV , the precursor F0 protein is activated by trypsin cleavage at a site located between the F1 and F2 proteins , and further conformational change by HN protein is required for viral envelope–cell or cell–cell fusion activity . To avoid the addition of trypsin , two additional protease cleavage sites were introduced , as described previously , to produce a SeV Fc protein that could induce cell–cell fusion without exogenous protease [35] . Co-expression of SeV Fc and HN induced the formation of syncytium that was morphologically similar to the syncytium induced by FAST-p10 protein at 12 and 24 h post transfection ( Fig 4G and S4E and S4F Fig ) . Immunostaining showed that expression of FAST-p10 or SeV Fc–HN prior to infection with rsMB-ΔFAST resulted in the formation of viral-antigen-positive syncytia similar to those seen in rsMB-infected cells ( S4G Fig ) . Expression of SeV Fc–HN caused a significant increase of 17 . 8-fold in rsMB-ΔFAST replication at 16 h post infection , compared with expression of SeV HN only ( Fig 4H ) . We were concerned that altered expression of p17 and sigmaC proteins of rsMB-ΔFAST ( Fig 1D ) affected viral replication; therefore , we performed the complementation assay using p17 and sigmaC expression plasmids . Overexpression of p17 or sigmaC did not enhance replication of rsMB-ΔFAST , indicating that the absence of FAST-p10 expression was responsible for the impairment of viral replication ( S4H , S4I and S4J Fig ) . We investigated whether FAST protein could enhance replication of viruses that do not encode fusogens . Expression of PRV FAST-p10 protein in trans significantly enhanced replication of other Reoviridae viruses , including MRV ( 63-fold ) and RVA ( 35-fold ) , in Vero cells , relative to mock-transfected cells ( Fig 5A and 5B ) . These observations were consistent with those of a previous study [27] . As expected , SeV F protein also enhanced replication of MRV ( 4 . 0-fold ) and RVA ( 9 . 3-fold ) ( Fig 5C and 5D ) . The enhancement of RVA replication by FAST-p10 protein was observed in the absence of trypsin , which is required for activation of newly synthesized RVA virion by cleavage of capsid VP4 protein into VP5 and VP8 fragments [36] , suggesting that enhancement of viral replication was not the result of secondary infection by released viruses . Notably , Vero cells are resistant to replication of RVA SA11 strain in the absence of exogenous FAST protein ( S6 Fig ) . FAST protein-dependent enhancement of viral replication was further investigated in viruses from different families , including infectious bursal disease virus ( IBDV , family Birnaviridae , two-segment dsRNA genome ) , encephalomyocarditis virus ( EMCV , family Picornaviridae , positive-sense single-stranded ( ss ) RNA genome ) , and vaccinia virus ( VV , family Poxviridae , dsDNA genome ) . Cell–cell fusion induced by FAST-p10 protein enhanced replication of IBDV ~5 . 5-fold ( Fig 5E ) . In common with reoviruses , IBDV is a segmented dsRNA virus , but whereas reoviruses maintain a transcriptional core particle that contains the dsRNA genome and RNA polymerase complex , the dsRNA of IBDV forms a transcriptionally active ribonucleoprotein complex with RNA-dependent RNA polymerase VP1 and RNA-binding polypeptide VP3 [37] . In contrast to IBDV , replication of EMCV and VV was not enhanced by FAST-p10 protein ( Fig 5F and 5G ) ; expression of FAST-p10 protein did not affect replication of EMCV and reduced that of VV in a dose-dependent manner . In summary , FAST protein enhanced replication of dsRNA viruses belonging to the families Reoviridae and Birnaviridae , but not of viruses belonging to the families Picornaviridae and Poxviridae . To investigate the role of FAST-p10 protein in vivo , the pathogenicity of wild-type rsMB and rsMB-ΔFAST was compared in a mouse model for lethal PRV infection [38] . Intranasal infection with rsMB caused lethal lung infection ( with progressive bodyweight loss in the early stages ) in inbred C3H/HeNCrl mice , with 80% mortality within 14 days post infection ( Fig 6A and 6B ) . By contrast , all the mice in the group infected with rsMB-ΔFAST survived , with no bodyweight loss . Viral titers in the lungs of mice infected with rsMB-ΔFAST were low , whereas rsMB replicated efficiently and had high viral titers ( Fig 6C ) . Histopathological analysis of lungs collected on day 5 post infection revealed that rsMB infection resulted in severely damaged lungs with excessive infiltration of lymphocytes and macrophages ( Fig 6D ) . Viral antigens were found in epithelial cells of bronchioles and lung alveolar cells . By contrast , lungs of mice infected with rsMB-ΔFAST were not apparently damaged . These results suggested that FAST-p10 protein enhanced viral replication in vivo , contributing to the high pathogenicity of PRV . The attenuation of FAST-deficient PRV in the mouse model suggested that FAST-deficient mutant viruses were promising candidates for the development of fusogenic-reovirus vaccines . To develop a live attenuated vaccine to protect host species against pathogenic PRV , C3H mice were infected intranasally with 2 . 0 × 105 PFU per animal of rsMB-ΔFAST either once or twice ( with a 1 week interval between infections ) ( Fig 7A ) . Control mice were intranasally inoculated with PBS . On day 14 after the first inoculation , mice were challenged with a lethal dose ( 4 . 0 × 105 PFU per animal ) of rsMB intranasally . Control mice challenged with rsMB underwent progressive loss of bodyweight , with 100% mortality by day 5 post infection ( Fig 7B and 7C ) . Mice immunized with a single dose of rsMB-ΔFAST underwent initial loss of bodyweight , with 67% mortality by day 6 post infection , but 33% of these mice survived and recovered , regaining the lost bodyweight ( Fig 7B and 7C ) . Mice immunized with two doses of rsMB-ΔFAST were protected against rsMB , with 100% survival and no loss of bodyweight ( Fig 7B and 7C ) . Similar results were obtained by immunization with rsMB-FAST/A30G , a mutant with a single amino acid substitution in FAST-p10 , resulting in impaired fusion activity and replication ( Fig 3B and 3C and S3A Fig ) . Mice immunized with a single dose of rsMB-FAST/A30G had a 33% survival rate 14 days after rsMB challenge , and surviving mice recovered and regained lost bodyweight ( S7A and S7B Fig ) . Mice immunized with two doses of rsMB-FAST/A30G were protected against rsMB , with 100% survival ( S7A Fig ) . The results demonstrated that attenuation of the cell–cell fusion induction activity of FAST protein is a novel strategy to develop live attenuated vaccine strains for fusogenic reoviruses , including PRV and ARV .
The FAST protein family consists of the only fusogenic proteins that have been found in non-enveloped viruses . Although the molecular biology of several recombinant FAST proteins , including PRV FAST-p10 , ARV FAST-p10 , RRV FAST-p14 , BRV FAST-p15 , and Aquareovirus FAST-p16 and FAST-p22 , has been extensively studied [1 , 3 , 4 , 6 , 8] , the functions of FAST proteins in viral replication have not previously been determined . By studying PRV mutants with deletion or alteration of FAST-p10 , we have now clearly shown that FAST proteins can act as enhancers of viral replication and virulence . Using a PRV reverse genetics system [28] , we generated FAST protein-deficient PRV , which was replication-competent , but which had significantly lower replication efficiency than wild-type PRV , indicating that cell–cell fusion enhanced viral replication . In the first 12 h after rsMB infection , we first observed cell–cell fusion , then enhanced ( relative to rsMB-ΔFAST ) synthesis of viral genomes and infectious virion production . The release of infectious virus into the culture medium was detected at 15 h post infection , suggesting that enhancement of viral replication was not associated with secondary infection by released viruses . The replication kinetics of rsMB and rsMB-ΔFAST at a high MOI and the results of the complementation assay using the FAST-p10 expression vector at a high MOI suggested that the spread of progeny virions did not contribute to the enhancement of replication by FAST protein . Syncytium formation by recombinant FAST-p10 protein recovered replication of rsMB-ΔFAST in a dose-dependent manner , demonstrating that impaired replication of FAST-deficient PRV was the result of a lack of cell–cell fusion activity . Similarly , the fusion activities of mutant PRVs with FAST-p10 variants were linearly correlated to enhancement of viral replication , and inhibition of cell–cell fusion by LPC suppressed the replication of rsMB ( but not rsMB-ΔFAST ) in a dose-dependent manner . FAST proteins from four different reoviruses all enhanced replication of rsMB-ΔFAST , and the level of enhancement for each was related to the cell–cell fusion activity . The finding that replication of rsMB-ΔFAST was enhanced by a paramyxovirus fusion protein supports the hypothesis that replication of PRV-MB largely depends on cell–cell fusion . However , the level of enhancement differed between FAST protein and paramyxovirus F protein ( Fig 4A and 4H ) , suggesting that FAST protein has an unknown function distinct from induction of syncytium formation . These results all confirmed the importance of cell–cell fusion for PRV replication and suggested that drugs or antibodies targeting FAST proteins could be developed to inhibit replication of fusogenic reoviruses . Unlike PRV , the non-fusogenic reoviruses MRV and RVA were capable of replication to high titers in the absence of syncytium formation . Notably , however , syncytium formation by PRV FAST-p10 enhanced the replication of MRV , RVA , and IBDV , which all have dsRNA genomes , but not replication of EMCV ( ssRNA ) or VV ( dsDNA ) . Although the replication cycle of PRV has not been studied , speculations can be made based on detailed studies of the replication of MRV and bluetongue virus ( BTV ) , which both belong to the family Reoviridae . In MRV , which is genetically close to PRV , viral positive-sense ssRNA is synthesized inside uncoated core particles and then released into the cytoplasm through the pore structures of the core particles , to function as messenger RNA or viral genomic RNA [39] . Notably , we observed enhancement of viral RNA synthesis soon after cell–cell fusion occurred ( as early as 5 h post infection ) , suggesting that cell–cell fusion enhanced primary RNA synthesis within the transcriptionally activated core particle in the cytoplasm . In vitro synthesis of BTV RNA has previously demonstrated that the core particle is activated by divalent cations , that it continuously synthesizes ssRNA [40] , and that RNA production requires nucleotide triphosphates and S-adenosyl-methionine for polymerase and capping reactions [41] . It is possible that core particles within syncytia are able to synthesize more viral RNA than those in discrete cells because more substrates for viral RNA synthesis are available in syncytia . A similar mechanism may be responsible for the enhancement of recovery of RVA by plasmid-based reverse genetics using FAST protein . Following production of the immature core particle after RVA plasmid transfection , production of RVA genome from the core would be enhanced by cell–cell fusion . To obtain a detailed understanding of the mechanisms involved in replication enhancement , further experiments will be required , focusing on ssRNA synthesis from core particles in syncytia . The difference between the spread of rsMB and rsMB-ΔFAST suggested that progeny viruses were efficiently released via disruption of cells following syncytium formation at a late stage of viral replication . Most non-enveloped viruses lack fusogenic proteins but efficiently replicate and egress . This suggests that a unique mechanism underlying release of progeny virions , which is largely dependent on FAST protein-induced syncytium formation , evolved in fusogenic reoviruses . In common with reoviruses , IBDV is a non-enveloped virus with a segmented dsRNA genome . However , IBDV lacks the T = 2 shell structure that is present in other dsRNA viruses , in which the dsRNA genome is isolated from host innate immune system sensors . The IBDV dsRNA genome instead forms a transcriptionally active ribonucleoprotein complex [37] . On the basis of its structural characteristics , IBDV is considered to be an evolutionary link between dsRNA and ssRNA viruses [42 , 43] . Replication of IBDV by FAST-p10 protein was only enhanced ~5 . 5-fold , compared with 15–64-fold for PRV rsMB-ΔFAST , 63-fold for MRV , and 35-fold for RVA , indicating that syncytium formation does not contribute as much to replication of IBDV as to that of other dsRNA viruses . Further research focusing on the common factors in Reoviridae and Birnaviridae viruses could help to uncover the mechanisms underlying FAST protein-dependent enhancement of viral replication . Replication of VV was not enhanced by cell–cell fusion , possibly because VV is coated by an envelope derived from the early endosomes or trans-Golgi network [44] , suggesting that FAST protein interfered with this lipid bilayer membrane . Although evidence suggests that cell–cell fusion occurring during the replication of enveloped viruses ( such as HIV ) would promote cell–cell viral transmission of viruses [45] , our result suggests that a high level of syncytium formation can inhibit the replication of enveloped viruses . In a small-rodent model , we demonstrated that FAST-p10 protein contributed to enhanced virus replication and pathogenesis in vivo . The low infectious virus titer of rsMB-ΔFAST in lung ( ~10 PFU per 100 mg of tissue ) indicated that replication and spread of PRV was severely impaired in the absence of FAST protein . By contrast , non-fusogenic MRV replicates and spreads efficiently in murine lung without a fusogenic protein [46] . We did not identify cell–cell fusion in the lungs of rsMB-infected mice by histological examination , suggesting that , if syncytia were present , disruption of the tissues made it difficult to find them in the tissue sections . Careful examination to identify syncytium formation in PRV-infected tissue ( e . g . , immunostaining of the plasma membrane in PRV-infected tissue sections ) may be needed to clarify the role of FAST protein in viral replication and pathogenesis in vivo . Our results demonstrated that rsMB-ΔFAST can be developed as a candidate for an attenuated vaccine strain . Since the first report of PRV infection in humans in 2007 , PRV has been detected in humans and bats in eastern and south-eastern Asian countries , suggesting that PRV is a bat-borne zoonotic virus [47] . Patients with PRV infections have a high fever , sore throat , cough , and/or diarrhea . Sero-epidemiological studies conducted in Malaysia and Vietnam reported that anti-PRV antibodies were detected in 4–13% of people [30 , 48] . Furthermore , the PRV genome was detected in 17% of patients with respiratory symptoms in Malaysia [49] . These studies suggest that PRV infections are common in south-eastern Asian countries and that PRV is an important respiratory virus . The strategy of developing a live attenuated vaccine strain by attenuation of FAST protein can also be applied to ARV , which causes arthritis and tenosynovitis in chickens . A recent epidemiological study of ARV in the USA and Canada reported the emergence of antigenic variant strains that are antigenically distinct from the vaccine strain [50 , 51] . Although single-dose infection with rsMB-ΔFAST did not protect animals effectively , two-dose inoculation completely protected mice from lethal infection . We assessed two PRV strains as immunogens , with complete ( rsMB-ΔFAST ) or moderate ( rsMB-FAST/A30G ) loss of fusion activity , and both strains replicated poorly in vitro . However , we produced a number of PRV mutants with different FAST-p10 mutations and different levels of replication in vitro , and one of these mutants with moderate replication might provide sufficient induction of neutralizing antibodies with minimal pathogenicity to enable its use as a single-dose immunogen . In this study , challenge infection was performed at 1 week post immunization because animals became resistant to PRV-MB infection over time . FAST-deficient viruses do not replicate efficiently in vivo; therefore , establishment of long-lasting immunity is a concern for vaccine development . The kinetics of specific antibodies following FAST-deficient virus infection should be evaluated in a future study . Moreover , as FAST protein is not essential for viral replication , foreign genes could be inserted into the position of the FAST ORF , enabling rsMB-ΔFAST to be developed as an attenuated transduction vector . The presence of fusogenic viruses in the different genera of reoviruses , including Orthoreovirus and Aquareovirus , suggests that the fusogenic characteristic was common among the ancestors of modern Reoviridae viruses . The FAST protein fusion-dependent mechanism may have conferred an evolutionary advantage on these viruses through enhanced efficiency of replication and transmission . However , this hypothesis raises the question of why only the limited group of reoviruses among all non-enveloped viruses acquired FAST proteins . One possibility is that fusogenic activity is too pathogenic to maintain sustainable host–pathogen relationships without some as-yet-unidentified control mechanism . Viral non-structural proteins support the viral replication cycle by interacting with host factors or other viral proteins , but the function of FAST proteins is dissimilar to known viral non-structural proteins . Thus , FAST protein could be categorized as a novel type of viral non-structural protein .
Monkey kidney epithelial Vero cells , murine fibroblast L929 cells , 293T cells , and canine kidney MDCK cells ( all from American Type Culture Collection , Manassas , VA ) were grown in high-glucose Dulbecco’s modified Eagle’s medium ( DMEM; Nacalai Tesque , Kyoto , Japan ) supplemented with 5% fetal bovine serum ( FBS; Gibco , Thermo Fisher Scientific , Waltham , MA , USA ) . Monkey kidney epithelial MA104 cells were provided by Dr . Hiroshi Ushijima ( Nihon University ) . Quail fibroblast QT6 cells were provided by Dr . Kazuyoshi Ikuta ( Osaka University ) . Chicken fibroblast DF1 cells were provided by Dr . Tsuyoshi Yamaguchi ( Tottori University ) . BSR cells , which are derived from baby hamster kidney cells , were provided by Dr . Polly Roy ( London School of Hygiene and Tropical Medicine ) . MA104 , QT6 , DF1 , and BSR cells were grown in DMEM containing 5% FBS . The U251 , CAKI-1 , HOP92 , UO-31 , LOX-IMVI , SF-295 , KM12 , TK-10 , SK-OV-3 , DU-145 , ADR-RES , H322M , and OVCAR-3 human cancer cell lines were provided by Dr . Toru Okamoto ( Walter and Eliza Hall Institute of Medical Research ) and were cultured in RPMI1640 ( Nacalai Tesque ) containing 10% FBS . Murine sarcoma S180-Meiji cells were kindly supplied by the Cell Resource Center for Biomedical Research , Institute of Development , Aging , and Cancer ( Tohoku University ) and were cultured in DMEM supplemented with 10% FBS . PRV strain Miyazaki-Bali/2007 ( PRV-MB ) was isolated from a Japanese patient who had returned from Bali , Indonesia , and been hospitalized with high fever , joint pain , sore throat , and cough [52] . A recombinant strain of PRV-MB ( rsMB ) generated by reverse genetics was amplified in L929 cells and used as wild-type virus [28] . MRV strain T1L and EMCV were amplified in L929 cells . Viral titers of PRV , MRV , and EMCV were determined by plaque assay using L929 cells . ARV strain 58–132 was amplified in QT6 cells , and viral titer was determined by plaque assay using QT6 cells . IBDV OKYMY strain [53] was amplified in DF1 cells , and viral titer was determined by 50% tissue-culture infectious dose ( TCID50 ) using DF1 cells . The recombinant RVA strain SA11 [27] was amplified in MA104 cells with 0 . 5 μg/ml trypsin , and viral titer was determined by plaque assay as described previously [27] . SeV strain Z [54] was amplified in MDCK cells , and viral titer was determined by plaque assay using MDCK cells . Attenuated recombinant VV expressing T7 RNA polymerase ( rDIs-T7pol ) was propagated in chick-embryo fibroblasts . Viral titer of rDIs-T7pol was determined by TCID50 in BSR cells . Virus stocks were kept at −80°C prior to use . Mouse antiserum against PRV-MB sigmaC was generated as described elsewhere [28] . To generate mouse antiserum against recombinant PRV-MB sigmaA and sigmaNS , Escherichia coli BL21 cells ( Takara , Mountain View , CA , USA ) transformed with pTrc-His-MB sigmaA or pTrc-His-MB sigmaNS plasmids were incubated with 1 mM isopropyl β-D-1-thiogalactopyranoside at room temperature for 8 h . Cell pellets were lysed in 1% Triton-X100 , and His-tagged viral proteins were purified from the soluble fraction using His-Select R Nickel Affinity Gel ( Sigma , St Louis , MO , USA ) according to the manufacturer’s instructions . The His-sigmaA or His-sigmaNS fusion protein was mixed with alhydrogel adjuvant 2% ( InvivoGen , San Diego , CA , USA ) according to the manufacturer’s instructions , and ICR mice ( CLEA Japan , Tokyo , Japan ) were immunized and boosted with the protein-adjuvant mixture . At 4 weeks after administration of the last booster , whole blood was collected and sera were separated by centrifugation . Antiserum to FAST-p10 was raised in rabbits immunized with peptides corresponding to residues 21–32 ( KNKAGGDLQATS ) of FAST-p10 ( Eurofins Genomics , Ebersberg , Germany ) . Antiserum to PRV-MB p17 was raised in rabbits immunized with peptides corresponding to residues 125–138 ( DDDPEHKRFAIRSI ) of PRV-MB p17 ( Sigma ) . Rabbit anti-pan-cadherin antibody ( C3678 ) and anti-mouse IgG-HRP conjugates ( A9044 ) were purchased from Sigma Aldrich ) . Goat anti-rabbit IgG-CF488 conjugate ( 20019 ) , goat anti-rabbit IgG-CF594 antibody ( 20113 ) , and goat anti-mouse IgG CF488 antibody ( 20018 ) were purchased from Nacalai Tesque . pT7-MB plasmids ( L1 , L2 , L3 , M1 , M2 , M3 , S1 , S2 , S3 , and S4 ) for reverse genetics of PRV-MB were prepared as described previously [28] . PRV-MB-p10 and ARV-p10 genes were amplified by RT-PCR using gene-specific primers , and inserted into the BglII site of the pCAGGS vector by homologous recombination using In Fusion HD cloning kit ( Clontech , Takara ) to create pCAG-MBFAST-p10 and pCAG-ARVFAST-p10 , respectively . BRV p15 gene ( AF406787 ) and RRV p14 gene ( AY238887 ) were synthesized ( Eurofins Genomics ) and cloned into pCAGGS plasmid to create pCAG-BRVp15-FAST and pCAG-RRVp14-FAST , respectively . Coding regions of PRV sigmaC , sigmaA , and p17 genes were amplified by RT-PCR and inserted into the EcoRI site of pEF-HA plasmid , which was modified from pEF-BOS plasmid using In Fusion HD cloning kit , to create pEF-HA-sigmaC , pEF-HA-sigmaA , and pEF-HA-p17 . SeV HN and F genes were amplified by RT-PCR using gene-specific primers and inserted into pEF-BOS vector to create pEF-SeV F and pEF-SeV HN plasmids . To create pEF-SeV Fc plasmid , nucleotides 5′-GCCAACAACAGAGCCAGAAGAGAG , corresponding to amino acids Ara-Asn-Arg-Ara-Arg-Arg-Glu , and 5′-AAGAAAAGGAAAAGA , corresponding to amino acids Lys-Lys-Arg-Lys-Arg , were inserted after Thr101 and Ser115 , respectively , in SeV F ORF by site-directed mutagenesis . Secretory NanoLuc luciferase ( secNLuc , Promega , Madison , WI , USA ) gene was amplified and inserted into T7 plasmid used for reverse genetics by replacing the viral genome with secNLuc gene to generate pT7-secNLuc . To generate recombinant protein expression vectors for protein purification in bacteria , coding regions of MB sigmaA and sigmaNS genes were amplified and cloned downstream of sequences encoding a poly-histidine ( His ) tag in the pTrc-HisA vector ( Life Technologies ) . Mutant pT7 plasmids for reverse genetics were generated by standard site-directed mutagenesis using KOD-Plus-NEO polymerase ( Toyobo , Osaka , Japan ) and DpnI restriction enzyme . Mutations encoding single amino acid substitutions in MB FAST-p10 gene were introduced into pT7 rescue plasmids by replacing one or two nucleotides to generate pT7-MBS1-FAST-p10/C5S , /V9G , /V9A , /V9R , /V11G , /V11R , /A24G , /G25A , /G26A , /D27E , /Q29N , /A30G , /T31S , /S32T , /V95E , /V95I , and /V95F . pT7 plasmids encoding truncated FAST-p10 genes were generated with mutations in the S1 gene segment to replace amino acids with a stop codon ( TAG ) , generating pT7-MB S1 p10/G83 stop ( TAG at nucleotides 273–275 ) , /V85 stop ( 279–281 ) , /P87 stop ( 283–285 ) , /A90 stop ( 294–296 ) , /T93 stop ( 303–305 ) , /S94 stop ( 306–308 ) , and /V95 stop ( 309–311 ) . The primers used to make FAST-p10 mutants are available upon request . The 10 PRV-MB plasmids ( L1–S4 ) for reverse genetics were prepared as previously described [28] . To rescue PRV-MB , monolayers of L929 cells were infected with rDIs-T7pol and incubated at 37°C for 1 h . The culture medium was replaced with DMEM supplemented with 5% FBS , and the cells were transfected with the 10 plasmids using 2 μl of TransIT-LT1 transfection reagent ( Mirus Bio , Madison , WI , USA ) per 1 μg of plasmid DNA . After incubation at 37°C for 5 days , cells were lysed by freeze-thawing and supernatant was transferred to a monolayer of L929 cells and overlaid with DMEM containing 0 . 8% agarose ( SeaPlaque Agarose , Lonza , Basel , Switzerland ) . At 4 days post infection , cells were stained with secondary overlay medium containing 0 . 8% agarose and 0 . 005% neutral red . Visualized plaques were transferred to L929 cells , and plaque-purified viral clones were amplified and used for experiments . The sequences of all the mutant and wild-type virus stocks used for experiments were confirmed . Briefly , dsRNA genomes were purified from virus stocks using Tri-reagent ( Life Technologies ) . cDNA was synthesized by Thermoscript reverse transcriptase with PRV-MB S1-specific primer that targeted the 3′ UTR . The S1 segment of each virus was then amplified with S1-specific primers ( pCAG-S1-F: 5′-CGCGCCGATATCTTAAGATCTGCTTATTTTTGTTCTCAAGT; pCAG-S1-R: 5′-GAGGAGTGAATTCGAAGATCTGATGAATAGCTGTCCTCAG , where underlined nucleotides indicate specific binding sites in the S1 UTRs ) and inserted into cloning plasmid using In Fusion HD cloning kit . Nucleotide sequencing was performed with Big Dye terminator ( Thermo Fisher Scientific ) . To avoid the possibility of contamination with rDIs-T7pol in recombinant PRV-MB stocks , PCR examination was performed using primers specific for the D1R gene of rDIs ( forward primer: 5′-TAAGCTAATAGAGCCCGTGAATGC; reverse primer: 5′-CACCTTCTGGTTGCTTTGGTAA ) . Briefly , DNA was purified from stocks of recombinant MB strains by standard phenol–chloroform extraction . As a control , a plasmid containing the D1R gene from rDIs-T7pol was prepared . Preliminary examination with PrimeSTAR GXL DNA Polymerase and the positive control plasmid demonstrated that the detection limit of the test was 10 ng of DNA/μl . To detect viral antigens , PRV-MB-infected cells were fixed with 3 . 8% formaldehyde and permeabilized with 0 . 1% Triton X-100 . PRV sigmaC antigens were detected by immunostaining with rabbit anti-sigmaC antibody followed by anti-rabbit IgG-CF488 . FAST-p10 was detected by immunostaining with rabbit anti-FAST-p10 peptide followed by anti-rabbit IgG CF594 conjugate . For co-staining of the plasma membrane and sigmaC antigen , virus-infected Vero cells were fixed with 100% ethanol . The plasma membrane and sigmaC were visualized by immunostaining with rabbit anti-pan-cadherin antibody and mouse anti-PRV-MB serum followed by anti-rabbit IgG CF594 antibody and anti-mouse IgG CF488 antibody , respectively . To count the numbers of cells per syncytium , Vero cells infected with PRV-MB were fixed in 3 . 8% formaldehyde at 16 h post infection . After permeabilization with 0 . 1% Triton X-100 , virus-infected foci were detected by immunostaining with mouse anti-PRV-MB serum followed by anti-mouse IgG-HRP conjugates and 3 , 3-diaminobenzidine tetrahydrochloride hydrate ( D5637; Sigma ) . In immunofluorescence assays , nuclei were stained with DAPI , which was included in VECTASHIELD mounting media ( H-1500 , Vector Laboratories , Inc . ) . Vero cells were infected with rsMB or rsMB-ΔFAST at MOIs of 0 . 01–10 PFU/cell . At 24 h post infection , whole-cell extracts were subjected to SDS-polyacrylamide gel electrophoresis . After transfer to a membrane , viral proteins were detected using rabbit anti-sigmaC , rabbit anti-p17 , and mouse anti-sigmaA primary antibodies and HRP-conjugated anti-rabbit or anti-mouse antibodies . As a positive control , lysates of 293T cells transfected with plasmid vectors encoding hemagglutinin ( HA ) -tagged sigmaC , p17 , and sigmaA were loaded . Viral proteins were detected with anti-sigmaC , anti-p17 , and anti-sigmaA antibodies . HA-tagged recombinant proteins were detected using mouse anti-HA antibody and anti-mouse IgG-HRP conjugate . Western blotting for β-actin was conducted as a loading control . Signals were detected using Pierce Femto Super-Signal reagent ( Pierce ) and a Luminescent Image Analyzer LAS-3000 ( Fujifilm , Tokyo , Japan ) . Confluent monolayers of Vero cells were infected with rsMB or rsMB-ΔFAST at a MOI of 0 . 01 PFU/cell and incubated at 37°C for 1 h . Cells were washed with PBS four times , which was replaced with DMEM supplemented with 5% FBS and various concentrations ( 0 , 50 , or 100 μM ) of L-α-LPC ( L4129 , Sigma Aldrich ) . At 1 , 12 , and 24 h post infection , cells were lysed by freeze-thawing and virus titers were determined by plaque assay . Confluent monolayers of Vero cells for PRV , MRV , RVA , ARV , and EMCV; BSR cells for rDIs-T7pol; and DF1 cells for IBDV grown in 24-well plates were transfected with 0 . 25 , 0 . 5 , 1 , or 2 μg of pCAG-FAST-p10 plasmid or pCAGGS empty plasmid using 2 μl of TransIT-LT1 transfection reagent per 1 μg of plasmid DNA . At 2–12 h post transfection , culture media were replaced with DMEM supplemented with 5% FBS and infected with MRV or RVA at a MOI of 0 . 001 PFU/cell . After adsorption at 37°C for 1 h , cells were washed with PBS six times and cultured in DMEM with 5% FBS for PRV or FBS-free DMEM for RVA . At 16 h post infection , cells were lysed by freeze–thaw cycles . Lysates of cells infected with RVA were incubated with 10 μg/ml trypsin at 37°C for 30 min before titration . Virus titers were determined by plaque assay except for rDIs-T7pol , for which viral titer was determined by TCID50 . Confluent monolayers of Vero cells grown in 24-well plates were transfected with 0 . 5 or 2 μg of the pCAG-p17 and pCAG-sigmaC plasmids using 2 μl of TransIT-LT1 transfection reagent per μg of plasmid DNA . The pCAGGS empty plasmid was used as a mock transfection control . At 2 h post transfection , culture media were replaced by DMEM supplemented with 5% FBS , and cells were infected with rsMB-ΔFAST at a MOI of 0 . 01 PFU/cell . After adsorption at 37°C for 1 h , cells were washed with PBS six times and cultured in DMEM with 5% FBS . At 16 h post infection , cells were lysed by freeze-thawing . Virus infectious titers in whole-cell lysates were determined by the plaque assay . Viral antigens in whole-cell lysates were detected by western blotting using rabbit anti-p17 , rabbit anti-sigmaC . and mouse anti-sigmaA sera . A mouse anti-β-actin IgG antibody was used as a loading control . Whole-cell lysates were clarified by centrifugation at 16 , 400 ×g for 2 min and the supernatant was collected . Viruses were concentrated by ultracentrifugation in a Beckman SW28 rotor at 89 , 450 ×g for 2 h . The pellet was resuspended in DMEM and sedimented through CsCl ( 0 . 39 g/ml ) using a Beckman SW55 rotor at 116 , 000 ×g for 16 h at 12°C . The fraction containing virions was diluted in DMEM and dialyzed against PBS . Purified virions were subjected to quantitative real-time PCR following dsRNA purification and the plaque-forming assay . Viral RNA was purified from whole-cell lysates or purified virions using a QIAamp Viral RNA Mini Kit . Viral cDNA was synthesized from positive-stranded RNA using RevertAid Reverse Transcriptase ( Thermo Fisher Scientific ) and a primer specific to the L1 gene positive-sense genome nucleotide position 1181–1204 ( 5’-ACTGAGGTTGCCAACGAACGGATG-3’ ) . Viral RNA copy numbers were quantified using TaqMan Fast Universal PCR Master Mix ( 2× ) , no AmpErase UNG ( Thermo Fisher Scientific ) , and the ViiA7 real-time PCR system ( Life Technologies ) . The following primers and probes specific to the PRV L1 gene segment were used: forward primer , 5’-ACGCATCCTTCTGTGGGTC-3’; reverse primer , 5’-GAGATGGAGATGAAAGGTGTGAGTG-3’; probe , 5’-FAM-CCAAAGCTATAACAGTACCGTCTC-TAMRA-3’ ( Eurofins Genomics , Ebersberg , Germany ) . The T7-MB L1 plasmid was used to generate a standard curve . mRNA of NanoLuc gene was synthesized from pT7-secNLuc using mMESSAGE mMACHINE T7 Ultra ( Ambion , Thermo Fisher Scientific ) according to the manufacturer’s instructions . Poly-A tailing was performed on synthesized mRNA , and products were purified by standard phenol–chloroform extraction . To examine the effect of FAST protein on mRNA translation , monolayers of 293T cells in 24-well plates were transfected with 0 . 5 μg of pCAG-FAST-p10 or empty pCAGGS plasmid with 2 μl of TransIT-LT1 transfection reagent per 1 μg of plasmid DNA . At 2 h post transfection , culture medium was replaced with DMEM containing 5% FBS , and cells were transfected with 100 ng of NLuc mRNA using 2 μl of TransIT-mRNA ( Mirus ) per 1 μg of RNA . At 8 , 12 , 24 , and 36 h post transfection of NLuc mRNA , NLuc activity in cell lysate was measured using Nano-Glo Luciferase Assay kit ( Promega ) and a luminometer ( AB-2200 , Atto , Tokyo , Japan ) according to the manufacturers’ instructions . Monolayers of Vero cells in 96-well plates were transfected with FAST-p10 expression vector or empty vector ( 0 . 1 μg/well ) and incubated for various durations . At the indicated time points post transfection , cell viability was determined by the wst-1 assay . In brief , 2 μl of cell proliferation reagents was added to each well and samples were incubated at 37°C for 1 h . Optical density at 440 nm was measured using a microplate reader ( Powerscan HT , DS Pharma Biomedical , Osaka , Japan ) . Male 4-week-old C3H/HeNCrl ( C3H ) mice were purchased from Charles River Japan Inc . ( Kanagawa , Japan ) . To obtain Kaplan–Meier survival curves , male C3H mice ( n = 10 / group ) were intranasally infected with 2 . 0 × 105 PFU per animal of rsMB or rsMB-ΔFAST and observed for up to 18 days post infection . Bodyweight was recorded every 1–2 days . To assess viral replication and to generate survival curves in mice , 4-week-old male C3H mice ( n = 5/group ) were intranasally infected with 2 . 0 × 105 PFU per animal of rsMB or rsMB-ΔFAST . Animals were euthanized 5 days after intranasal infection . Lungs were homogenized with a bead homogenizer ( BeadSmash 12 , Waken B Tech , Kyoto , Japan ) . Viral titers in lungs were determined by plaque assay . For histopathological study , C3H mice intranasally infected with 2 × 105 PFU per animal of rsMB or rsMB-ΔFAST were euthanized at day 4 and lungs were fixed in 10% neutral buffered formalin solution and processed for pathological examination . Tissue sections were examined by hematoxylin and eosin staining and immunohistochemical analysis with rabbit anti-PRV-MB sigmaC primary antibody , horseradish peroxidase-conjugated secondary antibody , and diaminobenzidine tetrahydrochloride . To evaluate the FAST mutant PRV as live attenuated vaccine , 3-week-old male C3H mice ( n = 6/group ) were intranasally infected with 4 × 105 PFU of rsMB , rsMB-ΔFAST or rsMB-FAST ( A30G ) once or twice with a 1 week interval . Control mice were intranasally inoculated with PBS . At 14 days after the first infection ( 7 days after the second infection ) , mice were intranasally infected with 4 × 105 PFU of rsMB . The survival rate and body weight were recorded for 2 weeks after challenge infection . To obtain anti-PRV-MB serum , C3H mice that survived after intranasal infection of 2 × 105 PFU of rsMB were intranasally re-infected two or four times with 2 × 106 PFU of rsMB . Statistical analyses were performed using GraphPad Prism ( v5 . 0 , GraphPad ) . Viral replication was compared between two groups using the two-tailed Student’s t-test . The number of nuclei per syncytium was compared between two groups using the two-tailed Student’s t-test and between three or more groups using a one-way analysis of variance with Dunn’s or Dunnett’s multiple comparison test . Linear regression lines and R-square values were calculated for fusion efficiencies and viral titers of FAST-p10 mutant viruses . Survival curves were compared with that of the control group using the log rank test . Experiments were repeated at least three times . The sample numbers provided represent biological replicates . p < 0 . 05 was considered statistically significant . The study was approved by the Animal Research Committee of Research Institute for Microbial Diseases , Osaka University ( Approval number: Bi-Dou-25-04-0 ) . The experiments were conducted following the guidelines for the Care and Use of Laboratory Animals of the Ministry of Education , Culture , Sports , Science and Technology , Japan . Chick-embryo fibroblasts prepared from 11-day-old chicken embryonated eggs were grown in DMEM supplemented with 10% chicken serum ( Life Technologies , Thermo Fisher Scientific ) . The PRV-MB strain , which was isolated from an anonymous donor [52] , was provided by the National Institute of Infectious Diseases ( Tokyo , Japan ) via a material transfer agreement . The usage of PRV-MB was approved by the Research Institute for Microbial Diseases , Osaka University ( Approval number: 24-Biken-362 ) . | Among diverse viral proteins of non-enveloped viruses , only FAST protein encoded by fusogenic reoviruses belonging to the family Reoviridae induces cell–cell fusion during viral replication cycle . Unlike enveloped viruses , non-enveloped viruses do not require fusion proteins to enter cells . Although the biochemical characteristics of FAST protein have been extensively studied , its biological function and its role in viral replication remain unknown . Here , we showed that cell–cell fusion induced by FAST protein dramatically increased replication of non-enveloped dsRNA viruses that did not encode FAST protein . We also demonstrated that FAST mutant viruses could be used to generate live viral vaccines . This study reports the unprecedented finding that a viral non-structural protein enhances replication of non-enveloped dsRNA viruses by inducing cell–cell fusion . | [
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] | 2019 | Cell–cell fusion induced by reovirus FAST proteins enhances replication and pathogenicity of non-enveloped dsRNA viruses |
In this study we present a detailed , mechanism-based mathematical framework of Drosophila circadian rhythms . This framework facilitates a more systematic approach to understanding circadian rhythms using a comprehensive representation of the network underlying this phenomenon . The possible mechanisms underlying the cytoplasmic “interval timer” created by PERIOD–TIMELESS association are investigated , suggesting a novel positive feedback regulatory structure . Incorporation of this additional feedback into a full circadian model produced results that are consistent with previous experimental observations of wild-type protein profiles and numerous mutant phenotypes .
Circadian rhythmicity is the product of a robust [1] , free-running , temperature-compensated [2] , and adaptable [3 , 4] biological clock found in diverse organisms ranging from bacteria to humans . The model organism Drosophila is commonly used to study this phenomenon due to the relative ease of experimentation and the similarities to the mammalian circadian clock ( reviewed in [5 , 6] ) . The Drosophila circadian clock is composed of two interlocking feedback loops , shown in Figure 1 . The first loop is composed of the negative feedback of period ( per ) and timeless ( tim ) , shown in red , which down-regulate their own expression by inhibiting the CLOCK–CYCLE ( CLK–CYC ) transcription factor . DOUBLE-TIME ( DBT ) binds to and phosphorylates PER , which dimerizes with TIM before localizing to the nucleus via an uncharacterized mechanism . Circadian rhythms are entrained by light through an increased degradation of TIM protein , shown in yellow . In the second loop , shown in blue , the expression of clk is regulated by vrille ( vri ) and PAR domain protein 1 isoform ɛ ( pdp ) . Both vri and pdp expression are activated by CLK–CYC . VRI represses the expression of clk , creating a negative feedback loop , whereas PDP creates a positive feedback loop through activating clk expression . Incorporating detail on interlocked feedback loops , recently shown to increase the stability and robustness of oscillations [7 , 8] , may be important to accurately capture the network behavior . Several mathematical models have been created to better characterize the network underlying circadian rhythmicity in Drosophila ( e . g . , [9–14] ) . These initial studies provided important insights into the molecular mechanisms of circadian rhythms and the ability to produce robust 24-hour oscillations . However , recent experimental observations have created a more detailed view of network interactions , including new critical aspects that are not described by previous models . It is thus necessary to establish whether a mathematical model of the current consensus network would provide robust oscillations . The nuclear localization of PER and TIM and the subsequent repression of CLK activity have been two particularly active areas of experimental research . The necessity of TIM for PER nuclear localization has been long established [15] and was assumed to occur through the nuclear transport of PER–TIM dimers . In contrast to this mechanism , recent experimental observations now suggest that PER and TIM localize to the nucleus in a primarily independent mechanism [16–21] . Additionally , while TIM is required for PER nuclear localization via a cytoplasmic “interval timer , ” the mechanism controlling this timer is independent of both TIM and PER concentration [21] . Thus , the way in which TIM affects PER nuclear localization is an open question . Once in the nucleus , PER ( and to a much lesser extent TIM ) repress CLK activity , recently observed to occur via PER-mediated phosphorylation of CLK by DBT [22 , 23] . These studies also provided evidence that total levels of CLK remain nearly constant [22 , 23] , in contrast to previously observed CLK oscillations [24–26] . It remains unclear whether constant total CLK concentration can coincide with stable oscillations in this new network . To address these questions , we first study the possible mechanisms underlying the PER/TIM cytoplasmic interaction in Drosophila S2 cell culture using mathematical models of this isolated ( arrhythmic ) network . Using the most likely candidate mechanism , one based on positive feedback , we created a detailed mathematical model of the wild-type Drosophila circadian network . This model incorporates post-translational modifications to the PER and CLK proteins in addition to including both interlocked feedback loops , without the use of explicit time delays . The results of this model are consistent with wild-type and mutant experimental observations , provide insight into recent network revisions , and suggest possible experimental directions to explore .
To investigate the six-hour delay created by the cytoplasmic interval timer observed in S2 cell culture by Meyer et al . [21] , the dynamics of the per/tim loop were isolated and studied independently of the remaining circadian gene network to mimic the environment within Drosophila S2 cells . The interactions constituting the three mathematical models studied are shown in Figure 2 . All models of the isolated per/tim loop include PER–TIM dimers in the cytoplasm that dissociate immediately prior to nuclear localization and re-association , but differ in the mechanism controlling the timing of this dissociation . The mass action model is the simplest isolated model and is based on the commonly accepted per/tim interactions shown in Figure 2A . In this model , PER–TIM dimers simply dissociate prior to independent nuclear transport . The dynamics of this model , shown as dotted lines in Figure 3A , were able to produce nuclear localization of PER six hours after inducing expression , but did not accurately capture the switch-like dissociation of PER–TIM observed experimentally [21] . Next , a model based on the sequential modification of PER–TIM dimers , termed the serial model , was created as shown in Figure 2B . The serial mechanism may represent the sequential phosphorylation of PER and/or TIM . To simplify the mathematics of this model ( see Materials and Methods ) , PER–TIM dimers were assumed to be initially associated before the proceeding series of modifications after which nuclear localization occurs . Interestingly , this model required hundreds of intermediates to produce a stable five-hour association followed by a precipitous dissociation , as shown in Figure 3B . Finally , a model based on positive feedback ( previously suggested to increase clock accuracy via the PDP loop of the full circadian network [27 , 28] ) was created as shown in Figure 2C . Consistent with experimental observations [21] , this model explicitly represents the cytoplasmic association of PER–TIM dimers and the subsequent localization of these dimers into discrete foci . Within the foci , a background level of activity creates a low amount of dissociation and PER nuclear localization . A nuclear-generated signaling molecule ( SM ) is then created in response to PER and is used to complete the positive feedback on the dissociation of PER–TIM in foci . This network is conceptually consistent with the observation that blocking nuclear export ( and thus preventing the SM in this model from exiting the nucleus and exerting the positive feedback ) delays nuclear localization [21] . The timing of PER nuclear localization in this model , shown as solid lines in Figure 3A , is consistent with experimental observations [21] . In addition to the feedback SM , this model incorporates another unknown component: the focus-binding mediator ( FBM ) molecule . The presence of this molecule at limiting concentrations creates a nuclear localization timer that is largely independent of the maximum PER and TIM concentration , as shown in Figure 4A . A model of the full circadian network was created based on a simplification of the positive feedback isolated per/tim loop model , the interactions of which are shown in Figures 1 and 5 . The expression of per , tim , clk , vri , and pdp mRNA and total protein are in excellent agreement with experimental observations , as shown in Figure 6 ( see references therein ) . The model results show a period of 24 . 0 hours under a light–dark cycle ( Figure 6 ) and 23 . 8 hours in constant darkness , also consistent with experimental observations . Our results show a per dosage dependence of the period length that is consistent with experimental observations [29 , 30] . A continuation analysis of the maximum transcriptional activation of per in the model demonstrates an inverse relation between per dosage and the period of circadian oscillation ( black lines and points in Figure 7 ) . In contrast , a continuation analysis of the maximum transcriptional activation of tim ( gray lines and points in Figure 7 ) revealed a profile which is similar to per dosage and thus not very consistent with experimental observations [17 , 31] . The results from the model are consistent with numerous homozygous mutant phenotypes , as shown in Table 1 ( see references therein ) . These results show that arrhythmic null mutants in the per/tim feedback loop ( i . e . , per01 and tim01 ) are unable to repress the activity of CLK–CYC resulting in constitutively high expression of unaltered per [32 , 33] , tim [24 , 26 , 33–35] , vri [36 , 37] , and pdp [36] . The decreased PER degradation in dbtP and dbtar mutants resulted in the stable repression of CLK–CYC activity and the constitutively low expression of per , tim , vri , and pdp mRNA and protein [34 , 38] . Similarly , when the level of active CLK–CYC is reduced by a knockout of CLK or CYC ( clkJrk and cyc0 ) or eliminating the activator of clk expression ( pdpP205 ) , the resulting levels of per , tim , vri , and pdp mRNA and protein are constitutively low [36 , 37 , 39–41] . Understanding the effects of these mutants provides key insights into the roles of specific genes in the network , and reproducing their behavior provides support for the model representation . The model accurately captures a majority of the published experimental observations . However , a number of mutant flies display behavior that is not completely consistent with the model results . For example , the low levels of tim mRNA in per01 , per mRNA in tim01 , and tim mRNA in tim01 from some publications [32 , 39] conflict with model results; however , experimental results from other publications on these same species do agree with our model results [32 , 33 , 37 , 39] . The low levels of per mRNA in per01 [32 , 33 , 39 , 42] , low levels of PER in tim01 [17 , 20 , 24 , 26 , 34] , and high levels of per mRNA and protein in dbtP/dbtar [34 , 38] observed experimentally conflict with the model results and experimental observations of other E-box mRNA and protein levels . The mathematical model lacks ability to produce nuclear CLK–CYC in clkJrk and cyc0 mutants , breaking the activation of E-box genes and producing no clk mRNA in contrast to experimental observations [27] . Also , the non–PER-mediated CLK phosphorylation in the model results are not able to produce low CLK levels [24 , 26] without nuclear PER in the per01 and tim01 mutants .
The isolated mass action model results ( dotted lines in Figure 3A ) are not consistent with the experimental observation of stably associated PER–TIM dimers and precipitous nuclear localization [21] . The serial model results ( Figure 3B ) show that hundreds of intermediates may be required to produce this behavior . This number of intermediates is larger than the potential phosphorylation sites on PER and TIM predicted by ScanProsite ( 22 Casein Kinase II sites on PER , 32 sites on TIM ) [43] . The progressive phosphorylation of PER and/or TIM may be observed as a change in electrophoretic mobility prior to nuclear localization in S2 cells . The positive feedback mechanism ( solid lines in Figure 3A ) is able to produce the correct delay and rapid dissociation , making it an attractive alternative to the serial model . The FBM in the positive feedback model , for which no direct experimental evidence currently exists , is responsible for controlling the onset of nuclear localization independent of PER and TIM concentrations . Without this molecule , the onset would be well correlated to experiment-to-experiment variability in the limiting concentration of PER and/or TIM ( unpublished data ) . The shaggy ( sgg ) kinase is a potential candidate because it has been previously shown to phosphorylate TIM , affecting the nuclear localization of PER [20 , 44] , and also bind to cytoskeletal elements [45] , a possible location of the cytoplasmic foci . A sgg knockout in S2 cells could be used to observe the possible disruption of PER/TIM accumulation in cytoplasmic foci , which would be consistent with this hypothesized role for sgg . No obvious candidate for the SM exists in the literature . Because small molecules have been shown to cause significant structural changes in PAS domains [46] , one possibility may be a small molecule binding to and elucidating a temporary conformational change in PER , allowing it to dissociate from TIM and localize to the nucleus . The feedback model is not consistent with all the data presented by Meyer et al . [21] . The rates of nuclear localization of PER and TIM are not completely independent ( unpublished data ) , and the conflict between rapid nuclear transport and well-controlled timing of nuclear localization results in a timing error that is double the observed seventy minutes [21] . These differences may be the result of additional regulatory structures not already identified . The full network results demonstrate that while total CLK levels do not change significantly during the course of a day , the oscillating phosphorylation of CLK can lead to significant and stable oscillations in mRNA ( see Figure 6 ) . These near-constant total CLK levels are generated by synchronized translation and degradation ( see Figure S1 ) . This result differs from previous mathematical models [11 , 12 , 14] which show a significant oscillation in CLK level ( consistent with prior experimental observations [24–26] ) , and suggest that the oscillation of CLK activity , not concentration , is necessary for circadian rhythmicity . We find that independent transfer of PER and TIM by simple mass action kinetics is inconsistent with experimental observations , but that an additional feedback loop ( or alternatively a large number of intermediate phosphorylated states ) is able to produce the switch-like dissociation of cytoplasmic PER–TIM underlying the interval timer [21] . This positive feedback was introduced into a mechanistic mathematical framework for Drosophila circadian rhythms which demonstrates excellent agreement with experimentally observed expression profiles of circadian genes and many circadian mutants . The framework is consistent with observations of the relationship between per dosage and circadian periodicity . Post-translational regulation is addressed , including the effect of phosphorylation on the transcriptional activation activity of CLK . Our results also show that the nuclear translocation of the PER and TIM can occur independently while producing stable oscillations when positive feedback is employed .
The simple mass action model of the isolated per/tim loop is represented by Equations 1–7 below . The serial model is represented by Equations 8–13 below , where N is the number of intermediates in the reaction series . To simplify the solution of the serial model , initial concentrations of monomeric PER and TIM in the cytoplasm were eliminated by assuming that their dimerization occurred quickly . This assumption allowed the analytic solution of the last PER–TIM dimer in the series of N reactions , and greatly reduced the number of equations for large N . The positive feedback model is represented by Equations 14–23 below . To represent the concentration effect of foci localization , a second-order term is used for slow dissociation of PER–TIM from the foci ( see Equations 15 , 17 , and 18 ) . Additionally , SM is assumed to catalyze the release of PER–TIM from the foci , and thus is not depleted by this reaction . The initial concentrations of cytoplasmic PER and TIM in the mass action and positive feedback models and the first PER–TIM dimer in the serial model were set to the maximum concentration of PER and TIM ( 10 , 000 molecules or approximately 104 nM ) . The initial concentration of FBM in the positive feedback model was set to 5 , 000 molecules . All other initial concentrations in the isolated models were set to zero . Additionally , the initial conditions of PER and TIM in the positive feedback model were varied in magnitude based on a log normal distribution fit to the data presented in Figure 1C of [21] . See Table S1 for a full list of reaction rate constants for the isolated models . A detailed mathematical framework of Drosophila circadian rhythms using the differential equations below was created based on the interactions shown in Figures 1 and 5 . This description does not use time delays and explicitly represents the post-translational modifications of PER and CLK . Illumination in light–dark cycles is modeled via Light , defined as a square wave in Equation 47 . Light acts upon the degradation of cytoplasmic and nuclear TIM . The transcriptional activation kinetics are borrowed from [47] , and described in Text S1 . FBM is not explicitly represented because the inclusion of this molecule at limiting concentrations did not significantly alter the presented results ( unpublished data ) . As shown in Figure 1 ( and based on the observations of [22 , 23] ) , the presence of nuclear PER and PER–TIM dimers causes the phosphorylation of CLK . Once phosphorylated , CLK cannot bind to DNA and is either degraded or exported into the cytoplasm where it can be degraded or dephosphorylated . Chemical reaction rate constants are the only adjustable parameters for which a set of biologically meaningful values was found ( see Parameter Estimation below ) . See Table S2 for a full list of reaction rate constants for the full circadian model . With the exception of the positive feedback model of the isolated per/tim loop , the mathematical models presented in this paper were solved using the LSODAR integrator as part of the SloppyCell package [48] . Periodic orbits were found through sequential integration cycles until a stable limit cycle was approached . For the continuation analysis of model parameters , AUTO 2000 was used . Since a small number of molecules may initiate positive feedback , the isolated per/tim loop positive feedback model was solved stochastically using Gillespie's algorithm . The model results are an ensemble of trajectories for a given parameter set ( a randomly selected subset which is shown in Figure 4B ) , with the trajectory closest to the experimentally observed mean nuclear onset time used in Figure 3A . The standard deviation of nuclear onset time was determined from this ensemble of trajectories . Several Drosophila mutant phenotypes were represented by the detailed mathematical model . The parameter changes used to represent the mutants described in the paper are shown in Table S3 . A typical result is shown for the arrhythmic dbtp/dbtar mutants in Figure S2 . The transient trajectory from a point on the wild-type constant darkness limit cycle illustrates the approach to a stable fixed point solution . Similarly , all arrhythmic mutants presented in Table 1 were found to approach stable fixed points ( unpublished data ) . The points and error bars presented in Figure 3 were the result of averaging the five trajectories for cytoplasmic PER–TIM dimers and nuclear PER shown in Figure 1B of Meyer et al . [21] . These trajectories were normalized to a minimum of zero and maximum of one prior to aligning the paired PER–TIM and nuclear PER trajectories by minimizing the root mean-squared distance . The mean onset time of the average of the aligned trajectories was then set to 340 min . The points and error bars in Figure 6 were adapted from the publications listed in Table S4 . With the exception of pdp mRNA and protein , multiple references were available . These datasets were interpolated and averaged to produce the means and standard deviations presented in Figure 6 . pdp mRNA and protein means and error bars were taken directly from [36] . The points and error bars in Figure 7 are the average and standard deviation of experimental observations of the period of oscillation in response to changes in per dosage [29 , 30] and tim dosage [17 , 31] . A Monte Carlo random walk , guided by importance sampling , adjusted model parameters to optimize a chi-squared value quantifying the consistency of the model results with available experimental observations ( discussed in the previous section and presented in Figures 3 , 6 , and 7 ) . Model parameters were manually adjusted to biologically meaningful values where necessary . | The ability of an organism to adapt to daily changes in the environment , via a circadian clock , is an inherently interesting phenomenon recently connected to several human health issues . Decades of experiments on one of the smallest model animals , the fruit fly Drosophila , has illustrated significant similarities with the mammal circadian system . Within Drosophila , the PERIOD and TIMELESS proteins are central to controlling this rhythmicity and were recently shown to have a rapid and stable association creating an “interval” timer in the cell's cytoplasm . This interval timer creates the necessary delay between the expression and activity of these genes , and is directly opposed to the previous hypothesis of a delay created by slow association . We use several mathematical models to investigate the unknown factors controlling this timer . Using a novel positive feedback loop , we construct a circadian model consistent with the interval timer and many wild-type and mutant experimental observations . Our results suggest several novel genes and interactions to be tested experimentally . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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"Methods"
] | [
"drosophila",
"biochemistry",
"computational",
"biology",
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] | 2007 | PERIOD–TIMELESS Interval Timer May Require an Additional Feedback Loop |
Sodium-Galactose Transporter ( SGLT ) is a secondary active symporter which accumulates sugars into cells by using the electrochemical gradient of Na+ across the membrane . Previous computational studies provided insights into the release process of the two ligands ( galactose and sodium ion ) into the cytoplasm from the inward-facing conformation of Vibrio parahaemolyticus sodium/galactose transporter ( vSGLT ) . Several aspects of the transport mechanism of this symporter remain to be clarified: ( i ) a detailed kinetic and thermodynamic characterization of the exit path of the two ligands is still lacking; ( ii ) contradictory conclusions have been drawn concerning the gating role of Y263; ( iii ) the role of Na+ in modulating the release path of galactose is not clear . In this work , we use bias-exchange metadynamics simulations to characterize the free energy profile of the galactose and Na+ release processes toward the intracellular side . Surprisingly , we find that the exit of Na+ and galactose is non-concerted as the cooperativity between the two ligands is associated to a transition that is not rate limiting . The dissociation barriers are of the order of 11–12 kcal/mol for both the ion and the substrate , in line with kinetic information concerning this type of transporters . On the basis of these results we propose a branched six-state alternating access mechanism , which may be shared also by other members of the LeuT-fold transporters .
Secondary active transporters are membrane proteins involved in the translocation of small organic molecules across the cellular membrane using the energy stored as transmembrane electrochemical gradient of ions ( mostly Na+ or H+ ) . Sodium symporters in particular use this alkali metal ion to cotransport a variety of substrates ( sugars , amino acids , neurotransmitters , nucleobases ) against their chemical concentration [1] , [2] . These symporters share a common structural core domain called ‘LeuT-fold’ [3]–[9] and they play a crucial role in the physiology of the brain , intestine , kidney , thyroid and skin , representing thus the target for therapeutic intervention in the treatment of depression , diabetes , obesity , etc [1] , [2] . The proposed transport mechanism of this kind of secondary active transporters is called ‘alternating access mechanism’ [10] . According to this mechanism , the symporters undergo a large conformational change switching from an outward-facing conformation , where the ligands bind from the extracellular medium , to an inward-facing conformation , where the ligands are released into the cytosol . In this work we focused on the Sodium-Galactose Transporter ( SGLT ) , a sodium symporter that accumulates sugars , like glucose or galactose , into cells . In humans this process is very important for a correct intestinal absorption and renal reabsorption , and it is nowadays a promising field for the development of a new class of drugs for the treatment of type 2 diabetes [11] . For Vibrio parahaemolyticus the crystal structure of the inward-facing conformation of the bacterial homologue of SGLT ( vSGLT ) was solved by Faham et al . [6] with galactose ( Gal ) bound inside the protein . While in the human transporter the substrate transport is driven by two Na+ ions , only one ion is required in the bacterial homologue . Yet , the Na+ was not solved in the X-ray structure ( PDB 3DH4 ) and a plausible ion-binding site , corresponding to Na2 site , was proposed , on the basis of a structural comparison with the LeuT structure [4] and by mutational analysis . Subsequent , molecular dynamics ( MD ) simulations studies suggested that this crystal structure represents an ion-releasing state of the transporter [12]–[14] . Thus , in a previous work , using MD and metadynamics ( MTD ) simulations , we identified a possible ion-retaining state of the vSGLT [15] . The dissociation mechanism of galactose has also been investigated by molecular simulations studies which showed that Gal release occurs either spontaneously or by applying an external force [13] , [14] , [16] . These studies lead to contradictory conclusions on a possible gating role of Y263 , on the exact conformational state of the transporter ( open or occluded ) captured crystallographically and on the free energy profile of Gal release [13] , [14] , [16] . Namely , Zomot et al . showed that Gal exited the protein only by using steered molecular dynamics ( SMD ) , after the rotameric transition of the side-chain of Y263 , which , according to this study , acts as a gate . A second gate represented by Y269 was also encountered later on along the path [13] . Consistently with these findings , Watanabe et al . showed that the sodium exit triggers the substrate release after the new rotameric conformation acquired by Y263 and that Gal has to overcome very small barriers ( kcal/mol ) along its exit pathway [14] . A different scenario was instead provided by Li and Tajkhorshid in 2011 . By combining MD and SMD simulations , they identified a curved translocation pathway for Gal release . In this path Gal moves around Y263 , requiring no gating event . This study pointed out that the crystal structure represents an open state of the transporter [16] . Unfortunately , experiments do not help solving the puzzle , as data on these controversial points as well as on order of dissociation of the two ligands are incomplete [17] . We here perform extended bias exchange metadynamics simulations ( BE-MTD ) [18]–[20] aimed at establishing the reciprocal influence of the Na+ and Gal in their dissociation mechanism and at characterizing the kinetics and thermodynamics of the process . Our study shows that ( i ) the Na+/Gal interplay along the dissociation path is minimal and it is limited only to the initial displacement of both Na+ and Gal from their binding sites; ( ii ) the dissociation of both Na+ and Gal occurs with free energy barriers of about 11–12 kcal/mol , and the rate limiting step is associated to conformations in which Na+ and Gal are more than 10 Å far apart from their binding sites; ( iii ) no gating role can be assigned to Y263 . Simulation of the Y263F mutant reveals a rather significant change in the binding site of Gal , confirming that this residue has an important functional role , even if it does not act as a gate .
The setup is the same we used in our previous work [15] . The model of vSGLT was built using the chain A of the 3 Å resolution crystal structure ( PDB accession code 3DH4 [6] ) . The first helix , partially solved , was reconstructed from a more recent crystal structure ( PDB code 2XQ2 [14] , 2 . 7 Å resolution ) . The missing atoms of side chains of residues K124 , V185 , R273 , K454 , K547 were built using SwissPDBViewer [21] application . Residues S31 to L46 , located between transmembrane helix ( TM ) 1 and TM2 , and residues Y179 to A184 , between TM5 and TM6 , were built using Loopy [22] program . We here number the helices like in Ref [6] . The final monomeric structure contained 539 residues ( S9 to K547 ) . The protein was embedded in a pure , pre-equilibrated 1-palmitoyl-2-oleilphosphatidylcholine ( POPC ) lipid model ( kindly supplied by T . A . Martinek ) [23] , [24] using the gmembed [25] tool of GROMACS4 [26] and then it was oriented following OPM [27] database model . Afterward the system was neutralized and solvated with TIP3P [28] water molecules ( 80969 total atoms in a box size of 97 . 6×96 . 7×85 . 1 Å3 ) . Simulations were carried out with GROMACS4 [26] package using Amber03 [29] force field for protein , GAFF [30] for galactose and for membrane the parameters supplied by T . A . Martinek [23] . For more details , see Supplementary Information ( S1 Text ) . The starting point of this study was the structure of vSGLT in an ion-retaining state obtained from our previous MTD simulations [15] . In order to study the binding/dissociation of galactose and its coupling with the binding/dissociation of sodium ion by using BE-MTD , we exploited nine different collective variables using the Plumed plugin [31] . The novelty of this technique with respect to standard MTD is that a large number of collective variables can be employed at the same time , allowing thus the study of complex ( bio ) chemical processes , as in this case the simultaneous dissociation of both Na+ and Gal . Seven of the CVs were reserved to the Gal . In particular , to assess the controversial role of Y263 we used 1 ) a combination of two dihedral angles of Y263 ( C-Cα-Cβ-C and Cα-Cβ-C -Cδ ) using the alpha-beta similarity keyword of Plumed [31] and 2 ) the hydrogen bonds ( H-bonds ) between Y263 and N64 . To focus on the dissociation of Gal from its binding site we used: 3 ) the distance of galactose from its binding site ( represented by the center of mass ( COM ) of selected residues , see S1 Table ) ; 4 ) the H-bonds between galactose and its binding site; 5 ) the H-bonds between galactose and the likely exiting pathway [13] , [14] , [16]; 6 ) the radius of gyration of a group of atoms belonging to the galactose binding site; 7 ) a path collective variable , which describes the progression of the galactose along its exit channel [32] . This variable , introduced in the simulation after a spontaneous exit of the galactose was observed biasing the Gal dissociation process with the other variables , is defined by a set of 7 reference conformations . Two additional variables were used to characterize the release mechanism of Na+: 8 ) the distance between the sodium ion and its binding site ( defined by the COM of selected residues , see S1 Table ) and 9 ) the coordination number between sodium and four residues of the binding site ( carbonyl oxygens of I65 and A361 and hydroxyl oxygens of S364 and S365 ) . Initially , each walker was biased by only one CV . After 740 ns , in order to improve the statistics and allowing a faster convergence of the free energy profile ( see S2 Text ) more walkers were added biasing the same CVs . In this way , we simulated a total of 1400 ns using at most 16 walkers . Parameters like gaussians width ( ds ) , intervals , walls were added/changed and adapted for a better and faster convergence of the simulation ( see S2 Text , S1 Table ) . Simulations of ligands dissociations were also performed in the absence of Na+ in order to assess the role of the ion . Moreover , we have performed an additional simulation starting from the Y263F mutant to clarify the role of this residue in shaping the free energy landscape . At this scope we elongated the BE-MTD simulation mutating Y263F ( for a total of 15 ns*16 CVs = 240 ns ) , maintaining all the CVs and their parameters used in the wild type ( WT ) simulation ( S3 Text ) . All structural and free energy analyses were performed using METAGUI [33] , a VMD [34] interface for analyzing metadynamics and molecular dynamics simulations . The error of the free energy profiles was calculated as the standard deviation of two different time averages of the biased potential in the first and the second part of the converged interval of the simulation . In order to assess the role of selected residues along the ligands dissociation paths we calculated the average interaction energies at the relevant minima and transition states . We considered the van der Waals and coulombic interactions . We remark that this analysis is qualitative and is meant only to provide a picture of the role of selected residues in the relative stabilization/destabilization of transition states and minima , as shown in other studies [35] , [36] . H-bond analysis was performed using Plumed [31] .
We first investigated the dissociation path of Gal . The projection of the free energy along the path collective variable , representing the progression of Gal along its exit channel , is reported in Fig . 1 . For sake of clarity , we add a subscript G for the states relative to the Gal exit path , and a subscript Na for those relative to the ion path . Initially , Gal is in the deepest minimum ( Min 1G ) , which is the same binding site identified in the crystal structure ( the RMSD of the heavy atoms of residues within 4 Å of Gal with respect to the conformation in the crystal structure is 1 . 0 Å ( ±0 . 1 ) ) . The substrate is stabilized by an extended H-bond network with E88 , Q428 , Q69 , E68 , N64 ( S2 Table ) . Residue Y263 ( OH ) interacts with N64 ( HN ) . There are 2-3 water molecules in the binding site , interacting with the substrate . While Gal is in its binding site , Na+ is coordinated by three water molecules and three residues ( A62 , I65 and S365 ) . This corresponds to the ion-retaining binding site of the inward-facing conformation of vSGLT identified in our previous work [15] . In analogy with our previous study we name this site LC1 . The next free energy minimum along the exit path of the substrate is Min 2G , where , consistently with the suggestion of Li et al . [16] , Gal undergoes a lateral displacement toward a position in which it is only partially shielded by the ring of the Y263 . From this point , the substrate will find its way out by rotating about 90 degrees ( assuming a conformation in which its ring is roughly parallel to the protein axis ) and continues his progression along a curved path beyond Y263 . This residue is at the edge of the hydrophilic cavity and the presence of water molecules confers flexibility to it , which hence is able to accommodate to the passage of Gal ( S1 Fig . ) . In this new position , several water molecules enter the binding site , while the substrate ( C6-O ) interacts with T431 ( HO ) and , through a water bridge , with N142 . In this minimum residue E68 assumes a new rotameric conformation . Indeed , its side chain , initially heading toward the Gal binding site , rotates toward the Na+ binding site , making one or two H-bonds with S66 , a conserved residue across the SSS family ( E68 ( O 1 ) with S66 ( HN ) and E68 ( O 2 ) with S66 ( HO ) ) . The interactions of N64 ( O ) with galactose ( HO-C2 ) and N64 ( HN ) with Y263 ( OH ) are still present , even if characterized by large fluctuations . A qualitative analysis of the interaction energies between the substrate and selected residues shows that in the first two minima the van der Waals interactions regard the substrate and Y263 , while the electrostatic interactions involve Gal-N64 ( S3 Table ) . Afterwards , the substrate , hydrated by 4–5 water molecules , enters into a narrow cavity created by the residues N267 , Q268 , W134 , T431 , V434 , transiently interacting with N142 and Y262 ( Min ) . The H-bonds of E68 ( O 1 ) with S66 ( HN ) and E68 ( O 2 ) with S66 ( HO ) contribute to stabilize Gal in this minimum ( S2 and S3 Tables ) . Y263 and N64 become very flexible as they can not form reciprocal H-bonds . After overcoming a transition state ( ) , where the substrate is partially hydrated , Gal finds another minimum ( Min ) . Here , it is almost fully hydrated , and surrounded by S368 , V185 , and the TM2-I , TM9 , TM6 , above loop TM5-6 and it is right below the sodium binding site , inside the hydrophilic cavity of the transporter . Gal ( C1-OH and C2-OH ) makes H-bonds with D189 ( O ) and Gal ( C6-OH ) with A184 ( carbonyl oxygen ) . Residue D189 is highly conserved throughout the SSS family and it has been experimentally seen to play an important role for a correct Na+-Gal cotransport and cation selectivity [37] , [38] . We see here that it is also involved in the exit path of galactose , contributing to the stabilization of this minimum . Here , the aromatic ring of Y263 maintains an orientation similar to the crystal structure , while the sidechain of N64 assumes a new conformation , pointing toward the carboxylic group of E68 . The H-bond between E68-S66 is present also when Gal is in this minimum ( S2 Table ) . In order to leave this site , moving deeper in the hydrophilic cavity , Gal has to overcome a transition state ( TS2G ) with ΔF# of 11 . 9±0 . 4 kcal/mol with respect to the minimum , which corresponds to the largest free energy barrier of the exit pathway . The breaking of the H-bonds between Gal and D189 contributes to the barrier , as suggested by the interaction energies among Gal and D189 along the path ( S2 and S3 Tables ) . At , the substrate is at the protein surface and , although being hydrated , it is still interacting with a few surface residues forming a H-bond ( Gal ( C3-OH ) with N371 ( O ) and hydrophobic interactions with other residues ( G181 , L182 ( on loop TM5-6 ) , V396 ( TM10 ) , N371 and T375 ( TM9 ) ( S2 and S3 Tables ) . Remarkably , the free energy barriers associated to the exit path of Gal are significantly higher than those calculated by Watanabe et al . [14] . This is most probably due to the fact that our simulations start from a stable ion-retaining state of the transporter , and since a subtle cooperativity between Na+ and Gal is observed at the very beginning of the path , MD runs starting from the crystal structure , as those of Watanabe et al . [14] , which corresponds to an ion-releasing state , may lead to simulate a different dissociation process . Since Y263F mutation has been observed to impair the transport mechanism , to further check the controversial role of Y263 in the dissociation of the substrate , we performed a BE-MTD simulation of the mutant using the same setup of the WT simulation . Looking at S2 Fig . , we can clearly see a different profile , where the second minimum becomes the global one , more stable and broader than the first minimum . In short , Min1-Y263F corresponds to Min1-WT , while Min2-Y263F is broad and thus characterized by different configurations of Gal ( containing among them Min2-WT minimum ) . Their relative free energy has changed , meaning that Y263 decides in this transporter the relative stability of the minima characterizing the releasing path of the substrate . Thus , this mutation has a role in reshaping the free energy surface of Gal exit path . The stabilization of the other minimum does not seem to influence the barrier height significantly , but it may hamper Gal from assuming a position necessary to undergo the inward-outward conformational change , affecting in turn the overall transport cycle , in line with experimental findings [14] . We remark that the change in the free energy profile of the releasing of Gal caused by this mutation does not imply a gating role for this residue . The dissociation path of Na+ is characterized by an overall free energy barrier of 11 . 1 ( ±0 . 7 ) kcal/mol and by the presence of a few faint metastable states ( see Fig . 2 ) . The most stable ion binding site is LC1 in which the ion is coordinated by three water molecules and three residues ( A62 , I65 and S365 ) . As soon as the ion starts moving toward the cytoplasm , it loses its coordination with I65; then , it approaches the mouth of the binding site keeping the interaction with A62 and coordinating S364 and D189 . This latter is often found to bind Na+ along the exit . This state was called PC in our previous work [15] . In this configuration the side chain of E68 rotates from a configuration in which it heads toward the Gal binding site to a new conformation in which it forms hydrogen bonds with S66 . In State the ion , at almost 5 Å from its binding site , has overcome D189 , moving deeper into the hydrophilic cavity , and it is fully hydrated . Then , it continues descending into the cavity coordinating G181 ( loop 5–6 ) and S368 ( on TM9 ) and four water molecules ( State ) . Remarkably , D189 interacts with the ion through water bridges , accompanying it from LC1 to State , confirming its important role in the exit pathway of [15] , [37] , [38] . After interacting with L182 ( loop 5-6 ) ( State ) , it reaches State , which is at approximately 1 . 8 Å from the binding site . Here , Na+ is coordinated by a few residues of loop 1–2 and by 3–4 water molecules . It finally overcomes , where it is still transiently coordinated by D43 , R400 , and even by a POPC molecule . Thus , the total free energy barrier is due to cumulative energy cost of small structural changes accompanying the Na+ release without the formation of any stable intermediate . After the Na+ is quite delocalized , in a vestibule mainly formed by loops TM9–10 , TM1–2 and loop TM5–6 . It is important to note that the two ligands , starting from different binding sites , exit the protein through the same hydrophilic cavity ( characterizing the inward-facing conformation ) communicating to the cytoplasm . In order to investigate possible cooperative effects in the release mechanism of Na+ and Gal , in Fig . 3 we report a projection of the free energy surface ( FES ) as a function of two CVs , the distance between the ion and its site and the path variable of Gal . It is possible to note that the deepest minimum for both exit pathways is the same , i . e . . Thus , it is labeled as Min 1 . A zoom of the free energy landscape in the region close to the binding sites is also reported . The shape of the free energy landscape clearly suggests an interplay between the two ligands . Indeed , upon displacement of Gal from its binding site to move toward Min , the Na+ loses its coordination with I65 and moves toward a site with a reduced number of coordinating residues , the PC site ( state ) . The residue linking the two binding sites is the E68 . Indeed , upon Gal displacement from Min 1 , E68 , initially heading toward the Gal binding site , rotates toward the Na+ binding site , establishing one H-bond with S66 ( HN ) ( S3 Table ) , a conserved residue across the SSS family . This functional rotation of E68 is also observed between the holo ( PDB 3DH4 ) and the apo ( PDB 2XQ2 ) forms of the vSGLT crystals . These results are in line with the previous hypothesis suggesting that the departure of Na+ from its stable putative ion-retaining site , LC1 , toward the PC site triggers the conformational changes at the basis of Gal displacement from the binding site , heading to the second metastable minimum of the path ( Min ) . However , the free energy barrier associated to this initial displacement is very small and the highest barriers lay further along the dissociation path of Na+ and Gal . This fact , along with the overall L-shape of the free energy for large values of the collective variables ( see Fig . 3 ) , indicates that the rate limiting steps of the release of the ion and the substrate are independent . Indeed , the values of the two CVs ( the path collective variable of Gal and the distance Na+ - binding site ) at the highest transition state ( ) of Gal exit are 6 . 2 and 3 Å . While , those at the of Na+ exit are 2 . 2 and 21 Å . Namely , at the transition state of Gal , Na+ is close to its binding site , and viceversa . In order to quantitatively verify this point , we computed the free energy of Gal exit in absence of Na+ . The free energy profile of Gal dissociation in the absence of Na+ is practically identical to the profile in the presence of Na+ , confirming unambiguously this important result ( Fig . 3 , panel C ) .
In this work we used BE-MTD to study the binding/dissociation mechanism of the two ligands of vSGLT symporter . We observed that the minimum free energy exit pathway of the galactose does not involve any rotameric transition of the side-chain of Y263 . Indeed , as already observed [16] , Gal circumnavigates the so-called inner gate Y263 and proceeds along the hydrophilic cavity . However , our simulation of the mutant points to a possible functional role of Y263 in determining the relative stability of the minima observed along the Gal exit path . The global free energy minimum for the mutant and for the WT turns out to be different . The main barriers characterizing the releasing mechanism are of the order of 11–12 kcal/mol for both the ion and the substrate . These barriers are significantly higher than those reported by Watanabe et al . [14] . This is possibly due to the fact that the free energy space explored in our case includes an occluded state of the transporter , with both ligands stable in their binding sites , while the work of Watanabe et al . [14] , starting from a different structure , may simulate a different process , the departure of the ion from an ion-releasing state . As also mentioned in our previous work , our starting structure is only a possible candidate of an ion retaining state . However , this binding site of Na+ is the same identified in our previous work [15] , by independent metadynamic simulations , demonstrating the reliability of our results . This ion-retaining site is also consistent with the observation of Faham et al . [6] pointing to an important role of S365 for the Na+-dependent transport of Gal . Moreover , Loo et al . [39] observed that a mutation of residue S392 of Na2 site of hSGLT1 ( that works with stoichiometry 2 Na+: 1 sugar ) affects the binding of both sugar and the second ion . In this case , a straightforward comparison with the corresponding S364 of vSGLT is not possible due to the different number of ions needed to activate Gal transport ( stoichiometry 1 Na+: 1 sugar ) . Concerning the reliability of the mechanism we propose , we remark that our barriers are affected by errors of the order of 0 . 7 kcal/mol due to the convergence of our BE-MTD simulations . Moreover , like in all the simulations based on classical molecular dynamics , our results could be affected by systematic errors due to the force field . These errors can alter the value of the barriers , but are unlikely to affect our main finding . Namely the fact that Na+ and Gal release are independent . Finally , we remark that we have not attempted computing the binding free energy ΔF of the two ligands . The value of the free energy at the maximum distance from the binding site is not a quantitative estimate of the ΔF , due to residual interactions of the ligands with the surface of the protein in the final states of our free energy profiles . The aim of our work was to reveal the interplay of Na+ and Gal during their dissociation mechanism focusing on the free energy barriers rather than on the binding free energies . As we already underlined , the barriers calculated in this study are large , possibly of the same order of magnitude of those associated with the inward-outward conformational switch . Indeed , the transition rate associated with the crossing of our barriers is approximately 1 s−1 ( assuming prefactor of 10−8 s−1 ) . This value is in line with kinetic experimental data . Indeed , the galactose turnover in vSGLT was estimated to be around 0 . 4 s−1 by Turk et al . [40] , or on the order of tens of ms in other works [11] , [41]–[43] . Lapointe and coworkers measured the turnover rate of hSGLT1 , finding values of 8 s−1 or , being near Vmax conditions , 13 s−1 . Moreover , a transition rate of 50–60 s−1 was found for hSGLT1 [17] , [42] . These values correspond to a free energy barrier in the range of 11–15 kcal/mol , consistently with our results . Importantly , our simulations provide for the first time direct insights on the possible cooperativity between Na+ and Gal for their release mechanism toward the cytoplasm . A small interdependence is observed only at the very beginning of the ligands release process , with residue E68 playing a central role in the communication between the two binding sites . Remarkably , this intercommunication occurs far from the point of the free energy profile associated to the highest free energy barriers . Our simulations , carried out in the absence of Na+ , reveal that the whole free energy profile of Gal exit is essentially unaffected , Fig . 3 ( green line , panel C ) . The lack of a marked cooperativity in the release mechanism of Gal and Na+ from the binding site is at first sight surprising . However , it is likely that the cooperativity might be associated to the first steps of the transport cycle , when the symporter , in the outward-facing conformation , binds the sodium ion and then the substrate , and their binding triggers the outward-to-inward facing conformational change , as observed in the LeuT-fold superfamily [8] , [44]–[47] . Due to this non-cooperativity in the Gal and Na+ release mechanism from the inward-facing conformation of vSGLT and to the almost identical rate limiting free energy barriers , we propose to extend the six-state kinetic model introduced by Wright and coworkers [11] , [48] by adding one more state , Fig . 4 ( blue region ) . Indeed , we suggest that , from the ligand bound inward-facing conformation , the transporter can follow independently two paths for Gal and Na+ release . The very similar free energy barriers observed for the Na+ and Gal dissociation from the inward-facing conformation may be in part responsible for the difficulties encountered experimentally in providing a detailed and clear picture for this part of the transport path of hSGLT [11] , [17] , [48] . We also observe that the crystal structure of the inward-facing conformation in the apo form of ( PDB code 2XQ2 ) [14] differs in the presence of a kink of a few degrees in TM2-I ( intracellular half ) and the side chain of E68 heading toward S66 . Remarkably , these structural features are observed during the Gal release pathway , suggesting that starting from a Na+ occluded and holo state of vSGLT ( PDB code 3DH4 ) [6] , we are able to visit these structural features of the apo state captured crystallographically ( PDB code 2XQ2 ) [14] . An important process associated to this transporter is the permeation of water , whose precise mechanism is still under debate [49] , [50] . The two mechanisms considered more viable are the active cotransport [49] , [51] , where water flux is coupled to ion/solute flux , or the passive permeation [52] , where the accumulation of the solutes near the intracellular side of the membrane during solute transport induces a flux of water as a response to the local osmotic gradient . A detailed analysis of this controversial issue is beyond the scope of our study . However , in line with the passive mechanism [53] , [54] , we observe that: ( i ) the water molecules permeate easily through the whole protein during the releasing process; ( ii ) the water is free to enter from the cytoplasm into the hydrophilic cavity and then into the binding sites . Consistently with Ref . [45] , [55] , in our simulations water molecules help the breaking of the H-bonds that keep the ligands bound to the protein , playing an important role in the whole releasing pathway . Their presence at the edge of the Gal binding site confers , indeed , flexibility to Y263 , facilitating the initial displacement of the substrate ( S1 Fig . ) . | Membrane proteins are crucial for the communication of the cell with the environment . Among these , symporters are in charge of the transport of molecules ( like sugars , amino acids , osmolytes ) inside the cells , exploiting the concentration gradient of an ion to perform the task . Here we investigate by atomistic simulations the transport mechanism of the Sodium-Galactose symporter . Our results allow constructing a detailed and quantitative model of the release process of the two ligands . Surprisingly , we find that the galactose is released to the cytosol independently from the ion , unambiguously indicating that the coupling in their transport mechanism is associated to the steps preceding the release process . A large family of symporters shares the same fold and potentially the same transport mechanism . As such our results are important also because they can provide insights on common mechanistic features of these transporters . | [
"Abstract",
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"sciences"
] | 2014 | Metadynamics Simulations Reveal a Na+ Independent Exiting Path of Galactose for the Inward-Facing Conformation of vSGLT |
Negative feedback loops ( NFLs ) for circadian clocks include light-responsive reactions that allow the clocks to shift their phase depending on the timing of light signals . Phase response curves ( PRCs ) for light signals in various organisms include a time interval called a dead zone where light signals cause no phase shift during daytime . Although the importance of the dead zone for robust light entrainment is known , how the dead zone arises from the biochemical reactions in an NFL underlying circadian gene expression rhythms remains unclear . In addition , the observation that the light-responsive reactions in the NFL vary between organisms raises the question as to whether the mechanism for dead zone formation is common or distinct between different organisms . Here we reveal by mathematical modeling that the saturation of a biochemical reaction in repressor synthesis in an NFL is a common mechanism of daytime dead zone generation . If light signals increase the degradation of a repressor protein , as in Drosophila , the saturation of repressor mRNA transcription nullifies the effect of light signals , generating a dead zone . In contrast , if light signals induce the transcription of repressor mRNA , as in mammals , the saturation of repressor translation can generate a dead zone by cancelling the influence of excess amount of mRNA induced by light signals . Each of these saturated reactions is located next to the light-responsive reaction in the NFL , suggesting a design principle for daytime dead zone generation .
Circadian clocks in various organisms are composed of cell autonomous gene expression rhythms with a nearly 24-hour period . Transcriptional-translational feedback loops ( TTFL ) in single cells drive such rhythmic gene expression [1–3] . One of the most important roles of circadian clocks is to entrain behavioral and physiological rhythms in organisms to the light-dark ( LD ) cycle on earth . A light signal shifts the phase of the clocks by affecting the biochemical reactions in the TTFLs that regulate circadian gene expression . Such phase responses of circadian clocks to light signals allow their entrainment to the LD cycle . Most organisms maintain their behavioral rhythms under constant dark ( DD ) conditions , indicating that their circadian clocks set subjective day and night . Subjective day and night under DD conditions can be defined by referring to the rhythms in an LD cycle . The phase responses of the circadian clocks to light signals have been examined by exposing organisms to short light pulses under DD conditions . Phase shift as a function of the timing of light exposure characterizes the entrainment property of the circadian clocks and is referred to as the phase response curve ( PRC ) [4 , 5] . Intriguingly , the PRCs of different organisms have several common features [4 , 6] . First , a light pulse at subjective morning advances the phase of the clock . Second , a light pulse at subjective evening and night delays the phase . Third , a light pulse at subjective daytime hardly changes the phase . This time window during daytime , when the phase of the clock is insensitive to light pulses , is referred to as the dead zone . Previous theoretical studies revealed the PRC shape that is optimal for robust light entrainment . This optimal PRC is similar to those observed in several organisms and , remarkably , includes a dead zone during daytime [6–9] . This is because the presence of a dead zone increases the robustness of the clock against external fluctuations , such as fluctuation of light intensity [6] and daylight length [9] , by reducing the responsiveness of circadian clock systems to external signals . However , the mechanism whereby a dead zone is created during daytime while preserving phase responses during the night remains unclear . Two possible mechanisms may underlie the creation of a dead zone during daytime . One possibility is the gating of light input to reduce its influence on circadian clock genes [10 , 11] . Gating is an elaborated mechanism , as it must be clock-dependent to distinguish day from night . Molecules that may be responsible for such gating have been identified previously [12 , 13] . Gating makes circadian clocks robust against external perturbation as described above by reducing input signals detrimental to clock gene expression [6 , 9] . However , gating is ineffective against internal perturbation that arises inside the gate , such as noise in gene expression and physiological states in cells [7 , 8] . The alternative and more beneficial mechanism is the use of biochemical reactions in the TTFL to directly decrease the responsiveness of the phase of the clocks to stimuli during daytime . A dead zone formed in this way can make the clocks resistant to both external and internal perturbation because of the unresponsiveness of the phase of the clocks [7 , 8] . Hence , some organisms should evolve to create a dead zone by biochemical reactions in TTFLs . Here we propose a design principle for creating such dead zones by analyzing the circadian clock systems of different organisms . The biochemical reactions modulated by light signals in the TTFL for the circadian oscillation differ between species . In some organisms , such as Drosophila , light signals increase the degradation of circadian clock proteins , which we refer to as the degradation response ( Fig 1A ) [14 , 15] . In Drosophila , the transcription of Period ( Per ) and Timeless ( Tim ) genes is induced by the CLOCK/CYCLE complex ( Fig 1A ) . The PER/TIM complex then represses the transcriptional activity of CLOCK/CYCLE , forming a negative feedback loop ( NFL ) [16] . By this NFL , the abundance of TIM protein oscillates under both LD and DD conditions [14 , 17] . Light signals activate Chryptochrome ( Cry ) and it degrades TIM protein [18–21] . This light-induced degradation of TIM allows the Drosophila clock to entrain to the LD cycle . As a result , the levels of TIM protein are lower during the day and higher during the night under LD conditions [14] . Differently , in other organisms such as mammals and Neurospora , light signals induce the transcription of repressor mRNA ( Fig 1B ) [10 , 11 , 22 , 23] . In mammals , the transcription of Per and Cry is induced by the CLOCK/BMAL1 complex ( Fig 1B ) . The PER/CRY complex then represses the transcriptional activity of CLOCK/BMAL1 , forming an NFL as in the case of Drosophila . This NFL generates self-sustained rhythms of Per expression under both LD and DD conditions . Light signals induce the transcription of Per genes through the activation of CREB ( Fig 1B ) [10 , 11 , 24–26] , allowing the mammalian clock to entrain to the LD cycle . In an LD cycle , Per expression levels are higher during the day and lower during the night [27] . We referred to this type of light response as an induction response . Although light-responsive reactions differ between Drosophila and mammals , the mechanism for phase shifting at night is predicted to be the same: light signals induce repressor mRNAs when their concentrations are decreasing due to the strengthened feedback repression by the abundant repressor proteins . In Drosophila , degradation of TIM protein by light signals relieves transcriptional repression , leading to the induction of Tim mRNA . In mammals , light signals induce the transcription of Per via CREB . Thus , the elevation in repressor mRNA levels by light signals leads to phase shifts during the night in both systems . On the other hand , there seems to be apparent differences in the effects of light signals on the dynamics of repressor mRNA and protein during daytime . In Drosophila , light signals increase the degradation of the repressor protein when its concentration is lower . In contrast , light signals in mammals increase repressor protein synthesis by inducing repressor mRNA when its concentration is already higher . These differences raise the question of whether the mechanisms for dead zone generation with different light responses are common or distinct . For the degradation response in Drosophila , several previous theoretical studies reproduced a dead zone without a clear explanation of its mechanism [28–30] . A reason for the elusiveness of this mechanism may be because an NFL with the degradation response can naturally create a dead zone without the inclusion of any additional reactions , as we discuss in this study . For the induction response in mammals , although various theoretical models have been proposed [29 , 31–35] , dead zone originated from unresponsiveness of phase of a clock has not been paid attention . Therefore , the mechanism underlying dead zone generation for the induction response observed in mammals should be clarified and compared with that for the degradation response observed in Drosophila to address the above questions . Here , we reveal that the saturation of a biochemical reaction in the repressor synthesis in an NFL is a common mechanism to cancel the effect of light signals and create a dead zone in different organisms with the distinct light responses . The location of the saturated reaction in the NFL depends on the types of light responses . It is the saturation of repressor transcription that generates a dead zone with the degradation response , whereas it is the saturation of its translation with the induction response . In short , these saturated reactions in the repressor synthesis are located next to the light-responsive reactions in the NFL , suggesting a design principle for the dead zone generation during daytime .
We start with dead zone generation with the degradation response as observed in the Drosophila circadian clock ( Fig 1A ) . As the neurons in the central pacemaker tissue are considered to determine the phase responses of individuals by entraining cells in peripheral tissues in general [36–39] , we model a negative feedback loop in these pacemaker neurons . Because rhythms of these neurons are synchronized with each other by intercellular interactions [36 , 40] , they can be approximated as a single oscillator for simplicity . Previous theoretical studies reported a dead zone in PRCs with the degradation response [28–30] . However , which reaction processes are critical for the dead zone generation has not been clarified yet . To reveal the key determinants of the dead zone , we first consider the following dimensionless three-variable Goodwin model ( Fig 1C ) : 1τdx ( t ) dt=11+ ( z ( t ) /K1 ) n−x ( t ) , ( 1 ) 1τdy ( t ) dt=γ1x ( t ) −γ2y ( t ) , ( 2 ) 1τdz ( t ) dt=γ2y ( t ) − ( γ3+γl ( t ) ) z ( t ) Km+z ( t ) , ( 3 ) where x , y and z are the concentrations of the repressor mRNA , repressor protein in cytoplasm and repressor protein in nucleus , respectively . In this model , the repressor protein is translated in the cytoplasm and transported into the nucleus . In Drosophila , these variables correspond to the levels of Tim mRNA and proteins in each cell compartment . K1 and n in Eq ( 1 ) are the threshold and Hill coefficient for transcriptional repression , respectively . γ1 is the translation rate and γ2 is the transport rate of repressor protein from the cytoplasm to the nucleus . We assume the saturation of nuclear protein degradation in Eq ( 3 ) . γ3 and Km in Eq ( 3 ) are the maximum degradation rate of nuclear repressor protein and Michaelis constant , respectively . γl is the rate of degradation induced by transient light pulses and is specified below . The time constant τ can tune the period of oscillation without affecting other properties of a limit cycle . The three-variable model in the absence of light signals can generate stable limit cycles ( Fig 2A ) . We set the origin of the horizontal axis in Fig 2 such that the levels of mRNA x take a minimum value at t = 0 . The levels of Tim mRNA in Drosophila become lowest around dawn ( ~ CT 0 ) [17] . Hence , t = 0 in Fig 2 corresponds to the subjective dawn . To examine the PRC , we consider the following form of light-induced perturbation in a reaction parameter ( Fig 1D ) : γl ( t ) ={εltl≤t≤tl+Td0elsewhere ( 4 ) where tl is the onset of a light pulse and Td is the pulse duration . The parameter εl represents the rate of a light-induced biochemical reaction . For Eq ( 3 ) , it is the rate of light-induced degradation of nuclear protein . We consider that εl reflects the strength of light . The value of εl becomes larger for a stronger light signal . To obtain the light-induced phase shift Δϕ , we compute the difference in peak timing between perturbed and unperturbed systems ( Fig 1E ) . A positive value of Δϕ indicates phase advance , whereas a negative value indicates phase delay . Typically , we run simulations for about 50 cycles after perturbation and measure the phase shift . Note that Δϕ quantifies the phase difference in terms of time . In this study , we examine the influence of each reaction parameter on the PRC . A change in the value of a reaction parameter may change the period of oscillation Tp . For clearer comparison of PRCs between different parameter values , we compute the phase shift normalized by the period of oscillation , Δϕ/Tp . In addition , the duration of the light pulse Td in Eq ( 4 ) is also scaled with the period Tp . PRCs obtained by the above procedure may depend on the functional form of light-induced perturbation in biochemical reactions γl ( t ) . Therefore , it is desirable to characterize phase responses to perturbation based solely on a limit cycle of the unperturbed system as a complement . If a perturbation by a light signal is sufficiently small , the properties of a PRC can be well characterized by the phase sensitivity of a limit cycle [6 , 41] . Phase sensitivity describes how a small increase in state variables at given time t shifts the phase of oscillation . Namely , the modulus of phase sensitivity represents the responsiveness of a clock to perturbation . Suppose φ is the phase of oscillation defined in radians ( 0 ≤ φ < 2 π ) and χ ( φ ) = ( xχ ( φ ) , yχ ( φ ) , zχ ( φ ) ) is a limit cycle solution of Eqs ( 1 ) – ( 3 ) in the absence of perturbation . See S1 Text for the details of the definition of phase in the entire state space . A small perturbation to the state variables at time t can be described as x ( t ) = χ ( φ ( t ) ) + μ η where η is a unit vector that specifies the direction of perturbation in the state space and μ is the modulus of the perturbation ( μ ≪ 1 ) . Then , the phase shift δφ caused by this perturbation reads: δφ=φ ( χ ( φ ) +μη ) −φ ( χ ( φ ) ) ≈μ∂φ∂x|x=χ ( φ ) ⋅η=μZ˜ ( φ ) ⋅η , ( 5 ) where Z˜ ( φ ) = ( Z˜x ( φ ) , Z˜y ( φ ) , Z˜z ( φ ) ) ≡∂φ ( χ ( φ ) ) /∂x . Thus , the 2π periodic function Z˜ ( φ ) specifies the magnitude and direction of phase shift and is referred to as phase sensitivity [41] . A positive ( negative ) value of Z˜i ( i ∈{x , y , z} ) indicates that an infinitesimal increase of the variable i advances ( delays ) the phase of oscillation . Note that Z˜ ( φ ) can be determined for a limit cycle in the unperturbed system . With this phase sensitivity , the phase shift Δϕ quantified by a peak phase difference between perturbed and unperturbed systems can be approximated as ( see S1 Text for details ) : Δϕ≈Tp2π∫tltl+TdZ˜ ( φ ( t ) ) ⋅G ( t , φ ( t ) ) dt , ( 6 ) where G ( t , φ ) is the perturbation in biochemical reactions by the light signal evaluated on the limit cycle χ . For example , G ( t , φ ) = ( 0 , 0 , –γl ( t ) zχ ( φ ) / ( Km+zχ ( φ ) ) ) for Eqs ( 1 ) – ( 3 ) . Hence , if Z˜z ( φ ) for Eqs ( 1 ) – ( 3 ) involves an interval where Z˜z ( φ ) ≈0 , the interval will form a dead zone in the PRC . Although a dead zone can be formed in an interval where Gz ( t , φ ) ≈ 0 as well , such dead zone formation was examined previously [9 , 42] and is out of the scope of the present study . Because the phase shift Δϕ is measured as the peak time difference , we introduce Z ( t ) ≡ ( Tp/2π ) Z~ ( φ ( t ) ) to quantify the phase sensitivity in a unit of time . We compute the phase sensitivity for a limit cycle with the adjoint method as described in S1 Text .
Fig 2B shows the PRC of the model Eqs ( 1 ) – ( 3 ) when a short light pulse is administered at each time point tl . We use pulse duration Td = 0 . 5 Tp/24 in Fig 2 . For example , if Tp = 24h , Td = 0 . 5h with this parametrization . During the night when the abundance of nuclear repressor protein z is higher , light signals shift the phase of oscillation ( Fig 2B ) . A light pulse delays the phase of oscillation at which the levels of mRNA x are near their peak and those of nuclear repressor protein z are increasing ( Fig 2B and S1A Fig ) . The reduction of repressor protein by a light signal during this time causes an increase in the transcription rate 1/ ( 1+ ( z/K1 ) n ) , resulting in excess accumulation of mRNA ( S1A Fig ) . Consequently , the nuclear repressor protein peaks later , delaying the initiation of the next cycle ( S1A Fig ) . In contrast , a light pulse near the peak of z advances the phase of oscillation ( Fig 2B and S1B Fig ) . The decrease in repressor protein during this time allows transcription to start earlier ( S1B Fig ) . Thus , light signals induce the transcription of repressor mRNA by relieving transcriptional repression . The magnitude of phase shifts within these time windows becomes larger as the rate of light-induced degradation εl increases ( Fig 2B ) . These results for the phase shifts are qualitatively consistent with previous experimental observations for the Drosophila circadian clock [14 , 43] . In contrast , during daytime when the abundance of z is lower , the phase of oscillation does not change with light-induced degradation , indicating the presence of a dead zone ( Fig 2B and S1C Fig ) . In this time window , the transcription rate of repressor is saturated at its maximum value due to the lower concentration of z ( Fig 1C and S1C Fig ) . This saturation of transcription cancels the effect of light signals ( S1C Fig ) , creating a dead zone . We also compute the phase sensitivity Z = ( Zx , Zy , Zz ) . Because the nuclear protein z is decreased by a light signal through enhanced degradation , |Zz| is relevant to the magnitude of a phase shift . We consider –Zz to match the phase advance and delay zones between phase sensitivity and the PRC ( Fig 2D ) . –Zz > 0 indicates phase advance by the decrease of z through light-induced degradation , while –Zz < 0 indicates phase delay . The magnitude of Zz is almost zero at the trough of nuclear protein concentration , confirming the existence of a dead zone ( Fig 2D ) . The presence of a dead zone in phase sensitivity Zz indicates that the dead zone in the PRC shown in Fig 2B is not created merely by the lower rate of light induced degradation εl z/ ( Km + z ) for z ≈ 0 , but , indeed , lower phase sensitivity of the limit cycle to perturbation . The continuous PRCs in Fig 2B are referred to as type 1 . As the rate of light-induced degradation εl further increases , the PRC becomes discontinuous ( S2A Fig ) , which is referred to as type 0 . In S2A Fig , the breaking point ( i . e . , transition point of Δϕ/Tp from –0 . 5 to +0 . 5 ) is at around tl / Tp = 0 . 85 , where the levels of nuclear protein are near their peak . Even in this stronger light-induced degradation , a dead zone is maintained . The breaking point and the dead zone length are rather insensitive to the change in εl once the type 0 PRC is created ( S2A Fig ) . With these larger values of εl , the levels of repressor protein z become almost zero immediately after receiving a light pulse , meaning that the effect of light signals is saturated . In addition , the model can be entrained to a LD cycle ( S2B Fig ) . The wave form of nuclear protein z peaks at night while reaching troughs in the daytime , which is consistent with experimental observations for TIM proteins under LD conditions [14 , 15] . We then study how the dead zone length depends on the parameters in Eqs ( 1 ) – ( 3 ) . We define the dead zone based on the magnitude of phase sensitivity Z . We detect a spanned time window where the phase of oscillation is insensitive to change in biochemical reactions induced by light signals: {t1≤t≤t2||Zi ( t ) |<θ , |Zi ( t1 ) |=|Zi ( t2 ) |=θ} , ( 7 ) where i ∈ {x , y , z} and θ is the threshold for phase irresponsiveness to perturbation . For the degradation response , phase sensitivity for nuclear protein Zz is relevant to phase shifts by light signals , i = z in Eq ( 7 ) . We set θ = 10−1 throughout the study . Any time window that satisfies Eq ( 7 ) is considered to be a dead zone and we measure its length Ld = ( t2 –t1 ) /Tp . Note that Ld is defined as the time interval normalized by the period of oscillation Tp . We first examine the dependence of the dead zone length Ld and the amplitude of phase sensitivity –Zz on the Michaelis constant for protein degradation Km ( Fig 3 ) . The amplitude of phase sensitivity decreases as the value of Km decreases ( Fig 3A and 3C ) . Instead , Ld monotonically increases as Km decreases ( Fig 3A and 3C ) . When the value of Km is smaller and degradation is strongly saturated , the minimum levels of repressor proteins at troughs zmin are close to zero , zmin/K1 ≪ 1 ( Fig 3B ) . The effect of light signals diminishes at that time because the light-induced degradation of nuclear protein does not further increase the transcription rate , 1/ ( 1+ ( z/K1 ) n ) ≈ 1 in Eq ( 1 ) ( Fig 3D , S1C and S3B Figs ) . Thus , the result confirms that the saturation of transcription is required for dead zone generation . The other requirement is quick recovery of the levels of nuclear protein after the light-induced degradation . If the recovery is slow , the duration of transcription is extended due to the lower levels of nuclear protein caused by light-induced degradation ( S3A Fig ) . This longer duration of transcription results in phase shifts . These requirements are more likely to be satisfied when zmin ≈ 0 . Thus , a dead zone tends to be long as the time interval where z ( t ) ≈ 0 becomes long . These results suggest that the strong saturation of TIM degradation lengthens the dead zone of the PRC in the Drosophila circadian clock . Next , we study the dependence of the dead zone length Ld on the other parameters in Eqs ( 1 ) – ( 3 ) ( S4 Fig; S1 Text ) . Typically , Ld depends on reaction parameters non-monotonically because the minimum value of nuclear protein zmin changes non-monotonically as the value of each parameter changes . In general , the amplitude of oscillation becomes smaller near a Hopf bifurcation point that sets the lower and upper bounds of an oscillatory parameter range . In the vicinity of Hopf bifurcation points , zmin is near the steady state and is more likely to be well above zero . Hence , Ld tends to be non-monotonic between the lower and upper Hopf bifurcation points . In addition , zmin becomes larger before the Hopf bifurcation points due to the accumulation of nuclear protein with , for example , faster nuclear protein transport ( larger value of γ2 ) and slower degradation ( smaller value of γ3 ) , further reducing the dead zone length . The details of the dependence of Ld and amplitude of phase sensitivity –Zz on each reaction parameter are described in S1 Text . For all the parameters , we find that Ld tends to be large when the values of z ( t ) at trough phase are close to zero . This observation suggests that each parameter influences the dead zone length by affecting the wave form of nuclear protein z ( t ) . In summary , the light-induced degradation of nuclear repressor protein induces transcription of the repressor mRNA . The elevation in mRNA levels result in phase shifts . A dead zone is formed if the light-induced degradation does not lead to a significant increase in x . Such time window arises when the nuclear protein concentration is significantly lower than the threshold for transcriptional repression K1 . Thus , it is the saturation of repressor transcription that cancels the effect of light signals and creates a dead zone for the degradation response . Reaction parameters in the NFL affect the dead zone length by modulating the wave form of nuclear protein z . To confirm the generality of the above results , we also analyze the dead zone in another model of Drosophila circadian clock [30] . The model includes interlocked feedback loops of PER/TIM and CLOCK/CYCLE . Qualitatively , the same results are obtained using this more complex Drosophila model ( S5A–S5D Fig; S1 Text ) . Furthermore , we also consider a biochemical oscillator other than circadian clocks . We adopt a repressilator model for the synthetic oscillator [44] . We obtain same results using the repressilator model ( S5E–S5H Fig; S1 Text ) , indicating that the proposed mechanism is robust and generic . Finally , we note that other previous models that realized dead zones also included the saturation of repressor mRNA synthesis and that of repressor degradation [28 , 32 , 45] . Thus , our current analysis highlights the relevance of saturation of repressor mRNA synthesis to dead zone formation . Next , we consider a model for the induction response ( Fig 1B ) . As in the case of the degradation response , we model a negative feedback loop in central pacemaker neurons . In mammals , neurons in the suprachiasmatic nucleus ( SCN ) in the brain receive light signals from the eyes and determine the phase responses of individuals by entraining peripheral clocks [37–40] . Light signals induce the transcription of Per genes in these neurons . We describe this light response with the following dimensionless differential equations: 1τdx ( t ) dt=1 ( 1+ ( z ( t ) /K1 ) n ) +γl ( t ) −x ( t ) ( 8 ) 1τdy ( t ) dt=γ1x ( t ) −γ2y ( t ) , ( 9 ) 1τdz ( t ) dt=γ2y ( t ) −γ3z ( t ) Km+z ( t ) , ( 10 ) where γl ( t ) in Eq ( 8 ) is the induction rate of a clock gene by a light signal . γl ( t ) is the same function as defined in Eq ( 4 ) . The light signal induces transcription of repressor mRNA at rate εl independent of the concentration of repressor protein in Eqs ( 4 ) and ( 8 ) . This may represent the induction of Per genes by CREB through CRE element in the mammalian circadian clock ( Fig 1B ) [24 , 25] . The inclusion of light-induced transcription in this form differs from previous theoretical studies [6 , 29 , 32] . These previous studies assumed that the nuclear repressor protein also repressed the light-induced transcription . Therefore , in these models , the effect of light signals was diminished when the protein levels were high . Because light signals only influence the transcription of repressor mRNA in Eqs ( 8 ) – ( 10 ) , phase sensitivity for x , Zx underlies phase shifts . The model Eqs ( 8 ) – ( 10 ) generates stable limit cycles with appropriate parameter sets ( Fig 4A ) . In Fig 4 , we set t = 0 to the time at which the levels of mRNA x are at the minimum value . In the mammalian SCN , the expression levels of Per genes are lowest at around CT20 [27] . Hence , the origin of the horizontal axis in Fig 4 may correspond to the subjective midnight . We then examine the phase shifts with the induction response ( Fig 4B and 4C ) . We do not find an extended dead zone in either the PRC ( Fig 4B ) or the phase sensitivity Zx ( Fig 4C ) of Eqs ( 8 ) – ( 10 ) . Rather , Δϕ and Zx intersect with zero steeply at a single time point . Phase delays are caused by light signals near the peak of mRNA . An increase in mRNA near its peak time results in an increase in the levels of nuclear protein and lengthens the duration of repression ( S6 Fig ) . We further examine whether a dead zone is formed in Eqs ( 8 ) – ( 10 ) with other different parameter sets . For this , we randomly generate 2000 parameter sets from uniform distributions with which Eqs ( 8 ) – ( 10 ) can generate stable limit cycle oscillations ( S1 Text ) . We compute the phase sensitivity Zx for each random parameter set and check the length Ld of the spanned time window that satisfies the condition Eq ( 7 ) . Ld of all the 2000 parameter sets examined is less than 1/24 ( e . g . , for Tp = 24h , Ld = 1/24 indicates a dead zone of 1h ) . Thus , our numerical results suggest that the NFL model Eqs ( 8 ) – ( 10 ) with the induction response does not form an extended dead zone in the PRC . The analysis of the degradation response described in previous sections implies that a dead zone can be formed when a light signal does not increase the levels of nuclear repressor protein . For this , cancellation of the influence of mRNA induction by light signals may be required . This consideration leads us to introducing a saturation of a biochemical reaction in the NFL . We first test the saturation of protein transport from the cytoplasm to the nucleus , but it does not generate a dead zone ( S7 Fig; S1 Text ) . We then test the saturation of mRNA degradation ( S8 Fig; S1 Text ) . In this case , although a dead zone is formed at night when the concentration of repressor mRNA is lower , a daytime dead zone is not generated ( S8 Fig ) . Finally , we introduce a Michaelis-Menten function in the translation process in Eq ( 9 ) : 1τdy ( t ) dt=γ1x ( t ) Kt+x ( t ) −γ2y ( t ) , ( 11 ) where Kt is the Michaelis constant for translation . Translational regulation by certain RNA binding proteins [46 , 47] may cause Michaelis-Menten type nonlinearity as assumed in Eq ( 11 ) . Although a previous theoretical study examined the effect of a saturated translation term mainly on the period of oscillation [48] , its effect on phase responses to light signals has not been studied . We simulate the model Eqs ( 8 ) , ( 10 ) and ( 11 ) , and find that it can generate sustained oscillations ( Fig 5A ) . Remarkably , the saturated translation can generate an extended dead zone in the PRC at subjective daytime when the levels of mRNA x ( t ) are near their peaks ( Fig 5B ) . The dead zone appears robustly even when we use different values of induction strength εl in Eqs ( 4 ) and ( 8 ) ( Fig 5B ) . To quantify the degree of saturation , we define a saturation index for translation sx = x ( t ) / ( Kt + x ( t ) ) [49] . The value of sx is close to 1 when the translation is saturated and close to 0 when less saturated . The time series of sx shows that the translation is indeed strongly saturated within the dead zone ( Fig 5C ) . We also find that the dead zone is present in the phase sensitivity Zx ( Fig 5D ) . We then check whether other parameter sets can create similar dead zones . Of 2000 randomly generated parameter sets , 54 ( 2 . 7% ) form similar dead zones of length Ld greater than 1/24 . The reason for this relatively small percentage of longer dead zones is that the values of Kt are large in most of those 2000 random parameter sets , meaning that translation is not saturated strong enough to generate a longer dead zone . Larger values of Kt are favored because the saturation of translation tends to suppress oscillations [49] , as we discuss in the discussion section . Changes in time series of nuclear protein z generate phase shifts . The induction by light at the early increase phase of mRNA can increase the levels of nuclear protein near its trough ( S9A Fig ) . Due to the excess amount of nuclear protein , the forthcoming peak of mRNA decreases . Accordingly , the levels of nuclear protein at the forthcoming peak are also decreased . Hence , the repression of mRNA synthesis relieves faster , advancing the phase . A light pulse at the late increase , the peak , and the early decrease phases of mRNA only weakly influences the levels of nuclear protein z due to saturation of translation ( S9B Fig ) . An unaltered time series of z does not cause a phase shift . The induction of mRNA by light at the late decrease phase of mRNA can increase the peak levels of nuclear protein ( S9C Fig ) . This lengthens the duration of transcriptional repression , delaying the phase . This model can realize type 0 PRC at stronger light intensity ( S10A Fig ) . Unlike the type 0 PRC in the degradation response ( S2A Fig ) , the breaking point and shape of the PRC change depending on the strength of light induction εl ( S10A Fig ) . Excess induction of mRNA lengthens the duration satisfying x > Kt . This allows for the production of excess protein and delays its peaks , resulting in phase delays of greater magnitude ( S10A Fig ) . We also confirm that the model can be entrained to an LD cycle ( S10B Fig ) . The levels of mRNA peak during daytime in the LD cycle , which is consistent with experimental observations of Pers in mammals . We then examine the parameter dependence of the dead zone length by computing the phase sensitivity Zx for Eqs ( 8 ) , ( 10 ) and ( 11 ) ( Fig 6 and S11 Fig ) . We start with the dependence of Zx on the Michaelis constant for translation Kt in Eq ( 11 ) ( Fig 6A ) . Smaller values of Kt lead to stronger saturation of translation when the levels of mRNA are higher ( Fig 6B ) . For each value of Kt , we measure the length of a time window Ld , within which the absolute value of phase sensitivity satisfies |Zx| < θ ( i = x and θ = 10−1 in Eq ( 7 ) ) . Ld is larger for smaller values of Kt and it decreases monotonically with an increase in Kt ( Fig 6C ) . The amplitude of Zx also monotonically decreases as Kt increases ( Fig 6D ) . Thus , the saturation of repressor translation increases both the dead zone length and phase sensitivity . We next study the dependence of the dead zone length Ld and the amplitude of phase sensitivity Zx on the other parameters in Eqs ( 8 ) , ( 10 ) and ( 11 ) ( S11 Fig ) . Overall , the dependence of Ld on each parameter is similar to that observed in the degradation response ( Fig 3 and S4 Fig ) . The reason for this observation is as follows . In the induction response , the larger amplitude and wider wave form of repressor mRNA x extend the dead zone by lengthening the time interval during which translation is saturated . To achieve this condition , the levels of nuclear protein z at its trough must be near zero . This common requirement underlies the similarity in the parameter dependence of Ld between the degradation and induction responses . Furthermore , as observed in the degradation response , the dead zone length Ld often changes non-monotonically as the value of a parameter increases ( S11 Fig ) . For example , Ld depends on the maximum translation rate γ1 non-monotonically ( S11A Fig ) . When γ1 is smaller , the amplitude of mRNA x is small , resulting in smaller Ld values . In contrast , when γ1 is larger , the amplitude of x is large whereas the width of its wave form narrows due to the higher levels of nuclear protein z . As a balance of these two contributions , Ld peaks near the lower Hopf bifurcation point ( S11A Fig ) . A similar trend can be seen in the dependence on the transport rate γ2 where Ld peaks near the lower Hopf bifurcation point ( S11B Fig ) and the maximum degradation rate of nuclear protein γ3 where Ld peaks near the upper Hopf bifurcation point ( S11C Fig ) . The dead zone length also depends non-monotonically on the threshold constant for transcriptional repression K1 ( S11D Fig ) , as the amplitude of mRNA x changes non-monotonically between the lower and upper Hopf bifurcation points . As in the degradation response , the dead zone length becomes longer monotonically for smaller values of the Michaelis constant for protein degradation Km ( S11E Fig ) . The result indicates that the stronger saturation of protein degradation is more likely to lead to the generation of a dead zone . Each reaction parameter also influences the amplitude of phase sensitivity Zx ( S11 Fig ) . Changes in the values of the maximum translation rate γ1 and protein degradation rate γ3 strongly influence the magnitude of phase delay rather than causing phase advancement ( S11A and S11C Fig ) . Changes in the values of the other parameters affect the magnitudes of both phase advance and delay ( S11B , S11D and S11E Fig ) . As observed in the degradation response , the amplitude of phase sensitivity becomes larger near the Hopf bifurcation points ( S11 Fig ) . To further study the effect of nonlinearity in translation on the dead zone generation , we extend the Michaelis-Menten function in Eq ( 11 ) into a Hill function: 1τdy ( t ) dt=γ1x ( t ) hKth+x ( t ) h−γ2y ( t ) , ( 12 ) where h is the Hill coefficient and the parameter Kt can now be interpreted as the threshold level of repressor mRNA required for translation to occur . Translation does not occur as long as x/Kt ≪ 1 for a large value of h . Time evolution of mRNA x and nuclear protein z are given by Eqs ( 8 ) and ( 10 ) . We study the dependence of the dead zone length on the Hill coefficient h with the same parameter set used in the analysis of the Michaelis-Menten translation function except for Km . For better illustration of the influence of h , we set Km = 0 . 053 in Fig 7 , which is larger than the value used in Fig 5 ( Km = 0 . 025 ) . We find that the increase in h lengthens the dead zone ( Fig 7A and 7B ) . This is because a larger h extends the interval of x where xh/ ( Kth+xh ) ≈ 1 . In addition , larger values of h increase the amplitude of x ( Fig 7C ) . The amplitude of the PRC also increases with h ( Fig 7A and 7D ) . The sharp transition of translational activity near x ~ Kt set by the Hill function more strongly influences the levels of nuclear protein z , resulting in a larger phase shift by a light signal . These results suggest that a switch in translation extends the dead zone in the PRC . Finally , to confirm the generality of the above results for the induction response , we analyze the dead zone in a more complex model of the mammalian circadian clock . The model includes the NFL of Per and Bmal1 and that between Bmal1 and Rev-erb ( S1 Text ) . Saturation of translation creates a dead zone in this complex model ( S12A and S12B Fig; S1 Text ) . In addition , we obtain a dead zone with the repressilator model including translational saturation and the induction response to external signals ( S12C and S12D Fig; S1 Text ) . In summary , when light signals increase the mRNA synthesis independent of the clock states , the saturation of translation is required to generate the daytime dead zone .
The biochemical reactions that are influenced by light signals in circadian clock systems vary between organisms . In this study , we considered the degradation and induction responses as observed in Drosophila and mammals , respectively . Despite the difference in light responses in these two different animals , light signals induce the transcription of the repressor and cause phase shifts during night as described below . In the degradation response , light signals increase the degradation of repressor protein , thereby relieving transcriptional repression . Subsequent elevation of mRNA levels determines the phase shifts of the clock ( S1 Fig ) . In the induction response where light signals directly induce the transcription of repressors , it is the subsequent increase in protein levels that determines the phase shifts ( S6–S9 Figs ) . In contrast , a dead zone is formed in both types of light responses in the daytime . Previous studies demonstrated the importance of a dead zone during the daytime to make the circadian clock systems resistant against internal and external perturbation [6–9] . However , whether and how the TTFLs for the circadian clocks create the dead zone has remained unclear . In this study , we revealed that the saturation of a biochemical reaction in repressor synthesis is a common mechanism for the different light responses to reduce the phase sensitivity of a limit cycle and create a dead zone ( Figs 2 , 3 and 5 ) . The degradation response requires the saturated transcription of repressor mRNA to generate a dead zone , whereas the induction response requires saturated repressor translation . Our theoretical results suggest that locating a saturated reaction in repressor synthesis next to a light-responsive reaction in an NFL is a design principle for dead zone generation . The saturation of biochemical reactions in an NFL influences the generation of oscillations [49–51] . For example , the saturation of translation and transport of repressor protein from the cytoplasm to nucleus suppresses oscillations [49] . Conversely , the saturation of mRNA and protein degradation facilitates oscillations . Therefore , the location of the saturated reaction for dead zone generation should affect the ability of the NFL to generate oscillations . It is ideal if the saturated reaction for dead zone generation can facilitate generation of sustained oscillation . However , because the location of the saturated reaction for dead zone generation is constrained by that of the light-responsive reaction in an NFL as described above , it may not be optimal in terms of rhythm generation in some organisms . For the degradation response , the saturation of transcription at its maximum rate is required to create a dead zone ( Figs 2C and 3D and S1C Fig ) . The saturated transcription can realize an effective transcriptional switch , as the resultant accumulation of repressor protein causes a subsequent rapid drop in the transcription rate ( Fig 3D ) . Such a switch in transcription is favorable to oscillation , as the Hill function with a larger Hill coefficient in transcription facilitates oscillation [50 , 52] . For the induction response , however , the saturation of translation is essential to create a dead zone ( Figs 5 and 6 ) . Although strong saturation of translation deprives the NFL of the ability to generate oscillation as described above ( S13 Fig; S1 Text ) [49] , other additional saturations such as that of protein degradation could compensate ( S13 Fig ) . Importantly , the saturation of protein degradation not only facilitates oscillations but also lengthens the dead zone in a PRC in both the degradation and induction responses ( Fig 3 , S3 and S11 Figs ) . A recent theoretical study proposed that the saturated degradation of molecules can be regarded as a positive feedback [51] . This suggests that there may be some positive feedbacks that support dead zone generation by the saturation of repressor synthesis , as the saturated degradation does . Crucially , our analysis of the induction response indicates that not every saturated reaction in an NFL can create a dead zone during daytime . For example , the saturation of repressor transport from the cytoplasm to the nucleus does not generate a dead zone in the PRC ( S7 Fig ) . The saturation of mRNA degradation does create a dead zone but only at night when the levels of mRNA are near the trough ( S8 Fig ) . According to these results , the translation of repressor protein is the most plausible reaction to saturate for dead zone generation during the daytime ( Fig 5 ) . Additionally , we note that nonlinear functions of protein translation other than saturation cannot create a dead zone in the daytime , as we demonstrate that a Hill function for translation with a larger threshold Kt results in the generation of a dead zone at night ( S14 Fig; S1 Text ) . A previous experimental study demonstrated that PER protein synthesis induced by vasoactive intestinal polypeptide ( VIP ) indeed saturated [53] . However , because the quantification was performed by the bioluminescence of the luciferase reporter fused with the functional PER2 protein , of which synthesis is under the control of endogenous transcriptional and translational regulations [54] , it remains open whether this saturation occurred at the translation step of the PER protein . Recent experimental studies have revealed the importance of posttranscriptional regulations in the circadian clock [55] . Several micro RNAs inhibit the translation of clock gene transcripts by leading them to degradation . For example , micro RNAs regulate the onset of circadian rhythms in mouse embryonic tissue by determining the localization of Clock mRNA [56] . On the other hand , some protein molecules are known to promote translation . For example , mammalian LARK binds to Per1 mRNA to promote translation of the PER1 protein [46] . Mouse heterogeneous nuclear ribonucleoprotein Q ( mhnRNP Q ) binds to 5'-UTR of Per1 mRNA , which is necessary for internal ribosomal entry site mediated translation [47] . The saturated translation assumed in the present study may be caused by these mRNA binding proteins . Interestingly , although the levels of Lark and mhnRNP Q mRNAs are constant , the protein levels of both are rhythmic [46 , 47] . Therefore , it is an important future work to reveal the significance of such rhythmic translational activities together with saturation in the phase responses of the circadian clock . In general , a dead zone in the PRC can be generated in several ways . One way is to gate the light input to the circadian clock genes at a particular phase of oscillation . Because light signals do not influence the expression of circadian clock genes in the presence of gating , the phase sensitivity of the limit cycle does not need to be near zero to form a dead zone [9 , 42] . Previous theoretical studies realized such gating by assuming that circadian clock proteins repress the light-induced transcription [9 , 29 , 42] . In mammals , light signals do not induce the expression of Per genes in the central pace maker tissue SCN during subjective day [10 , 11] , suggesting that the SCN gates the light input . However , the molecular mechanism of gating was not elucidated . Moreover , if the circadian system only utilizes gating , the system would remain susceptible to internal perturbation such as fluctuations in gene expression and physiological states in cells . In this study , we proposed an alternative mechanism of dead zone generation that is effective against both external and internal fluctuations [6–9] and functions in different organisms with different light responses . We note that the mechanism proposed in the present study can function together with gating and further improve clock precision . A dead zone like interval has been observed in the PRC of firing rate of neurons in the rat SCN explants treated with VIP for few minutes [57] . PRCs of single cells derived from peripheral tissues are typically type 0 [58–61] , most likely due to strong phase resetting effects of applied perturbations ( S10 Fig ) . A dead zone tends to be obscured in a type 0 PRC ( S10 Fig ) , making it hard to perform direct comparison with type 1 PRCs of the current study . A future study will address whether SCN can create a dead zone at a single cell level by the saturated translation of Per mRNAs . Treatment of lower concentration of VIP or forskolin , which also activates CREB , to a cell line derived from the rat SCN [62] can be used to realize type 1 PRCs in single cells and address this question . In conclusion , the saturation of a biochemical reaction in repressor synthesis is a simple and generic mechanism for the generation of a dead zone for light signals . Several environmental cues other than light signals change the phase of the circadian clock by influencing the rates of biochemical reactions . The PRCs for those cues may include a dead zone as observed in the responses to light pulses [63] . Our findings indicate that the saturation of biochemical reactions should also function in such dead zone generation . | Light-entrainable circadian clocks form behavioral and physiological rhythms in organisms . The light-entrainment properties of these clocks have been studied by measuring phase shifts caused by light pulses administered at different times . The phase response curves of various organisms include a time window called the dead zone where the phase of the clock does not respond to light pulses . However , the mechanism underlying the dead zone generation remains unclear . We show that the saturation of biochemical reactions in feedback loops for circadian oscillations generates a dead zone . The proposed mechanism is generic , as it functions in different models of the circadian clocks and biochemical oscillators . Our analysis indicates that light-entrainment properties are determined by biochemical reactions at the single-cell level . | [
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] | 2019 | A saturated reaction in repressor synthesis creates a daytime dead zone in circadian clocks |
In East Africa , epidemics of Rift Valley fever ( RVF ) occur in cycles of 5–15 years following unusually high rainfall . RVF transmission during inter-epidemic periods ( IEP ) generally passes undetected in absence of surveillance in mammalian hosts and vectors . We studied IEP transmission of RVF and evaluated the demographic , behavioural , occupational and spatial determinants of past RVF infection . Between March and August 2012 we collected blood samples , and administered a risk factor questionnaire among 606 inhabitants of 6 villages in the seasonally inundated Kilombero Valley , Tanzania . ELISA tests were used to detect RVFV IgM and IgG antibodies in serum samples . Risk factors were examined by mixed effects logistic regression . RVF virus IgM antibodies , indicating recent RVFV acquisition , were detected in 16 participants , representing 2 . 6% overall and in 22 . 5% of inhibition ELISA positives ( n = 71 ) . Four of 16 ( 25 . 0% ) IgM positives and 11/71 ( 15 . 5% ) of individuals with inhibition ELISA sero-positivity reported they had had no previous contact with host animals . Sero-positivity on inhibition ELISA was 11 . 7% ( 95% CI 9 . 2–14 . 5 ) and risk was elevated with age ( odds ratio ( OR ) 1 . 03 per year; 95% CI 1 . 01–1 . 04 ) , among milkers ( OR 2 . 19; 95% CI 1 . 23–3 . 91 ) , and individuals eating raw meat ( OR 4 . 17; 95% CI 1 . 18–14 . 66 ) . Households keeping livestock had a higher probability of having members with evidence of past infection ( OR = 3 . 04 , 95% CI = 1 . 42–6 . 48 ) than those that do not keep livestock . There is inter-epidemic acquisition of RVFV in Kilombero Valley inhabitants . In the wake of declining malaria incidence , these findings underscore the need for clinicians to consider RVF in the differential diagnosis for febrile illnesses . Several types of direct contact with livestock are important risk factors for past infection with RVFV in this study’s population . However , at least part of RVFV transmission appears to have occurred through bites of infected mosquitoes .
Rift Valley fever ( RVF ) is one of the major viral zoonoses in Africa . The disease is caused by the Rift Valley fever virus ( RVFV ) of the genus Phlebovirus in the family Bunyaviridae [1] , and it is transmitted to animals through infectious mosquito bites and other arthropod vectors [2] . People become infected either from mosquito bites or by direct or indirect contact with infectious material when exposed to blood , body fluids or tissues of viraemic animals when handling sick or dead animals as well as through aerosol transmission , consumption of raw milk , meat or blood [3–5] . The disease was first described in the Rift Valley of Kenya in the early 1900s and the etiological agent demonstrated in the early 1930s [6] . RVF epidemics occur in cycles of 5–15 years in the Eastern Africa region as a result of abnormally high precipitation , for example during the warm phase of the El Niño/Southern Oscillation ( ENSO ) phenomenon [7] . In other regions the disease has been driven by floods caused by other sources including construction of hydroelectric dams [8] . During the outbreaks the disease causes devastation in livestock populations and economies of livestock keepers as a result of morbidity , mortality in new-borns and abortions ( irrespective of gestation period ) with direct negative consequences in the next crop of new-borns [9] . Public health consequences during epidemics involve a wide range of clinical manifestation in people including mild illnesses characterized by fever , muscle pain , joint pain , and headache , which can cause RVF to be confused clinically with other febrile illnesses such as malaria . In mild cases , symptoms persist for about a week and subside without specific treatment . A small percentage ( 0 . 5–2% ) of patients may develop severe forms of the disease characterized by either ocular disease , meningo-encephalitis or haemorrhagic fever which last for 1–4 weeks after onset of symptoms [10 , 11] . People most at risk include those in close contact with infected animals and infectious materials [4] , but also those unprotected from infectious bites of infected mosquitoes . Apart from general supportive therapy , there is no established treatment for people , and a commercial vaccine for humans is not available either . The control of RVF therefore relies mainly on vaccination of livestock and preventive measures by humans ( including protection from mosquito bites and avoidance of contact with infected animals and infectious material during epidemics ) . [11] . Inter-epidemic transmission has increasingly been reported in recent years , including in our study area , but its consequences are not fully understood and its incidence not explored enough for future epidemic preparedness [8 , 12–16] . Relatively little is known regarding the natural history of RVF as the epidemics occur in remote areas inaccessible during heavy rains; on the other hand , inter-epidemic RVF transmission presents an opportunity for studying its natural history as it normally occurs when affected areas are accessible . In Tanzania , RVF with human involvement has been reported in the past [17 , 18] , with few studies demonstrating inter-epidemic transmission in livestock and people [12 , 19] . During the 2006/07 RVF epidemic in Tanzania , livestock and people in the Kilombero Valley were affected [20] , and a sero-survey in livestock indicated presence of inter-epidemic period transmission of RVF [12] . The Kilombero Valley is a seasonally inundated floodplain between the densely forested escarpment of the Udzungwa mountains to the northwest and the grass covered Mahenge mountains to the southeast . The annual floods in the valley mimic flooding that may occur elsewhere during ENSO years . In the Kilombero Valley , there has been intense malaria transmission due to abundance of the Anopheles gambiae complex , but other mosquito species including vectors of RVF virus ( e . g . Culex spp . , Aedes spp . and Mansonia spp . ) are present [21] . The current study therefore aimed to 1 ) determine whether people do acquire RVF during the inter-epidemic period in the Kilombero Valley and 2 ) evaluate the demographic , behavioural , occupational , and spatial determinants of recent and longstanding RVF sero-positivity in people .
We conducted the study in rural areas of the Kilombero River Valley , located in the Kilombero and Ulanga districts in south-eastern Tanzania [22] . The Kilombero Valley is characterized by seasonal flooding which supports reproduction of large numbers of mosquitoes including arbovirus vectors such as Aedes spp [21] . The inhabitants of the two districts engage mainly in smallholder farming , fishing , and livestock keeping . A serological survey was carried out from March to August 2012 in six villages , three from each study district , with a total population of 14 , 517 in 3716 households . About a quarter of households keep livestock [23] . We selected the villages from hotspots of RVF transmission in the livestock populations in the Kilombero Valley [12] . This aimed at maximizing the probability of detecting inter-epidemic virus activity in the human population , since the hotspots indicated presence of ecological features that promote RVF transmission . The sample size calculation took into account the fact that sampling was done in households ( clusters ) , with an average cluster size of 5 individuals per household considered appropriate for the valley [22] so a design effect of 3 was applied . The design effect adjusted sample size was further adjusted for the expected number of covariates we hoped to evaluate , which overall gave a sample size of 726 in 145 clusters . To ensure equal representation , we selected livestock keepers’ and farmers’ households independently as sampling units , because the two sub-populations are exposed in different ways to RVF risk factors [24] . In the four villages that were within the health and demographic surveillance system ( IHDSS ) of the Ifakara Health Institute , we randomly selected farmers’ households from the master list of IHDSS [23] . For farmers’ households in the other two villages and for livestock keepers’ households in all villages , we obtained the lists of households from the village office and manually picked every nth household from the list . We took blood samples from all members of the household who provided written consent to participate in the study . For children under 18 years the written consent was provided by parents or guardians . We collected blood samples into vacutainer tubes containing clot activator and after clotting , eluted the sera into cryovial tubes and kept these in a car fridge until transferred to the laboratory . We collected demographic characteristics and individuals’ exposure to risk factors to RVF through a structured questionnaire . We analysed the serum samples for presence of RVFV antibodies by two commercial enzyme-linked immunosorbent assay ( ELISA ) kits , an inhibition ELISA and a capture ELISA . The inhibition ELISA simultaneously detects immunoglobulin G ( IgG ) and immunoglobulin M ( IgM ) antibodies against RVFV in humans , domestic and wildlife ruminants ( Biological Diagnostic Supplies Limited , Dreghorn , United Kingdom ) [25] . We converted the net optical density ( OD ) reading for each sample to a percentage inhibition ( PI ) value using the equation: [ ( 100 – ( net OD of test sample / mean net OD of negative control ) x 100] . Test results producing PI values ≥38 . 6 are considered positive ( following the manufacturer’s recommendations ) whereas below that threshold is negative , with sensitivity and specificity being 99 . 5% and 99 . 7% respectively [25] . To determine recent infection , we then tested the positive samples for the presence of IgM using the capture IgM ELISA ( Biological Diagnostic Supplies Limited , Dreghorn , United Kingdom ) [26 , 27] . For this test , we used the two intermediate net OD values of positive controls ( C+ ) for the calculation of the net mean OD value of C+ . We then used this value in subsequent calculations of percentage positivity ( PP ) of C+ , C- and test sera as follows: [PP = ( net OD serum/net mean OD C+ ) x 100] . The cut off for positive samples’ PP values was ≥7 . 1 , with sensitivity and specificity being 96 . 4% and 99 . 6% respectively [27] . We analysed the data in STATA version 13 ( Stata Corp . , College Station , Texas , USA ) . Samples that were positive by inhibition ELISA were considered to give evidence of past infection in the individual , as IgG antibodies last long in persons infected in the past [26] . Samples that were positive by IgM ELISA were considered to indicate recent infection in the individual , as IgM antibodies are short lived following infection by RVF virus [26 , 28] . To examine risk factors of RVF virus infection and help identify households at higher risk for targeted public health interventions , we developed three separate mixed effect logistic models . We built two models for individual level risk factors for recent and past infection as outcome variables respectively and treated households as a random effect variable . We built a third model for household level factors with household sero-positivity as outcome variable and villages as random effect variable . For each model , we first determined the univariable association of individual factors with the outcome by fitting a logistic regression model . Variables with p-value <0 . 25 were selected as potential covariates in the multivariable analysis , where a p-value ≤ 0 . 05 was considered statistically significant . We performed manual forward model-building with subsequent models evaluated against sparser models by means of the Akaike Information Criterion ( AIC ) . We also tested two-way interactions between variables included in the model . Lastly , all factors that were dropped in the process of model building were later tested for any confounding effect . We considered factors to be a confounder if they led to a change of ≥25% in the coefficient estimates . We calculated the population attributable fraction ( PAF ) , a fraction of all cases in the study population due to exposure to a certain risk factor , as follows: PAF = ( Px* ( RR-1 ) ) / ( 1+ ( Px* ( RR-1 ) ) ) , where Px = estimated population exposure and RR = risk ratio . We obtained ethical approval from both the Institutional Review Board of the Ifakara Health Institute ( IHI-IRB ) and Medical Research Coordination Committee of the Tanzania’s National Institute for Medical Research for this study , permit number NIMR/HQ/R . 8a/Vol . IX/1101 . Prior to study procedures , participants were explained the study purpose and procedures and upon agreeing to participate , individual adult participants provided a written informed consent whereas parents or guardians provided written consent for the under-age participants .
The analyses were based on data from 606 participants in 141 households with complete questionnaire and laboratory results . We could not attain the a priori calculated sample size because of consenting issues among household members and because family size was smaller than expected . We do not anticipate this has introduced underrepresentation of participants with certain characteristics given the number of clusters involved . Out of 606 participants , 55 . 6% were females with age ranging between 2 and 90 years . Fifty four per cent and 46% of the participants originated from Kilombero and Ulanga districts respectively . The inhibition ELISA results indicated an overall RVF sero-prevalence of 11 . 7% ( 95% CI = 9 . 2–14 . 5 ) . There was a linear increase in sero-prevalence in the 10 year cohorts ( Fig . 1 ) . Evidence of recent infection by RVFV was found in 16 participants representing 2 . 6% overall ( n = 606 ) and 22 . 5% of inhibition ELISA positive individuals ( n = 71 ) . Four of 16 ( 25 . 0% ) IgM positives and 11/71 ( 15 . 5% ) of individuals with inhibition ELISA sero-positivity reported they had had no previous animal contact , suggesting that at least part of the transmission in the area occurred through infected mosquito bites . In the univariable analyses , factors associated with past RVF infection were history of participating in slaughter of animals ( odds ratio [OR] 1 . 85; 95% CI 1 . 01–3 . 42 ) , assisting birthing animals ( OR 2 . 02; 95% CI 1 . 12–3 . 63 ) , milking animals ( OR 2 . 45; 95% CI 1 . 35–4 . 45 ) , eating raw meat/blood ( OR 6 . 01; 95% CI 1 . 86–19 . 39 ) , disposing aborted foetus ( OR 2 . 04; 95% CI 1 . 13–3 . 67 ) and being older ( OR 1 . 03 per year; 95% CI 1 . 02–1 . 04 ) ( Table 1 ) . In the multivariable model , age ( OR 1 . 03; 95% CI 1 . 01–1 . 04 ) , milking animals ( OR 2 . 19; 95% CI 1 . 23–3 . 91 ) and eating raw meat/blood ( OR 4 . 17; 95% CI 1 . 18–14 . 66 ) remained significantly associated with past infection ( Table 2 ) . The PAFs of milking animals and eating raw meat in the past were 29% and 6% respectively . None of the risk factors studied were associated with recent infection ( results not shown ) . Though keeping livestock was not associated with individuals’ sero-positivity , households keeping livestock had a higher chance of having at least one member with past infection ( OR = 3 . 04 , 95% CI = 1 . 42–6 . 48 ) than households that do not keep livestock ( table 3 ) . Participant’s gender , eating meat from dead animals , drinking raw milk , bed net use , proximity to the main flood area , elevation and district were not associated with inhibition ELISA sero-positivity .
These findings , coupled with our previous report in livestock [12] , indicate persistent IEP transmission of RVFV in both livestock and human populations in the Kilombero Valley . The animal contact risk factors , especially milking and eating raw meat are important and present educational intervention targets for risk reduction . In the wake of declining malaria incidence [37] these findings underscore the need for clinicians to consider RVF in the differential diagnosis for febrile illnesses among Kilombero Valley inhabitants . This is relevant regardless of the person’s occupation , because part of the transmission likely happens through infectious mosquito bites . The findings also suggest the opportunity and need to further investigate the circulating RVFV strain as well as the main vectors responsible for IEP transmission . | Rift Valley fever ( RVF ) is a disease of animals and people that is caused by the RVF virus . During epidemics , humans get RVF through direct contact with animals or through mosquito bites . In East Africa , epidemics occur every 5–15 years following unusually high rainfall . In between epidemics , the transmission of RVF might occur at low level . In an epidemic-free period , we measured whether people in the Kilombero Valley in Tanzania had evidence of past and recent RVF infection in their blood sample , and studied risk factors . Three per cent of people had been infected recently , and 12% had evidence of past infection , with increased risk with age , among milkers and among people eating raw meat . Some people with past or recent infection reported they had not had contact with animals . Households keeping livestock had more members with evidence of past infection . The findings show that people get infected with RVF in between epidemics , and that various types of contact with livestock are important risk factors . There is also evidence that some people get infected with RVFV by mosquitoes in the epidemic free period . Clinicians in the Kilombero Valley should consider RVF in the differential diagnosis of patients with fever . | [
"Abstract",
"Introduction",
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"Discussion"
] | [] | 2015 | Inter-epidemic Acquisition of Rift Valley Fever Virus in Humans in Tanzania |
The degradation of small RNAs in plants and animals is associated with small RNA 3′ truncation and 3′ uridylation and thus relies on exonucleases and nucleotidyl transferases . ARGONAUTE ( AGO ) proteins associate with small RNAs in vivo and are essential for not only the activities but also the stability of small RNAs . AGO1 is the microRNA ( miRNA ) effector in Arabidopsis , and its closest homolog , AGO10 , maintains stem cell homeostasis in meristems by sequestration of miR165/6 , a conserved miRNA acting through AGO1 . Here , we show that SMALL RNA DEGRADING NUCLEASES ( SDNs ) initiate miRNA degradation by acting on AGO1-bound miRNAs to cause their 3′ truncation , and the truncated species are uridylated and degraded . We report that AGO10 reduces miR165/6 accumulation by enhancing its degradation by SDN1 and SDN2 in vivo . In vitro , AGO10-bound miR165/6 is more susceptible to SDN1-mediated 3′ truncation than AGO1-bound miR165/6 . Thus , AGO10 promotes the degradation of miR165/6 , which is contrary to the stabilizing effect of AGO1 . Our work identifies a class of exonucleases responsible for miRNA 3′ truncation in vivo and uncovers a mechanism of specificity determination in miRNA turnover . This work , together with previous studies on AGO10 , suggests that spatially regulated miRNA degradation underlies stem cell maintenance in plants .
Arabidopsis ARGONAUTE10 ( AGO10 ) , also known as ZWILLE ( ZLL ) or PINHEAD ( PNH ) , maintains stem cell homeostasis in the shoot apical meristem ( SAM ) and floral meristems through repression of miR165/6 activity [1–5] . The conserved miR165/6 family acts through AGO1 to downregulate the type III homeodomain-leucine zipper genes that are critical for stem cell maintenance , leaf polarity , and vasculature development [6–10] . AGO10 is expressed in the adaxial side of organ primordia and in the provasculature underneath the SAM to maintain stem cells in the SAM in a non-cell autonomous manner [3 , 4 , 11] , whereas miR165/6 is restricted to the abaxial side of organ primordia and excluded from the SAM [2 , 7] . As AGO10 binds miR165/6 with higher affinity than AGO1 , it was hypothesized that AGO10 , which accumulates in a highly restricted manner in the plant [3 , 4 , 11] , sequesters miR165/6 to prevent it from repressing its target genes through the ubiquitously present AGO1 protein [5 , 12] . AGO10 has also been implicated in repressing the accumulation of miR165/6 . In multiple ago10 loss-of-function mutants , the levels of miR165/6 are moderately increased ( to 1 . 5–2-fold of wild-type levels ) , as determined by northern blotting with whole seedlings or inflorescences [1 , 2 , 5] . Given that AGO10-expressing cells constitute only a tiny portion of the tissues used in these studies , the small increase is likely an underestimate for the ability of AGO10 to repress miR165/6 accumulation . In fact , in situ hybridization revealed that miR165/6 , which is normally excluded from the SAM , accumulates in the SAM in ago10 mutants [2] , suggesting that AGO10 not only sequesters miR165/6 but also represses its accumulation . The impact of AGO10 on miR165/6 contrasts the positive effects of AGO1 on the accumulation of microRNAs ( miRNAs ) , including miR165/6 [1 , 13] . The mechanism by which AGO10 reduces the levels of miR165/166 is currently unknown . The steady-state levels of miRNAs are determined by the balance between biogenesis and degradation . miRNA biogenesis is a multistep process . After the transcription of MIR genes into pri-miRNAs , DICER-LIKE1 ( DCL1 ) processes pri-miRNAs into pre-miRNAs and pre-miRNAs into the miRNA/miRNA* duplexes . The duplexes are methylated by HEN1 , and the miRNA strand is loaded into AGO1 , the major miRNA effector , to form the RNA-induced silencing complex ( RISC; reviewed in [14] ) . The mechanisms of miRNA degradation are not well understood . Degradation intermediates are hard to detect in the wild-type background , but they are readily detectable in hen1 mutants , in which miRNAs and small interfering RNAs ( siRNAs ) in plants and Piwi-interacting RNAs ( piRNAs ) in animals lose 2′-O-methylation on the 3′ terminal ribose and are more susceptible to degradation [15–25] . Studies of the consequences of loss of methylation in both plant and animal hen1 mutants revealed two molecular processes associated with small RNA degradation , namely 3′ truncation and 3′ uridylation [15 , 18 , 19 , 21 , 22] . The enzyme that causes miRNA 3′ truncation is presumably an exonuclease , but its nature is as yet unknown in Arabidopsis . Two nucleotidyl transferases , HESO1 and URT1 , play a major and minor role , respectively , in miRNA uridylation [26–29] . Both enzymes are able to uridylate AGO1-bound , unmethylated miRNAs in vitro , and they act in a partially redundant and synergistic manner to uridylate unmethylated miRNAs in vivo [26–29] . The sequence of events in miRNA degradation ( truncation followed by tailing or tailing followed by truncation ) is unknown . In Arabidopsis , the SMALL RNA DEGRADING NUCLEASE ( SDN ) family of 3′ to 5′ exonucleases consisting of five family members degrades short RNAs in vitro and limits the accumulation of miRNAs in vivo [30] . Prior in vitro enzymatic assays with SDN1 were performed with free RNA oligonucleotides as substrates , and , in these assays , SDN1 was able to reduce the size of its substrate RNA to a uniform and very small size [30] . This is apparently inconsistent with SDN1 being responsible for the observed miRNA 3′ truncation activity in vivo , as the truncated species lack a small and varying number of nucleotides from the 3′ end . However , in vivo , miRNAs are associated with , and protected by , AGO1; it is possible that the observed varying degree of 3′ truncation is due to the balance between protection by AGO1 and exonucleolytic degradation by SDN1 . It is unknown whether SDN1 is able to act on AGO1-bound miRNAs and , if so , whether the interplay between AGO1 and SDN1 leads to the truncation of a varying number of nucleotides . The answer to this question is critical in understanding how miRNAs are degraded in vivo , as SDN1 can act on methylated miRNAs [30] whereas HESO1 and URT1 cannot [26–29] , which makes SDN proteins the prime candidates in initiating the degradation of methylated miRNAs in vivo . In this study , we show that SDN1 and SDN2 are responsible for the 3′ truncation of a subset of miRNAs in the hen1 background and miR165/6 species in the wild-type background , thereby revealing an enzyme associated with the 3′ truncation process in vivo . We show that 3′ truncated miRNAs are further tailed by HESO1 to lead to their degradation , thus clarifying the relationship between the two miRNA degradation processes . Furthermore , we show that , in vitro , SDN1 acts on AGO1-bound , methylated miRNAs to produce 3′ truncated miRNAs of varying sizes , similar to those observed in vivo . These findings provide a molecular framework of miRNA degradation that acts on many miRNAs . Furthermore , we show that AGO10 promotes the degradation of its associated miR165/6 in vivo , and this effect requires SDNs . AGO10-bound miR165/6 is more susceptible to SDN1-mediated 3′ truncation than AGO1-bound miR165/6 in vitro . This study reveals an unexpected activity of an AGO protein , uncovers a mechanism of specificity determination in miRNA turnover , and implicates the importance of regulated miRNA degradation in stem cell maintenance .
In wild type , 3′ truncated miRNA species are rare , presumably because they are rapidly degraded . Thus , to determine whether SDNs are responsible for miRNA 3′ truncation , we resorted to a hen1 mutant , in which the lack of 3′ terminal 2′-O-methylation of miRNAs is associated with rampant miRNA 3′ truncation and 3′ uridylation . Truncated and/or tailed species of miRNAs are readily detectable by northern blotting [22 , 25] and quantifiable by small RNA high throughput sequencing ( sRNA-seq ) [26–29] . To ascertain whether SDNs are responsible for the production of 3′ truncated miRNA species in vivo , we generated the hen1-8 sdn1-1 sdn2-1 triple mutant ( hereafter referred to as hen1 sdn1 sdn2 ) and compared its miRNA profiles with those of the hen1-8 single mutant by sRNA-seq . To determine the sequence of events ( 3′ truncation versus 3′ tailing ) , we also examined published sRNA-seq data from hen1-8 and hen1-8 heso1-1 [29] . Reads corresponding to each miRNA were classified into the full-length ( FL ) , 3′ truncated-only ( TR-only ) , 3′ tailed-only ( TA-only ) , and 3′ truncated-and-tailed ( TR+TA ) categories and quantified [29] . For both pairs of genotypes , two biological replicates were performed or analyzed . To be consistent , we present the 23 most abundant miRNAs ( reads per million ( RPM ) > 10 ) across all eight libraries . We compared the levels of TR-only and TR+TA species in hen1 sdn1 sdn2 and hen1 as these species represented the miRNA 3′ truncation activity . Nine out of the 23 miRNAs showed a significant reduction in the levels of either TR+TA or TR-only species in hen1 sdn1 sdn2 ( Fig 1A , S2 Data , and S1 Fig ) . In addition , one miRNA ( miR167ab ) showed a significant reduction in TR+TA+TR-only species , although the reduction in either TR+TA or TR-only species was not statistically significant ( Fig 1A ) . Thus , ten of the 23 miRNAs showed reduced 3′ truncation . Many miRNAs also showed reduced 3′ truncation in both biological replicates but did not pass the p-value cutoff ( <0 . 05 ) , while few miRNAs showed increased 3′ truncation ( S2 Data ) . This indicates that SDN1 and SDN2 are responsible for the production of 3′ truncated species in vivo from at least some miRNAs . Functional redundancy with the remaining family members could be responsible for the lack of an observable effect on the 3′ truncation of other miRNAs . Alternatively , non-SDN exonucleases also cause miRNA 3′ truncation . Having shown that SDN1 and SDN2 cause the 3′ truncation of some miRNAs , we next sought to determine which occurred first , truncation by SDN1/2 or tailing by HESO1 . Either is theoretically possible—SDN1 truncates miRNAs and HESO1 uridylates truncated miRNAs , or HESO1 uridylates miRNAs and SDN1 acts on uridylated miRNAs to cause their 3′ truncation . In the hen1 heso1 double mutant , the levels of TR+TA species for many of the 23 miRNAs were reduced , and those of TR-only species were increased ( Fig 1B and S3 Data ) . Therefore , HESO1 tailed the TR-only species to generate the TR+TA species . In particular , for the ten miRNAs with a significant reduction in 3′ truncation in hen1 sdn1 sdn2 ( Fig 1A ) , nine showed a significant increase in TR-only species in hen1 heso1 ( Fig 1B ) . These data indicate that 3′ truncated species generated by SDNs are further uridylated by HESO1 for degradation . miR165/6 species were drastically affected in hen1 sdn1 sdn2 relative to hen1 . The proportion of FL species of miR165 and miR166 was much higher in hen1 sdn1 sdn2 than in hen1 , while those of TR-only or TR+TA species were much reduced ( Fig 1C and S1 Fig ) . The proportion of TA-only species was either unaffected ( miR166 ) or affected to a smaller extent ( miR165; Fig 1C and S1 Fig ) . This demonstrated a role of SDN1 and SDN2 in the production of 3′ truncated species of miR165/6 in the hen1 background . In the wild-type background , the proportions of TR-only , TR+TA , and TA-only miR165/6 species were much lower than those in the hen1 background ( Fig 1C and 1D ) . Nevertheless , a reduction in the proportions of TR+TA species was observed in sdn1 sdn2 relative to wild type ( Fig 1D ) . Therefore , SDN1 and SDN2 are responsible for the 3′ truncation of miR165/6 in vivo in both hen1 and wild-type backgrounds . In hen1 heso1 , the reduction in TR+TA miR165 or miR166 species is accompanied by an increase in TR-only species ( Fig 1E ) , indicating that HESO1 tails 3′ truncated miR165/6 species generated by SDN1/2 . Given the results above , an appealing model ( S2 Fig , right panel ) of miRNA degradation is that methylated , FL miRNAs are first truncated by SDN1/2 , which results in 3′ truncated miRNAs that lack 3′ terminal methylation . These 3′ truncated species are then tailed by HESO1 and URT1 to cause their complete degradation . One important question related to this model is whether SDN1/2 can act on AGO1-bound miRNAs ( S2 Fig , left panel ) , as mature miRNAs are associated with AGO1 in vivo . HESO1 and URT1 are able to uridylate AGO1-bound , unmethylated miRNAs in vitro [26–29] , but previous biochemical assays with SDN1 were only conducted with free RNA oligonucleotides as substrates [30] . Intriguingly , although SDN1 degrades RNA substrates to a uniform and very small size in vitro [30] , the 3′ truncated species that depended on SDN1/2 for accumulation in vivo had a small and varying number of nucleotides truncated from the 3′ ends ( S1 Fig ) . One possibility is that SDN1 cannot completely degrade AGO1-bound miRNAs but , instead , only cause their 3′ truncation . To test this , we conducted SDN1 assays with AGO1 immunoprecipates ( IPs; S3A Fig ) as the substrate under enzyme excess conditions . miR165/6 was detected by northern blotting before and after the reactions . While SDN1 was able to nearly completely degrade a free RNA oligonucleotide , it was largely ineffective in degrading miR165/6 in AGO1 IP ( S3B Fig ) . Thus , AGO1 protects miR165/6 from being degraded by SDN1 in vitro . However , we noticed that upon extended incubation ( >2 hr ) , weak signals representing shorter miR165/6 species were detectable in the AGO1 IP ( S3B Fig ) , suggesting that SDN1 caused miR165/6 3′ truncation at a low level . As northern blotting was not a sensitive method to detect such 3′ truncated species , we performed sRNA-seq to determine whether SDN1 caused 3′ truncation of AGO1-bound miR165/6 and other miRNAs . AGO1 IP was used as the substrate in assays with a mock ( no enzyme ) control , SDN1 , and a catalytic mutant ( SDN1D283A ) control for 1 hr . After the reactions , AGO1 was precipitated again , and the associated small RNAs were subjected to high throughput sequencing . Two biological replicates yielded highly similar results ( S4 Fig and S4 Data ) . For the quantification of miRNA 3′ truncation , the 3′ truncated species present in the mock reactions were subtracted from those in the SDN1 or SDN1D283A reactions . We analyzed the 3′ truncation status of all abundant miRNAs ( RPM > 10 in all six samples ) . Fifteen of 43 abundant miRNAs exhibited higher levels of 3′ truncation in the SDN1 reactions as compared to the SDN1D283A reactions ( Fig 2A ) . Several conclusions can be drawn from this in vitro study . First , SDN1 can act on AGO1-bound miRNAs , and , unlike its activities on free RNAs , it generates heterogeneous , 3′ truncated species from AGO1-bound miRNAs ( Fig 2D ) , consistent with the 3′ truncation observed in hen1 mutants in vivo . Second , miRNAs in AGO1 IP should be methylated . Thus , SDN1 can act on methylated , AGO1-bound miRNAs , consistent with its ability to degrade methylated RNA oligonucleotides [30] . Third , SDN1 was ineffective against many AGO1-bound miRNAs in vitro ( Fig 2A ) , suggesting that other factors assist SDN1 in miRNA 3′ truncation in vivo or that other exonucleases also cause miRNA 3′ truncation in vivo . Loss-of-function ago10 mutants , such as pnh-2 and ago10-13 , show increased levels of miR165/6 [1 , 2] . In northern blots with young seedlings of wild type , pnh-2 and ago10-13 , we consistently observed an increase in miR165/6 levels in six biological replicates ( Fig 3A ) . We examined whether the increase in miR165/6 accumulation in these mutants could be attributed to enhanced miR165/6 biogenesis . miR165/6 is encoded by two MIR165 and seven MIR166 loci . We designed primers that allowed the detection of the sum of pri- and pre-miRNA species from each of the nine loci . Reverse transcription PCR ( RT-PCR ) was performed to determine whether pri/pre-miR165/6 species from all nine loci were present in young seedlings in wild type . While PCR , using genomic DNA as the template , produced a specific band at each locus , RT-PCR produced a band at all nine loci except for MIR166g ( Fig 3B ) . The finding that eight MIR165/6 genes were expressed in young , wild-type seedlings was in agreement with findings from analyses of promoter activities of MIR165/6 genes [31] . We performed real-time RT-PCR in wild-type , pnh-2 , and ago10-13 seedlings for these eight loci . The levels of the eight pri/pre-miR165/6 species were not increased in the two ago10 mutants ( Fig 3C ) . Consistent with this analysis , northern blotting showed that the levels of pre-miR166a were similar in wild-type and pnh-2 seedlings ( Fig 3D ) . Therefore , loss of function in AGO10 resulted in an increase in the levels of mature miR165/6 but did not affect the transcription of MIR165/6 genes or the processing of pri-miR165/6 , suggesting that AGO10 represses miR165/6 accumulation at a step after precursor processing . AGO10 has been shown to recognize features of the miR165/miR165* or miR166/miR166* duplex during the loading of miR165/6 into AGO10 [5] . As miRNA/miRNA* duplexes are the substrates of HEN1 , we asked whether AGO10’s association with the duplex of miR165/6 and miR165/6* could compete with HEN1-mediated methylation . We immunoprecipitated AGO1 and AGO10 from a zll-1 ZLLp::YFP-ZLL line in which the YFP-ZLL ( AGO10 ) transgene driven by the ZLL ( AGO10 ) promoter fully rescues the morphological defects of zll-1 [11] . β-elimination assays that interrogated the methylation status of miR165/6 showed that both AGO1- and AGO10-bound miR165/6 species were fully methylated in vivo ( Fig 3E ) , suggesting that AGO10 does not affect the methylation status of this miRNA . Therefore , we conclude that AGO10 must repress the accumulation of miR165/6 at a step after its biogenesis . AGO10 is expressed in a highly restricted manner in meristems and developing organ primordia , and the expression domains of AGO10 and miR165/6 are largely exclusive [2–4 , 7 , 11] . We reasoned that , if AGO10’s association with miR165/6 leads to the degradation of the miRNA , AGO10 overexpression and ectopic expression should lead to further sequestration of miR165/6 from AGO1 and , consequently , a reduction in miR165/6 levels . To test this hypothesis , we introduced YFP-AGO10 , driven by the strong and constitutive Cauliflower Mosaic Virus 35S promoter into wild-type plants . Among independent T1 transgenic lines , most exhibited phenotypic alterations similar to what was previously observed to be associated with AGO10 overexpression [32] . The phenotypes were classified into the Weak , Moderate , and Strong categories ( Table 1 ) . Plants in the different categories were largely similar in size to wild-type plants , but they differed from wild type and from each other in the degrees of leaf hyponasty ( upward curling ) and serration , as shown in Fig 4A . We focused on an AGO10 overexpression ( AGO10 OE ) line in the Strong category ( referred to as AGO10 OE S1 ) for subsequent analyses . AGO10 mRNA levels were much higher in this line than in wild type ( S5A Fig ) . A large reduction in miR165/6 levels was observed in AGO10 OE S1 ( Fig 4B ) . Levels of pri/pre-miR165/6 from the eight genes with detectable expression in seedlings were unaffected ( S5B Fig ) . The levels of pre-miR166a were also unaffected by AGO10 overexpression in two independent transgenic lines ( one in the wild-type background [AGO10 OE S1] and the other in pnh-2; S5C Fig ) . These data indicated that AGO10 overexpression did not affect the biogenesis of miR165/6 . Among the six miRNAs examined by northern blotting in AGO10 OE S1 , miR173 was found to also accumulate at a lower level ( Fig 4B ) . This raised the possibility that the reduced abundance of miR165/6 and miR173 in AGO10 OE S1 was due to reduced AGO1 expression , as previous studies demonstrated the association between miR168 and AGO10 and implicated AGO10 in the repression of AGO1 expression at the posttranscriptional level through miR168 [1 , 33] . Real-time RT-PCR showed that the levels of AGO1 mRNA were reduced by about 20% in AGO10 OE S1 ( S5A Fig ) . AGO1 protein levels were reduced by about 50% ( S5D Fig ) . To evaluate whether AGO10 overexpression indirectly repressed the accumulation of miR165/6 and miR173 through inhibition of AGO1 expression , we introduced 4mAGO1 , which renders AGO1 resistant to miR168 [13] , into AGO10 OE S1 . Transgenic lines were screened by RT-PCR to obtain one in which AGO10 mRNA levels were comparable to those of AGO10 OE S1 , but AGO1 expression was elevated ( S5E Fig ) . AGO1 protein levels were also elevated in AGO10 OE 4mAGO1 ( S5F Fig ) . Elevated AGO1 expression in AGO10 OE 4mAGO1 failed to rescue the levels of miR165/6 or miR173 ( Fig 4C ) . Note that miR168 levels were comparable in wild type and AGO10 OE S1 and elevated in AGO10 OE 4mAGO1 ( Fig 4B and 4C ) , consistent with the previous observation that miR168 accumulation is tightly buffered by AGO1 [34] . In conclusion , although AGO10 overexpression led to reduced AGO1 expression , the reduced accumulation of miR165/6 and miR173 was not attributable to lower AGO1 expression . We evaluated whether there were any dosage effects of AGO10 overexpression on miR165/6 levels . We chose another four independent lines of AGO10 OE based on the severity of the morphological phenotypes ( Fig 4A ) . Lines S2 , M1 and M2 , and W1 were from the Strong , Moderate , and Weak categories , respectively . The levels of AGO10 mRNA in these lines were concordant with the severity of morphological defects ( Fig 4D ) . We conducted sRNA-seq for wild type and the four AGO10 OE lines ( S5 Data ) . The abundance of miR165 and miR166 was reduced in all four lines and was largely anticorrelated with the levels of AGO10 expression ( Fig 4E ) . As AGO10 loss-of-function and overexpression led to higher and lower levels of miR165/6 , respectively , without affecting miR165/6 biogenesis , we hypothesized that AGO10 promotes miR165/6 degradation . As miRNA degradation is manifested by the presence of 3′ truncated and/or 3′ tailed intermediates , we examined the status of miRNA 3′ truncation and tailing in wild type and AGO10 OE S1 with sRNA-seq ( three biological replicates ) . The proportion of FL miR165/6 reads was significantly reduced in AGO10 OE S1 ( Fig 5A , S6 Fig and S6 Data ) . Species of miR165/166 showing 3′ truncation , including both TR-only and TR+TA forms , were increased in AGO10 OE relative to wild type , suggesting that AGO10 overexpression enhanced the 3′-to-5′ truncation of miR165/6 ( Fig 5A , S6 Fig and S6 Data ) . Of the TA-only species , only miR165 was modestly increased in abundance ( Fig 5A and S6 Data ) . This suggested that 3′ truncation , but not 3′ tailing , was the primary event in miR165/6 degradation induced by AGO10 OE . To examine whether the enhanced degradation of miR165/6 in AGO10 OE was due to reduced AGO1 expression , we also sequenced small RNAs from AGO10 OE 4mAGO1 plants . The 3′ truncation of miR165/6 induced by AGO10 overexpression was not rescued by 4mAGO1 ( Fig 5B , S6 Fig , and S7 Data ) . Therefore , the degradation of miR165/6 induced by AGO10 overexpression was not attributable to reduced AGO1 expression . We examined whether AGO10 overexpression affected the status of 3′ truncation of other miRNAs . For the top 20 most abundant miRNAs in the sRNA-seq datasets ( Col and AGO10 OE S1 ) , miR165/6 were the only species with significant changes in 3′ truncation ( Fig 5C ) . No miRNAs showed reduced 3′ truncation . To elucidate how AGO10 overexpression repressed miR165/6 accumulation , we first tested whether AGO10 overexpression caused sequestration of miR165/6 from AGO1 . AGO1 was immunoprecipitated from wild-type and AGO10 OE S1 seedlings , and four associated miRNAs were examined by northern blotting . Note that AGO1 levels in AGO10 OE S1 were about 50% of those in wild type ( S5D Fig ) , but for northern blotting to detect AGO1-associated miRNAs , the amounts of AGO1 IP were adjusted such that AGO1 levels were similar in the two genotypes ( Fig 6A ) . Relative to a similar amount of AGO1 , miR165/6 was the only miRNA with reduced levels in AGO1 IP from AGO10 OE S1 ( Fig 6A ) . Thus , AGO10 overexpression caused further sequestration of miR165/6 from AGO1 . To obtain a global picture of the miRNAs associated with AGO1 and AGO10 in AGO10 OE S1 , we performed sRNA-seq from AGO1 and AGO10 IPs . Three biological replicates were performed for AGO1 and AGO10 IP from AGO10 OE S1 . The binding of AGO1 ( or AGO10 ) to a miRNA was quantified by the percentage of reads corresponding to this miRNA in total reads for all annotated miRNAs identified within the small RNA library from the AGO1 ( or AGO10 ) IPs . Results showed that the overall profiles of miRNAs associated with AGO1 or AGO10 in AGO10 OE S1 resembled those in wild type [5] . AGO1 associated with most miRNAs while AGO10 preferentially associated with miR165/6 ( Fig 6B and S8 Data ) . To determine whether AGO10 overexpression caused a further sequestration of miR165/6 from AGO1 , AGO1 IP was performed with wild type and AGO10 OE S1 with one biological replicate . Consistent with the northern blot results ( Fig 6A ) , reads for AGO1-associated miR165/6 were substantially reduced in AGO10 OE S1 as compared to wild type ( Fig 6C and S9 Data ) . The northern blotting and sRNA-seq results demonstrated that elevated AGO10 levels allowed AGO10 to compete more effectively with AGO1 for binding to miR165/6 . The only other miRNA that showed a similar effect was miR173 ( Fig 6D ) . We next examined whether there was any dosage effect of AGO10 overexpression on the sequestration of miRNAs from AGO1 . We conducted AGO1 IP from AGO10 OE S2 , M1 , M2 , and W1 lines and sequenced AGO1-associated small RNAs ( one biological replicate; S10 Data ) . Indeed , there was an AGO10 dosage-dependent reduction of miR165/6 levels in AGO1 IP ( Fig 6E ) . A similar dosage effect was found for miR173 but not for other miRNAs ( Fig 6F ) . Mechanistically , the enhanced degradation of miR165/6 by AGO10 could happen under several scenarios . First , 3′ truncation may happen more easily when miR165/6 is bound by AGO10 than when it is bound by AGO1 . Second , miR165/6 is dislodged faster from the AGO10 RISC than from the AGO1 RISC , and the degradation happens after miR165/6 is released from RISC . A third ( and hybrid ) model is that the initial 3′ truncation occurs on AGO10 RISC and triggers the dissociation of miR165/6 from AGO10 . In the first and the third model , we would expect the AGO10 RISC to contain more 3′ truncated miR165/6 than the AGO1 RISC . Under the second scenario , AGO1 and AGO10 RISCs are not expected to differ in terms of their association with 3′ truncated species . We examined the status of 3′ tailing and 3′ truncation of miRNAs that were present in AGO1 and AGO10 IPs from AGO10 OE S1 ( sRNA-seq in three biological replicates ) . We found that the most abundant species of miR165/6 in both AGO1 and AGO10 RISCs were the FL miRNA ( Fig 7A and S8 Data ) . However , the AGO10 IP showed a statistically significant increase in the TR-only miR166 species ( Fig 7A ) . An increase in the TR-only miR165 species was also found in AGO10 IP , although the increase did not pass the p-value cutoff ( Fig 7A ) . These data were consistent with the first or the hybrid model and suggested that 3′ truncation occurred , at least initially , on AGO10-associated miR165/6 . Interestingly , we observed a statistically significant increase in the levels of 3′ TR-only species in AGO10 IP for 16 out of 48 miRNAs at >1 RPM in any of the six libraries ( Fig 7B and S8 Data ) . Only one miRNA ( miR403 ) showed a significant reduction in 3′ TR-only species in AGO10 IP ( Fig 7B ) . This implies that AGO10 RISCs with many different resident miRNAs are more susceptible to miRNA 3′ truncation in vivo . The lack of an effect of AGO10 overexpression on the levels of most miRNAs is probably because these miRNAs are still mostly bound by AGO1 in AGO10 OE . To biochemically test whether AGO10 renders miR165/6 more susceptible to 3′ truncation , we conducted SDN1 assays in vitro . An ago10-3 His-Flag-AGO10 line [5] was used to immunoprecipitate AGO10 with anti-Flag antibodies and AGO1 with anti-AGO1 antibodies . Both IPs were successful as shown by western blotting to detect AGO1 and AGO10 as well as northern blotting to detect miR165/6 ( S3A and S3C Fig ) . Like for AGO1 IP , SDN1 was unable to degrade miR165/6 in AGO10 IP , as shown by northern blotting to detect miR165/6 before and after incubation with SDN1 under enzyme excess conditions ( S3B Fig ) . The lack of a large amount of AGO10 IP precluded the detection of miR165/6 3′ truncation by northern blotting ( S3B Fig ) . We resorted to sRNA-seq to compare the degree of SDN1-mediated truncation of miR165/6 in AGO1 IP and AGO10 IP . The AGO1 IP and AGO10 IP were incubated with buffer alone ( mock ) , SDN1 , or SDN1D283A . After the reactions , the AGOs were precipitated , and small RNAs were isolated and subjected to high throughput sequencing ( S4 Data ) . Two biological replicates gave reproducible results ( S4 Fig ) . Among 13 miRNAs present at >10 RPM in AGO10 IP ( in all six samples of mock , SDN1 , and SDN1D283A ) , five species , including miR165/6 , showed 3′ truncation by SDN1 ( Fig 2B ) . The AGO10 IP showed more pronounced miR165/6 3′ truncation than AGO1 IP ( Fig 2C and 2D ) , indicating that AGO10 rendered miR165/6 more susceptible to 3′ truncation by SDN1 than AGO1 . SDN1 and SDN2 mediate the 3′ truncation of some miRNAs including miR165/6 in the hen1 background and the 3′ truncation of miR165/6 in HEN1 backgrounds . This , together with the finding that SDN1 trimmed AGO10-bound miR165/6 in vitro , prompted us to test whether the increase in miR165/6 3′ truncation in AGO10 OE was mediated by SDNs . We generated a large population of primary transformants of 35S::YFP-AGO10 in sdn1-1 sdn2-1 ( hereafter referred to as sdn1 sdn2 ) and Col ( wild type ) backgrounds , identified lines that had comparable levels of AGO10 expression in the two genotypes , ( Fig 8A ) and performed sRNA-seq . Sequencing small RNAs from seedlings of one pair of lines ( AGO10 OE and sdn1 sdn2 AGO10 OE ) or inflorescences of another independent pair showed that increased 3′ truncation of miR165/6 in AGO10 OE was largely suppressed by sdn1 sdn2 ( Fig 8B , S7 Fig , and S11 Data ) . Therefore , AGO10 overexpression triggered SDN-dependent 3′ truncation of miR165/6 . The incomplete suppression of 3′ truncation of miR165/6 by sdn1 sdn2 was either due to the activities of other SDN family members or yet unknown nucleases that turnover miR165/6 . We also evaluated the effects of the sdn mutations on the severity of the developmental phenotypes caused by AGO10 overexpression . The percentage of primary transformants in each of the four phenotypic categories was documented and compared between wild type and sdn1 sdn2 ( Table 1 ) . The sdn1 sdn2 background had higher ratios of plants with wild type—like and weak phenotypes ( 36 . 8% and 43 . 9% ) , in contrast to the low ratios in the Col background ( 25 . 7% and 11 . 4%; Table 1 ) . The Col background had a higher ratio of plants with strong phenotypes as compared to that in the sdn1 sdn2 background ( 48 . 6% versus 8 . 8%; Table 1 ) . In conclusion , the degradation of miR165/6 triggered by AGO10 overexpression and the associated developmental consequences require SDN1 and SDN2 .
Universal small RNA decay processes in plants and metazoans appear to include 3′-to-5′ truncation and 3′ uridylation . In Arabidopsis , the nucleotidyl transferases HESO1 and URT1 are responsible for miRNA uridylation when miRNAs lack 2′-O-methylation , but the enzymes responsible for miRNA 3′ truncation were unknown , and the relationship between 3′ truncation and 3′ tailing was also unknown . In this study , we provided genetic evidence documenting a role of SDN1 and SDN2 in miRNA 3′ truncation in vivo . In a hen1 background , loss of function in SDN1 and SDN2 resulted in a reduction in miRNA 3′ truncation for some miRNAs . The lack of an effect on other miRNAs could be due to the presence of other SDN paralogs or other exonucleases . In addition , we observed that , in the hen1 heso1 background , 3′ truncated miRNAs accumulate at much higher levels than in the hen1 background . This supports the model ( S2 Fig ) that miRNA degradation is initiated by SDN-mediated 3′ truncation and followed by the uridylation of truncated species , which are further degraded by as yet unknown nucleases . Therefore , these genetic studies not only establish SDNs as one class of enzymes that causes miRNA 3′ truncation but also elucidate the relationship between miRNA 3′ truncation and 3′ tailing in miRNA turnover . While the observations discussed above were made in the hen1 background , genetic evidence also supports a role of SDN1 and SDN2 in miRNA 3′ truncation in the wild-type background . In the wild-type background , loss of function in SDN1 and SDN2 resulted in a reduction in the levels of TR-only and TR+TA miR165/6 . In AGO10 OE plants , loss of function in SDN1 and SDN2 reduced the levels of 3′ truncated miR165/6 and partially rescued the developmental abnormalities . These observations were especially important , as they suggest that SDN1 and SDN2 cause the 3′ trimming of miR165/6 when it is methylated ( as miRNAs are nearly completely methylated in HEN1 backgrounds ) . Furthermore , we provided biochemical evidence showing that SDN1 acts on AGO1-bound and methylated miRNAs in vitro . In fact , SDN1 had different effects on free and AGO1-bound miRNAs—it nearly completely degrades free miRNAs [30] but causes the truncation of a small and varying number of nucleotides from AGO1-bound miRNAs ( this study ) . The 3′ truncated , AGO1-bound miRNAs caused by SDN1 in vitro mimic the 3′ truncated species in hen1 mutants in vivo . This indicates that the 3′ trimmed miRNA species that accumulate in vivo result from the balancing act between AGO1-mediated protection and SDN1-mediated truncation . In summary , these genetic and biochemical observations support the following model of miRNA degradation . SDNs initiate miRNA degradation in wild type by 3′ truncation of AGO1-bound and methylated miRNAs to result in AGO1-bound , 3′ truncated-and-unmethylated miRNAs , which are uridylated by HESO1 and/or URT1 . The AGO1-bound , truncated-and-uridylated miRNAs are further degraded by an as yet unknown enzyme . However , we acknowledge that SDNs may not be the only exonucleases causing miRNA 3′ truncation . In addition , miRNA degradation may also entail mechanisms other than 3′ truncation and 3′ tailing . In addition to establishing a model of miRNA degradation , the study also uncovered an unexpected function of an AGO protein in destabilizing miR165/6 . In vivo , AGO10 overexpression caused further sequestration of miR165/6 from AGO1 , enhanced its 3′ truncation through SDN1/2 , and reduced its accumulation . In vitro , AGO10-bound miR165/6 species were more susceptible to SDN1-mediated 3′ truncation than AGO1-bound miR165/6 . The 3′ truncation of an AGO-bound miRNA should entail the displacement of the miRNA 3′ end from the binding pocket in the Piwi/Argonaute/Zwille domain [35] . Perhaps the 3′ end of miR165/6 is more accessible in an AGO10 RISC than in an AGO1 RISC . It is not known whether AGO10 confers 3′ end accessibility to its resident miRNAs in general . One observation consistent with this notion is that many miRNAs have higher levels of 3′ truncation in AGO10 IP than in AGO1 IP ( Fig 7B ) . However , AGO10 overexpression did not affect the abundance of most miRNAs . This is probably because most miRNAs are still bound by AGO1 despite AGO10 overexpression ( Fig 6B ) . Another miRNA that is reduced in abundance by AGO10 overexpression is miR173 ( Fig 4B ) . AGO10 overexpression caused a depletion of miR173 from AGO1 RISC relative to other miRNAs ( Fig 6D ) but not relative to AGO1 levels ( Fig 6A ) . Thus , the reduced abundance of miR173 by AGO10 overexpression was perhaps attributable to the lower levels of AGO1 . However , increasing AGO1 levels in AGO10 OE S1 could not restore miR173 accumulation ( Fig 4C ) . Intriguingly , miR173 happens to be the second most preferred miRNA by AGO10 for binding in a previous study [5] . Therefore , it is likely that AGO10 overexpression allowed AGO10 to better compete with AGO1 for binding to miR173 and lead to its degradation . This study , together with previous studies demonstrating the importance of AGO10-mediated repression of miR165/6 in meristem homeostasis [1 , 2 , 5] , provides an example of active miRNA degradation being employed as a mechanism to regulate stem cells in development . In developing seedlings , the spatial pattern of AGO10 protein accumulation is complementary to that of miR165/6 . We propose that AGO10 enhances the degradation of miR165/6 to help restrict this miRNA to cells not expressing AGO10 . The clearance of miR165/6 from the SAM by AGO10 is crucial for stem cell maintenance , as ago10 mutants accumulate ectopic miR165/6 in the SAM and fail to maintain the stem cell population [2] . But why is such a mechanism employed to clear miR165/6 from the SAM in addition to restricting the transcription of MIR165/6 from the stem cells ? This may have to do with the potential movement of this miRNA between cells . miR165/6 probably moves across a few cell layers from its site of synthesis in the root and in leaf primordia [36–41] . The non-cell autonomy means that cell type-specific transcription alone is not sufficient to restrict the miRNA from the SAM .
The pnh-2 and ago10-13 alleles [1 , 3] are in the Landsberg erecta ( Ler ) background . The hen1-8 allele and the sdn1-1 sdn2-1 double mutant are both in the Col background and were previously described [30 , 42] . The hen1-8 sdn1-1 sdn2-1 triple mutant was generated through a cross between hen1-8 and sdn1-1 sdn2-1 . zll-1 ZLLp::YFP-ZLL is a transgenic line in which the YFP-ZLL ( AGO10 ) transgene driven by the ZLL ( AGO10 ) promoter fully rescues the morphological defects of zll-1 [11] . The ago10-3 His-Flag-AGO10 line is in the Col background and is described [5] . Wild-type Columbia ( Col ) or sdn1-1 sdn2-1 plants were transformed with the 35S::YFP-AGO10 plasmid via the floral dipping method [43] to obtain AGO10 OE . The 4mAGO1 plasmid was obtained from Dr . Herve Vaucheret ( INRA , Versailles , France ) and introduced into AGO10 OE plants via the floral dipping method . When not specified , the plant materials used in this study were 12- to 13-d-old seedlings grown at 22°C under long day ( 16 hr light/ 8 hr darkness ) conditions . In only one instance ( mentioned in the text ) , inflorescences were used for small RNA sequencing in one pair of AGO10 OE and sdn1 sdn2 AGO10 OE lines . To generate the AGO10 overexpression construct , FL AGO10 coding region was amplified from cDNA using gene-specific primers containing sequences for TOPO reaction ( S1 Table ) . The AGO10 clone in the Gateway Entry vector was moved into pEarleyGate104 using LR reaction to produce 35S::YFP-AGO10 . The clone was sequenced to ensure the absence of mutations . For the expression of recombinant SDN1 protein , the FL SDN1 cDNA was amplified using primers SDN1 F and SDN1 R ( S1 Table ) and cloned into pET28-SMT3 ( pSUMO ) . The D283A mutation was introduced into SDN1 using the Stratagene QuikChange Site-Directed Mutagenesis Kit with a pair of primers , SDN1D283A F and SDN1D283A R ( S1 Table ) . The pSUMO-SDN1D283A clone was validated by sequencing . The pSUMO-SDN1 and pSUMO-SDN1D283A plasmids were transformed into the Escherichia coli strain BL21 Star ( DE3 ) for protein expression . The E . coli cells were cultured at 37°C until the OD600 reached 0 . 6 . Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) was added to a final concentration of 0 . 1 mM , and the cultures were incubated at 16°C for 16 hr . Cells were collected via centrifugation , resuspended in Lysis Buffer ( 20 mM Tris , pH 8 . 0 , 500 mM NaCl , 25 mM Imidazole , pH 8 . 0 ) , and sonicated on ice . The lysate was centrifuged again , and the supernatant was applied to a column containing preloaded nickel beads for purification . After two washes with Lysis Buffer , the homemade 6xHis-ULP in Dilution Buffer ( 20 mM Tris , pH 8 . 0 , 50 mM NaCl , 25 mM Imidazole , pH 8 . 0 ) was loaded to the column to remove the His-SUMO tag on the recombinant proteins . The free SDN1 and SDN1D283A proteins were then eluted with Elution Buffer ( 20 mM Tris , pH 8 . 0 , 100 mM NaCl , 25 mM Imidazole , pH 8 . 0 ) . For SDN1 enzymatic assay , AGO1 and AGO10 complexes were immunoprecipitated from ago10-3 His-Flag-AGO10 transgenic plants using anti-AGO1 ( Agrisera ) and anti-Flag ( Sigma-Aldrich ) antibodies , respectively . One-twelfth of the IP was used for western blotting to detect AGO1 and AGO10 , another 1/12 was used for northern blotting to detect miR165/6 , and the remainder was resuspended in reaction buffer ( 50 mM Tris , pH 8 . 0 , 150 mM NaCl , 1mM DTT , 2 . 5 mM MnCl2 , 1 mM ATP ) . The beads in the reaction buffer were evenly split into three parts for incubation with mock , SDN1 , and SDN1D283A . The reactions were carried out with 2 . 7 μM SDN1 or SDN1D283A and approximately 5 . 3 nM and 0 . 6 nM of small RNAs present in AGO1 IP and AGO10 IP , respectively ( see below for the estimation of sRNA concentrations ) . After incubation at room temperature for 1 hr , the beads were collected again for RNA extraction , followed by small RNA library construction . For the estimation of the amount of small RNAs in AGO1 or AGO10 IP , northern blotting was performed with the IPs and a miR165 oligonucleotide standard . The amount of miR165/6 in AGO1 and AGO10 IPs was deduced by comparing the signal intensities of miR165/6 in the IPs to those of the standard . Next , the amount of all sRNAs in the AGO1 and AGO10 IPs was estimated based on the proportions of miR165/6 reads in total small RNA reads from the AGO1 and AGO10 IPs ( as determined by sRNA-seq ) . Total RNA was extracted using TRI-reagent ( Molecular Research Center , Inc . TR 118 ) . For the detection of pri- and pre-miR165/6 species together , reverse transcription was performed with random primers , and real-time PCR was then performed as described [1] with gene-specific primers located within the pre-miRNAs from each locus . Values were obtained by normalizing to UBIQUITIN5 . Northern blotting to detect miRNAs or pre-miR166a was performed as described [44 , 45] . Antisense DNA oligonucleotides ( S1 Table ) were 5′-end labeled with γ32P-ATP to detect miRNAs . A DNA fragment amplified from genomic DNA using primers pre-miR166a-Nb F and pre-miR166a-Nb R ( S1 Table ) was randomly labeled with α32P-dCTP for the northern blotting to detect pre-miR166a . β-elimination followed by northern blotting to examine the methylation status of miR165/6 was performed as described [25] . Immunoprecipitation of AGO1 and AGO10 was performed as described [28] . In brief , 1 g of 12-day-old seedlings was ground in liquid nitrogen and dissolved in 1 . 5 ml IP buffer . The extract from AGO10 OE or zll-1 ZLLp::YFP-ZLL was incubated with anti-AGO1 antibodies ( Agrisera ) and anti-GFP antibodies ( Clontech ) , respectively . Then protein-antibody complexes were captured by Dynabeads-Protein-A ( Life Technologies ) . After washes , the beads containing AGO1 or AGO10 IPs were collected for small RNA analysis . Western blotting to determine AGO1 protein levels was performed with anti-AGO1 antibodies , and HSC70 ( Enzo Life Sciences ) was used as an internal control . The His-Flag-AGO10 protein was detected using anti-AGO10 antibodies ( Agrisera ) . Small RNA libraries were prepared using the Illumina Tru-Seq kit [29] and the NEBNext Multiplex Small RNA Library Prep Set for Illumina kit ( NEB ) and sequenced with Illumina's HiSeq2000 or Illumina NextSeq500 platform at the UCR Institute for Integrative Genome Biology ( IIGB ) genomic core facility . Bioinformatic analyses to categorize miRNA reads into the “FL , ” “TR-only , ” “TA-only , ” and “TR+TA” categories were performed as described [29] . All large-scale sequencing datasets generated in this study and a publicly available dataset used in this study are available in the Gene Expression Omnibus ( GEO ) database under series GSE35479 ( public dataset ) , GSE58138 ( this study ) , and GSE87355 ( this study ) . GEO GSE35479 ( public dataset ) sRNA-seq of hen1-8 and hen1-8 heso1-1 ( two replicates each ) GEO GSE58138 ( this study ) sRNA-seq of Col ( wild type ) , sdn1-1 sdn2-1 , hen1-8 , hen1-8 sdn1-1 sdn2-1 ( two replicates ) sRNA-seq of AGO1 IP from Col ( wild type ) and AGO10 OE ( one replicate ) sRNA-seq of AGO1 IP and AGO10 IP from Col ( wild type ) ( three replicates ) sRNA-seq of Col ( three replicates ) , AGO10 OE ( three replicates ) , and AGO10 OE 4mAGO1 ( one replicate ) sRNA-seq of AGO10 OE ( seedling ( I_s ) ) , sdn1 sdn2 AGO10 OE ( seedling ( I_s ) ) , AGO10 OE ( inflorescence ( II_f ) ) , and sdn1 sdn2 AGO10 OE ( inflorescence ( II_f ) ) ( one replicate ) GEO GSE87355 ( this study ) sRNA-seq of AGO10 OE lines with varying levels of AGO10 expression ( Col , S2 , M1 , M2 , W1 ) ( one replicate ) sRNA-seq of AGO1 IP from AGO10 OE lines with varying levels of AGO10 expression ( S2 , M1 , M2 , W1 ) ( one replicate ) sRNA-seq of AGO1 IP treated with mock , SDN1 or SDN1D283A ( two replicates ) sRNA-seq of AGO10 IP treated with mock , SDN1 or SDN1D283A ( two replicates ) | MicroRNAs ( miRNAs ) are 21–24 nucleotide regulatory RNAs that impact nearly all biological processes in plants and animals . The abundance of miRNAs is determined by their biogenesis and degradation . miRNA degradation is associated with trimming and tailing of their 3′ ends . The relationship between 3′ trimming and 3′ tailing as well as the enzyme ( s ) responsible for 3′ trimming was unknown in Arabidopsis . Mechanisms that protect miRNAs from degradation include 3′ terminal methylation and association with ARGONAUTE ( AGO ) proteins . In this study , we show that two members of the SMALL RNA DEGRADING NUCLEASE ( SDN ) family , SDN1 and SDN2 , are partly responsible for the miRNA 3′ trimming activities in Arabidopsis . We further elucidate the relationship between 3′ trimming and 3′ tailing—miRNAs are first trimmed by SDNs , and the 3′ trimmed-and-unmethylated miRNAs are tailed by the nucleotidyl transferase HESO1 . Furthermore , we demonstrate that SDN1 is able to act on AGO1-bound and methylated miRNAs . We also reveal a special function of AGO10 , which has the highest affinity for miR165/6 in vivo . We show that AGO10-bound miR165/6 is more susceptible to SDN-mediated degradation than AGO1-bound miR165/6 . This study provides a general molecular framework for miRNA degradation and an example of specificity determination in miRNA degradation . | [
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] | 2017 | ARGONAUTE10 promotes the degradation of miR165/6 through the SDN1 and SDN2 exonucleases in Arabidopsis |
Bacterial species , and even strains within species , can vary greatly in their gene contents and metabolic capabilities . We examine the evolution of this diversity by assessing the distribution and ancestry of each gene in 13 sequenced isolates of Escherichia coli and Shigella . We focus on the emergence and demise of two specific classes of genes , ORFans ( genes with no homologs in present databases ) and HOPs ( genes with distant homologs ) , since these genes , in contrast to most conserved ancestral sequences , are known to be a major source of the novel features in each strain . We find that the rates of gain and loss of these genes vary greatly among strains as well as through time , and that ORFans and HOPs show very different behavior with respect to their emergence and demise . Although HOPs , which mostly represent gene acquisitions from other bacteria , originate more frequently , ORFans are much more likely to persist . This difference suggests that many adaptive traits are conferred by completely novel genes that do not originate in other bacterial genomes . With respect to the demise of these acquired genes , we find that strains of Shigella lose genes , both by disruption events and by complete removal , at accelerated rates .
The wide variation in bacterial genome sizes was originally detected in the 1960s by DNA reassociation analyses [1] . And because bacteria have gene dense chromosomes , the differences in genome sizes implied that there were likely to be vast differences in the gene contents of bacterial species . With the current availability of hundreds of complete genome sequences , it is now possible to establish exactly which genes are present in , as well as those that are absent from , a genome . Among sequenced bacterial genomes , gene sets vary over 40-fold , ranging from 182 genes in the gammaproteobacterial symbiont Carsonella ruddii [2] to almost 8000 genes in the soil-dwelling acidobacterium Solibacter usitatus ( jgi . doe . gov ) . The wide variation in genome sizes and gene contents can also be observed between strains within individual bacterial genera or species . For example , isolates of Frankia that are more than 97% identical in their rRNA sequences–the conventional cutoff value for a bacterial species–can differ by as many as 3500 genes , which represents nearly half of their 7 . 5 Mb genome [3] . Even among bacterial strains of similar genome sizes , there can be substantial differences in gene repertoires [4] . Unlike mammals , in which only about 1% of the genes in a genome are unique to a taxonomic order ( e . g . , mouse vs . human [5] ) , the gene contents of bacterial genomes can change rapidly over relatively short evolutionary distances . The generation of novel gene repertoires is a consequence of the ongoing processes of gene acquisition and gene loss [6]–[10] . Although several mechanisms can generate new genes [6] , [11] , [12] , the novel gene sets observed in closely related bacterial strains result largely from gene transfer from distant sources , as duplications and gene rearrangement only rarely produce entirely unique genes in the short timescales in which bacterial gene sets evolve . Although homolog searches indicate that many genes arise from lateral transfer from other bacteria , most bacteria also contain genome-specific sets of genes that lack any homologs in the known databases ( termed “ORFans” ) [4] , [13] , [14] . Counteracting the augmentation of bacterial genomes by gene acquisition , gene loss occurs both through large-scale deletions [15] as well as by smaller changes that erode and inactivate individual genes [7] , [9] , [16] . As observed for acquired sequences , prokaryotes also contain genome-specific sets of inactivated genes ( i . e . , pseudogenes ) , which can comprise up to 41% of their annotated genes [17] . Taken together , these lineage-specific gene repertoires indicate the need to monitor bacterial genome dynamics–i . e . , the manner in which genes are gained and lost–over short evolutionary timescales . To this end , comparisons of closely related strains of Bacillus [18] , Staphylococcus aureus [19] and E . coli [7] , [20] have shown that gene acquisitions are prevalent at the tips of the phylogeny and that recently acquired genes seem to evolve more quickly . However , few studies have examined the fate of these genes within a bacterial lineage or have asked how many or which classes of genes , once acquired , are maintained , disrupted or removed from a genome . We address these questions by assessing the differences in gene repertoires among 13 sequenced strains of E . coli/Shigella clade . These strains are closely related , yet display substantial differences in genome size and gene content [21] , [22] , allowing us to pinpoint the introduction and persistence of genes in the lineages leading to these genomes .
We reconstructed the phylogeny of 13 sequenced strains of E . coli and Shigella species based on the concatenated sequences of 169 conserved , single-copy genes . The relationships and branching orders are well-resolved , well-supported , and congruent with previous studies [23] . The overall branching order of the resulting phylogeny is very similar to those based on other characters or for more limited sets of sequenced strains [24]–[26]: the uropathogenic E . coli ( UPEC ) form a monophyletic cluster at the base of the tree , and Shigella strains are polyphyletic , with a major lineage derived from the clade containing E . coli K-12 [27] . Based on this tree , we delineated 12 monophyletic clades of varied phylogenetic depths ( of which we designate the corresponding ancestral branches as S1 to S4 , SC , SCE , C1 , E1 , E2 , U1 , U2 and the ancestral branch , Figure 1 ) , which were used to trace the evolutionary history of all genes in these 13 genomes . By identifying the homologs of genes from the 13 E . coli and Shigella strains in each of 367 microbial genomes , and by mapping the gene distributions in a phylogenetic context , we could infer the ancestry ( vertical or horizontally acquired ) and dynamics ( incidence of acquisition or loss ) of genes among strains . Acquired genes were classified into two categories: ORFans , which are genes that have no homologs outside of the analyzed E . coli and Shigella strains , and HOPs , which are genes that have homologs outside of the analyzed E . coli and Shigella genomes but are not ancestral to all taxa containing the gene or whose phylogenetic distributions can not be most parsimoniously reconstructed solely through gene loss events . From the 13 sequenced strains within the E . coli/Shigella clade , we identified a total of 1443 ORFan gene families and 652 HOP gene families ( a family is a group of homologs ) . Gene family sizes ranged from one gene , for ORFans or HOPs present in a single genome ( representing 11% and 32% of the total number of families , respectively ) to 13 genes , for ORFans or HOPs with homologs present in all 13 genomes ( representing 13% and <1% of the total number of families , respectively ) . We inferred the branch on which ORFans and HOPs originated by reconstructing the most parsimonious series of events that would give rise to their present-day distributions . By this approach , all HOPs could be assigned to a particular clade , but only 1177 of the 1443 ORFan families were assigned unequivocally , and together these constitute the set considered in subsequent analyses . Only 8 ORFans ( <1% ) could be classified to a particular COG category , whereas 151 ( 23% ) of the HOP families could be assigned to a COG other than ‘poorly characterized’: these included Metabolism ( 10% ) , Cellular Processes and Signaling ( 8% , mostly in the category Cell Wall/Membrane/Envelope Biogenesis ) and Information Storage and Processing ( 5% ) ( Supplementary Table S1 ) . The numbers of acquired ORFans and HOPs vary substantially across strains and lineages ( Figure 1 ) , with the largest difference occurring in the gene set acquired by the ancestor to all tested strains in which ORFans are approximately four times more common than HOPs . This is in contrast to genes confined to a single E . coli or Shigella genome , where we identify ∼40% more HOPs than ORFans . This difference is not affected by the fact that 20% of ORFans could not be placed onto a specific branch , because singleton ORFans are among the easiest genes to assign . Taken together , these distributions suggest that HOP genes originate more frequently , but ORFans are more likely to persist . Overall , ORFans constitute between 9% and 14% of the protein coding genes per genome , and HOPs account for at most 5% of the protein coding genes per genome . Cumulatively , ORFans outnumber HOPs; however , HOPs represent a larger proportion of the acquired DNA in all strains as they are , on average , longer than ORFans ( 853 bp vs . 308 bp respectively ) ( Table 1 ) . There is an association between genome size and the amount of ORFan and HOP-derived DNA ( r2 = 0 . 75 and r2 = 0 . 72 , respectively ) per genome; however , it is not simply a matter that the strains with the largest genomes have acquired the most DNA . For example , Shigella dysenteriae and E . coli EDL933 have gained identical amounts of DNA from ORFans and HOPs despite an 800 kb difference in their genome sizes . ORFans are more A+T-rich than HOPs ( 44% vs . 47% G+C , respectively ) , and such differences in base composition are evident along most lineages ( Supplementary Figure S1 ) . When examining a single lineage at increasing phylogenetic depths , there is no clear trend towards increased G+C contents , G+C content of the third codon position or increased gene lengths of ORFans or HOPs with duration in the E . coli genome , although this has been observed previously for acquired genes assessed over substantially longer evolutionary timescales [20] . This indicates that the elapsed time since the divergence of the 13 tested strains from their common ancestor has been insufficient to adjust acquired genes to the nucleotide composition of their host genome . Recently acquired ORFans and HOPs occur more often in multigene clusters than do those assigned to older branches . For example , in E . coli CFT073 , which contains the largest numbers of both ORFans and HOPs , about half of the ORFans confined to this strain are adjacent to another ORFan of the same age . Going back to the next branch that subsumes this strain ( U2 ) , only a third of the ORFans reside next to another ORFan; and among those ORFans originating in the ancestor to the E . coli/Shigella clade , only 14% are situated next to another ORFan . The average cluster sizes of ORFans along these three branches are 1 . 59 , 1 . 34 , and 1 . 09 genes , indicating that ORFan genes are gained in clusters that subsequently shrink through fragmentation and gene loss . For the same lineages , a similar trend is observed for HOPs , although it is not as pronounced ( with 1 . 34 , 1 . 34 and 1 . 21 genes per cluster for singleton , U2-specific and ancestral HOPs , respectively ) . This decrease in gene cluster size is not due to the preferential insertion of new genes near older acquired genes , as we analyzed the cluster sizes of sets of ORFans and HOPs per introgression event ( i . e . , those originating on the same internal branch ) . A few of the clustered ORFans were located near genes of known phage functions , but a recent exhaustive study into viral ORFans has suggested that phages may play a lesser role in transferring ORFans to prokaryotes than previously thought [28] . Since the split from their common ancestor , the 13 E . coli and Shigella species have accumulated between 180 and 350 kb of foreign DNA per strain ( Table 1 ) . Aside from these additions , each of these strains has also lost between 30 and 190 kb of DNA that has been acquired and maintained in other strains . The two EHEC strains ( E . coli EDL and Sakai ) show the highest net gain of DNA , whereas the Shigella strains , E . coli K-12 and W3110 show the lowest . To compare the rates at which lineages vary in rates of DNA gain and loss , we calculated the amounts of DNA acquired and lost in relation to the branch lengths in the tree relating the 13 tested genomes . The rates on individual branches indicate that closely related strains can differ by over two orders of magnitude in the rates at which newly acquired DNA is gained and retained ( E . coli Sakai vs . S . dysenteriae ) but less than 20-fold in the rates at which such DNA is lost ( E . coli EDL933 vs . S . boydii ) ( Supplementary Table S2 ) . It should be noted that branch lengths can also vary for other reasons ( such as variation in substitution rates and differing rates of recombination ) , but these are most likely compensated due to the extensive gene set employed here . Gene acquisition rates for both ORFans and HOPs are higher on the internal branches leading to the EHEC and UPEC strains , and in contrast , rates of loss for acquired DNA are highest on all branches descending from the SC ancestor leading to the Shigella species . Taken together , strains that gain the lowest amounts of DNA , lose the highest amounts of acquired DNA with the result that their genomes have lower numbers of unique genes . There has been a continual gain and loss of ORFans and HOPs during the evolution and diversification of E . coli ( Supplementary Figures S2 and S3 ) , and based on the distribution of ORFans and HOPs in the 13 tested genomes , HOPs have a higher rate of origination , but ORFans are more likely to be retained . Since many , possibly most , genes are transient and not present in any contemporary genome , it is not possible to monitor the full complement of genes that are gained and lost in these lineages by comparing their present-day gene repertoires . However , the patterns of retention of genes assigned to evolutionary lineages of different ages offer a glimpse into the fate of acquired sequences . Among genes that originated in the ancestor to all 13 strains examined , 78% ( 61% of the ORFans and 95% of the HOPs ) were lost in one or more of the descendant lineages , whereas 96% ( 95% of the ORFans and 97% of the HOPs ) of the genes acquired on the next older branch , SCE , were lost . Overall , genes acquired on the ancestral branch have higher retention rates than those genes acquired on more recent branches . Combining the numbers of ORFans and HOPs , Shigella spp . ( including S . dysenteriae ) show significantly lower retention rates compared to E . coli strains ( 65% vs . 88%; p<0 . 01 ) , which is not surprising since Shigella species have the highest rates of loss of recently acquired genes . The lower retention rates in Shigella spp . result from both significantly more gene inactivations ( 11% vs . 5% in E . coli , p<0 . 01 ) and gene losses ( 24% vs . 8% lost in E . coli , p<0 . 01 ) , and though disruptions occur to a similar extent in both sets of acquired genes , HOPs are more frequently lost than retained as inactivated genes ( Table 2 , Supplementary Figure S3 ) . Also , ORFans are inactivated predominantly by truncations , whereas HOPs are more often disrupted by insertion sequences ( Supplementary Table S3 ) . Although pseudogenes have been shown to be largely genome-specific [7] , [9] , [16] , it was expected that some would be retained in multiple lineages over the short evolutionary time-span examined in this study . However , more than half of the inactivated ORFans and HOPs exist only in a single genome , whereas their functional homologs are usually present in several genomes ( data not shown ) . Similarly , over half of the losses of acquired genes are also genome specific ( i . e . , losses of the only member of a gene family ) , confirming the high turnover rate observed for inactivated DNA .
Gene gain and loss are ongoing processes in microbial genomes , resulting in a diversity in genome sizes , even among closely related strains within a bacterial species [3] , [29] . By comparing the genome contents of sequenced representatives of the E . coli/Shigella clade , and by mapping the phylogenetic distribution of every gene present in these genomes , we find that the rates of change in novel genes can differ over 200-fold between strains and lineages . Moreover , genes of different phylogenetic origins arise and persist at very different rates . For example , ORFan genes , i . e . , those with no homologs outside of the group of bacteria examined , emerge less frequently than do genes originating by acquisition from other bacteria ( termed “HOPs” ) , but are , on average , about eight times more likely to be maintained . Of the genes acquired on the ancestral branch , nearly 39% of the ORFans , but only 5% of HOPs , are present in all 13 genomes indicating that they now provide functions integral to all strains . The difference in the persistence of ORFans and HOPs is surprising because those genes acquired from other bacteria ( i . e . , HOPs ) typically encode functional proteins in the donor and could be immediately useful to the recipient , whereas the ORFans , whose origins are less certain , have probably never served a function in a cellular genome prior to their acquisition . The disparity in the types of properties conferred by these two classes of genes is supported by their assignment to known functional categories: whereas nearly a quarter of the HOPs could be designated a COG category , less than 1% of ORFans could . Although ORFans are often poorly annotated and resist functional characterization by comparative approaches ( partially due to their characteristically short length and atypical composition ) , several lines of evidence indicate that they encode functional proteins [20] , [30] , including structural in vitro analyses on E . coli ORFans ( unpublished data ) . Therefore , the retention of ORFans may reside in the fact that they confer truly novel ( but as yet unknown ) functions , as opposed to traits that are apt to be redundant to the recipient organism . Alternatively , as ORFans are generally thought to be derived from selfish mobile elements ( but see [28] ) , some might be perpetuated by encoding selfish functions themselves . The distributions of ORFans and HOPs show that sequences that do not provide a useful function are eliminated and that bacterial genomes are not repositories of non-functional genes . This parallels the situation observed for pseudogenes , which , due to their rapid removal , are largely strain- or genome-specific [7] . Because the most-recently acquired genes are the least likely to supply an immediately useful function , we might expect that the newest genes in a genome are the most rapidly removed [18] . Indeed , comparing the two oldest branches indicates that while 33% of the genes gained on the ancestral branch are lost in each extant genome , 42% of the genes gained on a younger branch ( SCE ) are lost . From the present dataset , it is difficult to assess how this trend continues because relatively few genes are introduced on each branch ( only 9 and 13 genes on SC and S4 respectively ) , and in younger clades , there are successively fewer genomes from which the gene can be eliminated . However , the low numbers of genes mapped to these internal branches probably reflects the fact that relatively few acquired genes are being maintained . The density of sequenced genomes has allowed the use of phylogenetic methods to assess the dynamics of gene contents within several bacterial species , and has shown that rates of DNA gain and loss are often strain or lineage specific . Based on the same genomes analyzed in the present study , Hershberg and co-workers [26] found that the rates of gene loss in Shigella species were consistently higher than in related strains of E . coli , presumably due to reduced selection brought about by their small effective population sizes . Our data agree with these findings , and additionally , show that Shigella species also have lower rates of gene acquisition and lower rates of retaining acquired genes . Taken together , the inactivation and subsequent deletion of resident genes coupled with decreased levels of gene acquisition and subsequent persistence accounts for the reduced size of Shigella genomes . Applying a similar approach , Vernikos and co-workers [31] analyzed the genes acquired by the strains of Salmonella enterica for which genome sequences are available . In Salmonella , most of the acquired genes have low GC-contents and are still “ameliorating” , i . e . , adjusting their base composition towards that of the host genome [31] , [32] , similar to results observed for acquired sequences in the Gammaproteobacteria as a whole [20] . That amelioration has been observed in studies on Salmonella and the Gammaproteobacteria , but not in E . coli , is due to the fact that the sequenced strains of E . coli span a much shorter timescale and have not yet accumulated sufficient numbers of mutations to noticeably alter the average base composition of genes . In addition to assessing genome dynamics by following the presence and absence of acquired genes , we also traced the formation of pseudogenes to more closely monitor the mechanisms by which genes are inactivated and eliminated from these genomes . Pseudogenes in our analyses have restricted distributions , and nearly half of the inactivated ORFans and HOPs occur in only a single genome . In that the formation of pseudogenes is an ongoing process , their very restricted distributions denote that inactivated genes are eliminated rapidly from the genome and imply that newly acquired genes that are not immediately functional are also subject to rapid removal . Although such assessments of gene contents are based only on those genes now present in contemporary genomes , the recognition of pseudogenes can provide additional insights into the evolution and dynamics of genomes . The inclusion of pseudogenes in the present analysis provides some indication that high numbers of genes are gained and lost without leaving traces of their introgression [7] , [33] . In conclusion , comparative genomics of multiple closely related strains provides high-resolution assessments and quantifications of gene fluxes in an evolutionary context [31] , [34] , and allows specific estimations of the processes of gene inactivation and deletion . Within the sequenced strains of E . coli and Shigella spp . , we detected large differences among closely related lineages in the rates of gene acquisition and loss , but also differences in gene retention rates due to the source of acquired genes . The higher retention rate observed in the functionally obscure ORFan genes suggests that there are unknown adaptive benefits to these small acquired genes .
To trace the history of each gene in the sequenced E . coli and Shigella genomes , it is first necessary to resolve the phylogenetic relationships among these 13 strains . We based this phylogeny on the core set of single-copy genes identified by Lerat et al . [35] as showing virtually no evidence of lateral gene transfer within the Gammaproteobacteria . The seven sequenced E . coli genomes ( E . coli K-12 [36] , E . coli W3110 , E . coli Sakai [37] , E . coli EDL933 [38] , E . coli CFT073 [21] , E . coli UTI89 [39] and E . coli 536 ) and six sequenced Shigella genomes ( S . flexneri 301 [40] , S . flexneri 2457 , S . flexneri 8401 [41] , S . dysenteriae [22] , S . boydii [22] and S . sonnei [22] ) were searched via BLASTP [42] for orthologs of these core genes , applying an E-value<1−10 and a match length >75% . Of the 203 genes identified by Lerat et al . [35] , 169 single copy genes met these criteria of orthology and were used for phylogenetic reconstruction . Concatenated sequences of these 169 genes from all 13 E . coli and Shigella genomes were aligned using MAFFT [43] and the alignment was edited to remove gaps using Gblocks [44] . A maximum likelihood tree ( using DNAML module of PHYLIP; http://evolution . genetics . washington . edu/phylip . html ) was generated using the concatenated orthologous sequences of Salmonella enterica as the outgroup . The genome sequences of the 367 prokaryotes ( 339 bacteria and 28 archaea ) available at the time of this study were retrieved from GenBank ( ftp . ncbi . nih . gov/genbank/genomes/Bacteria/; August 2006 ) , and an in-house database was created by extracting protein sequences from all but the 13 E . coli and Shigella genomes . Newly acquired genes can be of two types: ORFans , genes with no detectable homolog in the databases , and HOPs ( heterogeneous occurrence in prokaryotes [20] ) , genes with homologs in distantly related species . ORFans in each of the 13 E . coli and Shigella genomes were identified as described previously in Daubin and Ochman [20] . In brief , all protein sequences from these genomes were compared with the database using BLASTP , applying an E-value cutoff of 0 . 01 to uncover distant homologs . Those genes without a match at this relaxed cutoff were considered to be potential ORFans . To eliminate possible artifacts due to annotation errors , we queried gammaproteobacterial genomes with all putative ORFans using TBLASTN and excluded those with matches having E-value cutoff<10−5 and alignment lengths >50% . The distribution of all remaining ORFans among the 13 strains of E . coli and Shigella were obtained by comparing the ORFans from each E . coli and Shigella genome with the remaining 12 genomes using TBLASTN with an E-value cutoff of 10−5 . Based on their distribution among strains , ORFans were assigned to clades of the E . coli phylogeny . The orthology of ORFans present in more than one strain was confirmed by genome context . In contrast to ORFans , HOPs have homologs in other prokaryotic genomes . To qualify as a HOP , a protein must be restricted to an E . coli clade , absent from closely related genomes , and have a homolog in a more distantly related prokaryotic genome . We performed BLAST analyses to identify genes that displayed such sporadic distributions . For example , the 9 HOPs restricted to clade S1 ( Figure 1 ) were present in S . flexneri 301 and S . flexneri 2457 , lacked homologs in the other E . coli and Shigella genomes , but had homologs in some distantly related genomes . We mapped the branch on which a gene was acquired by reconstructing the parsimonious scenario that explains the present-day gene distribution [18] , such that the path that invokes the lowest number of events was viewed as the most evolutionarily plausible . In these reconstructions , gene gains and losses were viewed as individual and equally likely events . The genes acquired on each branch are listed in Supplementary Table S4 . Classification of the identified ORFans and HOPs to Clusters of Orthologous Groups ( COGs ) [45] was performed using in-house scripts . Pseudogenes were identified by using Ψ-Φ as described previously [7] , [9] , [16] . In this procedure , the annotated proteins from each genome were queried against the complete nucleotide sequence of every other strain with E-value cutoffs of 10−15 and sequence identities >75% . The Ψ-Φ program suite uses the TBLASTN output to return lists of predicted disrupted genes , which are manually curated . To identify gene-inactivating mutations , the predicted pseudogenes were aligned against their orthologs using CLUSTALW [46] . Gene-inactivating mutations were grouped into five classes: frameshifts ( insertions or deletions of 1 or 2 nucleotides in length ) , deletions ( >2 nucleotides in length ) , insertions ( >2 nucleotides in length ) , truncations ( large deletions at either or both ends of a coding sequence ) , nonsense mutations , or a combination of different classes . | Changes in genetic repertoires can alter the adaptive strategy of an organism , especially in bacteria , in which genes are continually gained and lost . Mapping the gains and losses of genes in the densely sequenced clade of Escherichia coli and Shigella shows that these genomes harbour two types of acquired genes: HOPs , which are those acquired genes with homologs in distantly related bacteria; and ORFans , which are genes without any known homologs . Surprisingly , the two classes of acquired genes display very different patterns of gain and loss . HOPs are acquired more frequently , though they rarely persist in the recipient genomes . In contrast , ORFans are much more likely to be maintained over evolutionary timescales , suggesting that despite their unknown origins , they will more often confer novel and beneficial traits to the recipient genome . | [
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] | 2008 | The Emergence and Fate of Horizontally Acquired Genes in Escherichia coli |
RIG-I like receptors ( RLRs ) recognize cytosolic viral RNA and initiate innate immunity; they increase the production of type I interferon ( IFN ) and the transcription of a series of antiviral genes to protect the host organism . Accurate regulation of the RLR pathway is important for avoiding tissue injury induced by excessive immune response . HSCARG is a newly reported negative regulator of NF-κB . Here we demonstrated that HSCARG participates in innate immunity . HSCARG inhibited the cellular antiviral response in an NF-κB independent manner , whereas deficiency of HSCARG had an opposite effect . After viral infection , HSCARG interacted with tumor necrosis receptor-associated factor 3 ( TRAF3 ) and inhibited its ubiquitination by promoting the recruitment of OTUB1 to TRAF3 . Knockout of HSCARG attenuated the de-ubiquitination of TRAF3 by OTUB1 , and knockdown of OTUB1 abolished the effect of HSCARG . HSCARG also interacted with Ikappa-B kinase epsilon ( IKKε ) after viral infection and impaired the association between TRAF3 and IKKε , which further decreased the phosphorylation of IKKε and interferon response factor 3 ( IRF3 ) , thus suppressed the dimerization and nuclear translocation of IRF3 . Moreover , knockdown of TRAF3 dampened the inhibitory effect of IFN-β transcription by HSCARG , suggesting that TRAF3 is necessary for HSCARG to down-regulate RLR pathway . This study demonstrated that HSCARG is a negative regulator that enables balanced antiviral innate immunity .
Innate immunity is the first line of defense for organisms to defeat disease-causing pathogens , such as viruses , bacteria , and parasites . The production of interferons ( IFNs ) is the core of the cellular antiviral response [1] . IFNs are secreted by virus-infected cells and are recognized by the interferon-α/β receptor in the cell membrane; they then promote the transcription of a series of antiviral genes through the Janus kinase-signal transducer and activator of transcription ( JAK-STAT ) pathway [2] . The induction of Type I IFNs is governed by both of the transcription factors NF-κB and IRF3/IRF7 that are activated by pattern recognition receptors ( PRRs ) [3] , [4] . Toll-like receptors ( TLRs ) and RIG-I like receptors ( RLRs ) are the main PRRs involved in the cell-specific regulation of Type I IFNs [5] . RLRs include retinoic acid inducible gene I ( RIG-I ) [6] and melanoma differentiation-associated gene 5 ( MDA5 ) [7] , which recognize viral RNA through the RNA helicase domain ( RLD ) and then recruit mitochondrial-associated virus stimulator ( MAVS , also known as IPS-1 , Cardif , or VISA ) [8]–[11] through CARD-CARD interaction . MAVS further recruits tumor necrosis receptor-associated factor 3 ( TRAF3 ) [12] and results in TRAF3 K63-linked auto-ubiquitination , which provides docking sites for the TANK binding kinase 1/I kappa-B kinase epsilon ( TBK1/IKKε ) complex [13] , [14] . This complex undergoes auto-phosphorylation-mediated activation , subsequently phosphorylates its substrate IRF3/7 , and then induces IRF3/IRF7 to form homodimers or heterodimers that translocate into the nucleus to bind the interferon promoter positive regulatory domains [15] , [16] . Besides , after TLR ligand stimulation , NF-κB is activated as well and starts type I IFN transcription through the MyD88-dependent or TRIF-dependent pathway [17] . More importantly , an aberrant RLR pathway is associated with multiple inflammatory diseases , so the intensity and duration of the antiviral response needs to be precisely regulated to prevent inflammation or autoimmune disease . Increasing evidence has shown that ubiquitination and de-ubiquitination of adaptor proteins such as RIG-I , MAVS , TRAF3 , and IRF3 are widely involved in the precise regulation of antiviral response activity [18]–[22] . TRAF3 belongs to the Ring finger ubiquitin E3 ligase family TRAF and is a versatile immune regulator with multiple functions in several signaling pathways [23] . It contributes to type I IFN production while attenuating mitogen-activated protein kinase activation and the alternative NF-κB signaling pathway through the recruitment of different complexes and various modes of auto-ubiquitination [14] . Triad3A is reported to conjugate TRAF3 with a K48-linked polyubiquitin chain , while it seems to generate a K63-linked polyubiquitin chain for itself [21] , [24] . So far , several deubiquitinases such as DUBA , OTUB1/2 , UCHL1 , and OTUD7B have been reported to cleave TRAF3 ubiquitin chains in response to viral infection [25]–[28] . However , how these regulators cooperate to orchestrate TRAF3 activity in specific contexts remains elusive . Besides , some novel proteins involved in the complicated fine-tuning of TRAF3 activity await discovery . HSCARG ( also named NmrA-like family domain-containing protein 1 or NMRAL1 ) is a newly-identified NADPH sensor and negative regulator of NF-κB [29] . It contains a Rossmann-fold in the N terminus and forms an asymmetrical dimer with only one subunit occupied by one NADP molecule [30] . In response to decreases in the NADPH/NADP+ ratio within cells , HSCARG interacts with argininosuccinate synthetase ( AS ) more potently , resulting in stronger inhibition of AS activity and NO production [31] . Besides , HSCARG negatively regulates TNFα-stimulated NF-κB activity by suppressing IKKβ phosphorylation and further blocking the degradation of IκBα [29] . Since NF-κB is an essential transcription factor in the cellular antiviral response , and RNA-seq analysis of HSCARG wild-type and HSCARG−/− HCT116 cells showed that HSCARG affects the mRNA level of several adaptor proteins in the RLR pathway , such as TBK1 , RIG-I , MDA5 , and MITA , we set out to investigate the function of HSCARG in cellular antiviral pathway . Here , we found that HSCARG dampens Sendai virus induced RLR signaling pathway activity through repressing K63-linked ubiquitination of TRAF3 . This decreases IFN-β production and attenuates cellular antiviral response to prevent excessive inflammation .
To investigate whether HSCARG functions in the cellular antiviral response , we assessed its effect on the activity of IFN-β promoter with a luciferase reporter assay . We found that HSCARG dose-dependently decreased the IFN-β reporter activity induced by Sendai virus ( SeV ) infection ( Figure 1A ) , similar to the positive control PCBP2 which promotes MAVS degradation by recruiting the HECT ubiquitin ligase AIP4 thus repressing IFN-β activity [20] . Consistently , knockdown of HSCARG strongly enhanced the IFN-β promoter activity ( Figure 1A , right ) . STK38 [20] was used as a negative control to confirm that the inhibitory effect of HSCARG is specific in the luciferase reporter assay system ( Fig . S1A ) . Next , in order to test the effect of HSCARG on various components of the RLR pathway , we respectively transfected HEK293T cells with plasmids expressing RIG-I-N ( N indicates the amino-terminal CARD domain; the full version is in a self-repressed state at rest ) , MDA5-N ( same as RIG-I-N ) , MAVS , TBK1 , IKKε , IRF3 , IRF7 with or without HSCARG , and measured the IFN-β promoter activity . The reporter assay results showed that HSCARG inhibited the IFN-β activity induced by almost all the components upstream of IRF3/7 ( Figure 1B ) , without affecting their stability ( Figure S1B ) ; whereas the absence of HSCARG significantly increased the RLR adaptors-mediated IFN-β promoter activity ( Figure 1C ) . Furthermore , we examined the mRNA level of IFN-β in wild-type and HSCARG−/− HCT116 cells . As expected , cells without HSCARG exhibited an increased IFN-β mRNA level in response to viral infection ( Figure 1D ) . Besides , we evaluated the secreted IFN-β level in the supernatant medium by ELISA and found that HSCARG clearly suppressed the IFN-β production and secretion triggered by SeV infection in a dose-dependent manner ( Figure 1E , left ) , as well as essential RLR adaptors-mediated IFN-β production ( Figure 1E , right ) . Finally , we performed the plaque assay in an attempt to detect the physiological importance of HSCARG in the cellular antiviral response . The results showed that cells overexpressing HSCARG impaired the antiviral response mediated by MAVS , TBK1 , TRAF3 and IKKε , decreased the resistance to vesicular stomatitis virus ( VSV ) infection , and led to increased VSV propagation , which was consistent with the PCBP2 positive control ( Figure 1F ) . Taking all the data together , HSCARG significantly inhibits the cellular antiviral response mediated by the RLR signaling pathway . As reported previously , HSCARG is a negative regulator of NF-κB that is important for IFN-β transcription . Therefore , it was necessary to detect whether HSCARG is solely dependent on NF-κB to accomplish its negative regulatory role in IFN-β production . We blocked NF-κB activation by knocking down p65 with a cocktail of p65 siRNA or using the specific NF-κB inhibitor , PDTC , and then assessed whether HSCARG lost its inhibitory effect on IFN-β promoter activity . The reporter assay results showed that blocking NF-κB activity by knocking down p65 only slightly impaired the inhibitory effect of HSCARG on the IFN-β and ISRE reporters ( Figure S2A ) . Besides , blocking NF-κB activity with PDTC gave consistent results without affecting ISRE activity ( Figure S2B ) . These data suggested that rather than via the NF-κB pathway , HSCARG down-regulates IFN-β by regulating IRF3 activation . Because IRF3 nuclear translocation is mainly governed by activating the RLR signaling pathway , in an attempt to test our hypothesis , co-immunoprecipitation ( Co-IP ) analysis was performed to identify the target proteins of HSCARG in the RLR pathway ( Figure S3A ) . The results revealed that HSCARG interacted most potently with TRAF3 ( Figure S3A ) , and this association was confirmed under physiological condition in which the interaction was enhanced by SeV infection ( Figure 2A ) . Furthermore , the essential domain of TRAF3 that binds with HSCARG was delineated by using a series of TRAF3 truncation constructs , and the zinc finger and isoleucine zipper domains of TRAF3 were found to be the crucial regions responsible for the association of TRAF3 with HSCARG ( Figure 2B ) . It is well-known that TRAF3 is modified with a poly-ubiquitin chain to provide a scaffold for complex formation; this promoted us to study the effect of HSCARG on TRAF3 ubiquitination . First , we found that SeV infection increased the level of endogenous TRAF3 ubiquitination ( Figure 2C ) . To explore the type of TRAF3 ubiquitin chains in response to early-phase viral infection , HEK293T cells transfected with TRAF3 were treated with or without the proteasome inhibitor MG132 , and the ubiquitination level of TRAF3 was monitored . Compared to the control , MG132 treatment did not change the TRAF3 ubiquitination level ( Figure 2D ) , suggesting that TRAF3 barely undergoes K48-linked ubiquitination at 6 h after viral infection . In addition , we constructed various ubiquitin mutants including K63R , K48R , K63 , and K48 to determine the TRAF3 poly-ubiquitin chain type . The level of TRAF3 ubiquitination evidently decreased when lysine 63 was mutated to arginine ( K63R ) , but showed no significant change when lysine 48 was mutated to arginine ( K48R ) ( Figure 2E ) . These data demonstrated that TRAF3 is mainly modified with K63-linked ubiquitination to transduce signals from MAVS to TBK1/IKKε in the early phase of viral infection . We further investigated whether HSCARG affected TRAF3 ubiquitination . His-ubiquitin pull-down assay showed that HSCARG markedly decreased TRAF3 ubiquitination . Consistently , TRAF3 poly-ubiquitination increased intensely in HSCARG−/− cells ( Figure 2F ) . In summary , these results demonstrated that HSCARG associates with TRAF3 and suppresses the K63-linked ubiquitination of TRAF3 . Because HSCARG does not belong to any known deubiquitinase family , it is reasonable to hypothesize that HSCARG works through recruiting a deubiquitinase to cleave the TRAF3 ubiquitin chain . We first screened the reported deubiquitinases for TRAF3 and found that HSCARG interacted with OTUB1 more potently than OTUB2 , UCHL1 , OTUD7B , and USP25 ( Figure 3A , Figure S3B ) , and this interaction was enhanced by viral infection ( Figure 3A ) . Hence , we focused on OTUB1 to further elucidate the regulatory mechanism of HSCARG in TRAF3 ubiquitination . Co-IP showed that overexpression of HSCARG enhanced the interaction of TRAF3 with OTUB1 , whereas in HSCARG−/− cells , this association was severely attenuated ( Figure 3B ) . These findings indicated that HSCARG promotes the recruitment of OTUB1 to TRAF3 . Furthermore , OTUB1 lost a majority of its de-ubiquitination function in HSCARG−/− cells ( Figure 3C ) , and its inhibition of IFN-β activity was also attenuated when HSCARG was knocked out ( Figure S3C ) , while in cells with depleted OTUB1 , HSCARG no longer inhibited TRAF3 ubiquitination ( Figure 3D ) . These results indicated that OTUB1 and HSCARG function cooperatively in down-regulating TRAF3 ubiquitination . It is known that IKKε and TBK1 are downstream kinases of TRAF3 , and they are recruited to MAVS-TRAF3 complex after TRAF3 ubiquitination . Inhibition of HSCARG on TRAF3 ubiquitination drove us to further explore the effect of HSCARG on the recruitment of IKKε/TBK1 . Co-IP analysis showed that endogenous HSCARG interacted with IKKε rather than TBK1 ( Figure 4A , Figure S4A ) . Consistent with previous reports , IKKε and TRAF3 formed a complex that was stabilized by SeV infection ( Figure S4C ) . Ectopic HSCARG impaired the interaction between TRAF3 and IKKε ( Figure 4B ) ; however , it had no effect on the stability of TRAF3-TBK1 complex ( Figure S4B ) . On the contrary , knockdown of HSCARG increased the association of TRAF3 with IKKε ( Figure 4C ) . After recruitment to the TRAF3 complex , IKKε is phosphorylated and sequentially phosphorylates its substrate IRF3 . As HSCARG interfered with the recruitment of IKKε , it probably dampened the phosphorylation of IKKε . To confirm this hypothesis , we investigated the effect of HSCARG on IKKε and IRF3 phosphorylation activity by performing in vitro phosphorylation assays . We found that HSCARG impaired both IKKε and IRF3 phosphorylation level in vitro ( Figure 4D ) . It is worth mentioning that TRAF3 was found to be attached to the enriched IKKε , suggesting that TRAF3 is important for HSCARG to regulate IKKε and IRF3 phosphorylation . Consequently , similar to the RNF5 positive control ( RNF5 is an ubiquitin ligase that down-regulates antiviral response through mediating the degradation of MITA [32] ) , HSCARG suppressed the IRF3 phosphorylation and dimerization induced by SeV in vivo ( Figure 4E , 4F ) . In addition , we assessed the nuclear translocation of IRF3 in HSCARG−/− and wild-type HeLa cells , respectively . In response to SeV infection , more IRF3 transported into the nucleus in the HSCARG−/− cells , suggesting that HSCARG decreases the translocation of IRF3 ( Figure 4G ) . Taken together , HSCARG interacts with IKKε and blocks the formation of the TRAF3-IKKε complex , which subsequently impairs the phosphorylation of IKKε and IRF3 and finally decreases IFN-β production . The above data showed that HSCARG blocks TRAF3 ubiquitination and so triggers a series of downstream effects . Hence we used the stable HEK293T cells with silenced TRAF3 or TRAF3 siRNA to detect whether TRAF3 is essential for the regulatory function of HSCARG . The IFN-β reporter assay showed that in cells with depleted TRAF3 , the induction of IFN-β was reduced greatly and the inhibitory effect of HSCARG was attenuated markedly ( Figure 5 ) , without affecting the stability of endogenous TRAF3 ( Figure S5 ) . These data suggested that TRAF3 is necessary for HSCARG to decrease IFN-β activity .
Previous studies have demonstrated that HSCARG is a negative regulator of TNFα- , IL-1β-induced NF-κB activity by targeting the canonical IκB kinase complex [29] . Here , we identified HSCARG as a critical component in the virus-triggered IRF3 activation pathway and the cellular antiviral response , and elucidated the underlying mechanism . TRAF3 is a key molecule in virus-triggered IRF3 activation and IFN induction [23]; it is the pivot of the TRIF- and MyD88-dependent , TLR-independent pathways [33] . To achieve its negative regulatory function in cellular antiviral response , HSCARG associates with TRAF3 and cooperates with OTUB1 to remove TRAF3 poly-ubiquitin chain that is important for the recruitment of TBK1-IKKε kinase complex . This further decreases the recruitment of IKKε , impairs IRF3 phosphorylation and dimerization , and results in a reduced level of IFN-β transcription ( Figure 6 ) . Recent studies have indicated that ubiquitination and de-ubiquitination play critical roles in regulating virus-triggered IFN production to assure that the antiviral response is modulated properly [34]–[36] . For example , when regulated by E3 ligases such as Triad3A and deubiquitinases including OTUB1/2 , DUBA , and UCHL1 , TRAF3 undergoes a biphasic ubiquitination that varies between the K63-linked and the K48-linked type in different phases after viral stimulation to maintain its activity in a suitable state [24]–[27] , [37] . However , the detailed mechanism of how these regulatory proteins function together in fine-tuning the TRAF3 activity , and whether other proteins are involved in this complicated regulation remained to be discovered . Here , we found that HSCARG selectively utilized OTUB1 to suppress TRAF3 ubiquitination ( Figure 3 ) . HSCARG neither interacted with DUBA nor promoted its recruitment ( Figure S6A , B ) , and depletion of DUBA had no effect on HSCARG in inhibiting TRAF3 ubiquitination ( Figure S6C ) . These data suggested that HSCARG specifically relies on OTUB1 to repress TRAF3 ubiquitination . Based on this , it is reasonable to speculate that the stimulus-specific regulation of TRAF3 ubiquitination is achieved through recruiting distinct E3 ligases or deubiquitinases mediated by specific adaptor proteins . Our data suggest that HSCARG is such an adaptor protein , which cooperates with a specific deubiquitinase to remove the TRAF3 ubiquitin chain in response to a viral stimulus , and to achieve accurate and timely modulation of TRAF3 activity . In response to different PAMPs , NF-κB and IRF3 promote pro-inflammatory cytokines and type I IFN production to initiate innate immunity [1] . There is close crosstalk between these two pathways at different steps to ensure a balanced and robust defense . For example , NEMO is not only the modulating subunit for the canonical IKK complex but also participates in the TBK1-IKKε complex [38] . Our previous studies demonstrated that HSCARG plays a critical role in TNFα- and IL-1β-induced NF-κB signaling . In this study , we further characterized the function of HSCARG in IRF3 signaling in response to viral invasion . Taken together , we propose that HSCARG functions via cross-talk to coordinate the activities of NF-κB and IRF3 , and to balance the production of pro-inflammatory cytokines and type I interferons . In response to different stimuli , HSCARG regulates the innate immune response either by inhibiting the NF-κB activity mediated by the canonical IKK complex or by modulating the IRF3 activity mediated by TRAF3-IKKε . Virus-triggered IFNs induction is important for the innate antiviral response . The intensity and duration of the antiviral response must be limited to an optimal range; otherwise it could cause inflammatory damage and finally lead to autoimmune disease . The production of IFN-β and relevant pro-inflammatory cytokines is regulated delicately through various molecules and distinct mechanisms in a specific spatiotemporal manner . Here we demonstrated that HSCARG plays an important role in the precise control of the cellular antiviral response by negatively regulating TRAF3 ubiquitination and IFN-β production , providing a potential target for the treatment of chronic inflammation and autoimmune disease .
Cells were transfected with the indicated plasmids using PEI ( Polyscience , USA ) following the manufacturer's instructions . Screen FectA ( S-3001 ) ( InCella , Germany ) was used for shRNA or siRNA transfection . The following commercial antibodies were used: monoclonal anti-Flag ( F3165 ) and anti-HA ( H9658 ) from Sigma ( USA ) ; anti-Myc ( M047-3 ) , anti-His ( D291-3 ) and anti-β-actin ( PM053 ) from MBL ( Japan ) ; anti-P-IRF3 pS386 ( 2562-1 ) and anti-TRAF3 ( 3555-1 ) from EPITOMICS ( USA ) ; anti-p65 ( 4764 ) from Cell Signaling Tech ( USA ) . Mouse polyclonal antibody against HSCARG and IRF3 were generated by immunizing mice with purified HSCARG and IRF3 proteins . NF-κB , IFN-β , and ISRE promoter reporter plasmids , mammalian expression plasmids for Flag-RIG-I-N , Flag-MDA5-N , HA-MAVS , HA-TRIF , Flag-TBK1 and Flag-IKKε were kind gifts from Profs . Zhengfan Jiang and Danying Chen; Flag-OTUB1 and Flag-OTUB2 were kind gifts from Prof . Hongbing Shu; Flag-USP25 was from Drs . Dong Chen and Bo Zhong; Flag-OTUD7B was from Dr . Hsiu-Ming Shih . HSCARG expression vectors tagged with Flag , HA or Myc were constructed previously . His-ubiquitin and its mutants K63R , K48R , K63 , and K48 were cloned to pEF1-HisA . HA-TRAF3 was sub-cloned to pCMV-HA , Flag-TRAF3 truncation constructs 1–108 , 109–347 , and 348–568 were inserted into pRK-Flag . The HSCARG−/− HCT116 cell-line was generated using the Cre/loxP system by inserting an additional sequence into the fourth exon and additional stop codons to disrupt HSCARG translation . The HSCARG−/− HEK 293T and HeLa cell-line was generated by the TALEN method using the FastTALE™ TALEN Assembly Kit ( SIDANSAI ) . TRAF3 knockdown HEK293T cells were generated by transfecting TRAF3 shRNA and selecting with puromycin ( 2 µg/ml ) for 3 weeks . HEK293T and HCT116 cells were cultured in Iscove's modified Dulbecco's medium ( Hyclone , USA ) with 10% fetal calf serum ( Genstar , China ) at 37°C in a 5% CO2 incubator . Sendai virus was from Prof . Danying Chen , propagated in chicken embryos , and titrated by the hemagglutination test . Cells were infected at 40 HAU/ml for the indicated times . Vesicular stomatitis virus ( VSV ) was from Prof . Zhengfan Jiang and was titrated by the plaque assay . Cells were infected at an MOI of 1 . HEK293T cells ( 1×105 ) were seeded on a 24-well plate and transfected with the IFN-β firefly luciferase reporter plasmid , Renilla reporter plasmid , and other plasmids as indicated . 24 h later , cells were harvested and luciferase assay was performed using the Dual-luciferase reporter assay system ( Promega ) . All the experiments were performed in triplicate and repeated at least three times . Data shown are mean ± S . D . from one representative experiment . The siRNA cocktail for p65 ( sc-29140 ) , OTUB1 ( sc-76014 ) and TRAF3 ( sc-29510 ) were purchased from Santa Cruz ( USA ) . TRAF3 shRNA plasmids were chosen from Sigma TRC1 . 0 shRNA library . The targeted sequences of shRNA used are as follow . TRAF3 shRNA 2: 5′-tgtcaagagagcatcgtta; TRAF3 shRNA 4: 5′-ttggccgtttaagcagaaa; HSCARG shRNA: 5′- accttcatcgtgaccaatt; DUBA shRNA: 5′-cagtggtgaatcctaacaa . The non-silencing sequences were used as negative controls . HEK293T ( 1×105 ) cells were seeded on a 6-well plate and infected with SeV at 40 HAU/ml for indicated time . Cells were harvested and the mRNAs were extracted with Trizol ( Invitrogen ) . The primers used are as follow . HSCARG: 5′-gaagctgctcgctgatctg ( forward ) , 5′-aaggctgagcaccacagga ( reverse ) ; IFN-β: 5′-ccaacaagtgtctcctccaa ( forward ) , 5′-atagtctcattccagccagt ( reverse ) ; Actin: 5′-aagtgtgacgtggacatccgc ( forward ) , 5′-ccggactcgtcatactcctgct ( reverse ) . HEK293T cells seeded in 24-well plates were transiently transfected with indicated plasmids . 24 h later , cells were washed gently with preheated PBS and infected by VSV at an MOI of 1 for 1 h . Cells were then washed and cultured in fresh medium for 12 or 24 h . The supernatants were collected and diluted from 1∶10 to 1∶108 , and infected the confluent BHK21 cells at a dilution from 1∶106 to 1∶108 at 37°C for 1 h . After washing with preheated PBS again , wells were covered with 1 . 5% methylcellulose DMEM . 3 days later , cells were fixed in 0 . 5% glutaraldehyde for 30 min and stained with 1% crystal violet dissolved in 70% ethanol . Plaques were counted , averaged , and multiplied by the dilution factor to determine viral titer as Pfu/ml . The experiments were performed in triplicate for three times . Data shown are mean ± S . D . from one representative experiment . HEK293 cells ( 2×105 ) were seeded on a 12-well plate and transfected with indicated plasmids . 24 h later , cells were harvested with cold PBS and lysed by 30 µl 1 . 5% TritonX-100 lysis buffer for 30 min on ice , and then centrifuged at 14000 rpm for 20 min . The supernatants were mixed with 30 µl loading buffer ( 62 . 5 mM Tris-HCl , pH 6 . 8 , 15% glycerol and 1% deoxycholate ) . Samples were applied to a 8% native acrylamide gel ( free of SDS ) that was pre-run at 40 mA for 30 min in running buffer ( 25 mM Tris and 192 mM glycine , pH 8 . 4 ) with or without 1% deoxycholate for the cathode and anode chamber , respectively . And then the samples were electrophoresed for 60 min at 25 mA in 4°C . HEK293T cells were seeded on a 24-well plate and transfected with the indicated plasmids for triplicates the following day . At 18 h after transfection , cells were infected with SeV ( 40 HAU/ml ) for 12 h , and then collected the supernatant and ELISA was performed using human interferon β ELISA kit ( RD , USA ) . HEK293T cells were transfected with the indicated plasmids and harvested at 48 h after transfection . Then Co-IP analyses were performed following the procedure described previously [39] . The band intensity of interest was quantified using Odyssey Infrared Imaging System and software Odyssey V3 . 0 ( LI-COR Biosciences , USA ) and normalized to corresponding enriched protein amount . HEK293T cells were transfected with His-ubiquitin and relevant plasmids for 24 h . Cells were harvested and His-ubiquitin pull-down analysis was performed following the procedures described previously [39] . GST-tagged IRF3 ( 131–426 ) and His-tagged HSCARG were purified from E . coli BL21 ( DE3 ) , Flag-IKKε was immune-precipitated with anti-Flag antibody . Reaction mixture contained 2 µg GST-IRF3 , enriched IKKε , 0 . 5 µCi [γ-32P]-ATP , and 20 µl kinase reaction buffer ( 1 mM DTT , 50 mM KCl , 2 mM MgCl2 , 2 mM MnCl2 , 10 mM NaF , 1 mM Na3VO4 , 25 µM ATP ) . The reaction solution was incubated at 25°C for 30 min , and stopped by adding 20 µl loading buffer , and then separated by SDS-PAGE and auto-radiographed using a storage phosphor screen ( GE ) . Each experiment was performed in triplicates and repeated at least three times , and values were represented as mean ± S . D . The student's t-test was used to determine the difference between experimental and control groups , requiring p<0 . 05 for statistical significance . The accession numbers in the UniProtKB/SwissProt database for the proteins in the manuscript are as followed . HSCARG , Q9HBL8; TRAF3 , Q13114; RIG-I , O95786; MDA5 , Q9BYX4; MAVS , Q7Z434; PCBP2 , Q15366; TBK1 , Q9UHD2; IKKε , Q14164; IRF3 , Q14653; IRF7 , Q92985; RNF5 , Q99942; OTUB1 , Q96FW1; OTUB2 , Q96DC9; UCHL1 , P09936; DUBA , Q96G74; USP25 , Q9UHP3 . | Innate immunity is critical for the host to defeat pathogen invasion , and the production of interferon ( IFN ) is the core of the cellular antiviral response , this is mediated by the Toll-like receptor ( TLR ) and RIG-I like receptor ( RLR ) signaling pathways in most cell types . As aberrant activity of the immune response leads to immune-deficiency or autoimmune disease , identification of the regulators involved in immune balance is particularly important . Accumulating evidence shows that ubiquitination plays a key role in regulating virus-triggered IFN production to assure that the antiviral response is modulated properly . Here , we demonstrated that HSCARG is a novel negative regulator in the precise control of antiviral innate immunity . HSCARG inhibited IFN-β production by suppressing TRAF3 ubiquitination with the help of OTUB1 , leading to disassociation of downstream IKKε and impairment of IRF3 activity . As the pivot of the TLR , RLR , and non-canonical NF-κB pathways , TRAF3 is an extremely versatile immune regulator . Our study sheds light on the mechanism of specificity and diversity achievement in the complicated regulation of TRAF3 activity , suggesting that HSCARG is a potential target for the treatment of inflammatory and autoimmune diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biology",
"and",
"life",
"sciences",
"immunology"
] | 2014 | HSCARG Negatively Regulates the Cellular Antiviral RIG-I Like Receptor Signaling Pathway by Inhibiting TRAF3 Ubiquitination via Recruiting OTUB1 |
A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing ( NGS ) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs . Current approaches are primarily based on mutation frequencies of single-genes , which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes . We propose a novel mutation network method , VarWalker , to prioritize driver genes in large scale cancer mutation data . VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors . These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network . We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples . In each cancer , we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways . These cancer-specific mutation networks were then validated using independent datasets for each cancer . Importantly , VarWalker prioritizes well-known , infrequently mutated genes , which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches . Utilizing VarWalker , we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data .
Next-generation sequencing ( NGS ) technologies have enabled genome-wide identification of somatic mutations in large scale cancer samples . One major challenge in interpreting the large volume of mutation data is to distinguish ‘driver’ mutations from numerous neutral ‘passenger’ mutations to facilitate the identification of targetable genes and new drugs . So far , the most widely adopted method is to search for highly frequently mutated genes within one cancer type [1] , [2] . Although effective in many cases , frequency-based approaches suffer from disadvantages such as lack of power to detect infrequently mutated driver genes and failure to incorporate functional interconnections and regulations among genes . Recently , many new methods have been reported . For a more comprehensive review , please refer to [3] , [4] . The complex features of mutations derived from NGS data present great challenges for computational approaches , both genetically and technically . First , the probability that a gene is mutated in a sample , i . e . , the gene-based mutation rate , is influenced by both genetic and environmental factors . In this study , we only consider single nucleotide variants ( SNVs ) and small insertions and deletions ( indels ) , and we define a mutant gene ( abbreviated as MutGene ) if it harbors at least one non-silent deleterious SNV or indel ( see Materials and Methods ) . Assuming that mutations occur randomly across the genome , long genes have a better chance of harboring mutations ( e . g . , the gene TTN ) . Other factors , including sequence context , GC content , replication timing , chromatin organization , and alterations in mutation repair systems [5] , [6] , [7] , as well as personal lifestyle and mutagen exposure period and level , have an impact on the gene-based mutation rate in an individual . Second , mutation ‘hotspot’ families , among other factors , often contribute many genes to the list of top candidate genes that are ranked by frequency . For example , genes from the olfactory receptor family are frequently mutated in many cases [1] , including both normal and disease samples [8] . However , it remains unknown whether these mutations , or only some of them , are disease-related . Finally , sequence errors exist; however , large scale validation is still a challenge in NGS projects that involve hundreds of cancer samples . Since all of these factors accumulate non-clinically related events in mutation data , these biases should be considered when developing new approaches to prioritizing driver mutations . An alternative approach to detect possible driver genes overlays the mutation genes in the context of biological pathways or protein-protein interaction ( PPI ) networks and then performs integrative analyses to identify significantly altered pathways or subnetworks . In cancer , functional pathways or biological networks are frequently interrupted in many patients [9] , and their gene components present mutually exclusive or co-occurring patterns [10] . To date , only a few studies have searched the cooperative mutation modules underlying cancer [11] , [12] . Notably , the incorporation of other large scale genetic and/or genomic data , such as mRNA abundance [13] and methylation data [14] , can greatly improve the detection of driver genes . However , these datasets are not always available for the same patient cohort in large-scale sequencing projects , creating both challenges and a high demand to develop comprehensive approaches that can prioritize driver genes from mutation data . In this work , we propose VarWalker , a network-assisted approach that aims to prioritize potential driver genes and better interpret mutation data in NGS studies . Our goal is to develop a tool that can address the huge variations among cancer samples as well as implement conventional approaches in modern NGS data analysis . VarWalker performs sample-specific filtering and implements the Random Walk with Restart ( RWR ) algorithm to search for frequently interrupted interactions between MutGenes and their interactors . We argue that if an interaction is interrupted by mutations in one or two of its linking proteins across many samples , this interaction has a higher chance to be biologically important than an interaction in which only one protein is disrupted by mutations . We demonstrated VarWalker in two recent large-scale NGS benchmark studies: one involving 183 matched tumor/normal LUAD samples [15] and the other involving 121 matched melanoma samples [16] . In each cancer , we derived a consensus mutation network , which was shown to be significantly enriched with known cancer genes and cancer-related functional pathways . Importantly , we not only identified highly recurrently mutated genes , but also well-known yet infrequently mutated genes , thereby demonstrating the usefulness of VarWalker to prioritize driver genes from NGS data .
The detailed description of the VarWalker algorithm is provided in Materials and Methods . It has four steps ( Figure 1 ) . The first three steps are implemented within each single sample , and the last step is across multiple samples . In step 1 , for each sample , VarWalker assesses the mutation probabilities of all human genes by fitting them to a generalized additive model based on the patient- ( or sample- ) specific mutational profile . A weighted resample-based test is then performed to filter passenger genes that occur largely due to random events across the genome . Genes occurring with a frequency of ≥5% in random datasets were suggested for filtration . Step 2 includes the execution of the RWR algorithm in each sample to search for the interactions among the filtered MutGenes in the human interactome . RWR has been proven to be sensitive in identifying disease candidate genes and has been successfully applied in disease-phenotype analyses [17] , [18] . Here , the introduction of RWR in mutation data analysis reinforces the recognition that driver MutGenes tend to converge in functional pathways and interrupt the same biological processes frequently , while passenger MutGenes are more likely to occur randomly in the genome ( as do their interactors in the whole interactome ) . This recognition enables us to consult both MutGenes and their close interactors and prioritize MutGenes based on their joint frequency . In step 3 , considering the complex topological features of human interactome data , we introduce a randomization-based test to evaluate the candidate interactors utilizing 100 topologically matched random networks . Candidate interactors that remain significant ( i . e . , pedge<0 . 05 ) are collected to form a universal candidate pool . This step is also implemented in each sample , respectively . Finally , a consensus mutation subnetwork is constructed ( step 4 ) by collapsing all sample-specific results . Using the overall implementation principles described above , we rigorously examined several factors that may influence the results as well as several parameter tunings that can potentially improve the performance . Text S1 in the Supporting Information provides a detailed description of these evaluations . We implemented our method in the network data from the Human Protein Reference Database ( HPRD ) , which serves as a good balance between completeness and biological inference . The Cancer Gene Census ( CGC ) [19] is a continuous effort to collect cancer genes with mutations that have been causally implicated in cancer . CGC genes are widely used in many cancer studies for benchmark evaluation . We first explored the topological features of CGC genes in HPRD . In our downloaded version ( 03/15/2012 ) , a total of 487 CGC genes are included , and 369 of them have protein interactions in HPRD . The examination of the distance ( measured by the shortest path ) among CGC genes and others showed that CGC genes tend to be located more closely to each other than other genes . Specifically , 263 out of the 369 ( 71 . 27% ) CGC genes are directly connected , 96 ( 26 . 02% ) have a shortest path of 2 from other CGC genes , and only 10 ( 2 . 71% ) have a shortest path ≥3 from other CGC genes . In contrast , in the whole HPRD network , 2931 ( 33 . 43% ) genes ( including 263 CGC genes ) directly interact with CGC genes , 4657 ( 53 . 11% ) genes have a shortest path of 2 from CGC genes , 1038 ( 11 . 84% ) have a shortest path of 3 , and the remaining 142 ( 1 . 62% ) genes have a shortest path >3 from CGC genes . In summary , 97 . 29% CGC genes are located within two steps from other CGC genes , whereas 86 . 54% of all human genes are located within this distance . Based on this observation , we conclude that known cancer genes such as CGC genes show a strong tendency to be more closely connected , which is consistent with previous observations that proteins involved in the same disease have an increased tendency to interact with each other [20] . Therefore , we implemented a filtering step to remove genes that are located far away from CGC genes ( e . g . , those with a shortest path ≥3 ) . We explored the number of MutGenes that are retained after each step . The largest proportion of MutGenes was removed during mapping of MutGenes onto the HPRD network . This removal resulted from a limitation of the current human PPI data knowledge . Specifically , during removal of genes located two steps away from CGC genes , an average of 88 . 06% ( range: 66 . 67–100% ) were kept in LUAD compared to the previous step . Similarly in melanoma , an average of 86 . 86% ( range: 72 . 22–100% ) were retained compared to the previous step . These results indicate that gene filtration based on distance from CGC genes does not filter a significant proportion of the MutGenes ( Figure S2 ) . We first explored long genes in the two working datasets: a LUAD patient cohort using mutation data from whole-genome sequencing ( WGS ) and whole-exome sequencing ( WES ) [15] and a melanoma patient cohort using WES data [16] . The LUAD dataset contains 183 samples , among which 182 had at least one non-silent deleterious mutation . This dataset involves a total of 11 , 306 MutGenes . A detailed mutational profile can be found in Figures S3 and S4 . We manually examined the MutGenes in these samples and observed the frequency-based approach has a strong preference towards long genes . As shown in Figure S5 , of the 10 most frequently mutated genes in the LUAD samples , with the exception of TP53 and KRAS , the remaining eight genes are relatively long when compared to the distribution of all human CCDS gene lengths . In contrast , we examined the least frequently mutated genes , i . e . , those mutated in one LUAD sample , and surprisingly pinpointed several important cancer genes , including MDM2 , RAC1 , AKT1 , and CDK4 . These observations suggest cancer genes could mutate in a broad range of frequency spectrums , making it difficult for the frequency-based filtering approach to be effective . We then systematically examined the 11 , 306 MutGenes in the 182 LUAD samples . Among these MutGenes , 6878 were mutated in at least two samples ( i . e . , “recurrent MutGenes” ) regardless of the mutation sites in these genes . Here , recurrent MutGenes differ from recurrent mutations , where the latter are defined as mutations that occur more than once at the same site . We hypothesize that genes that were mutated in only one sample are more likely to have their mutations attributable to random events . We then built two sets of MutGenes . Set one included all 11 , 306 MutGenes , and set two included all the recurrent MutGenes . We examined the gene length effects in these two MutGene sets by plotting the proportion of MutGenes versus their cDNA length . As shown in Figure 2 , both sets have positive correlations with the cDNA length , but the trend was relatively weaker in set two . This analysis revealed that ( i ) the probability of observing MutGenes is indeed positively correlated with cDNA length , with longer genes more likely to be MutGenes; and , ( ii ) the correlation is reduced in recurrent MutGenes , yet is nontrivial ( Figure 2A ) , indicating that even in recurrent MutGenes , random mutations still exist . The same pattern was observed in melanoma samples ( Figure 2B ) . A total of 121 melanoma patients had at least one non-silent deleterious mutation , involving 11 , 030 MutGenes that have CCDS IDs , 6852 of which were recurrent MutGenes . As shown in Figure 2B , both sets of MutGenes were positively correlated with cDNA length , and the recurrent MutGenes were less correlated , further supporting the necessity to perform gene length-based filtering . The same procedure that was used in LUAD was applied to the 121 melanoma samples , all of which had MutGenes . Using the same criteria , we constructed a melanoma consensus mutation network , which contains 331 MutGenes involved in 301 interactions . We found that 65 of these 331 MutGenes are CGC genes , indicating a significant enrichment of cancer genes in the network ( p-value<2 . 2×10−16 , Fisher's Exact test ) . Further examination showed 15 kinase proteins in the network , most of which overlapped with CGC genes . We also validated the melanoma consensus mutation network using somatic mutation data from the TCGA Skin Cutaneous Melanoma ( SKCM ) project . Many genes in the discovery consensus network were replicated ( Table S1 ) . In particular , 86 overlapping genes that account for 25 . 98% in the discovery dataset and 73 . 50% in the evaluation dataset were identified , which is significantly higher than expected by chance ( p-value<1×10−3 , Figure S6 ) . Similar to the case of LUAD , these results demonstrated that cancer-related genes are effectively prioritized by VarWalker . Functional enrichment analysis of the mutation network revealed many cancer-related signaling pathways ( Table S8 ) and biological processes ( Table S9 ) , further indicating that the resultant network is enriched with cancer-related genes and regulation . For example , 12 of the 19 top significant KEGG pathways ( pBonferroni<10−6 ) are cancer-related ( Table S8 ) . We compared our results with those from the single-gene-based strategy . In our application of VarWalker in LUAD , we selected interactions that occurred in ≥10 samples . This approach resulted in 367 genes , 70 of which are CGC genes ( 70/367 = 19 . 07% ) . Using the single-gene-based strategy , we also selected genes that were mutated in ≥10 samples . This step resulted in 426 genes , 16 of which are CGC genes ( 16/426 = 3 . 76% ) , much less than those observed in the consensus mutation network . In melanoma , we also selected interactions that occurred in ≥10 samples , generating a consensus mutation network with 331 genes , 65 of which are CGC genes . Using the single-gene-based strategy , we obtained 404 mutated genes in ≥10 melanoma samples , 23 of which are CGC genes . The proportion of CGC genes obtained by the single-gene-based strategy ( 23/404 = 5 . 69% ) is also smaller than the proportion obtained by VarWalker ( 65/331 = 19 . 64% ) . These comparisons clearly proved that our network-based approach is superior to the single gene frequency based strategy .
In cancer research , distinguishing between driver mutations , which contribute to tumorigenesis , and passenger mutations , which are mostly neutral and occur randomly , is extremely important to understand and design targeted therapies and treatments . We proposed an approach to prioritize candidate driver MutGenes and biological networks using individual or cohort NGS data . Our method VarWalker estimates the occurrence of mutation events in the genome according to approximated probabilities based on coding gene length . It implements gene-based filtering such that it can exclude genes that are mutated largely due to random events . VarWalker utilizes the Random Walk with Restart algorithm to search for interaction partners that are close to the mutation genes and assesses the resultant interactions using a comprehensive randomization test , thereby greatly reducing potential random interactors ( e . g . , those with high degrees ) . In summary , this method has the advantages of both filtering random mutation genes and detecting possible driver genes along with their functional interactions . Hence , it is promising for driver gene prioritization in the era of personalized medicine . The applications of our method to both LUAD samples and melanoma samples revealed a mutation network for each of them . These mutation networks include a large proportion of known cancer genes and show the interconnections among the protein products of mutant genes . Interestingly , in each of the subgraphs within the consensus mutation network , we observed key components involved in cancer-related signaling pathways and biological processes . For example , in the LUAD mutation network , the three largest subgraphs focused on ( i ) the EGF receptor signaling pathway , the regulation of nuclear SMAD2/3 signaling pathways , and the p53 signaling pathway; ( ii ) transmembrane receptors and receptor protein signaling pathways; and , ( iii ) the cell cycle and DNA repair systems , respectively . The subgraphs in the melanoma mutation network revealed featured pathways such as the Raf/MEK/ERK pathway and receptor signaling pathways ( e . g . , EGF/EGFR , FGF , PDGFR-beta signaling pathways ) . The diversity of the component mutation genes in the mutation networks confirms the multifactorial and multigenic mechanisms underlying cancer . These observations also demonstrated the advantages of network-based approaches over frequency-based approaches in prioritizing cancer genes and revealing their functional impacts . Comparison of the consensus mutation networks of LUAD and melanoma revealed 94 overlapping genes , 33 of which are also CGC genes ( Figure S10 ) . We performed a functional enrichment test of these 94 genes ( Table S11 ) and found that most of them are enriched in protein binding categories or cancer-related signaling pathways . The most highly enriched GO terms are involved in enzyme binding ( pBonferroni = 2 . 16×10−13 ) , receptor binding ( pBonferroni = 3 . 03×10−13 ) , phosphatase binding ( pBonferroni = 5 . 85×10−9 ) , and kinase binding ( pBonferroni = 1 . 76×10−6 ) . The most significant pathways include the pathway of “influence of Ras and Rho proteins on G1 to S transition” ( pBonferroni = 1 . 26×10−9 ) , “signaling events mediated by VEGFR1 and VEGFR2” ( pBonferroni = 1 . 74×10−8 ) , and “tumor suppressor Arf inhibits ribosomal biogenesis” ( pBonferroni = 1 . 01×10−7 ) . Collectively , these results suggested that the overlapping genes between LUAD and melanoma mainly function in cell signaling . The advantages of our approach are threefold . First , in contrast to single-gene-based mutation frequency , our method is based on the joint frequency of two interacting proteins; thus , at the same threshold of frequency , our method can detect moderately or even rarely mutated genes that fail the threshold individually . Second , our interaction-based method helps to filter out many randomly occurring passenger genes , as these genes are expected to be randomly distributed in the network and the chance that their interactors are mutation genes is smaller . Third , our mutation network shows the interactions and context of mutation genes , providing an interpretation to facilitate biological functional analysis in the future , such as further investigation of the novel gene RAC1 in melanoma . The limitations of our work , which could be improved in future investigations , are reflected in several factors that may impact the results . First , the method is sensitive to the reference network , though it could be flexibly selected . Currently , PPI network resources are comprehensive , but most of them are collected from large scale experiments [28] , [29] , [30] , [31] . Functional correlation networks are valuable when representing biological knowledge and correlations among genes but are generally limited to genes that have already been annotated . As shown in Figure S1 , a condensed mutation network was generated from the functional correlation network . This consensus network recruited 22 known LUAD genes , fewer than the 31 known LUAD genes that were recruited in the HPRD-based mutation network . Future expansion of biological networks is expected to improve the detection of mutation networks . Second , the threshold we used to select interactions , i . e . , 10 for both LUAD and melanoma samples , is a trade-off between accuracy and recall rate . Decreasing this threshold value would recruit more cancer genes , but it would also introduce false-positives . Currently , we propose to fit a linear regression model between the number of edges and the edge recurrence . This strategy works in most cases , including the independent TCGA LUAD and SKCM datasets . In practical applications , expert guidance could help to further refine the selection of candidate genes , e . g . , in the case of LUAD dataset [16] . In future work , we plan to optimize the selection of interactions by making it threshold-free . Third , we may improve the mutation recurrence ( mr ) index through the use of more sophisticated statistical tests and by including protein domain information ( details of the mr index is described in Methods and Materials ) . In our work , we examined the resultant mutation networks either with or without applying the criterion of mr<1 . 05 in LUAD samples . In the latter case , the recall rate of LUAD genes increased by 4%; however , this application also led to 20% more proteins recruited in the final mutation network and , correspondingly , greatly decreased the specificity . Taken together , the parameter mr performs satisfactorily in our work . In summary , we present a sample-specific mutation network analysis method to prioritize cancer driver genes using the mutation profiles generated in NGS projects . Our method will be useful for investigators who explore cancer genes through rapidly emerging NGS applications in cancer research and personalized medicine . It can also be applied to explore functional mutations in other complex diseases or traits . The source code in R is available at http://bioinfo . mc . vanderbilt . edu/VarWalker . html .
Figure 1 shows the workflow , which has the following four steps . Step 1 . Patient-specific assessment of MutGenes . The aim of this step is to filter out potential genes whose mutations likely occur by chance based on the patient- ( or sample- ) specific mutational profile . Note the data and model fitting in this step are both performed for each single sample . As aforementioned , the likelihood of a gene to be mutated in a sample relies on many factors , including both genetic and environmental factors , which makes it impractical to accurately estimate the mutation rate for each gene . Here , instead of a direct estimation , we tackled this problem by formulating a generalized additive model and estimated a relative mutation rate for each gene . Given a cancer sample with MutGenes , let the vector Y denote the mutation status of each CCDS gene , i . e . , yi = 1 if the ith gene is a MutGene in the sample and yi = 0 if it is not . A vector of X represents cDNA gene length . We formulate the following model to estimate the probability of a gene to be mutated as a function of its cDNA length , i . e . , where π is the proportion of MutGenes in the investigated samples ( i . e . , ) and f ( . ) represents an unspecified smooth function . The function is then solved using a monotonic cubic spline with six knots . Based on the successful fitting of the function , each gene is assigned a weight , which represents its relative probability to be a MutGene ( hereafter denoted as probability weight vector , or PWV ) and is used as the gene-specific weight in the follow-up weighted resampling process . PWV retains the relative weight of each gene in a particular patient genome and this relative weight changes in different samples . We then resampled random gene sets to build the null distribution of MutGenes occurring at random . Each random gene set has the same number of MutGenes . In this random selection procedure , each gene was selected from the genome following its probability weight as defined in the sample-specific PWV . The resampling process thus resembles the way in which MutGenes occur in a specific genome in random cases . The weighted resampling process was performed 1000 times in each sample , and a mutation frequency was computed for each gene using . Here , a freq≥5% indicates the gene likely occurs at random and a frequency <5% indicates the gene is highly unlikely to be mutated due to random events . Accordingly , we filter genes with freq≥5% . Upon completion of this step , we obtained a list of significant MutGenes for each sample . We attempt to fit sample-specific models using MutGenes for each sample such that the heterogeneous background of cancer patients can be properly considered . However , a practical challenge is to determine the minimum number of observations for reliable model fitting . For example , samples with very few MutGenes may not accomplish successful model fitting . Determination of the minimum number of observations remains an open question in statistics . In our case , to avoid arbitrary selection , we compared the results that were obtained using the sample-specific model with those obtained using the universal model . Here , the universal model was generated by using all MutGenes from the cohort . As shown in Figure S11 , the difference in the retained MutGenes was large when samples had more MutGenes . We therefore selected 50 as the cutoff . For samples with ≥50 MutGenes [128 ( 70% ) LUAD samples and 110 ( 91% ) melanoma samples , Figure S4] , we fitted a sample-specific model and obtained a sample-specific PWV . For other patients with fewer MutGenes , we performed a resample-based test using the universal PWV . As a positive control , we examined the performance of the resample-based strategy on CGC genes , which are well-studied cancer genes . We found that 96 . 30% CGC genes had a frequency <5% in random datasets . Only 3 . 70% CGC genes had a frequency ≥5% . This result indicates our resample-based strategy retains a high sensitivity as evaluated by CGC genes; thus , the filtered genes are more likely randomly-occurring genes . Based on this observation , we created a manual adjustment to always retain CGC genes , even if they were occasionally observed with ≥5% frequency in random datasets . In practice , the users may remove this inclusion criterion . Step 2 . Sample-specific application of the Random Walk with Restart algorithm to search candidate interactors and MutGenes . The RWR algorithm simulates a random walker's transition in the network from a starting node ( or a few starting nodes ) , with pre-defined starting probabilities , to its neighbors until it reaches a stable status . RWR allows for revisiting of the starting node ( s ) with revisiting probabilities . Given a network G with n nodes , we denote W as the column-normalized adjacency matrix for G; therefore , W is an n×n matrix . The RWR algorithm is formulated as:where r is the restart probability ( e . g . , r = 0 . 5 in this study ) , and p0 , pt , and pt+1 are vectors of size n . Each of the three parameters , p0 , pt , and pt+1 , represents a vector in which the ith element holds the probability that the walker is at node i at time steps 0 , t , and t+1 , respectively . In general , assuming that there are k initial genes from which the walker would start with equal probability , the initial vector p0 is defined as a vector , with the initial nodes having a probability of 1/k and the remaining nodes having a probability 0 , such that the sum of the probabilities equals 1 , i . e . , , where i = 1 , … , n . The RWR function is solved using this iteration process when the difference between pt and pt+1 is below a predefined threshold ( e . g . , 10−6 in our analyses ) . In each patient , we iteratively took each MutGene as the starting point to initiate the random walk and retained the top 1% ( i . e . , 10 ) of nodes that have the highest probabilities with which the walker would stay at a stable status as the highly accessible nodes for the initial node . Previous studies suggested various ways to select candidate nodes , e . g . , the most accessible node ( i . e . , top 1 ) [18] , top 5 [38] , top 10 [39] , [40] , top 20 [40] , and top 100 [41] , but no consensus rules have been made . In this work , we chose to retain the top 10 accessible nodes . Although this selection criterion is arbitrary , our strategy is based on the observation that , in real biological networks , especially PPI networks , each node often has more than one important interactor . For example , TP53 is inhibited by the protein MDM2 , but it is activated by ATM , both of which have a direct interaction with TP53 [36] . In such cases , consideration of only the most accessible interactor would overlook other important interactors . Taken together , the number of candidate interactors should not be too small ( e . g . , 1 ) , as it may miss many important interactors; however , it should not be too large either , as many irrelevant genes may be included . We tested the selection of the top 1 , top 5 , and top 10 interactors using the data in this study . Based on the assessment , we selected 10 as a balance between choosing too few informative genes ( e . g . , top 1 ) and too many genes . However , this criterion can be adjusted depending on the specific data . It is worth noting that these 10 nodes ( genes ) that are most highly accessible from the starting node ( gene ) may not always be statistically significant compared to mere chance and will be evaluated in the next step . Step 3 . Randomization-based evaluation of the candidate interactors . To evaluate whether the subnetworks generated by RWR in step 2 do not occur by chance , we generated 100 random networks , each of which maintains the topological characteristics of the original network ( e . g . , degree of each node ) . We adapted the switching algorithm proposed by Milo et al . [42] , which starts from the observed network and preserves the degree distribution in the generated random network . We also performed RWR for MutGenes in each of the 100 random networks and we extracted the top 10 nodes with the highest probabilities . For each node encoded by a MutGene , the 10 candidate interactors in the observed network , g1 , g2 , … , g10 , were assessed by computing an empirical p-value: , where π ( gi ) is a random network in which gi , i = 1 , … , 10 , was found as the top 10 candidate genes to the same initial node . The empirical p-value indicates the probability of a candidate interactor to be selected by chance . The interactors with pedge<0 . 05 are retained and denoted as significant interactors for the MutGene ( see Figure 1 ) . Step 4 . Construction of a consensus mutation subnetwork . After detecting MutGenes and their interactors in each sample , all significant interactions were pooled together , forming a universal candidate pool . This pool enabled us to better incorporate the information across multiple samples . After tabulating all edges , we explored the number of edges versus the edge occurrence ( Figure 3 ) . By fitting a linear regression model , we observed that the number of high frequency edges occurred more often than expected . A cutoff was selected according to the distribution ( e . g . , 10 for melanoma ) and was manually adjusted based on expertise ( e . g . , 10 for LUAD ) when necessary . Furthermore , we required both proteins involved in an interaction to be encoded by MutGenes . After pooling all the sample-specific MutGenes and their interactions , we implemented this step such that a pair of MutGenes and its interactor could be either mutated in the same patient or in different patients . In either instance , the interaction would be interrupted . Next , we defined a parameter called the mutation recurrence ( mr ) index for each gene , or a pair of genes whose proteins interact , to control the false positive rate . The mr index is defined as , where ‘# all mutations’ refers to mutations occurring in the gene across all samples , and ‘# unique mutations’ refers to the non-redundant set of ‘all mutations . ’ Redundancy was determined if two mutations shared the same genomic coordinate regardless of the derived alleles . The introduction of the mr index is based on the observation that mutations in driver genes typically occur in important domains ( e . g . , kinase domains ) and tend to cluster around ‘hotspots’ [43] . In contrast , mutations in passenger genes do not have particular features and may occur randomly across the whole gene . We removed interactions involving MutGenes whose mr<1 . 05 . This cutoff of mr ( <1 . 05 ) corresponds to MutGenes with >20 non-silent deleterious mutations in the cohort but none shared with any other ( i . e . , all are unique mutations ) . This filtering procedure resulted in a pool of high confidence interactions . Then , a consensus mutation network that was frequently mutated or revisited across many samples was derived by selecting the highly recurring interactions according to the overall distribution of the interaction pool . We used the online tools DAVID [23] and ToppGene [44] for functional analyses . Both tools provide comprehensive resources for biological pathway annotation ( e . g . , canonical pathways from KEGG [24] ) and biological processes ( e . g . , GO [25] terms ) . ToppGene also collected information from other databases , including BioCarta , BioCyc , Reactome , GenMAPP , and MSigDB . Wherever applicable , multiple testing correction using the Bonferroni method was performed to control the false discovery rate . | A cancer genome typically harbors both driver mutations , which contribute to tumorigenesis , and passenger mutations , which tend to be neutral and occur randomly . Cancer genomes differ dramatically due to genetic and environmental factors . A major challenge in interpreting the large volume of mutation data identified in cancer genomes using next-generation sequencing ( NGS ) is to distinguish driver mutations from neutral passenger mutations . We propose a novel mutation network method , VarWalker , to prioritize driver genes in large scale cancer mutation data . Applying our approach in a large cohort of lung adenocarcinoma samples and melanoma samples , we derived a consensus mutation subnetwork for each cancer containing significantly enriched cancer genes and cancer-related functional pathways . Our results indicated that driver genes occur within a broad spectrum of frequency , interact with each other , and converge in several key pathways that play critical roles in tumorigenesis . | [
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] | 2014 | VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data |
Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production . As a consequence , for any given protein , genetically identical organisms living in a constant environment will contain different amounts of that particular protein , resulting in different phenotypes . This phenomenon is known as “phenotypic noise . ” In bacterial systems , previous studies have shown that , for specific genes , both transcriptional and translational processes affect phenotypic noise . Here , we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes , using data for over 60% of all promoters in Escherichia coli . We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise . We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E . coli . In contrast to previous results in eukaryotes , we find no association between promoter-mediated noise and gene expression plasticity . These results are consistent with the hypothesis that , in bacteria , natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alone .
The phenotype of an individual is often considered to be a product of the individual's genotype and the environment in which it lives . However , significant phenotypic differences may exist between genetically identical individuals living in a homogeneous environment [1]–[7] . In the absence of genotypic differences or environmental cues , these differences often arise from random molecular processes during protein expression and development . In these cases , such variation is termed phenotypic noise . Although differences between individuals that are due to phenotypic noise are not encoded genetically , the level of phenotypic noise in a given gene may be subject to genetic control . One fundamental question is whether natural selection acts to control or promote phenotypic noise , and how organisms achieve this control . It is well established that selection acts strongly on mean expression level [8]–[12] . Additionally , there is good evidence that selection can also act on the variation of gene expression , that is , on the level of phenotypic noise . Many studies with bacteria and other microorganisms have identified genes with exceptionally high levels of phenotypic noise , and several studies have provided possible adaptive explanations . Both theoretical [13]–[17] and empirical studies [18]–[21] have shown that increased noise and bistable gene expression can allow organisms to persist in fluctuating environments , and that selection may thus in some cases increase phenotypic noise . Other studies have shown that it can promote the formation of specialized subpopulations that engage in division of labor [5] , [22] . However , there have been fewer studies on general patterns of gene expression noise , for example , across functional groups of genes . The best-established connection , and the only connection established for both eukaryotes and bacteria , is between mean expression level and variation in expression: strongly expressed genes have high levels of variation across cells [23] , [24] . However , mean expression level does not fully determine variation: analyses in yeast have shown that when mean expression level is accounted for , gene expression noise exhibits certain strong patterns: for example , there is a positive association between gene expression noise and gene expression plasticity ( i . e . , variation in gene expression across environments ) [24]; genes with TATA boxes exhibit high noise [24]; and those genes most critical for cell functioning exhibit lower levels of variation than other genes that are expressed at the same level [24]–[26] . This latter correlation is consistent with selection acting to decouple variation in expression from mean expression in order to decrease noise in important genes . However , this association is confounded by other correlations , such as the strong relationship between noise and expression plasticity . There is no data addressing the question of whether functionally important genes exhibit lower levels of noise in bacteria: only one analysis of variation in gene expression has been performed in bacteria [23] , which established that genes expressed at higher levels exhibit more extrinsic noise . This raises the question of whether these two properties can be decoupled , for example to lower noise in functionally important genes , even though these genes may be expressed at high levels . Thus , although there is good evidence in yeast that genes important for cell functioning have lower levels of gene expression noise , the interpretation of this result as evidence of selection acting to decrease noise has been complicated by the association between expression plasticity and noise . Additionally , there have been no analyses of whether the decoupling of mean expression level and variation in expression exhibits such general patterns in bacteria . Here , we investigate this possibility , and whether such decoupling exhibits patterns on a general , genome-wide level . In contrast to previous studies , which have examined protein expression noise , we carried out a comprehensive analysis of the noise conferred by the promoter regions alone in E . coli . Our goals were three-fold . First , we wanted to test whether the DNA sequence of the promoter region has a substantial and consistent effect on noise . Second , we asked whether differences in noise exhibit discernible patterns , for example across functional categories of genes . Finally , we assessed whether these patterns are consistent with selection acting to preventing or promoting phenotypic noise , or whether other explanations account equally well for the patterns we observe .
We used an E . coli promoter library [27] consisting of 1832 strains , in which each strain carries a low-copy number plasmid ( 3–5 copies per cell [28] , [29] ) with an E . coli promoter region inserted upstream of a gene for a fast-folding green fluorescent protein ( gfp ) . This library comprises about 75% of all E . coli promoters . The term ‘promoter region’ refers to the intergenic region between two open reading frames , together with 50–150 nucleotides of both the upstream and downstream open reading frame [27] . The mRNA that is produced consists of a transcriptional fusion between a short region of the 5′ end of the native mRNA , 31 bp that are identical for all promoters , and the open reading frame for GFP . A strong ribosome binding site ( RBS ) is located immediately upstream of gfp . As the 31 bp preceding the gfp start codon are identical for all constructs , effects from differences in the translation initiation rate should be minimal [30] , [31] . Additionally , as approximately 90% or more of the mRNA sequence is identical for each construct , in most cases , differences in mRNA half-lives between constructs are likely to be small . The GFP variant is quite stable , so decreases in protein concentration occur primarily through cell growth and division . For the above reasons , differences in the mean concentration of cellular GFP for different promoters are most likely due to differences in transcription ( see Text S1 ) . However , in many instances the promoter region may affect mRNA half-life or translation dynamics , since it contains a fraction of the native open reading frame . This experimental system removes several mechanisms that are likely to affect protein expression noise in the native context . Among these is the chromosomal context of the gene; the mRNA sequence content , affecting both mRNA half-life and translation; and the amino acid sequence , affecting protein degradation . In fact , the only variable among the constructs is the sequence of the promoter region . By definition , then , the effects on noise that we measure here are due to the promoter sequence alone . This experimental approach thus allows us to investigate whether and how the promoter sequence alone affects noise . Although this promoter-mediated noise contributes only partially to the total noise exhibited by a protein , it may play an important role , which we investigate here; later we use data on protein noise to explore other factors that contribute to affecting protein expression noise . To quantitatively measure variation in gene expression from each promoter , we grew a clonal population of each strain , and used flow cytometry to measure the GFP concentration in approximately 100'000 individual cells from each population . For each strain , we extracted a small gated subset of cells ( Figure S1; see Methods ) . This gating has the effect of minimizing extrinsic variation due to physiological differences among cells , such as cell cycle timing , slow growth , or other physiological stresses ( see Text S1 ) . For each of 1832 strains containing a promoter region from E . coli , we measured the mean and variance in fluorescence . 1522 of these yielded measurements significantly above background ( GFP vector lacking a promoter; see Methods ) . We use the data from these 1522 promoters for the remainder of our analyses . The mean and variance of fluorescence are highly repeatable measurements; when they were assessed for independent cultures , repeated measurements were extremely accurate ( r2 = 0 . 998 and 0 . 91 , for mean and standard deviation , respectively ) . This repeatability existed even when the cultures were grown in different laboratories , measured on different flow cytometry machines , and when different methods were used to filter events ( r2 = 0 . 92 and 0 . 51 for mean and standard deviation , respectively; see Methods and Figure S2 ) . Mean fluorescence levels varied over almost 3 orders of magnitude , qualitatively similar to the variation in mRNA levels observed in other studies [23] . Comparing our data on mean fluorescence level with published quantitative data , we also find that our data set correlates well with measured transcript levels , and is thus likely to capture an important aspect of mRNA transcription ( see Text S1 ) . We find a strong dependence of variation in expression on mean expression level for any particular promoter ( Figure 1 ) , as has been observed previously [23] , [24] , [32] . Because the primary effect of selection on gene expression occurs as stabilizing selection on mean expression level [8]–[11] , and mean and variation are closely coupled , we use a metric that decouples variation in expression from mean expression level . Modifying the method outlined by Newman et al . [24] we measured noise as the vertical deviation from a smoothed spline of mean log expression versus the coefficient of variation in log expression for all promoters in the library ( see Methods; Figure 1F; Text S1; Dataset S1 ) . When describing our findings , the term ‘phenotypic noise’ or ‘noise’ always refers to this metric in which variation is corrected for mean expression; such a measure allows us to assess whether variation in gene expression is controlled independently of the mean . We emphasize that we use the term ‘noise’ to refer to relative differences in variation when mean expression level is controlled for . Thus , it is a qualitative measure , and for this reason we emphasize comparative results of relative differences in promoter-mediated variation; also for this reason , we restrict our statistical analyses to non-parametric tests . We refer to this measure as ‘noise’ because it is a reflection of differences between cells that are likely to arise from stochastic events , but it is not a quantitative measure of the frequency or effect of those events . In addition , because we have functional data for genes only , and not promoters , when we refer to the noise of a ‘gene’ or the functional category of a ‘promoter , ’ we are referring only to the gene that lies directly downstream of the promoter , unless otherwise specified . When we calculate this noise metric for the entire library of promoters , we find excellent repeatability , even in different culture conditions . The correlations range from 0 . 50 ( Spearman's rho ) when using data from different labs , to 0 . 58 when using data collected in independent experiments in the same lab ( Figure S3 ) . These are lower limit estimates of repeatability , as in each of these comparisons different culture conditions were used ( see Methods ) . The repeatability of the noise metric implies that each promoter sequence has a consistent effect on variation in expression: thus , as suggested above , there are characteristics inherent to each promoter that result in different levels of noise . Noise in gene expression consists of different components [33] , [34] , and our experimental system mostly reports one of them , promoter-specific extrinsic noise . Since the promoter-gfp construct resides on a plasmid with several copies , the cellular GFP concentration is the sum of the contributions from individual promoters . Intrinsic noise – variation generated at the level of one single promoter – is therefore decreased . In addition , because the GFP protein has a longer half-life than mRNA , the sensitivity of these noise measurements to intrinsic noise events in transcription is decreased [35] . Finally , fluctuations in plasmid number , which are expected to increase noise in all strains equally , may decrease the sensitivity of this system . The noise that we measure is thus a qualitative and relative indication of the amount of promoter-specific extrinsic transcriptional noise [33] , [34] . If we measure high levels of noise in a protein controlled by a particular promoter , most likely this occurs because transcription from this promoter is controlled by factors ( or regulatory networks ) having higher noise , or that this promoter is more sensitive to global extrinsic noise factors ( e . g . variations in polymerase numbers ) than other promoters . This experimental system is thus useful to examine extrinsic promoter-mediated noise on a genome-wide scale , and to ask how the level of extrinsic noise differs among promoters . Even though , as discussed above , our plasmid-based system only captures some aspects of gene expression , we find that it gives similar results to chromosomally integrated systems in both mean and variation of expression . We measured the mean and variation in expression for nine chromosomally integrated promoter-gfp fusion constructs [36] , and found that both the mean and CV correlate well with the values that we find for the plasmid-based system ( rho = 0 . 85 , p = 0 . 006; rho = 0 . 77 , p = 0 . 016 for mean and CV , respectively; see Text S1 and Figure S4 ) . Given that the promoter sequence alone has a consistent influence on mRNA expression and noise levels ( above; Figure S3 ) , this raises the question of whether these levels of noise systematically differ for different classes , or types , of promoters . One broad division that can be made is between promoters that drive the expression of essential genes and those that drive the expression of non-essential genes ( we define a gene as essential if its deletion yields an inviable genotype in rich media [37] ) . We used data for 118 promoters that lie directly upstream of essential genes or operons [38] that contain at least one essential gene , out of 1456 promoters for whose downstream genes we have information about essentiality . We find that promoters of essential genes exhibit significantly lower levels of noise than other promoters: of the genes with the lowest level of noise ( first quartile ) , 13 . 1% are essential; of the genes with the highest level of noise ( fourth quartile ) , only 2 . 9% are essential ( p = 1 . 0e-6 , Wilcox rank sum test ) . This difference is not driven by any mechanisms relating to mean expression levels , since our measure of noise corrects for this . Thus , the promoter regions of genes that are essential in the laboratory environment have evolved such that essential genes have lower noise levels . Essentiality in the laboratory is an incomplete and potentially biased measure of a gene's importance in the natural environment . We thus also looked at gene conservation , which may capture additional aspects of functional importance [39] , [40] . Considering non-essential genes only , we found a negative relationship between noise and functional importance: non-essential genes that have high levels of conservation in the gamma-proteobacteria clade ( of which E . coli is a member ) have promoters conferring low levels of noise ( Spearman's rho = −0 . 19 , p = 7 . 2e-12 , n = 1350; Figure 2 and Figures S5 and S6 ) . Furthermore , this relationship between conservation and expression noise exists within functional categories: it does not depend on broad differences in conservation between genes of different function , for example between genes involved in RNA production ( expected to be more conserved and less noisy ) versus those involved in carbon metabolism ( expected to be less conserved and more noisy; Figure S7 ) . Together with the above data on essential genes , this suggests that the promoter regions of functionally important genes confer low levels of noise; given that the major effect of promoter sequence on protein level occurs through mediating transcription , this decrease in noise likely occurs through the control of transcriptional processes . The transcriptional regulation of some bacterial genes has been shown to be constructed such that increased noise is a result [41]; the data here suggest that on a genome-wide basis there is a tendency for functionally important genes to be controlled by less noisy transcriptional processes , that this trend extends beyond essential genes to conserved , non-essential genes , and that this trend persists within functional categories of genes . There are several possible explanations for the low levels of noise observed in essential and highly conserved non-essential genes , two of which we discuss here ( we explore a third explanation in the following section; however , this list is not exhaustive ) . First , it is possible that essentiality and gene conservation are good descriptors of the functional importance of a gene , and that selection has acted to decrease noise in such genes . This has been the explanation put forth in previous analyses . A second possible explanation is that low noise levels are difficult to evolve , and as conserved and essential genes have also spent more evolutionary time in a particular genome than non-conserved genes , selection has had more time to minimize noise in these genes . Either of these explanations could result in conserved and essential genes having lower noise . However , the results of our analysis suggest that the second explanation is less likely , for the following reasons . First , the correlation between gene conservation and noise exists even for genes that have been acquired very distantly in the past . We looked for an association between functional importance and noise considering only genes acquired before the divergence of the E . coli lineage from alpha-proteobacteria ( approximately 2 . 5 billion years ago [42] ) . These genes have had ample time for noise minimization . Thus , if the time a gene spends in a particular genome is a strong determinant of noise , there should be no relation between conservation and noise in this set of genes , as all have spent at least 2 . 5 billion years in the E . coli lineage . However , the correlation between conservation and noise within these anciently acquired genes remains strong ( Spearman's rho = −0 . 23 , p = 2 . 8e-4 , n = 249 ) . That the amount of noise minimization is related to the level of evolutionary conservation ( functional importance ) even in anciently acquired genes strongly suggests that the time that a gene spends in an organism has little to do with the level of noise it exhibits . Second , although horizontally transferred genes are generally enriched for genes of lesser functional importance , many genes important for cell functioning have been horizontally transferred ( e . g . some ribosomal genes ) . Among genes that have been recently horizontally transferred into E . coli [43] , strongly conserved genes have lower levels of noise ( correlation between noise and conservation: Spearman's rho = −0 . 22 , p = 6 . 9e-3 , n = 221 for genes transferred after the split of E . coli from Haemophilus; Spearman's rho = −0 . 25 p = 4 . 8e-4 , n = 171 , for genes transferred after the split of E . coli from Buchnera ) . When we consider very recent horizontal gene transfers the negative correlation remains ( Spearman's rho = −0 . 16 , p = 0 . 23 , n = 65 for genes transferred after the split of E . coli MG1655 from E . coli CFT073 ) . Although this correlation is not significant , there are only a small number of recently transferred genes , and these vary little in their levels of evolutionary conservation , decreasing the explanatory power of this variable . Given that the nucleotide divergence between MG1655 and CFT073 strains is approximately 2% [44] , finding a negative correlation of similar strength ( −0 . 16 vs . −0 . 19 for the entire data set ) is notable . Thus , the relationship between functional importance and noise does not appear to be related to the time that a gene has spent in a genome . The latter result also implies that the decreased noise observed in functionally important genes , if due to selection , can occur via a small number of mutations . Alternatively , it is possible that features of the promoter that influence noise act independently of the genetic background , so that genes retain characteristic levels of phenotypic noise even when horizontally transferred . We do find some support for this latter hypothesis: promoters of very recently horizontally transferred genes ( ORFan genes; e . g . [45] ) do not exhibit higher levels of noise than other promoters ( Wilcox rank sum , p = 0 . 69 , n = 37 ) . Our results , showing that functionally important genes exhibit lower gene expression noise , is consistent with the hypothesis that selection has acted to decrease noise in genes important for cell function . However , many other factors may potentially play a role in determining noise . A crucial determinant of noise in gene expression may be in how the gene is regulated: genes that exhibit large expression plasticity , meaning that they can undergo strong repression or activation across different environmental conditions , might be controlled in ways that makes them intrinsically more noisy . A very strong association between expression plasticity and noise has been found previously in yeast [24]–[26] . To investigate whether there is a similar association between noise and expression plasticity in E . coli , we gathered data on changes in gene expression across 240 pairs of environmental conditions [46] . For each pair of conditions , gene expression changes are expressed as the log ratio of expression in one condition relative to a reference condition; the value is positive for genes that increase their expression , and negative for genes that decrease their expression under the respective environmental condition . For each gene , we calculated the median of the absolute values of the expression changes . This value , which we term the expression plasticity , is high for genes whose expression frequently varies between two conditions , and low for genes whose expression is usually constant between two conditions , regardless of whether this occurs through repression or activation , or the nature of the reference condition . Surprisingly , we found no significant association between noise and expression plasticity in E . coli ( Spearman's rho = 0 . 030 , p = 0 . 27 , n = 1354 ) . It is possible that this correlation exists only in some growth conditions , and that these types of conditions are under-represented in the dataset . To account for this possibility , we grouped the condition pairs by their similarity in expression changes into 18 clusters , calculated the median of the absolute values of the expression changes , and again found no significant correlation ( Spearman's rho = −0 . 002 , p = 0 . 94 , n = 1354 ) . Performing a similar analysis for yeast yields a significant positive relationship between expression plasticity and noise ( data from [47]; unclustered analysis: Spearman's rho = 0 . 22 , p = 7e-26 , n = 2479 ) . Although the lack of a correlation in E . coli could be driven by differences in data quality , this is not a likely explanation ( see Text S1 and Figure S8 ) . Together , these data suggest that in yeast , a substantial fraction of gene expression noise might be a consequence of requiring dynamic control of gene expression [26] . However , in E . coli , high gene expression noise is not an unavoidable consequence of genes having high expression plasticity . Further supporting this conclusion is the association between functional importance and expression plasticity in E . coli: essential and conserved genes are the most dynamically regulated: 42% of essential genes are among the most dynamically regulated genes ( within the top quartile ) , while only 13% are among the least dynamically regulated ( bottom quartile ) ( p = 5e-6 , Wilcox rank sum for essential versus non-essential genes; Spearman's rho = 0 . 19 , p = 1 . 1e-11 , n = 1209 for the correlation between expression plasticity and conservation ) . Despite this , promoters of essential and conserved genes exhibit the lowest level of noise . Thus , in E . coli , there does not appear to be a constraint preventing promoters with high expression plasticity from having low noise . In contrast , there is a strong positive correlation between expression plasticity and noise in yeast , suggesting that for many genes , such a constraint may exist . Because essential genes in yeast have low expression plasticity ( see Text S1 ) , the previous finding that essential yeast genes exhibit low levels of noise might be a consequence of this association between expression plasticity and noise . We looked in more detail at how specific functional aspects relate to gene expression noise . We grouped genes according to the categories outlined by MultiFun [48] , and found substantial differences between genes having different functional roles ( Figure 3 ) . Relatively low levels of noise were exhibited in genes involved in DNA structure ( i . e . methylation , bending , and super-coiling ) and building block synthesis ( biosynthesis of amino acids , nucleotides , cofactors , and fatty acids ) . Low levels of noise in such housekeeping genes might be expected , given that normal cellular activities are probably compromised if these proteins are too abundant or not abundant enough , as has been suggested previously [49] , [50] . We also observed particularly low levels of noise in genes involved in protection ( from radiation , cell killing , drug resistance , or for detoxification ) . Finally , promoters annotated as having binding sites for σ32 ( control of heat shock genes ) have significantly lower levels of noise; several transcription factors are also associated with low noise ( Table 1 ) . Particularly high levels of noise are primarily found in genes involved in two functional groups: energy metabolism of carbon sources ( e . g . glycolysis , the pentose phosphate shunt , fermentation , aerobic respiration ) , and in adaptation to stress ( osmotic pressure , temperature extremes , starvation response , pH response , desiccation , and mechanical , nutritional , or oxidative stress ) . Finally , promoters with binding sites for σ38 ( control of starvation and stationary phase genes ) exhibit higher levels of noise than promoters containing binding sites for other sigma factors; several transcription factors were also associated with higher noise levels ( Table 1 ) . As the above analysis implied that high levels of noise are not simply a consequence of having high expression plasticity , the differences in noise between categories is consistent with differential selection ( although other factors may also be responsible ) . For example , one possibility is that some genes exhibit high levels of noise due to an absence of selection ( such that drift dominates the evolutionary process ) , in contrast to the majority of genes in the genome . A second possibility is these genes have experienced selection for high levels of noise . Variation in resource utilization between cells can sometimes increase the growth rate of clonal populations [19] , [51] by promoting the utilization of carbon sources that become newly available . Similarly , noise in genes involved in adaptation to stress could allow genotypes to persist under conditions where stressors appear quickly [13] , [52] , [53] . Alternatively , genes with high noise may also be constrained in their ability to evolve lower noise due to trade-offs with other functions that we have not measured . These results thus generate explicit and testable hypotheses about the possible adaptive functions of increased noise in gene expression . Our focus until now has been on how the nucleotide sequence of a promoter alone controls phenotypic noise in a plasmid-based context . Noise at the level of protein is possibly controlled through additional mechanisms acting at the post-transcriptional level . To include these mechanisms into our analysis , we used data from a recent study that measured variation in protein numbers between cells for a large number of the protein coding genes in E . coli [23] . This study was based on translational fusions of protein coding genes with YFP in the native chromosomal context . Using approximately 1'000 of these constructs , the authors used microscopy to measure the mean and variation in protein number per cell . This study thus provides us with information on the sum of intrinsic and extrinsic noise that occurs through both transcriptional and post-transcriptional processes . Using this dataset , we quantified protein expression noise in an analogous manner as for our data , removing genes with very low expression levels and correcting for mean protein expression level . Again , this decouples mean protein expression level from variation in protein expression . We find a significant but weak correlation between protein noise in this dataset and gene expression noise in our own ( Spearman's rho = 0 . 12 , p = 0 . 02 , n = 334 ) . A primary reason for this low correlation may be that the noise in protein expression was measured during late exponential phase , while we measured during early exponential phase growth ( see Text S1 ) . We find that , similar to the pattern observed for promoter-mediated noise , essential and conserved genes have low protein expression noise ( Wilcox rank sum , p = 3e-4 , n = 116 essential genes; Spearman's rho = −0 . 21 , p = 7 . 0e-9 , n = 645 non-essential genes ) . Using variation alone as a metric of noise , without the correction for mean expression level , gives the opposite result: essential genes have significantly higher levels of variation [23] , as they are expressed at higher levels , and variation is strongly positively correlated with mean expression . Finally , corroborating the lack of correlation between promoter-mediated noise and expression plasticity , protein expression noise and plasticity exhibit no significant correlation ( rho = 0 . 052 , p = 0 . 16 , n = 724 ) . We find that post-transcriptional processes play a role in controlling protein expression noise: genes with high protein expression noise have slightly higher rates of translation initiation ( Spearman's rho = 0 . 17 , p = 3 . 3e-6 , n = 730; computational predictions of ribosomal initiation rates from [30] , [54] , and slightly longer mRNA half-lives [55] ( Spearman's rho = 0 . 15 , p = 4 . 4e-5 , n = 689 ) . This is consistent with the idea that intrinsic noise in post-transcriptional mechanisms has a significant effect on total noise , as theoretical models have suggested [34] , [56]–[58] . However , the extent to which the cell actually employs these mechanisms has remained unknown . The data here suggest that these mechanisms affect the noise levels of many genes in E . coli . If this association has occurred through selection , this implies that although these mechanisms are quite costly for the cell [59] , the advantage of controlling intrinsic noise outweighs the energetic costs that it imposes .
We have shown here that by using a simple plasmid based system that different promoters consistently confer different levels of phenotypic noise . In particular , we find that functionally important genes have promoters that confer lower levels of gene expression noise , and certain functional categories are enriched or depleted for promoters that confer high noise . The noise metric we use accounts for mean expression level , so these patterns are not due to differences in expression levels between essential and non-essential genes , or to characteristics related indirectly to expression level ( for example , systematic differences in cellular stress levels due to GFP ) . Furthermore , these noise characteristics appear to extend across different growth conditions , as promoter-mediated noise is similar during growth in non-stressful ( arabinose and glucose ) and stressful ( low-levels of antibiotic ) conditions ( see Figure S3 ) . We have excluded several confounding factors from the association between noise and functional importance , including the age of the gene and the association with expression plasticity . The lack of association between promoter sequence and expression plasticity is surprising , given the strong relationship that has been observed in yeast [24] , and that promoter sequence is a strong determinant of transcript level ( see Text S1 ) . The low noise of promoters of functionally important genes is consistent with the hypothesis that natural selection acts to control against variation in proteins that are important for cellular functioning [60] . However , it is important to emphasize that we cannot exclude other factors being responsible for this pattern . We cannot yet determine the level at which the effects of promoter-mediated noise control extend to the protein level . Processes downstream from transcription may have significant effects on noise , and might sometimes overwhelm the effects arising on the transcriptional level . The association that we find between promoter-mediated noise and protein noise suggests that in many cases , transcriptional noise does correspond with the noise observed further downstream . However , we cannot say how strong this association is for all genes . As our noise metric largely excludes both intrinsic noise and global extrinsic noise , these results suggest that promoter-mediated noise is systematically reduced in functionally important genes through gene-specific mechanisms . Thus , it seems that the regulatory inputs for these promoters have evolved to minimize noise . This has been shown previously for single regulatory networks [61]; here we show that it also appears to occur for many different genes . In addition to promoter-mediated control of noise , we find that proteins that exhibit low levels of noise have short mRNA half-lives and low rates of translation initiation . Although previous work has shown that variation in expression is strongly positively associated with mean expression level [23] , the data here show that these two characters can be uncoupled , so that transcriptional noise can be controlled independently of the mean , and that this uncoupling is stronger for some types of genes ( those that are functionally important ) than others . Although it has been hypothesized previously that functionally important genes have been selected to exhibit low levels of noise [62] , it has been difficult to unambiguously show this . In particular , it has been difficult to separate the effects of expression plasticity and low noise , as all previous studies connecting noise and functional importance have been in yeast , where this association is quite strong [24]–[26] ( see Text S1 ) . The data shown here provide evidence that in E . coli , these two characteristics are unconnected . In eukaryotes , one of the dominant regulatory mechanisms associated with transcriptionally noisy genes is chromatin structure ( noisy genes tend to contain TATA boxes and are frequently regulated by SAGA [21] , [24] , [63] ) . A corollary of this is that in yeast there is a strong association between noise and expression plasticity , as dynamically regulated genes are often associated with chromatin remodeling factors . Much of this noise is thought to arise because of the two step process inherent in eukaryotic transcription , in which initial access to the DNA occurs through relaxation of histone binding , followed by transcription factor and polymerase binding [64] . Homologous mechanisms do not exist in bacterial systems; this may fundamentally affect the correlation between noise and expression plasticity . Despite these mechanistic differences , we do find a significant positive correlation between the promoter-mediated noise in E . coli genes and protein expression noise in their S . cerevisiae orthologues ( rho = 0 . 31 , p = 0 . 015 , n = 60; Figure 4 ) . Thus , although these organisms might differ in the mechanisms affecting gene expression noise , genes of similar function do exhibit similar levels of noise . However , protein expression noise , as calculated from [23] exhibits no correlation with gene expression noise in S . cerevisiae . The data presented here show that: ( 1 ) For many genes , the promoter region of a gene controls noise in a consistent manner; ( 2 ) Functionally important genes are controlled such that noise is decreased; ( 3 ) The lower noise observed in functionally important genes does not appear to result from these genes having been present in the genome for a longer period of time; ( 4 ) There is no correlation between the noise conferred by a promoter and the expression plasticity of mRNA expression that is controlled through that promoter . In particular , this latter observation implies that there may be fundamental differences between the mechanisms giving rise to phenotypic noise in bacterial versus eukaryotic systems .
All strains have been described previously [27] . Briefly , each strain in the library contains a plasmid with a ‘promoter region’ cloned upstream of a fast-folding GFP . These promoter regions consist of an intergenic region , together with 50–150 bp of the upstream and downstream genes . The inclusion of part of the upstream and downstream open reading frames ensures that the majority of transcriptional control elements are contained in the construct . The library contains all K12 intergenic regions longer than 40 bp . We note that although the system is plasmid based , copy-number variation is relatively low . The plasmid contains an SC101 replication origin , for which segregation is tightly controlled [29] . For this reason variation in plasmid number per cell is expected to be less than under a binomial distribution , although variation in plasmid numbers will contribute additional extrinsic noise . The strains with chromosomal integrations of the promoter-GFP fusions have been described previously [36] . Briefly , the promoter-GFP fusions were cloned and inserted into the attTn7 locus using a delivery plasmid containing a multiple cloning site surrounded by the terminal repeats of Tn7 [65] . All strains were grown in minimal media ( M9 ) supplemented with 0 . 2% arabinose . Overnight cultures grown in same media were diluted 1∶500 and allowed to grow to mid-exponential phase at 37°C , shaken at 200 rpm . The cells were incubated with Syto red 62 ( Molecular Probes ) to stain the chromosome . The filters used for cytometry were 488/530+/−15 for GFP and 633/660+/−10 for the nucleic acid staining . In calculating the repeatability of the noise metric ( Figure S3 ) , two additional growth conditions were used: M9 supplemented with 0 . 2% glucose , and M9 supplemented with 0 . 2% glucose and 2 . 5 ng/ml ciprofloxacin . The data were collected from a culture containing cells in different physiological states and quality . To minimize heterogeneity driven by these processes , we selected a small subset of cells with minimal CV . For the majority of promoters , the CV of the population was minimized between 5 , 000 and 10 , 000 cells , although gating had only a minimal effect on CV , decreasing it by 10–20% at most . Larger values than this generally contained cells of differing size and complexity , affecting the variance in fluorescence; smaller values contained too few cells to be a reliable indicator of the population . Thus , for all promoters , fluorescence data for 100 , 000 cells was collected and this data was subsequently filtered so that the fluorescence data from only 10 , 000 cells were analyzed further . These data were exported into text files and analyzed using the R statistical framework [66] ( the raw data is available at http://mara . unibas . ch/silander . html ) . The filtering process occurred in one of two ways . For the majority of the analysis , it occurred as follows: ( 1 ) the first 1000 acquisition events were excluded to minimize inaccuracies in fluorescence measurements resulting from sample crossover and initial inaccuracies in measurements that we observed; ( 2 ) extreme outliers ( all cells with red fluorescence values below ten and GFP values of one or less ) were removed; ( 3 ) to enrich for cells in similar physiological states and stages of the cell cycle , for each promoter , a kernel density was fitted to the log red fluorescence data ( indicative of the amount of nucleic acid in the cell ) , with Gaussian smoothing in which the density was estimated at 512 points using the method of Silverman for bandwidth selection [67] . The maximum value of this kernel density was determined , and 10 , 000 cells were selected from a symmetrical interval around this peak ( see Dataset S2 for simplified code ) . This number of cells minimized the variation in GFP signal due to external influences ( Figure S2 ) , while still allowing us to sample a large number of cells . The mean , median , and standard deviation for this population of cells were then calculated . For secondary confirmation of previous measurements , events were filtered on the basis of FSC and SSC alone: ( 1 ) again , the first 1000 acquisition events were excluded; ( 2 ) extreme outliers ( all cells with SSC , FSC or GFP values of one or less ) were removed; ( 3 ) a bivariate normal was fit to the log FSC and log SSC values , and values outside of two standard deviations were removed ( cellular debris ) ; ( 4 ) to enrich for cells in similar physiological states and stages of the cell cycle , a 2 d kernel density was fitted to the FSC and SSC data . The maximum value of this kernel density was determined , and 10 , 000 cells were selected from an elliptical gate around this point , oriented by the covariance between FSC and SSC ( Figure S1 ) . This gating was performed using the flowCore package [68] . Again , the mean , median , and standard deviation for this population of cells were calculated . Several promoters gave rise to distributions that appeared to be either bimodal or have extremely high variances . The promoters having the highest CV ( >0 . 6 ) , and all promoters exhibiting a bimodal expression pattern were reanalyzed by restreaking for single colonies and measuring fluorescence a second time . We found that for all promoters exhibiting bimodal patterns , the bimodality disappeared upon restreaking to obtain a single clone; a previous analysis of protein levels in E . coli cells confirms the rarity of bimodal distributions [23] . We thus concluded that the bimodal distributions were likely due to contamination from a second promoter construct . For this reason , these promoters were removed the analysis . Three samples were removed from the analysis , one on the basis of abnormal DNA staining , and two due to small sample sizes . We calculated a 95% confidence interval around the mean fluorescence of the empty vectors ( containing gfp , but no promoter ) , and excluded all promoters with a mean fluorescence less than this range from the analysis ( below 2 . 26 fluorescence units ) . There is thus only a 2 . 5% chance that the GFP signal for any promoter included in the analysis is due to only to autofluorescence . Our goal was to define a consistent metric of noise in mRNA expression that enabled comparison of genes with different mean expression levels ( in other words , to decouple mean from variation in expression ) . We thus followed a method similar to that outlined by Newman et al . [24] , in which noise is defined as the deviation from a sliding window of the median expression level versus the CV for each promoter . To more robustly estimate the deviation , we defined noise as the vertical deviation from a smoothed spline ( 6 degrees of freedom ) that covered a running median of mean log expression versus CV of log expression ( window of 15 data points ) ; a smoothed spline is not subject to the small deviations that a running median is ( Figure 1F ) . For simplicity , we refer to this deviation as noise in gene expression , or noise . We note that noise is homoscedastic across expression levels: mean expression level versus noise or the absolute value of noise gives no significant regression . This is not the case for two related metrics of noise based on vertical deviation from a smooth spline: if log mean expression versus CV of expression or mean log expression versus standard deviation of log expression are used , both result in highly expressed genes having extreme levels of noise ( either very high or very low ) ( Figure 1B , 1C , 1E ) . In contrast , for the metric of noise we use , genes having very high expression are not more likely to have extreme levels of noise . In addition , there is no significant correlation of noise with mean expression level ( rho = −0 . 035 , p = 0 . 17 , n = 1522 ) . Lastly , our results are robust when using similar noise metrics ( e . g . vertical deviation from the running median , Euclidean distance from the smoothed spline , or if different spline fits are used; see Text S1 ) . The noise metric is a highly reliable measure; for separate measurements of two independent cultures grown in different growth media yields a Spearman's rho value of 0 . 58 ( p<1e-120; Figure S3 ) . Data on gene essentiality was taken from the PEC dataset [37] . Promoters were considered essential if they drove the expression of an essential gene or an operon containing an essential gene . For conservation , only the immediate downstream gene was taken into account . Using data from Ragan et al . ( 2006 ) , for each gene that appeared to have experienced horizontal transfer , we used the median value of the estimated phyletic depth at which the horizontal transfer occurred . We then selected those genes that had been acquired after the divergence of E . coli from Haemophilus ( 220 genes ) , Buchnera ( 170 genes ) , or E . coli CFT073 ( 42 genes ) , and used these sets to calculate the relationship in recently transferred genes between noise and gene conservation . We calculated gene conservation using a reciprocal shortest distance strategy [69] to search for putative orthologues of E . coli genes in 105 fully sequenced gamma-proteobacteria or 58 alpha-proteobacteria [70] . We considered genes present in at least 30 out of 58 ( >50% ) fully sequenced alpha-proteobacterial taxa to have been acquired before the E . coli – alpha-proteobacteria divergence . Promoters were grouped by functional class according to the gene annotations for the immediate downstream gene , as outlined in MultiFun [48] into eight major categories: metabolism , information transfer , regulation , transport , cell process , cell structure , cellular location , and extra-chromosomal element; each major category contained up to eight subcategories . To test for the enrichment of low or high noise genes , for each major category , each subcategory was tested against the remaining genes in that major category for enrichment of promoters with higher or lower noise using a Wilcox rank sum test . Data on relative mRNA abundances and half-lives were taken from [55] . Data on relative mRNA expression levels ( i . e . expression ratios ) for 240 different conditions were taken from the E . coli Gene Expression Database ( http://genexpdb . ou . edu/ ) . These conditions were also grouped using hierarchical clustering into 18 clusters in which expression ratios were similar using the Lance-Williams formula as implemented by hclust in the R statistical package . Data on both operon structure and the binding sites of sigma factors was taken from RegulonDB ( http://regulondb . ccg . unam . mx/ ) . Orthologous genes in yeast were identified using a reciprocal best-hit analysis , with varying e-value cut-offs . The significance of the correlation , although low , is robust to changes in the stringency of the e-value cut-off ( we note that as the stringency of this cutoff is increased , the number of orthologues decreases , necessarily decreasing the significance: e-20: rho = 0 . 2 , p = 0 . 07; e-30: rho = 0 . 28 , p = 0 . 02; e-40: rho = 0 . 26 , p = 0 . 06; e-50: rho = 0 . 25 , p = 0 . 11 ) . Unless otherwise specified , all categorical comparisons were performed using a non-parametric two-sided Wilcox rank sum test and all reported correlations are non-parametric Spearman rank correlations . The p-values for the Spearman rank correlations were calculated using the default settings of the cor . test ( ) function in R , which uses an asymptotic t approximation . | Many biological processes in a cell involve small numbers of molecules and therefore fluctuate over time . As a consequence , genetically identical cells that live in the same environment differ from each other in many phenotypic traits , including the expression level of different genes . Our aim was to identify types of genes with particularly low or high levels of variation ( “noise” ) and to understand molecular and evolutionary factors that determine noise level . Working with the bacterium E . coli , we analyzed the expression—at the single cell level—of more than 1 , 500 different genes . We found particularly low levels of noise in genes that E . coli needs to live and genes that this bacterium shares with many related taxa . This suggests that cellular functions that are particularly important for this organism evolved towards low levels of variation . In contrast to previous results with yeast , we find that genes that change their expression levels in response to environmental signals do not have high levels of noise . This suggests that there may be fundamental differences in how noise is controlled in bacteria and eukaryotes . | [
"Abstract",
"Introduction",
"Results",
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] | [
"biology",
"microbiology",
"evolutionary",
"biology"
] | 2012 | A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli |
The evolutionary significance of hybridization and subsequent introgression has long been appreciated , but evaluation of the genome-wide effects of these phenomena has only recently become possible . Crop-wild study systems represent ideal opportunities to examine evolution through hybridization . For example , maize and the conspecific wild teosinte Zea mays ssp . mexicana ( hereafter , mexicana ) are known to hybridize in the fields of highland Mexico . Despite widespread evidence of gene flow , maize and mexicana maintain distinct morphologies and have done so in sympatry for thousands of years . Neither the genomic extent nor the evolutionary importance of introgression between these taxa is understood . In this study we assessed patterns of genome-wide introgression based on 39 , 029 single nucleotide polymorphisms genotyped in 189 individuals from nine sympatric maize-mexicana populations and reference allopatric populations . While portions of the maize and mexicana genomes appeared resistant to introgression ( notably near known cross-incompatibility and domestication loci ) , we detected widespread evidence for introgression in both directions of gene flow . Through further characterization of these genomic regions and preliminary growth chamber experiments , we found evidence suggestive of the incorporation of adaptive mexicana alleles into maize during its expansion to the highlands of central Mexico . In contrast , very little evidence was found for adaptive introgression from maize to mexicana . The methods we have applied here can be replicated widely , and such analyses have the potential to greatly inform our understanding of evolution through introgressive hybridization . Crop species , due to their exceptional genomic resources and frequent histories of spread into sympatry with relatives , should be particularly influential in these studies .
Hybridization and subsequent introgression have long been appreciated as agents of evolution . Adaptations can be transferred through these processes upon secondary contact of uniquely adapted populations or species , in many instances producing the variation necessary for further diversification [1] . Early considerations of adaptive introgression discussed its importance in the context of both domesticated and wild species [2] , [3] , viewing both anthropogenic disturbance and naturally heterogeneous environments as ideal settings for hybridization . More recently , studies of adaptation through introgression have focused primarily on wild species ( [4] , [5] but see [6] , [7] ) . Well-studied examples include increased hybrid fitness of Darwin's finches following environmental changes that favor beak morphology intermediate to that found in extant species [8] , [9] and the introgression of traits related to herbivore resistance [10] and drought escape [11] in species of wild sunflower [12] , [13] . Molecular and population genetic analyses have also clearly identified instances of adaptive introgression across species at individual loci , including examples such as the RAY locus controlling floral morphology and outcrossing rate in groundsels [14] ) and the optix gene controlling wing color in mimetic butterflies [15] , [16] . Despite long-standing interest in introgression , however , genome-wide analyses are rare and have been primarily conducted in model systems [17]–[22] . Studies of natural introgression in cultivated species have been limited in genomic scope and have largely ignored the issue of historical adaptive introgression , focusing instead on contemporary transgene escape and/or the evolution of weediness [23]–[27] . One notable exception is recent work documenting introgression between different groups of cultivated rice in genomic regions containing loci involved in domestication [19] , [28]–[30] . Few studies , however , have investigated the potential for introgression to transfer adaptations between crops and natural populations of their wild relatives post-domestication . Subsequent to domestication , most crops spread from centers of origin into new habitats , often encountering locally adapted populations of their wild progenitors and closely related species ( e . g . , [31]–[33] ) . These crop expansions provide compelling opportunities to study evolution through introgressive hybridization . Here , we use a SNP genotyping array to investigate the genomic signature of gene flow between cultivated maize and its wild relative Zea mays ssp . mexicana ( hereafter , mexicana ) and examine evidence for adaptive introgression . Maize was domesticated approximately 9 , 000 BP in southwest Mexico from the lowland teosinte taxon Zea mays ssp . parviglumis ( hereafter , parviglumis; [34]–[36] ) . Following domestication , maize spread to the highlands of central Mexico [34] , [37] , a migration that involved adaptation to thousands of meters of changing elevation and brought maize to substantially cooler ( ∼7°C change in annual temperature ) and drier ( ∼300 mm change in annual precipitation ) climes [38] . During this migration maize came into sympatry with mexicana , a highland teosinte that diverged from parviglumis ∼60 , 000 BP [39] . Convincing morphological evidence for introgression between maize and mexicana has been reported [40] , [41] , and traits putatively involved in adaptation to the cooler highland environment such as dark-red and highly-pilose leaf sheaths [42] , [43] are shared between mexicana and highland maize landraces [40] , [44] . These shared morphological features could suggest adaptive introgression [45] but could also reflect parallel or convergent adaptation to highland climate or retention of ancestral traits [46] . Though hybrids are frequently observed , phenological isolation due to flowering time differences [40] , [47] and cross-incompatibility loci [48]–[50] are thought to limit the extent of introgression , particularly acting as barriers to maize pollination of mexicana . Experimental estimates of maize-mexicana pollination success ( i . e . , production of hybrid seed ) are quite low , ranging from <1–2% depending on the direction of the cross [51] , [52] . Nevertheless , theory suggests that alleles received through hybridization can persist and spread despite such barriers to gene exchange , particularly when they prove adaptive [53] , [54] . Molecular analyses over the last few decades have provided increasingly strong evidence for reciprocal introgression between mexicana and highland maize landraces . Early work identified multiple allozyme alleles common in highland Mexican maize and mexicana but rare in closely related taxa or maize outside of the highlands [55] . Likewise , sequencing of the putative domestication locus barren stalk1 ( ba1 ) revealed a haplotype unique to mexicana and highland Mexican maize [56] . Multiple studies have found further support for bidirectional gene flow and have estimated that ∼2–10% of the genome of highland maize is derived from mexicana [34] , [57] and 4–8% of the mexicana genome is derived from maize [58] . A more recent study including several hundred markers revealed that admixture with mexicana may approach 20% in highland Mexican maize [36] . Similar to introgression studies in many other plant species ( e . g . , [31] , [59]–[62] ) , morphological and molecular studies have only provided rough estimates of the extent of introgression between mexicana and maize . Little is known regarding genome-wide patterns in the extent and directionality of gene flow . A genomic picture of introgression could greatly expand our understanding of evolution through hybridization , revealing how particular alleles , genes and genomic regions are disproportionately shaped by and/or resistant to these processes [63] , [64] . Additionally , assessment of introgression in crop species during post-domestication expansion can provide insight into the genetic architecture of adaptation to newly encountered abiotic and biotic conditions . Here , we provide the most in-depth analysis to date of the genomic extent and directionality of introgression in sympatric collections of maize and its wild relative , mexicana , based on genome-wide single nucleotide polymorphism ( SNP ) data . We find evidence for pervasive yet asymmetric gene flow in sympatric populations . Across the genome , several regions introgressed from mexicana into maize are shared across most populations , while little consistency in introgression is observed in gene flow in the opposite direction . These data , combined with analysis of environmental associations and a growth chamber experiment , suggest that maize colonization of highland environments in Mexico may have been facilitated by adaptive introgression from local mexicana populations .
To assess the extent of hybridization and introgression we collected nine sympatric population pairs of maize and mexicana and one allopatric mexicana population from across the highlands of Mexico ( Table S1; Figure 1 ) and genotyped 189 individuals for 39 , 029 SNPs ( see Materials and Methods ) . Genotype data at the same loci were obtained from Chia et al . [65] for a reference allopatric maize population . Average expected heterozygosity ( HE ) , percent polymorphic loci ( %P ) , and the proportion of privately segregating sites were higher in maize than mexicana ( t-test , p≤0 . 012 for all comparisons , Table S2 ) , likely influenced by the absence of mexicana from the discovery panel used to develop the genotyping platform [66] . However , substantial variation in diversity was observed across populations within taxa ( e . g . , %P ranged from 52–88% in maize and from 44–79% in mexicana ( Table S2 ) ) and meaningful comparisons can be made at this level . Our analysis of diversity identified the Ixtlan maize population as an extreme outlier , containing 31% fewer polymorphic markers than any other maize population . Discussion with farmers during our collection revealed that Ixtlan maize was initially a commercial variety whose seed had been replanted for a number of generations . Excluding this population , diversity in mexicana populations varied much more substantially than in maize ( e . g . , variance in %P across mexicana populations was 7-fold higher; Table S2 ) At the population level , summary statistics of diversity and differentiation were consistent with sympatric gene flow ( i . e . , local gene flow based on current plant distributions ) between maize and mexicana ( Figure 2 ) . First , %P was positively correlated between sympatric population pairs ( R2 = 0 . 65; p = 0 . 016; Figure 2A ) , though this trend could reflect local conditions affecting diversity in both taxa rather than gene flow . Second , in a subset of populations , the proportion of shared polymorphisms was higher ( Figure 2B ) and pairwise differentiation ( FST ) was lower ( Figure 2C ) between sympatric population pairs than in allopatric comparisons . Finally , an individual-based STRUCTURE analysis assuming two groups ( K = 2 ) revealed strong membership of reference allopatric individuals of maize and mexicana in their appropriate groups ( 96% and 99% respectively ) , yet appreciable admixture in sympatric populations ( Figure 2D ) . Four recent hybrids were identified ( 3 mexicana and 1 maize ) with <60% membership in their respective groups . STRUCTURE analysis also indicated that gene flow was asymmetric , with more highland maize germplasm derived from mexicana ( 19% versus 12% of mexicana germplasm from maize ) . Assignment at higher K values continued to indicate admixture in mexicana populations but not in maize , suggesting that gene flow from mexicana into maize may have been more ancient ( Figure S1 ) . Consistent with this interpretation , median values of the f3 statistic [67] for SNPs genome-wide were negative or zero for 8 of 9 sympatric maize populations ( Figure S2 ) ; only the Ixtlan maize population showed a positive median f3 signifying a lack of admixture . Collectively , these population-level summaries are suggestive of historical gene flow from mexicana into maize and , in a subset of populations , of ongoing sympatric gene flow from maize into mexicana . Meaningful information regarding the evolutionary significance of introgression can often be obscured in population-level summaries . However , the large number of SNPs in our data set allowed us to assess variation in the extent of introgression across the genome . We made use of two complementary methods . First , we employed the hidden Markov model of HAPMIX [68] to infer ancestry of chromosomal segments along the genomes of individuals from maize and mexicana populations through comparison to reference allopatric populations . Subsampling of the reference allopatric populations ( see Materials and Methods ) revealed considerable signal of introgression in the maize reference panel , particularly in low recombination regions of the genome near centromeres ( correction for this signal is illustrated in Figure 3 and Figure S3 ) . While this signal could represent genuine introgression predating allopatry , it could also indicate potential false positives in genomic regions with high linkage disequilibrium or less data . We therefore added a complementary analysis using the linkage model of STRUCTURE [69] , [70] to conduct site-by-site assignment across the genomes of mexicana and maize . Because STRUCTURE takes allele frequencies across all populations into account during assignment , the approach is robust to potential deviations of individual reference populations from ancestral frequencies . Both methods allowed quantification of introgression along the genome for individual samples . Rather than investigate every putative introgression , however , we focused further analyses on genomic regions with a high frequency of introgression , requiring an average of one chromosome or 50% assignment to the opposite taxon per individual in a given population ( Figure 3; Figure S3; referred to as “introgressed regions” hereafter ) . Approximately 19 . 1% and 9 . 8% of the genome met this criterion in the HAPMIX and STRUCTURE scans respectively for mexicana introgression into maize . In the opposite direction , we observed lower proportions at this threshold ( 11 . 4% in the case of HAPMIX and 9 . 2% using STRUCTURE ) , corroborating asymmetric gene flow favoring mexicana introgression into maize . Both scans showed a disproportionate number of introgressed regions shared across populations in mexicana-to-maize gene flow . Roughly 50% of regions introgressed from mexicana into maize were shared across seven or more populations in the HAPMIX scan , whereas only 4% of introgressed regions had this level of sharing from maize into mexicana; similar asymmetry was observed using STRUCTURE ( 12% versus <1% ) . By comparing composite likelihood scores from HAPMIX across individuals within each population , we were able to characterize relative times since admixture ( see Materials and Methods ) . We observed qualitative differences between maize and mexicana . The likelihood of the admixture time parameter began to decrease markedly after an average of 83 generations in mexicana populations , whereas the decrease in maize was much more gradual and did not occur until after an average of 174 generations ( Figure S4; averages exclude Ixtlan ) suggesting older introgression from mexicana into maize . A notable exception to this trend was observed in the Ixtlan sympatric population pair , where the maize population was likely derived in the recent past from a commercial variety and introgression appeared to be more recent from mexicana into maize ( Figure S4 ) . For further population genetic characterization , we focused on the subset of introgressed regions identified in both the HAPMIX and STRUCTURE scans , an approach that should be robust to the individual assumptions of the two methods . These regions spanned an average of 3 . 6% of the genome in the case of mexicana-to-maize introgression and 3 . 2% for maize-to-mexicana introgression ( Figure 3C; Figure S3 ) . As expected , differentiation between sympatric maize and mexicana was reduced in these introgressed regions in both directions of gene flow ( mean 25% reduction of FST mexicana-to-maize , 33% reduction maize-to-mexicana , t-test , p<0 . 001 for all population-level comparisons of introgressed vs . non-introgressed regions in both directions of gene flow ) . Introgressed regions also showed more shared and fewer fixed and private SNPs ( Table S3 ) , as well as longer tracts of identity by state ( IBS ) between maize and mexicana ( t-test , p<<0 . 001 ) . Consistent with these results , diversity in introgressed regions was generally different from non-introgressed regions in the recipient taxon and instead comparable to diversity in non-introgressed regions in the taxon of origin ( Table S3 ) . In total , we identified nine regions of introgression from mexicana to maize found by both methods and present in ≥7 sympatric population pairs ( Table S4 ) . Three of these shared regions of introgression span the centromeres of chromosomes 5 , 6 , and 10 ( Figure S3 ) , suggesting that maize from the highlands of Mexico may in fact harbor mexicana centromeric or pericentromeric sequence . No such shared introgressions were found in the opposite direction of gene flow ( maize into mexicana ) . Finally , we characterized regions of the genome notably lacking evidence of introgression . We refer to regions with ≤5% probability of introgression confirmed by both scans in ≥7 populations as being resistant to introgression ( Figure S5 ) . In both directions of gene flow , we found these genomic regions to have elevated differentiation , decreased diversity , fewer shared variants , more fixed differences , and a higher number of privately segregating SNPs in the opposite taxon ( Table S3 ) . Two non-mutually exclusive hypotheses of adaptive introgression can be readily discerned for gene flow between mexicana and maize: 1 ) as its natural habitat was transformed , mexicana received maize alleles conferring adaptation to the agronomic setting and 2 ) as it diffused to the highlands of central Mexico from the lowlands of southwest Mexico , maize received alleles conferring highland adaptation from mexicana , which was already adapted to these conditions . To evaluate evidence for the first hypothesis we gauged enrichment of 484 candidate domestication genes [71] in regions of introgression . We hypothesized that if maize donated alleles adaptive for the agronomic setting to mexicana , we would detect enrichment of domestication loci in regions introgressed from maize into mexicana . However , compared to the rest of the genome , introgressed regions in both directions of gene flow harbored significantly fewer domestication candidates ( permutation test , p≤0 . 001 ) , while regions resistant to introgression showed an excess of domestication candidates ( permutation test , p = 0 . 121 maize to mexicana , p = 0 . 008 mexicana to maize; Figure S5 ) . For example , two well-characterized domestication genes affecting branching architecture , grassy tillers1 ( gt1; [72] , [73] ) and teosinte branched1 ( tb1; [74] ) showed very little evidence of introgression ( Figure S5 ) . Introgression also appeared to be rare from maize into mexicana across much of the short arm of chromosome 4 , a span that includes the domestication loci teosinte glume architecture1 ( tga1; [75] ) , sugary1 ( su1; [76] ) and brittle endosperm2 ( bt2; [76] ) and the well characterized pollen-pistil incompatibility locus teosinte crossing barrier1 ( tcb1; [48] ) that serves as a hybridization barrier between maize and mexicana ( Figure S5 ) . These results suggest selection against introgression at loci that contribute to domestication and reproductive isolation . Several lines of evidence support the hypothesis that maize received introgression conferring highland adaptation from mexicana . Across the nine shared introgressed regions , five contained long stretches ( >300 kb ) of zero diversity across seven populations , implying a common introgressed haplotype ( Figure S6 ) . Given that these regions only have 5–15 SNPs , however , higher-density genotyping might resolve additional haplotypes . Additionally , we used the method of Coop et al . [77] to detect associations of population allele frequencies with 76 environmental variables ( see Materials and Methods ) . Environmental variables were reduced in dimensionality to four principal components that captured 95% of environmental variation . We found that loci associated with the second principal component ( loaded primarily by temperature seasonality ) were significantly enriched ( permutation test , p = 0 . 017 ) in genomic regions introgressed from mexicana into maize , but no significant enrichment was observed in regions introgressed from maize into mexicana . We then compared the nine regions of introgression found in ≥7 populations of maize to QTL for anthocyanin content and leaf macrohairs ( putatively adaptive traits under highland conditions ) identified in a previous study from a cross between parviglumis ( lowland teosinte ) and mexicana ( highland teosinte ) [42] . Six of the introgressed regions overlapped with five of the six genomic regions with QTL detected for these traits . Two of the shared introgressions that overlapped with QTL are of particular interest due to their previous characterization . One of these , on chromosome 4 , overlaps with QTL for both pigment intensity and macrohairs [42] , and maps to the same position as a recently identified putative inversion polymorphism showing significant differentiation between parviglumis and mexicana ( [78]; Figure 4A ) . The second region , on chromosome 9 , overlaps with a QTL for macrohairs [42] and includes the macrohairless1 ( mhl1 ) locus [79] that promotes macrohair formation on the leaf blade and sheath of maize ( Figure 4B ) . The two lowest elevation maize populations in our study ( Puruandiro and Ixtlan ) showed a conspicuous lack of introgression in these two genomic regions ( Figure 4A and 4B ) . Analysis of pairwise differentiation ( FST ) between these populations and two populations showing fixed introgression in the two genomic regions ( Opopeo and San Pedro; Figure 4A and 4B ) revealed substantial differentiation: the region on chromosome 4 contained the only fixed SNP differences genome-wide ( Puruandiro/Ixtlan versus Opopeo/San Pedro ) and a SNP in the region on chromosome 9 was an extreme FST outlier . To explore the potential phenotypic effects of these genomic regions we conducted growth chamber experiments including ten maize plants from each of these four populations . Under temperature and day-length conditions typical of the highlands of Mexico ( see Materials and Methods ) , the leaf sheaths of plants from populations where introgression was detected in the two genomic regions had 21-fold more macrohairs ( t-test , p = 0 . 0002; Figure 4C and 4D ) , and showed greater pigmentation ( t-test , p = 6E−06; Figure 4C and 4D ) . Introgressed plants were also ∼25 cm taller ( t-test , p = 6E−06; Figure 4D ) , a finding consistent with adaptation to highland conditions and potentially associated with increased fitness . No significant difference in plant height was observed in a separate experiment under lowland conditions ( t = test , p = 0 . 51 ) , and a significant interaction was observed between introgression status and environmental treatment ( ANOVA , F = 4 . 151 , p = 0 . 045 ) , with a disproportionate increase in plant height under lowland conditions in populations lacking introgression ( Figure S7 ) . While our scans for introgression clearly indicated that mexicana has made genomic contributions to maize landraces in the highlands of Mexico , the broader contribution of mexicana to modern maize lines remained unclear . Our HAPMIX and STRUCTURE analyses had low power to detect introgression distributed broadly in maize ( see Discussion ) . Therefore , to assess potential ancestral contribution of mexicana to modern maize , we evaluated patterns of IBS between mexicana , parviglumis [78] and a global diversity panel of 279 modern maize lines [80] , [81] using the program GERMLINE ( [82]; Figure 5 , Figure S8 and S9 ) . Substantial IBS was found between mexicana and modern lines at a number of genomic locations . To assess whether this IBS merely reflected shared ancestral haplotypes , we compared IBS between modern maize and parviglumis to IBS between modern maize and mexicana on a site-by-site basis , identifying regions in which various maize groups distinguished by Flint-Garcia et al . [81] showed stronger IBS with mexicana relative to parviglumis ( see Materials and Methods; Figure 5A; Figure S8 ) . As each of the groups identified by Flint-Garcia have distinct evolutionary histories , it is possible that mexicana contributed differentially to the founders of each group . For example , the tropical-subtropical , non-stiff-stalk , and mixed groups showed more genomic regions with stronger IBS with mexicana ( versus parviglumis ) than found in the stiff-stalk , popcorn , and sweetcorn groups ( ∼31% of sites with greater IBS with mexicana in the first group versus ∼23% in the latter group; Figure 5B and 5C ) .
Despite known pre-zygotic and phenological barriers to hybridization between maize and mexicana [47]–[50] , we have found evidence consistent with substantial reciprocal introgression . Based on our population genetic analyses , several observations regarding the nature of this gene flow can be made: 1 ) Gene flow appears to be ongoing and asymmetric , favoring mexicana introgression into maize . 2 ) Gene flow from mexicana into maize is generally older than gene flow in the opposite direction . 3 ) Haplotype diversity in nine genomic regions of mexicana-into-maize introgression shared across ≥7 populations suggests single , ancient introgressions followed by spread across the Mexican highlands . 4 ) Introgression from mexicana into maize is restricted at domestication loci but enriched at loci putatively involved in highland adaptation . 5 ) Genomic regions of mexicana/maize IBS within a global diversity panel of maize hint at a possible broader contribution of mexicana to modern improved maize . Several of these observations are in line with previous research . For example , the asymmetric gene flow we detect from mexicana to maize is consistent with findings of substantially higher pollination success in this direction [51] . Asymmetric gene flow would also be expected based on phenology: in Mexico , maize typically flowers earlier than mexicana [47] and pollen shed in both taxa precedes silking ( female flowering ) . Therefore , when maize silks are receptive , mexicana could potentially be shedding pollen , whereas when mexicana silks are receptive , maize tassels are more likely to be senescent . Under these conditions , F1 progeny would be more likely to have a maize seed parent and a teosinte pollen parent and subsequent inadvertent planting of F1's in maize fields would bias the direction of gene flow . Our data also provide support for previous assertions that shared morphological features between mexicana and maize represent adaptations derived from mexicana [45] rather than from maize [41] . For example , we have found significant environmental correlations in genomic regions of mexicana-to-maize introgression . We have also observed that overlap with QTL and fine-mapped loci for highland Zea traits ( e . g . , leaf sheath macrohairs and pigmentation ) are predominantly found in the direction of mexicana to maize gene flow . Two such regions , on chromosomes 4 and 9 , showed particularly strong evidence of introgression . Moreover , these genomic regions of introgression were more common in higher elevation maize populations in our sample , and maize populations with and without introgression in these regions showed differential morphology and greater plant height ( a proxy for fitness ) when grown under highland conditions . In contrast , we found little evidence of adaptive introgression in the opposite direction of gene flow . For example , domestication loci appeared resistant to gene flow from maize into mexicana , contradicting previous suggestions that gene flow from maize may have been required for mexicana to adapt to an agronomic setting [41] . Instead it appears likely that mexicana , like other wild teosintes [83] , was a ruderal species adapted to open and disturbed environments even before the transformation of its natural habitat by maize cultivation . Our detection of haplotype sharing between mexicana and a diverse panel of modern maize is consistent with previous findings suggesting the spread of introgressed mexicana haplotypes in maize outside of the highlands of Mexico [71] . Both the STRUCTURE and HAPMIX methods we used to identify regions of introgression would likely not detect introgression found ubiquitously in modern maize . Widespread mexicana introgression into maize would result in poor resolution between reference populations of these taxa in the HAPMIX analysis , and extensive haplotype sharing across maize and mexicana would result in a weak signature of introgression in STRUCTURE . Further analysis of representative panels of mexicana , parviglumis and maize haplotypes at greater marker density should help clearly distinguish mexicana from parviglumis haplotypes and determine whether mexicana haplotypes are indeed widespread in maize . While our results are consistent with previous research and the historical spread of maize , our power to detect introgression may be limited for a number of reasons . First , our analysis conservatively focused on regions of introgression identified by two independent methods and shared across individuals within populations , undoubtedly missing a number of genuine instances of more limited gene flow . Second , our markers were ascertained in a panel consisting entirely of maize . In addition to inflating the diversity of maize relative to mexicana , this ascertainment scheme likely limited our ability to distinguish among mexicana haplotypes and thus to detect local introgression from mexicana into maize . Third , the resolution of our data was on average one SNP per 80 kb , which could result in a bias toward detection of more recent introgression and introgression in low recombination regions of the genome . Finally , mexicana only rarely occurs allopatric from maize [40] , and most populations have likely experienced gene flow at some point in time , thus complicating estimation of ancestral mexicana haplotypes and allele frequencies . Many aspects of mexicana's contribution to highland adaptation in maize remain to be resolved . While our growth chamber experiment was suggestive of adaptive introgression , the loci conferring these traits are still ambiguous . Repetition of these experiments with mexicana/lowland maize near-isogenic introgression lines will be necessary to bolster the case for adaptive introgression . Additionally , a particularly interesting comparison can be made between highland maize in central Mexico , a geographic region sympatric with mexicana , and highland maize in the Andes of South America where no inter-fertile wild Zea species can be found . Future research should address whether highland adaptation in South American maize occurred in parallel to maize from Mexico [37] or whether pre-adapted highland maize was transported through Central America as some have suggested [84] . The potential for adaptive introgression during crop expansion is of course not limited to maize . Data from several crops ( e . g . , rice [19] , [85] , barley [86] , [87] , common bean [88] , and wheat [32] , [89] ) suggest defined centers of origin within a broader distribution of wild relatives . The distributions of these crop-wild pairs span continents and a wide range of environments , and many are known to hybridize ( for a review , see [24] ) . The methods we have applied here to maize and mexicana can therefore be replicated widely , perhaps revealing unexpected aspects of crop evolution and providing insight regarding the genetic architecture of local adaptation based on conserved regions of introgression . Crops and related wild taxa can also be seen more broadly as models for the study of evolution through hybridization . If crops are viewed as human-facilitated invasive species , clear connections can be made to theoretical work on introgression during invasion and range expansion . For example , our finding of asymmetric gene flow from mexicana into maize is consistent with simulations showing that invaders should receive much higher levels of introgression from local species than occurs in the opposite direction due to differences in population density at the time of invasion [90] , [91] . Theoretical research has also explored the divergence threshold for successful hybridization and introgression [53] , [92] . Crop expansions are ideal systems to test such predictions because , as ancient agriculturalists moved crops away from their centers of origin , these domesticates came into sympatry with relatives spanning a range of divergence times . For example , parviglumis , the progenitor of maize , has a divergence time from mexicana estimated at 60 , 000 years , from other members of the genus on the order of 100 , 000–300 , 000 years , and from the outgroup Tripsacum dactyloides of approximately 1 million years [39] . While parviglumis is currently physically isolated from these taxa and likely was at the time of domestication [38] , maize has subsequently come into sympatry with virtually all of its close relatives , providing extensive opportunities for hybridization . These newly-formed hybrid zones can be seen as testing grounds of the fitness of hybrids across a range of divergence and opportunities to study the evolution of barriers to hybridization .
Samples were collected from nine sympatric population pairs of mexicana and maize that spanned the known distribution of mexicana in Mexico , as well as a single allopatric population of mexicana ( Table S1; Figure 1 ) . Seed samples from 12 maternal individuals per mexicana population ( N = 120 ) were selected for genotyping . A single kernel was also sampled from each of 6–8 maize ears collected from sympatric maize fields ( N = 69 ) . The tenth kernel down from the tip of each ear was chosen to help control for potential variation in outcrossing rate along the ear . Seeds were treated with fungicide , germinated on filter paper and grown in standard potting mix to the five-leaf stage . Freshly harvested leaf tips were stored at −80°C overnight and lyophilized for 48 hours . Tissue was then homogenized with a Mini-Beadbeater-8 ( BioSpec Products , Inc . , Bartlesville , OK , USA ) and DNA was isolated using a modified CTAB protocol [93] . Purity of DNA isolations was determined with a NanoDrop spectrophotometer ( NanoDrop Technologies , Inc . , Wilmington , DE , USA ) . Samples with 260∶280 ratios ≥1 . 8 were deemed acceptable for genotyping . Concentrations of DNA isolations were determined with a Wallac VICTOR2 fluorescence plate reader ( Perkin-Elmer Life and Analytical Sciences , Torrance , CA , USA ) using the Quant-iT Picogreen dsDNA Assay Kit ( Invitrogen , Grand Island , NY , USA ) . Single nucleotide polymorphism genotypes were generated using the Illumina MaizeSNP50 Genotyping BeadChip platform and were clustered separately for the two taxa based on the default algorithm of the GenomeStudio Genotyping Module v1 . 0 ( Illumina Inc . , San Diego , CA , USA ) . Clustering for each SNP in each taxon was subsequently inspected and manually adjusted . Of the total of 56 , 110 markers contained on the chip , 39 , 029 SNPs that were polymorphic within the entire sample of maize and mexicana and contained less than 10% missing data in both taxa were used for further analysis . Observed ( HO ) and expected ( HE ) heterozygosities were summarized for each taxon in each sympatric population pair using the “genetics” package in R [94] . Polymorphisms were further characterized as shared , fixed , or segregating privately within one of each pair of sympatric populations using the sharedPoly program of the libsequence C++ library [95] . Pairwise differentiation between populations ( FST ) was calculated based on the method of Weir and Cockerham [96] using custom R scripts and the “hierfstat” package of R [97] . The f3 statistic for identification of admixture [67] was calculated using a custom R script . To characterize patterns of introgression across the genome in each population we used two complementary methods: 1 ) Identification of ancestry across chromosomal segments with the hidden Markov model approach of HAPMIX [68]; and 2 ) A site-by-site analysis of assignment probabilities using the Bayesian linkage model in the program STRUCTURE [69] , [70] . For both HAPMIX and STRUCTURE analyses , we used a subset of 38 , 262 SNPs anchored in a genetic map based on the Intermated B73×Mo17 ( IBM ) population of maize ( [66]; J . P . Gerke et al . , unpublished data ) . The IBM population has been widely used for genetic map development and for determining the genetic architecture of complex traits in maize [98] . Patterns of introgression were assessed using the program HAPMIX by comparing unphased data from putatively admixed individuals from our sympatric populations to phased data from reference ancestral populations . To represent ancestral mexicana haplotypes , we chose a population near the town of Amatlán , Morelos state , Mexico that is currently allopatric to maize . An Americas-wide sample of maize landraces collected largely outside the distribution of mexicana was chosen as the maize reference population [65] . In order to assess putative introgression and/or false positives in these reference populations , we removed each individual and evaluated introgression through comparison to remaining reference samples using a jackknife approach . Evidence for introgression was assessed in both putatively admixed and reference individuals using HAPMIX as described below . Initial estimates of ancestry proportions for HAPMIX models were based on a previous admixture analysis of mexicana and highland Mexican maize ( ∼20% introgression of mexicana into maize and ∼10% introgression of maize into mexicana; [36] ) . The number of generations since the time of admixture was varied from 1–5000 and the maximum likelihood across individuals in a population was used to compare relative time since admixture on a population-by-population basis ( Figure S4 ) . Subsequent analyses of HAPMIX output were based on introgression estimates from the highest likelihood run . Prior to analysis in STRUCTURE , SNP data were phased using the program fastPHASE ( version 1 . 4 . 0; [99] ) . Because STRUCTURE does not account for linkage disequilibrium ( LD ) due to physical linkage , SNPs were grouped into haplotypes separated by at least 5 kb . After grouping , our data set consisted of 20 , 035 loci with an average of 3 . 92 alleles per locus across all sympatric and reference allopatric individuals . We ran the linkage model in STRUCTURE with 5 , 000 steps of admixture burn-in , a total burn-in of 10 , 000 steps , and 100 , 000 subsequent steps retained for analysis . Convergence along the chain and consistency across replicate runs were assessed to ensure an adequate number of steps were included in the analysis . Assignment was carried out for K = 2 groups ( i . e . , maize and mexicana ) for each chromosome separately . Probability of assignment was summarized locus by locus across individuals from each population for each taxon . To identify SNPs associated with environmental variables , we employed the association method of BAYENV [77] , using a covariance matrix of allele frequencies estimated with 10 , 000 random SNPs to control for population structure . Seventy-six climatic and soil variables were summarized as four principal components that captured 95% of the variance among mexicana populations . BAYENV was run five times with 1 , 000 , 000 iterations for each SNP . A given SNP was considered a candidate if its Bayes factor was consistently in the 95th percentile across all five independent runs and its average Bayes factor was in the 99th percentile . Enrichment of significant SNPs in introgressed regions was determined based on bootstrap resampling for each environmental PC . Analyses of haplotype sharing/identity by state between mexicana , parviglumis , and modern maize lines were conducted using the program GERMLINE [82] with haplotypes generated by the program fastPHASE [99] from samples of parviglumis [78] and modern maize [80] . Shared haplotypes were identified with a seed of identical genotypes at five SNPs that were extended until mismatch . Analyses were then based on segments with a minimum size of 3 cM . Ten seeds were germinated from each of four maize populations showing little evidence of introgression ( Ixtlan and Puruandiro ) or fixed introgression ( Opopeo and San Pedro ) at two loci ( one on chromosome 4 and one on chromosome 9; Table S4 ) putatively linked to highland adaptation [42] , [78] and showing little evidence of false positives in our reference populations . Plants were grown under highland conditions with 12 . 5 hours of light at an intensity of 680 µmol/m2*s , a daytime temperature of 23°C and a nighttime temperature of 11°C . Daytime relative humidity was set at 60% and nighttime relative humidity at 80% . Height measurements were taken at 15 , 30 , and 50 days . Pigment extent was measured on the second leaf sheath from the top of the plant as the proportion of the total sheath showing pigment . Macrohairs were also measured on this leaf sheath as the total count one third of the way down from the leaf blade within the field of a dissecting microscope at 2× magnification . In order to contrast plant height from our highland treatment to those under conditions more comparable to the lowlands of western Mexico , we conducted a separate growth chamber experiment with a daytime temperature of 32°C and a nighttime temperature of 25°C and measured plant height at 30 days . All other conditions were identical to those of the highland treatment . | Hybridization and introgression have been shown to play a critical role in the evolution of species . These processes can generate the diversity necessary for novel adaptations and continued diversification of taxa . Previous research has suggested that not all regions of a genome are equally permeable to introgression . We have conducted one of the first genome-wide assessments of patterns of reciprocal introgression in plant populations . We found evidence that suggests domesticated maize received adaptation to highland conditions from a wild relative , teosinte , during its spread to the high elevations of central Mexico . Gene flow appeared asymmetric , favoring teosinte introgression into maize , and was widespread across populations at putatively adaptive loci . In contrast , genomic regions near known domestication and cross-incompatibility loci appeared particularly resistant to introgression in both directions of gene flow . Crop-wild study systems should play an important role in future studies of introgression due to their well-developed genomic resources and histories of reciprocal gene flow during crop expansion . | [
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] | 2013 | The Genomic Signature of Crop-Wild Introgression in Maize |
In plants and animals , nucleotide-binding and leucine-rich repeat domain containing ( NLR ) immune receptors are utilized to detect the presence or activities of pathogen-derived molecules . However , the mechanisms by which NLR proteins induce defense responses remain unclear . Here , we report the characterization of one basic Helix-loop-Helix ( bHLH ) type transcription factor ( TF ) , bHLH84 , identified from a reverse genetic screen . It functions as a transcriptional activator that enhances the autoimmunity of NLR mutant snc1 ( suppressor of npr1-1 , constitutive 1 ) and confers enhanced immunity in wild-type backgrounds when overexpressed . Simultaneously knocking out three closely related bHLH paralogs attenuates RPS4-mediated immunity and partially suppresses the autoimmune phenotypes of snc1 , while overexpression of the other two close paralogs also renders strong autoimmunity , suggesting functional redundancy in the gene family . Intriguingly , the autoimmunity conferred by bHLH84 overexpression can be largely suppressed by the loss-of-function snc1-r1 mutation , suggesting that SNC1 is required for its proper function . In planta co-immunoprecipitation revealed interactions between not only bHLH84 and SNC1 , but also bHLH84 and RPS4 , indicating that bHLH84 associates with these NLRs . Together with previous finding that SNC1 associates with repressor TPR1 to repress negative regulators , we hypothesize that nuclear NLR proteins may interact with both transcriptional repressors and activators during immune responses , enabling potentially faster and more robust transcriptional reprogramming upon pathogen recognition .
Plants have evolved a sophisticated immune system to fight against invading microbial pathogens that threaten their normal growth and development . Plant immunity is in part mediated by resistance ( R ) proteins that recognize pathogen proteins known as effectors [1]–[3] . The majority of R proteins are NLR receptors that contain leucine-rich repeats ( LRRs ) at the C-terminus , a central nucleotide-binding site ( NBS ) and either a Toll/Interleukin-1 receptor ( TIR ) or a coiled-coil ( CC ) domain at the N-terminus [4] . In Arabidopsis , genetically downstream of the R proteins are the EDS1 ( ENHANCED DISEASE SUSCEPTIBILITY 1 ) /PAD4 ( PHYTOALEXIN DEFICIENT 4 ) /SAG101 ( SENESCENCE-ASSOCIATED GENE101 ) complex and NDR1 ( NON-RACE-RESISTANCE 1 ) , which mainly mediate TIR-NB-LRR or CC-NB-LRR triggered defense responses , respectively [5]–[8] . While the mechanisms underlying effector recognition by R proteins have been intensively studied , little is known about the post-recognition events leading to defense activation . Recently , it has been shown that the nuclear pool of certain R proteins , including MLA10 ( MILDEW A LOCUS 10 ) in barley , N in tobacco , Pb1 ( Panicle blast 1 ) in rice , and RPS4 ( RESISTANT TO P . SYRINGAE 4 ) , RRS1 ( RESISTANT TO RALSTONIA SOLANACEARUM 1 ) and SNC1 ( SUPPRESSOR OF NPR1-1 , CONSTITUTIVE1 ) in Arabidopsis , is important for the activation of defense responses [9]–[14] . The latest discoveries on the interactions between some of these R proteins and their associating transcription factors ( TFs ) further shed light on the activation mechanism of nuclear R proteins . For example , MLA10 interacts with WRKY TFs to de-repress PAMP ( PATHOGEN-ASSOCIATED MOLECULAR PATTERN ) triggered basal defense [9] . The active state of MLA10 can also release MYB6 ( MYB DOMAIN PROTEIN 6 ) from WRKY suppression and promote its binding to cis-elements to initiate defense responses [15] . CC-type NLR Pb1 in rice interacts with WRKY45 and this interaction is believed to protect the TF from proteasomal degradation in the nucleus [16] . In addition , SNC1 associates with transcriptional co-repressor TPR1 ( TOPLESS RELATED 1 ) to negatively regulate the expression of known defense suppressors , thereby activating plant immunity [17] . Lately , studies on N in tobacco showed that it is able to associate with the TF SPL6 ( SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 6 ) upon effector recognition [18] . From these data , it has been hypothesized that some NLRs associate with TFs inside the nucleus to directly participate in transcriptional reprogramming to regulate downstream defense responses . In Arabidopsis , the gain-of-function NLR mutant snc1 constitutively expresses PATHOGENESIS RELATED ( PR ) defense marker genes and exhibits enhanced disease resistance against virulent bacteria Pseudomonas syringae pv . maculicola ( P . s . m . ) ES4326 and oomycete Hyaloperonospora arabidopsidis ( H . a . ) Noco2 [19] , [20] . As snc1 displays strong autoimmune phenotypes while remaining fully fertile , it has become a useful tool for dissecting NLR mediated resistance . Forward genetic screens designed to isolate positive regulators of immunity were conducted in the snc1 background and over a dozen Modifier of snc1 ( MOS ) genes have been identified . Characterizations of the MOS genes and their encoded protein products have revealed complicated regulatory events surrounding snc1 mediated autoimmunity , which include nucleocytoplasmic trafficking , RNA processing , protein modification and transcriptional regulation [21] , [22] . However , genetic redundancy and lethality may have prevented some essential positive regulators from being discovered through forward genetic approaches . Here , we employed a targeted reverse genetic screen to search for candidate TFs participating in the regulation of snc1-mediated defense . One basic Helix-loop-Helix ( bHLH ) type TF , which is a putative transcriptional activator , was isolated from the screen and found to be able to associate with NLRs to activate immunity .
Previously , SNC1 was found to participate directly in transcriptional reprogramming with TPR/MOS10 repressor proteins that do not directly bind DNA [17] . We did not find a DNA-binding TF that functions together with SNC1 from the MOS forward genetic screens , suggesting that multiple TFs may function redundantly in snc1-mediated immunity . To search for novel TFs regulating plant immunity , a reverse genetic screen was employed . As UV irradiation has been shown to induce resistance to pathogens and to induce transcription of defense related genes [23]–[25] , we selected 36 putative TFs which show >1 . 7-fold enhanced expression level upon UV treatment based on publically available microarray data from The Arabidopsis Information Resource ( Table S1 ) . The genomic sequences of these genes were cloned into a binary vector pCambia1305 containing C-terminus GFP and HA double tags . Using the floral dip method [26] , overexpression transgenic plants in snc1 and Col-0 backgrounds were generated . From the primary screen , we searched for transformants either suppressing or enhancing the dwarf morphology of snc1 or causing dwarfism in Col-0 background . Transgenic plants exhibiting heritable altered morphology were subject to a secondary screen , where altered resistance was examined using a Hyaloperonospora arabidopsidis ( H . a . ) Noco2 infection assay . Screening data for these candidate TFs are summarized in Table S1 . From the screen , we identified several TFs that displayed phenotypes in only snc1 or Col-0 background , but not in both when overexpressed ( Table S1 ) . However , overexpression of three TFs , At2g31230 , At2g14760 or At5g61590 , resulted in stunted growth in both the snc1 and Col-0 backgrounds ( Table S1 , Figure 1A and 1B ) . We selected two TFs with the strongest phenotypes for further analysis . At2g14760 encodes bHLH84 , a predicted basic helix-loop-helix TF , while At5g61590 encodes ERF107 , which belongs to the ethylene-response-factor ( ERF ) TF family . To further explore the functions of bHLH84 and ERF107 in plant immunity , we isolated homozygous overexpression transgenic lines in Col-0 background . As shown in Figure 1B , both OXbHLH84-GFP-HA and OXERF107-GFP-HA plants exhibited dwarf morphology compared with WT plants . We further examined defense marker PR gene expression in these transgenic plants using real-time PCR . As shown in Figure 1C , the expression of both PR1 and PR2 was significantly up-regulated , with about 100- and 35- fold changes , respectively , in OXbHLH84-GFP-HA , indicating that the defense responses were constitutively activated . In OXERF107-GFP-HA transgenic plants , both PR1 and PR2 were around 15-fold up-regulated . Consistent with PR gene expression , resistance against virulent pathogen H . a . Noco2 was enhanced in both OXbHLH84-GFP-HA and OXERF107-GFP-HA plants ( Figure 1D ) . As OXbHLH84-GFP-HA plants displayed more severe immune phenotypes than OXERF107-GFP-HA plants , we chose to focus solely on the functional study of bHLH84 . Consistent with its predicted TF function , bHLH84-GFP-HA fluorescence was detected in the nuclei when the OXbHLH84-GFP-HA seedlings were examined by confocal fluorescence microscopy ( Figure 1E ) . To further investigate how bHLH84 regulates plant immunity , we tested whether it is a bona fide transcription factor by conducting a previously established protoplast transcription activity transient assay [27] . In this assay , the β-glucuronidase ( GUS ) reporter gene is driven by 2×Gal4 DNA-binding sites ( DBS ) . Co-transformation of bHLH84 fused with the Gal4 DNA-binding domain ( DBD ) together with the reporter constructs in Arabidopsis mesophyll protoplasts resulted in drastically enhanced GUS expression ( Figure 2A ) compared to the control transfection , suggesting that bHLH84 functions as a transcriptional activator . bHLH TFs constitute one of the largest TF families in Arabidopsis , with 147 members including bHLH84 [28] . bHLH84 has three alternatively spliced variants according to available expressed sequence tag ( EST ) data ( Figure 2B ) . Based on sequence analysis , At2g14760 . 2 encodes a truncated protein without the C-terminal bHLH DNA binding domain , while the other two variants encode full-length proteins [28] . However , when the coding region of bHLH84 was amplified from cDNA of WT plants and sequenced , only At2g14760 . 1 was observed , suggesting that At2g14760 . 1 is the dominantly expressed version . To further investigate the contribution of bHLH84 in plant immunity , knock-out analysis of bHLH84 was carried out . A T-DNA allele of bHLH84 ( SALK_064296 ) was obtained from the Arabidopsis Biological Resource Centre ( ABRC ) . As shown in Figure 2B , the T-DNA inserts in the first exon of At2g14760 . 1 . As a consequence , the expression of bHLH84 was abolished ( Figure S1A ) . SALK_064296 was thus assigned as bhlh84 . When bhlh84 leaves were challenged with virulent bacterial pathogen Pseudomonas syringae pv maculicola ( P . s . m . ) ES4326 , they exhibited similar bacterial growth as WT ( Figure 2C ) , indicating that the immune response is not compromised in the knock-out mutant . To investigate whether genetic redundancy masks the function of bHLH84 , we carried out a phylogenetic analysis of bHLH84 and its paralogs . As RSL2 ( ROOT HAIR DEFECTIVE 6-LIKE 2 ) is the closest paralog of bHLH84 ( Figure 2D; [29] ) , a T-DNA knock-out line for this gene , SALK_048849 , was obtained from ABRC . As shown in Figure S1B , no expression of RSL2 was detectable in SALK_048849 , which was named as rsl2 . Double mutant bhlh84 rsl2 was created and subjected to pathogen infection experiments . As shown in Figure 2C , the bhlh84 rsl2 double mutant did not exhibit resistance defects either . As RSL4 ( ROOT HAIR DEFECTIVE 6-LIKE 4 ) is functionally redundant with RSL2 in regulating root hair growth [29] , we further created the triple mutant by crossing bhlh84 rsl2 with rsl4 rsl2 , which was characterized by Yi et al . , 2010 [29] . The triple mutant bhlh84 rsl2 rsl4 still did not exhibit obvious defects upon infection with P . s . m . ES4326 compared to WT plants ( Figure 2C ) , indicating that knocking out bHLH84 and its two paralogs does not compromise basal defense responses . Since no good T-DNA mutant line was available for bHLH139 , we were not able to test higher level of redundancy using knockout approach . To further examine the contribution of these TFs in specific R protein mediated immunity , we challenged single , double and triple mutant plants with Pseudomonas syringae pv tomato ( P . s . t . ) carrying either avrRPS4 or hopA1 , which are effectors recognized by TIR-NB-LRR proteins RPS4 and RPS6 , respectively . As shown in Figure 2E , significantly more P . s . t . avrRPS4 growth was observed in bhlh84 rsl2rsl4 triple mutant plant , while no detectable difference was observed when the TF mutants were challenged with P . s . t . hopA1 ( Figure S2 ) , suggesting that these bHLH TFs contribute redundantly to RPS4-mediated immunity . To investigate the biological function of bHLH84 and its paralogs in snc1-mediated immunity , we crossed bhlh84 rsl2 with snc1 and isolated triple mutant snc1 bhlh84 rsl2 . The dwarf phenotype of snc1 was not suppressed in the triple mutant ( Figure 3A ) . We further crossed snc1 bhlh84 rsl2 with rsl4 rsl2 [29] and isolated quadruple mutant snc1 bhlh84 rsl2 rsl4 from the F2 generation by genotyping bhlh84 , rsl4 and snc1 loci . The quadruple mutant plants were significantly larger than those of snc1 ( Figure 3A ) . Consistent with the morphological suppression , the expression of PR1 and PR2 in the quadruple mutant was significantly decreased compared to snc1 plants while only slight reduction was observed in the triple mutant ( Figure 3B ) . In addition , when the quadruple mutant seedlings were challenged with H . a . Noco2 and P . s . m . ES4326 , more pathogen growth was observed compared to snc1 , although the resistance was not restored to wild type levels ( Figure 3C and 3D ) . Taken together , the bhlh84 rsl2 rsl4 triple mutant partially suppresses snc1 , suggesting that bHLH84 and its paralogs are functionally redundant and required for the autoimmunity of snc1 . When we further isolated snc1 rsl2 rsl4 ( Figure S3A and S3B ) , the triple mutant was slightly larger than snc1 . Since snc1 bhlh84 rsl2 plants were indistinguishable from snc1 in size , it can thus be concluded that these three TFs are not equally redundant; RSL4 seems to play a slightly larger role than bHLH84 in snc1-mediated autoimmunity . To further test the redundant roles of bHLH84 and its paralogs , we overexpressed bHLH84 , RSL4 or RSL2 in Col-0 by transforming plants with the coding sequence of each gene without any epitope tags under the control of the 35S promoter . When screening T1 populations , multiple plants with extremely dwarf morphology were observed for each genotype ( Figure 4 ) . Intriguingly , plants of intermediate sizes were observed in the transgenic lines overexpressing bHLH84 , while the majority of the plants overexpressing RSL4 or RSL2 were tiny and gradually perished , presumably as a result of extreme autoimmunity . The phenotypic similarity in these overexpression progeny further supports the functional redundancy among these three TFs in regulating plant immunity . As with snc1 , the dwarf morphology of OXbHLH84-GFP-HA plants was largely suppressed when grown at 28°C ( Figure S4 ) [30] . This observation led us to ask whether SNC1 is required for the autoimmunity of OXbHLH84-GFP-HA . As shown in Figure 5A , the snc1-r1 allele ( a loss-of-function allele of SNC1 in which 8 bp of the first exon of SNC1 is deleted from fast neutron mutagenesis; [20] ) could largely suppress the dwarf morphology of OXbHLH84-GFP-HA . Consistent with the observed morphological suppression , defense response phenotypes conferred by OXbHLH84-GFP-HA , including up-regulation of PR gene expression and resistance to P . s . m . ES4326 and H . a . Noco2 , were significantly suppressed by snc1-r1 ( Figures 5B , 5C and 5D ) , indicating that a functional SNC1 is indispensable for the effects of bHLH84 overexpression . As CPR1 ( CONSTITUTIVE EXRPRESSER OF PR GENES 1 ) targets SNC1 for degradation [31] , we crossed OXbHLH84-GFP-HA with plants overexpressing CPR1 ( OXCPR1 ) . The dwarf morphology and enhanced resistance of OXbHLH84-GFP-HA were largely suppressed ( Figure 5 ) , providing further support that SNC1 contributes to the autoimmune phenotypes associated with OXbHLH84-GFP-HA . In addition , the bHLH84-GFP-HA protein level in snc1-r1 or OXCPR1 background was not changed ( Figure S5 ) , suggesting that SNC1 does not affect bHLH84 protein accumulation . To further dissect the function of bHLH84 in plant defense pathways , OXbHLH84-GFP-HA was crossed with various mutants of key components in plant immunity , including eds1-2 , sid2-2 , and ndr1-1 [8] , [32] , [33] . As shown in Figure 5A , eds1-2 and sid2-2 could fully and partially suppress the morphology of OXbHLH84-GFP-HA in terms of leaf shape and plant size , respectively , while ndr1-1 had little effect . The enhanced PR gene expression and resistance to H . a . Noco2 and P . s . m . ES4326 were fully suppressed by eds1-2 and partially by sid2-2 ( Figure 5B , 5C and 5D ) , indicating that EDS1 and SA are required for the autoimmunity in OXbHLH84-GFP-HA . In contrast , ndr1-1 was not able to suppress the enhanced PR gene expression , H . a . Noco2 and P . s . m . ES4326 resistance conferred by OXbHLH84-GFP-HA , indicating that the constitutive activation of defense responses in OXbHLH84-GFP-HA is NDR1-independent . As SNC1 is required for the constitutive activation of the defense responses of OXbHLH84-GFP-HA plants , we asked whether bHLH84 could directly regulate SNC1 transcription . We observed that the transcription and protein levels of SNC1 in OXbHLH84-GFP-HA plants were slightly higher than in WT ( Figure S6 ) . However , this up-regulation of SNC1 is probably due to the positive feed-back effect resulting from the high SA in the autoimmune transgenic plants [34] . To avoid interference from the feed-back up-regulation of SNC1 , we used OXbHLH84-GFP-HA eds1-2 plants to examine SNC1 transcription level . Real-time PCR showed that no significant change in SNC1 transcription was detected in OXbHLH84-GFP-HA eds1-2 compared to eds1-2 control plants ( Figure 6A ) . As a consequence , the SNC1 protein level in OXbHLH84-GFP-HA eds1-2 was similar to that of eds1-2 ( Figure 6B ) . In addition , we tested the transcript levels of selected R genes including RPS6 , RPS4 , RPP2 , RPP4 , RPS2 , RPS5 , and RPM1 in the OXbHLH84-GFP-HA eds1-2 background . Similar to SNC1 , none of the tested R genes showed over 1 . 2-fold transcriptional changes when compared to eds1-2 ( Figure S7A ) . In addition , no significant up-regulation of R genes was observed in OXbHLH84-GFP-HA snc1-r1 double mutant compared to snc1-r1 control plants ( Figure S7B ) . Taken together , bHLH84 does not seem to participate in the direct transcriptional regulation of SNC1 or other tested R genes , unless bHLH84 recruits both EDS1 and SNC1 for this regulation . As the dependence of OXbHLH84-GFP-HA on a functional SNC1 and the partial suppression of snc1 by bhlh84 rsl2 rsl4 resembles the genetic interactions between SNC1 and TPR1/MOS10 , and SNC1 interacts with TPR1 [17] , we further tested whether bHLH84 associates with SNC1 . We attempted a nuclear co-immunoprecipitation ( co-IP ) experiment using OXbHLH84-GFP-HA transgenic plants , which carry C-terminal GFP and HA double tags . Unfortunately , we were unable to detect the bait after immunoprecipitation in the elution , while all the proteins were found in the flow-through fraction ( Figure S8A ) . As an alternate approach , we transformed Arabidopsis plants with a construct expressing bHLH84 under its native promoter and containing an N-terminal GFP tag . The protein produced was functional , as the transgenic plants resembled the original OXbHLH84-GFP-HA plants ( Figure S8B ) . However , when they were used for co-IP with anti-GFP beads , the bait still could not be pulled down ( Figure S8C ) . The inability of bHLH84 to be pulled down using immunoprecipitation could be due to unknown structural complexity of the protein . Since we were not able to carry out a co-IP experiment with bHLH84 as bait using epitope-tagged bHLH84 transgenic plants , we decided to examine the interaction between SNC1 and bHLH84 using the Nicotiana benthamiana transient expression system [35] . Interestingly , when both proteins were expressed in N . benthamiana leaves , we consistently observed a faster hypersensitive response ( HR ) , which was obvious a few hours earlier compared to when SNC1-FLAG was expressed with the control vector ( Figure S9A and S9B ) . This was further confirmed by the ion leakage analysis of the infiltrated leaves ( Figure 7A ) . Both proteins were expressed efficiently in N . benthamiana ( Figure 7B ) . When co-immunoprecipitation was carried out , SNC1-FLAG could specifically pull down bHLH84-HA , but not an unrelated nuclear protein MAC5A-HA ( Figure 7C , [36] ) , indicating that bHLH84 can interact with SNC1 in planta . As bHLH84 is able to interact with SNC1 in planta , we further examined the interaction specificity between bHLH84-HA and other R proteins by conducting co-IP of bHLH84-HA with either RPS4-FLAG , RPS2-FLAG or RPS6-FLAG . As shown in Figure 7D , RPS4-FLAG could also immunoprecipitate bHLH84-HA , although not as efficiently as SNC1-FLAG . However , RPS2-FLAG or RPS6-FLAG could not pull down bHLH84-HA ( Figure S10 ) . Taken together , bHLH84-HA can specifically interact with SNC1-FLAG or RPS4-FLAG in planta . SNC1 was previously shown to interact with transcriptional co-repressor TPR1 , which does not contain a DNA binding domain [17] . Additionally , the SNC1-dependent phenotypes observed upon overexpressing bHLH84 are similar to those observed when TPR1 is overexpressed . We therefore asked whether bHLH84 interacts with TPR1 . As shown in Figure 7E , bHLH84-HA could not be pulled down by TPR1-FLAG , indicating that bHLH84 does not interact with TPR1 in planta . In addition , when we co-expressed SNC1-FLAG , bHLH84-HA and TPR1-HA in N . benthamiana , SNC1-FLAG was able to pull down both TPR1-HA and bHLH84-HA ( Figure S11 ) . The IP efficiency of TPR1-HA by SNC1-FLAG with all three proteins expressed was comparable to that with only TPR1-HA and SNC1-FLAG expressed . On the other hand , the IP efficiency of bHLH84-HA by SNC1-FLAG varied from trial to trial . Taken together , these data suggest that the interactions of SNC1-bHLH84 and SNC1-TPR1 in planta are independent , although whether there is competition between bHLH84 and TPR1 in associating with SNC1 is unclear . To further investigate whether bHLH84 is able to directly interact with SNC1 , we carried out yeast-two-hybrid experiment by co-transforming bHLH84 fused with AD and SNC1 fused with BD . Since we failed in making a full-length SNC1 construct , we made truncated SNC1 segments . As shown in Figure S12 , yeast cells transformed with bHLH84-AD and different truncated SNC1 fused with BD were not able to grow on the selection plates , suggesting that bHLH84 does not directly interact with the truncated SNC1 segments in yeast . Moreover , the interaction between bHLH84 and SNC1 probably demands a properly folded full-length SNC1 or an intermediate partner . As EDS1 is required for the function of bHLH84 and EDS1 was shown to interact with SNC1 [37] , we asked whether EDS1 or its interacting protein PAD4 [7] might be the intermediate partner . However , we did not detect interaction between bHLH84 and EDS1or bHLH84 and PAD4 ( Figure S13 ) , suggesting that EDS1or PAD4 is not likely mediating the interaction between SNC1 and bHLH84 .
Previous work on MLA , N , RRS1 and SNC1 suggests that the interactions between some nuclear R proteins and their associating TFs are essential in regulating defense responses [9] , [11] , [12] , [15] , [17] , [18] . Different approaches have been utilized to isolate TFs that are able to interact with nuclear R proteins . TPR1 , which associates with SNC1 to repress negative regulators of immunity , was isolated from a forward genetic screen for suppressors of snc1 [17] . Yeast-two-hybrid screens have been successfully used to identify TFs in plant immunity . For example , SPL6 was initially identified from a yeast-two-hybrid screen and was further confirmed to interact with N in tobacco [18] . In addition , identified from yeast-two-hybrid screens , MYB6 and WRKY1 were shown to interact with MLA in barley to initiate disease resistance signaling in an antagonistic manner [15] . In this study , we used an alternative reverse genetic screen and successfully identified a group of novel TFs that play critical roles in plant immunity . Our targeted reverse genetic approach has several advantages . Since plant defense to UV radiation is regulated by many of the same factors as pathogen resistance [23]–[25] , while UV treatment datasets exclude a large number of genes that are manipulated by pathogen effectors which are not directly related to defense responses [38] , the number of target genes we chose from the UV-induced database is more manageable for a reverse genetics screen . All the selected TFs were overexpressed in both Col-0 and snc1 backgrounds , facilitating rapid identification of both defense enhancers and suppressors ( Table S1 ) . Furthermore , the functional redundancy predicament often encountered in forward genetic screens can be effectively avoided by using the overexpression approach . Finally , our approach evades self-activation problems that are often associated with yeast-two-hybrid screens for transcriptional activators . Specifically , bHLH84 exhibits strong self-activation when fused with GAL4 binding domain in yeast ( data not shown ) , thus cannot be identified from a yeast-two-hybrid screen . However , our screen does rely on the availability of high-quality microarray data , which may still overlook TFs with relatively low expression level changes . As bHLH84 was shown to be a transcriptional activator , we attempted chromatin immunoprecipitation ( ChIP ) to identify target genes of bHLH84 . However , as with our co-IP experiments ( Figure S8 ) , the bHLH84-GFP-HA protein could not be pulled down when subjected to ChIP ( Figure S15 ) . Thus we were unable to identify the target DNA of bHLH84 in planta . Using yeast-one-hybrid assay as an alternative approach , we attempted to identify the DNA-binding sequences of bHLH84 . Many bHLH type TFs were shown to bind sequences containing a consensus core element E-box ( 5′-CANNTG-3′ ) , with the palindromic G-box ( 5′-CACGTG-3′ ) being the most typical form [39] . Some bHLH proteins bind to non-E-box sequences ( N-box ) , such as 5′-CACGc/aG-3′ and 5′-CGCGTG-3′ [40] , [41] . As shown in Figure S16A and Figure S16B , compared with the bHLH84 alone or cis-element alone negative controls , the most enhanced yeast growth was observed on SD-Leu-Trp-His media when AD-bHLH84 was co-transformed with pHIS2-N1-box , while considerably enhanced growth was observed when AD-bHLH84 was co-transformed with pHIS2-N2-box . No enhanced yeast growth was observed in G-box or N3 box co-transformations . These data suggest that bHLH84 is able to bind N1- and N2-boxes , but not N3- or G-boxes . These data are consistent with the prediction that TFs in this bHLH subfamily are non E-box binders [42] . Although the potential binding sites of bHLH84 have been revealed , it is still difficult to predict its target genes . More sophisticated ChIP experiments designed in the future may be able to solve this problem . The bHLH-containing proteins constitute a large conserved TF family in eukaryotes [43] , [44] . They have been studied intensively in yeast and humans , providing evidence for their regulatory functions in cell proliferation and cellular differentiation pathways [45]–[48] . While only a few bHLH proteins have been studied in detail in plants , they have been shown to serve regulatory functions in multiple biological pathways . For example , a group of bHLH TFs in Zea mays regulate the production of the purple anthocyanin pigments by interacting with R2R3-MYB TFs [49] . In Arabidopsis , GL3 ( GLABRA3 ) regulates trichome development through its interaction with MYB-like TF GL1 ( GLABRA1 ) [50] . Another small subfamily of bHLH TFs , referred to as phytochrome-interacting factors ( PIFs ) , have been shown to play diverse functions including regulating light signaling pathways , seed germination , seedling photomorphogenesis , and shade avoidance responses via their interactions with phytochromes [51]–[56] . In addition , JAM1 ( ABA-INDUCIBLE BHLH-TYPE TRANSCRIPTION FACTOR/JA-ASSOCIATED MYC2-LIKE ) , acts as a transcriptional repressor and negatively regulates JA signaling [57] . bHLH84 and its paralogs have previously been shown to regulate root hair elongation [29] , [58] . However , they are the first few bHLH TFs found to be involved in plant immunity . Since bHLH TFs form one of the largest TF families in plants , it is difficult to imagine that these three TFs are the only bHLHs involved in immune regulation . Lethality of the knockout mutants or redundancy could be the factors prohibiting others from being discovered . Future novel methods , such as our overexpression approach , may facilitate the functional studies of more TFs in large families . As one of the largest TF families in Arabidopsis with 147 members , bHLH TFs are further subdivided into 12 major subfamilies based on sequence similarity . bHLH84 and its paralogs belong to the VIIIc subgroup [28] . In this study , we have experimentally shown that bHLH84 , RSL2 and RSL4 redundantly regulate defense responses . Overexpression of any of these proteins results in constitutive activation of defense responses ( Figure 4 ) . Their redundancy was further demonstrated using the triple mutant of bhlh84 rsl4 rsl2 , which is able to partially suppress the autoimmune phenotypes of snc1 ( Figure 3 ) , and compromise RPS4-mediated defense responses ( Figure 2E ) . It is possible that additional members of the VIIIc subfamily are also functionally redundant with bHLH84 . Future construction of higher order bhlh mutants may provide insight into the additional redundant relationships among these family members . Typically , the bHLH domain contains approximately 60 amino acids and is comprised of a stretch of hydrophilic and basic residues at the N terminus , followed by two amphipathic alpha-helices connected by an intervening loop [44] . The helix-loop-helix and the basic region of the bHLH are required for DNA-binding , whereas the helix-loop-helix region alone often enables homo- or heterodimerization with other bHLH proteins . Since the single mutants of bhlh84 , rsl2 and rsl4 do not exhibit obvious phenotypes , we speculate that if dimerization occurs , it would most likely be homodimerization rather than heterodimerization . The dimerized bHLH84 or its paralogs may bind to the same DNA region , thus regulating immunity in a similar manner . In addition , bHLH TFs often associate with other types of TFs , including MYBs and bZIPs for transcriptional reprogramming [49] , [56] , thus we cannot exclude the possibility that there are more unknown TFs that are also involved in the bHLH84-SNC1 complex . As the expression level of SNC1 is comparable in eds1-2 and OXbHLH84-GFP-HA eds1-2 backgrounds ( Figure 6 ) , bHLH84 does not seem to regulate SNC1 expression . In addition , we did not observe transcriptional up-regulation of tested R genes in OXbHLH84-GFP-HA snc1-r1 or OXbHLH84-GFP-HA eds1-2 plants ( Figure S7 ) , suggesting that bHLH84 does not directly regulate the transcription of R genes . As we also detected attenuated immunity against P . s . t . avrRps4 in bhlh84 rsl2 rsl4 triple mutant ( Figure 2E ) , and interaction between RPS4 and bHLH84 in N . benthamina ( Figure 7D ) , bHLH84 and its paralogs seem to be not just specific to SNC1 . As both RPS4's and SNC1's nuclear localizations are critical to their defense activation [10] , [14] , we speculate that these bHLH TFs may work together with selective nuclear TIR-NB-LRRs to trigger downstream immunity . More in-depth investigations on the interactions of other nuclear TIR-NB-LRR proteins with these TFs might reveal more R proteins working together with these bHLH proteins . Overexpression of either bHLH84 or TPR1 results in SNC1-dependent autoimmunity , indicating that both bHLH84 and TPR1 positively regulate SNC1-mediated defense responses . Both bHLH84 and TPR1 were shown to associate with SNC1 , although no interaction was detected between bHLH84 and TPR1 , suggesting that bHLH84-SNC1 and TPR1-SNC1 probably function in distinct complexes ( Figure 7 , S11 and S14 ) . Their downstream target genes are probably different , as bHLH84 is a transcriptional activator while TPRs are repressors . Defense activation induced by SNC1 is likely achieved through a combination of activation of positive regulators and repression of negative regulators .
The genomic sequences of selected TFs , excluding the stop codon and including approximately 1 . 5 kb sequence upstream of the start codon , were amplified by PCR with two different restriction enzyme sites separately introduced at the two primer ends . The chosen restriction enzyme sites were KpnI , SalI , SacI , XbaI or PstI . The amplified fragments were then digested and ligated to modified pCambia1305 vectors harboring C-terminal GFP and HA tags . These constructs were transformed into snc1 and Col-0 using the floral dip method [26] . For overexpression of bHLH84 , RSL2 and RSL4 , coding sequences of the genes were amplified by PCR with two different restriction enzyme sites separately introduced at the two primer ends . The primer sequences can be found in Table S2 . The fragments were then digested and ligated to the pG229HAN vector with a 35S promoter . For the pCambia1300-35S-SNC1-FLAG , pCambia1300-35S-RPS4-FLAG and pCambia1300-35S-RPS6-FLAG constructs used in the transient expression in N . benthamiana , the genomic region of SNC1 , RPS4 or RPS6 without the stop codon , was cloned into the pCambia1300 vector with a 35S promoter and a C-terminus FLAG tag . For other pCambia1300 constructs used in the transient expression , the CDS regions of the genes were cloned into the corresponding vectors . The primer sequences can be found in Table S2 Approximately 0 . 4 g of T1 transgenic seeds for each construct were first plated on solid MS medium containing 30 µg/ml Hygromycin B . 48 one-week-old transformant seedlings per genotype were selected and subsequently transplanted on soil . Col-0 and snc1 seeds were planted on solid MS medium without any selection and transplanted on soil at the same time to serve as controls . Among the transgenic plants of each genotype , the transformants which showed varied sizes were kept , and T2 seeds from these plants were planted on Hygromycin B plates to analyze transgene copy number , check for the presence of the transgene and validate the background using primers specific to the SNC1 locus [20] . The transgenic plants with heritable phenotypes and with the correct backgrounds were then subjected to H . a . Noco2 infection to examine whether their altered morphology is correlated with altered resistance . Resistance was scored based on the degree of deviation from that observed in the control plants . More specifically , transgenic plants in Col-0 background showing similar sporulation as Col-0 were scored as no change ( NC ) . Plants showing less sporulation than Col-0 were scored as showing enhanced resistance phenotype with “+” . Plants exhibiting a little sporulation were scored as having more enhanced resistance phenotype with “++” , while the ones showing no sporulation were scored as the most enhanced resistance phenotype as “+++” . For transgenic plants in the snc1 background , plants showing more sporulation than snc1 were scored as suppressing phenotype with “−” , while the ones showing less sporulation than snc1 were scored as enhancing phenotype with “+” . Leaves from one-week-old seedlings were soaked in 1 mg/mL ( 1∶1 [g/v] ) propidium iodide ( PI ) for 3 minutes and rinsed briefly with water before visualization . Root tissues were submerged in 1 µg/ml ( 1∶1 [g/v] ) PI for 10 seconds and mounted in water . For GFP and PI visualization , a Nikon ECLIPSE 80i Confocal microscope was used under 488 nm and 543 nm filter sets . Transient protein expression in N . benthamiana was carried out as previously described [35] . The IP protocol was modified from [59] . Briefly , Agrobacteria containing the binary vector pCambia1300 constructed with the target genes and tags were cultured in LB media with kanamycin selection at 28°C overnight . The bacteria were inoculated into a new culture media ( 10 . 5 g/L K2HPO4 , 4 . 5 g/L KH2PO4 , 1 . 0 g/L ( NH4 ) 2SO4 , 0 . 5 g/L NaCitrate , 1 mM MgSO4 , 0 . 2% glucose , 0 . 5% glycerol , 50 µM acetosyringone , and 10 mM N-morpholino-ethanesulfonic acid ( MES ) ( pH 5 . 6 ) , 50 µg/mL Kanamycin ) by 1∶50 dilution and cultured for a further 8–12 hours . The bacteria were then harvested by centrifugation at 4000 rpm for 10 minutes and resuspended in MS buffer ( 4 . 4 g/L MS , 10 mM MES , 150 µM acetosyringone ) to a final concentration of OD600 = 0 . 2 for infiltration into four-week-old N . benthamiana leaves . For co-immunoprecipitation , 3 g of N . benthamiana leaves were collected at 36 hours post-infiltration and ground into fine powder in liquid nitrogen using a cold mortar and pestle . The powder was mixed with 6 ml extraction buffer ( 10% glycerol , 25 mM Tris pH 7 . 5 , 1 mM EDTA , 150 mM NaCl , 10 mM DTT , 2% w/v PVPP , protease inhibitor cocktail ) and homogenized by further grinding . All the following steps were carried out at 4°C . The samples were centrifuged at 15000 g for 10 minutes and the supernatants were transferred to new tubes . These two steps were repeated twice before NP40 ( Nonidet P-40 Substitute ) was added into each supernatant to a final concentration of 0 . 15% . 30 µl pre-washed protein A or protein G agarose beads were added into each supernatant and incubated for 30 minutes . The mixtures were centrifuged at 4000 rpm for 2 minutes to remove the beads . Each supernatant was incubated with 30 µl anti-FLAG beads or protein A agarose beads for 3 hours , and the beads were pelleted down by centrifuging at 8000 rpm for 1 minute and washed 8 times using extraction buffer containing 0 . 15% NP40 . Proteins specifically bound to the beads were competitively eluted using 100 µl 250 µg/ml 3×FLAG peptides . All the samples were boiled in SDS loading buffer for 5 minutes before running on SDS-PAGE gel . The isolation and transfection of Arabidopsis protoplasts and the reporter gene assay were previously described in [27] . Briefly , the Arabidopsis protoplasts were transfected with the reporter construct , the effector construct and the internal control construct as illustrated in Figure 2A . GUS expression was determined using MUG assay ( Acros Organics from Fisher Scientific ) . Fluorescence was measured using a fluorescence spectrophotometer ( 360/460 nm ) . The internal LUC expression was examined using a Dual-Luciferase reporter assay system ( Promega , E1910 ) . The ion leakage assay was performed as previously described [60] , with a few modifications . Briefly , twelve leaf discs ( 7 mm in diameter ) per measurement were punched from the infiltrated area at 23 hr post infiltration and placed in a 60 mm petri dish containing 10 ml of ddH2O . After 30 minutes , the water was removed and another 10 ml of ddH2O was added into the petri dish containing the leaf discs . Conductivity was measured using a 545 Conductivity Multi-purpose Cell ( VWR Scientific ) at the indicated time points . For yeast-one-hybrid assay , the pHIS2 derivatives ( harboring the N1- , N2- , N3- and G-box cis-elements ) were co-transformed with the construct of pAD-bHLH84 into the yeast strain Y187 . For each co-transformation of pAD-bHLH84 and pHIS2 derivatives , yeast cells co-transformed with pHIS2 empty vector ( EV ) and pAD-bHLH84 as well as yeast cells cotransformed with pAD EV and the pHIS2 derivatives were used as negative controls . The positive transformants were isolated from SD-Trp-Leu medium . The transformants were then analyzed on the SD-Trp-Leu-His medium supplemented with 60 mM and 100 mM 3-Amino-1 , 2 , 4-Triazole ( 3AT ) . For yeast-two-hybrid assays , the pGBKT7 derivatives containing various truncated SNC1 fragments were co-transformed with pAD-bHLH84 into yeast strain Y1347 . pGBKT7 EV cotransformed with pAD-bHLH84 was used as a negative control . The positive transformants were isolated from SD-Trp-Leu medium . The transformants were then analyzed on SD-Trp-Leu-His medium supplemented with 3 mM 3AT . | In plants and animals , NLR immune receptors are utilized to detect pathogen-derived molecules and activate immunity . However , the mechanisms of plant NLR activation remain unclear . Here , we report on bHLH84 , which functions as a transcriptional activator . Simultaneously knocking out three closely related bHLH paralogs partially suppresses the autoimmunity of snc1 and compromises RPS4-mediated defense , while overexpression of these close paralogs renders strong autoimmunity , suggesting functional redundancy in the gene family . In planta co-immunoprecipitation revealed interactions between not only bHLH84 and SNC1 , but also bHLH84 and RPS4 . Therefore bHLH84 family transcription factors associate with these NLRs to activate defense responses , enabling potentially faster and more robust transcriptional reprogramming upon pathogen recognition . | [
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] | 2014 | NLR-Associating Transcription Factor bHLH84 and Its Paralogs Function Redundantly in Plant Immunity |
Real-Time PCR-High Resolution Melting ( qPCR-HRM ) analysis has been recently described for rapid drug susceptibility testing ( DST ) of Mycobacterium leprae . The purpose of the current study was to further evaluate the validity , reliability , and accuracy of this assay for M . leprae DST in clinical specimens . The specificity and sensitivity for determining the presence and susceptibility of M . leprae to dapsone based on the folP1 drug resistance determining region ( DRDR ) , rifampin ( rpoB DRDR ) and ofloxacin ( gyrA DRDR ) was evaluated using 211 clinical specimens from leprosy patients , including 156 multibacillary ( MB ) and 55 paucibacillary ( PB ) cases . When comparing the results of qPCR-HRM DST and PCR/direct DNA sequencing , 100% concordance was obtained . The effects of in-house phenol/chloroform extraction versus column-based DNA purification protocols , and that of storage and fixation protocols of specimens for qPCR-HRM DST , were also evaluated . qPCR-HRM results for all DRDR gene assays ( folP1 , rpoB , and gyrA ) were obtained from both MB ( 154/156; 98 . 7% ) and PB ( 35/55; 63 . 3% ) patients . All PCR negative specimens were from patients with low numbers of bacilli enumerated by an M . leprae-specific qPCR . We observed that frozen and formalin-fixed paraffin embedded ( FFPE ) tissues or archival Fite’s stained slides were suitable for HRM analysis . Among 20 mycobacterial and other skin bacterial species tested , only M . lepromatosis , highly related to M . leprae , generated amplicons in the qPCR-HRM DST assay for folP1 and rpoB DRDR targets . Both DNA purification protocols tested were efficient in recovering DNA suitable for HRM analysis . However , 3% of clinical specimens purified using the phenol/chloroform DNA purification protocol gave false drug resistant data . DNA obtained from freshly frozen ( n = 172 ) , formalin-fixed paraffin embedded ( FFPE ) tissues ( n = 36 ) or archival Fite’s stained slides ( n = 3 ) were suitable for qPCR-HRM DST analysis . The HRM-based assay was also able to identify mixed infections of susceptible and resistant M . leprae . However , to avoid false positives we recommend that clinical specimens be tested for the presence of the M . leprae using the qPCR-RLEP assay prior to being tested in the qPCR-HRM DST and that all specimens demonstrating drug resistant profiles in this assay be subjected to DNA sequencing . Taken together these results further demonstrate the utility of qPCR-HRM DST as an inexpensive screening tool for large-scale drug resistance surveillance in leprosy .
Current leprosy control depends solely on case detection and treatment with multi-drug therapy ( MDT ) including dapsone ( DDS ) , rifampin ( RMP ) and clofazimine [1] . This strategy is based on the principle that identifying and treating chronic infectious diseases with combinations of bactericidal and bacteriostatic effective antibiotics reduces the bacterial numbers , and limits the emergence and spread of new or existing antibiotic resistant pathogens [2 , 3] . Although the rate of relapse following successful completion of the scheduled course of MDT is currently low for both paucibacillary ( PB ) leprosy ( 0 . 1% per year ) and multibacillary ( MB ) leprosy ( 0 . 06% per year ) [4] , several studies have documented drug-resistant Mycobacterium leprae strains in relapse cases [5–11] and also the emergence of primary drug resistance [12–15] . However , in contrast to what is known for tuberculosis , the current prevalence of primary and secondary resistance to anti-leprosy drugs is virtually unknown because of the inability to cultivate M . leprae on axenic medium [16] . Molecular drug susceptibility assays for M . leprae have been developed for DDS , RMP and ofloxacin ( OFX ) , a second-line drug for treatment of leprosy . These assays are based on PCR amplification and detection of mutant alleles in the drug resistance determining regions ( DRDRs ) of folP1 , rpoB and gyrA , respectively [16] . Among these assays , PCR followed by amplicon DNA sequencing is currently the ‘molecular gold standard’ for drug susceptibility testing ( DST ) in leprosy [17] . However , this assay format is laborious , expensive and sequencing capabilities are extremely limited in low resource facilities; these are limiting factors for routine drug resistance surveillance as well as prohibitive for large drug resistance sampling surveys , especially in resource-poor countries . A recent report has described a novel , ‘single-tube’ , Real-Time qPCR high resolution melt ( HRM ) analysis method for anti-leprosy drug susceptibility testing DST [12] . qPCR-HRM DST is based on the amplification of the DRDRs of folP1 , rpoB and gyrA genes , and a simple post-PCR step to exploit thermal characteristics of the amplicons for detection of sequence variants . As the amplicons are subjected to high temperatures , the wild type ( WT ) ( drug-susceptible ) and mutant ( drug-resistant ) DRDRs generate distinct HRM profiles . Li et al . ( 2012 ) demonstrated a strong correlation between qPCR-HRM DST results and that of PCR/direct DNA sequencing of M . leprae DRDRs from clinical specimens . It was recommended that DNA be purified using a column-based purification protocols such as DNeasy Blood and Tissue Kit ( QIAGEN , Valencia , CA ) . However , depending on the laboratory , various DNA purification protocols may be implemented to obtain DNA for molecular diagnostic assays . In addition , various types of specimens [e . g . fresh , frozen , formalin-fixed paraffin-embedded ( FFPE ) or tissues from Fite’s-stained FFPE sections on slides] may serve as the source of M . leprae DNA . Therefore , the purpose of the current study was to explore the usefulness of qPCR-HRM DST to predict DDS , RMP and OFX susceptibility in M . leprae from skin biopsy tissues using either a conventional phenol/chloroform DNA extraction or a column-based DNA purification protocols from fresh-frozen tissues , or FFPE tissue sections , or archival Fite’s-stained FFPE sections on glass slides . Results of this study support previous reported data that the qPCR-HRM DST assay strongly correlates with PCR/direct DNA sequencing for detection of M . leprae drug susceptibility directly from clinical specimens . Results also defined the specificity of the assay for M . leprae as well as demonstrated that conventional phenol based extraction and ethanol precipitation of DNA is a suitable method for this analysis . In addition to ethanol-fixed and fresh specimens , frozen , FFPE sections and Fite’s-stained FFPE sections from glass slides appear to be suitable specimens for this assay .
All procedures involving human subjects including biological sample collections and testing were performed following approval from the governing human subjects’ research ethical committees: in Brazil by the Institutional Review Board at the Federal University of Uberlandia; in the United States by the Institutional Review Board of the Tulane School of Medicine , New Orleans , LA . Written informed consent procedures were carried out in Brazil . In the United States the study was determined to be exempt from consent because archival diagnostic specimens were used , accessed through a de-identified database . Animal procedures were performed under a scientific protocol reviewed and approved by the NHDP Institutional Animal Care and Use Committee ( Assurance #A3032-01 ) , and were conducted in accordance with all state and federal laws in adherence with PHS policy and as outlined in The Guide to Care and Use of Laboratory Animals , Eighth Edition . The M . leprae Thai-53 drug-susceptible reference strain was propagated through serial passage in nu/nu mice ( Harlan Sprague-Dawley Inc . , Indianapolis , IN ) and freshly harvested bacilli purified from hind footpads were stored at 4°C and used within 48 hr of harvest , as previously described [18] . Briefly , mice were euthanized by CO2 asphyxiation and the hind feet were removed and soaked in 70% ethanol and Betadine to kill surface contaminants . The skin was removed aseptically and the tissue was excised , minced and homogenized in 10 ml of RPMI-10% FBS medium . Tissue debris was removed by slow speed centrifugation ( 50 x g for 10 sec ) and the bacilli-rich supernatant was enumerated by direct counting following staining using the Fite's variation of Ziehl-Neelsen method . The bacteria were resuspended for 8 min in 0 . 1N NaOH , and washed 3 times in Tris EDTA ( TE ) buffer to remove extraneous mouse tissue and DNA adsorbed to the bacilli . A panel of purified M . leprae DNAs from 19 M . leprae reference strains ( with confirmed drug susceptibility profiles by conventional mouse footpad phenotypic method and genotyping by DNA sequence analysis ) , including those containing the most common DRDR mutations leading to DDS , RMP and OFX resistance , and susceptible strains , were obtained from the Leprosy Research Centre ( LRC ) , National Institute of Infection Diseases , Tokyo , Japan , the Laboratory of Molecular Biology Applied to Mycobacteria ( LABMAM ) , FIOCRUZ , Rio de Janeiro , and the Institute Lauro de Souza Lima ( ILSL ) , Bauru , Brazil . These DNA samples were used to validate the qPCR-HRM assay for drug susceptibility in M . leprae . A panel of DNAs purified ( either from clinical specimens isolates or culture ) from other mycobacterial strains and bacteria often found in the skin , was obtained from the NHDP-LRB Biobank and used to test the specificity of the qPCR-HRM DST assay . All DNAs were tested for 16S rDNA PCR/direct DNA sequencing to confirm species and as a control for DNA amplification , and for the M . leprae-specific RLEP quantitative RT-PCR assay [16] . These included: M . lepromatosis ( patient specimen ) , M . avium , M . intracellulare , M . kansasii , M . lepraemurium , M . lufu , M . marinum , M . simiae , M . smegmatis , BCG-Pasteur , M . ulcerans , M . bovis , M . gordonae , M . fortuitum , M . haemophilum , M . tuberculosis , Staphylococcus aureus , Staphylococcus epidermidis , Streptococcus pyogenes , and Clostridium perfringens . In this study , 211 skin biopsy specimens obtained from untreated leprosy cases for routine diagnosis of leprosy from Brazil and the U . S . Remaining specimens were “blinded” using a coding system and sent to the NHDP for qPCR-HRM DST for M . leprae . The code was broken when all data was compiled for final analysis . These specimens were derived from multibacillary ( MB; n = 156 ) and paucibacillary ( PB; n = 55 ) leprosy patients . The procedures for collection and processing of the biopsy specimens are described below . Purified DNA from all types and formats of processed clinical specimens was initially evaluated for the presence of M . leprae DNA using the quantitative real-time PCR-RLEP assay ( qPCR-RLEP ) as previously described [19] . Briefly , 2 . 5 μl aliquots of each specimen were added to PCR master mix and MLRLEP primers and probe ( Table 1 ) in a final volume of 25 μl and tested in the qPCR-RLEP using a standard curve method for quantitation . Standard curve was established using serial 4-fold dilutions of crude cell lysates of mouse footpad-derived M . leprae after three cycles of freeze-thaw ( -80°C , 30 min/98°C , 10 min ) . Then 1 μl was added to PCR reagents to a 96-well plate and amplified . These dilutions represented 2 . 0 x 107–4 x 103 M . leprae/ml equivalents . All samples were run on an ABI 7500 Fast PCR Real-Time System ( Applied Biosystems , Foster City , CA ) in duplicate . Drug susceptibility testing of the 19 M . leprae reference panel of purified DNAs using the q-PCR-HRM DST assay was performed as previously described by Li et al . ( 2012 ) with the following modifications . The qPCR reaction included: 10 μl of MeltDoctor HRM Master Mix ( Applied Biosystems , Foster City , CA ) , forward and reverse primers ( 0 . 5 μl each of 10 μM stocks ) targeting DRDR fragments in the folP1 , rpoB or gyrA associated with mutations resulting in DDS , RMP , or OFX resistance , respectively ( Table 1 ) , nuclease-free water ( 8 μl ) , and ( 1 μl ) of DNA template . Reactions were set up in duplicate in a 96-well PCR plate . Preliminary experiments suggested that it was important to include a negative control ( containing no DNA ) , a drug-susceptible template for target gene being tested , and a mutation control ( drug-resistant mutant for gene target being tested ) on each plate ( each of these in duplicate ) . The target sequences were amplified using ABI 7500 Fast Real-Time PCR System using the following cycling parameters: 95°C , 2 min; then 45 cycles of 95°C , 10 sec; 60°C , 30 sec; and 72°C , 30 sec . The PCR products were then heated to 95°C , 10 sec and cooled to 60°C over a period of 1 min for hetero-duplex formation . Melting curves for the products were generated by heating the reaction from 60°C to 95°C ( at a ramp rate of 0 . 5°C/sec ) according to the instrument default parameters and the fluorescence was automatically recorded at each 0 . 1°C step . Post-qPCR HRM analyses of the melt curves were performed using High Resolution Melt Software v3 . 0 . 1 ( Applied Biosystems , Foster City , CA ) . The software assembles curves with similar profiles into distinct groups . The curves of the control WT and mutant strains were designated as reference profiles . Data that were similar to the WT reference control were assigned to the drug-susceptible group and data that resembled the mutant reference control were assigned to the “variant” ( V ) drug-resistant group . For better visualization the melting curves matching each group were color coded . After standardization , qPCR-HRM DST was performed on DNA from clinical specimens . The software default setting of automatic selection of the melting region ( between the pre- and post-melt temperatures ) , produced acceptable results for reference samples with purified DNA applications . Initially the start and end temperatures of the melting regions were established using these settings . These were then adjusted manually to increase the stringency of the software group assignment , particularly for clinical samples . The baseline settings of the pre-melt and post-melt temperature gates for the qPCR-HRM assay were as follows: folP1 DRDR pre-melt ( 80 . 8°C to 81 . 3°C ) and post-melt ( 83 . 4°C to 83 . 9°C ) ; rpoB DRDR pre-melt ( 85 . 4°C to 85 . 9°C ) and post-melt ( 88 . 1°C to 88 . 6°C ) ; gyrA DRDR pre-melt ( 81 . 2°C to 81 . 6°C ) and post-melt ( 83 . 4°C to 83 . 8°C ) . PCR for folP1 , rpoB and gyrA DRDRs was performed on all samples . PCR amplicons corresponding to the DRDRs in rpoB , folP1 and gyrA genes were investigated by direct sequencing of PCR amplicons obtained using a modification of the WHO guidelines for Global Surveillance of Drug Resistance in Leprosy [17] . Since OFX resistance in leprosy is of a lesser concern , the gyrA DRDR sequence was obtained from a subset ( 80% ) of the samples . Briefly , the PCR reaction mix included 25 μl of AmpliTaq Gold 360 PCR Master Mix ( Applied Biosystems , Foster City , CA ) , forward and reverse primers ( 2 . 5 μl each of 10 μM stocks ) , nuclease-free water ( 15 μl ) , and DNA from samples ( 5 μl ) . PCR cycling parameters were: 95°C for 2 min followed by 45 cycles of 95°C for 10 sec , 58°C for 30 sec , and 72°C for 30 sec and then a final extension step at 72°C for 7 min . PCR products were loaded in 2% agarose gel for confirmation of the fragment amplification . Amplified products were purified through QIAquick PCR Purification Kit ( QIAGEN ) . DNA concentrations were determined using NanoDrop 8000 spectrophotometer ( Thermo Fisher Scientific Inc . , Wilmington , DE ) and sequenced by capillary electrophoresis using BigDye Terminator v3 . 1 cycle sequencing kit in the ABI Prism 3130 Genetic Analyzer ( Applied Biosystems ) . Sequence data was analyzed using the nucleotide database in the Basic Local Alignment Search Tool ( BLAST ) ( http://blast . ncbi . nlm . nih . gov ) to identify mutations associated with drug resistance .
qPCR-HRM DST results from the analysis of 19 drug-susceptible ( n = 5 ) and drug-resistant ( n = 14 ) reference strains demonstrated that all characterized drug-resistant strains had distinct variant ( “V” ) HRM assignment group based on melt curve profiles from that of the drug-susceptible , WT M . leprae Thai-53 type strain ( Table 2 ) . In addition , all drug-susceptible reference strains generated the WT HRM profile . The genotypes of these strains were confirmed by DNA sequencing of the DRDRs . Interestingly , one strain ( Br-3 ) showed multiple missense mutations within the rpoB DRDR ( Fig 1 ) . The specificity of qPCR-HRM for detection of mutations in the M . leprae DRDRs for DDS , RMP and OFX was 100% . The specificity of the qPCR-HRM DST for detection of M . leprae was defined using purified DNA from a variety of other mycobacterial and bacterial species . Results demonstrated that only M . lepromatosis generated a PCR amplicon in the qPCR-HRM in folP1 and rpoB DRDRs , with no amplification in gyrA . In each of these two assays M . lepromatosis DRDR was assigned to a distinct “V” HRM profile from that of M . leprae ( Fig 2 ) . Alignment of DRDRs of folP1 and rpoB from M . leprae and M . lepromatosis demonstrated multiple base-pair mismatches between these two strains ( data not shown ) . It is of note that RLEP results demonstrated that M . lepromatosis presented no DNA amplification for the M . leprae-specific gene fragment , as observed for all mycobacterial and bacterial species tested by qPCR-RLEP . The M . leprae qPCR-HRM DST for DDS , RMP and OFX was conducted on 211 RLEP positive clinical specimens preserved using a variety of procedures including: freezing , FFPE , and Fite’s-stained FFPE sections on glass slides and processed using either conventional or column-based DNA purification protocols . Results demonstrated that 6/14 ( 42 . 9% ) TT and 29/41 ( 70 . 7% ) BT patient specimens and 154/156 ( 98 . 7% ) MB specimens gave HRM results for all three drugs ( S1 Table ) . The two negative specimens from the MB group were classified as BB patients ( B-154 , B-156; S1 Table ) and contained low numbers of bacteria as defined by qPCR RLEP . In contrast , amplicons for all three DRDRs were obtained from 4/14 ( 28 . 6% ) TT , 26/41 ( 63 . 4% ) BT and 143/156 ( 91 . 7% ) MB patient specimens using standard PCR assays for DNA sequencing . Although some specimens with as little as 33 M . leprae/ml ( enumerated by qPCR RLEP assay ) did yield HRM results , the majority of those containing ≤ 350 M . leprae/ml did not yield reliable results in the all three qPCR-HRM DST assays . When HRM profiles were initially compared to DNA sequencing results there was a 97% correlation between the two assays . Five specimens giving a “V” HRM profile in the qPCR-HRM assay gave the susceptible genotype in DNA sequencing ( B-07 , B-17 , B-47 , B-125 , and B-172; S1 Table ) . These samples were originally extracted using a non-column-based DNA purification protocol . These samples were subsequently re-extracted using the DNeasy Blood and Tissue kit . DNA was then subjected to qPCR-HRM DST . Wild-type HRM profiles were observed for all of these specimens . Four additional clinical specimens ( N-08 , N-21 , N-30 and N-33 ) generated distinct “V” HRM profiles in folP1 DRDR ( Table 3 ) . These mutations have been previously associated with DDS-R leprosy . One of these mutations ( ACC→GCC ) resulted in the substitution of an alanine amino acid residue for a threonine in the dihydropteroate synthase of M . leprae encoded by folP1 . This particular mutation increased the melting temperature ( Tm ) 0 . 2°C , which generated a HRM difference in fluorescence plot as a melting curve shape above the WT profile ( Fig 3 ) . This is a characteristic that has not been observed in any other SNP mutation in the folP1 DRDR associated with the DDS-R genotype evaluated thus far . One of the four DDS-R specimens also generated a “V” HRM profile for the rpoB DRDR , consistent with RMP-R M . leprae phenotype ( Table 3 ) . All specimens tested in the qPCR-HRM gyrA assay generated the WT for the gyrA DRDR , consistent with OFX-susceptible M . leprae genotype ( S1 Table ) . To further test the ability of qPCR-HRM DST to detect a mixed infection of drug-resistant mutants in a background of drug-susceptible M . leprae , RMP-R and RMP-S DNAs were combined using 10-fold dilutions of templates resulting in ratios 9:1 ( WT:RMP-R ) to 9:1 ( RMP-R:WT ) ratios . The DNA mixtures containing equimolar DNA concentrations were analyzed by both qPCR-HRM DST and PCR/direct DNA sequencing . Results demonstrated that the qPCR-HRM DST for RMP could detect as little as 10% of the RMP-R genotype in a background of 90% RMP-S genotype ( Fig 4A ) . In comparison , the minimal concentration of the RMP-R genotype to effectively detect a mixed allele by PCR/direct DNA sequencing was 30% ( Fig 4B and 4C ) .
The control of leprosy relies solely on early case detection and treatment . The success of MDT is also critical for preventing morbidities and disabilities associated with infection . Even though there are relatively low levels of relapse reported after MDT , the nearly stable incidence rate attests to continuing disease transmission . Studies have also documented the emergence of drug-resistant M . leprae strains in relapse cases [5–11 , 14] . However , the current prevalence of both primary and secondary resistance to anti-leprosy drugs is virtually unknown because of the inability to cultivate M . leprae on axenic medium and the limited availability of molecular DST in laboratories in low resource , highly endemic countries . The use of real-time PCR in combination with high-resolution melt technologies has been recently reported for DST of M . leprae to DDS , RMP and OFX [12] , which can increase throughput , reduce costs , and support the inclusion of more patients , including new and relapse cases . It is now possible to discriminate drug-resistant mutant loci by post-PCR analysis of the variations in the double-strand DNA dissociation temperatures of amplicon melting curves . The suggested requirements for qPCR-HRM are a highly purified DNA template , a compatible Real-Time PCR thermalcycler , a PCR mix containing appropriate enzymes , buffer , DNA-saturating dyes , and high-resolution melt software . For mutation analysis by HRM , there are no operator-dependent sample manipulations after the qPCR is assembled and no need for additional reagents . In addition , the HRM does not require allele-specific primers or expensive probes for detection of mutations associated with resistance of M . leprae to DDS , RMP and OFX in clinical specimens . It has been also strongly recommended that column-based DNA purification protocols be used to provide suitable template for HRM-based DST when frozen , ethanol-fixed clinical specimens were evaluated . In the current study we extended these observations by evaluating the effect of other DNA purification and specimen fixation protocols on HRM analysis . In addition , the specificity of qPCR-HRM DST of M . leprae for DDS , RMP and OFX was defined using other mycobacterial and bacterial DNAs and clinical specimens having mycobacterial infections . To standardize the qPCR-HRM DST of M . leprae for DDS , RMP and OFX for this study , a library of drug-resistant M . leprae isolates containing several mutations in each of three drug target genes ( folP1 , rpoB and gyrA , respectively ) was received from the LRC laboratory and used to establish the qPCR-HRM DST assay using the ABI 7500 Fast Real-Time PCR Instrument and ABI HRM software . In addition , four additional multi-drug-resistant isolates from the LABMAM laboratory were added to this library . Even though these mutants had not been previously evaluated in qPCR-HRM DST , our results demonstrated that all mutant strains within this combined collection were correctly identified by this technique . The mutations associated with drug resistance in the rpoB , folP1 and gyrA DRDRs of M . leprae have been associated with moderate to high levels of drug resistance in M . leprae using mouse footpad DST . No mutations have been identified in low level dapsone-resistant M . leprae . For DDS resistance folP1 missense mutations within codons 53 and 55 have been associated with the development of resistance [20 , 21] . We identified folP1 mutation types ( ACC→ATC ) in codon 53 and ( CCC→CTC and CCC→CGC ) in codon 55 . In addition , one of four clinical specimens generating a “V” folP1 profile in the current study contained a folP1 mutation type ( ACC→GCC ) in codon 53 . Its HRM curve was located above and right of that of the WT strains . This was most likely due to the substitution of a guanine base for an adenine base in codon 53 of the folP1 of this M . leprae strain , resulting in an increased melting temperature of its DNA heteroduplex . Together , these four folP1 mutation types cover 91% of the DDS-R mutants described worldwide [16 , 22] . The HRM profile of folP1 mutation type ( CCC→CGC ) in codon 55 was found in three characterized Brazilian DDS-R isolates . This mutant was difficult to discern by HRM , as it was initially assigned to the WT group . This was most likely due to the minimal change in the melting temperature of its duplex containing a substitution of a guanine base for a cytosine base . It was necessary to adjust software parameters for the gate region between the pre- and post-melt temperatures in order for this mutant to be grouped as a “V” profile . Thereafter , these adjustments defined the baseline settings as described in the methods section . The presence of double folP1 mutation types ( ACC→CTC ) in codon 53 and ( CCC→CTC ) in codon 55 , as well as , a mixed infection with WT type allele in codon 53 was also confirmed in one isolate . Together these data confirmed earlier observations that the q-PCR-HRM DST was highly specific for detection of DDS susceptibility in M . leprae [12] . For RMP resistance , rpoB mutation types ( ACC→ATC ) in codon 433 , ( GGC→CAC ) in codon 448 , ( CAC→TAC ) in codon 451 , and ( TCG→TTG ) in codon 456 have been previously associated with the RMP-R phenotype of M . leprae and have previously shown to generate distinct “V” HRM profiles [12] . Results from the current study support these observations and represent over 80% of the RMP-R mutants described worldwide[16] . In addition , one Brazilian RMP-R isolate that generated a distinct “V” HRM rpoB profile contained multiple rpoB mutation types including: ( ACC→ATC ) in codon 433 , ( GGC→CAC ) in codon 448 , and ( CAC→TAC ) in codon 451 . In addition , this isolate also had resistance to DDS ( folP1 mutation type CCC→CTC in codon 55 ) and OFX ( gyrA mutation type GCA→GTA in codon 91 ) . qPCR-HRM DST gyrA confirmed initial results that qPCR-HRM DST detects a mutation in codon 91 ( GCA→GTA ) in clinical isolates; which occurs in more than 90% of the reported mutations associated with the development of OFX resistance in M . leprae [16 , 23] . The HRM analysis was also sensitive in detecting the low levels of mutant alleles in the folP1 , rpoB and gyrA DRDRs in mixed infections of WT and resistant M . leprae . For example , in strain Ze-4 the melting curve for gyrA DRDR differed from that of the WT and the expected mutant . This was assigned to a “V” profile . DNA sequencing analysis of this mutant confirmed mixed alleles in codon 91 of the gyrA . In addition , the mixed infection of DDS-R and DDS-S M . leprae was also confirmed in Ze-5 . Thus , these results reinforce that HRM clustering can be sensitive to the presence of multiple alleles , as reported by Li et al . ( 2012 ) . Further analysis of mixed alleles demonstrated that as little as 10% resistant M . leprae genotype in 90% susceptible genotype was sufficient to convert the WT profile to “V” profile in the HRM assay for DST . In contrast , DNA sequencing DST required as much as 30% of the resistant allele for detection of the mixed genotypes . This confirms that qPCR-HRM DST analysis may enable detection of minor populations of mutant alleles in a WT background and thus the emergence of drug resistance . Taken together , these results confirmed the initial observations of Li et al . ( 2012 ) , demonstrating a strong correlation between qPCR-HRM DST results and that of PCR/DNA sequencing from characterized isolates . Future studies to explore the HRM capability in genotyping lower proportions of drug-resistant strains in mixed infections within the same patient are needed to understand the clonal complexity in the course of M . leprae infection , possibly in animal models experiments . This qPCR-HRM strategy for DST directly from clinical samples , could be potentially optimized to analyze RMP-R in M . tuberculosis , once it has been demonstrated that the conventional in vitro culture methods may allow one strain to predominate , hindering the detection of resistant strains [24] . qPCR-HRM DST results were obtained for all three drugs from 99% of all MB leprosy patient biopsies in which M . leprae DNA was detected using qPCR RLEP . The two MB specimens that did not produce qPCR-HRM DST results were from mid-borderline ( BB ) leprosy patients with low bacterial numbers . Four of the clinical specimens from MB patients generated “V” HRM profiles consistent with DDS-R leprosy . One of these specimens also contained an rpoB “V” HRM profile . qPCR-HRM DST results were obtained for all three drugs from 63% of all PB leprosy patient biopsies in which M . leprae DNA was detected using qPCR RLEP . Although results were obtained from PB specimens with as little as 30 M . leprae ( enumerated by qPCR-RLEP ) , more consistent results were obtained from samples with ≥ 350 bacteria . No drug resistance was detected in the PB group . DNA sequencing confirmed the genotypes of these clinical specimens and our data confirm the initial report of Li et al . [12] . Several other key factors were critical to the success of qPCR-HRM DST of M . leprae directly from clinical specimens . The first of these was the purity of the DNA in the samples to be analyzed for HRM analysis . A column-based DNA purification protocol has been recommended for sample processing for qPCR-HRM DST analysis [12] . However , our results demonstrated that the vast majority ( 97% ) of DNA samples prepared from skin biopsy tissues using a conventional DNA purification protocol ( Proteinase K treatment , phenol/chloroform extraction and ethanol precipitation ) provided suitable template for qPCR-HRM DST . While we concur that column-based DNA purification provides the best quality template for HRM , it is important to note that the conventional DNA purification protocol is also suitable for HRM as a low cost alternative for resource poor laboratories . However , we also recommend that qPCR-RLEP be performed on all specimens prior to qPCR-HRM DST for leprosy as well as all specimens with "V" HRM profiles be sequenced . The initial study also evaluated ethanol-fixed skin biopsy tissues as a source of DNA for the qPCR-HRM DST [12] . The current study extended this analysis to examine the effect of other methods for specimen storage or fixation on the qPCR-HRM DST . These included fresh-frozen specimens , specimens recovered from FFPE tissue sections , and from archival Fite’s stained formalin-fixed paraffin-embedded ( FFPE ) sections from glass slides . Fite’s stain is a modification of the Ziehl-Neelsen acid-fast staining procedure that preserves the precarious acid fastness of M . leprae and thereby is used extensively to stain for detection of M . leprae . Results demonstrated that the method of fixation does not appear to have an effect on the ability to generate qPCR-HRM DST results . The number of bacilli ( ~ 350 ) appears to be the limiting factor . Therefore , combining these data with that of previous published data suggest that tissues preserved for histopathology ( FFPE blocks , slides for histopathology ) , ethanol-fixed and fresh-frozen tissues are all suitable specimens for qPCR-HRM DST of M . leprae . Another key factor for qPCR-HRM DST of M . leprae directly in clinical specimens is the specificity of the qPCR amplification for M . leprae DRDRs . Since the dye used in this assay binds nonspecifically to dsDNA and therefore , any dsDNA PCR product can emit fluorescence , the current study characterized the specificity of the qPCR-HRM DST assays for M . leprae . After testing 20 other bacterial and mycobacterial DNAs and biopsy specimens containing some of these mycobacterial species ( M . avium , M . haemophilum , M . gordonae , and M . lepromatosis ) , M . lepromatosis was the only other mycobacterial species that generated amplicons in qPCR HRM DST . This was not surprising because M . leprae and M . lepromatosis are highly related mycobacterial species which both cause leprosy and are now referred to as the Leprosy Complex [25] . Analysis of the primer sequences for the RT-PCR HRM assays confirmed a high degree of homology of these primers to that of M . lepromatosis DRDRs ( S1 Fig ) . HRM analysis of these amplicons generated “V” HRM profiles distinct from but similar to that found for some drug-resistant M . leprae strains . However , this appeared to be due to mutations in other codons within the DRDRs of M . lepromatosis not associated with resistance in M . leprae . To avoid this potential specificity problem with M . lepromatosis , we recommend that all clinical specimens be “positive” in the qPCR-RLEP assay prior to being analyzed by qPCR-HRM DST . In addition , all specimens with a "V" HRM profile should be subjected to DNA sequencing . Proper gating of the HRM data for the difference plots was another key factor affecting the performance of the qPCR-HRM DST using clinical specimens . This was vastly improved by the addition of two dilutions of an appropriate known drug-susceptible and -resistant control DNAs on each qPCR-HRM plate . These controls consisted of the DRDRs of: Ze-4 containing the high frequency rpoB mutant allele S ( CTG ) 456L ( TTG ) ; Ai-3 containing the high frequency folP1 mutant allele , T ( ACC ) 53I ( ATC ) ; and Ry-6 containing the high frequency gyrA mutant allele , A ( GCA ) 91V ( GTA ) . Control samples were critical to establish reproducible derivative melting curve plots , which could then be used as reference peaks at the expected melting temperature for each specific DRDR fragment of M . leprae . The appropriate gate for each allele could be set and therefore further define the parameters for assigning WT and “V” profiles of the unknowns . In addition , to prevent the interference with the analysis of other samples on the plate , any sample that did not amplify before the Ct value = 36 in the respective qPCR assay for each of the DRDRs or that did not give the expected peak in the derivative melt curve plot were excluded from analysis . In summary the qPCR-HRM DST is a ‘single-tube’ assay that can identify genetic variants in drug “target” genes by post-PCR analysis of the shapes and melting temperatures of amplicon melting curves . Since PCR amplicons and melting curves are generated in the same instrument , there are no operator-dependent sample manipulations after the qPCR reaction is assembled and no need for additional reagents or supplies to determine drug susceptibility . This reduces the risk of amplicon cross contamination and the cost of DNA sequencing . In addition , 42 samples ( tested in duplicate ) can be investigated at the same time in a single 96-well plate within 3 hr . Therefore , qPCR-HRM DST lends itself to high throughput screening of leprosy drug resistance . The estimated cost of a single reaction using qPCR-HRM DST is ~ $3 ( U . S . dollars ) . In contrast , PCR/DNA sequencing DST includes post PCR amplicon purification and quantification prior to DNA sequencing . The estimated cost for analysis of a single specimen is $22 ( U . S . dollars ) . These data strongly suggest that qPCR-HRM DST for M . leprae has broad applicability and can dramatically reduce the cost and time involved with DNA sequencing by only sequencing those specimens that generate the “V” HRM profile , especially in resource poor endemic regions thereby providing valuable information to improve patient treatment outcome and to aid in the global context of leprosy drug resistance . | Despite three decades of effective treatment with multidrug therapy ( MDT ) , leprosy persists as a public health problem in many regions of the world . The recent increase in relapse cases , MDT treatment failures , and the emergence of drug-resistant strains of Mycobacterium leprae could undermine existing leprosy control measures . PCR/DNA sequencing is currently the method of choice for drug-resistance surveillance in leprosy; however , this technique is not available to most endemic communities and is not cost-effective for large sampling surveys . Therefore , there is a need for drug-susceptibility tests for M . leprae that could be applicable to large sampling studies , particularly in low-resource areas , where leprosy is endemic , resistance appears to be low , but drug resistance prevalence is likely to be underestimated . Our study demonstrated the utility of qPCR-HRM DST of qPCR-RLEP positive specimens as a reliable screening tool that improves the applicability in endemic regions and reduces cost and time for drug susceptibility screening from a variety of specimen types . To improve the overall reliability we recommend that all mutants be subjected to DNA sequencing , thereby providing valuable information to improve patient treatment outcome and to the global context of leprosy drug resistance monitoring . | [
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] | 2017 | qPCR-High resolution melt analysis for drug susceptibility testing of Mycobacterium leprae directly from clinical specimens of leprosy patients |
Navigation of cells to the optimal environmental condition is critical for their survival and growth . Escherichia coli cells , for example , can detect various chemicals and move up or down those chemical gradients ( i . e . , chemotaxis ) . Using the same signaling machinery , they can also sense other external factors such as pH and temperature and navigate from both sides toward some intermediate levels of those stimuli . This mode of precision sensing is more sophisticated than the ( unidirectional ) chemotaxis strategy and requires distinctive molecular mechanisms to encode and track the preferred external conditions . To systematically study these different bacterial taxis behaviors , we develop a continuum model that incorporates microscopic signaling events in single cells into macroscopic population dynamics . A simple theoretical result is obtained for the steady state cell distribution in general . In particular , we find the cell distribution is controlled by the intracellular sensory dynamics as well as the dependence of the cells' speed on external factors . The model is verified by available experimental data in various taxis behaviors ( including bacterial chemotaxis , pH taxis , and thermotaxis ) , and it also leads to predictions that can be tested by future experiments . Our analysis help reveal the key conditions/mechanisms for bacterial precision-sensing behaviors and directly connects the cellular taxis performances with the underlying molecular parameters . It provides a unified framework to study bacterial navigation in complex environments with chemical and non-chemical stimuli .
Living systems detect changes in the environment and try to find optimal conditions for their survival and growth . As one of the best-studied systems in biology , bacterial chemotaxis allows bacteria ( such as Escherichia coli ) to sense chemical gradients and navigate toward attractant or away from repellent [1]–[4] . This gradient sensing strategy makes cells move unidirectionally toward the extreme levels of stimuli . However , for other natural factors ( such as pH and temperature ) , the physiological optimum does not locate at the extreme but at some intermediate level in the respective gradient . To find such intermediate point , it requires a more sophisticated strategy , namely , precision sensing . Both pH taxis [5]–[9] and thermotaxis of E . coli [10]–[16] provide us inspiring examples of precision sensing . Amazingly , E . coli cells use the same signaling system to achieve these different navigation tasks . Different external signals are sensed by several types of transmembrane chemoreceptors , among which the Tar and Tsr receptors are the most abundant [17] . For chemotaxis , binding of attractant ( or repellent ) molecules to chemoreceptors triggers their conformational changes and affects the autophosphorylation of the histidine kinase CheA [3] , [4] . Analogous to ligand binding in chemotaxis , both temperature and pH affect the conformational state of chemoreceptors and hence the CheA activity . Regardless of the way of being activated , phosphorylated CheA transfers its phosphate group to the response regulator CheY in the cytoplasm . The phosphorylated CheY molecules ( denoted as CheY-P ) then bind to the flagellar motors , increase their probability of clockwise rotations , and cause E . coli to tumble . The resulted alternating run and tumble pattern can steer cells to advantageous locations . To make temporal comparisons of stimuli , a short-term memory ( or adaptation mechanism ) is required [4] , [18] , [19] . This is achieved by the slow methylation-demethylation kinetics , as catalyzed by two enzymes ( CheR and CheB ) that add and remove methyl group at specific sites of receptors , respectively . How does a bacterium navigate through its environment with different chemical and nonchemical cues by using the same signaling and motility machinery ? How do bacterial cells make decisions under competing chemical and/or nonchemical signals ? How accurately and reliably can bacteria find their favored conditions ( such as the preferred temperature or pH ) ? and how do they tune their preference for precision sensing ? We aim to address these questions under a unified theoretical framework , given that different taxis behaviors are based on the same sensory/motion machinery . To this end , we develop a multi-scale model which incorporates intracellular signaling pathways into bacterial population dynamics . The continuum population model reveals a simple theoretical result for the steady state cell distribution , which is found to be determined by the direction-dependent tumbling rates ( transmitted through intracellular signaling pathways ) as well as the dependence of the swimming speed on external factors ( such as temperature ) . This new finding enables us to systematically analyze bacterial navigation in chemical , pH , and temperature gradients . From each application , we have made quantitative comparison with the available experimental data and have gained new insights about the mechanisms of bacterial taxis . Our general model can be extended to study bacterial migration in complex environments ( e . g . , with a mixture of chemical and nonchemical stimuli ) and provide quantitative predictions to be tested by future population level experiments .
Our unified model for bacterial taxis is developed on the basis of a number of previous models at different scales [20]–[29] . We incorporate microscopic pathway dynamics into the macroscopic transport equations [22] , [28] and derive a closed-form solution for the steady state cell distribution in chemical and/or nonchemical gradients . In the following , we outline the main steps in obtaining this key result ( Eq . 5 ) , with more details about our model given in Text S1 . The architecture of our model is illustrated in Fig . 1 . The environmental signals ( such as chemoattractants , pH , and temperature ) , denoted by , can be sensed by different types of transmembrane chemoreceptors and converted into the total receptor-kinase activity , denoted by . This activity represents the internal state of the cell and is described by the Monod-Wyman-Changeux ( MWC ) two-state model [23]–[26] , [29]: ( 1 ) where measures the degree of receptor cooperativity and represents the free energy difference between the active and inactive receptor conformations . The total activity , , depends on the average methylation level of receptors , , which restores to the adapted level , , over a time scale . For simplicity , the methylation rate is taken to be linear in and hence the methylation dynamics can be described by: . Here , we do not distinguish the methylation dynamics for different types of receptors ( which appears to be regulated by the receptor-specific activity [30] ) and consider as the average methylation level of the whole receptor cluster . This treatment does not affect our main results since we are only interested in the total receptor-kinase activity , , of the entire receptor cluster . A swimming bacterial cell may change its direction due to two mechanisms: the active transition to the tumbling state and the passive rotational diffusion ( characterized by the rotational diffusion rate , ) . According to the measured flagellar CW bias [31] , the ( instantaneous ) rate of going into the tumbling state can be described as: , where is the duration time of the tumbling state , represents the activity level at which the CW bias is , and denotes the ultrasensitivity of the motor response to CheY-P . Combining these two effects , the effective tumbling rate is given as: ( 2 ) In response to environmental signals , a population of bacteria will move in the physical space . Different from purely passive Brownian particles , cells also “distribute” in the internal state space , as each cell carries its own internal activity when moving around . In the one-dimensional setup , let denote the probability to find a cell being in the internal state and moving in the “” or “” direction at . One can write down the master equation that governs the evolution of these probabilities ( Text S1 ) . As in many experiments , here we study the distribution of cells constrained in a finite chamber with a chemical or non-chemical gradient . The cell population distribution will equilibrate given enough time as the diffusion of cells balance the taxis drift effect . Using the zero-flux condition in the master equation leads to an exact expression for the steady state cell distribution ( Text S1 ) : ( 3 ) where is the normalization constant and represents the average tumbling rate for the right or left moving cells at the same position . It is clear from Eq . ( 3 ) that the cell distribution is determined by the two motility characteristics , tumbling rate ( ) and swimming speed ( ) . On one hand , the local cell density is inversely proportional to the local swimming speed which may depend on the external condition . Intuitively , it is easier for cells to leave a region if cells move faster there and thus cells spend more time in regions of low swimming speed . On the other hand , the cell density also depends on the accumulative ( integrated ) effect of the tumbling rate difference between the left and the right moving cells . For example , if , cells tend to move in the right ( ) direction on average because it is more difficult for cells to enter a region where they tumble more frequently . What is the origin of different tumbling rates ( ) for cells moving in different directions ? The tumbling frequency is controlled by the CheY-P level which is proportional to the total activity , . At any location , the internal activity is not fixed but distributed around its average , . In fact , the average activity of the left moving cells ( ) is different from that of the right moving cells ( ) as these two populations carry different average receptor methylation levels ( memories ) . This activity difference can be evaluated ( see Text S1 and Figure S1 for more details ) : which is proportional to the ( average ) run length and is valid as long as the adaptation time is much longer than the average run time . The activity difference can be used to evaluate the tumbling rate difference ( ) : ( 4 ) By using the above expression for in Eq . ( 3 ) , we finally obtain a simple expression for the steady state cell distribution : ( 5 ) The equation for is given in Text S1 . This general expression ( Eq . ( 5 ) ) for the cell distribution is the main theoretical result of our paper . It shows that the steady state cell distribution is determined by two separable effects , the local effect of swimming speed and the accumulative effect of the gradient-dependent tumbling rates governed by internal signaling dynamics . In a previous work [20] , [21] , a simple relation , , was derived by assuming that the tumbling rate directly depends on the local environment factor . This treatment , however , did not take into account the cell's internal state or memory . Therefore , although the dependence in Ref . [21] agrees with our Eq . ( 5 ) , the integrated effect of the intracellular signaling dynamics was not identified or captured before . The intracellular signaling response to specific stimuli and the motor response to the response regulator are characterized by the free energy function , and tumbling rate , respectively . These functions can be determined by molecular and cellular level experiments , such as Ref . [26] for and Ref . [31] for . Here , our model shows how population level ( macroscopic ) behaviors of cells can be predicted quantitatively based on these molecular level ( microscopic ) signaling and response characteristics . The general expression for steady state cell distribution , Eq . ( 5 ) , provides a unified framework to systematically study diverse bacterial navigation behaviors in response to different chemical and non-chemical gradients , as will be shown in the following . We will also compare our theoretical results with the available experiments and make quantitative predictions on population-level behaviors of bacteria in more complex environments . We first apply our unified model to the case of bacterial chemotaxis . For simplicity , the chemical gradient ( e . g . aspartate ) , denoted by , is specifically sensed by one type of receptors ( e . g . Tar ) whose average activity can be described by the two-state model , Eq . ( 1 ) . The free-energy difference between the active and inactive states is given by [25] , [29]: ( 6 ) where and denote the methylation- and ligand-dependent contributions , respectively . The prefactor is the free-energy change per added methyl group , is a reference methylation level , and and represent the dissociation constants for the active and inactive conformations , respectively . E . coli swimming speed ( ) does not vary with the aspartate concentrations and is treated as constant here . By Eq . ( 5 ) , it is easy to derive the steady-state cell distribution: ( 7 ) with a dimensionless factor that represents the effective sensitivity of the bacterial population to the environment: ( 8 ) The effective sensitivity is proportional to the signal amplification factors at both the receptor and the motor levels ( i . e . , and ) . It is dampened by the rotational diffusion ( ) , because random collision of cells with the medium reduces directed chemotaxic motion . The dependence of on the average activity is nontrivial: on one hand , an increase of could significantly boost the intrinsic tumbling ( ) and thus suppress the negative role of rotational diffusion; on the other hand , chemoreceptors become less responsive at a higher activity level . Consequently , has to be in a narrow optimal range in order to achieve high sensitivity . Our model allows for quantitative comparison with experiments . As shown in Fig . 2 , the same functional form given in Eq . ( 7 ) can be used to fit the cell distribution data [32] in different attractant gradients . The coefficient inferred from experiments ( Fig . 2 ) appears to decrease with the gradient steepness , indicating a higher population-level sensitivity in shallower chemical gradients . This trend agrees with the observation in our model that the average receptor activity tends to increase with the gradient steepness in closed geometry . This increase in is caused by the back flow of cells due to diffusion as is peaked at the boundary with the higher attractant concentration ( see Fig . 2 ) . Note that this is different from the open geometry case where the cell density is constant and there is no net diffusive back flow of cells . E . coli can sense pH changes in the environment . According to recent experiments [8] , Tar receptors exhibit an attractant response to a decrease of pH while an opposite response was observed for Tsr . The balance between the two opposing receptors leads to a preferred pH level for the wild-type E . coli , i . e . , precision sensing , as suggested by our recent model study [9] of intracellular pH responses . Here , we examine how accurately and how robustly a population of bacteria could find their preferred pH level . The extracellular pH modulates the receptor-kinase activity primarily by affecting the periplasmic domain of the Tar and Tsr receptors . The total receptor-kinase activity can be described by the generalized MWC model for heterogeneous types of receptors . The total free energy between the active and inactive states is given by ( 9 ) where and denote the fractions of Tar ( ) and Tsr ( ) in the receptor cluster , respectively . The dissociation constants and for the inactive and active receptors are expressed in the pH scale . The observed opposite responses to pH changes indicate that for Tar and for Tsr . Without loss of generality , we set and for numerical examples . As the E . coli motor speed does not vary significantly with the external pH [33] , we can take the swimming speed as constant here . Using Eq . ( 5 ) , one can easily obtain the cell distribution in a pH gradient: , with the effective potential . The competition between the opposing pH dependence of ( from Tar ) and ( from Tsr ) leads to the accumulation of cells at an intermediate ( preferred ) pH level , which can be analytically determined by the condition ; see Text S1 for more details . The preferred pH is mostly sensitive to the relative abundances of receptors ( ) and the values of and . Based on our analysis and simulations , we find an empirical equation ( Text S1 ) , ( 10 ) which shows the logarithmic dependence of the preferred pH on the relative abundance of Tar and Tsr ( Fig . 3A ) . The coefficient in Eq . ( 10 ) varies with the dissociation constants and can be interpreted as a measure of tunability of the preferred pH upon changing the Tar/Tsr ratio . Theoretically , this coefficient is close to one ( ) when ( Text S1 ) . Numerically , we also found that higher tunability ( ) can be achieved if and the opposite holds for ( Fig . 3A ) . Our results can be compared with the recent experiment [8] , where the pH preference point ( pH ) was observed to shift from to when the Tar/Tsr ratio ( ) changed from to ( symbols in Fig . 3A ) . Using these data with the empirical Eq . ( 10 ) yields and , which together indicate that . In addition to the preferred pH , our population model also tell us the dispersion ( or accuracy ) of bacteria seeking and aggregating around their favored pH , which can be quantified by the standard deviation of the cell distribution in the pH scale . As shown in Fig . 3B , the dispersion measure turns out to be minimal for the scheme , compared to the dispersion for either or . Therefore , one possible advantage for having ( Fig . 3A ) in E . coli is the optimal accuracy of pH sensing at population level , though with a tradeoff of a modest tunability ( ) for . Bacteria are able to sense thermal variations and migrate toward their favored temperature [10]–[15] , another example of precision sensing . However , unlike in pH sensing where two types of receptors , Tar and Tsr , respond in opposite ways to a pH change , temperature sensing can be achieved by a given type of receptor ( Tar ) which changes the sensing mode ( from being a warm sensor to a cold sensor ) as its methylation level increases across a critical level [12] , [13] . Added to the complexity is the fact that temperature affects many other aspects of motility , such as the swimming speed and the motor switch sensitivity . Here , we first demonstrate how a chemoreceptor acts as a thermal sensor that inverts response at some critical temperature . In the next section , we will study how all those temperature-sensitive factors affect thermotaxis at the population level . For simplicity , we consider E . coli cells that only express Tar receptors and migrate in a linear temperature gradient . In general , the total free energy for the Tar activity can be described as ( 11 ) where describes how temperature affects the total free energy and where refers to the free energy change per added methyl group ( in units of ) at a given reference temperature ( i . e . , . Note that a linear function with was used in a previous model of thermotaxis [16] . However , it is easy to verify that as long as , the Tar receptor switch from being a warm sensor ( ) when to a cold sensor ( ) when ; see Fig . 4A . It is observed experimentally that the adapted activity also changes with temperature [26] , [34] . This can be modeled by . For E . coli , it is reported that at room temperature and at , leading to an estimate of . The critical temperature at which Tar inverts its response is defined by . Using the condition , we can obtain the steady state methylation level as well as the critical temperature: , which is determined by the ( upstream ) receptor kinetics . This also leads to a simple relationship: ( 12 ) showing that the average methylation level relative to changes sign at the critical temperature ( Fig . 4A ) . According to Eq . ( 12 ) , when , the Tar methylation level is less than ( ) , so the Tar receptor is a warm sensor driving the cell towards from lower ; when , the Tar methylation level is greater than ( ) , now the Tar is a cold sensor driving the cell towards from higher . This is the basic mechanism for cell accumulation around . Besides the receptor-kinase activities , temperature also affects other aspects of the system . For example , it is observed for E . coli that both the motor dissociation constant , , and the swimming speed , , change with temperature [34] , [35] . It remains unclear whether and how different temperature-sensitive factors affect the performance of bacterial thermotaxis . Using Eq . ( 5 ) , one can derive the steady-state cell distribution over the temperature range : ( 13 ) where the function represents the effect of the direction-dependent tumbling rates governed by the thermosensory system . In Eq . ( 13 ) , is the effective sensitivity defined in Eq . ( 8 ) and has weak dependence on temperature through both and . The function is introduced for convenience and represents the effect of the sensory system . Eq . ( 13 ) shows that there are two independent channels affecting bacterial thermotaxis: one is the swimming speed , and the other is the sensory system that controls the rotational direction of flagellar motors . In contrast to the local speed effect which is direct and memoryless , the sensing effect is indirect ( channeled through signaling networks and motor control ) and relies on the slow adaptation dynamics which encodes memory for the system to sense the environment [19] , [29] . Near the critical temperature , we can compute by keeping only the leading order term in , that is , . The expression for the cell density follows: ( 14 ) where is a positive constant when and . It is clear from the above equation that for a constant cells will accumulate around the temperature ( Fig . 4B ) . The accumulation temperature can be shifted from by the dependence of the swimming speed on temperature , e . g . , if increases with temperature , cells tend to spend more time in regions of lower speed and thus aggregate at a lower temperature , i . e . ( Fig . 4B ) . The shift of from only weakly depends on , which indirectly affect in Eq . ( 14 ) through ( see Eq . ( 8 ) ) . In fact , even the shape of the distribution is not sensitive to , as shown in Fig . 4B . The insensitivity of thermotaxis to is due to the fact that only depends on the local temperature and is the same for different cells at a given position , regardless of their direction of motion . In other words , does not contribute to the tumbling rate difference that drives the directed migration ( taxis ) of cells . Our theory can help explain some recent experiments measuring cellular behaviors in shallow temperature gradients [36] , [37] . It was observed that even the mutant bacteria lacking all chemoreceptors are still able to migrate toward high temperature [36] , showing that there is an additional channel ( other than sensing ) in regulating bacterial thermotaxis . When the sensing channel ( i . e . the bacterial signaling machinery which translates temperature stimuli into tumbling bias ) does not work ( e . g . , due to deletion of the receptors ) , the temperature-dependent swimming speed can still cause the directed cell migration [36] . This is consistent with earlier work [20] , [21] and our model where mutant strains lacking all receptors can be described by which leads to as described by Eq . ( 13 ) . In Ref . [37] , the swimming speed for wild-type E . coli ( with functional chemoreceptors ) was measured at different temperatures . The speed profile appears to be a quadratic function of temperature and reaches its maximum at . We have quantitatively compared the inverse speed profile , , with the cell density data in Ref . [37] and found that the inverse speed profile alone could not account for the observation , especially the significant aggregation of cells at high temperatures ( Fig . 5 ) . This suggests that the thermosensory system may not be completely silent in shallow temperature gradients as suggested in [37] . We test this hypothesis by using Eq . ( 13 ) with the measured and the assumption for . It turns out that our model , which includes both channels ( speed and sensing ) , provides a better agreement with the observed data ( Fig . 5 ) . This result suggests that the thermosensory system may be active even in shallow temperature gradients . Further experiments are needed to examine and quantify the interplay between these two channels ( speed and sensing ) in shallow temperature gradients . Our unified model can be applied to study and predict bacterial taxis behaviors in more complex environments . As a final example , we investigate the behavioral response of the Tar-only mutant cells over the interval under a temperature gradient and an opposing chemoattractant gradient . The total free energy is modified by adding an additional ligand-depend free energy to Eq . ( 11 ) . In this case , the steady-state cell density is found to be ( Text S1 ) : ( 15 ) where the term describes the chemotactic drift , and the other term captures the interaction between the chemical and thermal signals . In the absence of attractant ( i . e . , ) , Eq . ( 15 ) recovers Eq . ( 13 ) for thermotaxis . Interestingly , for a uniform chemical background ( i . e . , and ) , the interference effect tends to suppress the accumulation of cells at high temperatures . Quantitatively , a uniform chemoattractant background can shift the preferred temperature from to a lower temperature . When there is an attractant gradient ( so that ) imposed against the temperature gradient , the accumulation point can be shifted further . Specifically , as the chemical gradient steepens ( increases ) , the chemotacic response of bacteria become stronger , leading to a positional shift of their aggregation toward lower temperatures , as shown in Fig . 6 . This example demonstrates the general capability of our model in making quantitative predictions on bacterial behaviors in complex environments ( with multiple and competing chemical and nonchemical stimuli ) , which can be used to guide future experiments .
From the population model , we can construct an effective potential function , which provides a useful scheme to visualize different cases of bacterial taxis , as summarized in Fig . 7 . The effective potential for chemotaxis decreases monotonically with the chemoattractant concentration and thus steer cells up the chemical gradient ( Fig . 7 ) . Our application to pH taxis illustrates how the competition between two pH sensors ( Tar and Tsr ) determines the preferred pH for the wild-type cells expressing both Tar and Tsr: a push-pull mechanism here creates a potential well for bacteria to accumulate ( Fig . 7 ) . In the case of E . coli thermotaxis , the push-pull mechanism is more subtle as the “push” and the “pull” are provided by the two sub-populations of Tar receptors with their methylation levels above or below the critical level ( ) . This leads to a well-defined critical temperature where cells tend to accumulate ( Fig . 7 ) . The push-pull mechanism is likely a general strategy for precision sensing . For example , it was found that two receptors , Tar and Aer , leads to a preferred level of oxygen for E . coli aerotaxis [38] , which may also be studied within our unified model . E . coli chemotaxis has served as a model system in studying robustness of biochemical networks [34] , [39] , [40] . Bacteria exhibit thermal robustness in their chemotaxis network output by counterbalancing temperature effects on different opposing network components [34] . For example , the dissociation constant for the motor switch is observed to increase with temperature [35] . This effect balances the increase of the adapted CheY-P level with temperature such that the motor switch is able to operate in a narrow optimal range with ultrasensitivity [31] . This , however , raises a question for bacterial thermotaxis: do those temperature-sensitive factors , such as and , hinder the thermotactic performance ? According to our model analysis , the steady state distribution of cells in a temperature gradient is mainly determined by two effects: the temperature-dependent swimming speed and the direction-dependent tumbling rates . The system is actually robust/insensitive to those instantaneous/local temperature-sensitive factors ( e . g . ) which do not contribute to the tumbling rate difference at any spatial point . The insensitivity of thermotaxis to , as shown from our model , is a highly desirable feature of the system as it allows robust thermotaxis without sacrificing motor-level sensitivity . In the natural environment , cells are often exposed to multiple chemical stimuli [41] . Our general model can be applied to study such cases ( with a specific example discussed in Text S1 ) . The density of cells subject to a multitude of chemical gradients shall be given by , where denotes the free energy contribution from all the chemical signals that are sensed by the type- receptors . Quantitative predictions can be made about how bacterial cells integrate and respond to mixed or competitive chemical signals and how their response changes with the composition and relative abundance of their sensors . More complex situations exist when different stimuli are interdependent and/or interfere with non-chemical factors . For example , the chemical environment can be modified through consumption and secretion by the bacteria , a dynamical process depending on the bacterial cell density [36] . In addition , temperature can change the metabolic rates of bacterial cells and create temperature-dependent chemical ( nutrients , oxygen ) gradients . How cells navigate under such complex circumstances and how such behaviors lead to survival/growth benefits remain unclear . Our model can be extended to study those phenomena and help address those fundamental questions . In sum , the work presented here provides a general model framework to study population behaviors in the presence of both chemical and non-chemical signals based on realistic intracellular signaling dynamics .
Numerical simulations and figures are generated using MATLAB 7 . 0 . | Bacteria , such as E . coli , live in a complex environment with varying chemical and/or non-chemical stimuli . They constantly seek for and migrate to optimal environmental conditions . A well-known example is E . coli chemotaxis which direct cell movements up or down chemical gradients . Using the same machinery , E . coli can also respond to non-chemical factors ( e . g . , pH and temperature ) and navigate toward certain intermediate , optimal levels of those stimuli . Such taxis behaviors are more sophisticated and require distinctive sensing mechanisms . In this paper , we develop a unified model for different bacterial taxis strategies . This multiscale model incorporates intracellular signaling pathways into population dynamics and leads to a simple theoretical result regarding the steady-state population distribution . Our model can be applied to reveal the key mechanisms for different taxis behaviors and quantitatively account for various experimental data . New predictions can be made within this new model framework to direct future experiments . | [
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] | 2014 | Behaviors and Strategies of Bacterial Navigation in Chemical and Nonchemical Gradients |
Elongating DNA polymerases frequently encounter lesions or structures that impede progress and require repair before DNA replication can be completed . Therefore , directing repair factors to a blocked fork , without interfering with normal replication , is important for proper cell function , and it is a process that is not well understood . To study this process , we have employed the chain-terminating nucleoside analog , 3’ azidothymidine ( AZT ) and the E . coli genetic system , for which replication and repair factors have been well-defined . By using high-expression suppressor screens , we identified yoaA , encoding a putative helicase , and holC , encoding the Chi component of the replication clamp loader , as genes that promoted tolerance to AZT . YoaA is a putative Fe-S helicase in the XPD/RAD3 family for which orthologs can be found in most bacterial genomes; E . coli has a paralog to YoaA , DinG , which possesses 5’ to 3’ helicase activity and an Fe-S cluster essential to its activity . Mutants in yoaA are sensitive to AZT exposure; dinG mutations cause mild sensitivity to AZT and exacerbate the sensitivity of yoaA mutant strains . Suppression of AZT sensitivity by holC or yoaA was mutually codependent and we provide evidence here that YoaA and Chi physically interact . Interactions of Chi with single-strand DNA binding protein ( SSB ) and with Psi were required to aid AZT tolerance , as was the proofreading 3’ exonuclease , DnaQ . Our studies suggest that repair is coupled to blocked replication through these interactions . We hypothesize that SSB , through Chi , recruits the YoaA helicase to replication gaps and that unwinding of the nascent strand promotes repair and AZT excision . This recruitment prevents the toxicity of helicase activity and aids the handoff of repair with replication factors , ensuring timely repair and resumption of replication .
All cells must balance ongoing DNA synthesis with repair reactions that are necessary to overcome problems in replication . Persistent , unreplicated single-strand gaps in DNA may be caused by lesions or by DNA structures that impede DNA polymerization in either the replication template or the nascent strand . Filling of single-strand DNA ( ssDNA ) gaps ( a process termed “post-replication gap repair” ) can be accomplished by translesion DNA synthesis , involving specialized translesion polymerases ( reviewed in [1] ) , by template-switching to enlist an undamaged sister-strand as template [2 , 3] or by homologous recombination ( reviewed in [4] ) . Recruitment of DNA processing enzymes including nucleases , helicases and topoisomerases to a persistent gap , signaled by the presence of single-strand DNA binding protein ( SSB ) , may also aid repair [5] . If left unrepaired , single-strand gaps are converted to potentially lethal double-strand breaks ( DSBs ) by endonucleolytic cleavage or by convergence of a new replication fork into the incompletely replicated region . To study the process of ssDNA gap repair in E . coli , we have employed the nucleoside analog 3’ azidothymidine ( AZT ) [6] , which acts as a chain terminator when incorporated into DNA by DNA polymerases . AZT arrests replication and causes single-strand gaps to accumulate in vivo , as evident by the cellular accumulation of foci of SSB protein . In addition , AZT promotes induction of the SOS DNA damage response , dependent on the RecFOR mediator proteins that promote RecA recombinase binding to single-strand gaps in DNA [6] . In E . coli , the genetic requirement for RecFOR is diagnostic for the formation of ssDNA gaps in adjoining duplex DNA and is distinct from single-strand DNA caused by resection of DSBs , which require RecBCD for processing and RecA loading ( reviewed in [4] ) . E . coli cells can tolerate a certain level of AZT monophosphate incorporation , which appears to be excised from the 3’ nascent strain by Exonuclease III [6] , a 3’ to 5’ exonuclease acting on duplex DNA from a nick or gap [7] . AZT also appears to elicit recombination via the RecAFOR pathway and to produce DSBs at some level , which are repaired via the alternative RecABCD recombination pathway [6] . To identify new functions that aid in AZT tolerance , we performed a genetic screen for expression-dependent suppression of AZT-sensitive phenotypes of several mutant strains . A plasmid library representing tac-promoter expressed E . coli open reading frames ( ORFs ) [8] was screened in pools for the ability to increase plating efficiency in the presence of normally-toxic levels of AZT . Two ORFs were recovered repeatedly: one encoding the putative Fe-S helicase , yoaA , and the other encoding an accessory protein of the replisome clamp loader ( χ ) , holC . Interestingly , these proteins have been reported to interact physically by pulldowns of affinity-tagged bait proteins , followed by mass spectrometric analysis of interacting peptides [9] . We confirm this interaction in this study . The association of a DNA helicase with a replisome component provides a potential way to target a repair factor to a stalled replication fork . In E . coli , the bulk of DNA replication is catalyzed by the DNA polymerase III holoenzyme , which participates in a plethora of protein interactions that regulate its activity and processivity ( reviewed in [10] ) . The DNA polymerase III core complex ( α , dnaE; θ , holE; and ε , dnaQ ) is tethered to its template strand by the processivity clamp , β ( encoded by dnaN gene ) . Loading and unloading of the clamp is facilitated by a clamp-loader complex , consisting of three subunits encoded by dnaX ( τ and/or γ ) , and one each encoded by holA ( δ ) and holB ( δ’ ) . This clamp loader complex binds through τ to both the fork helicase protein , DnaB , and to the α subunit of the core DNA polymerase III complex . Two additional proteins , χ ( encoded by holC ) and ψ ( encoded by holD ) , act in a dimeric complex as accessory proteins to the clamp loader [11] . Completing the cycle of interactions , ψ binds to γ or τ of the clamp loader [12] and χ binds to single-strand DNA binding protein , SSB , [13] , that coats ssDNA revealed as fork unwinding proceeds . The function of the accessory clamp loader proteins , χ and ψ , has been somewhat unclear since the core complex ( [τ/γ]3δδ’ ) is sufficient to load and unload β clamps . However , a number of functions have been suggested by biochemical studies . χψ may aid assembly of the core clamp loader complex by increasing the affinity of τ/γ with δ and δ’ [14] . χ assists DNA polymerization on SSB-coated substrates [15] and promotes 5’ strand displacement [16]; the SSB-χ interaction may also aid the stability of Polymerase III on its template . χ promotes the handoff of primers from primase to PolIII [17] . ψ may also enhance clamp-loader activity and enforce an order to clamp assembly [18] . In E . coli , χ is nonessential ( although mutants are quite sick and accumulate suppressors ) , and ψ’s essentiality can be suppressed by mutations that prevent the induction of the SOS response or by loss of translesion polymerases and the cell division inhibitor controlled by the SOS response [19] . The non-essentiality of χ and ψ are consistent with the hypothesis that χ and ψ are not obligatory for replication but replication in their absence causes ssDNA gaps to accumulate in the fork . This could result from defects in replication and/or the inability to elicit repair of gaps . Single molecule fluorescence microscopy of labeled replisome components suggests that χψ complexes are in excess of core polymerase complexes [20] . Therefore , χψ could be recruited independently through the SSB-χ interaction and provide a stand-alone , polymerase-independent function . Our genetic studies reported here implicate the previously uncharacterized YoaA protein in the repair of replication forks and AZT tolerance . YoaA is related in protein sequence to a known E . coli Fe-S helicase of the Rad3/XPD family [21] , DinG [22–24] . In addition , we show that a network of interactions is required for AZT tolerance: YoaA with χ , χ with SSB and ψ with χ . Our work suggests that persistent gaps in DNA accumulate SSB , which recruits YoaA . We propose that YoaA’s unwinding activity permits the proofreading exonuclease of Pol III to remove AZT from the nascent strand . Additionally , our work illustrates a new strategy by which replisome interactions enlist proteins directly to facilitate gap repair .
To identify new repair factors , we performed a genetic screen for expression-dependent suppressors of the AZT sensitivity ( AZTs ) of various mutants defective in repair functions or their regulatory pathways . A pNTR- mobile plasmid library expressing each E . coli ORF ( under the tac promoter ) was introduced by conjugation into various AZTs strains and those isolates that conferred increased tolerance to AZT were selected . We screened for suppressors of the following genetic mutants with different defects in repair: xthA ( AZT excision ) , recB ( DSB repair ) , lexA3 ( SOS regulatory response ) , relA ( stringent regulatory response ) , rpoS ( general stress response ) , parE-ts ( Topoisomerase IV ) . Two ORFs were found repeatedly as suppressors in multiple screens: yoaA , an ORF of unknown function predicted to encode an Fe-S helicase , and holC , the χ subunit of the replication clamp loader complex . Both ORFs were isolated as suppressors of xthA and parE-ts; holC was isolated as a suppressor of recB and yoaA as a suppressor of relA , rpoS and lexA3 . ( Please note that the suppressor screen was not performed to saturation , so the failure to isolate an ORF from any particular strain does not mean it is not a suppressor . ) Our interest in these suppressors was piqued by the fact that χ is a component of the replisome and has a reported physical interaction with the yoaA-encoded protein ( b1808 ) , documented in a high-throughput study of E . coli protein interactions [9] . The ability of holC and yoaA to suppress the extreme AZT-sensitivity of several strains was determined by reintroduction of the mobile plasmids by DNA transformation . Plasmid-borne expression of either holC or yoaA dramatically increased tolerance to chronic AZT exposure ( Fig 1 ) in a number of genetic backgrounds , including lexA3 ( defective in the SOS response ) , recA ( defective in the SOS response and homologous recombination ) and xthA ( defective in exonuclease III ) . Expression of holC or yoaA improved AZT tolerance even in wild-type strains , as judged by colony size on AZT-containing medium ( Fig 1 ) , although holC or yoaA expression did not alter plating efficiency ( i . e . the number of colonies that form ) . We hypothesize that holC and yoaA overexpression reduce the cellular burden of AZT by assisting in the removal of AZT-monophosphate from DNA . Expression of holD , encoding ψ , a partner to χ in the accessory clamp loader complex , or holA or holB , the δ and δ’ components of the clamp loader , did not alter AZT tolerance in the wild-type background ( see S1 Fig ) . Expression of dinG , a DNA helicase and paralog of yoaA , was toxic and therefore could not be tested for suppressor activity ( see S1 Fig ) . Knockout mutants of yoaA and its paralog dinG were tested for effects on tolerance of AZT chronic exposure . Mutants in yoaA were sensitive to AZT , whereas mutants in dinG were sensitive only to very high concentrations of AZT . The double yoaA dinG mutant exhibited strong synergistic sensitivity to AZT ( Fig 2 ) . Sensitivity of both yoaA and yoaA dinG strains could be complemented by pNTR plasmid-expressed yoaA ( Fig 3 ) . These data indicate that YoaA plays a key role in AZT tolerance in wild-type E . coli cells , with DinG providing a partial backup function . Because of the reported interaction between the YoaA and χ proteins , we tested whether yoaA suppression required holC and whether holC required yoaA ( Fig 3 ) . An E . coli strain with a deletion mutation in yoaA was strongly sensitive to AZT , with plating efficiency several orders of magnitude below wild-type strains ( Fig 2 ) . This phenotype was fully complemented by plasmid-expressed yoaA but holC expression did not suppress the phenotype ( Fig 3 ) . Likewise , a holC mutant poorly tolerates AZT exposure ( Fig 2 ) and is complemented by plasmid-expressed holC but not suppressed by yoaA ( Fig 3 ) . This stands in contrast to other AZT-sensitive strains that were strongly suppressed by both yoaA and holC expression ( Fig 1 ) . The codependence of holC and yoaA suppression indicates that a YoaA/χ complex mediates tolerance to AZT and that the formation or effectiveness of this complex is enhanced by increased expression of either the YoaA or χ component . Interestingly , we note that the holC mutant , as assayed by plating efficiency , was more sensitive to AZT than the yoaA mutant; in fact , this increased sensitivity was ameliorated by loss of yoaA ( Fig 2 ) —that is , functional YoaA leads to AZT-sensitivity in holC mutant strains . In addition , yoaA mutations appear to suppress the poor growth phenotype of the holC strain . Introduction of a YoaA-expressing plasmid into this yoaA holC strain re-sensitized it to AZT ( S2 Table ) . These data indicate that YoaA function is , in some way , deleterious in the absence of χ , but not when χ is present . A previous study employed mass spectrometry to identify E . coli proteins that interacted with 1000 TAP-tagged essential or conserved proteins [9] . This included holC-TAP , which copurified with a number of other proteins in the replisome and with YoaA ( designated as “b1808” in that study ) . This study did not detect YoaA as an interactor with any other member of the clamp loader complex ( including ψ , holD; γ/τ dnaX; δ , holA; or δ’ , holB ) , suggesting that the χ:YoaA interaction might be direct . We sought to confirm this interaction by two means: by protein pulldown assays and by yeast two-hybrid analysis . We expressed a His6-tagged χ ( HolC ) protein ( pCA24N-holC; [25] ) in E . coli AG1 and immobilized the protein on a Ni-NTA resin . Extracts of E . coli BL21/DE3 expressing a biotin-binding domain ( BBD ) -YoaA fusion protein were then applied to the His6-HolC bound resin . Protein from input , wash and bound fractions were resolved by SDS-PAGE and subjected to Western blot analysis with Neutravidin-conjugated horseradish peroxidase to detect BBD-YoaA ( Fig 4A ) . BBD-YoaA was indeed detected in the bound fraction ( Fig 4A , lane 9 ) , indicating a physical interaction , albeit possibly indirect , between χ and YoaA . A reciprocal pulldown experiment , using streptavidin-agarose resin to bind BBD-YoaA ( bait ) and a penta-His antibody to probe Western blots for His6-HolC ( prey ) , likewise detected interacting His6-HolC . ( S3 Fig ) . To detect a potential direct interaction , both holC and yoaA were subjected to yeast two-hybrid analysis ( Fig 4B ) . An interaction between YoaA and HolC relative to controls was confirmed by the His phenotype of the strain . Because no other E . coli protein was expressed in the yeast assay strain , this latter result supports a direct physical interaction between χ and the YoaA protein . Based on its amino acid sequence and similarity to DinG , YoaA is a predicted to possess 5’ to 3’ DNA helicase activity and a Fe-S cluster ( See S2 Fig ) . We mutated the pNTR-plasmid borne yoaA gene within several motifs including the Walker A box ( K51A ) , Walker B/DEAH box ( D225A ) and putative Fe-S coordination site ( C168A ) , all of which are predicted to affect helicase function . These plasmid alleles , and the wild-type yoaA control plasmid , were introduced into wild-type and yoaA mutant strains . We assayed these strains for AZT tolerance , with expression of the plasmid allele induced by IPTG ( Fig 5 ) . All three mutant alleles fail to complement the AZT sensitivity conferred by yoaA . In wild-type strains , although plating efficiency is not affected , the size of colonies formed on AZT medium was dramatically reduced by the three mutant alleles of yoaA , in comparison to yoaA+ . These data support the notion that YoaA possesses ATPase activity essential to its genetic function ( consistent with a helicase activity ) and is likely also a Fe-S cluster containing protein , as is DinG . The χ protein participates in two additional protein interactions: one with its partner ψ ( HolD ) and with SSB . We sought to determine whether these interactions were necessary for suppression of AZT sensitivity by holC expression . A previous study [26] identified χ residues essential for SSB interaction , including V117 , R128 and Y131 ( Fig 6A and 6B ) . We introduced the V117F , R128A and Y131L mutations demonstrated to abolish SSB interaction [26] into the pNTR-holC construct and assayed their ability , relative to holC+ , to suppress AZT-sensitivity of wild-type or holCΔ strains . The SSB-binding-defective mutants had reduced ability to suppress the AZT-sensitivity of holC , as illustrated by both plating efficiency and colony size on AZT-containing media . These SSB-interaction mutants , when expressed in holC mutant strains did show increase in plating efficiency relative to the control plasmid ( Fig 6A ) ; however colonies formed on AZT-medium were small and slow-growing ( Fig 6B ) . These mutants , particularly the V117F allele , also showed dominant reduction of AZT tolerance in wild-type strains , apparent by both plating efficiency ( Fig 6A ) and colony size ( Fig 6B ) . Assuming that these substitutions do not affect protein folding or expression levels ( which is consistent with their genetic dominance ) , this result confirms that the ability of χ to interact with SSB is critical to the mechanism that promotes AZT tolerance . Residues in χ that are required for its interaction with ψ have not been reported . However , the crystal structure of the χψ dimeric complex [11] suggests that χ-F64 is a good candidate since it appears buried in a hydrophobic pocket at the χ:ψ interface . We mutated pNTR-holC to carry a F64A mutation and showed that this mutation abolishes AZT tolerance promoted by pNTR-holC expression in both wildtype and holCΔ mutant strain backgrounds ( Fig 6 ) . To confirm that this mutation indeed affects the interaction between χ and ψ , we subjected holC and holC-F64A to yeast two-hybrid analysis with holD ( Fig 4B ) . HolC and HolD exhibit an interaction comparable to “strong interaction” controls , with HolC-F64A significantly reducing the interaction . Therefore , suppression of AZT sensitivity by HolC expression appears to require an intact χψ complex . In several bacterial groups including Gram-positives , the ortholog to YoaA/DinG helicase is fused to an N-terminal exonuclease domain from the DnaQ/ε 3’ exonuclease family [27] . Our prior genetic study of AZT tolerance in E . coli suggests that Exonuclease III , a dsDNA exonuclease that degrades a 3’ strand from nicks or gaps in DNA , is likely the enzyme that removes AZT monophosphate from the DNA chain [6] . Since YoaA and χ expression strongly suppressed AZT sensitivity of xthA mutant strains ( Fig 1 ) , we considered the possibility that χ recruitment of YoaA helicase to SSB-bound gaps might permit an alternative 3’ ssDNA exonuclease to remove AZT by unwinding the 3’ nascent strand from its template ( Fig 7 ) . In Gram-positive and other groups of bacteria ( see Discussion below ) , this exonuclease is conveniently fused to the helicase polypeptide . This hypothesis predicts that loss of a 3’ exonuclease would weaken the ability of YoaA or HolC expression to promote AZT tolerance . E . coli possesses 8 members of the DnaQ exonuclease family including the proofreading activities for DNA polymerases I ( polA ) , II ( polB ) and III ( dnaQ ) , exonuclease I ( xonA ) and exonuclease X ( exoX ) . DnaQ itself emerged as a strong candidate for the YoaA-associated 3’ exonuclease . Suppression of AZT sensitivity by YoaA or HolC was strongly reduced by the mutD5 allele of the dnaQ gene ( Fig 7 ) . ( The mutD5 allele of dnaQ is a T15I mutation adjacent to the catalytic glutamate in the Exo I motif of the protein [28] . This allele reduces exonuclease activity without affecting its association with the polymerase subunit α [29]; mutation of the catalytic glutamate is lethal [28] . ) Moreover , YoaA expression became somewhat toxic in mutD5 strains , evident by lower plating efficiency and smaller colonies of mutD5 strains , both on LB and LB AZT media . We explain the latter result by hypothesizing that single-strand DNA displaced by YoaA is deleterious when it fails to be degraded by DnaQ ( see Discussion below ) .
This work provides new evidence that the accessory proteins to the clamp loader complex play a role in coordination of DNA repair . Through a chain of protein interactions , the accessory clamp loader proteins χ and ψ may facilitate the binding of multiple repair and replication factors . Persistent unfilled gaps are likely to be bound by SSB , which recruits χψ . χ binds the YoaA helicase , which we hypothesize unwinds potentially damaged 3’ nascent ends such as those terminated by azidothymidine monophosphate . Our work suggests that YoaA permits ε , encoded by DnaQ , to degrade the 3’ nascent strand end . Whether this degradation occurs in the context of the fully assembled replisome , an αεθ Polymerase III core complex , or by a stand-alone ε subunit is not known . Through its ψ interaction with τ or γ , χψ recruits the clamp loader to the gap for ready assembly of β clamps at the site of the gap . These clamps may then bind repair factors such as MutLS , ligase , TLS polymerases or recruit DNA polymerase III to fill the gap . These interactions may aid rapid hand-off of repair intermediates , promoting maturation of a persistent gap into a repair substrate and then into a replicative complex . Association of YoaA with χ may also allow it to track with the fork , even in the absence of persistent SSB-bound gaps . We define here a new potential DNA repair function , encoded by the previously uncharacterized gene yoaA , that aids in tolerance of the chain-terminating nucleoside , azidothymidine . We confirm by protein pull-down and yeast two-hybrid analysis that YoaA binds χ of the accessory clamp loader complex . Genetic analysis demonstrates that YoaA’s putative ATPase activity is important for its in vivo activity . YoaA is likely to function in DNA repair as a 5’ to 3’ , Fe-S helicase , similar to its paralog , DinG . E . coli YoaA and DinG share 29% identity ( see alignment in S2 Fig ) over almost their entire length , including the four cysteines in the DinG Fe-S coordination site , C120 , C194 , C199 , C205 [24] , and helicase motifs . The dinG gene was discovered as a DNA damage-inducible gene , regulated by the LexA repressor as part of the SOS response [29–31] . DinG is a member of a large group of helicases , including the human excision repair factor XPD , and helicases FANCJ/BACH-1 , RTEL1 and ChlR1/2 [21 , 31] . Mutations affecting these human helicases are associated with a variety of human DNA repair-deficient genetic diseases ( reviewed in [27] ) . The translocation direction of E . coli DinG on ssDNA is 5’ to 3’ and its helicase activity is somewhat structure-specific , with highest activity in the unwinding of bifurcated , fork-like structures . Both RNA:DNA and DNA:DNA hybrid molecules can be unwound by DinG [23] . DNA helicases may help avoid the formation of replication gaps or facilitate their subsequent repair by several means . Helicases may unwind DNA structures , such as hairpins or G- quadruplex structures , which impede polymerization or fork progression . They may displace bound proteins , including RNA polymerase , unwind stable RNA:DNA hybrids ( “R-loops” ) or disassemble protein complexes formed during repair . Helicases may promote template-switching reactions that function in repair . Helicases also process branched DNA structures that are considered to be intermediates in homologous recombination such as D-loops and Holliday junctions and therefore function in recombinational DNA gap repair . Mycobacterial DinG has been reported to unwind G-quadruplex structures , in vitro [32] . DinG is one of several helicases , including Rep and UvrD , that are required in E . coli to overcome problems associated with DNA replication clashes with strong transcription [33] . YoaA likely differs from DinG in that YoaA is associated with the replisome , through its χ interaction , although it remains possible that the two helicases possess specialized binding affinities or activities . We observed expression-dependent suppression of AZT sensitivity by YoaA probably because of the increased likelihood of association with the χψ complex and timely recruitment to gaps when intracellular levels of YoaA are increased . An increase in intracellular χ might also make YoaA recruitment more efficient . In the study that identified the YoaA:χ interaction , DinG was not detected as a replisome-associated polypeptide [9] . DinG’s weak AZT-sensitive mutant phenotype and genetic synergy with YoaA suggest that it can play a similar role to YoaA in repair , albeit more inefficiently . This inefficiency may result from an intrinsic difference in the binding or helicase activities of the DinG and YoaA or from the fact that YoaA is more efficiently recruited to gaps through its χ interaction . The sites within YoaA that interact with χ will be of interest , but are currently unknown . Our pull-down assays lead us to suspect that the C-terminus will be important since what we infer is a C-terminally truncated BBD-YoaA form , detected in our bacterial cell extracts by Western blot analysis , did not appear competent to interact with χ . Our genetic analysis supports the notion that YoaA interacts with the χψ complex rather than with χ alone in the absence of ψ . The consequences of χ:YoaA binding on χψ ‘s other interactions , such as to the clamp loader or to SSB , will be important topics for future investigation . We may find , for example , that YoaA interactions with χψ are in competition with the DnaX clamp-loader interaction , or alternatively , they may be neutral or even enhance each other’s recruitment . A number of observations reported here support the idea that YoaA , when liberated from association with other factors , can be toxic . Mutants lacking χ are very sensitive to AZT exposure , a phenotype partially suppressed by loss of YoaA . In the absence of the χ targeting factor , YoaA may be deleterious to repair and cell survival . Additionally , induction of YoaA expression is toxic to cells with defective DNA Polymerase III proofreading ( mutD5 ) , particularly after AZT exposure . This toxicity may be related to genetic instability , the DNA damage response and/or toxic reactions promoted by accumulation of single-strand DNA . This situation may be analogous to the cold-sensitivity of ssDNA exonuclease mutants ( lacking RecJ , ExoI , VII and X ) , in which toxicity is dependent on UvrD helicase function [34] . The toxicity of ssDNA may result from constitutive induction of the SOS response [35] and/or by stimulation of low-homology recombination reactions [36] or template-switching [2] that lead to genetic mutations or chromosome rearrangements . We propose that the physical interactions that target YoaA to the replication fork and promote the handoff of repair intermediates to replication functions act to control the potential toxicity of strand unwinding . Many diverse bacterial genomes encode a putative helicase related to DinG and YoaA of E . coli , leading to the hypothesis that it evolved early in the history of life and plays an important role in bacterial cell fitness . Although E . coli and other γ-Proteobacteria encode two paralogs , many bacteria possess only a single member of this group . The β-Proteobacteria encode a single YoaA ortholog , with over 95% identity to E . coli YoaA and only about 40% to E . coli DinG . The α and δ-Proteobacteria encode a single protein more distantly related to both proteins ( about 40% identical to YoaA and 30% to DinG ) . In Gram-positive , Firmicutes bacteria ( such as Bacillus subtilis ) and in the Thermus-Deinococcus , green nonsulfur and Fusobacteria groups , the YoaA/DinG related helicase is fused to an N-terminal domain with homology to the DnaQ/ε ( DEDDh ) family of 3’ exonucleases . Although these proteins have been termed in various databases as DinG orthologs , protein sequence alignments support the notion that they are more strongly related to E . coli’s YoaA protein than its DinG protein . S2 Fig shows the pairwise BLAST alignment between the B . subtilis protein and E . coli YoaA , compared to the alignment of E . coli ( Eco ) YoaA and DinG , showing multiple regions that align to YoaA but not DinG . This homology is apparent in the gapped regions shared by Bsu ε and Eco YoaA , relative to Eco DinG , particularly in the region between helicase motifs II and III . Overall identity of Bsu ε with Eco YoaA or DinG is 29% and 25% , respectively , with 10% or 14% gaps . The Bacillus protein does not retain the conserved cysteine residues shared by Eco YoaA and DinG that are required to form the Fe-S cluster in this helicase family and so is unlikely to be an Fe-S protein . Our previous work implicated the 3’ dsDNA exonuclease , Exonuclease III , as an enzyme that removes AZT monophosphate ( AZT-MP ) from DNA in E . coli [6]: exonuclease III mutants are highly sensitive to normally sublethal concentrations of AZT and overproduction of the enzyme improves tolerance in wild-type strains . In this study , we found that mutants in the proofreading activity of DNA polymerases I or III show little or no sensitivity to AZT . This may be because AZT-MP is poorly proofread or , alternatively , because loss of proofreading is efficiently backed up by exonuclease III-mediated removal . We have no information about how efficiently AZT-MP can be removed in vitro from DNA by bacterial DNA polymerases . However , in both yeast and humans , AZT-MP is poorly proofread by DNA polymerase gamma , a DNA polymerase that readily incorporates AZT [37 , 38] . The work reported here suggests that YoaA DNA helicase activity aids removal of AZT-MP from 3’ termini by DnaQ , the proofreading subunit of DNA Pol III . When Pol III is bound to the paired template and nascent strand , AZT-MP may be poorly accessible to the exonuclease active site of the DnaQ because of AZT’s bulky 3’ azido group and/or because it pairs well with its template adenine residue . YoaA helicase may promote the dissociation of Pol III and/or unwind the nascent strain from its template ( as shown in Fig 7 ) , allowing it or a second enzyme to gain access to AZT at the 3’ nascent strand terminus .
E . coli K-12 strains isogenic to either the wild type MG1655 ( F- rph-1 ) or wild type AB1157 background ( S1 Text ) were grown as previously described at 37° in Luria-Bertani ( LB ) medium , with 1 . 5% agar for plates [39] . With the exception of lexA3 , parE-ts and mutD5 , all mutant strains used in this study carried deletions of the indicated gene . LB medium was supplemented as necessary with antibiotics including ampicillin ( Ap ) , chloramphenicol ( Cm ) , kanamycin ( Km ) , tetracycline ( Tc ) , streptomycin ( Sm ) or gentamycin ( Gm ) . Details of growth media and strain constructions are provided in the S1 Text . Details of the screen can be found in the S1 Text . Briefly , screens were performed with MG1655 isogenic strains carrying mutations that confer increased AZT sensitivity , including xthA , recB , rpoS , lexA3 , relA , parE-ts ( grown at 30° ) . The plasmid library of pNTR-based mobile plasmids carrying E . coli ORFs [8] was obtained from the National Bioresource Project at the National Institute of Genetics in Japan , which were introduced into tester strains in pools and screened for suppression of AZT sensitivity . These plasmids are based on a ColE1 replicon , with a copy number of approximately 30 per cell . Isolated mobile plasmids ( S1 Table ) were transformed into strains by electroporation [40] . For survival assays , strains were grown to OD595 0 . 3–0 . 7 , serial diluted in 56/2 buffer , and then plated on LB agar plates supplemented with azidothymidine ( AZT ) as indicated . Plates were incubated at 37° for 24–36 hours . Total colony forming units ( CFU ) counts were obtained , normalized to the NO AZT counts , and then log10-transformed to obtain Fractional Survival . Averages and standard deviations were calculated from the log10-transformed Fractional Survival data , and plotted as a function of AZT dosage . Strains containing mobile plasmids pNTR-Control , pNTR-HolC , or pNTR-YoaA were plated on growth medium supplemented with Ap , and expression was induced by the addition of 1 mM IPTG . Because of day-to-day variation of the potency of AZT , assays illustrated in the figure panels were conducted in parallel . Site-directed mutagenesis ( Quikchange , Agilent Technologies ) was used to construct mutant alleles of holC and yoaA on the pNTR mobile plasmids , as well as holC plasmid derived from pDONR221 by GATEWAY cloning ( Life Technologies ) . Primers used to construct the site-directed mutant alleles are listed in S1 Table . We used a commercially available yeast two-hybrid system ( ProQuest , Life Technologies ) to detect interactions between YoaA and HolC and between HolC and HolD when C-terminally fused to Gal4 DNA binding and activation domains . Details of the assays are provided in the Supplement . Readouts for transcriptional activation include URA3 , HIS3 and lacZ expression in yeast strain MaV203 . Controls include MaV203 “no interaction” strains containing pPC97 ( no insert ) + pPC86 ( no insert ) ; “weak interaction” strains containing pPC97-RB + pPC86-E2F1; “moderate interaction” strains containing pPC97-CH2S-dDP + pPC86-dE2F , and “strong interaction” strains containing pCL1 ( encoding full-length GAL4 ) + pPC86 . Primers used to construct GATEWAY cloned alleles of yoaA , holC , and holD are listed in S1 Table . Plasmid pSTL385 is a pET104 . 1-DEST based plasmid ( Life Technologies ) comprised of a N-terminal fusion of the biotin binding-domain ( BBD ) to yoaA , expressed from the T7 promoter . BBD-YoaA was expressed from E . coli B strain BL21 ( DE3 ) ( genotype fhuA2 lon ompT gal dcm hsdS λ DE3 ) , grown in LB + Ap medium , and induced by the addition 1 mM IPTG for 2 hours . Plasmid pSTL386 is a pCA24N-based plasmid comprised of a N-terminal His fusion to holC under the lac promoter [41] , which was transformed and expressed in strain AG1 ( recA1 endA1 gyrA96 thi-1 hsdR17 supE44 relA1 ) . The strain was grown in LB + Cm medium , induced by 1 mM IPTG for 2 hours . Cells of both overexpressing strains were harvested by centrifugation , resuspended and stored in Tris-sucrose buffer ( 50 mM Tris-HCl pH 7 . 5 10% sucrose ) at -70° . Crude cell extracts were prepared by lysozyme lysis as described previously [42] . Pulldown of His6-HolC was performed from crude cell extracts with Ni-NTA agarose beads ( Qiagen ) , equilibrated in wash buffer ( 50 mM Na2HPO4/NaH2PO4 pH 8 . 0 , 500 mM NaCl , 20 mM imidazole ) . Resin was mixed with an equal volume of the crude cell extracts from the His6-HolC expressing strain , allowed to incubate at 20° for 1 hour and was then washed five times with wash buffer . YoaA-BBD crude lysate was similarly mixed with the His6-HolC bound resin , incubated and washed . Samples were eluted by mixing the resin 1:1 in 2x Laemmli sample buffer ( 120 mM Tris-HCI , pH 6 . 8 , 4% SDS , 40% ( w/v ) glycerol , 0 . 02% bromophenol blue ) . Protein samples were subject to PAGE in 15% polyacrylamide gels and transferred to PVDF membrane transfer using a Mini Trans-Blot Electrophoretic Transfer Cell ( Bio-Rad ) and the methods provided by the manufacturer . The Western blot analysis was performed using the QIAexpress detection kit and protocol ( Qiagen ) and using detection of BBD-YoaA with a 1:10 , 000 dilution of NeutrAvidin Protein Horseradish Peroxidase Conjugated antibody ( Pierce ) . | During the replication of the cell’s genetic material , difficulties are often encountered . These problems require the recruitment of special proteins to repair DNA so that replication can be completed . The failure to do so causes cell death or deleterious changes to the cell’s genetic material . In humans , these genetic changes can promote cancer formation . Our study identifies a repair protein that is recruited to problem sites by interactions with the replication machinery . These interactions provide a means by which the cell can sense , respond to and repair damage that interferes with the completion of DNA replication . | [
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] | [] | 2015 | Connecting Replication and Repair: YoaA, a Helicase-Related Protein, Promotes Azidothymidine Tolerance through Association with Chi, an Accessory Clamp Loader Protein |
Symmetry breaking is involved in many developmental processes that form bodies and organs . One of them is the epithelial rotation of developing tubular and acinar organs . However , how epithelial cells move , how they break symmetry to define their common direction , and what function rotational epithelial motions have remains elusive . Here , we identify a dynamic actomyosin network that breaks symmetry at the basal surface of the Drosophila follicle epithelium of acinar-like primitive organs , called egg chambers , and may represent a candidate force-generation mechanism that underlies the unidirectional motion of this epithelial tissue . We provide evidence that the atypical cadherin Fat2 , a key planar cell polarity regulator in Drosophila oogenesis , directs and orchestrates transmission of the intracellular actomyosin asymmetry cue onto a tissue plane in order to break planar actomyosin symmetry , facilitate epithelial rotation in the opposite direction , and direct the elongation of follicle cells . In contrast , loss of this rotational motion results in anisotropic non-muscle Myosin II pulses that are disorganized in plane and causes cell deformations in the epithelial tissue of Drosophila eggs . Our work demonstrates that atypical cadherins play an important role in the control of symmetry breaking of cellular mechanics in order to facilitate tissue motion and model epithelial tissue . We propose that their functions may be evolutionarily conserved in tubular/acinar vertebrate organs .
Functional organ morphogenesis [1–3] has been linked to turns and rotations of epithelial sheets [4–9] relative to the organ or body anterior-posterior ( AP ) axis . The primary determinant of this chirality has been associated with the cytoskeleton in different species [10–14] . In rotating Drosophila organs such as the hindgut [9] and male genitalia [6] , the consistent handedness of epithelial rotation depends on myosinID ( myoID ) and utilizes asymmetric cellular intercalations . An alternative form of rotational movement has been recently identified in the Drosophila ovary [8] . Here , organ-like structures , called egg chambers , display rotation of an edgeless monolayered follicle epithelium together with underlying germline cells ( called nurse cells and the oocyte ) that all rotate along the surrounding rigid extracellular matrix ( ECM ) , called the basement membrane ( BM ) [8] ( Fig 1A ) . In contrast to the Drosophila hindgut and male genitalia where cell membranes adopt a specific form of asymmetry called planar cell chirality ( PCC ) , the follicle epithelium displays no apparent membrane PCC ( S1A Fig , Material and Methods ) , and different egg chamber units in one animal can rotate clockwise or anti-clockwise performing more than three full rotations around their AP axis during early and mid-oogenesis [8 , 15] . This suggests that an alternative , possibly myoID-independent , mechanism drives this collective cell behaviour . Interestingly , the basal surface of each follicle cell displays clear local chirality of actin-rich protrusions and chiral localization of several planar cell polarity ( PCP ) molecules that are genetically implicated in egg chamber rotation [16–23] . Epithelial rotation is initially slow during early oogenesis ( stages 2/3-5: average speed ~ 0 . 2 μm/min ) [24] , accelerates in mid-oogenesis ( stages 6–8: average speed ~ 0 . 5–0 . 6 μm/min ) [8 , 18 , 20 , 24] and stops at stage 9 [8] . It has been shown that microtubule ( MT ) growth predicts the direction of epithelial rotation in early and mid-oogenesis and that their planar symmetry breaking during rotation initiation ( stage 1/2 ) is regulated by the atypical cadherin Fat2 [24 , 25] . Fat2 is a key PCP regulator of the actin cytoskeleton [25] as well as BM components [16 , 19 , 26] and is required for the epithelial rotation and elongation of Drosophila egg chambers [20 , 25] . In addition , the Fat2 planar polarized ( zig-zag ) pattern at the basal lagging membrane surface of follicle cells depends on MTs during fast epithelial rotation [20] . There is no evidence that MTs represent the active force-generating mechanism that drives epithelial rotation , which recently has been shown to involve actin-rich protrusions [18 , 23] . However , non-muscle myosin II ( Myo-II ) , which generally provides contractility and force generation to the actin cytoskeleton , is missing on actin-rich protrusions [18] . Therefore , motivated by the observation that pharmacological depletion of ROCK activity ( Rho kinase inhibitor ) leads to no epithelial rotation [20] , we hypothesized that the basal actin filaments containing Myo-II are better candidates to fulfill this force generating function . To test this hypothesis we investigated the function of Myo-II , its connection to the PCP pathway in Drosophila epithelial rotation , and the role of their interplay in this epithelial tissue .
In order to understand the function of Myo-II in epithelial rotation , we employed ex vivo high-speed confocal live imaging to observe the behaviour of Myo-II regulatory light chains ( MRLC , called Spaghetti Squash , sqh in Drosophila ) at the basal surface of the follicle epithelium . In order to image the MRLC , we used a MRLC fusion protein ( MRLC::GFP ) and imaged in a null sqhAX3 mutant [27] to avoid competition with endogenous Myo-II . Using this method , we uncovered a very dynamic ‘dot-like’ pattern of Myo-II with an average size of 363 nm ± 0 . 05 nm ( n = 136 ) in a thin layer ( ≤1000 nm ) at the basal surface of the rotating follicle epithelium ( control stage 1/2 , control stage 4 and control stage 7 ) as well as in static fat258D/103C mutant egg chambers ( stages 1/2 and 7 ) , which have been previously shown to lack epithelial rotation [18 , 20] ( Fig 1B first row and S1–S5 Movies ) . Next , we calculated the average speed of MRLC::GFP movement in rotating egg chambers at the time of rotation initiation ( 2 . 1 μm/min ± 0 . 71 ( n = 80 ) for control stage 1/2 ) , slow ( 2 . 11 μm/min ± 0 . 79 ( n = 101 ) for control stage 4 ) and fast ( 2 . 44 μm/min ± 0 . 96 ( n = 105 ) for control stage 7 ) epithelial rotation . We found that these average speeds of MRLC::GFP movement did not significantly differ ( P = 0 . 11 ) from the average speeds of MRLC::GFP movement in static fat2 mutant egg chambers ( 2 . 23 μm/min ± 0 . 49 ( n = 81 ) for stage 1/2 and 1 . 99 μm/min ± 0 . 62 ( n = 100 ) for stage 7 ) . In addition , we identified large intense MRLC::GFP dots ( 1 . 01um ± 0 . 14 um , n = 50 ) , which were close to the lagging end of migrating follicle cells during fast epithelial rotation ( Fig 1B first row and S3 Movie ) and lost in the static fat2 mutant follicle epithelium of corresponding stage ( Fig 1B first row and S5 Movie ) . These findings are in contrast to the appearance of MRLC::GFP in fixed epithelial tissue of rotating and static egg chambers , where continuous filaments of Myo-II were seen and no large Myo-II dots were observed ( S2 Fig and [28] ) . Taken together , using high-speed confocal live imaging , we discovered that the MRLC of Myo-II displays a dot-like signal that is highly dynamic at the basal surface of the follicle epithelium and its speed of motion is independent of epithelial rotation during early and mid Drosophila oogenesis . Next , we investigated whether the small ( ~360nm ) MRLC::GFP dots move in a specific direction with respect to the egg chamber anterior-posterior ( AP ) axis . To do this , we quantified MRLC::GFP movement directions , expressed as angles ranging from 0° to 360° , where 0° represented the anterior and 180° the posterior of egg chambers . These MRLC::GFP directions were then assigned to four 90° quadrants: Anterior ( 315°≤45° ) , Up ( yellow , 45°≤135° ) , Posterior ( 135°≤225° ) and Down ( grey , 225°≤315° ) ( S1C Fig and Material and Methods ) . After quantification , we discovered that during rotation initiation ( control and fat2 mutant stage 1/2 ) MRLC:GFP showed a strong preference to move perpendicularly with respect to the AP axis of egg chambers ( i . e . within Up and Down quadrants ) . This preference was only moderate during slow epithelial rotation ( control stage 4 ) and strongly reinforced during fast ( control stage 7 ) epithelial rotation ( Fig 1B middle row and S3A Fig ) . In contrast , MRLC::GFP dots moved with no clear preferred direction in the epithelial plane in the static fat2 mutant egg chambers of stage 7 ( Fig 1B middle row ) . This data suggests that Fat2 is not required for Myo-II alignment during rotation initiation , but Fat2 is essential later for the maintenance of Myo-II alignment during epithelial rotation . Similarly , labeling actin filaments with a LifeAct [29] molecule fused to GFP ( LifeAct::GFP ) showed a strong preference for LifeAct::GFP movement perpendicular to the AP axis of egg chambers during fast epithelial rotation ( S1C and S3A Figs and S6 Movie ) . Indeed , the planar trend of MRLC::GFP and LifeAct::GFP signals , which were observed here using high-speed confocal imaging , corresponded to their signals in fixed controls and fat2 mutant egg chambers during early and mid Drosophila oogenesis ( S2 Fig ) . Having established the planar trend of Myo-II movement perpendicular to the AP axis of rotating egg chambers , we next asked whether individual MRLC::GFP dots moved randomly along this planar trend . In order to do this , we plotted frequencies of MRLC::GFP dots within each defined quadrant in individual egg chambers . Strikingly , our data revealed that although MRLC::GFP dots moved in agreement with this planar trend , in individual egg chambers they only moved in one specific direction ( i . e . either Up or Down ) during rotation initiation ( control stage 1/2 ) , slow ( control stage 4 ) and fast ( control stage 7 ) epithelial rotation ( Fig 1B last row ) . This Myo-II asymmetry was initially prominent during rotation initiation ( control stage 1/2 ) and comparable to the asymmetry shown during fast epithelial rotation ( control stage 7 ) , but was less prominent during slow epithelial rotation ( control stage 4 ) . Further to this , no obvious asymmetry was detected in fat2 mutant egg chambers of both stages 1/2 ( rotation initiation ) and stage 7 ( no epithelial rotation ) ( Fig 1B last row ) . These results show that Fat2 is required for the planar symmetry breaking of Myo-II prior to the onset of epithelial rotation ( during rotation initiation ) . This preference for unidirectional Myo-II movement within a plane perpendicular to the AP axis , led us to speculate whether the particular direction of movement could relate to the eventual direction of epithelial rotation . It has been recently observed that Drosophila egg chambers can rotate in two possible directions ( either clockwise or anti-clockwise ) relative to their AP axis [8] . From our perspective , the clockwise direction corresponded to the direction of Myo-II movement within the Down quadrant , and anti-clockwise to the direction of Myo-II movement within the Up quadrant . Thus , we separately plotted the average percentage of MRLC::GFP dots moving within Up and Down quadrants and unified the direction of epithelial rotations in the Up direction for all analyzed rotating egg chambers . Notably , we detected that on average 59% and 77% of MRLC::GFP dots moved against epithelial rotation during slow and fast epithelial rotation , respectively ( Fig 1C and S3B Fig ) . This was not true for the static fat2 mutant egg chambers ( stage 7 ) , where no preferred planar direction of MRLC::GFP was identified in individual egg chambers ( Fig 1C and Fig 1B ) . We also observed that actin molecules preferably moved ( 78% on average ) against fast epithelial rotation , based on LifeAct-GFP , and this movement was comparable to the MRLC::GFP movement during fast epithelial rotation ( S3B Fig , Fig 1C and S3 and S6 Movies ) . Importantly , based on our rotation initiation data , we observed that , although these egg chambers had not yet begun to rotate , MRLC::GFP dots strongly preferred to move ( 72% on average , comparable to control stage 7 ) towards one of the two possible directions ( Up or Down ) in individual egg chambers ( Fig 1C and Material and Methods ) . In contrast , egg chambers lacking Fat2 did not show any clear preference in MRLC::GFP movement towards Up or Down during rotation initiation ( Fig 1C ) . In total , these data reveal that planar polarized Myo-II moves preferentially against epithelial rotation ( henceforth called Myo-II retrograde movement ) . Notably , early planar symmetry breaking of Myo-II is Fat2-depenent and precedes the onset of epithelial rotation and , therefore , the decision whether egg chambers will rotate clockwise or anti-clockwise . Next , we sought to uncover how Fat2 breaks Myo-II symmetry in the follicle epithelium during rotation initiation and how it regulates Myo-II retrograde movement during epithelial rotation . We have shown here that Fat2 does not play a role in the planar alignment of Myo-II during rotation initiation ( Fig 1B middle row ) . Therefore , we assumed that Fat2 must use a different unrelated mechanism , in addition to the planar alignment of Myo-II movement , to break Myo-II symmetry in the follicle epithelium of young egg chambers during rotation initiation . To support this hypothesis , we took advantage of our finding that Fat2 regulates the planar alignment of Myo-II perpendicular to the AP axis of rotating egg chambers ( based on live imaging and fixed tissue , Fig 1 and S2 Fig ) , but does not seem to have an impact on local Myo-II alignment within follicle cells ( S2 Fig ) . To this end , we hypothesized that artificial planar alignment of individual fat2 mutant follicle cells perpendicularly to the AP axis of egg chambers should not in principle be sufficient to break the symmetry in the follicle epithelium . To simulate such a situation , we developed a computational angular correction approach , in which we assumed that such epithelial remodeling ( either by dissolving/reestablishment of adherens junctions or cell intrinsic regulation of the actomyosin cytoskeleton ) happens in the wild-type situation with minimal movement of the respective components ( i . e . cells or cytoskeleton would rotate 45 rather than 135 degrees to align ) . To mimic this , individual fat2 mutant follicle cells were angularly corrected for their predominant MRLC::GFP direction by the smallest possible angle ( i . e . < 90° ) to reach perpendicular alignment to the AP axis of individual egg chambers ( Fig 2A and Material and Methods ) . When angular correction was applied , we observed that although MRLC::GFP dots had moved perpendicular to the AP axis in the static fat2 mutant follicle epithelia ( stage 7 ) , it was only to the extent of the angularly corrected control stage 4 ( slow epithelial rotation ) Fig 2B . This is in contrast to control stage 7 , which displayed a strong planar trend for MRLC::GFP movement ( fast epithelial rotation , Fig 1B middle row ) , indicating that either Fat2 or epithelial rotation are responsible for reinforcement of planar Myo-II alignment during fast epithelial rotation . Notably , out of ten analyzed independent egg chambers only five clearly broke planar Myo-II symmetry ( at least comparable to the control stage 4 ) , three displayed very weak asymmetry and two remained symmetrical ( Fig 2B ) . These findings indicate that proper alignment of Myo-II movement perpendicular to the AP axis of rotating egg chambers is not sufficient in itself to break the planar symmetry of Myo-II during fast epithelial rotation . Therefore , it seems that to break Myo-II symmetry in the follicle epithelium prior to and during epithelial rotation , an unknown Fat2-dependent mechanism is necessary . Based on this finding , we hypothesized that Fat2 could potentially regulate Myo-II dynamics directly at the intracellular level . To support this hypothesis , we first analyzed the behaviour of MRLC::GFP dots in individual follicle cells of control and fat2 mutant egg chambers during rotation initiation , slow epithelial rotation , and fast epithelial rotation ( Fig 2C ) . To be able to compare between these situations , we plotted the direction of MRLC::GFP dots as the weighted ratio of those moving against epithelial rotation ( retrograde MRLC::GFP dots ) versus those moving with it ( anterograde MRLC::GFP dots ) ( Fig 2C , Material and Methods ) . In the case of static egg chambers ( fat2 mutant egg chambers of stages 1/2 and 7 ) , we plotted the weighted ratio of MRLC::GFP dots with strong preference towards one of the two possible directions ( Up or Down ) to those in the opposite direction ( Material and Methods ) . Only fat2 mutant egg chambers of stage 7 were angularly corrected , in order to compare with the other situations . Interestingly , this analysis of Myo-II behaviour at the intracellular level revealed that individual follicle cells of an egg chamber displayed different MRLC::GFP ratios ( Fig 2C ) . However , when considering all analyzed egg chambers of a given situation , MRLC::GFP ratios tended to show significantly higher ratios ( >3 , on log2 scale > 1 . 6 ) for rotation initiation ( control stage 1/2 ) and fast epithelial rotation ( control stage 7 ) . This was in contrast to slow epithelial rotation ( control stage 4 ) with a ratio preference between 1–2 ( on log2 scale 0–1 ) and static fat2 mutant egg chambers ( stage 1/2 and stage 7 ) , which displayed rather equal distribution of MRLC::GFP ratios when binned to categories ( <1 , 1–2 , 2–3 and >3 , Fig 2C and S4A Fig ) . Notably , we identified one or more follicle cells of individual egg chambers with symmetric MRLC::GFP movement or with anterograde direction ( opposite to the overall preferred direction ) during rotation initiation , slow epithelial rotation , and in static fat2 mutant egg chambers ( Fig 2C and S4A Fig ) . This was never the case during fast epithelial rotation when all egg chambers displayed only follicle cells with strong retrograde MRLC::GFP movement ( Fig 2C ) . Similarly , we observed the same , exclusively retrograde movement with LifeAct::GFP in individual follicle cells during fast epithelial rotation ( S4B and S4C Fig ) , indicating that MRLC::GFP movement reliably reflects LifeAct::GFP behaviour . In summary , the reason why 50% of static fat2 mutant egg chambers ( stage 7 ) did not clearly break planar symmetry of Myo-II ( Fig 2B ) is that they contained one or more follicle cells with strong MRLC::GFP movement in the opposite direction to the overall preferred one within the whole follicle epithelium ( Fig 2C ) . As a similar situation was observed in fat2 mutant egg chambers during rotation initiation , we conclude that the Fat2 intracellular function is likely to guarantee that Myo-II moves in the same preferred direction in all follicle cells within the follicle epithelium to break Myo-II symmetry . This finding shows that although neighbouring follicle cells in static fat2 mutant egg chambers can sense the planar alignment of actin filaments between their neighbours ( based on fixed tissues in the 0°-180° range [30] ) , when we use high speed live imaging and quantify in the 0°-360° range , it can be clearly seen that neighbouring follicle cells actually cannot sense the direction of Myo-II among each other ( often found in opposing direction Fig 2C ) . To prove that Fat2 specifically regulates Myo-II behaviour at the intracellular level , we generated mosaic egg chambers with substantially small fat2 mutant clones ( Material and Methods ) , which resulted in rotating egg chambers with a speed comparable to control egg chambers of similar stage ( S5A Fig and S7 Movie ) . Using high-speed confocal live imaging , we then analyzed the MRLC::GFP behaviour at the basal surface of these fat258D mutant follicle cells and compared it to the MRLC::GFP behaviour of their direct control neighbouring follicle cells ( Fig 2D and S7 Movie ) . Similarly as shown in Fig 2C , we plotted , on a log2 scale , MRLC::GFP movement as a ratio of MRLC::GFP dots moving against ( retrograde ) epithelial rotation versus those moving with ( anterograde ) epithelial rotation . This approach revealed that clonal control follicle cells contained Myo-II that displayed a significant preference for retrograde movement , whereas clonal fat2 mutant follicle cells showed no preferred direction with respect to epithelial rotation ( Fig 2D ) . In addition , follicle cells in the fat2 mutant small clones did not lose their intracellular Myo-II alignment perpendicular to the AP axis of mosaic egg chambers ( S4D Fig ) . Thus , our data provide evidence that it is not epithelial rotation per se that impacts Myo-II asymmetries ( both direction and magnitude ) , but instead , it is a specific function of Fat2 that is required to direct Myo-II movement against epithelial rotation and reinforce this retrograde Myo-II movement at the intracellular level of individual follicle cells upon the onset of fast epithelial rotation . Moreover , we also show that follicle cells do not need Fat2 to sense the planar Myo-II alignment of their neighbours in mosaic egg chambers with small fat2 mutant clones . Next , we wished to understand the role that epithelial rotation plays in the follicle epithelium . Epithelial rotation has been clearly linked to the PCP of the basement membrane and actin filaments , which is necessary for proper elongation of egg chambers along their AP axis [17 , 18] . In addition , integrin-based adhesions to the ECM can modulate the speed of epithelial rotation and impact the shape/stretching of follicle cells [17] . Therefore , we wondered whether epithelial rotation could define the shape of follicle cells . To investigate this , we measured the roundness parameter of the basal surface of follicle cells ( Material and Methods ) and found that follicle cells are on average significantly more elongated during fast epithelial rotation and less elongated during slow epithelial rotation . This was in contrast to the round follicle cells observed in static fat2 mutant egg chambers ( Fig 3A ) . We also pharmacologically perturbed epithelial rotation by using the actin-depleting drug , Latrunculin A , and the Arp2/3 complex-depleting drug , CK-666 , which have been shown to stop epithelial rotation [18 , 20] . Both these pharmacological experiments resulted in static follicle cells and the familiar round shape observed in fat2 mutant follicle cells ( Fig 3A and S5A Fig , S8 and S9 Movies ) , indicating that epithelial rotation is required for the elongation of follicle cells . To distinguish whether the round shape phenotype of fat2 mutant follicle cells is a result of the absence of epithelial rotation in analyzed fat2 mutant egg chambers or an actual Fat2 specific function that is required for cell elongation , we analyzed the follicle cells of mosaic egg chambers that contained small fat258D mutant clones and displayed similar speed to control egg chambers of a comparable stage ( S5A Fig , Material and Methods ) . To our surprise , clonal control cells elongated to the same extent as slowly migrating follicle cells ( control stage 4 ) , but were significantly less elongated than neighbouring clonal fat2 mutant follicle cells . These fat2 mutant follicle cells displayed the same elongation as the fast migrating follicle cells of control stage 7 egg chambers ( Fig 3A ) and indicated that Fat2 could be required to provide follicle cells with resistance against cell stretching at their basal side likely via Myo-II regulation ( Fig 2D ) . We further confirmed that weakening the attachment to the ECM via decreased integrin level led to accelerated speed of epithelial rotation ( S5A Fig , [17] ) and resulted in similar cell elongation to that found in clonal control follicle cells ( stage 7/8 ) and slow migrating follicle cells in egg chambers of control stage 4 ( Fig 3A ) . In addition , this clonal analysis suggested that fat2 mutant cells , with impaired resistance to cell stretching , influence the cell elongation of their neighbouring controls . As we observed that follicle cells were elongated along the direction of epithelial rotation in fast rotating egg chambers ( e . g . S3 Movie ) , we wished to know whether epithelial rotation could define the direction of elongation of follicle cells . We therefore quantified the alignment of cell elongation , expressed as the angular direction , in 20 degree bins , through a range of 0°-180° during slow , fast and no epithelial rotation . We found that follicle cells elongated mainly perpendicularly to the AP axis of egg chambers during fast epithelial rotation and that this was weaker during slow epithelial rotation ( Fig 3B ) . To understand how the elongation of follicle cells relates to the planar alignment of Myo-II in individual follicle cells , we calculated the relative angle between the planar alignment of follicle cell elongation and the MRLC::GFP pattern ( Material and Methods ) . We revealed that the direction of MRLC::GFP movements were in the plane of follicle cell elongation 65% of the time during slow epithelial rotation and this increased to 100% during fast epithelial rotation ( Fig 3B ) . The planar alignment of the MRLC::GFP pattern preceded that of follicle cell elongation during slow epithelial rotation , indicating that epithelial movement prefigures the elongation of follicle cells . In contrast , fat2 mutant follicle cells displayed random planar alignment in their elongation , MRLC::GFP pattern , and relative angle . Altogether , this data shows that epithelial rotation ( clockwise or anti-clockwise ) is required for the proper elongation of follicle cells and the planar alignment of their elongation in the direction of epithelial rotation ( henceforth called directed elongation ) perpendicular to the AP axis of rotating egg chambers . We next asked what impact epithelial rotation has on the Myo-II behaviour in the epithelial tissue of egg chambers . When we analyzed individual follicle cells within a fat2 mutant follicle epithelium , besides loss of planar Myo-II alignment and weak Myo-II asymmetries , we also observed spatially unequal ( i . e . anisotropic ) MRLC::GFP pulses ( Fig 4A–4C and S5B and S5C Fig ) along with the constant remodeling and deformation of cellular membranes ( Fig 4C and S5B Fig ) . Membrane deformation resulted in significant basal area contractions in fat2 mutant follicle cells compared to the corresponding control ( Fig 4C ) . However , average area stayed unchanged , indicating that these area contractions were asynchronous among neighbouring fat2 mutant follicle cells ( Fig 4C ) . We observed that the reduction in basal area followed ~6s after the increase of MRLC::GFP , based on our calculated cross-correlation coefficient ( Fig 4D and Material and Methods ) . However , this cross-correlation coefficient is weak , indicating that alternating anisotropic Myo-II pulses in the follicle epithelium are influenced by external forces generated in neighbouring follicle cells , which in turn have an impact on the shape of neighbouring membranes . In contrast , this pulsating behaviour was missing in the corresponding control ( stage 7 ) during fast epithelial rotation ( Fig 4A and 4B and S5C Fig ) . Importantly , our clonal analysis of rotating mosaic egg chambers that contained small fat2 mutant clones ( Fig 2D , Fig 3A , Material and Methods ) showed no significant Myo-II pulses and no area change in clonal fat2 mutant follicle cells ( Fig 4E ) . Although not significant , the area change of these cells appeared more frequent than that of control follicle cells . Thus , our data provide evidence that epithelial rotation suppresses anisotropic Myo-II pulses and cellular membrane contractions/relaxations to prevent deformations of epithelial tissue ( Fig 4F ) . This data also supports our previous observation where we saw basally deformed follicle cells in the fixed follicle epithelium of fat2 mutant egg chambers [25] . In addition , our observation shows that fat2 mutant follicle cells likely suffer from impaired cell retraction , as recently observed by others [28] .
In single animal cells , it is essential to break symmetry to establish intracellular polarization , which in turn defines the direction of cell locomotion . The motility of a cell can be provided by protrusive forces , as a result of actin and MT polymerization , and/or by tensile forces from myosin motors that contract cross-linked actin filaments [31] . Myosin motors that bind actin filaments generate contractile forces , resulting in actin deformations and allow actomyosin networks to behave like a flowing viscoelastic fluid [32] . Actomyosin flows can be classified as transient , sustained , and oscillatory based on their character [31] . Transient actomyosin retrograde flow can be observed , for example , in the one and four-cell C . elegans embryo [14] , where the actomyosin network provides active torque that generates force , which leads to symmetry breaking along the embryonic axes . Sustained actomyosin flows are often of a retrograde ( i . e . from the front to the rear of the migrating cell ) character and can be divided into two groups dependent on their style of cell locomotion . Firstly , this locomotion can be a result of high cortical contractility [33] , where no protrusions and no adhesion to a substrate is used , allowing cell mass to be pushed forward in front of the cell ( e . g . swimming cells ) . Secondly , an alternative type of locomotion observed classically in fish keratocytes [34] or neuronal growth cones [35] can be a result of low cortical contractility , when protrusions and substrate/ECM adhesions are used to pull the cell forward with retraction of the rear end [31] . Even though this type of actomyosin retrograde flow is typically observed in single motile cells , it has been also identified in the yolk cell of the zebrafish embryo during the time when the enveloping cell layer is spreading over the yolk cell by using a strong actomyosin contractile ring that combines contractile forces and flow-friction mechanisms based on actomyosin retrograde flow [36] . Here , we identified another , up to now unknown , polarized and dynamic actomyosin network that shows preferred actomyosin retrograde movement/flow at the basal surface of protrusive and ECM-adhesive follicle cells in the follicle epithelium of acinar-like Drosophila egg chambers . Interestingly , we found that the speed of Myo-II in Drosophila egg chambers corresponds very well to the anterograde flow of actomyosin observed during zebrafish gastrulation [36] . Given the fact that this preferred actomyosin retrograde flow covers the whole basal surface of follicle cells , we propose that follicle cells use directed basal actomyosin tensile contractility on their circumference as the main candidate force-generating mechanism , combined with supportive protrusive force , via actin-rich protrusions[18 , 23] , to actively move the whole mass of follicle cells in a collective manner . In light of our work , the previously proposed actomyosin-based ‘molecular corset’ that has been suggested to restrict the elongation of egg chambers [19 , 37] along their dorso-ventral axis , turns out to in fact be a highly dynamic and polarized actomyosin network that drives epithelial rotation in order to protect follicle cells from their deformations and only later serves as a restrictive mechanism that limits the oocyte expansion via polarized Myo-II pulses [38] . The third and final classification of actomyosin flows relates to oscillatory flows . These can be found in animal tissues , where the actomyosin network often oscillates and can change its direction . Such flows can be observed in junctional remodeling during Drosophila embryonic germband extension [39] , or neuroblast ingression , [40] and have been referred to as actomyosin pulses , for example , during dorsal closure and formation of the ventral furrow in Drosophila embryos [41 , 42] . Similar Myo-II oscillations were also observed at the cellular surface ( basal side ) of follicle cells during mid-late Drosophila oogenesis [38] . Although all these examples of actomyosin/Myo-II oscillations/pulses are tightly linked to proper embryogenesis/tissue morphogenesis , our work reveals that , at least in the Drosophila follicle epithelium , Myo-II pulses are not per se a guarantee of proper morphological process . This is evident because fat2 mutant egg chambers display Myo-II pulses , but do not properly elongate ( i . e . they remain round ) in late Drosophila oogenesis . We hypothesize that these observed non-physiological Myo-II pulses may be a consequence of a tug-of-war between neighbouring cells or the absence of communication between follicle cells and the ECM . The first is based on the existence of weak Myo-II asymmetries in fat2 mutant follicle cells that likely generate directed force . These forces are often in opposing directions ( Fig 2C ) and may result in Myo-II pulses as neighbouring cells fight as to which direction to collectively move . The second explanation is based on the possibility that communication with the ECM is defective or impaired . We speculate that actin-rich protrusions , which have previously been linked to epithelial rotation [18 , 23 , 28] , may have a mechanosensitive function and therefore are able to sense the behaviour of neighbouring cells , or even those at a distance through the ECM . In the absence of such ECM-cell communication the generated force needs to be released to the ECM if not utilized for the migration of follicle cells , resulting in the observed Myo-II pulses . Mechanical forces are essential for cell rearrangements and tissue morphogenesis during embryogenesis . For example , it has been shown recently that the movement of neighbouring tissues can establish friction forces at their interface , and these forces can be critical for forming correctly the shape of a zebrafish embryo [43] . Here we show that the level of attachment to the ECM is one determinant that defines to what extent follicle cells elongate . Thus , a similar friction-based mechanism is likely involved in the follicle epithelium , resulting in the directed elongation of follicle cells . In fact , the force generated by the collective movement of follicle cells could be plausibly transmitted through the ECM , and provide long-range signaling as previously suggested [31 , 44 , 45] . This view supports the recent finding that force can be transmitted between cells and within tissue , and is a critical factor for the organization of the actomyosin network during dorsal closure in the Drosophila embryo [46 , 47] and in the Drosophila wing [48] . The essential function of the ECM in collective cell migration and egg chamber morphogenesis has been recently confirmed [49 , 50] . Little is known about the mechanistic control of symmetry breaking in epithelial animal tissues . It has been only recently discovered that atypical cadherins can act via force generating molecules , such as the unconventional myosins , Dachs and MyoID [51 , 52] . Our data show that another atypical cadherin , namely Fat2 , controls symmetry breaking by regulation of cellular mechanics via conventional non-muscle Myo-II . We have discovered that Fat2 is required: ( i ) to unify Myo-II asymmetries in one direction in individual follicle cells in order to break planar symmetry of Myo-II prior to the onset of epithelial rotation; ( ii ) to guarantee the presence of unidirectional actomyosin contractility by correcting opposing Myo-II symmetries/asymmetries; ( iii ) to guarantee proper Myo-II alignment in individual follicle cells perpendicular to the AP axis of egg chambers during slow epithelial rotation and ( iv ) to reinforce retrograde Myo-II asymmetries and planar Myo-II alignment during fast epithelial rotation . Only then can unidirectional actomyosin contractility , at the central basal surface of follicle cells , together with actin-rich protrusions , at the front of the basal side of follicle cells , drive and likely predict the direction of epithelial rotation and subsequent directed cell elongation . As Fat2 is similarly required for the symmetry breaking of MTs prior to the onset of epithelial rotation [20 , 24] , it will be interesting to find out in the future , what interdependencies Myo-II and MTs display . It will also be interesting to identify why Fat2 breaks the symmetry of both these cytoskeletal components at the time of rotation initiation in Drosophila egg chambers in order to initiate the rotational movement of this acinar-like invertebrate organ . Thus , it appears that the atypical cadherin subfamily likely developed a prominent function to shape tissues in two ways , dependent on tissue character . Firstly , in migratory tissue via retrograde Myo-II movement and an ECM-dependent mechanism ( Fat2-Myo-II in Drosophila egg chambers in this work ) and , secondly , in moving non-ECM-migratory tissue via intercalations . For example , Dachsous-MyoID in the Drosophila hindgut [52] and Fat-Dachsous-Dachs in the Drosophila wing [53] . However , it remains unclear exactly which signal instructs cadherins to initiate the breaking of symmetry in these epithelial tissues and it needs to be addressed in the future whether the signal is of biochemical or physical ( cadherins are force-inducible as observed in the C-cadherin/keratin complex in Xenopus [54] ) character . Fat2 close homologs , namely Fat1-3 , exist in vertebrates [55] and have been implicated in cancer [56] and autism [57] . Surprisingly , the intracellular domain of mouse Fat3 , whose cell-autonomous function has been recently linked to intracellular actin cytoskeleton organization via the Ena/VASP complex in directed cell migration and trailing edge retraction of amacrine cell in the mouse retina [58] , resembles Drosophila Fat2 function in collective cell migration [18 , 20] , directed cell elongation ( this work ) and retraction of follicle cells [28] during Drosophila oogenesis . Thus , it is likely that a similar conserved mechanism is used to move and sculpt tissues of tubular/acinar organs in vertebrates .
The Drosophila MRLC ( myosin regulatory light chain of the non-muscle conventional Myosin II ) , encoded by spaghetti-squash ( sqh ) , was visualized by MRLC fused with eGFP [27] under the sqh promoter in a null sqhAX3 or sqhAX3/sqhAX3;; fat258D/fat2103C mutant background to avoid competition with the endogenous protein . The following stocks and genotypes were used: sqhAX3/ sqhAX3; sqh-MRLC::GFP/ sqh-MRLC::GFP ( on the II . chromosome ) and sqhAX3/sqhAX3;sqh-MRLC::GFP/ sqh-MRLC::GFP; fat258D/fat2103C were used in all figures except for S2 Fig , where the sqh-MRLC::GFP/ sqh-MRLC::GFP; fat258D/ fat2103C line was used for fat2 mutant egg chambers . For accelerated epithelial rotation , we used mysXB87 FRT18/FM7 . To visualize actin filaments by time-lapse life imaging , we used Act5C-Gal4 ( BL4414 , on II . chromosome ) >UAS-LifeAct::GFP ( on II . chromosome ) shown in S6 Movie . To label cell membranes we used CellMaskTM Deep Red ( Invitrogen ) . Mosaic egg chambers were generated using the FRT-Flp system [59] when 1–2 days old adult flies were exposed to a 38°C heat-shock bath for half an hour ( 2 times a day ) on three successive days . Ovaries were dissected 5–7 days after the last heat-shock . The analyzed genotypes were: y w hsp-flp/+; sqh-MRLC::GFP/sqh-MRLC::GFP; FRT80B , fat258D/FRT80B , ubi-mCherry ( Fig 2D , Fig 3A , Fig 4E and S5 Fig ) . Egg chambers were cultured and live imaging performed as described [20] . Notably , we used no agarose for egg chamber embedding and no cover slip to avoid any potential artificial forces . An inverted LSM 700 Zeiss confocal microscope was used with 63x/1 . 45 water immersion lens . Time-lapse movies were taken with an interval of 6s for 300s-600s . Adult fly ovaries were dissected in 1xPBS and fixed with 4% p-formaldehyde for 20 minutes . Immunostaining followed standard protocols . We used a polyclonal GFP tag antibody conjugated with Alexa Fluor 488 ( Molecular Probes ) in dilution of 1:100 and rhodamine-phalloidin in dilution of 1:200 . Images were acquired on an inverted LSM700 Zeiss confocal microscope with a 63x/1 . 45 oil immersion lens . To inhibit polymerization of actin filaments , Latrunculin A ( 10 μM in 1% DMSO , Enzo Life Sciences ) was used for ca . 10mins before direct imaging . To deplete actin protrusions , we used an Arp2/3 inhibitor ( CK-666 , 250 μM , Sigma ) for ca . 1h as described [18] . Measurement of the direction of Myo-II movement , size and velocity , epithelial rotation velocity , angular correction , actomyosin directionality , cell shape , Myo-II intensity and PCC are described in S1 Text . Rose diagrams , quadrant plots , histograms , bar and box plots were created in R studio http://www . rstudio . com , using various packages [60–63] . Error bars represent standard error of the mean ( S . E . M . ) . The Student double-sided t-test was used as indicated . | Movement of epithelial tissues is essential for organ and body formation as well as function . To facilitate epithelial movements , cells need an internal or external source of mechanical force and a collective decision in which direction to move . However , little is known about the underlying mechanism of collective cell movement in living and moving epithelial tissues . Using high-speed confocal imaging of rotating follicle epithelia in acinar-like Drosophila egg chambers , we find that individual cells polarize their actomyosin network , a potent force-generating source , at their basal surface . We show that the atypical cadherin Fat2 , a key regulator of planar cell polarity in Drosophila oogenesis , unifies and amplifies the polarized non-muscle Myosin II of individual follicle cells to break the symmetry of actomyosin contractility at the epithelial level . We propose that this is essential to facilitate epithelial rotation , and thereby directed cell elongation , at the basal surface of follicle cells . In contrast , a lack of unidirectional actomyosin contractility results in disrupted non-muscle Myosin II polarity within follicle cells and causes asynchronous Myosin II pulses that deform follicle cells . This demonstrates the critical function of Fat2 , in the planar symmetry breaking of actomyosin , in epithelial motility , and potentially in organ development . | [
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] | 2017 | Epithelial rotation is preceded by planar symmetry breaking of actomyosin and protects epithelial tissue from cell deformations |
The intracellular pathogen Legionella pneumophila translocates a large number of effector proteins into host cells via the Icm/Dot type-IVB secretion system . Some of these effectors were shown to cause lethal effect on yeast growth . Here we characterized one such effector ( LecE ) and identified yeast suppressors that reduced its lethal effect . The LecE lethal effect was found to be suppressed by the over expression of the yeast protein Dgk1 a diacylglycerol ( DAG ) kinase enzyme and by a deletion of the gene encoding for Pah1 a phosphatidic acid ( PA ) phosphatase that counteracts the activity of Dgk1 . Genetic analysis using yeast deletion mutants , strains expressing relevant yeast genes and point mutations constructed in the Dgk1 and Pah1 conserved domains indicated that LecE functions similarly to the Nem1-Spo7 phosphatase complex that activates Pah1 in yeast . In addition , by using relevant yeast genetic backgrounds we examined several L . pneumophila effectors expected to be involved in phospholipids biosynthesis and identified an effector ( LpdA ) that contains a phospholipase-D ( PLD ) domain which caused lethal effect only in a dgk1 deletion mutant of yeast . Additionally , LpdA was found to enhance the lethal effect of LecE in yeast cells , a phenomenon which was found to be dependent on its PLD activity . Furthermore , to determine whether LecE and LpdA affect the levels or distribution of DAG and PA in-vivo in mammalian cells , we utilized fluorescent DAG and PA biosensors and validated the notion that LecE and LpdA affect the in-vivo levels and distribution of DAG and PA , respectively . Finally , we examined the intracellular localization of both LecE and LpdA in human macrophages during L . pneumophila infection and found that both effectors are localized to the bacterial phagosome . Our results suggest that L . pneumophila utilize at least two effectors to manipulate important steps in phospholipids biosynthesis .
Legionella pneumophila , the causative agent of Legionnaires' disease , is an aerobic Gram-negative pathogen that multiplies intracellularly in human phagocytic cells and in freshwater protozoa [1] , [2] . The bacteria enter the cells by phagocytosis and reside within a unique phagosome , known as the Legionella containing vacuole ( LCV ) , that grows in size and changes its membrane lipids composition during infection [3] . During the onset of infection , the LCV does not fuse with the host cell lysosomes nor become acidic , but instead the bacteria actively recruit secretory vesicles to the LCV and establish a replication niche [4] , [5] . For the formation of the LCV , the bacteria utilize the Icm/Dot type IVB secretion system by which they translocate effector proteins that manipulate host cell processes during infection ( for reviews see [6] , [7] ) . A very similar Icm/Dot type IVB secretion system was also found in the obligate intracellular pathogen Coxiella burnetii , the etiological agent of Q-fever [8]–[11] . Similar to L . pneumophila , the Icm/Dot secretion system of C . burnetii was shown to be required for intracellular growth [8] . However , the intracellular lifestyle of these two pathogens is completely different [12] , [13] . Currently , about 300 Icm/Dot dependent effectors have been identified in L . pneumophila [6] using a variety of bioinformatics and genetic screens [14]–[19] . Several of the effectors were shown to influence different host cell processes , and some of these processes are targeted by several effectors ( for reviews see [7] , [20] ) . Six effectors were found to subvert host cell vesicular trafficking by manipulating the host small GTPase Rab1: SidM/DrrA was shown to recruit Rab1 to the LCV and it activates Rab1 by functioning both as a Rab1-GEF ( GDP/GTP exchange factor ) and as a Rab1-GDF ( GDI [GDP dissociation inhibitor] displacement factor ) [21] , [22] . SidM/DrrA was also shown to AMPylate Rab1 thus keeping it in its active state on the LCV [23] , and the effector SidD was shown to deAMPylate Rab1 and to counteract the AMPylation of SidM/DrrA [24] . In addition , AnkX was shown to phosphocholinate Rab1 , thus keeping it in its active state on the LCV [25] , and Lem3 was found to dephosphocholinate Rab1 and to counteract the phosphocholination mediated by AnkX [26] , [27] . An additional L . pneumophila effector , LidA was reported to bind Rab1 and render it active when bound to GDP or GTP [28] , [29] and to tether endoplasmic reticulum ( ER ) derived vesicles to the LCV [30] , while the effector LepB was shown to inactivate Rab1 by functioning as a Rab1-GAP ( GTPase activating protein ) [22] . Three L . pneumophila effectors ( LubX , AnkB and LegU1 ) have been shown to be involved in ubiquitination of host cell proteins; LubX possesses two eukaryotic U-box domains and it was shown to ubiquitinate the host cell cycle protein Clk1 and the L . pneumophila effector SidH [17] , [31] . AnkB possess a eukaryotic F-box domain and it was shown to functionally mimic eukaryotic F-box containing proteins and it exploit the host ubiquitination machinery via the conserved eukaryotic processes of K48-linked polyubiquitination and the proteasome machineries in order to generate free amino-acids for the bacteria [32]–[36] . LegU1 was also shown to mediate the ubiquitination of the host chaperone protein BAT3 involved in the regulation of the ER stress response [37] . Five other L . pneumophila effectors , including Lgt1/2/3 , SidI and SidL were shown to target the host translational machinery and block protein synthesis [38]–[40] and two additional effectors , LegK1 and LnaB , were shown to activate the host cell NF-kB pathway [41] , [42] . These observations clearly indicate that important host cellular processes are targeted by more than a single effector during L . pneumophila infection . Beside the effectors described above , several L . pneumophila effectors were shown to manipulate phospholipids . Four L . pneumophila effectors , VipD and its paralogs VpdA , VpdB and VpdC , are homologues to phospholipase A ( PLA ) , patatin-like , enzymes [43] , [44] . PLA enzymes hydrolyze the carboxylester bonds at the carbon-1 or carbon-2 positions of phospholipids and generate fatty acids and lysophospholipids [45] . VipD was shown to possess a PLA enzymatic activity in a yeast model [44] , VipD , VpdA and VpdC were reported to cause lethal effect on yeast growth when expressed , and VipD and VpdA were shown to cause secretory defects in yeast [15] . Another L . pneumophila effector , LegS2 , was shown to act as a sphingosine-1-phosphate lyase ( SPL ) , an enzyme that catalyze the irreversible degradation of sphingosine-1-phosphate , which is an important lipid secondary messenger , to phosphoethanolamine and hexadecanal [46] . Beside the effect on lipid composition , several L . pneumophila effectors ( SidC , SidM/DrrA and SdcA ) were shown to anchor to the LCV by specific binding to phosphatidylinositol-4 phosphate ( PI4P ) [47] , [48] , and other effectors ( LidA , SetA and LpnE ) were shown to preferentially bind phosphatidylinositol-3 phosphate ( PI3P ) [47] , [49] , [50] . Other bacterial pathogens have also been shown to manipulate host cell's phospholipids . Similar to L . pneumophila , Salmonella enterica resides in a unique phagosome known as the Salmonella containing vacuole ( SCV ) during infection . The S . enterica effector SseJ possesses a PLA and glycerophospholipid-cholesterol-acyltransferase activities . SseJ is localized to the SCV membrane where it esterifies cholesterol in order to promote infection [51] , [52] . Another S . enterica effector involved in phospholipids manipulation is SopB ( also known as SigD ) . SopB mediates the accumulation of PI3P on the SCV and affects multiple processes during the course of infection , including bacterial invasion , SCV formation and maturation [53]–[55] . SopB was shown to mediate PI3P accumulation by the recruitment of Rab5 to the SCV . Rab5 in-turn recruits and/or activates Vps34 which is a phosphatidylinositol ( PI ) 3-kinase that phosphorylates PI to produce PI3P [54] . Another example of phospholipids manipulation by a pathogen was shown in Mycobacterium tuberculosis which also replicates intracellularly in a phagosome [56] . The bacteria secrete the PI phosphatase SapM that specifically dephosphorylates PI3P to PI and lowers the levels of PI3P on the phagosomal membrane , thereby blocking phagosome fusion with late endosomes and lysosomes [57] . To date , L . pneumophila effectors were shown to be involved in the host cell's phospholipids regulation in two main aspects; i . Direct degradation of phospholipids by phospholipases ( such as VipD ) . ii . Anchoring of effectors to the LCV via specific PIs ( such as SidM/DrrA ) . In this work we present a novel strategy used by L . pneumophila to manipulate host cell phosphatidic acid ( PA ) , a main component in the host cell phospholipids biosynthetic pathway . We found that the L . pneumophila effector LecE manipulates the PA biosynthetic pathway by activating the host PA phosphatase protein family which results in the conversion of PA to diacylglycerol ( DAG ) . We also found that another L . pneumophila effector , LpdA , a phospholipase-D ( PLD ) enzyme , generates PA in mammalian cells and in this way it supplies additional substrate ( PA ) to the PA phosphatase which is activated by LecE . These findings suggest that L . pneumophila specifically manipulates the phospholipids composition of their phagosome to result in a successful infection .
The LecE protein ( Lpg2552 ) is 555 amino acids long , and is predicted to contain at least six hydrophobic domains which are most likely associated with membranes after translocation into host cells . To examine the involvement of LecE in L . pneumophila intracellular growth we constructed a deletion substitution mutant in the gene encoding for LecE and examined it for intracellular growth in Acanthamoeba castellanii and HL-60 derived human macrophage . Similarly to most of the L . pneumophila effectors , the deletion of lecE had no effect on the intracellular growth in both hosts ( Fig . 2A , and data not shown ) . In addition , similarly to other L . pneumophila effectors , the translocation signal of LecE was found to be located at the C-terminus , since a CyaA fusion of the 92 C-terminal amino acids of LecE was found to translocate into host cells with a similar efficiency like the full length protein ( Fig . 2B ) . To identify the cellular target of LecE we decided to use a S . cerevisiae high-copy number genomic library , and look for colonies that grow in the presence of LecE , at 37°C , under inducing conditions ( media supplemented with galactose ) . Several colonies where isolated ( see Materials and Methods ) , and most of them did not produce a full-length LecE ( data not shown ) , however one suppressor colony produced a full length LecE protein and the yeast cells were able to grow under LecE inducing conditions ( Sup13 in Fig . 3A ) . The library plasmid present in this suppressor colony was isolated and reintroduced into a yeast strain containing the galactose inducible lecE gene and similar suppression was obtained . Sequencing of the two edges of the plasmid insert revealed the genomic region responsible for the suppression observed ( Fig . 3B ) . Several subclones that were constructed ( Fig . 3B ) indicated that the dgk1 gene is the gene responsible for the suppression effect . To further confirm the results obtained , we cloned the dgk1 gene under the GAL1 promoter and both lecE and dgk1 containing plasmids were introduced into yeast . As can be seen in Fig . 3A , Dgk1 over expression showed clear suppression of the lethal effect caused by LecE . Dgk1 is a diacylglycerol-kinase enzyme that catalyzes the formation of phosphatidic acid ( PA ) from diacylglycerol ( DAG ) and counteracts the phosphatase activity of the enzyme Pah1 on PA ( Fig . 4A ) [63] . The activity of Pah1 has been shown to be dependent on its phosphorylation state , and it was shown to be active when de-phosphorylated [64] . The kinase-cyclin complex Pho85-Pho80 has been shown to phosphorylate Pah1 thus inactivating it [65] and the Nem1-Spo7 phosphatase complex has been shown to dephosphorylate Pah1 and activate it [64] . It is important to note that over expression of Dgk1 was found before as a single suppressor in two screens: i ) In a screen aimed at identifying yeast suppressors that can rescue the lethal effect caused by the over expression of Pah1-7P ( a constitutively dephosphorylated and therefore active Pah1 ) [63] and ii ) In a screen aimed at identifying yeast suppressors that can rescue the lethal effect caused by the over expression of the yeast Nem1-Spo7 phosphatase complex that dephosphorylates and therefore activates Pah1 [63] . In both screens , Dgk1 over expression suppresses a highly active Pah1 enzyme , what might indicate that this is also the outcome of the over expression of the L . pneumophila effector LecE . The Dgk1 suppression of the LecE lethal effect can be explained in several ways: i ) LecE might inhibit the function of Dgk1 , in this case higher levels of Dgk1 will result in some Dgk1 that will be left active in the cells; ii ) LecE might directly activate the function of Pah1 , in this case higher levels of Dgk1 , which performs the opposite enzymatic reaction , will suppress the effect of Pah1 activation by LecE . There are also two indirect ways by which Pah1 might be activated by LecE: iii ) LecE might activate the Nem1-Spo7 phosphatase complex , that activates Pah1 , and in this way it might activate Pah1 indirectly , and iv ) LecE might inhibit the Pah1 kinase-cyclin complex Pho85-Pho80 that inactivates Pah1 and in this way it might activate Pah1 indirectly . v ) An additional possibility might be that LecE itself possesses an enzymatic activity like Pah1 ( PA phosphatase ) and Dgk1 suppresses the effect of LecE simply because it performs the opposite enzymatic reaction . To sort between these possibilities we used several yeast deletion mutants and strains over expressing relevant yeast genes and the results of these analyses are presented in Fig . 4B , C , D , E and Fig . S1 . If LecE inhibits the function of Dgk1 then we would expect that a deletion mutant in dgk1 will be lethal to yeast , however it is known that a deletion in dgk1 is viable and show no yeast growth defects ( [66] and Fig . 4B ) . In addition , when we over expressed LecE in the dgk1 deletion strain the lethal effect of LecE was even stronger in comparison to the effect on wild-type yeast ( Fig . 4B ) indicating that LecE causes its lethal effect also in the absence of Dgk1 , therefore it is not possible that the lethal effect observed in the wild-type strain occurred due to inhibition of Dgk1 activity . Moreover , the result showing that LecE caused a stronger lethal effect in the dgk1 deletion mutant , in comparison to its lethal effect in the wild-type yeast ( Fig . 4B ) , supports the possibility that LecE activates the opposite reaction which is catalyzed by Pah1 . If LecE activates the function of Pah1 then its expression in a pah1 deletion mutant is expected to result with suppression of the LecE lethal effect because its target protein will be missing . Thus , LecE was over expressed in a pah1 deletion mutant and the result obtained was very clear , the deletion in the gene encoding for pah1 almost completely eliminated the lethal effect of LecE ( Fig . 4C ) , clearly showing that Pah1 is required in order for LecE to cause its lethal effect on yeast growth . In addition , when LecE and Pah1 were over expressed together the lethal effect of LecE was enhanced , even though Pah1 by itself had no effect on yeast growth ( Fig . 4C ) . The combined results indicate that Pah1 is activated by LecE and that this activation causes the observed LecE lethal effect on yeast growth . The fact that Pah1 was required for LecE to cause its lethal effect on yeast growth also indicates that lecE does not encode for a PA phosphatase enzyme by itself ( the Pah1 activity ) since in this case the deletion in pah1 should have had no effect on the lethal effect caused by LecE . In order to test whether LecE directly activates the Pah1 function or indirectly by targeting the Pah1 regulators , the relations between LecE and the Nem1-Spo7 phosphatase complex that activates Pah1 and the kinase-cyclin complex Pho85-Pho80 that inactivates Pah1 , were examined . To examine if LecE activates the Nem1-Spo7 phosphatase complex , LecE was over expressed in the nem1 deletion mutant ( nem1 encodes for the catalytic subunit of the phosphatase complex ) or together with the Nem1-Spo7 phosphatase complex . As shown in Fig . 4D , a deletion in nem1 weakly suppressed the lethal effect caused by LecE while the over expression of LecE together with the Nem1-Spo7 phosphatase complex enhanced the lethal effect compared to LecE or the phosphatase complex by themselves . Both results indicate that the Nem1-Spo7 phosphatase complex is not targeted by LecE , but that both LecE and the Nem1-Spo7 phosphatase complex perform a similar function that results with the activation of Pah1 ( see below ) . According to this hypothesis , when nem1 is missing some of the Pah1 protein remains inactive and therefore a weak suppression effect was observed , while when LecE was over expressed together with the Nem1-Spo7 phosphatase complex they both activate Pah1 , resulting with an enhanced lethal effect . An additional way for LecE to indirectly activate Pah1 is to inhibit the function of the Pah1 kinase-cyclin complex Pho85-Pho80 that was shown to phosphorylate Pah1 and in this way inactivate it [65] . To examine this possibility , LecE was over expressed together with the Pho85-Pho80 kinase-cyclin complex or in the pho80 deletion mutant . As shown in Fig . 4E , the over expression of the Pho85-Pho80 kinase-cyclin complex completely suppressed the LecE lethal effect on yeast growth while in the pho80 deletion mutant the lethal effect of LecE was enhanced ( comparable results were obtained when LecE was over expressed in the pho85 deletion mutant , data not shown ) . The enhanced lethality of LecE in the pho80 and pho85 deletion mutants indicates that the Pho85-Pho80 kinase-cyclin complex is not targeted by LecE . In addition , the suppression of the LecE lethal effect by the Pho85-Pho80 kinase-cyclin complex indicates that its function is opposite to the one of LecE . In conclusion , the analyses performed in the yeast system strongly indicate that LecE directly activates Pah1 . To further validate the possibility that LecE functions similarly to the Nem1-Spo7 phosphatase complex , we directly compared the effect of LecE and the Nem1-Spo7 phosphatase complex on yeast growth ( Fig . 5 ) . We found that over expression of LecE or the Nem1-Spo7 phosphatase complex are both lethal to yeast growth and their lethal effect was suppressed by over expression of Dgk1 ( Fig . 5A ) and by a deletion of the gene encoding for Pah1 ( Fig . 5B ) . In addition , to test whether the Pho85-Pho80 kinase-cyclin complex also suppresses the lethal effect of the Nem1-Spo7 phosphatase complex on yeast growth , a different yeast strain ( W303 ) that allowed the introduction of four plasmids , was used . Since this strain grows slowly at 37°C it was incubated at 30°C where the LecE lethal effect was less pronounced ( see above ) . Similarly to the over expression of Dgk1 and the deletion of pah1 , the over expression of the Pho85-Pho80 kinase-cyclin complex also suppressed the lethal effect on yeast growth of both LecE and the Nem1-Spo7 phosphatase complex ( Fig . 5C ) . These results indicate that LecE directly activates Pah1 similarly to the Nem1-Spo7 phosphatase complex . As indicated above , the S . cerevisiae dgk1 gene encodes for a DAG kinase enzyme that catalyzes the formation of PA from DAG . Unlike the DAG kinases from bacteria , plants , and animals , the yeast enzyme utilizes CTP , instead of ATP , as the phosphate donor in the reaction [67] . Point mutations of conserved residues within the Dgk1 CTP transferase domain were shown before to result in a loss of DAG kinase activity [67] . To determine if the enzymatic activity of Dgk1 is required for the suppression of the lethal effect caused by LecE , we generated two point mutations ( R76A and D177A ) in Dgk1 that were shown before to abolish the DAG kinase activity [67] ( Fig . 6A ) . As can be seen in Fig . 6B , both mutated Dgk1 proteins were unable to suppress the lethal effect of LecE in comparison to the wild-type Dgk1 , indicating that an enzymatically active Dgk1 is required for suppression . The same result was also obtained for the lethal effect caused by the over expression of the Nem1-Spo7 phosphatase complex ( Fig . 6C ) , further demonstrating the similar function of LecE and the Nem1-Spo7 complex . The S . cerevisiae Pah1 belongs to a highly conserved family of proteins , called lipins . This novel family of Mg+2-dependent PA-phosphatase enzymes catalyze a fundamental reaction in lipid biosynthesis , namely the dephosphorylation of PA to DAG . Lipins are highly conserved throughout the eukaryotic kingdom and exhibit similar overall primary organization [68] . They are relatively large proteins ( close to 100 kDa ) and contain a conserved amino-terminal domain ( N-LIP ) of unknown function , and a carboxy-terminal catalytic domain ( C-LIP ) harboring an invariable HAD-like phosphatase motif , the DXDXT motif [68]–[70] . To determine if the enzymatic activity of Pah1 is required for LecE to cause its lethal effect on yeast growth , we generated a point mutation ( D398E ) in the conserved DXDXT motif of Pah1 ( Fig . 7A ) that was shown before to be critical for the PA phosphatase activity of Pah1 [71] . To determine the outcome of this mutation on yeast cells in relation to LecE , we first constructed an HA-tagged wild-type Pah1 and introduced it into yeast containing a deletion in the pah1 gene and LecE . The introduction of the HA-tagged Pah1 restored the lethal effect of LecE on yeast cells ( Fig . 7B ) , however when the mutated HA-tagged Pah1 ( D398E ) was introduced instead of the wild-type Pah1 protein the lethal effect of LecE was not restored , indicating that an enzymatically active Pah1 is required to be present in the yeast cells in order for LecE to cause its lethal effect ( both the wild-type and mutated HA-tagged Pah1 proteins were expressed in the yeast cells examined , Fig . 7D ) . Like in the case of the mutated Dgk1 , a similar result to the one obtained with LecE was also obtained with the over expression of the Nem1-Spo7 phosphatase complex ( Fig . 7C ) , further demonstrating the similar function of LecE and this complex . The results described thus far , clearly demonstrate that LecE requires the presence of an enzymatically active Pah1 protein in the yeast cells in order to cause its lethal effect on yeast growth , and this requirement is identical to the one of the Nem1-Spo7 phosphatase complex . However , the mechanisms of action by which effectors activate host cell factors are often different than the ways by which these host factors are activated naturally ( see Introduction ) . To further determine the mechanism of activation of Pah1 by LecE , we examined the size of the Pah1 protein in yeast cells over expressing the LecE effector in comparison to the over expression of the Nem1-Spo7 phosphatase complex . Western analysis showed a clear reduction in the size of the Pah1 protein when the Nem1-Spo7 phosphatase complex was over expressed in yeast but no change in the apparent molecular weight of Pah1 was observed when LecE was over expressed ( Fig . S2 ) . These results indicate that LecE activates Pah1 in a different way than the Nem1-Spo7 phosphatase complex and it does not function as a phosphatase of Pah1 . It was shown recently that sometimes several L . pneumophila effectors affect the same host cell processes during infection ( see Introduction ) . To determine if there are additional effectors that affect PA and DAG levels , we examined seven additional effectors ( Table 2 ) that according to their sequence homology and/or sequence motifs are expected to be involved with or were shown to function in phospholipids biosynthesis [44] , [46] . We reasoned that yeast deletion mutants in specific host factors ( such as dgk1 and pah1 ) can be used in order to uncover additional effectors that target the same cellular process ( for example , other effectors that caused lethal effect on yeast growth might be suppressed by the same yeast strains ) . Moreover , effectors that originally show no lethal effect on wild-type yeast might cause lethal effect when they will be over expressed in the relevant yeast deletion mutants . Such a result might reveal effectors that target the same cellular process ( both effectors might activate or one of them might activate and the other inhibit the same process ) , during L . pneumophila infection . For this purpose we cloned the seven effectors listed in Table 2 under the control of the galactose-regulated promoter ( GAL1 promoter ) and expressed them in wild-type yeast ( Fig . 8 and Fig . S3 ) . Three of these effectors ( VipD , VpdA and VpdB ) caused strong lethal effect on yeast growth and one effector ( LegS2 ) caused a moderate lethal effect on yeast growth when expressed in wild-type yeast and they were not suppressed by the deletions in dgk1 or pah1 . However , interestingly , when lpg1888 ( an effector containing a PLD domain , that we named LpdA , see below ) was expressed in wild-type yeast no lethal effect was observed , but when it was expressed in the dgk1 deletion mutant clear lethal effect was observed , indicating that this specific yeast genetic background exposed the function of the effector . Eukaryotic enzymes containing a PLD domain where shown before to convert phosphatidylcholine ( PC ) to PA and free choline [72] , [73] , and the yeast Spo14 is a known PLD enzyme ( Fig . 4A ) . Thus the results obtained with LpdA can be explained in the sense that in the absence of Dgk1 there is no enzyme that can phosphorylate DAG back to PA and under these conditions the activity of LpdA was observed . LpdA was shown before to translocate into host cell , as part of a large screen , and its translocation level was very low ( only 5% of the cells show indication for translocation ) [19] . Therefore , we fused LpdA to the CyaA reporter and examined its translocation into host cell ( Fig . 9A ) . Our analysis confirms that LpdA translocates into host cells , its translocation levels were low in comparison to the other effectors examined in this study ( Fig . 1A ) , but no translocation was observed from an Icm/Dot deletion mutant ( Fig . 9A ) . To investigate the relations between LecE and LpdA we constructed a single deletion mutant in lpdA as well as a double deletion mutant of lecE and lpdA and examined the intracellular multiplication of these mutants in A . castellanii . As can be seen in Fig . 9B , no intracellular growth phenotype was observed for the single or double deletion mutants , as was shown before for most of the deletion mutants in L . pneumophila effectors . To further explore the relations between LpdA and LecE we expressed both proteins together in yeast . This analysis resulted with an additive effect on yeast growth , both effectors together were more lethal to yeast in comparison to LecE by itself ( Fig . 9C ) , indicating that both effectors function in the same direction ( LpdA by itself caused no yeast growth defect ( Fig . 9C ) ) . Our results reveal two conditions under which LpdA lethal effect on yeast growth can be observed: i ) in a dgk1 deletion mutant ( Fig . 8 ) and ii ) when LecE was expressed in the yeast cells ( Fig . 9C ) . Importantly , both these conditions have the same outcome on the yeast cell since in the first condition the yeast cell cannot convert DAG into PA and therefore DAG probably accumulates in the yeast cell . In the second condition there is high activity of Pah1 due to the expression of LecE that also leads to the accumulation of DAG . Thus , the results obtained with LpdA further supports the function of LecE as a Pah1 activator . LpdA was suggested to encode for a phospholipase-D due to sequence homology to eukaryotic ( fungal ) PLD enzymes . The PLD protein family is conserved from yeast to human and it comprises a conserved catalytic core ( HxK ( x ) 4D ) [74] . To determine if LpdA encodes a functional PLD enzyme we generated two point mutations ( K165R and K376R ) in two conserved lysine residues located in both predicted PLD conserved catalytic cores ( Fig . 10A ) . We then used the LpdA lethal effect observed in the yeast dgk1 deletion mutant ( Fig . 8 ) in order to examine these two mutants . As can be seen in Fig . 10B , over expression of the wild-type LpdA in the dgk1 deletion mutant caused lethal effect on yeast growth and this effect disappeared when the two LpdA mutants were used , and the yeast growth with these two mutants was similar to the one of the empty vector . These mutations did not detectably affect the stability of LpdA in yeast ( Fig . 10C ) , suggesting that the loss of toxicity was very likely due to the abolishment of the enzymatic activity of LpdA . Due to these results Lpg1888 was named LpdA for Legionella Phospholipase D . As indicated above , LpdA enhances the lethal effect caused by LecE on yeast cells ( Fig . 9C ) . To determine if this enhancement also requires the PLD activity of LpdA , LecE was expressed together with LpdA and it's two mutants ( K165R and K376R ) in yeast cells . As can be seen in Fig . 10D , the enhancement of the LecE lethal effect by LpdA requires its PLD activity , and the mutations in the PLD active site almost eliminated the enhancement of the lethal effect caused by LpdA . Also in this analysis the two mutations did not detectably affect the stability of LpdA ( Fig . 10E ) . The results obtained from the yeast analysis of LecE indicated that the function of this effector probably results in an increase in DAG levels in cells ( Fig . 4 ) . To determine if LecE affects DAG levels in-vivo in mammalian cells a system based on a DAG fluorescence biosensor was employed using live-cell imaging . The LecE effector was fused to the mCherry fluorescent protein ( Cherry-LecE ) and was ectopically expressed in COS7 cells together with a PKC-C1-DAG binding domain fused to GFP ( GFP-DAG ) that was validated before as a specific DAG sensor in several systems [75]–[78] . When the GFP-DAG sensor was expressed in COS7 cells it exhibited two localization patterns: in 59% of the cells the sensor was diffusely distributed throughout the cell but was also concentrated in a membranal peripheral nucleus area , while in 41% of the cells the GFP-DAG sensor showed a completely diffuse distribution ( Fig . 11A , B ) . In contrast , when the GFP-DAG sensor was expressed together with Cherry-LecE its distribution changed and in 88% of the cells the GFP-DAG sensor was mostly concentrated in the membranal peripheral nucleus area ( Fig . 11B ) . Moreover , a similar intracellular distribution was also obtained for Cherry-LecE ( Fig . 11C ) . Importantly , the Cherry-LecE induced changes of the GFP-DAG sensor was significant ( p<value 0 . 007 , Student's t-test; Fig . 11B ) . In addition , the effect of Cherry-LecE on the accumulation of the GFP-DAG sensor in the peripheral nucleus area was examined . In this analysis , the GFP-DAG sensor concentration at the peripheral nucleus area was significantly enriched in cells expressing Cherry-LecE in comparison to cells expressing the GFP-DAG sensor by itself , ( 2 . 35 fold , p<value 1 . 2×10−10 in Student's t-test; Fig . 11D ) . As a control , GFP was expressed in the presence or absence of Cherry-LecE , and no alterations to the mixed cytosolic and nuclear distribution of GFP were observed upon Cherry-LecE co-expression ( Fig . 11E and data not shown ) . This result further supports the conclusion that Cherry-LecE influences the distribution of the GFP-DAG sensor specifically . In addition , the specific localization of Cherry-LecE was examined and it was found to be localized to the cis-Golgi apparatus as it co-localized with GFP-KDEL-Receptor ( GFP-KDELR ) a well established cis-Golgi marker ( Fig . 11F ) [79] . Notably , a previous work done with a different GFP-DAG sensor ( based on the PKD-C1-DAG binding domain ) found it to be localized to the Golgi in HeLa cells [80] . The combined results presented demonstrate that LecE induces alternations in DAG content in COS7 cells and show co-localization of the ectopically expressed effector and its lipid product to the same sub-cellular compartment , the cis-Golgi . An analogous approach to the one described above was also applied to address the functionality of LpdA in mammalian cells and its ability to influence the levels and distribution of PA . For that purpose , LpdA was fused to the mCherry fluorescent protein ( Cherry-LpdA ) and ectopically expressed in COS7 cells together with GFP fused to a PA-binding domain from the yeast Spo20 SNARE protein ( GFP-PA ) . This domain was previously shown to function as a sensitive and specific PA sensor in mammalian cells [81] . As shown in Fig . 12A ( on the left ) , when the PA sensor was expressed by itself it accumulated in the cell nucleus . Several studies have shown before a similar accumulation of the GFP-PA sensor in resting cells , and it was found to be not specific [81] , [82] . In striking contrast , when Cherry-LpdA was expressed together with GFP-PA sensor it induced a punctuate distribution of GFP-PA throughout the cell's cytoplasm ( Fig . 12B ) , suggesting an effector-dependent generation of PA . Of note , the GFP-PA-labeled structures were highly mobile , resembling intracellular vesicles . The Cherry-LpdA effector itself showed a diffuse pattern in the cells with some punctuate distribution as well ( Fig . 12B ) . Importantly , when the GFP-PA sensor was expressed together with the LpdA PLD mutant , Cherry-LpdA-K165R , no change in the distribution of the GFP-PA sensor was observed ( Fig . 12C ) , what indicates that the PA production in the cells depended on the PLD activity of the Cherry-LpdA . In addition , it was demonstrated before that PA is usually dephosphorylated to DAG in-vivo [83] , [84]; thus , we examined the distribution of the GFP-DAG sensor ( described in the previous section ) when co-expressed with Cherry-LpdA , and found that it was also localized to motile puncta ( Fig . 12D ) , in sharp contrast to its distribution pattern when expressed alone ( Fig . 11A and Fig . 12A on the left ) . Importantly , when GFP was expressed with or without Cherry-LpdA it showed a diffuse distribution in the cells with some concentration in the cell nucleus ( Fig . 12E and data not shown ) . The combined results presented indicate that the changes observed with LpdA were specific to the PA and DAG sensors . These results substantiate LpdA as a PLD enzyme in the cells , where it generates PA which is further converted to DAG . To determine where in the host cell LecE and LpdA perform their function during L . pneumophila infection , we constructed plasmids that over express these effectors in L . pneumophila as a fusion to a myc-tag at their N-terminus , and infected U937-derived human macrophages with a wild-type L . pneumophila containing these plasmids and used confocal fluorescence microscopy to visualize the two effectors during infection . As can be seen in Fig . 13 , both effectors were found to be localized to the LCV during infection . Only intracellular bacteria show a signal with the anti-myc antibody directed against the effectors . Thus we conclude that LecE and LpdA are both localized to the LCV , where they probably manipulate the phagosome phospholipids composition during infection .
Up to date about 300 effector proteins were identified in L . pneumophila and the function of only several of them was uncovered . Effectors were found to affect diverse host cell processes which include vesicular trafficking , apoptosis , ubiquitination , translation and others [7] , [85] . In several cases , pairs of effectors were found to function together and one effector was found to counteract the function of another effector . The SidM/DrrA effector was found to AMPylate the host cell small GTPase binding protein Rab1 , thus keeping it in an active state which cannot be inactivated by host cell factors [23] , and the effector SidD was found to reverse this modification by deAMPylation of Rab1 [24] , [86] . Another pair of effectors also involved in Rab1 activation was described recently - AnkX and Lem3 . AnkX was found to phosphocholinate Rab1 and Lem3 was found to reverse this modification [25]–[27] . An additional effector that was found to affect another effector is LubX . LubX contains an E3 ubiquitin ligase domain and it was found to specifically target the bacterial effector protein SidH for degradation by the host cell proteasome [31] , thus affecting the time during infection when SidH is present in the host cell and performs its function . In this manuscript , we described a new pair of effectors that might function together – LecE and LpdA . This pair of effectors is different from the three pairs described above since both effectors function in the same direction and do not counteract the function of one another . The effector protein LpdA was found to contain a functional PLD domain and these enzymes were shown before to convert PC to PA and free choline [87] . The second effector – LecE was found to activate the yeast lipin homolog ( Pah1 ) which converts PA to DAG ( Fig . 14 ) . Both these effectors were found to be localized to the LCV during infection thus the combined lipid biosynthetic reactions that might occur on the LCV will include conversion of PC into PA ( by LpdA ) and then conversion of PA to DAG ( by LecE activated PA phosphatase ) a process which is expected to result in changes of the lipid composition of the LCV that can affect its fate in the host cell as well as the host proteins and bacterial effectors that will be recruited to the LCV ( see below ) . These results indicate that pairs or groups of L . pneumophila effectors function together and additional such effectors are expected to be found . The way by which LecE activates Pah1 is currently not known . The natural activation of Pah1 in yeast occurs via dephosphorylation , but our results indicate that this is not the way by which LecE activates Pah1 ( Fig . S2 ) . An important result regarding the mode of activation by LecE comes from the finding the over-expression of Pho80-Pho85 in yeast suppresses the lethal effect caused by LecE . This result suggests that the activity of Pho80-Pho85 is dominant on the activity of LecE , therefore it might be that LecE cannot perform its function when Pah1 is fully phosphorylated ( the expected state of Pah1 after Pho80-Pho85 over-expression ) . We hypothesize that LecE activates Pah1 by modifying one of its amino acids ( as was shown for SidM and AnkX in the case of Rab1 ) or by directly binding to it . Identification of pairs or groups of effectors that influence the same or related host cell processes is very important for the ability to understand the function of the enormous number of effectors translocated by L . pneumophila during infection . The approach that we used in this study , which led to the identification of LpdA as an effector that function with LecE , can help to discover such pairs and/or groups of effectors . Our approach takes advantage of yeast genetics as a tool to identify such groups of effectors . This approach can be applied in a very broad way in order to study effector proteins . When an effector that causes lethal effect on yeast growth is found and a yeast suppressor is identified , other effectors that cause yeast lethal effect and might be suppressed by the same yeast suppressor can be identified in case that they affect the same host factor in a similar way ( activation or inactivation ) . For example the lethal effect on yeast growth caused by AnkX was found to be completely suppressed by over expression of the yeast Ypt1 protein ( the yeast homolog of Rab1 ) [27] , it is possible that other effectors that cause lethal effect on yeast growth will be suppressed by over expression of Ypt1 , thus leading to the identification of the cellular process they affect . An even more interesting situation is the one described in this manuscript . LpdA causes no lethal effect on wild-type yeast , but when it was expressed in a yeast dgk1 deletion mutant clear lethal effect was observed . In this way , not only effectors that cause lethal effect on wild-type yeast can be sorted into functional groups but also effectors that cause no lethal effect on yeast growth can be sorted , since their effect can be uncovered by using different yeast genetic backgrounds . In the case of LecE and LpdA , over expression of Dgk1 suppresses the lethal effect of LecE and a deletion of dgk1 uncovered the lethal effect of LpdA thus indicating that both effectors function in the same direction . Our approach can also be expanded to other host cell processes expected to be affected by L . pneumophila effectors ( or any other pathogens ) . For example , yeast deletion mutants or strains over expressing genes related to trafficking ( such as vps ) or authophagy ( such as apg ) can be used to screen the collection of L . pneumophila effectors , both the ones that cause lethal effect on wild-type yeast as well as these that have no effect on wild-type yeast growth . In this way pairs or groups of effectors that affect similar host cell processes can be uncovered . The results presented in this study uncover another aspect of the involvement of phospholipids in L . pneumophila infection of host cells . It was shown before that several L . pneumophila effectors ( SidC , SidM/DrrA and SdcA ) specifically bind PI4P on the LCV [47] , [48] , [88] . However , it is known that PC constitutes the major phospholipid in eukaryotic membranes [89] . The results presented in this study show that the combined activity of the LpdA and LecE effectors is expected to result in the conversion of PC to DAG on the LCV . In addition , it was shown before that the presence of PI4P on the LCV is strongly dependent on the activity of the enzyme PI 4-kinase IIIβ ( PI4KIIIβ ) that converts PI into PI4P [47] . One way to recruit PI4KIIIβ to the LCV is by the activity of Arf1 [90] , however it was shown before that RalF that recruits Arf1 to the LCV is not required for SidC decoration of the LCV [47] . Another , major way to recruit PI4KIIIβ to membranes is by the action of protein kinase-D ( PKD ) . The recruitment of the latter to membranes is mainly mediated by its two DAG C1-binding domains [91] . The activation of PKD also requires phosphorylation by protein kinase-C ( PKC ) which is also recruited to membranes by DAG [92] . Thus , one way to increase the levels of PI4P on the LCV is by generating higher levels of DAG by the function of LpdA and LecE . The higher levels of DAG will result in the recruitment of PKC and PKD to the LCV , then PKC may phosphorylate PKD that will lead to the recruitment of PI4KIIIβ to the LCV that in turn will generate PI4P from PI . It is important to note that this is probably not the only way by which the LCV can recruit PI4KIIIβ since this enzyme can also be recruited from Golgi derived vesicles that fuse with the LCV . The results presented in this study uncovered an additional layer in the complex interaction between the L . pneumophila phagosome and the host cell , and show that changes in phospholipids composition are manipulated by L . pneumophila effectors in many ways to result with successful infection .
The L . pneumophila wild-type strain used in this work was JR32 [93] , a streptomycin-resistant , restriction-negative mutant of L . pneumophila Philadelphia-1 , which is a wild-type strain in terms of intracellular growth . In addition , mutant strains derived from JR32 , which contain a kanamycin ( Km ) cassette instead of the icmT gene ( GS3011 ) [11] , the lpg2552 gene ( RV-L6-45 ) ( this study ) , a gentamicin ( Gm ) cassette instead of the lpg1888 gene ( RV-L10-71 ) ( this study ) , and a double lpg2552/lpg1888 deletion ( RV-L10-77 ) ( this study ) were used . The E . coli strains used were MC1022 [94] and DH5α . The S . cerevisiae wild-type strains used in this work were BY4741 ( MATa his3Δ leu2Δ met15Δ ura3Δ ) [95] and W303 ( MATa leu2-3 , 112 trp1-1 can1-100 ura3-1 ade2-1 his3-11 , 15 ) [96] . In addition , mutant strains derived from BY4741 , which contain a G418 cassette instead of the pah1 gene ( RV-L8-59 ) ( this study ) , the dgk1 gene [97] ( a kind gift from Prof . Martin Kupiec , Tel-Aviv University ) and the nem1 gene ( RV-L8-54 ) ( this study ) were used . Plasmids and primers used in this work are listed in Table S1 and S2 . The pMMB-cyaA-C vector [98] was used to construct CyaA fusions . In addition , two plasmids were constructed to contain the pUC-18 polylinker , at the same reading frame like pMMB-cyaA-C , in order to generate C-terminal fusions . For the over expression of effectors in yeast the pUC-18 polylinker was cloned into pGREG523 [99] , between the EcoRI and HincII restriction sites to generate pRam ( this vector was used to construct 13× myc fusions under the yeast GAL1 promoter ) . For the effectors localization experiments the 13× myc tag was amplified by PCR from pRam using the primers Myc-F-NdeI and Myc-R-yeast , and the PCR product was digested with NdeI and EcoRI . The pUC-18 polylinker was digested from pMMB-cyaA-C with EcoRI and BamHI and the resulting inserts were cloned in a 3-way ligation into pMMB207-NdeI [98] , digested with NdeI and BamHI , to generate pMMB-13× myc ( this vector was used to construct 13× myc fusions under the bacterial Ptac promoter ) . The L . pneumophila genes examined were amplified by PCR using a pair of primers containing suitable restriction sites ( Table S2 ) . The PCR products were subsequently digested with the relevant enzymes , and cloned into pUC-18 . The plasmids inserts were sequenced to verify that no mutations were introduced during the PCR . The genes were then digested with the same enzymes and cloned into the suitable plasmids described above . Lpg1888 was also cloned into pGREG536 that contain the same reading frame as the above mentioned vectors to generate pGREG536-1888 ( generating a 7xHA fusion under the yeast GAL1 promoter ) . The pah1 gene was amplified by PCR with its native promoter using the Pah1-for and Pah1-rev primers . The PCR product was cloned into pUC-18 , sequenced , and then digested out from pUC-18 using XbaI and PvuII . C-terminal 3xHA tag was amplified by PCR with the primers HA-for and HA-rev using the pYM1 plasmid [100] as template , followed by cloning into pUC-18 , sequencing and digest with XbaI and SalI . Both inserts were then cloned into pGREG505 digested with Ecl136 and SalI , in a 3-way ligation , to generate pGREG505-Pah1-3xHA . The genes dgk1 , spo7 , nem1 , pho80 and pho85 were amplified by PCR using the DGK1-SpeI and DGK1-SalI primers for dgk1 , the SPO7-SpeI and SPO7-SalI primers for spo7 , the Nem1-SpeI and Nem1-SalI primers for nem1 , the Pho80-SpeI and Pho80-SalI primers for pho80 and the Pho85-SalI-for and Pho85-SalI-rev primers for pho85 ( Table S2 ) . The PCR products were cloned into pUC-18 , sequenced , and then digested out from pUC-18 using SpeI and SalI ( for pho85 only SalI was used ) , followed by cloning into different vectors from the pGREG series [99] digested with the same enzymes; pGREG506 for dgk1 to generate pGREG506-Dgk1 , pGREG505 or pGREG506 for nem1 to generate pGREG505-Nem1 and pGREG506-Nem1 , respectively , pGREG503 or pGREG505 for spo7 to generate pGREG503-Spo7 and pGREG505-Spo7 , respectively , pGREG504 or GREG505 for pho80 to generate pGREG504-Pho80 and pGREG505-Pho80 , respectively and pGREG506 for pho85 to generate pGREG506-Pho85 . The plasmid pmCherryC1-hMPV [101] , that contains an EcoRI site at the same reading frame like in pUC-18 , was used in order to construct C-terminal mCherry fusions under the viral pCMV promoter , using the same restriction enzymes as was mentioned above for both lpg2552 and lpg1888 , to generate the plasmids listed in the Table S1 . Fragments of 1 kb from the upstream and the downstream regions of the lpg2552 and lpg1888 genes were amplified by PCR using genomic L . pneumophila DNA as a template and pairs of primers containing suitable restriction sites ( Table S2 ) . The resulting fragments were digested with the appropriate enzymes and cloned into pUC-18 to generate pRV-lpg2552-UP and pRV-lpg2552-DW , respectively , for lpg2552 , and pRV-lpg1888-UP and pRV-lpg1888-DW , respectively , for lpg1888 , and sequenced . These two pairs of plasmids were then digested with the respective restriction enzymes and cloned into pUC-18 together with the Km resistance cassette digested with SalI to generate pRV-lpg2552-KM , for lpg2552 , or together with the Gm resistance cassette digested with EcoRV to generate pRV-lpg1888-GM , for lpg1888 . The two fragments containing the upstream region , the downstream region and the Km/Gm cassette between them were digested with PvuII or SmaI , respectively , and cloned into pLAW344 digested with EcoRV to generate pRV-lpg2552::KM-del and pRV-lpg1888::GM-del , for lpg2552 and lpg1888 , respectively . These two plasmids were used for allelic exchange as previously described [102] . For the construction of the double lpg2552::Km/lpg1888::Gm deletion mutant , pRV-lpg1888::GM-del was used to generate the lpg1888::Gm deletion in the lpg2552::Km deletion mutant ( RV-L6-45 ) . These strains were examined for intracellular growth in A . castellanii as previously described [103] . In order to construct yeast deletion mutants in the genes pah1 and nem1 , a KanMX resistance cassette was amplified by PCR from pM4754 [104] using primer containing the first and last 50 bp of each gene; Pah1-kanMX-for and Pah1-kanMX-rev for pah1 , and NEM1-kanMX-for and NEM1-kanMX-rev for nem1 . The PCR products were then ethanol precipitated , transformed into wild-type yeast using standard lithium acetate protocol [105] , spotted on YPD plates ( 20 gr glucose , 10 gr yeast-extract , 20 gr peptone in 1 L of distilled H2O ) that contained 200 µg/ml G418 and incubated for 2–3 days at 30°C , followed by replica plating on similar plates and incubation for additional 2–3 days at 30°C . Single colonies were then isolated on similar plates and the deletions were verified by PCR . In order to mutate specific amino acids in the active sites of lpg1888 , Pah1 and Dgk1 the PCR overlap-extension approach was used [106] , in a similar way as described before [98] . For the construction of site specific mutants in the putative PLD active sites of lpg1888 , the primers lpg1888-K165R-F and lpg1888-K165R-R were used to generate 1888-K165R and the primers lpg1888-K376R-F and lpg1888-K376R-R were used to generate 1888-K376R . For the construction of site specific mutant in the Pap1 active site of Pah1 , the primers Pah1-D398E-for and Pah1-D398E-rev were used to generate Pah1-D398E . For the construction of site specific mutants in the diacylglycerol kinase active sites of Dgk1 , the primers Dgk-R76A-F and Dgk-R76A-R were used to generate Dgk1-R76A and the primers Dgk-D177A-F and Dgk-D177A-R were used to generate Dgk1-D177A . For all protein fusions described above , the formation of a fusion protein with a proper size was validated by Western blot analysis using the anti CyaA antibody 3D1 ( Santa Cruz Biotechnology , Inc . ) in the case of the CyaA fusions , using the anti myc antibody 9E10 ( Santa Cruz Biotechnology , Inc . ) in the case of the 13× myc fusions or using the anti HA antibody F-7 ( Santa Cruz Biotechnology , Inc . ) in the case of the HA tag fusions . In all cases the primary antibody was diluted 1∶500 and goat anti-mouse IgG conjugated to horseradish peroxidase ( Jackson Immunoresearch Laboratories , Inc . ) diluted 1∶10 , 000 was used as the secondary antibody . Differentiated HL-60-derived human macrophages plated in 24-wells tissue culture dishes at a concentration of 2 . 5×106 cells/well were used for the assay . Bacteria were grown on CYE ( ACES-buffered charcoal yeast extract ) plates containing chloramphenicol for 48 h . The bacteria were scraped off the plates and suspended in AYE ( ACES-buffered yeast extract ) medium , the optical density at 600 nm ( OD600 ) was adjusted to 0 . 1 in AYE containing chloramphenicol , and the resulting cultures were grown on a roller drum for 17 to 18 h until an OD600 of about 3 ( stationary phase ) was reached . The bacteria were then diluted in fresh AYE medium to obtain an OD600 of 0 . 2 and grown for 2 h . IPTG was added to final concentration of 1 mM , and the cultures were grown for additional 2 h . Cells were infected with bacteria harboring the appropriate plasmids at a multiplicity of infection of 4 , and the plates were centrifuged at 180×g for 5 min , followed by incubation at 37°C under CO2 ( 5% ) for 2 h . Cells were then washed twice with ice-cold PBS ( 1 . 4 M NaCl , 27 mM KCl , 100 mM Na2HPO4 , 18 mM KH2PO4 ) and lysed with 200 µl of lysis buffer ( 50 mM HCl , 0 . 1% Triton X-100 ) at 4°C for 30 min . Lysed samples were boiled for 5 min and neutralized with NaOH . 110 µl of each sample was then transferred to a new tube and 220 µl of cold 95% ethanol was added . Samples were then centrifuged for 5 min at 4°C and the supernatant was transferred to a new tube and stored at −20°C until the next step was performed . The samples were dried in a speed-vac and suspended in 110 µl of sterile DDW . Samples were incubated at 42°C for 5 min , followed by 5 min incubation at room temperature . The levels of cyclic AMP ( cAMP ) were determined using the cAMP Biotrak enzyme immunoassay system ( Amersham Biosciences ) according to the manufacturer's instructions . L . pneumophila effectors encoding genes and S . cerevisiae encoding genes were cloned under the GAL1 promoter in the pGERG yeast expression vectors series as described above . Plasmids were transformed into yeast cells using standard lithium acetate protocol [105] , and transformants were selected for the appropriate prototrophy on minimal SD ( synthetic defined ) dropout plates ( 20 gr glucose , 6 . 7 gr yeast nitrogen base , 20 gr agar , 1 . 5 gr amino-acids mixture without the selective ones , in 1 L of distilled H2O ) . Resulting transformants were then grown over-night in liquid SD culture medium at 30°C , cell number was adjusted and a series of tenfold dilutions were made . The cultures were then spotted onto the respective SD dropout plates containing 2% glucose or galactose . Plates were incubated at 30°C or 37°C for 2–3 days and visualizes for differences in growth . Wild-type S . cerevisiae expressing lpg2552 from the GAL1 promoter ( pRam-lpg2552 ) was transformed with a Yep24 based , high copy number , yeast genomic library [107] . About 160 , 000 transformants were screened for their ability to suppress the toxicity of the lpg2552 over expression on galactose plates at 37°C for three days , and 23 suspected colonies were then isolated twice on similar plates . The suspected suppressors were then subjected to Western-blot analysis in order to confirm that lpg2552 is still intact , and only three suppressors gave a positive result , “Sup-1” , “Sup-13” and “Sup-14” . The library plasmid was recovered from each of these suppressor colonies and re-transformed into the original screening strain to verify the suppression effect . Two of these suspects- “Sup-13” and “Sup-14” , kept the suppressor phenotype at this stage . Sequencing of “Sup-14” reveled that the genomic fragment cloned in the plasmid contained the yeast HIS3 gene and therefore it was left out ( HIS3 was the marker that was used to keep lpg2552 plasmid in the yeast cells ) . “Sup-13” was sequenced and found to contain a fragment of the yeast genome and three sub-clones were constructed from it . Digestion of “Sup-13” with PvuII and self ligation generated pSup-13-sub-clone-1 . Digestion of “Sup-13” with SacI and self ligation generated pSup-13-sub-clone-2 . Digestion of pSup-13-sub-clone-1 with SmaI and BstEII , followed by treatment with Klenow fragment and self ligation generated pSup-13-sub-clone-3 . S . cerevisiae containing plasmids expressing lpg2552 , Spo7-Nem1 or a vector were grown on a roller drum over-night in the appropriate SD medium at 30°C . The following day the cultures were centrifuged and resuspended in SD medium containing 2% galactose and the cultures were grown on a roller drum for additional 6 h at 37°C . The cells were then harvested and subjected to SDS PAGE ( 0 . 8% ) followed by Western-blot analysis using the anti HA antibody . COS7 cells were transfected with the FuGENE ( Roche ) transfection reagent according to the manufacturer's instructions . Briefly , COS7 cells were grown in DMEM ( Invitrogen ) medium supplemented with 10% FBS . A day prior to transfection the cells were plated in 6-well plates containing 25 mm glass coverslips at a concentration of 3×105 cells per well . The next day the medium was replaced and the cells were transfected using a total of 1–2 µg DNA per well . Following 44–48 h of incubation at 37°C under CO2 ( 5% ) , the cells were used for live imaging . The intracellular distribution of the GFP-DAG sensor was classified through: ( i ) the visual inspection of 330 cells co-expressing Cherry-LecE and GFP-DAG and 473 cells expressing GFP-DAG alone , from three independent experiments; and ( ii ) the measurement of the ratio of peri-nuclear GFP-DAG to the total cell intensity of the GFP signal; in cells in which a peri-nuclear GFP-DAG signal could be identified . For this quantification , the entire cell volume was imaged , images were projected into two dimensions by summing the pixel intensities of each plane , and GFP signals were identified through intensity-based segmentation . Signal intensities were calculated with Slidebook . Two independent experiments , comprising 40 cells per condition , were performed . Infected cells were visualized by confocal microscopy . Coverslips were inserted into a 24-wells tissue culture dishes and incubated for 1 h with 10% Poly-L-Lysine ( Sigma ) diluted in PBS ( 1 . 5 M NaCl , 78 mM Na2HPO4 , 18 . 5 mM NaH2PO4·H2O ) , followed by three washes with PBS . U937 cells were then differentiated into human-like macrophages by addition of 10% normal human serum and 10 ng/ml of phorbol 12-myristate 13-acetate ( TPA ) ( Sigma ) at concentration of 0 . 5×106 cells per well , and incubated at 37°C under CO2 ( 5% ) for 48 h . Bacteria were grown as described above for the CyaA translocation assay . The cells were washed twice with RPMI supplemented with 2 mM glutamine and infected with the wild-type strain ( JR32 ) expressing either the 13×myc-tagged lpg2552 or lpg1888 at multiplicity of infection of 5 . Plates were then centrifuged at 180×g for 5 min , incubated at 37°C under CO2 ( 5% ) for 1 h , washed 3 times with PBS++ ( PBS containing 1 mM CaCl2 and 0 . 125 mM MgCl2 ) . The cells were fixed with ice-cold methanol for 5 min , washed twice with PBS and perforated with ice-cold acetone for 2 min . Coverslips were blocked for 10 min with PBS containing 10% BSA and stained with monoclonal chicken anti myc antibody ( Millipore ) diluted 1∶20 and mouse anti L . pneumophila antibody ( Santa Cruz Biotechnology , Inc . ) diluted 1∶100 in PBS containing 10% BSA for 1 h , followed by two 5 min washes in PBS containing 10% BSA . Coverslips were then stained with DAPI ( Sigma ) and with the secondary antibodies Alexa488 goat anti chicken ( Invitrogen Inc ) and Cy3 donkey anti mouse ( Jackson Immunoresearch Laboratories Inc ) diluted 1∶400 in PBS containing 10% BSA , followed by two 5 min washes in PBS containing 10% BSA . Coverslips were then mounted on glass slides using mounting solution ( Golden Bridge ) . Images were acquired using a motorized spinning-disc confocal microscope ( Yokogawa CSU-22 , Zeiss Axiovert 200 M ) . The confocal illumination was with 40 mW 473 nm and 10 mW 561 nm solid state lasers . Images were acquired with a 63× oil immersion objective ( Plan Apochromat , NA 1 . 4 ) For the effectors localization after infection , a Cool Snap HQ-CCD camera ( Photometrics ) was employed , with a typical exposure times of ∼1 s , images were acquired with 1×1 binning , yielding a pixel size of 0 . 065 µm . For presentation , fluorescence intensity values were corrected for the contribution of non-specific binding of the secondary/labeled antibody . For the PA and DAG sensors analysis , an Evolve EMCCD camera ( Photometrics ) was employed , typical exposures of 20–100 ms , 1×1 binning yielding a pixel size of 0 . 25 µm . Three dimensional image stacks were acquired by sequential acquisition of views recorded every 70–300 ms along the z-axis by varying the position of a piezo electrically controlled stage ( step size of 0 . 4 µm ) . All images were analyzed with SlideBook software ( version 5 . 0; Intelligent Imaging Innovations ) . | Legionella pneumophila is an intracellular pathogen that causes a severe pneumonia known as Legionnaires' disease . Following infection , the bacteria use a Type-IVB secretion system to translocate multiple effector proteins into macrophages and generate the Legionella-containing vacuole ( LCV ) . The formation of the LCV involves the recruitment of specific bacterial effectors and host cell factors to the LCV as well as changes in its lipids composition . By screening L . pneumophila effectors for yeast growth inhibition , we have identified an effector , named LecE , that strongly inhibits yeast growth . By using yeast genetic tools , we found that LecE activates the yeast lipin homolog – Pah1 , an enzyme that catalyzes the conversion of diacylglycerol to phosphatidic acid , these two molecules function as bioactive lipid signaling molecules in eukaryotic cells . In addition , by using yeast deletion mutants in genes relevant to lipids biosynthesis , we have identified another effector , named LpdA , which function as a phospholipase-D enzyme . Both effectors were found to be localized to the LCV during infection . Our results reveal a possible mechanism by which an intravacuolar pathogen might change the lipid composition of the vacuole in which it resides , a process that might lead to the recruitment of specific bacterial and host cell factors to the vacoule . | [
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] | 2012 | Identification of Two Legionella pneumophila Effectors that Manipulate Host Phospholipids Biosynthesis |
Arthritogenic alphaviruses , including Chikungunya virus ( CHIKV ) , are responsible for acute fever and arthralgia , but can also lead to chronic symptoms . In 2006 , a Chikungunya outbreak occurred in La Réunion Island , during which we constituted a prospective cohort of viremic patients ( n = 180 ) and defined the clinical and biological features of acute infection . Individuals were followed as part of a longitudinal study to investigate in details the long-term outcome of Chikungunya . Patients were submitted to clinical investigations 4 , 6 , 14 and 36 months after presentation with acute CHIKV infection . At 36 months , 22 patients with arthralgia and 20 patients without arthralgia were randomly selected from the cohort and consented for blood sampling . During the 3 years following acute infection , 60% of patients had experienced symptoms of arthralgia , with most reporting episodic relapse and recovery periods . Long-term arthralgias were typically polyarthralgia ( 70% ) , that were usually symmetrical ( 90% ) and highly incapacitating ( 77% ) . They were often associated with local swelling ( 63% ) , asthenia ( 77% ) or depression ( 56% ) . The age over 35 years and the presence of arthralgia 4 months after the disease onset are risk factors of long-term arthralgia . Patients with long-term arthralgia did not display biological markers typically found in autoimmune or rheumatoid diseases . These data helped define the features of CHIKV-associated chronic arthralgia and permitted an estimation of the economic burden associated with arthralgia . This study demonstrates that chronic arthralgia is a frequent complication of acute Chikungunya disease and suggests that it results from a local rather than systemic inflammation .
Chikungunya virus ( CHIKV ) is an arthropod-borne virus that belongs to the Alphavirus genus . Chikungunya disease is characterized by polyarthralgia , sometimes associated with rash . The articular symptoms , often debilitating , usually resolve within weeks , but have been reported to last for months , even though the natural history of this infection has not been precisely studied in prospective studies [1] , [2] , [3] . In 2005 , CHIKV emerged in islands of Indian Ocean including La Réunion , a French overseas department , and approximately one third of the inhabitants ( i . e . ∼300 , 000 ) was infected at the end of the outbreak in 2006 [4] , [5] . Compared to earlier outbreaks , this episode occurred in a highly medicalized area . Moreover previously unreported severe forms of Chikungunya were observed , such as encephalopathy [6] , [7] , and mother-to-child CHIKV transmission was demonstrated , leading to severe neonatal CHIKV infection [5] . In the wake of this outbreak , CHIKV also re-emerged in India with over 1 million cases [8] , [9] . In 2007 , CHIKV emerged for the first time in Europe , causing an outbreak in Italy [10] . We have described the clinical and biological features of acute CHIKV infection in a prospective cohort of patients with positive blood CHIKV RT-PCR [11] . It included all patients referred to the Emergency Department in Saint-Pierre de la Réunion with febrile arthralgia between March and May 2006 . As little is known about long-term outcome of CHIKV infection , we conducted a prospective longitudinal study to describe in details the specific clinical and biological features of chronic arthralgia , as well as clinical signs associated to this pathology . We evaluated the consequences of long-term arthralgia on patients' daily and social life , looked for risk factors associated with them and estimated their economic impact .
Ethical clearance was obtained from the «Comité de Protection des Personnes Sud-Ouest et Outre-Mer III» of Saint-Pierre , La Réunion , Paris ( CCP 2008/65 , n° 2008-A00999-46 ) . CHIK-IMMUNOPATH received approval from the ethical committee for studies with human subjects ( CPP ) of Bordeaux and the National Commission for Informatics and Liberty ( CNIL ) . Written informed consent was obtained from patients included in the CHIK-IMMUNOPATH study . The study respects the STROBE statement ( supporting information S1 ) . We studied a cohort of patients ( n = 180 ) enrolled for febrile arthralgia to the Emergency Department of the Groupe Hospitalier Sud Réunion between March 2005 and May 2006 [11] . Patients were interviewed by telephone 4 , 6 , 14 and 36 months ( M4 , M6 , M14 and M36 ) after the viremic phase , using the same questionnaire as that used at day 0 ( D0 ) ( supporting information S2 ) . At M36 after the acute phase , all patients who agreed to participate to a complementary study ( CHIK-IMMUNOPATH ) and who were arthralgic were interviewed and underwent clinical examination . Among them , 22 patients with arthralgia ( ART+ ) and 20 patients without persisting arthralgia ( ART− ) were randomly selected from the cohort . They signed a written consent and a blood sample was collected . Blood cell count was performed and viremia was tested by qRT-PCR [12] . Both serum anti-CHIKV IgG and IgM specific antibodies were screened . An enzyme-linked immunosorbent assay ( ELISA ) was performed with CHIKV antigen [12] . The avidity of anti-CHIKV IgG was tested by ELISA in the presence or absence of urea 8 M [13] . Geometric Mean Antibody Titer ( GMAT ) was calculated as previously described [14] . Plasmatic protein electrophoresis was performed and C-reactive protein ( CRP ) concentration was measured . The presence of anti-nuclear , anti-dsDNA , anti-endomysium autoantibodies , anti-cyclic citrullinated peptide antibody ( ACCP ) and cryoglobulinemia were investigated . Samples were transported at 37°C to research cryoglobulins . Sera were sent to Myriad RBM ( Austin , Texas ) and analyzed by Luminex using the inflammation MAP . Assays are run according to CLIA guidelines and in all cases , >100 beads per analyte were measured with CV <10% for values that are above the limit of quantification for the given assay . For each time point , the proportion of patients with monoarthralgia ( 1 site ) , oligoarthralgia ( 2–3 sites ) or polyarthralgia ( 4 sites or more ) were compared using a Chi-2 test . A logistic regression model was used to identify factors associated with long-term arthralgia , defined as presence of arthralgia at M14 . We studied factors at D0 , including demographic factors ( gender and age ) , biological markers , hospitalization and comorbidities , and factors measured at M4 ( arthralgia , treatment , and quality of life ) . All factors associated with long-term arthralgia with a p-value<0 . 15 in univariate analysis were entered in the multivariate model . A step-by-step backward procedure was then used to identify factors significantly associated with long-term arthralgia . A sensitivity analysis was also conducted , following the same procedure , defining long-term persistence of arthralgia as the presence of arthralgia at M36 . To address the lost of follow up at M36 , we looked for parameters that differentiate the patients who were lost between M14 and M36 and these who were followed . Statistical analyses were performed using the STATA software ( Stata Corporation , College Station , Texas , USA ) ; all significance tests were two-sided and p-values<0 . 05 were considered significant . Luminex data were mined using the Omniwiz software ( Biowisdom ) and Mann-Whitney analysis is reported . False discovery rate ( FDR or q-values ) were calculated as correction for multiple analyte testing . To characterize the spatiotemporal evolution of arthralgia , we considered different types of arthralgia . For a given site at a given time point , arthralgia was defined either as a “persistent symptom” if the affected site was the same as that reported during the previous time point , as “a relapse of an the acute symptom” if the site affected was the same as that at the acute phase , or as “a new symptom” if this site was not affected at the acute phase , or as a “migrating symptom” if the arthralgia was localized at a site distinct from that previously reported . For a given patient , these categories were not mutually exclusive . For modeling migratory arthralgia , we divided joints into three groups: upper limb joints ( hand , wrist , elbow ) , mid body joints , ( shoulder , spine , hip ) and lower limb joints ( foot , ankle , knee ) , and considered two migratory probabilities for migration to sites within to the same group ( e . g . , hand to wrist ) or to a different group ( e . g . , hand to foot ) ( Supporting information S3 and Table S1 ) .
At M4 , M6 , M14 and M36 after their inclusion as acute CHIKV-infected patients , all patients were interviewed using a questionnaire to monitor persistence of arthralgia , other clinical signs and treatments . The number of patients that participated is provided in Figure 1 . There is an important lost of follow up between M14 and M36 however we did not identify a bias associated with it . Among the 180 patients , 76 patients were followed at all time points of the study . The percentage of patients suffering from long-term arthralgia decreased after CHIKV acute infection and stabilized around 60% ( Figure 2A ) . Of note , all patients suffered from arthralgia at D0 . Among them , only 5 on 180 ( 2 . 8% ) suffered from joint pain prior to CHIKV infection . Most patients had intermittent arthralgia , with recovery and relapse . For each time point , 25 to 40% of patients complained of permanent arthralgia . Among the 76 patients that could be followed at each time point , 45% had arthralgia at all time , 24% experienced partial recovery at M4 , M6 or M14 then relapses , and 31% fully recovered from acute symptoms . Among patients who experienced chronic symptoms at M36 , 43 . 5% reported arthralgia triggered by a change in ambient temperature , 8% by physical effort . At M36 , arthralgia caused stiffness in 75 . 5% of patients with symptoms , and 67 . 7% of the patients reported a need of morning stretching ( time of 32 minutes , standard deviation ( SD ) 37 minutes , range 5–180 minutes ) . We monitored arthralgia in 9 anatomical sites ( Figure 2B ) . Arthralgia in upper limbs mostly affected fingers and wrists , while lower limbs arthralgia mostly affected knees and ankles . At each time point , these locations remain significantly the most affected ( Mac Nemar test for matched pairs of subjects ) . Importantly , arthralgia were typically symmetrical ( 90% ) . We then investigated whether the number of arthralgic sites diminished in patients still suffering from arthralgia among the 76 patients followed at all time points . The number of arthralgia sites decreased until M14 , with only 30% of patients suffering from polyarthralgia ( number of arthralgia sites >2 ) ( Figure 2C ) . Despite an increase of arthralgic sites at M36 , there is an overall significant decrease of the number of painful joints during the study period ( p<0 . 01 ) . We attempted to model the spatiotemporal evolution of arthralgia , as defined in the Materials and Methods section . We found that “persistent” symptoms had the strongest effect , as its probability of occurrence was three times higher than a “new” symptom ( Figure 2D ) . The probability of relapse of an “acute” symptom was twice the appearance of a “new” symptom . Finally , “migratory” symptoms tended to be intra-group as compared to inter-group migrations . Patients with arthralgia at M36 showed other clinical symptoms , including local swelling , cutaneous symptoms , myalgia and osteoligamentaous pain ( Table 1 ) . Local swelling localized to affected joints for 63% of patients . Moreover , sleep , memory or concentration disorders and asthenia or depression are significantly associated with arthralgic patients . The proportion of patients with arthralgia who attended a physician or received a treatment significantly increased between M4 and M36 ( p = 0 . 01 ) ( data not shown ) , and reached 80% ( Table 1 ) . Similarly , the number of patients receiving a treatment increased and these treatments are statistically associated with the arthralgic status of the patient ( p<0 . 001 ) . Arthralgia in patients at M36 were highly incapacitating for daily life tasks , professional life and spare-time activities ( Table 2 ) . To identify risk factors associated with long-term arthralgia , we performed univariate and multivariate statistical analyses at M14 , as the participation was higher than at M36 ( Table 3 ) . Gender was not associated with long-term arthralgia , age less than 35 years was protective . Risk of arthralgia was not associated with indicators of the disease severity during the acute phase ( viral load , duration of hospitalization or number of sites of arthralgia at D0 ) [11] , however it was weakly associated with C-reactive protein ( CRP ) level at D0 . Diabetes was the only comorbidity found to be a risk factor for long-term arthralgia . Interestingly , arthralgia at M14 was strongly associated with arthralgia at M4 , and even more if arthralgia was permanent at M4 . Memory and concentration disorders at M4 were also identified as risk factors for developing long-term arthralgia . Arthralgia , memory disorders and concentration disorders at M4 were the only risk factors significantly and independently associated with long-term arthralgia . When long-term arthralgia was assessed at M36 , results were very similar . At M36 , 22 patients with arthralgia ( ART+ ) and 20 patients without arthralgia ( ART− ) were randomly selected from the cohort to participate to the CHIK IMMUNOPATH study . Its aim was to titrate anti-CHIKV antibodies and identify a serum inflammatory or autoimmune signature associated with the arthralgia phenotype . All patients were negative for CHIKV RT-PCR , and exhibited anti-CHIKV IgGs in serum , while a minority ( 9 . 5% ) harbored measurable levels of anti-CHIKV IgM . The activity of CHIKV IgG ( GMAT ) was significantly higher in ART+ patients ( 30 ) than in ART− patients ( 20 ) , but antibody avidity was comparable in both groups ( mean ±SD: 31 , 6±20 , 4 in ART + patients and 33 , 7±19 , 8 in ART− patients ) . Although lymphopenia is a defining feature of acute CHIKV disease [11] , it was a rare finding at M36 ( data not shown ) . Plasma protein levels measured by electrophoresis and CRP concentration were within normal ranges . However , CRP levels were significantly higher in the ART+ group than in the ART− group ( mean ±SD: 3 . 35±3 . 00 mg/ml and 1 . 85±2 . 49 mg/ml , respectively; p = 0 . 04 ) . We used Luminex xMAP technology to assay analytes in the serum of patients . Most analytes were undetectable in both groups of patients ( Table 4 ) . Five inflammation markers were significantly elevated in ART+ patients: factor VII , C3 complement component , IL1α , IL15 and CRP ( Figure 3 ) . Ferritin level was significantly lower in ART+ patients than in ART− patients . These markers did not allow for the identification of a subgroup within the ART+ group , nor did they correlate one with another . No autoimmune marker and no anti-DNA antibody in the serum of ART+ patients were detected , although anti-nuclear antibodies were detected at low level in four ART+ patients . Three patients had elevated anti-nuclear antibodies , one in the ART+ group and two in the ART- group . As it has been reported that CHIKV could evolve into rheumatoid arthritis [15] , we screened for cyclic citrullinated protein antibodies . We also assayed for cryoglobulinemia and anti-endomysium IgA antibodies . All patient were found to be negative . We estimated the annual economic burden of long-term arthralgia by taking into account the cost of medical visits , therapeutic treatment and the cost for lost work time due to injury or pain ( using the population of La Réunion Island as a reference ) ( Table S2 ) . We found that arthralgia secondary to the CHIKV outbreak in La Réunion in 2005-06 has resulted so far in an estimated total cost of up to 34 millions euros per year . This corresponds to 250€ per year and per patient with long-term arthralgia . However , it should be noted that this sum might be overestimated due to the bias in our cohort selection , as our cohort is likely composed of the most severely affected patients who were referred to the hospital during the acute phase .
Our study is the first prospective cohort study on CHIKV long-term arthralgia that is based on the follow-up of patients who presented with acute CHIKV infection as the inclusion criterion . This study is also the first to define the evolution of CHIKV-induced arthralgia , mapping the frequency and location of arthralgic sites during a three year time period . We have also investigated the impact of CHIKV-chronic arthralgia on daily life of patients , identified clinical signs associated with arthralgia , and analyze biologic markers . Moreover we have evaluated associated risk factors and estimated the economic burden of this disease . Together , these data allow us to define the features of CHIKV-induced chronic arthralgia ( Table 5 ) , as compared to other viral arthritis [16] , and to establish a detailed understanding of the public health problem resulting from CHIKV-chronic arthralgia . Our data reveal that more than 60% of CHIKV-infected patients suffer from arthralgia , 36 months after acute infection . This high percentage of patients with long-term symptoms was also reported by other studies of Italian cohorts and French cohorts of La Réunion Island or metropolitan France [17] , [18] , [19] , [20] , [21] but is dramatically higher than documented in India and Senegal [1] , [22] , [23] , [24] , [25] . While this discrepancy may result from particular features of the CHIKV strain responsible for the La Réunion outbreak , data from Italy following the 2007 outbreak resulted from a CHIKV strain more closely related to the viral strain present in India [10] , with more than 60% of CHIKV patients in Italy having reported myalgia , asthenia or arthralgia 12–13 months after the acute disease [20] . Alternatively , reported differences may be a result of different genetic backgrounds of these populations . As joint pain is considered a subjective symptom , it might also reflect a difference in pain threshold of patients or reporting from physicians , thus reflecting differences in health care practices . Long-term CHIKV-associated arthralgia were mainly symmetrical , involving more than 2 different joints . Hand , wrist , ankle and knee were found to be the most affected , consistent with other studies [17] , [18] . Moreover , 60–80% of patients had relapsing arthralgia , while 20–40% had unremitting arthralgia . While some patients reported “migrating” arthralgia , most disease symptoms mapped to joints that were most painful during acute Chikungunya disease . Thus , symptoms at the chronic phase may be indirectly associated to virus replication at the time of acute infection [26] , [27] , [28] . In addition to arthralgia , many patients suffered from myalgia and cutaneous lesions and several cognitive dysfunctions . Although study patients did not display neurological symptoms at the acute phase of disease , we cannot exclude that cognitive dysfunctions result from CHIKV spread in the CNS , as it has been reported that CHIKV disseminates to the CNS in humans and in animal models [12] , [26] , [27] , [29] . Similar to other studies , chronic arthralgia are considered incapacitating for daily life tasks and impacted professional activities and quality of life [20] , [21] . Beside this impact on patient , the economic burden of this long-term pathology is also very significant , independently of the cost of the acute disease [30] . The longitudinal design of our study enabled us to identify risk factors for development of long-term arthralgia . Individuals over the age of 35 years or with diabetes were more likely to suffer from chronic arthralgia . The age has been reported to be a risk factor with some cohorts [21] , [31] , [32] , but not in others [18] , [33] . None of our available parameters to measure the severity of acute disease were associated with long-term arthralgia . This may be explained by differences in the way to measure disease severity in other studies [18] . Importantly , we show that the presence and intensity of arthralgia at M4 after the onset of the acute disease is a good predictor of long-term arthralgia . Our study did not identify positive markers for autoimmune or rheumatoid arthritis . Additionally , we failed to identify systemic biomarkers associated with the arthralgic phenotype . Nevertheless , a slightly more elevated inflammatory status is found in a subset of arthralgic patients who have detectable serum level of IL1α , IL15 and slight elevation in Factor VII , C3 and CRP . This signature differs from that observed at the onset of the infection , when circulating virus is detectable and type I interferon , IP10 , MCP1 , ISG15 are highly elevated [34] , [35] , [36] . Others have identified IL6 and GM-CSF or IL12 as being specifically associated with long-term arthralgia [32] , [33] . However , these studies were performed much earlier in the chronic phase ( 2–3 months and one year after disease onset ) . Our study shows that anti-CHIKV antibody titers were more elevated in ART+ patients than in ART- patients . This is in agreement with a recent study [37] . This higher level of antibodies could be associated with a more severe acute infection [37] . However , in our study , the level of antibody at M36 did not correlate with acute disease severity . Alternatively , this could reflect a persisting antigenic stimulation in ART+ patients ( see below ) . Interestingly , it has been reported that viremia level at the acute phase correlates with a faster appearance of neutralizing antibodies and a better recovery 2–3 months after the acute phase [38] . Similarly to CHIKV , other so called “arthritogenic” alphaviruses , notably Ross River virus ( RRV ) , are known to cause acute as well as chronic arthralgia [39] . Our data indicates that chronic symptoms are linked to the initial local joint inflammation and are not associated with markers of systemic inflammation or autoimmunity . A local inflammation of the joint could be maintained by the local persistence or delayed clearance of viral antigens . This is consistent with report of Hoarau et al . [32] who detected persistent CHIKV antigens within the synovial fluid of a patient suffering from chronic arthralgia . Moreover , experimental studies in CHIKV infected animal indicate that the joint is the most highly infected tissue , making it plausible that incomplete viral antigen clearance in this anatomical site may account for the long-term symptoms [26] . RRV has been shown to persist in vitro in mouse macrophages , and a model of RRV chronic arthritis suggests that viral persistence may account for chronic disease [40] . RRV and CHIKV have been shown to be weakly tropic for macrophages in vitro [41] , [42] but the presence of antibodies dramatically increased RRV entry into macrophage [42] . Further studies will be required to assess the role of macrophages , as well the role of persistent infection and antibodies in chronic arthritis caused by CHIKV . In sum , this study furthers our understanding of the pathophysiology of CHIKV chronic arthralgia , a prerequisite for the development of efficient therapeutic strategies and for assessing the burden of disease inflicted upon populations affected by epidemic Chikungunya disease . | Chikungunya virus ( CHIKV ) is transmitted to human by mosquitoes . It is a re-emerging virus that has a risk to spread globally , given the expanding dissemination of its mosquito vectors . Chikungunya disease is characterized by acute transient febrile arthralgic illness , but can also lead to chronic incapacitating arthralgia . We have conducted a prospective longitudinal study to investigate in details long-term outcome of CHIKV infection . We found that 60% of patients experienced arthralgia 36 months after the onset of acute disease . Arthralgia affected most often multiple sites and were usually incapacitating . In addition to arthralgia , many patients suffered from myalgia and cutaneous lesions and several cognitive dysfunctions . We also showed that age over 35 years and the presence of arthralgia 4 months after the onset of disease are risk factors for long-term arthralgia . Patients with long-term arthralgia did not display biological markers typically found in autoimmune or rheumatoid diseases . This study demonstrates that chronic arthralgia is a frequent complication of acute Chikungunya disease and suggests that it results from a local rather than systemic inflammation . | [
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] | 2013 | Chikungunya Virus-associated Long-term Arthralgia: A 36-month Prospective Longitudinal Study |
The prefrontal cortex ( PFC ) plays a crucial role in flexible cognitive behavior by representing task relevant information with its working memory . The working memory with sustained neural activity is described as a neural dynamical system composed of multiple attractors , each attractor of which corresponds to an active state of a cell assembly , representing a fragment of information . Recent studies have revealed that the PFC not only represents multiple sets of information but also switches multiple representations and transforms a set of information to another set depending on a given task context . This representational switching between different sets of information is possibly generated endogenously by flexible network dynamics but details of underlying mechanisms are unclear . Here we propose a dynamically reorganizable attractor network model based on certain internal changes in synaptic connectivity , or short-term plasticity . We construct a network model based on a spiking neuron model with dynamical synapses , which can qualitatively reproduce experimentally demonstrated representational switching in the PFC when a monkey was performing a goal-oriented action-planning task . The model holds multiple sets of information that are required for action planning before and after representational switching by reconfiguration of functional cell assemblies . Furthermore , we analyzed population dynamics of this model with a mean field model and show that the changes in cell assemblies' configuration correspond to those in attractor structure that can be viewed as a bifurcation process of the dynamical system . This dynamical reorganization of a neural network could be a key to uncovering the mechanism of flexible information processing in the PFC .
The prefrontal cortex ( PFC ) is believed to play crucial roles in flexible decision making and action planning that are essential for adapting to an ever-changing real world . Prefrontal neurons hold not only multiple sets of discrete information and parametric magnitudes of stimuli in their working memory but also transform online information to behaviorally relevant information that is required under a given behavioral context [1] , [2] , [3] , [4] , [5] , [6] , [7] . Such “representational switching” is observed in PFC neurons when subjects are undertaking various cognitive tasks , e . g . , “what–where” working-memory tasks [7] , location–object comparison tasks [6] , two-interval discrimination tasks [5] , duration-discrimination tasks [1] , and goal-oriented action-planning tasks [2] , [3] , [4] . These tasks usually require the holding of information as working memory during delay periods and the appropriate processing of information to guide behavior in a given context . For example , in the goal-oriented action-planning task , many prefrontal neurons initially encode a behavioral goal and then a part of these neurons subsequently encodes a future action [2] , [4] . This dynamical encoding by prefrontal neurons can be interpreted as the switching of mapping between patterns of neural activity and sets of information . We assume that a set of information ( e . g . , a set of goals or a set of actions ) is mapped onto an ensemble of neurons . Initially , one functional mapping may be manifested in local circuits and adaptively switched to another functional mapping toward the end of delay periods of the task . The PFC is seated on the highest level of a functional hierarchy of the sensation-action process and represents abstract aspects of complex sensory and action information [8] . The PFC contributes to planning and generation of actions with its internal dynamics , rather than with mere stimulus-response associations [9] . This ubiquitous adaptability to different functions in various tasks , which has been revealed by both electrophysiological and imaging studies , suggests that the mechanism of adaptive neural coding in the PFC may be general . However , little is known about the mechanism . In this study , we investigate the mechanism of representational switching by using a computational model of a prefrontal neural network . The abovementioned tasks require the storage of information in a delay period of a given task by using the working memory that is realized with sustained neural activity [10] , [11] . Stably sustained neural activity can be theoretically characterized by attractor dynamics [12] with a feedback mechanisms [13] , [14] , [15] . In a conventional attractor network , there generally exist multiple attractors , each of which distinguishes one discrete set of categories or information and shifts to another attractor by external inputs or noise depending on the required task [16] , [17] , [18] . However , such a state transition does not change the structure of the attractors in the state space that reflects the mapping between the attractors and information . Recent studies show , on the other hand , the possibility that the generation of representations is embedded in sequentially changing cell assemblies with the modulation of synaptic efficacy [19] , [20] , [21]; however , the underlying theoretical mechanism and the roles of this dynamical reorganization of cell assemblies in representational switching is still unclear . In the present study , we propose a switching network model based on the dynamical reorganization of attractor structure by internal changes in synaptic connectivity . In particular we used short-term synaptic plasticity [22] , [23] , [24] , [25] , [26] as a component of the model network for representational switching because synapses with short-term plasticity can facilitate global reorganization of functional cell assemblies in networks . Furthermore , because of gradual changes in synaptic efficacy , this network is able to hold one set of information before representational switching and another different set afterward , and endogenously generate representational switching of neuronal activity in a flexible manner . More specifically , we have developed a mathematical model of a lateral PFC network that performs the goal-oriented action-planning task ( Figure 1A ) . In this task , the PFC encodes dual information: goal positions and action directions as firing rates of prefrontal neurons [2] , [3] , [4] . Representations of the two different categories of information coexist and are endogenously transformed in the middle of the delay period [2] , [4] . The present model qualitatively reproduces these experimentally demonstrated responses in the PFC .
Figure 1 shows the temporal sequence of the goal-oriented action-planning task [2] , [3] , [4] and the responses of a lateral PFC neuron that were recorded from a monkey who was trained to move the cursor on the screen to a goal presented during a goal-display period . Neural activity depends on the phase of the task , the position of the goal , and the direction of the action . After the cursor is displayed at the start position , the final goal position is displayed during the goal-display period . If a prefrontal neuron prefers the displayed goal position , the firing rate of the neuron increases compared to that in the case when a non-preferred goal was displayed ( Figure 1B ) . Although the display of the goal position disappears in the next delay period , the goal-position-related activity in the prefrontal neuron persists until the middle of the delay period . It should be noted that although some neurons show goal-position-related activity in the entire period of the task , we focus here on such neurons that show representational switching from the goal mode to the action mode [2] , [4] . After the delay period , the first movement-initiation signal ( the “Go” signal ) appears , and the monkey is required to move the cursor stepwise to reach the goal . In this task , the neuron showed representational switching in the middle of the delay period and persistent activity depending on the action direction during the remaining delay period . Note that the representational switching precedes the Go signal [2] , [4] , suggesting that the representational switching is not triggered by an external signal or a sensory cue . Representational switching is more clearly shown by the selectivity measure ( Figure 1C ) , which is obtained by multiple linear regression analysis [4] , [27] ( see Methods for details ) . The selectivity measure indicates the switching from the goal-representation mode to the action-representation mode . Each neuron involved in the representational switching has a preference for both of a goal position and an action direction as shown in Figure 1D . In the goal representation mode , the activity of the neuron becomes high for the preferred goal , while it becomes low for the non-preferred goal . In the action representation mode , on the other hand , the neural activity becomes high for the preferred action direction , while it becomes low for the non-preferred action . What can neural mechanisms be inferred from this result ? Each neuron shows large responses for one of goals and one of actions , and switches its responsibility from the goal representation mode to the action representation mode in the middle of the delay period . For simplicity , suppose that two goals and two actions are involved in this task ( see Figure 1E ) . Possible patterns of neural activity in each representation mode are limited as in Figure 1E . In the goal ( action ) representation mode , possible combinations of sustained neural activity states are A&B or C&D ( A&D or B&C ) . Considering mutual connections between simultaneously activated neurons , the sustained neural activity in a specific group of neurons is also understandable with the conventional attractor framework . Mutually connected neurons form a cell assembly and the active state corresponds to an attractor . However , it is puzzling how the network dynamically reconfigures patterns of neural activity and switches representation modes . It is a natural idea that external stimuli trigger the transition among attractors , but if so , then an equivalently difficult problem of how such stimuli are generated by neural networks remains as unsolved . We propose a neural network model in which cell assemblies [19] , [28] that encode fragments of information are functionally structured and the formation of cell assemblies can be dynamically updated through the modulation of synaptic connections ( Figure 2A ) . The activity of the cell assemblies is triggered or read out by other networks that encode specific input or output information . If two sets of information such as goals and actions are represented in the neural network , the network can be characterized with two different formations of cell assemblies before and after representational switching . Each formation of cell assemblies corresponds to the formation of attractors that can be described on two characteristic axes ( Figure 2B ) . Each characteristic axis indicates the ability to represent information by the landscape . If two attractors coexist along the axis ( bistability ) and are mapped onto different fragments of information , e . g . , two different goal positions , the characteristic axis is able to discriminate between them . On the other hand , if the dynamics is monostable , no information is represented on the characteristic axis ( see Figure S1 ) . Therefore , we hypothesize that the formation of cell assemblies in the PFC is updated depending on the task context and that the switching of information representation on the axes is produced with the reorganization of the attractors . We have proposed that the representational switching is achieved in the PFC by the abovementioned mechanism . In the initial stage of the goal-oriented action-planning task , the PFC network stays in the goal-representation mode and is ready to discriminate which task-relevant goal will be displayed . When the goal position is specified by a sensory input , PFC neurons maintain this information as persistent activity of the goal-representation mode in the neural network , and then , the state of the network is switched to the action-representation mode due to short-term plasticity to be explained below . Consequently , one of the action directions is selected by the convergence of the network state into one of the attractors in the action-representation mode . The selected action in the PFC network will be read out by downstream neurons , which may correspond to neurons in the motor cortex . Here we assumed that the read-out neurons are activated when the state of the PFC network has converged to the action representing attractor , namely when most of neurons in the action representing cell assembly are activated . The sequences of capturing sensory information , maintaining goal information , and transforming it into an action direction are executed as dynamical processes in the PFC network . In the prefrontal network , neuronal responses are relatively diverse . Some neurons are involved in representing a specific goal or action during the entire task period , and others are involved in representing both of them and switching their representations during the task . Thus in general , the combination of functional cell assemblies may be more complicated ( see Figure S2 ) . However , in the present study , we have focused on essence of the observed phenomena and considered a minimal model . When one of the goals is displayed in the action-planning task of Figure 1A , the possible actions are actually limited to two directions . Therefore , for simplicity , we consider a PFC network that selects an action from the two possible actions cued by the displayed goal position . We implemented this mechanism to the dynamically reorganizable attractor network shown in Figure 2C . Each node in the figure indicates a population of neurons in the PFC ( A to D ) , sensory neurons ( G1 and G2 ) , and read-out neurons ( A1 and A2 ) . Four neural populations in the PFC ( A to D ) are assumed to be mutually connected with three different types of excitatory synapses with or without short-term plasticity: namely , facilitation , depression , and constant synapses [22] , [23] ( see Methods for the detailed network structure ) . A given presynaptic neuron can form depression synapses on one neuron and facilitation synapses on another [22] , [23] . The amplitude of the excitatory postsynaptic potential ( EPSP ) induced by a facilitation ( depression ) synapse increases ( decreases ) with successive presynaptic spikes , whereas constant synapses do not change the EPSP amplitudes . The excitatory neurons in the network are mutually connected and send excitatory output to a population of inhibitory interneurons through constant synapses . These interneurons send inhibitory synaptic outputs through constant synapses back to all excitatory neurons . We assume that , in the initial resting state of the network when the synapses are still neither depressed nor facilitated , populations A and B as well as populations C and D form cell assemblies with relatively strong synaptic connections and encode two goal positions ( see Figure 2D ) . These cell assemblies are mutually inhibiting via inhibitory interneurons , and thus the network should be bistable with the two active states of these cell assemblies [17] , [29] , as shown in the left of Figure 2D . The neurons in the nodes that form such a cell assembly are assumed to be predominantly connected by synapses with short-term plasticity such that when one of these cell assemblies of A&B or C&D is selectively activated by the goal display , the cell assembly temporally holds the displayed goal position as working memory but subsequently loses its stability because of the dynamic modulation in synaptic efficacy . We further assume that when a cell assembly encoding a goal position becomes unstable , the synaptic modulation reconfigures active cells such that a cell assembly of A&D or B&C that encodes the action directions emerges in the network as a dominant cell assembly in turn , as shown in the right of Figure 2D [30] . Before and after the reconfiguration of cell assemblies , the representational modes of goals and actions are characterized by different patterns of bi-stability among cell assemblies that are partially overlapped across different modes . Therefore , the representational switching is not simply a change of cell assemblies but rather a higher-ordered reorganization of partially overlapped dominant cell assemblies based on multiple stability in the neural network . We examined plausibility of this dynamical mechanism with two types of computational models , namely , a spiking neural network model and its mean field model . Moreover , we evaluated several connectivity patterns by combinations of depression , facilitation , and constant synapses , and confirmed that the abovementioned reorganization of cell assemblies is robustly realized in these different connectivity patterns ( see Methods ) . First , we consider a spiking neural-network model in which each population of the excitatory neurons in Figure 2C is replaced with 200 noisy and leaky integrate-and-fire neurons . In each neuron , the dynamics of the membrane potential and the three different types of synapses are simulated . When a neuron receives many excitatory inputs and its membrane potential reaches a threshold value , the neuron generates a spike , and the synapses on the axon terminals of the neuron are activated . If the neuron generates a series of spikes , the efficacy of each synapse is modulated by the amount of the available synaptic resources ( x ) and the utilization parameter ( u ) that defines the fraction of resources used by each spike [23] , [31] . The synaptic conductance induced by a synapse is determined by these two variables and a constant absolute value of the synaptic efficacy . The differences in the three types of synapses are based on the release probability of the neurotransmitter [23] , [32] , [33] , and modeled with the different recovery-time constants of the available resources and the utilization parameter ( see Methods for details ) . Figure 3A shows a typical response of the network consisting of all the three types of synapses , namely , facilitation , depression , and constant synapses when the goal position G1 was presented as a sensory input at the beginning of the goal-display period . The network shows a state transition from one active state of a cell assembly ( A and B ) to another ( A and D ) . After the state transition , the activation of the cell assembly ( A and D ) can be read out , which would activate motor neurons that encode the action direction A1 . In this network , goal and action encoding cell assemblies are predominantly connected by depression and facilitation synapses , respectively . During the goal-display period , the synaptic connections between A and B in the cell assembly activated by the goal display ( see Figure 2C ) were gradually depressed ( see the red curves in Figure 3A ) , and the connections from A to D and from B to C in the cell assemblies that represent actions were facilitated as shown in blue curves in Figure 3A . The time-varying synaptic efficacy in a cell assembly is quantified with the average peak synaptic conductance in a given cell assembly ( see Methods ) . Dominant cell assemblies that have greater synaptic efficacy were switched from the goal-cell assembly A&B to action cell assemblies A&D and B&C ( see the red arrow in Figure 3A bottom ) , and the active state that represents the goal position ( the cell assembly with A and B in Figure 3A ) is disbanded and another active state that represents the action ( the cell assembly with A and D in Figure 3A ) is formed . Because the connections among neurons in a cell assembly representing the action are facilitated , this active state is stable . Note that this stable action-representation state can be reset to the initial goal-representation state by decreasing the applied activation input . Figures 3B and 3C show that the model qualitatively captures the main features of the experimentally demonstrated responses and the representational switching as shown in Figures 1B and 1C . These results were obtained from average firing rates of populations of excitatory neurons in 40 simulation trials , including four possible patterns of state transitions consisting of all the combinations of two goals and two actions . Some disagreement between Figure 1B and 3B may be due to a diversity of the response properties of neurons in the PFC ( see also references [2] , [4] ) , whereas we assumed uniform parameter values for model neurons in the present model . This simulation was based on a network consisting of all three types of synapses . We confirmed that these simulation results that show the representational switching can be also obtained from a network consisting of only depression and constant synapses or of only facilitation and constant synapses . Even in these networks consisting of a single type of short-term plasticity , the switching of dominant cell assemblies from a goal to an action is also observed ( see Figure S3 ) . Next , we used a dimension-reduction formulation based on principal component analysis , which resulted in a multivariate trajectory of the population activity in the model transformed into its first and second principal components ( PCs ) ( see Text S1 ) . The trajectories of four different patterns of state transitions were separated in PCs ( Figures 4A–4C ) . The trajectories were distributed along the first ( second ) PC axis before ( after ) the representational switching ( Figures 4B and 4C ) . This result suggests that the first and second PCs can be regarded as the characteristic axis of goal positions and that of action directions , respectively . How does the short-term synaptic plasticity contribute to the representational switching ? We confirmed that the representational switching does not occur if the synaptic efficacy is fixed in the network . In the absence of any short-term plasticity , a sensory input triggered the activation of a cell assembly that encodes a goal position; however , the network did not show a state transition to another state that encodes an action direction ( Figures 4D and 4E ) . To examine the effect of the short-term plasticity on the network stability as well as a possibility to control the timing of the switching , we applied a small perturbation input to the network . Then , the state transition occurred earlier due to the perturbation input ( Figures 5A and 5B ) . The interval between the perturbation and the state transition was large immediately after the onset of the goal display . In contrast , the interval was small when the perturbation onset was close to the proper timing of the transition . These responses indicate that after the activation of the goal-encoding cell assembly , the network gradually lost stability and became increasingly susceptible to fluctuations in the neural activity . In real experiments , depending on the task , the delay period can be varied and animals can follow this change , suggesting flexible modification of the transition time . This modification can be realized by the abovementioned perturbation to the PFC network . Unexpected sudden appearance of the “Go” signal , for example , may affect the PFC network as a perturbation . In addition , the transition time can be also modulated by the common activation inputs . Greater activation inputs induced delayed transitions ( Figures 5C and 5D ) because the inputs may cause more stabilization in an already activated cell assembly . In the above results , we considered a case in which the connectivity in two cell assemblies encoding action directions are symmetric , implying that the two possible action directions were randomly determined with equal probability although a specific goal position generally has the tendency to lead to a specific action direction . We confirmed that such a general correlated tendency of the state transition can be implemented with asymmetric connectivity . If one of two action-representing cell assemblies has greater mutual connections than the other , the tendency of selection of this corresponding action is increased , and the transition time is reduced with increasing the asymmetric connectivity ( Figures 5E and 5F ) . The results above are based on a network model composed of spiking neurons , the dynamics of which is defined by thousands of variables . Thus , it is difficult to analyze the underlying population dynamics of this intricate spiking neural network . This difficulty can be alleviated with a mean field approach . Therefore , we considered the means of synaptic activity ( s ) and the variables that define the short-term plasticity: namely , the available synaptic resources ( x ) and the utilization parameter ( u ) . In each neural population in the model , the mean variables were dependent on a population-averaged firing rate that is given as a function of the conductance induced on the neural population ( see Methods for details ) . The responses in the mean field model are qualitatively similar to those in the spiking neural network ( Figures 6A–6C ) ; its trajectories are smoothened owing to the absence of noise . The underlying mechanism of the representational switching and the subsequent change of the neural activity might become clear by considering the stability that can explain the formation of attractors . The timescales of the dynamics of the membrane potential , the spike generation , and the synaptic activity are relatively faster than those of the synaptic modulation with the short-term plasticity . The time constants of the synaptic activity are less than 100 ms . In contrast , the recovery-time constants of the available resources and the utilization factors in the synaptic modulation are approximately 500–1000 ms [22] , [23] . Therefore , the fast dynamics is dominated by the slow dynamics of the synaptic modulation variables that work as bifurcation parameters , whereas the slow variables are also influenced by the fast dynamics . We analyzed how the stability of the fast dynamics is modulated by the slow dynamics . A dissipative dynamical system like neural network models can be generally characterized by the concept of attractors . We thus applied an equilibrium-finding algorithm and stability analysis to a dynamical system of the synaptic activity that is dominated by the synaptic modulation ( see Methods for details ) . On the coordinates of the first and second PCs , we traced how the stability of the attractors is modified by the synaptic modulation ( Figures 6D and 6E ) . Stable attractors were formed depending on the synaptic modulation , and the state of the synaptic activity follows the formation of the attractors . The system initially has three stable attractors: the resting state located on the origin of the first and second PC coordinates , and two attractors encoding the goal positions ( the red curves in Figure 6D ) . These two goal-encoding attractors were situated on the first PC axis , and correspond to the active state of populations A and B and that of populations C and D , respectively . The resting state was destabilized by the sensory input that reflects a goal position , and the state of the network moves to the attractor that represents the displayed goal position . When the state of the network approaches the goal-representation attractor , the goal-representation attractor starts to become unstable because of the synaptic modulation . Then , two stable attractors appears on the second PC axis , which represent actions ( the blue curves in Figure 6D ) . The state of the synaptic activity converges to an action-representation attractor and the attractor was maintained . Thus , the information representation of the network is ascribed to the dynamical formation of attractors , which can be updated by a sequence of stabilization and destabilization of the attractors due to the synaptic modulation .
Experimentally observed neural activity in the PFC implies that fragments of information about goals and actions are represented as sustained activity of PFC neurons and that possible patterns of neural activity are limited depending on goal- and action- representation mode ( see Figure 1E ) . Representations with cell assemblies dynamically vary with an internal mechanism of the PFC network . Conventional attractor frameworks can explain the representation of information with sustained neural activity [12] , [14] , [34] . Namely , an active state of a cell assembly constitutes an attractor and represents a fragment of information . In most such conventional views , the attractors structure is assumed to be static or time-invariant . Although an active state of whole the network shows a transition from one cell assembly to another one by external stimuli or noise produced by neural spiking ( see for example [18] ) , the attractors structure and the encoded information is invariant . However , in the representational switching observed in the PFC , a formation of cell assemblies and the encoded information in these cell assemblies are varying spontaneously depending on the task context without external signals . In the present model , the formation of cell assemblies is dynamically reorganized with synaptic modulation . In an initial stage of the task context , the information required by the task context is mapped onto some cell assemblies , the activity of which is triggered by a specific population of sensory neurons . Then , the reorganization of attractors with other cell assemblies is internally induced by the synaptic modulation . Finally , the state of the network move to the newly formed cell assemblies through bifurcations and its activity is read-out by another population of neurons , e . g . motor neurons . This dynamical reorganization of functional cell assemblies can be interpreted as reorganization of “synapsemble” [19] , or an assembly of synapses . The activation of the cell assembly induces depression of synapses in the cell assembly , and simultaneously induces facilitation of synapses among other neurons that form other cell assemblies . In this process , dominant cell assemblies switch due to change of synaptic efficacy ( see the red arrow in Figure 3A bottom ) ; this switching can be also achieved in networks consisting of only depression and constant synapses or of only facilitation and constant synapses as shown in Figure S3 . Accordingly , the initially required information is transformed to another information by forming the subsequent representational state , and this information is consecutively read out by another population of downstream neurons . These multiple representations of information are characterized by the dynamical attractor landscapes shown in Figure 6E . The synaptic modulation destabilizes initially required attractors and then stabilizes other attractors required at the subsequent stages of the task context . The positions of the attractors are situated , first on the initial representation axis and then on the subsequent representation axis . This functional degeneration of the dimension and switching of the dynamics on the characteristic axes are the essence of the representational switching . In contrast with the conventional attractor framework where transitions among static attractors are triggered by external stimuli or noise [18] , we proposed a new dynamical viewpoint that short-term synaptic modulation causes changes in attractors structure of the state space and that the state transition occurs through bifurcations on the basis of the dynamically changing attractor structure . As a future study , this mechanism should be verified with further experimental data . Generally speaking , nonlinear dynamical systems exhibit characteristic behavior just before the state transition by bifurcations , e . g . increases in fluctuation and correlation [35] . There are possibility to evaluate this characteristic behavior with further data collection and analyses . In the present model , the representational switching of the reorganization of cell assemblies relies on the inhomogeneous connectivity of the depression and facilitation synapses ( see Methods for details about this connectivity ) . How is this inhomogeneous connectivity acquired or learned from experience ? This can be achieved , for example , by Hebbian learning [18] , [36] as well as reinforcement-based learning [37] , [38] in which synaptic connections that contribute to achievement of rewardable behavior are selectively strengthened . In the initial stage of the learning , facilitation and depression synapses may constitute a random network with a homogeneous distribution ( see Figure S4 ) . Despite this homogeneous connectivity , the synaptic connections between pairs of neurons vary in type of synaptic connection ( i . e . , depression or facilitation ) and in the intensity of synaptic efficacy . The neural network shows diverse responses for each trial even under the same experimental condition because of the randomness of the timing of spike generation . In the process of learning , when the activities of a pair of neurons correlate with each other and contribute to achievement of rewardable behavior , the synaptic connections between this neuronal pair will be enhanced . Correlated activity among neurons enhances the synaptic connections between these neurons through Hebbian learning , and further , reward-based synaptic modulation may lead to more enhancements of synaptic connections through reinforcement learning . For example ( see Figure S4B ) , the goal encoding sensory neurons G1 activate some neurons in the PFC ( including candidates of neurons in populations A and B in Figure 3 ) . These neurons have correlated activity due to simultaneous activation , and thus the synaptic connections between them are enhanced by Hebbian learning . These neurons may have relatively strong connection to other PFC neurons ( including candidates of neurons in population C or D ) , which may be easily excitable due to these strong connections . Further , some of these PFC neurons ( e . g . , neurons in candidates of the population D ) may have reciprocal synaptic connections to the PFC neurons that are directly activated by the sensory input ( i . e . , a neuron in candidate of the population A ) . Moreover , some of these PFC neurons have connections to the read-out neurons that represent actions . Activity of these neurons initiated by the sensory neurons G1 can be correlated , and thus the synaptic connections between them tend to be enhanced by Hebbian learning . Furthermore , if the activity of the neurons contributes to obtaining a reward , namely , if facilitation synapses contribute to representation of action with stable activation of cell assembly and if depression synapses contribute representation of goal with temporal activation of cell assembly , reward-based learning will effectively reinforce synaptic connections that have contributed to the rewardable behavior , namely those connections that have been activated at the moment of or just before the reward acquisition [37] , [38] , [39] , [40] . Strengthened synapses with these learning rules may contribute to the obtainment of more reward on subsequent trials . Such learning rules in consecutive trials may result in the formation of functional subnetworks with inhomogeneous connectivity . Indeed , the abovementioned reciprocity with specific types of synapses is observed in the PFC [22] . Moreover , the timing of the representational switching is sensitive to the strength of synaptic connections ( e . g . , Figure 5E ) . Thus , the appropriate timing of the transition is also adjustable with these learning rules . It should be noted that neural network models can reproduce transitions from a retrospective to a prospective activity during a delay period through Hebbian learning and fluctuation [18] . A similar mechanism may also work to realize the representational switching , for example , from the cell assembly of populations A and B to that of A and D . In this sense , although our model provides a new mechanism for the representational switching based on a dynamical reorganization of the attractor landscape , which is different from the well-known mechanism of transitions among attractors in the static attractor landscape , detailed analysis of learning and neural dynamics on representational switching in the PFC still remains to be explored . The self-organized transition demonstrated by our dynamical model can help to understand recent studies on changes in the information representation coded by cell activity in the cerebral cortex [41] , [42] , [43] , including the frontal cortex [2] , [4] , [29] , [44] . The transition on the characteristic axes , which is an important aspect of our model , seems to be particularly consistent with the aspect of executive control of behavior , which is believed to be attributed to the PFC [45] , [46] , [47] . Such a transition can serve as a fundamental mechanism of the executive function that requires qualitative transformation between different categories of information . For example , the transition on the characteristic axes could correspond to set-shifting in the Wisconsin Card Sorting Test ( WCST ) . It should be noted that representational switching in WCST is also explained by a recurrent neural network model with neurons randomly connected both to the recurrent network and sensory inputs [48] . The prefrontal executive function might be a basis for our creativity [49] . Creativity almost always involves emergence of a novel axis or dimension in cognition and behavior , which is impaired by frontal-lobe damage [50] , [51] . Such aspects behind creativity can be the transition to a new representational axis demonstrated in our dynamical model , which may serve as a fundamental neuronal mechanism . The PFC is thought to be on the top of the functional hierarchy of voluntary actions and is making decisions about action generation with their internal process rather than with an external stimulus or with a signal from other parts of the brain . The representational switching occurs without external cues as shown in the present task ( see Figure 1 ) . If the representational switching is assumed to be triggered by an attentional signal from other part of the brain , it contradicts the fact that the PFC is on the top of the functional hierarchy and requires that the decision is performed by other parts of the brain . The interval between occurrence of the state transition and the Go signal onset ( ∼1 s ) is longer than time constants of the synaptic activity and modulation . The timing of the state transition is determined by the stability of the whole network dynamics rather than by the dynamics of individual neuron or synapse . Generally speaking , a nonlinear dynamical system becomes slowing down and increases in sensitivity to small fluctuation just before the state transition [35] . In the present model , the slowing down of network dynamics and the fluctuation in the neural activity lead to large deviation in the timing of the state transition and results in the correlation tendency between the mean and the deviation in timing of the state transition as shown in Figure 5 . Further , state transitions that occur with specific time delays from a cue onset may contribute to a representation of interval timing . The property of the timing of the state transition shown in the present model has an agreement with the scalar property [52] in which the mean and the standard deviation of the response time of an animal covary in an interval-timing task . Immediately before state transitions , nonlinear dynamical systems generally become sensitive to small perturbations and can easily trigger state transitions [35] , [53] . This idea has been applied to modeling of dynamical aspects of brain functioning [54] , [55] and is also demonstrated in our model ( Figure 5A and 5B ) . However , the “trigger” should not be mere noise , considering the nature of creativity or thought . Creativeness , or devising a new viewpoint and dimension , involves finding coherent relationship between internal and external information represented in the mind [56] , which may emerge as the transient synchrony of neural activity [57] , [58] . This idea was also supported by our previous study in which transient neuronal synchrony was enhanced around the representational switching of behavioral goals [2] , which is consistent with the results provided by our model ( compare Figure 5B with Figure 5C of Sakamoto et al . , 2008 ) . It is an important future problem to explore a comprehensive view of neuronal dynamics , including transitions and synchrony . In the present study , we have focused on the short-term synaptic plasticity as a component of the PFC network . However , the short-term plasticity is only one of many time-dependent properties that influence synaptic connectivity and have the potential to explain the representational switching . Other influential modulations in synaptic connections can be caused by monoaminergic neurotransmitters such as dopamine [34] and acetylcholine [59] as well as spike-timing-dependent plasticity [60] . Possible mechanisms of representational switching that include these components should be investigated in future . Here we have presented a minimal model to explain the representational switching between only two sets of binary information . In our hypothesis , the coexistence of multiple representations in a single network relies on the dynamical formation of cell assemblies . Thus , in principle , a single neural entity is capable of becoming more flexible and encoding more than two sets of information ( see Figure S2 ) . The present model is limited on the representation of sets of discrete information and an abstract aspect of sensory and motor information . Besides the discrete information , the PFC represents parametric information , e . g . intensity of a task related sensory stimulus or a coordinate of an arm movement . Transformation between such kinds of parametric information may contribute , for example , to a visio-motor coordinate transformation , which may be processed mainly on lower areas of the functional hierarchy of the brain , e . g . motor cortex or cerebellum . Representations of such parametric information may be achieved not only by attractors but also by trajectories of transitions among attractors [61] . The coordination between different areas of the functional hierarchy and between different kinds of discrete and parametric information remain to be further investigated . Moreover , the reorganization of functional cell assemblies can sequentially occur among more than two modes of representation . In our daily lives , we are required to handle many different categories of information as well as the step-by-step switching between them . Thus , our hypothesis should be further evaluated in such usual situations .
The physiological experiments were performed on animals cared for in accordance with the Guiding Principles for the Care and Use of Laboratory Animals of the National Institutes of Health , and the Guidelines for Animal Care and Use of Tohoku University . In the present study , we used two approaches for modeling neural networks in PFC , which are depicted in Figure 2C . The first approach is modeling with spiking neurons that simulate the generation of spikes , the synaptic activity , and the synaptic modulation with short-term plasticity , including their stochastic properties . The other approach is a mean field model that simulates the population averages of the variables and allows us to analyze a skeleton of the underlying population dynamics . To model the spiking network of PFC , we used noisy and leaky integrate-and-fire neurons with dynamic synapses that undergo synaptic plasticity . Each neural population from A to D and IN in Figure 2C consisted of 200 integrate-and-fire neurons; in total , 1000 neurons were used . The membrane potential of each neuron in each population of neurons , ( , with N = 200 ) varies according to the following equation [15]: ( 1 ) where is the membrane capacitance and is the conductance that induces leakage currents . and are the conductances on excitatory and inhibitory synapses , respectively , induced by other presynaptic neurons and external inputs . represent the corresponding reversal potentials . When reaches the threshold value of the membrane potential , the neurons generate an action potential or a spike , and the membrane potential is reset to the resting potential and maintained at this potential level during the absolute refractory period . represents Gaussian white noise that is applied for each neuron independently with mean 0 and standard deviation . When a neuron generates a spike , synapses on the axon terminals of the neuron are activated , and generate synaptic currents on the postsynaptic membranes . This postsynaptic current is modeled with a variable that represents the synaptic activity , or the ratio of open receptor channels in the postsynaptic terminal . The dynamics of the synaptic activity depends on excitatory or inhibitory synapses and the types of properties of short-term plasticity . In the present model , we used three types of excitatory synapses , namely , facilitation , depression , and constant synapses [22] , [23] , the synaptic activity values of which are denoted by , , and , respectively , where k and i are indices of a neural population and a neuron to which the synapses belong , respectively . The conductance induced by a synapse is given as the product of the weight of synaptic connection and the synaptic activity . We assumed that the absolute magnitude of the conductance and its synaptic type of short-term plasticity are common in all the synaptic connections from one population to another population , and that each neuron receives a constant bias input and a time-varying external input . Thus , the conductance is defined by the following equation: ( 2 ) In the first term on the right-hand side of equation ( 2 ) , denotes the weight of synaptic connection from population l to population k . specifies the type of excitatory synapses connected from population l to k , where F , D , and C indicate facilitation , depression , and constant synapses , respectively . is the transmission delay from the jth neuron in population l to the ith neuron in population k , which is uniformly distributed from 1 ms to 5 ms . In equation ( 2 ) , the first and second summations run over connected presynaptic neural populations and connected presynaptic neurons in these neural populations , respectively . The second term denotes the constant bias conductance . The third term is the time-dependent external input that describes both activation and sensory inputs . Activation inputs are commonly applied to the neural populations A to D in the form of a piece-wise linear function after the onset of the goal display during the task period . After the onset , the input magnitude linearly increases from 0 to until time , and remains at after . The activation input results in the active state of a goal-representing cell assembly by destabilizing the resting state . The sensory input is applied to a cell assembly representing one of the goals ( a pair of A and B or a pair of C and D ) from the onset of the goal display as a rectangular pulse with amplitude and width . The synaptic activity of an excitatory synapse is modulated by short-term plasticity and modeled as follows . Each presynaptic neuron triggers three types of synaptic activity . The synaptic activity is set to a peak value by a presynaptic spike , and exponentially decreases to zero with a time constant [29] as follows: ( 3 ) where is the Dirac delta function , and denotes the time of occurrence of the mth spike in neuron i in population k . In the case of constant synapses , the peak value of synaptic activity is fixed at unity . In contrast , the peak value of synaptic activity with either facilitation or depression plasticity is time-dependent , and is given by the product of the utilization factor that defines the fraction of resources used by each spike and the amount of available resources as follows [23] , [31]: ( 4 ) Variables and vary according to the following equations: ( 5 ) ( 6 ) Equations ( 5 ) and ( 6 ) describe dynamics of the facilitation ( X = F ) and depression ( X = D ) synapses . The difference in the types of the short-term plasticity is determined by the resting state of the utilization factor and the recovery-time constants from depression and facilitation , and , respectively . Regarding inhibitory synapses , all synaptic connections are derived from the population of interneurons , the synaptic activity of which is denoted by , and the conductance induced by the inhibitory neurons is given by ( 7 ) We assume that all inhibitory synapses are constant synapses for simplicity , i . e . , the peak value of synaptic activity is fixed at unity as follows: ( 8 ) where is the synaptic time constant . The synaptic efficacy in a cell assembly is defined as the average of the peak conductance of excitatory synapses on neurons in the cell assembly . The peak synaptic conductance in each neuron is given by ( 9 ) Figure 2C shows the overall network structure . The populations of neurons in the nodes of Figure 2C , which represent PFC neurons ( A to D and IN ) , are simulated with the spiking neuron model as explained above . The PFC neurons are driven by a sensory input that represents the goal positions ( G1 and G2 ) . The activity of the PFC neurons is read out by the populations of the neurons ( A1 and A2 ) . Although sensory and motor neurons are depicted as neural populations ( G1 , G2 , A1 , and A2 ) , their activity was not explicitly simulated; the activity of G1 and G2 is introduced by bias inputs for the PFC neurons and the activity of A1 ( A2 ) is given by summed activity of A and D ( B and C ) . Each population of neurons in the PFC network consists of 200 integrate-and-fire neurons . The neurons are sparsely connected within and across the nodes . Suppose that the connectivity ratio c is 0 . 2 , and each neuron receives randomly selected cN presynaptic connections from each presynaptic neural population . The weight of a synaptic connection from population l to k is defined as follows: , where is the summed weight of the connections from population l to k . Based on the integrate-and-fire neuron model , we constructed a mean field model that simulates the mean activity of a neural population , and allows us to define the overall dynamics of many populations of neurons and analyze changes in the stability . In the present model , the mean firing rates of excitatory and inhibitory neurons are denoted by and , which are the functions of excitatory and inhibitory input conductance and , respectively . The input conductance can be approximated as linear combinations of the excitatory and inhibitory conductances , namely , the firing-rate response function can be approximated with the coefficients and as follows [29]: ( 10 ) ( 11 ) In the present study , the approximated form of the firing-rate response function is given as the following Naka–Rushton formula [62] , which is generally used to fit intensity-response curves: ( 12 ) ( 13 ) The parameter values in equation ( 12 ) and ( 13 ) were determined to respectively fit the responses in the abovementioned excitatory and inhibitory spiking neurons . Using this formulation , the mean firing rate of population k in the present network is given by ( 14 ) Similarly to equation ( 2 ) , the excitatory conductance induced in the kth population of neurons is ( 15 ) where represents the mean synaptic activity that changes depending on the type of synapses . The mean activity of constant synapses was modeled as follows . When the neurons in a population fire asynchronously , the mean synaptic activity in the population should be almost stationary . When the mean synaptic activity changes because of changes in the firing rate , the synaptic activity will converge to the stationary value with a certain time constant . If each neuron fires with firing rate r and if the peak of the synaptic activity is unity , the temporal average of the synaptic activity is . Here we assume that the mean synaptic activity converges to this value with the time constant , i . e . , the synaptic activity obeys the following equation: ( 16 ) In the case of synapses that undergo modulation with short-term plasticity , can be denoted as a combination of the mean synaptic activity of constant synapses and a term of the synaptic modulation by the short-term plasticity as follows: ( 17 ) Similarly to equations ( 5 ) and ( 6 ) , the means of the utilization factor and the available resources change according to the following equations [31]: ( 18 ) ( 19 ) The inhibitory conductance and its synaptic activity are analogous to equations ( 7 ) and ( 8 ) as follows: ( 20 ) ( 21 ) The network structure of the meanfield model is the same as that of the spiking neuron model . The absolute strengths of all connections are denoted as the summed weight of connections , as shown in equation ( 15 ) . In the mean field model , the dynamics of the synaptic activity was defined by variables that indicate the synaptic modulation and . We traced the changes in the dynamical system for by identifying stable attractors . In each step of the numerical integration of the model , an equilibrium-finding algorithm ( the Newton–Raphson method ) and eigenvalue analysis were applied to identify the stability of attractors [63] . The differential equations were simulated by the Runge–Kutta method with the time step = 0 . 1 ms . The following parameter values were used for the spiking neuron [15] . For both excitatory and inhibitory neurons , = −52 mV , = −60 mV , = −5 mV , and = −75 mV . For excitatory neurons , = 0 . 5 nF , = 25 nS , = −70 mV , and = 2 ms . For inhibitory neurons , = 0 . 2 nF , = 20 nS , = −65 mV , and = 1 ms [15] . The stochastic term in equation ( 1 ) was simulated by adding a random variable following the normal distribution with mean 0 and variance at each integration time step . We set . For excitatory synapses , we assumed a long time constant = 100 ms for N-methyl-D-aspartate ( NMDA ) synapses , which may be important to maintain the active state in the PFC network [14] , [15] because this long time constant smoothens the destabilizing effect due to random spike activity . Although the dynamics of NMDA synapses is characterized by the long time constant and the membrane-voltage dependency , we used only the long time-constant aspect of NMDA synapses for simplicity . For inhibitory synapses , we set = 20 ms . The magnitude of the constant bias input was set such that the neuron stays just below the firing threshold or exhibits very low-frequency firing: for excitatory neurons , = 8 . 35 nS , and for inhibitory neurons , = 4 nS . For external inputs , the parameters of the activation inputs are set to = 0 . 35 nS and = 200 ms , and the amplitude and width of sensory inputs are = 0 . 2 nS and = 200 ms , respectively . For the short-term synaptic plasticity , we set = = 0 . 2 , = 20 ms , = 600 ms , = 600 ms , and = 100 ms . We constructed the following three types of network structures . The first consists of all the three types of synapses , and its simulation results are shown in Figures 3–6 . The second and third types of networks consist of only depression and constant synapses , and only facilitation and constant synapses , respectively; the results with these networks are shown in Figure S3 . For the first type of networks , the summed conductance and types of synapses are as follows . For connections among goal-representing cell assemblies , = 3 . 2 nS , X ( A , B ) = X ( B , A ) = X ( C , D ) = X ( D , C ) = D . For connections among action-representing cell assemblies , = 1 . 55 nS , X ( A , D ) = X ( D , A ) = X ( B , C ) = X ( C , B ) = F . For self-recurrent connections , = 1 . 7 nS , X ( A , A ) = X ( B , B ) = X ( C , C ) = X ( D , D ) = C . For connections from excitatory neurons to inhibitory interneurons , = 0 . 7 nS , X ( IN , A ) = X ( IN , B ) = X ( IN , C ) = X ( IN , D ) = C . For connections from interneurons to excitatory neurons , = 5 nS , X ( A , IN ) = X ( B , IN ) = X ( C , IN ) = X ( D , IN ) = C . For the second type of networks in which the short-term plasticity is driven only by the depression synapses , the summed conductance and types of synapses are as follows: = 3 nS , = D , = 0 . 5 nS , = C , = 1 . 8 nS , = C . = 0 . 7 nS , = C . = 5 . 5 nS , = C . For the third type of networks in which the short-term plasticity is driven only by the facilitation synapses , the summed conductance and types of synapses are as follows: = 0 . 8 nS , = C , = 3 . 1 nS , = F , = 1 . 9 nS , = C . = 0 . 7 nS , = C . = 7 . 5 nS , = C . This model exhibits the representational switching in a wide parameter range . Although the timing of switching is sensitive to the strength of the synaptic connections and the parameters in the dynamics of the short-term plasticity , the timing was easily adjustable in an experimentally plausible range as shown in Figure 5 . For the mean field model , the parameter values in the firing-rate response curve were specified to fit responses in the population of the integrate-and-fire neurons . We set M = 2 and obtained , for excitatory neurons , = 0 . 4611 , = 0 . 2561 , = 8 . 560 , and = 12 . 81 , and for inhibitory neurons , = 0 . 4611 , = 0 . 2561 , = 8 . 560 , and = 12 . 81 . Note that the units are in kHz for the firing rates and in nS for the conductances . The parameter values of the dynamic synapses and the connection strength are the same as those of spiking neurons , except for = 1 . 9 nS , because the spiking neural network shows an earlier state transition compared with the mean field model because of fluctuations in neural activity . Thus , we adjusted the connection strength such that the mean field model shows a state transition in a similar time range . If the model is completely symmetrical in its connectivity , the state of the network remains on a saddle point and does not show any state transition . Therefore , we applied very small perturbation inputs that induced state transitions at the beginning of the goal-display period . The perturbation input is applied to a cell assembly ( a pair of A and D or a pair of B and C ) representing one of the actions as a rectangular pulse with the amplitude of 0 . 01 nS and the width of 200 ms . The selectivity is determined using the firing rate , the goal position , and the action direction by the following equation: , where is an intercept , and the coefficients and indicate the goal and action selectivity , respectively . The regressors “Goals” and “Actions” indicated in parentheses are dummy variables [27] that represent preference or non-preference of the neuron for goals and actions . For example , in the case of the present model , there are two goal positions and two action directions . These discrete variables can be represented by dummy variables and as follows: The regression model with these dummy variables is given by , where F is the firing rate , and the goal and action selectivity and are obtained by the least square estimation with 40 simulation trials data at each time step . | The prefrontal cortex plays a highly flexible role in various cognitive tasks e . g . , decision making and action planning . Neurons in the prefrontal cortex exhibit flexible representation or selectivity for task relevant information and are involved in working memory with sustained activity , which can be modeled as attractor dynamics . Moreover , recent experiments revealed that prefrontal neurons not only represent parametric or discrete sets of information but also switch the representation and transform a set of information to another set in order to match the context of the required task . However , underlying mechanisms of this flexible representational switching are unknown . Here we propose a dynamically reorganizable attractor network model in which short-term modulation of the synaptic connections reconfigures the structure of neural attractors by assembly and disassembly of a network of cells to produce flexible attractor dynamics . On the basis of computer simulation as well as theoretical analysis , we showed that this model reproduced experimentally demonstrated representational switching , and that switching on certain characteristic axes defining neural dynamics well describes the essence of the representational switching . This model has the potential to provide unique insights about the flexible information representations and processing in the cortical network . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"biology",
"neuroscience"
] | 2011 | Representational Switching by Dynamical Reorganization of Attractor Structure in a Network Model of the Prefrontal Cortex |
Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes . Moreover , outcome of disease is highly variable even between patients with the same disease . Machine learning on transcriptome sequencing data could be a valuable new tool to understand differences between and within entities . Here we used machine learning analysis to identify novel diagnostic and prognostic markers and therapeutic targets for soft tissue sarcomas . Gene expression data was used from the Cancer Genome Atlas , the Genotype-Tissue Expression project and the French Sarcoma Group . We identified three groups of tumors that overlap in their molecular profiles as seen with unsupervised t-Distributed Stochastic Neighbor Embedding clustering and a deep neural network . The three groups corresponded to subtypes that are morphologically overlapping . Using a random forest algorithm , we identified novel diagnostic markers for soft tissue sarcoma that distinguished between synovial sarcoma and MPNST , and that we validated using qRT-PCR in an independent series . Next , we identified prognostic genes that are strong predictors of disease outcome when used in a k-nearest neighbor algorithm . The prognostic genes were further validated in expression data from the French Sarcoma Group . One of these , HMMR , was validated in an independent series of leiomyosarcomas using immunohistochemistry on tissue micro array as a prognostic gene for disease-free interval . Furthermore , reconstruction of regulatory networks combined with data from the Connectivity Map showed , amongst others , that HDAC inhibitors could be a potential effective therapy for multiple soft tissue sarcoma subtypes . A viability assay with two HDAC inhibitors confirmed that both leiomyosarcoma and synovial sarcoma are sensitive to HDAC inhibition . In this study we identified novel diagnostic markers , prognostic markers and therapeutic leads from multiple soft tissue sarcoma gene expression datasets . Thus , machine learning algorithms are powerful new tools to improve our understanding of rare tumor entities .
Soft tissue sarcomas are rare malignancies arising in the tissues that connect , support and surround other body structures , such as fat or muscle [1] . Soft tissue sarcomas annually affect approximately one per 50 million population , and represent <1% of all malignant tumors [2] . Soft tissue sarcomas can display different lines of differentiation , and as such are classified based on the tissue that they resemble most . More than 50 different subtypes have been described in the WHO classification . Even though these subtypes differ in prognosis and treatment , there is considerable morphological overlap between the different subtypes , making differential diagnosis both difficult and important . For instance , synovial sarcoma ( SS ) and malignant peripheral nerve sheath tumor ( MPNST ) can be morphologically identical , while also their immunohistochemical profile can overlap , making molecular testing for the presence of the SS specific SS18-SSX fusion essential for the final diagnosis ( which is laborious and time consuming ) . Over the last years there have been many large genetic studies generating open accessible gene expression datasets of sarcomas . One of the biggest soft tissue sarcoma sequencing projects to date is the Cancer Genome Atlas ( TCGA ) , which recently published a detailed analysis of the driving mutations in these cancers [3] . This data can be leveraged and analyzed with machine learning methodologies to better understand soft tissue sarcoma biology . Machine learning has been used previously to study gene expression patterns . Especially unsupervised algorithms , such as Principal Component Analysis ( PCA ) and more recently t-Distributed Stochastic Neighbor Embedding ( t-SNE ) , have been successfully used in gene expression studies to classify cancer patients [4] . Moreover , for classification of tumors , supervised algorithms such as random forest have been used previously . Gene expression signatures were shown to be effective at classifying breast cancer [5] . Later , it was shown that microRNA expression patterns could be used to distinguish between a number of different tumor subtypes , ranging from brain to colorectal cancer [6] . More recently , random forest analyses were used on DNA-methylation data to classify different brain tumor subtypes . The advantage of the latter is that it can be performed on paraffin embedded material [7 , 8] . Previously the French Sarcoma Group used a machine learning approach on a large cohort of soft tissue sarcomas to verify a set of 67 genes ( CINSARC ) , identified using differential expression analysis , that effectively predicted metastatic outcome in soft tissue sarcomas [9] . The identified CINSARC genes were more recently found to have prognostic value for other tumor types as well , such as breast cancer [10] . The CINSARC genes are mostly associated with cell proliferation and therefore lack tumor subtype specificity . Another approach to identify prognostic genes was used by the Pathology Atlas to identify tumor subtype specific prognostic genes . However , soft tissue sarcomas were not analyzed in this study [11] . In this study we used machine learning on open accessible expression data from soft tissue sarcomas to elucidate differences between and within the different entities . First , we investigated the overlap of gene expression patterns of soft tissue sarcomas with gene expression patterns of human tissues without malignancies from the GTEx project [12] using clustering with PCA and a deep neural network . Second , we identified novel diagnostic markers using a random forest approach . Third , we identified tumor subtype specific prognostic genes and showed , using a k-nearest neighbor analysis , that the identified prognostic genes are predictive of the metastasis-free interval . Last , we analyzed differential expression in the context of a regulatory network to identify novel therapies . We demonstrate that machine learning can be a powerful tool to identify novel diagnostic and prognostic biomarkers , as well as therapeutic targets , which will improve our understanding of rare soft tissue sarcomas .
All the specimens were coded and handled according to the ethical guidelines described in the Code for Proper Secondary Use of Human Tissue in the Netherlands of the Dutch Federation of Medical Scientific Societies as reviewed and approved by the LUMC ethical board ( B17 . 036 ) . The Cancer Genome Atlas ( TCGA ) RNA-seq count data was downloaded ( February 2018 ) from the NIH GDC data portal ( portal . gdc . cancer . gov/ ) . All clinical data corresponding to the soft tissue sarcoma samples in the TCGA was recently revised by the Cancer Genome Atlas Research Network which resulted in 206 revised cases with clinical data ( from the original 261 cases in the TCGA ) [3] . Soft tissue leiomyosarcoma ( STLMS ) was the most common sarcoma type with 53 samples and included cases of grade 1 ( n = 11 ) , grade 2 ( n = 35 ) and grade 3 ( n = 7 ) according to the Fédération Nationale des Centres de Lutte Contre le Cancer ( FNCLCC ) grading system . In addition , there were 27 uterine leiomyosarcoma ( ULMS ) cases . Furthermore , the TCGA included 50 dedifferentiated liposarcomas ( DDLPS ) , 44 undifferentiated pleomorphic sarcomas ( UPS ) , 17 myxofibrosarcomas ( MFS ) , 10 synovial sarcomas ( SS , both monophasic and biphasic ) and 5 malignant peripheral nerve sheath tumors ( MPNST ) . Second , the Genotype-Tissue Expression ( GTEx ) data ( v7 ) was downloaded ( gtexportal . org ) with corresponding annotations . The data consisted of transcriptome sequencing read counts for 9662 samples . The GTEx data included expression data for 31 different tissue types ( S1 Table ) . Third , DDLPS ( n = 62 ) and leiomyosarcoma ( LMS ) ( n = 84 ) expression array data from the French Sarcoma Group was downloaded from GEO ( ncbi . nlm . nih . gov/geo ) , deposited under accession number GSE21050 ( public in June 2010 ) , using GEOquery ( v3 . 6 ) in R [13] . Genes with low expression ( transcriptome sequencing read counts: cpm < 2; expression array: relative measured unit < 2 ) in all samples were removed . Thereafter , transcriptome sequencing read count and expression array data were normalized using Limma ( v3 . 6 ) R package . For normalization , the weighted trimmed mean of M-values was used [14] . Last , the data was log2 transformed and analyzed further . When indicated , data was combined and normalized . Where indicated samples were randomly subdivided into groups using the “sample” function in R . For the deep neural network TensorFlow ( v1 . 6 ) was used in combination with the Keras ( v2 . 1 . 4 ) R package to design a neural network with one converging invisible layer . t-SNE was performed using the Rtsne ( v0 . 13 ) R package . For t-SNE analysis a perplexity of 60 and a theta of 0 . 5 were used . Random forest analysis was performed on the normalized TCGA expression data . Data were analyzed according to Breiman’s random forest algorithm , using the randomForest ( v4 . 6 ) R package . Variable importance in the random forest analysis was calculated based on the Gini index , which is a measurement of variance for a given variable . For k-Nearest Neighbor analysis the Caret ( v6 . 0 ) R package was used . To resample the data , the “repeatedcv” option was used and k = 1–30 were tested . The EnrichR ( v1 . 0 ) R package was used for Gene Ontology ( GO ) term enrichment analysis . GO terms were selected from the “GO biological processes 2015” database and had adjusted p values lower than 1e-4 . As readout disease-free interval ( DFI ) was used , which was previously described as a strong measurement of outcome in soft tissue sarcomas [15] . DFI is the time until relapse , including distant metastasis and loco-regional recurrence . Prognostic genes were identified using the maxstat ( v0 . 7 ) R package . Maxstat determined the maximal rank statistic using a LogRank analysis , to determine the optimal gene expression cut-off . P values were calculated according to the Streitberg algorithm [16] . Version 18 of the Human Protein Atlas data was downloaded to cross-check prognostic genes identified in other tumor types ( proteinatlas . org/about/download ) . This dataset included genes and their association with disease outcome in common cancer types . Immunohistochemistry ( IHC ) was performed on one existing tissue microarray ( TMA ) and one newly constructed TMA . The TMA was constructed as previously described by our group [17] . Clinicopathological details are summarized in S2 Table . In total , seventy leiomyosarcomas could be scored for HMMR protein expression and had available clinicopathological information . The cases originated from two cohorts: the first contained 32 cases that could be scored and has been previously described by our group [17] , the second consisted of 38 cases that could be scored . IHC was performed simultaneously on all cases to enable comparison between the cohorts . The 70 cases consisted of 43 females and 27 males , with a mean age of 62 years at diagnosis . Five patients had uterine LMS , the rest were soft tissue LMS . Soft tissue LMS were graded according to the FNCLCC grading system , including 10 grade 1 , 23 grade 2 , 31 grade 3 and for 1 grading was not available . HMMR was detected with a polyclonal rabbit antibody ( Sigma-Aldrich; HPA040025 ) . The HMMR antibody was titrated on normal testis tissue , the optimal antibody dilution was found to be 1:1000 in PBS/1%BSA/5%/non-fat dry milk . Microwave antigen retrieval was performed using citrate ( pH 6 . 0 ) and immunohistochemistry was performed according to standard protocols [18] . Scoring was performed using ImageJ ( v1 . 5 ) in which color deconvolution was used to separate haematoxylin and 3 , 3'-Diaminobenzidine ( DAB ) staining . Haematoxylin was used to identify the core and intensity of the DAB was quantified and compared between cores . A cut-off score of 20 was used to define high and low expressing cores . The second cohort was also scored manually by a pathologist ( JVMGB ) blinded towards clinicopathological data and results of the automatic scoring , in which staining intensity was scored as weak ( 1 ) , moderate ( 2 ) or strong ( 3 ) . For the analysis , the average of the three cores per tumor were used . Frozen tissue from five SS and four MPNSTs was retrieved from our archive and anonymized . All selected MPNSTs were either associated with a nerve , were NF1 related or had reported loss of H3K27me3 at immunohistochemistry [19 , 20] . All selected synovial sarcomas were proven to be positive for the SS18-SSX translocation . RNA was isolated using the Direct-zol RNA isolation kit ( Zymo research ) . cDNA was made using iScript cDNA Synthesis Kit ( Bio-Rad ) . Real-time PCR was performed using Sybr Green ( Bio-Rad ) on a CFX384 real-time PCR Detection System ( Bio-Rad ) . Real-time PCR Ct values were normalized to housekeeping gene HPRT1 expression . The following primers were used , noted as 5’ to 3’: NEURL1_Fw GCATCCTCGGCTCCACTATC NEURL1_Rv CTGAGCAAGGGGTCAGACAG SCD_Fw CTTGCGATATGCTGTGGTGC SCD_Rv CCGGGGGCTAATGTTCTTGT NPAS1_Fw CAGCTGCTACCAGTTTGTCCAC NPAS1_Rv ACCCTTGTCCAGCAAGTCCAC HPRT1_Fw TGACACTGGCAAAACAATGCA HPRT1_Rv GGTCCTTTTCACCAGCAAGCT Cells were cultured in RPMI 1640 medium ( Gibco ) supplemented with 10% FBS . Cells were tested for mycoplasma and Short Tandem Repeats were characterized for authentication . One SS cell-line was used ( SYO-1 ) [21] . Three LMS cell lines were included ( JA192 , LMS04 and LMS05 ) . Quisinostat ( Selleckchem ) and trichostatin A ( Selleckchem ) were used for HDAC inhibition . Both compounds were dissolved in DMSO . Cells were seeded in triplicates on a 96-well plate and compounds were added after 24 hours . Cell viability was measured after 72 hours incubation with the compounds by adding PrestoBlue Cell Viability Reagent ( Life Technologies ) according to the manufacturers protocol . Fluorescence was measured reading the plate at 590 nm on a fluorometer ( Victor3V , 1420 multi-label counter ) . Viability was determined in three independent experiments in triplicate . For Connectivity Map ( CMAP ) analysis the regulatory network was first determined using expression2kinase ( maayanlab . net/X2K ) based on the differentially expressed genes that were identified . Potential targeted therapies were identified based on the proteins in the regulatory network . The pipeline for identification of transcription factors and kinases is described in literature [22] . R statistical software ( v3 . 4 . 4 ) was used for all statistical tests [13] . Network plots were generated with igraph ( v1 . 2 . 1 ) R package and formatted with Cytoscape ( v3 . 6 . 0 ) [23] . Chord diagrams were generated with GOplot ( v1 . 0 . 2 ) [24] . All further graphs were generated with R package ggplot2 ( v2 . 2 . 1 ) . Cox regression was performed with the “coxph” function from the survival ( v2 . 43 ) R package .
Since soft tissue sarcomas are histologically classified according to their line of differentiation , we compared gene expression data from 206 soft tissue sarcoma samples in The Cancer Genome Atlas ( TCGA ) ( Table 1 ) with normal tissues from the Genotype-Tissue Expression ( GTEx ) project . For this we used a deep neural network approach , enabling us to find similarities between normal tissues and tumors identified through hidden layers that would not be obvious in a direct comparison ( such as a PCA analysis ) . First the TCGA and GTEx data were combined and normalized together ( S1a Fig ) . Principal components were calculated for all samples , the principal components ( 9868 in total ) for the GTEx data was used to train a neural network resulting in a prediction accuracy of 98% ( S1b Fig ) . The neural network was then applied to the principal components from the TCGA sarcoma data . As might be expected , ULMS was the only sarcoma subtype showing overlap with the expression patterns of normal uterus tissue as well as normal cervical tissue ( S1c Fig ) . Moreover , STLMS was the only subtype showing similarity to blood vessel , which may be explained by the fact that a subset of STLMS are presumed to arise from small to medium sized veins [25] . However , both ULMS and STLMS also showed overlap with skin and brain tissue which is more difficult to understand at this point . Interestingly , we found large similarities between MPNST and SS , showing expression patterns very similar to tissue derived from the nervous system ( brain and nerve ) . In addition SS showed some overlap with salivary gland which might be explained by the fact that 2 out of 10 SS were biphasic , of which the glandular epithelial elements may have caused the found similarity with salivary gland ( Fig 1a ) . Surprisingly , MFS , and to a lesser extent UPS , showed a large overlap with normal adipose tissue . The overlap with adipose tissue in MFS and UPS is larger than found in DDLPS , which could be due to the selective sampling of DDLPS including the dedifferentiated component . For the other soft tissue sarcoma subtypes similarities were more dispersed since no specific normal tissue showed a large overlap with the tumor gene expression ( S1c Fig ) . To study the gene expression patterns of soft tissue sarcomas the TCGA expression data was normalized and differentially expressed genes ( DEGs ) were identified ( Benjamini-Hochberg adjusted p value < 0 . 05 and logFC > 0 ) for all soft tissue sarcoma subtypes using Limma and Voom , comparing the subtypes to the other samples ( S1d Fig ) . The number of DEGs per subtype ranged from 331 to 7784 ( in STLMS and DDLPS respectively , 3156 DEGs on average ) ( S1e Fig ) . The DEGs were used to generate a heat map showing differences between soft tissue sarcoma subtypes . MFS and UPS showed the largest overlap in DEGs ( 1201 genes ) followed by STLMS and ULMS ( 210 genes ) ( Fig 1b ) . Using EnrichR we tested for functional enrichment of the DEGs to identify GO terms associated with each of the subtypes . The DEGs from STLMS and MPNST showed a clear relation to differentiation; GO terms for STLMS related to muscle development and for MPNST the GO terms related to neuronal development . The top GO terms associated with ULMS were not related to muscle differentiation , but with cell cycle processes . However , significant GO terms associated with muscle differentiation were identified such as “muscle system process” ( adjusted p = 6e-4 ) and “muscle contraction” ( adjusted p = 3e-3 ) matching with the GO terms found in STLMS , which suggests that proliferation was more pronounced than differentiation in the ULMS compared to the STLMS samples . We did not identify GO terms related to differentiation for DDLPS , but , as can be seen in the heat map , we found that many of the identified GO terms associated with DDLPS , UPS and MFS overlapped . These included GO terms associated with the immune system which may reflect the presence of an inflammatory infiltrate in these tumors ( Fig 1b ) . To investigate the similarities of the molecular profiles of the different soft tissue sarcoma subtypes we performed a t-SNE analysis on the expression data ( S2a Fig ) . The average of the first two components for the different subtypes is shown in Fig 2a . In the t-SNE analysis , three clusters of soft tissue sarcoma subtypes were identified . MFS , UPS and DDLPS clustered together , in line with the undifferentiated sometimes pleomorphic morphology of these tumors . ULMS and STLMS also cluster together . The third cluster consisted of MPNST and SS , for which distinction based on morphology alone is often impossible . As a deep neural network is not informative on the biological differences between these subtypes , we therefore used a random forest machine learning approach to identify subtype defining genes . The samples were divided into test and training groups at random . The resulting random forest reached a subtype prediction accuracy of over 95% for all groups , except in differentiating between MFS and UPS ( where it reached an accuracy of 88% ) ( Fig 2b ) . Differentially expressed genes ( adjusted p<0 . 05 ) were used to generate the random forest . Important genes were identified based on their variable importance index ( Fig 2c ) . Top genes in group 1 ( STLMS and ULMS ) included HOXA11 and its anti-sense RNA ( HOXA11-AS ) were identified . HOXA11 and HOXA11-AS have both been described to be important regulators of uterine development and homeostasis [26] . For group 2 ( MPNST and SS ) genes related to neural differentiation such as NEURL1 and NPAS1 were identified , which were found to be upregulated in synovial sarcomas , while SCD , an enzyme involved in fatty acid biosynthesis , is more highly expressed in MPNST . For the third group ( DDLPS , UPS and MFS ) , we first compared DDLPS with the UPS and MFS together . As previously described and already widely implemented in routine diagnostics , expression of MDM2 and CDK4 ( which is part of the 12q13-15 amplification characteristic of DDLPS ) were identified as diagnostic markers to identify DDLPS [27] . FRS2 , TSPAN31 and CTDSP2 are located near the amplified MDM2 on chromosome 12 and therefore most likely also part of the same amplified region that characterizes DDLPS . In Fig 2d , we visualized gene expression levels of the genes with the highest variable importance scores for each of the four comparisons . JADE2 showed the highest variable importance score for the differentiation between UPS and MFS although expression still somewhat overlapped , confirming the large molecular and morphological similarity between the two entities ( Fig 2d ) . To verify the diagnostic markers that were identified for group 2 ( MPNST and SS ) using the random forest algorithm we used qRT-PCR on an independent cohort of nine samples . Indeed , the expression patterns of NEURL1 , SCD and NPAS1 were similar in the independent cohort ( Fig 2e ) . We identified prognostic genes for all annotated soft tissue sarcoma subtypes , except MPNST ( with only five samples available ) . First , the optimal gene expression cutoff was calculated for all the 24168 genes that met the defined thresholds in the TCGA soft tissue sarcoma expression data . Next , disease-free interval ( DFI ) ( time to local recurrence or distant metastases ) was tested using the Hothorn and Lausen statistical test; DFI was used as the read-out . In total 429 genes were found to be strong predictors ( favorable or unfavorable ) of DFI ( p < 0 . 001 ) ( S3 Table ) . Most genes were identified for SS ( 166 genes ) while 74 and 34 genes were identified for STLMS and ULMS respectively . Interestingly , there was very little overlap between the prognostic genes for the different subtypes . Two overlapping prognostic genes ( KLF6 and MT1F ) were found for UPS and SS and one ( NPM2 ) for ULMS and MFS . No overlapping prognostic genes were found between STLMS and ULMS ( Fig 3a ) . Furthermore , only one gene ( CDCA3 identified in STLMS ) was found to overlap between the 67 described CINSARC genes and the soft tissue sarcoma subtype specific prognostic genes identified in the current study . From the 429 identified prognostic genes 201 were new , 228 had however been previously identified in other ( non-sarcoma ) tumor types in the Protein Atlas database ( S3a Fig ) . To cross-check the identified prognostic genes identified for LMS , DDLPS and UPS , we used expression data from the French Sarcoma Group [9] . The French Sarcoma Group array data was first normalized ( S3b Fig ) . The data contained information on the metastasis-free interval but not DFI as was used by us for the TCGA data . The French Sarcoma Group data was split in two groups . Genes that were significant prognostic genes for DFI in the TCGA and the metastasis-free interval in the first French Sarcoma Group cohort ( both with p < 0 . 05 ) were considered for further analysis ( S4 Table ) . From the identified genes , strong prognostic genes were used in a k-nearest neighbor analysis . For LMS HMMR , MXD4 and BRCA2 were identified , for DDLPS KLF6 was found to be a strong prognostic gene while for UPS PCMTD2 , TNXA , TMEM65 , SNRNP48 were identified . The k-nearest neighbor algorithm was trained on the first group and tested on the second group in the French Sarcoma samples . The k-nearest neighbor algorithm was a significant predictor for the metastasis-free interval for LMS , DDLPS and UPS in the second group ( p = 0 . 045 , p = 0 . 02 and p = 0 . 012 respectively ) ( Fig 3b ) , outperforming the reported CINSARC classification in the second cohort ( LMS p = 0 . 24 , DDLPS p = 0 . 14 and UPS p = 0 . 038 ) ( S3c Fig ) . HMMR was identified as a significant ( p<0 . 05 ) prognostic gene for DFI and the metastasis-free interval in LMS . In an independent validation set of 70 LMS cases , we verified using immunohistochemistry with automated scoring that high protein expression of HMMR was associated with a shorter DFI ( p = 0 . 0061 ) ( Fig 3c & 3d ) . For the second cohort , manual scoring was compared with automated scoring and results were similar . Prognostic value of HMMR was further compared to the FNCLCC grading system . In a multivariate Cox-regression it was found that the HMMR staining ( p = 0 . 0039 ) retained significance and was a better predictor than FNCLCC histological grade ( p = 0 . 285 ) . To identify novel targeted therapies gene expression data was used to infer the regulatory transcription factors and kinases in the different soft tissue sarcoma subtypes . First , the signature genes for each soft tissue sarcoma subtype were used to infer the transcription factors that were most likely to regulate those genes based on data from the ChIP-seq Enrichment Analysis ( ChEA ) database [22] . The most important kinases regulating these transcription factors were inferred using the Kinase Enrichment Analysis [22] . Based on the identified transcription factors and kinases , tumor subtype specific drugs were identified based on the Connectivity Map ( CMAP ) drug data ( with kinases and transcription factors as input ) . Doxorubicin , which is commonly used as systemic treatment for STS , was identified as a potentially effective therapy for most soft tissue sarcoma subtypes , validating our analysis approach . Trichostatin A , a HDAC inhibitor , was predicted to be potentially efficient in all soft tissue sarcoma subtypes , while another HDAC inhibitor , Vorinostat , was identified for UPS and ULMS . Tanespimycin was identified for UPS , ULMS and MPNST , which is an inhibitor of Hsp90 and currently used in clinical trials for solid tumors ( Fig 4a and S5 Table ) . While sensitivity to HDAC inhibition is known for translocation driven tumors like synovial sarcoma [28] , for LMS this has not been extensively studied . We thus performed cell viability assays on three LMS cell lines ( JA192 , LMS04 and LMS05 ) , treated with two HDAC inhibitors ( quisinostat and trichostatin A ) , with one SS cell line ( SYO-1 ) as positive control ( Fig 4b ) . For both compounds the half maximal inhibitory concentration ( IC50 ) was determined . For trichostatin A ( TSA ) an IC50 ranging from 39 to 474 nM was found ( JA192: 474 nM; LMS04: 229 nM; LMS05: 178 nM; SYO-1: 39 nM ) . Although all cell-lines were sensitive to TSA , SYO-1 was more sensitive compared to the LMS cells . However , for quisinostat a low IC50 was found for all cell-lines; between 15 and 41 nM ( JA192: 41 nM , LMS04: 34 nM; LMS05: 39 nM; SYO-1: 15 nM ) . These results indicate that LMS and SS cell lines are highly sensitive to HDAC inhibition by quisinostat .
Accurate diagnosis and prediction of biological behavior is a challenge for soft tissue sarcoma pathologists . These tumors are rare and often overlap in their morphology , while subtype specific diagnostic and prognostic markers are scarce . As an increasing amount of transcriptome sequencing data becomes available , even for rare cancers such as soft tissue sarcomas , new methods need to be developed to identify novel diagnostic and prognostic biomarkers for these tumors from existing data . Here we used machine learning algorithms to identify similarities and differences between soft tissue sarcoma subtypes and normal human tissue from the GTEx data . Using a deep neural network , we demonstrate that SS and MPNST mostly correspond to neural related tissues . MPNST often arises from or within nerves; therefore , it is likely a tumor originating from neural related tissue , while for synovial sarcoma the cell of origin and line of differentiation have been unclear . Our observation of the neural related tissue as a potential tissue of origin confirms previous suggestions [29] . The deep neural network also identified that cervix and uterine tissue showed the largest overlap with ULMS as is expected . Other findings however illustrate the limitations in comparing gene expression of normal tissue with tumor , such as the large overlap in gene expression between skin and adrenal gland with ULMS or the large overlap found between SS and salivary gland ( that could be explained due to the biphasic SS samples displaying epithelial elements ) . These findings in part could be explained by the fact that the sequencing is performed on tissue containing many different cell types , including immune and stromal cells . Single cell sequencing and projects such as the Human Cell Atlas [30] could in the future shed more light on the tissue of origin for soft tissue sarcomas . Using a random forest analysis , we identified subtype specific genes that can be used as diagnostic markers within the three groups of soft tissue sarcoma subtypes that were identified based on their molecular profile and morphology . For instance , NEURL1 was one of the genes highly expressed in SS as compared to MPNST . NEURL1 is an important determinant of neural tissue differentiation and functions as a tumor suppressor which is inactivated during malignant progression of astrocytic tumors [31] . In line with this , the lower expression of NEURL1 could be explained by recurring losses of chromosome 10 in 48% of MPNST [32] . SCD was found to be highly expressed in MPNST compared to SS . SCD has been found to associate with a poor prognosis in breast and lung cancer . Moreover , SCD can be directly inhibited with the small molecule MF-438 which sensitized adenocarcinoma cells to cisplatin treatment [33 , 34] . It was previously found that when SS was treated with a HDAC inhibitor , neural differentiation was induced [28] . Furthermore , treatment with BMP4 or FGF2 restored expression of neural tissue related genes in SS [35] . Our study further confirms neural differentiation in SS , as shown using hidden layers in a deep neural network . Future validation studies should indicate whether the diagnostic biomarkers that we identified here can also be used immunohistochemically in the differential diagnosis . We identified subtype specific prognostic genes using Kaplan-Meier analysis on all individual genes combined with a k-nearest neighbor algorithm to accurately predict the disease-free interval ( DFI ) . DFI was previously shown to be one of the strongest outcome measurements for soft tissue sarcomas [15] . For all genes the cut off was determined first and the DFI for high and low expression was calculated . This Kaplan-Meier approach was previously used on 17 other cancer types , not including soft tissue sarcomas [11] . Although this method results in tumor subtype specific prognostic genes that can predict outcome , a major challenge is to correct for multiple testing . Here we used an independent cohort from the French Sarcoma Group to validate strong prognostic genes for LMS , DDLPS and UPS . However , for this independent cohort only data on metastasis were available , whereas the TCGA also contained data on loco-regional recurrence . Using both data sets , overlapping prognostic genes were identified which could be considered strong prognostic genes . For the other tumor subtypes , to our knowledge , there are no available expression data sets with accurate follow up data to perform cross-validation . Interestingly we found only one gene , CDCA3 , overlapped between the prognostic genes we identified in the TCGA soft tissue sarcoma data and the CINSARC prognosticator . We likely did not identify a larger overlap because the CINSARC study aimed to identify a general prognosticator for soft tissue sarcomas , which is not subtype specific . In addition , the outcome used was different; we used DFI as an outcome measurement while in the CINSARC study metastasis was used . Moreover , we identified subtype specific prognostic genes using a Kaplan-Meier approach which does not only take outcome but also time to events into account . Here we showed that subtype specific prognostic genes outperformed general prognostic genes . For one of the identified genes , HMMR , we confirmed that high protein expression was associated with poor outcome of LMS . Further we confirmed that HMMR expression outperformed the FNCLCC histological grading to predict outcome . Recently it was shown that LMS displays hallmarks of “BRCAness” through identification of mutation signatures and alterations in genes related to homologous recombination [36] . Here we identified strong prognostic genes for LMS , two of which were related to homologous repair ( BRCA2 and HMMR ) . HMMR forms a complex with BRCA1 or BRCA2 together with other proteins , and high expression of HMMR was associated with poor survival in liver , pancreatic and lung cancer [11] . Possibly , defects in the homologous repair pathway could result in over-expression of HMMR in an attempt to compensate for other defective proteins . The involvement of genes related to “BRCAness” and to disease outcome warrants further studies . A regulatory network reconstruction combined with the CMAP drug data revealed not only the commonly used drug doxorubicin , but also indicated that HDAC inhibitors could be a potential treatment for many different soft tissue sarcoma subtypes . Recent studies indeed suggest that HDAC inhibitors may be effective in treating soft tissue sarcomas . In liposarcoma it was shown that HDAC inhibitors increase apoptosis and anti-proliferation effects [37] . In SS HDAC inhibitors cause disruption of the SS18-SSX oncoprotein resulting in apoptosis [28] . Another study found HDAC inhibitors lead to apoptosis in SS cell-lines [38] . In other sarcoma subtypes HDAC inhibitors have not been studied extensively . One uterine LMS cell line was tested and shown to be sensitive to the pan HDAC inhibitor ITF2357 with a synergistic effect when combined with doxorubicin [39] . In this study we further investigated LMS sensitivity to HDAC inhibition using quisinostat and trichostatin A . We included three LMS cell-lines , one ULMS ( LMS04 ) and two STLMS ( LMS05 and JA192 ) . As SS was previously found to be sensitive to HDAC inhibition we also included one SS cell-line ( SYO-1 ) as a positive control . SS showed a greater sensitivity to TSA , however , quisinostat showed a very low IC50 ( 15–41 nM ) in all cell lines . Thus , quisinostat might be further explored as a potential therapy for both ULMS and STLMS . In conclusion , three groups of soft tissue sarcoma subtypes included in the TCGA study were identified based on similarities in their expression profiles , corresponding to their overlapping morphology . Using a random forest analysis , novel diagnostic markers were identified that may distinguish between soft tissue sarcoma subtypes within these three groups , including NEURL1 that was highly expressed in SS as compared to MPNST . Next , using a Kaplan-Meier analysis , prognostic genes were identified . Of these , HMMR protein expression was confirmed to be associated with poor outcome in an independent cohort of LMS from our archives . A network reconstruction combined with CMAP data revealed that HDAC inhibitors could be effective therapy in different soft tissue sarcoma subtypes , which we confirmed in LMS and SS cell-lines . In conclusion , machine learning algorithms uncovered diagnostic biomarkers , prognostic genes and identified potential novel therapeutic targets for soft tissue sarcomas . This study thereby illustrates the power of different machine learning algorithms to improve our understanding of rare cancers using existing datasets . | Soft-tissue sarcomas are a group of rare cancers that can be challenging to diagnose and treat . The morphology of the different soft-tissue sarcoma subtypes can overlap and the prognosis differs significantly between , and also within , the different subtypes . Moreover , targeted therapies are often not available . In this study we used transcriptome sequencing data from The Cancer Genome Atlas , containing 206 soft-tissue sarcoma samples which we analyzed using different machine learning algorithms to gain novel insights . When possible , we verified our findings in independent datasets or in cell lines . First , we found that both synovial sarcomas and malignant peripheral nerve sheath tumors show the largest overlap with normal tissue derived from the nervous system . This link with neural differentiation for synovial sarcoma was not well established until now . Second , genes were identified whose expression could be used to differentiate between the different soft-tissue sarcomas where the morphology overlaps . Third , novel prognostic genes were identified for separate subtypes . One gene , HMMR , which we found as a strong prognostic gene for leiomyosarcoma , was verified with immunohistochemistry on samples from our archives . Last , using a network analysis new potential therapies were identified . HDAC inhibitors were identified as a potential strong therapy for sarcomas , including leiomyosarcomas , which we verified in cell lines . | [
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] | 2019 | Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas |
The relative importance between additive and non-additive genetic variance has been widely argued in quantitative genetics . By approaching this question from an evolutionary perspective we show that , while additive variance can be maintained under selection at a low level for some patterns of epistasis , the majority of the genetic variance that will persist is actually non-additive . We propose that one reason that the problem of the “missing heritability” arises is because the additive genetic variation that is estimated to be contributing to the variance of a trait will most likely be an artefact of the non-additive variance that can be maintained over evolutionary time . In addition , it can be shown that even a small reduction in linkage disequilibrium between causal variants and observed SNPs rapidly erodes estimates of epistatic variance , leading to an inflation in the perceived importance of additive effects . We demonstrate that the perception of independent additive effects comprising the majority of the genetic architecture of complex traits is biased upwards and that the search for causal variants in complex traits under selection is potentially underpowered by parameterising for additive effects alone . Given dense SNP panels the detection of causal variants through genome-wide association studies may be improved by searching for epistatic effects explicitly .
There exists a paradox in evolutionary biology . Despite a near-ubiquitous abundance of genetic variation [1] traits under selection often evolve more slowly than expected and , contrary to expectation , genetic variation is maintained under selection . This problem is known as ‘stasis’ [2] , [3] , and it is particularly evident in fitness-related traits where the genetic variation tends to be highest [4] yet there is commonly no observed response to selection at all [5]–[7] . There are a number of mechanisms by which this might arise , amongst which the most commonly cited are various forms of constraints [8] , [9] or stabilising selection [10] . Because stasis is widespread its properties may reveal insights into the genetic architecture of complex traits related to fitness and thus inform the strategies that are employed to detect their underlying genetic variants . After hundreds of genome-wide association ( GWA ) studies [11] a picture is emerging where the total genetic variation explained by variants that have been individually mapped to complex traits is vastly lower than the amount of genetic variation expected to exist as estimated from pedigree-based studies , a phenomenon that has come to be known as the problem of the ‘missing heritability’ [12] . Again , there are probably numerous contributing factors , and ostensibly the most parsimonious explanation is that complex traits comprise many small effects that GWA studies are underpowered to detect [13] , [14] , but whether this is the complete story deserves exploration . With respect to the fields of both the aforementioned issues , it is typical to model genetic variation using an additive framework , such that each allele affecting a trait acts in an independent , linear , cumulative manner . For many practical applications this is a very useful approach ( e . g . [15] , [16] ) , but there does exist a popular school of thought that suggests that the mechanisms of gene action , and the architecture of complex traits , are actually much more complex than the additive model allows ( e . g . [17]–[20] ) . Epistasis , defined in functional terms as the event whereby the effect of one locus depends on the genotype at another locus , is one source of non-additive genetic variation . How it contributes to both the paradox of ‘stasis’ and the problem of the ‘missing heritability’ will be the focus of this study . The importance of epistasis in complex traits has proven to be a particularly divisive issue throughout the history of quantitative genetics . Recently it has been suggested that epistasis might form part of the answer to the ‘missing heritability’ [21]–[24] , but how this might manifest is not immediately obvious . When heritability estimates are reported for complex traits they typically pertain to the narrow-sense ( , the proportion of the phenotypic variance that is due to additive genetic effects ) . Calculating the broad-sense heritability ( , or the proportion of variance that is due to both additive and non-additive genetic effects ) , is an intractable problem for non-clonal populations [25] , thus we have little knowledge of how much epistasis exists in human and animal traits . In this light one might suggest that we are actually dealing with two problems: the ‘missing heritability’ , and the ‘unknown heritability’ . By definition epistasis will form a part of the ‘unknown heritability’ , but theory shows that epistatic interactions could also contribute to estimates . This could arise through two possible mechanisms , firstly by generating real additive variation as marginal effects from higher order genetic interactions [26]–[29]; or secondly by creating a statistical illusion of additive variance through confounding between non-additive and common environment effects in twin study based estimates [24] , [30] . Beyond the realm of complex trait genetics it appears that epistasis does appear to be common . For example in molecular studies it is routine to observe ‘phenotypic rescue’ where the phenotypic effect of a gene knockout can be masked by a second knockout ( e . g . [31] ) . Another commonly encountered form of epistasis is ‘canalisation’ [32] , where phenotypes exhibit robustness to the knockout of one gene , requiring a second knockout to elicit a response ( e . g . [33] ) . At the macroevolutionary scale , epistasis is also of central importance , for example it has recently been shown that an advantageous substitution in one species is often found to be deleterious in others , thus the substition's effect on fitness is dependent upon the genetic background in which it is found [34] . The mechanisms behind pathway-level [32] , [35] , [36] or species-level epistasis [20] , [34] , [37] are widely explored , and yet at the intermediate , within-population level there is a distinct lack of evidence for any widespread importance of epistasis arising from natural variation , and most genetic variation appears to be additive [28] . Nevertheless some convincing examples of epistasis have been reported , for instance there are a number of cases of canalisation in Homo sapiens [38] , [39] , Gallus gallus [40] , Drosophila melanogaster [41] , Saccharomyces cerevisiae [42] , and Arabidopsis thaliana [43] to name but a few . At the statistical level , for a pair of single nucleotide polymorphisms ( SNPs ) that exhibit epistasis , in addition to interaction terms between the two loci , the total genetic effect is likely to also include marginal additive or dominance effects at each locus [28] , [44] . The proportion of additive to non-additive genetic variation will depend both on the genotype-phenotype map ( G-P map ) , and the allele frequencies at each locus . In turn these frequencies will depend on selection acting on the phenotype . Thus , if epistasis contributes towards fitness then how selection acts is highly dependent on the particular genotype-phenotype map in question [45] . Ostensibly , the additive framework that is used in GWA studies follows Occam's razor , employing the hypothesis that introduces the fewest new assumptions ( i . e . non-additive variation cannot be estimated , thus SNPs are not modelled to have non-additive effects ) . But whether the phenomenon of stasis can accommodate a purely additive genetic model remains an open question . The premise of this study is centred around finding common ground between the problems of stasis and the missing heritability . Given that fitness related traits often exhibit stasis then the underlying genetic architecture may not solely comprise independent additive effects . Through theory and simulations we demonstrate that epistasis will maintain additive genetic variation under selection at higher levels than independent additive effects , and that by extending GWA studies to search for epistasis directly we could improve statistical power to detect additive genetic variation .
Our results demonstrate that for many of the patterns of epistasis that we assayed , deleterious effects can be maintained at intermediate frequencies over long evolutionary time periods ( Figure 1 patterns 1–3 , and Figures S1 , S2 and S3 ) . As might be expected , a number of G-P maps that maintained genetic variation at intermediate frequencies also exhibited over-dominance , or some form of heterozygote advantage ( e . g . Figure 1 patterns 4–6 ) . However , most patterns of epistasis that we assayed ( Figure S1 ) do not exhibit heterozygote advantage , and these can also effectively temper the rate of extinction of deleterious alleles . Conversely , some level of under-dominance is required for variation to be maintained , for example although the classic pattern of epistasis ( pattern 52 , Figure S1 ) can theoretically avoid fixation when both loci are at allele frequencies of ( Figure S2 ) , drift provides sufficient perturbation to prevent it from being maintained at equilibrium ( Figure S3 ) . The consequences of these results are examined below . In summary , we show that a small amount of additive variation is maintained by epistasis but most genetic variation is non-additive; that there is a strong bias in GWA studies that lead to an overestimation of additive effects at QTLs; and that , perhaps counterintuitively , the most powerful way to uncover additive variation under selection is to parameterise the search to include epistatic effects using dense genotype information . The genetic variance of a G-P map depends on the allele frequencies of the loci involved , and selection drives these allele frequencies to minimise the directional effect of each locus . From this we can calculate the expected changes in genetic variance over time . For many of the patterns of epistasis studied they maintain genetic variance over long evolutionary periods ( Figure 2a and Figure S5 ) , as often their allele frequencies can be maintained at intermediate levels . However the majority of this variance is non-additive , with almost all of the additive variance eventually disappearing ( Figure 2b and Figure S6 ) . Although this study assesses a large number of G-P maps , because the parameter space of epistatic G-P maps is effectively infinite the question of how much additive variance can possibly be maintained under selection by a two-locus system still remains . To answer this we used a genetic algorithm to heuristically search the parameter space of the two-locus GP-map , with the objective of finding epistatic patterns that maintain high additive variance over evolutionary time . We should note that the purpose of this exercise is not to identify biologically feasible patterns per se , rather it is to assess the propensity for additive variance to be maintained under selection . The epistatic patterns that emerged with the highest level of maintained additive variance , as a proportion of total genetic variance , are shown in Figure 1 ( patterns 4–6 ) . The main feature of these patterns is that they exhibit overdominance , and that even in these extreme cases where the algorithm attempts to generate the G-P maps with the largest possible maintained additive variance , it is clear that the majority of genetic variance that is maintained will still be non-additive . While it appears that additive variance is difficult to maintain either through independent additive effects or through epistatic interactions , it is clear that most causal effects that have been discovered through GWA studies appear additive in nature [11] . This paradox may result from both ascertainment and confirmation biases that arise when there is incomplete linkage disequilibrium between underlying causal variants and the observed SNPs in a GWA study . Figure 2b and Figure S6 show that although estimated genetic variance at observed SNPs decreases as LD with causal variants decreases , the estimated proportion of the variance that is additive actually increases . To illustrate this further Figure 3 shows how estimates of epistatic GP-maps change when LD is reduced , and two important biases can be shown . Firstly , the higher order variance components ( rows 3–5 ) rapidly haemorrhage genetic variance ( see Figure S8 ) , such that even at LD of the genotype class means are close to identical . This means that detection is strongly dependent upon high or complete LD , even when effect sizes are large , so most epistatic mutations will remain undetected and their prevalence underestimated . Hence , because additive variance decays linearly with LD [46] , at low LD they remain detectable leading to an ascertainment bias for additive vs non-additive effects . Secondly , with the patterns of canalisation ( rows 1–2 ) , as LD reduces although some genetic variance is maintained , the G-P map appears entirely additive . Thus functional maps that confer epistatic effects that can be detected at relatively low LD are likely to be interpreted as being entirely additive . Thus , researchers who attempt to quantify the contribution of non-additive variance from SNPs associated with a trait are liable to incorrectly confirm that most variants act additively . Typically GWA studies test each SNP one at a time for additive effects . To explicitly include interaction terms in the search this approach can be extended from one dimension ( 1D ) to two dimensions ( 2D ) , where every pair of SNPs is tested jointly for an association [26] , [47] . Given that we know the trajectory of allele frequencies under selection , it is possible to ask what the best GWA strategy for detecting evolutionarily likely variants might be . Two main search methods were tested , 1D scans ( as are typically performed in GWA studies ) , and exhaustive 2D scans ( previously computationally unfeasible until the availability of more advanced software [47] ) . For each search method various different model parameterisations were also tested . We used a Bonferroni correction for all methods , so 2D scans had a much more severe multiple testing penalty than 1D scans . Broadly , the results show two important points . Firstly , there is no single method that is always superior under the conditions that were tested . Secondly , it is very rare that parameterising for additive effects is the most powerful method ( Figure 4a and Figure S7 ) . Rather , if LD is no higher than , for example , between causal variants and observed SNPs then one dimensional scans , although not particularly powerful in absolute terms , are most effective provided that both additive and dominance effects are modelled ( 2 d . f . test ) . In the case of very high LD ( e . g . dense marker panels , imputed data , sequence data ) , a strong advantage in power to detect variants at evolutionarily likely frequencies can be conferred by using exhaustive two-dimensional scans and modelling whole genotype effects ( 8 d . f . test ) . A more detailed view of this relationship between LD and detectability is shown in Figure 4b ( patterns 4–6 ) . When LD between causal variants and observed SNPs is high , although additive variation exists , much greater power can be achieved in its detection if the search focuses on the larger non-additive variance components . However , as LD decreases , the proportion of the genetic variance that is additive increases , thus one dimensional scans gain an advantage . Nevertheless , as one might expect at low LD there is in general very little power from any method . It may seem surprising that despite the much larger multiple testing penalty , the 2D scans perform well in terms of power . But there exists a trade-off between the extra variance explained by extending the search into higher dimensions , and the amount of variance required to be detected in order to overcome the multiple testing correction . The results show that because non-additive variance components can be maintained under selection the 2D strategy is conferred an advantage in this trade-off .
The architecture of genetic variation must be understood if we are to make progress in fields such as disease risk prediction , personalised medicine , and animal and crop breeding . This study sought to examine the potential for epistasis to maintain genetic variation under selection , and thus to inform GWA strategies based on these results . We investigated to what extent deleterious mutations could be maintained as common polymorphisms under selection . A large sample of potential G-P maps were assayed [48] in order to develop a broad picture of the general behaviour of epistasis under selection , and this was extended further by heuristically searching through the parameter space of epistatic G-P maps . It was demonstrated that the maintenance of genetic variation at intermediate frequencies , for traits under selection over evolutionary time , could be achieved through a wide range of two-locus epistatic models . By definition , such is not the case for independent additive effects ( Figures S2 and S3 ) . Following on , it was demonstrated that even in the best case scenario , where G-P maps were generated to maximise additive variance , total genetic variance was mostly composed of non-additive components ( Figure 2b and Figure S6 ) . This finding is in disagreement with a recent study [28] , which showed that for various two-locus epistatic models , the deterministic partitions of genetic variance calculated across different frequency distributions were largely comprised of the additive component . Here we show that those allele frequencies at which additive variance is high ( a large proportion of the frequency spectrum ) , are evolutionarily unstable , thus should epistatic variants be affecting fitness traits then the majority of the variance will be non-additive . Ultimately there is no simple mechanism whereby two-locus epistasis will significantly contribute towards the missing heritability , unless estimates have been contaminated by non-additive genetic components or common environment effects . This is a well-known potential problem with full-sib based estimates and twin studies [30] . Indeed , a recent examination of this problem showed that additive variance estimates could be inflated significantly when complex traits are controlled by epistasis [24] . The results suggest that we should expect significant levels of non-additive variation to be maintained in fitness-related traits . While non-additive variance components are often considered to be nuisance terms in quantitative genetics [49] , their existence can be levered to actually improve the detection of additive variance . Here the premise is that if additive variation is observed then there is likely to be an accompanying non-additive genetic component that allows it to persist in the population . Power comparisons were made between 1D and 2D scans , as well as different model parameterisations , with a view to testing the power to detect variants under selection at evolutionarily likely frequencies . Surprisingly , the simplest and most widely used parameterisation , modelling for additive effects in one dimension , was seldom the most powerful approach . On the contrary , because other forms of genetic variance are co-precipitated along with additive variance , by parameterising the tests to include them the power was seen to improve . However , it was observed that even with modest reductions in LD between causal variants and observed SNPs all testing strategies tended to decline in performance rapidly . This leaves researchers in a difficult situation , where the strategy of increasing SNP panel densities as an intuitive response to improve LD coverage comes at a quadratic cost ( in the two-locus case ) in computation time and multiple testing penalties . An important outcome here is that there is no single test with consistently superior performance , and this resonates with the idea of the “no free lunch” theorem , which states that although competing algorithms will behave differently under different conditions , they will have identical performance when averaged across all conditions [50] . The key in such a situation is to know which conditions are most likely to manifest in the data , and here our argument is that for fitness traits non-additive effects are likely to exist at frequencies where additive variance is minimised . Although the intention behind the use of the genetic algorithm in this study was to explore the potential for a two-locus system to maintain additive variance , rather than to necessarily identify biologically feasible maps , those maps that emerged did not appear biologically untenable . In fact they can be supported by reports in the literature due to their tendencies for exhibiting heterozygote advantage [51] , [52] . The example of the single locus case , overdominance , is central to processes of heterosis and inbreeding depression [52] , [53] , and has been identified in molecular studies also [54] , [55] . Indeed , heterozygote advantage plays an important role in evolutionary theory , as it confers segregational load on a population , and this type of load cannot be purged due to balancing selection , potentially rendering populations susceptible to accumulating a critical mass of such polymorphisms [56] . The idea of a critical mass of deleterious mutations has been widely explored in amictic haploid populations , particularly in the context of Muller's ratchet , and in this case synergistic epistasis has been suggested as a mechanism that could alleviate the problem in some situations [57] , [58] . This study may offer a similar answer for the analogous problem of segregational load in diploid populations , because it can be observed that while patterns of overdominance ( Figure S3 , pattern 55 ) form a stable equilibrium , small perturbations to this G-P map through the introduction of an interacting locus ( e . g . patterns 45 , 47 , 53 ) could destabilise the equilibrium and lead to eventual fixation . It is important to note that the processes underlying stasis and missing heritability are unlikely to be caused by any single factor . For example , a compelling argument is that though most traits exhibit genetic variation , selection acts upon multidimensional trait space in which there is no genetic variation [59] , and this will hold under an additive model of genetic variation . It is also important to consider the manner in which traits of interests , such as human diseases , are involved in fitness . For example in an assessment of selection signatures on SNPs implicated in type 1 diabetes it has been shown that the causal alleles have undergone positive selection to a greater extent than protective alleles , while with Crohn's disease the converse is true [60] . In the case of both diseases more variants are being discovered as sample sizes increase [61] , [62] , but given that only a small proportion of the total heritability has so far been explained , and the search has concentrated on additive variants only , inferences about the genetic architecture cannot be made . Occam's razor can be invoked to justify the additive paradigm used in GWA studies [28] . But the analyses presented here demonstrate that perhaps rejecting more complex models in favour of simple ones should not always be the automatic choice . With sample sizes growing and with the tools now available to search for epistasis in a computationally efficient manner ( e . g . [47] , [63]–[66] ) it should be possible to explore the genetic architecture of complex traits in directions that were not previously possible .
The evolutionary fate of an arbitrary two-locus epistatic fitness pattern can be characterised by the allele frequencies and recombination fraction of the two loci as a Markovian process . Therefore it is straightforward to calculate the trajectory of allele frequencies over evolutionary time for a wide range of epistatic patterns . For each G-P map , deterministic simulations were performed with varying conditions for initial allele frequencies ( 25 initial allele frequencies enumerating the set over both loci ) and linkage disequilibrium between the linked and causal SNPs ( ) . Variance components and expected test statistics for different parameterisations and under different assumed search strategies were calculated . The purpose of genetic algorithms is to heuristically search a large solution domain for optimal model parameters whilst avoiding a computationally prohibitive exhaustive search [72] . In this case , the algorithm is used to search for two-locus epistatic fitness patterns that simultaneously maximise additive genetic variance and avoid fixation through selection , where is a G-P map whose values represent the fitness associated with each two-locus genotype . In this case the building blocks are the nine two-locus genotype class means that comprise the G-P map . To consider the potential impact of genetic drift and random noise on the conclusions from the deterministic simulations , similar conditions were recreated heuristically on randomly generated populations . For each epistatic pattern we generated 300 populations of 1000 individuals . Each individual has a two-locus genotype and a corresponding phenotype such that ( 28 ) where ( 29 ) and and were the fitness values for indvidual corresponding to the G-P map . The non-genetic variance of the trait was defined at generation 0 as in equation 15 and remained constant at each generation . The heritability , , was set to at generation 0 . Each generation 500 individuals were sampled from a discrete probability distribution where the individual's phenotype was the relative probability of being sampled , and from these 250 random pairings were made to produce 1000 offspring for the next generation . Phenotypes for each new individual were created at each generation as in equation 28 , and simulations continued until at least one locus reached fixation . The initial allele frequencies were 0 . 5 for each locus , and the simulations ran for 200 generations or until at least one locus became fixed . The code for this algorithm is available at https://github . com/explodecomputer/epiFit/ . | In this study we have shown that two independent problems may have a common cause . Why do traits under selection exhibit additive genetic variance , and why is the proportion of the heritability explained by additive effects much smaller than the total heritability estimated to exist ? Our results indicate that epistatic interactions can allow deleterious mutations to persist under selection and that these interactions can abate the depletion of additive genetic variation . Furthermore , a much larger element of non-additive genetic variance is maintained , which supports the notion that the heritability estimated from family studies could be a mixture of both additive and non-additive components . We show that searching directly for epistatic effects greatly improves the discovery of variants under selection , despite the multiple testing penalty being much larger . Finally , we demonstrate that common practices in genome-wide association studies could lead to both an ascertainment bias in detecting additive effects and a confirmation bias in perceiving that most of the genetic variance is additive . | [
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] | 2013 | An Evolutionary Perspective on Epistasis and the Missing Heritability |
Modification defects in the tRNA anticodon loop often impair yeast growth and cause human disease . In the budding yeast Saccharomyces cerevisiae and the phylogenetically distant fission yeast Schizosaccharomyces pombe , trm7Δ mutants grow poorly due to lack of 2'-O-methylation of C32 and G34 in the tRNAPhe anticodon loop , and lesions in the human TRM7 homolog FTSJ1 cause non-syndromic X-linked intellectual disability ( NSXLID ) . However , it is unclear why trm7Δ mutants grow poorly . We show here that despite the fact that S . cerevisiae trm7Δ mutants had no detectable tRNAPhe charging defect in rich media , the cells constitutively activated a robust general amino acid control ( GAAC ) response , acting through Gcn2 , which senses uncharged tRNA . Consistent with reduced available charged tRNAPhe , the trm7Δ growth defect was suppressed by spontaneous mutations in phenylalanyl-tRNA synthetase ( PheRS ) or in the pol III negative regulator MAF1 , and by overexpression of tRNAPhe , PheRS , or EF-1A; all of these also reduced GAAC activation . Genetic analysis also demonstrated that the trm7Δ growth defect was due to the constitutive robust GAAC activation as well as to the reduced available charged tRNAPhe . Robust GAAC activation was not observed with several other anticodon loop modification mutants . Analysis of S . pombe trm7 mutants led to similar observations . S . pombe Trm7 depletion also resulted in no observable tRNAPhe charging defect and a robust GAAC response , and suppressors mapped to PheRS and reduced GAAC activation . We speculate that GAAC activation is widely conserved in trm7 mutants in eukaryotes , including metazoans , and might play a role in FTSJ1-mediated NSXLID .
During biogenesis , tRNAs acquire extensive post-transcriptional modifications that are important for their function as an adaptor molecule during translation . Modifications in the main body of the tRNA generally affect folding or stability of specific tRNAs [1–3] , whereas modifications in and around the anticodon loop play crucial roles in translation , including promoting accuracy in charging [4 , 5] , reading frame maintenance [6–9] and decoding [10–13] . Indeed , modification is particularly extensive in the anticodon loop region comprising the loop itself and the 31–39 closing base pair , with an average of 2 . 72 modifications per eukaryotic cytoplasmic tRNA [14] . Defects in anticodon loop modification frequently lead to impaired growth in the yeast Saccharomyces cerevisiae and to a number of human disorders , particularly neurological disorders or mitochondrial syndromes [15 , 16] . For example , yeast TAD2 and TAD3 are required for inosine modification of the wobble nucleotide A34 and are essential [10] , and a mutation in the corresponding human ADAT3 gene is associated with intellectual disability and strabismus [17] . Similarly , yeast pus3Δ mutants have growth defects due to lack of pseudouridine ( Ψ ) at U38 and U39 and are temperature sensitive due to tRNAGln ( UUG ) [18] , and a mutation in the corresponding human PUS3 gene is associated with syndromic intellectual disability and reduced pseudouridine [19] . In addition , yeast elongator mutants lacking the carbonylmethyl-U34 family of modifications ( xcm5U34 ) have a number of phenotypes due to reduced function of two or three tRNA species [20–22] , while Caenorhabditis elegans elongator mutants are associated with neurological and developmental dysfunctions [23] , and human elongator mutations are linked to familial dysautonomia [24–26] . Although the molecular mechanisms linking tRNA modification defects to human diseases remain largely unknown , the causes are amenable to study in model organisms . One such unsolved problem is why it is important for eukaryotes to have 2'-O-methylated C32 ( Cm ) and N34 ( Nm ) in their tRNAs , catalyzed by Trm7 family members . In S . cerevisiae , a trm7Δ mutant grows poorly due to reduced function of tRNAPhe , but not its other two substrates , tRNALeu ( UAA ) and tRNATrp ( CCA ) , and in the phylogenetically distant yeast Schizosaccharomyces pombe , the near lethal phenotype of a trm7Δ mutant is rescued by overproduction of tRNAPhe [27–29] . In humans , seven different alleles of the human TRM7 homolog FTSJ1 have been linked to non-syndromic X-linked intellectual disability ( NSXLID ) [30–34] , and lymphoblastoid cell lines ( LCLs ) derived from patients with two different FTSJ1 alleles had tRNAPhe with undetectable levels of Cm32 and Gm34 [34] . In eukaryotes , modification of tRNAs by Trm7 involves conserved partner proteins for each modification and a conserved circuitry for tRNAPhe anticodon loop modification . In S . cerevisiae , Trm7 interacts separately with Trm732 and Trm734 for formation of Cm32 and Nm34 respectively in each of its three tRNA substrates , and the presence of Cm32 and Gm34 in tRNAPhe drives the formation of wybutosine ( yW ) from 1-methylguanosine ( m1G ) modification at G37 [28] . S . pombe Trm732 and Trm734 have the same functions in Cm32 and Gm34 modification of tRNAPhe and , as in S . cerevisiae , Cm32 and Gm34 drive formation of yW37 in tRNAPhe [29] . Moreover , available evidence suggests that this circuitry is conserved in humans . tRNAPhe from patient LCLs with an FTSJ1 deletion or a splice site mutation had substantially reduced peroxywybutosine ( o2yW37 ) , as expected if o2yW37 formation is stimulated by Cm32 and Gm34 [34] . Furthermore , expression of either FTSJ1 or the TRM732 ortholog THADA complements the corresponding S . cerevisiae mutants , as does expression of S . pombe trm7+ and the Drosophila TRM7 homolog ORF CG5220 [29] . However , despite the extensive studies of Trm7 in different organisms , the biological consequences of lacking Cm32 and Gm34 modifications on tRNAPhe remain unclear . We investigate here why Cm32 and Gm34 modifications are critical for tRNAPhe function and healthy growth in yeast . We provide evidence that despite the lack of an obvious charging defect , trm7Δ mutants activate a robust general amino acid control ( GAAC ) response in both S . cerevisiae and S . pombe , each in a manner suggesting the sensing of uncharged tRNA . Moreover , in each organism we find that suppressors of the trm7Δ growth defect frequently map to subunits of phenylalanyl tRNA synthetase ( PheRS ) and reduce the GAAC response toward that in wild type cells . These results argue for a conserved Trm7 biology in eukaryotes and argue that subtle changes in tRNAPhe charging have dramatic effects on cell physiology .
To begin to elucidate why Trm7 and 2’-O-methylation at C32 and N34 of tRNAs were important , we isolated and analyzed spontaneous suppressors that improved the slow growth phenotype of S . cerevisiae trm7Δ mutants . This slow growth phenotype is apparent by analysis of growth of trm7Δ [URA3 CEN TRM7] mutants on media containing 5-FOA [28] , and by growth analysis of trm7Δ mutants on rich media and minimal media immediately after loss of the [URA3 CEN TRM7] plasmid ( S1 Fig ) , and in all of these conditions , the growth defect is fully suppressed by overproduction of tRNAPhe ( S1 Fig , [28] ) . We isolated 21 genetically independent faster growing suppressors after plating trm7Δ cells on YPD ( rich ) medium , and found that 19 of them had a dominant mutation in either FRS1 or FRS2 ( S1 Table ) , which encode the two subunits of PheRS [35] . This result was surprising since we had shown previously that tRNAPhe from trm7Δ mutants had no obvious charging defects , and was present at similar overall levels in WT cells [28] . Indeed , analysis of tRNA isolated under acidic conditions to preserve charging [36 , 37] showed that tRNAPhe from three independent freshly derived trm7Δ isolates grown in rich media had no discernible charging defect ( 65 ± 1% charging ) , compared to tRNAPhe from WT cells ( 67 ± 2% ) or tyw1Δ mutants ( 66 ± 2% ) ( Fig 1A ) . tyw1Δ mutants , like trm7Δ mutants , have m1G37 instead of yW37 [38] , and migrate identically on acidic gels . Similarly , no charging defect was seen in the other two Trm7 substrates , tRNALeu ( UAA ) and tRNATrp , or in the non-substrate tRNAGly ( GCC ) . Furthermore , no increase in charging was observed in each of three representative suppressors of the trm7Δ growth defect ( frs1-E415K , frs1-A549T , and frs2-L265V ) for any of the tRNA species examined ( 65 ± 1% , 64 ± 0% , 65 ± 1% respectively for tRNAPhe ) . By contrast , in synthetic minimal medium a more prominent charging defect was observed by acidic Northern analysis of tRNAPhe from trm7Δ cells ( Fig 1B ) . Under this growth condition , we found that tRNAPhe charging levels were reduced to 55 ± 0% in trm7Δ mutants , substantially below those of WT cells ( 68 ± 1% ) and tyw1Δ mutants ( 77 ± 2% ) . Moreover , the three trm7Δ suppressors all restored tRNAPhe charging to levels similar to charging observed in tyw1Δ mutants ( 75 ± 2% , 74 ± 1% , 73 ± 1% respectively for the frs1-E415K , frs1-A549T , and frs2-L265V mutants ) . Consistent with a tRNAPhe charging defect in minimal media , we found that limiting phenylalanine exacerbated trm7Δ growth defects . After deletion of PHA2 ( encoding prephenate dehydratase ) to confer phenylalanine auxotrophy [39] , we found that trm7Δ pha2Δ mutants showed an exacerbated growth defect compared to trm7Δ mutants in the presence of 10 mg/L phenylalanine , and this defect was complemented by re-introduction of the PHA2 gene on a plasmid; by contrast , under the same conditions pha2Δ mutants showed no discernable growth defect compared to a WT ( trm7Δ [TRM7] ) strain ( Fig 1C ) . Since acidic Northern analysis of trm7Δ mutants revealed no detectable tRNAPhe charging defect in rich media , but a distinct charging defect in minimal media that was suppressed by each of three suppressors , we examined in vivo charging in both rich and minimal media by analysis of the general amino acid control ( GAAC ) response [40] . In yeast and other eukaryotes , uncharged tRNAs arising from amino acid starvation or lack of functional tRNA synthetases bind to Gcn2 and activate its kinase domain , resulting in phosphorylation of eIF2α , de-repression of GCN4 translation , and transcriptional activation of nearly one tenth of the yeast genome , including numerous amino acid biosynthetic genes [41–43] . We reasoned that if there was a subtle accumulation of uncharged tRNAPhe in trm7Δ mutants , this might result in a GAAC response . Indeed , RT-qPCR analysis of mRNA from cell pellets collected from the same cultures as those used in the acidic Northerns ( Fig 1A and 1B ) revealed that the mRNA levels of two known GCN4 target genes , HIS5 and LYS1 , were significantly increased in trm7Δ mutants ( relative to ACT1 ) , compared to WT cells . In rich media , relative levels of HIS5 and LYS1 mRNA increased 27 . 8-fold and 90 . 9-fold respectively , and in minimal media relative levels increased 17 . 1-fold and 43 . 2-fold ( Fig 2A , S2 Table ) . These GAAC activation levels in trm7Δ mutants were comparable to those in WT His+ cells treated for 1 hour with 10 mM or 100 mM 3-amino-1 , 2 , 4-triazole ( 3-AT ) ( Fig 2B , S2 Table ) , a competitive inhibitor of His3 that has been used extensively to induce the yeast GAAC response [40 , 44] . This robust constitutive GAAC response in trm7Δ mutants provided initial evidence that charged tRNA was limiting in vivo . Further analysis showed that the trm7Δ-mediated induction of the GAAC response is occurring through Gcn2 , which senses uncharged tRNA [45] . The GAAC pathway can be induced by Gcn2 or by a pathway independent of Gcn2 [46–48] , which is not well understood . As expected of the Gcn2-mediated GAAC response , we found that trm7Δ gcn2Δ strains completely abolished transcriptional activation of the HIS5 gene , as did the control trm7Δ gcn4Δ mutants ( Fig 2C , S2 Table ) . These results provided compelling evidence that the GAAC response observed in trm7Δ mutants arose from uncharged tRNA . We note that the slow growth of S . cerevisiae trm7Δ mutants appears to be due to both lack of available charged tRNAPhe and to activation of the GAAC response itself , since either a gcn2Δ or a gcn4Δ mutation partially improved growth of a trm7Δ strain in both rich and minimal media ( Fig 2D ) . Nonetheless , the increased stress on trm7Δ mutants associated with activation of the GAAC response must be a secondary consequence of the lack of available charged tRNAPhe required to initiate the response . Further analysis demonstrated that activation of the GAAC response was closely tied to the growth phenotype of trm7Δ related strains . Thus , as measured by HIS5 mRNA levels , the GAAC pathway was not activated by the lack of Cm32 in a trm732Δ mutant , by lack of Nm34 in a trm734Δ mutant , or by lack of yW37 in a tyw1Δ mutant , or by a trm732Δ tyw1Δ double mutant or a trm734Δ tyw1Δ double mutant ( Fig 2E , S2 Table ) , all of which are healthy strains [28] . By contrast , a trm732Δ trm734Δ strain fully activated the GAAC response , with relative HIS5 mRNA levels comparable to those of trm7Δ mutants , consistent with our previous finding that trm732Δ trm734Δ strains phenocopied the growth defect of trm7Δ mutants [28] . Strikingly , each of 18 trm7Δ suppressors we examined reduced the magnitude of the GAAC response from relative HIS5 mRNA levels of 37 . 3-fold and 36 . 1-fold in trm7Δ mutants ( Fig 3A , left side and right side respectively , S3 Table ) to levels approaching those observed in WT cells ( 1 . 1- to 5 . 5-fold ) . These suppressors included 16 with mutations in PheRS subunits , as well as two that did not have mutations in PheRS . The co-reversion of the trm7Δ growth defect and the GAAC response further implied that lack of available charged tRNA was the cause of the growth defect . Consistent with the interpretation that the poor growth of trm7Δ mutants is caused by defective charging , we found that overproduction of PheRS on a [PGAL-FRS1 PGAL-FRS2] plasmid improved trm7Δ growth , compared to that of a trm7Δ strain with a plasmid expressing either subunit of PheRS , or an empty vector ( Fig 3B ) . Furthermore , overexpression of both FRS1 and FRS2 improved tRNAPhe charging ( S2 Fig ) and reduced relative HIS5 mRNA levels in trm7Δ mutants from 17 . 8 to 4 . 9 , while overexpression of either FRS1 or FRS2 had no effect ( Fig 3C , S3 Table ) . Since elongation factor 1A ( EF-1A ) binds to and delivers aminoacylated tRNA to the ribosomes A-site , we speculated that its overexpression might result in more charged tRNAPhe available for use in translation , thereby improving trm7Δ growth . To test this hypothesis , we introduced an extra copy of TEF1 or TEF2 , which encode identical copies of EF-1A , into a trm7Δ strain . We found that elevated levels of EF-1A moderately rescued the growth defect ( Fig 4A ) , and partially suppressed the GAAC activation , with p values of 0 . 012 and 0 . 055 respectively ( Fig 4B , S4 Table ) , while deletion of TEF2 in a trm7Δ strain exacerbated the slow-growth phenotype ( Fig 4C ) . The improved growth of trm7Δ mutants with increased EF-1A levels presumably reflects increased availability of aminoacylated tRNAPhe for translation after charging by PheRS , rather than increased tRNAPhe charging , which is not significantly altered ( S3 Fig ) . Similar results are obtained by treatments expected to increase the population of charged tRNAPhe . Thus , overexpression of tRNAPhe on a high copy plasmid , which is known to suppress the trm7Δ growth defect ( S1 Fig , [28] ) and results in 4 . 2-fold overproduction of tRNAPhe ( S4A Fig ) , reduced the relative HIS5 mRNA levels to values similar to WT cells , while overexpression of other control tRNAs had no effect ( Fig 5A , S5 Table ) . Furthermore , whole genome sequencing showed that trm7Δ suppressor 12 ( Fig 3A ) had a mutation in MAF1 , a negative regulator of pol III transcription [49] . Since this maf1-C299Y mutation alters a highly conserved residue in the Box C region of Maf1 , we inferred that this mutation behaved as a null mutation [49–51] . To test this inference , we introduced a MAF1 deletion into the trm7Δ [URA3 TRM7] strains and tested for growth on media containing 5-FOA to select against the URA3 plasmid . The resulting trm7Δ maf1Δ strain grew better than the control trm7Δ mutants ( Fig 5B ) , and had increased levels of tRNAPhe and tRNAPhe charging , in both log phase and stationary phase ( S4B Fig ) . To determine if GAAC activation is a common theme among tRNA anticodon loop modification mutants , we examined the GAAC response in several other mutants , including strains lacking isopentenyladenosine ( i6A37 ) , due to a mod5Δ mutation [52]; 3-methylcytidine ( m3C32 ) , due to a trm140Δ mutation [53 , 54]; the cm5U moiety of xcm5U34 , due to a kti12Δ mutation [55]; the 2-thiouridine moiety ( s2U ) of mcm5s2U34 , due to a uba4Δ mutation [56]; and Ψ38 and Ψ39 , due to a pus3Δ mutation [57] . Among these mutants , only pus3Δ mutants had a substantial increase in relative HIS5 mRNA levels ( 15 . 7-fold ) , albeit much less than in trm7Δ mutants ( 116-fold in this experiment ) , whereas other modification mutants had only slightly increased HIS5 mRNA levels ( 1 . 8- to 3 . 2-fold ) ( Fig 6 ) . Thus , robust GAAC activation , as observed in trm7Δ mutants , is not a general theme among modification mutants . To investigate the evolutionary implications of our results , we examined the charging status and GAAC response in S . pombe trm7Δ mutants , which as in S . cerevisiae , grow poorly due to lack of sufficient tRNAPhe [29] . Since Sp trm7Δ strains are barely viable , we assayed tRNA charging and GAAC induction after growth of an Sp trm7Δ [Pnmt1 trm7+] strain in minimal ( EMM ) medium , followed by addition of thiamine to repress Sp Trm7 expression [29] . As in S . cerevisiae trm7Δ mutants grown in rich medium , we found that Sp tRNAPhe charging levels were comparable in Sp trm7Δ [Pnmt1 trm7+] grown in repressing conditions to deplete Trm7 ( 77 . 0 ± 2 . 6% ) , compared to the same strain in permissive conditions ( 79 . 3 ± 3 . 5% ) or to WT strains ( 76 ± 5 . 6% ) ( Fig 7A ) . ( Note that a similar S . pombe Trm7 depletion experiment could not be done in rich ( YES ) medium due to the presence of thiamine in this medium . ) However , examination of mRNA from cell pellets collected in parallel from the same cultures revealed that Sp trm7Δ [Pnmt1 trm7+] strains grown in repressing conditions induced the GAAC response , with significantly increased relative mRNA levels of three Gcn2 dependent GAAC-regulated genes ( lys4+ , aro8+ ( SPBC1773 . 13 ) , and aro8+ ( SPAC56E4 . 03 ) ) [58] , compared to WT cells ( 14 . 7-fold , 6 . 9-fold and 22 . 8-fold increase respectively ) ; whereas Sp trm7Δ [Pnmt1 trm7+] strains grown under permissive conditions had relative mRNA levels very similar to WT cells . The GAAC induction levels in the Sp trm7Δ [Pnmt1 trm7+] strains grown in repressing conditions were similar to those when WT S . pombe cells were treated with 10 mM or 30 mM 3-AT for 4 hours ( Fig 7B , S7 Table ) . Thus , depletion of Trm7 in S . pombe resulted in little , if any , detectable defect in tRNAPhe charging , but a robust induction of the GAAC pathway . As in S . cerevisiae , S . pombe mutants lacking Cm32 or Gm34 of tRNAPhe have GAAC responses that tracked with the growth defect . Sp trm734Δ strains grow relatively poorly [29] , but not nearly as poorly as trm7Δ mutants , and had a partially activated GAAC response , with 6 . 5-fold and 6-fold increased relative mRNA levels of lys4+ and aor8+ ( SPAC56E4 . 03 ) respectively , compared to 9 . 0-fold and 12 . 9-fold for Sp trm7Δ [Pnmt1 trm7+] strains grown under repressive conditions . By contrast , Sp trm732Δ mutants have no obvious growth defect at 30°C—37°C [29] , and had near wild type relative mRNA levels for lys4+ and aor8+ ( SPAC56E4 . 03 ) ( Fig 7C , S7 Table ) . Further analysis showed that rescue of the growth defect of S . pombe trm7 mutants reduced the GAAC response toward WT levels . As in S . cerevisiae , overproduction of Sp tRNAPhe reduced the GAAC response as measured by relative mRNA levels of lys4+ and aor8+ ( SPAC56E4 . 03 ) ( Fig 7C , S7 Table ) , consistent with the rescue of the Sp trm7Δ growth defect we previously observed [29] . Furthermore , suppressors of the Sp trm7Δ growth defect behaved as in S . cerevisiae . We isolated Sp trm7Δ suppressors by plating Sp trm7Δ [Pnmt1 trm7+] cells on media containing FOA , and each of two suppressors we analyzed had mutations in PheRS , and the suppressor strains in each case reduced the induction of the GAAC response ( Fig 7C , S7 Table ) . These results suggest that lack of sufficient charged tRNAPhe is also the main problem causing slow growth in Sp trm7Δ cells , despite the lack of detectable charging defect in acidic Northerns . Based on the conserved induction of the GAAC response in S . cerevisiae and S . pombe trm7Δ mutants , we examined human lymphoblastoid cell lines with mutations in FTSJ1 for an induced GAAC response by measuring mRNA levels of two Gcn2 dependent GAAC-regulated genes , CTH and GADD153 [59] . Although WT control cell lines treated with the prolyl tRNA synthetase inhibitor halofuginone induced a significant GAAC response ( S5A Fig ) [60 , 61] , we obtained equivocal and inconclusive results for GAAC induction in the human lymphoblastoid FTSJ1 cell lines , compared to the WT cell lines ( S5B Fig ) .
Although standard acidic Northern analysis did not reveal significant reduced tRNAPhe charging in S . cerevisiae trm7Δ mutants grown in rich media , we provided four lines of evidence supporting the conclusion that the growth defect of trm7Δ mutants is caused by reduced available charged tRNAPhe . First , contrary to our observations in rich media , in minimal media acidic Northern analysis revealed distinctly reduced tRNAPhe charging in trm7Δ cells , charging was restored in each of three suppressors analyzed , and limiting phenylalanine exacerbated the trm7Δ growth defect . Thus , it seemed plausible that there was a subtler charging defect in rich media . Second , trm7Δ mutants activated a robust GAAC response in both rich media and minimal media , and activation of the GAAC response in rich media depended on Gcn2 , which is known to sense uncharged tRNA [45 , 62] . Third , each of 18 tested trm7Δ suppressors isolated in rich media suppressed the activation of the GAAC response found in trm7Δ mutants , and the vast majority had mutations that mapped to PheRS , arguing for the importance of increased charging for suppression of both the trm7Δ growth defect and the GAAC activation . Fourth , overproduction of PheRS also suppressed both the trm7Δ growth defect and GAAC activation , further implying that more charged tRNAPhe could overcome the phenotypes of trm7Δ mutants . Ascribing the trm7Δ growth defect to reduced tRNAPhe charging is also consistent with our previous observation that the steady state levels of tRNAPhe were normal in trm7Δ mutants [28] . The effects of manipulation of EF-1A , MAF1 , or tRNAPhe gene dosage on suppression of S . cerevisiae trm7Δ phenotypes can also be interpreted in terms of tRNAPhe charging or availability . The rescue of both the trm7Δ growth defect and GAAC activation by an extra copy of TEF1 or TEF2 could be due to the increased availability of the EF-1A:phe-tRNAPhe complex for the translation machinery , achieved by increased overall binding of charged tRNAPhe to EF-1A relative to PheRS , due to the tight binding constant of EF-1A for charged tRNA [63] , or by preventing spontaneous deacylation of charged tRNAPhe not bound by EF-1A , as demonstrated for EF-Tu [64] . The rescue of both the trm7Δ growth defect and GAAC activation by a maf1 mutation is likely due to the observed increase in tRNAPhe levels , consistent with the role of Maf1 as a negative regulator of pol III [49 , 65] , resulting in more charged tRNAPhe . Similarly , the rescue of both the trm7Δ growth defect [28] and GAAC induction by overexpression of tRNAPhe is due to the 4 . 2-fold increase in tRNAPhe , and the commensurate increase in charged tRNAPhe . We note that there is also an increase in the ratio of charged:uncharged tRNAPhe that occurs when tRNAPhe is overexpressed or in a maf1Δ mutation; this likely results from the decreased relative usage of tRNAPhe during translation when it is overproduced . We also note that the increase in uncharged tRNAPhe that occurs when tRNAPhe is overexpressed or in a maf1Δ mutation does not provoke the GAAC response . This result is consistent with the prevailing model that Gcn2 activation occurs in concert with the Gcn1-Gcn20 complex at the ribosome , triggered by entry of uncharged cognate tRNA at the A site independent of EF-1A [66–68] . Based on this model , the increased pools of charged tRNA would effectively outcompete the increased pool of uncharged tRNA for binding at the A-site when both are available , thus preventing activation of the GAAC response . We have also shown that depletion of Trm7 in S . pombe resulted in a severe growth defect , and induced a robust GAAC response with no obvious alteration of tRNAPhe charging as measured by acidic Northerns , and that suppressors of the growth defect reduced induction of the GAAC response and mapped to PheRS . Since the genes we assayed respond to the GAAC pathway when it is activated by uncharged tRNA , but not by other stimuli [58] , we infer that S . pombe trm7Δ mutants , like S . cerevisiae trm7Δ mutants , behave as if they have uncharged tRNA . It is intriguing that there was no discernible tRNAPhe charging defect detected in acidic Northerns from S . cerevisiae trm7Δ mutants grown in rich media and in S . pombe trm7Δ [Pnmt1 trm7+] mutants grown in repressing conditions , whereas mRNA levels analyzed from the same cultures showed robust induction of the GAAC response . There are at least three reasonable explanations of this observation . First , the tRNAPhe charging defects may be too subtle to be detected by the acidic Northern assay , but can be effectively captured by the sensitive GAAC response . Acidic Northern analysis has been used extensively to measure charging since its initial description [36 , 69] . However , quantification of uncharged tRNA might be particularly difficult for tRNAPhe because of the higher background of uncharged tRNAPhe in most RNA preps ( Fig 1A; [70 , 71] ) and because of the possibility of incomplete yW modification of tRNAPhe in WT cells grown in different conditions [72] , which could interfere because of small mobility differences between uncharged tRNAPhe with yW , and charged tRNAPhe without yW . In this regard , it is not clear how much uncharged tRNA in the cell is required to activate the GAAC response for a given tRNA species [40] . Second , it is possible that tRNAPhe is efficiently charged in vivo , but is sequestered from use in translation by some tRNA binding proteins , resulting in an increased probability that uncharged tRNAPhe will bind at the ribosome A site and trigger the GAAC response . The tRNAPhe might be sequestered in the nucleus by retrograde tRNA nuclear import [73 , 74] or as an Msn5:EF-1A:phe-tRNAPhe complex [75] somehow triggered by lack of the modifications , or perhaps sequestered in a stress granule [76] . However , it seems unlikely that charged tRNAPhe is sequestered by binding as a product to PheRS , since overproduction of PheRS suppresses the growth defect and the GAAC response . Whether tRNAPhe is subtly undercharged or is charged but effectively sequestered , the concordance of the trm7Δ growth defect and the robust GAAC response is striking in both S . cerevisiae and S . pombe , and suggests that they have the same root cause: lack of available charged tRNAPhe . A third explanation is that lack of Trm7 modifications causes ribosome stalling independent of uncharged tRNA , as reported in mouse mutants deficient in tRNAArg ( UCU ) and GTPBP2 , a ribosome rescue factor [77] . The finding that a gcn2Δ or a gcn4Δ mutation partially improved growth of an S . cerevisiae trm7Δ strain indicates that some combination of the massively re-programmed expression pattern during the GAAC response [78] increases the stress on the trm7Δ mutants . This interpretation is consistent with models suggesting that constitutive activation of the GAAC response is deleterious to yeast [79 , 80] , as it may also be in metazoans based on the observation that inactivation of the GAAC response relieves TDP-43 toxicity in Drosophila and in mammalian neurons [81] . The GAAC activation we observed in S . cerevisiae trm7Δ mutants was more robust than each of the other anticodon loop modification mutants tested . The much more modest GAAC activation found in kti12Δ or uba4Δ mutants was very similar in magnitude to the Gcn2-independent GAAC activation found previously for disruption of the same mcm5s2U34 modification [82] , and a similar modest GAAC activation was also detected in mod5Δ and trm140Δ mutants . These more modest GAAC activation levels are associated with mutants that have no obvious growth defect under these conditions; by contrast , the more substantial GAAC induction found in pus3Δ mutants is consistent with the known growth defect of pus3Δ mutants [18 , 57] . Since a pus3Δ mutation impairs function of at least 3 of its 19 or more tRNA substrates in S . cerevisiae [18] , it is possible that more than one tRNA is responsible for the GAAC induction . The extent of GAAC induction observed in these anticodon loop modification mutants is consistent with a recent study on transcriptome-wide analysis of roles for tRNA modifications by ribosome profiling [83] . It is unclear from our results why the frs1 or frs2 mutations that we identified from S . cerevisiae trm7Δ suppressors were all genetically dominant . Dominant PheRS mutations would be expected if trm7Δ mutants had a charging defect , since gain of function mutations are expected to be dominant . However , the frs1 mutations map throughout the body of the protein , based on the human PheRS structure [84] , bringing up the question of how scattered mutations all improve the function of the synthetase in trm7Δ mutants . As none of the frs1 mutations localized to the editing domain , it is unlikely that these PheRS mutations reduce PheRS editing to inhibit GAAC induction , as observed for an frs1 editing mutant grown under conditions of excess tyrosine relative to phenylananine [70 , 71] ) . Two models of PheRS function could explain the widespread locations of dominant frs1 mutations among the S . cerevisiae trm7Δ suppressors . First , PheRS function could be reduced in trm7Δ mutants because of decreased recognition and binding of the hypomodified tRNA to PheRS , in which case the scattered frs1 gain-of-function mutations would all act to improve interactions with tRNA . This model is plausible , and consistent with the principle of weak binding for efficient catalysis [85] . It is also formally possible in this model that the frs1 mutations improve interaction between the two PheRS subunits , or that they improve stability or expression of the PheRS subunits , but these possibilities seem less likely to us because the mutations map all over the Frs1 subunit . Second , the charging activity of PheRS could be reduced in trm7Δ mutants because of increased binding of PheRS to hypomodified phe-tRNAPhe and the consequent slow release of product , reducing the rate of multiple turnover reactions . Although in bacteria rate limiting product release is found in class I synthetases rather than class II synthetases like PheRS [86] , this mechanism is in principle plausible for eukaryotic PheRS acting on tRNAPhe lacking 2'-O-methylation . In this case , the scattered gain-of-function frs1 mutations would all reduce interactions between PheRS and hypomodified tRNAPhe , promoting more effective release of charged tRNAPhe from PheRS and increased overall charging . Both of these models call for specific interactions between the anticodon loop and PheRS , consistent with the known PheRS recognition of G34 [87] , but the effects of Cm32 and Gm34 have not been tested [88 , 89] . It is remarkable that in both S . pombe and S . cerevisiae the poor growth of trm7Δ mutants is associated with apparently complete tRNAPhe charging but a robust GAAC response . Since these species diverged ∼330 to 420 million years ago [90] , this result implies its generality among eukaryotes , to go along with the previously established conserved importance of tRNAPhe as a Trm7 substrate in S . pombe and S . cerevisiae , the conserved anticodon loop modification circuitry of tRNAPhe in S . pombe , S . cerevisiae , and humans , and the conserved favored importance of Gm34 in S . pombe and humans [28 , 29 , 34] . Moreover , all eukaryotic PheRS species appear to have similar recognition sets , since human and S . cerevisiae PheRS each recognize the same five residues [91] , and tRNAPhe from wheat germ or S . pombe is charged by S . cerevisiae PheRS nearly as effectively as the native substrates [92] . Although our preliminary analysis of the GAAC response in human lymphoblastoid cell lines with mutations in FTSJ1 yielded equivocal results , this analysis might require specialized cell types to explain the non-syndromic nature of NSXLID [30–34] , or the cell lines may have accumulated secondary lesions that mask the GAAC induction . Based on this high degree of conservation of the biology of Trm7 and PheRS , we speculate that GAAC activation will be widely conserved in trm7 mutants in eukaryotes , including metazoans , and might play a role in NSXLID due to lesions in human FTSJ1 .
Yeast strains used in this study are listed in S1 and S8 Tables . trm7Δ supp 1 to 10 were isolated from yMG105 ( MATa , trm7Δ::bleR ) strain , and supp 11 to 21 from yMG107 ( MATα , trm7Δ::bleR ) strain . For all other experiments , trm7Δ mutants were freshly derived from yMG348-1 trm7Δ::bleR [CEN URA3 TRM7] each time before use , by growing yMG348-1 in YPD media overnight followed by streaking on media containing FOA to select against the URA3 plasmid . The WT His+ strains were derived from BY4741 by PCR amplification of HIS3 with its 5' and 3' flanking sequence , followed by linear transformation , selection on SD-His and PCR verification . PHA2 , GCN2 , KTI12 , UBA4 , MAF1 , and TEF2 were deleted by PCR amplification of DNA from the appropriate YKO collection kanMX strains using oligomers containing sequences 5' and 3' of the gene [93] , followed by linear transformation and selection on YPD media containing 300 mg/L geneticin . GCN4 was deleted by PCR amplification of the hygR marker , followed by linear transformation and selection on YPD media containing 300 mg/L hygromycin B . All trm7Δ double-mutant strains were constructed similarly by PCR amplification , linear transformation into yMG348-1 trm7Δ::bleR [CEN URA3 TRM7] , and selection against the URA3 plasmid by streaking on media containing FOA . The haploid S . pombe trm7Δ::kanMX [ura4+ Pnmt1 trm7+] ( yMG1052A ) strain was generated as previously described [29] and used for isolation of suppressors . The haploid trm7Δ::kanMX [LEU2 Pnmt1 low strength trm7+] ( yMG1541 ) strain was generated by transformation of yMG1052A with a LEU2 Pnmt1 low strength sp trm7+ plasmid ( pMG527B ) , and selection against the ura4+ plasmid by streaking on media containing FOA , and was used for experiments in which Trm7 was depleted with thiamine . For all experiments in which two or more strains with the same genotype are analyzed , these samples are biological replicates . Plasmids used in this study are listed in S9 Table . Plasmids for FRS1 and/or FRS2 expression were derived from pBG2619 , which is a [2μ PGAL1 , 10 LIC] dual ORF expression plasmid . In this plasmid , expression of one ORF is under PGAL1 control with a C-terminal PT tag , containing 3C site-HA epitope-His6-ZZ domain of protein A , and expression of the second ORF is under PGAL10 control with no tag [94] . CEN plasmids were constructed by ligation-independent clone ( LIC ) of genes containing their own 5' and 3' flanking sequence into pAVA581 ( LEU2 ) or pAVA579 ( URA3 ) [94] . S . cerevisiae strains were grown at 30°C to mid-log phase in either rich media or minimal media as indicated . S . pombe strains were grown at 30°C to mid-log phase in EMM supplemented with 225 mg/L adenine , lysine , histidine , leucine , and uracil . To analyze WT and trm7Δ [Pnmt1 trm7+] strains under repressive growth conditions , thiamine was supplemented to the media at 5 mg/L . For either S . cerevisiae or S . pombe , bulk RNA was prepared from ~4 OD pellets using glass beads , and RNA was resolved on acrylamide gels and analyzed by hybridization as previously described [3] . For analysis of charging , RNA was prepared and resolved under acidic conditions as described [3] . Strains were grown in triplicate to mid-log phase as described above for Northern blot analysis . Bulk RNA was prepared from 5–10 OD pellets using glass beads , treated with DNase , reverse transcribed , and the resulting cDNA was amplified and analyzed as previously described [95] . Whole genome sequencing was done at the UR Genomics Research Center at a read depth of greater than 100-reads . | The ubiquitous tRNA anticodon loop modifications have important but poorly understood functions in decoding mRNAs in the ribosome to ensure accurate and efficient protein synthesis , and their lack often impairs yeast growth and causes human disease . Here we investigate why ribose methylation of residues 32 and 34 in the anticodon loop is important . Mutations in the corresponding methyltransferase Trm7/FTSJ1 cause poor growth in the budding yeast Saccharomyces cerevisiae and near lethality in the evolutionarily distant fission yeast Schizosaccharomyces pombe , each due to reduced functional tRNAPhe . We previously showed that tRNAPhe anticodon loop modification in yeast and humans required two evolutionarily conserved Trm7 interacting proteins for Cm32 and Gm34 modification , which then stimulated G37 modification . We show here that both S . cerevisiae and S . pombe trm7Δ mutants have apparently normal tRNAPhe charging , but constitutively activate a robust general amino acid control ( GAAC ) response , acting through Gcn2 , which senses uncharged tRNA . We also show that S . cerevisiae trm7Δ mutants grow poorly due in part to constitutive GAAC activation as well as to the uncharged tRNAPhe . We propose that TRM7 is important to prevent constitutive GAAC activation throughout eukaryotes , including metazoans , which may explain non-syndromic X-linked intellectual disability associated with human FTSJ1 mutations . | [
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] | 2018 | Lack of 2'-O-methylation in the tRNA anticodon loop of two phylogenetically distant yeast species activates the general amino acid control pathway |
Enteropathogenic and enterohemorrhagic Escherichia coli ( EPEC and EHEC ) are related strains capable of inducing severe gastrointestinal disease . For optimal infection , these pathogens actively modulate cellular functions through the deployment of effector proteins in a type three secretion system ( T3SS ) -dependent manner . In response to enteric pathogen invasion , the Nod-like receptor pyrin domain containing ( NLRP ) inflammasome has been increasingly recognized as an important cytoplasmic sensor against microbial infection by activating caspase-1 and releasing IL-1β . EPEC and EHEC are known to elicit inflammasome activation in macrophages and epithelial cells; however , whether the pathogens actively counteract such innate immune responses is unknown . Using a series of compound effector-gene deletion strains of EPEC , we screened and identified NleA , which could subdue host IL-1β secretion . It was found that the reduction is not because of blocked NF-κB activity; instead , the reduction results from inhibited caspase-1 activation by NleA . Immunostaining of human macrophage-like cells following infection revealed limited formation of inflammasome foci with constituents of total caspase-1 , ASC and NLRP3 in the presence of NleA . Pulldown of PMA-induced differentiated THP-1 lysate with purified MBP-NleA reveals that NLRP3 is a target of NleA . The interaction was verified by an immunoprecipitation assay and direct interaction assay in which purified MBP-NleA and GST-NLRP3 were used . We further showed that the effector interacts with regions of NLRP3 containing the PYD and LRR domains . Additionally , NleA was found to associate with non-ubiquitinated and ubiquitinated NLRP3 and to interrupt de-ubiquitination of NLRP3 , which is a required process for inflammasome activation . Cumulatively , our findings provide the first example of EPEC-mediated suppression of inflammasome activity in which NieA plays a novel role in controlling the host immune response through targeting of NLRP3 .
Enteropathogenic and Enterohemorrhagic Escherichia coli ( EPEC and EHEC ) are major causative agents of food poisoning worldwide [1] . EPEC causes infantile diarrhea , and EHEC causes bloody diarrhea and hemolytic uremic syndrome ( HUS ) in patients who ingest contaminated food [2] . These invading bacteria colonize the surface of the epithelial cells lining the intestinal tract and cause localized damage to the intestinal microvilli and rearrangement of host cytoskeletal proteins under the intimately attached bacterial colonies [3 , 4] . These characteristic histopathological lesions are referred to as “attaching and effacing lesions ( A/E lesion ) ” , and EPEC/EHEC are known as “A/E pathogens” [5] . A/E lesion formation depends on a chromosomal region named the locus of enterocyte effacement ( LEE ) , which is the key to EPEC/EHEC pathogenicity . LEE encodes the regulators , an adhesin ( intimin ) , the chaperones , a translocator , the effector proteins and the type three secretion system ( T3SS ) components . The T3SS is a needle-like apparatus that is responsible for delivering the effector proteins into the cytosol of the host cells [6] . Effector proteins then modulate various aspects of cellular function and optimize bacterial infection . Based on the sequenced genomes of EPEC ( E2348/69 ) and EHEC ( O157:H7 ) , more than 30 effector genes have been predicted , and at least 17 of these genes are found in both strains [7 , 8] . Several effector genes typically form clusters , and there are several of these clusters , known as pathogenicity islands , scattered in the genome; those effector genes located outside of the LEE are referred to as non-LEE-effectors . The functions of each effector protein are incompletely understood . Some effector proteins of the A/E pathogens have been shown to disrupt vital host cellular functions such as the cellular structures , cell death , proliferation and inflammatory responses [9] . Recently , the observation of pathogen-induced suppression of host inflammatory responses has led to the discoveries of multiple NF-κB ( nuclear factorκ-light-chain-enhancer of activated B cells ) pathway-inhibiting effector proteins , including NleB , NleC , NleE , NleH , EspL , and Tir [10] . Host cells are equipped with a variety of receptors on the membrane surface and within the cytoplasm to detect conserved bacteria-originated antigens as well as danger-associated molecular patterns ( DAMPs ) released from infected and damaged cells [11 , 12] . The ligand-receptor engagement elicits many downstream signaling events that result in cellular output of antimicrobial peptides , inflammatory cytokines , and chemokines required for further recruitment of innate and adaptive immune cells . Recently , the inflammasome , a multimeric protein complex consisting of Nod-like receptor ( NRLs ) , ASC and caspase-1 , was shown to be important in the maturation and secretion of interleukin ( IL ) -1β and related family members [12] . In particular , the NLRP3 inflammasome has been extensively studied and can be activated by a variety of stimuli including microbial infection [13–15] . Live non-pathogenic and pathogenic Escherichia coli are known to elicit the NLRP3 inflammasome [16–18] , and such activation has been shown to be essential for clearance of Citrobacter rodentium , a mouse model pathogen of EPEC , in an animal infection model [19] . Because activation of the inflammasome leads to production of IL-1β and escalation of inflammation for enhanced pathogen clearance , counter measurements to this type of pathway appear to be vital for the survival of and successful infection by A/E pathogens . There have been no reports of A/E pathogen-mediated suppression of the inflammasome . In this study , we identified NleA and NleE through screening of the effector genes of EPEC responsible for inhibiting IL-1β secretion . In contrast to NleE , NleA represses caspase-1 activation without affecting NF-κB activity . This inhibition is a result of reduced formation of inflammasomes containing NLRP3 , ASC and total caspase-1/active caspase-1 . Further examination revealed that NleA directly interacts with NLRP3 and affects the de-ubiquitination of NLRP3 , which is known to control its activity .
The inflammasome plays a major role in the secretion of IL-1β , one of the essential inflammatory cytokines for host defenses against enteropathogens . To determine whether pathogenic Escherichia coli could modulate inflammasome activity with effectors , we first induced THP-1 to differentiate into a macrophage-like cells and primed cells with LPS to promote synthesis of pro-IL-1β . LPS-primed dTHP-1 ( differentiated THP-1 ) were uninfected ( UI ) or infected with wild type EPEC E2348/69 ( WT ) , a ΔescF isogenic mutant ( T3SS-defective strain ) , and TOE-A6 ( an E2348/69 strain that lacks all non-LEE-effector genes ) ; we then measured the amount of IL-1β using an ELISA assay . Compared with WT , we found that although the ΔescF mutant reduced the amount of secreted cytokine , TOE-A6 caused an increase in IL-1β secretion ( Fig 1A ) , despite of comparatively lesser degree of bacterial exposure of TOE-A6 ( S1 Fig ) . The reduction of IL-1β secretion from the ΔescF mutant-infected cells is consistent with previous reports in which the T3SS of EPEC/EHEC could elicit an inflammasome response [20] . The significant increase in IL-1β release from the cells infected by TOE-A6 suggested that deleted non-LEE effector ( s ) might participate in the suppression . To elucidate potential inhibitory effector ( s ) , we performed a screening experiment by infecting dTHP-1 with the EPEC derivatives TOE-A1 to TOE-A6 , which are strains with serial deletions of clusters of effector genes [21] . After 1 hr of infection followed by 6 hrs of further incubation , we used an ELISA method to measure the IL-1β secretion from cells infected with TOE-A1 to TOE-A6 strains . We found that there was a significant increase in the TOE-A4 and TOE-A5 strains compared with TOE-A3 and TOE-A4 , respectively ( Fig 1B ) . TOE-A4 was derived from TOE-A3 by the deletion of the IE6 region , which contains a cluster of effector genes ( nleE , nleB1 , and espL ) , resulting in a lack of a total of nine effector genes ( nleB2 , nleH1 , espJ , nleG , nleC , nleD , nleE , nleB1 and espL ) . TOE-A5 was generated upon the removal of PP6 from TOE-A4 , leading to additional loss in the effector genes nleH , nleA , and nleF . The results of the screening suggested that some of nleE , nleB1 , espL , nleH , nleA , and nleF might be involved in the interference of IL-1β secretion . To identify the inhibitory effectors , we performed the rescue test by using plasmids expressing one of these effectors . Plasmids harboring each individual effector gene with a FLAGx3 epitope at the C-terminus were introduced into TOE-A4 or TOE-A5 , yielding TOE-A4/pFLAG3-NleE ( TOE-A4/nleE ) , TOE-A4/ pFLAG3-nleB1 ( TOE-A4/nleB1 ) , TOE-A4/ pFLAG3-espL ( TOE-A4/espL ) , TOE-A5/ pFLAG3-nleH ( TOE-A5/nleH ) , TOE-A5/ pFLAG3-nleA ( TOE-A5/nleA ) , and TOE-A5/ pFLAG3-nleF ( TOE-A5/nleF ) . We then infected dTHP-1 with these established strains and compared the level of IL-1β secretion to their respective parent strains . As shown in Fig 1C and 1D , TOE-A4/nleE and TOE-A5/nleA exhibited significant suppression of IL-1β release from the host . The expression of other effectors in TOE-A4 or TOE-A5 failed to reduce the secretion levels . These results indicated that at least two non-LEE-effectors , NleA and NleE , are capable of inhibiting host IL-1β secretion . The IL1B gene is a regulatory target of NF-κB transcription factors ( NF-κBs ) , and the synthesized cytokine requires enzymatic processing by active caspase-1 for secretion [22] . We found a significant reduction of cellular pro-IL-1β in the presence of NleE but not NleA in infected cells ( S2 Fig ) . Because NleE has been known to be a potent inhibitor of the NF-κB pathway [23–26] , it is likely that NleE reduces IL-1β production by inhibiting the activation of NF-κB . Indeed , we found infection of cells with the pathogen expressing NleE hampers the IκB degradation , which is necessary for the activation of NF-κB , but such inhibition was not seen in cells infected with NleA-expressing strain ( Fig 2A ) . We focused on NleA because this effector has not yet been reported to be involved in the regulation of host inflammation . In a steady state , NF-κBs are inhibited by IκB ( Inhibitor of κB ) in the cytoplasm; however , the NF-κBs are translocated into the nucleus after IκB is degraded upon immune challenge [27] . We further explored the effect of NleA on nuclear translocation of RelA , a subunit of NF-κBs in the infected cells . By immunostaining the cells that were infected with WT , TOE-A5 , and TOE-A5/nleA , we found the WT-infected cells had relatively weak nuclear RelA staining , which is in accordance with previous observations [21] . The majority of the cells infected with TOE-A5 or TOE-A5/nleA exhibited strong nuclear RelA staining ( Fig 2B ) . By enumerating the percentage of cells showing strong nuclear RelA signals , we concluded that NleA does not affect RelA nuclear translocation ( Fig 2C ) . Finally , we examined the effect of NleA on the ability of NF-κBs to produce other inflammatory cytokines . We infected dTHP-1 with WT , TOE-A5 , and TOE-A5/nleA and measured the concentrations of TNF-α , another NF-κB-dependent cytokine , and IL-1β from the identical cell culture medium . We found that TNF-α secretion was unaffected in the cells infected with TOE-A5/nleA compared with the cells infected with TOE-A5 ( Fig 2D ) . A significant impediment in IL-1β secretion occurred in the cells infected with TOE-A5/nleA and not in the cells infected with TOE-A5 ( Fig 2E ) . Unlike TNF-α , IL-1β secretion is independent of the conventional ER-Golgi secretion pathway and requires additional enzymatic cleavage by active caspase-1 [28] . Therefore , we speculated that NleA might negatively influence IL-1β-specific processing or secretion pathways , such as the activation of caspase-1 . To examine this possibility , we infected dTHP-1 with WT , TOE-A5 , TOE-A5/nleE , and TOE-A5/nleA and analyzed the p20 subunit of active caspase-1 in the cell culture medium by immunoblotting . As shown in Fig 2E , the p20 subunit was noticeably decreased in the cells infected with TOE-A5/nleA compared with the cells infected with TOE-A5 ( Fig 2F ) . To further confirm that NleA alone is sufficient for the reduced caspase-1 activation , we infected dTHP-1 with WT , ΔnleA , and the nleA complemented strain ΔnleA/nleA . We found that the EPEC lacking nleA gene resulted in increase of caspase-1 p20 in the culture medium compared to WT , and that such increase was diminished by introducing the nleA gene ( Fig 2G ) . Taken together , these results suggest that NleA might directly or indirectly downregulate caspase-1 activation , and less active caspase-1 contributes to less processing of IL-1β for secretion . Unlike NleA , NleE did not show an effect on the production of active caspase-1 , indicating that NleA reduces IL-1β secretion through a mechanism completely different from that of NleE . These results showed that NleA did not interfere with the NF-κB pathway; however , NleA reduces the amount of active caspase-1 required for IL-1β secretion . The observation of reduced active caspase-1 by NleA prompted us to further investigate the mechanism by which this effector affects the processes leading to the generation of active caspase-1 , predominantly the inflammasome pathway . Active caspase-1 is generated from the autoprocessing of its precursor protein ( pro-caspase-1 ) following recruitment into the inflammasome complex [29 , 30] . Because the inflammasome consists of the NLR protein , ASC , and caspase-1 , it could be visualized as a speckled structure or foci after the immunofluorescent staining of its constituent proteins [31] . We first examined the number of formed active caspase-1 foci in the uninfected ( UI ) , TOE-A5-infected , and TOE-A5/nleA-infected dTHP-1 cells at 3 and 6 hrs post-infection . Labeling the infected cells with FAM-YVAD-FMK , a fluorescent irreversible inhibitor probe of active caspase-1 , revealed minimal formation of foci in the uninfected dTHP-1 cells and a comparatively higher number of foci in cells infected by TOE-A5 ( Fig 3A ) . This result is consistent with the finding that infection with TOE-A5 increased the p20 subunit production ( Fig 2E ) . However , when comparing the TOE-A5/nleA- infected dTHP-1 cells to the cells infected with TOE-A5 , we observed significantly fewer foci in the presence of NleA ( Fig 3A and 3D ) . This observation is consistent with the result of reduced active caspase-1 production by TOE-A5/nleA-infected cells . Next , we examined the inflammasome that contains any form of caspase-1 ( Pro- and active forms ) by immunofluorescent staining with an anti-caspase-1 antibody . As shown in Fig 3B and 3E , the number of foci containing total caspase-1 foci was consistently lower in the dTHP-1 cells infected with TOE-A5/nleA than in the cells infected with TOE-A5 ( Fig 3B and 3D ) , indicating that NleA blocks the recruitment of pro-caspase-1 into the inflammasome . Because ASC functions in the formation of the inflammasome by bridging NLRP and caspase-1 , we next examined the ASC foci in the infected dTHP-1 cells . As shown in Fig 3C and 3F , fewer ASC foci were observed in the TOE-A5/nleA-infected cells than in the TOE-A5-infected cells . Fernandes-Alnemri et al . previously showed that the stimulation and activation of the inflammasome leads to the formation of a large ASC oligomer complex , and this complex could be fractionated by centrifugation [32] . To compare the formation of such complexes , we isolated the complexes by the centrifugation of the lysates of uninfected , TOE-A5- , and TOE-A5/nleA-infected cells following protein-protein cross-linkage . As shown in Fig 3G , we observed a decrease in the total amount of ASC and ASC-dimers and oligomers in complexes in TOE-A5/nleA-infected cells compared with the TOE-A5-infected cells ( Fig 3G ) . These results indicated that NleA hinders the formation of the ASC-containing inflammasomes and their oligomerization . We showed that NleA causes a reduction in active caspase-1 by prohibiting the formation of the inflammasome . The NLRP3 inflammasome is known to be triggered by infection with A/E pathogens and shows a non-redundant role in caspase-1 activation when mouse macrophages ( mBMDM ) are challenged with Citrobacter rodentium , a mouse A/E pathogen possessing the nleA homologous gene [19] . Therefore , we hypothesized whether NleA might limit NLRP3 inflammasome formation . After infecting dTHP-1 cells with TOE-A5 or TOE-A5/nleA for 1 hr followed by 3 hrs of incubation , the cells were co-immunostained with antibodies against NLRP3 and total caspase-1 to detect the mature NLRP3 inflammasome . We observed fewer NLRP3 inflammasomes in the cells infected with TOE-A5/nleA than in those infected with TOE-A5 ( Fig 4A and 4B ) . The majority of the caspase-1 foci overlapped with the NLRP3 foci . This result suggested that EPEC infection induces the formation of the NLRP3 inflammasome in human macrophage-like cells; however , NleA negatively regulates the formation of the NLRP3 inflammasome . Ubiquitin modification controls NLRP3 protein activity [33 , 34] . It has been shown that primary or secondary signal alone can each trigger certain degree of de-ubiquitination in NLRP3 without caspase-1 activation; and , it is the combination of two signals induce the greatest extend of de-ubiquitination leading to the assembly of the NLRP3 inflammasome and caspae-1 processing , reflecting the nature of strict regulations on this pathway [33] . We found that cells stimulated with LPS for 15 min or 2 hrs had reduction in ubiquitinated NLRP3 , but still retained a significant amount ( S3 Fig ) . Furthermore , these LPS treatments alone produced below the detection level of caspase-1 processing . Because TOE-A5/nleA infection inhibited the assembly of the NLRP3 inflammasome , we examined the degree of de-ubiquitination of NLRP3 . The cells were treated with LPS for 2 hrs and then infected with bacteria for 1 hr , followed by 3 hrs of incubation . The endogenous NLRP3 were isolated by immunoprecipitation using an anti-NLRP3 antibody , and the ubiquitinated NLRP3 were detected with an anti-ubiquitin antibody . As shown in Fig 4C , compared to uninfected cells , while the infection of TOE-A5 caused reduction in ubiquitinated NLRP3 , the amount of the ubiquitinated NLRP3 in the TOE-A5/nleA-infected cells did not decrease but increased , indicating that NleA directly or indirectly influence this step of inflammasome activation . We also tested whether NleA can hinder the NLRP3 inflammasome activation in infected cells treated with nigericin , an agonist of NLRP3 inflammasome . We found that the elicitation of caspase-1 activation to be less when NleA was available ( S4 Fig ) . Taken together , these results showed that NleA specifically targets NLRP3 inflammasome activation eliciated by infection and that the effector prevents the assembly of the NLRP3 inflammasome , possibly through interfering the change of ubiquitination in NLRP3 during infection . To identify potential targets of NleA involved in the inflammasome pathways , a cytosolic extract of THP-1 was applied through columns packed with purified MBP or MBP-NleA-bound resins . Potential inflammasome-related proteins were analyzed with specific antibodies by immunoblotting . We found , upon probing with anti-NLRP3 , the immunoblot revealed that NLRP3 specifically interacts with MBP-NleA . We failed to detect NLRC4 , which is another activator of caspase-1 , in the NleA-bound fraction . This result indicated that NleA interacts with NLRP3 and not with NLRC4 ( Fig 5A ) . To further validate the association between NleA and NLRP3 , we co-transfected HeLa cells with plasmids expressing EGFP-NleA and KGC-NLRP3 and performed a co-immunoprecipitation assay . As shown in Fig 5B , EGFP-NleA , but not EGFP , was able to interact with KGC-NLRP3 , confirming the association between NleA and NLRP3 . To further determine whether this interaction is direct , we examined the binding of a purified MBP-NleA fusion protein with a purified GST-NLRP3 fusion protein . To perform the assay , we applied MBP-NleA to columns packed with GST or GST-NLRP3-bound resins . We found that GST-NLRP3 , but not GST , could interact with MBP-NleA , indicating a direct interaction between the effector and the host factor ( Fig 5C ) . NLRP3 consists of the following three domains: the PYD , NACHT , and LRR domains . Additionally , to determine the domain of NLRP3 that NleA binds , we constructed bacterial plasmids that express GST-PYD , GST-NACHT , and GST-LRR ( Fig 5D ) . After purification and immobilization on resins in the columns , purified MBP-NleA was applied to each column . After extensive washing and the elution of the bound proteins , we analyzed the presence of MBP-NleA in the eluates of these columns by immunoblotting; we found that MBP-NleA interacts with the GST-PYD and GST-LRR constructs ( Fig 5E ) . These results indicate that NleA could directly target NLRP3 through interactions with the PYD and LRR domains . In unstimulated cells , NLRP3 is ubiquitinated; however , NLRP3 undergoes de-ubiquitination upon LPS stimulation [33] . Because infections with NleA-producing bacteria reduced the de-ubiquitination of NLRP3 compared with that of the NleA-negative bacteria , it is likely that NleA binds to ubiquitinated NLRP3 and inhibits de-ubiquitination . In prior co-immunoprecipitation assays between EGFP-NleA and KGC-NLRP3 ( Fig 5B ) , we did not observe any higher-molecular-weight KGC-NLRP3 . This result might be because of an insufficient amount of ubiquitinated KGC-NLRP3 in the transfected cells . To improve these results , we scaled up the transfection and expressed KGC-NLRP3 with or without HA-ubiquitin and EGFP or EGFP-NleA in HeLa cells . Cell lysates were immunoprecipitated with an anti-GFP antibody; the pulldown products were probed with an antibody against a KGC-epitope to detect the total KGC-NLRP3 . As shown in Fig 6A , higher-molecular-weight KGC-NLRP3 was co-precipitated with EGFP-NleA from the lysate of HA-ubiquitin expressing cells . In addition , KGC-NLRP3-Ubi was detected in the pulldown products from cells without HA-ubiquitin , although the amount was much smaller . Given that NleA reduces the de-ubiquitination of NLRP3 during infection and that NleA binds to ubiquitinated NLRP3 in a co-immunoprecipitation assay , we speculated that NleA could inhibit the de-ubiquitination of NLRP3 upon stimulation . To verify the inhibitory effect of NleA on NLRP3 de-ubiquitination , we conducted an in vitro de-ubiquitination assay . Total KGC-NLRP3 proteins were purified from the plasmid-transfected HeLa cells either expressing KGC-NLRP3 alone or co-expressing KGC-NLRP3 and HA-ubiquitin , and were immobilized on magnetic beads with an anti-KGC antibody . The beads were pre-mixed with assay buffer or MBP or MBP-NleA followed by addition of the cell lysates extracted from the LPS-primed dTHP-1 cells . The reaction was performed at 0°C or 37°C for 1hr before being terminated with SDS sample buffer . Samples were analyzed by anti-HA antibody to detect changes in HA-ubiquitinated KGC-NLRP3 . Incubation with dTHP-1 lysate greatly reduced the HA-ubiquitinated KGC-NLRP3 in samples pre-incubated with the buffer or MBP , while , the sample pre-incubated with MBP-NleA had partially lost HA-ubiquitinated KGC-NLRP3 but the degree was not as extensive as those of buffer- or MBP-treated sample ( Fig 6B , compare Lane 4 to lanes 5 and 6 ) . Taken together , these results strongly suggest that NleA can bind to both non- and ubiquitinated NLRP3 and that it interferes the alteration of the amount of ubiquitinated NLRP3 in infected cells .
The sensor molecule NLRP3 and its family members are central to the inflammasome in response to a wide range of stimuli , whereby are responsible for the activation of caspase-1 and the secretion of inflammatory IL-1β and related cytokines for amplifying inflammation . Prior studies have demonstrated that infections with EPEC , EHEC and Citrobacter rodentium could elicit inflammasomes in macrophages and epithelial cells [17 , 19] , but our study is the first to show that A/E pathogens could actively suppress IL-1β production and that a T3SS-dependent effector , NleA , is used to inhibit host inflammasome formation in macrophage-like cells . NF-κB and the inflammasome regulate transcription and secretion of IL-1β , respectively [22] . Interference in each or both pathways will result in an overall reduction in the output of IL-1β . We initially suspected that the TOE-A4 strain would lose suppression of IL-1β production because most of the reported NF-κB suppressors , including NleC , NleB , NleE , and EspL are absent . Significant de-repression was seen in TOE-A4 , which is similar to that observed in our previous study [21] . The subsequent screening of effectors restoring TOE-A4 ( TOE-A4/nleE , TOE-A4/nleB , and TOE-A4/espL ) only indicated that NleE inhibited IL-1β . This finding may be because cells were only being challenged by added pathogens during infection , instead of the addition of other stimuli such as TNF-α , whose signal pathway has been shown to be inhibited specifically by NleB [24] . Because bacterial effectors are versatile and frequently possess multi-functions , we investigated whether NleE affects the inflammasome pathway by examining the amount of active caspase-1 in infected cells . The activation of caspase-1 appeared to be unhindered by the presence of NleE , implicating that the suppression of NF-κB is likely the determinant event in the NleE-mediated reduction of IL-1β . NLRP3 and NLRC4 are the most frequently studied NLRs , and they each respond to different sets of agonists . Upon activation , NLRP3 depends on ASC to assemble a mature inflammasome , whereas NLRC4 could directly bind and activate pro-caspase-1 [35] . In bacterial infections , there appears to be a cell-context dependent requirement of NLRC4 in protection from A/E pathogen infection , whereas Nordlander et al . showed that NLRC4 is dispensable for eliciting caspase-1 activation in macrophages , Liu et al . showed that NLRC4 is pivotal for caspase-1 activity in non-hematopoietic cells [19 , 36] . Infection of the mouse A/E pathogen C . rodentium has been shown to activate the NLRP3 inflammasome , which is critical for the production of IL-1β and IL-18 and protection in mouse macrophages [19] . In our initial study , we found that the deletion of escF , a major component of the T3SS injectosome , renders the mutant strain inert to stimulate IL-1β secretion from THP-1 cells , which implicates the contribution of NLRC4 to host cell defense [20] . However , in our pulldown assay using MBP-NleA and THP-1 lysates , we found that the effector associates with NLRP3 and that it does not associate with NLRC4 . Because infection of THP-1 cells with EPEC induced the formation of the NLRP3 inflammasome and NleA reduces the formation of the caspase-1 containing inflammasome , activation of the NLRP3 inflammasome might play a critical role in caspase-1 activation in THP-1 cells . Infection of A/E pathogens , such as EPEC and EHEC , might activate NLRC4 and NLRP3 inflammasomes . However , because NLRC4 requires accessory factors to recognize agonists before activation , NleA might target these co-factors , such as NAIP [37] . Prevention of inflammasome formation by NleA contributes to the reduction of the secretion of IL-1β and IL-18 and the inflammatory response of the host . Recently , Juliana et al . showed that the de-ubiquitination of NLRP3 is critical for inflammasome activation following PAMP/DAMP stimulation [33] . This result coincides with our finding that de-ubiquitination in NLRP3 and inflammasome formation were both reduced in cells infected with NleA-expressing EPEC compared with cells infected with non-expressing strains . More ubiquitinated NLRP3 is present when NleA is available . This result suggests that NleA alters the process of ubiquitin modification of NLRP3 . Although the precise lysine residues and the degree of ubiquitination in endogenous NLRP3 that might account for the inactivation remain unclear , Py et al . demonstrated that a mix of K48- and K63-polyubiquitin chains are present and that modifications occur at regions of the NATCH and LRR but not PYD domains [34] . We found that NleA is able to directly bind to the LRR and PYD domains of NLRP3 . Binding to these domains might inhibit access of the de-ubiquitinating enzyme to polyubiquitin at the LRR domain . In addition , because the PYD domain of NLRP3 is responsible for mediating interactions with ASC , it is plausible that NleA might inhibit such an association and prevent further assembly of the NLRP3 inflammasome ( Fig 7 ) . Bacterial subversion of the inflammasome is common among other enteric pathogens . Diverse strategies by effector proteins have been described; for example , YopK of Yersinia spp . associates with components of the T3SS to prevent recognition by host sensing molecules [38] , and YopM of Yersinia spp . and OspC3 of Shigella spp . directly bind to inhibit Caspases-1 and -4 activity , respectively [39 , 40] . In comparison , the inhibitory mechanism of NleA , which directly targets and suppresses the ubiquitination modification of NLRP3 , presents a novel strategy among bacterial pathogens . In viral pathogens , however , targeting NLRs has been previously reported; for example , Orf63 of Kaposi’s sarcoma-associated herpes virus and V protein of Measles virus have been reported to target NLRP [41 , 42] . In particular , Orf63 is a viral homolog of several NLRs proteins and could interact with NLRP3 and with NLRP1 , resulting in the inhibition of NLRs oligomerization [41] . However , whether Orf63 could additionally bind to the ubiquitinated form of NLRP and influence changes in the modification is unknown . We found no amino acid sequence homology between NleA and members of NLR proteins or between NleA and Orf63; this result suggests that NleA is a novel type of virulence factor targeting NLRP3 . Although they are different in their inhibitory mechanisms , these viral and bacterial proteins have evolved independently to target a common host factor and highlight the critical roles of NLRP3 in host defenses against bacterial and viral pathogens . NleA has been shown to localize to Golgi [43] and its C-terminal end of 40 amino acids interacts with several members of Sec24 paralogues [43 , 44] . The association affects COPII formation , ER-Golgi transportation and the maintenance of tight junctions [43–45] . In current study using THP-1 cells , we found that bacteria-delivered NleA-FLAG proteins localized diffusedly throughout cytoplasm with a fraction overlapping with Golgi apparatus ( S5 Fig ) . Furthermore , compared to the full length NleA , we have also shown that NleA lacking C-terminal end of 33 or 62 amino acids is not as effective as the full length in inhibition of caspase-1 activation nor IL-1β secretion ( S6 Fig ) . These findings suggest that the C-terminal end of NleA is involved in the interference of host Caspase-1 activity . At the present time , the precise molecular mechanism responsible for observed reduction in de-ubiquitinated NLRP3 by NleA still remain to be determined . It is plausible that the association of NleA to ubiquitinated NLRP3 hinders the access of host de-ubiquitinase or that the binding of NleA promotes the ubiquitination of NLRP3 by the effector itself or a third unknown factor , or that the association enforces a closed conformation of NLRP3 , which remains inactive ( Fig 7 ) . NleA has been shown to be required for infections with Citrobacter rodentium in the mouse [46] . A lack of NleA in Citrobacter rodentium resulted in attenuated symptoms of milder hyperplasia in the large intestine and in reduced intestinal inflammation . Reduced severity of disease in NleA-deficient strains could be caused by the reduced efficiency of colonization in the colon [46] . Because the absence of NleA resulted in higher IL-1β secretion by macrophages , this might implicate a more efficient inflammatory response from host cells at early stages of infection to increase the clearance of bacteria . Recently Song-Zhao et al . and Wlodarksa et al . each reported the critical roles of NLRP3 and NLRP6 in intestinal epithelial and goblet cells , respectively , against the infection of enteropathogens [47 , 48] . Defects in these NLRs results in increase in epithelial colonization of C . rodentium in infected mouse . Therefore , NleA may , in addition to reported function of disrupting tight junctions , also help increasing the colonization by targeting NLRP3 and potentially NLRP6 in nonhematopoietic cells . In conclusion , we showed that NleA negatively modulates host NLRP3 inflammasome activity by interfering with the de-ubiquitin modification of NLRP3 and directly targeting the NLRP3 protein upon bacterial infection . Because the inflammasome is increasingly being recognized as an important sensor and reactor to offending pathogens , our study showing how NleA interferes with this pathway provides important insights into efficient bacterial strategies for evading host immune responses and demonstrates the critical role of NLRP3 for the infection of A/E pathogens .
HeLa cells were maintained in MEM ( Sigma ) supplemented with 10% FCS and 1x NEAA ( Gibco ) . RPMI-1640 medium containing 10% FCS and 1x NEAA was used for THP-1 culture . THP-1 cells were differentiated into adherent macrophage-like cells in a time course of 4 to 5 days before being used in experiments . On day 0 , cells were first seeded at 2x105 cell/ml in 24 well plates and stimulated with Phorbol 12-myristate 13-acetate ( PMA ) ( Wako ) at concentration of 100 ng/ml . On day 1 , cells were washed with PBS once and fresh medium without PMA was added . Cells were then allowed to differentiate for the next 72–96 hrs . Half-medium change was carried out every 48 hrs . The bacterial strains and plasmids used in this study are described in S1 Table . Primers used for cloning are listed in S2 Table . Construction of nleA deletion mutant , ΔnleA , followed published protocols [49] . Genes encoding effectors were PCR amplified directly from EHEC Sakai ( Accession No . NC_002695 ) and E2348/69 ( Accession No . FM180568 ) chromosomal DNAs . All generated DNA fragments were digested with designated restriction enzymes and subcloned into bacterial or mammalian expression plasmids as indicated . One day prior to the infection experiment , bacterial strains to be used were inoculated in selective LB broth overnight with constant agitation at 30°C . On the day of the experiment , bacterial cultures at the stationary phase were diluted 20-fold in serum-free DMEM ( Sigma ) and cultured with agitation at 37°C for 2 hours . Two hours before the actual infection , differentiated THP-1 was washed once with PBS and cell medium was changed to serum-free RPMI containing LPS at 1 μg/ml ( Sigma ) . Upon completion of 2 hours of bacteria culture , bacteria were added to cell culture at m . o . i ( multiplicity of infection ) of 20 or as indicated and cells were subjected to 10 min of spin-infection at 1600 x rpm to synchronize the start of infection . Following 1 hr of incubation at 37°C , 5%CO2 , gentamycin ( Wako ) was added at concentration of 0 . 1 mg/ml to terminate further infection by extracellular bacteria . Cells were further incubated and samples ( cells or cell culture medium ) were harvested for analysis at time indicated . THP-1 were seeded at 2x105 cells/ml in the 24 well plate and induced for differentiation as mention above . After the initial spin-infection and subsequent one hour of co-culturing , the live bacteria were terminated by addition of gentamycin . Cells were further cultured at 37°C , 5%CO2 for 6 hrs . The culture medium were collect and centrifuged once to pellet down bacterial debris as well as the non-adherent cells . Commercial ELISA kits ( IL-1β and IL-8 , both from eBioscience; caspase-1 , R&D ) were used to determine cytokine concentration in the medium , following the manufacture’s protocol . THP-1 were seeded at 2x105 cell/ml on the coverslips in the 24 well plate and induced for differentiation by PMA as described above . At the end of infection experiment , cells were fixed by 4% paraformaldehyde ( PFA ) and permeabilized with 0 . 1% Triton-X in PBS . Cells were blocked with 1%BSA in PBS and stained with primary antibodies overnight at 4°C , followed by treatment with appropriate Alexa Fluo-555 or Alexa-Fluo-488 secondary antibodies at room temperature for 1 hr ( Invitrogen ) . Slides were mounted with SlowFade Gold antifade reagent with DAPI ( Invirogen ) . For FLICA staining of active caspase-1 , 1 hr prior to the fixation with 4% PFA , cells were washed with warm PBS to remove non-adherent bacteria and cell debris , and treated with FAM-YVAD-FMK peptides ( immunochemistry ) at concentration of 5 mM per manufacturer’s suggestion . Upon completion of one hour staining in 5%CO2 chamber at 37°C , cells were washed with PBS to remove non-binding reagents and fixed for subsequent immuno-staining procedure . Samples were visualized under 60x oil immersion lens of Olympus FV10i-DOC . For each sample slide , 9 randomly selected fields of views were examined and image recorded with z-axis stacking mode . Raw images were exported by FLUOVIEW Viewer software ( ver4 . 0 , Olympus ) in the format of TIFF . The exported files were processed by Adobe Photoshop CS5 ( Adobe System ) . For quantification of active caspase-1 , total caspase-1 and ASC foci , the number of cells contained at least one spherical or speckle-like structures were counted , divided by the number of cells per field , and expressed in percentage . For identification of mature NLRP3 foci , cells were co-stained with NLRP3 and caspase-1 specific antibody . At each indicated time point , cells that show co-localized signals in a speckle-like structure were counted . The percentage of foci formation was then calculated as the number of cells with foci divided by number of cells in the field , expressed in percentage . HeLa cells were seeded at 0 . 8x105 cell/ml in the 24 well plate or 4x105 cell/well in the 6 well plate or 3x106 cell/10-cm dish one night prior to the transfection . On the day of transfection , cell culture were transfected using Lipofectamine 2000 per manufacture’s suggestion ( Life Technologies ) . Cells were further cultured for indicated time before being collected for subsequent experiments . For the whole cell lysate , cells were washed with PBS and directly lysed in 2x SDS sampler buffer and sonicated . For assessment of active caspase-1 , collected cell culture medium were centrifuged at 800xg for 5 min to sediment cells and the supernatant were transferred to new eppendorf tubes for subsequent TCA precipitation . The precipitated proteins were dissolved in 1x SDS sampler buffer and were analyzed by immunoblotting . Prepared samples were separated on SDS-PAGEs and procedures of immunoblotting were performed according to standard protocol . For Immunoprecipitation and co-immunoprecipitation assay , cells were washed and lysed in NP-40 lysis buffer ( 20 mM HEPES-KOH pH7 . 6 , 150 mM NaCl , 1% NP40 , 10% glycerol , 5 mM NaF and Proteinase inhibitor cocktail ( Sigma ) ) . Cells in the buffer were incubated on ice for 15 min , and centrifuged at 12 , 500xg for 15 min . The supernatant collected as the cytosolic fraction was added with species-matched control immunoglobulin G ( IgG ) or specific antibodies coupled to Dynabeads Protein G ( Invitrogen ) and allowed to mix at 4°C for overnight . The bound proteins were washed with lysis buffer and eluted in 1x SDS Sample buffer . For detection of NLRP3 ubiquitination , cells were lysed in RIPA buffer ( 50 mM Tris-HCl pH7 . 6 , 150 mM NaCl , 1 mM EDTA , 0 . 05% sodium deoxycholate , 1% Triton X-100 , 0 . 01% SDS , 10 mM N-ethylmaleimide ) . Endogenous NLRP3 proteins were immunopricipitated with anti-NRLP3 ( Cell signaling technology ) coupled to Dynabeads IgG ( Invitrogen ) . After 3 hours of gentle agitation at 4°C , Dynabeads bound proteins were washed with RIPA buffer and eluted with 1x SDS sampler buffer . Bacterial expression plasmids containing each coding sequence were transformed into E . coli BL21 ( DE3 ) strain . Freshly transformed colonies were picked and cultured in selective broth LB one night before the induction . On the day of induction , overnight cultured bacteria was first diluted 500-fold in fresh LB and cultured with constant agitation at 37°C until O . D600 ~0 . 4 . IPTG was then added to induce protein expression for the next 3 hours . Bacteria were collected and soluble proteins were extracted . Amylose resin and glutathione agarose beads were used to purify MBP-fusion and GST-fusion proteins , respectively , following manufacture’s suggestion ( New England Biolabs , GE Life Sciences ) . To assess amount of bound proteins , known concentration of BSA standard and samples of purified proteins were separated on SDS-PAGEs and stained by CBB ( Wako ) . To determine the direct binding between MBP-NleA and GST-NLRP3-FL ( full length ) , equal amount of purified GST or GST-NLRP3-FL bound by the glutathione beads ( total of 2 . 5 μg each ) were first packed into columns at beads bed volume of 500 μl . Purified MBP-NleA at concentration of 5 μg/ml were applied to the column and allowed to flow-through by gravitation . Columns were washed with 0 . 1% Triton-X/PBS and eluted with high salt elution buffer ( 1x PBS supplemented with 850 mM NaCl ) . The eluted products were precipitated by TCA as described above . Concentrated samples were applied and separated on SDS-PAGE , followed by immunoblotting with anti-MBP antibody . Similar procedures were taken for determining NleA-interacting domains on NLRP3 . For determination of interacting domains , same steps described above were taken . Total KGC-NLRP3 was isolated by anti-KGC antibody ( MBL ) coupled magnetic beads from lysates of HeLa cells transfected with only the plasmids of KGC-NLRP3 or co-transfected with plasmids of KGC-NLRP3 and HA-ubiquitin . MBP and MBP-NleA fusion proteins were obtained as described in the section of Direct binding assay . To obtain LPS-primed dTHP-1 lysate , dTHP-1 was stimulated with LPS ( 1 μg/ml ) for 2 hrs , lysed in assay buffer ( 25 mM Tris-Cl pH7 . 9 , 100 mM NaCl , 1 mM DTT , 0 . 1% Triton X-100 ) and centrifuged at 15000xrpm at 4°C for 20 min . to obtain cytosolic fraction containing de-ubiquitin enzymes . For the de-ubiquitination assay , total KGC-NLRP3 bound on the magnetic beads were first mixed with MBP ( 2 μg ) or MBP-NleA ( 2 μg ) at 4°C for 16 hours . Then beads were washed twice with assay buffer to remove unbound proteins and re-suspended in 200 μl of assay buffer in new tube . Primed dTHP-1 lysates were added at 1/100 reaction volume . The de-ubiquitination reaction was allowed to proceed at 0°C or 37°C for 1 hr . At the end of reaction time , magnetic beads from each sample was washed three times with assay buffer and eluted in 1x SDS sampler buffer . Anti-caspase-1 ( for immunoblotting; #2225 , Cell Signaling ) , Anti-caspase-1 ( for immunostaining; #3866 , D7F10 clone , Cell Signaling ) , anti-ASC ( D086-3 , MBL ) , anti-GFP ( AsOne ) , anti-Flag ( M6 , Sigma ) , anti-NF-κB ( #4764 , C22B4 clone , Cell Signaling ) , anti-IκBα ( #4814 , L35A5 clone , Cell Signaling ) , anti-NLRP3 ( Cryo-2 clone , Adipogen ) , anti-NLRC4 ( #12421 , D5Y8E clone , Cell signaling ) , anti-ubiquitin ( Ubi-1 clone , Millipore ) , anti-KGC ( 21B10 clone , MBL ) , anti-GFP mAb-Magnetic beads ( RQ2 clone , MBL ) , and anti-Tubulin ( Sigma ) were used in this study . The statistical analysis was calculated using the build-in mathematical function of Numbers ‘09 ( Apple Inc . ) . Difference between two groups was identified using the Student’s t-test under the condition of two-tails/two-sample unequal for all analysis . Data are presented as means with standard error of mean ( S . E . M ) and statistical significance was recognized when p<0 . 05 . All experiments were repeated for reproducibility and the representative data was shown in figures . | Enteropathogenic Escherichia coli ( EPEC ) and enterohemorrhagic E . coli ( EHEC ) cause severe intestinal dysfunction , including watery diarrhea or severe bloody diarrhea , and acute kidney failure ( hemolytic uremic syndrome ) . Transmitted through ingestion of contaminated food , these pathogens colonize and disrupt the linings of intestinal epithelial cells . EPEC and EHEC interrupt many cellular functions , including the inflammation response , to increase their chances of proliferation and survival in the intestine . Upon detection of the invasion , epithelial cells and immune cells secrete inflammatory cytokines to further boost the immune response for efficient clearance of the pathogens . IL-1β is an important inflammatory cytokine , and its secretion is regulated by a multimeric protein complex , termed the inflammasome , in host cells . In this study , we discovered that EPEC injects a bacterial effector protein , NleA , to inhibit the secretion of IL-1β . Exploring the potential mechanisms , we found that NleA does so by directly associating with NLRP3 ( Nod-Like Receptor 3 ) , one of the three basic components of the inflammasome , and that the presence of NleA interrupts the de-ubiquitination of NLRP3 , which is a prerequisite for the assembly of the inflammasome . As a result , NleA reduces the formation of the NLRP3 inflammasome and negatively regulates the secretion of IL-1β . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Enteropathogenic Escherichia coli Uses NleA to Inhibit NLRP3 Inflammasome Activation |
Schistosomiasis affects millions of people in developing countries and is responsible for more than 200 , 000 deaths annually . Because of toxicity and limited spectrum of activity of alternatives , there is effectively only one drug , praziquantel , available for its treatment . Recent data suggest that drug resistance could soon be a problem . There is therefore the need to identify new drug targets and develop drugs for the treatment of schistosomiasis . Analysis of the Schistosoma mansoni genome sequence for proteins involved in detoxification processes found that it encodes a single cytochrome P450 ( CYP450 ) gene . Here we report that the 1452 bp open reading frame has a characteristic heme-binding region in its catalytic domain with a conserved heme ligating cysteine , a hydrophobic leader sequence present as the membrane interacting region , and overall structural conservation . The highest sequence identity to human CYP450s is 22% . Double stranded RNA ( dsRNA ) silencing of S . mansoni ( Sm ) CYP450 in schistosomula results in worm death . Treating larval or adult worms with antifungal azole CYP450 inhibitors results in worm death at low micromolar concentrations . In addition , combinations of SmCYP450-specific dsRNA and miconazole show additive schistosomicidal effects supporting the hypothesis that SmCYP450 is the target of miconazole . Treatment of developing S . mansoni eggs with miconazole results in a dose dependent arrest in embryonic development . Our results indicate that SmCYP450 is essential for worm survival and egg development and validates it as a novel drug target . Preliminary structure-activity relationship suggests that the 1- ( 2 , 4-dichlorophenyl ) -2- ( 1H-imidazol-1-yl ) ethan-1-ol moiety of miconazole is necessary for activity and that miconazole activity and selectivity could be improved by rational drug design .
Schistosomiasis is a helminthiasis caused by trematode worms of three main schistosome species , Schistosoma mansoni , S . haematobium , and S . japonicum . The disease is responsible for approximately 280 , 000 deaths annually and significant morbidity in more than 200 million people [1 , 2] . Schistosomiasis belongs to a class of neglected tropical diseases whose control has been given limited attention by the pharmaceutic industry because they affect poor people in developing nations . Currently , praziquantel ( PZQ ) is the only treatment for schistosomiasis [3] . However , studies indicate that PZQ-resistant laboratory strains can be isolated and clinical isolates with increased PZQ resistance have been reported [4] . Therefore , it is a matter of time before resistance fully evolves . In addition , PZQ is much less active against juvenile worms and often results in incomplete cures [5–8] and its mechanism of action , including its biotransformation are not fully understood [3] . Biotransformation pathways play vital roles in providing essential molecules for cell survival and to modify harmful molecules in order to facilitate their elimination . Xenobiotic biotransformation occurs in three phases . Phase I metabolism involves the oxidative , reductive , or hydrolytic transformations of xenobiotics , of which the most important are catalyzed by CYP450 enzymes . In phase II transformation , metabolites undergo conjugation reactions with endogenous compounds such as glutathione , glucuronic acid , amino acids , and sulfate in reactions mainly catalyzed by glutathione S-transferases ( GSTs ) , UDP-glucuronosyltransferases , N-acetyltransferases , methyltransferases and sulfotransferases . Phase III transformations utilize membrane-bound transport proteins , which carry modified molecules across membranes for excretion [9] . There has been an extensive study of phase II metabolizing enzymes including the glutathione S-transferase family in schistosomes . For example , the main GSTs identified in S . mansoni have been shown to bind to several commercially available anthelmintics [10] and are currently important vaccine candidates [11] . Recently , a sulfotransferase was implicated in the mechanism and selectivity of action of oxamniquine in schistosomes [12] . In addition , Phase III biotransformation proteins , including the ATP-binding cassette ( ABC ) transporters , have been identified and their role in praziquantel susceptibility , immunoregulation within the host , parasite egg development and maturation , and translocation of important signaling molecules such as glyco- and phospholipids is being studied [13] . However , very little is known about phase I metabolizing CYP450 enzymes in schistosomes . CYP450s are heme-containing monooxygenases . In concert with NADPH CYP450 reductases , the heme group of CYP450s serves as a terminal oxidase , i . e . , a source of electrons to split molecular oxygen , with one oxygen atom added to the substrate and the other atom accepting reducing equivalents from NADPH to form water [14] . Characterized CYP450 reductase proteins are well conserved and occur as single copy genes in individual organisms . However , the CYP450 proteins are quite diverse , with most organisms having multiple CYP450 genes ( Table 1 ) [9 , 15] . Analysis of the S . mansoni genome database has identified only one potential CYP450 gene [16] . In a previous study , extracts of adult S . mansoni and S . haematobium were shown to metabolize some typical CYP450 substrates and immunoblotting experiments with an anti-rat CYP450 antibody had cross-reactivity with both S . mansoni and S . haematobium homogenates with a specific band at ~50 kDa , well within typical CYP450 molecular weight range [17] . In addition to biotransformation activities , CYP450 proteins are involved in the metabolism of many essential endobiotic compounds . Synthesis of membrane sterols , cholesterol and ergosterol depends on CYP450s as does synthesis and degradation of steroid hormones [18 , 19] . Cellular levels of retinoic acid , the active metabolite of vitamin A , which is essential for embryonic development , postnatal survival , and germ cell development , are regulated and metabolized by several CYP450 proteins [18] . Other CYP450s are involved in the metabolism of prostaglandins , prostacyclins , and leukotrienes [19] , all derivatives of fatty acids and important for cell signaling and immune response . In Caenorhabditis elegans CYP450 proteins are thought to be involved in meiosis , egg polarization , and egg shell development [20] . In this study , we hypothesize that the single CYP450 gene present in schistosomes is essential for worm survival and that blocking its function would lead to worm death and/or interference in parasite development . We used both genetic and pharmacological approaches to test this hypothesis . Treating larval parasites with SmCYP450-specific double-stranded RNA led to significant decreases in CYP450 mRNA and resulted in worm death . Screening a collection of CYP450 inhibitors ( Fig 1 ) we found that low micromolar concentrations of imidazole antifungal CYP450 inhibitors had schistosomicidal activity against adult and larval worms and blocked embryonic development in the egg . We conclude that SmCYP450 is essential for parasite survival and egg development , and it is proposed as a novel target for antischistosomal drug development , with miconazole analogs as starting points in drug discovery .
In all of the experiments involving the use of animals , maintenance and use of these animals were performed in accordance with protocols approved by the Institutional Animal Care and Use Committee ( IACUC ) at Rush University Medical Center ( IACUC number 14–080; DHHS animal welfare assurance number A3120-01 ) . Animals were euthanized with a lethal dose of Nembutal . CYP450 inhibitors ( Fig 1 ) were purchased from Sigma Aldrich ( miconazole , clotrimazole , ketoconazole , posaconazole , triadimenol , sertaconazole , bifoconazole , econazole , butoconazole , dafadine , fluconazole ) , Santa Cruz Biotechnology ( piperonyl butoxide , tioconazole , fenticonazole , prochloraz , sulconazole , oxiconazole , anastrozole , letrozole , aminoglutethimide ) , and Cayman Chemical Company ( abiraterone acetate ) . Sulfaphenazole was synthesized according to published procedures [21 , 22] . A Puerto Rican strain of S . mansoni maintained in Biomphalaria glabrata snails and the same strain of S . mansoni maintained in NIH Swiss mice was supplied by the Biomedical Research Institute ( Rockville , Maryland , USA ) . All adult worms , schistosomula , and egg cultures were incubated in Basch’s Media 169 [23] . Basal Medium Eagle was from Life Technologies; glucose and fungizone were from Fisher Scientific; hypoxanthine , serotonin , insulin , hydrocortisone , triiodothyronine were from Sigma Aldrich; MEM vitamins , Schneider’s Drosophila Medium , and gentamicin were from Gibco; HEPES buffer from Mediatech , Inc . ; penicillin/streptomycin from Cellgro; and fetal bovine serum was from HyClone Laboratories , Inc . Cercariae were shed from infected Biomphalaria glabrata snails and mechanically transformed to schistosomula as described [24] . To collect liver-stage , juvenile parasites mice were perfused 23 days post infection and to collect adult worms mice were perfused 6–7 weeks after infection with Dulbecco’s modified Eagle’s medium ( Gibco ) using methods described previously [24] . Live worms were washed thoroughly with DMEM . Eggs were obtained from the livers of the mice 7 weeks post infection . Livers were placed in ice-cold PBS and stored at 4°C overnight and processed the following day as described [24] . Parasite material was stored at -80°C for later use in stage specific SmCYP450 mRNA quantitation . The CYP450 open reading frame was amplified from adult mixed cDNA using P450_5' and P450_3' ( all primers listed in Table 2 ) and GoTaq Flexi DNA Polymerase ( Promega ) . PCR product was cloned into pCRII ( Invitrogen ) and plasmids were purified ( Plasmid Mini Kit ( QIAGEN ) and sequenced at the University of Illinois-Chicago Core Sequencing Center ( UIC ) . Alignment of the obtained open reading frame with the genome sequence was done using the Needleman-Wunsch Global Sequence Alignment Tool ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) . Prediction of the molecular weight of the encoded protein was done at the Swiss Institute of Bioinformatics Resource Portal ( http://web . expasy . org/compute_pi/ ) . Internal Coordinate Mechanics ( ICM ) homology modeling tool ( http://www . molsoft . com ) [25 , 26] was used to generate a CYP3050A1 model based on the CYP2C5 ( PDB ID 1nr6 ) template and the structure-superimposition-guided sequence alignments performed using the iterative dynamic programming and superimposition steps implemented in the ICM Homology Modeling module [27] . Alignments were further adjusted manually to preserve integrity of the a-helices and b-sheets , patterns of positive ( blue ) and negative ( red ) charges , aromatic ( purple ) and hydrophobic ( green ) functionalities , and finally , proline ( ochre ) and cysteine ( yellow ) side chains . Global optimization was performed using the Biased Probability Monte Carlo ( BPMC ) conformational search combined with the electrostatic energy term [28] . Loop search and side chain refinement was conducted for up to 100 , 000 iterations , which included full energy minimization at each step , to result in a model with satisfactory local strain parameters [29] . To determine if a subset of CYP450 mRNAs was trans-spliced , the S . mansoni trans-spliced leader sequence was used in PCR with either CYP450-R1 or CYP450-R3 specific internal primers ( Table 2 ) . A modified 5’ rapid amplification of cDNA ends ( RACE ) with Q5 DNA polymerase ( New England Biolabs ) was done in a nested PCR using an adult cDNA library ( kindly provided by Dr . Philip LoVerde ) as the template and vector primer T3 + gene-specific CYP450-revComF2 in the first stage and the vector primer SK + gene-specific SmCYP450-Rev ( Table 2 ) for the second stage . The product of the second PCR was cloned into pCR4 ( Invitrogen ) . To determine if the SmCYP450 mRNA is alternatively spliced , the complete ORF was amplified using Q5 DNA polymerase from adult male , adult female , and egg cDNA ( synthesized as described below ) with P450_5' and P450_3' . PCR products were cloned into pCR4 . Plasmid DNAs were isolated ( GeneJET Plasmid Miniprep Kit , Thermo Scientific ) and sequenced at the UIC sequencing core . To determine the activity of CYP450 inhibitors , 10 worm pairs in 5 ml Basch’s media per well in 6-well plates were cultured overnight at 37°C and 5% CO2 and the following day CYP450 inhibitors ( Fig 1 ) were added to each well . The media were replenished every 48 hr with fresh media and inhibitors . Dead worms were identified as those that showed no motility when observed for several minutes . For larval worms , 300–400 freshly prepared schistosomula were placed in each well in a 24-well plate containing 1 ml Basch’s Media and incubated overnight at 37°C and 5% CO2 . The following day compounds were added to each well and the parasites observed for several days without changing the media or adding fresh compounds . Live and dead parasites were classified as before . To monitor the effects of miconazole on egg development we followed a recently published method [32] . Freshly perfused adult worm pairs were incubated in Basch’s media overnight . The following day worms were removed and miconazole ( 5 or 10 μM ) or an equal volume of DMSO was added to the eggs produced . Eggs were further incubated a total of 72 hr in the presence of miconazole . Each group of treated eggs was then collected and centrifuged ( 500 x g , 5 min ) and the supernatant discarded . The egg pellets were each washed in excess PBS and centrifuged . The eggs were then fixed in 100% methanol at room temperature for 10 min . After removing the methanol the eggs were incubated in DAPI ( 4’6-diamidino-2-phenylindole ) Fluoromount-G ( SouthernBiotech ) overnight at 4°C for nuclear staining . Images were captured using Zeiss Axiovert Z1 imaging microscope and analyzed with AxioVision software LE ( release 4 . 8 . 2 SP3 , 2013 ) . To see if their activities had additive effects , schistosomula were treated with dsCYP450 RNA at a concentration that alone did not kill schistosomula ( 10 μg/mL ) and miconazole at concentrations that resulted in minimal killing ( 2 . 5 or 5 μM ) or each alone . Schistosomula cultures were set up as described above . A control experiment was set up with irrelevant dsRNA with and without 5 μM miconazole . Parasites were observed as described above . Total RNA was isolated from frozen worm and egg samples using the TRIzol Reagent ( Life Technologies ) per the manufacturer’s recommendation in a 2 ml Lysis Matrix Tubes ( MP Biomedicals ) containing 500 μl TRIzol reagent . Tubes were shaken three times for 20 seconds each using a tissue homogenizer ( FastPrep-24 5G Instrument , MP Biomedicals ) . The samples were incubated on ice for 5 minutes in between each lysis process . After lysis , another 500 μl TRIzol Reagent was added to each sample , mixed and incubated at room temperature for 5 min . The resultant sample was spun at 13 , 000 x g for 1 min to pellet cellular debris . Following centrifugation , supernatants were transferred to a new 1 . 5 ml microfuge tube and extracted with chloroform/isopropanol according to the manufacturer’s instructions . The gelatinous , white RNA precipitate obtained after the chloroform/isopropanol extraction was resuspended in DEPC treated water in 75% ethanol and spun at 6500 x g for 5 min at 4°C . After centrifugation the supernatant was removed and the RNA pellet briefly air-dried and re-suspended in DEPC-treated water , heated briefly at 55°C quantified on a Nanodrop spectrophotometer . Total RNA was used for cDNA synthesis ( iScript , BIO-RAD ) per the manufacturer’s recommendation . The synthesized cDNA for each sample was quantified by Nanodrop spectrophotometry and stored at -20°C . Primers used for qPCR are shown in Table 2 . α–tubulin ( GenBank accession M80214 ) was used to normalize the results . The reactions were each carried out in a 20 μl reaction using ROX Passive Reference Dye ( Bio-Rad ) according to the manufacturer’s protocol . The amplification was monitored in a 7900HT Fast Real-Time PCR Machine ( Applied Biosystems ) under the following cycle conditions: ( stage 1 , 95°C 30 sec , stage 2 , 95°C 5 sec , 60 C 30 sec ) x 50 , plus a one cycle dissociation curve . Fold differences were calculated using the 2-ΔΔCT as described [33] with α–tubulin transcript levels serving as the internal standard . Reactions were done in triplicate . Semi-quantitative RT-PCR was used to assess the relative abundance of SmCYP450 mRNA after RNAi silencing using Platinum Taq DNA polymerase ( Life Technologies . Glyceraldehyde 3-phosphate dehydrogenase ( GenBank accession M92359 ) was used as a control gene ( primers GAPDH_S . mansoni FWD and GAPDH_S . mansoni REV ) and SmCYP450 cDNA was amplified with primers CYP450-interF2 and ORF_CYP450 Reverse .
Cloning and sequence analysis shows that the SmCYP450 coding sequence is 1452 base pairs encoding a protein of 483 amino acids with a predicted molecular weight of 55 . 28 kDa . The family assignment as CYP3050A1 was made by Dr . David R . Nelson according to the CYP450 nomenclature [34 , 35] . The sequence was found to be longer than the sequence reported in GeneBank ( Smp_156400 , 1245 base pairs , ) due to a miscalled junction of the 5th intron/6th exon during genome annotation . The sequence obtained was submitted to GenBank with the accession number KT072747 . The gene is composed of 7 exons and 6 introns spanning 15 , 378 base pairs ( not including 5’ and 3’ noncoding sequences ) . Sequence analysis shows it to be comparable to CYP450 proteins from other organisms . The signature heme-binding motif [14 , 36] , [FW]-[SGNH]-x-[GD]-{F}-[RKHPT]-{P}-C-[LIVMFAP]-[GAD] , is present ( the bold , underlined residues are present in SmCYP450 ) ( Fig 2 ) . The ‘P450-signature’ sequence , [AG]-G-X-[DE]-T-[TS] , which forms a channel for electron transfer [36] , is also present in the SmCYP450 peptide . The protein has an N-terminal membrane spanning region followed by the poly-proline domain , which is important for protein folding and structural integrity [37] . The turns by the poly-proline region provide a junction between the transmembrane region and the main catalytic domain typical for most CYP450 proteins [37] . The organization of the predicted secondary structure of the SmCYP450 protein sequence follows other CYP450 proteins , beginning from helix A in the N-terminal region of the protein sequence and ending with helix L , which contains the heme-binding sequence ( Fig 2 ) . Likewise , with the exception of the absence of the J and J’ helices , the tertiary structure of SmCYP450 protein is predicted to be similar to known CYP450 proteins ( Fig 3 ) . It is possible that a diversity of CYP450 proteins or alternative subcellular targeting of SmCYP450 results from alternative splicing or post-transcriptional modifications of the mRNA produced from the single S . mansoni CYP450 gene . This was addressed by analyzing cDNAs from a variety of developmental stages and by using modified 5’RACE and PCR with the schistosome spliced leader sequence to search for multiplicity of CYP450 mRNAs . We analyzed 33 clones from adult female worm cDNA , 23 clones from adult male worm cDNA , 11 clones from egg cDNA , and 28 clones generated by 5’ RACE and all sequences were identical . Therefore , we found no evidence for alternative splicing or other sequence variations . PCR with the spliced leader sequence and two different internal CYP450-specific primers resulted in no PCR products; therefore , the SmCYP450 mRNA does not appear to be trans-spliced . Therefore , it appears that the SmCYP450 gene encodes a single CYP450 protein . Using qRT-PCR we found that SmCYP450 mRNA was present at all developmental stages investigated and that it is differentially present during development ( Fig 4 ) . Eggs , the larval stages of development ( cercariae and schistosomula ) and adult female worms had higher mRNA levels than adult male worms . Liver stage parasites had the lowest SmCYP450 mRNA expression levels , about 50% that of adult males . To determine if SmCYP450 is essential for schistosomula survival we used RNAi to silence SmCYP450 expression . Treating worms with 10 μg/mL or 30 μg/mL SmCYP450 specific dsRNA for two or three days resulted in a dose-dependent reduction in SmCYP450 message ( Fig 5A ) . No change was seen in SmCYP450 mRNA after treatment with 30 μg/mL irrelevant dsRNA or in GAPDH mRNA abundance after treatment with either dsRNA ( Fig 5A ) . Treatment with 30 μg/mL SmCYP450 specific dsRNA resulted in 80% schistosomula survival by day 3 , 40% survival by day 5 , and 15% survival by day 7 . In contrast , 95% and 94 . 5% of schistosomula were alive on day 7 after treatment with 30 μg/mL irrelevant dsRNA or 10 μg/mL SmCYP450 specific dsRNA , respectively ( Fig 5B ) . CYP450 enzymes are inhibited by numerous anti-infective and anticancer agents . We next asked if clinically relevant CYP450 inhibitors ( Fig 1 ) affected parasite survival . Several antifungal imidazoles ( miconazole , clotrimazole , ketoconazole ) but not closely related triazole antifungals ( fluconazole , posaconazole and triadimenol ) were active against both larval and adult worms ( Fig 6A and 6B and Table 3 ) . Miconazole , clotrimazole , and ketoconazole had ED50 ( Effective Dose producing 50% worm death ) values of 10 μM , 20 μM , and 40 μM , after 5 day treatments against adult worms and 12 . 5 μM , 27 . 5 μM , and 30 μM after 2 day treatments against schistosomula , respectively . Other CYP450 inhibitors , such as prochloraz , sulfaphenazole , piperonyl butoxide , dafadine , letrozole , aminoglutethimide , abiraterone acetate , and anastrozole had no significant schistosomicidal activity against either larval or adult worms ( Table 3 ) . Expansion of the anti-fungal imidazole series was done to generate preliminary structure activity relationships of this compound series . Our studies revealed that imidazoles that retained the 1- ( 2 , 4-dichlorophenyl ) -2- ( 1H-imidazol-1-yl ) ethan-1-ol moiety of miconazole had significant schistosomicidal activity against both larval and adult worms , while those which lacked this moiety had much reduced or no schistosomicidal activity ( Table 3 , Fig 6C ) . Does the potent schistosomicidal activity of miconazole act through inhibition of worm CYP450 or does it have other targets in the worm ? To address this question we tested low doses of miconazole against worms treated with 10 μg/mL dsRNA CYP450 , which caused no significant worm death itself . While 5 μM miconazole alone resulted in 80% survival after 6 days , combinations of 5 μM miconazole and 10 μg/mL SmCYP450-specific dsRNA resulted in 60% survival ( p = 0 . 0042 ) . Combining 2 . 5 μM miconazole ( 90% survival alone ) and 10 μg/mL SmCYP450-specific dsRNA resulted in 75% survival ( p = 0 . 007 ) ( Fig 7A ) . Addition of 30 μg/mL irrelevant dsRNA treatment had no effect on killing by 5 μM miconazole ( Fig 7B ) . These results strongly suggest that miconazole schistosomicidal activity is specific for SmCYP450 . To determine if miconazole interferes with egg development and maturation we treated eggs deposited by freshly perfused adult worm pairs with miconazole and monitored embryo development using a recently described method [32 , 39] . Egg development was scored based on the number and arrangement of cell nuclei ( Fig 8A ) . Our results indicate that there is a general interference of egg development and accumulation of early embryonic stages ( I , II and III ) and decrease in late stage embryos ( IV and V ) in the miconazole treatments compared to the DMSO controls . Only 30% ( 18/62 ) of eggs treated with 5 μM miconazole and 18% ( 10/56 ) treated with 10 μM miconazole reached the latter stages of egg development ( stages IV and V ) compared to 64% ( 35/55 ) in DMSO control ( Fig 8B ) . These results indicate that miconazole affects embryonic development .
Because schistosomiasis control relies on a single drug and there is field evidence for the evolution of drug resistance [3 , 4] , there is an urgent need to identify new , druggable worm targets . In this study we present the first detailed characterization of the CYP450 from S . mansoni and provide strong evidence that it is an essential and druggable target in the worm . The SmCYP450 exists as a single copy gene in the S . mansoni genome [16] . This is in stark contrast to humans , which have 57 genes and alternative splicing and genetic variations that can lead to the production of many more distinct protein species [40 , 41] , and to the free-living flatworm Schmidtea mediterranea , which has at least 39 CYP450 genes [15] . The loss of CYP450 family members in parasitic helminths has been noted previously [42] . However , the fact that parasitic flatworms have retained one CYP450 signifies that it plays an important and perhaps essential function . We add here that in S . mansoni there appears to be no post-transcriptional modifications ( alternative or trans-splicing , RNA editing ) to the mRNA . Therefore , it is likely that a single protein product is produced from the SmCYP450 gene . Since there was no evidence for alternative splicing to insert different leader sequences at the N-terminus , the protein product is likely only targeted to the endoplasmic reticulum . The predicted protein has generally low sequence identity with the other CYP450s; the highest identity to human CYP450 proteins is 22% to CYP2C9 . Importantly , the CYP450 consensus motif responsible for heme-binding and interaction with molecular oxygen and the relevant substrates and the ‘P450-signature’ sequence are conserved in the SmCYP450 protein sequence . Curiously , SmCYP450 lacks a number of motifs found in many characterized CYP450 . The majority of CYP450s contain an ‘EXXR motif’ in helix K . The glutamic acid and arginine residues form a charge pair with a third amino acid more distant in the meander region . This is frequently an arginine in the so-called ‘PERF motif’ . Putative functions of the EXXR motif and PERF motif may be to associate heme with the newly synthesized CYP450 polypeptide and/or to maintain the CYP450 tertiary architecture [43] . This is key to the structural fold of CYP450s and previous studies in which mutagenesis directed at the side-chains of glutamic acid or arginine in the EXXR motif or at the invariant cysteine in the L-helix resulted in completely inactive and misfolded proteins [44] . However , these motifs are not present in CYP450s from parasitic Trematodes ( e . g . , Schistosoma , Clonorchis sinensis ) and Cestodes ( e . g . , Echinococcus multilocularis ) [42] . Their absence is not without precedent as the EXXR motif is also absent in most members of a CYP157 subfamily in Streptomyces spp [45] . The Trematode CYP450 proteins also lack the J and J’ helices , which occur to the N-terminal side of and include the EXXR motif . How these differences affect protein structure and function remains to be determined . CYP450s function in an electron transport chain in which electrons are passed from NADPH through a flavoenzyme either directly to the CYP450 heme or indirectly through cytochrome b5 or ferredoxin . In the endoplasmic reticulum , the flavoenzyme is NADPH CPY450 reductase . Additional partners of CYP450s in the endoplasmic reticulum include cytochrome b5 and cytochrome b5 reductase . In mammals , ferredoxin reductase and ferredoxins ( also known as adrenodoxin reductase and adrenodoxins ) are found in the mitochondria and are involved in steroid hormone synthesis mediated by CYP450s . The S . mansoni genome contains one CYP450 reductase , two cytochrome b5s , two cytochrome b5 reductases , one ferredoxin reductase , and two ferredoxins with potential to support SmCYP450 activity . Previous studies in schistosomes have found that ferredoxin reductase is mitochondrial and likely functions there in redox defenses [46 , 47] . Since we currently have no evidence for mitochondrial targeting of SmCYP450 protein , it is not likely that it functions in concert with ferredoxin reductase/ferredoxins . Unlike the only previously characterized trematode CYP450 , which showed highest expression in adult hermaphrodites [42] , SmCYP450 is expressed at the highest levels in larval and egg stages . It is important to note that the developmental cycles and tissue locations of these organisms are significantly different . After active host localization and penetration , S . mansoni has extensive interactions with host skin , lungs , liver , and vascular epithelia , while Opisthorchis worms reside in the bilary ducts after excysting from metacercariae in the duodenum . As sequence identity between S . mansoni and O . felineus CYP450 proteins is only 37% it is quite possible that CYP450s have different functions in the worms . The function of the SmCYP450 is not yet known . Different development stages may require different CYP450 metabolites and/or experience different immunological stresses . For instance larval parasites penetrate the skin of human host and begin migration through the skin and other tissue and may encounter different stress and immunological responses than adult worms in the mesenteric system . Larval schistosomes synthesize and secrete eicosanoids [48–53] , which are signaling molecules derived from arachidonic acid , some of which are produced by CYP450s . The eicosanoids produced by schistosomes may down modulate host immune function [54 , 55] . Eicosanoids produced by adult worms may control other functions such as vasodilator activity , and/or vasoconstrictive action [55] . Other potential functions of SmCYP450 are in the metabolism of cholesterol and steroid hormones . Adult worms have been shown to convert cholesterol into several metabolites including pregnenolone , the first committed metabolite in steroid hormone biosynthesis [56 , 57] . Male worms transfer cholesterol and uncharacterized cholesterol metabolites to female worms [56] and synthetic steroids have been shown to affect worm egg production in vivo [56] . More recently , a catechol-estrogen conjugate ( downstream products of CYP450 metabolism of estradiol and estrone ) , which has anti-estrogen affects , was identified in schistosome worm extracts and in the serum of infected humans [58] . Retinoic acid is essential for embryonic development in all metazoan organisms investigated , including free-living flatworms [59] . Retinoic acid activity is controlled through its tightly regulated synthesis from vitamin A ( all-trans retinol ) in a 2-step process by retinol dehydrogenases to all-trans retinal and by retinaldehyde dehydrogenases to all-trans-retinoic acid and is terminated via its breakdown by CYP450s [18 , 60] . Although retinoic acid signaling or metabolism in schistosomes is largely unknown , they have enzymes involved in retinoic acid metabolism ( 10 retinol dehydrogenases and 2 retinaldehyde dehydrogenases ) and nuclear receptors related to retinoic acid receptors [61–64] . Ecdysteroids are hormones involved in insect molting and development and CYP450s are involved in their synthesis and transformation from farnesyl diphosphate and cholesterol . Ecdysteroids have been detected in schistosomes and their levels shown to vary during development [65 , 66] . S . mansoni synthesizes ecdysone and 20-OH ecdysone , which were shown to be potent stimulators of growth and vitellogenesis [67] . β-Ecdysterone was found to be effective in stimulating host location activities in S . mansoni miracidia [68] . Worms have two nuclear receptors related to insect ecdysone receptors , but their function in ecdysteroid signaling has not been determined [69 , 70] . Identification of the function of SmCYP450 will be targeted in future studies . Our findings indicate S . mansoni has a single CYP450 protein , with highest sequence identity to human CYP450s CYP2C9 and CYP1A1 . In order to compare the differences between S . mansoni CYP450 and human CYP450s we tested several different classes of CYP450 inhibitors . Although miconazole and structurally related imidazoles had schistosomicidal activity against adult and larval worms , other CYP450 inhibitors did not . These observations gave rise to an early exploration to investigate the structure-activity-relationships ( SAR ) of imidazole class of compounds , especially miconazole analogs ( Fig 6C ) . Miconazole analogs were obtained by substituting the ( 2 , 4-dichlorophenyl ) methanol moiety with different aryl groups . Sertaconazole , which results from substitution with a ( 7-chlorobenzo[b]thiophen-2-yl ) methanol group , was equipotent to miconazole against adult and larval worms . Replacement with ( 4-chlorophenyl ) methanol group results in econazole . Replacement of the oxygen by a sulfur in the econazole led to sulconazole . Modification of the econazole by substitution of a phenylthio group for the 4-chloro led to fenticonazole . Replacement with an oxime moiety into the miconazole gave oxiconazole . Econazole , sulconazole , fenticonazole and oxiconazole were less potent than miconazole . Substitution with ( 2-chlorothiophen-3-yl ) methanol moiety results tioconazole , which is much less active . Our results indicate that miconazole constitutes a promising scaffold for targeting schistosome worms . Evidence that schistosomicidal activity of miconazole and analogs resides in the 1- ( 2 , 4-dichlorophenyl ) -2- ( 1H-imidazol-1-yl ) ethan-1-ol moiety of miconazole suggests routes to improved activity by rational drug design in future studies . Miconazole had previously been included in a medium throughput phenotypic screen against schistosomula in an effort to repurpose approved drugs [71] . In , this study , compounds were screened at 1 μM against schistosomula and miconazole was found to be inactive , which is consistent with our results . However , for our screening purposes we tested compounds at higher concentrations and therefore , identified the schistosomicidal activity of this class of compounds . Although the concentrations required for worm killing activity in vitro may not be attained in vivo due to low biological availability , improved pharmacological properties can be incorporated into miconazole analogs to overcome these limitations . Our results indicate that the schistosomicidal activity of miconazole is due to inhibition of SmCYP450 . Low concentrations of miconazole alone resulted in low schistosomicidal activity and partial reduction of SmCYP450 mRNA alone resulted in no larval worm death . However , combination treatments produced more than an additive response: 10% death in 2 . 5 μM miconazole alone increased to 20% with partial mRNA silencing and 20% death in 5 μM miconazole alone increased to 40% with partial mRNA silencing . The simplest explanation for this effect is that partial mRNA silencing results in decreases in SmCYP450 protein , which although it is not lethal to the worms itself , results in increased activity of miconazole due to a reduction in its protein target abundance . This strongly suggests that both SmCYP450 dsRNA and miconazole target the same pathway . In schistosomes , egg development is a multi-stage process . Within the host mesentery and vasculature , a mature female releases approximately 300 encapsulated embryos ( pre-mature eggs ) per day [72 , 73] . Prior to that and within the mature female the early development of eggs occurs in several pre-zygotic and post zygotic stages [74] . Using methods recently developed to facilitate monitoring egg development [32 , 38] we investigated the effect of miconazole on egg development and maturation . Treatment with miconazole resulted in a dose-dependent impairment of ex vivo egg development , with most miconazole-treated eggs remaining at the early stages of embryonic development ( Stages I-III ) compared to control treatments , in which most eggs reached later stages of embryonic development ( stages IV and V ) . CYP450 proteins are known to be involved in egg development in C . elegans , with CYP31A2 and CYP31A3 essential for the production of lipids required for egg shell development [20] . In addition , retinoic acid is essential for embryonic development in all metazoan organisms investigated , including free-living flatworms , and as indicated above , retinoic acid metabolism is mediated by CYP450 proteins . Inhibition of retinoic metabolism by miconazole could interfere with embryogenesis and egg development . There has not been a direct identification of SmCYP450 protein in eggs [75] . The newly oviposited egg is not fully formed and undergoes embryonic and subshell envelope development [76] . It is not known if SmCYP450 functions in embryonic or subshell envelope development or both , but our work shows for the first time that miconazole can block egg development . Schistosomiasis remains a challenging disease to people living in endemic areas . In spite of many years of praziquantel use , the prevalence of infection remains high . The specter of evolving resistance to praziquantel , the only drug available for disease treatment , calls for the identification of new protein targets , the discovery of lead compounds and the development of new drugs for the treatment of the disease . The S . mansoni CYP450 exists as a single gene in the parasite genome . Our work shows that it is essential for parasite survival and could be an ideal drug target . In addition , select anti-fungal azoles could be promising starting points for future studies towards identifying new therapies for schistosomiasis . | Over 600 million people in endemic countries are at risk of contracting schistosomiasis , which results in over 200 , 000 deaths each year and significant illness to most people that are infected . There are concerns that the drug widely used for the treatment of schistosomiasis , praziquantel , may be losing efficacy due to evolution of drug resistant worms . Since the disease mainly affects the poor in developing countries , pharmaceutical companies have little interest in developing new drugs and none are currently being tested . In this paper we focus on a novel parasite protein , cytochrome P450 , which we propose to be a new drug target . Worms are unusual in having only one cytochrome P450 gene; humans have 57 cytochrome P450 genes . By using reverse genetic and chemical approaches we found that the schistosome cytochrome P450 is essential for worm survival and egg development and , therefore , is an essential and druggable target . Drugs that target fungal cytochrome P450s and are already in use for treating several human diseases were identified as potential hits for further development for schistosomiasis treatment . | [
"Abstract",
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] | [] | 2015 | The Schistosoma mansoni Cytochrome P450 (CYP3050A1) Is Essential for Worm Survival and Egg Development |
The Ecohealth strategy is a multidisciplinary data-driven approach used to improve the quality of people’s lives in Chagas disease endemic areas , such as regions of Central America . Chagas is a vector-borne disease caused by the parasite Trypanosoma cruzi . In Central America , the main vector is Triatoma dimidiata . Because successful implementation of the Ecohealth approach reduced home infestation in Jutiapa department , Guatemala , it was scaled-up to three localities , one in each of three Central American countries ( Texistepeque , El Salvador; San Marcos de la Sierra , Honduras and Olopa , Guatemala ) . As a basis for the house improvement phase of the Ecohealth program , we determined if the localities differ in the role of sylvatic , synanthropic and domestic animals in the Chagas transmission cycle by measuring entomological indices , blood meal sources and parasite infection from vectors collected in and around houses . The Polymerase Chain Reaction ( PCR ) with taxa specific primers to detect both , blood sources and parasite infection , was used to assess 71 T . dimidiata from Texistepeque , 84 from San Marcos de la Sierra and 568 from Olopa . Our results show that infestation ( 12 . 98% ) and colonization ( 8 . 95% ) indices were highest in Olopa; whereas T . cruzi prevalence was higher in Texistepeque and San Marcos de la Sierra ( >40% ) than Olopa ( 8% ) . The blood meal source profiles showed that in Olopa , opossum might be important in linking the sylvatic and domestic Chagas transmission cycle , whereas in San Marcos de la Sierra dogs play a major role in maintaining domestic transmission . For Texistepeque , bird was the major blood meal source followed by human . When examining the different life stages , we found that in Olopa , the proportion bugs infected with T . cruzi is higher in adults than nymphs . These findings highlight the importance of location-based recommendations for decreasing human-vector contact in the control of Chagas disease .
Chagas disease or American trypanosomiasis , is a neglected , zoonotic vector-borne disease , transmitted by insect vectors in the subfamily Triatominae ( Hemiptera: Reduviidae ) known colloquially as “kissing bugs” [1 , 2] . The disease , caused by the parasite Trypanosoma cruzi ( Kinetoplastea: Trypanosomatida ) , is endemic throughout Latin America with some autochthonous cases reported to the southern United States [3–6] . It has been estimated that in 2010 , 5 . 7 million people from 21 Latin America countries were infected with T . cruzi [1 , 2] . Among the different pathways to acquire the disease ( e . g . , mother-fetus , oral , blood transfusion , organ transplant ) , insect vector transmission is the most common , and there are over 150 species of triatomine vectors distributed across the endemic area [7] . For Central America , Triatoma dimidiata became the most important vector in the human Chagas transmission cycle after vector control strategies successfully eliminated Rhodnius prolixus , a vector indigenous to South America that had been introduced into Central America [8–10] . In its introduced range , R . prolixus was exclusively domestic , whereas T . dimidiata , a vector native from southern Mexico through Central America and into northern South America , is a complex of subspecies found in sylvatic and domestic habitats [11–16] . International health organizations ( i . e . Pan American Health Organization , PAHO and World Heath Organization , WHO ) identify reducing vector infestation as the main way to reduce human Chagas disease[17] . To this end , a multifactorial Ecohealth strategy combining insecticide spraying and house improvements has been developed that eliminates vector hiding places within and around houses to decrease the presence of bugs , reduce vector-human contact , and improve the quality of people’s lives [18–20] . The biology and ethology of T . dimidiata have been well studied [20–25] and its domiciliary infestation has been associated with different bio-socio-ecological factors . These bio-socio-ecological factors include: I ) the presence of domestic and synanthropic animals , such as bird ( e . g . , chicken , turkey and duck ) , rodents ( mouse and rat ) , dog , and opossum , [20 , 22–24 , 26]; II ) house construction with natural materials specifically adobe or bajareque , and house wall conditions including rustic , unplastered walls or cracks in the wall plastering [27]; III ) the location of chicken coops ( next to the house or away from the house ) or evidence of animals inside the house ( i . e . rodent or bird nests ) , and IV ) household characteristics including: the presence of dirt floors , poor hygiene ( e . g . , clutter ) , and signs of triatomines inside the house ( insect feces , exuviae , eggs or dead insects ) [26–30] . House improvements that target these factors include not only replacing dirt floors with concrete and plastering the walls using local materials , but also removing blood meal sources by removing clutter and relocating chicken coops and other domestic animals outside and away from the house . An essential component of the Ecohealth approach is the collection of data before , during , and after intervention ( data-driven intervention ) , to enable data-driven evaluation . This research-based approach was developed and tested in Jutiapa , Guatemala [24] . Before and after the house improvements , two common entomological indices were used to assess vector abundance: infestation and colonization indices [18] . Entomological surveys have shown these indices are reduced by the interventions of the Ecohealth strategy [26] . To identify risk factors and assessing the success of the Ecohealth interventions in a single village in Jutiapa , seven potential blood meal sources of T . dimidiata before and after house improvements were compared [20] . Within the framework of the Implementation Science approach [31] , recent efforts have scaled up the Ecohealth program from the single locality in Jutiapa to three new locations in three different countries , Texistepeque , El Salvador; San Marcos de la Sierra , Honduras and Olopa , Guatemala . These localities differ in ecology , culture , ethnicities and social administrative structure . The first step of Ecohealth interventions is assessing the initial conditions at the locations targeted for vector control . Establishing the baseline conditions of an ecosystemic intervention enhances vector control success by identifying risk factors for house infestation , and has been well reviewed [31] . The risk factors associated with domestic infestation for these localities have been identified [27] . Based on these risk factors , the aim of the present study was to: 1 ) document and understand the role of domestic , synanthropic and sylvatic animals in the occurrence of Chagas vector infestation in each location , and 2 ) use a research-based approach to determine if the locations vary in T . cruzi transmission risk factors and infestation indices prior to the Ecohealth interventions . With these goals , we surveyed the totality of houses at the three locations and collected all vectors found during half an hour , to estimate the infestation and colonization indices . From these vectors , we identified the blood source profiles based on the seven blood meal sources previously studied [20] and assessed T . cruzi infection prevalence in vectors for each of the three locations . Our data analysis tested for differences among locations evaluated in the metrics of vector infestation and infection , including vector developmental stage and ecotope of collection , as well as the association between vectors infected with T . cruzi and the various blood sources detected .
This study was part of the project “Ecohealth interventions for Chagas Disease prevention in Central America” [27] . Using the Implementation Science approach [31] to increase what is known about Chagas disease and reduce transmission , interventions that mitigate factors associated with vector infestation were scaled up from one locality in Guatemala to localities in three different countries ( Texistepeque , El Salvador; San Marcos de la Sierra , Honduras and Olopa , Guatemala ) in Central America ( Fig 1 ) . The localities were identified by local ministry of health officials as having high incidence of vector infestation . The environmental differences among the three locations are described below . In the municipality of Texistepeque , Santa Ana , El Salvador , is hot and dry with average annual temperature of 24 . 4°C and precipitation of 1 , 653 mm . The ecosystem is deciduous forest characterized by sandy soil with low fertility . The forest has been altered with extensive cutting and the introduction of non-native species . Most people work in agriculture ( mainly growing peanuts ) and small business . The houses in El Salvador are grouped in small “Caseríos” within “Cantones” , where most of the houses are constructed of adobe , block , wood or corrugated aluminum . Within Texistepeque , 928 houses from two cantons , El Jute and Chilcuyo were examined [27] . Information about prior vector control interventions ( e . g . insecticide spraying ) is not available for these cantons . From the 16 acute cases reported for El Salvador for 2012 , seven ( 43 . 7% ) came from the department of Santa Ana which includes Texistepeque [32] . The municipality of San Marcos de la Sierra , Intibucá , Honduras , is hot with average annual temperature of 21°C and average annual precipitation of 1 , 943 mm . The ecosystem is mountainous pine-oak forest and sandy soil with low fertility . In this area , subsistence farming is mostly corn and beans . The houses in this locality are further apart than those examined in El Salvador , most are constructed of adobe blocks with clay tile roofs . In Intibucá , we examined 613 houses from four cantones [27] . Information about prior insecticide spraying or disease prevalence is not available for this part of Honduras . In the municipality of Olopa , Chiquimula , Guatemala , we examined five villages along a forested altitudinal gradient with the average temperature of 20°C and annual precipitation of 1 , 439 . 4 mm . The cloud forest at the highest altitude has cold weather and water year-round , while the lowest village is in humid tropical forest . Along the gradient , crops include shade grown coffee interspersed with plantations of bananas along with remnants of the original forest . Most of the houses are constructed of adobe bricks or bajareque . In Olopa we examined 1 , 140 houses from the five villages El Amatillo , La Prensa , El Cerrón , El Guayabo and Paternito [27] . For the five villages , the most recent insecticide sprayings prior to this study were: El Amatillo in 2004 , La Prensa in 2000 , El Cerrón in 2001 , El Guayabo in 2001 and El Paternito in 2004 ( Personal communication , Vectores program , Ministry of Health , Chiquimula , Guatemala ) . Information about the number of acute cases is not available for these villages , however for 2003 a Chagas disease seroprevalence of 6 . 52 on school-age children was reported for the department of Chiquimula which includes Olopa [33] . Animal household practices are similar for the three locations; the most frequent domesticated animals are birds ( usually chickens ) , followed by dogs , cats and pigs , only a few houses have beasts ( e . g . cows , sheep ) . Prior to our Ecohealth interventions , over 75% of people at the three localities kept chickens inside the house at night to prevent theft , wandering off , or predation . Dogs are almost exclusively outside guarding the house at night . Over 50% of the houses at Texistepeque and Olopa showed signs of synanthropic animals ( e . g . mice and/or rats ) inside the house , while only 25% of the houses at San Marcos de la Sierra reported traces of synanthropic animals . No records are available related to the presence and abundance of sylvatic reservoirs at the three localities . The insect vectors examined in this study were collected during the baseline survey of all the houses in each locality conducted during August-October 2011 , prior to interventions which included insecticide spraying and house improvement ( for more details refer to [27] ) . Surveys were conducted by professionals of the Ministries of Health ( El Salvador , Guatemala and Honduras ) , members of the Laboratory of Applied Entomology and Parasitology ( Guatemala ) , and personnel from the Center of Health Research and Development ( CENSALUD ) in El Salvador . Collection of triatomines was performed using the person-hour method , with two people searching all areas of a house for half an hour each using flashlight and forceps [34] . The house areas inspected included the intradomicile and peridomicile , where the peridomicile could include structures close to the house , e . g . chicken coops , as well as piles of wood or other accumulated material . All insects collected were transported to laboratories in their respective countries in mesh covered plastic bottles labeled with the collecting information ( House ID , village , date , ecotope , stage ) . Upon arrival , collection information for each insect was recorded in an electronic database , and insect vectors were placed in 95% ethanol + 5% glycerol and stored at room temperature until subsequent DNA extraction for blood source and T . cruzi parasite detection . The sample sizes for each component of the study is shown in Fig 2 . To determine the blood meal sources of insect vectors collected at houses from the three locations , we used seven PCR reactions for each individual to test for the presence of DNA from bird , dog and pig ( domestic animals ) ; rat and mouse ( synanthropic animals ) , opossum ( sylvatic reservoir ) and human . For bird , we did not distinguish between domesticated ( chicken , duck , turkey ) and sylvatic birds . For specimens collected in Olopa and San Marcos de la Sierra , DNA was extracted in Guatemala , while for Texistepeque DNA extractions were carried in that country . All DNA extractions used the last three segments of the insect abdomen with the E . Z . N . A Tissue DNA kit ( Omega Bio-Tek , Georgia , GA , USA ) , following the manufacturer’s tissue protocol for the first two steps , and the blood protocol for the remaining steps , with an additional incubation time at 65°C of 10 min followed by 95°C for 5 min after the third step . Positive controls for the blood meal sources were obtained from the tissue of chicken ( for the bird assay ) and pig , while blood was used for rat , mouse , dog , opossum , and human . All subsequent PCR assays were done in Guatemala . Extracted DNA was used in 12 μL PCR reactions consisting of 4 . 5 μL H2O ( molecular grade , DNase and RNase free ) , 0 . 5 μL of each primer ( 10ng/μL ) , 6 . 5 μL of 2X master mix ( EconoTaq PlusGreen , Lucigen Corporation , Middleton , WI , USA , or REDTaq ReadyMix PCR Reaction Mix , Sigma , St . Louis , MO , USA ) and 0 . 5 μL of genomic DNA ( concentration not determined ) . Assay conditions for the T . cruzi major nuclear repetitive element followed the protocol of [35]; bird ( “avian” ) , rat , mouse were based on [36] , pig was based on [37] and human was based on the protocol of [38] ( see also [20 , 39] ) and dog assay was based on [40] . A new assay for opossum was developed: forward primer: 5’ GATGGAGATTAGTGGCTCTG 3’ , reverse primer: 5’ GAAGGCAGAGAATTCCAAGA 3’ with a PCR product size of 243 bp . PCR conditions for opossum were: denaturation at 94°C for 2 min followed by 30 cycles at 95°C ( 30 sec . ) , 50°C ( 30 sec ) and 70°C ( 45 sec ) ; followed by a final extension at 72°C for 5 minutes ( S1 Text ) . The PCR reactions for T . cruzi , bird , dog , human , mouse , rat , and pig were carried out using a PTC-100 thermocycler ( MJ Research , California , CA , USA ) . For the opossum PCR reactions we used the SimpliAmp Thermal Cycler ( Life Technologies Corporation , Carlsbad , CA , USA ) . Electrophoresis of the amplified DNA used 1% agarose gels with 10 μg/mL of ethidium bromide in TBE ( 90 mM Tris-borate , 1 mM EDTA , pH 8 , 0 ) , followed by UV transillumination to observe the DNA bands . The opossum assays were run in 2% agarose gels stained with 2 . 8 μL/100 ml Syber green DNA gel stain ( ThermoFisher , Waltham , MA , USA ) . The results ( absence or presence of taxa-specific size bands ) for each blood source were recorded in the electronic database . Recent studies have shown that the lack of blood source detection by PCR can indicate either a recent blood meal from taxa not included in the survey or no recent blood meal rather than PCR inhibition [4 , 20 , 41] . Because of this , a subset of bugs with no blood meal detected by PCR , but positive for T . cruzi , were assayed by PCR with the universal 12S ribosomal gene vertebrate primers as in [4] . Samples with an appropriate sized band were sent for DNA sequencing in one direction ( Beckman Coulter Genomics , now GeneWiz , Cambridge , MA , USA ) as in [42] . Trace files were edited using Sequencher v5 . 3 ( Gene Codes Corporation , Ann Arbor , MI USA ) and taxonomically identified based on >98% match for 130 bp using NCBI-BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) . This study received ethical clearance for the three countries from the Panamerican Health Organization ( ID: PAHO-2011-08-0017 . R1 ) . All household adult participants and parents or legal guardians of minors provided informed consent . The sample sizes for each component of the study is shown in Fig 2 . Our sample sizes differ slightly from those reported in the study of socioeconomic and house construction factors for these same houses [27] because we included dead insects in the analysis . Although we sampled all the houses within each village , canton or caserio at each of the three localities; because of sample sizes differences among villages and caserios/cantones we pooled the data into the three localities ( Fig 1 ) . Thus for example , we cannot infer that birds were the most common blood source in every canton in Texistepeque compared to every canton in San Marcos de la Sierra , but we can conclude that on average bird blood meals are more common in Texistepeque than San Marcos de la Sierra . All statistical analyses were run using the software JMP Pro 13 . 0 . 0 ( 64-bit , SAS Institute Inc . , Cary , NC , 1989–2017 ) .
Of the 2681 houses surveyed across the three locations , Olopa had significantly higher infestation than San Marcos de la Sierra and Texistepeque ( χ2 = 13 . 84; p = 0 . 001 ) ; Olopa also had a significantly higher colonization index than San Marcos de la Sierra and Texistepeque ( χ2 = 32 . 76; p < 0 . 001 ) . For all three locations , bugs were found in both the intradomestic and peridomestic ecotope , whereas dead bugs were only found inside houses ( Table 1 ) . Overall , the distribution of potential blood sources at the three locations shows little variation . There are between 4–5 people per house at each locality and on average at least 2 dogs per house . Pigs were the least frequent overall at houses , with an average number between 2–3 per house , however the average number of birds was 9–15 per house ( this includes chickens ) . For the three locations , more than 70% of the total houses surveyed had chickens . From the houses with chickens , 82% , 56% and 35% ( Texistepeque , San Marcos de la Sierra and Olopa , respectively ) do not have a facility to keep them ( e . g . chicken coops ) , instead birds were kept inside the house . In contrast , for the 70% of houses with chickens and chicken coops , the chicken coops are located away from the house ( Table 2 ) . In addition to chicken coops , two other peridomestic risk factors were frequent at the three localities , these were firewood piles and construction materials . Firewood piles were present in more than 50% of the total houses surveyed in Texistepeque and Olopa and located next to the house; whereas only 33% of the total houses surveyed in San Marcos de la Sierra had firewood piles and 65% of them were located next to the house . Construction materials were present in less than 29% of the total houses surveyed in the three locations , however more than 67% of the houses with construction materials had those materials next to the house ( Table 2 ) . The frequency of bugs from which any blood source was detected was different among localities ( χ2 = 35 . 19; p < 0 . 001 ) . For Texistepeque , 19 of 71 bugs ( 27% ) had at least one blood source detected , the value for San Marcos de la Sierra was 52 of 84 ( 62% ) and for Olopa 170 of 568 ( 30% ) ( Fig 3 ) . Usually a single blood source was detected; however , two blood sources were detected in some bugs , [Texistepeque: 3 of 19 ( 16% ) , Olopa: 12 of 170 ( 7% ) and San Marcos de la Sierra: 10 of 52 ( 19% ) , S1 Table column P] . More than two blood sources were not detected at any locality probably because blood meals are detected by these primers for only about 12–28 days post feeding [41] . Each of the locations has a different blood source profile ( Fig 4 ) . All three localities had human , domestic animal and synanthropic animal blood sources , but opossum , an important blood source because of its relation to the sylvatic transmission cycle , was only detected in Olopa , in both , intradomestic and peridomestic bugs ( S1 Fig ) . For Texistepeque , bird , human and mouse were found to be the most frequent blood sources detected ( 16%-59% ) . In contrast to Texistepeque , dog was the main blood source detected followed by human and mouse ( 19%-52% ) in San Marcos de la Sierra . In Olopa , dog , bird , and human were the most detected blood sources ( 17%-45% ) . Statistical comparison of blood sources among locations indicated significant differences in bird , dog , mouse and opossum; specifically , dog detection in Texistepeque is significantly lower than in the other two localities , yet bird is significantly lower in San Marcos de la Sierra , and mouse in Olopa . When the blood meal sources are examined by ecotope , intradomestic bugs follow the same blood source profiles described above for each location . However , the low sample size of peridomestic bugs do not allow us to make statistical inference . The 12S sequencing of samples that were positive for T . cruzi indicates additional blood meal sources: cow ( in Olopa and San Marcos de la Sierra ) and frog ( San Marcos de la Sierra ) ; however , the overall frequency of these new taxa was low [3 of 52 ( 5 . 7% ) ] . Because frogs were never seen inside houses or in the peridomicile we take this as evidence of sylvatic blood meal sources at the three locations . For Texistepeque , no new blood sources were detected , but we did find one opossum had gone undetected with opossum PCR . These results are detailed in the supplemental material ( S1 Table ) . Bug infection prevalence with T . cruzi is similar for Texistepeque [30 of 71 bugs ( 42% ) ] and San Marcos de la Sierra [45 of 84 ( 54% ) ] , but significantly lower in Olopa [43 of 568 ( 8% ) ] ( χ2 = 136 . 3; p < 0 . 001 ) . In addition , 6–25% of infected bugs either did not have a recent blood meal or had recently fed on taxa we did not assay ( 25% Texistepeque , 6% Olopa , and 16% San Marcos de la Sierra ) ( Fig 3 , diagonal stripe area ) . The factors associated with T . cruzi infection vary among locations ( S2 Table ) . The three localities show significant differences with respect to only one of the four factors examined . Within each locality , the likelihood for a bug to be infected was not associated with having at least one blood source detected ( S2 Table ) . Ecotope only has an effect in Texistepeque where peridomestic bugs are more likely to be infected than bugs collected inside houses . For Olopa and San Marcos de la Sierra , there is no difference in the likelihood of T . cruzi infection for bugs collected from the peridomicile compared to the intradomicile ( Table 3 ) . The sex/stage factor had a significant effect on bugs being infected , but only in Olopa , with adults being more likely to be infected than nymphs; on the contrary there are no differences between males and females ( female vs male ( OD = 0 . 6137 , 95% CI = 0 . 2836–1 . 3279 , p = 0 . 2151 ) . In Texistepeque and San Marcos de la Sierra , there is no difference in the likelihood of T . cruzi infection among nymphs , males and females ( Table 3 ) . Only in the case of San Marcos de la Sierra was T . cruzi infection associated with the recent blood meal source . Bugs with evidence of human blood meals were significantly less likely to be infected with T . cruzi than bugs that fed on domestic animals ( dog and pig ) and blood meals from synanthropic animals ( rat and mouse ) ( Table 3 ) .
Although all three locations are known to have persistent T . dimidiata infestation both entomological indices clearly show that in Olopa the proportion of houses with bugs is higher than in the other two localities ( Table 1 ) . The infestation index ( presence of adults or nymphs , or both ) only indicates the possibility of a colony establishing inside a house or in a peridomestic area , or bugs migrating from the sylvatic to the intradomestic or peridomestic environment; however , the values of the colonization index ( presence of nymphs with adults , or just nymphs ) represents the number of houses with reproducing bugs per location . It has been shown that risk factors for triatomine infestation differ among regions [43] . Infested houses with T . dimidiata at the three locations ( Table 1 ) and previous studies in Jutiapa , Guatemala show most bugs were found in the intradomestic ecotope and intradomestic infestation is largely associated with poor wall conditions ( no plaster or deteriorated , cracked plaster ) [27 , 30] . Even though house construction material ( adobe , bajareque ) , house wall condition ( no plaster or deteriorated , cracked plaster ) , signs of infestation ( e . g . , eggs , feces ) and other factors ( See also Table 1 from [27] ) are predictors of infestation , for these three locations , this same study [27] showed that adding a random location effect improved model fit , thus confirming the variation among localities . In contrast , peridomestic infestation tend to be associated with chicken habitats ( coops or nests ) [26 , 44] piles of firewood [44] , and coffee trees [26] in close proximity to a house , as well as tiled roofing [44] . As shown in Table 2 and [27] , the most frequent peridomicile risk factor at the three locations are: chicken coops , firewood piles and construction material accumulation . Chicken coops were present in less than 45% of the houses with chickens at Texistepeque and San Marcos de la Sierra . However , more than 70% of the chicken coops were away from the house at the three localities . In addition , the frequency of having both , firewood piles and construction material accumulation , inside-adjacent to the house , were more frequent at the three localities ( more than 57% and 67% respectively ) . In the context of Implementation Science for Ecohealth data-driven interventions , the infestation and colonization information reported here suggest that for infested houses , spraying followed by house improvements such as wall plastering and cement floors should be performed as has been shown by [45] . This is especially a concern for Olopa , the locality with the highest infestation and colonization indices ( Table 1 ) . However , in addition to information from the entomological indices and house risk factors , our data on blood meal source profiles and T . cruzi infection of the insect vectors also show differences among regions and provide information to guide and prioritize interventions as described below . We found relatively high feeding on humans at all three locations , highlighting the potential for T . cruzi transmission in all locations . Although there was no significant difference of human blood meals at the three locations , further study is needed to determine if the seroprevalence of T . cruzi in people is correlated to human blood meals and T . cruzi detection in vectors . This is especially a concern for Texistepeque were recently a high incidence of acute cases has been reported for Santa Ana , El Salvador [32 , 46] . Host accessibility shapes a vector’s blood source profile [47] and we found that the blood meal source profiles in T . dimidiata at the three locations show statistically significant differences in bird , dog , mouse and opossum ( Fig 4 ) . Although only mammals can transmit the parasite , birds are frequent and adequate blood sources for the insect vector . The statistical analysis shows bugs feeding more frequently on birds at Texistepeque and Olopa ( above 30% ) than at San Marcos de la Sierra ( 12% ) . In Olopa and San Marcos de la Sierra , few bugs feeding on chickens are infected with T . cruzi , thus they appear to play a role in maintaining bug population numbers but not the parasite; whereas in Texistepeque , that many bugs that had recently fed on birds and were also positive for T . cruzi , indicates that those bugs had previously fed from an infected mammal ( human or non-human ) ( Fig 4 ) . With respect to the Ecohealth data-driven interventions , the frequency of houses with birds ( including chickens ) with no chicken coops ( 82% in Texistepeque , Table 2 ) , suggests that more attention should be focused on bird location , in particular moving the chickens into coops away from houses . With this in mind , it would be interesting to examine the effect of both chicken coop construction material and chicken coop location with respect to the house on reducing other mammal blood meals and thus T . cruzi infection as has been shown by [20] for T . dimidiata in Guatemala . Although overall there are similar number of dogs per house in the three locations ( Table 2 ) , dogs appear to play a more important role in Olopa and San Marcos de la Sierra . For both locations , the prevalence of dog as a blood source ( 40% ) was significantly above that in Texistepeque ( 5% ) . In studies from numerous locations across several countries , dogs are reported as the most important reservoirs of T . cruzi and are a host that coexists with people ( Argentina [48] , Venezuela [49] and the USA [50] ) . Recent studies [26] show that the number of dogs ( in particular >2 per house ) is an important risk factor for house infestation with T . dimidiata , and for others vectors ( Triatoma infestans ) dogs are preferred as a blood meal source over chickens [51] . In addition to being a domestic reservoir , it has been shown that dogs can become infected when roaming into sylvatic environments [52] , suggesting that the prevalence of the parasite in dogs could result from vectors in both domestic and sylvatic ecotopes . In fact , the wooded areas in San Marcos de la Sierra and Olopa are more preserved than in Texistepeque ( Monroy in field , personal observation ) . Even though overall there is on average at least one dog per house at each location ( Table 2 ) , among the houses with dogs , there is on average 2 dogs per house ( SD = 1 . 07 ) . Since our data show dogs are more important in the transmission cycle in San Marcos de la Sierra , for this location we suggest prioritizing controlling dog reproduction ( e . g . spay or neuter ) to reduce dog populations as well as the major blood source for the vector because the population abundance of a T . cruzi reservoir would be reduced . Interestingly , opossum was only detected as a recent blood meal in Olopa ( 7% ) ( Fig 4 ) . Opossum is considered an “ancient” host of the parasite , because it can be a reservoir and host at the same time , opossum is one of the most important sylvatic reservoirs in Chagas transmission [53] . The importance of opossum as a blood meal source for T . dimidiata was also reported for Costa Rica by [22] , where rat and opossum blood meal sources were common in T . cruzi positive bugs . In Olopa , no information is available related to the abundance of sylvatic reservoirs , however , no signs of opossum were evident during our examination inside houses , this and our findings support that opossum might be moving from sylvatic to domestic ecotopes , highlighting its role as a link between the sylvatic and domestic cycle of Chagas disease . Although mouse was significantly higher in Texistepeque and San Marcos de la Sierra ( > 16% ) compared to Olopa ( 1% ) , because the frequency of mouse blood meal detection is smaller than bird and dog , targeting this blood meal source would be lower priority . Although the percent of vectors with recent blood sources was significantly different among locations , overall there was a surprisingly low percent of recent blood sources , especially in Texistepeque and San Marcos de la Sierra ( T . 27% , S 38% and O 70% ) . These values are higher than reported in a recent study for a nearby location in El Tule , Jutiapa , Guatemala with ~15% of vectors having no recent blood source before interventions and 40% after [20] . The 12S sequencing assay indicates bugs are feeding on additional blood meal sources at all three locations: frog for San Marcos de la Sierra , cow for Olopa , and opossum for Texistepeque ( S1 Table ) . As mentioned before , the lack of blood source detection by PCR can indicate either a recent blood meal from taxa not included in the survey or no recent blood meal [4 , 20 , 41] . Strong support for no recent blood meal is provided by recent studies based on mass spectrometry [54 , 55] including domestic vectors from El Salvador that show DNA based methods have a short window for blood meal detection [54] , it would be interesting to examine T . dimidiata where no blood sources were detected by PCR from these three localities to strengthen the Ecohealth strategies proposed by this study . Establishing if T . cruzi infection in the vector is associated with: I ) the ecotope where the bug was collected , II ) the ecological association of the blood source with human , or III ) the stage/sex of the bug , also provides information for in shaping Ecohealth data-driven interventions . This study shows that the different factors associated with T . cruzi infection support different recommendations for the three locations . For Texistepeque , peridomestic bugs are significantly more likely to be infected than intradomestic bugs ( S2 Table ) . All 12 peridomestic bugs tested were collected from piles of construction material or an accumulation of firewood in the peridomicile . This reinforces other studies showing an association between infestation and accumulation of firewood in the peridomicile [57] . Since firewood piles were present in 57% of the houses surveyed in Texistepeque , Ecohealth interventions ensuring firewood piles are located away from the house should be prioritized for this location . In contrast , for San Marcos de la Sierra , we found bugs that had fed on domestic and synanthropic animals were more likely to be infected than bugs that had fed on humans . Because 52% of the bugs had fed on dogs , Ecohealth interventions targeting dogs should be prioritized . For Olopa , we found adult insect vectors were more likely to be infected compared to nymphs ( S2 Table ) . This fact has been well supported elsewhere [56 , 57] and this is because adults have had more time to become infected and their mobility , adult bugs can fly and are more mobile and thus can feed from a variety of blood sources , whereas nymphs likely move less and perhaps have only fed on blood sources available at the site of collection . For Olopa that the dispersing adult stage is more likely to be infected highlights the importance of Ecohealth interventions to make human dwellings less attractive to migrating bugs . For Texistepeque and San Marcos de la Sierra the likelihood of T . cruzi infection in adult insect vectors was not significantly higher either because of small sample sizes or adults moving less at these locations . If increased sampling indicated adults are more likely to be infected than nymphs at Texistepeque and San Marcos de la Sierra ( S2 Table ) house improvements ( e . g . wall plastering and dirt floor replacement by concrete ) as suggested by [27] would be recommended for all three locations ( see also [27] ) . Because firewood piles and construction materials have shown to be associated with triatomine infestation and at the same time were frequently located next to the house at all three localities ( Table 2 ) , we suggest that in addition to house improvements , peridomestic reorganization should be prioritized .
To our knowledge , this is the first study that aims to use local information to implement an Ecohealth data-driven intervention to reduce vector transmission of T . cruzi . Our results related the presence of bugs ( described by entomological indices ) at both intradomestic and peridomestic ecotopes across locations and suggest that intervention is needed to decrease vector-human contact . Blood meal source analysis has shown to be a good measure of the impact of Ecohealth interventions for Jutiapa , Guatemala by [20] and similar analysis was applied to the Central American locations surveyed here with the same goal . In this study , variation among locations in the blood source profiles and correlates with T . cruzi prevalence highlight the regional importance of domestic and synanthropic animals in disease transmission and a link between sylvatic and domestic transmission cycles . Dog is the most frequent blood source for San Marcos de la Sierra and Olopa , while humans and birds ( which are blood sources but cannot become infected ) in Texistepeque . In San Marcos de la Sierra , controlling dog reproduction through neutering and spaying is suggested to reduce dog populations and thus reduce the risk of the house infestation , but most important because 52% of bugs that fed on dog were T . cruzi positive then it would reduce disease incidence . It is important to remember that blood source profiles are not generalizable and represent only a snapshot in time . However , blood source profiles do provide important information to make locally relevant recommendations for Ecohealth interventions . Our results combined with those from previous studies on blood source profiles can be used by policy makers to consider a wider breadth of vector control measures and target limited resources to locally-identified , high-impact intervention . | Blood feeding insects from the subfamily Triatomine are involved in the transmission of Chagas disease , caused by the protozoan parasite Trypanosoma cruzi , a neglected tropical disease endemic from southern Mexico through Central to northern South America . Chagas disease mostly affects rural areas and especially people living in houses made of low-cost , natural materials such as bajareque or adobe that have mud walls and a dirt floor . A multidisciplinary data-driven Ecohealth vector control program that includes house improvements ( wall plastering and cement flooring ) , as well as insecticide spraying , was developed in Jutiapa department , Guatemala , and has been shown to decrease vector-human contact . Because Chagas vectors feed on a wide variety of vertebrates , knowing the local feeding profiles of the insect vectors before interventions can strengthen Ecohealth program development . To facilitate scaling up the Ecohealth program developed in Jutiapa to three new locations in three different countries , Texistepeque , El Salvador; San Marcos de la Sierra , Honduras and Olopa , Guatemala , and with distinct ecological scenarios , we assessed the entomological indices , feeding profiles and parasite infection of vectors collected in and around houses in the new locations prior to any interventions . Our results show all three metrics varied among locations . The results highlight the importance of domestic , synanthropic and sylvatic blood meal sources on the disease transmission cycle and the need to consider local conditions for vector control . | [
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] | 2018 | Implementation science: Epidemiology and feeding profiles of the Chagas vector Triatoma dimidiata prior to Ecohealth intervention for three locations in Central America |
Buruli ulcer ( BU ) is the third most frequent mycobacterial disease in immunocompetent persons after tuberculosis and leprosy . During the last decade , eight weeks of antimicrobial treatment has become the standard of care . This treatment may be accompanied by transient clinical deterioration , known as paradoxical reaction . We investigate the incidence and the risks factors associated with paradoxical reaction in BU . The lesion size of participants was assessed by careful palpation and recorded by serial acetate sheet tracings . For every time point , surface area was compared with the previous assessment . All patients received antimicrobial treatment for 8 weeks . Serum concentration of 25-hydroxyvitamin D , the primary indicator of vitamin D status , was determined in duplex for blood samples at baseline by a radioimmunoassay . We genotyped four polymorphisms in the SLC11A1 gene , previously associated with susceptibility to BU . For testing the association of genetic variants with paradoxical responses , we used a binary logistic regression analysis with the occurrence of a paradoxical response as the dependent variable . Paradoxical reaction occurred in 22% of the patients; the reaction was significantly associated with trunk localization ( p = . 039 by Χ2 ) , larger lesions ( p = . 021 by Χ2 ) and genetic factors . The polymorphisms 3’UTR TGTG ins/ins ( OR 7 . 19 , p < . 001 ) had a higher risk for developing paradoxical reaction compared to ins/del or del/del polymorphisms . Paradoxical reactions are common in BU . They are associated with trunk localization , larger lesions and polymorphisms in the SLC11A1 gene .
The neglected tropical disease Buruli ulcer ( BU ) is the third most frequent mycobacterial disease in immunocompetent persons after tuberculosis and leprosy [1–2] . It is caused by Mycobacterium ulcerans . Central to the pathogenesis is the immunosuppressant and necrosis inducing toxin mycolactone . During the last decade , an antibiotic regimen of eight weeks of streptomycin and rifampicin was introduced [3 , 4] . Earlier studies reported the success of this antimicrobial treatment with or without surgery [5–7] . A clinical trial showed that antimicrobial treatment was highly effective in patients with small lesions ( cross-sectional diameter < 10 cm ) , of which 96% healed without surgery [8] . However , during or after antibiotic treatment the BU lesions may worsen . This could be caused by treatment failure [9–11] , but might also be due to an inflammatory response caused by treatment-induced recovery of the immune system , i . e . a paradoxical reaction . Paradoxical reactions have been described in tuberculosis and in leprosy [12 , 13] . Recent studies have recognized the existence of paradoxical reactions in BU [11 , 14] . In Australia , one in five BU patients appear to have a paradoxical reaction . Most cases occurred between three and ten weeks after the start of treatment [9] . In a trial in Ghana , most of the cases with a paradoxical reaction ( >30% ) were reported at week eight after the beginning of antimicrobial treatment [15] . The diagnosis of paradoxical response is difficult; no serological markers have been identified to differentiate paradoxical reactions from treatment failure [15] . Paradoxical reactions can be defined clinically by worsening of existing lesions , or the appearance of new lesions , and histologically by the appearance of intense inflammation in lesions [9] . Importantly , in most areas endemic for BU , histology is not available . In Africa , very few studies have addressed paradoxical reactions in BU [10 , 14] as well as its risk factors . In Australia , edematous lesions , use of amikacin and age above sixty years old were strongly associated with paradoxical reactions . In addition to sociodemographic and clinical features , we suggest genetic factors may influence the occurrence of paradoxical reactions as well . As paradoxical reactions are hypothesized to reflect an exaggerated immune response , genes involved in the immune response in infectious diseases might play a role . For BU , a polymorphism in the innate immune SLC11A1 gene ( formerly known as NRAMP1 ) was previously found to be associated with increased susceptibility to BU [16] . Furthermore it has been shown that 1 , 25 ( OH ) 2D3 suppresses the Th1 response by down-regulating the production of pro-inflammatory cytokines [17–19] . So it is possible that polymorphisms in SLC11A1 gene as well as vitamin D are also related to paradoxical reactions . In West Africa , most of the patients are below age 15 [20] and amikacin is not used to treat BU but very few patients receive antimicrobial treatment without streptomycin , the parent aminoglycoside drug . As the patient demographics and treatment regimen in West-Africa are widely different from that of Australia , it is important to look at the risk factors for developing paradoxical reactions in BU in this region . In Ghana , paradoxical reactions were described in patients with M . ulcerans infection with early lesions ( duration < 6 months ) , limited to 10 cm cross-sectional diameter [14]; large lesions that are common in west Africa were not included in that study . Our study focuses on the risk factors associated with paradoxical reactions in patients with both small and large BU lesions , during and after antimicrobial treatment , and examines the influence of genetic factors as well .
In the present study , we included participants of two randomized clinical trials in Ghana and Benin . The BURULICO drug trial with patients enrolled between 2006–2008 , was a randomized controlled trial for the treatment of early ( duration less than 6 months ) , limited ( cross-sectional diameter , 10 cm ) M . ulcerans infection [clintrials NCT00321178] . In this trial , patients were randomized to receive either 8 weeks of streptomycin and rifampicin or 4 weeks of streptomycin and rifampicin followed by 4 weeks of clarithromycin and rifampicin . Participants in this study that had their BU lesions healed at time point 52 weeks after initiation of antimicrobial treatment were earlier studied for possible paradoxical reactions [14] . The second trial is a randomized trial on timing of the decision on surgical intervention for BU patients treated with rifampicin and streptomycin [clintrials NCT01432925] . All included patients ( 2011–2015 ) had confirmed M . ulcerans infection by direct microscopy following acid-fast staining or Polymerase Chain Reaction ( PCR ) , and all received 8 weeks of antimicrobial therapy with rifampicin and streptomycin . For both trials , patients who were pregnant , children below five years old , patients not compliant with the antibiotic therapy , and patients with osteomyelitis , were excluded from the study . For the current study population , 150 of 241 participants of the BURULICO study , and 91 of the Burulitime study contributed ( S1 Dataset ) . For all patients , we recorded demographics and clinical data from the trial databases . In addition , we recorded the progression of the size of the lesion size by measurement at regular intervals . For both trials , measurements were available for the first 12 weeks at two-week intervals . In the BURULICO trial , lesions were measured at 14 , 21 , 27 weeks after start of treatment , and for the timing of surgical intervention trial , measurements were available at 16 , 20 , and 28 weeks after starting treatment . For analyses , the measurements at 14 and 16 weeks , at 21 and 20 weeks , and 27 and 28 weeks were considered to be equivalent time points . We considered an increase in lesion area of more than 5% between two consecutive measurements as a clinically relevant change . We defined a paradoxical reaction as 2 consecutive increases in lesion size after 1 initial decrease . We additionally performed all analyses ( post-hoc ) using a less strict definition of two consecutive increases without an initial decrease . For associations of clinical and patient characteristics with paradoxical responses , we used t-tests or Mann-Whitney U tests for accordingly and Χ2 tests for categorical variables . For testing the association of genetic mutations and variants with paradoxical responses , we used a binary logistic regression analysis with the occurrence of a paradoxical response as the dependent variable . The protocol was approved by the Committee on Human Research , Publication , and Ethics of the Kwame Nkrumah University of Science and Technology and the Komfo Anokye Teaching Hospital , Kumasi ( CHRPE/07/01/05 ) , by the Ethical Review Committee of Ghana Health Services ( GHS-ERC-01/01/06 ) and by the provisional national ethical review board of the Ministry of Health Benin , nr IRB00006860 . Written and verbal informed consent or assent was obtained from all participants aged ≥12 years , and consent from parents , caretakers , or legal representatives of participants aged ≤18 years . All data were analyzed anonymously .
A total of 241 patients were included , 150 from Ghana , and 91 from Benin; 61% were female . The mean ( SD ) age was 16 . 2 ( 13 . 2 ) years . On presentation , 45% of patients had an ulcer , 23% had a plaque , and 13% had a nodule; 29% had a WHO category I lesion , 55% a category II lesion , and 16% a category III lesion . The median ( IQR ) surface area of the lesion on presentation was 20 . 6 ( 6 . 6; 43 . 5 ) cm2; 49% had a lesion on the lower limb , 43% on the upper limb , and 8% on the trunk . Paradoxical reactions , as defined by an initial decrease of the lesion followed by two consecutive increases occurred in 22% of cases . Most paradoxical reactions occurred between weeks 8 and 12 ( Fig 1 ) . When using a definition that did only require two consecutive increases without an initial decrease , 26% of patients had a paradoxical response , and the frequency distribution of the initiation week of paradoxical reaction did not differ substantially . All cases that had a paradoxical reaction healed without additional treatment . Paradoxical reactions were significantly related to the site of lesion ( p = . 039 by Χ2 ) : 44% of patients with a lesion on the trunk had a paradoxical response , compared to 24% of patients with a lesion on the upper limb , and 17% with a lesion on the lower limb . Paradoxical reactions were also significantly related to WHO category at presentation . Ten percent of patients with a category I lesion had a paradoxical response , compared to 27% , and 23% of patients with a category II and category III lesion , respectively ( p = . 021 by Χ2 ) . Paradoxical reactions were not significantly related to patient age or gender . They were also not related to the type of lesion , duration of lesion before presentation , or white blood cell count at presentation ( Table 1 ) . For the participants in the BURULICO trial , paradoxical reactions were not related to treatment arm ( 8 week streptomycin vs 4 weeks streptomycin followed by 4 weeks clarithromycin ) . The pulse and temperature at the time of paradoxical response did not differ from the pulse at presentation by paired samples t-test , and did not differ from the average pulse and temperature of those not classified as having a paradoxical response at the respective week . Using the less strict definition , the same pattern of results emerged , where paradoxical reactions were significantly related to the site of the lesion ( p = . 024 by Χ2 ) and WHO category at presentation ( p = . 009 by Χ2 ) , but to none of the other variables . Vitamin D deficiency was found in 15% of participants . The mean ( SD ) vitamin D level was 66 . 5 ( 19 . 1 ) for the patients who had paradoxical reaction and 68 . 3 ( 17 . 1 ) for those who did not; 38% of patients with a vitamin D deficiency had a paradoxical reaction , compared to 23% of patients without a deficiency ( p = . 134 by Χ2 ) . In the post-hoc analysis using the less strict definition of a paradoxical response , 33% of patients with a vitamin D deficiency had a paradoxical reaction , compared to 17% of patients without a deficiency ( p = . 082 by Χ2 ) . 31% of patients with a 3’UTR TGTG ins/ins polymorphism had a paradoxical response , compared to 13% of patients with a ins/del or del/del polymorphism ( OR 0 . 14 , 95% CI: 0 . 05–0 . 44 ) . 5’ ( CA ) n microsatellite length , INT4 G/C polymorphism and D543N G/G polymorphism were not significantly related to paradoxical responses ( Table 1 ) . Using the less strict definition of a paradoxical response in a post-hoc analysis , a similar pattern of results emerged .
This is the first prospective study in West Africa addressing risk factors associated with paradoxical reaction in BU . In our sample , paradoxical reactions were common , and significantly associated with trunk localization , larger lesions and genetic factors . Currently , there is no standard definition of paradoxical reactions in BU . Histological aspects [9] suggested from Australia is not feasible in rural West Africa where most BU cases occur [2] . All patients included in this study healed without changes in therapy ( no change in antibiotics , no corticosteroids ) . This strongly supports our suggested definition and suggests that cases in our study represent true paradoxical reaction and not progressive disease secondary to antibiotic failure . We found a 22% incidence of paradoxical reactions ( 2 consecutive increases after 1 initial decrease and healing without surgery or a change in antimicrobial therapy ) , which is similar to a previous study from Australia [9] . In our study , most paradoxical reactions occurred between week 8 and 12—slightly later than the Australian study , where most paradoxical reactions occurred between week 3 and 10 [9 , 11] . In the case reports from Benin paradoxical reactions occurred between 12 and 409 days after completion of antibiotic treatment [10] . Mycolactone , the exotoxin produced and secreted by M . ulcerans , has been proposed as the major cause of immune suppression [28–32] . Perhaps , the period between week 8 and 12 in which most paradoxical reactions occurred coincides with the elimination of most M . ulcerans organisms , with an arrest in the production and subsequently , a strong decrease in tissue concentration of mycolactone . The increase of the lesion then reflects an inflammatory response against the microbes—or microbial antigens of dead bacilli—already present in tissue which initially failed to elicit a host immune response [25–27 , 30] . We found several risk factors associated with paradoxical reactions . The incidence appeared to increase in larger lesions . One explanation of this may be that smaller lesions heal before eight weeks when most of the paradoxical reaction occurs . Another possibility is that larger lesions have a higher bacterial load than small lesions . We showed that lesions localized on trunk were significantly associated with paradoxical reaction , even when controlling for the size of the lesion . More than 4 out 10 patients ( 44% ) with lesion on the trunk had paradoxical reaction compared to 24% and 17% for the upper limb and lower limb respectively . The increased incidence of paradoxical reactions on the trunk might be due to a difference in local immune responses and body temperature . Our study shows that paradoxical reactions were not significantly associated with patient age or type of lesion . This finding contrasts with Australian patients in whom associations between paradoxical reactions and age and edema were reported [9] . This might be due to differences in the study populations . In affluent countries like Australia , with a steeper population pyramid , BU mainly affects the elderly in Australia [31] , while in West Africa , most patients are children [32] . Paradoxical reactions were not associated with the white blood cell count or patients’ vital parameters such as the temperature and the pulse rate . We argue that an increase of pulse , temperature or white blood cell count is indicative of an additional disease or super-infection , which should be further investigated . Whether paradoxical reactions were associated with aminoglycoside use , as has been shown for amikacin in Australia , could not be examined for streptomycin use because all study participants had been exposed to this drug , for 4 or 8 weeks . One might speculate that this effect seen in amikacin might in fact reflect a decrease in paradoxical reactions by using antimicrobial drugs like macrolides that have been associated with immuno-modulatory effects [33] . We also show for the first time that paradoxical reactions to M . ulcerans infection are associated with genetic factors . Carrying the homozygous ins/ins genotype of 3’UTR TGTG polymorphism in the SLC11A1 increases the risk of paradoxical reactions in BU . Earlier studies have shown that genetic factors can influence the innate immune response to mycobacterial antigens , such as infectious disease susceptibility genes , e . g . , SLC11A1 , HLA-DR , vitamin D3 receptor , and mannose binding protein [34 , 35] . In BU no associations were found with the 3’UTR TGTG ins/del polymorphism and developing BU [16] . However in tuberculosis , it was reported that participants who were heterozygous for two SLC11A1 polymorphisms ( INT4 and 3’UTR ) were at highest risk of tuberculosis [35] . A meta-analysis [35] has shown that the TGTG ins/ins 3’UTR genotype protected against tuberculosis , compared to the del/del genotype . We interpret our data such that the protective TGTG ins/ins 3’UTR genotype in the SLC11A1 gene may induce a stronger immune response during M . ulcerans infection . In turn , this stronger immune response might increase the risk of paradoxical reactions once BU develops . It has been reported that genetic variation in SLC11A1 affects susceptibility to others mycobacterial diseases such as leprosy and tuberculosis [35–37] . However , no study addressed the genetic risk factor for paradoxical reaction in tuberculosis or leprosy . In this study , we report for the first time that paradoxical reactions are not associated with vitamin D level . Vitamin D deficiency has been found to be associated with susceptibility to tuberculosis [38] . Very few studies address vitamin D and paradoxical reactions in tuberculosis . Clearing of pathogens with anti-tuberculosis treatment and a delayed negative feedback on macrophage activation due to low 1 , 25 ( OH ) 2D production from vitamin D deficiency can lead to excessive granuloma formation and an exacerbated inflammatory response [39] . In our sample , the means of vitamin D level in patients with or without paradoxical reactions were similar . All included patients in this study healed without any change in therapy . In earlier studies corticosteroids were used to treat paradoxical reactions [9 , 40 , 41] . We would indeed caution for use of corticosteroids West Africa , as other infections like tuberculosis and strongyloidiasis may worsen . This study has some limitations . There are no standard definitions of paradoxical reactions in BU that we could use to validate our definition . Our definition is clinical and did not include histological aspects , which may lead to a lack of accuracy . However we believe that our cases accurately represent paradoxical reactions since all patients healed without any additional therapy . Secondly , we excluded co-infected patients with Buruli ulcer and HIV . This may have reduced the incidence and severity [42] . Paradoxical reactions are common in BU–and it is important that these should be differentiated from antimicrobial treatment failure . These paradoxical reactions are associated with trunk localization , larger lesions and certain polymorphisms in the SLC11A1 gene . There was no apparent need to change therapy or add steroids . | Buruli ulcer is an infectious disease of skin , subcutaneous fat and sometimes bone , mainly affecting children in West Africa . It is considered as one of the Neglected Tropical Diseases but the disease occurs also in moderate climates like South East Australia and Japan where it may also affect adults . Once a patient has started antibiotic treatment , lesions may increase in size even if the antimicrobial treatment is effective; this is highly confusing for doctors and patients as they may think that treatment actually fails . The cause of Buruli ulcer is Mycobacterium ulcerans , related to other mycobacteria that cause disease in man , like leprosy and tuberculosis . Using data from two different studies in West Africa , we show that these paradoxical reactions are associated with trunk localization and that they occur more often in larger lesions . The chance to develop these reactions appeared partly inherited: carrying the homozygous ins/ins genotype of 3’UTR TGTG 285 polymorphism in the SLC11A1 gene increased the risk of paradoxical reactions . Vitamin D is important for the immune defense against infections by mycobacteria . Vitamin D blood concentrations were not associated with paradoxical reactions; patients generally did well , and we did not need corticosteroid immune suppression to overcome these reactions . | [
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] | 2016 | Genetic Susceptibility and Predictors of Paradoxical Reactions in Buruli Ulcer |
Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry ( MALDI-TOF MS ) has been shown to be an effective tool for the rapid identification of arthropods , including tick vectors of human diseases . The objective of the present study was to evaluate the use of MALDI-TOF MS to identify tick species , and to determine the presence of rickettsia pathogens in the infected Ticks . Rhipicephalus sanguineus and Dermacentor marginatus Ticks infected or not by R . conorii conorii or R . slovaca , respectively , were used as experimental models . The MS profiles generated from protein extracts prepared from tick legs exhibited mass peaks that distinguished the infected and uninfected Ticks , and successfully discriminated the Rickettsia spp . A blind test was performed using Ticks that were laboratory-reared , collected in the field or removed from patients and infected or not by Rickettsia spp . A query against our in-lab arthropod MS reference database revealed that the species and infection status of all Ticks were correctly identified at the species and infection status levels . Taken together , the present work demonstrates the utility of MALDI-TOF MS for a dual identification of tick species and intracellular bacteria . Therefore , MALDI-TOF MS is a relevant tool for the accurate detection of Rickettsia spp in Ticks for both field monitoring and entomological diagnosis . The present work offers new perspectives for the monitoring of other vector borne diseases that present public health concerns .
Ticks are obligate hematophagous arthropods that parasitize vertebrates in almost all regions of the world and are currently considered to be the second-most important vectors of human infectious diseases worldwide , after mosquitoes [1] . Tick-borne rickettsioses are caused by obligate intracellular bacteria belonging to the spotted fever group of the genus Rickettsia . These zoonoses are among the oldest known vector-borne diseases , and include Mediterranean spotted fever , which is caused by Rickettsia conorii conorii and transmitted by the brown dog tick Rhipicephalus sanguineus . Additionally they include most of the emerging tick-borne diseases such as the infection caused by R . slovaca which is transmitted by Dermacentor spp [1 , 2] . When removing an attached tick from the human body , patients and physicians may have two questions: 1 ) is the tick a known vector of human infectious disease , and 2 ) is the tick infected by a pathogenic agent ? Identifying the species of the tick may alert the physician to the diseases that may appear , and knowledge of the infectious status of the tick is a key to evaluating the risk of disease transmission . Both pieces of information , if obtained quickly may be clinically helpful , particularly with regard to decisions about the use of antibiotic prophylactic treatment to prevent tick-borne diseases . The routine method of identifying Ticks has traditionally been morphological identification using taxonomic keys , entomological expertise and specific documentation [1] . In the past decade , molecular tools have been developed to identify Ticks but these techniques also have their limitations including the selection of ideal primers , the requirement for technically time-consuming and expensive of PCR assays , and the availability of gene sequences in GenBank [1 , 3] . More recently , we implemented the use of Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry ( MALDI-TOF MS ) in our laboratory as an effective tool to rapidly identify arthropods including Ticks [4–7] . Furthermore , with the creation of a database of reference spectra MALDI-TOF MS profiling of tick leg protein extracts will allow the rapid , cost-effective and accurate identification of Ticks . For the detection and identification of Rickettsia species in infected Ticks , the most widely available tools remain molecular methods [1] , and several Rickettsia DNA sequences can be detected and precisely identified in Ticks by different PCR methods [1] . However , to date , no system allows for the rapid and accurate identification of both the tick species and the Rickettsia spp that the Ticks harbor . Although the MALDI-TOF MS approach has emerged as a routine method for the identification and classification of bacteria for clinical diagnostics [8] , no reference spectrum is available for the identification of intra-cellular Rickettsia in the commercial reference spectra database . The aim of the present study was to determine whether it is possible , to simultaneously identify the tick species and the presence of an associated intra-cellular pathogen in a single assay . To test this , Rh . sanguineus and D . marginatus Ticks that were infected or not , by R . c . conorii or R . slovaca , respectively , were used as experimental models .
Adult laboratory-reared Rh . sanguineus ( n = 15 ) and D . marginatus ( n = 20 ) were used , including rickettsia free specimens and specimens infected by R . c . conorii and R . slovaca respectively . Rh sanguineus were collected in France and Algeria and maintained at the URMITE laboratory . The Rh . sanguineus infected by R . c . conorii were obtained from specimens collected in the field , which were initially infected naturally by R . c . conorii . The vertical transmission of the Rickettsia in these Ticks during their laboratory rearing maintained the presence of R . c . conorii in this colony from generations to generation [9] . The presence of R . c . conorii was regularly confirmed by molecular biological analyses . The laboratory specimens were reared in an environmental incubator ( 19°C for D . marginatus and 25°C for Rh sanguineus with a relative humidity of 80–90% ) and successive generations were obtained by allowing the Ticks to feed on rabbits as previously described [10] . The Ticks infected by Rickettsia spp were maintained in a biosafety level 3 laboratory ( BSL-3 ) . D . marginatus Ticks were also collected on dead wild boars killed by hunters in Southern France in order to obtain specimens infected by R . slovaca ( see below ) . They D . marginatus Ticks were morphologically characterized using standard taxonomic keys [11] . For further analysis , each specimen was placed in 1 . 5 mL microcentrifuge tubes and immobilized or anesthetized at -20°C for 30 min . Whole Ticks were rinsed once with 70% ethanol for 2 min followed by 2 washes with distillated water . After air-drying , all of the legs were removed and two- to four-legs were used either for DNA extraction or sample preparation for MALDI-TOF MS analysis . Additionally , infected Ticks removed from patients including 2 specimens of Rh . sanguineus infected with R . c . conorii , 1 specimen of Rh . sanguineus infected with R . massiliae and 1 specimen of D . marginatus infected with R . slovaca were used . The presence of Rickettsia spp was previously confirmed by qPCR [4] . All processing of infectious Rickettsia spp was carried out in a BSL 3 laboratory . R . c . conorii ( ATCC N° VR613 ) and R . slovaca ( CSUR N° R154 ) were grown into the cell line L929 ( ATCC N° CCL-1 ) for approximately 7 days ( +/- 2 days ) at 32°C as previously described [12] . ] . To purify each Rickettsia strain , the infected L929 cells were centrifuged at 11650x g for 10 min . The pellets were rinsed twice in 30 mL of phosphate-buffered saline ( PBS ) ( BIOMERIEUX/France ) and centrifuged again at 11650x g for 10 min . The pellets were harvested in 18 mL of sterile PBS , vortexed , diluted in 12 mL of 2 . 5% concentrated Trypsin ( Gibco® ) and incubated at 37°C for 60 min . The suspensions were vortexed every 15mn and centrifuged at 11650x g for 10 min . This washing step was repeated three times using sterile PBS; the final suspensions were centrifuged and the pellets were collected in 1 mL of PBS . To eliminate the last cellular debris , two filtrations were performed using 5 μm and 0 . 8 μm filters ( Millipore/France ) . The purity level and the quantification of the Rickettsia strains was evaluated by Gimenez staining [13] to detect residual cellular debris and to determine bacteria concentration . After purification , serial dilutions of each purified strain was performed in PBS and 10μL of each Rickettsia sample was applied to a 18 Well microscope slide ( THERMO Cel-Line Diagnostic 6mm well ) , fixed by heat during 15min at 100°C , and stained by the Gimenez method [13] . Whole cells or cell debris were stained green and bacteria stained red . The purification rate was determined visually based on the absence of green labelling and the presence of red staining reflecting the individual purified bacteria . Bacteria concentration was estimated by counting all the bacteria in 5 different fields by well at two dilutions under microscopy . After purification Rickettsia counting was also performed using flow cytometry ( BD Accuri C6 ) . The combination of side scatter ( SSC ) and forward ( FSC ) correlates with the cell size and the density of the particles of the sample analyzed . In this manner , a bacterial population can be distinguished according to the differences of its size and density without any fluorescent staining . In addition , flow cytometry allowed us to control for the purity of the bacterial based on the absence of whole cells or cell debris . Serial dilutions of each purified Rickettsia bacteria strains in PBS buffer were performed to determine the optimal concentration for MALDI-TOF MS analysis . The rickettsial strain suspensions were then either immediately used for MALDI-TOF MS analysis or stored overnight at 4°C before MS analysis . DNA extractions were performed with one or two legs of each tick specimen included in the present study ( laboratory and field specimens ) using the EZ1 DNA Tissue kit ( Qiagen , Hilden , Germany ) . Rickettsial DNA detection was performed by quantitative PCR using a CFX 96 Real Time System ( BIO-RAD , Singapore ) and the Eurogentec MasterMix Probe PCR kit ( Qiagen , Hilden , Germany ) following the manufacturer’s instructions . The presence of R . c . conorii and R . slovaca was determined using the primers R_conorii_6967 and R . slo_7128-R , respectively , which target tRNA intergenic spacers as previously described [14 , 15] . A negative control ( sterile water containing DNA extracted from uninfected Ticks maintained in laboratory colonies ) and a positive control using DNA from R . c . conorii or R . slovaca strains were included in each respective test . Ticks . Two to four legs of Rickettsia-infected and uninfected Ticks were homogenized manually in 40 μL of 70% formic acid ( Sigma , Lyon , France ) and 40 μL of 100% acetonitrile ( VWR Prolabo ) using pellet pestles ( Fischer Scientific ) . All homogenates were centrifuged at 6700 x g for 20 sec and 1 μL of each supernatant was spotted onto a steel target plate ( Bruker Daltonics ) in quadruplicate . Then , 1 μL of matrix suspension composed of saturated α-Cyano-4-hydroxy-cinnamic acid ( CHCA ) ( Sigma ) , 50% acetonitrile , 10% trifluoroacetic acid ( Sigma ) and HPLC water was directly spotted onto each sample on the target plate . Following the drying of the matrix at room temperature , the target plate was immediately introduced into the MALDI-TOF MS instrument for analysis . Rickettsia species . For protein extraction from each Rickettsia species , a suspension of 500 μL of purified bacteria was centrifuged for 5 min at 14 , 000 x g . The supernatant was discarded and the pellet was washed twice in 500 μL of pure water , vortexed and centrifuged for 5 min at 14 , 000 x g . The pellet was then homogenized with 7 . 5 μL of 70% formic acid and 7 . 5 μL acetonitrile; after centrifugation at 14 , 000 x g for 5 min , 1 μL of supernatant was deposited on the target plate in quadruplicate and overlaid with 1 μL of CHCA matrix buffer . L929 cell line . Uninfected cells were treated with 0 . 05% trypsin ( 1X ) , counted with Kova-Slide and washed twice in 10 mL of PBS; the cells were then centrifuged for 10 min at 262 x g . The pellet was homogenized in 1 mL of buffer to obtain a final concentration of 107cells/mL . After a centrifugation at 14 , 000 x g for 5 min , 1 μL of the supernatant was deposited on the target plate in quadruplicate and overlaid with 1 μL of CHCA matrix buffer , as described above . The mass spectrometer was calibrated using the Bruker Bacterial Test Standard in the mass range of 2–20 kDa . Protein mass profiles were acquired using a Microflex LT spectrometer ( Bruker Daltonics ) with Flex Control software ( Bruker Daltonics ) . The spectra were recorded in a linear , positive ion mode with an acceleration voltage of 20 kV , within a mass range of 2 , 000–20 , 000 Da . Each spectrum corresponds to an accumulation of 240 laser shots from the same spot in six different positions . To control the loading on the steel target , the matrix quality and the MALDI-TOF apparatus performance , the matrix solution was loaded in duplicate onto each MALDI-TOF plate with or without Bacterial Test Standard ( Bruker Protein Calibration Standard I ) . The spectrum profiles obtained were visualized with Flex analysis v . 3 . 3 software and exported to ClinProTools version v . 2 . 2 and MALDI-Biotyper v . 3 . 0 ( Bruker Daltonics , Germany ) . MALDI-TOF MS spectra from the leg protein extracts of 9 D . marginatus infected or not by R . slovaca , and 10 Rh . sanguineus infected or not by R . c . conorii were imported into ClinProTools v . 2 . 2 ( Bruker Daltonics , Germany ) to identify the specific peaks related to the infection status of the tick . The parameters for ClinProTools software analysis were similar to those previously described [4] . An average spectrum was generated for each condition ( i . e . , tick species infected or not by Rickettsia spp ) , using the algorithm “average peak list calculation” tool within the range of 2–20 kDa . The detection of discriminating peak masses was performed by comparison of the average spectrum generated between two classes . The Genetic Algorithm ( GA ) model of the ClinProTools software was then used to automatically display a list of discriminating peak masses . Based on the selected peak masses , the values of Recognition Capability ( RC ) and Cross Validation ( CV ) were determined [16 , 17] . The presence or absence of each discriminating peak masses generated by the model was verified by the comparison of each peak mass contained in the peak report created for each species , with the total average spectrum created from all the replicates between two classes ( i . e . , Rickettsia-infected and uninfected ) for each tick species . Additionally the peak mass lists of each Rickettsia strain were retrieved from the Flex analysis v . 3 . 3 software . The accuracy of MALDI-TOF MS for the detection both of the Ticks and pathogens was assessed in a validation step involving a blind test using other tick specimens that were infected or not by Rickettsia spp , including Ticks collected in the field or removed from patients . MALDI-TOF MS spectra from the leg protein extracts of 3 uninfected D . marginatus , 3 D . marginatus infected by R . slovaca , 2 uninfected Rh . sanguineus and 4 Rh . sanguineus infected with R . c . conorii , were used for a blind test ( Blind test 1 ) with 1 to 4 new specimens per species against our laboratory’s database of reference spectra for ( Database 1 ) . This database includes the leg protein spectra of 6 rickettsia free tick species ( Amblyomma variegatum infected by R . africae , Rh . sanguineus , Hyalomma marginatum rufipes , Ixodes ricinus , D . marginatus and D . reticulatus ) , 30 mosquito species ( Anopheles gambiae molecular form M and An . gambiae molecular form S , An . funestus , An . ziemanni , An . arabiensis , An . wellcomei , An . rufipes , An . pharoensis , An . coustani , An . claviger , An . hyrcanus , An . maculipennis , Culex quinquefasciatus , Cx . pipiens , Cx . modestus , Cx . insignis , Cx . neavei , Ae . albopictus , Aedes excrucians , Ae vexans , Ae . rusticus , Ae . dufouri , Ae . cinereus , Ae . fowleri , Ae . aegypti , Ae . caspius , Mansonia uniformis , Orthopodomyia reunionensis , Coquillettidia richiardii and Lutzia tigripes , ) , and other arthropods including louse ( Pediculus humanus corporis ) , triatomine ( Triatoma infestans ) and bedbugs ( Cimex lectularius ) , as well as the spectra obtained from the bodies ( without the abdomens ) of 5 flea species ( Ctenocephalides felis , Ct . canis , Archaeopsylla erinacei , Xenopsylla cheopis and Stenoponia tripectinata ) [4–7] . Then , MALDI-TOF MS spectra from uninfected D . marginatus ( n = 4 ) , D . marginatus infected by R . slovaca ( n = 4 ) , uninfected Rh . sanguineus ( n = 4 ) and Rh . sanguineus infected with R . c . conorii ( n = 5 ) were added to our database; this upgraded database is referred to as Database 2 . The same specimens of D . marginatus , D . marginatus infected by R . slovaca , uninfected Rh . sanguineus and Rh . sanguineus infected with R . c . conorii , were tested in a blind test against Database 2 ( Blind test 2 ) . Additionally , the spectra from the leg protein extracts of 3 Ticks removed from 3 patients were also tested against Database 2 . The presence of Rickettsia spp was previously confirmed by qPCR including 1 specimen of Rh . sanguineus infected with R . c . conorii ( Ct = 22 ) , 1 specimen of Rh . sanguineus infected with R . massiliae ( Ct = 24 ) , and 1 specimen of D . marginatus infected with R . slovaca ( Ct = 19 ) ( Table 1 ) [4] . The reliability of the identification was estimated based on the Log Score values ( LSVs ) exhibited by the MALDI-Biotyper software , between 0 and 3 . These LSVs correspond to the degree of homology between the query mass spectra and the reference spectra . An LSV was obtained for each spectrum of the samples tested . The maintenance of laboratory colony of Rhipicephalus sanguineus and Dermacentor marginatus Ticks [18] has been approved by the Institutional Animal Care and Use Committee of the Faculty of Medicine at Aix-Marseille University , France . The collection of Dermacentor marginatus Ticks in the field did not involve privately owned , wildlife , national park or other protected areas and endangered or protected species .
When the legs of 15 Rh . sanguineus specimens including 8 specimens presumably infected with R . c . conorii and 7 Rickettsia-free specimens from the laboratory colony were tested by qPCR , R . c . conorii DNA was detected in 100% ( 8/8 ) of the Rh sanguineus legs predicted to be infected by this bacterium , with a mean Ct ± SD value of 28 . 76 ±3 . 27 ( Table 1 ) . As expected , R . c . conorii DNA was not detectable in the Rh . sanguineus Rickettsia-free specimens . When the legs of 12 D . marginatus collected in the field were tested by qPCR , 58% ( 7/12 ) of the tick legs tested positive for the presence of R . slovaca with a mean Ct ± SD value of 23 . 93 ± 5 . 62 ( Table 1 ) . Additionally , the absence of R . slovaca from the laboratory reared D . marginatus colony was confirmed by quantitative PCR . Gimenez straining was performed to determine the purity and concentration of each Rickettsia strain ( S1A and S1B Fig . ) . The absence of green labelling indicated that the purified bacteria samples were free of cells and cell debris . The purity of the samples was confirmed by flow cytometry ( BD ACCURI C6 instrument ) to detect a homogeneous population of bacteria . Serial dilution of the purified bacteria samples was performed to determine the Rickettsia concentration . Flow cytometry and direct counting on slides by Gimenez labelling led to similar results ( S1C and S1D Fig . ) . The concentration of each purified strain was of 1 . 6 x107 bacteria /mL and 1 . 35 × 107 bacteria /mL for R . c . conorii and for R . slovaca , respectively ( S1E Fig . ) for the MALDI-TOF MS analysis . Legs from a total of 19 Rickettsia-infected and 13 uninfected specimens belonging to Rh . sanguineus ( n = 17 ) and D . marginatus ( n = 15 ) were subjected to MALDI-TOF MS analysis ( Table 1 ) . Although one leg of adult tick was sufficient to generate an accurate MS spectra , to increase the rate of identification , at least two adult tick legs should be included in the preparation for mass spectra analyses ( Yssouf et al 2013 ) . Similar MALDI-TOF MS spectra profiles from the leg protein extracts were obtained for each tick species and infectious status . Representative MS profiles with high intensities peaks in the range of 2–20 kDa are presented in Fig . 1 . Using Flex analysis software , the alignment of the leg MALDI-TOF MS spectra of 2 uninfected specimens of R . sanguineus and 2 specimens of Rh . sanguineus infected by R . c . conorii , confirmed the reproducibility of the spectra and also revealed changes in the MS pattern according to the infectious status . Comparable results were obtained from MS spectra of D . marginatus specimens infected or not by R . slovaca . Although several protein peaks were conserved in the spectra from specimens belonging to the same species , modifications of the MS patterns were detectable in Rickettsia-infected specimens compared to uninfected specimens ( Fig . 2 ) . Technical and biological replicates yielded reproducible spectra ( Fig . 1 ) . The spectra of at least 4 specimens of each species ( infected and uninfected ) were added to our arthropod database ( Database 1 ) in MALDI-Biotyper 3 . 0 , which was designated as Database 2 . In parallel , MALDI-TOF MS spectra of each Rickettsia strains were compared to that of the L929 cell line . The alignment of the spectrum profiles of the strains with the cell line using Flex analysis software revealed the absence of peaks with identical masse-to-charge ratios , supporting the conclusion that Rickettsia strains were not contaminated by L929 cell proteins and that the MS spectra corresponded to the Rickettsia strains . To determine whether the mass spectra data were suitable for the identification of discriminating peaks ( m/z-values ) according to the Rickettsia-infectious status , 16 to 20 MS spectra per group were selected for further analysis and loaded into the ClinProTools software . Among the Rh . sanguineus and D . marginatus Ticks that were infected or not , by R . c . conorii or R . slovaca , respectively , 76 spectra from 19 specimens that were selected for the MALDI-Biotyper database were imported into the ClinProTools software . The Genetic Algorithm model displayed the peak masses that discriminate between the Ticks that were infected or not by Rickettsia spp with RC and CV values of 100% for both comparisons . After verification of the peak report in the averaged spectrum of the Rh . sanguineus species , 30 biomarker masses were identified that could distinguish Rh . sanguineus specimens that were infected or not by R . c . conorii ( Table 2 ) . Among them , 22 peak masses were observed uniquely in the R . conorii-infected specimens and 8 peak masses were associated with the uninfected Rh . sanguineus specimens ( Table 2 ) . To confirm the specificity of several of these discriminant biomarker masses , a comparison of the MSP between Rh . sanguineus infected by R . c . conorii and the purified R . c . conorii strain was performed ( Table 2 ) . Twelve peak masses were common to both samples , and they were localized in the spectra of Rh . sanguineus infected by R . c . conorii using Flex analysis software ( Fig . 3A ) . Using a comparable strategy for D . marginatus specimens , 35 discriminating peak masses were identified , among which 21 peak masses were specific to spectra from D . marginatus infected by R . slovaca ( Table 3 ) . Moreover , among these 21 specific peak masses , 4 were shared between D . marginatus infected by R . slovaca and the purified R . slovaca strain . These 4 peak masses were localized on the spectra profiles of infected D . marginatus using the Flex analysis software ( Fig . 3B ) . A total 15 specimens , including uninfected and Rickettsia-infected Ticks , were queried successively against the MS reference Database 1 and Database 2 ( i . e . , Database 2 = Database 1 plus the spectra from Rickettsia-infected Ticks ) . Using Database 1 , the blind test yielded 100% correct identification at the species level for the specimens tested irrespective of their infectious status and their origin of collection ( i . e . , Ticks that were laboratory-reared , collected in the field or removed from patients ) . The LSVs of the first top-ranking hits against Database 1 varied from 1 . 756 to 2 . 449 ( Table 1 ) . Interestingly , the tick specimens infected by Rickettsia spp had lower LSVs than the uninfected specimens . The same specimens were then tested against Database 2 , and 100% of the specimens tested possessing a corresponding reference spectrum in Database 2 were correctly identified at the levels of tick species and infectious status ( Table 1 ) . Moreover , with the exception of the Rh . sanguineus specimen infected by R . massiliae , only the LSVs from Rickettsia-infected Ticks were increased , and all of these specimens had an LSV larger than 1 . 85 . Interestingly , no association was observed between the cycle threshold value of qPCR and the LSVs . Although no reference spectrum was included in the database for the Rh . sanguineus specimen infected by R . massiliae , it was correctly identified at the level of the tick species as an uninfected Rh . sanguineus specimen , with an LSV greater than 2 .
The present study shows that MALDI-TOF MS can be used to reliably identify tick species infected or not by Rickettsia spp without the use of a molecular method requiring DNA sequence information . It is important to note that no Rickettsia spp spectrum is available in the Bruker reference database and that this is the first analysis of Rickettsia strain by MALDI-TOF MS . This work also demonstrated that MALDI-TOF MS could be applied for the rapid detection of Rickettsia spp in Ticks removed from patients . The rapid determination of a tick’s identity and it infectious status should guide decisions related to specific patient monitoring or the administration of preventive treatment . Additionally , the low consumable costs , minimal time required for sample preparation and rapid availability of the results of MALDI-TOF MS could be useful for epidemiological studies and the monitoring of tick-borne diseases via the dual identification of vectors and their borne pathogen in one step . The main obstacle to the use of the MALDI-TOF MS approach is the cost of acquiring the machine , but its use is cost effective thereafter [29] . These results also open new doors for the monitoring and management of other vector-borne diseases that are of importance for public health in human and veterinary medicine . For example , it would be advantageous to test whether MALDI-TOF MS , which has been shown to be a relevant tool for the identification of mosquito species [5 , 7 , 29 , 30] , could be useful for detecting the Plasmodium-infectious status of mosquito malaria vectors . | Tick-borne rickettsioses include mild to life-threatening diseases in humans worldwide . When removing an attached tick from the human body , patients and physicians may have two questions: 1 ) is the tick a known vector of a human infectious disease , and 2 ) is the tick infected by a pathogenic agent that could have been transmitted during the attachment period ? The morphological identification of Ticks is difficult , and requires expertise and specific documentation . The use of Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry ( MALDI-TOF MS ) has recently emerged as an effective , rapid and inexpensive tool to identify arthropods including Ticks . Here , we show the utility of MALDI-TOF MS for the dual identification of tick species and the rapid detection of Rickettsia spp in Ticks . Such results can be used to guide decisions related to specific patient monitoring or the administration of preventive treatment . Additionally , the low consumable costs , the minimum time required for sample preparation and the rapid availability of the results of MALDI-TOF MS could be useful for epidemiological studies and tick-borne disease monitoring via the dual identification of vectors and the pathogens they carry in one step . These results present new opportunities for the management of other vector-borne diseases that are of importance to public health . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Detection of Rickettsia spp in Ticks by MALDI-TOF MS |
Borrelia burgdorferi sensu lato ( Bbsl ) , the causative agent of Lyme disease , establishes an initial infection in the host’s skin following a tick bite , and then disseminates to distant organs , leading to multisystem manifestations . Tick-to-vertebrate host transmission requires that Bbsl survives during blood feeding . Complement is an important innate host defense in blood and interstitial fluid . Bbsl produces a polymorphic surface protein , CspA , that binds to a complement regulator , Factor H ( FH ) to block complement activation in vitro . However , the role that CspA plays in the Bbsl enzootic cycle remains unclear . In this study , we demonstrated that different CspA variants promote spirochete binding to FH to inactivate complement and promote serum resistance in a host-specific manner . Utilizing a tick-to-mouse transmission model , we observed that a cspA-knockout B . burgdorferi is eliminated from nymphal ticks in the first 24 hours of feeding and is unable to be transmitted to naïve mice . Conversely , ectopically producing CspA derived from B . burgdorferi or B . afzelii , but not B . garinii in a cspA-knockout strain restored spirochete survival in fed nymphs and tick-to-mouse transmission . Furthermore , a CspA point mutant , CspA-L246D that was defective in FH-binding , failed to survive in fed nymphs and at the inoculation site or bloodstream in mice . We also allowed those spirochete-infected nymphs to feed on C3-/- mice that lacked functional complement . The cspA-knockout B . burgdorferi or this mutant strain complemented with cspA variants or cspA-L246D was found at similar levels as wild type B . burgdorferi in the fed nymphs and mouse tissues . These novel findings suggest that the FH-binding activity of CspA protects spirochetes from complement-mediated killing in fed nymphal ticks , which ultimately allows Bbsl transmission to mammalian hosts .
Lyme disease is caused by spirochetes of Bbsl and is transmitted to humans by the bites of infected Ixodes ticks . It is the most common vector-borne disease in North America and Europe [1 , 2] . Upon blood feeding , spirochetes migrate from the ticks’ midguts to salivary glands , where they are transmitted to the host’s skin at the tick bite sites [2 , 3] . In humans , Lyme borreliae initiate local skin infection often leading to erythema migrans , commonly known as a “bull’s-eye” rash [1 , 2] . If left untreated , spirochetes are capable of entering the bloodstream and spreading to multiple tissues and organs , leading to arthritis , carditis , neuroborreliosis , and acrodermatitis chronica atrophicans [4] . The three main Lyme disease causing species , B . afzelii , B . garinii , and B . burgdorferi sensu stricto ( hereafter B . burgdorferi ) , survive not only in humans , but also in other vertebrate animals [5] . These spirochete species tend to be associated with different vertebrate hosts: B . afzelii is typically isolated from small mammals , B . garinii from birds , and B . burgdorferi from both hosts [6 , 7] . Specific spirochete-host associations are thought to be caused by the selective ability of these spirochetes to evade innate immune responses of different hosts . One such immune response , complement , is the first-line defense mechanism in humans and other vertebrates [7 , 8] . The fluid-phase of complement is comprised of serum proteins , which are sequentially activated in response to invading pathogens [9 , 10] . Complement can be initiated by three different pathways ( the classical , alternative , and lectin pathways ) which result in the formation of two distinct C3 convertases , C4b2a and C3bBb [11] . These C3 convertases recruit other complement components to generate C5 convertases , resulting in the release of pro-inflammatory peptides ( C3a and C5a ) , the deposition of opsonins ( C3b and iC3b ) on microbial surfaces , and in the case of gram-negative organisms , lysis via insertion of the pore-forming membrane attack complex ( C5b-9 also known as MAC ) [11] . Humans and other vertebrates produce complement regulators to down-regulate the excessive complement activity to prevent host cell damage from non-specific attack by complement [9] . For example , factor H ( FH ) and factor H-like protein-1 ( FHL-1 , the spliced form of FH [12] ) bind to C3b to promote its cleavage to iC3b , which is hemolytically inactive [9] . Interestingly , FH sequences from different vertebrate animals are diverse ( e . g . 60 to 70% identity between mice and humans ) , suggesting a selective adaptation of FH to efficiently regulate the host-specific complement [13 , 14] . Invading pathogens produce a variety of surface components such as capsules , lipopolysaccharides ( e . g . , O-antigens ) , and complement regulator-binding proteins to combat complement-mediated killing [15–18] . These complement regulator-binding proteins recruit these complement regulators to promote pathogen survival in the blood or serum ( also known as serum resistance ) [19 , 20] . Like other pathogens , Lyme borreliae produce surface proteins that bind complement regulators to block the formation of lethal pores generated by the MAC . The resulting localization ( and hence locally high concentration ) of complement regulators on the spirochete surface permits survival of Lyme borreliae despite high concentrations of complement in the blood [21 , 22] . B . burgdorferi produces five complement regulator acquiring surface proteins ( CRASPs ) including CspA ( CRASP-1 ) , CspZ ( CRASP-2 ) , ErpP ( CRASP-3 ) , ErpC ( CRASP-4 ) , and ErpA ( CRASP-5 ) [23] . These CRASP proteins share activities in binding to FH ( for all 5 CRASPs ) and FHL-1 ( for CspA and CspZ ) [23] and degrading C3 or C5 convertases by binding to plasminogen ( for all 5 CRASPs ) [24 , 25] . Additionally , CspA also inhibits complement by binding to C7 and C9 to block the formation of MAC [24 , 25] . Unlike other CRASP genes , cspA is expressed when Lyme borreliae are in both fed and unfed nymphal ticks or at the inoculation site immediately after infection , suggesting that CspA may play a unique role in tick-to-mammal transmission [26 , 27] . Ectopic production of CspA confers resistance to human serum in an in vitro gain-of-function study [24 , 25] , while deleting cspA from an infectious and serum-resistant B . burgdorferi strain makes this strain susceptible in human serum in vitro [28 , 29] . These results indicate that CspA functions as a key factor to promote spirochete survival in serum . CspA is highly conserved within B . burgdorferi ( greater than 90% identity ) but displays less than 50% sequence identity across different spirochete species [25] . Consistent with this variation , CspA variants from B . burgdorferi , B . spielmannii , or B . afzelii exhibit varying capacities to bind human FH and differ in their abilities to resist human serum [25 , 30 , 31] . These findings led us to hypothesize that the CspA-mediated FH-binding activity promotes spirochete serum survival in a vertebrate animal-specific manner . In addition , while the role that CspA plays in vitro has been extensively characterized , the function of this protein for Lyme borreliae in the enzootic cycle is still unclear . In this study , we elucidate the role of CspA-FH interactions in promoting complement evasion in vitro and tick-to-host transmission of Lyme borreliae .
The sequences of CspA variants among B . burgdorferi sensu lato are extremely polymorphic ( S1 Fig ) [25] . We thus sought to examine whether CspA polymorphisms account for variant-to-variant differences in their abilities to bind to FH from different vertebrate species . Thus , we tested the ability of recombinant CspA proteins derived from cspA sequences of B . burgdorferi strain B31 ( CspAB31 ) , B . afzelii strain PKo ( CspAPKo ) , or B . garinii strain ZQ1 ( CspAZQ1 ) to bind to FH from different vertebrate animals . These animals include mouse ( Mus musculus ) , Coturnix quail ( rodent or avian model of Lyme disease [32 , 33] ) , human ( incidental host ) , and horse ( dead-end host ) . We used ELISA and surface plasmon resonance ( SPR ) to evaluate the FH-binding affinity of CspA variants . A CspA mutant , CspAB31L246D , in which the leucine at position 246 was replaced by aspartic acid rendering it incapable of binding to human FH [34 , 35] , was included as negative control . As expected , while the irrelevant negative control protein B . burgdorferi DbpA did not bind to FH from these animals , both methods for assessing binding affinity indicated three distinct binding patterns of CspA variants in a host-specific manner ( S2 and S3 Figs , and Table 1 ) : ( 1 ) CspAB31 exhibited a versatile binding pattern to FH from all tested species ( ELISA: KD = 0 . 23–0 . 92 μM , SPR: KD = 0 . 07–0 . 55 μM ) ; ( 2 ) CspAPKo possessed less flexible binding , with preference for human and mouse FH ( ELISA: KD = 0 . 16–0 . 76 μM , SPR: KD = 0 . 11–0 . 46 μM ) , but not horse or quail FH; ( 3 ) CspAZQ1 bound only to FH from quail ( ELISA: KD = 0 . 36 μM ) . As expected , the recombinant CspAB31L246D , which retained a secondary structure similar to wild type CspAB31 by far-UV CD analysis ( S1 and S4 Figs and S1 Table ) , was unable to bind to FH from any of the tested species ( S2 and S3 Figs and Table 1 ) . These results indicate that CspA variants from different Lyme borreliae species bind variably to FH from different animals , and that leucine-246 of CspAB31 is essential for its ability to bind to FH from all tested animals . To examine whether the FH-binding characteristics of CspA proteins described above are maintained when these variants are produced on the surface of the spirochetes , we transformed shuttle plasmids encoding cspAB31 , cspAPKo , cspAZQ1 , or cspAB31L246D under the control of the cspA promoter from B . burgdorferi strain B31 into B . burgdorferi strain B31-5A4NP1ΔcspA ( 5A4NP1ΔcspA ) , a cspA deficient strain previously generated from an infectious background B . burgdorferi strain B31-5A4NP1 ( S2 Table ) [28] . Because strain 5A4NP1ΔcspA was identified to have lost the plasmid lp21 , B . burgdorferi strain B31-5A15 ( B31-5A15 ) , which has the same plasmid profile as strain 5A4NP1ΔcspA and is fully virulent , was used as a wild type ( WT ) positive control [28 , 36] . We first used flow cytometry analysis to verify that the CspA variants or mutants produced in strain 5A4NP1ΔcspA are localized on the surface of spirochetes and at levels similar to the WT strain B31-5A15 ( Fig 1 ) . We then incubated each of the CspA-producing 5A4NP1ΔcspA-derived strains , strain 5A4NP1ΔcspA harboring the vector alone ( hereafter termed 5A4NP1ΔcspA-V ) , or the WT strain B31-5A15 , with FH purified from human , mouse , horse , or quail . We then determined their FH-binding activity using flow cytometry . A high passage and non-infectious B . burgdorferi strain B313 ( B313 ) harboring the shuttle vector alone was also included as a negative control as this strain does not encode cspA and does not bind human FH [37] . Consistent with previous findings [37] , strain B313 ( vector alone ) did not bind human FH ( Fig 2A right panel ) , or to FH from mouse , horse , and quail ( Fig 2B to 2D right panel ) . The WT strain B31-5A15 bound FH from all these animals ( Fig 2A to 2D right panel ) , in agreement with findings in previous studies [21 , 28] . Strain 5A4NP1ΔcspA-V displayed undetectable levels of human FH-binding activity ( Fig 2A ) , similar to previous observations [28] . Note that the infectious B . burgdorferi strain B31 , the background strain of 5A4NP1ΔcspA-V , produces other human FH-binding proteins when cultivated in vitro [23 , 26 , 38] . However , these FH-binding proteins are either produced in extremely low levels [26] or display lower levels of FH-binding ability , compared to CspA [28 , 39 , 40] . These previous studies support our finding that nearly no human FH-binding activity was observed in this strain . Additionally , strain 5A4NP1ΔcspA-V did not show binding ability to mouse , horse , or quail FH ( Fig 2B to 2D ) . These data indicate that CspA is essential for this infectious B . burgdorferi strain to bind to FH from all tested animals . Further , expression of CspA variants in strain 5A4NP1ΔcspA restores the levels of binding to FH in a host-specific manner , reflecting the results obtained with the recombinant proteins ( Fig 2 ) : ( 1 ) The production of CspAB31 restored binding to FH from human , mouse , horse , and quail; ( 2 ) CspAPKo increased binding to human and mouse FH but not to horse or quail FH; ( 3 ) CspAZQ1 promoted binding only to quail FH but not to FH from other animals . Additionally , the cspAB31L246D-complemented strain showed no detectable binding to FH from all four animals tested ( Fig 2A to 2D right panel ) . We also incubated the above-mentioned spirochete strains with C3-depleted human or mouse serum ( C3-depleted horse or quail serum is not available ) to verify the FH-binding activity in the context of serum components . C3-depleted serum was used because the strains that show reduced FH-binding would have greater levels of C3b deposited on their surfaces [28] , which in turn would bind FH thereby confounding interpretation of results . We observed binding of human and mouse FH in C3 depleted sera as with purified FH ( S5 Fig ) . These results indicate that the leucine-246 of this protein plays an essential role in facilitating spirochete binding to FH from these animals . We next aimed to determine the role of host-specific FH-binding of CspA variants in inhibiting complement deposition on the spirochete surface . We first incubated human , mouse , or horse serum with strain 5A4NP1ΔcspA-V or this strain producing CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D as well as the WT strain B31-5A15 or the negative control strain B313 . The levels of C3 fragments ( C3b and iC3b ) and the MAC bound by spirochetes were quantified using flow cytometry . Quail serum was not included as antibodies against quail C3b or MAC were not available . FH-depleted serum was not used as the lack of FH in serum causes an uncontrollable complement activation , which consumes C3 resulting in no C3b/iC3b deposition on spirochete surface [41] . Consistent with previous observations [42] , a significant amount of C3b and MAC was detected on the surface of the strain B313 carrying vector alone upon incubation with human ( Fig 3B and 3D top panel ) , mouse , or horse serum ( Fig 3B and 3D middle and bottom panels ) . Conversely , incubation of the WT strain B31-5A15 with serum from these animals resulted in at least 2-fold reduction of C3b deposition compared to strain B313 , and virtually undetectable MAC deposition ( Fig 3B and 3D , p < 0 . 05 ) . Strain 5A4NP1ΔcspA-V had similar levels of C3b and MAC deposition compared to strain B313 ( Fig 3B and 3D p > 0 . 05 ) [28] . These results indicate that CspA is required to inhibit human and non-human complement bound to the spirochete surface . We also observed a correlation between the origin of the serum and the ability of CspA variants to inhibit deposition of C3 fragment or MAC: ( 1 ) Compared to strain 5A4NP1ΔcspA-V , expression of cspAB31 significantly decreased levels of C3b and MAC deposition on the spirochete surface in human serum ( Fig 3B and 3D top panels , consistent with a previous finding [25] ) and in mouse or horse sera ( Fig 3A and 3C , the middle and bottom panels of Fig 3B and 3D ) . ( 2 ) Expression of cspAPKo in the strain 5A4NP1ΔcspA significantly reduced human and mouse C3b and MAC deposition compared to strain 5A4NP1ΔcspA-V ( Fig 3A and 3C , top and middle panels of Fig 3B and 3D ) , in agreement with a previous observation [25] , but not in horse serum ( bottom panels of Fig 3B and 3D ) . ( 3 ) Expression of cspAZQ1 resulted in similar C3b and MAC deposition as the strain 5A4NP1ΔcspA-V in all three sera ( Fig 3A to 3D ) . We also compared the complement activating abilities of isogenic 5A4NP1ΔcspA producing either CspAB31 or the FH-binding deficient point mutant CspAB31L246D . Although CspAB31 has also been shown to bind complement C7 and C9 , and plasminogen [24 , 25 , 43] , CspAB31L246D was selectively defective in FH-binding , but still bound to C7 , C9 , or plasminogen at levels similar to CspAB31 ( S1 Table and S6 Fig ) . Thus , the CspAB31L246D producing strain showed significantly greater levels of C3 and MAC deposition than the strain producing CspAB31 ( Fig 3B and 3D ) , suggesting that the FH-binding activity of CspA mediates inhibition of complement deposition on the surface of B . burgdorferi . The ability of pathogens to inhibit the host complement in the bloodstream correlates with their ability to survive in the serum [7] . Thus , we sought to investigate how species-to-species variation of CspA promotes bacterial survival in serum from different vertebrate hosts . We incubated WT strain B31-5A15 , as well as strain 5A4NP1ΔcspA-V or this plasmid encoding cspAB31 , cspAPKo , cspAZQ1 , or cspAB31L246D with serum from human , horse , or quail and negative control serum ( C3-depleted or heat treated human serum ) for four hours . Mouse serum was not used , as its complement is highly labile ex vivo [44] , and the ability to kill spirochetes in vitro has not been consistently observed [45 , 46] . More than 75% of the strain B31-5A15 survived in human serum or serum without active complement ( C3-depleted serum ) ( Fig 4A and 4B ) . In addition , this strain survived in horse and quail serum , with average spirochete serum survival above 75% ( Fig 4C and 4D ) . Less than 20% of the strain 5A4NP1ΔcspA-V remained motile after incubation with human serum ( less than the survival percentage of WT strain B31-5A15 , p < 0 . 05 ) , but more than 85% of the strain 5A4NP1ΔcspA-V survived in the C3-depleted or heat inactivated human serum ( Fig 4A and 4B ) . Similarly , 5A4NP1ΔcspA-V was killed by horse and quail serum , indicating that CspA plays an essential role in promoting spirochete survival in not only human serum as shown previously [28] , but also in horse and quail serum ( Fig 4C and 4D ) . We also found that CspA variants produced on the strain 5A4NP1ΔcspA confer serum survival in a host-specific manner: ( 1 ) More than 75% of motile cspAB31-complemented B . burgdorferi were detected when incubated with sera from all these animals ( Fig 4A , 4C and 4D ) . ( 2 ) A cspAPKo-complemented strain was able to survive in human serum ( Fig 4A ) , but less than 50% of the spirochetes survived when incubated with horse or quail sera ( Fig 4C and 4D ) . ( 3 ) Less than 20% of a cspAZQ1-complemented strain survived following the treatment with human or horse serum ( Fig 4A and 4C ) , but over 75% of this strain survived in quail serum ( Fig 4D ) . Additionally , less than 25% of cspAB31L246D-complemented strain remained motile after treatments with human , horse , or quail serum , which was about three-fold lower than the cspAB31-complemented strain ( Fig 4A , 4C and 4D ) . Conversely , more than 75% of cspAB31L246D-complemented strain survived in either C3-depleted or heat treated-serum , suggesting that the FH-binding ability of CspA promotes spirochete survival in serum ( Fig 4A to 4D ) . We and others have demonstrated the binding of FH to CspA in vitro and the role of CspA in inactivating complement on spirochete surface and serum survival [25 , 28 , 30 , 31] ( Figs 2 to 4 ) . How CspA promotes Lyme infection in vivo is still unclear . Thus , we examined regulation of CspA throughout the enzootic cycle ( S7B Fig ) . The naïve larval Ixodes scapularis ticks were first allowed to feed on mice previously infected with the infectious B . burgdorferi strain B31-5A15 . After the engorged and infected larval ticks molted into nymphal ticks , they were placed on naïve C3H/HeN mice for blood feeding . We then determined the expression levels of spirochete genes cspA , recA , and flaB in ticks and mouse tissues using quantitative RT-PCR and calculated the expression levels of cspA and recA relative to that of flaB . recA expression levels relative to flaB levels remained unchanged throughout the enzootic cycle ( Fig 5A top panel ) . As reported previously [26] , cspA expression was detected in both larval and nymphal ticks , as well as at tick bite sites on the mouse skin at 72 hours post feeding , but not in mouse tissues after spirochetes disseminate ( Fig 5A bottom panel ) . B . burgdorferi in flat nymphs expressed more than 2-fold increased levels of cspA compared to levels prior to molting ( replete larvae , p < 0 . 05 ) , but the spirochetes’ cspA expression was reduced approximately 2-fold in nymphs following 24 hours of feeding , compared to expression levels in flat nymphs ( Fig 5A bottom panel , p < 0 . 05 ) . We next sought to investigate CspA production in different stages of ticks . A previous study reported that the production of CspA could not be visually detected in spirochetes using fluorescence microscopy when spirochetes are present in nymphs [47] . One possibility for this discrepancy is that visual detection using microscopy may be subjective and not be sensitive enough to detect small differences in protein production [26] . We therefore gently lysed different stages of ticks , sorted B . burgdorferi from the tick lysates by their granularity and size , and quantitated the levels of CspA ( and a constitutively produced protein FlaB ) in the spirochetes using flow cytometry ( S8 Fig ) . The strain B313 , which does not carry the plasmid encoding cspA , was included as a negative control . There was no significant difference of FlaB production by strain B31-5A15 and the negative control strain B313 cultured in vitro ( Fig 5C top panel ) . Additionally , we did not observe a significant difference of FlaB production by strain B31-5A15 in different stages of ticks ( Fig 5C top panel ) . In spirochetes cultured in vitro , CspA production was detected in B31-5A15 , but not in the strain B313 ( Fig 5C bottom panel ) . Interestingly , we were able to detect B . burgdorferi CspA production in all examined stages of the ticks ( significantly greater MFI values than strain B313; p < 0 . 05 ) ( Fig 5C bottom panel ) . Further , the CspA production more than quadrupled in flat nymphs compared to fed larvae ( p < 0 . 05 ) , but thereafter halved after nymphs had fed for 24 hours ( Fig 5B and 5C bottom panel ) . Our findings on the spirochetes’ CspA protein production in ticks are consistent with the dynamic changes of cspA mRNA levels ( Fig 5A ) [26] , suggesting that CspA is up-regulated after larval ticks molt but is down-regulated after nymphal ticks feed . We then examined the role of CspA to facilitate spirochete survival in the enzootic cycle . As expected , when we inoculated C3H/HeN mice by subcutaneous needle infection with strain 5A4NP1ΔcspA-V or the WT strain B31-5A15 ( S7A Fig ) , these strains colonized tissues to similar degrees ( S9 Fig , 14 days post infection ) . Similarly , at early stages of infection , strain 5A4NP1ΔcspA-V colonized the skin of the mouse inoculation site and triggered bacteremia with a burden similar to the WT strain B31-5A15 ( S10 Fig , 4 days post infection ) . These results indicate that CspA is not essential during early and disseminated stages of mouse infection through needle inoculation . WT strain B31-5A15 and the strain 5A4NP1ΔcspA-V display similar levels of infectivity at 14 days post needle infection in mice ( S9 Fig ) . Thus , to examine whether CspA plays a role in vivo during tick infection , we allowed the larval ticks to feed until replete on mice previously infected for 14 days with either WT strain B31-5A15 or strain 5A4NP1ΔcspA-V ( S7B Fig ) . Nymphal ticks that developed after replete larval ticks molted were fed on naïve C3H/HeN mice until removed or replete , and the bacterial burdens in the nymphs and nymph-infected mouse blood and tissues were determined ( S7B Fig ) . The bacterial loads of the strain 5A4NP1ΔcspA-V did not differ from the WT strain B31-5A15 in fed larvae or flat nymphs ( Fig 6A and 6B ) . However , strain 5A4NP1ΔcspA-V did not survive in nymphs fed for 24 , 48 , or 72 hours or in replete nymphs ( approximately 1000-fold less than the WT strain , Fig 6C and 6D and S11 Fig ) . Further , this strain was incapable of surviving in the mouse blood and colonizing tissues at 7 and 14 days post nymph feeding ( at least 40-fold less than the WT strain , Fig 6E and 6F and S12 Fig ) . These results indicate that CspA enables the spirochete to survive in fed nymphs , which subsequently permits spirochete transmission to mice . We then investigated the ability of CspA variants to promote spirochete survival in infected nymphs and in mice infected by tick infection . Mice were initially infected by needle injection with the strain 5A4NP1ΔcspA producing CspAB31 , CspAZQ1 , or CspAPKo . All strains exhibited similar levels of tissue colonization ( S9 Fig ) . After the larval ticks fed on these mice and molted into nymphs , the infected nymphs were placed on naïve mice ( S7B Fig ) . All strains displayed similar levels of survival in fed larvae and flat nymphs ( Fig 6A and 6B ) . The isogenic strain producing CspAB31 or CspAPko was able to survive in fed nymphs , colonize the mouse inoculation site , and survive in blood at the levels at least 150-fold more than the strain 5A4NP1ΔcspA-V ( P < 0 . 05 ) ( Fig 6C to 6F ) . Conversely ( and similar to 5A4NP1ΔcspA-V ) , the isogenic strain producing CspAZQ1 did not survive in nymphs fed for 24 hours or in replete nymphs and was incapable of surviving in the mouse blood and colonizing tissues ( p > 0 . 05 , Fig 6C to 6F ) . We then compared the burdens of the isogenic B . burgdorferi strain producing CspAB31 or the point mutant CspAB31L246D at different stages of tick and mouse infection . Similar levels of both strains were seen in fed larvae as well as in flat nymphs ( Fig 6A and 6B ) . Interestingly , no CspAB31L246D-producing isogenic spirochetes were detectable in fed nymphs or at mouse inoculation site and in the bloodstream ( similar strain 5A4NP1ΔcspA-V , p >0 . 05 ) ( Fig 6C to 6F ) . These findings suggest that the in vitro FH-binding ability of CspA is correlated with spirochete survival in fed nymphs and with subsequent transmission to the mammalian host . We have demonstrated that CspA is critical to promote spirochete survival in nymphal ticks in the first 24 hours of feeding ( Fig 6 ) . At this time point , the small amount of blood and the interstitial fluid that contain complement components enter nymphs’ midguts [48 , 49] . The fact that the majority of B . burgdorferi have been found in the tick midgut at this time point ( and is thus in contact with the host blood and interstitial fluid ) [48 , 50] led us to hypothesize that CspA-mediated complement inactivation facilitates host complement evasion by spirochetes in fed nymphs . Thus , we fed the nymphs infected with the WT strain B31-5A15 or the strain 5A4NP1ΔcspA-V on a mouse strain deficient in C3 , the central molecule of complement required for opsonophagocytosis and formation of MAC ( S7C Fig ) [51] . WT BALB/c mice , which the C3-deficient mice were back-crossed into , served as controls . The spirochete burdens in the nymphs prior to and post feeding on mice were determined using qPCR . Consistent with infection in C3H/HeN mice , strain 5A4NP1ΔcspA-V was undetectable in the nymphs fed on WT BALB/c mice ( Fig 7A and 7B , left panel ) and at the bite site of the mouse skin , or in the blood ( Fig 7C and 7D , left panel ) . However , this strain survived in nymphs fed on C3-deficient mice for 24 hours or to repletion at levels indistinguishable from the WT strain B31-5A15 ( p > 0 . 05 , Fig 7A and 7B , right panel ) . Strain 5A4NP1ΔcspA-V was also observed at bite sites and in the blood of C3-deficient mice at 7 days post nymph feeding ( Fig 7C and 7D , right panel ) . These results suggest that CspA plays an essential role in evading the complement present in fed nymphs and for tick-to-mammal transmission . We then examined the ability of B . burgdorferi strain B31-5A4NP1ΔcspA complemented with cspAB31 , cspAZQ1 , or cspAPKo to survive in nymphs fed on WT BALB/c or C3-deficient mice . In agreement with findings using WT C3H/HeN mice , the cspAB31- or cspAPKo- but not cspAZQ1-complemented B . burgdorferi was detectable in the nymphs fed on WT BALB/c mice ( Fig 7A and 7B , left panel ) and at the bite sites of the skin and in blood of these mice ( Fig 7C and 7D , left panel ) . Interestingly , the strain 5A4NP1ΔcspA-complemented with each of these three cspA alleles exhibited nearly identical bacterial levels in nymphs fed on C3-deficient mice for 24 hours or to repletion ( Fig 7A and 7B , right panel ) . These cspA-complemented strains also colonized the tick bite sites and bloodstream at 7 days after the onset of nymph feeding at levels similar to WT bacteria ( Fig 7C and 7D , right panel ) . These results suggest that the CspA variants differentially promote spirochete complement evasion in fed nymphs , which is required for spirochete transmission from ticks to mammals . Nymphs infected with the cspAB31- or cspAB31-L246D-complemented strain were also allowed to feed on WT or C3-deficient BALB/c mice to test how FH binding by CspA promotes complement evasion in fed nymphs and facilitates tick-to-mammal transmission . We observed that the cspAB31L246D-complemented strain displayed approximately 5000-fold lower burdens than the cspAB31-complemented strain in nymphs fed on BALB/c mice for 24 hours or to repletion ( Fig 7A and 7B , left panel ) . Similar to C3H/HeN mice , the cspAB31L246D strain was not detectable at the bite site or blood of BALB/c mice ( Fig 7C and 7D , left panel ) . However , this mutant strain was detected in nymphs fed on C3-deficient mice for 24 hours , replete nymphs ( Fig 7A and 7B , right panel ) , and at the bite sites and blood of C3 deficient mice at 7 days post feeding at levels similar to the cspAB31-complemented strain ( Fig 7C and 7D , right panel ) . Our findings thus strongly suggest that the in vitro FH-binding activity of CspA is correlated with the ability of B . burgdorferi to evade mouse complement in fed nymphs , which would promote spirochete survival in those ticks and facilitate transmission from ticks to mammalian hosts .
Each of the Lyme borreliae species has been associated with specific vertebrate host ( s ) [7 , 52 , 53] . This spirochete-host association has been correlated with the ability of B . burgdorferi to survive in the blood ( or serum ) from the corresponding hosts [7 , 54–56] , but the mechanism that drives this association is still unclear . An attractive hypothesis is that the spirochetes exhibit host-specific immune evasion , which leads to the observed spirochete-host association . This could be due to variable spirochete outer surface proteins that interact with components of host complement in a host-specific manner . One such protein is CspA , which displays variant-to-variant differences in binding to human FH [25] . However , the recombinant CspA protein from B . burgdorferi strain B31 is incapable of binding to any other vertebrate animals’ FH , when the FH has been subjected to SDS-PAGE followed by a far-western blot [57] . In contrast , FH from the serum of multiple animals including human and mouse recognizes CspA variants run on a similar blot [58 , 59] . This discrepancy may be due to structural alternations of animals’ FH on SDS-PAGE and the following far-western blot [8 , 22 , 23] . Therefore , we further verified the binding of CspA to FH from mouse , horse , and quail by demonstrating that the CspA of three main Lyme borreliae ( B . burgdorferi , B . afzelii , or B . garinii ) bind to purified FH in a host-specific manner . Additionally , we observed that the ability of different CspA variants to bind to FH correlates with their ability to inhibit complement activation on the spirochete surface and facilitate spirochete survival in serum in a host-specific manner . We found that these correlations not only apply to human complement ( consistent with a previous study [28] ) but also to complement of other animals . Further , the high-resolution structure of CspA suggests the recombinant version of this protein forms a dimer [35] . Two FH-binding regions have been localized on the central cleft of the CspA dimer and on the C-termini of this protein including leucine-246 [34 , 60] . By ectopically producing a CspA point mutant ( CspA-L246D ) selectively lacking FH-binding ability on spirochetes , we further demonstrated that the CspA-mediated FH-binding activity contributes to the inactivation of host complement and spirochete survival in sera . Note that similar non-polar features of this position ( leucine-246 ) of CspAB31 and the equivalent location of other CspA variants ( phenylalanine-237 of CspAPKo and leucine-252 of CspAZQ1 ) suggests a possibility that this amino acid is critical for the FH-binding activity of these variants ( S1 Fig ) . FH is polymorphic between humans and mice ( 61% amino acid identity ) [13 , 14] . However , we have demonstrated a similar ability of CspA to bind to both human and mouse FH as well as inhibit complement deposition of both human and mouse sera on the spirochete surface . These findings imply that CspA plays a similar role in mouse and human during infections . Thus , the murine model was selected to test the role of the CspA-mediated FH binding activity . In addition , we have demonstrated that spirochetes require CspA to survive in fed nymphs during tick-to-mouse transmission . This result is consistent with the fact that cspA is uniquely expressed when spirochetes are in fed nymphs [26] . In contrast , CspA was not essential for spirochetes to be transferred from mouse to larvae or establish infection in mouse skin at early stages of infection , even though cspA expression was detectable at these stages . This may be due to the previous observation that other genes encoding functionally redundant FH-binding proteins ( e . g . cspZ , erpP , and erpA ) are co-expressed with cspA in spirochetes at these stages [26] . Unexpectedly , CspA was detected in spirochetes when spirochetes are in flat nymphs even though this protein was not required for B . burgdorferi to survive in these ticks . One possibility is that spirochetes may need to maintain certain levels of CspA in preparation to survive in the first 24 hours of feeding when the small amount of blood and interstitial fluid containing complement components enter the ticks’ midgut . This possibility is supported by the observations that a cspA deficient B . burgdorferi is eliminated from human serum within 1 hour [28] , but a significant shift in gene expression is not detectable until the spirochetes are treated with blood for 48 hours [61] . In addition to CspA , B . burgdorferi requires other proteins to promote persistent survival in fed nymphs and to be transmitted to vertebrate animals [62–66] . Some of these proteins have been thought to be important for nutrient acquisition and metabolism in ticks [62 , 67–72] , while the functions of the other proteins are still unclear . Blood and interstitial fluid from vertebrate hosts contain diverse innate immune defense mechanisms , including complement [9] . In fact , B . burgdorferi displays increased infectivity in C3-deficient mice , which lack the ability to deposit opsonic C3 fragments or to generate the pore-forming MAC that can lyse spirochetes [73 , 74] . This finding suggests that spirochetes need to evade hosts’ complement to survive in vertebrate animals . Moreover , Rathinavelu et al . reported that the blood meals from either WT or C3-deficient mice do not eliminate the WT B . burgdorferi in fed ticks [75] , raising the possibility that spirochetes produce factors to facilitate the complement evasion in fed ticks . In this study , we observed a clear correlation of CspA variants or mutants in their FH-binding activity with their ability to promote spirochete survival in nymphs fed on WT mice and tick-to-mouse transmission . Conversely , this correlation was not detectable when these ticks fed on C3-deficient mice . These findings identify that CspA-mediated FH-binding activity is necessary for the spirochetes’ evasion of complement in fed nymphs and eventually to be transmitted to mammalian hosts . It is noteworthy that Woodman et al . reported that FH-deficient mice were susceptible to WT B . burgdorferi infection , leading to the conclusion that spirochetes did not require FH-binding activity to evade mouse complement [74] . However , the lack of the FH leads to the spontaneous complement activation and subsequent complement consumption in these mice , rendering them functionally complement deficient [41] . Thus , FH-deficient mice cannot be used to study the role of FH-binding microbial proteins in complement evasion . It is noteworthy that B . garinii ZQ1 was originally isolated from ticks [76] . Therefore , the infectivity of this strain in vertebrate animals is still unclear . We found that variant to variant differences of CspA-mediated FH-binding activity are correlated with these variants’ ability to confer spirochete transmission from nymphs to mice . Particularly , the cspA-ZQ1-complemented strain infected C3-deficient mice but not wild type mice via ticks . Thus , our observation of CspA-ZQ1 selectively binding to quail FH implies that this variant may promote spirochete infectivity in quail by evading this animal’s complement . ( Note that the complement among different avian hosts may vary . Thus , that one CspA variant promotes infectivity in quail may not necessarily indicate that the same variant contributes to the infectivity in other birds ) . More in-depth studies on avian hosts promoted by CspA-ZQ1 are warranted . In this study , we demonstrated the molecular mechanisms by which CspA of B . burgdorferi facilitates complement evasion of spirochetes in fed nymphal ticks ( Fig 8 ) . The host-specific differences in FH-binding capabilities conferred by CspA variants illuminate the possibility of a complement-driven host-specificity and selective transmission of Lyme disease spirochetes .
All mouse experiments were performed in strict accordance with all provisions of the Animal Welfare Act , the Guide for the Care and Use of Laboratory Animals , and the PHS Policy on Humane Care and Use of Laboratory Animals . The protocol was approved by the Institutional Animal Care and Use Committee ( IACUC ) of Wadsworth Center , New York State Department of Health ( Protocol docket number 16–451 ) , and University of Massachusetts Medical School ( Protocol docket number 1930 ) . All efforts were made to minimize animal suffering . C3H/HeN , BALB/c and Swiss-Webster mice were purchased from Charles River ( Boston , MA ) and Taconic ( Hudson , NY ) , respectively . C3-/- mice ( C57BL/6 ) purchased from Jackson Laboratory ( Bar Harbor , ME ) were backcrossed for 11 generations into BALB/c background . Mice were genotyped for the C3 allele by PCR analysis of mouse tail DNA . Ixodes scapularis tick larvae were purchased from National Tick Research and Education Center , Oklahoma State University ( Stillwater , OK ) or obtained from BEI Resources ( Manassas , VA ) . B . burgdorferi-infected nymphs were generated as described in the section “Mouse infection experiments by ticks . ” The Borrelia and Escherichia coli strains used in this study are described in S2 Table . E . coli strains DH5α , M15 , and derivatives were grown in Luria-Bertani ( BD Bioscience , Franklin lakes , NJ ) broth or agar , supplemented with kanamycin ( 50 μg/mL ) , ampicillin ( 100 μg/mL ) , or no antibiotics where appropriate . All B . burgdorferi , B . afzelii , and B . garinii strains were grown in BSK-II completed medium supplemented with kanamycin ( 200 μg/mL ) , streptomycin ( 50 μg/mL ) , gentamicin ( 50 μg/mL ) , or no antibiotics ( see S2 Table ) . The open reading frames lacking the putative signal sequences of bba68 ( cspAB31 ) from B . burgdorferi strains B31 or zqa68 ( cspAZQ1 ) from B . garinii strain ZQ1 were amplified using primers listed in S3 Table to generate recombinant histidine-tagged CspA proteins . In addition , an altered open reading frame encoding CspAB31L246D ( residue 26 to 252 of CspAB31 with leucine-246 replaced by aspartate ) was amplified using the primers described in S3 Table . Amplified fragments were engineered to encode a BamHI site at the 5’ end and a stop codon followed by a SalI site at the 3’ end . PCR products were sequentially digested with BamHI and SalI and then inserted into the BamHI and SalI sites of pQE30Xa ( Qiagen , Valencia , CA ) . The plasmids were transformed into E . coli strain M15 , and the plasmid inserts were sequenced ( Wadsworth ATGC core facility ) . The resulting M15 derived strains and the M15 strain carrying the plasmid encoding the open reading frames lacking the putative signal sequences of bafPKo_A0067 ( cspAPKo ) from B . afzelii strains PKo [25] were used to produce respective recombinant CspA variants or mutants ( S2 Table ) . The histidine-tagged CspA variants or mutants were produced and purified by nickel affinity chromatography according to the manufacturer’s instructions ( Qiagen , Valencia , CA ) . Antisera against CspAB31 , CspAPKo , or CspAZQ1 were generated by immunizing four-week-old Swiss Webster mice with each of the CspA proteins as described previously [77] . The ability of each of these antisera to recognize CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D was verified using ELISA . Basically , one microgram of the above-mentioned CspA variants or mutant proteins , or BSA ( negative control ) was immobilized on microtiter plates ( MaxiSorp , ThermoFisher ) . The antisera raised from the mice immunized with each of these CspA variants ( 1: 1 , 000x ) were added to the wells . The pre-immune mouse serum was also included as a negative control . HRP-conjugated goat anti-mouse IgG ( 1: 1 , 000x ) ( ThermoFisher ) was then added as antibody to detect the binding of the mouse anti-sera to CspA variants . The plates were washed three times with PBST ( 0 . 05% Tween 20 in PBS ) , and 100 μL of ortho-phenylenediamide dihydrochloride solution ( Sigma-Aldrich ) were added to each well and incubated for five minutes . The reaction was stopped by adding 50 μL of 2 . 6M hydrosulfuric acid to each well . Plates were read at 405nm using a Tecan Sunrise Microplate reader ( Tecan , Morrisville NC ) . The anti-sera of CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D exhibited similar levels of binding to each of these CspA variants or mutant proteins ( S13 Fig ) . CD analysis was performed on a Jasco 810 spectropolarimeter ( Jasco Analytical Instrument , Easton , MD ) under nitrogen . CD spectra were measured at room temperature ( RT , 25°C ) in a 1 mm path length quartz cell . Spectra of CspAB31 ( 10 μM ) or CspAB31L246D ( 10 μM ) were recorded in phosphate based saline buffer ( PBS ) at RT , and three far-UV CD spectra were recorded from 190 to 250 nm for far-UV CD in 1 nm increments . The background spectrum of PBS without proteins was subtracted from the protein spectra . CD spectra were initially analyzed by the software Spectra Manager Program ( Jasco ) . Analysis of spectra to extrapolate secondary structures and the prediction of the spectrum using the amino acid sequences of CspAB31 were performed by Dichroweb ( http://dichroweb . cryst . bbk . ac . uk/html/process . shtml ) using the K2D and Selcon 3 analysis programs [78] . The procedure to purify FH from serum of various vertebrate animals has been described previously [79] . Basically , the serum collected from horses originally from New Zealand , ( ThermoFisher , Waltham , MA ) or Coturnix coturnix quail ( Canola Live Poultry Market , Brooklyn , NY ) was centrifuged to remove aggregates prior to being diluted with two volumes deionized water . Then , 6g of cyanogen bromide ( CNBr ) -activated Sepharose 4B resin ( GE Healthcare , Piscataway , NJ ) was mixed with 100mg of Trinitrophenyl-Bovine Serum Albumin ( TNP-BSA ) ( LGC Biosearch Technology , Petaluma , CA ) for 2 hours followed by incubation with the blocking buffer ( PBS with 100mM ethanolamine-HCl , 150mM NaCl at pH8 . 5 ) at room temperature for 2 hours . The TNP-BSA CNBr resin was then equilibrated with PBS and packed into a column . After the diluted serum was applied into the TNP-BSA CNBr column , the column was washed by PBS until the OD280 values of the effluent below 0 . 04 Arbitrary Unit . Bound proteins were then eluted by the elution buffer ( PBS with 0 . 5mM EDTA and 1M sodium chloride at pH7 . 4 ) . The eluent was subsequently applied to a NAb Protein G Spin Column ( ThermoFisher ) according to the manufacturer’s instruction to remove the immunoglobulin in the serum . The purified factor H was confirmed by ELISA [77] . A sheep anti-FH polyclonal IgG ( ThermoFisher ) ( 1:200x ) , which has been shown to recognize horse FH [58] or a mouse anti-FH monoclonal antibody VIG8 ( 1: 200x ) , which has been observed to recognize avian FH [80] was used as a primary antibody . A horse radish peroxidase ( HRP ) conjugated donkey anti-sheep ( 1:2 , 000x ) ( ThermoFisher ) or goat anti-mouse ( 1: 1 , 000x ) was used as a secondary antibody . Quantitative ELISA for FH , C7 , C9 , or plasminogen binding by CspA proteins was performed similarly to that previously described [78] . For FH binding , one microgram of BSA ( negative control; Sigma-Aldrich ) or FH from human ( ComTech , Tyler , Texas ) , mouse ( MyBiosource , San Diego , CA ) [81] , horse , or quail was coated onto microtiter plate wells . For FH binding , one hundred microliters of increasing concentrations ( 0 . 03125 , 0 . 0625 , 0 . 125 , 0 . 25 , 0 . 5 , 1 , 2 μM ) of histidine-tagged DbpA from B . burgdorferi strain B31 ( negative control ) or a CspA variant or mutant , including CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D was then added to the wells . Mouse anti-histidine tag ( Sigma-Aldrich , St . Louis , MO; 1:200 ) and HRP-conjugated goat anti-mouse IgG ( 1: 1 , 000x ) were used as primary and secondary antibodies , respectively , to detect the binding of histidine-tagged proteins . The plates were washed three times with PBST ( 0 . 05% Tween 20 in PBS ) , and 100 μL of tetramethyl benzidine ( TMB ) solution ( ThermoFisher ) were added to each well and incubated for five minutes . The reaction was stopped by adding 100 μL of 0 . 5% hydrosulfuric acid to each well . Plates were read at 405 nm using a Tecan Sunrise Microplate reader . To determine the dissociation constant ( KD ) , the data were fitted by the following equation using GraphPad Prism software ( Version 7 , La Jolla , CA ) . Interactions of CspA with FH were analyzed by a SPR technique using a Biacore 3000 ( GE Healthcare ) . Ten micrograms of FH from human , mouse , or horse were conjugated to a CM5 chip ( GE Healthcare ) as described previously [78] . A control flow cell was injected with PBS without FH . For quantitative SPR experiments to determine FH-binding , ten microliters of increasing concentrations of CspA variants or mutants , including CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D , were injected into the control cell and flow cell immobilized with different animals’ FH , human plasminogen ( Sigma-Aldrich ) , C7 ( ComTech ) , or C9 ( ComTech ) at 10 μL/min , 25°C . To obtain the kinetic parameters of the interaction , sensogram data were fitted by means of BIAevaluation software version 3 . 0 ( GE Healthcare ) , using the one step biomolecular association reaction model ( 1:1 Langmuir model ) , resulting in optimum mathematical fit with the lowest Chi-square values . cspAB31 , cspAPKo , cspAZQ1 , or cspAB31L246D was first PCR amplified with the addition of a SalI site and a BamHI site at the 5’and 3’ ends , respectively , using Taq polymerase ( Qiagen ) and the primers ( see S3 Table ) to generate the plasmids encoding cspA alleles . The unpaired nucleotides at 5’ and 3’ end of the amplified DNA fragments were removed by exonuclease from CloneJet PCR cloning kit ( ThermoFisher ) and then inserted into the vector pJET1 . 2/blunt ( ThermoFisher ) . The plasmids were then digested with SalI and BamHI to release the cspA alleles , which were then inserted into the SalI and BamHI sites of pBSV2G ( S2 Table ) [82] . The promoter region of cspA from B . burgdorferi B31 , 400bp upstream from the start codon of cspA , was also PCR amplified . SphI and SalI sites were added at the 5’and 3’ ends of amplified DNA , respectively , using primers pcspAfp and pcspArp ( S3 Table ) . Promoter fragments were then inserted into the SphI and SalI sites of pBSV2G to drive the expression of cspAB31 , cspAPKo , cspAZQ1 , and cspAB31L246D . Electrocompetent B . burgdorferi B31-5A4NP1ΔcspA prepared as described [77 , 83] was transformed separately with 80 μg of each of the shuttle plasmids encoding cspAB31 , cspAPKo , cspAZQ1 , or cspAB31L246D ( S2 Table ) and cultured in BSK II medium at 33°C for 24 hours . Liquid plating transformations were performed in the presence of antibiotic selection ( 50 μg/mL gentamicin , 200μg/mL kanamycin , 50μg/mL streptomycin , as required ) , as described previously [84 , 85] . After incubation at 33°C in 5% CO2 for two weeks , kanamycin- , gentamicin- , and streptomycin-resistant colonies of cspA-complemented B . burgdorferi were obtained and expanded at 33°C in liquid BSK II medium containing these antibiotics , followed by genomic DNA preparation as previously described [86] . PCR was performed with primers ( S3 Table ) specific for aphI ( encoding the kanamycin resistance gene ) , aacC1 ( encoding the gentamicin resistance gene ) , and aadA ( encoding the streptomycin resistance gene ) to verify their presence in the transformants . The plasmid profiles of the cspA deficient mutant complemented with cspA alleles were examined as described previously [36] . The plasmid profiles of these strains were found to be identical to those of the parental strain 5A4NP1ΔcspA and the strain 5A4NP1ΔcspA-V . The determination of surface localization of CspA by Flow cytometry has been described previously [77 , 87 , 88] . Basically , 1 x 108 B . burgdorferi cells producing CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D were washed three times with HBSC buffer containing glucose and BSA ( 25 mM Hepes acid , 150 mM sodium chloride , 1 mM MnCl2 , 1 mM MgCl2 , 0 . 25 mM CaCl2 , 0 . 1% glucose , and 0 . 2% BSA , final concentration ) and then resuspended into 500 μL of the same buffer . To prepare permeabilized spirochetes , 1 × 108 B . burgdorferi was incubated with 100% methanol for an hour , followed by washing three times with HBSC buffer containing glucose and BSA . A mixture of mouse antisera raised against CspAB31 , CspAPKo , or CspAZQ1 or monoclonal mouse antibody against B . burgdorferi FlaB ( negative control ) was used as a primary antibody ( 1:250x ) . An Alexa 647-conjugated goat anti-mouse IgG ( ThermoFisher ) ( 1:250x ) was used as a secondary antibody . Three hundred microliters of formalin ( 0 . 1% ) were then added for fixing . Surface production of CspA was measured by flow cytometry using a Becton-Dickinson FACSCalibur ( BD Bioscience ) . All flow cytometry experiments were performed within two days of collection of B . burgdorferi samples . Spirochetes in the suspension were distinguished on the basis of their distinct light scattering properties in the flow cytometer equipped with a 15 mW , 488 nm air-cooled argon laser , a standard three-color filter arrangement , and CELLQuest Software ( BD Bioscience ) . The mean fluorescence index ( MFI ) of each sample was obtained from FlowJo software ( Three-star Inc , Ashland , OR ) representing the surface production of the indicated proteins . Mean Fluorescence Index ( MFI ) normalized to that of CspA from permeabilized B . burgdorferi obtained from S5 Fig was used to compare the surface production of CspA in different strains . These results represent the mean of three independent determinations ± the standard deviation of mean . Each standard deviation of mean value was no more than 7% of its mean value . To quantitatively determine the ability of B . burgdorferi strains producing CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D in binding to FH , 1 x 107 B . burgdorferi strains were washed twice by PBS , resuspended into 100μL of the same buffer , and then incubated with FH from human , mouse , horse , or quail ( 1 μg per reaction ) or C3-depleted human serum ( ComTech ) or serum from BALB/c C3-/- mice ( Final concentration: 20% ) at 25°C for 1 hour . Following incubation , the spirochetes were washed three times with PBS and resuspended in 100μL of HBSC buffer containing DB . A sheep anti-FH polyclonal IgG ( ThermoFisher ) ( 1:250x ) , which has been shown to recognize FH from human , mouse and horse [58] or a mouse anti-FH monoclonal antibody VIG8 ( 1:250x ) , which has been observed to recognize avian FH [80] were used as primary antibodies . An Alexa 647-conjugated donkey anti-sheep IgG ( ThermoFisher ) ( 1: 250x ) or goat anti-mouse IgG ( 1:250x ) was used as secondary antibodies . Three hundred microliters of formalin ( 0 . 1% ) was then added for fixing . The mean fluorescence index ( MFI ) of each B . burgdorferi strain was measured to determine the FH-binding capability promoted by CspA variants or mutants using a Becton-Dickinson FACSCalibur and analyzed by FlowJo software as described above . The deposition of C3b and MAC on the surface of B . burgdorferi producing CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D was quantitatively determined by flow cytometry as revised from previous reported methodologies [89] . B . burgdorferi strains were washed twice , resuspended in PBS , and then incubated with serum from human ( MP Biomedical , Santa Ana , CA ) , mouse ( Southern Biotech , Birmingham , AL ) , horse , or quail ( Final concentration: 20% ) at 25°C for 1 hour . Twenty percent serum was used in this study because more than 80% of B . burgdorferi strains were capable of surviving in this concentration of serum [28] , but C3b and MAC have been constantly detected on spirochete surface when these strains are incubated with 20% serum [28] . Prior to being mixed with B . burgdorferi , those sera were screened with the C6 Lyme ELISA kit ( Diamedix , Miami Lakes , FL ) to determine whether the individual from which it was collected had prior exposure to B . burgdorferi by detecting the antibody against the C6 peptide of a B . burgdorferi protein VlsE [90] . Then , the spirochetes were washed three times with PBS and resuspended in HBSC buffer containing glucose and BSA . A guinea pig anti-C3 polyclonal IgG ( ThermoFisher ) ( 1:250x ) , which has been shown to recognize C3 from human , mouse and horse , was used as a primary antibody to detect C3b . A mouse anti-C5b-9 monoclonal antibody aE11 ( ThermoFisher ) ( 1:250x ) , which has been observed to recognize MAC from human and horse , and a rabbit anti-C5b-9 polyclonal IgG ( Abcam , Cambridge , MA ) ( 1:250x ) , which has been verified to bind to MAC from human and mouse , were used as primary antibodies . An Alexa 647-conjugated goat anti-guinea pig IgG ( ThermoFisher ) ( 1:250x ) , goat anti-mouse IgG ( ThermoFisher ) ( 1:250x ) , or goat anti-rabbit IgG ( ThermoFisher ) ( 1:250x ) were used as secondary antibodies . Three hundred microliters of formalin ( 0 . 1% ) were then added for fixing . The mean fluorescence index ( MFI ) of each B . burgdorferi strain was measured to determine the levels of C3b or MAC deposition on the surface of B . burgdorferi strains using a Becton-Dickinson FACSCalibur and analyzed by FlowJo software as described above . The serum sensitivity of B . burgdorferi strain B31-5A15 , B31-5A4NP1ΔcspA-V and this cspA mutant strain producing CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D was measured using a published procedure [91] . Briefly , triplicate samples of each strain were grown to mid-log phase and diluted to a final concentration of 5×106 bacteria per milliliter into BSKII medium without rabbit serum with a final concentration of 40% normal serum from human , mouse , horse , or quail or C3-depleted human serum . We also included the spirochetes mixed with 40% heat-inactivated serum from these vertebrate hosts , which was incubated at 55 °C for 2 hours prior to being mixed with spirochetes . At 0 and 4 hours after the addition of serum , an aliquot was taken from each condition and counted by Petroff-Hausser counting chamber ( Hausser Scientific , Horsham , PA ) using a Nikon Eclipse E600 darkfield microscope ( Nikon , Melville , NY ) . Though the strain B31-5A4NP1ΔcspA has been shown to be eliminated by incubating human serum ( final concentration 40% ) in one hour [28] , the survival of the B31-5A4NP1ΔcspA-derived strains was evaluated at 4 hours post incubation to delineate the potential partial serum survival of these strains . The percentage of survival for those B . burgdorferi strains was calculated using the number of mobile spirochetes at 4 hours post incubation normalized to that prior to the incubation with serum . Four-week-old female C3H/HeN mice were used for experiments involved in needle infection of B . burgdorferi . Mice were infected by intradermal injection as previously described [77] with 105 of different strains of B . burgdorferi strain B31-5A15 , B31-5A4NP1ΔcspA-V or this cspA mutant strain producing CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D . The plasmid profiles and the presence of the shuttle vector of each of these B . burgdorferi strains were verified prior to infection as described to ensure the stability of the vector and no loss of plasmids ( S7A and S7B Fig ) [36] . Mice were sacrificed at 14 days post-infection , the inoculation site of the skin , the tibiotarsus joints , ears , bladder , and heart were collected to quantitatively evaluate levels of colonization during infection . The procedure of the tick infection has been shown in S7B and S7C Fig and described previously [92] . Basically , four-week-old male and female C3H/HeN mice were infected with 105 of B . burgdorferi strain B31-5A15 , the cspA knockout mutant strain B31-5A4NP1ΔcspA-V or this cspA mutant strain producing CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D by intradermal injection as described above . The ear punches from those mice were collected and placed into BSKII medium at 7 days post infection , and the spirochete growth in the medium was evaluated to confirm the infection of these mice . At 14 days post infection , the uninfected larvae were allowed to feed to repletion on those B . burgdorferi-infected C3H/HeN mice as described previously [92] . Approximately 100 to 200 larvae were allowed to feed on each mouse . The engorged larvae were collected and allowed to molt into nymphs in 4 to 6 weeks in a desiccator at room temperature and 95% relative humidity in a room with light dark control ( light to dark , 12: 12 hours ) . DNA was extracted from engorged larvae and post molting flat nymphs to examine the plasmid profiles and the presence of the shuttle vector the B . burgdorferi strains carried by these ticks as described to ensure no loss of plasmids during acquisition and molting ( S7B Fig ) [36] . The flat nymphs molted from larvae were placed in a chamber on four to six-week old male and female C3H/HeN , BALB/c , or C3-/- mice in BALB/c background as described [93] . Ten nymphs were allowed to feed on each mouse . After the nymphs were forcibly removed by forceps at 24 , 48 , or 72 hours post feeding , the rest of the ticks were allowed to feed to repletion . The mice were then euthanized at 7 or 14 days after tick feeding , and the blood , the feeding site of the skin , the tibiotarsus joints , bladder , ears , and heart were collected . The B . burgdorferi strain B31-5A15 , ticks , or mouse tissues were mixed with glass beads and then homogenized by a Precellys 24 High-Powered Bead Mill Homogenizer ( Bertin , Rockville , MD ) . RNA was extracted from these homogenized bacteria , ticks or tissue samples using Direct-Zol RNA MiniPrep Plus Kit ( Zymo Research , Irvine , CA ) according to the manufacturer’s instructions . Contaminating DNA was removed using RQ1 RNase-Free DNase ( Promega , Madison , WI ) following vendor’s instruction . cDNA was synthesized from 1 μg of RNA measured by spectrophotometer using a qScript cDNA SuperMix ( Quanta Bioscience , Beverly , MA ) according to the manufacturer’s instructions . Then , the quantification of cspA , flaB , or recA expression from cDNA was performed using an Applied Biosystems 7500 Real-Time PCR system ( ThermoFisher ) in conjunction with PowerUp SYBR Green Master Mix ( ThermoFisher ) , based on amplification of the B . burgdorferi cspA , flaB , or recA gene using primers BBCspAfp and BBCspArp ( for cspA ) , BBFlaBfp and BBFlaBrp ( for flaB ) , or BBRecAfp and BBRecArp ( for recA ) as described previously [94] ( S3 Table ) , respectively . Cycling parameters for SYBR green-based reactions were 50°C for 2 minutes , 95°C for 10 minutes , 45 cycles of 95°C for 15 seconds , and 60°C for 1 minute . Melting curve analysis for purity was performed on each sample by performing 80 cycles of increasing temperature for 10 seconds , each beginning at 55°C . Three biological replicates were included . Each biological replicate was run in duplicates and checked for intra-run variation . The gene expression of cspA or recA was normalized to that of flaB using the ΔCT method , where the relative expression of target ( cspA or recA ) , normalized to the expression of flaB , is given by 2−ΔCT , where Ct is the cycle number of the detection threshold ( see Eq 2 ) . All analyses and calculations were performed using the Applied Biosystem sequence detection software version 7 . 5 . 1 ( ThermoFisher ) . Ticks mixed with Enzyme Free cell dissociation buffer ( ThermoFisher ) were gently disrupted by pipette tips and then incubated at 37°C for 30 minutes to release the spirochetes into the buffer . The ticks-spirochetes mixtures were subsequently spun down , washed by PBS , and permeabilized by incubating the mixture with 100% methanol for one hour . After these mixtures were washed three times with HBSC buffer containing glucose and BSA , they were incubated with mouse antisera raised against CspAB31 ( 1:250x ) or monoclonal mouse antibody against FlaB ( 1:250x ) as primary antibody followed by an Alexa 647-conjugated goat anti-mouse IgG ( 1:250x ) as a secondary antibody . Three hundred microliters of formalin ( 0 . 1% ) were then added for fixing . The spirochetes were first sorted using a FACSAria cell sorter II equipped with FACSDiva software ( BD Bioscience ) . The purity of sorted populations was greater than 70% in all experiments ( S8 Fig ) . Then , the production of CspA and FlaB ( negative control ) in these spirochetes was measured by this equipment and software . The mean fluorescence index ( MFI ) of each sample was obtained from FlowJo software representing the production of the indicated proteins . The “ΔMFI” values are the mean fluorescence index obtained from each of these strains subtracting that obtained from the strains stained only by the secondary antibody . The production levels of CspA and FlaB in different stages of the enzootic cycle and in vitro cultured B . burgdorferi were presented as “ΔMFI” ( Fig 5C ) . These results represent the mean of three independent determinations ± the standard deviation of mean . The ticks collected from the chambers on the mice were mixed with glass beads and then homogenized by a Precellys 24 High‑Powered Bead Mill Homogenizer ( Bertin , Rockville , MD ) . The DNA from mouse tissues or blood or homogenized ticks was extracted using the EZ-10 Genomic DNA kit ( for mouse tissues and blood , Biobasic , Amherst , New York ) or the insect DNA kit ( for ticks , OMEGA Biotek , Norcross , GA ) . The quantity and quality of DNA for each tissue sample have been assessed by measuring the concentration of DNA and the ratio of the UV absorption at 280 to 260 using a nanodrop 1000 UV/Vis spectrophotometer ( ThermoFisher ) . The amount of DNA used in this study was 100 ng for each sample , and the 280:260 ratio was between 1 . 75 to 1 . 85 , indicating the lack of contaminating RNA or proteins . Quantitative PCR ( qPCR ) was then performed to quantitate bacterial loads , using 100 ng of DNA per reaction . B . burgdorferi genomic equivalents were calculated using an Applied Biosystems 7500 Real-Time PCR system ( ThermoFisher ) in conjunction with PowerUp SYBR Green Master Mix ( ThermoFisher ) , based on amplification of the B . burgdorferi recA gene using primers BBRecAfp and BBRecArp ( S3 Table ) , as described previously [77] . Cycling parameters for SYBR green-based reactions were 50°C for 2 minutes , 95°C for 10 minutes , 45 cycles of 95°C for 15 seconds , and 60°C for 1 minute . The number of recA copies was calculated by establishing a threshold cycle ( Ct ) standard curve of a known number of recA gene extracted from B . burgdorferi strain B31 , then comparing the Ct values of the experimental samples . To determine that the shuttle vectors expressing CspA variants is not missing during tick-mouse studies , the colE1 gene on the shuttle vector was amplified using primers BBColE1fp and BBColE1rp ( S3 Table ) and the same cycling parameters used to amplify recA described above . The bacterial burdens determined using colE1 primers were compared with the burdens obtained using primers to amplify the recA gene , the gene on the chromosome of spirochetes . The shuttle vectors were not missing as the bacterial loads determined using colE1 primers is close to 100% of the bacterial loads determined using recA primers . To assure the low signals were not simply a function of the presence of PCR inhibitors in the DNA preparation , we subjected 5 samples from blood , tibiotarsus joints , and bladder of the mice infected by B . burgdorferi strain B31-5A15 , B31-5A4NP1ΔcspA-V or this cspA mutant strain producing CspAB31 , CspAPKo , CspAZQ1 , or CspAB31L246D to qPCR using mouse nidogen primers mNidfp and mNidrp ( S3 Table ) as an internal standard [77] . As predicted , we detected 107 copies of the nidogen gene from 100ng of each DNA sample , ruling out the presence of PCR inhibitors in these samples . Significant differences between samples were determined using Student’s T test or the one-way ANOVA with post hoc Bonferroni correction . P-values were determined for each sample . A P-value < 0 . 05 ( * ) or ( # ) was considered to be significant . | Lyme disease , the most common vector-borne disease in the United States , is caused by the bacterium , Borrelia burgdorferi . This bacterium is transmitted to humans via the bite of a tick , and then spreads from the bite site to multiple tissues . Tick-to-human transmission of this bacterium requires bacterial survival in fed ticks because the blood and body fluid that ticks consume contain complement , an important mechanism that kills bacteria . To prevent host cell damage by this powerful mechanism , vertebrate animals produce factor H to inhibit complement . Lyme disease bacteria produce a surface protein CspA that binds to factor H to inhibit complement , but the role that CspA-mediated factor H-binding activity plays in tick-to-human transmission remains unexplained . To investigate this , we infected mice with Lyme disease strains that were identical except for the CspA variant or mutant with no factor H-binding ability they produced . We found that the factor H-binding activity of CspA appears to prevent the bacteria from being killed by complement in fed nymphal ticks . Such ability further facilitates bacterial transmission to mice . These results will promote the development of strategies against CspA to intervene in Lyme disease bacterial transmission from ticks to humans . | [
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] | 2018 | Polymorphic factor H-binding activity of CspA protects Lyme borreliae from the host complement in feeding ticks to facilitate tick-to-host transmission |
The spatial arrangements of secondary structures in proteins , irrespective of their connectivity , depict the overall shape and organization of protein domains . These features have been used in the CATH and SCOP classifications to hierarchically partition fold space and define the architectural make up of proteins . Here we use phylogenomic methods and a census of CATH structures in hundreds of genomes to study the origin and diversification of protein architectures ( A ) and their associated topologies ( T ) and superfamilies ( H ) . Phylogenies that describe the evolution of domain structures and proteomes were reconstructed from the structural census and used to generate timelines of domain discovery . Phylogenies of CATH domains at T and H levels of structural abstraction and associated chronologies revealed patterns of reductive evolution , the early rise of Archaea , three epochs in the evolution of the protein world , and patterns of structural sharing between superkingdoms . Phylogenies of proteomes confirmed the early appearance of Archaea . While these findings are in agreement with previous phylogenomic studies based on the SCOP classification , phylogenies unveiled sharing patterns between Archaea and Eukarya that are recent and can explain the canonical bacterial rooting typically recovered from sequence analysis . Phylogenies of CATH domains at A level uncovered general patterns of architectural origin and diversification . The tree of A structures showed that ancient structural designs such as the 3-layer ( αβα ) sandwich ( 3 . 40 ) or the orthogonal bundle ( 1 . 10 ) are comparatively simpler in their makeup and are involved in basic cellular functions . In contrast , modern structural designs such as prisms , propellers , 2-solenoid , super-roll , clam , trefoil and box are not widely distributed and were probably adopted to perform specialized functions . Our timelines therefore uncover a universal tendency towards protein structural complexity that is remarkable .
The polypeptide chains of proteins generally fold into highly ordered and well-packed three-dimensional ( 3D ) atomic structures [1] . These protein folds represent spatial arrangements of more or less wound helices ( generally α-helices ) and extended chain segments ( β-strands ) that are separated by flexible loops and relatively rigid regions in the form of turns and coils . Helices are stabilized by local main-chain ( backbone ) hydrogen bonding interactions . In turn , β-strands establish main-chain interactions with other strand elements that are distant . Parallel and antiparallel arrangements of β-strands form β-sheets , which often curve to form open and closed barrel structures . Folds are generally defined by the composition , architecture and topology of their core ‘helix’ and ‘sheet’ secondary structure elements [2] . The satisfaction of the hydrogen bonding potential of main-chains gives rise to regular secondary and super secondary structural elements in globular proteins . Analysis of protein folds indicates that those that occur frequently tend to adopt regular architectures , such as the αβ Rossmann folds , α/β-barrels , β-sandwiches , and bundles [3] . Main-chain hydrogen bonding is also important for the formation of complex turns and coils that link α-helices and β-strands . Protein domains are compact , recurrent , and independent folding units of protein structure that sometime combine with other domains to form multi-domain proteins . They are considered evolutionary units and are the basis for several protein structure classification schemes . Two of them , CATH and SCOP , are accepted as gold standards and share a number of common features [4] . SCOP [5] is a largely manual collection of protein structural domains that aims to provide a detailed and comprehensive description of the structural and evolutionary relationships of proteins with known structures . In contrast , CATH [6] uses a combination of automated and manual techniques , which include computational algorithms , empirical and statistical evidence , literature review and expert analysis . Both classifications are hierarchical but dissect 3D structure differently , focusing more on either evolutionary or structural considerations [4] . SCOP unifies domain structures that are evolutionarily related at sequence level ( >30% pairwise residue identities ) and are unambiguously linked to specific molecular functions into fold families ( FFs ) , FFs with common structures and functions with a common evolutionary origin into fold superfamilies ( FSFs ) , FSFs with similarly arranged and topologically connected secondary structures ( not always evolutionarily related ) into folds ( Fs ) , and finally Fs that share a general type of structure into classes . CATH unifies domain structures hierarchically ( bottom-up ) into sequence families ( SFs; analogous to FFs ) , homology superfamilies ( Hs; analogous to FSFs ) , topologies ( Ts; analogous to Fs ) , architectures ( As ) , and protein classes [6] ( see also Figure 1 for comparisons of SCOP and CATH levels of structural abstractions ) . Multi-linkage clustering groups domains into SFs based on sequence similarity . SFs with structures that are thought to share common ancestry and can be described as homologous are grouped into Hs . H structures sharing patterns of overall shape and connectivity of secondary structures are grouped into Ts . T structures that share and overall shape of the domain structure according to the orientations of the secondary structures but ignoring their connectivity are unified into As . Finally , A general shapes are grouped into four protein structural classes , mainly-alpha , mainly-beta , alpha-beta and few secondary structures [6] . Protein structures are evolutionarily conserved and capable of preserving an accurate record of genomic history [1] , [7] . They represent ‘relics’ of molecular evolution [2] and express the greatest levels of redundancy and reuse that exist in molecular biology [8] . Many studies have been conducted to unfold the evolution and diversification of protein domain structures and proteomes of extant organisms [1] , [9]–[11] . Structural phylogenies describing the evolutionary relationship of SCOP F , FSF and FF domains were built by data-mining the census of structures in hundreds of genomes [12]–[15] . Timelines of F , FSF and FF appearance were derived from the phylogenetic trees and revealed the existence of three epochs in protein evolution , ‘architectural diversification’ , ‘superkingdom specification’ and ‘organismal diversification’ . A universal core of domain structures that is central for cell function was the first to unfold in the timelines during the architectural diversification epoch . During the superkingdom specification epoch , patterns of reductive evolution in the domain repertoire consistently segregated the archaeal lineage from the ancient community of organisms and established a first organismal divide . Finally , the appearance of eukaryotic and archaeal signature domains marked the start of the organismal diversification epoch and the rise of domain structures specific to proteome lineages . Finally , trees of proteomes ( i . e . trees of life ) placed Archaea at the root and confirmed this organismal supergroup represents the most ancient superkingdom of life [7] , [16] . While we have studied how F , FSF and FF domains appeared and distributed in the world of organisms , we have not embarked in a systematic study of the origin and evolution of general structural designs . Here we study how these designs evolve in trees of domain structures , this time focusing on the CATH classification . The appearance and diversification of general protein structural designs at A-level ( e . g . , sandwiches , bundles , barrels , solenoids , propellers ) and published literature define in this study a unique chronology of structural innovation . Structural phylogenies of domains at T and H levels of structural abstraction uncover global evolutionary patterns of structural distribution in the world of organisms . The study benchmarks previous phylogenetic analysis of SCOP-defined domains and again reveals the early origin of the archaeal superkingdom . Congruent patterns of diversification derived from protein structure provide remarkable support to the ancient history of the cellular world , and trees of life confirm the primordial evolutionary patterns .
Domain structures are unevenly distributed in the world of proteins and proteomes [1] . They distribute differently in superkingdoms Archaea ( A ) , Bacteria ( B ) and Eukarya ( E ) and can be pooled into seven taxonomical groups depending on whether they are unique to a superkingdom ( A , B and E ) or are shared by two ( AB , AE and BE ) or three superkingdoms ( ABE ) . The taxonomical groups can be visualized in a simple Venn diagram ( Figure 2 ) . Bias in the relative number of domains structures corresponding to each taxonomical group persists regardless of the classification used ( CATH or SCOP ) or the level of structural abstraction of the classification scheme ( Figure 2 ) . This bias cannot be attributed to non-vertical patterns of inheritance ( e . g . the effect of horizontal transfer ) since research groups have confirmed independently that convergent evolution is relatively rare ( ∼2–12% ) at these high levels of structural conservation ( e . g . , [17] , [18] ) . Distribution biases among taxonomical groups show some striking features . First and as expected , higher taxonomical levels show higher levels of structural sharing between superkingdoms ( especially ABE ) than lower taxonomical levels , confirming the contention that they are evolutionarily more conserved . Second , the highest level of structural abstraction ( CATH A ) does not contain a single superkingdom-specific taxonomic group , suggesting that these groups represent sets of structures that are late evolutionary additions to the protein repertoire . Finally , ABE and BE domain structures are consistently the dominant taxonomic groups at all hierarchical levels , from FF to A . This final observation suggests they represent the most ancestral and common taxonomical groups . The most parsimonious corollary of these evolutionary patterns of domain distribution is that the ancient BE taxonomical group must arise by loss of archaeal-specific domain structures , suggesting Archaea is the most ancient superkingdom . As we will now show , this suggestion can be confirmed by phylogenomic reconstruction . We generated phylogenomic trees describing the phylogenetic relationship of 38 A , 1 , 152 T and 2 , 221 H domain structures ( Figures 3 and 4 ) . Tree distribution profiles and metrics of skewness suggest significant cladistic support ( P<0 . 01 ) . The trees were well resolved . However , internal branches for trees of Hs and Ts were poorly supported by bootstrap analysis , an expected outcome with trees of this size . Chronologies of evolutionary appearance [7] of CATH domain structures were derived directly from the phylogenomic reconstructions . The relative age of domains ( nd ) was measured on the trees as a relative distance in nodes from the hypothetical ancestor of domains at the base of the trees , and used to build the timelines . Since our method produces rooted trees that are highly unbalanced and reject the Yule and random speciation models [19] and since molecular speciation in our trees has clock-like behavior and does not depend on changes in domain abundance [20] , nd was considered a good and most-parsimonious proxy for time . To study how domain structures distribute in proteomes , we calculated a distribution index ( f ) , the number of species that use each structure given on a relative 0–1 scale . The f index was plotted along the timelines of domain structures , i . e . against nd ( Figure 5 ) . Three As ( ndA = 0–0 . 068 ) , fifteen Ts ( ndT = 0–0 . 061 ) and fifteen Hs ( ndH = 0–0 . 049 ) were present in all proteomes examined ( f = 1 ) and were the most ancient in the timeline . A list of the fifteen Hs is given in Table S1 . The f of As decreased with increasing age . The f of Ts and Hs decreased with their increasing age until f approached zero at ndT = 0 . 55 and at ndH = 0 . 55 , respectively . We term these ages “crystallization points” of the T and H structural chronologies , borrowing the idea of a phase transition from physics . At these time points , a steady decrease in f results in a large number of structures being specific to a small number of organisms . After crystallization , an opposite trend takes place , in which Ts and Hs increase their representation in genomes . In contrast , the architectural chronology that describes the appearance of As remained unaffected by the crystallization event since the losing trend of As started at ndA = 0 . 56–0 . 60 but rarely reached zero ( Figure 5 ) . To uncover hidden patterns of organism diversification in our dataset , we divided structures according to their distribution in superkingdoms and constructed three separate structural chronologies for the genomes of each superkingdom at A , T and H levels of structural abstraction ( Figure 5 ) . Taxonomical groups of domain structures were identified in the time plots with different colors . We previously observed that a superkingdom must ‘lose’ a significant number of SCOP structures before the evolutionary appearance of the first superkingdom-specific ‘signature’ structure [7] . In our study , this loser trend of domain structures was also observed for the CATH annotated genomes in each superkingdom . This observation strengthens our claim of reductive evolution in protein domains of the lineages that emerge from the cellular urancestor ( the last universal common ancestor; LUCA ) that we find is functionally complex [11] . The loser trend of SCOP and CATH structures reveals the primordial birth of Archaea followed by the birth of Bacteria and Eukarya . For example , the complete loss of Hs first starts in Archaea ( ndH = 0 . 176 ) with the membrane-bound lytic murein transglycosylase D ( chain A ) H domain ( 3 . 10 . 350 . 10 ) . Its appearance is congruent with the loss of the first SCOP FSF in Archaea ( ndFSF = 0 . 174 ) , the LysM domain ( d . 7 . 1 ) , observed in previous studies [7] . Both domain definitions are very much similar in how they describe functions in the cell . Analysis of domain distribution in Archaea shows that the vast majority of ancient Ts and Hs that were lost in proteomes were present in all superkingdoms ( ABE; colored grey ) . These were followed by AB ( orange ) , A ( wine ) and few AE ( red ) structures , most of which started to appear after the crystallization point and during the superkingdom specification and organismal diversification epochs [7] . Clear decreases in structural representation ( f-value ) also occurred in Bacteria and Eukarya , but involved fewer and younger structures . Analysis of domain distribution in Bacteria shows that AB and B structures ( dark yellow ) started to increase representation after the crystallization point , leading towards their diversification and specification . Similarly , the eukaryotic chronology showed that comparatively younger architectures [e . g . BE ( blue ) and E ( green ) ] increased their popularity among the eukaryal lineages . The appearance and distribution of the seven taxonomical groups of T and H structures was unfolded in the timelines using boxplots describing the range of ndT and ndH values and measures of central tendency for each group ( Figure 6 ) . Only domains shared by the three superkingdoms ( ABE ) span the entire chronology , from the origin of proteins ( nd = 0 ) to the present ( nd = 1 ) . These structures represent instantiations of the domain content of the urancestor but their late appearance may also indicate events of horizontal transfer between lineages . Boxplots for BE , AE and AB explain relationships among superkingdoms over time . The BE boxplot is the most ancient of the three , suggesting Archaea diversified early by reductive evolution . The A , B and E boxplots reflect the history of ‘signature’ structures that are unique to individual superkingdoms . These signatures appear first in Bacteria and then concurrently in Archaea and Eukarya , an observation that is congruent with timelines derived from SCOP domains [7] . Despite its early specification , Archaea tends to acquire Archaea-specific structures very late in evolution and their number is limited when compared to Bacteria and Eukarya . This may stem from very strong adaptive pressures that were historically imposed by lifestyle . Archaea are very simple organisms that usually live in harsh and extreme environments [21] . We believe their extremophilic lifestyles impose constraints on their molecular make up that: ( i ) limit the possibility of acquiring new structures , and ( ii ) induce a constant selective pressure to maintain a minimal structural set necessary for survival . We therefore propose that Archaea maintained a minimal set of structures while losing structures by strong reductive evolution . We note that signature As exhibit very low f values , suggesting these molecular designs were acquired as adaptations to new environments and lifestyles . The appearance of structures shared by only two superkingdoms was also revealing . For example , the AE boxplot's upper whisker approached ndH = 1 , implying a recent relationship between Archaea and Eukarya . Comparatively , the nd values for SCOP FSFs for the AE taxonomical group was ndFSF = 0 . 85 , supporting the late appearance of the interaction [7] . Note that a sister relationship between Archaea and Eukarya is usually used to claim the canonical bacterial rooting of the tree of life [22] , but that in our studies this relationship is only supported by domain structures that are quite derived . It is also noteworthy that the early loser trend in the BE taxonomic group , made explicit by smooth decreases in f-values in the timeline , occurs in the absence of signature domain structures specific to superkingdoms ( Figure 5 ) . This weakens other evolutionary scenarios of superkingdom origin , including chimerism mediated by massive horizontal gene transfer ( endosymbiosis or fusion ) processes , and the possibility that phylogenetic signal of these events ( e . g . those between Bacteria and Eukarya ) would make Archaea appear artificially ancient in phylogenomic reconstructions ( see below ) . We previously reconstructed trees of proteomes from a genomic census of SCOP domains and made inferences about the rooting of the tree of life [7] , [11] , [16] . We found trees of proteomes reconstructed from ancient domain structures were rooted paraphyletically in Archaea while trees reconstructed using derived structures exhibited the canonical rooting with Bacteria emerging at their base . We also revealed how parasitic and symbiotic lifestyles can complicate phylogenetic interpretation [7] , [16] . The proteomes of organisms that are parasitic or that establish symbiotic relationships with other organisms have frequently experienced reductive evolution , discarding enzymatic and cellular machineries in exchange for resources from their hosts . Since their inclusion can lead to incorrect phylogenetic trees , we excluded proteomes from all but 295 free-living ( FL ) organisms and reconstructed rooted trees that most parsimoniously describe their evolution . The FL set included 41 archaeal , 189 bacterial , and 65 eukaryotic organisms . The tree of FL proteomes reconstructed from a census of H domain structures supported the trichotomy of the superkingdoms ( Figure 7 ) . The number of bacterial proteomes was however overrepresented in the FL-tree and could cause long-branch attraction during phylogenetic reconstruction possibly leading to incorrect deep phylogenetic relationships . Since taxon sampling can also affect phylogenomic inference [23] , we randomly sampled equal numbers of proteomes per superkingdom ( a maximum of 41 ) and generated replicated trees of proteomes . Reconstruction of equally sampled FL proteomes improved tree resolution and bootstrap support values of deep branches . More importantly , the trees consistently showed a paraphyletic rooting in Archaea and the derived placement of monophyletic Bacteria and Eukarya ( Figure 7 ) . We also reconstructed trees of FL proteomes from three subsets of phylogenetic characters: ancient H structures common to all superkingdoms corresponding to the architectural diversification epoch ( ndH<0 . 176 ) , H structures of intermediate ancestry corresponding to the superkingdom specification epoch ( 0 . 176<ndH<0 . 55 ) and H structures that are derived and reflect the organismal diversification epoch ( 0 . 55<ndH ) . The proteome tree reconstructed from the most ancient H structures was rooted paraphyletically in Archaea , reflecting their early segregation through the minimalist strategy . Reconstructions from H structures of intermediate ancestry produced trees with three clades corresponding to the three superkingdoms that were rooted in Archaea . Finally , reconstructions from H structures that were derived yielded the canonical tree of life rooted in Bacteria . It is noteworthy that the rooting of these trees reflects the early appearance of Bacteria-specific domain structures ( Figure 7 , see trees reconstructed using most ancient , ancient and younger characters sets ) . We note the split of Archaea in three groups in the tree reconstructed from ancient H structures . We believe this anomaly stems from using subsets of characters in phylogenomic reconstructions and from the existence of a ‘modern effect’ [11] imposed by relatively recent changes in abundance of domain structures belonging to the ABE taxonomic group . Both factors impoverished phylogenetic signal and obscured deep phylogenetic relationships . The modern effect is an embodiment of recent evolutionary processes affecting ancient repertoires , the effects of which must be identified and removed when reconstructing the set of domain structures present in the urancestor [11] . The structural chronology , especially at H level , unveils a relatively recent ( perhaps ongoing ) sharing of protein architectures between archaeal and eukaryal genomes . The timeline reveals that while AE domain structures appeared for the first time when Archaea and Eukarya acquired their superkingdom-specific signature structures , the vast majority of them appeared quite late in evolution ( e . g . , Figure 6D ) . This was unanticipated . This finding inspired us to resolve the phylogenetic contribution of each structural character set in the tree of proteomes . Interestingly , characters that are shared by archaeal and eukaryal genomes exhibited high retention index ( RI ) values ( Figure 8 ) , indicating that the sharing pattern did not result from annotation artifacts . The RI measures the amount of synapomorphy ( features that are shared and derived ) expected from a data set that is retained as synapomorphy on a cladogram . Boxplots of structural character sets shared by the seven taxonomical groups were also plotted ( Figure 8 ) . Since low RI values indicate high levels of homoplasy ( i . e . non-vertical phylogenetic signal ) , the low values of bacterial signature structures confirm the high incidence of horizontal gene transfer that exists in the bacterial superkingdom . In turn , the relatively high RI levels of the common ABE group is surprising . Most members of the group include very ancient structures ( Figure 8 ) , many of which were part of the urancestor . High RI levels in this taxonomical group challenge the common assumption that horizontal transfer was rampant during early life [22] . These RI boxplots are powerful enough to explain the relationships of superkingdoms in our tree of proteomes . The AE boxplot is the only one exhibiting very high RI values . In turn , bacteria-specific characters had the most dispersed RI boxplot . Hence , archaeal and eukaryotic lineages share good signal characters that are very recent and are widely present; their high f values indicate for example their presence in most of archaeal and eukaryotic proteomes ( Figure 5C ) . More than 30 years ago , Woese and Fox [24] defined the existence of three ‘aboriginal’ lines of descent – superkingdoms Archaea , Bacteria and Eukarya . The microbial Archaea and Bacteria lines were conceptualized as ‘urkingdoms’ of deep origin that were qualitatively different from the eukaryotic kingdoms . This prompted reconstructions of a tripartite tree of life and later proposals of the early rise of Bacteria with rooting determined using paralogous gene couples ( e . g . , EF-Tu/EFG ) . This classical ( canonical ) tree topology induces sister lineages corresponding to Archaea and Eukarya and an exclusive common ancestor of both . Many archaeal components involved in informational systems ( e . g . translation , replication and transcription ) and transmission of genetic information show a higher sequence similarity with their eukaryotic homologue than their bacterial homologue [25] , [26] . For instance , more than 30 ribosomal proteins are shared between the Archaea and Eukarya that are not present in Bacteria [27] . Moreover , Archaea and Eukarya also share a similar base excision repair system that is different than the system in bacteria [28] . If the phylogenetic signal in the sequence of these RNA and protein molecules adequately depicts history , these findings would explain the evolutionary link between Archaea and Eukarya and the topology of the canonical tree of life that emerges in some phylogenetic studies from their close relationship . However , many genes do not share the archaeal and eukaryal link and the canonical root must be considered contentious . Remarkably , the tree of proteomes reconstructed using the modern structural character set in our experiments ( Figure 7 , epoch III or younger character sets ) is the only tree with the canonical topology that places the root branch in Bacteria . This topology mostly results from protein domain structures of very recent origin that are shared between Archaea and Eukarya . We contend that these very recent domains retain good phylogenetic signal , especially in their sequences , and will be less affected by processes of mutation saturation . Consequently , the close evolutionary relationship of Archaea and Eukarya in trees of life derived from analyses of these sequences [22] , [24] can be considered an artifact of the focus on sequence . Current trees of life built for example from sequence concatenation , such as those in refs . [29] , [30] , include genes encoding for multidomain proteins ( e . g . aminoacyl-tRNA synthetases ) . Some of these domains are of recent origin and may fall within the derived domain set we have analyzed . We claim that strong phylogenetic signal in the sequence of these domains likely drives the reconstructed topologies . Instead , weak phylogenetic signal embedded in the sequences of older and universal domains is swamped by the recent archaeo-eukaryotic signal that is in part responsible for the canonical tree . Our focus on CATH domain structure ( not gene sequence ) can dissect the differential contribution of old and recent protein domains that belong to the proteome-encoding gene repertoire . A similar focus on deep phylogenetic signal in RNA structure has also shown the basal placement of Archaea in phylogenetic reconstructions from tRNA , RNase P RNA and 5S rRNA [31]–[35] , including analysis of paralogy in tRNA [35] . For example , a timeline of accretion of helical RNA substructures of RNase P complexes showed the most ancient substructures were universal and harbored the core catalytic activities of the endonuclease [34] . However , the first substructures that were lost were specific to Archaea and this episode occurred before molecules were accessorized with superkingdom-specific substructures . The early origin of Archaea was also shown in trees that describe the structural evolution of RNase P RNA , which placed archaeal molecules at its base . These results obtained by studying the evolution of RNA structure clearly parallel the evolutionary patterns of CATH domain accumulation of this study . Clearly , deep phylogenetic signal in protein and RNA structure is free from the limitations of gene sequence and associated non-vertical patterns arising from horizontal gene transfer but more importantly from domain rearrangement and can therefore reveal historical patterns without bias [36] . Here we show the importance of considering the age heterogeneity of a biological repertoire , in this case the proteome , when making phylogenetic statements . The architectural chronology of As is evolutionarily more conserved than chronologies of Hs and Ts ( Figure 5A ) . The timeline shows that As are widely shared and are refractory to loss in genomic lineages . In fact , very few As are lost in superkingdoms ( 4 in Archaea , and one each in Bacteria and Eukarya ) and are thus very old and popular in the world of organisms . The 3-layer ( αβα ) sandwich ( 3 . 40 ) is the most abundant and ancient of all proteins . The orthogonal bundle ( 1 . 10 ) and the α/β-complex ( 3 . 90 ) are equally abundant and are the second and third most ancient architectures . Remarkably , the phylogenomic tree of As shows that comparatively simpler shape structural designs are more favored than complex designs and in general are more ancient , appearing at the base of the tree . Architectural complexity was here evaluated on empirical grounds by focusing on the topology and regularity of spatial arrangements of secondary structures in a structural design . For example , the most ancient 3 . 40 and 1 . 10 architectures involve simple arrangements of secondary structure that can be very diverse in different structural variants while more recent shape designs are spatially more convoluted and regular ( Figures 3 ) . As time progresses the complexity in architectural make up of structural designs also increases ( Figure 3 ) . The few As that are lost in superkingdoms are quite complex and as expected their appearance is quite derived . The first loss occurred in Eukarya ( ndA = 0 . 76 ) with the very complex Clam architecture , and then in Archaea and Bacteria . We note that Archaea loses four As quite late and in a row , showing that the pervasive reductive trends of Archaea described above extend almost to the present . This also reflects the conservative nature of extremophilic Archaea , which are not in need of modern structural designs . Bacteria loses the most recent A structural design , Box ( 2 . 80 ) , at ndA = 1 , which is shared by both archaeal and eukaryal genomes . Box is involved in nucleotide excision repair , a molecular function that has a unique place in cellular defense because of its wide substrate range and its ability to virtually remove all base lesions from a genome . Ögrünç et al . [28] reported a similar base excision repair system used in Archaea and Eukarya and argued that a different set of proteins are employed by the bacterial nucleotide repair system . Interestingly , the f index for Box in Archaea ( f = 1 ) and Eukarya ( f = 0 . 96 ) again indicates a recent sharing of structural designs between archaeal and eukaryal organisms . Architectures constitute the second highest level of structural abstraction in CATH , and because of their high conservation it is difficult to clearly delimit the three epochs of the protein world . In contrast , our results indicate CATH H and SCOP FSF are the most suitable levels to uncover the evolution of domain structures in genomes . These levels of abstraction are structurally and evolutionarily conserved . They preserve deep phylogenetic signatures and are variable enough to dissect evolutionary history of proteomes and molecular functions . To obtain a detailed view of architectural discovery and usage over time , we grouped As into 10 major structural designs: sandwiches , bundles , barrels , prisms , horseshoes , rolls , solenoids , propellers , complexes and other ( a category with structural designs that could not be clearly grouped into the main categories ) ( Table 1 and Figure 9 ) . We found that most sandwiches , bundles , barrels , complexes and rolls have high f values ( f∼1 ) and rather simple structural designs ( Figure 9 ) . In turn , structural designs such as propellers , horseshoes , solenoids ( 2 Solenoid , 2 . 150 ) , prisms , trefoil and box , have low f values ( f = 0 . 85–0 . 10 ) and are very complex . Under the assumption that widespread and abundant designs are old , complex folds appear to have evolved later than simpler folds . We also mapped the appearance of T and H structures harboring individual A designs , plotting ndH and ndT values for Hs and Ts belonging to each of the 38 known As ( Figure 10 ) . The structural makeup of the most ancient 3-layer ( αβα ) sandwich ( 3 . 40 ) architecture ( Figure 3 ) represents the central theme of the most ancient SCOP FFs [37] . These structures consist of repeating α-β-α supersecondary units , such that the outer layer of the structure is composed of helices packing against a central core of parallel β-sheets . Many enzymes , including most of those involved in glycolysis , are α/β layered proteins and are cytosolic [38] . These α/β structures harbor repeats of the α-β-α arrangement ( e . g . , the α-β-α-β-α sequence ) . The β-strands are parallel and hydrogen bonded to each other , while the α-helices are all parallel to each other but are antiparallel to the strands . Thus the helices pack against the sheet forming a sandwich-like structure . We note that the β-α-β-α-β ( αβα ) subunit , often present in nucleotide-binding proteins , represents the Rossmann structural motif found in proteins that bind nucleotides , especially the cofactor NAD ( H ) [39] . The orthogonal bundle ( 1 . 10 ) and α-β-complex ( 3 . 90 ) appear immediately after the 3-layer ( αβα ) sandwich ( 3 . 40 ) design . The orthogonal bundle consists of a 3–4 α-helix bundle and is found in a number of different proteins , most of which associate with membranes . Due to physical constraints imposed by the lipid bilayer of membranes the list of possible membrane protein structures is limited to either bundles [40] , [41] or barrels [42] , [43] . In many cases the α-helices are part of a single polypeptide chain and are connected to each other by three loops . In the 4-helix bundle proteins the interfaces between the helices consist mostly of hydrophobic residues while polar side chains on the exposed surfaces interact with the aqueous environment . A number of cytokines consist of 4-helix bundles , such as interleukin-2 , interleukin-4 , human growth hormones , and the granulocyte-macrophage colony-stimulating factor ( GM-CSF ) [38] and DNA binding proteins ( e . g . , transcription factors , repressors proteins ) [44] . CATH has grouped the complex shaped structures into the ‘complex’ bin , until alternative assignment methods are developed . The α/β-complex architecture groups together all those designs that include significant α and β secondary structural elements in a mixed fashion . Examples of α/β-complex proteins include bacterial and mammalian pancreatic ribonucleases [45] , Zn metallo-proteases and DNA topoisomerases [46] . Two kinds of barrel structures are the most ancient and abundant in the protein world , the α/β-barrel ( 3 . 20 ) and the β-barrel ( 2 . 40 ) [6] , and both appeared at about the same time ( ndA = 0 . 13 ) . The α/β-barrel is composed of eight α-helices and parallel β-strands that alternate along the peptide backbone . The α/β-TIM barrel is the most prominent example of α/β-barrel and is widely present in enzymes of central metabolism [47] . A β-barrel is a large β-sheet that twists and coils to form a closed structure in which the first strand is hydrogen bonded to the last . β-strands in β-barrels are typically arranged in an antiparallel fashion . Barrel structures are commonly found in porins and other proteins that span cell membranes and in proteins that bind hydrophobic ligands in the barrel center , such as lipocalins [48] . The roll is a complex nonlocal structure in which 3–4 pairs of antiparallel β-sheets , only one of which is adjacent in sequence , are ‘wrapped’ in 3D space to form a barrel shape [49] . Rolls appear for the first time at ndA = 0 . 3 . A number of distinct and more complex architectures appear later on in the chronology , including solenoids , horseshoes , prisms , propellers and trefoils . Solenoid proteins , with their arrays of repeating motifs , tend to have elongated structures that contrast with the majority of globular proteins whose polypeptide chains follow more complex trajectories [50] . These are constructed from tandem structural repeats arranged in superhelical fashion , a feature that is important for many cellular processes [51] . Solenoid proteins constructed from HEAT repeats [52] and armadillo repeats [53] , [54] constitute the principal transport receptors . A key structural property that differentiates solenoid proteins from other structured proteins is the lack of contacts between distal regions of protein sequence ( sequence-distal contacts ) . For this reason , solenoid proteins are often more flexible than other structured proteins and this flexibility is an important feature of their specific functions [50] . The solenoid structure appears for the first time at ndA = 0 . 46 . The α-horseshoe protein appears at ndA = 0 . 4 , is a super helical structure made up of a number of three α-helical orthogonal bundle repeats . The α-β horseshoe appeared at ndA = 0 . 56 , consists of several α/β-repeating units [55] . The structure of the ribonuclease inhibitor , a cytosolic protein that binds strongly to any ribonuclease that may leak into the cytosol , takes the concept of the repeating α/β unit to the extreme [55] . The structure is made of a 17-stranded parallel β-sheet curved into an open horseshoe shape , with 16 α-helices packed against the outer surface . Prisms are similar to solenoids in geometry but completely different in connectivity . A more self-contained β-sheet forms each face of a triangular prism . They appear late at ndA = 0 . 86 . The trefoils consist of an unusual β-sheet formed by six β hairpins arranged with three fold symmetry into ‘Y’ like structures [56] and are also quite derived ( ndA = 1 ) . The most ancient and popular architecture , the 3-layer ( αβα ) sandwich ( 3 . 40 ) , harbors the most ancient and abundant topology , the Rossmann fold ( 3 . 40 . 50 ) and the most ancient and abundant superfamily , the P-loop containing nucleotide triphosphate hydrolases ( 3 . 40 . 50 . 300 ) . Despite differences of topology and ranking within databases [57] , this H structure of CATH is analogous to the “P-loop containing nucleotide triphosphate hydrolase” FSF ( c . 37 . 1 ) of SCOP [4] , since both have Rossmann fold topology and also agree on their keyword definitions . A careful analysis of CATH and SCOP structures phylogenies show that the ancient domains structures at T ( 3 . 40 . 50 ) and H ( 3 . 40 . 50 . 300 ) levels are in global agreement with timelines of F ( c . 37 ) and FSF ( c . 37 . 1 ) structures [7] . Despite differences in domain definitions of tertiary structure in CATH and SCOP , the remarkable conservation of evolutionary signal indicates both classification systems effectively preserve evolutionary information in protein structure and uncover global patterns of origin and diversification that are for the most part congruent . We note that levels of structural abstraction above H and FSF unify domains that may not be necessarily homologous . In other words , T and A in CATH and F in SCOP may show episodes of structural convergence . This could complicate evolutionary interpretations . The fact that the same evolutionary patterns observed using H domain structures in this study ( and FSF structures in previous studies; reviewed in [1] ) could be recovered at higher levels of the structural hierarchy is encouraging and suggests that the influence of convergent processes at these higher levels is limited and that the classifications do in general a good job in capturing true evolutionary information . In this study we follow the history of protein fold structures and proteomes in the tripartite world of organisms . Instead of generating trees of life from protein sequence with standard methods , we use a genomic structural census and robust cladistics methods to build trees of domain structures and proteomes . Structural phylogenies describing the evolution of CATH domains at A , T and H levels of structural abstraction revealed patterns of reductive evolution and the three epochs in the evolution of the protein world that were previously proposed [7] . Structural diversification patterns match those observed in the analysis of SCOP domain structures [7] , [16] , [58] . Reconstruction of phylogenomic trees of proteomes describing the evolution of lineages confirms Archaea is the most ancient superkingdom . Provided assumptions of our phylogenomic method are considered valid , six major findings summarize novel results and take advantage of the ability of CATH to better describe topological features of protein structure: We note that these conclusions entrust CATH with the ability to properly apportion domain structures in fold space and are only valid if assumptions of character argumentation are valid . Our trees of domain structures define timelines that trace back the history of innovation , diversification and distribution of protein structural designs . Our finding that protein architectures tend to become more complex in evolution is very significant . In a previous study , analysis of β-barrel structures revealed that the curl and stagger and complexity of the connectivity of supersecondary structures increases in evolution [12] . The very early appearance of multilayered sandwich structures is also compatible with the finding that the most ancestral folds share a common architecture of interleaved β-sheets and α-helices [12] . An even more recent study shows that 36 out of the 54 most ancient FFs harbor α/β/α-layered sandwich structures [37] . The very early appearance of the P-loop hydrolase motif in the first FF , the ABC transporters , was associated with a built-in lateral bundle , which resembles the trans-membrane domains of transporter proteins . This suggests that first proteins contained sandwich and bundle structures and were associated with the membranes of primordial cells . Remarkably , P-loop hydrolase folds and bundles make up important membrane complexes , such as ion channels and transporters . Their very early origin highlights a crucial links between the origin of proteins and the origin of cells .
Phylogenomic trees describing the evolution of domain structures and proteomes were reconstructed using a census of domain abundance in proteomes using PAUP* version 4 . 0b10 [59] . Figure S1 presents a flowchart of the methodology . CATH annotations for the proteomes of 492 fully sequenced genomes ( 42 Archaea , 360 Bacteria and 90 Eukarya ) were retrieved from Gene3D [60] . We used CATH version 3 . 3 and its corresponding Gene3D assignments . Table S2 lists the organisms studied and Table S3 lists the subset that is free-living and was used in phylogenomic analyses . Gene3D is a repository of manually curated HMM predictions with a false positive prediction rate of only 0 . 2–0 . 6% . As with SUPERFAMILY [9] , [61] , a repository of SCOP domain predictions , proteomes deposited in Gene3D were searched against HMM libraries using the iterative Sequence Alignment and Modeling System ( SAM ) method . Data matrices of genomic abundance ( G ) of domains at A , T and H levels were assembled for phylogenetic analysis . Empirically , G values represent numbers of multiple occurrences of an A , T and H domain in a genome , ranging from 0 to thousands and resembling morphometric data with large variances . Because existing phylogenetic programs can process only tens of phylogenetic character states depending on user's CPU performance , the space of G values in the matrix was reduced using a standard gap-coding technique with the following formula:in which denote either an A , T or H domain structure , a genome , and the abundance of in . indicate maximum values for all genomes . The round function normalizes G values on a 0–20 scale ( ) . These values define character states , which are encoded as linearly ordered multistate phylogenetic characters using an alphanumeric format of numbers 0–9 and letters A–K that is compatible with PAUP* . A ‘by hand’ generic example of data normalization and encoding is shown in Protocol S1 . The actual raw matrix describing the A-level domain census is shown in Dataset S1 as an example . Transposition of the data matrix ( switching characters and taxa ) allowed reconstruction of trees of either proteomes or domain structures . Trees of A , T and H domains were built by polarizing states from ‘K’ to ‘0’ using the ANCSTATES command in PAUP* , with ‘K’ being ancestral . Trees of proteomes were built by polarizing character states from ‘0’ to ‘K’ , with ‘0’ being ancestral . The trees were rooted without invoking outgroup taxa using the Lundberg method , which positions the most ancient proteomes and domain structures at the base of their corresponding trees . Assumptions of character argumentation have been discussed in previous publications [1] , [7] , [12] , [15] . Our model of structural evolution ( ‘K’ to ‘0’ polarization ) considers that the abundance of individual domain structures increases progressively in nature , even when expanding domain levels suffer loss in individual lineages or are selectively constrained during evolution ( we consider that character state transformation is reversible ) . Consequently , ancient structures are more abundant and widely present than younger ones . In contrast , our model of proteome evolution ( ‘0’ to ‘K’ polarization ) assumes proteomes have built their structural repertoires progressively , increasing both the diversity and abundance of their structural make up . While character argumentation considers that domain structures that appear early in evolution are prominent in genomes and that their numbers increase in steps corresponding to the addition or removal of a homologous gene in a family , the model is agnostic about how changes occur . For example , duplications of domains with simple structural motifs that occur in multiples may involve the entire array , and if these tandem duplicates confer selective advantage , they can be retained in the lineage and can distribute throughout proteome lineages . This is the case for example with proteins that contain tandem repeats of several domains from a same family that are common in Eukarya [62] . While this mechanism of domain gain in not accounted by the model , evolutionary statements relate to domain taxa and their definitions , which generally consider domains as structural and evolutionary modular units . Phylogenomic trees were reconstructed using the maximum parsimony ( MP ) optimality criterion in PAUP* with 1 , 000 replicates of random taxon addition , tree bisection reconnection ( TBR ) branch swapping , and maxtrees unrestricted . Phylogenetic confidence was evaluated by the nonparametric bootstrap method with 1 , 000 replicates ( resampling size matches the number of the genomes sampled; TBR; maxtrees , unrestricted ) . The degree of phylogenetic signal for taxa was measured using the skewness ( g1 ) test with a tree length distribution obtained from 1 , 000 random trees . Since trees of domain structures are rooted and are highly unbalanced , we unfolded the relative age of protein domains directly for each phylogeny as a distance in nodes ( node distance , nd ) from the hypothetical ancestral architecture at the base of the trees in a relative 0–1 scale . nd was calculated by counting the number of internal nodes along a lineage from the root to a terminal node ( a leaf ) of the tree on a relative 0–1 scale with the following formula:where a represents a target leaf node ( either an A , T or H domain ) , r is a hypothetical root node , and m is a leaf node that has the largest possible number of internal nodes from node r . Consequently , the nd value of the most ancestral taxon is 0 , whereas that of the most recent one is 1 . Node distance can be a good measure of age given a rooted tree because the emergence of protein domains ( i . e . , taxa ) is displayed by their ability to diverge ( cladogenesis or molecular speciation ) rather than by the amount of character state change that exists in branches of the tree ( branch lengths ) . In this study we have not compared phylogenies recovered using different versions of CATH . However , our experience with SCOP definitions over the years has shown that tree topologies do not change significantly and that evolutionary inferences stand despite biases in the databases [8] and addition of new domain structures to the known repertoire of proteins [1] . We note that the atomic structures of most protein folds have been acquired ( ∼1 , 200 out of 1 , 500 expected ) [63] . Consequently , new domain structures are by definition either rare in genomes or intrinsically difficult to recover . Since important evolutionary patterns obtained using CATH definitions match those derived from SCOP , we do not expect CATH updates will change the central conclusions of our study . | Proteins are vital and central macromolecular players necessary for the functioning of the cell . The redundant and highly conserved structural makeup of proteins reflects their ability to act as genomic repositories of evolutionary history . These structures are fundamental subjects for the study of molecular evolution . Structural biologists have demonstrated the existence of a wide array of compact 3-dimensional fold structures , the protein domains . Their classification resulted in hierarchical taxonomies that describe protein fold space , most notable SCOP , CATH and FSSP . Studies have shown that certain types of protein shapes are more abundant than others and this uneven distribution implicates processes by which new shapes are discovered . Our evolutionary genomic research explores the evolution of protein domains at the deeper levels of classification . However , we have not embarked in a systematic study of the origin and evolution of general structural designs . These designs include topologies such as sandwiches , bundles , barrels , prisms , solenoids , and propellers . The appearance and diversification of general structural designs and their confirmation from published literature defines a unique chronology of structural innovation . The study also uncovers a recent trend of architectural sharing between Archaea and Eukarya and benchmarks the phylogenomic analysis of CATH domains with SCOP domains . | [
"Abstract",
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] | 2013 | Origin and Evolution of Protein Fold Designs Inferred from Phylogenomic Analysis of CATH Domain Structures in Proteomes |
Natively unstructured or disordered protein regions may increase the functional complexity of an organism; they are particularly abundant in eukaryotes and often evade structure determination . Many computational methods predict unstructured regions by training on outliers in otherwise well-ordered structures . Here , we introduce an approach that uses a neural network in a very different and novel way . We hypothesize that very long contiguous segments with nonregular secondary structure ( NORS regions ) differ significantly from regular , well-structured loops , and that a method detecting such features could predict natively unstructured regions . Training our new method , NORSnet , on predicted information rather than on experimental data yielded three major advantages: it removed the overlap between testing and training , it systematically covered entire proteomes , and it explicitly focused on one particular aspect of unstructured regions with a simple structural interpretation , namely that they are loops . Our hypothesis was correct: well-structured and unstructured loops differ so substantially that NORSnet succeeded in their distinction . Benchmarks on previously used and new experimental data of unstructured regions revealed that NORSnet performed very well . Although it was not the best single prediction method , NORSnet was sufficiently accurate to flag unstructured regions in proteins that were previously not annotated . In one application , NORSnet revealed previously undetected unstructured regions in putative targets for structural genomics and may thereby contribute to increasing structural coverage of large eukaryotic families . NORSnet found unstructured regions more often in domain boundaries than expected at random . In another application , we estimated that 50%–70% of all worm proteins observed to have more than seven protein–protein interaction partners have unstructured regions . The comparative analysis between NORSnet and DISOPRED2 suggested that long unstructured loops are a major part of unstructured regions in molecular networks .
One central paradigm of structural biology is that the intricate details of 3-D protein structures determine protein function [1 , 2] . In the last few years , many studies have shown that often the lack of a unique , native 3-D structure in physiological conditions can be crucial for function [3–21] . Such proteins are variously called disordered , unfolded , natively unstructured , or intrinsically unstructured proteins . A typical example is a protein that adopts a unique 3-D structure only upon binding to an interaction partner and thereby performs its biochemical function [3–6] . The better our experimental and computational means of identifying such proteins , the more we realize that they come in a great variety: some adopt regular secondary structure ( helix or strand ) upon binding , and some remain loopy . Some proteins are almost entirely unstructured , and others have only short unstructured regions . The more we can recognize short unstructured regions , the more we realize that the term “unstructured protein” would be misleading , as most unstructured proteins have relatively short unstructured regions . There is no single way to define unstructured regions . Here , we define an unstructured region as that which lacks unique 3-D structure by one of the following experimental techniques: circular dichroism ( CD ) spectroscopy , nuclear magnetic resonance ( NMR ) spectroscopy , X-ray crystallography , or proteolysis experiments [7–9] . Thanks to the outstanding data collection by the Dunker group , we could also describe this as regions that are the minimal common denominator between all proteins collected in DisProt [10] . However , as we learned from prediction methods , DisProt and similar databases cover only a small fraction of all unstructured regions ( Figure 1 ) , and as we learned from recent experiments [11–13] , there are many unstructured regions covered neither by these databases nor by existing prediction methods . Methods that predict unstructured regions from sequence are mushrooming . Fast methods identify regions with high net charge and low hydrophobicity [14 , 15] , monitor the differences in amino acid propensities between unstructured and other regions ( GlobPlot ) [16] , or identify motifs associated with regions depleted of regular structure [17 , 18] . Most methods are based on a different definition of disordered region that has been introduced by the Dunker group [19]: residues for which X-ray structures do not have coordinates are considered as disordered . Methods based on this concept used neural networks [19–23] or support vector machines [24] . The meetings for the Critical Assessment of Structure Prediction ( CASP ) have exclusively assessed disorder predictions on subsets of the “noncoordinate” data [25 , 26] . The major drawback of this approach is that the Protein Data Bank ( PDB ) is biased toward proteins for which structures can be determined; natively unstructured proteins are underrepresented in the PDB [5 , 10 , 24 , 27] . This may be one reason why most prediction methods tested by Oldfield et al . [11 , 12] missed a substantial number of the proteins with unstructured regions identified in a large-scale NMR study spinoff from structural genomics . Other sequence features are predictive of disorder . For example , functionally flexible regions are identified from known structures through molecular dynamics simulation and can be generalized through machine learning . The Wiggle method provides predictions that overlap with unstructured regions even though it is focused on a different aspect of protein flexibility [28] . Our group identified long regions with no regular secondary structure ( NORS ) , which are stretches of 70 or more sequence-consecutive surface residues with few or no predicted helices and strands [27] . NORS regions showed considerable overlap with proteins predicted to have long unstructured regions by various disorder predictors . NORS regions are overrepresented in eukaryotes ( over five times more than in prokaryotes ) , overrepresented in regulatory and interacting proteins [27 , 29] , and share biophysical properties with unstructured regions . In addition , when natively unstructured regions are cocrystallized with their binding partner , they are still enriched in nonregular structure compared with globular proteins; ∼45% and ∼31% of the residues are in coils , respectively [4] . Somewhat surprisingly , the method for predicting regular secondary structure in NORS regions , PROFsec ( a profile-based neural network secondary structure predictor ) [30–32] , accurately predicts the secondary structure state in unstructured regions [4] . NORS regions capture only one particular aspect of unstructured regions ( Figure 1 ) . The major advantages of our focus on NORS regions are that this definition implies a simple structural interpretation , and that we can reliably identify thousands of such regions by scanning entire organisms . The thresholds for the minimal length ( 70 residues ) and for the definition of “largely loop” were optimized in order to minimize the identification of any of these stretches in the PDB [27] . This procedure does not explicitly use any information about a protein other than its prediction of secondary structure and solvent accessibility . Thus , it mainly identifies extreme cases ( e . g . , highly exposed and long loop regions ) . Since many unstructured regions are shorter , one of our objectives was to capture much shorter NORS-like regions while ascertaining that we would not confuse long , well-structured loops with unstructured regions . One disadvantage of our focus on NORS was that some unstructured regions contain secondary structure elements ( helix or strand ) [4]; i . e . , not all unstructured regions are captured by NORS ( Figure 1 ) . One goal of structural genomics is the determination of a 3-D structure representative for every protein family [33 , 34] . Unstructured regions have not impeded structural genomics so far because almost all consortia have focused on bacterial proteins in order to increase the structure-to-clone ratio . However , consortia that focus on eukaryotes , such as the Northeast Structural Genomics ( NESG ) Consortium , or the Center for Eukaryotic Structural Genomics ( CESG ) have to carefully exclude such problematic targets [35 , 36] . More than 10 , 000 proteins have been cloned and more than 3 , 000 proteins have been purified by NESG . Many of these did not adopt regular structure , possibly because they have unstructured regions that were not filtered out by our original filter , which discarded targets containing NORS regions [29] . To speed up structure determination we need to increase the sensitivity in identifying unstructured regions [11] ( i . e . , one goal of the development was to end up with a method that would be complementary to existing methods for the identification of unstructured regions ) . Our first hypothesis was that NORS regions share commonalities that distinguish such long unstructured loops from well-structured loops . If so , we should be able to somehow distinguish between the two types of loops at least in the sense that all loops predicted to be unstructured by our method ought to have different average features from other loops . We assumed that the neural network would pick up local correlations in amino-acid preferences for the different structural states . Our second hypothesis was that what distinguishes NORS regions from regular loops is exactly what makes regions become unstructured . If so , our method for the identification of NORS regions would also accurately predict unstructured regions . Here , we describe NORSnet , a new method that extends our NORS concept to also detect shorter ( 30–70 residues ) NORS-like regions . The method was developed without ever using proteins with experimentally known unstructured regions . Instead , it was optimized to distinguish predicted NORS from all other regions . This unique approach , unprecedented in any machine learning method competing in a real-life application with other methods , has three important advantages . First , the data used for development and testing do not overlap . Since NORS regions were predicted from sequence , we can identify thousands of such regions . Our dataset was “dirty” in the sense that it contained many false negatives ( all residues in PDB were considered to be well-structured during training ) as well as some false positives ( incorrect NORS predictions ) . This was the second major advantage: the positives ( unstructured regions ) sampled entirely sequenced organisms without any major bias with respect to this particular flavor of unstructured regions . Thereby , we identified unstructured regions that were missed by methods trained on more specialized datasets . The third advantage was that the resulting method explicitly focused on one feature of unstructured regions with a structural interpretation , namely that they are loops . Although we could have assessed NORSnet on any existing dataset due to the lack of overlap , we added a new set with experimental data about unstructured regions different from existing data . Note that both sets differed from each other as well as from the set used for development . Our three major results confirmed our hypothesis: ( 1 ) training on predictions succeeded in developing a powerful prediction method; ( 2 ) long loops are a major component of what is picked up by existing methods predicting unstructured regions; and ( 3 ) well-ordered and unstructured loops differ . In conjunction with existing methods , the one that we introduce here will allow the focus on particular structural aspects .
We trained our system on NORS regions that had been predicted by our previous high-accuracy/low-coverage method [27 , 29] for the identification of very long regions depleted of predicted helices and strands ( NORSp; see Methods ) . Technically , the task was to separate between all residues predicted to be in a NORS region and all residues in the PDB . As we used neural networks for this task , the typical assessment of accuracy usually involves a cross-validation experiment . For the first time in our work , we did not do this . In fact , we completely ignored the performance of the network on the task it optimized . Our hypothesis simply was that the only aspect that consistently separates extreme NORS regions from all residues in the PDB are the building blocks for a particular type of unstructured regions , namely the NORS-like loopy ones . Therefore , we measured performance on rather different datasets and separation tasks . First , we established success by predicting well-structured loops and NORS-like loops for DisProt , which consists of proteins with experimentally characterized unstructured regions . A total of 88% of the residues predicted by NORSnet were also predicted to be loops by PROFsec , while only 51% of the residues predicted as loops in DisProt also appeared NORS-like . In other words , most regions identified by NORSnet appeared to be in loops . Conversely , many loops were not predicted by NORSnet . Since residues in loops were identified through prediction , this difference may have been caused by prediction mistakes . To rule this out , we collected a set of 45 sequence-unique proteins that had been added to the PDB after we had completed developing our method ( September 2005 to June 2006 ) . We found that NORSnet classified only 1% of loop residues ( Dictionary of Secondary Structure of Proteins states T , S , L ) [37] as natively unstructured regions . In other words , NORSnet largely succeeded for these new proteins . In fact , it predicted only one region in these structures to be unstructured , namely a stretch in the HIV type 1 P6 protein of 52 residues [38] , the NMR structure of which indicated depletion of regular secondary structure . This protein has been shown to undergo conformational changes [38] , suggesting that our method correctly identified it as unstructured . Very long NORS regions differ statistically from regularly structured or well-ordered loops [27] . In general , unstructured regions that are not NORS-like tend to be more loopy than well-structured regions [4] . Here , we showed that our ability to distinguish between well-ordered and unstructured loops was also successful for much shorter loops . Medium-length ( 30–70 residues ) unstructured loops differed from well-structured loops ( Figure 2 ) . NORSnet precisely distinguished between unstructured and well-structured loops . Although the amino acid composition of unstructured loops was similar to that in long disordered regions [39] , it was unique ( Figure 2 ) . For instance , the regions identified by our method contained significantly more cysteines than other PDB proteins and , within these , more than the set of residues unresolved in electron density maps . Thus , methods trained on unresolved residues , such as DISOPRED2 , are likely to miss these regions . Furthermore , methods using pairwise energy potentials , such as IUPred , to predict unstructured regions are also likely to miss these regions , as many cysteines typically coincide with many paired cysteine bonds that significantly contribute to protein stability [40 , 41] . About 30%–60% of all eukaryotic proteins have been estimated to contain unstructured regions [24 , 42] . However , DisProt [10] , the largest resource of experimentally verified unstructured regions , contains only a few hundred eukaryotic proteins , and thus covers a small fraction of sequence space ( Figure 1 ) . Moreover , this small fraction is not representative , as many unstructured regions described experimentally are missing from existing databases and are not identified by prediction methods [11] . NORSnet attempted to solve both problems by sampling sequence space exhaustively ( trained on all positives from entirely sequence organisms ) and focusing on unstructured loops . To assess the accuracy of NORSnet and to estimate to what extent unstructured loops dominate our current identification of unstructured regions , we investigated two different datasets . The first was built around the DisProt database used previously in the literature; the second originated from careful NMR measurements and has not been used in many previous analyses . The structural plasticity of proteins with unstructured regions may enable its binding to many proteins , i . e . , may typify a protein–protein interaction hub ( a protein with many binding partners in an interaction network ) [6 , 56–59] . Several detailed studies have specifically identified unstructured regions in hub proteins that are involved in signaling [3 , 5 , 6 , 60–62] . Natively unstructured regions are also predicted to be abundant in other regulatory processes ( e . g . , alternative splicing [63] and transcription [64] ) and in cancer-associated signaling proteins [65] . We addressed this point by correlating sustained large-scale datasets of physical protein–protein interactions ( see Methods ) with predictions for unstructured regions . We applied NORSnet , DISOPRED2 , and IUPred to all proteins in the worm ( Caenorhabditis elegans ) proteome and considered only predictions at thresholds corresponding to 100% accuracy . The subset of interacting proteins resulted from the high-throughput experiment by Vidal et al . [66] and from IntAct [67] . Predictions for unstructured regions for all three methods correlated with the average number of interacting partners; in other words , proteins with more unstructured regions had more binding partners ( Figure 7 ) . Since we used two different datasets to determine the thresholds for what constituted reliable predictions ( DisProt and NESG ) , we also obtained two different thresholds for each method . For the purpose of fishing for hubs in protein–protein networks , we counted the number of proteins with unstructured regions according to any of those thresholds . Using DisProt to tune thresholds , DISOPRED2 predicted more proteins with unstructured regions than did NORSnet ( 1279 ± 88 versus 899 ± 76 ) ; using the NESG dataset , NORSnet predicted many times more proteins with unstructured regions than did DISOPRED2 ( 1282 ± 87 versus 321 ± 46; Figure 7 ) . These results agreed with recent studies that estimated hub proteins to be enriched in unstructured regions [57–59] . However , could NORSnet identify any new unstructured regions in hub proteins ? We chose the cutoff that yielded the highest number of unstructured regions ( NORSnet , 1 , 279; DISOPRED2 , 1 , 282 ) for each method and checked whether the two methods predicted unstructured regions in the same hub proteins . Both methods predicted unstructured regions in most ( 74 ) of the proteins observed with more than ten partners ( 140 ) . DISOPRED2 predicted unstructured regions in another 13 of the promiscuous proteins , and NORSnet in another 21 proteins . If the reliable predictions of both methods are correct , 77% of all promiscuous proteins in the worm ( 74 + 13 + 21 = 108 of 140 ) have unstructured regions . While these data do not suffice to identify hubs from sequence , we undoubtedly showed that methods such as NORSnet and DISOPRED2 clearly have some capability in the identification of unstructured regions that will adopt 3-D structures upon binding . While this finding was not new , our particular perspective was that the differences between DISOPRED2 and NORSnet resulted from the difference in the focus of the two . NORSnet focuses more on loopy regions than DISOPRED2 , and it also identified more hub proteins . Similar results were obtained when we compared NORSnet and IUPred predictions on the same dataset . Again , IUPred identified the hub signal but much less clearly than did NORSnet ( Figure S3 ) . All these observations suggested that the aspect of unstructured regions most relevant to hubs might actually be the unstructured loops . While NORSnet has some ability to identify unstructured regions that are often involved in binding ( Figure 6 ) , it may miss many of these regions due to their enrichment in regular secondary structure ( helix , strand ) in their bound form . We may therefore wonder why NORSnet identified so many worm hub proteins to have unstructured regions in the first place . Interestingly , many of the hubs had several modules/domains , some of which were predicted not to contain unstructured regions . Some of these modules were DNA-binding domains ( such as Homeobox domains ) or protein–protein interaction binding motifs ( such as EGF repeats ) . The majority of the unstructured regions predicted by NORSnet in these hubs bridged connections between well-structured domains: these bridges were often on the surface ( unpublished data ) . At first glance , the fact that these regions were predicted to be unstructured might seem biologically unimportant . However , there are several possible biological consequences of the abundance of hubs with unstructured loops . These exposed unstructured/loopy regions might serve as sites for proteolysis , allowing some parts of the protein to undergo proteolytic degradation under different cellular conditions . Such differential degradation could allow different modules of the same protein to be functional under different conditions . Alternatively , these long connecting loops might function as extremely flexible connecting linkers that facilitate the modules to adopt different orientations , thereby allowing the binding of different targets or binding similar targets in different fashion . Each of these alternatives could be at the heart of a different function . These two hypotheses may explain some of the regulatory characteristics of hub proteins . The intricate details of protein 3-D structures are crucial for their functional role; i . e . , structure determines function . Natively unstructured regions do not necessarily contradict this structure–function paradigm . Nevertheless , a variety of proteins require unstructured regions in order to function as domain linkers , filling material , and detergents . For other proteins with unstructured regions , changes in the environment ( e . g . , pH change , presence of target ) or posttranslational modifications can trigger the formation of a regular 3-D structure that will then again determine function . In an evolutionary sense , the required changes/modifications constitute an integral part of the function and are therefore likely to be somehow encoded in the sequence of such proteins . The unusual aspect is that the key structural feature of these proteins is to keep regions natively unstructured or adaptable . The experimental and in silico identification and characterization of proteins with unstructured regions is evolving into an increasingly important challenge for structural biology . In facing this challenge , it becomes increasingly clear that the term “unstructured” describes a rather mixed bag of phenomena from regions that alter between different conformations to those that remain molten globule-like , and from regions that adopt regular helices and strands to those that remain intrinsically loopy . Here , we present NORSnet , a neural network–based method that revisited the task of identifying unstructured regions from a different angle than that taken by other methods . It focuses on the distinction between unstructured and well-structured loops . The success in this undertaking confirmed our initial hypothesis , namely that short unstructured loops resemble very long unstructured loops ( NORS regions ) . Our application of machine learning was rather unconventional in two ways . First , we trained on positives ( predicted NORS ) that did not contain the feature we sought to predict ( short unstructured loops ) and on negatives ( all regions in the PDB ) that contained regions that we wanted the method to predict as positives; i . e . , we implicitly hoped that our development would fail for many cases . Second , we did not optimize any parameters on the dataset used for assessing the performance of our method . Due to the difference in our approach , NORSnet complemented existing methods that optimize on previous datasets of unstructured regions . Consequently , NORSnet will enable the application of additional filters for structural genomics . Last , through a comparison between our new and other prediction methods , we confirmed the importance of unstructured regions for protein–protein interactions . Moreover , we specifically touched on the importance of unstructured loops for network complexity .
We created our dataset of residues in natively unstructured regions ( “positives” ) in the following way . We grouped all proteins from 62 entirely sequence proteomes into domain-like families using CHOP and CLUP [35 , 71 , 72] . We identified proteins with long NORS regions by the application of NORSp; i . e . , all residues that are located in a stretch of >70 consecutive residues with <12% predicted helix or strand [27 , 29] by PROFsec [30–32] and have at least one contiguous segment longer than ten residues predicted to be on the protein surface [73] . The hope was that all residues in this pool have commonalities that we could extract through machine learning , and that will also be shared by proteins with unstructured regions much shorter than 70 residues . Due to the fact that PROFsec is especially accurate for natively unstructured regions [4] , the noise in these data that originated from the prediction mistakes was likely very low . To distinguish between proteins with and without unstructured regions , we needed a set of “negatives” ( i . e . , residues that are well-structured ) . For this , we chose a sequence-unique subset of globular protein structures from the PDB . Technically , this sequence-unique subset was taken from the EVA server [74 , 75] . Specifically , the sequence redundancy was removed above HSSP ( a measure for sequence-proximity ) similarity values of 0 [76 , 77] ( corresponding to <22% pairwise sequence identity for long alignments ) . Any pair of sequences between training and testing sets that could be aligned at PSI–BLAST [78] E-values of <10−3 according to our standard procedure of three automated iterations [79] was also removed . To further amplify the signal from well-structured regions in the negative set , we also excluded all loops longer than 30 residues . Our datasets were not fully clean in the sense that our negative set of well-structured PDB proteins certainly contained some residues that did not appear in the X-ray structure ( which were implicitly treated as well-structured ) , and that the positive set ( predicted NORS ) might contain some regular , ordered regions . However , due to the immense size of both datasets and to our use of neural networks , we did not worry about such outliers . In fact , our particular generation of a prediction-based training set that is more than ten times larger , and certainly more representative , than sets used previously might be the most important difference to all previous methods . In the context of a different problem , we showed how beneficial the use of prediction-based sets with errors might be [80] . To optimize the parameters of the method , we trained the network on 90% of the sequences and tested it on the remaining 10% . Note that these data were only used for the development of the method . We never reported the performance of the method on these data . The datasets on which we did assess NORSnet had no overlap ( HSSP-value <0; i . e . , <22% pairwise sequence identity for 250 aligned residues ) with any of the proteins used for development . In particular , NORSnet was not optimized in any way on DisProt and the NESG dataset , as these were solely used to assess its performance . After optimizing our method to predict NORS regions ( as described below in the prediction method section ) , we assessed NORSnet performance on different sets without any further optimization . In the first benchmark , we used DisProt proteins that have unstructured regions longer than 30 residues as positives and a sequence-unique subset of 173 PDB X-ray structures as negatives . The latter subset was taken from the EVA server [74 , 75] , and did not include sequences that were in the original training set . One particular advantage of testing our method on DisProt was that we did not have to run any additional cross-validation experiment since we used different proteins; respectively , the same proteins with different labels ( all residues from PDB in DisProt were explicitly treated as “well-structured” by our training procedure ) . To further validate the method , we tested it on a set of proteins from the NESG consortium . The positive set included 30 proteins that were identified to have unstructured regions ( “NESG unfolded” ) , and the negative set included 170 recently determined protein structures . Both sets were identified as such by the NESG consortium . The definition of “unstructured region” was as follows: ( 1 ) HSQC ( heteronuclear single quantum correlation ) was high signal to noise and very low dispersion; and ( 2 ) hetNOE ( heteronuclear Overhauser effect ) data was clean negative ( G . T . Montelione , personal communication ) . Using this set contributed to the removal of two types of biases in DisProt and similar databases . ( 1 ) Structure determination method: the negative set was almost equally divided between X-ray and NMR structures . ( 2 ) Length bias: while usually sequences selected for NMR structure determination are shorter than for X-ray determination , the NESG consortium reduced this artifact by using both methods in parallel to determine the structures of the same sequences . Thus , the length distribution of the NESG unfolded set is similar to the one of the folded set , in contrast to DisProt database , which consists of some much longer sequences ( see Table S1 ) . For the large-scale predictions of proteins that are involved in protein–protein interactions , we used the IntAct database ( http://www . ebi . ac . uk/intact ) . IntAct includes both large- and small-scale experiments for different organisms [67] . Specifically , we used proteins from interactions that were detected in a large-scale yeast two-hybrid screen of C . elegans ( worm ) proteins [66] . The set included 2 , 622 proteins that participate in 4 , 039 interactions . We used a standard feed-forward neural network described elsewhere in more detail [30 , 32 , 73 , 81] The crucial novelty for the given task was the choice of input information . This choice was largely influenced by what we found to succeed in different contexts , namely for the prediction of normalized B-values [82] and protein–protein interfaces [83] . Local input information was taken from a sliding window of 13 sequence-consecutive residues ( the prediction was for the central residue in that window ) . For each residue , we used the evolutionary profile ( from PSI-BLAST alignments according to our standard protocol [79] ) , the three-state secondary structure predicted by PROFsec [30–32] , the two-state solvent accessibility state predicted by PROFacc ( a profile-based neural network predictor of solvent accessibility ) [73] , and the two-state flexibility prediction by PROFbval [82 , 84] . Global input information was represented by the global amino acid composition ( 20 units ) , the composition in predicted secondary structure ( three units ) , and solvent accessibility ( two units ) , as well the length of the protein/domain-like fragment ( three units as in [82] ) , and the mean hydrophobicity divided by the net charge as was first suggested by Uversky et al . [14] . We downloaded the DISOPRED2 package from http://bioinf . cs . ucl . ac . uk/disopred and installed it locally . The package included DISOPRED2 V0 . 2 and PSIPRED Version 2 . 45 ( from November 2003 ) . To assess its performance on our datasets , we ran the program using the default parameters . The prediction for casein precursor in Figure 5A was taken from the DISOPRED2 server . We ran FoldIndex using the server at http://bip . weizmann . ac . il/fldbin/findex ( in September 2006 ) with default parameters . We ran IUPred using the server at http://iupred . enzim . hu/index . html ( in December 2005 and January 2006 ) with default parameters .
The Protein Data Bank ( http://www . rcsb . org/pdb ) accession numbers for the structures discussed in this paper are HIV type 1 P6 protein ( 2c55_A ) , Methanobacterium thermoautotrophicum 1615 ( 1eij ) , the conserved domain common to the transcription factors TFIIS , elongin A , and CRSP70 ( 1eo0 ) , and DFF40 ( 1ibx ) . The DisProt ( http://www . disprot . org ) accession number for bovine Kappa-casein precursor is DP00192 . | The details of protein structures are important for function . Regions that do not adopt any regular structure in isolation ( natively unstructured or disordered regions ) initially appeared as a curious exception to this structure–function paradigm . It has become increasingly clear that unstructured regions are fundamental to many roles and that they are particularly important for multicellular organisms . Structural biology is just beginning to apprehend the stunning diversity of these roles . Here , we focused on unstructured regions dominated by a particular type of loop , namely the natively unstructured one . We developed a method that succeeded in the distinction between well-structured and natively unstructured loops . For the development , we did not use any experimental data for unstructured regions; when tested on experimental data , the method performed surprisingly well . Due to its different premises , the method captured very different aspects of unstructured regions than other methods that we tested . We applied the new method to two different problems . The first was the identification of proteins that may be difficult targets for structure determination . The second was the identification of worm proteins that have many interaction partners ( more than seven ) and unstructured regions . Surprisingly , we found unstructured regions of the loopy type in more than 50% of all the promiscuous worm proteins . | [
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] | 2007 | Natively Unstructured Loops Differ from Other Loops |
Many thousands of endoparasitic wasp species are known to inject polydnavirus ( PDV ) particles into their caterpillar host during oviposition , causing immune and developmental dysfunctions that benefit the wasp larva . PDVs associated with braconid and ichneumonid wasps , bracoviruses and ichnoviruses respectively , both deliver multiple circular dsDNA molecules to the caterpillar . These molecules contain virulence genes but lack core genes typically involved in particle production . This is not completely unexpected given that no PDV replication takes place in the caterpillar . Particle production is confined to the wasp ovary where viral DNAs are generated from proviral copies maintained within the wasp genome . We recently showed that the genes involved in bracovirus particle production reside within the wasp genome and are related to nudiviruses . In the present work we characterized genes involved in ichnovirus particle production by analyzing the components of purified Hyposoter didymator Ichnovirus particles by LC-MS/MS and studying their organization in the wasp genome . Their products are conserved among ichnovirus-associated wasps and constitute a specific set of proteins in the virosphere . Strikingly , these genes are clustered in specialized regions of the wasp genome which are amplified along with proviral DNA during virus particle replication , but are not packaged in the particles . Clearly our results show that ichnoviruses and bracoviruses particles originated from different viral entities , thus providing an example of convergent evolution where two groups of wasps have independently domesticated viruses to deliver genes into their hosts .
Polydnaviruses ( PDVs ) are unique viruses symbiotically associated with endoparasitic wasps belonging to the families Braconidae and Ichneumonidae . Virus particles produced in the ovaries [1] are injected into lepidopteran hosts during wasp oviposition . The PDV genomes packaged in the particles are composed of circular dsDNA molecules that harbor from 60 to 200 genes [2]–[5] . These genes are expressed in infected caterpillar tissues , and their products ensure successful parasitism by abolishing host immune responses and/or altering host larval development [6]–[9] . Viruses belonging to a given family typically share a set of conserved genes ( core genes ) involved in DNA replication , transcription of viral genes and particle morphogenesis . Strikingly , PDV genomes packaged in the particles lack such typical virus genes . This is not completely unexpected given that no PDV replication takes place in the caterpillar; rather , replication is confined to the wasp ovary where viral DNAs destined for packaging are generated from proviral copies maintained within the wasp genome [10] , [11] . Thus the genes involved in particle replication are not required within the particles . We recently identified genes encoding structural components of PDVs associated with braconid wasps ( Bracoviruses or BVs ) . These genes derive from an ancestral nudivirus ( nudiviruses are a sister group of baculoviruses ) , but instead of being packaged in BV virions they are transcribed from the wasp genome [12] , [13] . Overall the data support the hypothesis that a nudivirus integrated its own genome into that of the ancestor of bracovirus-associated wasps , which lived ∼100 million years ago , according to a recent estimation based on the age of fossils in amber [14] . Since their integration into the wasp genome , the original nudivirus genes that were not essential to the parasitoid host interaction appear to have been replaced , in the packaged BV genome , by genes contributing to the success of parasitism . The nudivirus-like genes are specifically expressed in the calyx region of the wasp ovaries , where BV virions are produced , and they have been maintained in the ancestor wasp lineage and selected for their contribution to the success of parasitism as gene transfer agents for ∼100 million years . PDVs associated with ichneumonid wasps ( Ichnoviruses or IVs ) share many features with BVs: they deliver genes in the parasitized host that are necessary for the successful development of the parasite , their packaged genome is composed of dsDNA circles and their virions are specifically produced in the calyx . However , IVs do not appear to derive from a nudivirus and their origin remains unclear . Indeed , the analysis of cDNAs from the wasp Hyposoter didymator did not lead to the identification of nudivirus genes expressed in the ovaries [12] . Moreover no set of genes having significant similarities with known viral genes could be identified . The recent discovery of a new lineage of insect viruses [15] , [16] , sharing few genes with other viruses , indicates that the extent of diversity of insect viruses is not completely known . This could explain our inability to identify core genes of viral origin in ichneumonid genomes . To overcome this problem , we searched for genes involved in IV particle production by analyzing the protein components of purified H . didymator ichnovirus ( HdIV ) particles and we studied the protein-coding DNA organization in the wasp genome . IV particles have an ovocylindrical shape and a large nucleocapsid ( 330×85 nm ) surrounded by two envelopes [17] , [18] . Whereas the inner envelope is acquired de novo in the nucleus of calyx cells , where the particles are produced , the outer envelope is acquired from the cell membrane , during particle exocytosis into the oviduct lumen [1] . As expected from this complex structure , purified particles have a protein profile comprising several dozens of components [19] , [20] . So far , only two structural proteins associated with the IV virions of the wasp Campoletis sonorensis ( CsIV ) have been characterized: p12 ( gi|4101554 ) and p44 ( p53 gene product , gi|4101552 ) [21] , [22] . They display no overall resemblance to known proteins , although some similarities based on secondary structure analyses between domains of p44 and of an ascovirus protein have recently been described [23] . Here we report the characterization of 40 genes involved in HdIV virus particle production . Strikingly they are densely clustered in specialized regions of the wasp genome that we named “Ichnovirus Structural Protein Encoding Regions ( IVSPERs ) ” . These genes are specifically expressed in the calyx of H . didymator and at least 19 of them encode components of virus particles , including homologues of the p12 and p53 genes originally identified in C . sonorensis . Furthermore , we showed that 11 homologues of HdIV IVSPER genes are expressed in the ovaries of the ichneumonid wasp Tranosema rostrale , indicating that the set of structural genes is conserved among wasps associated with IVs . Unexpectedly , IVSPERs are amplified during virus replication in H . didymator ovaries despite not being packaged in the particles . Altogether , IVSPER genomic structure , replication properties and involvement in particle production suggest they originated from a common set of genes , which could correspond to the genome of an ancestral virus .
In order to identify genes expressed during HdIV virus particle production , 5636 clones from H . didymator ovary cDNA libraries were sequenced , resulting in the identification of 1956 non redundant coding regions . No significant similarities were detected with genes of conventional viruses , but two genes similar to CsIV p12 ( named H . didymator p12-1 and p12-2 ) and two similar to CsIV p53 ( named p53-1 and p53-2 ) were identified . Quantitative RT-PCR ( qPCR ) analyses indicated that transcript levels of these four H . didymator genes were at least 25 times higher in the calyx , where HdIV particles are produced , than in the ovarioles of female pupae ( Table S1 ) . This strongly suggested that these genes encode structural particle components , as shown for the CsIV p12 and p53 proteins , but also that sequences of additional IV structural genes were likely present in the libraries . Several nudivirus-like genes involved in BV particle production are clustered in the wasp genome [13] . We hypothesized previously that this cluster corresponds to a remnant of the nudivirus genome acquired by the braconid ancestor wasp . To determine whether HdIV structural genes were similarly clustered , we isolated H . didymator genomic DNA clones containing the p12-2 , p53-1 and p53-2 genes by screening a wasp bacterial artificial chromosome ( BAC ) library using gene-specific probes ( BAC clones BQ , BR and BT; Figure 1 ) . Sequencing of these clones revealed that the p12-2 , p53-1 and p53-2 genes reside in genomic regions characterized by a high density of coding sequences ( exon density: 62 . 2% ) , making them atypical compared to the rest of the wasp genome ( exon density: 21% ) . There was a significant difference in the mean length of intergenic sequences between these atypical regions ( 638 bp ) and other portions of the wasp genome ( 1669 bp ) . Moreover the 40 genes in these regions consist of a single exon while a large majority of wasp genes are predicted to contain multiple exons . The atypical regions seemed to harbor virus structural genes since , in addition to p12-2 , p53-1 and p53-2 , they also contained p12-1 and another p12 homolog , designated p12-3 . We therefore named them “IchnoVirus Structural Proteins Encoding Regions” ( IVSPER , Figure 1 ) . Strikingly , two IVSPERs were located respectively 3 kb upstream and 4 kb downstream the chromosomal form of an HdIV genome segment that is packaged in the particles ( Figure 1 ) . These two HdIV sequences ( SH-BQ and SH-BR ) did not show significant similarity to each other when compared at the nucleotide level but both contained a member of the N-gene family that is conserved among IVs [5] . Interestingly , an N-gene was also present in each of the three IVSPERs ( N-1 , N-2 and N-3; Figure 1 ) . As H . didymator IVSPERs contain genes ( p12 and N ) that are related to coding sequences known to be present in packaged CsIV or HdIV DNA , we examined the possibility that IVSPERs may be part of the packaged genome as well . PCR experiments were thus conducted using specific primers and template consisting of either wasp genomic DNA or DNA extracted from purified HdIV particles . Using HdIV particle DNA , no amplification could be obtained with several primer pairs corresponding to sequences scattered along the IVSPERs , whereas amplification products could be obtained with primers specific for the SH-BQ viral segment ( Table S2 ) . This showed that the IVSPERs are not packaged in the particles but are expressed in calyx cells at the time of virus production . To confirm that the identified p12 and p53 genes encoded structural components of HdIV particles and to assess the possibility that IVSPERs contained other structural genes , proteins extracted from purified HdIV particles were analyzed by mass spectrometry ( LC MS/MS ) . After separation of HdIV proteins by SDS-PAGE , more than 70 bands were detected , ranging from 10 to 250 kDa ( Figure 2 ) . Among them , the 16 most intense bands were selected and trypsin digested to produce peptides . Strikingly , comparison of peptides identified by LC MS/MS with translated coding sequences showed that 19 IVSPER predicted gene products were components of virus particles ( Figure 2; Table S3 ) . They included the p53-2 and p12-1 proteins and the product of the N-2 gene . Products of p53-1 and of other p12 genes were not detected , but could be present in the less intense bands ( not analyzed by LC MS/MS ) as other IVSPER proteins . Altogether the results obtained indicate that at least half of the IVSPER genes ( 19/40; Figure 3 ) encode virion structural proteins and that the IVSPERs constitute clusters of HdIV structural genes . The 19 genes shown to encode components of the particles are expected to be transcribed in the tissue producing the particles , i . e . , the calyx . To verify this prediction and to determine whether the other 21 IVSPER genes might also be involved in the production of virus particles we analyzed the expression of these genes by qRT-PCR . All the 25 IVSPER genes examined were found to be specifically transcribed in calyx cells at levels at least 13 times higher than in the ovarioles ( Figure 3 ) . In accordance with these results , blast similarity searches against sequence database generated from H . didymator ovarian cDNA libraries , using IVSPER gene sequences as queries , identified 26 different IVSPER-derived cDNAs ( Table S1 ) , thus verifying our initial prediction that cDNAs from genes involved in IV viriogenesis were present in the libraries . Altogether these results suggest that all IVSPER genes are likely to be involved in virus particle production , either directly by encoding structural proteins or indirectly by promoting their production . The IVSPER gene products display no significant similarity to protein sequences deposited in public databases , and only one conserved domain has been identified in IVSPER proteins: a cyclin domain present in the U12 protein ( Tables S3 and S4; Text S1 ) . In addition , the U22 product shows weak similarity with a baculovirus P74 envelope protein ( gi|48843584| ) . The presence of the P74 domain was confirmed when conserved structural signatures in IVSPER products were searched for using HHPred ( Table S3; Text S1 ) . The P74 protein is an envelope protein involved in the entry of baculovirus virions into midgut cells and is conserved among nudiviruses and bracoviruses . However the presence of a single gene is not sufficient to draw conclusions as to the nature of the IV ancestor; rather , the H . didymator IVSPER gene products appear to constitute a set of proteins specific to IVs . In addition to the p12 , p53 and N-gene families found in the IVSPERs , we identified members of four new gene families , named IVSP1 to IVSP4 ( for “IchnoVirus Structural Protein”; Figure 1 ) . Altogether members of these seven gene families represent 40% ( 16/40 ) of the IVSPER genes , and proteins within a given family display >60% sequence similarity ( Table S5 ) . The observation that IVSPERs share a combination of related genes suggests they may have originated from a common ancestor having this set of genes . IVs are associated with species from the Campopleginae and Banchinae subfamilies of ichneumonid wasps . The different features of the virions and the fact that PDVs have not been recorded in species from several groups separating Campopleginae and Banchinae [24] , suggest that two distinct ancestral wasp-virus associations may have arisen during the diversification of ichneumonid wasps . In this context , if one assumes that the associations in Campopleginae have a common origin , the genes encoding structural proteins expressed in H . didymator are predicted to be conserved in wasps from this subfamily . We thus searched for IVSPER homologs by sequencing cDNAs ( 4992 clones ) generated from the ovaries of Tranosema rostrale ( Campopleginae ) , which carries the ichnovirus TrIV . As observed for H . didymator , no significant similarities were found with known virus genes , except with those described in IVs [4] . Strikingly , a similarity search allowed the identification of 11 genes expressed in T . rostrale ovaries whose products display significant similarity ( 60 to 93% similarity ) to those of H . didymator IVSPERs ( Table S1 ) : seven were homologs of genes shown to encode HdIV structural proteins ( U1 , U3 , IVSP4-1 and 2 , p12-1 , U23 , N-2 ) and four to other IVSPER genes ( N-1 , U10 , U16 , U19 ) . Interestingly these genes were not identified in the packaged genome of TrIV [4] , indicating that , like HdIV IVSPER genes , they reside in the wasp genome . These results strongly suggest that HdIV IVSPER genes are conserved among campoplegine wasps and point to a common origin of the set of IV structural genes . Unlike the cluster of nudivirus-like genes involved in BV particle production , two IVSPERs are located in the vicinity of the integrated form of a viral DNA sequence packaged in the particles ( Figure 1 ) . This linkage could have a role in the coordinated expression of genes involved in IV virion production . To assess whether IVSPER DNA could be amplified with the packaged DNA we studied the level of IVSPER DNA during particle production using qPCR . The levels of nine genes chosen in the three IVSPERs and of packaged DNA ( SH-BQ , Vinnexin gene ) were measured in calyx cells from wasps just after their emergence , when particle production is highest and in adult wasp ( 24h hours after emergence ) . As shown in Figure 4 , the results indicated that the nine IVSPER genes examined are amplified in calyx cells at a level comparable to that of the viral DNA packaged in the particles . It is noteworthy that the IVSPER-3 genes , which do not appear linked to a packaged DNA sequence , are also amplified . Relative to the levels measured in 2 h-old females , there was a coordinated drop in the amplification of both HdIV segment and IVSPER DNA in females one day after emergence ( Figure 4 ) , further confirming the existence of a direct correlation between the level of amplification of these two groups of genes in the calyx . Altogether these results indicate that IVSPERs have retained an important property of virus DNA: they are amplified during virus particle production . Another link between IVSPERs and packaged viral DNAs is the phylogenetic relationship between IVSPER-2 and CsIV viral segment SH-C ( Figure 5 ) . The comparison of nucleotide sequences revealed important similarities which encompass 7014 nt in H . didymator IVSPER-2 and 5328 nt in CsIV SH-C . They consist in a succession of comparable ( 65 to 77% identity ) and more divergent sequences ( less than 10% similarity ) . The highest similarities concern regions containing coding sequences , and 5 homologs of the HdIV structural genes ( including p12 gene ) are encoded , in CsIV , by a viral segment . This suggests that CsIV SH-C and H . didymator IVSPER-2 have a common ancestor sequence and that during evolution , the CsIV segment has retained the ability to be encapsidated whereas the HdIV segment has lost this ability and is now expressed in the calyx but not packaged .
Because PDV packaged genomes lack typical viral genes , their relationship to conventional viruses has been a subject of debate . We recently identified genes encoding structural components of PDVs associated with braconid wasps , based on their mRNA expression in the ovaries . Present in the wasp genome and expressed specifically in the calyx , these structural protein genes resemble protein-coding genes of nudiviruses , a sister group of baculoviruses [12] . These data strongly suggest that PDVs from braconid wasps originated from a nudivirus . The same approach performed using the ovaries of H . didymator did not lead to the identification of coding sequences showing significant similarity to the core genes of a known virus . To overcome this problem , we conducted mass spectrometry analyses of purified virion proteins to identify genes encoding HdIV particle components . We discovered that the proteins associated with HdIV particles are encoded by genes located in specialized regions of the wasp genome , the IVSPERs . A subset of 19 IVSPER gene products were identified as components of viral particles and the other IVSPER genes were shown to be highly expressed in the tissue where HdIV particles are produced , suggesting that IVSPER proteins contribute directly ( as structural proteins ) or indirectly to HdIV particle production . Thus , the IVSPERs clearly encode the protein machinery involved in HdIV viriogenesis . Consistent with this key role and the hypothesis that wasp-IV associations in this group have a common origin , IVSPER genes are conserved among IV-associated campoplegine wasps: in addition to the p12 and p53 genes first described in CsIV , 11 homologs of H . didymator IVSPER genes were found to be expressed in T . rostrale ovaries and four H . didymator IVSPER-2 genes have homologues in CsIV segment SH-C . Analysis of the gene content of IVSPERs points to a relationship between some of the genes they contain and those packaged in virus particles , a situation that differs from that described for BVs where the packaged genome does not contain genes that are similar to those involved in particle production . More specifically , we found that IVSPERs contain members of the N-gene family , also present on HdIV segments and previously described in the packaged DNA of CsIV [5] , Hyposoter fugitivus IV and TrIV [4] . The presence of related genes in IVSPER and packaged DNA , along with the absence of some CsIV genes including the p12 gene in the packaged HdIV genome may reflect the fact that different IV genomes are at different stages of their evolution . Except for the eight proteins encoded by the p53 , p12 and N-gene families , U12 , which contains a cyclin domain , and U22 , which displays a weak similarity with a baculovirus P74 protein , the other IVSPER gene products do not resemble any previously described protein . In particular , we did not find similarity with ascovirus sequences or structures , a finding that does not support the hypothesis that IVs have an ascovirus origin , as previously suggested [23] . However , the absence of conserved proteins among IV structural protein genes is not completely surprising since several sequencing programs focusing on viral genomes have led to similar findings . For example , the Mimivirus genome consists of 1262 putative open reading frames , among which only 10% exhibit significant similarity to proteins of known functions [25] . Similarly , in a comparison of the herpes virus infecting oysters and those infecting vertebrates , only the structure of the genome was found to be conserved [26] . Although they are not packaged in virus particles , IVSPERs are physically , functionally , and phylogenetically related to the packaged IV DNA and could thus be considered as an integral part of the IV genome . First , we have shown that IVSPERs are amplified in calyx cells during virus production at a level comparable to that measured for packaged segments . The genomic proximity and comparable amplification of IVSPERs and packaged segments strongly suggest they belong to common viral replication units , whereas the IVSPER-3 , not in the close vicinity of an HdIV segment and flanked by wasp genes , may constitute an independent unit . A second source of evidence for a close relationship between IVSPERs and packaged IV DNA is the synteny between IVSPER-2 and CsIV segment SH-C , suggesting a common origin of these DNA regions . A simple explanation could be that during evolution of the H . didymator lineage , IVSPER-2 ( but not the corresponding region of CsIV ) may have lost the ability to be packaged . Conceptually , IVSPERs could thus be considered as elements of the IV genome that no longer require encapsidation . Due to the exclusive vertical transmission of the IV chromosomally integrated genomes , structural protein genes are not required on the viral segments injected into the host , but their amplification may have been selected for to allow production of high levels of virion structural components in the calyx . This appears to differ from the situation described for braconid wasps where the high production of structural proteins is presumed to be effected by a nudiviral RNA polymerase expressed in the calyx [13] . In addition to their functional role in particle production , IVSPERs display other notable features , including ( i ) their high exon density relative to regions of the wasp genome containing cellular genes , and ( ii ) the simple structure of their genes ( made of a single exon ) , which is more typical of virus genes than of wasp genes , which more often consist of multiple exons . Strikingly , this organization resembles that of the “nudivirus cluster” in the genome of the wasp Cotesia congregata , which is thought to constitute a remnant of the ancestral nudivirus genome integrated into the genome of the ancestor of BV-associated wasps . This cluster contains 10 genes made of a single exon , is densely packed ( exon density: 50% ) and the products of five of its genes display similarities to conserved proteins of nudiviruses . The similar organization of IVSPERs suggests that they constitute , like the nudivirus cluster , remnants of foreign DNA integrated into the wasp genome . Altogether , IVSPER genomic structure , gene content , replication properties and involvement in particle production suggest they originated from a virus , belonging to an uncharacterized or extinct group . The nature of the ancestral virus genome could not be established using viral sequences currently available in public databases: sequences of the ancestor group are missing or IV sequences have diverged to such an extent that a relationship is undetectable . However it is interesting to note that IVSPERs contain a combination of related genes that are members of seven families . We hypothesize ( Figure 6 ) that the IV ancestor possessed a member of each gene family . After duplications , different copies of this ancestral genome may have diversified , leading to the current IVSPERs , containing both common and specific genes that cooperate to produce HdIV particles . Clearly IVs associated to campoplegine wasps originate from an entity that differs from that of the nudiviral BV ancestor , demonstrating that the association between wasps and viruses arose at least twice during the evolution of parasitic wasps . The use of PDVs by two groups of wasps to deliver genes into the host thus represents an example of convergent evolution . Recently a PDV from a banchine wasp has been described and was proposed to belong to a third group , based on its unusual features , in particular the morphology of the particles and the content of its packaged genome [3] . It will be of interest to determine whether this association constitutes a third event of viral capture by parasitoid wasps . Given that these associations between viruses and eukaryotic organisms have only been described for parasitic wasps , one may ask whether they are specific to these insects because of their unusual life-style , i . e . larvae living within the body of a caterpillar , or whether they occur more commonly . One might predict that virus domestication allowing gene transfer has arisen several times in the course of evolution in situations where interactions between organisms are both intimate and antagonistic .
Hyposoter didymator wasps were reared in laboratory and Tranosema rostrale wasps were obtained from the field as described [1] , [6] . The libraries were constructed as described [12] . Briefly , ovaries were dissected from H . didymator pupae of different developmental stages and total RNA was extracted using the Qiagen RNeasy Mini Kit . The cDNA synthesis was performed using the Creator SMART cDNA Library Construction Kit ( Clontech ) from 2 µg of total RNA . The cDNAs were cloned into the pDNR-LIB vector ( Clontech ) . A total of 5636 clones were sequenced from the 5′-end . The sequences cleaned from vector stretches were subjected to clustering using the TIGR software TGI Clustering tool ( TGICL ) , as described [27] . They corresponded to 597 clusters ( containing more than one sequence ) and 1359 singletons , and thus to 1956 non redundant sequences . To identify similarities with known proteins , the sequences were searched using the Blastx algorithm against a local non-redundant protein database ( NCBI , release july 15 , 2008 ) with no cut-off for the E-value . Ovaries were dissected from adult wasps shortly after emergence , and total RNA was extracted using the RNeasy Mini Kit ( Qiagen ) . 250 ng of RNA was treated with amplification-grade DNAse I ( Invitrogen ) [28] and reverse transcribed using an oligo dT primer , followed by a second strand synthesis and ligation of an adapter . Using a distal adapter primer and the oligo dT primer , the cDNAs were amplified and then ligated into the pGEM-T-Easy vector ( Promega ) . A total of 4992 colonies were selected and sequenced from both ends at the Genome Sciences Centre , BC Cancer Agency ( Vancouver , Canada ) . To obtain a H . didymator BAC library , high molecular weight DNA was extracted from larval nuclei and partially digested with HindIII . The fragments thus obtained were ligated into the pBeloBAC11 vector . High-density filters ( 18 , 432 clones spotted twice on nylon membranes ) were screened using specific 35-mer oligonucleotides . Positive BAC clones were analyzed by fingerprint . One genomic clone was selected for each probe and sequenced by a shotgun method . Coding sequences were predicted using Kaikogas ( http://kaikogaas . dna . affrc . go . jp/ ) . A Blastn similarity search against the ovary EST libraries was performed with no cut-off for the E-value . The sizes of the intergenic regions within the IVSPER and other available genomic regions ( over 1 . 40 Mb of wasp genome ) were compared using a Student T-test ( t = 4 . 552 , df = 49 . 238 , p-value = 3 . 497e-05 ) . Total RNA from H . didymator calyx and ovariole fractions was extracted using the Qiagen RNeasy Mini Kit and treated with the Turbo DNAse kit ( Ambion ) . First strand cDNA was synthesized from 3 to 5 µg of RNA using the Invitrogen Superscript III Reverse Transcriptase . Absence of DNA contamination and first-strand cDNA synthesis were verified by PCR with primers specific to Elongation Factor EF1-α ( Table S6 ) . The qPCR was performed using the Applied Biosystem 7000 sequence detection system in 96-wells PCR plates ( ABgene ) that comprised triplicates of 2 or 3 biological replicates . Primer pairs ( Table S6 ) were designed using the Primer ExpressTM software ( Applied Biosystems ) to generate 51 bp amplicons . The final qPCR reaction volume of 25 µl contained an amount of cDNA equivalent to 20 ng of total RNA , 0 . 4 µM of primer pairs , and the Platinum SYBR Green qPCR SuperMix-UDG with ROX ( Invitrogen ) . The dissociation curve method was applied to ensure the presence of a single specific PCR product . The data were analyzed either with the classical CT method or with an alternative assumption-free method [29] . The latter gives the relative N0 values corresponding to the initial transcript levels of each gene in a given tissue . Four endogenous reference genes ( EF1-α , ribosomal L55 , cytochrome VIIC and histone H1 ) were used for normalization . A first purification was performed by filtration from 300 dissected ovaries as described [30] , and the viral particles were further purified on a sucrose gradient ( 20–50% ) . Centrifugation was performed at 154 , 324 g during 1 . 5 h at 4°C in a Beckman L7 ultracentrifuge , using a SW-41 swing-out rotor . Viral fractions were collected , diluted in saline buffer ( PBS ) and submitted to a second centrifugation ( 154 , 324 g during 1 h at 4°C ) in order to pellet the viral particles . The resulting pellet was re-suspended in PBS and submitted to dialysis during two days at 4°C . The presence of viral particles was verified by TEM followed by SDS-PAGE . Gel electrophoresis was carried out as described [31] on a 12% acrylamide gel . After gel staining with colloidal blue ( Fermentas ) , gel slices were cut out , washed with 50% acetonitrile , 50 mM NH4HCO3 and incubated overnight at 25°C ( with shaking ) with 15 ng/µl trypsin ( Gold ) in 100 mM NH4HCO3 . The tryptic fragments were extracted with 1 . 4% ( v/v ) formic acid . Samples were analyzed online using a nanoESI LTQ-OrbitrapXL mass spectrometer ( Thermo Fisher Scientific ) coupled with an Ultimate 3000 HPLC ( Dionex ) . Details are given in Text S1 . Data were acquired using Xcalibur software ( v 2 . 0 . 7 , Thermo Fisher Scientific ) . Identification of proteins was performed using the Mascot v 2 . 2 algorithm ( Matrix Science Inc . ) , by searching against the entries of H . didymator sequences . The data submission was performed using ProteomeDiscoverer v 1 . 0 ( Thermo Fisher Scientific ) . Peptides with scores greater than the identity score ( p<0 . 05 ) were considered as significant . All spectra were manually validated for proteins identified with less than three different peptides . Presence of selected genes in the HdIV packaged genome was verified by PCR using gene-specific primers ( Table S6 ) . Templates consisted of either 20 ng of viral DNA or 100 ng genomic H . didymator DNA . HdIV DNA was extracted from viral particles purified on a sucrose gradient ( see above ) . Genomic wasp DNA was extracted with the Promega Wizard Genomic DNA Purification System . The 50 µl reactions were conducted using the GoTaq Flexi DNA Polymerase ( Promega ) following standard PCR protocol . | The polydnaviruses ( PDVs ) are a unique virus type used by an organism ( a parasitic wasp ) to manipulate the physiology of another organism ( a lepidopteran host ) in order to ensure successful parasitism . The evolutionary origin of these unusual viruses , found in ∼17 , 500 braconid wasps ( Bracoviruses ) and ∼15 , 000 ichneumonid wasps ( Ichnoviruses ) , has been a major question for the last decade . We thus undertook an exclusive work aiming at investigating this origin via the characterization of genes encoding structural components for both types of PDVs . The present paper constitutes the first report on the identity and genome organisation of the viral machinery producing Ichnovirus virions . Our results strongly suggest that Ichnoviruses originated from a virus belonging to a group as yet uncharacterized that integrated its genome into that of an ichneumonid wasp ancestor . More importantly , our results demonstrate that the ancestor of Ichnoviruses differs from that of Bracoviruses , which originated from a nudivirus . We have now identified , for the two types of PDVs , the non packaged viral genes and their products involved in producing particles injected into the host during oviposition . Together , these data provide an example of convergent evolution where different groups of wasps have independently domesticated viruses to deliver genes into their hosts . | [
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] | 2010 | Analysis of Virion Structural Components Reveals Vestiges of the Ancestral Ichnovirus Genome |
The free-energy principle has recently been proposed as a unified Bayesian account of perception , learning and action . Despite the inextricable link between emotion and cognition , emotion has not yet been formulated under this framework . A core concept that permeates many perspectives on emotion is valence , which broadly refers to the positive and negative character of emotion or some of its aspects . In the present paper , we propose a definition of emotional valence in terms of the negative rate of change of free-energy over time . If the second time-derivative of free-energy is taken into account , the dynamics of basic forms of emotion such as happiness , unhappiness , hope , fear , disappointment and relief can be explained . In this formulation , an important function of emotional valence turns out to regulate the learning rate of the causes of sensory inputs . When sensations increasingly violate the agent's expectations , valence is negative and increases the learning rate . Conversely , when sensations increasingly fulfil the agent's expectations , valence is positive and decreases the learning rate . This dynamic interaction between emotional valence and learning rate highlights the crucial role played by emotions in biological agents' adaptation to unexpected changes in their world .
Free-energy is an information theoretic quantity that upper bounds the surprise on sampling some data , given a generative model of how data were caused . The free-energy principle assumes that biological agents encode a probabilistic model of the causes of their sensations , and postulates that any adaptive agent that resists a tendency to disorder must minimize its free-energy [1] , [2] . Under simplifying ( Gaussian ) assumptions , free-energy minimization can be understood as the minimization of the prediction error between the actual and predicted sensory inputs . In order to comply with the free-energy principle , adaptive agents have two tactics at their disposal: ( 1 ) adjusting their internal states to generate more accurate predictions and ( 2 ) acting on the environment to sample sensations that fulfil their predictions . The principle is based upon the realization that perceptual inference and learning [3] , [4] and active inference [5] , [6] rest on the same Bayesian scheme . Perceptual inference refers to inferring the states of the world causing sensory inputs . Perceptual learning relates to learning the relationship between inputs and causes . Active inference corresponds to acting on the world to fulfil prior expectations about sensory inputs . The computational implementation of the free-energy principle has been shown to replicate in many aspects neural mechanisms and the cortical organization of the brain [4] , [7] . Crucially , when inferring and learning the causes of their sensations in a changing world , adaptive agents need to deal with different forms of uncertainty , namely estimation uncertainty [8] , volatility [9] , [10] and unexpected uncertainty [11] , [12] . Estimation uncertainty refers to the known estimation variance of states of the world causing sensory inputs and can be reduced through learning . Volatility refers to slow and continuous changes in states of the world , and has usually been modelled by making estimation uncertainty follow some latent stochastic process [13] . Finally , unexpected uncertainty arises from surprising sensory inputs caused by discrete and fast changes in states of the world , and calls for forgetting the past and restarting learning from new sensory data . Dealing with different forms of uncertainty is fundamental to Bayesian models of learning in a non-stationary environment [14] . In fact , a critical challenge faced by many recent Bayesian schemes of human learning is how to dynamically update beliefs about states of the world in order to optimize predictions in a changing environment [9]–[12] , [15]–[18] . Despite the major role attributed to emotions in influencing the content and the strength of the agent's beliefs and the resistance of these beliefs to modification [19] , emotion has not been considered in - much less integrated into - these computational models . The concept of emotional valence , or simply valence , has been used among emotion researchers to refer to the positive and negative character of emotion or some of its aspects , including elicitors ( events , objects ) , subjective experiences ( feeling , affect ) and expressive behaviours ( facial , bodily , verbal ) [20] . The valence of feelings has been argued to be a pivotal criterion for demarcating emotion [21] and a core dimension of the subjective experience of moods and emotions [22] . Traditionally , mood has been defined , in contrast to emotion , as an affective state that lacks a clear referent , changes slowly and lasts for an extended period of time [23] . In the present paper , we propose a mathematical definition of emotional valence in terms of the negative rate of change of free-energy over time . As we shall see later , this formalism entails the dynamic attribution of emotional valence to every state of the world that an adaptive agent might visit and prescribes the dynamics of basic forms of emotion such as happiness , unhappiness , hope , fear , disappointment and relief . We will first introduce the free-energy principle and present our computational model of emotional valence . We then demonstrate this scheme by simulating and comparing two artificial agents . One agent explicitly optimises posterior beliefs about volatility and does not use its internally generated emotional valence signal . The other does not estimate volatility but instead implements the emotional regulation of estimation uncertainty . The contribution of this work is two-fold . First , we provide a phenomenological account of emotion in terms of changes in free energy - as a proxy for changes in the quality of how the world is being modelled during inference and learning . Second , emotion is coupled back to inference using a form of regularization or meta-learning . In other words , changes in the quality of inference are used to regularize the rate of evidence accumulation to provide adaptive learning rates . These learning rates correspond to expected uncertainty about inferences , under hierarchical models of the world .
In statistical physics , variational free-energy minimization is a method for approximating a complex probability density by a simpler ensemble density that is parametrized by adjustable parameters [24] . In neuroscience , the free-energy principle assumes that biological agents encode the parameters of an arbitrary recognition ( ensemble ) density of environmental quantities that are the presumed causes of their sensations [2] . The recognition density is an approximation to the true posterior density of , given the sampling of some sensory data and the generative model entailed by the agent . The environmental quantities may be any forces or fields that act upon the agent , such as heat or light-stimulating sensory receptors . In more complex agents , may also refer to very abstract quantities such as ‘social rank’ or ‘moral norms’ . The learning of the environmental quantities and inferences about their states rest on empirical Bayes and hierarchical generative models [3] , [4] . In this framework , perceptual learning corresponds to estimating the parameters of the recognition density after many sensations , whereas perceptual inference corresponds to inferring the state of after a single sensation . In a hypothetical environment , learning could refer to the estimation of the categories associated with sensations while inference would be the classification of a particular sensation into one of these categories . In what follows , we shall see how free-energy minimization can account in a unified way for perception , learning and action . The divergence from the recognition density to the true posterior density is measured by the Kullback-Leibler ( KL ) divergence , which can be decomposed into two quantities known as free-energy and surprise: ( 1 ) The first term on the right side of the equation is the free-energy that may be efficiently treated by adjusting the parameters in order to minimize the divergence . The second term is surprise , which informs about the probability that some data has been generated under the assumptions of the model . In Bayesian model selection , the marginal likelihood is also known as the evidence for the model . Rearranging ( 1 ) , one obtains a formulation of free-energy in terms of divergence plus surprise: ( 2 ) The free-energy principle states that any adaptive agent that is at equilibrium with its environment must minimize its free-energy [1] , [2] . Minimizing free-energy with respect to reduces divergence , thereby making the recognition density an approximate posterior density . Notice that divergence depends on while surprise does not . Because the divergence is always non-negative ( Gibb's inequality ) , free-energy is said to be an upper bound on surprise . Crucially , biological agents can minimize free-energy not only by changing their beliefs but also by changing their sensations through acting on the environment . The dependency of sensation on action is expressed by . A new rearrangement of ( 1 ) shows more clearly what acting on the environment to minimize free-energy implies ( here , we replace the dependency of free-energy on sensation by expressing it directly as a function of ) : ( 3 ) where means the expectation under the density . In this second formulation , free-energy is expressed as complexity minus accuracy . Complexity is the divergence from the recognition density to the true prior density of the causes . Accuracy is the surprise about sensations that are expected under the recognition density; note that accuracy depends on action whereas complexity does not . This means biological agents will act to minimise free-energy through maximising accuracy . That is , biological agents will act in the environment to sample sensations that are expected by their recognition density . This perspective on behaviour contrasts with the traditional one in Pavlovian and instrumental conditioning , where behaviour is chiefly understood in terms of maximizing expected reward or pleasure ( or conversely minimizing expected loss or pain ) [25] , [26] . In active inference , behaviour is driven by an attempt to fulfil the sensory expectations of posterior beliefs ( recognition density ) . This prevents the dichotomization of the states of the world in terms of pairs of opposites , such as ‘reward-loss’ or ‘pleasure-pain’ , and implies that the notion of desired states is replaced with that of expected states . States with high probability under the recognition density ( low surprise ) are more frequently approached whereas states with low probability ( high surprise ) are avoided by the agent . Agents that expect to visit states that are noxious to their structure will compromise their chances of survival and transmitting their phenotype to future generations ( e . g . , a rabbit that expects to visit foxes ) . The adaptive fitness of a phenotype is thus the negative surprise averaged over all the states the agent visits [2] . In order to harmonize the principled assumption that any biological agent that is at equilibrium with its environment must minimize its free-energy [2] and the traditional notion that humans approach pleasure and avoid pain [27] , we related positive and negative valence to the decrease and increase of free-energy over time , respectively . In a continuous time domain , the rate of change of free-energy is the first time-derivative of free-energy at time . We thus formally define the valence of a state visited by an agent at time as the negative first time-derivative of free-energy at that state or , simply , . Here , we recall that adaptive agents encode a hierarchical generative model of the causes of their sensations [3] , [4] . States of the world of increasing complexity and abstraction are encoded in higher levels of the hierarchy , whereas sensory data per se are encoded at the lowest level . Free-energy is minimized for each level of the hierarchy separately , and the quantity corresponds to the free-energy associated with the hidden state at the i-th level of the hierarchical model . According to our definition of emotional valence , when is positive ( i . e . , free-energy is increasing over time at level i of the hierarchy ) the valence of the state at this level i is negative at time . When is negative ( i . e . , free-energy is decreasing over time at level i ) the valence of the state at this level i is positive at time . When is zero ( i . e . , free-energy is constant at level i ) the valence of the state at this level i is neutral at time . Importantly , free-energy is an upper bound on surprise , and neutral valenced states may also be characterized by low or high levels of surprise . The factorization of emotional valence across levels of the hierarchical model means that positive and negative valence can be independently attributed to each state in the model , and thus positive and negative valences can be concurrently elicited for the same sensation . Note that free-energy and the rate of change of free-energy are functions not just of current sensations but the posterior beliefs about the causes of those sensations . This means that the free-energy can change in a way that is context-sensitive , depending upon ( different ) current beliefs about ( exactly the same ) sensations . Cognitive theories of emotion have widely relied on degrees of belief about states of affairs ( environmental states ) for their analyses of some basic forms of emotion . It has been suggested that a large group of emotions , which includes happiness , unhappiness , relief and disappointment , is related to certain ( firm ) beliefs that states of affairs obtain , while a second smaller group of emotions , mainly represented by hope and fear , is related to uncertain beliefs [28]–[31] . These two classes of emotions have been referred to as factive and epistemic , respectively [29] . In philosophy , states of affairs are formally said to either obtain or not whereas beliefs can be true or false ( see [32] ) . Henceforth , we will adopt this terminology . To illustrate the difference between factive and epistemic emotions , imagine the case of Lucia who is waiting for a train at the station . Lucia is happy that the train is on time ( state of affairs ) , if she desires and is certain ( i . e . , firmly believes ) that obtains . Conversely , Lucia is unhappy that , if she does not desire and is certain that obtains . However , Lucia hopes that , if she desires but is uncertain that obtains; and , alternatively , Lucia fears that , if she does not desire but is uncertain that obtains . On the other hand , relief and disappointment are better related to the transition from uncertain to certain beliefs [31] . For instance , Lucia is relieved that not-p if she does not desire and up to now was uncertain about p , but now is certain that not-p obtains; conversely , Lucia is disappointed that not-p if she desires and up to now was uncertain about , but now is certain that not-p obtains . Beliefs and desires can be intuitively related to bottom-up conditional expectations and top-down predictions , respectively , in a predictive coding scheme of free-energy minimization [2] . In this formulation , states of affairs cannot be directly assessed but must be inferred from sensory inputs . Assigning absolute certainty ( or zero uncertainty ) to any belief impairs the learning of new relationships between sensory inputs and their causes . Here , we consider it more appropriate to circumvent the assumption of certain beliefs proposed in cognitive theories of factive and epistemic emotions , and present a new formulation that relies only on the dynamics of free-energy without any explicit reference to uncertainty . Later , we shall see that factive and epistemic emotions are indeed associated with low and high levels of uncertainty , respectively , but this comes as a consequence and not as a necessary condition of their definition ( see Results ) . In a continuous time domain , the rate of change of the first time-derivative of free-energy at the i-th level of the hierarchical model is the second time-derivative of free-energy . By analogy with mechanical physics , and can be understood as the velocity and acceleration of free-energy at time , respectively . Our proposal stands on the assumption that , when both and are negative ( i . e . , free-energy is decreasing ‘faster and faster’ over time ) the agent hopes to be visiting a state of lower free-energy in the near future at this level i . However , when is negative and is positive ( i . e . , free-energy is decreasing ‘slower and slower’ over time ) the agent is happy to be currently visiting a state of lower free-energy than the previous one at this level i . Equivalently , when and are positive ( i . e . , free-energy is increasing ‘faster and faster’ over time ) the agent fears to be visiting a state of greater free-energy in the near future at this level i . However , when is positive and is negative ( i . e . , free-energy is increasing ‘slower and slower’ over time ) the agent is unhappy to be currently visiting a state of higher free-energy than the previous one at this level i . Additionally , when the rate of change of free-energy changes sign from negative to positive , the agent is disappointed not to be visiting a state of lower free-energy than the current one at this level i . Conversely , when changes sign from positive to negative , the agent is relieved not to be visiting a state of higher free-energy than the current one at this level i . Finally , when and are zero ( i . e . , free-energy is constant over time ) the agent may be low or high neutrally surprised in this level i . This analysis is summarized in Table 1 . Note that since free-energy is minimized for each level of the hierarchical model separately , our formulation also predicts that different emotions can occur concurrently . The dynamics of free-energy reveal an interesting temporal dependency among the basic forms of emotion . Figure 1 illustrates two hypothetical dynamics of free-energy ( top and bottom rows ) that elicit distinct patterns of emotion over time ( left column ) . From the two-dimensional space defined by the first and second time-derivatives of free-energy ( right column ) , it becomes clear that transitions from negative to positive emotions can only occur by passing through relief , and transitions from positive to negative emotions can only occur by passing through disappointment , but transitions between negative ( e . g . , fear and unhappiness ) or positive ( e . g . , hope and happiness ) emotions can occur bidirectionally . More importantly , each basic form of emotion is mapped onto a particular region of this two-dimensional space . So far , we have described how emotional valence and some basic forms of emotion can be elicited by the dynamics of free-energy . What , however , is the function of these quantities in a scheme originally developed to explain perception , learning and action ? We propose that valence , computed as the negative rate of change of free-energy , is crucial because it informs biological agents about unexpected changes in their world . When valence is positive , sensory inputs fulfil the agent's expectations and the probability of unexpected changes is low . However , when valence is negative , the agent's expectations are violated and unexpected changes in the world are likely to have taken place . In settings where recent information is a better predictor of states of the world than past information , that is , in a changing world , recent information must be more heavily weighted and , therefore , the learning rate should be high [14] . Conversely , in a stationary world , in which past and recent information are equally informative , the learning rate should be low in order to take into account both past and recent information . We formalise this notion in terms of emotional meta-learning in which estimation uncertainty is determined not just by free-energy but by the rate of change of free-energy . More specifically , when the free-energy associated with posterior beliefs about states at a particular level in the agent's hierarchical model is increasing , the posterior certainty about these states decreases . In other words , the agent interprets decreasing evidence for its estimates of states of the world as evidence that it is too confident about those states . This can be implemented fairly simply with the augmented Bayesian update: ( 4 ) ( 5 ) ( 6 ) Here , the variances and correspond to the posterior estimation uncertainty with and without emotional regulation , respectively . The variance is the one that changes to minimize the free-energy at the i-th level of the generative model . The quantity denotes the Shannon entropy , which in this case is a measure of the uncertainty associated with the states at level i in the recognition density . The parameter can be interpreted as the sensitiveness or ‘awareness’ of the agent to its emotional valence signals , which informs the agent about changes in the world . The parameter represents a long-lasting valenced level that lacks a clear referent , which we thus interpret as mood [23] . The parameters and are both state and agent dependent . They can also be interpreted as the agent's meta-cognition about the extent to which the agent knows that it does not know the structure of the world . We have framed the emotional regulation of uncertainty as meta-learning to emphasise that learning ( the update ) is informed by the consequences of learning , here , the rate of change of variational free-energy . Note that this is a very general scheme that is not tied to any particular generative model . Crucially , expectations about various states , which define them as surprising or not , rest upon prior beliefs that are themselves optimised with respect to variational free-energy; either at an evolutionary timescale or during experience dependent learning . From equation 4 , one can see that positive and negative valence exponentially decreases and increases , respectively , the estimation uncertainty about states of the world . The mood induces a constant level of over or under-confidence in the estimates of states irrespective of how surprising the sensory inputs may be . In a negative mood ( ) , the agent overweights recent inputs , tracking more easily any volatility in the environment . In a positive mood ( ) , the agent overweight past inputs , becoming more attached to past information and less susceptible to tracking environmental changes . This emotionally regularized update scheme may appear a bit ad hoc . However , there are several important heuristics in the optimisation literature that are closely related to Equation 4 . These are generally described as regularization schemes - for example Levenberg Marquardt Regularization - in which the gradient descent or learning rate is generally decreased when the objective function being optimized does not change as expected . Usually , this regularization can be cast as changing the relative precision of the data at hand . In short , like our scheme , regularization schemes detect a failure in optimization in terms of adverse changes in the objective function ( here the free energy ) and respond by making more cautious updates - through changing the expected uncertainty about data or prior beliefs . We will see later that , in a hierarchical setting , this can lead to an adaptive change in the rate of optimization or learning at various levels of a hierarchical model .
Mathys et al . [10] have proposed a generic hierarchical Bayesian scheme that accounts for learning under multiple forms of uncertainty and environmental states . The environmental states can be either discrete or continuous , and the uncertainty can range from probabilistic relations between environmental and perceptual states ( perceptual ambiguity ) to environmental volatility . Here , we focus only on the simplest discrete and deterministic ( i . e . , without perceptual ambiguity ) environment which nevertheless includes volatility . In our example of a discrete and deterministic environment , we simulate an agent that learns the probability of a slot machine ( one-armed bandit ) to generate outcomes ( ) equal to either $1 ( ) or $0 ( ) . The agent's sensations ( ) of the outcomes ( ) are unambiguous , meaning that for both and . The reward probability of the slot machine is governed by the tendency ( ) of the machine to generate $1 . In the dynamic perceptual model , the agent knows that the reward tendency may change over time and therefore they also estimate its volatility ( ) . This discrete and deterministic environment can be formalized with the statement that the sensory input is binary and the environmental state is the deterministic cause of input at trial . The likelihood of state given sensory input has the following form ( for simplicity , we omit the trial reference ) : ( 7 ) Therefore , for both and . At the next level of the hierarchy , the tendency of ( i . e . , outcome equal to $1 ) is defined by the state . The probability of approaches zero when and approaches one when . The mapping from the tendency to the probability of is defined by the following empirical ( conditional ) prior density: ( 8 ) where is the sigmoid function: ( 9 ) It is also assumed that the state at trial is normally distributed around its value at the previous trial with variance . In other words , evolves in time as a Gaussian random walk: ( 10 ) where the parameters and are agent dependent . The state determines the log-volatility of the environment and is represented at the third level of the model . Again , also evolves in time as a Gaussian random walk but with a step size defined by the constant that may also differ among agents: ( 11 ) This structure defines a four-level generative model , where is represented at the last level , and its inversion corresponds to optimizing the posterior densities over unknown hidden states and parameters . Here , states and parameters are distinguished in terms of the timescale at which they change . More specifically , states change quickly and parameters change either slowly or not at all for the duration of the observations . Alternatively , we propose a generative model that does not explicitly estimate the volatility ( e . g . , ) of some environmental states ( e . g . , ) but instead makes use of emotional valence ( i . e . , the negative rate of change of free-energy over time ) to assess unexpected changes in the environment . For that purpose , we implement the static perceptual model proposed by Daunizeau et al . [15] with two modifications . First , we consider unambiguous sensory inputs as in Mathys et al . [10] and , second , we use valence to update the posterior variance ( estimation uncertainty ) of states according to equation 4 . At the first level of the hierarchy , the dynamic model and static perceptual model with valence are exactly the same . At the second level , the static model assumes that the tendency of outcome to be equal to $1 is constant across trials: ( 12 ) After inverting this generative model using variational free-energy minimization as described in [10] , [15] , we obtain the updated equations of the posterior distribution of , which can be used to investigate the behaviour of the agent on a trial-by-trial basis: ( 13 ) ( 14 ) ( 15 ) where the following definitions have been used: ( 16 ) ( 17 ) ( 18 ) ( 19 ) Here , and are the posterior expectations of and after sensory input , which can be interpreted as the expected probability and the expected tendency of reward , respectively . Accordingly , the uncertainty is the posterior variance of . The prediction error at the first level is the difference between the expectation and the prediction before seeing the input . Equivalently , is the variance of the prediction before seeing the input . In order to adapt to unexpected changes in the environment , the agent needs to update the posterior variance proportionally to the valence of the state at time . In discrete time , the valence of the state is , by definition , the negative first backward difference of free-energy at time : ( 20 ) Specific to the proposed generative model , the free-energy of state is: ( 21 ) where the expectation is taken under the approximate posterior densities and . The parameters and are constant and dependent on the agent . They represent the sensitiveness to emotional valence and the mood of the agent , respectively . According to our assumptions , the uncertainty of a hidden state should increase or decrease when its valence is negative or positive , respectively . Therefore , is constrained within the interval . Notice that , when and are equal to zero , the static perceptual model with valence becomes the same as the standard static perceptual model described in [15] . Having defined the two competing schemes , we implemented two agents under the dynamic perceptual model ( DP ) and the static perceptual model with valence ( SPV ) , hereafter referred to as the DP agent and the SPV agent . These agents were exposed to 320 sensory inputs ( outcomes ) sampled from a three-stage reference scenario as proposed in [10] . In the first stage ( low volatility ) of the scenario , the agents were exposed to a sequence of 100 outcomes where the probability of ( outcome equal to $1 ) was 0 . 5 . In the second stage ( high volatility ) , the probability that alternated between 0 . 9 and 0 . 1 every 20 inputs . Finally , in the third stage ( low volatility again ) , the first 100 outcomes were repeated in exactly the same order . The initial values of the hidden states and were , and for both the DP and SPV models . In the DP model , the initial values of the hidden state were and . We replicated the results reported by Mathys et al . [10] for the DP model with the same parameters , and ( see Figure 2 ) . Overall , the posterior expectation of , which is the reward probability , fluctuated around the true probability of both in the low and high volatility stages . Nevertheless , one can observe increasing instability during the third stage relative to the first , even though the inputs were presented exactly in the same order in both of them . Mathys et al . [10] explained this in terms of a strong tendency for the agent to increase its posterior expectation of log-volatility in response to surprising stimuli ( given the parameters used in the reference scenario ) . The increase of was followed by an increase in the posterior variance of state , which regulates the learning rate at the second level . Despite the different levels of volatility in each stage , the posterior variance smoothly increased with a constant rate during the whole scenario . We first evaluated the SPV model setting both the sensitiveness and mood equal to zero . In this case , the agent learns according to a standard static perceptual model and is completely insensitive to any volatility or unexpected change in the environment . As illustrated in Figure 3 , the posterior expectation of converges to 0 . 5 , which is the true probability of across the three ( low and high volatility ) stages . Concomitantly , the posterior variance ( estimation uncertainty ) asymptotically decreases toward zero , reflecting the decreasing uncertainty of the estimates across sensory inputs . When setting the parameters and to values different than zero , the agent becomes sensitive to changes in its environment . In Figure 4 , one can observe the effect of mood alone . When is set to −0 . 13 and is kept equal to 0 , a negative mood is sufficient to make the SPV model reactive to the volatility of the environment similar to the DP model . Importantly , the dynamic model also has a constant parameter that is agent dependent , which has a similar function to in our model . Nevertheless , the SPV does not show the increasing instability in the last ( low volatility ) stage observed in the DP model . In fact , the posterior variance returns to a stable baseline even after the increased fluctuation during the high volatility stage . With the addition of emotional valence to the model , the agent becomes even more reactive and is able to track fast changes in the environment . In Figure 5 , the sensitiveness is set to 0 . 8 . The posterior variance now changes more quickly in response to surprising sensory inputs and there is a clear distinction between the low and high volatility stages . More specifically , the elicitation of negative valence is the main cause of increases in , whereas positive valence causes to decrease . Despite the phasic reaction to unexpected changes during the high volatility stage , the agent returns again to a fairly stable baseline similar to the first low volatility stage in the last low volatility stage . Critically , an optimal tracking of environmental volatility requires mood to be set to some appropriate negative value . An extremely low mood , characterized by a large negative tau , would cause a very large increase in estimation uncertainty , consequently impairing discrimination between high and low volatility stages . We also investigated the estimation uncertainty associated with the factive ( happiness or unhappiness ) and epistemic ( fear or hope ) emotions in the reference scenario . It is noteworthy that we defined these emotions simply in terms of the dynamics of free-energy without any assumptions about uncertainty , contrary to the traditional analysis of these emotions in psychology and philosophy ( see [28]–[31] ) . For this purpose , we performed 100 realizations of the reference scenario ( i . e . , we repeated the simulation with the reference scenario 100 times , sampling new sensory inputs at each time ) and we computed the mean of the posterior variance ( estimation uncertainty ) of state immediately after the onset of factive and epistemic emotions . The posterior variance represents the change in estimation uncertainty after the elicitation of the emotion and before the observation of the next sensory input ( see equation 19 ) . For this analysis , we set the sensitiveness to an intermediate value equal to 0 . 4 and we kept the mood equal to −0 . 13 . The distribution of the mean across simulations grouped within the low and high volatility stages of the reference scenario is shown in Figure 6 . In both the low and high volatility stages , the mean was higher on average for the epistemic ( low volatility: M = 0 . 68 , SD = 0 . 03; high volatility: M = 1 . 07 , SD = 0 . 19 ) than the factive ( low volatility: M = 0 . 58 , SD = 0 . 02; high volatility: M = 0 . 69 , SD = 0 . 06 ) emotions . Furthermore , the mean was also higher on average during the high ( M = 0 . 88 , SD = 0 . 24 ) than the low volatility ( M = 0 . 63 , SD = 0 . 06 ) stages .
In this paper , we have proposed a biologically plausible computational model of emotional valence inspired by the free-energy principle . The mathematical definition of emotional valence in terms of the negative rate of change of free-energy not only accounts for how beliefs determine emotions but also provides a formal account of how emotions determine the content and the degree of posterior beliefs ( see [19] ) . In our framework emotional valence regulates estimation uncertainty signalling unexpected changes in the world , thereby performing an important meta-learning function . The relationship between emotional valence and state transition also finds support in previous studies of emotion ( see [33]–[36] ) . Batson et al . [35] have argued that the shift from a less valued state ( i . e . , high free-energy ) to a more valued state ( i . e . , low free-energy ) is accompanied by positive affect , while a shift in the opposite direction is accompanied by negative affect . Likewise , Ben-Zeev [36] has suggested that emotions are generated when the level of stimulation we have experienced for long enough to get accustomed to it changes , and the change , rather than the general level , is of emotional significance . Accordingly , in the words of the same author , “loss of satisfaction does not produce a neutral state , but misery , and loss of misery does not produce a neutral state either , but happiness” [36] . Similar situations can also be found when people are entertained by magicians or humorists . In both cases , following the surprise elicited by the apparent violation of the physical laws in magic [37] or the incongruity of the situation in humour [38] , greatest pleasure is experienced when the trick or the joke is understood . Our suggestion is that pleasure is elicited in the transition from a state of high to low surprise . Critically , magic tricks are performed on a stage where people know that there is no real violation of the physical laws; if such surprising events would happen in everyday life , they would probably be experienced as quite disturbing and unpleasant . According to our scheme , emotional valence is not estimated itself by the agent but emerges naturally from the process of estimating hidden states by means of free-energy minimization . One could eventually hypothesize that some living organisms , such as humans , explicitly represent valence as one of the causes of their sensations . This means that these agents should also estimate valence ( and its uncertainty ) like any other hidden state in their generative model . Nevertheless , the explicit representation of valence is not a requirement for emotional valence to exist in our scheme and to play an important role in the adaptation of biological agents to unexpected changes in their world . To put our valence-based meta-learning scheme to a test , we compared two competing agents in a non-stationary environment . The SPV agent with valence replicated the behaviour of the DP agent that explicitly estimated the volatility of the environment [10] . Nevertheless , the adaptive fitness of the SPV agent to unexpected changes was achieved with the representation of only two hidden states and two parameters , whereas the DP agent required three hidden states and three parameters . More importantly , the two parameters and of the SPV agent have a clear psychological interpretation in terms of sensitiveness to emotional valence and mood , respectively . The mood was shown to be important for tracking slow and continuous changes in the environment , known as volatility , whereas the sensitiveness was shown to be crucial for tracking fast and discrete changes , known as unexpected uncertainty . The proposed scheme is very general and does not rely on any particular generative model of how sensory inputs are caused , meaning that it can account for any internal model of the world that defines a particular agent ( see [7] ) . We also investigated the relationship between estimation uncertainty and factive ( happiness as well as unhappiness ) and epistemic ( hope and fear ) emotions . Although psychologists and philosophers have traditionally relied on degrees of belief ( uncertainty ) in their analyses of these families of emotion [28]–[31] , we alternatively relied only on the dynamics of free-energy . In agreement with these more traditional analyses , we found that epistemic emotions are indeed more related to higher levels of ( estimation ) uncertainty than factive emotions . However , at the algorithmic level , we reiterate our claim that the computational quantity that unambiguously distinguishes between factive and epistemic emotions is not degrees of belief , as previously proposed [31] , but rather the temporal dynamics of free-energy . More important for psychological perspectives on emotion , the trajectory invariant representation of emotions in the state space defined by the first and second time-derivatives of free-energy also recapitulates the dimensional view of emotion [39] . Although the first time-derivative of free-energy has been intuitively related to the dimension of valence , it is still unclear how to interpret the second time-derivative in terms of a psychological construct . The emergence of some forms of emotion , tentatively labelled as happiness , unhappiness , hope , fear , disappointment and relief , also provides support for the notion of basic emotions [40] , in the sense that these emotions are exclusively related to very precise dynamics of free-energy . Furthermore , our scheme also encompasses important aspects of cognitive models of emotion [31] , [41] , [42] , in the sense that states of the world ( e . g . , agents , objects , events ) , which are relevant for the diversity and complexity of human emotions , can be accounted for within the hierarchical generative model entailed by the agent . To illustrate , happiness ( unhappiness ) has been related to the negative ( positive ) first time-derivative and the positive ( negative ) second time-derivative of the free-energy of some state in the generative model . When the state under consideration is the fate of another person , this can be understood as a specific form of happiness ( unhappiness ) usually known as ‘joy for another’ ( pity ) [31] . The concept of value has been largely related to valence in social and affective psychology ( see [43] ) . Our definition of emotional valence in terms of the rate of change of free-energy also provides a formal distinction between valence and value . In the free-energy principle , value is the complement of free-energy in the sense that minimizing free-energy corresponds to maximizing the probability that an agent will visit valuable states , where the evolutionary value of a phenotype is the negative surprise averaged over all the ( interoceptive and exteroceptive ) sensory states it experiences [2] . This formulation parallels a recently proposed reinforcement learning theory for homeostatic regulation [44] , which attempts to integrate reward ( valence ) maximization with the minimization of departures from homeostasis ( free-energy ) . Our scheme is also broadly compatible with the predictive coding model of conscious presence [45] , which claims that interoceptive inference is the constitutive basis of the subjective experience of emotions . Although our formulation treats interoceptive and exteroceptive predictions ( and their uncertainty ) on an equal footing , one might imagine that prediction of interoceptive states would be a particularly important target for emotional regulation . This is because , from an evolutionary perspective , it is important to maintain a physiological homeostasis and respond adaptively to any unpredicted changes in the internal milieu . Furthermore , the putative emphasis on interoception provides a close link between ( literally ) ‘gut feelings’ and the computational ( inferential ) role of emotion that we have described above . An apparent paradox that might emerge from our definition of emotional valence is related to the common sense notion that both the violation and the fulfilment of expectations can be either positive or negative . As we stated before , according to our scheme , the fulfilment of expectations must always elicit positive emotions whereas the violation of expectations must always elicit negative emotions . Therefore , how can the subjective experience of positive surprises and negative expectations be accounted for within our scheme ? In our perspective , these experiences emerge from a confound between the fulfilment and the violation of expectations across different levels of the hierarchical generative model . To illustrate this , we first need to recall that in the Bayesian brain formulation , agents encode a hierarchical generative model of the causes of their sensations , where states of the world of increasing complexity and abstraction are encoded in higher levels of the hierarchy and sensory data per se are encoded at the lowest level . Let us imagine the case of an old friend who suddenly steps in our door . This unexpected visit can be intuitively related to the experience of a very positive and surprising emotion . However , a more careful analysis can unveil which aspects of this experience are indeed surprising and which are just as expected , given a hierarchical generative model of how sensations are caused . Assuming that our friend has moved to a distant city many years ago , the sudden apparition of this friend certainly violates any expectation about the physical causes of sensations . It would be very surprising to meet a friend at our door when they are expected to be miles away - no matter how beloved they might be . Such a surprising sensation should elicit unpleasantness at the corresponding levels of the model where physical causes of sensations are encoded . Concomitantly , this same sensation should also fulfil more abstract expectations that we might have of being close to beloved ones . The fulfilment of these expectations should conversely elicit pleasantness at higher levels of the generative model where these more abstract causes of sensations are probably represented . With the formalism of a hierarchical generative model , the causes of sensations can be clearly defined and their respective valence properly investigated . In the example above , we would thus consider it more precise to say that ‘we are surprised about the unexpected visit of a friend but happy to be close to a beloved one’ . Here , our explanation rests upon the assumption that the subjective experience of emotion usually confounds the increasing fulfilment ( pleasantness ) and violation ( unpleasantness ) of expectations across different levels of the hierarchical model . In another example , the reasoning above also can help us to explain how our scheme may account for sensations that are expected but of negative valence ( e . g . , the expectation of an eminent injury ) . Let us imagine the case of someone who is walking on the street and suddenly sees a cyclist riding a bicycle dangerously . As the cyclist gets closer , the person becomes increasingly confident that they will be hit by the bicycle . In this situation , the movement of the bicycle fulfils the expectations of the person about how physical bodies should move in the world and , therefore , it elicits pleasantness at those levels of the generative model . Indeed , it would be very surprising ( and unpleasant at these levels ) if the bicycle suddenly disappeared or made an unexpected movement that violated the physical laws of motion . Nevertheless , the approach of the bicycle also violates other expectations regarding the safety of walking down the street , which are probably represented at different levels of the hierarchical model . At these levels , the approach of the bicycle is very unpleasant and becomes even more unpleasant when the person is indeed injured by the bicycle . Again , in this case , we would consider more precise to say that ‘the person expects the bicycle to hit them - under such environmental conditions - but they do not expect to be injured when walking down the street’ . The flexibility of our scheme to accommodate different generative models may raise some concerns regarding the falsifiability of our theory . However , we would like to clarify that hypotheses derived from our theory should be tested conditional on a particular generative model . Especially given the known diversity of phenotypes in nature , we consider that this flexibility is more a strength than a weakness . Furthermore , generative hierarchical models and free-energy minimization provide a principled way to represent the relationship between hidden states and to understand their dynamics . Nevertheless , further empirical work is still required to better understand at which levels of the hierarchical generative model the violation of expectations might be more closely related to the subjective experience of surprise and emotional valence . Our intuition is that the subjective experience of surprise is more closely related to violations at lower levels of the hierarchy , whereas the subjective experience of emotional valence is more closely related to violations at higher levels . The distinction between violation and fulfilment of expectations across different levels of the generative model might also help us to further disambiguate the subjective experience of other emotions such as fear and anxiety , which have an important role in psychopathology . One of the ways in which cognitive theories of emotion have distinguished fear from anxiety is based on the physical and existential aspect of their causes . Fear involves threats that are concrete and sudden , whereas anxiety is related to threats that are more symbolic , existential and ephemeral [41] , [42] . Nevertheless , both fear and anxiety are related to the prospect of visiting unpleasant states in the future , which in our scheme has been related to a ‘faster and faster’ increase of free-energy over time . To illustrate , let us imagine the case of a spider-phobic person who is presented with a spider . The subjective experience of fear in this case could be explained as the product of ( 1 ) a ‘slower and slower’ increase in the violation of the expectations about the more physical causes of sensations , which encodes the physical recognition of the spider , eliciting unhappiness at these levels; and ( 2 ) a ‘faster and faster’ increase in the violation of the expectations about more abstract causes of sensations , such as the increasing probability of being bitten by the spider , eliciting fear at these levels . However , in the case of anxiety , there seems to be incongruence between the violation of expectations about the physical and the existential causes of sensations . Therefore , in our perspective , the subjective experience of anxiety should be expressed as the product of ( 1 ) a stationary violation of the expectations about the physical causes of sensations ( i . e . , the environment is physically perceived as usual ) bringing neutrality to these levels , and ( 2 ) a ‘faster and faster’ increase in the violation of the expectations about more abstract/existential causes of sensations , eliciting fear at these levels . This incongruence of violation across levels of the generative model could explain the difficulty that anxious people have to attribute concrete causes to their fears . Our formulation of emotional valence might also be of importance in the investigation of affective and other mental disorders , such as depressive and anxiety disorders [46] . For instance , when we use our model to explain major depressive disorder ( MDD ) , which is a complex debilitating psychiatric condition that is largely characterized by persistent low mood and decreased interest or pleasure in usually enjoyable activities [47] , we immediately find the crucial role played by our mood model parameter . In our meta-learning scheme , when mood is low ( ) , the estimation uncertainty of environmental states is overestimated and top-down predictions become under confident . Theoretical computational simulations has shown that pathological under confidence in top-down predictions can impair behaviour due to a failure in eliciting sufficient sensory prediction errors [48] . Consequently , the agent reacts less vigorously toward , or away from stimuli that might have been previously evaluated as pleasant or unpleasant . In fact , several studies have reported that clinically depressed individuals spend significantly more time looking at negative stimuli [49]–[52] . A subsequent , and cyclical , increase in mood ( ) could eventually explain manic episodes in bipolar disorders [53] . Manic episodes are characterized by a distinct period during which patients experience abnormally and persistently elevated , expansive , or irritable mood [54] . In fact , a pathological increase in the precision of top-down predictions has also been shown to induce perseverative behaviours [48] . It would be interesting to investigate how mood induction in healthy subjects might affect their performance on tasks where tracking volatility is necessary . According to our theory , we would predict that subjects with mood levels below and above the optimum for tracking some particular level of environmental volatility should benefit from positive and negative mood induction , respectively . More precisely , an inverted U-shaped performance curve is predicted with depressed and manic patients found at the lowest and highest extremes of the mood range . A reasonable approach to test hypotheses derived from our theory would be to invert a generative model ( i . e . , estimate the unknown model parameters ) for the experimental task at hand using variational Bayes [55] . The free-energy computed during this inversion process can then be exploited to estimate the emotions at different levels of the hierarchical generative model according to our scheme . A complete characterization of the generative model could eventually be relaxed if a direct measure of the free-energy or , under simplifying assumptions , prediction error is also available . Indeed , the quantity that matters for testing our emotional valence hypothesis is the rate of change of free-energy rather than the generative model itself . Future empirical work should investigate the correlation between the estimated emotional valence ( i . e . , the first time-derivative of free-energy ) and verbal-reports of valence for a variety of experimental conditions . As previously mentioned , free-energy is an upper bound on surprise and its minimization also entails prediction error reduction . In this perspective , recording prediction error signals in the brain , computing their temporal derivatives and correlating them to verbal-reports of valence could be a suitable procedure . Human neuroimaging studies have shown that the orbitofrontal cortex plays an important role in linking different types of reward to hedonic experience ( see [56] ) . Orchestrated with the striatum [57] , which has been traditionally implicated in reward prediction error [58] and saliency [59] , those two regions might be of particular relevance to the investigation of our scheme in the brain . In biologically plausible implementations of free energy minimisation , precision ( i . e . , the inverse of uncertainty ) is encoded by the gain of cells reporting prediction error [2] . This directly implicates the classical ascending neuromodulatory transmitter systems like dopamine , acetylcholine and norepinephrine in the encoding of uncertainty . The diverse and complex interactions between these neurotransmitters and their role in encoding different forms of uncertainty are still far from being clearly understood [11] , [60] , [61] . Future work will address how our meta-learning scheme , which links the rate of change of free-energy ( prediction error ) to estimation uncertainty ( precision ) , can help in elucidating the complex interaction between these neurotransmitters and the activity in their target brain areas . To conclude , by providing a general framework in which different perspectives on emotion can be formally interrelated , and by demonstrating how emotional valence can dynamically regulate uncertainty , we hope to contribute to paving the way for future computational studies of emotion in learning and uncertainty . | Emotion plays a crucial role in the adaptation of humans and other animals to changes in their world . Nevertheless , emotion has been neglected in Bayesian models of learning in non-stationary environments . The free-energy principle has recently been proposed as a unified account of learning , perception and action in biological agents . In this paper , we propose a formal definition of emotional valence ( i . e . , the positive and negative character of emotion ) in terms of the rate of change of free-energy or , under some simplifying assumptions , of prediction error over time . This formalization leads to a straightforward and simple meta-learning scheme that accounts for the complex and reciprocal interaction between cognition and emotion . We instantiate this scheme with an emotional agent who is able to dynamically assign emotional valence to every new state of the world that is visited and to experience basic forms of emotion . Crucially , our hypothetical agent uses emotional valence to dynamically adapt to unexpected changes in the world . The proposed scheme is very general in the sense that it is not tied to any particular generative model of sensory inputs . | [
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] | 2013 | Emotional Valence and the Free-Energy Principle |
Bartonella infections were investigated in seven species of bats from four regions of the Republic of Georgia . Of the 236 bats that were captured , 212 ( 90% ) specimens were tested for Bartonella infection . Colonies identified as Bartonella were isolated from 105 ( 49 . 5% ) of 212 bats Phylogenetic analysis based on sequence variation of the gltA gene differentiated 22 unique Bartonella genogroups . Genetic distances between these diverse genogroups were at the level of those observed between different Bartonella species described previously . Twenty-one reference strains from 19 representative genogroups were characterized using four additional genetic markers . Host specificity to bat genera or families was reported for several Bartonella genogroups . Some Bartonella genotypes found in bats clustered with those identified in dogs from Thailand and humans from Poland .
Bats ( Order: Chiroptera ) are hosts of a wide range of zoonotic pathogens . Their significance as reservoirs of emerging infectious diseases , predominantly of viral origin , has been increasinglyecognized during recent decades [1 , 2] . In contrast , the study of bacterial infections in bats hasprogressed more slowly [3] . Bacteria of the genus Bartonella are small and slow-growing Gram-negative aerobic bacilli . These bacteria parasitize erythrocytes and endothelial cells of a wide range of mammals . During the last six years , diverse Bartonella strains were identified in bats from Europe [4–6] , Africa [7–12] , Asia [13 , 14] , and Latin America [15–19] . Recent studies have demonstrated significant patterns of evolutionary codivergence among bats and Bartonella , demonstrating that strains of Bartonella in bats tend to cluster according to bat families , superfamilies , and suborders [20 , 21] . Host specificity and codivergence have also been documented in rodent-associated Bartonella strains [20 , 22] and bat-associated Leptospira strains [23] . Despite their apparent host associations , Bartonella spp . can spillover into phylogenetically distant hosts , including humans [24 , 25] . A recent human case of endocarditis in the US Midwest was associated with a novel Bartonella species ( B . mayotimonensis; [26] ) , which later was isolated in bats in Europe [5] . This human case has demonstrated the zoonotic potential of bat-borne Bartonella and underscores the need for extended surveillance and studies of these pathogens . The goal of the present work was to identify prevalence and diversity of Bartonella in bats in theRepublic of Georgia ( southern Caucasus ) with the following objectives: 1 ) to compare prevalence of Bartonella infection in diverse bat species from different geographic locations within Georgia; 2 ) to determine the genotypes of obtained strains by variation in gltA sequences , a gene commonly used for discrimination of Bartonella species; 3 ) to characterize reference strains representing diverse genogroups by variation of multiple genetic loci; and 4 ) to evaluate the links between identified Bartonella genogroups and bat hosts .
All animal work has been conducted according to relevant NCDC , national , and international guidelines . Bats were collected from two distinct parts of Georgia in June 2012 . Four locations are situated in Eastern Georgia: three sites in the Kakheti region near Davit Gareja , one site in the Kvemo Kartli region in Gardabani district . The other four locations are in Western Georgia: two sites in the Samegrelo-Zemo Svaneti region ( Martvili district and Chkhrotsku district ) and two sites in the Imereti region ( Terjola district and near Tskaltubo town ) . The number of captured bats from each site is shown in Table 1 . Bats were captured manually or using nets from different roosts in caves and buildings ( attics , cellars , and monasteries ) . The list of bat species and the number of animals per roost or colony availablefor sampling was approved by the Ministry of Environmental and Natural Resources Protection ofGeorgia . Species of captured bats were identified based on external morphological characteristics . Captured bats ( n = 236 ) were delivered to the processing site in individual cotton bags where they were processed . Bats were anesthetized with the use of ketamine ( 0 . 05–0 . 1 mg/g body mass ) and exsanguinated by cardiac puncture . All bats were sexed and measured . The procedures of handling animals were performed in compliance with the protocol approved by the CDC Institutional Animal Care and Use Committee ( protocol 2096FRAMULX-A3 ) . Blood specimens were transported on dry ice to the NCDC Laboratory , Tbilisi where they were stored at -80°C until they were shipped on dry ice to the CDC’s laboratory , Fort Collins , Colorado . Upon arrival at CDC , the samples were stored at -80°C until they were analyzed . Bat blood was diluted 1:4 in Brain Heart Infusion ( BHI ) with 5% Fungizone ( amphotericin B ) , and 100μl of the sample was placed on a chocolate agar plate following the protocol published previously [27] . Inoculated plates were incubated at 35°C in a 5% CO2 environment for up to five weeks . Plates werechecked periodically , and bacterial colonies that morphologically resembled those typical for Bartonellawere passaged onto a new plate to obtain pure cultures . In an attempt to capture possible Bartonella coinfections , all morphologically unique colonies growing from a single sample were sub-passaged and sequenced . All resulting isolates were collected in a 10% glycerol solution . Crude DNA extracts were obtained from isolates by heating a heavy suspension of themicroorganisms for 10 minutes at 95°C . Polymerase chain reactions ( PCR ) with the gltA primersBhCS781 . p ( 5’-GGGGACCAGCTCATGGTGG-3’ ) and BhCS1137 . n ( 5’-AATGCAAAAAGAACAGTAAACA-3’ ) [28] were performed using PCR Thermal Cycler Dice ( Takara Bio Inc . , Japan ) and C1000 Touch Thermal Cycler ( Bio-Rad , Berkeley , CA ) . Positive ( B . doshiae ) and negative ( nuclease free water ) control samples were included in each PCR assay to evaluate the presence of appropriately sized amplicons and to rule out contamination of reagents , respectively . Positive PCR products were purified using QIAquick PCR purification Kit ( Qiagen , Valencia , CA ) and sequenced with an ABI 3130 Genetic Analyzer ( Applied Biosystems , Foster City , CA ) . Forward and reverse reads were assembled into consensus sequences with the SeqMan Pro program in Lasergene v . 11 ( DNASTAR , Madison , WI ) . A BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) search of the GenBank database was performed with all assembled gltA sequences to verify their Bartonella origin . Positive sequences were aligned with Bartonella reference sequences available in GenBank which included sequences obtained from various bats in previous studies . Brucella abortus sequence was used as outgroup . Alignment was performed with MAFFT v7 . 187 using the local , accurate L-INS-i method [29] . The optimal evolutionary model for the aligned sequences was determined by jModelTest2v2 . 1 . 6 [30] using Akaike information criterion corrected for finite sample sizes ( AICc ) for modelselection [31] . For our dataset , the best model was the generalized time-reversible substitution model with four gamma-distributed categories and a proportion of invariant sites ( GTR+Γ+I ) . We implementedthis model for the Bayesian phylogeny of our sequences with BEAST v1 . 8 . 3 [32 , 33] . Since our goal was only to reconstruct the evolutionary topology of the sequences and not any demographic parameters , we assumed a constant population size for all branches . Similarly , we chose a strict molecular clock because the Bartonella sequences from Georgian bats were all isolated at the same date and thus could not be used for calibration of another clock model; furthermore , our analysis did not seek to accurately deduce branch times , and the strict clock was adequate . No codon partitioning was used due to the fact that gltA sequences represent only a 367 base pair fragment of the entire gene; codon partitioning with limited genetic information can substantially reduce the effective sample size of estimated parameters forseparate codon positions [34] . All priors were kept at the default , diffuse settings ( see Appendix ) and the number of Markov chain Monte Carlo ( MCMC ) iterations was set to 1 . 2E8 with states sampled every 1 . 2E4 steps . Three independent chains were run and effective sample sizes and convergence ofparameters during MCMC sampling were assessed using Tracer v1 . 6 [32] . TreeAnnotator was used to find the most probable tree with burning 10% of the initial trees . The selected tree was then visualizedand edited in FigTree v1 . 4 . 2 [35] . Sequence alignment with MAFFT and phylogenetic analysis withBEAST were run using XSEDE supercomputing resources [36] , accessed through the CIPRES ScienceGateway [37] . A quantitative threshold for demarcation of sequences into genogroups was set at 96%nucleotide identity following recommendations by La Scola et al . proposed for demarcation of Bartonella species [38] . Based on this clustering scheme , branches on the phylogenetic tree were collapsed and annotated with the number of sequences included in each genogroup and the range of DNA identity values . Five genetic loci ( ftsZ , gltA , nuoG , rpoB , and groEL ) that have been previously used for bartonellacharacterization [9 , 39 , 40] were additionally investigated in 21 isolates representing 19 diverse genogroups identified based on variation of the gltA gene . Genogroups Vesp-7 , Vesp-13 , and Rhin-3 were not analyzed by MLST , while three isolates of Vesp-6 were selected for analysis to examine within-genogroup variation . The primers and cycle conditions used to generate sequences for each loci have been previously published [28 , 41–44] . Sequences were aligned with those of the reference Bartonella species and other Bartonella sequences obtained from bats with MAFFT v7 . 187 using the L-INS-i method [29] . Evolutionary model selection was performed for each marker separately and for the concatenated sequences using jModelTest2 v2 . 1 . 6 [30] based on AICc [31] . Again , the best available model for all sequences was GTR+Γ+I . A Bayesian tree was inferred using BEAST v1 . 8 . 3 [33] with the same settings and resources as for the gltA tree as described above . Separate maximum likelihood gene trees were generated using the GTRCAT model in RAxML [45] . A network phylogeny was created using the NeighborNet algorithm in SplitsTree v4 . 13 . 1 [46] and the pairwise homoplasy index [47] was calculated to test for evidence of recombination among genogroups . All unique sequences were uploaded to GenBank with accession numbers KX300105-KX300201 ( Table 2 ) . Data available from the Dryad Digital Repository: http://dx . doi . org/10 . 5061/dryad . f0k4j A logistic model was used to examine important predictors of Bartonella prevalence in Georgian bats . For this analysis , we included such variables as bat species , sex , capture location , and capture region . Additional size measurements ( weight and forearm length ) , were collapsed into a single principlecomponent that explained 95% of variation in size . However , bat size was strongly predicted by batspecies ( F = 534 . 6 , p-value = 2E-16 ) and sex ( F = 25 , p-value = 1 . 3E-6 ) , so size was not included as acovariate in the global model . Model selection was based on AICc [31] . Additional tests , including Waldtests of fixed effects and calculation of the area under the receiver operating characteristic curve ( AUC ) , were performed on models within two AICc of the top model ( ΔAICc ) [48 , 49] . Binomial confidenceintervals for Bartonella prevalence among bat species , capture locations , and bat sexes wereapproximated with the Agresti-Coull method [50] . All statistical tests were performed in R [51] andvalues were considered significant for P < 0 . 05 . Additional details of the statistical tests can be found inthe Appendix .
A total of 236 bats were sampled from eight field sites in four regions of Georgia . The sampled batsincluded eight species: common serotine , Eptesicus serotinus ( Vespertilionidae; n = 20 ) ; Schreibers's long-fingered bat , Miniopterus schreibersii sensu lato ( Miniopteridae; n = 29 ) [52]; lesser mouse-eared myotis , Myotis blythii ( Vespertilionidae; n = 75 ) ; Geoffroy's myotis , Myotis emarginatus ( Vespertilionidae; n = 42 ) ; whiskered myotis , Myotis mystacinus ( Vespertilionidae; n = 1 ) ; soprano pipistrelle , Pipistrellus pygmaeus ( Vespertilionidae; n = 13 ) ; Mediterranean horseshoe bat , Rhinolophus euryale ( Rhinolophidae; n = 29 ) ; and greater horseshoe bat , Rhinolophus ferrumequinum ( Rhinolophidae; n = 27 ) . The number of species and specimens obtained varied per site ( Table 1 ) . A total of 212 bats of seven species were available for Bartonella testing . The amount of blood from thesingle captured My . mystacinus was not sufficient for culturing . Except for this , bartonellae weresuccessfully cultured from all bat species tested ( Table 1 ) . Bartonella colonies became visible within 3–28 days after plating . All plates remained free of contamination for the entire five week period and only Bartonella-like colonies were observed . Most of the isolated colonies appeared small , circular , and raised , with smooth or rough morphology . The number of Bartonella-like colonies observed per plate ranged from 1 colony to “too numerous to count” ( TNTC ) . All the harvested colonies were confirmed as Bartonella by PCR and sequencing of gltA fragments . The overall prevalence of Bartonella in bats by culturing was 49 . 5% ( 105/212 ) . Bartonella isolates were obtained from each of the eight sites . The prevalence of culture-positive bats varied from 16 . 7% at the Lavra site in Davit Gareja to 64 . 6% at Gliana Cave in Tskaltubo . The range of prevalence varied from 16 . 7% in P . pygmaeus to 88 . 9% in Mn . schreibersii . The best model based on AICc included bat species only with a good amount of predictive power ( AUC = 0 . 71 ) [49] . Based on the Wald test , there were significant differences among bat species ( χ2 = 26 . 9 , df = 6 , p-value = 1 . 5E4 ) in Bartonella prevalence . Prevalence of Bartonella in My . blythii ( odds ratio = 3 . 4 , 95% CI = [1 . 1 , 13] , p-value = 0 . 044 ) , Mn . schreibersii ( odds ratio = 30 . 7 , 95% CI = [6 . 9 , 188 . 4] , p-value = 3 . 7E-5 ) , and R . euryale ( odds ratio = 9 , 95% CI = [2 . 4 , 40] , p-value = 0 . 0017 ) was significantly higher . Culture observations from 16 bat samples revealed morphology differences among bacterial colonies . From these samples , Bartonella-like colonies were observed with morphologies that visually varied by size ( small , large ) and/or texture ( rough , smooth ) . The number of visually different colonies per plate varied from one unique colony among TNTC similar colonies to an equal number of two unique colony morphologies . We did not attempt to estimate colony forming units ( CFU ) for individual bats suspected of coinfection . Sequencing analysis confirmed a coinfection with two different Bartonella sequences from these 16 samples ( Table 1 ) . Of those , seven were detected in Mn . schreibersii , three in My . blythii , one in My . emarginatus , two in R . euryale , and three in R . ferrumequinum ( Table 1 ) . The Bayesian analysis indicated that most gltA sequences from Georgian bats cluster closely with eachother as distinct genogroups from known Bartonella species Based on a sequence identity threshold of 96% , we identified 22 distinct genogroups . Nucleotide sequence identity values varied between 97–100% within the identified genogroups . ( Fig 1 ) Results from BLAST searches for each Bartonella genogroup from Georgian bats are compiled in Table 3 . In some cases , Georgian bat sequences matched very closely with other bartonella sequences from related bats ( same genus or family ) , but from distant locations . Other sequences , notably from genogroups Mini-1 . 1 , Mini-3 , and Vesp-6 , clustered with bartonella sequences identified in dogs from Thailand [53] and in humans ( forest workers ) from Poland [54] . The phylogeny based on concatenated sequences from five genetic loci ( ftsZ , gltA , nuoG , rpoB , and groEL ) confirmed that most Bartonella genogroups from Georgian bats formed well-supported clades ( posterior probability > 90% ) with other Bartonella genogroups identified in bats . ( Fig 2 ) Genogroups Mini-1 , Mini-1 . 1 , Mini-2 , Mini-3 , Rhin-2 , Rhin-4 , Rhin-5 , and Vesp-10 formed a well-supported clade with other Bartonella genogroups found in African pteropodid ( Eidolon helvum andRousettus aegyptiacus ) [7 , 9] , hipposiderid ( Hipposideros sp . and Triaenops persicus ) [7] , andemballonurid ( Coleura afra ) [7] bats . Genogroups Mini-1 and Mini-1 . 1 clustered with anotherBartonella genogroup found in Miniopterus schreibersii from Taiwan [13] . Genogroups Vesp-6 , Vesp-8 , Vesp-9 , and Vesp-11 formed a second clade related to Candidatus Bartonella naantaliensis found in Myotis daubentonii from Finland [5] . These two clades were linked together by a node in the phylogeny; however , the posterior probability support for this node was only 53% . Genogroups Rhin-1 , Vesp-4 , and Vesp-5 clustered with genogroup Ew from Eidolon helvum [7] . Genogroups Vesp-1 , Vesp-2 , and Vesp-3 clustered with Bartonella mayotimonensis isolated from a human endocarditis patient [26] and from European vespertilionid bats ( Eptesicus nilssonii and Myotis daubentonii ) [5] . These two clades were linked by a node , including Bartonella vinsonii subspecies , with low posterior probability support ( 50% ) . Finally , genogroup Vesp-12 clustered with genogroup E4 from Eidolon helvum [9] , as well as with Bartonella clarridgeiae and Bartonella rochalimae . The network phylogeny ( Fig 3 ) indicated that most genogroups form distinct lineages , although there is some reticulation among related genogroups . In these cases , homologous recombination might be occurring among genogroups infecting a single bat species or a group of species . However , the pairwise homoplasy index [47] did not indicate significant evidence for recombination ( mean = 0 . 6 , variance = 1 . 7E-5 , p-value = 0 . 5 ) , suggesting that the reticulations in the network did not have a strong influence on the evolutionary history of these genogroups .
This report is the first to describe the prevalence , geographic patterns , and genetic characteristics ofBartonella species found in bat communities within the southern Caucasus . Several interestingconclusions can be drawn from the study . First , we provided the evidence that Bartonella infections arewidespread and highly prevalent in all seven bats species tested . This observation is comparable to the investigations of Bartonella species in bats from other geographic regions ( e . g . , Kenya , Guatemala , and Peru ) where high prevalence and diversity of Bartonella strains have been reported [7 , 15 , 16] . However , in our study the prevalence of infection varied greatly between bat species ( nearly 89% in Mn . schreibersii and below 17% in P . pygmaeus ) as well as between study sites . The difference inprevalence between locations can be likely explained by bat community composition ( Table 1 ) . For example , P . pygmaeus was only captured at one location whereas Mn . schreibersii was collected from many sites , and the bat colony at John the Baptist Cave in Davit Gareja consisted solely of My . blythii . ( Fig 4 ) . These sampling biases should be considered when interpreting Bartonella prevalence values . We alsocannot exclude other factors , including the level of ectoparasite infestation in bats that may influence theprevalence of Bartonella in each bat species and locations . We observed several coinfections among sampled bats . The phenomenon of coinfections with two or three different Bartonella species or genotypes in blood has been described previously for rodents [55] . Interestingly , a high rate of coinfection was observed in one particular bat species , Mn . schreibersii . Seven of the 27 ( 26% ) Mn . schreibersii tested were coinfected with two different Bartonella genotypes ( Patterns of codivergence of Bartonella with their bat hosts have varied among studies and aroundthe world [7 , 15 , 16 , 20] . For Bartonella genogroups found in Georgian bats , some general patterns of hostspecificity at the genus and family level are apparent . Nearly all of the isolates ( 33/35 ) from Mn . schreibersii aligned with genogroups Mini-1 , Mini-1 . 1 . , Mini-2 , or Mini-3 ( Table 3 ) . Based on sequence identity at the gltA gene , all of these genogroups closely matched Bartonella sequences from otherMiniopterus spp . ( e . g . , Mn . griveaudi , Mn . aelleni , and Mn . gleni ) from Madagascar [11] . Thirty-seven of 38 isolates obtained from Rhinolophus spp . ( R . euryale or R . ferrumequinum ) belonged to genogroupsRhin-1 , Rhin-2 , Rhin-3 , or Rhin-4 . Genogroups Rhin-2 and Rhin-3 cluster with Bartonella sequences identified in R . acuminatus and R . sinicus from Vietnam [14] . Most isolates ( 54/60 ) obtained from vespertilionid bats ( Eptesicus , Myotis , and Pipistrellus spp . ) were members of genogroups Vesp-1 to Vesp-12 with closely matching sequences found in other vespertilionid bats [4–6 , 17 , 56] . Despite these general host associations , specificity of genogroups at the genus or family levelwas not strict , with some instances of apparent spillover of Bartonella into atypical hosts . For example , isolates of Bartonella from genogroup Mini-1 were found in E . serotinus , My . blythii , and P . pygmaeus , and isolates of Bartonella from genogroups Rhin-1 and Rhin-3 were found in My . emarginatus and My . blythii , respectively ( Table 3 ) . Though infrequent , these spillover events can be explained by the co-occurrence of these bat species in the same roosts ( Table 1 ) , wherein transmission may be facilitated by shared vectors . Ectoparasites were collected from bats at the sampled sites in Georgia in 2012 , but have not yet been identified and are thus not included in this study . However , there are numerous ectoparasite species reported on our seven focal bat species in the literature . While some ectoparasite species preferentially feed on specific bat hosts , they can also be found infrequently on other bat hosts , which may lead to transmission of bacteria . For example , bat flies ( Diptera: Nycteribiidae ) can be closely associated with one or a few bat hosts: Basilia nana with Myotis bechsteinii [57] , Basilia nattereri with Myotis nattereri [58] , Nycteribia schmidlii and Penicillidia conspicua with Miniopterus schreibersii [59] , and Phthiridium biarticulatum with Rhinolophus spp . [60] . Nevertheless , there are recorded incidents of these bat flies on other bat hosts , including the focal species in this study: Basilia nana recorded on My . blythii and My . emarginatus [61] , Basilia nattereri recorded on E . serotinus [62] , Nycteribia schmidlii recorded on My . blythii , My . emarginatus , R . euryale , and R . ferrumequinum [61 , 63] , Penicillidia conspicua on My . blythii [61] , and Phthiridium biarticulatum on E . serotinus , Mn . schreibersii , and My . emarginatus [61 , 64] . Other ectoparasites can have broader and more evenly distributed host ranges , and may be found infesting our focal bat species . Argas vespertilionis ( Ixodida: Argasidae ) has been collected from E . serotinus , My . blythii , P . pygmaeus , and R . ferrumequinum [61 , 65 , 66] . Cimex pipistrelli ( Hemiptera: Cimicidae ) has been reported parasitizing E . serotinus , My . blythii , My . emarginatus , P . pygmaeus , and R . ferrumequinum [67 , 68] . Additionally , Spinturnix myoti ( Mesostigmata: Spinturnicidae ) has been recorded on E . serotinus , Mn . schreibersii , My . blythii , R . euryale , and R . ferrumequinum [69–71] . This short review of the literature is not exhaustive , but is meant to illustrate that nonspecific parasitism by Bartonella genogroups in some bat hosts can potentially be explained by sharing of ectoparasites . Future analyses exploring the influence of ectoparasite distributions on sharing of Bartonella genogroups among bats are in progress . The sequence characterization of five house-keeping genes ( ftsZ , gltA , nuoG , rpoB , and groEL ) along with the network phylogenetic analysis strongly indicated that many genogroups characterized in our study can be segregated into new Bartonella species according to established demarcationcriteria considering loci separately [38] , with sequence identity >95% based on concatenated loci for most pairwise comparisons within each Bartonella genogroup . The host associations observed for most of identified genetic clusters also supports the biological basis for discrimination of the species . As was reasoned previously [72] , a refined approach that combines data from multiple genetic markers with ecological information about host specificity provides more reliable and tangible demarcations of Bartonella species compared to sequence analysis alone . For example , genogroups Vesp-1 , Vesp-2 , and Vesp-3 share 92% , 93% , and 92% nucleotide identity , respectively , with Bartonella mayotimonensis , the bacterial species discovered in a human patient in the United States [26] . However , B . mayotimonensis is closest ( 95% ) at the gltA locus to a sequence identified in a bat fly Anatrichobius scorzai taken from a bat Myotis keaysi in Costa Rica [17] . It is likely that clusters Vesp-1 , Vesp-2 , Vesp-3 , and the bat fly strain from Costa Rica can be assigned to the B . mayotimonensis species , but using the gltA locus alone creates an artifactual split among the genogroups . When all five concatenated loci were considered , genogroups Vesp-1 , Vesp-2 , and Vesp-3 shared pairwise sequence identities between 96 . 9–98 . 11% . Considering their relatedness and apparent specificity to vespertilionid bats ( Eptesicus , Myotis , and Pipistrellus spp . ) [5] , all of these genogroups may be included as one species . The pairwise identities of these genogroups with B . mayotimonensis ranged 95 . 1–95 . 5% , which is near the previously established minimum threshold for distinguishing between Bartonella species ( 95 . 4% for rpoB sequences [38] ) and we argue it should be considered synonymous with Vesp-1 , Vesp-2 , and Vesp-3 . Similarly , genogroups Vesp-6 and Vesp-8 were 95 . 9% identical and considering their apparent specificity to vespertilionid bats ( Eptesicus and Myotis ) [5] they may also constitute a single Bartonella species . This is also true for genogroups Vesp-4 and Vesp-5 found in one bat species , My . blythii ( 96 . 3% sequence identity ) and genogroups Mini-1 and Mini-1 . 1 found in Mn . schreibersii ( 96 . 6% sequence identity ) . The most intriguing and important results from this study is the identification of bat-borneBartonella , which are similar to Bartonella strains previously reported in humans and in dogs . Thepublic health relevance of bat-borne Bartonella infection has been discussed since the identification ofsuch bacteria in bats from Kenya [7] . Our results highlight the importance of Bartonella surveillance inbats , as it can help to identify potential wildlife reservoirs of human cases . Although some sequences of Bartonella found in Georgian bats clustered with B . mayotimonensis , the genetic distances were relatively long , as noted above . We might speculate that Bartonella more closely related to thishuman case are circulating in vespertilionid bats in the North and South America rather than in Europe . Even more unexpected was the discovery of Bartonella strains in Georgian bats which wereidentical or very similar to ones reported in forest workers from Poland . The study in Poland wasconducted to evaluate the level of exposure of 129 forest workers to diverse tick-borne pathogens [54] . Bartonella antibodies were reported in about 30% of tested individuals , but more importantly , threeserologically-positive samples were also positive for Bartonella nucleic acids by PCR and sequencing . The gltA sequences identified in that study were distinct from all previously reported . They were closest ( 90% similarity ) to B . koehlerae , B . clarridgeiae and a genotype from an arthropod from Peru . They were deposited in GenBank ( accessions HM116784 , HM116785 , and HM116786 ) as uncultured Bartonella spp . [54] . All strains identified in our study as genotype Vesp-6 were 100% identical by gltA sequences to the HM116785 sequence . Vesp-6 is the largest genogroup found in bats from Georgia containing 18 sequences from My . blythii ( n = 15 ) , My . emarginatus ( n = 2 ) , and E . serotinus ( n = 1 ) . All of these bat species are listed as occurring in southern Poland where the investigation of forest workers was conducted [73–75] . Another surprising discovery was that Bartonella strains observed in this study were closely related to those identified in stray dogs from Thailand . , Bai et al . [53] provided evidence of common Bartonella infections and diverse Bartonella species in the blood of stray dogs from Bangkok and Khon Kaen ( northeastern province of Thailand ) . Besides two Bartonella species ( B . elizabethae and B . taylorii ) detected in stray dogs from Khon Kaen , the authors also reported two genotypes ( KK20 and KK61 ) that could potentially represent a new species [53] . Performing the analysis of Bartonella strains found in bats from Georgia , we found that sequences of the strains from genogroup Mini-1 . 1 obtained from Mn . schreibersii ( n = 7 ) and R . euryale ( n = 1 ) were 99% similar to those dog sequences from Thailand ( strain KK61 , GenBank accession FJ946852 ) . Likewise , seven sequences from Mn . schreibersii ( genogroup Mini-3 ) were 99% similar to the sequences of the strain KK20 from stray dogs from Khon Kaen , Thailand ( GenBank accession FJ946854 ) . Bat species belonging to the genus Miniopterus ( e . g . , Mn . magnater and Mn . pusillus ) are present in Thailand [76] . The identification of diverse Bartonella strains in Georgian bats , which are identicalor similar to the strains previously described in humans and in companion animals in other geographic areas grants special attention in future studies to evaluate their role as potential zoonotic agents . Aparticular question remains regarding the route of transmission of bat-associated Bartonella to people . Itis easier to speculate how stray dogs , which may scavenge for grounded bats , can become infected withbat-associated Bartonella , but the question concerning transmission of bat-borne strains to humans ismore challenging [77] . However , the human case of endocarditis linked to a bat-associated Bartonellaspecies [5 , 26] suggests that such transmission can occur . Some bat ectoparasites are known tooccasionally bite humans , including Argas vespertilionis and Cimex pipistrelli [78–80] . Thus , Bartonella surveillance should include not only mammals , but also their vectors whenever possible to better understand the risks of disease transmission . | Bacteria of the genus Bartonella parasitize erythrocytes and endothelial cells of a wide range of mammals and recently were reported in bats from Africa , Asia , America , and northern Europe . A human disease case in the USA was associated with a novel Bartonella species , which later was identified in bats in Finland . This human case has demonstrated the zoonotic potential of bat-borne Bartonella and underscores the need for extended surveillance and studies of these pathogens . The present work assesses prevalence and diversity of Bartonella in bats in the country of Georgia ( southern Caucasus ) , characterizes reference strains representing diverse genogroups by variation of genetic loci , and evaluates the links between identified Bartonella genogroups and bat hosts . Importantly , some Bartonella genotypes found in bats were close or identical to those identified in dogs and humans . The data indicate that the public health impact of Bartonella carried by bats should be investigated . | [
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] | 2017 | Prevalence, diversity, and host associations of Bartonella strains in bats from Georgia (Caucasus) |
We analyzed two sets of human CD4+ nucleosomal DNA directly sequenced by Illumina ( Solexa ) high throughput sequencing method . The first set has ∼40 M sequences and was produced from the normal CD4+ T lymphocytes by micrococcal nuclease . The second set has ∼44 M sequences and was obtained from peripheral blood lymphocytes by apoptotic nucleases . The different nucleosome sets showed similar dinucleotide positioning AA/TT , GG/CC , and RR/YY ( R is purine , Y - pyrimidine ) patterns with periods of 10–10 . 4 bp . Peaks of GG/CC and AA/TT patterns were shifted by 5 bp from each other . Two types of promoters in H . sapiens: AT and GC-rich were identified . AT-rich promoters in apoptotic cell had +1 nucleosome shifts 50–60 bp downstream from those in normal lymphocytes . GC-rich promoters in apoptotic cells lost 80% of nucleosomes around transcription start sites as well as in total DNA . Nucleosome positioning was predicted by combination of {AA , TT} , {GG , CC} , {WW , SS} and {RR , YY} patterns . In our study we found that the combinations of {AA , TT} and {GG , CC} provide the best results and successfully mapped 33% of nucleosomes 147 bp long with precision ±15 bp ( only 31/147 or 21% is expected ) .
A nucleosome is the basic unit responsible for packing eukaryotic DNA into chromatin . Nucleosome position is determined during chromatin assembly and related to regulatory elements of gene expression such as promoters , transcription factors and others . The precise position of a nucleosome is important for correct interaction and function of numerous gene regulatory elements . A nucleosome consists of a segment of DNA ( ∼146 bp ) wound around a histone octamer protein core . One of the main factors that determine nucleosome position is a nucleosome DNA sequence pattern or variety of patterns . Specific patterns reflect DNA affinity to histone octamers and places nucleosomes at an essential position . Several models of nucleosome positioning have been published . The static model ( model of DNA signal ) describes the nucleosome affinity to specific periodical DNA patterns . Trifonov et al . [1] first obtained the 10 . 2 bp periodicity of certain dinucleotides in nucleosome DNA sequence . Specific patterns and/or periodicity were found in a variety of eukaryotic organisms such as Saccharomyces cerevisiae [2]–[6] , Caenorhabditis elegans [7] , Homo sapiens and Drosophila melanogaster [8] , [9] . In addition to the periodicity , G+C content and the frequency of some AT-rich tetra-nucleotides influence nucleosome occupancy in vitro and in vivo in a manner comparable to Kaplan's model [5] . G+C content is dominant , alone explaining ∼50% of the variation in nucleosome occupancy in vitro [10] . Apoptotic DNA fragmentation is a key feature of apoptosis , a type of programmed cell death . Apoptosis is characterized by activation of endogenous endonucleases with subsequent cleavage of chromatin DNA into nucleosomal fragments of roughly 180 base pairs ( bp ) and multiples thereof ( 360 , 540 etc . ) . The enzyme responsible for apoptotic DNA fragmentation is the Caspase-Activated DNase ( CAD ) . CAD cleaves the DNA at the internucleosomal linker sites between the nucleosomes , protein-containing structures that occur in chromatin at ∼180 bp intervals [11] . Micrococcal nuclease ( MNase ) also can be used for cleavage of chromatin DNA to fragments of ∼150 bp , and has been often used for nucleosome mapping . Both enzymes display sequence specificity in their preferred cleavage sites , nucleosomes produced by CAD nuclease are 8–10 bp longer than those produced with MNase . The CAD cleavage sites tend to be 4–5 bp further away from the nucleosomal dyad than MNase cleavage sites [12] . We suggest that degradation of DNA in apoptotic cells is not a random process but is controlled by a specific chromatin related system . For this reason we have investigated and compared chromatin structure and nucleosome distribution in normal and apoptotic T-cells .
Previous analysis of nucleosomal position around TSS in human showed that nucleosomes were well phased around TSS [8] . We analyzed nucleosomal distribution in the normal and apoptotic T-cells to figure out the stability and phasing of nucleosomes during lysis . Two sets of DNA nucleosomal sequences were matched on 32 , 038 human promoters ( Fig . 1A ) . A well phased +1 nucleosome is observed in both data sets but its occurrence is low around TSS in apoptotic cells . We also performed the same analysis of four sets of human CD4+ cells obtained from [13] . We divided 32 , 038 promoters into two groups by distribution of nucleosome occupancy around TSS in apoptotic cells , using a K-means clustering method . Two different groups were clearly observed: the first group had the same distribution of nucleosomes around TSS as in normal CD4+ cells [8] while the second group had very low occupancy of nucleosomes around TSS ( Fig . 1B ) . The first group of promoters had a similar occupancy of nucleosomes in both normal CD4+ cells and apoptotic lymphocytes , where a +1 nucleosome ( the first nucleosome after TSS ) was phased as well . In apoptotic cells the position of +1 nucleosome was shifted 50–60 bp downstream compared to normal cells . The second group of promoters had a significant difference between normal CD4+ and apoptotic cells: promoters from the second group of apoptotic cells had less than 20% of nucleosomes around TSS than in normal CD4+ cell cells , Z-score = −4 . 4 , P<0 . 001 ( Fig . 1C ) . Further analysis of each group revealed that the two groups had a different AT/GC ratio . The first group of promoters was AT-rich whereas the second was GC-rich ( Supplementary Fig . S1a and S1b respectively ) . Data of nucleosome occupancy in AT-rich ( group 1 ) promoters in CD4+cells obtained by [13] have the similar distribution to the data obtained by [8] . With that nucleosome occupancy in GC-rich ( group 2 ) promoters obtained from [13] has intermediate value between nucleosome occupancy in normal CD4+ cells obtained from [8] and nucleosome occupancy in the apoptotic lymphocytes obtained from [14] . ( Supplementary Fig . S2 ) . A genome-wide analysis of nucleosome positioning in human apoptotic lymphocytes revealed the same phenomenon: AT-rich DNA retained nucleosomes whereas GC-rich DNA lost 80% , Z-score = −4 . 4 ( P<0 . 001 ) of nucleosomes during apoptosis ( Supplementary Fig . S3 ) . Statistical significance was estimated by bootstrap method ( see Methods ) . A periodical distribution of ∼10 bp of AA-TT , GG-CC , WW-SS ( A , T and G , C ) and RR-YY ( R is Purine , Y is Pyrimidine ) dinucleotides were present in both sets ( Fig . 2 and Supplementary Fig . S4 , Fourier transformation ) . This observation was consistent with previous publications [15] , [16] . The positions of the individual dinucleotide peaks were the same on the patterns of both sets . However , there were two significant differences between the dinucleotide distributions of the two sets . The first difference was that nucleosomal DNA sequences of apoptotic cells were more AT-rich than those of normal cells . This difference could be explained by the loss of nucleosome from GC-rich DNA during apoptosis . The second disparity was a convex curved gradient in the patterns of the normal CD4+ cells . GG , CC and SS ( guanine or cytosine ) dinucleotide patterns had maximum values at the symmetry dyad , whereas AA , TT and WW ( adenine or thymine ) dinucleotides had minimum values in this area . Purine-pyrimidine patterns of the two sets also had similarities: both have periodicity of ∼10 bp but did not have a convex curved gradient as WW-SS dinucleotides in normal cells ( Supplementary Fig . S5 ) . We performed analysis of order and distances between dinucleotides of nucleosome patterns obtained from normal CD4+ cells by correlation between AA-TT; GG-CC; WW-SS ( week and strong ) , and YY-RR ( purine and pyrimidine ) dinucleotide patterns ( Supplementary Fig . S6 ) . Correlation between all dinucleotides had a periodicity of 10–11 bp long . This may be because the short periodical nucleotide sub patterns 10–11 bp long form conventional nucleosome patterns 146 bp long . Alternation of purine and pyrimidine and guanine and cytosine has 5 bp and was consistent with previous publication [16] , [17] . Adenine and thymine tended to form trinucleotide TTA , ATT , TAA , and AAT rather than tetranucleotide sequences AATT or TTAA . Alternation of AA and TT dinucleotide was 10 bp . This alternation and presence not only AAT and ATT but TAA and TTA trinucleotide was different from result obtained in [16] where observed patterns contained only TnAn motif but not AnTn motif . Guanine and cytosine tended to form only GGC and GCC trinucleotides but neither CGG or CGG trinucleotides , nor GGCC or CCGG tetranucleotides . Nucleosomes were phased around TSS , especially the first ( +1 ) nucleosome downstream from TSS . Position of nucleosomes can be effected by various factors such as affinity of histone octamer to specific DNA patterns and other factors related to transcription for example transcription factor binding proteins or chromatin structure remodelling complex [18] , [19] . +1 nucleosome is adjacent to TSS and is involved in the process of gene regulation , so we anticipated that +1 nucleosome had different dinucleotide pattern than regular nucleosomes . We extracted a set of +1 nucleosome sequences ( distance 90±50 bp downstream of TSS ) from both cell types to test our hypothesis . Dinucleotide patterns of +1 nucleosome differed from other nucleosome patterns and varied between normal and apoptotic cells and between AT and GC-rich promoters as well ( Fig . 3 ) . Two sets of nucleosome sequences , the first from [8] and the second from [14] were obtained using different methods . They had different length and number of flanking sequences of each DNA fragment . The set obtained by [8] contained short non paired ( only one flank ) sequences 24–25 bp long . The set obtained by [14] had paired ( two flanks ) sequences 120 bp long . There was a need to find the precise position of dyad symmetry of each set to obtain nucleosome patterns . To determine the position of the dyad symmetry of the non paired ends set we calculate the correlation between patterns obtained from two strands of DNA 146 bp long . The position with maximum correlation corresponded to the position of the nucleosome . The set obtained by [8] had dyad symmetry at 75 bp from beginning of flank sequence; the set obtained by [14] had dyad symmetry at the 85 bp . This result is consistent with previous publications [12] . The average length of DNA fragments obtained from [8] was 150 bp long and 170 bp in the set obtained from [14] ( Supplementary Fig . S7 ) . With two flanked sequences as in the set from [14] we can obtain the position of dyad symmetry using two methods: first by calculating maximum correlation between patterns of two DNA strands or by determining the midpoint between the two paired flanks . Analysis of fragments with different lengths revealed that the positions of dinucleotide patterns' peaks differ . Positions of the peaks were shifted correspondingly to the change in length of the fragments . For example if the length of a fragment increased by 10 bp the peaks shifted 5 bp to the left on the left side and 5 bp to the right on the right side ( Supplementary Fig . S8 ) . We suggest that nucleosomal patterns are dependent on the end of a fragment rather than on the midpoint between two ends . For this reason we aligned paired and non paired nucleosome DNA sequences by the 5′-ends of fragments from experimental data to obtain nucleosome patterns . We matched two sets of nucleosome and one set of promoter sequences with the whole human genome and separated nucleosome sequences 313 bp long ( including flank regions ) from 32 , 038 promoter regions ( ±1000 bp from TSS ) , obtaining 711 , 873 nucleosomes from apoptotic and 581 , 507 nucleosomes from normal CD4+ cells . We obtained two 147 bp long 16-dinucleotides patterns from each set of nucleosome sequences ( see Methods ) to predict nucleosome positions around TSS . These two sets were mapped by the patterns {AA , TT , AT} , {GG , CC , GC} , {WW-SS} , and {RR , YY} ( two last patterns are not shown ) . Patterns {AA , TT , AT} and {GG , CC , GC} detected 33% of nucleosomes with precision ±15 bp , {WW} , {SS} 29% and {RR-YY} 18% respectively . Combination of patterns {AA , TT , AT} and {GG , CC , GC} had the best result and could predict around 50% of experimental nucleosomes . Only 5% of nucleosomes were predicted by both {AA , TT , AT} and {GG , CC , GC} patterns . The maximum number of predicted nucleosomes matched with the center of nucleosome sequence , 60% more than with shuffled sequence , Z score = 10 ( Supplementary Fig . S9 ) . We matched the set of nucleosome sequences from human CD4+ cells [8] with the whole human genome and separated 183 , 372 sequences 313 bp long ( including flank regions ) of the most precise nucleosomes ( StDev = 0 ) . This set was mapped by two {AA , TT} and {GG , CC} patterns obtained from normal human CD4+ cells and two {AA , TT} dinucleotide patterns of yeast nucleosomes obtained by [4] ( Supplementary Fig . S10a–b ) . {AA , TT} and {GG , CC} patterns of normal human CD4+ cells predicted 50% and 48% of nucleosomes respectively in the interval ±36 bp ( one fourth of the length of a nucleosome sequence ) , similarly to results for yeast [3] , [13] . The two patterns predicted 71% of nucleosomes but only 26% of nucleosomes were predicted by both patterns ( Table 1 ) . Two {AA , TT} patterns of yeast [4] did not have significant correlations with human nucleosome sequences ( Supplementary Fig . S10b ) . By comparison , alternative nucleosome positioning code model ( duration Hidden Markov model , HMM ) proposed by [20] , [21] predicted 41% of nucleosomes in the interval ±36 bp . 15% of sequences were predicted as a nucleosome free region ( NFR ) using this model . The maximum of predicted nucleosomes using HMM and human dinucleotide patterns match with the center of nucleosome sequences , 4 times more than with shuffled sequences , Z score>20 ( Supplementary Fig . S10c ) . We separated experimental nucleosome sequences 146 bp long of normal CD4+ and apoptotic T-cells obtained by [8] and [14] from 32 , 038 promoter regions 2000 bp long ( ±1000 bp from TSS ) . Each set of sequences was divided into two subsets according to normal ( one nucleosome per 200 nucleotides , 16670 TSS ) or low ( one or less than one nucleosome per 1000 nucleotides , 15368 TSS ) nucleosome occupancy in promoters . We calculated the correlation of {AA , TT , AT} and {GG , CC , GC} dinucleotide patterns with two groups of promoters with normal and low nucleosome occupancy ( Supplementary Fig . S11 ) . Distribution of correlation between {AA , TT} patterns and promoter sequences in the two kinds of promoters with normal or low nucleosome occupancy were similar . The distribution had two peaks: upstream and downstream from TSS . The position of the downstream peak was 50 bp from TSS . It was located 40 bp upstream from the position of +1 nucleosomes received experimentally from normal CD4+ cells . The position of the upstream peak was 50 bp from TSS and matches to the site known as nucleosome free region of human promoters . Distribution of correlation between {GG , CC} patterns and promoter sequence differed in the two kinds of promoters with normal or low nucleosome occupancy . Distribution of correlation between {GG , CC} pattern obtained from normal cells had two peaks similar to the {AA , TT} pattern . With that the position of the downstream peak was closer to the position of +1 nucleosome . Distribution of correlation between {GG , CC} pattern and DNA obtained from apoptotic and non-apoptotic promoter sequences had only one peak 50 bp upstream from TSS and did not have peaks related to +1 nucleosome . We observed correlation between dinucleotide patterns and DNA sequence at the position of the nucleosome ( Supplementary Fig . S11 ) . However , distribution of correlation differed from the actual positions of the nucleosomes around TSS: a peak that matched with a nucleosome free region and a shift of 40 bp between downstream peak and position of +1 nucleosome ( Supplementary Fig . S11 ) . Therefore we assume that other transcription elements factors such as transcription factor binding proteins , RNA polymerase II and chromatin remodeling complex are related to displacement and/or removal of nucleosomes from preferable positions around TSS . We examined 68 promoters of 52 genes related to apoptosis obtained from [22] . The genes have positive or negative changes of expression during apoptosis ( Supplementary Table S1 ) . Promoter sequences were obtained from DBTSS ( see methods ) and data on expression of the genes was obtained from [22] . We found that promoters of genes with a positive trend of expression had much less nucleosomes during apoptosis than they had in CD4+ cells under normal conditions ( Fig . 4b ) . At the same time , promoters of genes with a negative trend of expression during apoptosis had a similar number of nucleosomes to those in CD4+ cells in normal conditions ( Fig . 4a ) . Average numbers of nucleosomes in promoters with a negative trend of expression were 7 . 3 and 4 . 5 in normal CD4+ cells and apoptotic lymphocytes , respectively . These promoters lost one third of nucleosomes during apoptosis . Average numbers of nucleosomes in promoters with a positive trend of expression were 6 . 5 and 1 . 5 in normal CD4+ and apoptotic lymphocytes , respectively . The promoters of highly expressed genes lost much more nucleosomes ( 77% ) during apoptosis than promoters of low expressed genes ( Table 2 ) . These promoters lost almost all nucleosomes on the downstream region from TSS ( Fig . 4d ) . Promoters of genes with a negative trend of expression mostly retained nucleosomes during apoptosis and had synchronized nucleosomes downstream from TSS ( Fig . 4c ) . Analysis of intrinsic DNA curvature of nucleosomal sequences revealed that DNA sequences of nucleosomes from normal cells predicted by {AA , TT} pattern have sites that are more curved at ±55 and ±65 bases from dyad symmetry ( Supplementary Fig . S12 ) . We used 11 bp window to calculate DNA curvature . DNA sequences of nucleosomes from apoptotic cells predicted by {AA , TT} pattern are more curved . We suggest that the curved sites at the end of nucleosome sequences stabilize nucleosomes during apoptosis . DNA sequences of +1 nucleosomes predicted by {AA , TT} pattern have higher gradient of curvature than the entire nucleosome set . DNA sequences of nucleosomes predicted by {GG , CC} pattern have different distribution of curvature . They have a less sharp gradient of curvature but a higher curvature site at ±15 bases from the dyad symmetry . Sites ±55 and ±65 bases from the dyad match with kinked DNA in nucleosome as observed in X-ray experiments [23] and theoretically [24] whereas sites ±15 bases from the dyad match with predicted kinked DNA in theoretical analysis only [24] .
Analysis of apoptotic and normal T-cells revealed that almost 50% of promoters lost nucleosomes ( Fig . 1 and 2 ) during apoptosis . The promoters that lost nucleosomes were GC-rich whereas promoters retaining nucleosomes were AT-rich ( Supplementary Fig . S1 ) . It was shown that GC-rich DNA is more favorable to formation of nucleosomes [10] , [25] . GC-rich chromatin is more flexible [25] and GC-rich genes tend to be more active . GC-rich chromatin contains more modified histones such as acetylated histones H3 and H4 [26] . We observed that it has high level of disassembling during apoptosis . Process of disassembling GC-rich chromatin is fast and starts before apoptotic nuclease digests significant amounts of chromatin . The yield of digesting GC-rich chromatin is 20% of the AT-rich one . Not only GC-rich promoters but all GC-rich DNA sequence lost most of its nucleosomes during apoptosis ( Supplementary Fig . S3 ) . The first explanation is that nucleosome-free DNA is easier to degrade . But that does not explain why AT-rich DNA saved most nucleosomes when GC-rich DNA lost them . We observed that when genes related to apoptosis have tendency for high expression during apoptosis they lost nucleosomes while when they have tendency for low expression they kept nucleosomes . One of the possibilities why gene expression increases when promoters lose nucleosomes is because they have less negative regulation factors ( the nucleosomes ) . On the other hand plasticity of expression of these genes is low . It is possible that high expression of apoptosis related genes is more important than plasticity during apoptosis because of it being the final process of a dying cell . Other parameters of regulation do not matter; the goal is to finish the process as quickly as possible . It's well known that +1 nucleosome is better phased than other nucleosomes [8] , [27] . Not only DNA pattern determines position of +1 nucleosome but other regulatory elements as RNA polymerase or chromatin remodelling complex [8] , [18] ) . We observed a shift of +1 nucleosome from preferable position determined by nucleosomal patterns . Patterns obtained from +1 nucleosomes were periodical with a period of about 10 bp as in canonical nucleosome patterns . We suggest that the periodicity of 10 bp of the 200 bp long region downstream of TSS [28] is essential for shifting of +1 nucleosomes and remodelling chromatin around TSS . Periodical TSS downstream region is longer than a nucleosome sequence . Nucleosomes are not shifted randomly but shifted by multiples of 10 nucleotides as 10 , 20 , 30 bp etc . from the pattern-preferable position . RNA polymerase evicts nucleosomes from NFR and shifts +1 nucleosomes downstream from TSS by tens of bases during transcription [18] . This observation is consistent with the lack of nucleosomes upstream from TSS ( NFR ) and in a short interval downstream from TSS where we observe high correlation between nucleosome patterns and sequences ( Supplementary Fig . S11 ) . New position of +1 nucleosome is determined by other non-canonical patterns . {AA , TT} pattern of +1 nucleosome has an unusual peak at dyad symmetry . This central peak could be explained by the fact that +1 nucleosomes are moved along DNA sequence with periodical AA-TT dinucleotides . In this case AA-TT dinucleotides can be relocated to dyad symmetry which is not observed in canonical AA-TT pattern . All AA and TT dinucleotides are located in places where the DNA minor groove faces the histone octamer . Six of these positions 16/17 , 26/27 , 38/39 , 109/110 , 121/122 , and 131/132 are identified as kinked [23] . Two other positions 57/58 and 88/89 were predicted as kinked too [24] . The minor groove in the two last positions is extremely narrow . On the other hand it is known that 10 bp periodical AA-TT dinucleotides have the largest DNA curvature [29] . The bent sites of DNA are delocalized throughout the nucleosome core region and have varying degrees of intrinsic curvature [30] . Intrinsic curvature of DNA around kinked sites assists forming and stabilizing nucleosome structure . {GG , CC} pattern of +1 nucleosome from AT-rich promoters have different changes . CC dinucleotide follows GG dinucleotide instead of their nonadjacent alternating arrangement in canonical pattern . GG-CC dinucleotides are flexible [25] and located between the deformed kinked sites of DNA sequence of +1 nucleosome ( Fig . 3 ) unlike conventional nucleosomes where they alternate every 5 nucleotides ( Fig . 2 ) . Analysis of distribution of three and tetra nucleotides revealed that AAT , ATT , TTA , TAA , AATT , and TTAA have the same position as AA and TT dinucleotides . GGC , GCC and GGCC have the same position as GG and CC dinucleotides , other CCG , CGG , and CCGG oligomers were not found . Human nucleosomes have a periodical pattern for each of the 16 dinucleotides . Combinations of {AA , TT} and {GG , CC} are the patterns that have the best correlation with nucleosomal sequences . Both of these patterns have two peaks of maximum correlation between dinucleotide patterns and DNA sequences in human promoters . The peaks are positioned 50 bp upstream and 50 bp downstream from TSS . Upstream peak matches with nucleosome free region ( NFR ) , and the downstream peak relates to +1 nucleosome . Despite of the upstream region having maximum correlation with {AA , TT} and {GG , CC} nucleosome patterns it has a NFR . Apparently not only DNA sequence determines nucleosome position but other transcription regulatory factors as well . Transcription binding proteins and RNA polymerase displace nucleosomes from NFR and shift +1 nucleosomes by tens of bases downstream from preferable position [8] . Both {AA , TT} patterns obtained from normal and apoptotic cells have the same position of peaks ( maximum correlation ) around TSS while {GG , CC} patterns have different distribution of correlation with promoter sequences . Distribution of correlation between {GG , CC} pattern obtained from normal T-cells and promoter sequences has similar two peaks around TSS as {AA , TT} patterns . Downstream peak is slightly closer to the position obtained by experiment . {GG , CC} pattern obtained from apoptotic lymphocytes had only one peak at the upstream region and did not have a downstream peak . This means that the apoptotic cells do not have +1 nucleosomes in GC-rich promoters . Apoptotic lymphocytes lost 80% of nucleosomes in GC-rich promoters ( 50% of all promoters ) and GC-rich DNA compared with normal CD4+ cells . The rate of disassembling chromatin of GC-rich promoters and all GC-rich DNA was high; thus they lost these nucleosomes before defragmentation of DNA by apoptotic nuclease takes place . For this reason , nucleosomes from apoptotic cells had higher occurrence of AA and TT dinucleotides than from normal CD4+ cells . Apoptotic related genes such as caspase cascade or P53 signaling pathway were highly expressed and their promoters lost 80% of nucleosomes . We compared nucleosome occupancy around TSS in apoptotic lymphocytes and normal CD4+ cells obtained by [8] , [13] , [ and 14] , and we could see similarities and differences between these sets . GC-rich promoters lost nucleosomes during apoptosis like highly expressed genes in [13] . These two groups of promoters have the same genes ( Supplementary Fig . S2 ) . AT-rich promoters in [13] have three times higher nucleosome occupancy than GC-rich , ( Z-score = 10 , Pe<0 . 001 ) . With that nucleosome occupancy in two groups of promoters obtained by [14] ) do not have so significant difference ( Figure 1 ) . All three sets have clear visible maxima of +1 nucleosomes at the distance 100–120 bp downstream from TSS . We suggest that genes with GC-rich promoter have higher expression than AT-rich in normal and apoptotic conditions . Both sets of nucleosomal sequences possessed similar dinucleotide distributions . All dinucleotide distributions: AA , TT , GG , CC and WW , SS ( W = A , T and S = G , C ) and RR , YY ( R is Purine , Y is Pyrimidine ) , had periodicity ∼10 bp ( Supplementary Fig . S4 and S5 ) . +1 nucleosomes from normal T-cells had alternating phase of GG and CC dinucleotides whereas +1 nucleosomes from apoptotic lymphocytes had adjacent GG-CC peaks . {AA , TT} pattern of +1 nucleosomes from normal CD4+ cells had a central peak of AA and TT dinucleotides matching with dyad symmetry .
We analyzed two sets of human nucleosome DNA fragments . The first set was an isolated mononucleosome-sized DNA from MNase-digested chromatin . This set was sequenced using Solexa sequencing technology by [8] . Unique non paired nucleosomal sequences 24–25 bp long were obtained as described previously [31] from the normal CD4+ T lymphocytes . The raw data consists of roughly 40 million non-paired reads 24–25 bases long . Data was obtained from the Short Read Archive ( SRA , Illumina sequencing of Human resting genomic fragment library , NCBI ) [http://www . ncbi . nlm . nih . gov/sra/SRX000168] , under accession number SRA000234 . The second set was chromatin DNA of peripheral blood lymphocytes ( from a human male ) cleaved by apoptotic nucleases and fractionated by electrophoresis on an agarose gel [14] . Bands of ∼200 bp length consisting of mononucleosomal DNA were excised . These fragments were ligated to GAII paired end adaptors by the standard Illumina protocol , amplified by PCR and sequenced on a 2nd Gen Illumina GAII sequencer . The raw data consisted of roughly 44 million paired reads of 120 bases long . Positions of raw data sequences 24–25 bases long [8] and 120 bases long [14] were matched to the H . sapiens genome by BLAT [http://genome . ucsc . edu/cgi-bin/hgBlat ? command=start] . The genome sequence of male H . sapiens ( 22 paired , X and Y chromosomes ) of total length 2 , 865 , 822 , 365 bp . was obtained from NCBI genome archive [ftp://ftp . ncbi . nih . gov/genomes/H_sapiens] , BUILD . 37 . 2 , 22 November 2010 . 32 , 038 human promoter sequences 600 bp long ( 500 bp upstream and 100 bp downstream ) were obtained from DBTSS [http://dbtss . hgc . jp/] . Positions of promoter sequences 600 bases long ( promoter sequences , DBTSS ) were matched on the H . sapiens genome by BLAT [http://genome . ucsc . edu/cgi-bin/hgBlat ? command=start] . Then positions of TSS were aligned with experimental nucleosome positions from [8] , [13] , [ and 14] on referral human genome . The sequences with 100% identity were used in further analyses . Nucleosome sequences 147 bp long were cut from referral human genome starting from 5′ end of experimental position and aligned . Then occurrence of all 16 dinucleotides was calculated for each position . The dinucleotide shuffling for each of 16 dinucleotide pairs was performed in order to estimate statistical significance of the results . The swap algorithm [32] was used to conserve dinucleotide composition because the used models of DNA property as bendability , intrinsic curvature , and sequence periodicity of dinucleotides depend on the dinucleotide composition of DNA . The calculations of the confidence interval using the classical estimation methods are not applicable if the estimator does not have an approximately normal distribution or the type of distribution is unknown . Some computational methods were developed for this case . One of useful methods is bootstrapping [33] . Correlation of the dinucleotide patterns to particular DNA sequences was calculated according to the protocol described in [15] . Nucleosome positions were predicted as a maximum correlation between DNA sequence and dinucleotide nucleosomal patterns . DNA curvature was calculated as described by [29] . We used window 11 bp long to calculate the DNA curvature . | We analyzed nucleosomal DNA of human CD4+ T normal and apoptotic lymphocytes . Dinucleotide positions ( pattern ) of AA/TT , GG/CC , WW/SS ( W is adenine or thymine , S is guanine or cytosine ) and RR/YY ( R is purine , Y - pyrimidine ) of nucleosome sequences in both cell conditions are similar and have period 10–10 . 4 bp . We successfully mapped 33% of nucleosomes with precision ±15 bp by combination of {AA , TT} , {GG , CC} , {WW , SS} and {RR , YY} patterns . We identified two types of promoters in H . sapience: AT and GC-rich . AT-rich promoters keep nucleosomes around transcription start site when GC-rich promoters lost 80% of nucleosomes during apoptosis at the same region . | [
"Abstract",
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] | [
"genetics",
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] | 2014 | Apoptotic Lymphocytes of H. sapiens Lose Nucleosomes in GC-Rich Promoters |
The Kato-Katz thick smear ( Kato-Katz ) is the diagnostic method recommended for monitoring large-scale treatment programs implemented for the control of soil-transmitted helminths ( STH ) in public health , yet it is difficult to standardize . A promising alternative is the McMaster egg counting method ( McMaster ) , commonly used in veterinary parasitology , but rarely so for the detection of STH in human stool . The Kato-Katz and McMaster methods were compared for the detection of STH in 1 , 543 subjects resident in five countries across Africa , Asia and South America . The consistency of the performance of both methods in different trials , the validity of the fixed multiplication factor employed in the Kato-Katz method and the accuracy of these methods for estimating ‘true’ drug efficacies were assessed . The Kato-Katz method detected significantly more Ascaris lumbricoides infections ( 88 . 1% vs . 75 . 6% , p<0 . 001 ) , whereas the difference in sensitivity between the two methods was non-significant for hookworm ( 78 . 3% vs . 72 . 4% ) and Trichuris trichiura ( 82 . 6% vs . 80 . 3% ) . The sensitivity of the methods varied significantly across trials and magnitude of fecal egg counts ( FEC ) . Quantitative comparison revealed a significant correlation ( Rs >0 . 32 ) in FEC between both methods , and indicated no significant difference in FEC , except for A . lumbricoides , where the Kato-Katz resulted in significantly higher FEC ( 14 , 197 eggs per gram of stool ( EPG ) vs . 5 , 982 EPG ) . For the Kato-Katz , the fixed multiplication factor resulted in significantly higher FEC than the multiplication factor adjusted for mass of feces examined for A . lumbricoides ( 16 , 538 EPG vs . 15 , 396 EPG ) and T . trichiura ( 1 , 490 EPG vs . 1 , 363 EPG ) , but not for hookworm . The McMaster provided more accurate efficacy results ( absolute difference to ‘true’ drug efficacy: 1 . 7% vs . 4 . 5% ) . The McMaster is an alternative method for monitoring large-scale treatment programs . It is a robust ( accurate multiplication factor ) and accurate ( reliable efficacy results ) method , which can be easily standardized .
Infection with soil-transmitted helminths ( STH ) , including Ascaris lumbricoides , Trichuris trichiura and hookworm ( Ancylostoma duodenale and Necator americanus ) are of major importance for public health in tropical and subtropical countries [1] , [2] . Current approaches proposed for controlling STH infections entail periodic large-scale administration of anthelmintic drugs , particularly targeting school-aged children [3] , [4] . Since such large-scale interventions are likely to intensify as more attention is given to these neglected tropical diseases [5] , monitoring drug efficacy will assume increasing importance for assessment of drug efficacy [6] and for detection of the emergence of resistance [7] , [8] . A weakness of published studies reporting anthelmintic efficacy in human trials has been the focus on qualitative diagnosis of infections ( presence/absence of STH eggs in stool ) after treatment , that is , on the cure rate . Quantitative studies , reporting the reductions in the number of eggs excreted are published more rarely ( fecal egg count reduction ( FECR ) ) [9] , yet are likely to provide the best summary measure for assessment of anthelmintic efficacy in large-scale treatment programs [10] . Although this implies the need for methods to accurately quantify egg excretion levels , studies where more than one coprological method based on fecal egg counts ( FEC ) has been used , are scarce . In addition , little is known about the variability in qualitative and quantitative diagnosis by these methods between different laboratories [11] or about the accuracy of the methods for estimating drug efficacies in monitoring programs . To date , the Kato-Katz thick smear method ( Kato-Katz ) is the diagnostic method recommended by the World Health Organization ( WHO ) for the quantification of STH eggs in human stool [12] , because of its simple format and ease-of-use in the field . The chief limitation of the Kato-Katz method , however , arises when it is used with the objective of simultaneous assessment of STH in fecal samples from subjects with multiple species infections . This is because helminth eggs of different species of helminths appear at different time intervals ( clearing times ) . In addition , hookworm eggs rapidly disappear in cleared slides , resulting in false negative test results if the interval between preparation and examination of the slides is too long ( >30 min ) . These properties have impeded standardization of the Kato-Katz method in large-scale studies at different study sites [13]–[15] . Moreover , quantification of the intensity of egg excretion is based on a fixed volume of feces , rather than the mass of feces examined . Its quantitative performance is , therefore , questionable , as the intensity of eggs excreted is expressed as the number of eggs per gram of stool ( EPG ) [16] , and the density of feces can vary . This potential bias in the value of FEC is likely to be important in programs monitoring drug efficacy by the Kato-Katz , where it may introduce additional variation in the results of FECR and broaden the confidence levels of the resulting statistical parameters . A recent study in non-human primates , demonstrated that the McMaster egg counting method ( McMaster ) holds promise for the assessment of the efficacy of anthelmintics by FECR [17] , as it provided accurate estimates of FEC , and was very easy to use , making it particularly suitable for use in poorly equipped and often short-staffed laboratories . However , despite the fact that McMaster is the method of choice for efficacy monitoring programs in veterinary medicine [18] , its performance for the detection and enumeration of STH eggs in human public health remains unknown . Therefore , a multinational study was conducted to evaluate the relative performance of the McMaster and Kato-Katz methods for monitoring drug efficacy in STH in humans . To this end , these methods were compared for both qualitative and quantitative detection of STH in human populations in Brazil , Cameroon , India , Tanzania and Vietnam . The three specific objectives of the current study were ( i ) to assess the consistency of the performance of these two methods in trials conducted in these different countries located in three continents; ( ii ) to validate the fixed multiplication factor employed in the Kato-Katz method; and ( iii ) to assess the accuracy of both methods for estimating drug efficacies based on FECR .
The overall protocol of the study was approved by the ethics committee of the Faculty of Medicine , Ghent University ( Nr B67020084254 ) , followed by a separate local ethical approval for each study site . For Brazil , approval was obtained from the institutional review board from Centro de Pesquisas René Rachou ( Nr 21/2008 ) , for Cameroon from the national ethics committee ( Nr 072/CNE/DNM08 ) , for India from the institutional review board of the Christian Medical College ( Nr 6541 ) , for Tanzania ( Nr 20 ) from the Zanzibar Health Research Council and the Ministry of Health and Social Welfare , for Vietnam by the Ministry of Health of Vietnam . All subjects included in the study , or the parents in the case of school-aged children , signed an informed consent form . The clinical trial in this study was registered under the ClinicalTrials . gov identifier NCT01087099 . The study was undertaken in five countries across Africa ( Cameroon , Tanzania ) , Asia ( India , Vietnam ) and South America ( Brazil ) . For Brazil , Cameroon , Tanzania , and Vietnam , the subjects involved also participated in a multinational trial of the efficacy of a single-dose albendazole ( 400 mg ) against STH infections , which has been presented elsewhere [10] . It is important to note that here we do not make comparison between countries as such , but rather between five distinct trials conducted in five countries in geographically contrasting regions of the world , and reference to country is only for the purpose of distinguishing between specific trials . For this multinational efficacy trial , only subjects meeting the required criteria were included: attending school , aged of 4–18 years , not experiencing a severe concurrent medical condition or diarrhea at time of first sampling . For the trial conducted in India , stool samples of patients presented at the Christian Medical College hospital in August 2009 were included . A subset of at least 100 subjects ( first screened ) from each site was included in the analysis . This sample size was based on available prevalence data [19]–[23] , and was sufficient in size to enable analysis by logistic regression modeling ( 10 infected subjects per predictor included in the model ) [24] . All stool samples were processed by the McMaster and the Kato-Katz methods as described below . For each stool sample , both methods were applied on the same day by experienced laboratory technicians blinded to any preceding test results . As described below both diagnostic methods were compared qualitatively ( sensitivity and negative predictive value ( NPV ) ) and quantitatively ( FEC ) for each of the three STH species . In addition , the validity of the fixed multiplication factor for the Kato-Katz was examined . Finally , the accuracy of both methods for estimating drug efficacy by means of FECR was assessed . Both the qualitative and quantitative comparisons for each of the three STH separately were based only on subjects meeting the following inclusion criteria: ( i ) excreting STH eggs and ( ii ) originating from a trial were a minimal of 30 infected subjects were detected at the initial survey . The number of subjects enrolled , the occurrence of STH and the number of subjects included for this qualitative and quantitative comparison are shown in Figure 1 .
The prevalence and the agreement in qualitative test results ( sensitivity and NPV ) between Kato-Katz and McMaster are summarized in Table 1 . Overall , each of the three STH showed similar prevalence , ranging from 20 . 3% for hookworm over 21 . 7% for A . lumbricoides to 26 . 1% for T . trichiura . The Kato-Katz method ( 88 . 1% ) was more sensitive for the detection of A . lumbricoides infections compared to the McMaster method ( 75 . 6% ) ( z = 4 . 01 , p<0 . 001 , n = 312 ) . For hookworm ( 78 . 3% vs . 72 . 4% ) and T . trichiura ( 82 . 6% vs . 80 . 3% ) , the difference was non-significant resulting in a p-value of 0 . 10 ( z = 1 . 65 , n = 290 ) and 0 . 43 ( z = 0 . 78 , n = 345 ) , respectively . The NPV for both methods was higher than 93% for all three STH . There was a large overlap in 95% CI between the two methods , except for A . lumbricoides where there was no overlap in 95% CI . There was considerable variation between the different trials ( countries ) in prevalence , sensitivity and to a lesser extent in NPV . A . lumbricoides was the most prevalent species in Cameroon ( 53 . 5% ) , but eggs of this parasite were rarely detected in the Vietnamese trial ( 12 . 3% ) . T . trichiura ( 53 . 8% ) and hookworm ( 58 . 3% ) were the most prevalent STHs in Tanzania , whereas in Vietnam they were less prevalent ( 22 . 1% ) and even relatively rare ( 6 . 6% ) , respectively . The explanation for the significant differences in prevalence was beyond the scope of the present study . The sensitivity of the McMaster method varied from 67 . 9% ( Brazil ) to 85 . 2% ( Cameroon ) for A . lumbricoides , from 60 . 7% ( Vietnam ) to 91 . 2% ( Tanzania ) for T . trichiura , and from 66 . 7% ( Cameroon ) to 76 . 9% ( India ) for hookworm . The sensitivity of the Kato-Katz method ranged from 67 . 6% ( Vietnam ) to 100% ( Brazil ) , from 60 . 7% ( Vietnam ) to 97 . 0% ( Cameroon ) , and from 51 . 0% ( Vietnam ) to 95 . 2% ( Brazil ) for A . lumbricoides , T . trichiura and hookworm , respectively . In Brazil ( 100% vs . 67 . 9% , n = 81 , z = 6 . 09 , p<0 . 001 ) and Tanzania ( 92 . 7% vs . 81 . 3% , n = 74 , z = 2 . 09 , p = 0 . 04 ) , significantly more A . lumbricoides infections were diagnosed with Kato-Katz , than with McMaster . For T . trichiura , this was the case in Cameroon ( 97 . 0% vs . 83 . 6% , n = 67 , z = 2 . 69 , p = 0 . 01 ) . For hookworm , a significant difference in sensitivity between the two methods was found only in Brazil ( 95 . 2% vs . 71 . 4% , n = 84 , z = 4 . 4 , p<0 . 001 ) . This variation in sensitivity of both methods could be largely explained by the magnitude of the FEC and ‘trials’ ( more than 80% of the outcome was correctly predicted ) . The predicted sensitivity of the McMaster and Kato-Katz methods for the detection of STH in the different trials is illustrated by Figure 2 . For the McMaster method , the sensitivity was equally affected by FEC at all trials for A . lumbricoides ( χ21 = 112 . 6 , p<0 . 001 ) and T . trichiura ( χ21 = 78 . 0 , p<0 . 001 ) , but not for hookworm ( χ21 = 1 . 0 , p = 0 . 31 ) , where the effect of FEC on the sensitivity differed between the different trials ( lines for trials in different countries cross one another ) ( two-way interaction FEC x trial , χ23 = 36 . 9 , p<0 . 001 ) . A significant difference between trials was found for A . lumbricoides ( χ23 = 17 . 3 , p<0 . 001 ) and hookworm ( χ23 = 33 . 5 , p<0 . 001 ) , but not for T . trichiura ( lines close to one another and overlapping ) ( χ22 = 0 . 9 , p = 0 . 64 ) . Analysis of the Kato-Katz method yielded similar models , but they differed from the results of the analysis of the McMaster method in four ways . First , the effect of intensity of FEC was less pronounced ( flat curves for A . lumbricoides and T . trichiura: χ21 = 22 . 4 , p<0 . 001 and χ21 = 3 . 9 , p<0 . 05 , respectively ) . Second , high FEC contributed significantly to the ability of Kato-Katz to detect hookworm ( χ21 = 22 . 0 , p<0 . 001 ) . Third , significant differences between trials occurred with T . trichiura ( χ22 = 27 . 8 , p<0 . 001 ) . Finally , a drop in sensitivity was observed at high FEC in the trial in Vietnam for hookworm ( χ23 = 16 . 4 , p<0 . 001 ) . Figure 3 shows the differences in predicted sensitivity between the two methods . Overall , the McMaster method often failed to detect infection when the intensity of egg excretion was low , but performed at least as well as Kato-Katz as the FEC increased . This decrease in differences in sensitivity across increasing FEC was also found more or less for each of the three STH in all trials . An exception was Vietnam , where the McMaster method was more sensitive compared to Kato-Katz as FEC increased . The NPV of both methods was high ( >80% ) for each of the three STH in all trials ( Table 1 ) , except for the detection of T . trichiura ( McMaster: 65 . 2%; Kato-Katz: 63 . 5% ) and hookworm ( McMaster: 73 . 4%; Kato-Katz: 72 . 9% ) in Tanzania . In the majority of the cases , there was a large overlap in the 95% CI of both diagnostic methods , except in the Brazilian trial for the detection of A . lumbricoides ( McMaster: [87 . 8–94 . 2%] vs . Kato-Katz: [100–100%] ) and hookworm ( [88 . 3–94 . 7%] vs . [96 . 9–99 . 7%] ) and in Cameroon for T . trichiura ( [70 . 4–90 . 5%] vs . [89 . 4–100%] ) . In each of these trials , the overlap was either small or absent . Overall there was a significant correlation between the FEC of the McMaster and those obtained by Kato-Katz ( A . lumbricoides: Rs = 0 . 70 , n = 312 , p<0 . 001; T . trichiura: Rs = 0 . 49 , n = 345 , p<0 . 001; hookworm: Rs = 0 . 32 , n = 290 , p<0 . 001 ) ( Table 2 ) . Assessment of egg excretion intensity by the Kato-Katz resulted in significantly more eggs of A . lumbricoides ( 14 , 197 EPG vs . 5 , 982 , n = 312 , p<0 . 001 ) , but not for hookworm ( 468 EPG vs . 409 , n = 290 , p = 0 . 10 ) and T . trichiura ( 784 EPG vs . 604 , n = 345 , p = 1 . 00 ) . However , these findings were not consistent across the different trials . A significant positive correlation between both methods was found for each of the three STH in all countries ( Rs = 0 . 28–0 . 88 , p<0 . 05 ) , except for trials in Tanzania and Vietnam . In Tanzania , no significant correlation was found between the two methods for the quantification of hookworm eggs ( Rs = −0 . 05 , n = 116 , p = 0 . 56 ) , while in the trial in Vietnam , a significant negative correlation was found for T . trichiura ( Rs = −0 . 24 , n = 107 , p = 0 . 01 ) and hookworm ( Rs = −0 . 49 , n = 51 , p<0 . 001 ) . A significant difference in the enumeration of STH eggs between the Kato-Katz and McMaster methods was found for Brazil , Cameroon , and Vietnam . In both the Brazilian and Cameroonian trials , the Kato-Katz method yielded higher FEC compared to the McMaster method . In the Vietnamese trial , the McMaster method resulted in detection of more T . trichiura and hookworm eggs . In trials in India and Tanzania , no significant differences between the methods were found . Overall , there was a fair agreement ( 0 . 2≤κ<0 . 4 ) between the methods in the assignment of the samples to the three levels of egg excretion intensity as recommended by WHO ( A . lumbricoides: κ = 0 . 37 ( n = 199 , p<0 . 001 ) ; T . trichiura: κ = 0 . 39 ( n = 217 , p<0 . 001 ) ; hookworm: κ = 0 . 34 ( n = 147 , p<0 . 001 ) . As shown in the Figure 4 , the McMaster method often assigned the samples to a lower level of egg excretion intensity compared to the Kato-Katz method . The mass of feces was measured in 207 Kato-Katz thick smears ( Cameroon , n = 107; Tanzania , n = 100 ) in order to assess the validity of the multiplication factor used . Overall , the adjusted multiplication factor was 23 . 7 , but it was subject to considerable variation ( 95% CI: [14 . 3–66 . 7] ) . This variation was observed in both trials ( Cameroon 23 . 3 [13 . 4–83 . 3] , and Tanzania 23 . 7 [15 . 3–54 . 3] ) ( p = 0 . 82 ) . Table 3 summarizes the quantitative agreement between the FEC based on the fixed and adjusted multiplication factor , respectively . There was a high correlation between both approaches ( Rs = 0 . 98 , n = 39–146 , p<0 . 001 ) , regardless of in which country the trial was based . However , FEC obtained on the fixed multiplication factor were significantly higher compared to those adjusted for the mass of feces examined for A . lumbricoides ( 16 , 538 EPG vs . 15 , 396 EPG , n = 99 , p<0 . 001 ) , T . trichiura ( 1 , 490 EPG vs . 1 , 363 EPG , n = 146 , p<0 . 001 ) , but not for hookworm ( 351 EPG vs . 301 EPG , n = 39 , p = 0 . 05 ) . These findings were confirmed in both countries , though not significant in the case of A . lumbricoides in Tanzania . Despite the differences in FEC , there was a substantial to almost perfect agreement in the assignment to the different levels of egg excretion intensity between both approaches ( κA . lumbricoides = 0 . 93 , n = 99 , p<0 . 001; κT . trichiura = 0 . 89 , n = 146 , p<0 . 001; κhookworm = 0 . 93 , n = 39 , p<0 . 001 ) . Overall , the mean bias ( departure from the TDE in either direction ) was 1 . 7% for McMaster and 4 . 5% for Kato-Katz . The bias for each of the two methods by trials ( different countries ) , by pre-DA FEC and by TDE are illustrated in Figure 5 . The bias for McMaster did not exceed 5% . Differences in bias across trials were small ( Cameroon: 0 . 3–4 . 6%; Tanzania: 0 . 1–3 . 6%; Vietnam: 0 . 3–4 . 7% ) , but there was a decrease in bias across both pre-DA FEC ( 100 EPG: 0 . 3–4 . 6%; 250 EPG: 0 . 3–3 . 8%; 500 EPG: 0 . 2–4 . 7%; 750 EPG: 0 . 1–2 . 1%; 1 , 000 EPG: 0 . 1–2 . 6% ) and TDE ( 90%: 0 . 1–4 . 7%; 95%: 0 . 7–2 . 4%; 99%: 0 . 1–0 . 5% ) . The bias for Kato-Katz ranged from 0 . 01% to 25 . 7% , and decreased when pre-DA FEC increased ( 100 EPG: 5 . 3–25 . 7%; 250 EPG: 0 . 2–8 . 0%; 500 EPG: 0 . 5–4 . 4%; 750 EPG: 0 . 3–4 . 0%; 1 , 000 EPG: 0 . 1–4 . 0% ) . Across trials ( Cameroon: 0 . 3–14 . 8%; Tanzania: 0 . 4–20 . 9%; Vietnam: 0 . 1–25 . 7% ) and TDE ( 90%: 0 . 5–25 . 7%; 95%: 0 . 2–17 . 9%; 99%: 0 . 1–20 . 9% ) , the bias remained largely unchanged . McMaster was significantly more accurate in estimating FECR compared to Kato-Katz ( p = 0 . 006 ) . Yet , these differences in accuracy of FECR between the methods became non-significant when only pre-DA FEC above 100 EPG were considered ( p = 0 . 40 , McMaster: 1 . 6% ( range: 0 . 01–4 . 7% ) , Kato-Katz: 2 . 0% ( range: 0 . 01–8 . 0% ) ) . A detailed overview of the calculations made is available in Table S1 .
In the present study , the McMaster and Kato-Katz were compared for both qualitative and quantitative detection of STH infections in human populations on a scale that is unprecedented in the literature . Moreover , we assessed ( i ) the consistency of the performance of these two methods across five trials in different countries , ( ii ) the validity of a fixed multiplication factor for the Kato-Katz , and ( iii ) the ability of both methods to estimate a ‘true’ drug efficacy . The qualitative comparison revealed that Kato-Katz was more sensitive for the detection of A . lumbricoides , but not for hookworm and T . trichiura . These differences in sensitivity can be explained to some extent by the intrinsic properties of the methods . In the Kato-Katz method , a larger quantity of stool is examined ( Kato-Katz: 41 . 7 mg , McMaster: 20 mg ) . Moreover , this quantity of stool is determined after the larger items in fecal debris have been removed by sieving , whereas the initial quantity of stool used in the McMaster method includes large items of debris . Finally , the McMaster method is based on the flotation of eggs , but it is clear that the buoyancy of eggs differs between the different STHs . For example , it was noticed that unfertilized eggs of A . lumbricoides ( heavier than fertilized ones ) were rarely detected in McMaster chambers , even when a high numbers of eggs was being excreted . For both methods there was a considerable variation in sensitivity between the different trials . This variation was largely explained by intensity of egg excretion ( FEC ) and factors inherent to the different laboratories involved in the trials and the countries where they were located . The probability of the diagnosis of STH infections increased as the number of eggs excreted increased . Although this finding is not unexpected , it highlights the importance of quantifying infection intensity in future studies comparing diagnostic methods . This will enable ready comparison of the sensitivity reported in different studies . The differences between countries/laboratories are not easily explained and are likely multi-factorial . An important factor , which may have contributed to this difference , is human error . Although we employed standardized methods throughout based on identical written protocols , small differences in processing samples and/or examination of the slides between laboratories/countries cannot be ruled out . This is particularly the case in the use of the Kato-Katz , for which the time between processing and examination is extremely difficult to standardize ( in the present study ranging from 30 to 60 min ) , yet crucial for the detection of hookworm eggs [12] . Similar major inter-laboratory differences also became apparent when their performance of diagnostic testing for STH was compared between European and African laboratories [11] . Therefore in future , rigorous quality control for similar studies is recommended to minimize human error . A set of control samples from the same source could have been examined independently by the different laboratories involved ( so-called ring test ) . However , this would have required preservation of the samples , which may itself have thwarted the interpretation of the quality control , and dispatch to the laboratories involved would have resulted in different time periods between collection of sample from the donor and fixation , and eventual assessment of FEC , adding yet more variables and uncertainties to the outcome . Preservation ( e . g . , formaldehyde ) is known to alter the morphology/density of eggs , resulting in false negative test results and an underestimation of FEC [29] . Moreover , when preserved by the addition of a preservant in a liquid formulation , it would no longer be possible to process samples as fresh samples , as normally done under field conditions , because then centrifugation would have to be implemented to discard the preservant prior to assay . This additional step , therefore , is likely not only to generate extra variation in the test results , but also to concentrate the eggs , hence increasing the sensitivity and FEC [30] . Other factors which cannot be excluded are differences in fecundity of worms [31] , the number of samples containing unfertilized eggs ( A . lumbricoides ) , the diet of subjects or the proportion of N . americanus/A . duodenale . The diet varied considerably across the five participating countries , and thus differences in the quality of food consumed would have created differences in fat and roughage content , which may have influenced the buoyancy of helminth eggs , particularly for the McMaster method as it is based on flotation of the eggs . Our study did not distinguish between N . americanus and A . duodenale eggs , yet it was remarkable that the effect of magnitude of FEC on sensitivity differed markedly between countries only for hookworm ( interaction term ) , suggesting that sensitivity may also vary between hookworms species . At present , it remains unclear which factor ( s ) is ( are ) causing the observed variation across laboratories/countries , however , differences in sensitivity between countries for the McMaster were less pronounced compared to Kato-Katz , indicating that the McMaster is a more robust method under field conditions . The quantitative comparison revealed an overall positive correlation . Yet , the Kato-Katz method resulted in significantly higher FECs than the McMaster method for A . lumbricoides , but not for T . trichiura or hookworm . These findings partially confirm previous studies summarized by Knopp et al . ( 2009 ) [32] , where differences in FEC between Kato-Katz and FLOTAC ( a derivative of the McMaster method ) were more pronounced for A . lumbricoides and hookworm , than for T . trichiura . It is clear that intrinsic aspects of both methods explaining the discrepancy in sensitivity for STH will also contribute to the discrepancy in FEC . In addition , it is important to bear in mind that the Kato-Katz method does not include the homogenization of a large mass of the stool sample ( 41 . 7 mg compared to 2 g for the McMaster ) prior to examination , that in certain cases may result in higher counts , as eggs are not equally distributed among the sample [33] , [34] . The level of quantitative agreement was not consistent across the different trials involved , but this can be explained mostly either by a small number of samples containing STH ( type error II ) or differences in sensitivity . The present study also confirms that the use of a fixed multiplication factor of 24 for the Kato-Katz should be revised to enable more accurate quantification of the eggs excreted [16] . Although the mean of the multiplication factor adjusted for the mass of feces examined ( 23 . 7 ) approached the conventially used 24 , there was considerable variation in the multiplication factor across the different samples ranging from 11 to 100 . Moreover , FECs based on the fixed multiplication factor resulted in significantly higher FECs compared to those based on a multiplication factor adjusted for the actual mass of feces examined , which may explain the above described difference in FEC between McMaster and Kato-Katz . The statistical simulation revealed that both methods provide reliable estimates of drug efficacies , supporting the use of both methods for monitoring large-scale treatment programs implemented for the control of STH in public health . However , the McMaster method has several advantages when a large number of samples need to be examined because the microscopy is readily performed , and all parasites can be examined simultaneously , in contrast to the Kato-Katz method where different clearing times for the different STH require re-examination at times optimal for different species [15] . These findings also confirms that FECR is preferred as a summary measure for assessment of drug efficacy , since it allows an accurate and realistic comparison of FECR across laboratories or the locations where the trials have been conducted , and this regardless of differences in sensitivity between trials . In conclusion , this multinational study highlights considerable variation in the performance of two methods used for the diagnosis of STH , particularly for the commonly used Kato-Katz . Both the McMaster and the Kato-Katz methods are valid methods for monitoring large-scale treatment administration programs . Yet , the McMaster method seems more suitable for further standardization because of its robust multiplication factor , and allowing for simultaneous detection of all species of STH . | Currently , in public health , the reduction in the number of eggs excreted in stools after drug administration is used to monitor the efficacy of drugs against parasitic worms . Yet , studies comparing diagnostic methods for the enumeration of eggs in stool are few . We compared the Kato-Katz thick smear ( Kato-Katz ) and McMaster egg counting ( McMaster ) methods , which are commonly used diagnostic methods in public and animal health , respectively , for the diagnosis and enumeration of eggs of roundworms , whipworms and hookworms in 1 , 536 stool samples from children in five trials across Africa , Asia and South America . The Kato-Katz method was the most sensitive for the detection of roundworms , but there was no significant difference in sensitivity between the methods for hookworms and whipworms . The sensitivity of the methods differed across the trials and magnitude of egg counts . The Kato-Katz method resulted in significantly higher egg counts , but these were subject to lack of accuracy caused by intrinsic properties of this method . McMaster provided more reliable estimates of drug efficacies . We conclude that the McMaster is an alternative method for monitoring large-scale treatment programs . It allows accurate monitoring of drug efficacy and can be easily performed under field conditions . | [
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] | 2011 | A Comparison of the Sensitivity and Fecal Egg Counts of the McMaster Egg Counting and Kato-Katz Thick Smear Methods for Soil-Transmitted Helminths |
The hypothesis that evolvability - the capacity to evolve by natural selection - is itself the object of natural selection is highly intriguing but remains controversial due in large part to a paucity of direct experimental evidence . The antigenic variation mechanisms of microbial pathogens provide an experimentally tractable system to test whether natural selection has favored mechanisms that increase evolvability . Many antigenic variation systems consist of paralogous unexpressed ‘cassettes’ that recombine into an expression site to rapidly alter the expressed protein . Importantly , the magnitude of antigenic change is a function of the genetic diversity among the unexpressed cassettes . Thus , evidence that selection favors among-cassette diversity is direct evidence that natural selection promotes antigenic evolvability . We used the Lyme disease bacterium , Borrelia burgdorferi , as a model to test the prediction that natural selection favors amino acid diversity among unexpressed vls cassettes and thereby promotes evolvability in a primary surface antigen , VlsE . The hypothesis that diversity among vls cassettes is favored by natural selection was supported in each B . burgdorferi strain analyzed using both classical ( dN/dS ratios ) and Bayesian population genetic analyses of genetic sequence data . This hypothesis was also supported by the conservation of highly mutable tandem-repeat structures across B . burgdorferi strains despite a near complete absence of sequence conservation . Diversification among vls cassettes due to natural selection and mutable repeat structures promotes long-term antigenic evolvability of VlsE . These findings provide a direct demonstration that molecular mechanisms that enhance evolvability of surface antigens are an evolutionary adaptation . The molecular evolutionary processes identified here can serve as a model for the evolution of antigenic evolvability in many pathogens which utilize similar strategies to establish chronic infections .
The ability of a biological trait to evolve by natural selection , or evolvability , varies substantially among species , among populations within species , and even among traits within populations . The hypothesis that differences in evolvability result from past natural selection acting on the ability to evolve , however , remains highly controversial for two primary reasons [1] , [2] , [3] . First , evolvability is a population-level phenotype and thus must be favored by the relatively weak forces generated by natural selection at the population level [4] . Second , selection on evolvability suggests the unlikely scenario that natural selection has the evolutionary foresight to adapt a population to future environmental contingencies [1] . These issues complicate the interpretation of studies which suggest that differences in evolvability arise as a result of natural selection [5] , [6] , [7] , [8] , [9] . The major objections to the evolvability-as-adaptation hypothesis lose force when applied to many microbial pathogens as a result of two biological features of these organisms . First , microbial pathogen cells within a host are often nearly clonal such that the fitness interests of individuals and groups are closely aligned [10] , [11] . The exceedingly high degree of genetic relatedness among individuals in a host has resulted in compelling evidence of selection on population-level traits that are vital to the life-histories of microbial pathogens [10] , [12] , [13] , [14] . Second , the history of consistent environmental uncertainty caused by the dynamic immune response is likely to select for antigenic novelty . Indeed , even critics of the evolvability-as-adaptation hypothesis agree that plausible examples of natural selection that promote evolvability are most likely to be found in antigenic variation loci of microbial pathogens [1] . To date , however , empirical evidence of natural selection acting to promote evolvability is primarily correlative and indirect [6] , [15] . Here we provide direct evidence of natural selection acting to promote antigenic evolvability in a well characterized microbial pathogen system . During vertebrate host infections , the immune system repeatedly eliminates lineages expressing antigens that are not sufficiently different from those previously expressed in the host . Lineages with greater potential to produce novel antigens – lineages with greater antigenic evolvability – are likely to be favored by natural selection due to their ability to rapidly adapt to the immune response of the host . In the Lyme disease bacterium , Borrelia burgdorferi , rapid evolution of the surface antigen , VlsE , is required for immune evasion and long-term infection in vertebrate hosts [16] , [17] , [18] , [19] , [20] . In vivo studies have shown that the vlsE antigen expression locus is indeed highly evolvable; a near-complete replacement of vlsE alleles occurs every 14–28 days in experimentally infected mice [21] . Novel VlsE antigens are generated through unidirectional recombination of a segment of one of the several unexpressed , paralogous vls cassettes into the vlsE expression site; six regions in the unexpressed vls cassettes are known to vary among cassettes and correspond to the antigenically important extracellular loop structures of the VlsE protein [18] , [22] , [23] . Previous studies have shown that novel VlsE antigens produced by recombination between vls cassettes and vlsE are not recognized by antibodies that target previously detected VlsE antigens [24] . Importantly , the evolvability of the vlsE locus during infection is tightly correlated with the amount of diversity among the unexpressed vls cassettes , as mutations in the vlsE locus are rare except by recombination ( Fig . 1 ) [21] . Natural selection could therefore promote evolvability of the VlsE antigen by favoring lineages with greater genetic diversity among the vls unexpressed cassettes . The evolutionary history of the vls antigenic variation system in B . burgdorferi is experimentally tractable as the reservoir of unexpressed cassettes maintains an historical record of past natural selection . Additionally , the reading frame in the unexpressed cassettes and the vlsE expression site is conserved making it possible to predict the amino acid sequence that would result due to recombination into vlsE . The proportion of ‘synonymous’ and ‘non-synonymous’ differences in the predicted reading frame of the unexpressed cassettes can be used to test whether natural selection preferentially favors among-cassette diversity that would alter the amino acid sequence of VlsE after recombination . We compared synonymous and non-synonymous differences among unexpressed cassettes within each of twelve B . burgdorferi strains [25] to statistically test the hypothesis that natural selection favors genetic diversity among the vls unexpressed cassettes in order to promote antigenic evolvability . We also examined evolutionary sequence changes in the unexpressed cassettes during experimental infections of laboratory animals . We use these findings to address the hypothesis that natural selection promotes antigenic evolvability and propose a model for the evolution and evolvability of antigenic variation systems of microbial pathogens .
Diversity among the unexpressed vls cassettes is correlated with the evolvability of the vlsE expression locus ( Fig . 1 ) . This relationship results from the fact that nearly all of the sequence evolution at vlsE is generated through unidirectional recombination of a segment of the unexpressed vls cassettes into vlsE [21] . Thus , evidence of selection for increased diversity among vls cassettes would also be evidence that natural selection favors elevated antigenic evolvability at the vlsE expression locus . To test the hypothesis that natural selection has favored increased antigenic evolvablity of VlsE in B . burgdorferi , we analysed the unexpressed vls cassette sequences in 12 independent strains of the bacterium for signatures of intragenomic diversifying selection . Such diversifying selection was strongly supported by three lines of evidence . First , non-synonymous differences ( per non-synonymous site ) are substantially more frequent than synonymous differences ( per synonymous site ) among the six regions of the unexpressed vls cassettes that correspond to the antigenically important loop regions of VlsE ( Fig . 2A ) . This pattern was observed in all 12 strains analyzed and was statistically significant in 10 strains despite inclusion of data from the highly conserved regions of the cassettes that correspond to the alpha helical domains of VlsE in the statistical analyses ( see Table S2 ) . In contrast , synonymous differences were more common than non-synonymous differences in the regions of the unexpressed cassettes homologous to the sequences encoding alpha helices on the expressed protein ( Fig . 2B ) . Taken together , these observations indicate that selection favors the potential for amino acid diversity at regions of the unexpressed cassettes that encode antigenic epitopes upon recombination into vlsE whereas regions of the unexpressed cassettes that are homologous to the alpha helices of VlsE are evolving under neutral or purifying selection . Second , and consistent with these results , codon-by-codon inferences which use a Bayesian posterior distribution to assign confidence to the ratio of non-synonymous and synonymous substitutions at each codon identified a large proportion of amino acid residues under positive selection in regions of the cassettes that correspond to the antigenically important loop domains on the surface of VlsE; most residues in the cassettes that correspond to the alpha helical domains in VlsE show signatures of stabilizing selection ( Fig . 3 ) . Finally , a codon substitution model allowing for heterogeneous selective pressures among sites in the cassettes was significantly more likely in all strains when sites under positive selection were permitted in the model [26] compared to a model allowing only purifying selection and neutral evolution [27] ( Likelihood ratio test , p<0 . 001 ) . The evidence of selection for diversity among the vls cassettes provides evidence of selection for elevated antigenic evolvability at VlsE . Diversity among the cassettes is further increased by the presence in all strains of highly-mutable tri-nucleotide tandem-repeat motifs in regions homologous to the antigenically important loop structures on VlsE ( Fig . 4A ) . These repeats are associated with a high frequency of insertion-deletion ( indel ) mutations ( Fig . 4B , Fig . S6 ) that occur as triplets in-line with the reading frame and therefore do not result in frameshift or nonsense mutations when recombined into the vlsE expression locus . Length variation due to tandem repeats is a common source of sequence diversity in vls unexpressed cassettes of B . burgdorferi ( Fig . 4C ) . In fact , indel events were significantly more likely in antigenic loop regions that contained tandem repeats compared to those in which no repeats were detected ( Permutation test , p<0 . 02 ) ( Fig . S1 ) . Interestingly , tandem repeat structures are maintained in the unexpressed cassettes of all strains despite an almost complete absence of sequence identity between strains , making them one of the only conserved features among the antigenically important domains of the vls unexpressed cassettes ( Fig . 4A ) . The tri-nucleotide repeats are highly conserved at the first and third codon positions but are significantly more variable at second codon positions resulting in differences in the amino acids they encode ( Kruskal-Wallis test , p<0 . 0001 , Fig . 4C , Fig . S2 ) . Thus , expansion and contraction of the tri-nucleotide tandem-repeat motifs results in length variation in regions of the cassettes homologous to the antigenic loop domains of VlsE but does not produce tracts consisting of a single amino acid residue . The unexpressed vls cassettes , like all repeated sequences , are susceptible to homogenization through gene conversions , duplications , and deletions [28] , [29] , [30] , [31] , [32] . This is supported by a strong phylogenetic signature of concerted evolution in the cassettes – a pattern of diversity in which sequence divergence among the cassettes is far greater between strains than within strains ( Fig . S3 ) . Indeed , amino acid sequence divergence among the unexpressed cassettes within strains is very low at regions homologous to the alpha helical domains of VlsE ( 2–8% ) and moderate ( 26–48% ) in regions homologous to the surface-exposed antigenic loops ( Fig . S3B , diagonal ) . By contrast , cassettes from different strains are very divergent at both alpha-helical regions ( 27–42% amino acid divergence; Fig . S3B , below diagonal ) and the surface-exposed antigenic loop regions ( 71–88%; Fig . S3B , above diagonal ) . Although recombination from the cassettes into the vlsE expression locus is unidirectional and does not affect the sequence of unexpressed cassettes [33] , gene conversion between cassettes will homogenize the sequences and could explain the observed pattern of concerted evolution . The signal of diversifying selection that we detect among the cassettes within strains is all the more remarkable given the clear tendency for gene conversion to eliminate differences among the cassettes and thus eliminate evidence of past natural selection for increased diversity . Sequence alterations in the vls unexpressed cassettes are much more common during the course of infections than at other genetic loci . The vls unexpressed cassettes of three clonal isolates sequenced after one year in experimentally infected mice had diverged from the inoculating strain by a total of 23 substitutions and 2 large deletions ( Fig . S4 , Fig . S5 ) . In contrast , no mutations were observed at the ospA or IGS loci ( this study ) , nor the ospC or erp loci [34] , [35] of the same isolates ( Table S3 ) . These and other surface exposed proteins are not expected to experience diversifying selection as they are either not expressed during vertebrate infections or are down-regulated once antibodies against them are developed by the host [36] , [37] , [38] , [39] . The majority of the substitutions introduced in the cassettes were identical to homologous sites from a different cassette and are likely to have resulted from gene conversion events between cassettes . Two single-nucleotide substitutions occurred in the unexpressed cassettes during experimental evolution that were not attributable to gene conversion: one in a region homologous to the antigenically important loop structures in VlsE and one in a conserved alpha helical region ( Fig . S4 ) . Interestingly , the substitution that occurred in an antigenically important region ( cassette 2 of derived isolate 1 ) was non-synonymous , whereas the substitution that occurred in an alpha helical region ( cassette 15 of derived isolate 3 ) was synonymous . These data are consistent with our analyses supporting diversifying selection on amino acid composition in the unexpressed cassettes .
Many pathogens rely on continual genetic changes to their antigens to rapidly adapt to the immune response and persist in their hosts . In B . burgdorferi , the rate of genetic change , or antigenic evolvability , at VlsE is tightly correlated with the amount of genetic diversity contained in the unexpressed vls cassettes ( Fig . 1 ) [21] . Antigenic evolvability at VlsE is required for long-term infection in vertebrate hosts [16] , [17] , [18] , [19] , [20] . Previous studies have demonstrated that novel VlsE antigens , generated through recombination between vls cassettes and the vlsE expression site , are favored by natural selection because they are not recognized by antibodies that target previously detected VlsE antigens [21] , [24] . Thus , B . burgdorferi lineages with greater diversity among the vls cassettes will have a selective advantage as they will be more antigenically evolvable ( better able to repeatedly generate novel antigens ) and thus be more likely to persist within hosts [19] , [21] . The hypothesis that natural selection favors diversity among the vls cassettes and promotes antigenic evolvability was supported by molecular evolutionary analyses of the cassettes of 12 B . burgdorferi strains ( Fig . 2 , Fig . 3 ) . Polymorphisms among the unexpressed cassettes that would result in non-synonymous changes in the antigenic loops of VlsE were up to eight times more frequent than expected by random mutation alone ( Table S2 ) , and numerous codons showed strong signatures of diversifying selection ( Fig . 3B ) . In addition , mutation-prone tandem repeats were conserved in all strains despite a near complete sequence divergence among the vls cassettes ( Fig . 4A ) . Conservation of these mutation-prone structures is consistent with the hypothesis that natural selection acts to maintain mutable sequence structures that promote diversity among unexpressed cassettes . Diversification among vls cassettes promoted by natural selection and mutable repeat structures is detectable despite the tendency for gene conversion to eliminate differences among the cassettes and thus eliminate evidence of past natural selection for increased diversity . These results support the hypothesis that natural selection favors those mutations that increase the diversity among the unexpressed cassettes in order to promote antigenic evolvability in a primary surface antigen of B . burgdorferi . Despite the limited sequence identity at the vls unexpressed cassettes among strains , six clearly identifiable regions of high variability were maintained in all strains . When recombined into the vlsE expression locus , these regions are expressed as antigenically important loop structures on the surface of the bacterium [21] , [23] ( Fig . 3A ) . Our analyses revealed that diversity among the unexpressed cassettes is elevated by natural selection favoring mutations that code for amino acid changes in the antigenically important regions ( Fig . 2 , Fig . 3 ) , thus elevating antigenic evolvability at VlsE . The regions of the unexpressed cassettes that correspond to antigenically important loop regions contained significantly more non-synonymous polymorphisms than synonymous polymorphisms , supporting the hypothesis that variation in the cassettes is maintained by diversifying selection . This conclusion was supported by three independent statistical tests of diversifying selection on the cassettes . Importantly , these signatures of selection were strong enough to overcome acknowledged detection limitations resulting from averaging frequencies of non-synonymous polymorphisms over both antigenic loop and conserved alpha helical regions , using samples from a single species [40] , and using analytical methods which yield conservative estimates [41] . The high rate of non-synonymous polymorphisms in the unexpressed cassettes likely results from random mutations that are favored by natural selection if they enhance antigenic evolvability at VlsE , as no mutational mechanism that is biased toward amino acid substitutions has been described . Antigenic evolvability at VlsE is also elevated by insertion-deletion ( indel ) mutations at unstable tandem-repeat motifs , which are present in all B . burgdorferi lineages analyzed . These repeats promote diversity in regions of the unexpressed cassettes that correspond to the antigenic loop domains in VlsE ( Fig . 4 , Fig . S1 ) . Tandem repeats are prone to length mutations caused by slipped-strand mispairing during DNA replication [9] , [42] , accounting for the high frequency of indel mutations observed in these regions ( Fig . 4B ) . Tandem repeats have previously been reported in and around antigens of numerous pathogens where the resulting increase in mutation rate is hypothesized to be an adaptation to facilitate rapid adaptation to the host immune response [6] , [43] . Their presence in the unexpressed vls cassettes of B . burgdorferi coincides with strong signatures of diversifying selection among the cassettes and provides additional empirical evidence for the adaptive significance of tandem repeats in pathogens . All repeats observed in B . burgdorferi occur as triplets in line with the reading frame and thus have the potential to alter antigenic epitopes , when recombined into VlsE , without introducing stop codons or frameshifts that would have deleterious effects on the protein structure . The tri-nucleotide repeats show little variation at the first and third codon positions but are significantly more variable at second codon positions resulting in differences in the amino acids they encode ( Fig . 4A , Fig . S2 ) . Thus , expansion and contraction of the tri-nucleotide tandem-repeat motifs results in length variation in antigenic loop regions of the cassettes but do not produce tracts consisting of a single amino acid residue . Further experimental data are needed to establish that tandem repeats in the unexpressed cassettes are maintained by natural selection in evolving populations . Nevertheless , the presence of the highly-mutable tandem repeat motifs in all strains despite the absence of sequence homology ( Fig . S3B ) suggests that mutable sequences may be selectively maintained as a mechanism to generate the genetic diversity among the cassettes that is needed to elevate antigenic evolvability at VlsE . This explanation is consistent with the analyses supporting diversifying selection in the unexpressed cassettes . Ascertaining whether evolvability is a byproduct of selection on other phenotypes or is , itself , the object of natural selection presents an empirical challenge , especially in natural populations in which the consequences of putative evolvability differences cannot be tested directly [1] , [2] , [5] , [7] , [8] , [43] . The antigenic variation system of B . burgdorferi provides a measurable phenotype , however , that can be used to test whether evolvability has been the object of natural selection . The amino acid diversity among the unexpressed vls cassettes determines the rate of evolutionary change , or evolvability , at vlsE ( Fig . 1 ) . Thus , the population genetic analyses described above provide clear evidence of selection in favor of amino acid diversity at the vls cassettes that enhances evolvability at VlsE . It is unlikely that among-cassette diversity generated by point mutations or indels is directly favored by natural selection or is favored as a byproduct of a function unrelated to the evolvability of the vlsE expression locus , because the cassettes are not expressed and serve no known function aside from recombination with vlsE . Rather , B . burgdorferi lineages with greater among-cassette diversity are more likely to persist evolutionarily because of their increased capacity for rapid sequence evolution , or evolvability , at vlsE . There are two potential scenarios that could account for our observation that natural selection promotes increased antigenic evolvability in B . burgdorferi . Selection could favor populations that can rapidly generate novel VlsE antigens during an infection because this enables rapid adaption to changes in the immune response and persistence within a host [19] , [20] , [24] . Alternatively , selection could favor individual cells which produce offspring that tend to be antigenically different because this increases the likelihood that offspring will survive the host immune response . These two possibilities—population-level and individual-level—are nearly indistinguishable in pathogens like B . burgdorferi because infections are likely to be derived from a small number of highly related cells , which has the effect of aligning the fitness interests of individuals and populations [10] , [11] . In either scenario , moreover , more diverse sets of cassettes are expected to prevail over less diverse cassettes via the well-understood population genetic process of hitchhiking , rising to high frequency as a consequence of their association with VlsE antigens that escape immune surveillance . Sexual eukaryotic pathogens conceivably experience fluctuating selective pressures similar to those experienced by B . burgdorferi and might be expected to exhibit signatures of selection on evolvability similar to those we have described here . In sexual populations , however , recombination will tend to separate the genetic drivers of rapid sequence evolution ( analogous to vls cassette diversity in B . burgdorferi ) from the beneficial alleles they create ( VlsE escape antigens ) , thereby inhibiting hitchhiking [1] , [44] . For this reason , the evolution of unambiguous signatures of selection on evolvability such as those we have reported here is likely to be restricted to cases of tight genetic linkage . The data reported here exhibit the genetic signatures expected given selection on antigenic evolvability . Future experimental evolution assays can be used to experimentally validate these conclusions and to further dissect the molecular mechanisms involved . For example , assays competing isogenic B . burgdorferi strains that differ only in the diversity among vls cassettes in a repeated mouse-tick-mouse transmission cycle would provide an experimentally-controlled evaluation of the strength of selection on evolvability in this system . Analyses of the mutations introduced into the cassettes of each strain at each mouse-tick-mouse transmission could also elucidate the role of tandem repeats and other mechanisms that result in greater diversity among cassettes . In particular , comparing experimental infections in immunologically active and immunocompromised mice can determine the role of cassette diversity in establishing and maintaining persistent infection during an adaptive host immune response . Our results support a model of evolution in the vls unexpressed cassettes in which strong diversifying selection leads to elevated amino acid diversity in regions that correspond to antigenically important domains in order to promote the evolvability at VlsE that allows for continual immune evasion . Such selection for cassette diversity could be a common strategy for maintaining antigenic evolvability in a diverse range of pathogens that generate antigenic variation by intragenomic recombination [45] , [46] , [47] , [48] , [49] , [50] , [51] . For example , polymorphisms in the semi-variable and hyper-variable regions of the unexpressed cassettes of the pilE locus of Neisseria gonorrhoeae are maintained despite common gene conversion events [49] , possibly due to evolutionary processes similar to those described here . Similar analyses to those conducted in this study can be used to establish whether the model of cassette evolution proposed here maintains antigenic evolvability in other pathogens . Further , the finding that selection promotes antigenic evolvability in microparasites may offer an explanation for numerous observations of sophisticated variation systems used to adapt to rapidly changing environments [43] , [45] , [46] , [47] .
We analyzed the genetic diversity of the vls unexpressed cassettes both within and among B . burgdorferi strains for which genome sequence data was available [25] ( Table S1 ) . Amino acid sequences of individual cassettes from all strains were aligned using MAFFT [52] and converted into the corresponding codon alignment using PAL2NAL [53] . The hypothesis that diversity among unexpressed cassettes is favored by natural selection to promote antigenic evolvability was tested using three molecular evolutionary analyses . First , the proportion of non-synonymous polymorphic nucleotides ( dN ) and synonymous polymorphic nucleotides ( dS ) were estimated [54] for all pair-wise comparisons among unexpressed cassette alignments within each strain . The average across the pair-wise comparisons for dN and dS were calculated for both antigenic loop and alpha helical regions to test for evidence of selection . Evidence of selection was detected using a Z-test of the hypothesis that dN is significantly different than dS across the complete alignment ( antigenic loop and alpha helical regions ) in each strain [55] using the MEGA v . 5 . 5 software [56] with variance estimates calculated using 1000 bootstrap replicates . Second , codon-by-codon analyses of positive selection among cassettes within each strain was conducted using the Selecton v . 2 . 4 server [41] . Codons under positive or purifying selection were identified based on the 95% confidence interval of Bayesian posterior dN/dS estimates at each codon in the alignment . The hypothesis of positive selection was further tested via likelihood ratio tests comparing of the likelihood of the M8a model of evolution which allows only stabilizing selection and neutral evolution [26] to the likelihood of the M8 model which also allows positive selection [27] . Tandem-repeat motifs were identified using the mreps server [57] and majority-rule consensus sequences of the repeats were reported ( 75% threshold ) . The frequency of insertion-deletion ( indel ) mutations was calculated for each strain as the proportion of nucleotide sequences containing gaps at each site in the multiple sequence alignments of the cassette regions ( excluding large indel mutations that are not the result of tandem repeat length variation ) . Structural models of VlsE in B . burgdorferi strains JD1 and N40 were predicted using the SWISS-MODEL server [58] based on homology to the 1L8W chain A of the resolved protein structure in strain B31 [23] . Structural models for VlsE in B . burgdorferi strain JD1 were determined using the reported VlsE sequence ( Genbank [CP002306] ) , whereas the protein structure in strain N40 was predicted by replacing the cassette region of vlsE in B31 with the unexpressed cassettes of N40 . A nucleotide Neighbour-Joining ( NJ ) phylogeny ( Jukes Cantor distance , 1000 bootstrap replicates ) was constructed in Geneious v . 5 . 3 [59] . Pair-wise amino acid distance matrices were produced by averaging the Hamming distances of nucleotide sequence alignments within and among strains at both the antigenic loop and alpha helical regions . A clonal isolate of B . burgdorferi strain N40 [60] was intradermally inoculated into three C3H/HeN mice and re-isolated after 12 months from the blood ( derived isolate 1 ( 36B ) , derived isolate 2 ( 44B ) , and derived isolate 3 ( 39B ) ) as previously described [34] . The clonal N40 parent isolate ( cN40 ) and each of the derived isolates were grown from frozen stocks at 34°C in BSK-H medium supplemented with 6% rabbit serum ( Sigma Aldrich ) to a density of ∼5*107 cells/ml and the genomic DNA was purified using the DNeasy Blood and Tissue Kit protocol for gram negative bacteria ( Qiagen; Valencia , CA ) . The vls cassette region from each isolate was cloned into BigEasy v2 . 0 Linear Cloning Kit ( Lucigen; Middleton , WI ) by either 1 . ligating total genomic DNA treated with Mung Bean Nuclease and DraI ( New England Biolabs ( NEB ) ; Beverly , MA ) into the cloning vector ( derived isolate 2 ( 44B ) ) or 2 . ligating a long-range PCR fragment containing the unexpressed cassettes into the cloning vector ( isolates cN40 , 36B , and 39B ) . Long-range PCR amplification was conducted using the primers N40-vlsLR-R ( 5′ Phos - GCT GGA CTT GAA TTT GGT AGG GAT TC 3′ ) and N40-vlsLR-F ( 5′ Phos - GGT GAT GGT GCC GAT TCA AAA TCT GG 3′ ) which anneal to the unique conserved regions flanking the unexpressed cassettes . The PCR reactions contained 2–6 ng/µl of genomic DNA , 0 . 2 mM of each dNTP , 0 . 4 µM of each primer , 7% DMSO , 1× GC buffer and 0 . 02 U/µl of Phusion Hot Start II DNA Polymerase ( NEB ) and were amplified with 25 cycles of 30 s at 98°C and 90 s at 72°C . BigEasy vectors containing an intact cassette region were amplified in E . coli and isolated using a Qiagen Mini-prep kit . The TSA cell line used in transformation of the BigEasy vector ( Lucigen ) contains RecA and EndA mutations that make them recombination-deficient and minimize the chance that gene-conversion was introduced during cloning . Purified plasmid DNA was sheared using a Nebulizer ( Invitrogen; Carlsbad , CA ) to 500–3000 bps , purified by ethanol precipitation , and subcloned using the Zero Blunt PCR cloning kit for sequencing ( Invitrogen ) . Additionally , the ospA and rrs-rrlA IGS loci were PCR amplified from each culture as previously described [61] and sequenced for comparison . Each shotgun sequencing fragment was aligned independently to the sequence reported for N40 plasmid lp36-1 [25] ( Genbank [CP002230] ) to minimize errors in shotgun sequencing assembly . All regions with reported sequence changes received between 6× and 11× coverage in the assembly . Additional notes on the cassette sequencing methodology are provided with supplemental Figure S3 . | The hypothesis that natural selection shapes the ability of a population to evolve is highly controversial due primarily to a paucity of empirical evidence . However , the ability to constantly and rapidly evolve may be selectively adaptive in pathogens due to the constantly changing environment created by the vertebrate immune response . The Lyme disease bacterium , Borrelia burgdorferi , evades immune detection through a system consisting of numerous unexpressed ‘cassette’ sequences that alter the expressed protein via recombination . Importantly , the potential for evolutionary change in the expressed protein , and thus its ability to repeatedly escape a directed immune response , is correlated with the diversity among the cassettes . We analyzed genetic data from diverse B . burgdorferi strains to test the hypothesis that natural selection favors diversity among the unexpressed cassettes in order to promote evolvability . The data provide significant evidence of natural selection acting to increase among-cassette diversity , but only in regions that are targeted by immune antibodies when expressed . Further , these antigenically-important regions contain mutation-prone DNA sequence structures that are conserved despite high levels of sequence divergence among strains . The empirical evidence of selection favoring mutation-prone sequences and favoring among-cassette diversity supports the hypothesis that natural selection can shape the evolvability of populations . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Natural Selection Promotes Antigenic Evolvability |
HCV ( hepatitis C virus ) research , including therapeutics and vaccine development , has been hampered by the lack of suitable tissue culture models . Development of cell culture systems for the growth of the most drug-resistant HCV genotype ( 1b ) as well as natural isolates has remained a challenge . Transfection of cultured cells with adenovirus-associated RNAI ( VA RNAI ) , a known interferon ( IFN ) antagonist and inhibitor of dsRNA-mediated antiviral pathways , enhanced the growth of plasma-derived HCV genotype 1b . Furthermore , persistent viral growth was achieved after passaging through IFN-α/β-deficient VeroE6 cells for 2 years . Persistently infected cells were maintained in culture for an additional 4 years , and the virus rescued from these cells induced strong cytopathic effect ( CPE ) . Using a CPE-based assay , we measured inhibition of viral production by anti-HCV specific inhibitors , including 2′-C-Methyl-D-Adenosine , demonstrating its utility for the evaluation of HCV antivirals . This virus constitutes a novel tool for the study of one of the most relevant strains of HCV , genotype 1b , which will now be available for HCV life cycle research and useful for the development of new therapeutics .
Hepatitis C virus ( HCV ) , a member of the Flaviviridae family , is an enveloped , positive-sense RNA virus that infects approximately 170 million people worldwide . Chronic HCV infection can lead to serious liver disease , including cirrhosis and hepatocellular carcinoma . Current therapy with pegylated interferon ( IFN ) and ribavirin is expensive , associated with serious side effects and only effective in about 50% of treated patients . Of the six major genotypes of HCV , the relatively IFN-resistant genotypes 1a and 1b predominate in the United States , Japan and Western Europe [1] . Recent developments have advanced the HCV research field whereby a single virus isolate ( cloned from a patient with a rare case of fulminant hepatitis C ) , JFH-1 , or derivatives of that isolate have been shown to robustly replicate in the human hepatoma cell line , Huh7 [2] , [3] . Full-length replicons constructed by adding the structural coding regions from another genotype 2a virus , J6 [2] , were shown to not only replicate in culture , but to efficiently produce infectious viral particles [2]–[6] . Replication of the J6/JFH-1 virus in Huh7 cells was more robust in a derivative cell line , termed Huh7 . 5 , which was selected from replicon-containing Huh7 cells after curative treatment with IFN [6] , [7] . An infectious system based on the use of a Vero cell line and the pHCV-WHU-1 consensus clone ( genotype 1b ) was reported to produce high levels of HCV genome ( >108 copies/ml ) with the aid of T7 polymerase provided by recombinant vaccinia virus vTF7-3 [8] . While the current cell culture systems utilize viruses that were initially replicon-derived from the JFH-1 isolate [2]–[4] , [9]–[15] , from HCV genotype 1b consensus clones [8] , [16] or from the HCV genotype 1a prototype virus ( H77-S ) [10] , there remains the need for a system that would be permissive for a wide variety of HCV strains found in nature . Human hepatocytes ( including fetal hepatocytes ) have been reported to support virus replication after RNA transfection or infection with patient sera [17] , [18] . However , the use of primary cells has several technical limitations because they proliferate poorly in vitro and divide only a few times . Primary cultures could be maintained for longer periods of time only if the cells were immortalized by introducing oncogenes , a procedure that typically results in changes of the hepatocyte characteristics and function [17] . One approach to overcoming the obstacle of limited HCV growth in culture is to identify the mechanism of restriction . Activation of alpha/beta interferon ( IFN-α/β ) production is a key step in the innate response to viral infection and to the presence of double-stranded RNA ( dsRNA ) synthesized during replication of many viruses [19] . Several cellular dsRNA-binding proteins have been implicated in the IFN-response to infection . For instance , we have previously identified the adenosine deaminase that acts on dsRNA ( ADAR1 ) as an IFN-α/β-induced protein that is a potent inhibitor of HCV replicon growth in cell culture [20] . ADAR1 converts adenosines in viral RNA to inosine [21] , rendering the RNA inactive [20] . Both ADAR1 and the IFN-induced dsRNA-activated protein kinase ( PKR ) are inhibited by the small adenovirus-associated RNA ( VA RNAI ) [20] , [22] , [23] . When VA RNAI is transfected into replicon containing Huh7 cells , it increases replication by 40-fold [20] , suggesting that these IFN-induced proteins impose critical limitations to HCV replication . In this study , we achieved growth of an HCV genotype 1b isolate by inoculating IFN-deficient cells with human plasma from an infected patient . Viral replication was stimulated further with the addition of VA RNAI , and led to the creation of a cell line persistently infected with HCV . More interestingly , the virus isolated from these cultures has the potential to induce cytopathic effects in the persistently infected VeroE6 cells and cause massive cell death in Huh7 . 5 cells .
Based on our previous finding that VA RNAI enhanced HCV replication in the replicon system [20] , we hypothesized that virus growth in cell culture may also be inhibited by IFN-induced pathways . Our approach was to employ VeroE6 cells , which contain a homozygous-allelic deletion of the IFN-α/β genes [24] , [25] , yet retain the ability to express IFN-induced genes such us ADAR1 and PKR , which can be activated during virus infection . Cells were transfected with a plasmid encoding VA RNAI ( pVA; [26] , [27] ) , and then inoculated once with HCV genotype 1b infectious human plasma , LB [28] , [29] ( Figure 1 ) or with genotype 1a infectious chimpanzee serum [30] ( see Text S1 and Table S1 ) . Normal human serum was used as a negative control . Infected cells were passaged ( division ratio of 1∶6 ) every seven days for 20 weeks with weekly pVA re-transfection ( Table 1 ) . HCV RNA was detected sporadically in the virus-infected cells after week 20 . Nevertheless , passages were continued weekly in the absence of VA RNAI . Surprisingly , after 2 years of passage in culture in the absence of VA RNAI , HCV RNA was detected consistently , indicating that the virus was able to establish a persistent infection . No virus was detected after 20 weeks in the control experiment that was infected with normal human serum . The possibility that the positive PCR results were due to RNA carry-over is extremely low since the cells had been diluted ∼1 . 94×1096 after 2 years in culture . LB-plasma persistently infected VeroE6 cells ( LB-piVe cells ) were screened with anti-human- and anti-monkey-specific primers to ensure that the cultures were not contaminated with human cells ( data not shown ) . Sequence analysis showed that the persistent virus ( LB-piVe virus ) shares 99 . 7% amino acid homology with the parental genotype 1b virus and contains only 10 amino acid changes in the nonstructural region . Sequences have been deposited into GenBank . A representative nucleotide sequence of the LB-piVe virus , aligned with the parental virus sequence and a prototype genotype 1b virus is shown in Figure S1 . The complete sequence alignment and reverse genetics studies are being conducted and will be presented for publication in the future . To visualize HCV antigen expression , we stained LB-piVe fixed cells with polyclonal anti-HCV serum [30] ( Figure 1A and B ) or anti-NS5A monoclonal antibodies ( Figure 1C and D ) . To increase the sensitivity of the immunofluorescence assay , we enriched the cell culture by selecting virus-containing , antigen-expressing cells ( Figure 1B and D ) using a cell panning procedure ( see Materials and Methods ) . LB-piVe cells expressed HCV antigens in both perinuclear and cytoplasmic regions of the cells as expected ( Figure 1A–D , right panels ) . The results suggest that the addition of VA RNAI may broaden cellular tropism by allowing persistent growth and replication of HCV from plasma in non-hepatic VeroE6 cells . Western blot analysis of LB-piVe ( after two rounds of cell panning ) and J6/JFH-1-infected cell extracts demonstrates that HCV proteins were expressed at detectable levels ( Figure 1E and F ) . Because the proportion of immunofluorescent cells was low , we then compared the levels of viral RNA in filter-clarified supernatants from LB-piVe panned cells versus J6/JFH-1-infected Huh7 . 5 cells ( Figure 1G ) . J6/JFH-1 yielded 9 . 2×107 RNA copies/ml , while LB-piVe yielded 1×104 RNA copies/ml; ( see Figure 1G ) . Persistent infection could only be maintained at a low viral titer , as attempts to obtain the higher viral yields by cell panning ( Figure 1B and D ) resulted in viral instability due to cell cytolysis ( data not shown ) . Interestingly , we observed evidence of CPE in LB-piVe cells after 2 years in culture ( Figure 2A , right ) . To demonstrate that the virus from the persistently-infected cells was infectious , filter-clarified culture supernatants from LB-piVe cells were used to inoculate naïve Huh7 . 5 cells . The infected Huh7 . 5 cells demonstrated enhanced CPE compared to the parental LB-piVe cells , and resulted in gross cell death after 5 days ( Figure 2B , right ) . Viral antigens were detected at 3 days post-transfer of supernatants by immunostaining the infected Huh7 . 5 cells ( Figure 2C , right ) and also by immunoblotting Huh7 . 5 cell extracts ( Figure 2D ) with anti-NS5A antibody . The level of CPE observed in Huh7 . 5 cells ( Figure 2E , micrographs ) was directly related to the amount of viral RNA in the inoculum ( Figure 2E , histogram ) . Taken together , our results show that viral infectivity can be transferred from the persistently infected cell line , LB-piVe , to naïve hepatic cells and that the level of CPE correlates with the level of input viral RNA . Based on these unique characteristics of LB-piVe , we developed a CPE-based end-point dilution assay for quantification of viral titers . Naïve Huh7 . 5 cells were plated in 96-well plates and then infected with serial dilutions of virus-containing filter-clarified supernatants ( see Materials and Methods ) . Five days post-infection ( dpi ) , cells were observed by light microscopy and those wells showing CPE were assigned a positive result . The 50% tissue culture infectious dose ( TCID50 ) was calculated using the method of Reed and Muench [31] . To further confirm that the CPE was linked to virus infection , we employed the end-point dilution assay ( based on visualization of cell death ) to study virus neutralization . Huh7 . 5 cells were first incubated with antibodies to the putative viral receptor CD81 [32]–[34] and then infected with serial dilutions of filter-clarified supernatants of LB-piVe ( Figure 3A ) or J6/JFH-1 ( Figure 3B ) . Viral titers were determined as described in Materials and Methods . This study showed that anti-CD81 antibodies reduced genotype 1b LB-piVe viral titers by ∼1×log10 ( Figure 3A ) , similar to that observed for the genotype 2a virus J6/JFH-1 ( Figure 3B ) . Pre-incubation of LB-piVe virus with HCV-specific immunoglobulin intravenous ( HCIGIV ) [35] ( Figure 3C ) or anti-E2 monoclonal antibodies [36] ( Figure 3D ) also inhibited virus growth similarly . However , pre-incubation of LB-piVe virus or J6/JFH-1 with normal IGIV or an isotype-matched negative control antibody did not affect viral titers . It may be noted that the anti-E2 monoclonal antibodies were generated to genotype 1a recombinant E2 proteins , including the hypervariable region . Consequently , their ability to neutralize a genotype 1b virus could be limited to some extent as reflected by the 60% decrease in viral titers observed . These neutralization experiments demonstrate that Huh7 . 5 cell death resulted from the transfer of virus from the LB-piVe cells , and that infection and viral spread in Huh7 . 5 cells was blocked by the addition of HCV-specific antibodies . We then explored the utility of the CPE-based assay to screen therapeutics by treating virus-infected cells with HCV inhibitors . We used a well characterized inhibitor of the HCV polymerase , 2′-C-Methyl-D-Adenosine ( 2′-C-Me-A ) [37] . J6/JFH-1 and LB-piVe infected cells were incubated with complete growth medium containing a range of 2′-C-Me-A , from 0 . 05 to 1 µM . Titers were determined as described in Materials and Methods . The results showed that LB-piVe growth was reduced after treatment with 2′-C-Me-A ( Figure 4A ) , with a 50% effective concentration ( EC50 ) value in the nanomolar range , comparable to that observed for J6/JFH-1 ( Figure 4B ) . Additionally , we tested an HCV-specific small inhibitory RNA to knock-down viral titer ( siRNA 313; [38] , for details see Materials and Methods ) . In this assay , siRNA 313 inhibited CPE caused by LB-piVe virus by ∼80% ( Figure 4C ) , while J6/JFH-1 was inhibited by >90% ( Figure 4D ) . When LB-piVe cells were treated with IFN , the virus continued to replicate . We measured LB-piVe viral titers in cells that were treated with 0 , 10 , 100 or 1000 IU/mL of IFN for 24 , 48 and 72 hr ( Figure 4E ) . LB-piVe titer decreased slightly only at 72 hr with 100 or 1000 IU/mL , but the values were not significantly lower than for 48hr . In contrast , J6/JFH-1 titer decreased by 5-fold when treated with 10 IU/mL for 48 hr and there was no detectable virus with 1000 IU/mL ( Figure 4F ) . To ensure that the LB-piVe cells had not become insensitive to IFN treatment , we measured LB-piVe titers in Huh7 . 5 cells that were also treated with IFN ( Figure 4G ) . There was no significant effect on LB-piVe titers by treating the Huh7 . 5 cells with IFN for 5 days at 10 IU/mL and no change after treatment with 1000 IU/mL . A decrease of 0 . 2 log10 was observed when comparing 10 IU/mL vs 1000 IU/mL over 5 days ( Figure 4G ) . This small difference may be attributed to the long incubation period . These results indicate that the LB-piVe virus and not the persistently infected cells are relatively IFN resistant as would be expected for a natural genotype 1b virus isolate . To ensure that the virus had not acquired IFN resistance through passage in culture , we compared the effects of IFN treatment on the parental virus ( LB ) with the persistent virus in VeroE6 cells ( Figure 4H ) . There was little effect after treating the LB virus for 24 , 48 or 72 hr , demonstrating that this parental genotype 1b strain was relatively IFN resistant , as expected . These inhibition studies demonstrated that the LB-piVe virus was sensitive to HCV-specific inhibitors and that the CPE-based assay provides an easy and quantitative method for measuring the efficacy of antiviral compounds . Furthermore , the LB-piVe virus behaved like the wild-type parental virus and maintained its relative IFN resistance . VeroE6 cells express the putative receptors for HCV [39] . To elucidate the general growth properties of HCV in these cells , we tested their ability to support replication of the IFN-sensitive genotype 2a virus ( Figure 5 ) . Cells were mock infected ( Figure 5A and B , left ) or J6/JFH-1-infected ( Fig . 5A and B , right ) , and immunostained at 4 dpi with anti-NS5A antibody . J6/JFH-1 was infectious and replicated in many Huh7 . 5 cells ( Figure 5B , right ) and fewer VeroE6 cells ( Figure 5A , right ) . An increase in the number , size and intensity of the foci in J6/JFH-1-infected Huh7 . 5 cells was observed in the presence of wild-type VA RNAI ( WT ) ( Figure 5C , right panel ) but not mutant VA RNAI ( dl1 ) ( Figure 5C , center panel ) , demonstrating that J6/JFH-1 growth was improved by VA RNAI . These data suggest that while the paracrine and autocrine IFN pathways may be defective in VeroE6 cells , additional cellular factors antagonized by VA RNAI are limiting for HCV growth . We also determined the viral titer of the J6/JFH-1-infected Huh7 . 5 cells , which were transfected with mutant VA RNAI ( dl1 ) or wild-type VA RNAI ( WT ) ( Figure 5D ) . The results showed that wild-type VA RNAI led to an increase in J6/JFH-1 viral titers by >1 . 5 log units ( Figure 5D , WT ) , and further confirms that VA RNAI enhances both growth and replication of this genotype 2a virus , probably through inhibition of dsRNA-activated pathways . The amount of J6/JFH-1 RNA was quantitated ( relative to GAPDH ) by real-time RT-PCR . Replication of J6/JFH-1 increased by 15-fold ( ±3-fold SEM ) in Huh7 . 5 cells with VA RNAI , whereas VeroE6 cells containing VA RNAI yielded 7-fold ( ±2-fold SEM ) more viral RNA than cells with mutant ( dl1 ) VA RNAI ( Figure 5E ) . While the exact mechanism is unknown , a 15-fold increase in viral RNA suggests that the fate of viral RNA in the cells may be affected by the presence of VA RNAI , consistent with our previous findings that ADAR1 was inhibited in replicon cells containing VA RNAI [20] . J6/JFH-1 RNA titers were enhanced by VA RNAI twofold in Huh7 . 5 cells ( Figure 5E , left ) over VeroE6 cells ( Figure 5E , right ) , illustrating preferential growth of J6/JFH-1 in Huh7 . 5 cells and demonstrating that HCV genotype 2a growth , in addition to genotypes 1a and 1b , is enhanced by VA RNAI . VA RNAI allowed the establishment of a persistently infected cell line and increased growth of LB-piVe ( Figure 6A ) and J6/JFH-1 ( Figure 5A–E ) . To evaluate the effects of VA RNAI on the parental virus during the first few days of infection , we examined its effect on HCV RNA stability by comparing the relative increase in viral RNA in VeroE6 cells to that in Huh7 . 5 cells that were inoculated with the same HCV-positive human plasma ( LB , [28] , [29] ) that was used to establish the persistently infected cell line LB-piVe . Naïve VeroE6 and Huh7 . 5 cells were transfected with pVA before inoculation with LB plasma ( transient infection ) ( Figure 6B–D ) . RNA was extracted from cell lysates on the days indicated ( Figure 6B , C ) and HCV RNA was measured by quantitative RT-PCR . The relative amount of HCV RNA in pVA-transfected cells versus pVA-untransfected cells ( Figure 6B ) increased 60-fold after 8 days of transient infection in VeroE6 cells . However , in transiently infected Huh7 . 5 cells , the relative amount of HCV RNA did not increase with the addition of VA RNAI over 8 days ( Figure 6B ) , consistent with our inability to obtain a persistently infected Huh7 . 5 cell line . It may be noted here that the CT values of GAPDH employed as a normalization control in these experiments , were consistent among cells in the presence or absence of pVA . Thus , the dramatic increase in viral RNA in VeroE6 cells may be due to factors other than the variation in transcript levels of GAPDH reported in liver cells [40]–[43] . Furthermore , when the results were expressed in terms of absolute HCV RNA copy number ( using an HCV RNA standard curve and measuring copies per ml; Figure 6C ) , the number of RNA copies remained stable in Huh7 . 5 cells , suggesting that the level of replication may be equal to the degradation of HCV RNA , with or without the addition of VA RNAI . In contrast , a precipitous decline in HCV RNA copy number was observed in transiently infected VeroE6 cells in the absence of VA RNAI , while the levels remained relatively stable in cells that contained VA RNAI ( Figure 6C ) , thus indicating that VA RNAI has an effect on viral RNA over 8 days in VeroE6 cells . We speculate that this effect may be due to; ( i ) altering the RNA synthesis rate , ( ii ) altering the degradation rate of HCV RNA molecules other than the input RNA , or ( iii ) inhibition of an RNA degradation pathway . Incubation of transiently infected cells with an RNA polymerase inhibitor helped to assess the level of viral RNA in the absence of viral replication . We treated the parental virus with 2′-C-Me-A , which resulted in similar HCV RNA levels when cells were transfected with either WT- or mutant-VA RNAI ( Figure 6D ) . Initially there was a decrease in viral RNA ( time 0 = input RNA ) . VA RNAI was not able to stimulate replication in the presence of an inhibitor of HCV polymerase . Additionally , the level of viral RNA did not increase in the presence of wild-type VA RNAI , suggesting that viral RNA stability was also not affected by the presence of VA RNAI in the absence of replication . Figure 6 shows that in the presence of VA RNAI , viral RNA titer goes down and then levels off; while in the absence of wild-type VA RNAI it continues to decrease ( Figure 6C ) . When the experiment is done in the presence of 2′ , C-Me-A , the RNA titer decreases and levels off , independent of VA RNAI ( Figure 6D ) . This is consistent with the mechanism of 2′ , C-Me-A , which inhibits new RNA synthesis , however , in this experiment the viral RNA is not degraded 100-fold ( c . f . , Figure 6C and 6D ) . We interpret these data as follows: 1 ) in the absence of viral replication ( in 2′ , C-Me-A-treated cells ) , there is less degradation of the viral RNA; 2 ) in the absence of viral replication ( in 2′ , C-Me-A-treated cells ) there is also the absence of dsRNA ( positive strand plus negative strand ) ; and 3 ) therefore , dsRNA-activated proteins , including ADAR1 , would not be activated , leaving VA RNAI with no effect on stability . This is consistent with our ongoing studies that show that only wild-type VA RNAI ( that which can bind PKR or ADAR1 ) is capable of stimulating the replicon ( Taylor , unpublished results ) . Taken together , these results suggest that VA RNAI in the early stages of infection may affect the stability of the viral RNA , either by altering the degradation rate of new HCV RNA molecules or by inhibition of an RNA degradation pathway that may be modulated by viral replication . However , we also cannot exclude the possibility that VA RNAI alters the HCV RNA synthesis rate in the absence of a polymerase inhibitor .
In this study , we have demonstrated that the addition of VA RNAI , a known IFN antagonist and inhibitor of dsRNA-mediated antiviral pathways , permitted the persistent growth of a plasma-derived HCV in a cell line that lacks IFN genes . Most of the current knowledge of HCV biology and pathogenesis has been derived from the use of the unique JFH-1 cell culture system , which now allows the study of the complete virus life cycle , including entry , assembly and release . The limitation of this model , however , is that robust viral growth is restricted only to hepatic-derived cell lines such as Huh7 . 5 and Huh7 cells [44] and only by a genotype 2a replicon-derived virus . The establishment of an alternative model to characterize other HCV genotypes from infected individuals is still needed and is critical for the development of efficient viral therapies to control the disease . By passaging genotype 1b virus-infected VeroE6 cells for 20 weeks in the presence of VA RNAI and more than 2 years without VA RNAI , we generated a persistently infected cell line that expresses HCV antigens at levels high enough to be detected by immunofluorescence and Western blot ( Figure 1A–F , Table 1 ) . We found that the LB-piVe virus is highly cytotoxic , and is capable of inducing massive Huh7 . 5 cell death ( Figure 2B , C and E ) ; indicating that the virus produced in the persistently infected cells is infectious to hepatocytes . CPE could be blocked by antibodies to CD81 ( Figure 3A ) , by anti-HCV-specific immunoglobulins ( Figure 3C ) and by anti-E2 monoclonal antibodies ( Figure 3D ) , confirming the link between cell death and viral infection . While neutralization was not as potent using the anti-E2 monoclonal antibodies , we believe that this may be due to the antibodies being raised against recombinant genotype 1a proteins . The genotype differences in the E2 proteins ( including hypervariable domains ) may be reflected in loss of epitope recognition , thus explaining the 0 . 6 log10 decrease in viral titer . The LB-piVe virus-mediated CPE has the advantage that it can be assessed visually , and quantified easily and rapidly . This represents a significant improvement over the current genotype 2a HCVcc systems that utilize FFU assays , RT-PCR or reporter assays for quantitation , which are both laborious and time-consuming [45] . In addition , we have demonstrated the utility of this system in virus neutralization studies ( Figure 3A , C and D ) and in testing virus inhibition by well characterized HCV-specific antivirals ( Figure 4A , C , E and G ) . CPE was observed in VeroE6 cells and more-exaggerated CPE was found when filterable supernatants were used to infect Huh7 . 5 cells . While it was possible to enhance viral titer by panning the LB-piVe cells , and effectively increasing the number of virus-infected cells , the new culture could not survive after several passages . We suspect that the virus cannot be maintained in a culture that demonstrates massive CPE , such as that seen in Huh7 . 5 cells . This may be the reason that we were unable to obtain persistently infected Huh7 . 5 cell line , while VeroE6 cells can support persistent HCV infection due to a low-level display of CPE . HCV- associated cell death has also been reported in Huh7 . 5 . 1 cells after infection with JFH-1 when HCV RNA levels reached a maximum [46] . Gene expression profiling of HCV-infected Huh7 . 5 cells showed both the presence of activated caspase-3 and induction of cell death-related genes , suggesting an association of virus infection with cytopathic effects . Although not yet resolved , it has been postulated that HCV could mediate direct apoptosis by deregulating the cell cycle , which may contribute to liver injury in infected individuals [46] . While still requiring further studies and more comparisons between human pathology and cell culture , we suggest that the LB-piVe system may very well mimic a natural HCV infection in humans and could represent a useful tool to study the intricate process of viral pathogenesis . VeroE6 cells were also permissive for replication of genotype 2a J6/JFH-1 virus [2] ( Figure 5A–C ) . VA RNAI boosted replication and spread in these cells , as shown by the increase in the HCV RNA yield ( Figure 5D , E ) . This may be attributable to an increase in viral RNA stability and possibly reflects the type of interplay between host and virus . The presence of VA RNAI allowed for broadened cell tropism by HCV to include non-hepatic cells ( Tables 1 and S1 ) , perhaps due to its ability to circumvent the IFN-induced antiviral response . The full-extent of the mechanisms employed by VA RNAI towards overcoming the negative effects of IFN is currently unknown . VA RNAI is important to adenovirus infection and confers virus stability in the presence of IFN and IFN-induced proteins . It has been suggested that VA RNAI has an effect on HCV RNA stability by inhibition of the IFN-induced protein , ADAR1 [20] . When we compared the relative amount of HCV RNA in VA RNAI-transfected cells versus -untransfected cells ( Figure 6B , C ) that were infected with HCV-positive human plasma LB , we observed a 60-fold increase in VeroE6 cells . Interestingly , a precipitous decline in HCV RNA was observed in these cells in the absence of VA RNAI ( Figure 6C ) . Thus , VA RNAI has an effect in the VeroE6 cells , at least during the first 8 days of infection . We have yet to evaluate possible defects in the RIG-I pathway observed previously in Huh7 . 5 cells and likely to play a role in early infection [47] . The fact that we did not observe any increase in the relative amount of HCV RNA in Huh7 . 5 cells after VA RNAI transfection followed by infection with the parental genotype 1b serum-derived virus ( Figure 6B and C ) , was unexpected . We suspect that the relatively stable amount of viral RNA reflects extremely low viral replication of the LB virus in Huh7 . 5 cells . These findings are supported by the evidence that we could not establish a persistently infected cell-line with Huh7 . 5 cells , suggesting that VeroE6 cells were more permissive for persistent infection , perhaps due to the lack of IFN genes . VA RNAI was not able to rescue the virus during 2′-C-Me-A treatment and does not stimulate replication nor does it protect the virus from an antiviral that targets the HCV polymerase . We used this RNA polymerase inhibitor to evaluate RNA stability in the absence of viral RNA replication . Since VA RNAI only increased the HCV RNA in the presence of viral replication , we believe that it may perhaps inhibit cellular factors that are activated during viral replication ( e . g . , dsRNA-binding proteins ) and cause instability of the virus . It's possible that VA RNAI interacted with , and therefore blocked ADAR1 and PKR pathways . This would be consistent with our previous findings showing that the HCV replicon was stimulated by knock-down or inhibition of ADAR1 or PKR [20] . Additionally , the inhibition of RNA replication ( including loss of negative strand RNA ) should inhibit the formation of dsRNA intermediates , thus avoiding the activation of dsRNA-activated proteins that can lead to viral instability [48]–[54] . Again , we cannot exclude the possibility that VA RNAI enhanced viral replication in the absence of the polymerase inhibitor . Taken together , these data suggest that VA RNAI may possibly contribute to establishing a persistent infection in VeroE6 cells; however , the presence of VA RNAI alone is not enough to overcome the cellular antiviral response in Huh7 . 5 cells . IFN-deficient VeroE6 cells probably provide a more ideal environment for a virus that is , usually , IFN responsive . We suspect that this may be due to the decreased expression of IFN-induced proteins which may actively inhibit HCV replication [20] . Both Huh7 . 5 cells and VeroE6 cells express PKR and ADAR1 , but only the VeroE6 cells lack the IFN genes that induce these proteins . We found that even the persistent virus was stimulated by the presence of VA RNAI , suggesting that some of the dsRNA-activated proteins were still expressed and were inhibitory to the virus . In patients , in general , genotype 2 and 3 viruses are more sensitive to current antiviral therapy than the genotype 1 viruses [55] . Genotype 1b is thought to be the most IFN resistant and the most prevalent in North America , Europe and Japan . However , the HCV replicons ( genotype 1b ) and J6/JFH1 virus are sensitive to IFN in cell culture . It is not clear why viruses respond to IFN differently in vivo versus in vitro . Since HCV grows well in VeroE6 cells , especially when assisted by VA RNAI , we suggest that endogenous IFNs may limit HCV replication in cell culture . We suspect that the LB-piVe virus , like the parental LB from which it was derived , was relatively resistant to IFN ( Figure 4E , H ) , a property that has not yet been reported in infectious cell culture ( Figure 4G ) . While it warrants further investigation , it may be possible that we were able to obtain this virus because VA RNAI was present in the early stages of infection and inhibited the antiviral response generated by viral RNA replication . Our results on the enhancement of virus replication by VA RNAI are clearly consistent with evasion of the antiviral response , and correlate with the observation that susceptibility of human primary hepatocytes to HCV infection could be improved by impairing expression of other IFN signaling factors such us interferon regulatory factor-7 ( IRF-7 ) [17] . We suggest that in the early stages of cell culture infection , before viral proteins are in sufficient quantity , the innate immune pathways are active and control infection ( RIG-I , PKR , ADAR1 , RNaseL , etc . ) . However , once the virus is given a chance to accumulate , it can overcome these mechanisms of host control , either through the E2 , NS5A or NS3 proteins [56]–[58] . Our findings have raised some interesting questions . Future studies with IFN-sensitive viruses , complemented with known IFN-resistant HCV proteins ( such as NS5A and E2 ) using sequences from the LB-piVe virus , are planned . Additionally , the LB-piVe virus will be ideal for evaluating the genes responsible for conferring IFN resistance . We plan to construct an infectious clone and a replicon based on this virus with the aim of evaluating individual genes . At the same time , alignments with the IFN-resistant parental strain of LB with IFN-sensitive genotype 1b replicons may enable the identification of important amino acids that determine IFN resistance . Transient transfection experiments complementing the IFN-sensitive replicons will be among the experiments that will provide insights into the identification of the features that may confer IFN resistance by this genotype 1b virus . In summary , here we demonstrate that wild-type HCV genotype 1b viruses from human plasma can replicate in African green monkey kidney cells , VeroE6 , and that replication of viral genotypes 1a and 2a can be stimulated by the presence of VA RNAI . This is a new approach to culturing HCV and the first report of a cell culture system that represents a convenient assay for studying genotype 1b . This is an improvement in terms of utility for research , as the virus can be titrated without employing error-prone , quantitative RT-PCR methods nor arduous immunocytochemistry-based focus forming assays . The availability of the LB-piVe virus raises an exciting possibility; potentially opening a new era of HCV research through the use of a new model system . Moreover , a persistently infected cell line that exhibits CPE provides a novel assay that may be conducive to high throughput development and screening of new antivirals .
pVA containing the adenovirus 2 virus-associated RNA I ( VA RNAI ) sequence , and VA RNAI mutant dl1 ( pVAdl1 ) plasmids , were provided by M . B . Mathews [26] , [27] . VeroE6 cells ( ATCC ) were maintained in complete Dulbecco's modified Eagle's medium ( DMEM; Invitrogen , Carlsbad , CA ) containing 10% heat-inactivated Fetal Bovine Serum ( FBS; Hyclone ) at 37°C with 5% CO2 . Huh7 . 5 cells were provided by C . M . Rice ( Rockefeller University , NY ) and maintained in complete DMEM containing 10% FBS and non-essential amino acids ( Invitrogen ) . FBS was screened by RT-PCR to ensure the absence of bovine viral diarrhea virus ( BVDV ) . Multiple lots of VeroE6 cells were infected to check for reproducibility . Cells were cultured before transfection in T25 flasks or 6-well plates at a density to provide an overnight confluence of 35% , and transfected with 15–30 µg plasmid vector pVA [26] , [27] using DMRIE-C per the manufacturer's specifications ( Invitrogen , Carlsbad , CA ) . Virus stocks for J6/JFH-1 were prepared by inoculating 1×108 Huh7 . 5 cells with 1 ml culture supernatant ( 103 FFU/ml ) in serum-free medium [2] . Inoculated cells were grown at 37°C for 12 days . Filter-clarified culture supernatants were obtained as described above . LB-piVe stocks were prepared by growing the persistently infected cells for 8 days at 37°C in T75 flasks and supernatants were collected as for J6/JFH-1 . Huh7 . 5 cells were plated at a density of 5×103 per well in 96-well plates to obtain 60% confluence after 24 hr . Cells were incubated with anti-CD81 ( BD Pharmingen , San Diego , CA ) or isotype-matched control anti-flag M2 ( Sigma , St . Louis , MO ) antibodies for 1 hr at 37°C . and subsequently infected with serial dilutions of J6/JFH-1 or LB-piVe filter-clarified supernatants . After 6 hr at 37°C , cells were washed and supplemented with fresh media . Three dpi , J6/JFH-1-infected cells were immunostained with anti-Core antibodies [59] . Wells were scored positive if at least 1 positive cell was detected . LB-piVe-infected cells were observed by light microscopy at 5 dpi . Wells showing CPE were assigned a positive result and titers were calculated as described above . 8×102 TCID50 LB-piVe were treated for 1hr at 37°C with 5-fold dilutions of a cocktail of anti-E2 monoclonal antibodies [36] or 5µg/ml of isotype-matched control anti-flag M2 ( Sigma , St . Louis , MO ) antibody . The anti-E2 monoclonal antibodies were produced by hybridomas obtained after immunization of BALB/c mice with E1 and E2 glycoproteins expressed in insect cells [36] . Huh7 . 5 cells growing in 96-well plates were inoculated with serial dilutions of the neutralization reaction products and incubated for 5 days . LB-piVe titers were determined by a CPE-based end-point dilution assay . To test LB-piVe virus neutralization by immunoglobulins prepared from human plasma , virus was incubated with HCIGIV [35] or HCV-negative IGIV [35] , before titrating on naïve Huh7 . 5 cells . 2′-C-Methyl-D-Adenosine ( 2′-C-Me-A ) was obtained from Carbosynth Ltd . ( Berkshire , UK ) and resuspended at 100 mM [37] in dimethylsulfoxide ( DMSO ) . 5×103 Huh7 . 5 cells were infected with filter-clarified supernatants containing 100 FFU of J6/JFH-1 for 12 hours , washed , and incubated with complete growth medium containing a range of 0 . 05 to 1 µM 2′-C-Me-A . Three dpi , J6/JFH-1 infected cells were immunostained with anti-Core antibodies [59] . Fluorescent foci were counted in triplicate wells , and titers were calculated as the mean number of foci per ml ( FFU/ml ) . To measure inhibition of LB-piVe growth , 5×105 LB-piVe cells were grown in T25 flasks . After 3 days , 2′-C-Me-A was added to the growth media and cells were incubated for 3 additional days . Filter-clarified culture supernatants from treated LB-piVe cells were titrated in a CPE-based end-point dilution assay as described above . HCV growth in the absence of 2′-C-Me-A was set at 100% . The percentage reduction in the inhibitor treated cells relative to the untreated control was plotted against 2′-C-Me-A concentrations , employing GraphPad Prism 3 . 0 software . 50% effective concentration ( EC50 ) value values were interpolated from the resulting curves . To measure the level of viral RNA in the presence of 2′-C-Me-A , Vero E6 cells growing in 6-well plates were transfected with either WT- or mutant-VA RNAI . Four hours post-transfection , cells were treated with media containing 1 µM 2′-C-Me-A overnight and then infected with the parental LB virus . Total cellular RNA was extracted at 0 , 2 , 4 , 6 and 8 days post-infection . Determination of HCV RNA titers was performed by real-time RT-PCR analysis as described previously [62] . A chemically synthesized irrelevant oligo , termed siIRR [64] [5′-AAGGACUUCCAGAAGAACAUCTT-3′] and an HCV-specific oligo , termed si313 [38] [5′-CCCGGGAGGUCUCGUAGACTT-3′ ] , were obtained from Dharmacon , Lafayette , CO . Huh7 . 5 cells ( 2×104 ) were transfected with 100 nM of siIRR or si313 using DharmaFECT Transfection reagent 1 ( Dharmacon , Lafayette , CO ) following the manufacturer's protocol and recommendations . One day after siRNA transfection , cells were infected with 200 FFU of J6/JFH-1 or 200 TCID50 LB-piVe . Three dpi , J6/JFH-1 infected cells were washed , fixed and stained using an indirect immunofluorescence assay with anti-Core antibodies [59] . Fluorescent foci were counted in triplicate wells , and titers were calculated as the mean number of foci per ml ( FFU/ml ) . LB-piVe infected cells were detected by observing CPE by light microscopy . Culture supernatants of LB-piVe infected cells were titrated by a CPE-based end-point dilution assay on naïve Huh7 . 5 cells as described above . Viral titers were calculated using the method of Reed and Muench [31] . LB-piVe cells were grown in T25 flasks at a density to provide an overnight confluence of 40% , and transfected with pVA . One day post-transfection , cells were treated with 0 , 10 , 100 and 1000 IU/ml of Universal Type 1 IFN ( PBL Interferon Source , Piscataway , NJ ) for 24 , 48 and 72 hr . Filter-clarified culture supernatants were prepared and titrated by a CPE-based end-point dilution assay on naïve Huh7 . 5 cells as described above . Viral titers were calculated using the method of Reed and Muench [31] . Alternatively , LB-piVe titers were determined in naïve Huh7 . 5 in the presence of 0 , 10 , and 1000 IU/ml of IFN . To show the effect of VA RNAI on J6/JFH-1 , Huh7 . 5 cells were transfected with plasmid vectors pVA or pVAdl [26] , [27] , and infected the following day with 105 TCID50 . J6/JFH-1 ( filter-clarified culture supernatants from Huh7 . 5 cells that were transfected with wild-type or mutant VA RNAI plasmids ) . The infected Huh7 . 5 cells were titrated by end-point dilution in 96-well plates as described above . Wells were scored positive if at least 1 positive cell was detected . The TCID50 was calculated using the method of Reed and Muench [31] . Sequences can be accessed from GenBank through the NCBI website: H77 ( AF009606 ) ; JFH-1 ( AB047639 ) ; adenovirus 2 ( AC000007 ) ; LB-piVe ( FJ976045 , FJ976046 and FJ976047 ) . | Hepatitis C virus ( HCV ) causes a persistent infection that can lead to hepatocellular carcinoma and liver cirrhosis . Interferon ( IFN ) -based treatments are ineffective for some HCV genotypes . HCV research has been hampered by the lack of suitable cell culture systems . With the discovery of a unique HCV genotype 2a isolate that can replicate in the human liver cell line Huh7 , some obstacles were overcome . However , there remains the need of systems to grow IFN-resistant genotypes and serum-derived isolates . Here we show that the presence of adenovirus-associated RNAI ( VA RNAI ) , a known IFN antagonist , permitted establishment of a persistent infection of genotype 1b in VeroE6 cells that were passaged weekly for more than 2 years . The persistent virus induces strong cytopathic effect ( CPE ) , a feature that allowed the development of a CPE-based assay to test HCV-specific inhibitors , neutralization by anti-HCV immunoglobulins and by anti-CD81 antibody , and HCV-specific siRNA . Our system provides the first persistent culture of genotype 1b virus and a convenient assay that can be used for therapeutics development . | [
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] | 2010 | Persistent Growth of a Human Plasma-Derived Hepatitis C Virus Genotype 1b Isolate in Cell Culture |
We previously showed that HIV-1 subtype C viruses elicit potent but highly type-specific neutralizing antibodies ( nAb ) within the first year of infection . In order to determine the specificity and evolution of these autologous nAbs , we examined neutralization escape in four individuals whose responses against the earliest envelope differed in magnitude and potency . Neutralization escape occurred in all participants , with later viruses showing decreased sensitivity to contemporaneous sera , although they retained sensitivity to new nAb responses . Early nAb responses were very restricted , occurring sequentially and targeting only two regions of the envelope . In V1V2 , limited amino acid changes often involving indels or glycans , mediated partial or complete escape , with nAbs targeting the V1V2 region directly in 2 cases . The alpha-2 helix of C3 was also a nAb target , with neutralization escape associated with changes to positively charged residues . In one individual , relatively high titers of anti-C3 nAbs were required to drive genetic escape , taking up to 7 weeks for the resistant variant to predominate . Thereafter titers waned but were still measurable . Development of this single anti-C3 nAb specificity was associated with a 7-fold drop in HIV-1 viral load and a 4-fold rebound as the escape mutation emerged . Overall , our data suggest the development of a very limited number of neutralizing antibody specificities during the early stages of HIV-1 subtype C infection , with temporal fluctuations in specificities as escape occurs . While the mechanism of neutralization escape appears to vary between individuals , the involvement of limited regions suggests there might be common vulnerabilities in the HIV-1 subtype C transmitted envelope .
Neutralizing antibody ( nAb ) responses which target the Env of HIV-1 and block viral entry develop in most HIV-1 infected individuals , reaching detectable levels within a few months of infection when measured against the autologous Env [1] , [2] , [3] , [4] . Much of the variation that occurs in the Env during early infection is thought to be the result of pressure exerted by autologous nAbs , which is testimony to the potency of such responses [3] , [4] , [5] . Neutralization escape has been documented in HIV-1 subtype B viruses [3] , [4] , [6] , [7] , [8] , [9] , [10] , [11] , [12] and in SIV [13] , [14] , [15] with contemporaneous viruses showing less sensitivity to autologous neutralization than earlier viruses . Even in virus controllers with relatively low levels of antigenic stimulation of B cells , continuous viral selection and escape from autologous nAbs occurs [16] . However , the dynamic nature of the autologous neutralizing response is exemplified by the fact that escape variants are sensitive to de novo nAb responses generated to new variants . The nature and timing of the novel responses , or whether initial autologous nAbs are maintained or decay is not clear . It seems likely that early nAbs will wane as escape occurs , when the antigen responsible for elicitation of such responses is replaced by escape variants , which would presumably no longer stimulate existing antigen-specific B cells . Escape from autologous nAbs may occur through amino acid substitutions resulting in mutational variation at epitopes [17] , insertions and deletions ( indels ) in the Env [18] , [19] , and through an “evolving glycan shield” , where a shift in the number and position of glycans prevents access of nAbs to their cognate epitopes [4] , [19] , [20] . The relative importance of each mechanism of escape is not clear , and in many cases , a global view of envelope mutations and indels in escaped variants has not allowed precise elucidation of the genetic basis of escape [3] , [12] , [17] . Furthermore , the specificities , number and kinetics of the antibodies driving escape are largely unknown . The autologous nAb response in subtype C infection appears to differ somewhat from that in subtype B viruses and is less well-characterized . In subtype C , these antibodies develop to higher titer and are particularly type-specific with little or no cross-neutralizing activity within the first year of infection [1] , [2] . The type-specificity of autologous nAbs implies that they target variable regions , and indeed we have shown that nAbs directed at the V1V2 , V4 and V5 regions contributed to autologous neutralization in some HIV-1 subtype C infected individuals [1] , [21] . The role of V1V2 in shielding neutralization determinants is well-recognized [19] , [20] , [22] , [23] , [24] , [25] , [26] . V1V2 may also act as a neutralization target in some laboratory adapted HIV isolates [27] and primary HIV isolates [1] , [18] , [21] , [28] , [29] , [30] , [31] , [32] , [33] . Furthermore , use of reciprocal V1V2 chimeras suggested that the V1V2 region was principally responsible for the strain-specific AnAbs detected in plasma from SHIV-infected monkeys [34] . In subtype C , variable regions ( V1 to V4 ) have also been implicated in shielding neutralization determinants , and infection may be mediated by viruses with relatively short variable loops and high sensitivity to neutralization by donor sera [35] . The role of V4 and V5 in neutralization resistance is less clear , although these regions may impact on envelope conformation and glycan packing [4] , [15] , [36] , thereby sterically limiting accessibility of neutralization determinants . In addition to the variable regions , the C3 region , located in the outer domain of gp120 slightly upstream of the V3 loop has been implicated in neutralization escape in subtype C viruses [37] . The C3 region of subtype C viruses is under strong diversifying pressure [38] and there are distinct structural differences between subtypes B and C in the alpha 2 ( α2 ) -helix of C3 [37] suggesting increased exposure of this region in subtype C viruses . It has therefore been proposed that nAbs directly target the α2-helix in subtype C viruses [37] , [39] . We recently showed that the region is a major target of autologous neutralizing antibodies in subtype C HIV-1 infection [21] . Here we investigated neutralization escape in 4 individuals from the CAPRISA 002 Acute Infection cohort , including one virus controller , 2 individuals classified as intermediate progressors , and one rapid progressor . Neutralization profiles against the infecting virus from three of the four individuals have been described previously and were shown to differ in timing and magnitude [1] , [21] . In this study , representative envelopes were derived by single genome amplification ( SGA ) from multiple time points during the first year of infection , and tested against autologous plasma spanning the first 2 years of infection . We examined the specificities of autologous nAbs using chimeric envelopes . Potential escape mutations were identified by examination of sequences from multiple time points , and tested by generating chimeric and/or mutant envelopes representative of polymorphisms and measuring the acquisition of neutralization sensitivity in resistant envelope clones obtained at later time-points . Our data suggest that antibodies targeting only one or two epitopes , predominantly in V1V2 and C3 , drive escape in some individuals in the first year of infection in HIV-1 subtype C infection .
CAP88 , an intermediate progressor , first showed a neutralizing response at 15 weeks p . i . [1] . Here we derived SGA envelope sequences from CAP88 at 1 month ( enrolment ) , 6 months and 12 months post-infection ( p . i . ) . Selected amplicons chosen to represent each major clade in the phylogenetic tree were cloned and tested against autologous plasma spanning the first 2 years of infection . The neutralization curve when measured using the earliest available clone , 88 . 1m . c17 , had a biphasic shape , with one peak at approximately 26 weeks p . i . and a second peak at 81 weeks p . i . ( Figure 1A ) . The neutralization curves for the 6 and 12 month clones were shifted incrementally to the right ( Figure 1A ) indicating neutralization escape . Escaped variants , however , remained sensitive to later neutralizing antibody responses as evidenced by the high titers using later plasma samples . Furthermore , the varied curves shown by the multiple 12 month clones ( Figure 1A ) suggested different pathways to escape . We have previously probed the specificities of antibodies in CAP88 using heterologous chimeras , where regions of interest in CAP88 were transferred into an unrelated envelope , CAP63 [21] . Using this approach , serum from CAP88 was shown to contain at least 2 antibody specificities within the first year of infection targeting the C3 and the V1V2 regions . Here , longitudinal analysis using the heterologous chimeras over the first 2 years of infection suggested that each of the 2 peaks in the overall nAb response comprised a single specificity ( Figure 1B ) . The neutralization curve using the heterologous chimeric envelope 63/88/63-C3 ( where the C3 region from CAP88 at 1 month p . i . was transferred into the unrelated envelope , CAP63 ) mapped exactly to the first peak of the overall nAb response , while the neutralization curve of 63/88/63-V1V2 mapped to the second peak . These data suggested that the autologous response in CAP88 consisted of an initial response to the C3 region , peaking at 26 weeks p . i . with a titer of ∼1∶4 , 000 , and then waning so that by 54 weeks p . i . the titer had dropped to approximately 1∶700 . The second specificity targeting the V1V2 region developed from a titer of 1∶60 at 26 weeks p . i . to peak at 81 weeks p . i . with a titer exceeding 1∶5 , 500 , thereafter it too waned to a titer of ∼1∶2 , 000 by 108 weeks p . i . ( Figure 1B ) . We were interested in the relationship between these 2 defined specificities and the neutralization escape occurring in CAP88 . Comparison of the neutralization curves of representative clones from 1 month , 6 months and 12 months p . i . with the neutralization data obtained using the heterologous chimeras showed that the 1 month clone ( in yellow ) was sensitive to both the anti-C3 and anti-V1V2 antibodies ( Figure 1B ) . In contrast the 6 month clone matched the second peak indicating that this clone had escaped the initial anti-C3 response but remained sensitive to the anti-V1V2 antibodies . The 12 month clones had shifted still further and had therefore escaped both the anti-C3 and anti-V1V2 responses ( Figure 1B ) , but remained sensitive to yet another unidentified response . Analysis of the C3 region of the 6 month amplicons showed that each contained 2 potential escape mutations in the α2-helix; I339N plus E343K or I339N plus E350K ( Figure 2 ) . Back mutation of the I339N and E350K changes in a representative clone , CAP88 . 6m . c10 , to create the mutant envelope CAP88 . 6m . c10 N339I , K350E resulted in the resistant 6 month clone acquiring complete neutralization sensitivity , matching the 1 month clone ( Figure 1C ) . The I339N mutation in all 6 month amplicons created a potential N-linked glycolsyation site [40] , [41] , [42] suggesting the possibility of glycan shielding as a mechanism of escape . However , both the E343K and E350K mutations resulted in charge switches from negatively charged glutamic acid residues to positively charged lysine residues . The charge alteration in each clone ( at either position 343 or 350 ) may be in response to conformational changes resulting from glycosylation at the 339 residue . However mutagenesis studies in 88 . 6m . c10 showed that the charge change at position 350 , independently of the N339 PNG site ( 88 . 6m . c10 N339I ) also conferred some sensitivity ( titer of 548 , Table 2 ) , though much less profoundly than the sensitivity conferred by both mutations ( titer of 2 , 221 , Table 2 ) . This suggested that charge changes in the absence of glycans may also contribute to neutralization escape . The continuing pressure on the C3 region remained evident at 12 months p . i . where despite the waning anti-C3 levels , there was maintenance of the I339N glycosylation site , in some cases still with either E343K or E350K , plus an additional deletion in most clones at N355 ( Figure 2 ) , the significance of which is not known . Overall , by altering two amino acids in the C3 region the virus in CAP88 escaped the anti-C3 antibodies that arose within the first 6 months of infection . A similar approach was used to investigate neutralization escape in the 12 month clones which were resistant to both specificities . Since the number of mutations in V1V2 precluded testing all changes by site-directed mutagenesis we used the whole V1V2 region to generate chimeras . Sequential autologous chimeras were constructed using a 12 month clone , CAP88 . 12m . c2 , firstly introducing the V1V2 region from the sensitive early virus to create 88 . 12m . c2-V1V2s then back-mutating the C3 region to make 88 . 12m . c2-V1V2s , N339I , K350E . Use of these chimeras in neutralization assays showed that 88 . 12m . c2-V1V2s became sensitive to the second peak comprising anti-V1V2 antibodies , but remained resistant to anti-C3 antibodies ( Figure 1D ) , with the neutralization curve of 88 . 12m . c2-V1V2s matching the 6 month clone . The double chimera , 88 . 12m . c2-V1V2s , N339I , K350E shifted still further , becoming as sensitive as the earliest virus to both anti-C3 and anti-V1V2 antibodies ( Figure 1D ) , suggesting that in this 12 month clone , escape was mediated by changes in V1V2 in addition to the maintenance of escape mutations in C3 . These included a 2 amino acid deletion in V1 as well as 3 substitutions in V2 . Examination of all SGA amplicons at 12 months p . i . showed the addition of a PNG in V2 in 8 of 13 amplicons , suggesting a role for glycan shielding . The remaining 5 clones all contained a 2 amino acid deletion in the same region of V2 which could perhaps re-orientate the loop resulting in ablation of the epitope ( Figure 2 ) . Therefore , the mechanism of escape from an anti-V1V2 response varied between clones present in the same individual , utilizing either deletions or glycan shielding . The concept of evolving nAb specificities which drive sequential escape mutations was supported by data from a second individual , CAP177 . Envelope SGA amplicons were derived from CAP177 at 2 weeks ( preseroconversion ) , 6 months and 12 months p . i . Use of the transmitted envelope , inferred from the consensus sequence at preseroconversion , 177 . 2wk . cA3 , showed the development of autologous nAbs by 19 weeks p . i . , peaking at 1∶5 , 500 at about 50 weeks p . i . ( Figure 3A ) . Like CAP88 , clones obtained at 6 months and 12 months p . i . all exhibited neutralization escape ( Figure 3A ) . Due to the relatively high levels of variability across the envelope we again made use of autologous chimeric viruses , where we exchanged either V1V2 , C2 , C3 , V4 or C3-V4 ( regions exhibiting changes in all 12 month clones ) between the sensitive preseroconversion envelope and a representative clone from 6 months and 12 months p . i . When the C3 region from the preseroconversion envelope was transferred into a 6 month clone , 177 . 6m . c8B to form 177 . 6m . c8B-C3s , neutralization sensitivity increased markedly , with titers higher than those seen with 177 . 2wk . cA3 , although the shapes of the curve were remarkably similar ( Figure 3B ) . The increase in sensitivity of the 177 . 6m . c8B-C3zs envelope suggested that , as with CAP88 , escape initially occurred via changes in the C3 region . Four changes were noted in the C3 of 177 . 6m . c8B ( Figure S2 ) , one of which resulted in a shift in the position of a PNG , though the overall number of PNGs in the region remained unchanged . All four changes were charge changes from neutral or negatively charged residues to positively charged residues . This was similar to CAP88 although the fact that mutated residues differed between clones at 6 months p . i . in both CAP88 and CAP177 suggested multiple mechanisms of escape within C3 even in a single individual . When we examined neutralization escape at 12 months p . i . , transfer of the V1V2 region from the preseroconversion envelope into 177 . 12m . c1 to form 177 . 12m . c1-V1V2s resulted in a shift leftwards of the neutralization curve , with the chimeric envelope becoming as sensitive to neutralization as the 6 month clone ( Figure 3C ) . All of the 12 month clones contained at minimum a 3 amino acid insertion in the V1 loop , resulting in the addition of either one or two novel PNGs ( Figure S2 ) . It is likely that the extended loop length in addition to the novel PNGs mediated escape through shielding of neutralization epitopes . These data support the notion that waves of nAb specificities drive sequential escape mutations over the first year of infection . A role for autologous nAbs targeting V1V2 , with escape mutations occurring directly in V1V2 was also shown in CAP210 . The autologous neutralizing response in CAP210 , a rapid progressor , was of low magnitude and only became consistently detectable at 46 weeks p . i . when measured against the transmitted envelope ( yellow ) ( Figure 4A ) . Neutralization escape in CAP210 occurred late , with the 1 month and 6 month clones showing identical sensitivity to the earlier envelope despite occasional sequence changes ( Figure S3 ) . This was unsurprising considering the absence of measurable nAbs at these time points . In contrast the 12 month clones , from a time-point very shortly after the development of measurable autologous nAbs did exhibit neutralization escape with a shift to the right in the neutralization curves . As with CAP88 , we constructed a heterologous chimera for the V1V2 region ( where the majority of amino acid changes were located in 12 month clones ) to assess whether anti-V1V2 antibodies mediated autologous neutralization . We observed transfer of neutralization sensitivity using the heterologous 84/210/84-V1V2 chimera ( where the V1V2 region from CAP210 . 2wk . cTA5 was transferred into the unrelated envelope CAP84 ) suggesting that anti-V1V2 antibodies were present in CAP210 sera from 54 weeks p . i . , coinciding with the timing of detectable autologous nAbs ( Figure 4B ) . In order to assess whether changes in V1V2 also mediated escape in CAP210 , as we had shown in CAP88 and CAP177 , we created an autologous chimera , transferring the V1V2 region from the early sensitive clone into a 12 month resistant clone , 210 . 12m . c12 . The autologous V1V2 chimeric envelope , 210 . 12m . c12-V1V2s showed a shift left in the neutralization curve , becoming as sensitive as the early preseroconversion envelope ( Figure 4C ) . While all CAP210 12 month SGA sequences contained an A153V mutation at the beginning of V2 , the presence of this change in the sensitive 6 month clone 210 . 6m . cC9 and other amplicons from 6 months p . i . , suggested that this mutation was not involved in neutralization escape ( Figure S3 ) . In resistant clone 210 . 12m . c12 there were no changes in PNG sites , although a 2 amino acid deletion at position 180 between 2 PNGs could perhaps alter the arrangement of the PNGs in this clone . Alternatively , deletion may alter the conformation or directly ablate the epitope independently of glycosylation . In contrast , clones 210 . 12m . c47 and 210 . 12m . c66 ( which had superimposable neutralization curves and were slightly more sensitive than 210 . 12m . c12 ) both had the addition of a single PNG in V1 at position 132 , as well as an E181K change between two PNGs . Overall , these data suggested that the initial autologous nAb response in CAP210 was comprised solely of anti-V1V2 antibodies , which drove neutralization escape via mutations within V1V2 , likely via shifting glycans . In a fourth individual , CAP45 , previous analyses suggested that autologous nAbs targeted C3-V4 and V1V2 [21] . CAP45 who was a slow progressor developed a robust autologous response by 9 weeks p . i . peaking at titers exceeding 1∶6 , 000 at 43 weeks p . i . when measured against the transmitted envelope ( Figure 5A ) . Envelope clones obtained at 4 months , 8 months and 12 months p . i . escaped the early nAbs as shown by the increasing shift right in the neutralization curves . Inspection of SGA sequences showed potential escape mutations in V1V2 , however despite data suggesting the C3-V4 region was a target , there were no changes in C3-V4 indicating that escape occurred by a more indirect mechanism . The level of genetic diversity in CAP45 was very low , and so it was possible to examine all changes using site-directed mutagenesis . We back-mutated each of the 8 changes observed in a 12 month clone , 45 . 12m . c7 to match the preseroconversion motifs . Mutations in the V1V2 region contributed marginally to neutralization sensitivity in CAP45 ( Figure 5B ) . The S147N change in V1 , which resulted in the shift by one amino acid of a PNG ( Figure 5B , green line and Figure S4 ) , and the mutations observed in V2 ( K181R , G186E , neither of which impacted on PNG formation ) ( Figure 5B , blue line ) both increased neutralization sensitivity with relatively low titers of ∼1∶500 and ∼1∶900 respectively . This did not conflict with data obtained using the heterologous chimera which suggested only low level anti-V1V2 nAbs in CAP45 serum , becoming detectable at 35 weeks p . i . and not exceeding a titer of 1∶800 ( data not shown ) . However neither of these changes in V1V2 accounted for complete restoration of neutralization sensitivity . The most significant escape mutations observed in CAP45 were located in V5 ( K460E , D462G ) , and neither affected glycosylation . Simultaneous back-mutation of these 2 residues to match the preseroconversion motif resulted in almost complete restoration of sensitivity ( Figure 5C , pink line ) . No other mutations observed in resistant 12 month clones impacted on neutralization sensitivity when back-mutated to preseroconversion motifs ( data not shown ) . Making use of heterologous chimeras described previously [21] , we saw no evidence for anti-V5 antibodies in CAP45 , suggesting that V5 is unlikely to be a direct target of nAbs in CAP45 . It seems more likely that V5 , and escape mutations therein , were responsible for exposure of epitope ( s ) in other regions of the envelope , perhaps in C3-V4 which is in relatively close proximity to V5 ( Gnanakaran , pers comm ) , and which we proposed to be a nAb target in CAP45 [21] . Nevertheless , the existence of one significant mechanism of escape in CAP45 at 12 months p . i . suggests that , as with CAP210 , a single neutralizing antibody was solely responsible for driving escape in this individual . Given that a single amino acid change ( I339N ) was associated with escape from the anti-C3 antibody in CAP88 , this allowed us to measure the timing of the emergence of the escape mutation relative to the appearance of the antibody . We made use of quantitative allele-specific real-time PCR to investigate the development of the I339N change in the C3 over time . Figure 6A shows the decline in the wild-type ( I339 ) residue from 100% to approximately 10% of the total circulating population , with a concomitant increase in the proportion of the I339N mutation by 26 weeks p . i . Interestingly , there was a 7 week gap between detectable neutralizing antibodies and the appearance of escape mutations . This suggested that relatively high levels of autologous nAbs were needed to impact on the evolution of genetic escape mutations . Development of the anti-C3 response corresponded temporally with an 86% decrease in viral load from 93 , 400 RNA copies/ml at 11 weeks p . i . to 12 , 800 copies/ml by 15 weeks p . i . ( Figure 6B ) . At this stage ( 15 weeks p . i . ) 90% of circulating virus remained genotypically wild-type . The viral load rebounded to reach a level of 50 , 500 copies/ml at 22 week p . i . , at which stage the escape mutant dominated the viral population . Extrapolation of the real-time PCR data to determine the wild-type and mutant viral load suggested that the wild-type virus continued to decline reaching levels of approximately 1 , 900 copies/ml at 26 weeks p . i . This suggested that the potent nAbs identified in vitro and through analyses of genetic escape impacted at least in the short-term on viral levels in vivo .
This study aimed to identify the antibody specificities mediating autologous neutralization in HIV-1 subtype C infection during the first year of infection , and investigate the mechanisms of viral escape from these antibodies . We showed that neutralization escape occurred shortly after the appearance of nAbs , and was mediated by relatively few amino acid changes , including substitutions , indels , and glycan shifts , commonly in the V1V2 and C3 regions . A limited number of specificities were responsible for driving genetic escape and these antibodies appeared sequentially and waned as escape mutations emerged . A schematic representation of the targets of autologous nAbs as well as the regions or residues involved in neutralization escape are shown in Figure 7 . The investigation of neutralization escape in conjunction with data examining the specificity of the circulating nAbs is a strength of this study . We examined neutralization escape in four individuals , with varying disease status and viral genetic diversity . In CAP45 and CAP210 , where genetic diversity at 1 year p . i . was low , use of chimeric and mutant envelopes indicated the development of only a single nAb specificity resulting in neutralization escape . The low levels of viremia in CAP45 who was classified as a controller ( Table 1 ) , may have resulted in minimal antigenic stimulation of B cells , perhaps accounting for the development of only a single specificity . In contrast , in CAP210 who was a rapid progressor with a high viral load , it is somewhat surprising that the high antigenic load did not result in many more specificities and much earlier development of a nAb response . It is possible that the high viral load in CAP210 impacted on B cell dysfunction , delaying the development of nAbs ( Table 1 ) . In CAP88 and CAP177 , both of whom exhibited higher genetic diversity after 1 year of infection , two distinct nAb specificities were involved in driving escape within the first year of infection . It is not clear whether the increased diversity was responsible for generating multiple antibody specificities , or whether the reverse is true . However , it was interesting to note that a very small proportion of these changes were directly involved in neutralization escape . CAP88 had an envelope CTL response targeting C2 ( Clive Gray , pers comm ) which resulted in genotypic changes in this region . Thus , nAb and CTL pressure directly accounted for 7 of the 14 mutations found in the CAP88 envelope , with the remaining 7 mutations playing no obvious role in immune escape . Thus , attributing bulk variation in envelope sequences largely to immune pressure may overstate the direct effect of early nAbs on envelope variation . Of course , it is likely that other mutations may indirectly affect neutralization sensitivity via e . g . changes in fitness or entry efficiency which could increase the overall neutralization resistance , but which should be differentiated from specific changes mediating neutralization escape from single antibody specificities Changes within the V1V2 region were implicated in neutralization escape in all 4 individuals ( Figure 7 ) although in CAP45 this was minor . This observation is perhaps not particularly surprising , as the role of V1V2 in shielding neutralization determinants is well-recognized [19] , [20] , [22] , [23] , [24] , [25] , [26] . Furthermore , we and others have proposed that the V1V2 may serve as a neutralization target in some cases [1] , [18] , [21] . Here we show clearly in 2 cases ( CAP88 and CAP210 ) , using heterologous chimeric viruses , evidence for nAbs which directly target the V1V2 region with neutralization escape occurring via changes within this region . Such changes were variable even within a single individual and involved multiple mechanisms . In CAP88 , the unique V1V2 sequences in each of the 13 amplicons obtained at 12 months p . i . suggested that escape from the anti-V1V2 response occurred via either glycan shifts , indels or substitutions ( Figure 2 ) . Similarly , in CAP210 , virtually every amplicon was unique within the V1V2 ( Figure S3 ) . Although for both CAP88 and CAP210 , phenotypic testing was only performed on selected clones , the presence in plasma of these multiple variants suggests the likelihood that such sequence changes also confer escape . While changes within V1V2 also conferred neutralization escape in CAP177 we could not determine whether V1V2 was a direct antibody target in this case ( Figure 7 ) . Collectively , these observations suggest that V1V2 utilizes many pathways to escape , even when the selection pressure is induced by a single antibody specificity , almost certainly reflecting the extreme plasticity of the V1V2 region . We previously showed that the C3-V4 region is a major target of autologous nAbs in subtype C [21] . In general , the α2-helices of subtype C viruses have more defined polar and non-polar faces than those in subtype B which are more hydrophobic [37] . This amphipathicity , characteristic of surface helices , suggests that the α2-helix in subtype C may be more exposed . Indeed , it has been proposed that nAbs directly target the α2-helix in subtype C viruses , a possibility supported by our recent data [21] . Furthermore , the C3 region of the HIV-1 subtype C envelope , including mutational patterns within the α2-helix , has been implicated in neutralization resistance [39] . Here we show that in CAP88 and CAP177 , neutralization escape from anti-C3 nAbs was mediated by changes in the α2-helix of the C3 region ( Figure 7 ) . In CAP88 we were able to confirm that the C3 region was a direct target of nAbs . However , in both cases , neutralization escape was associated with charge changes within the α2-helix , all of which were located in the solvent-exposed portions of the helix maintaining the amphipathic structure . Neutralization escape in these 2 individuals was associated with an overall increase in positively charged residues within the α2-helix . While switching between oppositely charged residues within the α2-helix has been proposed to be mediate immune escape in subtype C viruses [37] , [39] , the precise mechanism whereby charge changes abrogate neutralization is unclear . Charge changes may simply disrupt the electrostatic interactions between nAbs and their epitopes if the C3 is a direct target , as we have shown in CAP88 . Alternatively , since there are strong interactions between the N-terminus of the α2-helix and the C-terminus of the V4 region [37] , with the conservation of a charge anti-correlation between the two regions , charge changes within the α2-helix may affect the conformation of the V4 loop with respect to the α2-helix , affecting the exposure of nAb targets in the C3 region or elsewhere . The sequential development of nAb specificities , sometimes requiring months of infection was intriguing considering that the targets of these antibodies may have been present in the infecting virus . This is most clearly exhibited in CAP88 where an initial anti-C3 response developed , waned and was subsequently replaced by an anti-V1V2 response . The V1V2 epitope that was the target of this secondary response was present in the earliest virus , cloned at 1 month p . i . Despite the ongoing presentation of this epitope , an anti-V1V2 response was only detectable at 26 weeks p . i . , 11 weeks after the detection of the initial anti-C3 response , suggesting the possibility of an immunological hierarchy . Delayed development of selected responses would be in line with the hierarchical binding antibody responses which develop in the very early stages of infection [43] , prior to the development of nAbs . It is also possible that other changes which develop across the entire envelope during the first 6 months of infection affected the conformation and exposure of the secondary nAb target , V1V2 , facilitating presentation of this region to the immune system only at a later time-point . Of interest was the observation that after escape occurred , although specific responses waned , they continued to be maintained at somewhat lower levels . This was difficult to discern when looking at overall neutralization levels as novel responses replaced waning responses . However , the use of heterologous viruses in CAP88 clearly showed that the anti-C3 response was maintained , albeit at lower levels ( declining from a peak of >1∶3 , 000 to stabilize at approximately 1∶700 during the second year of infection ) . How this response was maintained is not clear as by 6 months p . i . , 8/8 envelope clones contained escape mutations which presumably no longer stimulated the B cells responsible for producing anti-C3 antibodies . It is possible that even after escape has occurred , low levels of sensitive variants remain in the lymph nodes and stimulate maintenance of low levels of antibodies . The persistence of such sensitive variants in the face of potent nAbs as reported by Mahalanbis et al [16] , may result from continuous reversion of less fit escape variants , or persistent release of pre-escape variants from cell reservoirs . Alternatively , long-lived memory or plasma B cells may be responsible for the maintenance of specific responses in the absence of antigenic stimulation . The identification of a single amino acid change associated with initial neutralization escape in CAP88 afforded us the opportunity to investigate the kinetics of the development of neutralization escape with respect to the timing of the autologous neutralization response . The relationship between percentage neutralization at a 1∶45 plasma dilution and development of the initial escape mutation , showed a lag of 7 weeks in the development of genetic escape . This suggested a threshold requirement , whereby relatively high titers of nAbs were needed before sufficient pressure was exerted on the overall population , forcing escape to occur . Maturation of the antibody response , in terms of affinity and avidity may also play a role in the duration of time required for escape to occur . The decrease in the viral load which occurred as the autologous neutralizing antibody response developed was intriguing , suggesting the possibility that autologous nAbs may in the short term impact on viral load , with this effect abrogated by the development of neutralization escape mutations . The observation of the sequential development of anti-C3 antibodies followed by anti-V1V2 antibodies suggests that both of these regions are exposed and immunogenic on the HIV-1 subtype C envelope , possibly due to unique structural features of this viral subtype . Further studies are needed to determine if this is a common pattern , and whether emergence of these autologous antibodies is associated with decreases in HIV-1 viral load as seen in one individual who developed an anti-C3 specific nAb . However , as shown here , escape variants emerged as a result of a few genetic changes , not unlike the scenario with anti-retroviral monotherapy . The ease with which escape occurred , and the multiple pathways used to escape autologous responses further supports the notion that these responses , while driving considerable variation in the envelope region , have no long-term role in containing viral replication . Nonetheless , neutralization escape is reflective of active and ongoing replication in the face of an evolving and initially very low titer response , considerably different to a possible future vaccine scenario with pre-existing antibodies . Overall , these data provide insight into how a focused antibody response targeting limited regions of envelope in early subtype C infection drives sequential waves of neutralization escape .
The CAPRISA Acute Infection study was reviewed and approved by the research ethics committees of the University of KwaZulu-Natal ( E013/04 ) , the University of Cape Town ( 025/2004 ) , and the University of the Witwatersrand ( MM040202 ) . All participants provided written informed consent for study participation . Participants were from the CAPRISA 002 Acute Infection study , a cohort of 245 high risk HIV negative women which was established in 2004 in Durban , South Africa for follow-up and subsequent identification of HIV seroconversion [44] . The 4 individuals studied here included one controller ( CAP45 ) , one rapid progressor ( CAP210 ) and 2 individuals classified as intermediate progressors ( CAP88 and CAP177 ) . Clinical profiles indicating viral loads and CD4 counts of each are shown in Figure S1 . CAP45 , CAP177 and CAP210 were all infected by single transmitted variants [45] , with the transmitted envelope sequence inferred from the consensus sequence at the earliest available timepoint . The JC53bl-13 cell line , engineered by J . Kappes and X . Wu , was obtained from the NIH AIDS Research and Reference Reagent Program . 293T cells were obtained from Dr George Shaw ( University of Alabama , Birmingham , AL ) . Both cell lines were cultured in D-MEM ( Gibco BRL Life Technologies ) containing 10% heat-inactivated fetal bovine serum ( FBS ) and 50 ug/ml gentamicin ( Sigma ) . Cell monolayers were disrupted at confluency by treatment with 0 . 25% trypsin in 1 mM EDTA . HIV-1 RNA was purified from plasma using the Qiagen Viral RNA kit , and reverse transcribed to cDNA using Superscript III Reverse Transcriptase ( Invitrogen , CA ) . The env genes were amplified from single cDNA copies [46] and amplicons were directly sequenced using the ABI PRISM Big Dye Terminator Cycle Sequencing Ready Reaction kit ( Applied Biosystems , Foster City , CA ) and resolved on an ABI 3100 automated genetic analyzer . The full-length env sequences were assembled and edited using Sequencher v . 4 . 0 software ( Genecodes , Ann Arbor , MI ) . The number of potential N-linked glycosylation sites ( PNGs ) was determined using N-glycosite ( http:/www . hiv . lanl . gov/content/hiv-db/GLYCOSITE/glycosite . html ) . Multiple sequence alignments were performed using Clustal X ( ver . 1 . 83 ) and edited with BioEdit ( ver . 5 . 0 . 9 ) . Pairwise DNA distances were computed using Mega 4 [47] . Selected amplicons were cloned into the expression vector pCDNA 3 . 1 ( directional ) ( Invitrogen ) by re-amplification of SGA first-round products using Phusion enzyme ( Finn Enzymes ) with the EnvM primer [48] and directional primer , EnvAdir [21] . Env-pseudotyped viruses were obtained by co-transfecting the Env plasmid with pSG3ΔEnv [4] using Fugene transfection reagent ( Roche ) as previously described [1] . Chimeric Env were created using an overlapping PCR strategy with the inserts and flanking regions amplified in separate reactions . After linkage , the 3 Kb chimeric PCR fragments , generated using EnvAdir and EnvM primers [48] , were cloned into the pCDNA 3 . 1 ( directional ) ( Invitrogen ) and screened for function as previously described [49] . Chimerism was confirmed by sequence analysis . Site-directed mutagenesis was performed using the Stratagene QuickChange II kit ( Stratagene ) Neutralization was measured as described previously [1] by a reduction in luciferase gene expression after single round infection of JC53bl-13 cells with Env-pseudotyped viruses [50] . Titers were calculated as the reciprocal plasma dilution ( ID50 ) causing 50% reduction of relative light units ( RLU ) . Real-time PCR was performed on RNA extracted from sequential plasma samples of CAP88 using the QIAamp Viral RNA Mini Kit ( Qiagen ) . HIV-1 RNA was reverse transcribed to cDNA using the Superscript III Reverse Transcriptase System ( Invitrogen ) using the primer OFM19 as described [51] . cDNA was used in real-time PCR , making use of the following primers designed to detect the 339I and 339N residues: 88AS-PCR-T-for ( CAT TAC TAA AGA CAG ATG Gtt ) for detection of the 339I , 88AS-PCR-A-for ( CAT TAC TAA AGA CAG ATG Gta ) for the detection of 339N . Control primers were 88AS-PCR-control ( GAG ATA TAA GAC AAG CAC ATT G ) and 88AS-PCR-A/T-rev ( CTA TGT GTT GTA ACT TCT AGG ) . The reaction was performed using ABI PowerSYBR Green PCR master mix , in the ABI 7500 Real Time PCR System . Primer concentrations were 300 nM , final volume 25 ul . Cycling was performed as follows: 95°C for 10 minutes followed by 45 cycles of 95°C for 15 seconds , 60°C for 15 seconds , and 72° for 1 minute , for a total of 45 cycles . Relative quantification of mutation frequency was determined by calculating the number of copies of wild type and mutant populations relative to an internal control . | Most HIV-1 infected individuals develop neutralizing antibodies against their own virus , termed an autologous neutralizing response . It is known that this response exerts pressure on the envelope of HIV , the target of such antibodies , resulting in neutralization escape . Here we have identified the targets of these antibodies and the precise genetic basis of neutralization escape in 4 individuals infected with HIV-1 subtype C . We show that V1V2 is commonly involved in escape , and that the C3 region is also a target in some cases . The latter observation confirms this region is exposed in subtype C , unlike subtype B . We show that neutralization escape is conferred by a few amino acid mutations , some of which are outside the antibody target site . Moreover , escape from these limited specificities even within a single individual occurs via a variety of different pathways involving substitutions , indels and glycan shifts . The finding in 2 individuals that an anti-C3 response developed first , followed by an anti-V1V2 response , suggests there may be specific regions of envelope particularly vulnerable to antibody neutralization . Overall , we propose a mechanistic explanation for how HIV-1 epitopes drive sequential waves of neutralization escape in early subtype C infection . | [
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] | 2009 | Limited Neutralizing Antibody Specificities Drive Neutralization Escape in Early HIV-1 Subtype C Infection |
Phytochelatin synthase ( PCS ) is a protease-like enzyme that catalyzes the production of metal chelating peptides , the phytochelatins , from glutathione ( GSH ) . In plants , algae , and fungi phytochelatin production is important for metal tolerance and detoxification . PCS proteins also function in xenobiotic metabolism by processing GSH S-conjugates . The aim of the present study is to elucidate the role of PCS in the parasitic worm Schistosoma mansoni . Recombinant S . mansoni PCS proteins expressed in bacteria could both synthesize phytochelatins and hydrolyze various GSH S-conjugates . We found that both the N-truncated protein and the N- and C-terminal truncated form of the enzyme ( corresponding to only the catalytic domain ) work through a thiol-dependant and , notably , metal-independent mechanism for both transpeptidase ( phytochelatin synthesis ) and peptidase ( hydrolysis of GSH S-conjugates ) activities . PCS transcript abundance was increased by metals and xenobiotics in cultured adult worms . In addition , these treatments were found to increase transcript abundance of other enzymes involved in GSH metabolism . Highest levels of PCS transcripts were identified in the esophageal gland of adult worms . Taken together , these results suggest that S . mansoni PCS participates in both metal homoeostasis and xenobiotic metabolism rather than metal detoxification as previously suggested and that the enzyme may be part of a global stress response in the worm . Because humans do not have PCS , this enzyme is of particular interest as a drug target for schistosomiasis .
Phytochelatin synthase ( PCS ) proteins are γ-glutamylcysteine dipetidyltranspeptidases ( EC 2 . 3 . 2 . 15 ) known for their ability to synthesize phytochelatins , which have the general structure ( γ-Glu-Cys ) n-Gly ( n≥2 , where PC2 is a polymer with n = 2; PC3 , n = 3; etc . ) , from glutathione ( γ-Glu-Cys-Gly; GSH ) . They have been widely studied in plants , yeasts , algae , and fungi [1]–[6] . More recently they have been identified in bacteria [7] , [8] and animals , especially in worms [9]–[12] . Surprisingly , many animals , but not vertebrates , have PCS genes . A number of studies implicate the activity of phytochelatins in metal detoxification . Phytochelatins are immediately produced after exposure to a range of metal ions , the metal that seems unequivocally involved is cadmium , and their production transforms the metal-sensitive organism to a metal tolerant one [4] . Organisms deficient in PCS display hypersensitivity to cadmium or become unable to tolerate cadmium toxicity [4] , [12] . Essential heavy metals such as copper and zinc are required cofactors in redox reactions , ligand interactions and a number of other reactions . However , non essential metals , such as arsenic , cadmium , lead , and mercury are highly reactive and can be toxic through the displacement of endogenous metal cofactors from their binding sites in proteins and in the formation of reactive oxygen species through the Fenton reaction or by the inhibition of enzymes involved in reducing oxidative stress [13] . To control the cellular uptake and to respond to the accumulation of metals , organisms produce metal-binding ligands such as metallothioneins and phytochelatins [2] . Phytochelatin synthases belong to the papain superfamily of cysteine proteases with a mechanism of deglycination of GSH involving a catalytic triad Cys-His-Asp [14] . Most eukaryotic PCS proteins are composed of two domains: i ) the N-terminal domain , the catalytic domain for which high sequence homology is found among organisms and ii ) the C-terminal domain that has been suggested to function in metal regulation of activity [15] . Most prokaryotic PCS proteins are constituted only of the N-terminal domain . Phytochelatin synthesis involves two distinct reaction steps: the first step involves the cleavage of the glycine from a GSH molecule and generates a γ-Glu-Cys-modified enzyme . In the second step , the γ-Glu-Cys unit is transferred to an acceptor molecule that is either GSH or an oligomeric phytochelatin peptide to generate PCn+1 [16] , [17] . In addition to their role in metal detoxification , PCS proteins are also involved in xenobiotic metabolism and detoxification in plants and fungi [18] , [19] . After conjugation of GSH to electrophilic compounds by GSH S-transferase ( GST ) , GSH S-conjugates are excreted or further metabolized by PCS and/or other enzymes of the phase II xenobiotic degradation pathway . The product of Reaction 2′ can be further processed by γ-glutamyl transpeptidase ( γ-GT , EC 2 . 3 . 2 . 2 ) or cellular proteases prior to excretion as the mercapturic acid derivatives ( Cys ( S-X ) ) . Recently , we identified for the first time a PCS in a parasitic organism and a causative agent of schistosomiasis , Schistosoma mansoni [11] . Schistosomiasis affects more than 200 million people in more than 70 tropical and sub-tropical countries and causes more than 200 , 000 deaths annually [20] . Chemotherapy is the major control measure for schistosomiasis and currently only a single drug , praziquantel , is available [21] , [22] . Monotherapy for such a widespread and prevalent disease raises serious concerns about the selection of drug resistant parasites [21] . Since schistosomes , but not humans , have PCS its potential as a drug target for the treatment of the disease schistosomiasis has been suggested [11] . Recombinant expression of S . mansoni PCS in a yeast system leads an increased cadmium tolerance due to the synthesis of phytochelatins [11] . Because schistosomes , as well as other parasitic flatworms , do not appear to have genes encoding metallothioneins , we have proposed that PCS is the major defense against metal toxicity in this parasite [11] , [23] . In addition , because there is a lack of evidence regarding the involvement of phase I detoxification enzymes ( e . g . cytochrome P450 ) in xenobiotic metabolism of trematodes , it is thought that the hydrolytic phase II pathway involving GST conjugation activity to eliminate xenobiotics is mainly used [24] , [25] . In this regard , enzymes acting downstream of GST , such as PCS and γ-GT would likely participate in xenobiotic metabolism [26] . However , such activities have yet to be described in the worm to support this hypothesis . In this paper we have further investigated the molecular mechanisms and the possible functions of PCS in S . mansoni ( SmPCS ) . In vitro experiments using recombinant SmPCS proteins showed the bifunctionality of the enzyme: SmPCS can synthesize phytochelatins through a thiol-dependant and metal-independent mechanism and can act as a peptidase on GSH S-conjugates . To the best of our knowledge , this is the first time that the possible participation of PCS in xenobiotic detoxification in an animal is described . We showed that PCS expression in the parasite is affected by various compounds and that the enzyme may be part of a global stress response . Interestingly , we found PCS transcripts in the esophageal gland suggesting that the action of the enzyme may be for protection against compounds ingested by the worms . Our findings indicate that S . mansoni PCS may participate in metal homoeostasis and xenobiotic metabolism rather than metal detoxification .
This study was approved by the Institutional Animal Care and Use Committee at Rush University Medical Center ( IACUC number 11-064; DHHS animal welfare assurance number A3120-01 ) . Rush University Medical Center's Comparative Research Center ( CRC ) is operated in accordance with the Animal Welfare Act ( Public Law ( P . L . ) 89–544 ) as amended by P . L . 91–579 ( 1970 ) ; P . L . 94–279 ( 1976 ) ; P . L . 99–198 ( 1985 ) ; and P . L 101–624 ( 1990 ) , the Public Health Service's Policy on Humane Care and Use of Laboratory Animals ( revised , 2002 ) , the Guide for the Care and Use of Laboratory Animals ( revised , 2011 ) and the U . S . Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research and Training . The CRC is registered with the Animal and Plant Health Inspection Service ( APHIS ) arm of the United States Department of Agriculture ( USDA ) . The Institution has an Animal Welfare Assurance on file with the National Institutes of Health , Office of Laboratory Animal Welfare ( OLAW ) , A-3120- 01 . The facilities are accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC International ) . The CRC is directed by the Senior Director of the CRC , a Doctor of Veterinary Medicine ( D . V . M . ) and a Diplomate of the American College of Laboratory Animal Medicine ( ACLAM ) , who reports to the Associate Provost and Vice President for Research , who is also the Institutional Official for Animal Care and Use . The SmPCS ORF was amplified as previously described [11] and cloned into the pET100 expression vector ( Invitrogen ) . Two other sequences were also cloned into pET100: the N-truncated form corresponding to the amino acids 66 to 590 and the N- and C-truncated form corresponding to the amino acids 66 to 300 ( Fig . 1 ) . The resulting constructions ( pET100PCS1–591; pET100PCS66–591 and pET100PCS66–300 ) were used to transform BL21 Star™ ( DE3 ) Escherichia coli ( Invitrogen ) . Recombinant PCS proteins were expressed and purified as described . An overnight culture in Luria broth ( LB ) containing 50 µg/mL carbenicillin was used to inoculate 1 L LB plus 50 µg/mL carbenicillin culture and the cells were grown at 37°C . After reaching an optical density at 600 nm of 0 . 5 , isopropyl 1-thio-β-D-galactoside was added to 0 . 1 mM and the culture was incubated overnight at 24°C to express the recombinant protein . After centrifugation at 4500×g for 25 min at 4°C , the cells were resuspended in lysis buffer ( 50 mM potassium phosphate ( pH 7 . 8 ) , 400 mM NaCl , 100 mM KCl , 10% glycerol , 0 . 5% Triton X-100 , 30 mM imidazole , 1 mM dithiothreitol , 1 mM phenylmethylsulfonyl fluoride , 1 mg/mL lysozyme ) and disrupted by sonication . After centrifugation at 14000×g for 40 min at 4°C , the supernatant was filtered and applied to a 1 mL HisTrap™ HP column ( GE Healthcare Life Sciences ) first equilibrated with binding buffer ( 50 mM potassium phosphate ( pH 7 . 8 ) , 400 mM NaCl , 100 mM KCl , 10% glycerol , 0 . 5% Triton X-100 , 30 mM imidazole ) . The column was washed with 20 mL of binding buffer , contaminant proteins were eluted with 10 mL of elution buffer A ( 20 mM sodium phosphate ( pH 7 . 5 ) , 500 mM NaCl , 100 mM imidazole , 10 mM β-mercaptoethanol ( β-Me ) ) and the recombinant protein was eluted with 3 mL of elution buffer B ( 20 mM sodium phosphate ( pH 7 . 5 ) , 500 mM NaCl , 5% glycerol , 500 mM imidazole , 10 mM β-Me ) . The elution fraction containing the recombinant protein was dialyzed against the same buffer without imidazole . Homogeneity of the purified proteins was confirmed by SDS PAGE . The concentration of proteins was measured according to the method of Bradford [27] . PCS activity was measured according to the method of Oven et al . [28] . The reaction mixture ( 180 µL ) contained 200 mM Tris-HCl ( pH 8 ) , 10 mM β-Me , 10 mM GSH , 0 . 1 mM CdCl2 and 3 µg of SmPCS . PCS activity for PC2 synthesis was measured in the same condition but in a final volume of 60 µL . PCS activity was expressed as the amount of PC2 synthesized by 1 mg of protein per minute and was measured in the first 15 min when the synthesis of PCn ( with n>2 ) was negligible . In the conditions without added metals , the chelating agent diethylene triamine pentaacetic acid ( DTPA ) ( 6 . 3 mM ) was added to the reaction mix to chelate any metals carried over from expression and purification of the protein . To measure PCS activity with GSH S-conjugates , the reaction mixture ( 60 µL ) contained 200 mM Tris-HCl ( pH 8 ) , 10 mM β-Me , 1 mM GSH S-conjugate , 0 . 1 mM CdCl2 and 3 µg of SmPCS . For the metal sensitivity experiments , various concentrations of CdCl2 or ZnCl2 were used . All the reactions were incubated at 37°C and were stopped by addition of an equal volume of 0 . 2 N HCl . After a centrifugation at 12 , 000×g for 5 min , the supernatants were analyzed by LC-MS . For the synthesis of GSH S-bimane ( GS-bimane ) , the reaction mixture contained 10 mM monobromobimane prepared in 100% acetonitrile , 10 mM GSH , 200 mM HEPPS buffer ( pH 8 ) containing 6 . 3 mM DTPA . The mixture was incubated at 45°C for 30 min in the dark and the reaction was terminated by the addition of 1 M methane sulfonic acid to achieve a final concentration of 0 . 1 M . The reaction was then dried in a speed vacuum and the pellet resuspended in 25 mM Tris-HCl pH 8 and filtered to remove the excess of monobromobimane . For the synthesis of GSH S-ethacrynic acid ( GS-EA ) , the reaction mixture contained 10 mM GSH , 0 . 2 mM ethacrynic acid prepared in 70% ethanol and 100 mM potassium phosphate buffer pH 7 . The mixture was incubated at 37°C for 30 min and used without further purification . Twenty µL of each sample was injected onto a reverse-phase column ( Eclipse Plus C18 , Agilent ) attached to an Agilent 1946A LC-MSD system , an Agilent 1100 HPLC LC system coupled to a single quadrupole LC-mass spectrometer equipped with electrospray ionization ( ESI ) . Thiol compounds were separated by using solvent ( A ) 0 . 1% TFA and ( B ) 100% acetonitrile at a flow rate of 0 . 5 mL/min . The gradient was from 2% to 20% of solvent ( B ) over 20 min . Before injecting a new sample , the column was washed ( 4 min 98% B ) and equilibrated ( 4 min 2% B ) . The integrated peak area was used to quantify the PC levels after calibration with chemically synthesized PC2 , PC3 , and PC4 ( AnaSpec ) . PCS activity was expressed as the amount ( µmol ) of phytochelatins synthesized by 1 mg of enzyme . For the analysis of the hydrolysis reaction of GSH S-bimane ( or GSH S-EA ) , the gradient used was from 30% to 45% of solvent ( B ) over 8 min . The integrated peak area of the extracted ion chromatogram was used to quantify γ-Glu-Cys-S-bimane levels after calibration with synthetic GSH S-bimane . PCS activity was expressed as the amount ( µmol ) of γ-Glu-Cys-S-bimane synthesized by 1 mg of enzyme . Perfusions of adult worms ( 6–7 wk post infection ) from mice were done as previously described [29] . Adult worms collected from mice were incubated in complete DMEM medium ( Difco ) containing 100 U/mL penicillin , 100 µg/mL streptomycin and 10% heat-inactivated FBS . For the treatments , six pairs of worms per well were incubated in 6-well tissue culture plates at 37°C with 5% CO2 . Metals ( CdCl2 , FeCl3 and ZnCl2 ) were added at a final concentration of 100 µM . Worms were exposed to 30 µM monobromobimane ( Sigma ) , 1 µg/mL praziquantel ( Sigma ) , 10 µM ethacrynic acid ( MP Biomedical Inc . ) , or 100 µM H2O2 . Six hour after addition of the compounds , worms were collected , washed twice in PBS , flash frozen in a dry ice/ethanol bath and stored at −80°C . Parasites without treatment were incubated the same length of time and used as controls . First strand cDNA was synthesized from adult worm total RNA . RNA was extracted from worms using TRIzol® ( Invitrogen ) according to the manufacturer's instructions . cDNA was synthesized using the iScript cDNA synthesis kit ( Bio-Rad ) using 1 µg of total RNA . For quantitative PCR experiments , 1 µg of cDNA was used with the SsoFast™ EvaGreen® Supermix in an ABI PRISM 7900 sequence detection system ( Applied Biosystems ) . qPCR primers were designed for SmPCS SmGST26 , SmGST28 , SmγGT and SmγCGL and for the housekeeping gene β-tubulin using PrimerQuest ( IDT ) ( Table 1 ) . Fold differences were calculated using the 2−ΔΔCt method [30] . Riboprobes were synthesized according to previously published methods [31] . Briefly , probes were synthesized from restriction enzyme digested DNA according to the orientation of the transcript in pCRII , using the Riboprobe synthesis kit ( Promega ) with SP6 or T7 polymerases and the digoxigenin ( DIG ) RNA labeling kit ( Roche ) . WISH was also done according to previously published methods [31] . For preparation of the worm extracts the lyophilized worm homogenates were extracted 1∶4 with 0 . 1 N HCl , vortexed , centrifuged ( 9 , 500×g , 12 min , 4°C ) , filtered and immediately analyzed by HPLC . Prior to UPLC-MS 500 mM tris ( 2-carboxyethyl ) phosphine were added to the worm extracts in the ratio 1∶29 in order to reduce potentially present phytochelatins . HPLC ( Ellman ) was conducted on a RP18 column ( Bischoff , Leonberg , ProntoSil C18 AQ , 5 µm 120 Å; 250×4 . 6 mm ) via a Merck-Hitachi La Chrom HPLC system ( Darmstadt , interface d-700 , pump L-7110/L-7100 , autosampler L-7200 , UV/Vis detector L-7420 ) . All solvents used were helium degassed . The mobile phase A consisted of trifluoroacetic acid in water ( pH 2 ) and a mobile phase B of acetonitrile with a flow rate of 1 mL min−1 . The injection volume of the samples was 70 µL . A linear gradient from 2 to 20% B during 20 min followed by isocratic elution at 20% B during 5 min was applied . For thiol-specific detection , postcolumn derivatization was carried out by adding via a T-piece 0 . 4 mL min−1 300 µM Ellman's reagent ( DTNB ) in 50 mM KH2PO4 , pH 8 . 0 , 1-mL reaction loop ) . Detection was performed at λ = 410 nm . For UPLC-MS/MS analysis a nanoUPLC – MS system from Waters ( Eschborn , Germany ) was operated in “single pump trapping” mode . Therefore , analytes were preconcentrated on a C18 trapping column during 1 min with an eluent flow rate of 5 µL min−1 under isocratic conditions ( 1% acetonitrile and 0 . 1% trifluoroacetic acid in water ) . Via a nano-Tee-valve the eluent was directed into the waste . Afterwards the flow rate was decreased to 0 . 3 µL min−1 and nano-Tee-valve was switched directing the eluent on the analytical column . A linear gradient from 3 to 50% acetonitrile during 30 min was used . Analytes in the UPLC eluent were ionized by electrospray at 2 . 5 kV and introduced into the mass spectrometer . Ions were scanned in the range m/z 100–1500 for acquiring MS spectra . Selected masses were fragmented by collision induced dissociation with energies between 10 and 60 V . The acquired data was analyzed with the Waters MassLynx 4 . 1 update 3 software .
Three different forms of the S . mansoni PCS protein were produced in E . coli: the full length protein ( PCS1–591 ) , the N-truncated protein ( PCS66–591 ) and the C-truncated protein ( PCS66–300 ) ( Figure 1 ) . All three purified , six-His-tagged recombinant proteins were successfully expressed and purified to homogeneity at 1 mg/L culture and formed homodimers as determined by size exclusion chromatography ( data not shown ) . Recombinant PCS1–591 , corresponding to the unprocessed , mitochondrial form of the protein [11] , was enzymatically inactive for the production of phytochelatins . This suggests that the presence of the predicted mitochondrial signal peptide in the protein when expressed in E . coli may prevent correct protein folding . Recombinant SmPCS66–591 , corresponding to the cytoplasmic form of the protein , was found to be active in phytochelatin synthesis ( Figure 2 ) . In the first 15 min of the reaction , SmPCS66–591 synthesizes PC2 , PC3 appears after 20 min incubation , and PC4 after 90 min when PC2 synthesis reaches a plateau . PC5 , PC6 , and PC7 were detected after 180 min indicating that SmPCS can synthesize high molecular weight species of phytochelatins ( data not shown ) . During the time course analysis , no accumulation of γ-Glu-Cys was detected . This suggests that once cleaved from GSH , γ-Glu-Cys is immediately consumed for the synthesis of phytochelatins . To compare the catalytic mechanism of SmPCS to PCS proteins from other organisms , the reaction was also carried out with 1 mM synthetic PC2 as the sole substrate . In this case , we were able to detect PC3 , γ-Glu-Cys and GSH after 60 min incubation ( data not shown ) . Therefore , as previously described for other PCS proteins [8] , SmPCS synthesizes PC3 through the cleavage of PC2 into γ-Glu-Cys and GSH followed by the conjugation of γ-Glu-Cys to another PC2 molecule . To define the catalytic domain of SmPCS we used a C-truncated form of the protein , SmPCS66–300 . Phytochelatin synthesis ( PC2 , PC3 , and PC4 ) by SmPCS66–300 was found to be the same as by the full length protein ( PCS activity for PC2 synthesis by SmPCS66–591 = 0 . 88±0 . 14 µmol min−1 mg−1 and by SmPCS66–300 = 1 . 2±0 . 14 µmol min−1 mg−1 ) . Therefore , the N-terminal domain is sufficient for efficient PC synthesis and the C-terminal domain does not enhance the catalytic activity of SmPCS in vitro . To investigate the influence of metals on SmPCS activation , we followed PC2 synthesis with either SmPCS66–591 or SmPCS66–300 with cadmium added to the reaction for the condition with metal or with DTPA added to chelate any residual metals for the condition without metal . Surprisingly , we found that that enzymatic activity was the same in the absence of metals ( +DTPA ) as in the presence of 100 µM of cadmium ( −DTPA ) and PC2 synthesis is inhibited in the absence of a reducing agent ( Fig . 3 ) . To investigate the importance of the reducing agent for the enzyme activity , we used S-methyl-GSH as sole substrate for the enzyme since the GSH through its thiol group can play the role of reducing agent . We found that both forms of enzyme were capable of synthesizing S-methyl-phytochelatins from S-methyl-GSH only in the presence of β-Me or the non-thiol reducing agent tris ( 2-carboxyethyl ) phosphine ) and independently of the presence of cadmium ( data not shown ) . Therefore , the presence of a reducing agent , but not the presence of metal , is necessary for SmPCS activity . To further investigate the effects of metals on PCS activity , we followed PC2 synthesis by SmPCS66–591 and SmPCS66–300 with increasing concentrations of cadmium and zinc ( Table 2 ) . For the full length protein , we found that PC2 synthesis decreased at higher concentrations of cadmium ( 69±7% of activity with 1 mM CdCl2 in the reaction mixture ) . With addition of zinc , full activity was obtained with only 20 µM of metal and above this concentration the activity decreased significantly . Activity for the C-truncated form of the enzyme did not change significantly with increasing concentration of metal . These results suggest that in addition to the lack of a requirement for a metal cofactor , metals can inhibit PCS activity at high concentrations . We next investigated whether the enzyme was able to act as a peptidase on GSH S-conjugates . Monobromobimane ( bimane ) , a compound that labels thiols , was used . This compounds is known to conjugate to GSH both enzymatically ( by GST ) and non-enzymatically , in vitro as well as in vivo . The GSH S-conjugates , GS-bimane , was synthesized and tested as substrate for SmPCS66–300 and SmPCS66–591 . The reactions were analyzed by LC-MS for the appearance of the product γ-Glu-Cys-S-bimane . We found that both SmPCS66–591 and SmPCS66–300 cleaved the glycine from the GSH conjugate to give the corresponding γ-Glu-Cys-S-conjugate . Indeed , the mass spectra of the control reactions ( substrate alone ) exhibited the [M+H] ion of GS-bimane ( m/z 498 ) ( Fig . 4A ) . In the presence of SmPCS66–591 and SmPCS66–300 , the mass spectra displayed an additional peak at m/z 441 corresponding to γ-Glu-Cys-S-bimane ( Fig . 4B and 4C , respectively ) . Using extracted ions chromatogram we could measure the respective amount of GS-bimane and γ-Glu-Cys-S-bimane that are co-eluted by LC-MS . We found that SmPCS66–300 and SmPCS66–591 activity for γ-Glu-Cys-S-bimane synthesis was 109 . 6±5 . 3 and 11 . 7±4 . 2 µmol min−1 mg−1 respectively . This suggests that the N-terminal domain of the enzyme alone is more efficient in the catalytic cleavage of GS-conjugates than the full length enzyme . Production of the hydrolyzed products was detected in the presence and in the absence of added metals but no cleavage occurred in the absence of the reducing agent β-Me ( data not shown ) . Similar results were obtained with GS-EA ( data not shown ) . Phytochelatin synthase activity is closely related to metal abundance through the synthesis of the metal-chelating PC peptides and to xenobiotic metabolism through its peptidase activity on GS-conjugates . To understand the function of the enzyme in the parasite , we investigated the transcriptional regulation of the enzyme by analyzing the level of SmPCS mRNA transcripts in adult worms exposed to metals and xenobiotics . Worms were cultured in the presence of stressors for 6 h and the abundance of SmPCS mRNA was evaluated by quantitative real-time reverse transcription-PCR ( qPCR ) . The level of mRNA of SmPCS increased 5 . 9±1 . 6 fold in the presence of cadmium and 6 . 4±3 . 6 fold with iron compared to the control ( Fig . 5a ) indicating that worms respond to metal exposure by synthesizing PCS . The time course of induction was similar with both metals ( Fig . 5b ) . Some xenobiotics are known to be conjugated to GSH by GSTs and thus potentially substrates of PCS . Therefore , monobromobimane and ethacrynic acid ( EA ) acid were tested for their ability to alter SmPCS expression . We found that both compounds caused an increase in mRNA levels of SmPCS after 6 h , 2 . 3±1 . 1 and 3 . 8±0 . 2 , respectively , suggesting that adult worms synthesize PCS also in response to xenobiotics . The drug used clinically to treat schistosomiasis , praziquantel , was also tested since it is known to increase the expression of many genes in the worms [32] . Interestingly , although praziquantel is not believed to be processed by GST conjugation and may trigger different pathway of elimination , it was found to increase SmPCS mRNA abundance ( 9 . 9±3 . 5 ) . To test whether oxidative stress would have an effect on PCS mRNA abundance , worms were treated with H2O2 and PCS transcripts were found to be increased 1 . 8±0 . 2 fold compared to the control . Taken together , our data demonstrate that PCS transcripts are upregulated by obvious inducers as well as less evident stressors . We next investigated if changes in PCS expression were coordinated with the expression of other genes involved in GSH metabolism . Our hypothesis was that the worms produce PCS not specifically to detoxify metals but rather as part as a systemic stress response . Changes in the mRNA abundance of two GSTs , GST26 and GST28 , γ-GT , γ-glutamylcysteine synthetase ( γ-GCS ) , the rate-limiting enzyme in GSH synthesis , and thioredoxin glutathione reductase ( TGR ) in worms treated for 6 h with different stressors were monitored by qPCR ( Fig . 6 ) . Most of the stressors that were found to increase PCS mRNA abundance also increased γ-GT and γ-GCL mRNA levels . γ-GCS transcript levels increased in response to all treatments indicating that GSH synthesis is required to cope with metal and xenobiotic exposures and oxidative stress . Although H2O2 did not enhance TGR mRNA level as might be expected since it is an antioxidant enzyme , it did increase PCS and γ-GCS mRNA levels suggesting that GSH synthesis may be the first cellular response to oxidative stress . Interestingly , praziquantel was found to increase the level of all transcripts tested , suggesting that as previously mentioned , schistosomes exposed to praziquantel may undergo a transcriptomic response similar to that observed during oxidative stress [32] . In addition , some of the treatments ( cadmium , EA , monobromobimane ) also triggered GST transcription and in particular , GST28 . This suggests that the cell copes with these stressors by triggering GSH metabolizing pathways . Overall , this set of data support the hypothesis that SmPCS is part of the protective response of the organism utilizing GSH . Cadmium-treated and control worm samples were investigated for the presence of phytochelatins . Classic thiol detection by HPLC and Ellman post-column derivatization as well as modern phytochelatin analysis by UPLC-MS was applied . The latter method offers highly specific and ultrasensitive phytochelatin detection by their exact masses in MS and fragment ions in MS/MS . Detection limits down to 5 and 17 nM for PC2 and PC3 , respectively , were achieved in a previous study [33] . In the present study both methods were optimized with a PC2 standard . HPLC ( Ellman ) confirmed the presence of GSH in worms , but no phytochelatins could be detected . UPLC-MS was optimized for phytochelatin detection , but not for GSH , showed no signal for phytochelatins in the investigated worm samples although with the same system traces of PC2 could be recently detected in chromium stressed algae ( unpublished data ) . In conclusion , phytochelatins could not be detected down to the nM level in metal treated worms . Whole mount in situ hybridization ( WISH ) was used to localize PCS transcripts in adult worms . Digoxigenin labeled probes designed to the C-terminal sequence of PCS were generated and used for WISH . PCS transcripts localize to the gut epithelium and esophageal gland in adult male and female worms ( Fig . 7 ) .
In this study , we set out to understand the catalytic processes of SmPCS and its function in the parasite . Analysis of recombinant SmPCS allowed us to highlight the characteristics of this unusual enzyme . The recombinant full length protein did not have enzymatic activity . It is likely that the mitochondrial peptide prevents the correct folding of the protein in E . coli . The N-truncated protein , SmPCS66–590 , corresponding to the cytoplasmic form of the enzyme in the parasite , lacks the amino acids corresponding to the predicted mitochondrial targeting peptide [11] . We showed that SmPCS is capable of synthesizing phytochelatins as previously suggested [11] . We found that the enzyme also functions as a peptidase using GSH S-conjugates as substrates . Although those two activities have been described for PCS enzymes from other organisms , significant differences in the molecular mechanisms between SmPCS and other PCS proteins were observed . The molecular mechanism of PCS catalysis is not clear and there are contradictions between studies regarding metal activation and the role of the C-terminal domain . Vatamniuk et al . used as a model the recombinant flag-tagged PCS from Arabidopsis thaliana ( AtPCS-flag ) and described that during the first step acyl-enzyme intermediate formation occurs at two distinct sites [16] . They proposed that the first acylation occurs at the N-terminal domain of the enzyme and does not require metal and that the second acylation occurs at a site located in the C-terminal domain and is metal dependant . Romanyuk et al . suggested that the C-terminal domain is required for second site acylation and metal sensing and augments the catalytic process [34] . Although it is clear that the N-terminal domain is sufficient for the catalysis [8] , [15] , [34] , the position of the second acylation site is not well established . The second point of contradiction is the involvement of metals in PCS activity . It has been suggested that although the enzyme requires metal to synthesize phytochelatins , it is not a metal in a free state that interacts directly with the enzyme leading to its activation but the formation of a metal complex with GSH ( e . g . , GS-Cd2 ) that functions as the substrate [17] . However , other studies also using AtPCS as a model have demonstrated a direct interaction of the enzyme with the metal [28] , [35] . Although previous studies investigating PCS activity showed that PCS proteins with both N- and C-terminal domains have an absolute requirement for metals for GSH-dependent phytochelatin synthesis activity [4] , PCS from cyanobacteria , which has only the N-terminal domain , has been shown to function without added metals [14] . Our results indicate that neither the SmPCS protein with both the catalytic and C-terminal domains ( SmPCS66-591 ) nor the N-terminal-only protein ( SmPCS66-300 ) require metal for its activity . In our experiments , chelation of metals by DTPA did not affect SmPCS activity and thus it is likely that SmPCS activity is independent of metal ions . However , the fact that both SmPCS and AtPCS , can catalyze the synthesis of S-methyl PC from S-methyl GSH independently of metal suggests that no direct interaction of the metal with the enzyme is required [17] , [34] . In the case of AtPCS , blocked GSH ( S-methyl GSH or GS-metal complexes ) appear to serve as substrates , while this does not appear to hold for SmPCS . It is noteworthy that an increase in metal concentration was found to inactivate SmPCS66–591 but not SmPCS66–300 , suggesting that metals can interact with the C-terminal domain resulting in enzyme inactivation . It has been suggested that AtPCS possesses a binding site where metal binds resulting enzyme inhibition [35] , but whether or not this is true for SmPCS remains to be determined . Our data show that the C-terminal domain of SmPCS does not participate in metal sensing as previously suggested for other PCS enzymes [15] or provide a second acylation site since SmPCS66–300 retains the same phytochelatin synthetic activity as SmPCS66–590 . In our previous study , it was found that a truncated form of the protein ( deletion of the 100 C-terminal amino acids ) did not provide cadmium tolerance when expressed in yeast suggesting that no PC synthesis occurred [11] . In this study , the deletion was made so that the C-truncated form would match with the sequence of Nostoc PCS . It is possible that the 100 amino acid C-terminal truncated SmPCS protein studied previously does not fold correctly or is unstable when expressed in yeast . Another possibility could be that the C-truncated form does not behave similarly in vitro and in vivo as is the case for AtPCS [8] . A cysteine residue ( Cys109 in AtPCS ) is conserved in most of the plant , algal , and animal cadmium-dependant PCS proteins [11] . This particular residue is not found in SmPCS ( Lys ) nor in Nostoc PCS ( Lys ) , PCS proteins that appear to have metal-independent activity . This particular cysteine may be involved in metal sensing and could be a cadmium-dependant acylation site . Mutant PCS proteins with a deletion of this cysteine still have phytochelatin synthesis activity [16] , but no investigations concerning the metal-dependent activity of these mutants have been done . The role of the C-terminal domain in the synthesis of phytochelatins by SmPCS is not clear , but it appears to be dispensable . Although the role of reducing agents in PCS activity has not been extensively documented , previous studies have shown that AtPCS has lower activity in the absence of a reducing agent while Nostoc PCS activity is independent of added thiols [8] , [28] . We found that a reducing agent was required for SmPCS activity . AtPCS and SmPCS display , respectively , seven and eight cysteine residues in their N-terminal domain and eight cysteine residues in their C-terminal domain and require a reducing agent for their activity . Interestingly , Nostoc PCS has only one cysteine ( the catalytic residue ) and does not require a reducing agent to be active [8] . Therefore , the abundance in cysteine in both domains may be correlated with a requirement for a reducing agent; the enzyme must be in a reduced state to be active . In this regard , it would be interesting to study the sensitivity of the Cys mutant proteins generated by Tsuji et al . or Vatamaniuk et al toward reducing agents [8] , [16] . Our observation that in high concentrations of metals SmPCS is inhibited suggests that the enzyme with reduced thiols may chelate metals and adopt an inactive conformation , perhaps similar to the oxidized form of the protein . Thus , reducing agents for the schistosome enzyme and metals for other PCS proteins act similarly resulting in protein activity; perhaps reducing agents mimic cadmium , which normally binds to the auxiliary cysteines stabilizing protein function . The active site amino acid triad – Cys-His-Asp – is contained in SmPCS66–300 , full-length Nostoc PCS , and AtPCS1–221 , the C-truncated form of AtPCS [14] . SmPCS and Nostoc PCS catalyze PC2 synthesis at the similar rates , 1 . 1 µmol SH mg−1 protein min−1 [8] and 0 . 84 µmol SH mg−1 protein min−1 ( Fig . 3 ) , respectively . The full length AtPCS activity is 14 µmol SH mg−1 protein min−1 but is only 5 µmol SH mg−1 protein min−1 for the C-truncated form . This suggests that the enzymes that do not require cadmium are less active than those that are cadmium-dependant . Or , as suggested by Vatamaniuk et al . , the C-terminal domain of those enzymes may contain the acylation site ( cadmium-dependant ) that would enhance the activity . The less-conserved Cys residues in the C-terminal domain , often presented in pairs in cadmium-dependant PCS proteins , may have a role as binding sensors for metals [36] . Interestingly , we found that SmPCS can catalyze the cleavage of the glycine residue from GSH S-conjugates . This is the first time that the peptidase activity of a PCS from animal has been described . We found that metal and reducing agent requirements for the peptidase activity are similar to that of the transpeptidase activity . The C-truncated form of the protein appeared to be more efficient in the cleavage reaction . This is probably due to a better access of the large substrates that represent GS-conjugates in the binding pocket . The peptidase activity was detected with compounds known to be processed in vivo by GST in the process of xenobiotic metabolism . Because in schistosomes the main elimination pathway is thought to be through GST conjugation [37] it is likely that in vivo PCS acts in tandem with γ-GT to process GSH S-conjugates . GSH conjugation and subsequent peptidase processing are also involved in the metabolism of endogenous compounds such as leukotrienes [38] . Whether SmPCS peptidase activity is involved in vivo in xenobiotic detoxification or in the synthesis of endogenous compounds has yet to be determined . In vivo , we have shown that metals such as cadmium and iron can increase steady-state levels of PCS mRNA . In certain plants and fungi , increases in PCS expression upon metal exposure seems to be organism specific [2] , but metal exposure is necessary to induce PCS expression and to produce phytochelatins for detoxification [1] , [39] . Although we found an increase in PCS transcripts after exposure to metals , we could not detect any accumulation of phytochelatins down to the nM level . Because SmPCS enzyme activity is not influenced by cadmium ( or other metals ) , it is likely that PCS functions constitutively and does enhance phytochelatin synthesis in the presence of metals . Moreover , the phytochelatin synthesis activity of the schistosome enzyme is low compared to plant PCS proteins ( Fig . 2 ) . Although it is clear from this and previous studies [11] that SmPCS can synthesize phytochelatins , it does not appear that in the parasite phytochelatins are synthesized for metal detoxification . Therefore , if phytochelatins are produced in vivo , they may be produced at low levels and may be involved in metal homeostasis rather than detoxification . In some organisms the cadmium detoxification system appears to be the result of a balance between antioxidant systems ( antioxidant enzymes and GSH ) and metal-specific detoxification systems ( phytochelatins , metallothioneins ) [40] . A number of studies have shown that metal stress is not necessarily counteracted by phytochelatin production [4] . Indeed , It is well known that although cadmium in not a redox metal , it can generate oxidative stress [13] . Iron is known to be a redox active metal [41] . Hence , it is likely the increase in mRNA level of PCS may be due to the oxidative stress triggered by cadmium and iron . Because SmPCS can act as a peptidase on GSH conjugates , we followed transcriptional regulation of SmPCS in worms treated with compounds known to be eliminated through GST conjugation . We found that PCS mRNA levels increase in response to exposure suggesting that PCS is required to process those compounds . However praziquantel , which interacts with GST but is not processed by GST [42] , [43] , was also found to increase PCS level . Therefore , PCS seems to be up-regulated by an array of compounds and not only by those potentially directly involved in its activity . Studies have demonstrated that large gene sets are induced in response to various stressors/toxicants [44] . In general these studies have been used to identify particular genes involved in the detoxification process . In particular , cadmium exposure induces MAPK pathways that affect the expression of genes that detoxify related stressors notably in plants [45] and in invertebrates [46] . MAPK activation induces c-jun and c-fos and triggers , among others , genes related to metal trafficking and antioxidant defense . If those stressors activate MAPK pathway , and PCS is affected in response to this activation , it is likely that genes other than PCS are also affected . GSH is involved in metal , antioxidant , and xenobiotic defenses through direct metal-thiol interactions , as a cofactor to transfer reducing equivalents to glutathione peroxidases , and through xenobiotic conjugation by GST [47] . We thus thought that PCS could be regulated as part of a comprehensive antioxidant GSH-dependent defense mechanism [22] . Metals have been shown to affect the transcript levels of γ-GCS in Saccharomyces cerevisiae [48] and in plants [49] and of PCS [50] . H2O2 was also found to trigger γ-GCS transcription in S . cerevisiae [48] . To verify this hypothesis , we looked at transcripts of proteins involved in GSH metabolism to see if PCS would follow the same trend of regulation . Our data show that various stressors ( metal , xenobiotic , oxidative stress ) enhanced γ-GCS transcription , supporting an increase of GSH metabolism under stress in the parasite , as it has been described in plants and yeast [51] . In schistosomes , GSH may be responsible for the direct detoxification of metal , which may explain why at high concentrations of metals PCS is inhibited in vitro . Since PCS uses GSH to synthesize phytochelatins , the cell spares GSH so that it can be use for direct metal detoxification and for counteracting the oxidative stress triggered by metals . Other genes were found to be up-regulated with the stressors tested: γ-GT , as well as GSTs and TGR . Mammalian GSTs have been described to be up-regulated by products of oxidative stress [38] . Aragon et al . showed that praziquantel increases GST transcript abundance as well as other enzymes involved in antioxidant defense [32] . They suggested that the ‘anti-oxidant’ response may actually be induced by praziquantel . Interestingly , we found that praziquantel enhanced the transcription of the genes involved in GSH metabolism . All those data taken together tend to indicate that PCS may be up-regulated as part of the anti-oxidant response of the worms . Using WISH , we localized PCS transcripts to the gut epithelium and esophageal gland in both adult male and female worms . This localization pattern suggests that PCS functions similarly in male and female schistosomes . It is well known that many proteases localize in the gut of S . mansoni where they participate in hemoglobin proteolysis [52] and cathepsin-L has been used as a gut epithelium marker [53] . The esophageal gland is responsible , among other activities , for secretions promoting the lysis of red cells after their ingestion by the parasite [54] . We hypothesize that because PCS mRNA , and possibly protein , localize to the esophageal gland and digestive tract that it is likely that PCS protein functions in the defense response triggered by compounds ingested by adult worms . In contrast , tissue-specific PCS expression in the nematode Caenorhabditis elegans is essentially nonoverlapping with that found S . mansoni . In C . elegans , expression was localized to the hypodermis , the pharyngeal grinder , the pharyngeal-intestinal valve , the bodywall and vulval muscles , and coelomocytes [55] . Different patterns of tissue expression suggest different functions for PCS proteins in nematodes and trematodes . To conclude , we suggest that SmPCS synthesizes phytochelatins in vivo likely for metal homeostasis rather than detoxification . This is consistent with the fact that , whereas plants or earthworms live in soil contaminated with metals need such a system , S . mansoni does not face high concentration of metal during its residence in the human blood stream . Our data suggest that SmPCS may work in tandem with GSTs and γGT for xenobiotic degradation . SmPCS may be necessary to detoxify xenobiotics and/or oxidative stress that may be present in human blood . It is not clear if depriving the parasite of such a defense or control system may be deleterious for the parasite . Due to this bi-functionality , the role of SmPCS in GSH metabolism is however of great interest . Because PCS is absent from the human genome , understanding its function in the parasite may help to elucidate how the parasite deals with external stress and to target suitable enzymes or pathways . | Schistosomiasis is a chronic , debilitating disease that affects hundreds of millions of people . The treatment of schistosomiasis relies solely on monotherapy with praziquantel and there is concern that drug-resistant parasites will evolve . Therefore , it is imperative to identify new drugs for schistosomiasis treatment . In this study our goal was to characterize the function of the phytochelatin synthase of Schistosoma mansoni , previously suggested as a candidate for drug targeting to control schistosomiasis . Phytochelatin synthase catalyzes the production of metal chelating peptides , the phytochelatins , from glutathione ( GSH ) . In plants , algae , and fungi phytochelatin production is important for metal tolerance and detoxification . PCS proteins also function in the elimination of xenobiotics by processing GSH S-conjugates . We found that SmPCS expressed in bacteria could both synthesize phytochelatins and hydrolyze various GSH S-conjugates . We found the enzyme works through a thiol-dependant and , notably , metal-independent mechanism for both transpeptidase ( phytochelatin synthesis ) and peptidase ( hydrolysis of GSH S-conjugates ) activities . The expression of the PCS gene in adult schistosome worms was increased by exposure to a number of metals and xenobiotics . In addition , these treatments were found to increase the expression of other enzymes involved in GSH metabolism . Highest levels of PCS transcripts were localized in the esophageal gland of adult worms . Taken together , these results suggest that S . mansoni PCS participates in both metal homoeostasis and xenobiotic metabolism rather than metal detoxification as previously suggested and that it may be part of a global stress response in the worm . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"biochemistry",
"biology",
"zoology",
"parasitology"
] | 2013 | Towards an Understanding of the Function of the Phytochelatin Synthase of Schistosoma mansoni |
Balanced maternal and paternal genome contributions are a requirement for successful seed development . Unbalanced contributions often cause seed abortion , a phenomenon that has been termed “triploid block . ” Misregulation of imprinted regulatory genes has been proposed to be the underlying cause for abnormalities in growth and structure of the endosperm in seeds with deviating parental contributions . We identified a mutant forming unreduced pollen that enabled us to investigate direct effects of unbalanced parental genome contributions on seed development and to reveal the underlying molecular mechanism of dosage sensitivity . We provide evidence that parent-of-origin–specific expression of the Polycomb group ( PcG ) gene MEDEA is causally responsible for seed developmental aberrations in Arabidopsis seeds with increased paternal genome contributions . We propose that imprinted expression of PcG genes is an evolutionary conserved mechanism to balance parental genome contributions in embryo nourishing tissues .
Polyploidy , the presence of more than two complete sets of chromosomes within an organism , is known to be common in plants and in some animals such as amphibians , fish and reptiles [1] , [2] . The widespread occurrence of polyploids among plant species suggests that polyploidy is evolutionary beneficial and represents a major mechanism for plant adaptation and speciation [2]–[6] . The additional sets of chromosomes may originate from the same species ( “autopolyploid” ) , or from the hybridization of two different species ( “allopolyploid” ) . Polyploids can arise spontaneously by the fusion of a diploid gamete with a normal haploid gamete , leading to the formation of a triploid zygote . Diploid gamete formation resulting from failure of reduction during meiosis occurs in several plant species and can give rise to triploids that serve as a bridge to the formation of stable polyploids with an even set of chromosomes [4] . In most flowering plants the fusion of one sperm cell with the haploid egg cell is accompanied by the fusion of a second sperm cell with the homodiploid central cell nucleus , resulting in the formation of the triploid endosperm with a 2∶1 ratio of maternal to paternal genomes . The endosperm is a nourishing tissue supporting embryo growth [7] . Double fertilization occurs also in polyploids , resulting in the formation of embryo and endosperm with proportionally increased ploidies . However , crosses between plants of different ploidy often fail because seed development does not proceed normally and non-viable seeds are produced , a phenomenon that has been termed “triploid block” [8] . It is assumed that abnormalities in growth and structure of the endosperm are the cause of triploid seed failure [9] , consistent with the proposed role of the endosperm in reproductive isolation and angiosperm speciation [10]–[12] . In many species the 2∶1 ratio of maternal to paternal genomes in the endosperm is required for normal seed development [12] , [13] , giving rise to the hypothesis that gene dosage effects and imprinting of regulatory genes in the endosperm is the underlying cause for developmental failure in seeds with deviating parental contributions [9] , [14] , [15] . Genomic imprinting is the mitotically stable inheritance of differential expression states of maternal and paternal alleles caused by different epigenetic modifications of the alleles . Genomic imprinting renders maternal and paternal genomes non-equivalent , and balanced contributions of maternal and paternal genomes are therefore essential for post-fertilization development [15]–[17] . The penetrance of the interploidy hybridization barrier varies within and between species . Whereas maize is particularly sensitive to changes in the ratio of maternal to paternal genome contributions in the endosperm [13] , [14] , in Arabidopsis thaliana a substantial accession-dependent variation in the degree of postzygotic seed lethality has been observed in crosses between individuals of different ploidy [18] , [19] . The phenotype of viable seeds resulting from reciprocal interploidy crosses is different , depending on which parent contributes the higher genome dose . Whereas seeds developing from a cross of a diploid female with a tetraploid male develop larger endosperm , the reverse is true when the maternal plant contributes the double genome dose [19] . Increased endosperm growth in seeds with increased paternal genome contribution is associated with an increased number of mitotic divisions and delayed cellularization [19] . This phenotype is strikingly similar to the endosperm phenotype of Arabidopsis fertilization independent seed ( fis ) mutants that are characterized by an uncellularized endosperm with increased number of nuclei [20] , [21] . The FIS genes MEDEA ( MEA ) , FERTILIZATION INDEPENDENT ENDOSPERM ( FIE ) , FIS2 and MSI1 encode components of an evolutionary conserved Polycomb group ( PcG ) complex that deposits repressive chromatin modifications on a defined set of target genes [21]–[26] . The FIS PcG complex has been implicated in the regulation of imprinted genes [27] , and the FIS components MEA and FIS2 are themselves regulated by genomic imprinting with only the maternal alleles being expressed [28]–[30] . In this work , we addressed the question whether seeds with increased paternal genome contribution have reduced FIS PcG activity causing developmental aberrations in the endosperm . We identified a novel mutant forming unreduced pollen that enabled us to investigate direct effects of unbalanced parental genome contributions on seed development . We show that parent-of-origin specific expression of MEA is causally responsible for seed developmental aberrations in triploid Arabidopsis seeds . Seeds containing triploid embryos and tetraploid endosperm ( referred to as “triploid seeds” ) are characterized by increased expression of FIS PcG target genes during late stages of seed development and increased transgene induced MEA expression in the endosperm can significantly suppress developmental defects of triploid seeds . Our findings reveal the underlying molecular mechanism of dosage sensitivity and suggest that imprinted expression of PcG genes is an evolutionary conserved mechanism to balance parental genome contributions in embryo nourishing tissues .
PHERES1 ( PHE1 ) is a direct target gene of the Arabidopsis thaliana FIS PcG complex and is predominantly expressed in the endosperm during early stages of seed development [31] . PHE1 expression ceases around the time of endosperm cellularization , when the embryo has reached late heart stage . We performed a genetic screen aimed at identifying novel regulators of PHE1 expression and made use of a previously established reporter line containing the PHE1 promoter fused to the β-GLUCURONIDASE ( GUS ) reporter [31] . The identified jason ( jas-1 ) mutant had strongly increased GUS staining in the endosperm starting at 5 to 6 days after pollination ( DAP ) , when embryos had reached late heart stage , whereas the GUS staining pattern was indistinguishable from the wild type during early seed development ( Figure 1A–1H ) . Increased GUS staining was restricted to the endosperm , apparent staining of the embryo is a consequence of the endosperm overlaying the embryo . We tested whether increased expression levels of the reporter gene were reflected by increased expression of the endogenous PHE1 gene and analyzed PHE1 expression at defined DAP ( Figure 1I ) . Consistent with the results from the PHE1 reporter construct , increased endogenous PHE1 expression was observed from 5 DAP onwards . PHE1 is an imprinted gene that is predominantly paternally expressed [27] . Therefore , we investigated whether increased expression of PHE1 in jas mutant seeds is caused by breakdown of PHE1 imprinting . We crossed the Arabidopsis accession C24 as female with pollen from Landsberg erecta ( Ler ) wild type or jas mutant and analyzed allele-specific expression of PHE1 in seeds resulting from these crosses . However , no increase in maternally derived PHE1 transcripts was detected in jas mutant seeds and increased PHE1 transcript levels were solely established by increased expression of the paternal PHE1 allele ( Figure 1K ) . Development of jas seeds is delayed compared to wild-type seeds: whereas wild-type seeds reached maturity at 10 DAP , jas seeds reached maturity only after about 18 DAP ( Figure 1G and 1H ) . Mature jas seeds were significantly increased in size ( Figure 2A and 2B ) , resembling seeds derived from 2n×4n crosses [19] . Therefore , we tested whether the jas mutation caused triploid seed formation by measuring the ploidy levels of the progeny of diploid homozygous jas plants . Indeed , among the jas progeny we found diploid and triploid ( 45% ) but no tetraploid seedlings ( 10 triploids among 22 plants; Figure 2C ) . Seedlings grown from size-selected enlarged jas seeds were all triploid , indicating that enlarged seeds are indeed triploid ( n = 51 ) . These data suggest that the jas mutation causes diploid gamete formation at high frequency , however , the presence of diploid seedlings as well as normal sized seeds among the progeny of jas plants ( Table 1 ) indicates that the jas mutation is not completely penetrant . The jas mutant is sporophytic recessive; mutant plants were detected at a ratio of about 25% among segregating F2 plants ( n = 185; ( χ2 = 0 . 134<χ20 . 05[1] = 3 . 84 ) ) , indicating normal transmission of the jas allele through male and female gametes . Also , JAS does not appear to have a general role in seed development , as abnormal seed development was not observed in seeds from selfed jas heterozygous plants , even though 25% of the seeds are homozygous for the jas allele . In order to test whether the jas mutation affects male or female gametogenesis , we reciprocally crossed jas plants with Ler wild-type plants and analyzed seeds developing from these crosses . Enlarged seed formation was only observed when the jas mutation was paternally transmitted ( Table 1 and Figure S1 ) , suggesting that jas pollen is diploid . Conversely , we did not detect significant numbers of abnormally-sized seeds when the jas mutation was maternally transmitted ( Figure S1 ) , strongly suggesting that jas only affects male gametogenesis . Additional support for this conclusion was derived from the finding that all F1 seedlings derived from crosses of jas plants with wild-type pollen were diploid ( n = 40 ) . Together , triploid seed formation in the identified jas mutant is caused by a defect during male gametogenesis leading to unreduced gamete formation . Diploid pollen is significantly larger than haploid pollen [32] , and consistent with our hypothesis that jas pollen is diploid , we observed 62% enlarged pollen in jas plants ( n = 144; Figure 3A and 3B ) . Furthermore we measured DNA content of sperm cells from wild-type and enlarged jas pollen . The mean fluorescence from the enlarged pollen was approximately twice that of the wild-type and of normal-sized jas pollen , indicating that the former has twice the DNA content and is thus diploid ( Figure 3C ) . To define the stage of pollen formation affected by the jas mutation , we analyzed the meiotic products of jas microspore mother cells . Whereas wild-type plants almost exclusively formed tetrads , jas plants formed dyads and triads at high frequency ( 64% dyads , 19% triads , n = 307 Figure 3D–3F ) . Thus , JAS is required in the sporophyte to regulate male meiosis and a meiotic defect is responsible for the formation of diploid pollen in jas . Consistent with this view we frequently observed ten chromosomes in dyad microspores , in contrast to the five chromosomes observed in tetrad microspores ( Figure 3G and data not shown ) . The subsequent mitotic divisions are not affected by the jas mutation; all pollen grains formed by jas mutant plants contained two sperm cells and one vegetative cell ( Figure 3B ) . To define the meiotic stage affected by the jas mutation , we analyzed chromosome behavior during male meiosis in jas mutants . Chromosome spreads showed that meiosis in the jas mutant progressed normally and was indistinguishable from wild-type meiosis until the end of telophase I . Synapsis was complete and chiasmata as well as bivalents formed ( Figure S2A , S2B , S2C , S2G , S2H , S2I ) . At metaphase II , however , we observed differences to wild-type meiosis . Whereas wild-type chromosomes aligned into two well separated metaphase II plates ( Figure S2D ) , jas chromosomes failed to align properly ( Figure S2J ) , likely causing a failure in chromatid separation at the second meiotic division ( Figure S2K ) and the formation of dyads ( Figure S2L ) and triads ( Figure S2M ) , in contrast to predominantly occurring tetrad formation in wild-type ( Figure S2N ) . We map-based cloned the JAS gene and found the jas mutation to cause a premature stop codon in the fifth exon of the At1g06660 gene , encoding an as yet unknown protein without domains of described functions ( Figure 4A and 4B ) . BLAST searches of Arabidopsis proteins revealed a single related protein ( At2g30820; JAS-LIKE ) with 64% similarity . JAS was predominantly expressed during reproductive development , whereas JAS-LIKE has a broader expression domain ( Figure 4C ) , suggesting partially redundant functions of both genes . JAS homologs were not identified in animals; however , JAS is conserved throughout the plant kingdom and contains a highly conserved domain at the C-terminus ( Figure 4D ) . We identified three independent T-DNA lines containing insertions in intron 1 ( jas-2 ) , exon 5 ( jas-3 ) and exon 3 ( jas-4 ) . Mutant alleles jas-3 and jas-4 caused dyad and triad formation at comparable frequency as the jas-1 mutant ( Figure 3E ) , confirming that the identified mutation is indeed the cause of the jas phenotype . The frequency of dyad formation in the jas-2 allele was approximately half that of the other alleles ( data not shown ) , indicating that the jas-2 mutant allele is not completely penetrant . We also generated and analyzed F1 jas-1/jas-3 plants . While plants heterozygous for either the jas-1 or jas-3 allele did not produce dyads , we clearly observed dyad formation in F1 plants containing both alleles ( Figure 3E ) . The presence of dyads in multiple jas alleles and in plants containing both the jas-1 and jas-3 alleles confirms that mutations in the JAS gene are indeed the cause of the jas phenotype . PHE1 is a direct target gene of the FIS PcG complex [31] , and we wondered whether increased paternal genome contribution would cause a reactivation of other FIS target genes as well . To investigate this question , we profiled transcriptomes of seeds derived from crosses of wild-type plants with pollen from either jas plants or tetraploid plants as well as mutants lacking the FIS subunit FIS2 [33] , which were manually self-pollinated . Consistent with the finding that jas pollen is diploid , we observed a strong overlap among genes that were deregulated in seeds derived from crosses of wild-type plants with pollen from either jas or tetraploid plants ( p = 9 . 11E-99 for up-regulated genes and p = 1 . 61E-62 for down-regulated genes; Figure 5A and Table S1 ) . The lower number of deregulated genes in seeds of jas pollen parents is attributed to the fact that only about half of these seeds are triploid ( Table 1 ) , reducing the statistical power to detect small expression changes , whereas all seeds derived from tetraploid pollen parents are triploid . Strikingly , 83% of up-regulated and 41% of down-regulated genes in seeds derived from jas pollen parents were also up- or down-regulated in mutants lacking FIS function ( p = 7 . 84E-106 for up-regulated genes and p = 7 . 56E-49 for down-regulated genes; Figure 5A ) . Similarly , 89% of up-regulated and 54% of down-regulated genes in fis2 mutants were as well up- or down-regulated in triploid seeds derived from tetraploid pollen parents ( p = 9 . 38E-164 for up-regulated genes and p = 2 . 86E-57 for down-regulated genes; Figure 5A ) . There was a strong linear relation with a slope close to 1 when comparing fold changes of jas or fis2 seeds to fold changes of seeds derived from tetraploid pollen parents ( Figure S3 , slope parameter = 0 . 87 and 0 . 88 , respectively ) . This demonstrates that not only was the set of differentially expressed genes very similar between the analyzed samples , but that also the magnitude of change was very similar in seeds with increased paternal genome contribution and in seeds lacking FIS PcG function . These findings strongly suggest that increased paternal genome contribution causes global deregulation of direct or indirect target genes of the FIS PcG complex . To substantiate these findings , we tested whether genes with altered expression in triploid seeds were enriched for H3K27me3 [34] . Indeed , genes that were up-regulated in triploid seeds or in seeds lacking FIS PcG function were significantly enriched for H3K27me3 , supporting the idea that direct FIS PcG target genes are deregulated in triploid seeds ( Figure 5B ) . Interestingly , genes commonly up-regulated in the three datasets had a preferential expression in the endosperm and were found in all three endosperm domains ( micropylar , peripheral and chalazal domains; Figure 5C ) . These findings suggest that increased paternal genome contribution as well as lack of FIS PcG function predominantly affects endosperm development . Furthermore , these data indicate that FIS PcG function is required to suppress genes in the endosperm that are required during the earlier stages of endosperm development . In contrast , genes that were commonly down-regulated in all three datasets were expressed in various plant organs , suggesting that developmental perturbations in triploid and fis2 mutant seeds are caused by reduced expression of genes that have a general role during plant development . This idea is supported by the observation that down-regulated genes in triploid seeds are significantly enriched for genes with functions in cellularization and cell cycle control ( Table S2 ) , two processes that have a general role during plant development . Among the genes commonly up-regulated in all three datasets we found PHE1 as well as MEIDOS ( MEO ) , a gene that we previously identified among genes with deregulated expression in fis mutants [31] . Furthermore , we identified the potential FIS target gene AGL62 [35] as well as several other AGL genes ( AGL40 , AGL36 , AGL90; Table S1 ) with as yet unknown function which , however , were previously shown to interact with PHE1 ( AGL40 , AGL62 ) or with interaction partners of PHE1 ( AGL36 , AGL90 ) in yeast two-hybrid studies [36] . We tested MEO and AGL62 expression in jas seeds at 5–8 DAP and found strongly increased MEO and AGL62 expression levels ( Figure 6A and 6B ) , supporting the results obtained from transcriptome profiling studies . Taken together , these findings strongly support the idea that increased paternal genome contribution causes de-repression of FIS target genes during late seed development . Although the FIS subunit MEA is expressed until late stages of seed development in embryo and endosperm [28] , a role for the FIS complex has only been established to date for female gametophyte and early seed development [37] , [38] . Our results suggest that the FIS complex is also required for gene repression in the endosperm during late seed development and that increasing the paternal genome contribution interferes with FIS function . One possible explanation for this finding is that imprinted components of the FIS complex that are only expressed maternally become limited in the endosperm containing an increased paternal genome contribution . We tested this hypothesis by analyzing expression of the paternally imprinted gene MEA [28] , [30] . We confirmed expression of MEA in wild-type seeds 5–8 DAP and , importantly , observed decreased expression levels of MEA in seeds of self-fertilized jas plants starting at 5–6 DAP ( Figure 6C ) . Because transcript levels were normalized to ACTIN11 with bi-allelic expression , the increased paternal genome contribution in jas is expected to cause an apparent reduction in transcript levels for genes with only maternal expression; thus , the measured changes in relative MEA transcript levels were in the expected range . Reduced MEA transcript levels were not observed at earlier seed developmental stages , most likely because activation of the paternally contributed genome occurs with a delay of 3–4 days [39] and therefore , increased paternal genome contributions are unlikely to impact on the transcript level of maternally expressed genes . MEA is biallelically expressed in the embryo , but exclusively maternally expressed in the endosperm [28] . As embryo development in triploid jas seeds is delayed , we wondered whether decreased transcript levels of MEA in jas seeds were caused by reduced MEA expression in the embryo . Therefore , we tested allele-specific MEA transcript levels in seeds derived from crosses of accession RLD with wild-type Ler or jas pollen . We detected strongly decreased transcript levels of the maternal MEA allele and only marginally decreased transcript levels of the embryo-derived paternal MEA allele in jas seeds ( Figure 6D ) . Thus , the observed decreased MEA transcript levels in the endosperm were caused by reduced maternal-specific MEA transcripts . FIS2 encodes a subunit of the FIS complex and is also regulated by genomic imprinting with only the maternal FIS2 allele being expressed in the endosperm [29] , [40] . We also analyzed expression of the FIS2 gene that like MEA had a second expression peak during late seed development ( Figure 6E ) . However , in contrast to reduced MEA transcript levels in jas seeds , relative FIS2 transcript levels were increased ( Figure 6E ) . We wondered whether increased FIS2 expression is caused by activation of the paternal FIS2 allele . Therefore , we tested allele-specific FIS2 transcript levels in seeds derived from crosses of accession C24 with wild-type Ler or jas pollen . However , FIS2 remained imprinted in wild-type and jas seeds ( Figure 6F ) , suggesting different regulatory modes of MEA and FIS2 regulation in triploid seeds . Together , our data suggest that developmental changes observed in triploid seeds are caused by lack of sufficient MEA rather than FIS2 expression . If decreased MEA transcript levels are causally responsible for developmental aberrations in triploid seeds , increased MEA expression levels should normalize development of triploid seeds and partially suppress the jas phenotype . To test this hypothesis , we overexpressed MEA under control of the RPS5a promoter , which is expressed in embryo and endosperm [41] . Complementation of the mea/+ mutant by a hemizygous RPS5a::MEA transgene is expected to result in a reduction of the seed abortion ratio to 25% , as half of the mea mutant gametophytes will inherit the transgene . Expression of MEA under control of the RPS5a promoter in the mea/+ mutant ( mea/+;RPS5a::MEA/+ ) suppressed the mea seed abortion phenotype , with 6 out of 14 lines showed seed abortion ratios between 29% ( χ2 = 2 . 34<χ20 . 01[1] = 6 . 64 ) and 31% ( χ2 = 6 . 52<χ20 . 01[1] = 6 . 64 ) , which is close to the expected seed abortion ratio . This suggests that the RPS5a promoter is active at the correct stage and at sufficient strength and should be suitable to compensate reduced MEA transcript levels in most triploid seeds . We selected three transgenic lines being hemizygous for single locus insertions of the RPS5a::MEA construct in the jas mutant background that had either wild-type-like or strongly increased MEA expression levels ( Figure 7A ) and assayed the number of enlarged seeds . Consistent with our hypothesis , all lines had significantly less enlarged seeds ( Figure 7B , Table S3 , Figure S4 ) , with two of the lines having less than half the number of enlarged seeds compared to the jas mutant . We hypothesized that normalization of seed size by RPS5a::MEA expression would be associated with a progression of triploid embryo development . We tested this hypothesis by comparing developmental stages of jas and jas;RPS5a::MEA seeds . Gynoecia of jas and jas;RPS5a::MEA transgenic lines were manually pollinated and seed development inspected after 10 DAP . Indeed , all three RPS5a::MEA containing jas lines had reduced numbers of developmentally delayed seeds compared to the jas mutant not containing the transgene ( Figure 7C ) , indicating that increased MEA expression can normalize seed development in triploid jas seeds . In jas mutant plants , only segregating wild-type seeds had reached maturity at 10 DAP; whereas triploid jas seeds were either in the torpedo or walking stick stage ( Figure 7D ) . We were not able to phenotypically differentiate mature triploid seeds containing the RPS5a::MEA transgene from diploid transgene containing seeds ( Figure 7D ) , suggesting that triploid seeds with increased MEA expression can complete seed development at similar pace as diploid seeds . Finally , we asked whether increased MEA expression would normalize PHE1 expression in triploid seeds . Again , consistent with our hypothesis , PHE1 expression levels were significantly reduced by increasing MEA expression in the jas mutant ( Figure 7E ) . Together we conclude that reduced MEA transcript levels are causally responsible for seed developmental aberrations in triploid jas seeds .
Misbalanced expression of imprinted genes has long been implicated as the cause of seed development defects after interploidy crosses [9] , [14] , [15] Our study provides strong evidence in favor of this hypothesis and demonstrates that MEA imprinting is a major origin of developmental defects caused by increased paternal genome contributions . In this work we took advantage of the jas mutant that forms unreduced diploid pollen at high frequency , which allowed us to create first generation polyploids and to investigate direct effects of chromosome doubling on seed development . Lack of JAS function causes failure of chromatid segregation during meiosis II , leading to second division nuclear restitution . This mechanism is different to the formation of unreduced pollen by parallel spindles during meiosis II or omission of the second meiotic division , as it occurs in the recently identified Atps1 and osd1 mutants , respectively [42] , [43] . It also clearly differs from the male defect of the switch1 ( dyad ) mutant that is defective in prophase I , leading to aberrant segregation of chromatids during the first meiotic division [44] . Thus , the identified JAS gene provides molecular insights into a novel mechanism of unreduced pollen formation in plants and will further our understanding of the underlying molecular mechanisms of polyploidy formation . Similarities of the endosperm phenotype in triploid jas seeds and seeds lacking components of the FIS PcG complex let us to propose that FIS function is impaired in seeds with increased paternal genome contribution . We show that FIS genes MEA and FIS2 are expressed at later stages of seed development , concomitantly with the time of endosperm cellularization [45] . Endosperm cellularization in triploid Arabidopsis seeds with paternal excess is delayed or fails completely [18] , [19] , consistent with increased expression of FIS target genes like PHE1 and its proposed interaction partner AGL62 [36] , which is implicated to suppress endosperm cellularization [35] . This suggests that the FIS PcG complex has a function during later stages of seed development to suppress inhibitors of endosperm cellularization . We did not observe deregulation of FIS PcG target genes during early development of triploid jas seeds , indicating that only this later function of the FIS PcG complex is impaired by increased paternal genome dose in the endosperm . This suggest that the number of accessible FIS target sites increases at the time of endosperm fertilization , consistent with our finding that the paternal PHE1 allele becomes targeted and silenced by the FIS PcG complex at this time . MEA and FIS2 are regulated by genomic imprinting and are only maternally expressed [24] , [28] , [30] . Therefore , we hypothesized that transcript levels of both genes could become limiting in triploid seeds with increased paternal genome contribution . The paternal MEA allele is silenced by the MEA containing FIS complex [46]–[48] . Therefore , reduced MEA transcript levels in tetraploid jas endosperm could potentially cause a breakdown of MEA imprinting , leading to a reactivation of the paternal MEA allele . However , we show that MEA remains imprinted in tetraploid endosperm , suggesting that MEA is able to recruit sufficient FIS complexes leading to stable silencing throughout seed development , while many other FIS target genes including MEO , AGL62 and the paternal PHE1 allele , are not . This implicates that different FIS target genes have different binding affinities for the FIS PcG complex , which is consistent with observations made in Drosophila that dependent on the genomic context , PcG proteins have different binding affinities to their targets [49] . At the PHE1 locus , different binding affinities of the FIS PcG complex to maternal and paternal alleles might be caused by a differentially methylated region located downstream of the PHE1 locus that is required for repression of maternal PHE1 alleles [50] . Although FIS2 remained imprinted in triploid jas seeds , FIS2 transcript levels were increased compared to wild-type seeds . This suggests that activation of maternal FIS2 alleles requires transcriptional activators that are induced by increased paternal genome dose in the endosperm . Support for this idea comes from a recent study of Jullien and colleagues [51] , who propose the requirement of additional activating factors for FIS2 expression in the endosperm , based on the finding that lack of DNA methylation does not lead to FIS2 activation in vegetative tissues . Increased MEA expression normalized triploid seed development and caused triploid embryo development to progress at similar pace like wild-type embryos . About half the number of triploid jas seeds were affected by increased MEA expression , indicating that either MEA expression levels were not sufficiently enhanced in all seeds , or , alternatively , that there are additional factors required to restore normal seed development . Expression of MEA under control of its endogenous promoter [25] as well as under control of the RPS5a promoter ( this study ) significantly suppressed abortion of mea mutant seeds; however , there was a deviation from the expected seed abortion ratio of 4 to 7% , indicating that indeed transgene-derived MEA expression levels are not in all seeds sufficient to restore wild-type seed development . Interestingly , postygotic lethality of hybrids between A . thaliana and A . arenosa seems to depend on reduced expression of the FIS2 and increased expression of AGL family members AGL62 and AGL90 , indicating that disturbed FIS complex function might contribute to hybrid seed failure as well [52] . Indeed , increased maternal genome dose strongly suppressed hybrid incompatibility in crosses of tetraploid A . thaliana and A . arenosa [53] , suggesting that increased transcript levels of maternal-specific FIS genes permit normal seed development . Consistently , several other studies reported that there are reciprocal differences in interploidy crossing success , with unreduced eggs being more effective in polyploid formation than unreduced pollen [4] , suggesting that increased dosage of PcG complexes is less detrimental for endosperm development than lack of sufficient PcG function . Whether indeed increased maternal genome dose causes repression of FIS PcG target genes will be subject of future investigation . Maize endosperm is highly dosage sensitive and deviations from the 2∶1 maternal to paternal genome dose will ultimately cause seed abortion [13] , [14] . The MEA homolog Mez1 is also imprinted in the maize endosperm [54] , suggesting that dosage sensitivity in the endosperm is caused by a conserved mechanism involving imprinted expression of PcG genes . Finally , the Sfmbt2 PcG gene has recently been shown to be imprinted in trophoblast tissues of mouse embryos [55] . Trophoblast tissues are particularly sensitive to perturbations in genomic imprinting , reflected by dysmorphic trophoblast development in interspecies hybrids [56] and uniparental embryos [57] . Thus , it is possible that imprinting of PcG genes in embryo-nourishing tissues of flowering plants and mammals is an evolutionary conserved system ensuring correct parental genome contributions in the developing progeny .
Plants were grown in a growth room at 70% humidity and daily cycles of 16 h light at 21°C and 8 h darkness at 18°C . The jas-1 allele was induced in the Landsberg erecta ( Ler ) accession by ethyl methanesulfonate mutagenesis and harbors a premature stop codon at amino acid position 294 ( C-to-T nucleotide substitution at position +1960 of the genomic sequence ) . Unless otherwise stated , all experiments were performed with the jas-1 allele . Additional alleles in the Columbia ( Col ) accession were found in T-DNA insertion libraries: jas-2 ( SALK_083575 ) , jas-3 ( SAIL_813_H03 ) and jas-4 ( SALK_042866 ) harboring insertions in intron 1 , exon 5 , and exon 3 , respectively . The fis2-1 mutant ( Ler accession ) has been previously described [33] . Tetraploid lines of Ler were obtained form the Nottingham Arabidopsis Stock Centre . The RPS5a::MEA overexpressing lines were generated by Agrobacterium tumefaciens mediated transformation into jas-1 heterozygous plants and five transgenic lines homozygous for the jas-1 mutation were analyzed . The PHE1::GUS line has been described previously [31] . This line was used as the corresponding wild-type control for allele-specific expression analysis . For crosses , designated female partners were emasculated , and the pistils hand-pollinated one day after emasculation . For RNA expression analysis , three siliques were harvested at each of the indicated time points . The PHE1::GUS line , mutagenized by ethyl methanesulfonate ( EMS ) treatment , was screened for mutants by selecting M2 plants that showed GUS activity during late stages of seed development . For genetic mapping of the jas mutation , we established an F2 mapping population by crossing jas with the Col-0 accession . Analyzing 280 jas F2 plants using PCR-based polymorphisms , the mutation was located on chromosome 1 in an area of 570 kb between polymorphisms SM104_106 , 6 and PAI1 . 2 on BACs T21E18 and F24B9 , respectively . Open reading frames within this region were PCR-amplified and analyzed using the SURVEYOR Mutation Detection Kit ( Transgenomic ) . A polymorphism was detected in At1g06660 ( JAS ) and confirmed by sequencing . Siliques were harvested for GUS staining at the indicated time points . Staining of seeds to detect GUS activity was done as described previously [31] . Mature pollen nuclei were visualized after staining with 4′ , 6-diamidino-2 phenylindole ( DAPI ) as described previously [58] . Buds were harvested for microscopic analysis of tetrad formation and fixed overnight in 3∶1 ethanol∶acetic acid for about 24 h . Buds were then separated and anthers dissected to release pollen into clearing solution ( 67% chloralhydrate in 8% glycerol ) or into DAPI staining solution ( 100 mM sodium phosphate [pH 7 . 0] , 1 mM EDTA , 0 . 1% Triton X-100 , and 0 . 4 mg/ml DAPI , high grade; Sigma ) . Microscopy imaging was performed using a Leica DM 2500 microscope ( Leica ) with either bright-field or epifluorescence optics . Images were captured using a Leica DFC300 FX digital camera ( Leica ) , exported using Leica Application Suite Version 2 . 4 . 0 . R1 ( Leica Microsystems ) , and processed using Photoshop 7 . 0 ( Adobe ) . For chromosome spreads , inflorescences were harvested and fixed in 3∶1 ( ethanol∶acetic acid ) at −20°C overnight . Flower buds ( 0 . 3–0 . 8 mm ) were fixed , equilibrated in citric buffer ( 10 mM sodium citrate , pH 4 . 8 ) and incubated with 1% cytohelicase , 1% pectolyase and 1% cellulase in citric buffer for 3–4 hours at 37°C . Squashes made in 45% acetic acid were air-dried and mounted in antifade containing 4′ , 6-diamidino-2-phenylindole ( DAPI ) . Slides were analyzed with a Zeiss Axioscope fluorescence microscope ( Zeiss , Germany ) equipped with a cooled CCD camera ( Visitron , Germany ) . Images were acquired using MetaView software ( Universal Imaging Corporation , USA ) . The ploidy levels of leaf cell nuclei were determined by flow cytometry using a PA ploidy analyzer ( Partec ) . Leaves were chopped with a razor blade in CyStain extraction buffer ( Partec ) , filtered through a 30-µm CellTrics filter ( Partec ) into a sample tube , and stained with CyStain Staining buffer ( Partec ) . Data were collected for approximately 10 , 000 nuclei per run and were presented on a linear scale . The 1 . 6 kb upstream sequence of the RPS5a translational start was cloned into pB7WG2 vector replacing the 35S promoter . The MEA cDNA was cloned into pENTR/D-TOPO ( Invitrogen ) . The RPS5a::MEA overexpressing construct was generated by clonase reaction ( Invitrogen ) between pB7WG2/Rps5a and pENTR/D-TOPO/MEA RNA extraction and generation of cDNAs were performed using RNAeasy Plant Mini Kit ( Qiagen ) according to the supplier's instructions . Quantitative PCR was done on an ABI Prism 7700 Sequence Detection System ( Applied Biosystems ) using SYBR Green PCR master mix ( Applied Biosystems ) according to the supplier's recommendations . Quantitative RT-PCR was performed using three replicates and results were analyzed as described [59] using ACTIN11 as a reference gene . Briefly , mean expression values and standard errors for the reference gene as well as for the target genes were determined , taking into consideration the primer efficiency that was determined for each primer pair used . Relative expression values were determined by calculating the ratio of target gene expression and reference gene expression and error bars were derived by error propagation calculation . The primers used in this study are specified in Table S4 . Primers for PHE1 , MEA and FIS2 allele specific expression analysis are specified in Table S2 . Allele-specific FIS2 expression analysis was done by crossing C24 and Ler accessions . The amplified products were digested with AflIII and analyzed on a 2 . 5% agarose gel . Allele-specific MEA and PHE1 expression was analyzed as described previously [27] , [28] . | Crosses between plants of different ploidy often fail because seed development does not proceed normally and non-viable seeds are produced . It is assumed that abnormalities in growth and structure of the endosperm ( the nutritional tissue of the seed ) are the cause of triploid seed failure , consistent with the proposed role of the endosperm in reproductive isolation and angiosperm speciation . In many species , the ratio of maternal to paternal genomes in the endosperm is important for normal seed development , giving rise to the hypothesis that parent-of-origin–specific gene expression ( imprinting ) of regulatory genes in the endosperm is the underlying cause for developmental failure in seeds with deviating parental contributions . We tested this hypothesis using the jason mutant that forms unreduced male gametes and triploid seeds with increased paternal genome dosage . Based on the results of our study , we propose that imprinting of the FIS component MEDEA serves as a dosage sensor for increasing paternal genome contributions , establishing the molecular basis for dosage sensitivity . Our study provides strong evidence supporting the hypothesis that misbalanced expression of imprinted genes is the cause of seed development defects after interploidy crosses and demonstrates that MEDEA imprinting is a major origin of developmental defects caused by increased paternal genome contributions . | [
"Abstract",
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"genetics",
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] | 2009 | Imprinting of the Polycomb Group Gene MEDEA Serves as a Ploidy Sensor in Arabidopsis |
Obesity is one of the largest health problems facing the world today . Although twin and family studies suggest about two-thirds of obesity is caused by genetic factors , only a small fraction of this variance has been unraveled . There are still large numbers of genes to be identified that cause variations in body fatness and the associated diseases encompassed in the metabolic syndrome ( MetS ) . A locus near a sequence tagged site ( STS ) marker D6S1009 has been linked to obesity or body mass index ( BMI ) . However , its genetic entity is unknown . D6S1009 is located in the intergenic region between SLC35D3 and NHEG1 . Here we report that the ros mutant mice harboring a recessive mutation in the Slc35d3 gene show obesity and MetS and reduced membrane dopamine receptor D1 ( D1R ) with impaired dopamine signaling in striatal neurons . SLC35D3 is localized to both endoplasmic reticulum ( ER ) and early endosomes and interacts with D1R . In ros striatal D1 neurons , lack of SLC35D3 causes the accumulation of D1R on the ER to impair its ER exit . The MetS phenotype is reversible by the administration of D1R agonist to the ros mutant . In addition , we identified two mutations in the SLC35D3 gene in patients with MetS , which alter the subcellular localization of SLC35D3 . Our results suggest that the SLC35D3 gene , close to the D6S1009 locus , is a candidate gene for MetS , which is involved in metabolic control in the central nervous system by regulating dopamine signaling .
The worldwide prevalence of obesity ( OMIM 601665 , http://www . ncbi . nlm . nih . gov/omim/ ) is increasing ( data from the International Obesity Taskforce ( IOTF ) website , http://www . iaso . org/iotf/obesity/ ) . This has resulted in a significant increase in morbidity and mortality associated with the metabolic syndrome ( MetS , OMIM 605552 ) . Obesity and associated MetS or body mass index ( BMI , OMIM 606641 ) are regarded as complex traits influenced by both additive genetic effects and environmental factors [1] . It has been estimated that genetic factors explain 67% of the variance in human obesity [2] . Currently , more than 150 loci have been implicated in the development of monogenic obesity , syndromic obesity and polygenic obesity . However , only about 2% of the variance in this trait has been explained [3] , [4] . About 200 cases of severe obesity have been reported to be associated with a single gene mutations in a cohort of 11 genes [5] . Studies on extremely obese children have been successful in the characterization of the causative genes for monogenic obesity . However , progress with this approach has been very slow , and is expected to be faster in the era of whole exome sequencing . On the other hand , the identification of the FTO gene as an obesity gene is an example of loci uncovered by genome-wide association or linkage studies [6] . It remains a challenge to uncover genes responsible for mild or moderate obese phenotypes , especially those which develop in adulthood . Genome-wide linkage analyses have revealed that a locus on chromosome 6q23-25 is linked to obesity in the Framingham Heart Study , with a major locus near the sequence tagged site ( STS ) marker D6S1009 [7]–[9] . D6S1009 is located within the intergenic region between SLC35D3 ( 55 , 419 bp apart at the centromere side ) and NHEG1 ( 867 bp apart at the telomere side ) in the NCBI Map Viewer ( http://www . ncbi . nlm . nih . gov/mapview/ ) . NHEG1 ( neuroblastoma highly expressed 1 ) is a predicted gene with unknown function . No association with obesity of this gene has been documented . SLC35D3 ( solute carrier family 35 , member D3 ) is predicted as an orphan nucleotide sugar transporter or a fringe connection-like protein with 10 transmembrane domains . Previous studies have characterized the recessively inherited ros-/− mutant mouse ( ros hereafter ) , which has a spontaneous intracisternal A particle ( IAP ) insertion at the first exon of the Slc35d3 gene to disrupt its function [10] . Platelet dense granules are absent in the ros mutant , suggesting that SLC35D3 is involved in the biogenesis of platelet dense granules [10] , [11] . This function seems not to do with the solute carrier , and requires further investigation . Mouse Slc35d3 is specifically expressed in the brain as determined by multiple tissue Northern blots [10] , suggesting it has specific roles in the central nervous system . In addition , Slc35d3 expression was restricted to the striatonigral medium spiny neurons ( MSNs ) expressing dopamine receptor D1 ( D1R ) rather than the striatopallidal MSNs expressing dopamine receptor D2 ( D2R ) [12] . Interestingly , during the breeding of this mouse , we observed that the adult ros mice gained weight progressively . Here we have characterized the ros mutant as a mouse model of MetS and obesity . In addition , we found two MetS patients with mutations of the SLC35D3 gene . Our results suggest that SLC35D3 is a candidate gene for obesity-related MetS , which is involved in metabolic control in the central nervous system by regulating dopamine signaling .
During the breeding of the ros mutant mice , we observed that adult ros mice became obese compared to sex and age-matched wild-type ( WT ) mice ( Fig . 1A ) . Growth curves showed progressive and significant weight gain of ros mice relative to WT controls , starting at 8 weeks in males , which is similar to the features of late-onset obesity in humans . However , the expression levels of SLC35D3 in the striatum did not change at different postnatal stages within 6 months ( Fig . S1 ) . Based on this observation , we chose mice at 12 weeks of age for behavior and molecular tests , mice at 24 weeks of age for phenotypic analyses of MetS . At 24 weeks of age , ros males were 31 . 5% heavier than age-matched WT males ( Fig . 1B ) , while the naso-to-anal body length was increased by 6 . 2% in ros mice relative to WT controls ( Fig . 1C ) . To determine whether the increased weight of ros mice reflects body composition changes , we dissected and weighed two distinct fat pads , epididymal and perirenal white adipose tissue ( WAT ) . Both epididymal and perirenal fat mass were enormously increased in ros mice ( Fig . 1D ) . Serum cholesterol and triglycerides levels were increased about 19% and 61% respectively compared with the WT controls ( Figs . 1E and 1F ) . Blood glucose levels in ros mice were increased about 43% ( Fig . 1G ) . In addition , serum insulin was increased about 4 . 2-fold in ros mice ( Fig . 1H ) , while the insulin tolerance test ( ITT ) and glucose tolerance test ( GTT ) showed ros mice were insulin resistant and glucose intolerant ( Figs . 1I and 1J ) . Taken together , the ros mice exhibited multiple features of the MetS with late-onset obesity , hyperlipidemia , hyperglycemia and hyperinsulinemia . The development of obese ros mice may result from elevated energy intake and/or decreased energy expenditure . To assess whether ros mice were hyperphagic , daily food intake was monitored in animals fed a standard chow diet ad libitum for 7 consecutive days at the age of 24 weeks when ros mice were already obese . There was no significant difference in daily food intake between WT and ros mice ( Fig . 2A ) . This suggests that energy intake is unaffected in ros mice . On the other hand , ros mice had significantly decreased physical activity including decreased movement distance , average velocity and movement duration ( Figs . 2B–D ) . The decreased physical activity in ros mice prompted us to measure daily energy expenditure over 5 day periods using an Oxymax system ( details in the Materials and Method and Fig . S2 ) . Total energy expenditure ( resting plus activity metabolism ) in the ros mice at age 2–3 months , prior to development of obesity , was significantly lower than in the WT mice using analysis of covariance ( ANCOVA ) [13] ( Fig . 2E ) . However , there was no difference between the genotypes in their resting metabolic rates , independent of how the resting metabolism was evaluated ( Figs . 2F and 2G ) . This indicated that the difference in energy expenditure between the genotypes was contributed to only by the differences in physical activity expenditure . When we included distance traveled in the respirometry chambers as a covariate in the analysis of total metabolic rate , this only marginally reduced the effect of genotype ( F1 , 12 = 11 . 48 , P = 0 . 05 ) , suggesting the impact of the mutation is on the total amount of activity as well as the energy costs of locomotion . The brain-specific expression pattern in the multiple Northern blots [10] indicated a restricted expression and specific function of SLC35D3 in the brain . We detected no mutant SLC35D3 protein in striatum , substantia nigra and olfactory bulb of the ros mutant with an antibody to mouse SLC35D3 ( Fig . 3A ) , although transcription was upregulated in mutant tissues [10] . SLC35D3 protein was readily detectable in these tissues of WT mice ( Fig . 3A ) . Although the expression of SLC35D3 in the Allen Brain Atlas ( http://www . brain-map . org/ ) shows a wider distribution , in either WT or mutant mice , we did not detect the SLC35D3 protein in other brain sub-regions and especially in the obesity-related brain tissues such as thalamus and hypothalamus ( Fig . 3A ) , as well as in several organs involved in energy homeostasis such as adipose tissue , pancreas , liver and skeletal muscle ( Fig . 3B ) . In addition , no apparent morphological changes or fat accumulation was observed in these organs ( Figs . 3C–3F ) . Considering the enlargement of WAT fat pads ( Fig . 1D ) , the adipocytes in adult ros mice exhibited hyperplasia ( increase in number ) , rather than hypertrophy ( increase in size ) , to contribute mainly to the weight gain . The presence of SLC35D3 in non-neuronal tissues is only known so far in platelets and it plays a role in the biogenesis of platelet dense granules [10] , [11] . These results suggest that SLC35D3 is selectively expressed in certain types of neurons with particular enrichment in the basal ganglia , and that ros mice allow us to investigate the phenotypes related to its dysfunction in these neurons . It has been reported that Slc35d3 is specifically expressed in the striatonigral MSNs expressing D1R rather than the striatopallidal MSNs expressing D2R [12] , therefore , we investigated whether SLC35D3 regulates the function of D1 neurons . Immunohistochemical analysis using an antibody to D1R showed that numerous intensely immunoreactive cell bodies were present in ros striatum ( Fig . 4A ) , which is similar to the intracellular accumulation or internalization of D1R after D1R agonist treatment [14] . To confirm this , in immuno-electronic microscopic ( IEM ) pictures labeled by anti-D1R , we quantified the gold-labeled particles located on the plasma membrane ( PM ) and endomembrane structures ( EnM ) ( Fig . 4B ) . The proportion of D1R in EnM was significantly higher ( 66% ) in ros striatum than that ( 48 . 3% ) in wild-type ( Fig . 4C ) . We then tested cyclic AMP ( cAMP ) production to detect functional D1R activation at the cell surface . Stimulation of D1R by the specific agonist SKF82958 ( 10 µM ) produced an accumulation of cAMP in both groups . Consistently , cAMP production in ros striatum was reduced about 36% compared with the WT controls ( Fig . 4D ) , which may be attributable to the reduction of plasma membrane D1R . Western blotting of striatum lysates showed that total D1 receptor expression levels were similar between wild-type and ros samples ( Fig . 4E ) , indicating the total number of striatal D1 neurons in ros mice is not changed . In comparison , total D2R and the fraction of D2R on the plasma membrane were unchanged in the striatum of ros mutant mice ( Fig . S3 ) , consistent with the observation that SLC35D3 is not expressed in the D2 neurons [12] . This indicates that SLC35D3 plays a role in D1R trafficking in the striatal D1 neurons , but does not affect D2R trafficking in the striatal D2 neurons . Taken together , our results suggest that loss of SLC35D3 in ros striatum causes intracellular accumulation of D1R and reduces D1 receptors on the plasma membrane . To ascertain the underlying mechanism of the accumulation of D1R within the ros neurons , we first examined the subcellular localization of mouse SLC35D3 . Consistent with a recent report [11] , the EGFP-SLC35D3 protein was selectively localized to the ER and early endosomes , but not to the Golgi apparatus or late endosomes/lysosomes ( Figs . 5A–5D ) . We then investigated the intracellular location of accumulated D1R in ros striatal neurons . We performed immuno-EM by double-labeling with anti-D1R and anti-SEC61B ( an ER marker ) and found that the D1R particles co-residing with SEC61B in ros striatal neurons ( 19 . 6% ) were significantly higher than that in wild-type ( 6 . 8% ) ( Fig . 5E ) . This indicates that the increased proportion of ER-retained D1R ( 12 . 8% , Fig . 5E ) may mostly account for the reduction of plasma membrane D1R ( 17 . 7% , Fig . 4B ) in ros mice . Our OptiPrep gradient assays further confirmed the shift of D1R from plasma membrane ( fractions 16–25 ) to intracellular fractions 2–10 mainly corresponding to ER in ros striatum compared with the wild-type ( Fig . 5F ) . We then tested whether there is a physical interaction between SLC35D3 and D1R by co-immunoprecipitation . Indeed , we observed that Myc-SLC35D3 co-precipitated with Flag-D1R ( Fig . 6A ) . Reciprocally , Myc-D1R co-precipitated with Flag-SLC35D3 ( Fig . 6B ) . In addition , we found that the N-terminal portion of SLC35D3 ( 1–241aa ) interacted with the C-terminal region of D1R ( 217- 446aa ) ( Figs . 6A , 6B ) . Taken together , our results indicate that SLC35D3 is likely involved in the membrane trafficking of D1R on its ER exit , and that loss of SLC35D3 leads to the intracellular D1R retention mainly on ER , thus reducing the amount of plasma membrane D1R receptors and their signaling . The above findings in ros mice prompted us to investigate whether there are mutations in the orthologous human SLC35D3 gene in patients with MetS . We screened 363 Chinese Han patients with MetS and 217 unaffected individuals by sequencing the two exons and adjacent exon/intron boundaries together with 1 kb untranslated sequence upstream of the start codon of the SLC35D3 gene . Two variants of SLC35D3 leading to the frame-shift of the coding sequence were found in two unrelated patients . These variants were absent in the control group or the NCBI SNP database for the SLC35D3 gene ( Locus ID 340146 , http://www . ncbi . nlm . nih . gov/SNP/ ) . In patient #1 ( Male , Age: 55 , BMI: 26 . 1 , waist circumference: 109 cm , blood pressure: 135/85 mmHg , TG: 4 . 23 mmol/L , Chol: 5 . 28 mmol/L , Gluc: 4 . 4 mmol/L ) , a heterozygous ΔK404 was identified ( Fig . 6C ) . The mutated SLC35D3 showed the miscolocalization to LAMP3-positive late endosomes/lysosomes in transfected cells compared with WT protein ( Fig . 6D ) , suggesting its subcellular localization has been altered . In patient #2 ( Male , Age: 51 , BMI: 27 . 1 , waist circumference: 100 cm , blood pressure: 120/80 mmHg , TG: 2 . 52 mmol/L , Chol: 5 . 94 mmol/L , Gluc: 5 . 2 mmol/L ) , a heterozygous insL201 was identified ( Fig . 6C ) . Similarly , the mutant insL201 colocalized with LAMP3 , but not EEA1 or SEC61B ( Fig . 6D ) , also suggesting that these mutations alter the subcellular localization of SLC35D3 . The residues around L201 are conserved in human , chimpanzee , dog , mouse and rat . However , the residues around K404 are less conserved in these species . The mislocalization of these two variants ( insL201 and ΔK404 ) implicates localization or sorting signals may lie on these mutational sites . We did not find a second mutation in the SLC35D3 gene in these two patients after excluding possible large deletions , suggesting that both patients are likely affected in the heterozygous state . Both patients were diagnosed as having MetS with central obesity according to the guidelines of International Diabetes Federation ( IDF ) [15] and central obesity in China [16] . Similarly , we observed moderate weight gain in heterozygous ros+/− mice at 5 months of age compared with WT littermates ( WT: 29 . 4±0 . 38 , n = 7; ros+/−: 30 . 9±0 . 24 , n = 8; P<0 . 01 ) . The more severe weight gain in homozygous ros−/− mice at the same age is suggestive that the SLC35D3 mutation may have a gene dosage effect on D1R trafficking . It is unknown whether patients with homozygous or compound mutations may have more severe phenotypes . Unfortunately , we were not able to get access to the blood samples of the family members of these two patients , which precluded us to explore the penetrance of the mutations . This study suggests that mutant human SLC35D3 does not function properly in the ER exit of D1R , thus likely impairing the membrane trafficking of D1R and D1 signaling in the patients in a similar mechanism as revealed in ros mice . To test whether the pathological phenotype in obese ros mice is reversible by the treatment with a D1R agonist , adult male mice received a daily intraperitoneal injection of D1 receptor agonist SKF38393 . Following the 12-day treatment period , we observed that body weight loss of ros mice ( 13% ) was significantly higher than that of the wild-type ( 7% ) . In contrast , body weight changes of saline-treated wild-type mice or ros mice were not significant ( Fig . 7A ) . Treatment with SKF38393 did not change the levels of serum lipids and glucose compared with saline-treatment in wild-type mice . Strikingly , serum cholesterol and triglycerides levels were significantly decreased for SKF38393-treated ros mice to levels that were similar to those of wild-type mice . Blood glucose levels in ros mice were significantly reduced after the treatment of SKF38393 ( Figs . 7B–7D ) . Thus , administration of SKF38393 caused body weight loss and rescued the hyperlipidemia in ros mice . In addition , physical activity was increased significantly after SKF38393 treatment in ros mice compared with WT ( Fig . 7E ) . These results suggest that impaired D1R signaling could be reversible by D1R agonists in ros mice and likely in patients with MetS who carry SLC35D3 mutations .
Obesity is caused by perturbations of the balance between energy intake and energy expenditure , which in turn is regulated by a complex physiological system that requires the coordination of several peripheral and central signals in the brain [17]–[19] . Dopaminergic signaling pathways are involved in the regulation of food intake and energy expenditure , including the mesolimbic pathway in food reward , the mesohypothalamic pathway in satiety and the nigrostriatal pathway in energy expenditure [20]–[22] . Both the D1 and D2 dopamine ( DA ) receptors act synergistically in the regulation of the basal ganglia function in the striatal MSNs [23] , [24] . Dysregulation of DA signaling has been previously implicated in the development of obesity [25] . However , the precise mechanism by which DA receptors regulate energy balance is still unclear [26] . Positron emission tomography revealed that striatal D2 receptor availability is lower in obese humans compared to lean individuals [25] , [27] , but to date no human imaging studies have assessed the involvement of D1 receptors in obesity . Reduction of DA receptors on the cell surface could result from 1 ) increased internalization , 2 ) reduced reinsertion to the plasma membrane due to increased degradation , and 3 ) reduced trafficking or expression ab initio . Previous extensive studies have focused on understanding the internalization of DA receptors following agonist occupancy , including agonist-elicited receptor desensitization , endocytosis , and resensitization or degradation [28]–[31] . Unlike D2R , which is generally trafficked to the lysosomes for degradation [32] , endocytosed D1R is recycled back to the plasma membrane [14] , [33] . However , mechanistic studies of the trafficking of D1R to the cell surface are limited [34] , and its relevance of this trafficking to metabolic disorders has not been reported . Transit out of the ER has been shown to be a critical control point and rate-limiting step in the expression of D1 receptors at the cell surface [35] , [36] . A number of DA receptor-interacting proteins have been identified [37] . One ER protein , DRiP78 , acts as a chaperon for D1 receptor trafficking [35] . Similarly , our data have shown that SLC35D3 is localized to the ER and endosomes , where it interacts with D1R . Loss of SLC35D3 in ros mice blocks the ER exit of D1R , thus leading to retention in the ER and reduced D1R distribution on the cell surface , thereby impairing D1R signaling . We have not completely excluded the possibility that D1R trafficking from early endosomes to plasma membrane is also blocked . Given that the ER-retained fraction of D1R accounts for the greatest proportion of reduced plasma membrane D1R ( Figs . 4B and 5E ) , we speculate that SLC35D3 plays a major role in the ER exit of D1R . Likewise , in human patients , the mutant SLC35D3 ( insL201 or Δ404K ) is mistargeted to late endosomes/lysosomes and therefore is likely unable to function properly in the ER exit of D1R . Therefore , SLC35D3 is identified as a novel regulator of D1R membrane trafficking from ER . The restricted expression of SLC35D3 in the brain and the absence of expression in other peripheral organs ( except for the platelets ) laid the foundation of our hypothesis that the MetS phenotype in ros mice is attributable to lesions in the central nervous system . Since D2R distribution ( Fig . S2 ) is unaffected , and SLC35D3 was not expressed in D2 neurons [12] , the impaired D1R signaling perturbs the D1R/D2R balance , which likely led to the reduced movement and energy expenditure due to the dysfunction of the basal ganglion DA loop . No apparent obesity phenotypes have been documented in several D1r-knockout ( KO ) mouse lines as listed in the MGI database ( MGI:99578 , http://www . informatics . jax . org/ ) . However , reduced spontaneous locomotor activity was reported in a line of D1r-KO mutants [38] . Although the number of plasma membrane D1R in ros mice is about 65% ( 34%/51 . 7% ) of the WT mice ( Fig . 4C ) , the ros mutant does not mimic the D1r+/− mice as the total number of D1R is unchanged but redistributed mainly from the plasma membrane to ER . The other significant difference is that SLC35D3 is selectively expressed in striatal D1R-expressing neurons which may manifest specific effects related to D1R reduction . The D1r-KO mice in contrast may have additional defects given the wider expression of D1R in both neuronal and non-neuronal tissues . In fact the D1r-KO mice showed postnatal growth retardation [39] . Thus , complex multiple interacting effects may preclude the development of obesity in the D1r+/− or D1r−/− mice . In other words , the ros mutant mouse mimics a D1-neuron specific knockdown of plasma membrane D1R , rather than mimicking the conventional D1r−/− or D1r+/− mouse . In contrast to the ob/ob mice which develop obesity from the age of weaning [40] , the ros mice exhibited progressive weight gain starting from 2 months of age . The delayed weight gain in ros mice with late-onset obesity is still a mystery given that the indicated protein is present in early stages after birth ( Fig . S1 ) . In addition , the two patients with SLC35D3 mutations were diagnosed with adult central obesity , which also suggests a late-onset obesity phenotype in humans . Our studies have elucidated the underlying genetic entity of a long-standing unresolved linked locus near the marker D6S1009 . Both mouse and human mutations of the SLC35D3 gene are associated with MetS , suggesting that SLC35D3 is a novel candidate gene for MetS . Considering that obesity affects 10∼25% of the European population and nearly one third of the US population [41] , a mutational screen of SLC35D3 in the obese population would be cost-effective as a precursor to potential D1R agonist treatment . Obese children receiving D1R agonist treatment reverse weight gain [42] . Similarly , administration of D1R agonist reversed most of the phenotype of MetS in ros mice . This effect may be caused by the stimulation of the residual D1R on the plasma membrane of ros striatum , or redirection of the ER-retained D1R to plasma membrane for its signaling . In addition , the reversible phenotypes upon D1R agonist treatment suggest that the reduced D1R numbers on the plasma membrane could be the primary cause of MetS in the ros mutant mice , although we have not excluded the effects on the substantia nigra and olfactory bulb where SLC35D3 are also expressed ( Fig . 3A ) . SLC35D3 deficiency caused obesity primarily via effects on physical activity levels , supporting that genetic factors could be a component of low physical activity [43] . As reduced physical activity is the primary consequence of impaired D1R signaling , encouraging elevations in physical activity in these patients might be an alternative way to prevent or alleviate their symptoms of obesity [44] .
The ros mutant ( ros−/− ) [10] and control C3H/HeSnJ mice ( wild-type , WT ) were originally obtained from Dr . Richard T . Swank's laboratory and bred in the animal facility of the Institute of Genetics and Developmental Biology ( IGDB ) , Chinese Academy of Sciences . To ensure the genotypes of ros−/− and wild-type , we developed a PCR method of genotyping based on the nature of the insertional mutation in the Slc35d3 gene [10] . For the locomotion tests , only male mice of each genotype at 12 weeks old were selected to control for potentially confounding hormonal effects during the estrous cycle in females . Mice were housed in a room with a 12-hr light/dark cycle ( lights on at 7:30 a . m . and off at 7:30 p . m . ) with access to food and water ad libitum . For other phenotypic analyses , males at 24 weeks of age were used except for those specified in figure legends or materials and methods . We recruited 363 unrelated Chinese Han patients with MetS from The Affiliated Hospital of Qingdao University Medical College and The Affiliated Children's Hospital of Nanjing Medical University , and 217 unaffected individuals from Beijing Tongren Hospital of Capital Medical University . The patients were diagnosed as MetS according to the guidelines of International Diabetes Federation ( IDF ) [15] and central obesity in China [16] . In brief , MetS is diagnosed as abdominal obesity ( or central obesity ) with any two of the following parameters , 1 ) TG>1 . 7 mmol/L , 2 ) HDL<1 . 03 mmol/L ( male ) or <1 . 29 mmol/L ( female ) , 3 ) blood pressure >130/85 mmHg , 4 ) fasting plasma glucose >5 . 6 mmol/L . For the diagnosis of abdominal obesity in Chinese population , we choose either 1 ) BMI >28 as general obesity or 2 ) BMI between 24 to 28 as overweight , and waist circumference >90 cm ( male ) or >85 cm ( female ) . Eight mililiter peripheral blood samples were collected from all subjects participating in this study . We designed primers for amplifying the two exons and about 1 kb upstream of the human SLC35D3 gene . Amplified PCR products were subjected to direct sequencing by an ABI PRISM 3700 automated sequencer ( Applied Biosystems , Foster City , CA ) . Growth curves for males were obtained by measuring body weight once a month from 4 to 24 weeks of age . For determination of body length , mice were anesthetized and fully extended to measure the naso-anal distance . Epididymal and perirenal fat pads were harvested from male mice and weighed . Blood was collected by cardiac puncture after an overnight fast for measuring blood glucose , serum cholesterol and triglycerides by colorimetric kit assays ( Leadman , Beijing , China ) and analyzed using an automatic biochemical analyzer ( Hitachi , Tokyo , Japan ) . Insulin was measured by a rat/mouse insulin enzyme-linked immunoassay ( ELISA ) Kit on non-fasted mice ( Millipore , Bedford , MA , USA ) . For the insulin tolerance test ( ITT ) and glucose tolerance test ( GTT ) , fasting plasma glucose levels were measured ( 16 hours fast , blood taken from the tail vein ) using a glucosimeter ( Teromo , Japan ) . Then insulin ( Roche Diagnostics , Switzerland ) was injected intraperitoneally ( 1 U/kg ) and blood glucose was measured again at 15 , 30 , 60 , 90 and 120 min post injection . Alternatively , D-glucose ( Sigma-Aldrich , St . Louis , Missouri , USA ) was injected intraperitoneally ( 2 g/kg body weight ) and blood glucose was measured again at 15 , 30 , 60 and 120 min post injection . Mice were pre-exposed to the chamber before testing to allow environmental habituation , and activity was monitored under indirect dim light and sound-attenuated conditions . A single mouse was placed in a chamber ( 40 cm length×40 cm width×45 cm height ) for 30 min . Total distance traveled , average velocity and total movement duration measured spontaneous activity . All these parameters were measured by JLBehv software ( JLGY , Shanghai , China ) . Behavioral testing was performed between 8:00 and 12:00 a . m . Before measurement of daily food intake , mice fed ad libitum were individually housed for 3 days to allow environmental habituation . Food was measured at 3:00 p . m . each day for 7 consecutive days . Mice aged 8 to 12 weeks ( prior to development of obesity ) were measured using an indirect calorimetry system ( Oxymax , Ohio , USA ) . Oxygen consumption , CO2 production and physical activity ( beam breaks ) were recorded at 30-min intervals for 5 consecutive days ( 48 times a day ) . Volume of oxygen consumption ( VO2 ) and carbon dioxide production ( VCO2 ) were measured using electrochemical and spectrophotometric sensors respectively . Oxygen consumption data were converted to energy expenditure ( Watts ) using the measured RQ values using procedures outlined in Arch et al [45] . To report the total energy expenditure we averaged the 48 measurements collected each day across days 2 to 5 , allowing the animals to acclimate during the first 24 h in the chambers [13] . We recorded simultaneously the physical activity levels of the mice . Typical temporal patterns of oxygen consumption and physical activity are shown in Figs . S2A and S2B respectively . To establish the resting metabolic rate we used two different strategies . First we summarized all the half hourly oxygen consumption data in a histogram and then calculated the mean of the lowest 5% of values . Typical histogram for the data shown in Fig . S2A is shown in Fig . S2C . A second method was to use the regression approach outlined by Nonogaki et al [46] . This involved plotting the time matched data for VO2 and physical activity levels ( Fig . S2D ) and then evaluating the resting metabolic rate from the intercept of a fitted linear regression model . The relationship between total metabolic rate and body weight for each genotype is shown in Fig . 2E , and that for the two resting metabolic rate approaches in Figs . 2F and 2G respectively . Following derivation of the total and resting rates of metabolism we corrected for the potentially confounding effects of body weight ( Figs . 2E–2G ) as recommended in Arch et al [45] and Tschop et al [13] . Twenty-four to 25-week-old male mice of each genotype received a daily intraperitoneal injection of dopamine D1 receptor SKF38393 ( 20 mg/kg ) ( Sigma-Aldrich ) for a period of 12 days . Saline-treated mice served as controls . Body weight in all mice was measured on the fourteenth day . Blood glucose , serum cholesterol and triglycerides , and locomotor activities were measured as described above . The dorsal striatum of mice was dissected as above and homogenized in Buffer A ( 10 mM Tris pH 7 . 4 , 1 mM EDTA , 30 µM leupeptin , 1 µM pepstatin A ) with 10% sucrose . Membranes were isolated by centrifugation ( 65 min at 100 , 000 g ) onto a cushion of Buffer A with 44 . 5% ( w/v ) sucrose . The membranes at the interface were transferred to a new tube and washed twice with Buffer A and collected by centrifugation ( 30 min at 100 , 000 g ) . Protein concentrations were determined with Protein Assay ( Bio-Rad , Hercules , CA , USA ) . Adenylyl cyclase activity was determined by incubating membrane protein ( 20 µg ) at 30°C for 10 min in 0 . 1 ml of buffer containing 10 mM imidazole ( pH 7 . 4 ) , 0 . 2 mM EGTA , 0 . 5 mM MgCl2 , 0 . 5 mM DTT , 0 . 1 mM ATP , 0 . 5 mM IBMX , and 10 µM D1 receptor agonists SKF82958 ( Sigma-Aldrich ) . Reactions were terminated by placing the tubes into boiling water for 2 min . The cAMP concentrations were measured using the Direct cAMP Enzyme-linked Immunoassay Kit ( Sigma-Aldrich ) following the manufacturer's instructions . Optical density was measured at 405 nm by a microplate reader ( Bio-Rad ) . The polyclonal rabbit anti-mouse SLC35D3 antiserum ( 1∶1000 ) for immunoblotting ( WB ) was prepared using the purified C-terminal 322–422aa peptide as an antigen . The monoclonal mouse anti-GFP ( 1∶1000 ) and polyclonal rabbit anti-Myc ( 1∶1000 ) were obtained from Santa Cruz Biotechnology ( Santa Cruz , CA , USA ) . Monoclonal mouse anti-Flag antibody ( Sigma-Aldrich ) was used for WB ( 1∶7000 ) and immunocytochemical ( ICC ) analysis ( 1∶3000 ) . Mouse monoclonal antibody against calnexin ( 1∶200 ) for ICC was purchased from Abcam ( Cambridge Science Park , Cambridge , UK ) . Mouse monoclonal antibody against D1 receptor ( Chemicon , Temecula , CA , USA ) was used for WB ( 1∶600 ) and ICC ( 1∶300 ) . Polyclonal rabbit anti-D2 receptor ( Millipore ) was used for WB ( 1∶2000 ) . Mouse polyclonal antibody against GM130 for ICC ( 1∶500 ) was a kind gift from Dr . S . Bao ( IGDB , CAS , China ) . Polyclonal rabbit anti-LAMP3 ( Chemicon ) was used for ICC ( 1∶200 ) . Rabbit polyclonal anti-SEC61B antibody was purchased from Millipore and used for ICC ( 1∶600 ) . Mouse monoclonal anti-EEA1 was from BD Biosciences ( Franklin Lakes , NJ , USA ) for ICC ( 1∶500 ) . Rabbit polyclonal anto-GluR1 was from Millipore for WB ( 1∶1000 ) . Goat polyclonal anti-BIP was from Santa Cruz Biotechnology for WB ( 1∶400 ) . Mouse monoclonal anti-β-actin ( Sigma-Aldrich ) was used for WB ( 1∶10000 ) . Alexa Fluor 594-conjugated donkey anti-mouse and donkey anti-rabbit IgG ( H+L ) were purchased from Molecular Probes ( Invitrogen , Carlsbad , CA , USA ) . Cell lysates , immunoprecipitates or tissue lysates were combined with loading buffer and subjected to 8–12% SDS polyacrylamide gel electrophoresis ( SDS-PAGE ) . Proteins were blotted onto polyvinylidene difluoride membranes in phosphate buffer with 0 . 1% Tween-20 ( PBST ) , blocked for 1 h in 5% non-fat dry milk/PBST and probed for 2 h with primary antibodies at room temperature . The membranes were rinsed three times ( 10 min each ) with PBST prior to incubation with appropriate peroxidase-conjugated secondary antibodies ( Santa Cruz Biotechnology ) and developed with enhanced chemiluminescence ( Amersham Biosciences , Piscataway , NJ , USA ) . We prepared constructs for co-immunoprecipitation and immunofluorescence experiments . Mouse entire coding regions of wild-type Slc35d3 ( RefSeq , NM_029529 , http://www . ncbi . nlm . nih . gov/refseq/ ) , D1r ( RefSeq , NM_010076 ) , D2r ( RefSeq , NM_010077 ) , and human SLC35D3 ( RefSeq , NM_001008783 ) were subcloned into the pEGFP-C2 or -N2 vector ( with GFP-tag ) , pCMV-tag2B vector ( with Flag-tag ) and pCMV-tag3B vector ( with Myc-tag ) as specified in the figures . The fragments of mouse SLC35D3 and mouse D1R specified in figure legends were generated by subcloning . The mutant human SLC35D3 constructs ( ΔK404 and insL201 ) were generated by site-directed mutagenesis ( Takara , Japan ) using human wild-type SLC35D3 construct . Transfected HEK-293T cells grew to confluency on 6-well plates . Cells were harvested and lysed in 50 mM Tris-HCl ( pH 7 . 4 ) , 150 mM NaCl , 1 mM EDTA , 1% Triton X-100 and protease inhibitors . Cell lysates were centrifuged at 18 , 000 g for 10 min , and the supernatant was collected and recentrifuged . The supernatant was incubated overnight with 3 µg mouse monoclonal anti-FLAG M2 antibody ( Sigma-Aldrich ) and washed 6 times with ice-cold wash buffer ( 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl ) . The samples were eluted with elution buffer ( 5 µg/µl 3× FLAG peptide ) and subjected to SDS-PAGE and Western blotting with anti-Myc or anti-Flag antibody as described above . Mice were perfused through the heart with 4% paraformaldehyde in 0 . 1 M phosphate buffer ( pH 7 . 4 ) under deep pentobarbital anesthesia . The brains were removed , and 20 µm frozen sections in the coronal plane were prepared for hematoxylin and eosin ( H–E ) or immunohistochemical staining ( IHC ) . The H–E staining followed routine procedures . A standard H–E staining protocol was applied to sections of adipose tissue , liver , pancreas and skeletal muscle . For IHC , the endogenous peroxidase activity was blocked by treatment with 0 . 3% hydrogen peroxide in methanol , sections were blocked with 0 . 01 M PBS containing 10% goat serum and were then incubated overnight at 4°C with the mouse monoclonal antibody against D1R ( 1∶400 ) . Following 0 . 01 M PBS rinses , sections were incubated in a biotinylated secondary antibody ( Zhongshan Goldenbridge , Beijing , China ) for an hour at room temperature , treated for another hour at room temperature with peroxidase-ligated streptavidin . The results were captured by a TS100 microscope ( Nikon , Tokyo , Japan ) . HEK293T cells transfected with EGFP-SLC35D3 ( wild-type or mutant or other constructs as specified in the results or figure legends ) were grown on glass cover slips in 24-well plates until 30–50% confluence . 18–20 hrs after transfection , they were fixed with freshly prepared 4% paraformaldehyde for 10 min . Cells were washed 3 times with 0 . 01 M phosphate buffer ( pH 7 . 4 ) . The permeabilization of cells was carried out in the presence of 0 . 3% Triton X-100 in PBS for 10 min . After blocking in 0 . 01 M PBS containing 1% BSA for 1 hr at 37°C , fixed cells were incubated with various antibodies as indicated in the results overnight at 4°C . Cells were then washed 3 times in 0 . 01 M PBS containing 0 . 1% Triton X-100 before incubating with Alexa Fluor 594-conjugated secondary antibody at 1∶2000 dilution for 1 h at 37°C . Cells were then washed 3 times before glass cover slips were mounted . Images were acquired with an ×100 lens on a D-ECLIPSE-si confocal microscope ( Nikon ) . Mice were anesthetized with pentobarbital ( 0 . 1 g/kg , Sigma-Aldrich ) . Brains were separated and cut by a vibratome ( DSK , model DTK-1000 , Japan ) . The striatum was fixed with 2% paraformaldehyde , 2 . 5% glutaraldehyde and 0 . 1% tannic acid in 0 . 1 M natrium cacodylicum . Then sections were rinsed and postfixed with 1% osmium tetroxide for 30 min . After washing , the sections were dehydrated in an ascending series of dilution of acetone and impregnated in Epon 60°C , 24 hours . Ultrathin ( 70 nm ) sections were collected on nickel grids , rinsed and incubated with mouse anti-D1R ( 1∶10 ) or rabbit anti-SEC61B ( 1∶50 ) in 1% BSA buffer overnight at 4°C , washed and incubated with 10 nm gold-anti-mouse IgG or 15 nm anti-rabbit IgG ( 1∶50 ) . Sections were observed in JEM 2000 electron microscope ( Japan ) . All the reagents were purchased from Electronic Microscope Science ( EMS , Hatfield , PA , USA ) . The dissected striatum was immediately homogenized with 1 ml HB lysis buffer ( 250 mM sucrose , 20 mM Tris-HCl , pH 7 . 4 , 1 mM EDTA ) . The sample was placed onto the top of an 11 ml continuous 5%–20% Optiprep ( Axis-Shield , Norway ) gradient in HB buffer . The gradient was centrifuged at 200 , 000 g ( 34100 rpm ) for 14 hours in a Beckman SW41 rotor . Twenty-six fractions ( 400 µl each ) were collected from the top using auto-collector ( BioComp , USA ) . Equal aliquots from each fraction were analyzed for immunoblotting . For the fractionation assay , the dissected striatum was immediately homogenized with 200 µl HB lysis buffer . 800 µl 20% Optiprep and 800 µl 5% Optiprep in HB buffer were placed into the tube constitutively . The tissue lysate was placed onto the top of the gradient . The sample was centrifuged at 28 , 000 rpm ( TLS-55 , Beckman , USA ) for 14 hours . Eighteen fractions ( 100 µl each ) were collected from the top . Based on the pilot Western assay , the fractions were combined as the 1–10th tube ( mostly cytoplasm fraction ) and the 11–18th tube ( mostly plasma membrane fraction ) respectively . Equal aliquots from each fraction were analyzed for immunoblotting . cAMP ELISA was performed in duplicate and was repeated three or four times . The standard curves were generated using non-linear regression curve fitting . The specific protein bands on Western blots were scanned and analyzed using the software program NIH Image J . All data were obtained from at least three independent experiments . Data were expressed as mean ± SEM and statistical significance was tested by Student's t-test . Data from the calorimetry system was tested by ANCOVA . Distribution of gold-labeled particles in immuno-EM pictures were counted and statistical significance was tested by Chi-square test . Intensities of immunofluorescence in cultured cells were analyzed using NIH Image J . All mouse procedures were approved by the Institutional Animal Care and Use Committee of IGDB ( mouse protocol KYD2006-002 ) . The study of human subjects was approved by the Bioethic Committee of IGDB , Chinese Academy of Sciences ( IRB approval number , IGDB-2011-IRB-002 ) . The study was conducted according to the Declaration of Helsinki Principles . Written informed consents were obtained from all subjects . | Genome-wide linkage analyses have revealed that an STS marker D6S1009 ( about 55 kb from the SLC35D3 gene ) is linked to obesity or BMI in the Framingham Heart Study , but its genetic entity is unknown . Here we characterized the features of obesity and metabolic syndrome with reduced physical activity levels in a previously identified ros mouse mutant , carrying a homozygous Slc35d3 mutation . These ros phenotypes were caused by the intracellular accumulation of D1R mostly on ER in the striatal neurons , impairing D1R signaling and reducing energy expenditure . In addition , we identified two mutations of SLC35D3 in patients with metabolic syndrome which are subcellularly mislocalized . We propose that the SLC35D3 gene is likely a novel candidate gene for MetS and obesity . | [
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] | 2014 | Mutation of SLC35D3 Causes Metabolic Syndrome by Impairing Dopamine Signaling in Striatal D1 Neurons |
Heterotrimeric G proteins are an important group of signaling molecules found in eukaryotes . They function with G-protein-coupled-receptors ( GPCRs ) to transduce various signals such as steroid hormones in animals . Nevertheless , their functions in plants are not well-defined . Previous studies suggested that the heterotrimeric G protein α subunit known as D1/RGA1 in rice is involved in a phytohormone gibberellin-mediated signaling pathway . Evidence also implicates D1 in the action of a second phytohormone Brassinosteroid ( BR ) and its pathway . However , it is unclear how D1 functions in this pathway , because so far no partner has been identified to act with D1 . In this study , we report a D1 genetic interactor Taihu Dwarf1 ( TUD1 ) that encodes a functional U-box E3 ubiquitin ligase . Genetic , phenotypic , and physiological analyses have shown that tud1 is epistatic to d1 and is less sensitive to BR treatment . Histological observations showed that the dwarf phenotype of tud1 is mainly due to decreased cell proliferation and disorganized cell files in aerial organs . Furthermore , we found that D1 directly interacts with TUD1 . Taken together , these results demonstrate that D1 and TUD1 act together to mediate a BR-signaling pathway . This supports the idea that a D1-mediated BR signaling pathway occurs in rice to affect plant growth and development .
Brassinosteroids ( BRs ) are a class of polyhydroxylated sterol derivatives , structurally similar to animal steroids , which appear to be ubiquitously distributed throughout the plant kingdom [1] . As a group of growth-promoting steroid hormones , they play pivotal roles in promoting cell expansion and division , regulating senescence , male fertility , fruit ripening and modulating plant responses to various environmental signals [1] , [2] . Extensive studies in Arabidopsis have identified a nearly complete BR signaling pathway starting with BRI1 ( BRASSINOSTEROID INSENSITIVE 1 ) as the cell membrane receptor which perceives and binds to BRs [3] , initiates a phosphorylation-mediated cascade involving BSK1 ( BR SIGNALING KINASE 1 ) , BSU1 ( BRI1-SUPPRESSOR1 ) , BIN2 ( BRASSINOSTEROID INSENSITIVE2 ) , and PP2A ( Protein phosphates 2A ) and which subsequently transduces the extracellular steroid signal to the transcription factor BZR1 ( BRASSINAZOLE RESISTANT 1 ) [1] , [2] , [4] , [5] . In rice ( O . sativa ) , the BRI1-mediated BR pathway appears to be conserved with Arabidopsis as several important components of this signaling pathway such as OsBRI1 , OsBZR1 and 14-3-3 have the same function as their Arabidopsis orthologs [6] , [7] , [8] . Further , numerous BR-insensitive mutants in tomato , barley and pea have been identified as mutations in BRI1 orthologs [9] , [10] , [11] , [12] , indicating that the BRI1 pathway is conserved in flowering plants [7] , [8] . However , a major question for both Arabidopsis and rice remains; how do G proteins fit into this cascade ? Several previous reports indicate that the canonical heterotrimeric Gα of rice and Arabidopsis are involved in a BR response but are apparently not related to the BRI pathway [13] , [14] , [15] . Heterotrimeric G proteins consist of three subunits , Gα , Gβ and Gγ , which play essential roles in various biological processes in many eukaryotes [16] . Once a ligand binds to a GPCR , the GPCR undergoes a conformational change which activates the G proteins by promoting the exchange of GDP/GTP associated with the Gα subunit , leading to its dissociation from Gβ/Gγ . Subsequently , Gα acts in its cascade while Gβ/Gγ regulate their own downstream effectors [17] , [18] . In contrast to mammals that possess 16 Gα , 5 Gβ and 14 Gγ genes [19] , Arabidopsis has 1 Gα , 1 Gβ and 3 Gγ ( 1 atypical ) genes , while rice has 1 Gα , 1 Gβ and 5 Gγ ( 3 atypical ) genes [18] , [19] , [20] . Despite this limited number of plant G protein members , their functions are diverse and have multiple roles in various hormone responses [16] , [21] . In particular , a loss-of-function mutant in the rice Gα gene D1/RGA1 , displays dwarfism , erect leaves , compact panicle and small round seeds . The d1 mutant was originally identified as a gibberellic acid signaling mutant and exhibited reduced growth and a highly hypersensitive response to infection by a fungus [22] , [23] , suggesting that D1 is involved in both GA signaling pathway and disease resistance . However , several recent studies have shown that the phenotypic characteristic of the d1 mutants are more similar to that of BR-deficient mutants , displaying shortened second internodes , erect leaves , constitutive photomorphogenic growth in darkness and decreased sensitivity to the brassinosteroid 24-epibrassinolide ( 24-eBL ) [13] . Importantly , double mutants obtained from crossing d1-7 and d61-1 ( an OsBRI1 allelic mutant ) showed no epistasis in many organs except in seed length and seed weight [14] , [24] . In addition , the expression patterns of several BR biosynthetic genes were not altered by brassinosteroid in d1 mutants . These results indicated that there may exist a BR signaling pathway in rice which involves Gα , but which is different from the canonical BRI1 pathway [25] . This idea is in agreement with the results for the Arabidopsis Gα gene ( GPA1 ) . The mutant gpa1 shows less sensitivity to 24-eBL and double mutants between Gα-deficient mutants and BR-deficient mutants had additive effects in many organs and tissues [15] . Thus , it is important to understand this potentially novel Gα-mediated BR pathway and to show how it controls BR-mediated growth responses . Recent studies have shown that the ubiquitin-proteasome system ( UPS ) is an integral part of auxin , GA , jasmonic acid ( JA ) , ethylene and abscisic acid signaling or biosynthetic pathways [26] . UPS is regarded as one of the most prominent mechanisms which regulates protein degradation to modulate protein levels in plants to efficiently alter their proteomes and so ensure proper developmental responses and environmental adaptations [27] . Ubiquitin is a 76 amino acids polypeptide that is covalently attached to a protein target through an enzymatic cascade comprising a ubiquitin-activating enzyme ( E1 ) , a ubiquitin-conjugating enzyme ( E2 ) , and a ubiquitin ligase ( E3 ) . The E3s are key factors that define substrate specificity . In plants four main types of E3s have been classified according to their mechanisms of action and subunit composition: HECT , RING , U-box and Cullin-RING ligases ( CRLs ) [26] . U-box E3 ligases are grouped based on a conserved 70 amino acid motif , that lacks characteristic zinc-chelating cysteine and histidine residues , and so uses intramolecular interactions to maintain the U-box scaffold [28] , [29] . Yeast and humans have 2 and 21 U-box genes , respectively . In contrast , Arabidopsis and rice have 64 and 77 U-box genes , respectively [30] , [31] . The expansion of the plant U-box gene family suggests that they are key in controlling diverse cellular processes , with possibly many being specific to plants . The biological functions of over 25 U-box E3s have been reported , involving hormone responses , biotic stress , abiotic stress and self-incompatibility [32] , [33] , [34] , [35] . Here we characterize a new U-box E3 function . We have identified the U-box E3 ligase gene , TAIHU DWARF1 ( TUD1 ) and shown its genetic interactions with the rice Gα subunit D1 . TUD1 is a functional E3 ligase and acts as a BR signaling activator . Furthermore , TUD1 and D1 physically interact and together define a D1-mediated BR signaling pathway which may parallel or partly overlap with the canonical BRI1 pathway .
To dissect additional components involved in D1-dependent BR responses [13] , [14] , we took a genetic approach to identify mutants similar to d1 . Among 250 reduced plant height mutants of rice , a dwarf mutant similar to d1 was identified and subsequently shown to be non-allelic to d1 . We named this dwarf mutant taihu dwarf1 ( tud1 ) . Five allelic mutants ( tud1-1 to -5 ) were further identified from our dwarf mutant collection . To examine the genetic relationship of d1 and tud1 , we crossed a weak d1 allele with a strong tud1 allele ( tud1-5 ) with dm-type dwarfism ( see below ) in the same background of Nipponbare [36] . The phenotype of the F1 was normal , suggesting that they were non-allelic . The phenotype of the double mutant in the F2 was similar to tud1-5 with a specific reduction of the second internode length , erect leaves and shortened grain lengths ( Figure 1A–1C ) , indicating that tud1-5 was epistatic to d1-c ( Table S1 ) . In addition , tud1-4 and d61-2 ( an OsBRI1 allelic mutant ) were crossed and the double mutant showed that tud1 and d61 had an additive effect on rice growth and development ( 9∶3∶3∶1 , χ2:0 . 677 , p>0 . 05 Figure 1D ) . These results showed that TUD1 acts in the same genetic pathway as D1 but different from that involving the rice BRI1 ortholog D61 . To examine the type of dwarfism of tud1 , we compared the gross morphology of 9-week-old wild-type and tud1 plants ( Figure 2A ) . The plant heights of tud1 mutants were significantly shorter than their corresponding wild type , and tud1-5 showed a severe dwarf phenotype . The internode elongation was inhibited in all of the mutants . Lengths of the individual internodes of plants were measured and expressed as a relative value ( Figure 2B ) . Among them , tud1-1 , tud1-2 , and tud1-5 showed a specific internodal inhibition; the second internode was severely shortened relative to other internodes . This pattern was typically classified as a dm-type elongation pattern [36] . In tud1-3 and tud1-4 , the lengths of each internode were almost uniformly shortened , resulting in an elongation pattern similar to that of wild type . According to Takeda [36] , tud1-3 and tud1-4 showed dn-type dwarfism ( Figure S1 ) . To our knowledge , rice mutants showing different patterns of inhibition of internode elongation were only reported in BR-insensitive mutants of d61 and d1 [6] , [14] , suggesting that tud1 is also involved in a BR pathway . In addition , either unhulled or hulled seeds were specifically shortened in the vertical direction in tud1 mutants ( Figure 2C ) . Compared with their corresponding wild type , the grain lengths of tud1 mutants were reduced by 30 to 44% . Their leaves were also shortened , erect and dark-green , similar to d1 ( 24 ) , but their severe rugose ( curled ) nature appeared to be different from d1 ( Figure 2D ) . To determine whether dwarfism in tud1 was due to cell division , cell elongation or both , we measured the cell length and number in the third leaf sheath , the third internode and the lemma of tud1-2 and its wild type ( Figure 3A–3C ) . Overall we found that the total number of cells , in all given organs , was reduced in the mutant compared to wild type ( Table S2 ) . The length of the third leaf sheath in tud1-2 was decreased by 57% compared to wild type , while the average cell length was similar . Thus , the estimated cell number in the third leaf sheath was reduced by about 43% compared with the wild type , indicating that the shortened leaf sheath in tud1-2 was due to a reduction in cell number rather than cell length ( Figure 3D , 3E ) . Similarly , the calculated cell number of the third internode and the lemma in tud1-2 was reduced by about 67% and 36% respectively ( Figure 3F–3H ) . These results indicated that tud1 has a significantly reduced cell number in these aerial plant organs . We analyzed more closely the changes in the second internode and adaxial leaf surfaces . In the wild type , cells in the second internode were elongated and well-organized into longitudinal files , but in tud1-2 and tud1-1 , cell files were disorganized and not elongated ( Figures S2 , S3 ) . In the wild type , the epidermal cells of leaf blade almost run parallel to the vertical vascular tissues , but in tud1-2 , all of the vertical tissues were waved and the leaf epidermal cells had a disorganized arrangement ( Figure S4 ) . However , it was clear that the extent of the overall disorganization of leaf epidermal cells was less severely affected in tud1-2 than in the second internodal cells . These results showed that the deficiency of cell division and the arrangement of poorly organized cell files leads to dwarfism in tud1 plants . Because of its dwarf phenotype , d1 was initially classified as a gibberellin ( GA ) -insensitive mutant [22] . To examine whether tud1 was a GA-deficient mutant , we performed GA and paclabutrazol ( PAC , an inhibitor GA biosynthesis ) application assays for tud1-2 and wild type . Treatment of tud1-2 plants with 1 µM GA3 or 30 µM PAC had effects similar to those seen in wild type ( Figure 4A ) . Similarly , the effect of different concentration of GA3 on increase of seedling plant height was also similar between the wild type and tud1-2 ( Figure 4B ) , indicating that tud1-2 plants have a normal sensitivity to GA . To further confirm this , we performed an immunoblot analysis of SLENDER1 ( SLR1 ) protein , which is a repressor in the GA signaling pathway [37] , [38] , and found that SLR1 protein levels remained similar in tud1-2 and wild type plants treated by either GA3 or PAC ( Figure 4C ) . Meanwhile , an α-amylase activity assay ( diagnostic for GA responses ) also showed that wild type and tud1-2 responded similarly to GA ( Figure 4D ) . Additionally , a new eui1-d ( elongation uppermost internode1-d ) mutant , which accumulated exceptionally large amount of biologically active GAs in the uppermost internode [39] in tud1-2 background , was recovered and showed additive phenotypes ( Figure 4E ) , indicating that tud1 did not influence the GA biosynthesis . Furthermore , a tud1-2/slr1-l double mutant exhibited additive plant height phenotypes ( Figure 4F ) . Taken together , these results showed that tud1-2 is not a GA-deficient mutant . To ascertain whether or not the tud1 is a cytokinin ( CK ) -related mutant , the seeds of tud1-2 and its wild type were germinated on agar plates with or without the cytokinin 6-benzyladenine ( 6-BA ) . The growth of the seminal ( main ) root of both wild type and tud1-2 were inhibited and the degree of reduction in root length was similar in both . Also , the relative expression of three CK-related genes OsIP4 , OsRR1 , and OsHP2 ( Table S4 ) [40] were not significantly altered between wild type and tud1-2 ( Figure S5 ) . Additionally , the degree of the alteration in the relative expression level of OsIP4 , OsRR1 , OsHP2 were also found to be similar in wild type and tud1-2 with treatment of 6-BA when compared to no treatment ( Figure S6A , S6B , S6C ) . These results showed that tud1-2 is not a CK-deficient mutant and its phenotype is not directly associated with CK . To examine whether tud1 was involved in BR responses , we first performed a mesocotyl elongation experiment in the dark . The tud1-2 mutant showed a typical deetiolated phenotype , similar to the BR-insensitive mutant d61-2 [6] ( Figure 5A and 5B ) . Next , seeds of wild type and tud1-2 mutant plants were germinated on agar plates containing different concentrates of 24-eBL and examined for the length of the seminal root after one week . The growth of the seminal root of wild type was inhibited by 24-eBL in a dose-dependent manner , but the tud1-2 mutant showed a significantly reduced response to 24-eBL ( Figure S7A and S7B ) . The growth of the seminal root was inhibited 43% in the wild type , but only 27% in the tud1-2 mutant in the presence of 10−6 M 24-eBL ( Figure S7B ) , suggesting that tud1 is much less sensitive to exogenous BL than the wild type . Furthermore , a lamina joint test was performed using tud1-2 and wild type [41] . In wild type , the lamina joint bending was increased in a dose-dependent manner , from 14 . 2° to 178 . 5° with 0 to 1000 ng 24-eBL ( Figure 5C and 5D ) . In tud1-2 plants , the degree of bending of the leaf lamina also increased with increased concentrations of 24-eBL , but the effect of the bent angles was much smaller than that of wild type under the same condition ( Figure 5C and 5D ) . We compared the effects of 24-eBL on the size of cells in the lamina regions of the wild type and tud1-2 . Under no treatment of 24-eBL , the adaxial cell number in the lamina regions of the wild type was similar to that in the tud1-2 . However , in the presence of 24-eBL , the adaxial cells in this region of wild type were greatly expanded while the cells in tud1-2 were only expanded slightly ( Figure S8 ) . These results indicated that the cells of lamina region in tud1-2 were less insensitive to 24-eBL than that in the wild type . Therefore , these results show that tud1 is a BR insensitive mutant . The results of histological analysis above showed that the main cause of dwarfism in the second internode and leaf blade appeared to be different from that in most of the other aerial organs of tud1-2 . To understand what is the molecular mechanism of retardation in these two type organs , we checked the relative expression levels of several BR-related genes as tud1-2 is affected in BR responses . We analyzed the uppermost ( first ) and second internode of tud1-2 and its wild type , and the seedling leaf blade of wild type , d1-c , tud1-5 and d1-c/tud1-5 with and without treatment of 24-eBL . The q-RT-PCR results showed that the amount of BRD1 , OsDWARF4 and D61 [6] , [42] , [43] , [44] expression differed significantly between the uppermost internode and the second internode; expression of all BR-related genes was stronger in the second internode of tud1-2 than that in the uppermost internode ( Figure 5E , 5F , 5G ) . Similarly , the q-RT-PCR results also indicated that relative expression level of D61 was higher in the seedling leaf blade of the d1-c/tud1-5 double mutant compared with its parents ( Figure S9 ) . The expression level of D61 was also found to be higher in the second internode and seedling leaf blade in tud1-2 . These results suggested that mutation in TUD1 impairs the OsBRI1-mediated BR pathway signal transduction . This affect has consequences with altered feedback regulation of the expression of BRD1 and OsDWARF4 occurring in the second internode of tud1-2 mutant . To examine its molecular function , we isolated TUD1 by map-based cloning . The TUD1 locus was first mapped to the short arm of chromosome 3 between markers s1193411 and s32681 ( Figure 6A , Table S5 ) . TUD1 was further localized to an 18 . 457 kb region containing three open reading frames ( Figure 6A , Table S5 ) . These reading frames were sequenced and the second one , annotated as a U-box protein ( LOC_03g13010 , Os03g0232600 ) , was found to be mutated in all the five allelic mutants ( Figure 6A ) . To further confirm the identity of TUD1 , a DNA fragment of ∼6 . 4 kb in size including the entire sequence of the putative gene was introduced into tud1-2 by Agrobacterium-mediated transformation [45] . Transformants with the TUD1 gene-containing vector showed phenotypes similar to wild-type plants , while transformants with the control vector containing no target gene did not ( Figure 6B ) . Thus , tud1 was caused by a loss-of-function mutation in a U-box gene . On the basis of public data ( www . tigr . org and http://cdna01 . dna . affrc . go . jp/cDNA/ ) and our results of 3′ RACE ( 3′ end rapid amplification cDNA ends ) , we found that TUD1 is an intronless gene corresponding to an ORF ( open reading frame ) of 1380 bp , which is predicted to encode a protein containing 459 amino acids residues with a U-box motif near its N-terminus ( Figure 6C ) . All five allelic mutants were shown to be mutated in the coding sequence ( CDS ) , but at different locations . Despite their differences in genetic background , the tud1-5 , tud1-1 and tud1-2 were easily grouped as strong alleles by their mutant phenotypes , showing the smallest leaf angles and most dwarfism in elongation of internodes when compared to wild type . In contrast , tud1-3 and tud1-4 exhibited mild mutant phenotypes , with respect to the elongation pattern of internodes . The degree of mutant phenotype strength appeared to be correlated to the severity of mutation in TUD1 . tud1-5 was identified as a null allele due to the change of 341G into 341A leading to a premature stop codon . tud1-1 has a “G” inserted into the CDS of TUD1 , near the initiator codon ( ATG ) , and generates an aberrant truncated protein compared to TUD1 ( Figure 6A , 6C ) . Although there is a single base substitution mutation both in tud1-2 and tud1-4 , the mutant phenotype of tud1-2 was more severe than that of tud1-4 , suggesting that the 381S amino acid in TUD1 is very important ( Figure 6A , 6C ) . tud1-3 has a mild phenotype , likely due to a mild defective function in TUD1 with a 62 bp deletion in the CDS of TUD1 , not entirely abolishing its function in vivo , but showing no in vitro ubiquitination activity ( see below ) . Blast searches revealed that the deduced TUD1 protein sequence has high similarity to several sequences in other plant species: 90% identity to 01g042180 protein from Sorghum bicolor , 90% to LOC100281502 and 88% to LOC100383857 proteins from Zea mays , 60% to AT3G49810 and 56% to AT5G65920 proteins from Arabidopsis ( Figure S10 ) . Therefore , the function of TUD1 may be conserved in higher plants . We examined whether TUD1 possesses a ubiquitination E3 ligase activity as a predicted U-box protein . Ubiquitination activity was observed for the purified GST-TUD1 , compared to 2 well-characterised E3 ligases RMA1 and CIP8 proteins [46] ( Figure 6D ) . In addition , tud1-1 , tud1-3 and tud1-4 proteins did not possess any apparent E3 ligase activity ( Figure 6E ) , showing that the ubiquination activity of TUD1 is essential for its function . To investigate the subcellular localization of TUD1 , we conducted an in-vivo targeting experiment using fusions of TUD1 with synthetic green fluorescent protein ( sGFP ) as a fluorescent marker in a transient transfection assay . The TUD1::sGFP fusion protein in rice protoplasts was mainly associated with the plasma membrane ( Figure 7A ) , similar to that of D1 [20] . Together with the result that tud1 is epistatic to d1 , we wondered whether TUD1 would physically interact with D1 . We examined this possibility in three ways . First , we used a biomolecular fluorescence complementation ( BiFC ) assay [47] to test the interaction between the TUD1 and D1 in rice protoplasts . Cyan fluorescence protein ( CFP ) fluorescence was reconstituted when the full-length TUD1 and D1 proteins were co-expressed in rice protoplasts ( Figure 7B ) , showing that they physically interact with each other in vivo . Second , yeast two-hybrid assays [48] were performed using the D1 or TUD1 and the interaction between TUD1 and D1 was subsequently detected ( Figure 7C ) . Third , we performed a glutathione S-transferase pull-down assay [48] for which we also determined whether TUD1 preferentially interacts with the GTP-bound form of D1 . Thus , D1-tagged His and TUD1-tagged GST were expressed and purified from E . coli by nickel and glutathione S-transferase columns , respectively . Purified D1 fusion protein was incubated in buffer with either GDP or GTPγS or blank for 2 hours before adding the GST-TUD1 to the binding assay buffer . We subsequently detected that both the GDP- and GTPγS-bound forms of D1 have similar binding ability to GST-TUD1 , whereas no binding occurred to GST alone ( Figure 7 ) . Based on these results , we concluded that TUD1 physically interacts with D1 .
Heterotrimeric G proteins , consisting of α , β and γ subunits ( Gα , Gβ and Gγ ) , function as signal mediators at the cell plasma membrane in mammals and higher plants [16] , [17] . These G protein complexes dissociate into an α subunit and the βγ dimers upon activation of the complex by signal perception in mammals , yeast and higher plants [49] . In rice , previous studies demonstrated that Gα ( D1 ) , Gβ , Gγ1 , and Gγ2 are not only localized in the plasma membrane , but are also present in a large protein complex ( ca . 400 kDa ) [19] , [20] . As the molecular mass of the αβγ trimer is 100 kDa , the rice 400 kDa G protein complex should contain additional proteins [20] . TUD1 is also localized in the plasma membrane and directly functions with D1 in the plasma membrane . TUD1 , therefore , could be associated with the large G protein complex in rice . It is well known that the active Gα forms are free from βγ dimmers , and Gα-GTP monomer and a Gβγ dimmer regulate their own downstream effectors , respectively . However , in our study , pull-down assays showed that TUD1 physically interacts with both the GDP- and GTPγS-bound form of D1 , indicating that TUD1 may interact with either inactive or active form of D1 . To better understand how D1 and TUD1 interact with each other in vivo , further studies including Co-IP ( co-immuoprecipitation ) and expressing a constitutively active form of D1 ( Q233L ) in the tud1 mutant would be interesting . Gα subunits and E3 ligases act as integral components of signaling pathways; Gα in the G protein complex and E£ ligase in the ubiquitin-proteasome system ( UPS ) [17] , [23] , [27] , [28] , [32] , [33] , [34] , [35] . However , there was no previous evidence to link Gα with an E3 ligase in any signaling pathway in plants . Now , in our study , we have demonstrated that first link; TUD1 is a functional U-box E3 ligase and directly acts downstream of the Gα subunit D1 . Further , TUD1 and D1 mutants show impairment in BR responses . Together these results suggest we have uncovered a novel signaling pathway controlling rice growth , whereby a BR signal , mediated by heterotrimeric G protein , is then potentiated into the UPS . It is well-known that a signal perceived by G proteins is generally primary , and so we suggest that the D1-TUD1 interaction could mediate a very early BR response . This proposal is also consistent with the observation of the defective phenotypes in d1 and tud1 being at early developmental stages . As yet , we cannot discern whether the BR signal mediated by D1-TUD1 originates from the OsBRI1- mediated BR signaling pathway or other unknown BR receptor ( s ) . It will be interesting to isolate the target ( s ) of TUD1 to further define this pathway . The well-characterized BRI-mediated BR signaling pathway is conserved among several plant species [1] , [2] . Gα also appears to be involved in a BR signaling pathway , but as yet has not been linked to BRI1 [13] , [14] , [15] . In rice , it is presently regarded as the D1-mediated BR pathway . One possibility is that the D1-mediated BR pathway is parallel to the BRI-mediated BR signaling pathway . The second possibility is that D1 may amplify some BR responses that are initiated by OsBRI1 [14] . In our study , tud1 mutant was classified as a BR-deficient mutant in the broad sense , with its phenotype more close to d1 than other reported BR-deficient mutants , such as d61 , brd1 , and d2 [6] , [42] , [44] . The tud1 and d1 mutants have common , characteristic phenotypes of BR-related mutants , but also show short , compact panicles and more specifically decreased length in the vertical direction of grain shape . Histological observation showed that the dwarfism in most of aerial organs of tud1 was mainly due to a decreased cell proliferation , which is similar to the cause of dwarfism in d1 . Furthermore , tud1 is completely epistatic to d1 and TUD1 functions together with D1 in vivo . Based on these results , we conclude that TUD1 is a direct downstream factor of D1 signaling and mediates a G-protein signaling pathway for BR . Despite TUD1 and D1 showing differences to BRI1 , we found that the dwarfism in the second internode and leaf blade of tud1-2 ( due to disorganized cell files and failure of normal cell elongation ) is similar to the phenotypes in BR-deficient mutants , such as d61 and brd1 . In addition , q-RT-PCR results showed that mutations in TUD1 may lead to an impairment of the OsBRI1-mediated BR pathway transduction . Thus , the D1-TUD1-mediated BR signaling pathway might have an overlapping function with the OsBRI1-mediated BR pathway in the second internode and leaf blade . In rice , dm-type mutants are commonly identified as BR-deficient mutants [6] , [14] , [42] , [43] , [44] , but why the second internode is specifically inhibited remains unknown . It is difficult to explain if it is simply due to differential OsBRI1 expression in different internodes , leading to differences in BR signal strength , through only one OsBRI1-mediated BR pathway . Rather , it suggests there should be additional signal pathway ( s ) involved in the second internode elongation [6] . Our results support this idea , as we show that the OsBRI1- and D1-TUD1-mediated BR signaling pathways may both have important roles in regulating the second internode elongation in rice . However , there are many questions remaining . For example , how do these two BR signaling pathways function together in the second internode ? How are the BR signals perceived and transduced by the D1-TUD1-mediated pathway if not via BRI1 ? Is the D1-TUD1-mediated BR pathway involved in the signal amplification of some responses that originate from OsBRI1 ? Despite the simple repertoire of G protein signaling elements in plants , multiple signals can be propagated through the G-proteins , to mediate diverse physiological responses [19] , [20] . It is well-known that some physiological responses are mainly accounted for by Gα , whereas others are predominantly mediated by Gβγ . In particular , any given G protein component may have multiple differential roles during plant growth and may have different responses to biotic and abiotic stress in a cell-type- or developmental-stage-specific manner [50] , [51] . In this context , two important rice yield QTLs , GS3 and DEP1 , were recently identified as atypical G protein Gγsubunits [52] , [53] , [54] . This could serve as a good example for selection and their uses in practical cereal breeding . In our study , D1 and TUD1 function together not only to promote plant height , panicle development and seed length increase , but also to control a hypersensitive response to infection by avirulent races of a rice blast fungus ( our unpublished data ) . Based on the D1-TUD1-mediated pathway having dual roles in promoting plant growth and resistance to rice blast , it may be feasible to manipulate the components of this pathway to enhance rice yield . Furthermore , similar genes to TUD1 are found in both Arabidopsis and several cereal species , such as Sorghum bicolor and Zea mays ( Figure S10 ) . As the D1-TUD1-mediated BR signaling pathway is likely conserved across flowering plants , it may provide a way to manipulate the G protein pathway that leads to increased plant productivity .
The dwarf1 ( d1-c ) , slender1 ( slr1-l ) , d61-2 mutants were kindly provided by Dr . Chengcai Chu , Da Luo and Makoto Mastsuoka , respectively . Among them , d1-c and slr1-l were new allelic mutants . The tud1-1 was derived from tissue culture of an indica cultivar-MH63 ( Oryza sativa cv . MingHui63 ) , and tud1-2 , tud1-3 , and tud1-4 were isolated from spontaneous mutations of indica cultivars , ZhangYe ( ZY ) , SHuHui163 ( SH163 ) , and TeTePu ( TTP ) , respectively . The tud1-5 was derived from chemical mutagenesis with ethyl methylsulfonate of japonica cultivar Nipponbare . Double mutants were isolated by phenotype observation and verified by genotyping ( the primers used for genotyping are listed in Table S3 ) . For ascertaining the genetic relationship between tud1-5 and d1-c , we successively investigated the segregation ratio of mutations in F2 and F3 populations of tud1-5/d1-c . The phenotypic segregation ratio of F2 fitted to 9 ( normal ) ∶3 ( d1-c ) ∶4 ( tud1-5 ) . This result was further confirmed by analyzing the F3 seeds from homozygous d1-c and tud1-5 plants . F3 progeny of d1-c plants occasionally segregated the tud1-5 phenotype in 1∶3 ratio , but F3 progeny of tud1-5 never showed the d1-c phenotype ( Table S1 ) . Mutants and wild-type rice plants were planted in paddy fields under natural conditions or greenhouse at 30°C ( day ) and 24°C ( night ) . Longitudinal sections of the third leaf sheaths and the third internodes were analyzed by light microscopy . Cells of the inner epidermal tissues of lemma were analyzed by scanning electron microscopy . Cell lengths were measured in each organ , both in WT and in tud1-2 and the total cell number in each organ was estimated . For light microscopy , leaf sheaths and internodes were fixed with formalin: glacial acetic acid: 70% ethanol ( 1 ∶ 1 ∶ 18 ) and then dehydrated in a graded ethanol series . Fixed tissues were embedded in paraplast ( Sigma ) , and cut using a microtome into 12 µm thick sections and then applied to glass slides . The sections were counter-stained by 0 . 005% ( w/v ) safranin . Tissues were observed under a light microscope ( BX51; Olympus , Tokyo , Japan ) . The inner epidermal cells of lemma were observed by scanning electron microscopy ( SEM ) ( S-3000N; Hitachi , Tokyo , Japan ) . The samples were photographed under the microscopes and the size of >50 cells in each tissue were measured . Total RNA was extracted from the third leaf , the uppermost and the second internode using a Invtrogen Extraction kit . Total RNA was treated with RNase-free DNase ( Promega; http://www . promega . com ) and first strand cDNA was synthesized using SuperScriptII reverse transcriptase ( Invtrogene ) . Real-time PCR was performed using 2×SYBR Green PCR Master Mix ( Applied Biosystems ) on an Applied Biosystems 7900HT Real-Time PCR System with at least three PCR replicates for each sample . The PCR conditions were 2 min at 50°C , then 10 min at 95°C , followed by 40 cycles of 15 s at 95°C , and 1 min at 60°C . In addition , methods for GA and BR sensitivity test , map-based cloning , and assays for E3 ubiquitin ligase , subcellular localization , yeast two-hybrid , BiFC and pull-down are provided in Text S1 . | Rice is an important and staple grain food . Understanding the molecular basis of rice growth and development is crucial to safeguarding our food security . Hormone signaling pathways are key regulators of plant growth and development . Heterotrimeric G-protein complexes serve as signal transducers between cell surface receptors and downstream effectors . In plants , the repertoire of G-protein signaling elements is smaller than in mammals , but there are many examples of G protein components mediating important physiological responses . In rice , the heterotrimeric G protein α subunit known as D1/RGA1 appears to be involved in gibberellin and brassinosteroid ( BR ) responses , but it remains unclear how D1 functions in these responses . Here we discovered a D1 genetic interactor Taihu Dwarf1 ( TUD1 ) that encodes a U-box E3 ubiquitin ligase . Such ligases are important regulators of cell functions . Genetic and molecular analyses revealed that D1 and TUD1 genetically and physically interact with each other and function together to regulate BR-mediated growth in rice . Furthermore , similar genes to TUD1 are found in both Arabidopsis and other cereal species , such as Sorghum bicolor and Zea mays . G-protein signaling mediated by TUD1 , therefore , may be conserved across flowering plants . Such key signaling molecules may provide a target to increase plant productivity by modulating the strengths of signals controlling growth in different tissues such as the seed grain . | [
"Abstract",
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] | [
"agriculture",
"biology"
] | 2013 | The U-Box E3 Ubiquitin Ligase TUD1 Functions with a Heterotrimeric G α Subunit to Regulate Brassinosteroid-Mediated Growth in Rice |
Chagas disease is endemic in the rural areas of southern Peru and a growing urban problem in the regional capital of Arequipa , population ∼860 , 000 . It is unclear how to implement cost-effective screening programs across a large urban and periurban environment . We compared four alternative screening strategies in 18 periurban communities , testing individuals in houses with 1 ) infected vectors; 2 ) high vector densities; 3 ) low vector densities; and 4 ) no vectors . Vector data were obtained from routine Ministry of Health insecticide application campaigns . We performed ring case detection ( radius of 15 m ) around seropositive individuals , and collected data on costs of implementation for each strategy . Infection was detected in 21 of 923 ( 2 . 28% ) participants . Cases had lived more time on average in rural places than non-cases ( 7 . 20 years versus 3 . 31 years , respectively ) . Significant risk factors on univariate logistic regression for infection were age ( OR 1 . 02; p = 0 . 041 ) , time lived in a rural location ( OR 1 . 04; p = 0 . 022 ) , and time lived in an infested area ( OR 1 . 04; p = 0 . 008 ) . No multivariate model with these variables fit the data better than a simple model including only the time lived in an area with triatomine bugs . There was no significant difference in prevalence across the screening strategies; however a self-assessment of disease risk may have biased participation , inflating prevalence among residents of houses where no infestation was detected . Testing houses with infected-vectors was least expensive . Ring case detection yielded four secondary cases in only one community , possibly due to vector-borne transmission in this community , apparently absent in the others . Targeted screening for urban Chagas disease is promising in areas with ongoing vector-borne transmission; however , these pockets of epidemic transmission remain difficult to detect a priori . The flexibility to adapt to the epidemiology that emerges during screening is key to an efficient case detection intervention . In heterogeneous urban environments , self-assessments of risk and simple residence history questionnaires may be useful to identify those at highest risk for Chagas disease to guide diagnostic efforts .
Chagas disease has historically occurred in poor rural settings of Latin America [1] , [2] , [3] . In the rural areas of the Department of Arequipa in southern Peru , reports of Chagas disease and its vectors date back to the early 20th century [4] , [5] , [6] , where the disease has persisted in an endemic state [7] . However , recent case reports [8] and epidemiologic studies [9] , [10] have documented emerging vectorial transmission of Trypanosoma cruzi , the etiologic agent of Chagas disease , in communities of the urban capital of Arequipa . As T . cruzi and Triatoma infestans , the sole insect vector in this setting , spread through the city of Arequipa ( pop . 864 , 250 ) [11] , [12] , several hundred thousand people are at risk of infection . Most individuals have mild or no symptoms during the acute phase of Chagas disease , and pass into the chronic phase without having the infection detected . In the chronic phase , most infected individuals show no signs or symptoms and are considered to have the indeterminate form of the disease . An estimated 20–30% of infected individuals will later develop the cardiac or digestive forms of chronic Chagas disease . Advanced cardiac or digestive disease cannot be reversed and can be fatal [13] , [14] , [15] . However , for patients with the indeterminate form or early cardiomyopathy , antitrypanosomal treatment is reported to decrease the probability of disease progression [15] . It is important to detect individuals with chronic T . cruzi infection because they need clinical attention , may be candidates for treatment , and pose risk for further transmission – either vectorial , congenitally or through blood donation [16] , [17] , [18] . Diagnostics for Chagas disease in southern Peru are expensive relative to local income and the limited government budget for vector-borne disease control [19] . Alarmed by the urban encroachment of T . infestans , the Arequipa regional Ministry of Health ( MOH ) recommends universal testing of all residents of communities with T . cruzi-infected T . infestans . However , such wide-scale testing is too costly for practical implementation . Population screening is further challenged by the very low sensitivity of field-applicable rapid tests in Arequipa , and screening must employ high sensitivity conventional tests such as enzyme-linked immunosorbent assays [20] . Given the large population of Arequipa city and limited health resources , targeted interventions are the only viable option to screen for chronic T . cruzi infection in this population . Although mass vector-control campaigns have had success in Latin America [21] , [22] , numerous authors call for improved screening strategies and expansion of treatment for persons with Chagas disease [19] , [23] , [24] , stressing the importance of cost-efficiency , sustainability , and integration of sectors [22] , [25] . Several cost-effectiveness studies of Chagas disease interventions have focused on devising optimal insecticide application and blood donor testing schemes [25] , [26] , [27] , [28] , but few have examined strategies for human serologic testing in endemic or epidemic areas . A study by Mott et al . [29] indicated the potential for targeted screening around detected positive children under 5 years of age in a rural area of Brazil . Gurtler et al . [19] , [30] highlight the potential efficiency of linking serologic screening to vector control campaigns . It remains unclear how to implement cost-effective , targeted screening programs across a large and diverse urban environment with dynamic vector infestation . In one periurban community of Arequipa that was heavily infested with T . infestans , 5 . 3% of children had T . cruzi infection [31] . Age-prevalence curves of infection [10] and the spatial distribution of cases suggested that the disease was in an epidemic phase in this community [31] . A retrospective analysis of household vector data carefully collected during an insecticide-application campaign prior to serologic testing , indicated that a two-step targeted screening intervention based on household entomologic risk factors and ring testing around identified cases would have captured 83% of infected children , while minimizing the testing of negative children [31] . In the present operational research study , we test 4 targeted screening strategies based on similar household entomologic data , and one adaptive spatial strategy , in 18 periurban communities of Arequipa , Peru . We compare the performance and cost of each , and evaluate the patterns of infection they reveal . Our objective was to test the operational feasibility of routinely collected data from vector control campaigns for human Chagas disease detection on a larger urban scale .
This cross-sectional study was conducted in 3 districts of the city of Arequipa . Each district is composed of several communities , which span a gradient of development . In general , there are higher quality housing materials and infrastructure in the parts closer to the urban center and more recent , less developed settlements towards the periphery . As part of a coordinated Chagas control campaign , Ministry of Health ( MOH ) vector control teams applied two rounds of deltamethrin insecticide ( 5% Wettable Powder; K-Othrine , Bayer; target dose of 25 mg a . i . /m2 ) , spaced six months apart , to houses in 92 communities across the 3 study districts between 2005 and 2009 . The insecticide has an immediate repellent effect , and a delayed lethal effect , on the triatomine bugs . Technicians were trained by the MOH to collect as many emerging triatomine bugs as possible from each house at the time of spraying , thereby obtaining a sample of vectors infesting the house . All technicians were overseen by a brigade chief experienced in Chagas disease vector control . The insects were placed in containers coded by household and delivered to the study laboratory in Arequipa for microscopic examination for T . cruzi , as described previously [9] . Of the 92 communities participating in the insecticide campaign , T . cruzi-infected triatomines were detected in only 18 communities ( 19 . 6% , 95% CI: 11 . 5–27 . 6 ) . We limited our targeted screening strategies to residents of these 18 communities in which the etiologic agent of Chagas disease had been documented . We tested the following four targeted and mutually exclusive strategies for use in the initial screening step , based on household vector data collected at the time of the MOH spray campaign in the 18 communities . Each strategy grouped households on a gradient of risk for human T . cruzi infection based on information from prior studies [10] , in the order listed below . Figure 1 presents a flowchart of the process by which vector data were used to separate the sprayed households into each targeted screening strategy . Sampling percentages for each strategy were chosen to stay within the sample size of the study . Random selection of houses was carried out using a random number generator code in Stata 10 ( StataCorp ) applied to a list of all houses in each category . Following this initial screening step , the second step consisted of adaptive ring sampling in which testing was offered to the inhabitants of all houses within 15 meters of a seropositive individual detected through one of the above strategies . City block layouts are such that houses are contiguous ( share one or more walls ) . A radius of 15 meters would allow to capture , on average , 5 immediate neighbors per index house ( two lateral neighbors , one neighbor behind the house and two diagonal neighbors ) . We subsequently tested another 15 meter radius around any secondary seropositive individuals detected during the adaptive sampling . There had been no insecticide-application campaigns in any of the study sites prior to the 2005–2009 campaigns described here . Houses that were not sprayed during the vector-control campaign could not be assigned to the vector-based strategies , and were excluded . However , they were eligible for the adaptive ring testing . Within the randomized strategies , households that refused to participate in the serology were replaced by additional random selections until reaching the enrollment goal for each strategy . Refusal events were recorded and some reasons for refusal noted . Household participation was defined as participation by at least 1 member . Households were mapped using GoogleEarth ( Google Inc . ) and ArcGIS 9 . 3 . 1 ( ESRI , 1999–2008 ) . The study was approved by the human subjects research ethics committees of Johns Hopkins Bloomberg School of Public Health and by the Universidad Peruana Cayetano Heredia in Peru . Signed informed consent was obtained prior to participation by all adults and the parents of all participating children <18 years . In addition to their parent's consent , children ≥7 years old provided signed informed assent prior to participating . Trained field workers approached each selected house and explained the entire study . In addition , they informed residents about the specific vector data obtained from their house during the spray campaign . For example , residents of households in the infected vector strategy were informed that triatomines collected from their homes carried the parasite that causes Chagas disease . Each house's vector data was kept confidential and not shared with any other household; potential participants were only informed of the community-level risk , for example that T . cruzi carrying bugs had been found in other homes of their community . All individuals ≥1 year old were invited to participate . Field workers conducted a brief household census with one adult member per house , and a questionnaire about demographics and exposure to Chagas disease risks with each individual study participant . Demographic variables included sex , age , and education level . Exposure variables focused on the residential history of participants . Starting with their place of birth , participants were asked to list and categorize each previous place of residence ( stays longer than 1 month ) as rural , urban or periurban , and to recall the presence or absence of “chirimachas” , the local name for T . infestans . Participant recall was aided by the fact that T . infestans is the sole Chagas disease vector in southern Peru and by the heightened awareness resulting from widespread radio , print and interpersonal messaging during the spray campaigns . This element of the questionnaire was not designed to test participant knowledge or ability to identify T . infestans , but to examine if recall through a simple questionnaire could be operationally useful to identify infected or high-risk individuals . In addition , for the purposes of informing future feasibility of this type of screening , we collected data on the costs of fieldwork for obtaining blood samples , specimen processing and testing , data management and quality control . All data was double digitated and managed in Microsoft Access® . We logged person-hours for each of these activities , the number of visits made to each house , and the cost of materials and overhead . This data would yield a simple estimate of the cost of screening strategy implementation for operational purposes . A venous blood specimen ( 3 ml for children younger than 5 years , 5 ml for participants 5 years or older ) was collected from each consenting participant and transported to a Chagas disease field laboratory of the Universidad Peruana Cayetano Heredia ( UPCH ) located in Arequipa . At the field laboratory , specimens were centrifuged , aliquoted , and stored at −20°C until the time of diagnostic testing using the Chagatek ELISA kit following the manufacturer's instructions for cutoffs ( BioMérieux ) . 100% of specimens with positive results by ELISA and a randomly selected 10% with negative results were tested using immunofluorescence assay ( IFA ) according to published methods [20] . Aliquots of the same specimens were processed in parallel in the UPCH Microbiology Laboratory in Lima for quality control . Individuals with positive results by both ELISA and IFA were considered to have confirmed infection . Those with positive ELISA results and negative IFA results were considered discordant . However , previous research has shown that ELISA-positive , IFA-negative specimens likely indicate true infection in Arequipa [32] , and therefore for the purposes of our data analysis only we considered individuals with discordant test results as T . cruzi infected . Regardless of this grouping , all ELISA-positive participants were referred to the MOH along with their lab results , for a case-by-case evaluation of infection status and clinical management by physicians based on national guidelines . Participants' demographic and exposure variables described above were used to calculate frequencies and means , and to fit regression models on the outcome of detected T . cruzi infection . The total number of lifetime locations each participant had lived in was tabulated from the migration histories , as were each participants' total number of years lived in a rural , periurban , or urban locations , and in a location recalled as being infested with T . infestans ( regardless of rural , urban or periurban ) . Because rates of infection were low in our sample , we used Poisson regression to evaluate associations between covariates and infection status using the prevalence ratios ( PrR ) [33] , [34] . Since the data were neither over- nor under-dispersed with respect to the outcome , no adjustments were required [34] . We fit models with a random effect term ( gamma distributed ) to consider correlation among participants of the same household . For the regression analyses , education level was considered only for adults . All variables with p<0 . 2 in univariate regression analysis were considered in fitting a multivariate model . Statistical tests were conducted using Stata 10 ( StataCorp ) .
At the time of insecticide application in our 18 study communities , 1980 of 7739 ( 25 . 6% , 95% CI: 24 . 6–26 . 6 ) sprayed households were found to be infested with triatomine vectors , with technicians capturing between 1–301 T . infestans per household . The 90th percentile cut-off between the high-density and low-density vector houses was determined separately for each community and ranged from 11–85 T . infestans captured per household . Out of the 1980 households in which vectors were detected , eighty-one ( 4 . 1% ) had vectors carrying T . cruzi . In total , 923 people from 249 households participated in the serological testing across the 18 communities in 3 districts . There were no statistically significant differences in demographics by district ( data not shown ) . Participants had a mean age of 34 years ( range: 1–94 ) , and 78 . 9% were adults of age 18 or over . Of the total participants , 21 ( 2 . 28% ) tested positive according to ELISA , and were considered T . cruzi infected for this analysis . Nineteen of these also had positive results by IFA , while 2 did not . All 21 infected individuals were over the age of 18 . Table 1 displays the results across strategies . Neither demographic characteristics nor infection prevalence differed significantly among participants recruited through the four alternative strategies . Although not statistically significant , the highest prevalence was observed among those screened in houses with no infestation detected ( 3 . 07% ) . However , we encountered high refusal rates during participant recruitment from the houses with no infestation detected . Of 97 households approached , only 59 ( 61% ) participated . By contrast , 98% of households with T . cruzi-infected vectors detected participated in the study . In addition , the within household participation rate was lower for households in which no infestation was detected versus households in the infected vector group , 55% versus 70% participation , respectively . These data suggest a self-selection bias in participation among people living in houses with no infestation detected . Potential reasons for this are explored in the discussion . The 923 participants had lived in a mean of 2 . 5 locations ( range:1–15 ) . Mean residence in a periurban location was 26 . 97 years ( CI 25 . 98 , 27 . 97 ) , while mean residence in an urban location was 3 . 61 years ( CI 3 . 11 , 4 . 12 ) , and 3 . 39 years ( CI 2 . 91 , 3 . 88 ) in a rural location . The T . cruzi infected human cases we detected had lived longer , on average , in rural places than non-cases ( mean 7 . 20 years vs . 3 . 31 , respectively; p = 0 . 0184 ) . The results of univariate analysis on T . cruzi infection status are shown in Table 2 . The probability of infection with T . cruzi increased by 2% per year of age ( p = 0 . 02 ) , by 2% per year lived in a periurban location ( p = 0 . 185 ) , by 4% per year lived in a rural location ( p = 0 . 04 ) , and by 4% per year lived in a place with triatomine bugs ( p = 0 . 008 ) . Multivariate models with combinations of these variables did not fit the data better than the univariate models ( data not shown ) . Given inherent dependence of all four of the above variables on calendar time , it is likely that all are describing a similar experience of risk . Table 1 also displays the results of the secondary case detection by 15 meter radii . The adaptive ring sampling of houses within 15 meters of the houses of the 17 index cases yielded 4 additional cases out of 158 individuals tested . Although employed in seven different communities , all four of the secondary detected cases lived in the same community , Simón Bolívar . Of the 10 total cases found in Simón Bolívar , 7 had lived only in urban districts of Arequipa , and 5 only in Simón Bolívar itself , suggesting a local , urban site of transmission . The estimated costs for each study activity are shown in Table 3 . The fixed overhead cost per participant was $7 . 14 , including field materials , telecommunications , transportation , data management , and diagnostic testing , and this was standard across all strategies . However , the recruitment cost per participant varied greatly across strategies , being a function of both the sampling framework and differential participation rates for each . For the households with infected-vectors detected , our field team made 1 . 32 household visits per participant recruited , versus 2 . 63 visits to houses in which no infestation was detected , reflecting the higher refusal rate of the latter . Each household visit required an average of 1 . 54 person-hours , at which rate , the testing in houses where no infestation was detected was the most costly strategy per participant ( $14 . 62 USD ) , and the infected-vector houses strategy the least costly per participant ( $10 . 91 USD ) .
Chagas disease is a growing problem in Arequipa , Peru . A previous study in the city uncovered micro-epidemics of transmission associated with high density of vectors , suggesting that targeting screening based on entomologic information could be an efficient means of detecting T . cruzi infected individuals [31] . In this prospective field trial , we uncovered important differences in the association between vector-parasite distribution and human Chagas disease infection , which merit consideration in the design of screening programs for Chagas in Arequipa and other urban settings . There are numerous reasons why the vector-based targeted screening strategy designed for a previously studied community ( Guadalupe ) , yielded fewer cases when applied elsewhere in the city . In Guadalupe , parasite infections in both humans and vectors were clustered , and the age-prevalence curves suggested established epidemics of T . cruzi transmission [10] , [31] . The present study sites , much closer to the city center , also contained clusters of parasite-infected vectors , but these consisted of fewer households . These smaller clusters are likely indicative of a relatively short history of vectorial transmission , leading to few locally acquired cases . In addition to epidemiology and ecology , social and demographic phenomena may also affect patterns of human Chagas disease [35] , [36] . Frequent migratory movement between rural and metropolitan Arequipa may bring parasite into the city [36] , [37]; the cases of infection we detected were most associated with a history of triatomine exposure and residence in rural areas , where Chagas disease is historically endemic [4] , [5] , [6] . It is possible that the few clusters of parasite are due to introductions from outside that did not manage to spread beyond a handful of households in the new urban environment . That periurban time of residence did not significantly contribute to overall risk of infection in this study is another indication of minimal vectorial disease transmission in the study communities . Interestingly , one of the 18 communities studied , Simón Bolívar , did display patterns of infection reminiscent of the micro-epidemic hotspots observed in Guadalupe . In Simón Bolivar , the 4 secondary cases detected within 15 meters of index cases may suggest significant rates of local vector-borne transmission at the time of insecticide application . The dissimilar migration history of the cases found in Simón Bolívar allow us to rule out that this clustering may be due to a group of infected migrants from the same sending community settling on the same city block , a phenomenon that has occurred in periurban settlements of Arequipa [37] , [38] . Importantly , there was no obvious a priori evidence in Simón Bolívar to expect transmission to differ from the other 17 communities considered . This study adds to growing evidence of an uneven distribution of T . cruzi infection in the city of Arequipa [9] , [10] , not uncommon to this parasitic disease [39] . Other similar pockets of vector-borne transmission may exist in the city; the implementation of small pilot studies in infested areas followed by spatial adaptive sampling around human cases can help uncover them and determine the appropriate screening strategy for each setting . Where urban vectorial transmission is present , this adaptive strategy identifies secondary cases efficiently . When employed in an area without vectorial transmission , adaptive sampling should return few or no secondary cases , such that additional screening is curtailed and expenditures capped . Although harder to obtain , longitudinal entomological data , as opposed to the cross-sectional data utilized here , may be informative in locating these mini-epidemics . We are currently exploring community-based recognition and alert systems as a promising mechanism for obtaining this longitudinal vector data . In addition , our disease screening activities took place between 5 months and 4 years after household insecticide application and collection of the entomologic data . It is not clear what the effect of this time lapse may be on the association between vector data and human infections detected . The strategies may work much better if these delays were eliminated . However , considering that exposure risk is greatly reduced by the elimination of vectors , we can reasonably expect that there were little new infections prior to our testing . Another possible cause of error is the inability of migration histories to capture participants' short visits to endemic areas . While a potentially important source of exposure , there are methodological and recall challenges to documenting travel history at such a fine level . We found that screening based on personal risk assessment and residence-exposure history ( ie , time lived in rural and/or infested areas ) is advantageous for capturing high-risk individuals . Studies of blood donors in Canada and the US have found promising usefulness and operational feasibility of residence history questionnaires [26] , [27] , [40] . Our study consisted of operational research in a large heterogeneous city . In the wake of vector control campaigns , the target population was well-aware of their risk of Chagas disease . Residents of houses where no infestation was detected could have been at risk of vectorial transmission due to proximity to infested houses . However , during our fieldwork , it became clear that those who participated in this optional Chagas disease screening did so because they believed themselves to be at risk due to prior exposure . Participants from houses with no infestation detected often expressed concern about a previous infestation in their current or prior homes . In contrast , those who refused often reported being uninterested in testing because they did not consider themselves at risk , or because in their memory they had no contact with a vector . As a result , this study suffered from a self-selection bias according to perceived risk among participants living in vector-free houses at the time of the spray campaign . This bias likely caused an overestimate of the Chagas disease prevalence in houses with no infestation detected . Although not directly comparable , our observed overall prevalence of 2 . 28% is much higher than the 0 . 73% reported in a 2004–05 cross-sectional screening of pregnant women in Arequipa , which included populations from communities also studied here [41] . In contrast , we believe the prevalence estimate of 2 . 60% among the infected vector group to be quite accurate for persons living in this risk category due to the 98% participation rate . Factors affecting participation need to be taken into consideration to design an economically optimal algorithm for targeted screening of Chagas disease . The high refusal rate among houses with no infestation detected required double the number of household visits per participant recruited than for the infected-vector group , making it the most expensive strategy in our cost calculation . If similar entomologic data from vector control campaigns are used to guide a human Chagas disease case-finding strategy , minimizing costs for fieldwork while still detecting cases would be optimal . Targeting screening according to the presence of T . cruzi-infected T . infestans was less expensive and similarly effective compared to the other strategies . Further , coupling screening to ongoing vector control campaigns can improve participation [19] with the added advantage of eliminating the time lapse between entomologic data and human testing that we experienced in some communities of this study . In large cities like Arequipa , where T . cruzi exposure is highly heterogeneous , a targeted screening program is necessary for the prompt diagnosis of indeterminate Chagas disease , the prevention of future transmission and the maximization of limited resources [19] . A greater knowledge of the patterns of infection can be obtained by pilot studies , and would improve the design of screening strategies . Rural , urban and periurban places have different ecologies , and it is reasonable to expect different epidemiologies of Chagas disease in these settings [42] . The flexibility to adapt to the epidemiology that emerges during pilot screenings is key to an efficient case detection intervention . Finally , this data can help develop a brief residence history questionnaire for referring to diagnostic testing those at greatest risk of Chagas disease . Self-assessment of risk and triatomine exposure time is a potentially useful tool for screening; volunteer screening programs at local health posts or fairs may be very effective ways to capture infections in heterogeneous periurban communities subsequent to informative vector control campaigns . | In the wake of emerging T . cruzi infection in children of periurban Arequipa , Peru , we conducted a prospective field trial to evaluate alternative targeted screening strategies for Chagas disease across the city . Using insect vector data that is routinely collected during Ministry of Health insecticide application campaigns in 3 periurban districts of Arequipa , we separated into 4 categories those households with 1 ) infected vectors; 2 ) high vector densities; 3 ) low vector densities; and 4 ) no vectors . Residents of all infected-vector households and a random sample of those in the other 3 categories were invited for serological screening for T . cruzi infection . Subsequently , all residents of households within a 15-meter radius of detected seropositive individuals were invited to be screened in a ring case-detection scheme . Of 923 participants , 21 ( 2 . 28% ) were seropositive . There were no significant differences in prevalence across the 4 screening strategies , indicating that household entomologic factors alone could not predict the risk of infection . Indeed , the most predictive variable of infection was the number of years a person lived in a location with triatomine insects . Therefore , a simple residence history questionnaire may be a useful screening tool in large , diverse urban environments with emerging Chagas disease . | [
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] | 2012 | A Field Trial of Alternative Targeted Screening Strategies for Chagas Disease in Arequipa, Peru |
The functional role of ELR-positive CXC chemokines in host defense during acute viral-induced encephalomyelitis was determined . Inoculation of the neurotropic JHM strain of mouse hepatitis virus ( JHMV ) into the central nervous system ( CNS ) of mice resulted in the rapid mobilization of PMNs expressing the chemokine receptor CXCR2 into the blood . Migration of PMNs to the CNS coincided with increased expression of transcripts specific for the CXCR2 ELR-positive chemokine ligands CXCL1 , CXCL2 , and CXCL5 within the brain . Treatment of JHMV-infected mice with anti-CXCR2 blocking antibody reduced PMN trafficking into the CNS by >95% , dampened MMP-9 activity , and abrogated blood-brain-barrier ( BBB ) breakdown . Correspondingly , CXCR2 neutralization resulted in diminished infiltration of virus-specific T cells , an inability to control viral replication within the brain , and 100% mortality . Blocking CXCR2 signaling did not impair the generation of virus-specific T cells , indicating that CXCR2 is not required to tailor anti-JHMV T cell responses . Evaluation of mice in which CXCR2 is genetically silenced ( CXCR2−/− mice ) confirmed that PMNs neither expressed CXCR2 nor migrated in response to ligands CXCL1 , CXCL2 , or CXCL5 in an in vitro chemotaxis assay . Moreover , JHMV infection of CXCR2−/− mice resulted in an approximate 60% reduction of PMN migration into the CNS , yet these mice survived infection and controlled viral replication within the brain . Treatment of JHMV-infected CXCR2−/− mice with anti-CXCR2 antibody did not modulate PMN migration nor alter viral clearance or mortality , indicating the existence of compensatory mechanisms that facilitate sufficient migration of PMNs into the CNS in the absence of CXCR2 . Collectively , these findings highlight a previously unappreciated role for ELR-positive chemokines in enhancing host defense during acute viral infections of the CNS .
Inoculation of the neurotropic JHMV strain of mouse hepatitis virus ( a positive-strand RNA virus and member of the Coronaviridae family ) into the CNS of susceptible strains of mice results in an acute encephalomyelitis , characterized by wide spread infection and replication within astrocytes , microglia , and oligodendrocytes , while relatively sparing neurons [1] . Mechanisms associated with control of viral growth are dictated by the infected host cell . Astrocytes and microglia are susceptible to perforin-mediated lysis by cytotoxic T lymphocytes [2] , whereas IFN-γ suppresses viral replication within oligodendrocytes [3] . Although a robust cell-mediated immune response occurs during acute disease , sterilizing immunity is not achieved , resulting in viral persistence [4] . While virus-specific CD8+ T cells are retained within the CNS of persistently infected mice and lytic activity is muted , these cells retain the capacity to secrete IFN-γ that limits viral replication in oligodendrocytes [3] , [5]–[7] . Histological features associated with viral persistence include the development of an immune-mediated demyelinating disease similar to the human demyelinating disease multiple sclerosis ( MS ) , with both T cells and macrophages being important in amplifying disease severity by contributing to myelin damage [8] , [9] . Chemokines are rapidly secreted within the CNS in response to JHMV infection and contribute to host defense [10]–[14] and disease progression [10] , [15]–[17] . The ELR+ ( glutamic acid – leucine – arginine ) CXC chemokines CXCL1 and CXCL2 are up-regulated within the brains of JHMV-infected mice [11] , [18] , [19] , yet little is known regarding their biological significance or cellular targets . CXCL1 and CXCL2 are potent chemoattractants for PMNs , binding and signaling through their receptor CXCR2 [20]–[22] . Moreover , PMNs have been shown to enhance CNS inflammation by disrupting blood brain barrier ( BBB ) integrity in animal models of spinal cord injury ( SCI ) [23] , [24] , autoimmune demyelination [25] , and JHMV-induced encephalomyelitis [26] . In addition , blocking or silencing of CXCR2 signaling mutes inflammation and tissue damage in mouse models in which PMN infiltration is critical to disease initiation , including SCI [23] , inflammatory demyelination [25] , bacterial infection of the CNS [27] , and viral infection or injury to the lung [28]–[32] . With regards to JHMV infection , depletion of PMNs increases mortality due to abrogated BBB permeabilization and subsequent diminished T cell infiltration into the CNS , however the relationship between CXCR2 signaling and PMN migration during viral infection of the CNS has yet to be determined . The present study was undertaken to characterize the functional role of ELR+ chemokines in either host defense or disease following viral infection of the CNS . Using JHMV infection as a model of viral-induced encephalomyelitis , we demonstrate a protective role for ELR-positive chemokines in promoting PMN migration to the CNS and subsequent BBB degradation , facilitating the entry of T lymphocytes and control of viral replication .
To evaluate the early immune response following JHMV infection , C57BL/6 mice were intracerebrally ( i . c . ) inoculated with JHMV , and the accumulation of neutrophils within the blood and the CNS was monitored . Within the brain , JHMV titers peaked at day 3 p . i . and gradually declined to below the level of detection ( ∼100 PFU/g ) by day 15 p . i . ( Figure 1A ) . Elevated numbers of neutrophils were observed within the blood as early as day 1 p . i . PMN peaked at day 3 p . i , before rapidly returning to sham levels by day 7 p . i . ( Figure 1C ) . Similarly , neutrophil infiltration into the CNS peaked at day 3 p . i . and subsequently returned to baseline levels by day 7 p . i . ( Figure 1E ) . The chemokine receptor CXCR2 was expressed on the majority of PMNs within the blood ( Figure 1D ) and brain ( Figure 1F ) , as assessed by flow cytometry . The pattern of neutrophil infiltration into the CNS was unique , as the infiltration of inflammatory leukocytes ( CD45high ) and macrophages ( F4/80+CD45high ) steadily increased to day 7 p . i . ( Figure 1B ) . As the majority of neutrophils were CXCR2-positive , we next determined the expression pattern of chemokine ligands capable of binding and signaling through CXCR2 within the brain during JHMV infection . Expression of transcripts for the ELR-positive chemokines CXCL1 , CXCL2 , and CXCL5 , as well as CXCR2 , paralleled PMN recruitment into CNS , peaking at day 3 p . i . and returning to baseline levels by day 12 p . i . ( Figure 2A ) . Immunostaining for CXCL1 and the astrocyte marker GFAP at day 3 p . i . revealed dual-positive cells located within the parenchyma and associated with the microvasculature ( Figure 2B ) . Staining was concentrated within the hippocampal region of the brain . The majority ( >80% ) of CXCL1-positive cells were astrocytes , as defined by GFAP staining , although endothelial cells also appeared positive , given the enriched CXCL1 signal intensity surrounding the vessel wall ( Figure 2B ) . These results are consistent with previous results [11] , [33]–[35] that astrocytes , as well as endothelial cells , are capable of producing this CXCR2 ligand . These data indicate that JHMV infection and replication results in regulated expression of ELR+ chemokines and CXCR2 within the CNS that parallels neutrophil mobilization into the blood and migration into the CNS . To assess the role of CXCR2 during acute viral encephalomyelitis , JHMV infected C57BL/6 mice were treated with neutralizing polyclonal CXCR2 antiserum or control rabbit serum ( NRS ) , and the effect on PMN accumulation within the CNS determined . As shown in Figure 3A , representative FACS dot plots revealed an almost complete absence of PMN accumulation at day 3 p . i . within the CNS of mice treated with anti-CXCR2 blocking antibody . Enumeration of total PMN infiltration to the brain showed a significant reduction compared to mice treated with control serum at days 1 ( p<0 . 05 ) and 3 p . i . ( p<0 . 01 ) ( Figure 3A ) . CXCR2 neutralization also retarded the accumulation of total inflammatory cells ( Figure 3B ) and macrophages ( Figure 3C ) at day 3 p . i . Further , treatment with CXCR2 antiserum resulted in a significant ( p<0 . 05 ) reduction of neutrophil numbers within the blood at day 3 p . i . ( Figure S1 ) . Importantly , anti-CXCR2 treatment did not induce complement-mediated lysis or neutrophil depletion from bone-marrow ( data not shown ) . Previously , neutrophils have been deemed partly responsible for the permeabilization of the BBB following JHMV infection [19] , [26] , therefore , we assessed BBB integrity in the brains of mice treated with either anti-CXCR2 or NRS . Indeed , compared to NRS treatment , CXCR2 neutralization significantly ( p<0 . 05 ) reduced BBB breakdown , as measured by Evans Blue uptake ( Figure 3D ) . However , BBB permeability in anti-CXCR2 treated mice was not completely eliminated compared to sham – infected mice ( Figure 3D ) . Previous studies indicated that MMP-9 expression by PMN is associated with BBB permeabilization in response to JHMV infection [19] , [26] . Consistent with reduced numbers of neutrophils in the brains of anti-CXCR2 treated mice , MMP-9 activity was significantly ( p<0 . 001 ) muted within the brains , whereas detectable levels of enzyme activity were observable within the brains of NRS treated mice ( Figure 3E & 3F ) . These data indicate that CXCR2 signaling promotes the directed migration of neutrophils from the periphery to the CNS , thus facilitating the degradation of the BBB in response to JHMV infection . To better assess the functional role of CXCR2 in host defense , JHMV-infected mice were treated with anti-CXCR2 beginning day −1 p . i . and continuing throughout acute disease . Mice began to die at day 4 p . i . and 100% of mice treated with anti-CXCR2 were dead by day 11 p . i . , while greater than 90% of infected mice treated with control serum survived until day 12 p . i . ( Figure 4A ) . Anti-CXCR2-treated mice were also unable to control viral replication within the brain , as evidenced by the significantly elevated viral titers at days 7 ( p<0 . 05 ) and 9 p . i . ( p<0 . 01 ) , compared to mice treated with control serum ( Figure 4B ) . Sham – infected mice treated with anti-CXCR2 did not exhibit any signs of morbidity or mortality ( data not shown ) . To further explore the consequences of blocking CXCR2 signaling early following infection , neuroinflammation was assessed by H&E staining of brains from JHMV-infected mice treated with either anti-CXCR2 or control sera at day 9 p . i . Such analysis revealed limited inflammatory cell infiltration , as demonstrated by an overall reduction in the size of the meningeal and perivascular infiltrates ( Figure 4D ) , compared to mice treated with control serum ( Figure 4C ) . Consistent with the reduced inflammation observed histologically , immunophenotyping the cellular infiltrate in the brain revealed an overall reduction in CD45high cells present within the brains of infected mice treated with anti-CXCR2 ( Figure 4E ) . Moreover , blocking CXCR2 reduced CD8+ ( Figure 4F ) and CD4+ ( Figure 4G ) T cell infiltration , as well as reduced the numbers of virus-specific CD4+ and CD8+ T cells ( Figure 4H ) within the brains compared to control-treated mice . In contrast , if administration of anti-CXCR2 was delayed until 2 days p . i . , mortality , T cell infiltration , and viral burden were unaffected , when compared to control-treated mice ( Figure S2A–D ) . Notably , a single treatment of CXCR2 antiserum at day +2 p . i . significantly ( p<0 . 01 ) reduced neutrophil infiltration into the CNS at day 3 p . i . ( anti-CXCR2 , 8 . 3×103±1 . 7×103 cells/g , n = 5 ) compared to control-treated mice ( 5 . 0×105±1 . 2×105 , n = 5 ) ( Figure S2E ) . However , compared to mice that were treated with CXCR2 antiserum beginning at day −1 p . i . , the levels of neutrophils within the CNS at day 3 p . i . was approximately 5 times greater [anti-CXCR2 , 1 . 7×103±2 . 7×102 , n = 5; control-treated , 3×105±1 . 8×104 , n = 3 ) ( Figure 3A ) . These findings indicate that CXCR2 signaling is protective; however the period of protection is confined to the earliest days following JHMV infection , coinciding with dramatic mobilization of PMNs from the blood and subsequent migration into the CNS . We next determined if CXCR2 signaling was important in the generation of virus-specific T cells . C57BL/6 mice were treated with either anti-CXCR2 or control serum beginning at day −1 prior to being infected i . c . with JHMV , and the presence of virus-specific T cells within the draining cervical lymph nodes ( CLN ) [36] was determined at day 7 p . i . by evaluating ex vivo responses to defined CD4+ and CD8+ T cell-specific viral epitopes [37]–[39] . Similar numbers of virus – specific CD8+ T cells were generated in mice treated with either anti-CXCR2 or control sera as determined by measuring S510–518 MHC class I tetramer – reactive cells ( Figure 5A ) and intracellular staining for IFN-γ within cultured cells pulsed with either the S510–518 ( Figure 5B ) or S598–605 ( Figure 5C ) peptides . Similarly , there were no differences in the numbers of CD4+ T cells recognizing the M133–147 peptide in anti-CXCR2-treated mice compared to control mice ( Figure 5D ) . It has been reported that the absence of CXCR2 abrogates peripheral immune responses to pathogens [40]–[46] , therefore we also sought to determine whether antiviral responses were muted following CXCR2 neutralization following intraperitoneal ( i . p . ) challenge with JHMV . C57BL/6 mice were treated with either anti-CXCR2 or control serum beginning at day -1 before being infected i . p . with virus . The presence of virus – specific T cells within the spleen was assessed as described above . Similar frequencies and numbers of virus – specific CD8+ and CD4+ T cells were generated in both control and anti-CXCR2 antiserum treated animals ( Figure 6 A–D ) . Moreover , no difference in the expression of the T cell activation markers CD25 , CD127 , and CD44 was observed upon splenic T cells from mice treated with CXCR2 antiserum ( Figure 6E and F ) . Finally , i . p . infection of CXCR2−/− mice with JHMV did not affect the generation of virus-specific CD4+ or CD8+ T cells compared to infected CXCR2+/+ mice ( Figure S3 ) . Therefore , these data clearly indicate that CXCR2 signaling does not influence the generation or expansion of virus – specific T cells following JHMV infection . We next utilized mice in which CXCR2 signaling was genetically silenced ( CXCR2−/− mice ) to further assess the functional role of CXCR2 in host defense in response to JHMV infection of the CNS . First , we sought to ensure that CXCR2 –deficient neutrophils were unresponsive to defined CXCR2 chemokine ligands . Our findings confirmed that PMNs isolated from the bone-marrow of CXCR2−/− mice did not express CXCR2 ( Figure 7A ) , and , unlike CXCR2+/+ PMN , CXCR2−/− PMN did not migrate in vitro in response to recombinant mouse CXCL1 , CXCL2 , or CXCL5 ( Figures 7 B–D ) , consistent with previous reports [47] , [48] . CXCR2−/− mice inoculated i . c . with JHMV exhibited an approximate 60% reduction ( p<0 . 05 ) in PMN migration to the CNS compared to wildtype littermates at day 3 p . i . ( Figure 8A ) . However , neutrophil infiltration into the CNS of CXCR2−/− mice was not completely eliminated , as previously observed following anti-CXCR2 treatment of JHMV-infected wildtype mice ( Figure 3A ) . Correspondingly , there was no difference in mortality ( Figure 8B ) or brain viral titers ( Figure 8C ) between JHMV-infected CXCR2−/− mice and CXCR2+/+ mice . Additionally , both CXCR2−/− and CXCR2+/+ displayed similar amounts of Evans Blue extravasation into the brain at day 3 p . i . ( Figure 8D ) . To assess potential compensatory mechanisms that may allow for CXCR2−/− neutrophils to enter the CNS , we evaluated expression of CXCR1 , an alternate receptor for ELR+ chemokines [49] , on neutrophils . Neutrophils isolated from the bone marrows of CXCR2−/− mice exhibited enriched levels of CXCR1 mRNA transcripts compared to CXCR2+/+ neutrophils suggesting that , in vivo , this may be used as an alternate receptor for migration to the CNS ( Figure 8E ) . In order to demonstrate that the CXCR2 anti-serum did not have any off – target effects , JHMV – infected CXCR2 – deficient and wildtype littermates were treated with anti-CXCR2 or NRS beginning at day -1 p . i . and PMN migration into the CNS at day 3 p . i . was determined . As shown in Figure S4A , anti-CXCR2 treatment did not affect neutrophil accumulation into the brains of CXCR2−/− animals , compared to CXCR2−/− mice receiving control serum . Similar numbers and frequencies of PMNs were also present within the CNS of knockout mice treated with either anti-CXCR2 antiserum or control serum ( Figure S4A ) compared to untreated CXCR2−/− mice infected with JHMV ( Figure 8A ) . In contrast , anti-CXCR2 treatment of JHMV-infected CXCR2+/+ mice reduced ( >96% ) PMN migration to the CNS ( Figure S4B ) . These findings indicate that the CXCR2 blocking antibody is specific and suggest that genetic deletion of CXCR2 allows for compensatory mechanisms to emerge , such as utilization of CXCR1 , that support PMN trafficking into the CNS in response to viral infection .
We have examined the role of ELR+ chemokines in host defense following infection of susceptible mice with the neurotropic coronavirus , JHMV . Early following JHMV infection , CXCR2-positive neutrophils are mobilized into the bloodstream and migrate to the CNS in response to elevated expression of the ELR+ chemokines CXCL1 , CXCL2 , and CXCL5 . Early administration of a blocking antibody specific for CXCR2 to JHMV-infected mice reduced >95% of PMN migration into the CNS , and this corresponded with increased mortality and uncontrolled viral replication . Subsequent studies revealed that anti-CXCR2 treatment prevented PMN-mediated BBB permeabilization , associated with muted MMP-9 activity , and ultimately resulted in the impaired accumulation of virus-specific T cells within the CNS . These findings emphasize the importance of rapid neutrophil recruitment to the CNS in response to viral infection of the CNS to facilitate control of viral replication . Moreover , our findings highlight that the numbers of neutrophils recruited to the CNS is critical in defense as JHMV-infection of CXCR2−/− mice or delayed anti-CXCR2 treatment resulted in efficient control of viral replication , and this was associated with greater numbers of neutrophils within the CNS compared to mice treated prior to infection with blocking antibody . Collectively , our studies support and extend others highlighting the functional role of neutrophils in promoting vascular permeability in response to infection or injury to the CNS [25] , [26] , [50] . This is elegantly illustrated in the studies by McGavern and colleagues [50] that defined the importance of myelomonocytic cell recruitment to the CNS in response to LCMV infection with regards to contributing to fatal viral meningitis via promoting massive vascular injury . Importantly , interventional therapies targeting myeloid cell trafficking to the CNS during acute viral infection may offer a powerful approach to dampen neuroinflammation and decrease fatalities associated with viral encephalopathies . In agreement with previous reports [51] , we have also observed that CXCR2 signaling is important for neutrophil release from the bone marrow into the blood . Blocking CXCR2 dramatically reduced neutrophil mobilization into the bloodstream in response to JHMV infection . Notably , neutrophil entry into the blood was not completely inhibited , indicating that there may be additional signaling components that aid neutrophil release such as CXCL12 downregulation or G-CSF induction [51] , [52] . CXCR2 neutralization also reduces circulating levels of neutrophils within uninfected mice ( data not shown ) , suggesting that CXCR2 ligands contribute to both normal neutrophil homeostasis and emergency release following infection with a neurotropic virus . Our studies highlight a previously unappreciated functional role for ELR+ chemokines in host defense during viral-induced encephalomyelitis , rapidly recruiting PMNs into the blood with subsequent infiltration into the CNS , thus enhancing protection by contributing to the disruption of the BBB and facilitating anti-viral inflammatory T cell access . The protease MMP-9 is specific for the structural components of the BBB , including type IV collagen and laminin , and it is associated with BBB breakdown in a mouse model of cerebral ischemia [53] . Although MMP-9 is associated with the loss of BBB integrity following JHMV infection , it is likely that additional PMN-associated molecules including β2-integrin , azurocidin , and glutamate participate in BBB damage , as these components can also contribute to the disruption of endothelial cell adherens junctions [54]–[58] . Additionally , MMP-9 may also be contributing to the degradation of the parenchymal basement membranes and glia limitans that regulate leukocyte infiltration from the perivascular space into the parenchyma [59] . Infection of CXCR2−/− mice with JHMV did not recapitulate our observations with infected wildtype mice treated with CXCR2 antiserum . While neutrophil infiltration into the CNS was reduced by approximately 60% at day 3 p . i , CXCR2 – deficient mice experienced no deficits in survival , viral clearance , or BBB degradation . Our demonstration that PMN migration to the CNS of JHMV-infected CXCR2−/− mice was not altered following administration of anti-CXCR2 signaling emphasizes the specificity of this reagent and suggests that compensatory mechanisms allow for sufficient numbers of neutrophils to migrate to the CNS that enable BBB breakdown and infiltration of virus-specific T cells . However , it is notable that our observations are in contrast to previous reports utilizing CXCR2−/− mice in models of host defense following infection of the CNS . For example , Del Rio et al . [40] demonstrated elevated numbers of T . gondii cysts within the brains of CXCR2−/− mice , yet all of the immune defects occurred within the periphery , suggesting CXCR2-mediated protection occurs in a manner independent of leukocyte trafficking . Additionally , Kielian et al . [27] have shown that in a model of S . aureus experimental brain abscess neutrophil extravasation into the brain is impaired in CXCR2−/− mice , resulting in moderately increased bacterial burdens and pathology . However , the authors did not assess long-term bacterial clearance in the CXCR2 – deficient mice , nor quantitate total neutrophil or other immune cell infiltration that may have been impacted . Moreover , in experimental brain abscesses , neutrophils have direct anti-bacterial roles [60] , while during JHMV infection , neutrophils are responsible for the permeabilization of the BBB [26] , so functional differences of these cells in host defense may explain differences between the model systems . Our assessment of CXCR2 staining on neutrophils isolated within both the blood and brain of wildtype mice revealed that a small population of neutrophils was unreactive to CXCR2 antiserum . These findings argue for either transient receptor internalization following ligand binding [61] or alternative mechanisms of neutrophil attraction in the absence of CXCR2 signaling . Neutrophils have been reported to be chemotactic to cleaved complement products [48] , leukotrienes [62] , and other chemokines [63]–[65] in vitro and in vivo . Moreover , a functional murine homolog for CXCR1 has recently been identified and this may serve to mediate PMN trafficking to the CNS in response to JHMV infection [49] . Indeed , neutrophils enriched from the bone marrow of CXCR2 – deficient mice expressed noticeably increased transcripts specific for CXCR1 , whereas transcripts were barely detectable within neutrophils from wild type mice , indicating that CXCR2−/− neutrophils may be compensating through increased expression of CXCR1 . In addition , we have observed elevated numbers of PMNs residing within the CNS of CXCR2−/− mice in the absence of infection ( data not shown ) , suggesting potentially highly dysregulated myeloid cells in these mice . The fact that these cells are present prior to JHMV-infection suggests the possibility that viral infection results in local activation that contributes to BBB breakdown and may account for the muted CD11b expression upon CXCR2−/− neutrophils within the CNS . Recent evidence indicates that neutrophils are capable of influencing the generation of an adaptive immune response following infection . Neutrophil activation results in secretion of the cytokines IL-12/23 p40 that are associated with tailoring T cell-specific responses following antigenic challenge [66] , [67] . Furthermore , neutrophils are capable of secreting chemokines such as CCL3 and CCL4 that also influence dendritic cell function , and thus they have been suggested to regulate T cell polarization via dendritic cell activation [68] . Additionally , the generation of TH1 immune reponses to a variety of pathogens is negatively impacted within neutropenic mice [41]–[46] . However , our findings clearly indicate that in the absence of CXCR2 signaling there are no deficiencies in either the frequencies or numbers of JHMV – specific T cells or T cell activation markers , regardless of whether mice were challenged within the CNS or peripherally . Our results complement previous work that has highlighted the importance of ELR-positive chemokines and PMNs in promoting vascular permeability and subsequent immune cell infiltration into the CNS . CNS-specific transgenic expression of CXCL1 was associated with disruption of the BBB and neutrophil infiltration into the brain parenchyma [69] . Moreover , blocking CXCR2 signaling inhibits neutrophil-mediated BBB damage and dampens CNS inflammation in autoimmune demyelination and spinal cord injury [23]–[25] . Although the mechanisms associated with induction of ELR+ chemokines expression within the CNS of JHMV-infected mice have yet to be characterized , intracerebral administration or localized transgenic expression of IL-1β within the brain enhances CXCL1 expression , inducing neutrophil accumulation and subsequent BBB breakdown [70] , [71] . Collectively , these findings have implications for other neurotropic viruses , including West Nile virus , as PMN represent an early and predominant inflammatory infiltrate in response to viral infection [72] . Therefore , understanding the signaling mechanisms governing inflammation in response to viral infection raises the possibility of selectively muting specific pathways associated with BBB breakdown and CNS inflammation which may have therapeutic benefits within the context of human neuroinflammatory diseases that arise in the apparent absence of an infectious trigger .
Age-matched 5–6 week old C57BL/6 ( H-2b , National Cancer Institute , Frederick , MD ) or 5–9 week old CXCR2−/− ( H-2b , Cleveland Clinic , OH ) mice were infected intracerebrally ( i . c . ) with 500 plaque forming units ( PFU ) of JHMV strain J2 . 2v-1 in 30 µl of sterile HBSS . Control ( sham ) animals were injected with 30 µl of sterile saline alone . CXCR2 deficient mice [47] were originally backcrossed to C57BL/6 mice for 11 generations , and CXCR2−/− mice used for experimental purposes were obtained from heterozygous breeder pairs . All pups derived from breeders were genotyped as previously described [73] . Age and sex – matched littermate CXCR2+/+ were used as controls for all experiments with CXCR2−/− mice . To assess the generation of JHMV – specific T cells , C57BL/6 mice were infected intraperitoneally ( i . p . ) with 2 . 5×105 PFU of JHMV strain DM . For analysis of viral titers , one-half of each brain was homogenized and used for standard plaque assay on the DBT mouse astocytoma cell line [74] at the indicated days post-infection ( p . i . ) . All experiments were approved by the University of California , Irvine Institutional Animal Care and Use Committee . Rabbit polyclonal antiserum was generated to a 17-amino acid portion of the amino-terminus ligand binding domain of CXCR2 ( MGEFKVDKFNIEDFFSG ) [75] . In our hands , the CXCR2 antiserum specifically blocks CXCR2 dependent infiltration of neutrophils into the peritoneum of mice following thioglycollate irritation [76] and does not bind rabbit complement and deplete neutrophils in vitro . For in vivo neutralization during JHMV infection , 0 . 5 ml of anti-CXCR2 or control normal rabbit serum ( NRS ) was administered intraperitoneally ( i . p . ) on days −1 , 1 , 3 , 5 , 7 , 9 , and 11 p . i . or days 2 , 4 , 6 , 8 , and 10 p . i . Total cDNA from the brains of sham and JHMV infected mice at days 1 , 3 , 7 , 12 , 15 , 18 , and 21 p . i . was generated as previously described [77] . Real-time Taqman analysis for HPRT , CXCR2 , CXCL1 , CXCL2 , and CXCL5 was performed using a BioRad ( Hercules , Ca ) iCycler with previously described primers and probes [78] , [79] . CXCR2 and CXCL1 , −2 , and −5 expression was normalized to HPRT . Probes were purchased from Integrated DNA Technologies ( Coralville , IA ) , and primers were purchased from Invitrogen ( Carlsbad , CA ) . iQ Supermix ( BioRad ) was used for all reactions . Assay conditions were as follows: a 4 . 5 min initial denaturation at 95°C , and 45 cycles of 30 sec at 95°C and 1 min at 58°C . Data were analyzed with BioRad iCycler iQ5 and quantified with the Relative Expression Software Tool [80] . Flow cytometry was performed as previously described [12] , [17] , [36] . Isolated cells were Fc blocked with anti-CD16/32 1∶200 ( BD Biosciences , CA ) and immunophenotyed with fluorescent antibodies ( BD Biosciences ) specific for the following cell surface markers: CD4 ( L3T4 ) , CD8b ( 53–5 . 8 ) , CD8a ( 53–6 . 7 ) , CD11b ( M1/70 ) , Ly6G ( 1A8 ) , Ly6G/C ( RB6–8C5 ) , CD44 ( IM7 ) , CD25 ( 7D4 ) , CD127 ( A7R34 , E Biosciences , Ca ) , CD45 ( 30-F11 , E Biosciences ) , and F4/80 ( CI:A3-1 , Ab Direct , NC . Appropriate isotype antibodies were used for each antibody . For determination of viral specificity , isolated CNS cells , splenocytes , or cervical lymph node cells ( CLN , isolated at day 7 p . i . ) were either stained with H-2b-S510 tetramer ( Beckman Coulter , CA ) or stimulated ex vivo for 6 h with 5 µM of the immunodominant CD4 epitope M133–147 [38] , immunodominant CD8 epitope S510–518 [39] , or the subdominant CD8 epitope S598–605 [37] and GolgiStop ( Cytofix/Cytoperm kit , BD Biosciences ) , and the production of IFNγ was determined by intracellular staining . Cells were Fc blocked with CD16/32 and stained with FITC or APC conjugated CD4 , CD8a , or CD8b antibodies ( BD Biosciences ) before being fixed and permeablized with the Cytofix/Cytoperm kit and stained with PE conjugated IFNγ ( XMG1 . 2 , BD Biosciences ) . Appropriate isotype antibodies were used for each antibody . For CXCR2 staining isolated Fc – blocked cells were stained rabbit CXCR2 anti-serum ( 1∶1000 ) for 1 hour . NRS at 1∶1000 was used as a serum control . Cells were then washed and stained with APC – conjugated donkey anti-rabbit antibodies ( 1∶200 , Jackson Immuno ) for 30 min . Cells were run on a FACStar flow cytometer ( BD Biosciences ) and analyzed with FlowJo software ( TreeStar , OR ) . Brains and spinal cords from 4% paraformaldehyde perfused mice were removed and fixed overnight in 4% paraformaldehyde at 4°C . Tissues were embedded in paraffin and stained with hematoxylin and eosin to determine the extent of inflammation . Brains and spinal cords from 4% paraformaldehyde perfused mice were removed and fixed overnight in 4% paraformaldehyde at 4°C and cryoprotected in 20% sucrose . Tissue sections ( 7 µm ) were fixed in 4% paraformaldehyde and blocked in 10% normal donkey serum , 0 . 3% Triton X 100 . Immunostaining for CXCL1 and GFAP was performed serially using polyclonal goat anti-CXCL1 ( 2 ug/ml , R&D Systems , MN ) and polyclonal chicken anti-GFAP ( 1∶500 Abcam , MA ) overnight at 4°C . Cy-2 or Dylight 549 conjugated donkey secondary antibodies ( 1∶200 , Jackson ImmunoResearch , PA ) were used for visualization . Hoechst 33342 ( Invitrogen ) was used to stain nuclei prior to mounting coverslips . At day 3 p . i . , mice were injected with 200 µl of 2% ( w/v ) Evan's Blue in sterile PBS intraorbitally . Two hours later , brains and kidneys from PBS perfused mice were removed and homogenized in formamide ( 20 ml/g wet weight ) . Homogenates were incubated overnight , clarified by centrifugation , and assayed at 620 and 720 nm . CNS tissue turbidity was calculated [−log ( OD620 ) = 0 . 964 ( −log ( OD740 ) −0 . 0357] and subtracted from the original A ( 620 ) [25] , [81] . Relative permeability was calculated as the ratio of Evans blue extravasation ( µg/ml per gram of tissue ) of brain to kidney homogenates . Brains from PBS perfused mice were homogenized in 50 mM Tris-HCL 0 . 5% TritonX-100 pH 7 . 6 and clarified by centrifugation . 15 µg of homogenate were separated on polyacrylamide gels containing 1% gelatin ( BioRad ) in the absence of reducing agents . Gels were washed for 20 minutes and incubated in developing buffer ( BioRad ) for 2 days at 37°C . Gels were stained with Coomassie R-250 and destained in 10% acetic acid and 10% methanol . Quantification was performed with Image J software ( v 1 . 42l , NIH ) and expressed relative to sham activity . Neutrophils were enriched from bone marrow as previously described [82] . Femurs and tibias were dissected from CXCR2+/+ or CXCR2−/− mice . Marrow was flushed , and red blood cells lysed . Neutrophils were enriched using a 5 step percoll cushion: 45% , 50% , 55% , 62% , and 81% . Cells were collected from between the 81% and 62% percoll layers . Cells were washed , warmed at 37°C for 10 min , and 106 cells were plated into the top well of a prewarmed 6 . 5 mm 5 µm transwell plate ( Corning , NY #93421 ) . Chemokine dilutions ( Peprotech ) at the indicated concentrations were made in the bottom well . Plates were incubated for 3 hours before cells in the bottom well were collected and counted . Data is presented as % input . Neutrophils were enriched from the bone marrows of CXCR2+/+ and CXCR2−/− mice and cDNA was generated as previously described [77] . PCR was performed upon the generated cDNA with primers specific for CXCR1: forward 5′-GGGTGAAGCCACAACAGATT , reverse 5′-CGGTGTGTCAAAACCTCCTT and rpL32: forward 5′-AAGCGAAACTGGCGGAAACC , reverse 5′-CGTAGCCTGGCGTTGGGATT . Reaction conditions for CXCR1 were as follows: step 1 , initial denaturation at 94°C for 3 min; step 2 , denaturation at 94°C for 30 s; step 3 , annealing at 58°C for 1 min; step 4 , extension at 72°C for 30 s . Steps 2–4 were repeated 39 times for a total of 40 cycles . Reaction conditions for rpL32 were the same as above , except the annealing step was performed at 53°C . Sequencing of PCR amplicons confirmed primer specificity . All data is presented as average ± SEM . Statistically significant differences between groups of mice treated with anti-CXCR2 or NRS were assessed by one way ANOVA . Statistically significant differences between CXCR2−/− and CXCR2+/+ mice were assessed by a two-tailed Mann Whitney U Test . Statistical significance on survival was performed with a Fisher Exact test . P values less than 0 . 05 were considered significant . | Consequences of viral infection of the central nervous system ( CNS ) can range from encephalitis and paralytic poliomyelitis to relatively benign infections with limited clinical outcomes . The localized expression of proinflammatory chemokines within the CNS in response to viral infection has been shown to be important in host defense by attracting antigen-specific lymphocytes from the microvasculature into the parenchyma that control and eventually eliminate the replicating pathogen . However , the relationship between chemokine expression and recruitment of myeloid cells , e . g . neutrophils , to the CNS following infection with a neurotropic virus is not well characterized . Emerging evidence has indicated that the mobilization of neutrophils into the blood and recruitment to the CNS following microbial infection or injury contributes to permeabilization of the blood-brain-barrier that subsequently allows entry of inflammatory leukocytes . Therefore , we have defined the chemokines involved in promoting the directional migration of neutrophils to the CNS in response to viral infection . Using the neurotropic JHM strain of mouse hepatitis virus ( JHMV ) as a model of acute viral encephalomyelitis , we demonstrate a previously unappreciated role for members of the ELR-positive CXC chemokine family in host defense by attracting PMNs bearing the receptor CXCR2 to the CNS in response to viral infection . | [
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] | 2009 | A Protective Role for ELR+ Chemokines during Acute Viral Encephalomyelitis |
Respiratory syncytial virus ( RSV ) is an RNA virus in the Family Paramyxoviridae . Here , the activities performed by the RSV polymerase when it encounters the viral antigenomic promoter were examined . RSV RNA synthesis was reconstituted in vitro using recombinant , isolated polymerase and an RNA oligonucleotide template representing nucleotides 1–25 of the trailer complement ( TrC ) promoter . The RSV polymerase was found to have two RNA synthesis activities , initiating RNA synthesis from the +3 site on the promoter , and adding a specific sequence of nucleotides to the 3′ end of the TrC RNA using a back-priming mechanism . Examination of viral RNA isolated from RSV infected cells identified RNAs initiated at the +3 site on the TrC promoter , in addition to the expected +1 site , and showed that a significant proportion of antigenome RNAs contained specific nucleotide additions at the 3′ end , demonstrating that the observations made in vitro reflected events that occur during RSV infection . Analysis of the impact of the 3′ terminal extension on promoter activity indicated that it can inhibit RNA synthesis initiation . These findings indicate that RSV polymerase-promoter interactions are more complex than previously thought and suggest that there might be sophisticated mechanisms for regulating promoter activity during infection .
Respiratory syncytial virus ( RSV ) is the major cause of respiratory tract disease in infants and young children worldwide , causing 3 . 4 million cases of severe acute lower respiratory infection , and between 66 , 000 and 199 , 000 deaths per annum [1] . As yet , there is no vaccine available to prevent RSV disease , or effective antiviral drug to treat it [2] , [3] . RSV has a single stranded , negative sense RNA genome and is classified in the Order Mononegavirales , Family Paramyxoviridae . In general terms , RSV shares the strategy for gene expression and genome replication that is used by all non-segmented negative strand ( NNS ) RNA viruses [4] . The RSV genome acts as a template for transcription , to generate subgenomic mRNAs , and RNA replication , to generate an antigenome RNA . The antigenome in turn acts as a template for genome RNA synthesis ( reviewed in [5] ) . Both the genome and antigenome RNAs are encapsidated with multiple copies of nucleoprotein ( N ) as they are synthesized , such that each N molecule binds seven nucleotides ( nts ) [6] . These RNAs are never completely uncoated and so it is this N-RNA structure that acts as a template for the viral RNA dependent RNA polymerase ( RdRp ) . To perform transcription and replication , the RdRp engages with promoter sequences that lie at the 3′ ends of the genome and antigenome RNAs [7] . The 44-nt leader ( Le ) promoter region at the 3′ end of the genome is responsible for directing initiation of mRNA transcription and antigenome synthesis , and the 155-nt trailer complement ( TrC ) promoter at the 3′ end of the antigenome directs genome RNA synthesis [8] . The organization of the Le and TrC promoters has been studied extensively using the RSV minigenome system [9]–[12] . These studies indicate that the minimal promoters are located within nts 1–11 of the genome and antigenome termini , with additional downstream sequences required for production of full-length RNA products . Although the promoter regions of RSV and other NNS RNA viruses have been thoroughly mapped and the viral proteins involved in RNA synthesis have been identified , a detailed understanding of the molecular mechanisms underlying transcription and genome replication initiation lags significantly behind that of other RNA viruses . In part , this is due to the lack of tractable assays for studying polymerase behavior . Although the minigenome system is a valuable tool , because it is an intracellular assay it is largely limited to studying the final , stable products of these processes , and cannot be used to examine unstable RNA intermediates . It is also not possible to manipulate intracellular conditions to isolate specific steps in RNA synthesis initiation . In addition , from a drug discovery perspective , it is an expensive and time-consuming assay that is not readily applicable to a high-throughput screening approach . Study of positive strand RNA viruses has been helped enormously by the development of in vitro assays that reconstitute RNA synthesis using purified components e . g . [13] . Using this approach , template sequences , the polymerase and available substrates can be manipulated to perform detailed mechanistic analyses . A major hurdle to applying this approach to the NNS RNA viruses is that the natural template for their RdRp is encapsidated RNA . Although there are some reports indicating that it is possible to reconstitute N-RNA complexes in vitro for the rhabdoviruses [14]–[16] attempts to reconstitute RSV N-RNA complexes in vitro have been unsuccessful . However , available data indicate that the N protein must be locally and transiently displaced to allow the RdRp to engage the RSV RNA template in its active site [6] , [17] , suggesting that it might be possible to use a naked RNA oligonucleotide to recapitulate the events that occur once N protein has been locally removed from the promoter . This approach was recently applied to studying RNA synthesis initiation by the RdRp of another NNS RNA virus , vesicular stomatitis virus ( VSV ) [18] , and now we show that it is possible to utilize this technique for studying RSV RNA synthesis initiation . Importantly , experiments with this assay , combined with analysis of RSV RNA generated in infected cells , revealed that the RSV RdRp has a far more complex behavior on its promoter than previously realized , or than has been described for VSV , with the capability of initiating RNA synthesis from two different sites on the promoter , and extending the 3′ end of the TrC RNA using a back-priming mechanism .
To enable detailed analysis of the mechanisms involved in RSV RNA synthesis initiation , an assay was developed in which RSV RNA synthesis was reconstituted in vitro using isolated components . To date , the only recombinant NNS virus RdRps that have been expressed and purified in functional form are those of VSV , Chandipura and Sendai virus [18]–[23]; the purification of recombinant RdRp of RSV or any other human pathogens in the paramyxovirus family has not been described . Therefore , a strategy for purification of recombinant RSV RdRp from baculovirus infected insect cells was developed . Based on previous studies it was known that the catalytic domain for RSV RNA synthesis is located in domain III of the 250 kDa large ( L ) protein [24] , [25] , and that in infected cells , L forms a complex with the viral phosphoprotein ( P ) , which is thought to act as a bridge between the L protein and the N protein of the nucleocapsid template [26]–[28] . Purification of the RSV L protein proved challenging for two major reasons . First , numerous attempts to express L without P were unsuccessful , indicating that whereas the VSV , Chandipura and Sendai virus L proteins can be expressed in isolation , in the case of RSV , the P protein might be necessary to stabilize L . Second , expression of L protein using the RSV gene sequence resulted in very poor expression of full-length L protein . This problem was overcome by using a codon-optimized version of the L open reading frame . By co-expressing codon-optimized L with P , it was possible to purify microgram quantities of L/P complex to near homogeneity . Figure 1B shows characteristic examples of isolated L/P complexes , with the bands corresponding to the correct migration pattern for full length L and P indicated . Note that the 27 kDa P protein has previously been shown to migrate anomalously [29] , [30] . Analysis of these and other bands from a representative gel by excision , trypsin digestion and mass spectrometry , determined that the bands indicated with an asterisk or dots contained L and P specific polypeptides , respectively . The smaller L fragment may arise as a consequence of premature translation termination or proteolytic cleavage of the full length L protein . The relative abundance of this band compared to full-length L protein varied depending on the preparation . The P protein is known to be differentially phosphorylated and to exist as a highly stable oligomer [30] , which could account for the multiple P bands present . The band migrating between 70 and 80 kDa was also consistently observed and identified as Hsp70 and/or HSC70 by Western blot analysis ( Figure 1C ) . Hsp70 has previously been shown to affect RSV RdRp activity in an assay involving an infected cell extract [31] , but its relevance to RSV RdRp function in the in vitro RNA synthesis assay described here is not yet known . Because the L/P preparations were not completely pure , and because there was variation in the relative levels of full-length and truncated L proteins , the experiments described in Figures 1 and 2 were performed with three independent preparations of wt and mutant L/P complexes and essentially identical results were obtained with each preparation . To determine if the isolated RdRp was capable of performing RNA synthesis on a naked RNA template , L/P complexes were incubated with an RNA oligonucleotide representing the 3′ terminal 25 nts of the TrC promoter ( Figure 1A ) in the presence of all four NTPs and an [α-32P]ATP label . Although the M2-1 protein has been shown to bind P and RNA and affect transcription of mRNAs longer than ∼200 nts , it was not included in these experiments because it has been shown to have no effect on either transcription or replication initiation [32] . RNA products were analyzed by denaturing gel electrophoresis alongside a molecular weight ladder corresponding to nts 1–25 of the anticipated Tr RNA product , followed by autoradiography ( Figure 1E ) . A number of labeled products were detected , ranging from 8 to 23 nts in length , with dominant bands of ∼8–10 nts and 21 nts ( Figure 1E , lane 2 ) . Some products longer than 25 nts could be detected at a very low level , and these are discussed in the following sections . No products were observed in reactions containing an RdRp preparation in which the L protein contained a substitution in the catalytic GDNQ motif , LN812A [25] ( Figure 1D; Figure 1E , lane 3 ) , confirming that the RNA synthesis activity observed was that of the RSV RdRp . It should be noted that the bands of the molecular weight ladder do not align perfectly with the products of the RSV RdRp . For the smaller RNAs this might be in part because the RNA transcripts in the ladder contained a monophosphate group at the 5′ terminus , whereas the terminal triphosphate was removed from the products of the RSV RdRp with calf intestinal phosphatase . In addition , the ladder was designed to represent RNA initiated from +1 of the TrC promoter , but as described below , it is likely that most or all of the RSV RNA synthesis products were generated from a +3 initiation site , and so the sequences and migration patterns might not have been identical . Importantly , reactions containing an RNA template consisting of the complement of the promoter sequence ( i . e . the 5′ terminal 25 nts of Tr ) did not yield RNA products ( Figure 1E , lane 4 ) . This finding shows that the isolated L/P complex had RNA synthesis activity with specificity for an RNA template containing RSV promoter sequence . To determine if similar results were obtained with a different NTP label , reactions were performed , as described above , using [α-32P]GTP rather than [α-32P]ATP . In this case , the in vitro RNA synthesis reaction also resulted in products of 8–10 and 21 nts in length ( Figure 2A , lane 2; note that these bands are faint in this experiment due to the relatively low NTP concentration; see Figure 3 ) . However , dominant products of 26 , 27 and 28 nts were also detected , specifically in reactions containing wt RSV RdRp and the TrC RNA . The fact that these products were larger than the input template suggested that they might have been generated as a result of the RdRp adding nts to the 3′ end of the template RNA , as has been shown for a number of other viral RdRps in in vitro reactions [33]–[43] . To test this possibility , reactions were performed with GTP as the only NTP source , to prevent de novo RNA synthesis from the TrC promoter . Under these conditions , a 26 nt band was observed ( Figure 2B , lane 3 ) . This result indicated that the 26 nt band was the result of nt addition to the 3′ end of the TrC template and was not a product of de novo RNA synthesis . In addition , RNA containing 3′ puromycin ( PMN ) in place of the 3′ hydroxyl group was tested in a reaction containing all four NTPs . The presence of 3′ PMN should abrogate 3′ terminal nt addition , while not preventing the ability of the RdRp to use the RNA as a template . The 3′ PMN TrC RNA generated significant levels of the RNAs≤23 nts , but the 26–28 nt RNA products were not detected ( Figure 2C , lane 3 ) . These results show that the RNA products smaller than 25 nts were generated by de novo RNA synthesis from the promoter , whereas the products longer than 25 nts were generated by addition of nts to the 3′ end of the template . In summary , the data presented in Figures 1 and 2 show that the RSV RdRp had two distinct RNA synthesis activities in vitro: one in which it used the TrC RNA as a template for de novo synthesis of RNA products , yielding a dominant product of 21 nts , minor products of 22 and 23 nts , and a series of smaller RNAs , and another in which it added additional nts to the 3′ end of the TrC RNA to generate products of 26–28 nts in length . Having identified these activities , we set out to examine the mechanisms by which they occurred . As a step towards optimizing the RNA synthesis assay , the NTP concentration in the reaction was varied from 200 µM to 1 mM of each NTP . At 200 µM NTP concentration , the de novo RNA synthesis products could be barely detected , whereas the 3′ extension products were produced at a relatively high level ( Figure 3 , lane 2 ) . As the NTP concentration was increased , RNA synthesis became much more efficient ( Figure 3 , compare lanes 2 , 3 , and 4 ) . These data show that de novo RNA synthesis and 3′ extension are differentially affected by NTP concentration , with de novo RNA synthesis depending on a higher NTP concentration than 3′ nt addition . Experiments were performed to characterize the initiation and termination sites of the products of de novo RNA synthesis . During RSV infection , the TrC promoter directs synthesis of genome RNA , which is the full-length complement of the antigenome . Therefore , it would be expected that the RdRp would initiate RNA synthesis from the 3′ terminal nt of the TrC promoter , the +1 position , and continue RNA synthesis to the end of the template to generate a 25 nt product . The finding that the major de novo RNA synthesis product from the 25 nt TrC template was 21 nts in length indicated that the RSV RdRp either initiated internally and/or failed to extend to the end of the template RNA . To identify the initiation site ( s ) , the RNA synthesis reaction was performed without UTP . As shown in Figure 1A , the first A residue in the template is at position +14 , so omission of UTP should inhibit the RdRp from continuing RNA synthesis beyond nt 13 . Reactions were performed with either [α-32P]ATP or [α-32P]GTP as a label ( Figure 4 , panels A and B , respectively ) . In both cases , omission of UTP resulted in a dominant band of 11 nts in length , and another band of 13 nts . However , products longer than 13 nts , including the 21 nt band , were still detectable , particularly in reactions containing [α-32P]ATP ( Figure 4A and B , lane 2; note that there are more A than G residues in the Tr product which greatly increases the sensitivity of the [α-32P]ATP label ) . The presence of these bands suggested that either the NTP stocks were impure , or that the RdRp had poor fidelity in this assay , allowing it to insert an alternative NTP instead of UTP . Products less than 11 nts in length could also be detected , but their abundance was not affected by the presence or absence of UTP , indicating that these were premature termination products , rather than RNA initiated from downstream sites ( Figure 4A and B , compare lanes 1 and 2 ) . The fact that the 11 nt product was dominant specifically in reactions lacking UTP indicated that the RSV RdRp could initiate RNA synthesis opposite the position +3 of the TrC template . On the other hand , the 13 nt product could either be RNA that was initiated at +1 and terminated at the first A in the template at position +14 , or RNA initiated at +3 and extended to the second A in the template at position +16 , due to misincorporation of an NTP opposite position +14 . Therefore , as a second step to identify the initiation sites of the 11 and 13 nt products , reactions were performed using [γ-32P]ATP or [γ-32P]GTP as a label . A [γ-32P]NTP label can only be incorporated into the 5′ terminal nt of the product . Thus , it would be expected that RNA initiated at +1 would incorporate [γ-32P]ATP , whereas RNA initiated at +3 would incorporate [γ-32P]GTP . Despite multiple experiments with different NTP concentrations , it was not possible to clearly detect incorporation of [γ-32P]ATP into RNA synthesis products ( data not shown ) . In contrast , RNA products labeled with [γ-32P]GTP were readily detected . In reactions lacking UTP , a product of 11 nts could be detected ( Figure 4D , lane 2 ) , providing confirmatory evidence that the RdRp could initiate opposite the C residue at position +3 . A 13 nt band could also be detected ( Figure 4D , lane 2 ) . This suggested that the 13 nt RNA was generated if the RdRp initiated at position +3 and then terminated when it reached position +16 . These data indicate that under these in vitro assay conditions , the majority of detectable RNA transcripts were initiated at nt +3 . Having identified that RNA was initiated at position +3 , it was possible to deduce how far it could be extended . In reactions containing a [γ-32P]GTP label and all four NTPs , a product of 21 nts was generated , although smaller amounts of 22 and 23 nt products could also be detected ( Figure 4C , lane 2 ) . This indicated that the RdRp frequently paused or terminated at nt 23 , with less frequent extension to the end of the template . In summary , the data from these experiments show that during de novo RNA synthesis , the RdRp initiated from +3 , and that while initiation at +1 might have occurred , RNA initiated from this site was not readily detectable . The data also show that the RdRp tended to pause or terminate at nt 23 . In addition , the data suggest that the RSV RdRp had low fidelity under these assay conditions . Although initiation at the +3 site of the TrC promoter has been observed previously in experiments using the RSV minigenome system [44] , it has never been described during RSV infection and the size of the RNA generated from this site has not been determined precisely . Examination of the TrC sequence showed that positions +3 to +12 are almost identical to the gene start signal sequence that lies at the beginning of the RSV L gene ( Figure 5C ) , suggesting that initiation at +3 could occur by a mechanism analogous to transcription initiation at the gene start signals that lie internally on the RSV genome . To determine if the +3 initiation site is used during infection , RNA purified from wt RSV infected cells was analyzed by primer extension using TrC-sequence specific primers . Analysis using a primer that hybridized at positions 13–35 relative to the 5′ end of the Tr sequence clearly identified two bands , corresponding to initiation at positions +1 and +3 ( Figure 5A , left panel , lane 4 ) . This finding was consistent with the results obtained with the in vitro RNA synthesis assay , and indicates that nt +3 is a bona fide initiation site . Analysis with a primer that hybridized to positions 32–55 of Tr detected RNA initiated from +1 but not from +3 , indicating that whereas the RNA initiated from +1 could be elongated , the RNA generated from the +3 initiation site was not extended far enough to hybridize to this primer ( Figure 5A , right panel , lane 4 ) . To determine the size of the RNA generated from the +3 site more precisely , RNA from RSV infected cells was also analyzed by Northern blotting with a probe specific to nts 5–32 of Tr , using conditions optimized for examination of RNA of 10–500 nts in length . This analysis identified an apparently abundant RNA transcript of ∼21–25 nts ( Figure 5B , lane 2 ) . This length is consistent with the primer extension analysis of the RNA generated from the +3 site , although the data do not exclude the possibility that some of the small RNA was initiated at +1 . These data show that the RSV TrC promoter has the unusual property of having two closely positioned initiation sites , one at +1 that is required to generate genome RNA , and another at +3 that yields small RNA transcripts . The data presented in Figure 2 show that in addition to generating newly synthesized RNA , the RSV RdRp could add nts to the 3′ end of the TrC RNA . Experiments were performed to determine which nts could be added , and to establish if they were added in a specific order . Reactions were performed containing each NTP label , either alone , or in combination with the other unlabeled NTPs . As described above , incubation with GTP in the absence of other NTPs showed strong incorporation into a 26 nt band , but no detectable incorporation into longer RNAs ( Figure 6C , lane 3 ) . If other NTPs were included in the reaction , a 27 nt band could be detected ( Figure 6C , lane 2 ) . This indicated that a different nt was added after the G to generate the 27 nt RNA . Labeled CTP was also incorporated into a 26 nt band in the absence of other NTPs , and yielded dominant bands of 26 and 28 nts when all four NTPs were present ( Figure 6B , compare lane 3 with lane 2 ) . In contrast , when UTP was used as a label , no incorporation was detected with UTP alone , but a 27 nt band was dominant when the other NTPs were present and a 28 nt band could be faintly detected ( Figure 6D , compare lane 3 with 2 ) . Similarly to the results shown in Figures 1 and 4 , ATP showed only very weak incorporation into RNA longer than 25 nts , either in the presence or absence of other NTPs ( Figure 6A ) . These data suggest that nts were incorporated onto the 3′ end of the TrC RNA with some specificity . Based on these data it can be deduced that either a G or C residue could be added to the −1 position at the 3′ end of the TrC RNA; a U residue could only be efficiently added after G , resulting in the 27 nt bands detected with either the GTP or UTP label , but not detectable with a CTP label; a C residue could then be added to the U to generate the 28 nt band , detected with CTP , and UTP , and to a lesser extent with a GTP label ( see also Figures 2 , 3 and 4 ) . Thus , the sequence of nts most frequently added to the 3′ end of the TrC RNA was G , GU , GUC , or C only; other nt sequences , such as an A tract , might also have been added to a lesser extent . This experiment also revealed that ATP and CTP could be incorporated into the 3′ end of the Tr sense RNA also ( Figure 6A and B , lane 5 ) , but the CTP label showed that this occurred less frequently than addition to the 3′ end of the TrC RNA ( Figure 6B , compare lanes 3 and 5 ) . The mechanism by which the nts were added to the TrC RNA was investigated . There were two potential mechanisms by which 3′ nt addition could occur: terminal transferase activity , or back-priming ( also known as template dependent priming ) . In back-priming , the 3′ end of the RNA interacts with an internal sequence to form a hairpin structure , and the RdRp adds nts to the 3′ terminus using the folded RNA as a template [38] , [45] . Visual inspection of the TrC RNA sequence showed there was possibility for two alternative hairpin loop structures to form in which nt 1U could base pair with either nts 14A or 16A , and nt 2G could base pair with either nts 13C or 15C . Pairing of nts 1 and 2 with 13 and 14 and extension by one to three nts would allow the RdRp to add a G , GU , or GUC , to the 3′ end of the TrC RNA by using nts 15C-17G as a template , whereas pairing of nts 1 and 2 with 15 and 16 would allow the RdRp to add a C ( Figure 7A ) . This model was consistent with the results shown in Figure 6 . To investigate this model , nts 1 or 14 and 16 in the 25 nt TrC RNA were substituted ( Figure 7A ) and NTP incorporation at the 3′ end of the RNA was examined using either a GTP or ATP label . Substitution of position 1U with an A caused a significant decrease in the levels of the 26–28 nt RNAs , suggesting that the identity of the 3′ terminal nt was significant for 3′ nt addition to occur ( Figure 7B , compare lanes 1 and 2 ) . Surprisingly , substitution of positions 14A and 16A with U residues did not block 3′ nt addition , but caused an alteration in the number and sequence of incorporated nts , with A being added , and G only being incorporated into longer products ( Figure 7B , compare lanes 1 and 3 , and 4 and 6 ) . Thus , disruption of possible base-pairing between the 3′ terminus and nts 13 and 14 , or 15 and 16 did not prevent 3′ addition , but altered the sequence of added nts . These results show that modification of the 3′ end of the TrC RNA involves an internal sequence , consistent with a back-priming mechanism , rather than terminal transferase activity . Having shown that nts were added to the 3′ end of the TrC RNA in vitro with some specificity , it was of interest to determine if this occurred during RSV infection . In the context of an RSV infection , the TrC sequence is at the 3′ end of the antigenome . To our knowledge , no one has previously identified additional sequences at the 3′ terminus of the RSV antigenome . However , antigenome 3′ terminal sequences are rarely determined directly , but instead are inferred from the genome sequence [7] , [46]–[48] . In one paper in which antigenome RNA was analyzed , only a small number of individual clones were sequenced [49] . Thus , prior sequencing analyses did not exclude the possibility that nts are added to the TrC region of a subpopulation of antigenome RNAs during RSV infection . To examine this possibility , antigenome RNA from RSV infected cells was tailed with either A or C residues , transcribed into cDNA by 3′ rapid amplification of cDNA ends ( 3′ RACE ) and sequenced . Direct sequence analysis of the cDNA population showed that there was a mixed population of sequences , with a significant proportion of antigenomes containing additional nts of G , U and/or C at the −1 , −2 , and −3 positions relative to the 3′ end of the TrC promoter , respectively ( Figure 8B , left panels ) . Sequencing of individual cDNA clones showed that while 10/19 clones contained wt antigenome sequence with no additional nts , 7/19 clones contained a 3′ G , 3′ UG , or 3′ CUG at the end of the antigenome ( Figure 8C; note that 2/19 clones did not fall into either category ) . These sequence additions are consistent with a back-priming event involving interaction of nts 1 , 2 and 13 , 14 of the TrC RNA and extension by 1–3 nts in a template dependent manner , as illustrated in Figure 8A ( left panel ) . Examination of the Le promoter sequence at the 3′ end of the genome showed that it also has the potential to form a secondary structure that could be used to direct back-priming . Indeed , in this case , a significantly stronger secondary could be formed than by the TrC sequence ( Figure 8A , right panel ) . However , analysis of the same RNA preparation using Le specific probes showed that there was no additional sequence at the 3′ end of the Le promoter in the genome RNA ( Figure 8B , right panels ) , demonstrating that the 3′ end of the Le is unmodified . These findings suggest that in addition to being able to use the TrC RNA as a promoter , the RdRp also facilitates a back-priming event to allow a precise sequence of nts to be added to the 3′ end of the antigenome . The presence of additional sequence at the 3′ end of almost half of the antigenome sequences that we examined indicated that the 3′ extension plays a role in RSV replication . The only known function of antigenome RNA is as a template for RNA synthesis . Therefore , we examined if the additional nts at the 3′ end of the TrC sequence affected promoter activity . The 1–25 TrC RNA template was compared to a “+CUG” RNA template , which contained 1–25 nts of TrC sequence and a 3′ CUG extension , using the in vitro RNA synthesis assay . Both RNA templates contained a 3′ terminal PMN group to ensure that neither was subject to further 3′ modification . Analysis of the RNA generated from these templates showed that the presence of a 3′ terminal CUG extension was highly deleterious to RNA synthesis , indicating that the 3′ extension inhibited access of the RdRp to the promoter ( Figure 9 , compare lanes 2 and 3 ) . We considered the possibility that the extension might increase initiation from the +1 position , but there was no evidence of incorporation of a [γ-32P]ATP label into RNA synthesized from the +CUG template ( data not shown ) . Thus , these data indicate that the 3′ terminal extension can inhibit antigenome promoter activity .
It is accepted that normally the RSV template RNA is encapsidated with N protein . However , the fact that our experiments showed that the RSV polymerase was able to recognize the RNA in a sequence specific manner in the absence of N , and modify the TrC RNA apparently by using an RNA secondary structure , reveals insight into the molecular details of the polymerase-template complex . These findings suggest that although the antigenome RNA is normally encapsidated with N protein , there are occasions during the RSV replication cycle when the RdRp can interact with the RNA directly . The ability of the RSV RdRp to recognize the promoter in the absence of N protein is not necessarily surprising , as prior studies have shown that there is no requirement for the RSV promoter to be in phase with N protein [52] . Likewise , the VSV RdRp has also been shown to be able to recognize a specific initiation signal on a naked RNA template [18] , and also does not follow an integer rule [53] . The situation might be different for other NNS RNA viruses , which require their genomes to be a particular integer length [54]–[60] . In these cases , the promoter is clearly recognized in the context of N protein [61]–[63] and it might be that the polymerase would either not recognize naked RNA as a template , or that there would be little or no sequence specificity on naked RNA . While RdRps are known to be error prone , the data obtained from the –UTP experiments suggest that the RdRp might have particularly low fidelity in this system ( Figure 4A ) , which would not be tenable during infection . It is possible that during RSV infection , the N-RNA template opens to allow the RdRp to make direct contacts with the promoter RNA and initiate RNA synthesis , but that because RNA elongation is dependent on release of the RNA from the downstream N molecules , the RdRp structure is slightly altered , allowing for greater accuracy . The TrC promoter would be expected to direct RNA synthesis initiation from the +1 position , to yield the genome RNA , and RNA initiated from +1 could be readily detected in RSV infected cells . However , RNA initiated at +3 was also detected . Primer extension analysis showed that this RNA was truncated within a short distance from the promoter and consistent with this , RNA transcripts of 21–25 nts in length could be readily detected in infected cells . The function of the small RNA initiated from +3 is not yet known . However , previous studies suggest that Tr-specific RNA might play a role in subverting the cellular stress granule response [64] , [65] . If this RNA does play a functional role , it would indicate that the TrC promoter is not limited to initiating RNA replication , but also has a role in RSV transcription , albeit directing synthesis of a small RNA transcript rather than mRNA . In this study , we showed that initiation at +3 occurs by a de novo initiation mechanism , and apparently does not depend on prior initiation at +1 ( Figure 4 ) . This is consistent with previous minigenome experiments that showed that mutations that inhibited initiation at +1 augmented initiation at +3 [44] . In contrast , we were unable to convincingly demonstrate initiation at the +1 position in the in vitro assay by incorporation of a [γ-32P]ATP label , despite numerous experiments aimed at optimizing NTP concentration for +1 initiation . A band of 25 nts in length could occasionally be detected ( e . g . Figure 9 , lane 2 ) indicating that a low level of initiation at +1 might occur . Failure to detect +1 initiation using [γ-32P]ATP could reflect differences in the NTP concentrations required for initiation at +1 versus +3 . It was possible to detect +3 initiation with [γ-32P]GTP by using a relatively low concentration of unlabeled GTP in the reaction so that the proportion of labeled GTP in the total GTP pool was not too low . If initiation at +1 required a particularly high concentration of ATP , it might be impossible to identify conditions that allow +1 initiation , without out-competing the [γ-32P]ATP label with unlabeled ATP . It is also possible that initiation at +1 requires different conditions , or an additional factor that is missing in the in vitro assay , or that this initiation event was too inefficient to be detected with a [γ-32P]ATP label . In previous minigenome studies , we showed that if deletions or substitutions were introduced at position +1U of the TrC or Le promoter , almost all the detectable replication product was restored to wt sequence in a single round of replication , indicating that during initiation at the +1 site , the initiating NTP was selected independently of the 3′ terminal nt of the template [44] , [66] . Based on these findings , we proposed that the RSV RdRp becomes preloaded with a primer for initiation at +1 [66] . If this model were correct , it would be expected that ATP might be required at a particularly high concentration to generate a primer and/or that other factors might also be involved . Thus , it is not surprising that the +1 initiation event could not be detected or reconstituted as readily as +3 initiation . It is unusual for an RdRp initiate from two sites within the same promoter region . One explanation for how this occurs is that the RdRp binds to a sequence within nts 3–11 of the promoter and either recruits GTP to initiate at position +3 , or is preloaded with a 5′ AC or 5′ ACG primer to initiate RNA synthesis from position +1 . An in vitro RNA synthesis assay was also recently established for the VSV RdRp [18] . The VSV study utilized the Le , rather than the TrC promoter sequence , preventing direct comparison with the results obtained here . However , the RSV Le promoter also contains a gene start-like sequence at nts 3–12 , and has been shown to direct RNA synthesis initiation from positions +1 and +3 in the minigenome system [66] . In contrast , the VSV RdRp only initiated RNA synthesis at the +1 position on the wt template , and unlike the situation with RSV , RNA initiated at this site could be readily detected using a [γ-32P]ATP label [18] . Thus , the existing data suggest a significant difference in the functional properties of the RSV and VSV promoters , and in their mechanisms for RNA synthesis initiation . In the experiments shown here , there were dominant RNA synthesis products of ∼8–10 nts ( e . g . Figure 1E ) . The reason why RNAs ∼8–10 nts in length were generated at a relatively high level is likely due to abortive synthesis in which the RdRp failed to escape from promoter and released the nascent RNA transcript . This is a common feature of initiation by RNA polymerases , which has been well documented [67] . In addition , the RdRp was inefficient at extending transcripts to the end of the template , frequently halting RNA synthesis at nt 23 of the TrC RNA ( e . g . Figure 1E ) . Examination of RNA synthesis products from two shorter templates indicated that in one case the RdRp was able to extend to the end of the template , whereas in another it terminated either at the terminal or penultimate nt ( data not shown ) . Therefore , it is possible that the polymerase was influenced by the 5′ terminal sequence of the template , as has been shown for two bromovirus replicases [68] . The RdRp might also terminate due to a termination signal or inherent instability at this position , as the short RNAs detected in RSV infected cells were ∼21–25 nts in length ( Figure 5 ) . The data also revealed that the RSV RdRp could add nts to the 3′ terminus of the TrC RNA both in vitro and during RSV infection , apparently using a back-priming mechanism ( Figures 2 , 6 , 7 and 8 ) . These results are similar to findings for Borna disease virus , in which it has been shown that nts are added to both the genome and antigenome RNAs with an apparent 100% efficiency [51] . The finding that RSV shares a back-priming activity with Borna disease virus is surprising , as Borna disease virus is somewhat distinct from RSV and the other NNS RNA viruses . Interestingly , in the experiments described here , none of the RNA oligonucleotides tested in the in vitro assay possessed a stable secondary structure that would allow base-pairing between the 3′ terminus and an internal sequence , as predicted by Mfold analysis [69] . Furthermore , there was no evidence for addition of nts to the 3′ end of the RSV genome , despite the 3′ end of the Le region having the potential to form an inherently stronger RNA secondary structure ( Figure 8A and B ) . Therefore , the RNA secondary structure to facilitate back-priming on the antigenome presumably is stabilized by the RdRp ( or an associated protein ) at a point when the antigenome RNA is not fully encapsidated . One possible model is that nt addition to the 3′ end of the TrC sequence occurs as the RdRp completes synthesis of the antigenome RNA . In this scenario , when the RdRp reaches the end of the antigenome , it folds the RNA into the back-priming structure and adds the additional 3′ nts before the nascent RNA becomes completely encapsidated . Alternatively , the 3′ end of the antigenome might become unencapsidated prior to RdRp binding the promoter . Then depending on RdRp orientation when it accesses the promoter , it could either modify the 3′ terminus or initiate de novo RNA synthesis . It remains somewhat unclear what role the 3′ terminal extension plays in RSV replication . Unfortunately , because of the multifunctional nature of the TrC promoter in directing RNA synthesis and encapsidation , it is probably not possible to generate a mutant virus in which the 3′ terminal extension activity is ablated without also affecting other aspects of genome replication . Therefore , we can only speculate on the significance of antigenome 3′ terminal extension during RSV infection . In the case of Borna Disease virus , the function of the additional nts at the genome and antigenome ends is to compensate for cleavage of the 5′ ends of replication products , which allows the virus to avoid detection by RIG I [70] . It is unlikely that 3′ nt addition fulfills a similar function in the case of RSV , as the data suggest that only a subpopulation of replication products are modified ( Figure 8 ) . Furthermore , RIG I binds to RSV RNAs and is activated early in RSV infection [71] , [72] . A second possibility is that having additional sequence and a double stranded RNA structure at the ends of the RSV RNA provides some protection of the promoter sequence from cellular exonucleases . However , if this were the case , it is not clear why there was heterogeneity in the antigenome population . We have also investigated if back-priming activity could allow repair of a template in which the 3′ terminal nt was deleted , but we were unable to detect incorporation of labeled UTP into this template in the in vitro RNA synthesis assay , indicating that deletion nt 1 of the TrC promoter prevents the back-priming event from occurring ( data not shown ) . However , it is possible that repair can occur through a back-priming mechanism in a cellular environment . Finally , it is possible that the 3′ terminal additions are part of a regulatory mechanism to modulate promoter activity . Consistent with this idea , the data shown in Figure 9 indicates that the 3′ terminal extension inhibits RNA synthesis , at least in the context of the in vitro assay . It is important to note that in this assay the RNA was naked , and so the effect of the 3′ extension might not reflect the situation in RSV infected cells . For example , in the in vitro assay , the 3′ extension is likely to have created an RNA secondary structure that prevented access of the RdRp to the promoter . In contrast , in infected cells the antigenome RNA would be expected to become completely encapsidated in N protein , eliminating RNA secondary structure . Comparison of wt and mutant templates containing the extensions using the minigenome system ( in which the template RNA does become encapsidated ) indicated that , the 3′ terminal UG and CUG additions slightly increased RNA synthesis from both the +1 and +3 positions , while a G extension had no effect ( data not shown ) . However , we also found that the 3′ termini of the input wt and mutant minigenome templates had the potential to be modified in the transfected cells , and so it is not clear how much weight can be attributed to these results . Nonetheless , it is interesting to speculate that the ability of the RdRp to add nts to the antigenome terminus and the effect of the nt extension are linked by the encapsidation status of the antigenome RNA . For example , one possibility is that 3′ terminal extension occurs when encapsidation of the newly synthesized antigenome RNA lags behind RNA synthesis . In this scenario , the putative hairpin structure formed by 3′ terminal extension might function to prevent initiation of de novo RNA synthesis until the antigenome RNA becomes fully encapsidated , at which point it might become an even more efficient promoter . In summary , the findings presented here indicate that the behavior of the RSV RdRp at the TrC promoter sequence is more complex than previously realized , directing initiation of RNA synthesis from two sites , and having the capacity to add nts to the 3′ end of the antigenome template by a back-priming mechanism . We speculate that the advantage of having greater complexity in polymerase-promoter interactions is that it might offer an opportunity for temporal or environmental regulation of RSV RNA expression .
A codon-optimized version of the RSV ( strain A2 ) L protein ORF was chemically synthesized ( GeneArt ) and the mutant LN812A was generated by QuickChange site-directed mutagenesis ( Aligent Technologies ) . RSV L or LN812A , were cloned in the pFastBac Dual vector ( Invitrogen ) together with the RSV A2 P ORF , which was tagged with a hexahistidine sequence , separated from the ORF by a tobacco etch virus ( TEV ) protease cleavage site . Recombinant baculoviruses were recovered using the Bac-To-Bac system ( Invitrogen ) and used to infect Sf21 cells in suspension culture . RSV L/P protein complexes were isolated from cell lysates by affinity chromatography , TEV protease cleavage , and size exclusion . Isolated L/P complexes were analyzed by SDS-PAGE and PageBlue staining ( Fermentas ) and the L protein concentration was estimated against bovine serum albumin reference standards . The bands migrating between 160 to 250 kDa and ∼35 and 50 kDa were confirmed to be RSV L and P polypeptides by excising each band and performing trypsin digestion followed by liquid chromatography , tandem mass spectrometry ( LC/MS/MS ) . The MS/MS spectra were analyzed using SEQUEST software using the RSV A2 sequence as a reference . The identity of a band migrating between 70 and 80 kDa was determined by performing SDS-PAGE and Western blot analysis using an hsp70/HSC70 specific antibody ( Santa Cruz ) . Codon optimized wt and LN812A ORFs were introduced under the control of the T7 promoter in pTM1 . Minigenome RNA analysis was performed using plasmid MP-28 , which encodes a replication competent dicistronic CAT minigenome template [10] . Minigenome transcription and RNA replication were reconstituted in BSR-T7 cells as described previously [66] . Minigenome specific RNAs were analyzed by Northern blot using CAT-specific probes , as described previously [73] . RNA oligonucleotides representing nts 1–25 of either wt or mutant TrC promoter sequence or its complement ( Tr sequence ) were purchased ( Dharmacon ) . All oligonucleotides contained an –OH group at the 3′ terminus , unless stated otherwise , and an –OH group at the 5′ terminus . The RNA oligonucleotides were combined with RSV L/P ( containing ∼100 ng of L protein ) in transcription buffer containing 50 mM Tris HCl at pH 7 . 4 , 50 mM NaCl , 5 mM MgCl2 , 5 mM DTT , 40 U RNase inhibitor , and NTPs , including either 1 µl of [α-32P]ATP , CTP , GTP or UTP ( as indicated in the legend ) or [γ-32P]GTP ( ∼10 µCi ) , in a final volume of 50 µl . The RNA and NTP concentrations used for each experiment are indicated in the figure legends . Reactions were incubated at 30°C for 3 h , heated to 90°C for 3 min to inactivate the RdRp and cooled briefly on ice . Reactions containing [α-32P]NTP were combined with 10U calf intestinal alkaline phosphatase , incubated at 37°C for 1 h and RNA was isolated by phenol-chloroform extraction and ethanol precipitation . Reactions containing [γ-32P]GTP were combined with 7 . 5 µl 10% SDS , 0 . 5 µl 500 mM EDTA and 10 µg proteinase K and incubated at 45°C for 45 min before phenol-chloroform extraction and ethanol precipitation . The RNA was analyzed by electrophoresis on a 20% polyacrylamide gel containing 7 M urea in tris-borate-EDTA buffer , followed by autoradiography . On each autoradiogram , the nt lengths of the RNA products were determined by comparison with a molecular weight ladder generated by alkali hydrolysis of a 32P end-labeled RNA oligonucleotide representing the anticipated 25 nt Tr RNA product . This marker is shown in Figure 1E , Figure 2 , Figure 3 , Figure 6 , and Figure 9 , and the same marker was used for the remaining experiments . The bottom of each gel is cropped to eliminate the non-specific signal from unincorporated radiolabeled NTPs that were not always efficiently removed during RNA purification and electrophoresis . HEp-2 cells were infected with RSV at an MOI of 5 or mock infected and incubated at 37°C for 17 h . Total intracellular RNA was isolated using Trizol ( Invitrogen ) . Primer extension reactions were carried out as described previously [44] using primers that hybridized at positions 13–35 or 32–55 relative to the genome 5′ terminus . The sizes of the labeled cDNA products were compared to 32P end labeled DNA oligonucleotides of sequence and length equivalent to cDNAs corresponding to RNAs initiated at positions +1 and +3 on the antigenome . The method for detecting small genome sense RNA by Northern blot analysis was adapted from a protocol described by Varallyay and co-workers [74] . Briefly , total intracellular RNA was subjected to electrophoresis in a 6% urea-acrylamide gel alongside low-range ssRNA and miRNA molecular weight standards ( NEB ) . The lanes containing the molecular weight standards were excised from the gel prior to Northern transfer , stained with ethidium bromide and the standards were detected with UV light . The remainder of the gel was transferred to Nitran-N positively charged Nylon membrane ( Sigma-Aldrich ) using the Whatmann TurboBlotter downward capillary transfer system ( Sigma-Aldrich ) in 8 mM NaOH , 3 mM NaCl . Following transfer , blots were neutralized in 6× SSC and UV-crosslinked . Blots were prehybridized for 1 h in 5× Denhardt's Solution , 6× SSC , 0 . 1% SDS , and 0 . 01% NaPPi at and hybridized for 12–18 h with a 32P end-labeled locked nucleic acid modified DNA oligonucleotide specific to nts 5–32 ( relative to the 5′ terminus ) of genome sense RNA ( 5′- GAGATATTAGTTTTTGACACTTTTTTTC - 3′ ) in the same buffer at 62°C . Blots were washed with 6× SSC twice for 15 minutes at room temperature , and twice for 10 minutes at 62°C , and the RNA was detected by autoradiography . 3′ RACE and sequence analysis of antigenome and genome RNA ( Figure 8 ) was performed using RNA isolated from infected cell extracts enriched for RSV ribonucleoprotein ( RNP ) complexes [75] . Briefly , 8×106 HEp2 cells were infected at an MOI of 5 . At 17 h post infection , the supernatant was replaced with media containing 2 µg/ml actinomycin D and cells were incubated at 37°C for a further 1 h . Following an ice cold PBS wash , cells were treated for 1 min with PBS supplemented with 250 µg/ml lyso-lecithin , on ice . Cells were scraped into 400 µl of ice cold Buffer A ( 50 mM tris-acetate pH 8 , 100 mM K-acetate , 1 mM DTT , 2 µg/ml actinomycin D ) , disrupted by repeated passage through an 18G needle and incubated on ice for 10 min . Following centrifugation at 2400× g for 10 min at 4°C , the resulting pellet was disrupted in 200 µl of ice cold Buffer B ( 10 mM tris-acetate pH 8 , 10 mM K-acetate , 1 . 5 mM MgCl2 , 1% triton X-100 ) by repeated passage through an 18G needle and then incubated on ice for 10 min . The sample was centrifuged and the resulting pellet was disrupted in 200 µl of Buffer B supplemented with 0 . 5% deoxycholate , 1% tween 40 as described above . Following a 10 min incubation on ice and a repeat centrifugation , the supernatant enriched for viral RNPs was collected and RNA was extracted using Trizol ( Invitrogen ) . The purified RNA was tailed with either A or C residues using E . coli poly A polymerase ( NEB ) , followed by heat inactivation of the enzyme , according manufacturer's instructions . First strand cDNA synthesis was performed using primers 5′ GAGGACTCGAGCTCAAGCATGCATTTTTTTTTTTTTTT , or 5′ GAGGACTCGAGCTCAAGCATGCATGGGGGGGGGGGGGGG , which hybridized to the poly A or poly C tail , respectively , and Sensiscript reverse transcriptase ( Qiagen ) , according to manufacturer's instructions . To determine the sequence of the antigenome 3′ terminus , purified cDNA was PCR amplified using primer SLNQi 5′-GAGGACTCGAGCTCAAGC and a TrC specific primer Tr1: 5′-GCAGCACTTTTAGTGAACTAATCC . The resulting product was subjected to a second round of hemi-nested PCR using primer SLNQi and primer Tr2: 5′-GCAGTCGACCATTTTAATCTTGGAG . PCR products were gel purified and either sequenced directly or cloned into a pGEM vector for sequencing of individual cDNA clones . Analysis of the genome 3′ terminus was performed as described above , using the same cDNA preparation and primer SLNQi , but with NS1 specific primers: 5′-GCACAAACACAATGCCATTC and 5′-GCAGTCGACGTATGTATCACTGCCTTAGCC . | Respiratory syncytial virus ( RSV ) is a major pathogen of infants with the potential to cause severe respiratory disease . RSV has an RNA genome and one approach to developing a drug against this virus is to gain a greater understanding of the mechanisms used by the viral polymerase to generate new RNA . In this study we developed a novel assay for examining how the RSV polymerase interacts with a specific promoter sequence at the end of an RNA template , and performed analysis of RSV RNA produced in infected cells to confirm the findings . Our experiments showed that the behavior of the polymerase on the promoter was surprisingly complex . We found that not only could the polymerase initiate synthesis of progeny genome RNA from an initiation site at the end of the template , but it could also generate another small RNA from a second initiation site . In addition , we showed that the polymerase could add additional RNA sequence to the template promoter , which affected its ability to initiate RNA synthesis . These findings extend our understanding of the functions of the promoter , and suggest a mechanism by which RNA synthesis from the promoter is regulated . | [
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] | 2012 | The Respiratory Syncytial Virus Polymerase Has Multiple RNA Synthesis Activities at the Promoter |
For a given gene , different mutations influence organismal phenotypes to varying degrees . However , the expressivity of these variants not only depends on the DNA lesion associated with the mutation , but also on factors including the genetic background and rearing environment . The degree to which these factors influence related alleles , genes , or pathways similarly , and whether similar developmental mechanisms underlie variation in the expressivity of a single allele across conditions and among alleles is poorly understood . Besides their fundamental biological significance , these questions have important implications for the interpretation of functional genetic analyses , for example , if these factors alter the ordering of allelic series or patterns of complementation . We examined the impact of genetic background and rearing environment for a series of mutations spanning the range of phenotypic effects for both the scalloped and vestigial genes , which influence wing development in Drosophila melanogaster . Genetic background and rearing environment influenced the phenotypic outcome of mutations , including intra-genic interactions , particularly for mutations of moderate expressivity . We examined whether cellular correlates ( such as cell proliferation during development ) of these phenotypic effects matched the observed phenotypic outcome . While cell proliferation decreased with mutations of increasingly severe effects , surprisingly it did not co-vary strongly with the degree of background dependence . We discuss these findings and propose a phenomenological model to aid in understanding the biology of genes , and how this influences our interpretation of allelic effects in genetic analysis .
In any given genetic pathway , or even in a single gene , different mutations ( whether they are natural variants or lab-induced lesions ) can have a wide range of phenotypic effects , and these effects are often modulated by the environment and alleles at other genes throughout the genome ( the genetic background ) [1–7] . These interactions , however , remain poorly understood . Most studies on genetic background and environmental influences on the phenotypic expression of an allele have examined a single allele of a single gene . As a consequence , a number of questions remain unanswered about how these factors interact to give rise to phenotypic variation , in particular with respect to predictability of those effects [8] . Given our knowledge of context dependence for one allele , can we predict the degree of background dependence for other alleles in that gene or among genes with related functions [9] ? Or are the background effects so complex and multifaceted that this remains an impossible task ? Moreover , can variation in the phenotypic consequences of multiple alleles , and variable expression of a single allele in different contexts , be explained by similar developmental underpinnings ? Answering these questions is essential for understanding the relationships between genotype and phenotype at a mechanistic level . This also has implications for genetic analysis , per se . Many of the genetically based definitions of a “gene” depend on patterns of non-complementation [10] . Likewise , the study of the phenotypic consequences of different alleles of a gene ( an allelic series ) is necessary for gene structure-function analysis . Investigations of allelic complementation patterns led to discoveries of mechanisms for gene regulation such as pairing-dependence ( transvection; [11–13] ) , position effects [14] , and dominant negative interactions [15] . Such genetic inferences—e . g . , on gene identity , structure-function relationships , and phenomena such as transvection—rest on the assumption that the phenotypic outcomes of genetic lesions are a function solely of the mutations themselves . However , this view ignores the importance of context dependence , which is relevant when making inferences regarding “higher order” genetic effects . The genetic definition of a gene becomes convoluted if two alleles complement each other only under certain conditions . Likewise , it could be difficult to draw conclusions about the functions of different structural domains in a gene based on a series of alleles with lesions in different locations , if the phenotypic expression of a series of alleles depends on the genetic background or environment . Ignoring the context dependence of allelic effects can influence inferences from mutational analyses designed to dissect the mechanisms connecting genetic lesions to mutant phenotypes . Nevertheless , the majority of studies examining the influence of genetic background and environment have been limited to a single allele in a given gene . Thus , it is difficult to evaluate how genetic background might influence these higher-level attributes necessary for genetic analysis . Recent studies have highlighted that interactions among mutations can be heavily dependent on the wild-type genetic background in which they are examined [16–21] . Surprisingly , given its central importance in genetics , there has been almost no examination of the influence of wild type genetic background on the ordering of allelic effects or patterns of complementation among mutations within a gene . There are at least two possible explanations that have been discussed for how genetic background and environment influence penetrance and expressivity; these explanations are not necessarily mutually exclusive and may be viewed as endpoints along a continuum . On one end , context dependence may be unpredictable and highly specific to certain alleles; in other words , knowing how the genetic background and environment influence the expression of one allele does not tell us anything at all about how the same context will influence the penetrance or expressivity of other mutations , even if those mutations affect the same gene or pathway . On the other end , the degree of context dependence is determined by the developmental or physiological constraints of the particular genetic network or trait , not on the unique properties of specific alleles [22 , 23] . This perspective on allelic expressivity can be considered an extension of the molecular model for dominance proposed by Kacser & Burns [24] , with the robustness being a potentially intrinsic property of a genetic system with thresholds for phenotypic effects . According to this model , the expression of alleles with weak effects ( low expressivity ) should show little context-dependence , as they minimally perturb the system . Likewise , alleles with very strong effects ( e . g . , null alleles ) should also show little context dependence , because their large effects cause a complete loss of gene function , leaving little room for variability in phenotypic effects due to genetic background or environment . The phenotypic effects of alleles with intermediate effects , on the other hand , are most likely to be sensitive to context . An important corollary to this model is that variability in expressivity and penetrance across genetic backgrounds , and variability within genetic backgrounds , might be correlated; that is , variability across genetic backgrounds and within genetic backgrounds should both be highest for alleles of moderate effect . Yet despite this reasoning , there is definite evidence for genetic background effects along the spectrum of severity of alleles , including many null alleles [25–28] . At face value , this may suggest that the “intrinisic threshold” model of genetic function may be insufficient to explain genetic background effects . However , this may also reflect that , to our knowledge , no studies have systematically examined an allelic series of mutations that vary along the spectrum of phenotypic effects with respect to genetic background . Here , we describe a comprehensive , systematic analysis of the effects of genetic background and environmental context on the ordering of allelic series and patterns of complementation using the Drosophila melanogaster wing as a model system . We introduced multiple mutations in two genes , scalloped and vestigial , into two commonly used wild-type genetic backgrounds . Because these two genes interact in a common pathway , this experimental design maximizes the chances of uncovering common influences of genetic background and environment; if genetic background effects are uncorrelated across these two genes , they are not likely to be correlated for other genes , either . We examined how the ordering of allelic effects and patterns of complementation are affected by wild-type genetic background and rearing environment . We demonstrate that the observed genetic background effects are not a property just of specific alleles , but extend across multiple alleles and genes . Genetic background and environmental effects were common , and these influences were most prominent for alleles with intermediate phenotypic effects . However , variability across genetic backgrounds for a given genotype was not strongly correlated with variability within genetic backgrounds . While the relative ordering of allelic effects was consistent across wild type backgrounds , patterns of complementation ( intragenic interaction ) were consistent only in some instances , differing dramatically in others . Variation in cell proliferation in the developing wing imaginal disc was congruent with phenotypic variation in adult wings among different alleles of each gene . Surprisingly , however , these cellular markers did not always reflect adult phenotypic variation across genetic backgrounds . Our results therefore suggest that multiple distinct mechanisms may underlie variation in expressivity among mutant alleles of the same gene and among genetic backgrounds . We discuss these results both within the broadening context of the biology of the gene , and how to exploit such variation to address fundamental questions in genetics .
To assess how both wild-type genetic background and rearing environment influenced the expressivity of mutations we used a set of mutations in the scalloped and vestigial genes ( Table 1 ) that had been repeatedly backcrossed into two common wild type strains , Samarkand ( SAM ) and Oregon-R ( ORE ) both marked with the eye color marker white ( see Materials and Methods ) . After introgression the strains were genotyped for ~350 anonymous markers throughout the genome including ~100 that distinguished the two wild-type strains . With a few exceptions for particular alleles on particular chromosome arms , introgressions appeared close to complete ( S1 Table , S1 Fig ) . We observed a striking pattern of genetic background effects related to the overall degree of perturbation caused by the mutations . Some weak and all of the moderate hypomorphic ( loss of function ) alleles showed genetic background dependence for both genes and at both rearing temperatures . However , the strongest hypomorphs for both sd and vg showed little or no evidence for sensitivity to the effects of genetic background ( Figs 1 and 2 , S2 Fig ) . Despite the considerable variation in expressivity of the mutations due to genetic background effects , we generally saw consistent ordering of allelic effects . That is , genetic background did not cause substantial changes to the rank ordering of the series of alleles within each gene ( Fig 2 , S2 Fig ) . While there was no overall switching of rank order , certain alleles had largely equivalent phenotypic effects in one genetic background but not in the other , e . g . , sd1 , sdETX4 , and sdE3 . In some cases , this pattern was seen in some environments but not others ( e . g . , vg2a33 and vg21-3 show similar effects in Oregon-R but not Samarkand at 18°C , but non-overlapping effects in both backgrounds at 24°C ) . These results are inconsistent with a model where the wild type genetic background has a constant influence with respect to mutations in the gene . Instead , some non-linear function of both the degree of perturbation and likely the details of the lesion ( regulatory vs . coding , etc . ) are interacting with genetic background in as yet unexplained ways . To test whether these results were generalizable to other genetic backgrounds , we crossed the sd alleles to 16 randomly selected additional wild type strains that are part of the DGRP collection of sequenced strains [29] . There are a few key differences between this experiment and the more in-depth examination of SAM and ORE . Notably , we did not introgress the mutations; instead , we crossed SAM sd mutant females to wild-type males , to obtain F1 flies hemizygous for the sd mutation and heterozygous for genetic background alleles on chromosomes 2 , 3 , and 4 . Therefore , the effects of autosomal recessive genetic background modifiers , and all X-linked background modifiers , would not be detected , and we measured background dependence only in hemizygous males . Nevertheless , we observed a similar pattern as before ( Figs 1 and 2 ) : while the rank ordering of alleles did not change , some pairs of alleles had similar effects in certain backgrounds and distinct effects in others ( S3 Fig ) . Thus , this result is likely generalizable across wild-type genetic backgrounds , and depends at least in part on background modifiers with additive and/or dominant effects . To understand gene structure-function relationships , the analysis of patterns of complementation among alleles of a gene is essential . We adopt the definition of complementation used by [30] , in which two mutant alleles complement if , when crossed , they result in phenotypes that quantitatively overlap with wild-type . We investigated how wild type genetic background and rearing temperature influence such patterns ( Figs 3 and 4 , and S4 and S5 Figs ) . The effect of crossing direction on phenotypes was small in magnitude ( S6 and S7 Figs ) , so for these analyses we treated reciprocal crosses as equivalent . As we observed with the allelic series , the quantitative complementation data suggest that the influence of rearing temperature was relatively modest ( S4 and S5 Figs ) . In some instances , background dependence has a fairly “constant” effect ( same patterns of complementation across backgrounds , but different “intercept” ) , such as that seen for patterns of complementation between sdE3 and most other sd alleles ( Fig 3A ) . Interestingly , this pattern was not exclusively observed . Indeed , we observed some cases where alleles failed to complement each other in one wild type background , but complemented in the other ( produced near wild type phenotypes ) ; e . g . , at 24°C sdETX4 and sdE3 complement in Samarkand but not Oregon-R ( Fig 3B ) . Perhaps most interestingly , we observed several cases where hetero-allelic combinations showed background dependent phenotypes , despite lack of background dependence in the homozygotes ( Fig 3C ) ; e . g . , neither sd1 homozygotes nor sdG0309 homozygotes show much background dependence , but genetic background has a strong influence on phenotypes in sd1/sdG0309 trans-heterozygotes . In general , we observed that hetero-allelic combinations that resulted in broadly intermediate phenotypic effects were the most background dependent , while those with relatively weak or severe effects had relatively weak background dependence . Thus these results were generally consistent with the observation for homozygous and hemizygous effects of individual alleles for scalloped and vestigial . Given how generally important the distinction between complementation and non-complementation can be , this pattern seems strikingly and potentially important for inferences in genetic analysis . Two particular alleles of vg ( vg1 and vg83b27 ) have been used extensively to study transvection [31] , i . e . pairing-dependent regulation of gene expression as they have mutations in introns 3 and 2 respectively ( each providing distinct regulatory sequences ) . Among these previous studies there was considerable variation observed in the degree of complementation ( compare [3 , 31] with [13] ) . Thus , we decided to also examine vg allelic combinations including vg1 and vg83b27 for background dependence . As homozygotes both mutations have among the strongest phenotypic effects for viable vg alleles ( Fig 2 , S2 Fig ) , yet in combination they have been shown to complement partially or completely [13 , 32] . We observe largely the same pattern as that reported in the literature ( Figs 1 and 2 ) , with surprisingly weak influences of genetic background ( S2 Fig ) . It also seems as if trans-heterozygotes between other vg alleles and vg83b27 also show near complete complementation consistent with this pairing-dependent effects ( Fig 4A ) . This same pattern was not observed in most other hetero-allelic combinations of vg ( Fig 4B and 4C , S5 Fig ) . One of these alleles ( vg1 ) is known to be temperature sensitive; when flies are reared at “low” temperatures ( 17–20°C ) the expressivity of the mutation is strongest , while rearing at higher temperatures ( above 25°C ) has been reported to result in phenotypes that overlap with wild type [33] . We examined the phenotypic effects of the two most severe ( but homozygous viable ) alleles of vg ( vg1 and vg83b27 ) in both SAM and ORE reared at three temperatures ( 18 . 5 , 24 and 28°C ) . As expected wild type flies ( both ORE and SAM ) reared at higher temperatures were smaller for wing size ( S8 Fig , squares ) . However , we observed an intriguing pattern for the vg1 allele . Flies reared at 18°C and 24°C both demonstrated similar ( and severe ) phenotypic expressivity with respect to wing size and morphology ( S8 Fig , circles ) . However vg1 flies reared at 28°C did show almost completely wild type phenotypes , but only in the SAM background in male flies ( S8 Fig , purple circles ) . Female SAM vg1 showed a subtle suppression of the phenotypic effects ( green circles ) , while the ORE vg1 showed only a slight ( and non-significant ) reduction in phenotypic effects ( red and blue circles ) . We also observed weak evidence for temperature sensitivity for the vg83b27 allele ( triangles ) . This suggests that the previously observed temperature sensitivity is not only a function of the vg1 allele , but also depends on how the allele interacts with genetic background and sex . As we noted above , the alleles of moderate phenotypic effect seemed to show the strongest degree of background dependence when measured as either homozygotes or hemizygotes ( for sd males ) . This suggested a potential relationship between expressivity and sensitivity to conditional effects , or a particular property of the specific alleles . To examine this , we analyzed all of the genetic data ( including homozygotes , hemizygotes , and trans-heterozygotes ) estimating the variability across wild type genetic background and the average phenotypic effect of the genotype . We observed a pattern resembling an inverted hourglass , where genotypes with weak or severe phenotypic consequences have relatively little background dependence ( Fig 5 ) . This includes cases where by themselves the alleles showed no background dependence , but did in combination ( Fig 3C ) , as well as the weakened induction of the vg-RNAi ( with the NP6333-GAL4 ) at the lower rearing temperature ( Fig 2 ) . This suggests that the pattern is a result of the magnitude of the genotypic effects overall , not a function of specific alleles . Given that we observe variation among genetic backgrounds is greatest for intermediate allelic effects ( hemizygous , homozygous or hetero-allelic combinations ) , we can ask whether this pattern holds with respect to variation within each genotype , i . e . whether phenotypic variability due to micro-environmental variation or developmental noise is also greatest for alleles of intermediate effects . It is well known that variability is higher for many ( but not all ) mutants relative to their corresponding wild type [22 , 34] . However , it is not clear what relationship is expected for within-genotype variability as a function of the severity of allelic effects . We assessed this by estimating the median form of Levene’s statistic to quantify variability among individuals , but within genotype and background [35] . While we did observe a relationship between severity of phenotypic effect and variability , there was no evidence that it was highest for intermediate allelic effects . Indeed the highest within-genotype variability was generally observed for the most severe allelic effects ( S9B Fig ) , although there was considerable variation even in such cases . This demonstrates a degree of independence between genetic background effects and the intrinsic sensitivity to micro-environmental variation or developmental noise within genotypes . To assess how the background dependence of these allelic effects are reflected in the underlying developmental processes during wing development , we examined both the size and patterns of proliferation in wing imaginal discs from mature third instar larvae . Previous work has suggested that mutations in these genes mediate the wing phenotype through their effects on cell proliferation [36 , 37] . If this is true , we predict that patterns of cell proliferation will vary consistently with severity of both allelic and background effects . We observed that the Oregon-R wild type wing disc pouches are on average 1 . 25 times larger than the corresponding Samarkand wild type ( S10 Fig ) . Yet ( and consistent with the observed adult phenotype ) the effects of the mutations decrease the size of the pouch far more dramatically ( relative to the wild type ) in Oregon-R than in Samarkand ( S10 Fig ) . The effects of sd and vg alleles on cell proliferation are consistent with previous observations , and with overall magnitude of allelic effects ( based on adult wing size ) . However , cell proliferation in the imaginal disc does not correlate directly with degree of background dependence for adult wing size ( compare Fig 2 with Fig 6C and 6E ) . Yet despite this , the influence of genetic background is already manifest as shown by the extent of Wingless expression ( WG ) at the presumptive margin in the wing imaginal disc ( Fig 6D and 6F ) .
Collections of alleles are commonly used in experiments designed to dissect gene functions at a fine scale . For instance , using a set of alleles with lesions in different regions allows geneticists to assign functions to specific structural domains or regulatory regions by examining the effects of those mutations on organismal phenotypes ( e . g . , [45 , 46] ) . Our results suggest that in some cases , repeating such experiments in novel contexts would not necessarily alter overall conclusions , as we did not see any major re-ordering of alleles based upon phenotypic effects . Nevertheless , there were some intriguing differences . For example , some alleles had essentially equivalent effects in one genetic background , but showed differences in the other , and in some cases this difference also depended ( albeit weakly ) on the rearing temperature ( Fig 2 ) . This is interesting given that several vg alleles ( including vg1 ) are known to show patterns of temperature sensitivity [33] . While vg1 did not show changes in expressivity or background sensitivity at rearing temperatures of 18°C or 24°C , we did observe almost wild type-like phenotypes in the SAM vg1 males reared at 28°C , with much weaker phenotypic suppression for females ( Fig 2 , S8 Fig ) . This suggests that the temperature sensitivity is not a function of just the allele , but due to an interaction between genetic background , sex , and rearing temperature . We also observed weaker phenotypic effects and an increase in background sensitivity in vg21-3 flies when reared at lower temperatures ( Fig 2 ) . This allele shows context-dependent phenotypic effects for wing morphology based upon its P-element cytotype [4 , 47] . While we observed concordance between the effects of vg and sd across backgrounds , this may not be the norm even for functionally related genes . There is little evidence of correlation across backgrounds for mutational effects of genes involved in the PAR network for lethality associated with early embryogenesis in C . elegans [48]; that is , the genetic background modifiers had gene-specific effects , rather than acting similarly on interacting genes . Likewise , the influences of genetic background were incongruent on the phenotypic expression of sevenless and Egfr mutants in Drosophila melanogaster [49] , even though these two genes act in related signaling pathways involved in eye development . However , mutations influencing vulval cell induction in C . elegans do appear to show moderate concordance across wild type genetic backgrounds [50] . We can think of at least two ( non-mutually exclusive explanations ) for these contradictory observations . First , as observed in this current study , alleles of different magnitudes can vary in degree of background dependence despite overall concordance between the allelic series in vg and sd . If previous studies used alleles of large phenotypic effect for some genes , and small-effect for others , then differences in the degree of perturbation may explain the incongruent effects of genetic background , in particular given that perturbation in each gene was evaluated with only one allele or “dose” ( for RNAi knockdown ) . An alternative explanation may be due to subtle aspects of pleiotropy and developmental “degrees of freedom” . In other words , when testing whether the influences of genetic background on expressivity are congruent for different genes , it is important to be sure that the same trait is measured . While the PAR genes discussed above [48] influence the first two cell divisions of embryogenesis in C . elegans , the observed lethality may be a result of distinct pleiotropic effects . Similarly , the phenotype scored in the Drosophila eye was surface “roughness” [49] , which can be caused by multiple developmental changes , not just transformation of photoreceptor identity [51–54] . This interplay between environmental and genetic context dependence and explaining the degree of phenotypic concordance remains an important area for further research . A potential concern with some studies , including our own is that the genetic perturbations have mostly titrated the amount of transcript or protein ( via regulatory mutations or RNAi ) , and thus may only represent a subset of effects ( i . e . informational suppressors [30] ) and thus may not fully represent the degree of modifiability of alleles . As with patterns in the allelic series , while the results of complementation were comparable across contexts in some cases , there were several instances where they differed drastically between genetic backgrounds ( Figs 3 and 4 ) . Although certain genetic backgrounds and rearing temperature have similar effects on overall phenotypes across alleles and genes—for example , in this case mutant phenotypes are generally more severe in Oregon-R than in Samarkand—this pattern does not seem to extend to patterns of complementation . Instead , where complementation depends on the genetic background , the outcome seems to involve a complex interaction between the background and the two alleles in question . For instance , sdE3 and sdETX4 complemented only in Samarkand , and not in Oregon-R . Complementation was observed between vg83b27 and most other vg alleles , but not between other pairs of vg alleles ( similar to [32] ) , suggesting that transvection is a unique property of this allele or the location of this lesion ( vg83b27 is the only mutation located in intron 2; Table 1 ) . However , we did not observe strong evidence of differences across wild type genetic backgrounds for these allelic combinations , as observed at the Men locus [55] , although this is likely due to the almost complete complementation observed in combination with vg83b27 ( Fig 4 ) . These results have important implications for interpreting genetic analyses , especially if we rely on complementation tests to determine genetic identity , and an important challenge is to determine what causes these differences in complementation . It is important to note that we used a standard definition of complementation ( i . e . where the hetero-allelic combination quantitatively overlaps with wild type sensu [30] ) . Arguably complementation could also be defined as any phenotype for the trans-heterozygotes that is quantitatively more like wild type than the homozygotes ( i . e . over-dominance ) . However , this definition does not substantially alter our conclusions . We examined several developmental and cellular correlates of the phenotypic effects: patterns of cell proliferation , overall size of the developing wing , and development of the future margin in the mature third instar wing imaginal discs . Previous work has described the patterns of reduced cell proliferation [36 , 37 , 56] associated with mutations in the sd and vg genes . Indeed the associations between the effects in the wing disc and the morphology of the adult wing for these mutations may suggest a causal relationship , as viewed across the allelic series in either background . However , the results presented here caution against making such causal inferences too quickly ( Fig 6 ) . A clear relationship exists across alleles between the severity of the adult phenotype and the amount of proliferation in the imaginal disc ( Fig 6C and 6E ) . Yet it is clear that the strong background dependence of the observed effects on adult morphology is not reflected in this cellular correlate during development . For example , both sdETX4 and vg2a33 genotypes have much smaller wings in Oregon-R compared with the Samarkand background ( Figs 1 and 2 ) . Yet cell proliferation in the wing disc is indistinguishable between backgrounds for these genotypes ( Fig 6C and 6E ) . In contrast , differences among the genetic background are observed using WG expression in the imaginal disc ( concurrent with the marker for cell proliferation ) , showing that genetic background effects are already present ( Fig 6D and 6F ) . Thus variation in proliferation accounts for only part of the effects observed for adult wing morphology . This is reminiscent of earlier work with mutations in the mushroom body miniature gene , which showed that a mutation's effects on the size of the mushroom bodies ( part of the insect brain ) and its effects on learning were in fact separable across two different wild type genetic backgrounds [57] . Indeed , studies of mutational effects across wild-type genetic backgrounds may be a useful tool to distinguish true causality from so-called epistatic pleiotropy [58] . With the advent of tools using RNAi , over-expression , full gene knockouts , targeted deletions and direct allelic replacements , the experimental capabilities of most geneticists have substantially expanded in recent years . In comparison the “legacy” mutational toolkits that were available for the first 80+ years of genetics research analysis were generated in a less standardized fashion , with uncertainty remaining for many alleles with respect to many aspects of function . Given the costs associated with maintaining such collections it may seem like allelic variants ( such as those used in the current study ) may not be important to maintain . Nevertheless , if the newer toolkits are used at the exclusion of “legacy” mutations , the new tools may ironically lead to a decrease in the diversity of genetic research . Without many of the baroque facets of mutational approaches that have been employed in the past , it is possible that phenomena like transvection and position effect variegation may have never been discovered . These in turn have had a profound influence on our understanding of gene regulation .
The two wild-type strains used for this study were Oregon-R ( ORE ) , and Samarkand ( SAM ) , both marked with a white ( w ) allele and are maintained as inbred lines , and are regularly genotyped to avoid any contamination , and maintain homozygosity [17 , 19 , 59] . The origin of mutant alleles used in this study can be found in Table 1 . The alleles are largely regulatory , and are not expected to alter protein function per se . Introgression of the alleles was performed largely as previously described for other alleles [17 , 19] . However , in several instances , balancer chromosomes ( that had been previously introgressed into each wild-type background ) were used to transfer entire chromosomes , followed by backcrossing to complete introgression . After completion of introgression all strains were genotyped for ~350 markers , of which ~100 explicitly distinguish the two wild-type strains used in this study ( S1 Table , S1 Fig ) . While most alleles showed upwards of 90% introgression , some alleles still showed some degree of the ancestral background . Thus , we recognize that in additional to the focal alleles , there are some small genomic regions from the ancestral backgrounds that will contribute to the observed effects . We argue that this final step ( genotyping ) should be employed whenever possible , as there is no guarantee of successful introgression of the majority of the genome , and residual genomic fragments from the progenitor background may result in artifacts . While almost all alleles used in this study represent “classic” alleles ( in that the effect is a result of mutation at the native locus ) , for vg we were concerned about having insufficient phenotypic coverage ( in terms of severity ) . Thus we introgressed a UAS-vg . RNAi ( VDRC ID 16896 ) and NP6333 ( Pen ) -GAL4 into both Samarkand and Oregon-R . However , it is worth noting that the temperature dependent effects of this UAS-GAL4 combination likely reflect the temperature sensitivity of GAL4 per se . To determine whether the patterns observed across the Samarkand and Oregon-R wild type backgrounds could be generalized , we crossed three of the sd alleles ( sd1 , sdE3 & sd58d ) as well as the corresponding SAM wild type to 16 of the sequenced wild type lines that are part of the DGRP collection [29] . As the original SAM sd58d was lost between the first and subsequent experiment , this mutations was re-introgressed independently ( starting from the ORE sd58d ) . After introgression , the phenotypic values were compared and found to be indistinguishable from the original estimates of SAM sd58d . All mutant and wild-type flies in Oregon-R as well as Samarkand genetic background were separately raised at low density , in bottles with ~50ml fly food at both 24°C and 18°C with 65% relative humidity and 12 hr light/dark cycle ( Percival , Model: I41VLC8 , incubator ) . After maintaining the fly strains in these conditions for at least 2 generations , virgin females and males were collected and housed separately in vials with 40 individuals/ sex/ genotype/ background at 24°C and 18°C respectively . For every strain , this collection was performed for a total period of 4 days upon eclosion , under CO2 anesthesia and the flies were maintained in vials ( as above ) for an additional 3 days to remove any residual effects of the CO2 . Following this , all the flies in a given treatment were randomized ( within genotype ) and 40 pairs were then allowed to lay eggs on 35mm x 10mm cell culture plates with grape juice agar ( 2% agar in 50% grape juice:water ) for 15–22 hours . For generating the allelic series data , 40 pairs of flies per allele were crossed among themselves and to generate the intragenic complementation data , 40 pairs of chosen genotypes were crossed to each other . Eggs collection was performed from multiple such plates per treatment and 4 replicates were created with each replicate having 40 eggs in a vial with ~10-12ml fly food/ cross/ background at both 24°C and 18°C respectively . These eggs were allowed to develop at the respective temperatures , and upon eclosion the adults were stored in 70% ethanol for further analyses . However , we have no data for some specific genotypes , as they were lethal in one or more combinations of background and rearing temperature . All of these experimental crosses were performed simultaneously using a single batch of media . While for the vast majority of experimental crosses our design provided sufficient samples ( i . e . at least 10 individuals per genotype/replicate vial ) , several alleles showed partial lethality , and thus for some alleles and allelic combinations we were only able to collect a small number ( ~3–5 ) of individuals . For experimental crosses that did fail to yield F1 progeny during the primary experiment , an additional block of crosses ( Block 3 ) was setup . As controls for block level effects , wild type and a few ( randomly chosen ) mutant crosses that were successful in the initial experiment were also setup in this block . For this additional setup , wild type and relevant mutant flies in both backgrounds were grown , and virgin females and males were subsequently collected as described above . Four replicate vials , each containing six virgin females and four males of the appropriate genotypes were created . The flies were allowed to mate and lay eggs for 3–4 days following which the adults were discarded and the vials were allowed to develop and were further processed as described above . For crosses of the DGRP strains to the sd mutations introgressed into Samarkand ( and the corresponding Samarkand control ) we crossed females bearing the sd alleles to males of each of the 16 DGRP lines and reared these in the incubator described above at 24°C . When the F1 progeny emerged the male F1 sd hemizygotes ( sd is X-linked ) were stored in 70% ethanol for phenotyping . To further investigate the joint effects of rearing temperature and genetic background on the phenotypic expressivity of the vg1 and vg83b27 alleles we reared the strains bearing these mutations in both Oregon-R and Samarkand ( as well as the corresponding wild types ) at 18°C , 24°C and 28°C . For the 28°C treatment a Fisher Scientific incubator ( Model 3070C ) was used , but with otherwise similar settings to the Percival incubators . A single wing was dissected from at least 5 individuals/sex/genotype for 2 replicates and mounted in 70% glycerol for a total of at least 10 observations/ genotype . Images of the wings were captured using an Olympus DP30BW camera mounted on an Olympus BW51 microscope using DP controller image capture software ( v3 . 1 . 1 ) . The wing area was then obtained using a custom macro in ImageJ software ( v1 . 43u ) . Measures of wing area can be confounded by variation for body size . Furthermore , some mutations have subtle effects only causing bristle loss at the wing margin , but with no influence on wing size . Both of these issues are of particular concern for weak hypomorphic alleles ( Fig 1 ) . Therefore , in addition to using wing area , we also utilized an ordinal scale to measure severity of the phenotypic effect ( on a scale of 1–10 ) . This approach [19 , 60] has been used successfully and correlates well with wing area . For the crosses to the DGRP lineages , wings were imaged using a Leica M125 Microscope ( 50X total magnification ) and captured with a Leica DFC400 Camera . For the additional crosses of the vg1 and vg83b27 alleles the images were captured on an Olympus microscope with an Olympus DP80 digital camera ( 40X total magnification ) . For all estimates we generated genotypic means and 95% confidence intervals . To examine the amount of variation within genotypes , we used two forms of Levene’s Statistic , using the genotypic medians , and then use the absolute value of the differences ( or log transformed values of these values ) . See [35] for further discussion on these methods . We did not scale the among background effects for the primary experiment ( i . e . using CV ) given some of the known assumptions that are severely violated ( i . e . lack of a linear relationship between standard deviation and the mean under perturbation for a trait ) , see [22 , 61] for more details . Thus we used two alternative approaches to confirm our results . First using a semi-quantitative , ordinal scale for severity of wing perturbation . Previously [19] we have demonstrated that this is linearly correlated with wing area , and only deviates near wild type values , where wing area cannot capture subtle perturbations ( i . e . loss of margin bristles ) . We also confirmed the results with the data on a natural log scale , in particular for the main experiments , because we had only two genetic backgrounds , estimates of the among background variances would be poor . However , for the subsequent follow up experiment with the 16 DGRP strains we did estimate the between-background variability using Levene’s statistic as described above . 10–15 larval heads with the wing imaginal discs attached were dissected from wandering third instar larvae in 1x Phosphate Buffered Saline ( PBS ) per genotype . These were then fixed in 4% paraformaldehyde dissolved in 1x PBS for 20 minutes at room temperature . This was followed by repeated gentle washing ( 4x ) for 15 minutes each with a PBT solution consisting of 1x PBS and 0 . 1% Tritonx100 at room temperature . After washing , the tissues were treated with a PBTBS blocking solution consisting of PBT , 0 . 1% Bovine serum albumin and 2% Goat serum for 2–3 hours at 4°C . This was then followed by overnight ( minimum of 4 hours ) incubation with the primary antibody at 4°C on a shaker followed by washing as above . This was followed by blocking ( 2 hours ) , overnight ( minimum of 4 hours ) incubation with secondary antibody ( at 4°C ) and washing as above . Post-washing , the tissues were incubated with Hoechst stain ( Sigma ) at 1:15000 dilution ( blue color ) , for ~1 hour , the discs separated and mounted on a slide . Images were captured using Olympus DP30BW camera mounted on an Olympus BW51 microscope using the DP controller image capture software ( v3 . 1 . 1 ) . The images were merged using the ImageJ software ( v1 . 43u ) . The primary antibodies used were mouse anti-WG ( 4D4 , 1:500 dilution ) or anti-CT ( 2B10 , 1:100 ) from the Developmental Studies Hybridoma Bank used in conjunction with rabbit phospho-histone H3 ( 1:500 , Cell Signalling Technology ) . Secondary antibodies were anti-mouse FITC ( 1:1000 ) and anti-rabbit DyLight 594 ( 1:500 ) . At least 10 wing discs were imaged for each genotype x background combination ( 1 image per channel ) on the same microscope described above . We then applied custom ImageJ macros to the image stacks to estimate the area of the wing pouch ( defined by the proximal ring of WG expression ) , and count phospho-histone H3 positive cells in that region . Since sd and vg influence the development of the wing margin , we measured the length of the central part of the imaginal disc as defined by the central WG expression band ( the future wing margin ) by using the line tool in ImageJ . In cases where the bands were incomplete or contained gaps , a sum of lengths of all the individual bands was used and for discs that lacked the central band , we measured the length of the central part . We then determined the proportion of wing margin length to the length of the complete margin ( the curve from anterior to posterior ) for each of the sd and vg alleles . Refer to Fig 6A to see a complete WG expressing margin . | Different mutations in a gene , or in genes with related functions , can have effects of varying severity . Studying sets of mutations and analyzing how they interact are essential components of a geneticist's toolkit . However , the effects caused by a mutation depend not only on the mutation itself , but on additional genetic variation throughout an organism's genome and on the environment that organism has experienced . Therefore , identifying how the genomic and environmental context alter the expression of mutations is critical for making reliable inferences about how genes function . Yet studies on this context dependence have largely been limited to single mutations in single genes . We examined how the genomic and environmental context influence the expression of multiple mutations in two related genes affecting the fruit fly wing . Our results show that the genetic and environmental context generally affect the expression of related mutations in similar ways . However , the interactions between two different mutations in a single gene sometimes depended strongly on context . In addition , cell proliferation in the developing wing and adult wing size were not affected by the genetic and environmental context in similar ways in mutant flies , suggesting that variation in cell growth cannot fully explain how mutations affect wings . Overall , our findings show that context can have a big impact on the interpretation of genetic experiments , including how we draw conclusions about gene function and cause-and-effect relationships . | [
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] | 2017 | How well do you know your mutation? Complex effects of genetic background on expressivity, complementation, and ordering of allelic effects |
Homologous recombination ( HR ) is essential for the repair of blocked or collapsed replication forks and for the production of crossovers between homologs that promote accurate meiotic chromosome segregation . Here , we identify HIM-18 , an ortholog of MUS312/Slx4 , as a critical player required in vivo for processing late HR intermediates in Caenorhabditis elegans . DNA damage sensitivity and an accumulation of HR intermediates ( RAD-51 foci ) during premeiotic entry suggest that HIM-18 is required for HR–mediated repair at stalled replication forks . A reduction in crossover recombination frequencies—accompanied by an increase in HR intermediates during meiosis , germ cell apoptosis , unstable bivalent attachments , and subsequent chromosome nondisjunction—support a role for HIM-18 in converting HR intermediates into crossover products . Such a role is suggested by physical interaction of HIM-18 with the nucleases SLX-1 and XPF-1 and by the synthetic lethality of him-18 with him-6 , the C . elegans BLM homolog . We propose that HIM-18 facilitates processing of HR intermediates resulting from replication fork collapse and programmed meiotic DSBs in the C . elegans germline .
DNA double-strand breaks ( DSBs ) can arise in various ways , including as a result of the collapse of stalled replication forks , exposure to DNA damaging agents and the formation of programmed meiotic DSBs [1] . The importance of DSB repair is therefore highlighted by its critical roles in replication fork restart , the maintenance of genomic integrity and promoting faithful meiotic chromosome segregation . Homologous recombination ( HR ) provides an efficient and accurate repair of DSBs , in part through the use of an intact donor template for repair . Current models of HR propose that following DSB formation , the DSB ends are resected in a 5′ to 3′ orientation generating 3′ single-stranded DNA ( ssDNA ) tails [2] , [3] . Repair can then proceed through different pathways , one of which involves the association of the Rad51 recombinase protein with the ssDNA tails . This generates a nucleoprotein filament that engages in strand invasion with either an intact sister or homologous chromosome resulting in the formation of a D-loop structure . Subsequent second end capture , DNA synthesis and ligation , results in the formation of two four-way DNA junction intermediates referred to as Holliday junctions ( HJ ) . Double HJ ( dHJ ) intermediates can be resolved through cleavage by HJ resolvases , which results in either a crossover ( CO ) or noncrossover ( NCO ) product , or undergo dissolution mediated by RecQ helicases in combination with topoisomerase activity , resulting in NCOs . Identifying components involved in the processing of HR intermediates has been critical to understand the molecular mechanisms of HR . HJ resolvases have been identified in poxviruses ( A22R ) , bacteriophages ( T4 endonuclease VII and T7 endonuclease I ) , and E . coli ( RuvC ) [4] . In S . cerevisiae and S . pombe , Cce1 and Cce1/Ydc2 , respectively , were identified as HJ resolvases that act in the mitochondria [5] , [6] . Recently , H . sapiens GEN1 and its homolog in S . cerevisiae , Yen1 , have been reported to resolve HJs in vitro via a canonical RuvC-like symmetrical cleavage that does not require further processing [7] . However , their in vivo function remains unclear . Moreover , HJ processing can also be achieved through asymmetric cleavage , as exemplified by the biochemical activity of the MUS81-EME1 complex in eukaryotic cells [8] , [9] . The identification of proteins involved in HJ processing has been challenging due to the crossfunctionality observed for nucleases throughout different HR pathways . This crossfunctionality can be partly due to changes in complex composition that may modulate specificity of recognition and processing of a given intermediate . In D . melanogaster , where 90–95% of meiotic COs do not require MUS81 [10] , COs are dependent on a protein complex consisting of MEI-9 , an ortholog of the mammalian XPF and S . cerevisiae Rad1 nucleotide excision repair ( NER ) endonucleases , ERCC1 , an XPF interaction partner , MUS312 and HDM , a member of a superfamily of proteins with ssDNA binding activity [11]–[16] . However , ERCC1 and HDM are only required for a subset of CO events [12] , [13] . In S . cerevisiae , SLX4 was first identified in a synthetic-lethal screen for genes required for viability in the absence of SGS1 , a member of the RecQ family of DNA helicases implicated in the human Bloom and Werner syndromes [17] . Slx4 binds to two structure-specific endonucleases , Slx1 and Rad1 , in a mutually exclusive manner [17] , [18] . The Slx1-Slx4 complex cleaves multiple branched DNA substrates in vitro , particularly 5′-flap , simple-Y , HJ and replication fork structures [19] , and functions in rDNA maintenance during S phase [20] , [21] . Meanwhile , the Slx4-Rad1 complex is required for the single-strand annealing ( SSA ) HR pathway , in which Mec1/Tel1 dependent phosphorylation of Slx4 is essential [18] , [22] . Furthermore , an in vivo nuclease activity of Slx4 without Slx1 or Rad1 is proposed [19] , [23] , [24] . Here , through a functional genomics approach in the nematode C . elegans , we identified HIM-18 , which shares sequence similarity with S . cerevisiae Slx4 and D . melanogaster MUS312 . HIM-18 is localized to mitotic nuclei at the premeiotic tip and to meiotic nuclei from late pachytene through diakinesis in wild type germlines . The accumulation of HR intermediates stemming from replication fork collapse in mitosis and from SPO-11-dependent programmed meiotic DSBs in mid to late pachytene observed in him-18 mutant germlines , coupled with the localization pattern of HIM-18 , suggests a role in both mitotic and meiotic DSB repair . This is further supported by the observation of reduced CO frequencies and the premature disassembly of bivalent attachments at prometaphase I resulting in increased chromosome nondisjunction in him-18 mutants . Moreover , HIM-18 interacts with SLX-1 and XPF-1 , and him-18 mutants show similar DNA damage sensitivity and synthetic lethality with him-6/BLM as observed in slx4 mutants in yeast . Taken together , our analysis suggests that HIM-18 is required for the maintenance of genomic integrity in germline nuclei . We propose a model in which HIM-18/SLX-4 promotes the processing of late HR intermediates in the germline resulting from replication fork collapse in mitotic nuclei and programmed DSBs in meiotic nuclei .
him-18 ( open reading frame T04A8 . 15 ) was identified in an RNA interference ( RNAi ) screen performed as in [25] and designed to detect meiotic candidates from among germline-enriched genes [[26] and M . Colaiácovo , unpublished results] . The HIM-18 protein harbors two DNA binding motifs ( zinc finger and SAP ) and three protein binding motifs ( coiled-coil , BTB and leucine-zipper ) ( Figure 1A ) . Homology searches revealed HIM-18 orthologs from yeast to humans ( Figure 1B ) . Specifically , the SAP motif is highly conserved throughout these orthologs , with the exception of MUS312 in D . melanogaster . This conservation is particularly interesting because the SAP motif , present in the S . pombe mitochondrial HJ resolvase Cce1/Ydc2 , is implicated in promoting HJ binding and resolution [27] , [28] . Furthermore , the mammalian orthologs , named BTBD12 , also contain BTB and zinc finger motifs . Therefore , this sequence analysis suggests that HIM-18 is conserved among eukaryotic organisms and shares homology with proteins predicted to function in the processing of HR intermediates . The him-18 ( tm2181 ) mutant , obtained from the Japanese National Bioresource Project , carries a 394 bp out-of-frame deletion encompassing parts of exons 5 and 6 ( Figure 1A ) . This deletion results in a premature stop codon and the loss of the predicted coiled-coil , BTB , SAP and leucine-zipper motifs . The analysis of DAPI-stained germlines of him-18 ( tm2181 ) /+ hermaphrodites , and a genetic analysis of both embryonic lethality and the incidence of males among the progeny of these worms , indicated that these heterozygotes were indistinguishable from wild type ( Table 1 ) . This indicates that tm2181 is a recessive allele of him-18 . A similar analysis indicated that tm2181 homozygotes were indistinguishable from transheterozygotes for tm2181 and sDf121 , a deficiency encompassing the him-18 locus ( Table 1 ) . Moreover , an affinity purified HIM-18 antibody raised against the first 166 amino acids present in HIM-18 , failed to detect a HIM-18 signal on immunostained whole mounted gonads from him-18 ( tm2181 ) mutants ( Figure S1 ) . Taken together , these studies suggest that him-18 ( tm2181 ) is a null . To investigate the localization of HIM-18 in the germline , dissected gonads from wild type hermaphrodites were immunostained with the affinity purified HIM-18 N-terminal antibody ( Figure 1C ) . HIM-18 is first observed in mitotic nuclei at the distal tip region of the germline ( premeiotic tip ) . However , the HIM-18 signal is abruptly reduced as nuclei enter into meiotic prophase , being barely visible in the transition zone ( leptotene/zygotene ) or in early to mid pachytene nuclei . HIM-18 signal is then clearly detected once again in late pachytene nuclei , persisting through the end of diakinesis . At a higher resolution , the HIM-18 signal is observed as nuclear foci ( mainly around the chromosomes ) in the premeiotic tip , late pachytene and diplotene/diakinesis stages ( Figure 1C ) . Although the HIM-18 signal is reduced from transition zone through mid-pachytene , a low level of HIM-18 foci , both on and around chromosomes , is still apparent at these stages upon longer exposure ( Figure 1C ) . Finally , HIM-18 is no longer observed localizing onto chromosomes after diakinesis ( data not shown ) . Analysis of him-18 ( tm2181 ) homozygous mutants and following depletion of him-18 by RNAi revealed phenotypes suggestive of errors in meiotic chromosome segregation such as an increased embryonic lethality ( 79 . 9%; n = 2748 , and 72 . 5%; n = 436 , respectively ) and a high incidence of males ( 11 . 9% and 5 . 5% , respectively ) among their surviving progeny ( Table 1 and Figure S2A ) . Errors in meiotic chromosome segregation can stem from earlier defects in homologous chromosome pairing , axis morphogenesis or synapsis . We examined homologous pairing via fluorescence in situ hybridization ( FISH ) and immunofluorescence analysis , by monitoring both the establishment and maintenance of pairing , as chromosomes enter into meiotic prophase at the transition zone and progress into pachytene where they are fully synapsed . Specifically , we utilized a FISH probe to the 5S ribosomal DNA region ( chromosome V ) and observed wild type levels of pairing between homologs in him-18 mutants both in the transition zone and pachytene nuclei ( Figure 2A , 2B , and 2E and Figure S3 ) . 98% ( n = 107 ) of the pachytene nuclei examined in him-18 mutants carried fully paired homologs in this analysis , compared to 97% ( n = 101 ) in wild type . We obtained a similar result by immunostaining both wild type and him-18 mutant gonads with a HIM-8 antibody , which specifically localizes to the pairing center end of the X chromosome [29] ( Figure 2C and 2D and data not shown ) . Therefore , homologous pairing is normal for autosomes and the X chromosome in him-18 mutants . We determined that axis morphogenesis was indistinguishable from wild type , as exemplified by the normal kinetics and pattern of localization of axis-associated proteins such as the meiosis specific cohesin REC-8 and the cohesin SMC-3 ( Figure S4 ) . We then examined the formation of the synaptonemal complex ( SC ) , the proteinaceous scaffold that forms between fully paired and aligned homologous chromosomes during meiosis . Immunostaining for SYP-1 , a SC central region protein , revealed a SYP-1 localization between paired homologous chromosomes that is indistinguishable from wild type both at the transition zone and pachytene ( Figure 2 and data not shown ) . Taken together , these results indicate that events occurring upon entrance into meiosis , such as homologous pairing , axis morphogenesis and synapsis do not require HIM-18 . To investigate whether the defects in meiotic chromosome segregation observed in him-18 mutants reflect defects in DSB repair , we performed a quantitative comparison of the levels of RAD-51 foci in the germlines of both wild type and him-18 mutants ( Figure 3A and Figure S5 ) . Since nuclei are positioned in a temporal-spatial gradient throughout the germline in C . elegans , proceeding in a distal to proximal orientation from mitosis into the various stages of meiotic prophase I , levels of RAD-51 foci were assessed both in mitotic ( zones 1 and 2 ) and meiotic ( zones 3–7 ) nuclei ( Figure 3A and 3B , Figure S5 , S6 , S7 , S8 and [30] ) . In wild type , only a few mitotic RAD-51 foci were observed at zones 1 and 2 ( 5 . 8% and 1 . 2% of nuclei contained 1 and 2–3 RAD-51 foci , respectively; Figure 3A and Figure S5 ) . In these mitotic nuclei , RAD-51 foci are thought to be mainly derived from single-stranded DNA gaps formed at stalled replication forks or resected DSBs resulting from collapsed replication forks [31] . During meiotic prophase , as a result of SPO-11-dependent programmed meiotic DSB formation , levels of RAD-51 foci start to rise at the transition zone ( zone 3 ) , then accumulate maximally at early to mid-pachytene ( zones 4 and 5; nearly 90% of nuclei contain an average of 3 . 3 RAD-51 foci ) , and are reduced at late pachytene ( zones 6 and 7 ) [30] . In him-18 mutants , levels of RAD-51 foci were higher than those observed in wild type germlines , both in mitotic ( 23% , 14% and 3% of nuclei contained 1 , 2–3 and 4–6 RAD-51 foci , respectively , in zones 1 and 2; P<0 . 0001 for both zones ) and meiotic nuclei ( an average of 5 RAD-51 foci/nucleus in zones 4 and 5; P<0 . 0001 and P = 0 . 0021 , respectively ) ( Figure 3A and Figure S5 ) . Moreover , higher levels of RAD-51 foci persisted through late pachytene in him-18 mutants compared to wild type ( 3 . 2 RAD-51 foci/nucleus compared to 0 . 9 , P<0 . 0001 , and 0 . 9 foci/nucleus compared to 0 . 1 , P = 0 . 0001 , in zones 6 and 7 , respectively ) suggesting either a delay in meiotic DSB repair or an overall increase in the levels of DSBs formed during meiosis . Interestingly , larger RAD-51 foci were observed throughout both mitotic and meiotic nuclei in him-18 mutants compared to wild type ( Figure S8 ) , some of which were still present in pachytene nuclei in him-18; spo-11 double mutants . However , these SPO-11-independent RAD-51 foci failed to result in physical attachments between homologs ( chiasmata ) . Specifically , instead of six bivalents , corresponding to the six pairs of homologous chromosomes held together by chiasmata , observed in DAPI-stained diakinesis oocytes in wild type , an average of 11 . 9 DAPI-stained bodies were observed in him-18; spo-11 double mutants ( n = 36 ) , similar to spo-11 single mutants ( 11 . 7 DAPI-stained bodies; n = 44 ) ( Figure 3B and 3C ) . We also observed elevated levels of germ cell apoptosis in both him-18 and him-18; spo-11 double mutants , compared to wild type ( P<0 . 0001 and P = 0 . 0009 , respectively ) and spo-11 mutants ( P<0 . 0001 , respectively; Figure 4A and 4B ) . Furthermore , the elevated germ cell apoptosis in both him-18 and him-18;spo-11 mutants was suppressed following depletion of cep-1/p53 by RNAi ( Figure S2C and Figure S9 ) , suggesting that the elevated apoptosis stems from the activation of the DNA damage checkpoint in late pachytene as a result of damage incurred both during mitosis and meiosis . Altogether , these observations suggest that SPO-11-independent mitotic RAD-51 foci persist into pachytene and that these unresolved recombination intermediates contribute in part to the increase in germ cell apoptosis observed in him-18 mutants . However , the increased RAD-51 foci and germ cell apoptosis observed in mid to late pachytene , also support a role for HIM-18 specifically in HR during meiosis and are not simply a result of DNA damage being carried over into meiosis from defects in mitosis . This is further supported by the observation that events occurring upon entrance into meiosis were indistinguishable from wild type , and that despite homologous pairing and synapsis , repair of SPO-11-dependent DSBs was impaired . To gain further support for a role for HIM-18 in DNA damage repair , young adult hermaphrodites were exposed to different types of DNA damage and embryonic viability was monitored as an index of sensitivity ( see Experimental Procedures ) . Embryonic viability after DNA damage treatment was plotted as a percentage of the hatching after DNA damage normalized by that in untreated him-18 mutants . him-18 mutants were hypersensitive to DSBs induced by γ-irradiation ( IR ) . Only 60% , 5% and 0% hatching was observed in him-18 mutants exposed to 10 , 50 and 100 Gy , respectively , compared to wild type worms where a high level of hatching was observed even following the highest IR exposure level ( 83% at 100 Gy ) ( Figure 4C ) . Exposure to nitrogen mustard ( HN2 ) , which induces DNA interstrand crosslinks ( ICLs ) that obstruct essential cellular processes such as transcription and replication , resulted in significantly decreased hatching levels in him-18 mutants ( 5% hatching at 100 µM HN2 ) compared to wild type ( 95% hatching at 100 µM HN2 ) ( Figure 4D ) . To further examine the role of HIM-18 in responding to lesions that affect replication fork progression , worms were exposed to camptothecin ( CPT ) , which inhibits the detachment of topoisomerase I from DNA , thus preventing DNA re-ligation at a single-strand nick , which in turn results in a single ended DNA double-strand break when collision of a replication fork occurs at the lesion [32] . Treatment with CPT resulted in a decrease in hatching in him-18 mutants ( 17% hatching at 500 nM CPT ) compared to wild type ( 96% at 500 nM CPT ) ( Figure 4E ) . In addition , both wild type and him-18 mutants were examined following exposure to the ribonucleotide reductase inhibitor hydroxyurea ( HU ) , which results in a checkpoint-dependent cell cycle arrest ( Figure S10C ) . him-18 mutants showed hypersensitivity to HU , further suggesting that him-18/slx-4 is required to resolve stalled replication forks . Furthermore , the levels of RPA-1 foci were indistinguishable between both wild type and him-18 germlines ( Figure S10A ) , in contrast to the increase in RAD-51 foci observed at the premeiotic region in him-18 mutants compared to wild type ( Figure 3A and Figure S5 ) . This observation suggests that the frequency of replication stalling is similar between wild type and him-18 mutants , but that there is a defect in the recovery from stalled replication forks in him-18 mutants . Mitotic germ cell nuclei with larger nuclear diameters are observed in him-18 mutants compared to wild type even prior to HU treatment , further suggesting replication stress is occurring in this background ( Figure S10B and Table S1 ) . Following HU treatment , larger nuclear diameters were observed in mitotic germ cell nuclei in both wild type and him-18 mutants , suggesting that the S phase checkpoint is intact in him-18 mutants . Since the checkpoint is apparently intact , but reduced survival was still observed in him-18 mutants , this implies a DNA repair rather than a checkpoint defect . Taken together , a drastic reduction in relative hatching frequencies was observed in him-18 mutants compared to wild type following exposure to all four kinds of genotoxic agents . These results suggest that HIM-18 is required for DSB repair , ICL repair and recovery from replication fork collapse . Budding yeast SLX4 was first identified in a synthetic-lethal screen for genes that are essential in an sgs1 mutant background [17] . Given that HIM-18 and Slx4 share sequence homology and the analysis of DSB repair progression and DNA damage sensitivity suggest a functional conservation , we investigated whether him-18 mutants show synthetic lethality with loss of him-6 , the C . elegans homolog of BLM , the Bloom syndrome helicase gene [33] . The total number of eggs laid either by him-18 or him-6 single mutants were only moderately reduced compared to wild type ( 66% and 79% of wild type levels were observed , respectively ) , and among those eggs laid , 20% and 40% , respectively , hatched ( Table 1 ) . In contrast , him-18;him-6 double mutants showed a drastic reduction in brood size ( only 3% of wild type ) and all the eggs laid failed to hatch ( Table 1 ) . To further investigate whether the increased embryonic lethality observed in the him-18;him-6 double mutants correlated with defects in DSB repair , we quantified the levels of RAD-51 foci in their germlines ( Figure 3A and Figure S5 ) . Indeed , both mitotic and meiotic RAD-51 foci levels were drastically increased in him-18;him-6 double mutants compared to either single mutant . On average , a 2 . 7- and 19-fold increase in RAD-51 foci/nucleus was observed in mitotic nuclei at zone 1 in him-18;him-6 double mutants compared with him-18 and him-6 single mutants , respectively ( P<0 . 0001 ) , and a 2 . 9- and 2 . 3-fold increase in meiotic mid-pachytene nuclei at zone 5 ( P = 0 . 0001 ) ( Figure S6 ) . Furthermore , germ cell apoptosis was elevated nearly two-fold in him-18;him-6 mutants compared to either single mutant ( P<0 . 0001 , respectively; Figure 4A and 4B ) . We also detected thin DAPI-stained threads ( hereafter referred to as chromatin bridges ) , frequently stained with RAD-51 , connecting nuclei at the premeiotic tip in him-18;him-6 mutants . Specifically , 9 out of 11 gonads contained pairs of nuclei connected by chromatin bridges . Chromatin bridges were observed in 8 . 6% ( n = 19/221 ) of nuclei in zone 1 . Moreover , between 1 and 25 RAD-51 foci were observed on 95% of these chromatin bridges ( Figure S11 ) . These data suggest that accumulation of unresolved toxic recombination intermediates results in synthetic lethality in him-18;him-6 double mutants . Mus81 has been shown to be required for meiosis in fission yeast [8] , [34] , [35] and has been implicated in HJ processing in eukaryotic cells [8] , [9] . To determine whether it plays a role with HIM-18 in DNA repair in the C . elegans germline , we examined phenotypes suggestive of errors in chromosome segregation and the levels of RAD-51 foci in mus-81 and mus-81;him-18 double mutants ( Table 1 , Figure 3A , and Figures S6 , S7 , S8 ) . The total number of eggs laid by mus-81 mutants was reduced compared to wild type ( 47% of wild type levels ) , and 20 . 7% of the eggs laid failed to hatch . This embryonic lethality was not accompanied by an increase in the frequency of males among the surviving progeny , suggesting that MUS-81 is not required for the proper disjunction of the X chromosome at meiosis I and in agreement with [36] . In mus-81;him-18 mutants embryonic lethality , but not the incidence of male progeny , was elevated compared to him-18 single mutants ( 87 . 8% of the eggs laid failed to hatch; 5 . 6% males; P<0 . 0001 and P = 0 . 2272 , respectively , by the Fisher's Exact test ) . Levels of RAD-51 foci in mus-81 mutants were only increased in nuclei in the premeiotic region compared to wild type ( levels of RAD-51 foci were similar during pachytene ) . However , levels of RAD-51 foci were increased in both premeiotic and pachytene nuclei in mus-81;him-18 double mutants compared to either single mutant ( Figure 3A , Figure S5 , S6 , S7 , S8 ) . Therefore , while MUS-81 may not play a critical role during meiosis in C . elegans , most of the him-18 phenotypes are aggravated by mus-81 . These results suggest that MUS-81 may have additive roles with HIM-18 during repair both in mitosis and meiosis . Slx4 interacts with Slx1 , Rad1/XPF , Rtt107/Esc4 and Cdc27 in S . cerevisiae [17] , [37]-[39] . In D . melanogaster , MUS312 interacts with MEI-9/XPF [16] . To determine whether HIM-18 interacts with SLX-1 , XPF-1 or ERCC-1 , which forms a heterodimer with XPF-1 in C . elegans [40] , we tested the full length and various regions of HIM-18 for interactions with these proteins using the yeast two-hybrid system ( Figure 5 ) . We divided HIM-18 into three parts , namely HIM-18N ( amino acids 1 to 166 ) , HIM-18M ( amino acids 165 to 437 ) , and HIM-18C ( amino acids 420 to 718 ) . HIM-18N contains the zinc finger domain , HIM-18M contains the coiled-coil and BTB domains , and HIM-18C contains the SAP and leucine zipper motifs . DB-HIM-18M showed self-activation precluding further analysis with this construct . Both HIM-18 full length and HIM-18C interact with SLX-1 in either orientation in the yeast two-hybrid system indicating that SLX-1 binds to the C- terminal region of HIM-18 . HIM-18 full length also interacts with XPF-1 in either orientation although this interaction is weaker than those observed between HIM-18-SLX-1 or XPF-1-ERCC-1 . Similar to D . melanogaster , where an interaction between MUS312 and ERCC1 was not detected [16] , we also failed to observe an interaction between HIM-18 and ERCC-1 ( data not shown ) . We obtained similar results using different combinations of yeast strains and plasmids ( Figure S12 ) . Thus , HIM-18 physically interacts with SLX-1 and XPF-1 in C . elegans , similar to the interactions observed involving Slx4 in S . cerevisiae [17] , and its orthologs in S . pombe [20] and D . melanogaster [16] . To further refine the region of HIM-18 required for the interaction with SLX-1 , we specifically examined two domains contained within the C-terminus defined based on recent studies of the mammalian SLX4/BTBD12 protein [41]–[43] . Specifically , we examined the conserved C-terminal domain ( CCD ) [41] , [42] and the helix-turn-helix ( HtH ) region contained within the CCD [43] . We observed that HIM-18CCD , but not HIM-18HtH , binds to SLX-1 ( Figure 6 ) . These results are in agreement with the analysis of the human SLX4/BTBD12 [41] [42] . The S . cerevisiae Slx4-Slx1 complex can cleave branched DNA substrates such as HJs in vitro and both MUS312 and MEI-9/XPF in D . melanogaster have been implicated in HJ resolution . Since both xpf-1 ( e1487 ) mutants and xpf-1 ( RNAi ) worms showed embryonic lethality ( 20 . 2% , n = 2348 , and 7 . 8% , n = 2890 , respectively ) , a high incidence of males ( 4 . 5% and 3 . 5% , respectively ) ( Table 1 and Figure S2B ) and elevated levels of RAD-51 foci at late pachytene ( zone 6 ) ( 3 . 1 and 2 . 1±0 . 29 foci/nucleus , respectively; P<0 . 0001 and P = 0 . 0028 by the two-tailed Mann-Whitney test; 95% C . I . ) compared to wild type ( 0 . 9 foci/nucleus ) and control ( RNAi ) ( 0 . 9±0 . 15 foci/nucleus ) ( Figure 3A and Figure S5 ) , it is possible that XPF-1 may also play a role in meiotic recombination in C . elegans . Given that meiotic COs require HJ resolution , we assessed the role of HIM-18 and XPF-1 in meiotic CO formation by comparing CO frequencies along both chromosomes I and X between wild type and mutants for him-18 and its interaction partner xpf-1 ( Figure 7A and Table S2 ) . We were precluded from performing this analysis for slx-1 mutants due to the lack of an available strong loss-of-function mutant ( T . Saito and M . Colaiácovo , unpublished results ) . On chromosome I , a 55 . 6cM interval corresponding to 96% of this chromosome's whole length ( interval A to E ) was assayed using 5 snip-SNPs . The CO frequency in this interval was reduced to 69 . 8% ( P = 0 . 0005 ) and 79% ( P = 0 . 0302 ) of wild type , respectively in him-18 and xpf-1 mutants . On chromosome X , a 44cM interval corresponding to 76% of this chromosome's whole length ( interval A to E ) was assayed using 5 snip-SNPs . The crossover frequency observed in this interval was reduced to 50 . 5% ( P<0 . 0001 ) and 76 . 1% ( P = 0 . 0434 ) of wild type , respectively in him-18 and xpf-1 mutants . As in wild type , double COs were not detected in either mutant for either chromosome , indicating that CO interference was not impaired . Finally , in wild type C . elegans , CO distribution is biased towards the terminal thirds of autosomes and is more evenly distributed along the X chromosome [44] . Our analysis suggests that these distribution patterns are not altered among the remaining COs observed in him-18 and xpf-1 mutants ( the reduction in COs observed for interval D-E on the right end of the X chromosome in him-18 mutants was not significant compared to wild type; P = 0 . 0553 ) . Taken together , these data suggest that HIM-18 and XPF-1 do not play a role in CO positioning , but are required for normal levels of CO formation in the autosomes and the X chromosome . The fact that the frequency but not the position of meiotic crossover events is affected in him-18 and xpf-1 mutants suggests that HIM-18 and XPF-1 may be required very late in the process of crossover formation . To further assess this , we examined the genetic interaction between him-18 and msh-5 ( Figure 3B and 3C and Table 1 ) . MSH-5 and HIM-14/MSH-4 function downstream of the chromosomal association of the RAD-51 strand-exchange protein , but upstream of dHJ resolution during meiosis [30] . We observed approximately 12 DAPI-stained bodies in diakinesis oocytes in both msh-5 and him-18;msh-5 mutants , compared to the nearly 6 DAPI-stained bodies observed in both wild type and him-18 mutants . These results suggest that HIM-18 may act downstream of MSH-5 , after dHJ formation . However , until further studies address the nature of the bivalent connections observed in him-18 mutants , we cannot exclude the possibility that HIM-18 may also act on a completely different pathway from MSH-5 . Given that CO recombination results in chiasmata that persist until the metaphase I to anaphase I transition during meiosis , it was intriguing that despite the reduction in meiotic CO recombination frequencies observed in him-18 mutants , we mostly observed 6 pairs of attached bivalents in him-18 diakinesis oocytes ( Figure 3B and 3C ) . However , careful examination revealed that the morphology of the DAPI-stained bivalents observed in him-18 mutants was distinct from wild type in diakinesis and prometaphase I , although this was more clearly apparent in the latter ( Figure 7B and 7C ) . Specifically , homologs were loosely attached and 84% ( n = 16/19 ) of the oocytes examined had at least one bivalent held together by a thin DAPI-stained thread at prometaphase I . The “fragility” of the connections between homologs is further highlighted by the localization of the LAB-1 and AIR-2 proteins on bivalents at prometaphase I . LAB-1 is a protein recently implicated in the protection of sister chromatid cohesion and is restricted to the longer axes of the bivalents from late prophase I through the metaphase I to anaphase I transition [45] . AIR-2 is the C . elegans Aurora B kinase and is restricted to the mid-bivalent from late diakinesis through metaphase I . However , in him-18 mutants the mid-bivalent is observed separating prematurely at prometaphase I as indicated by the partially separated ( V-shaped ) AIR-2 ring-like signals in 68% of the oocytes examined ( n = 13/19; P<0 . 0001 by the two-sided Fisher's Exact test , 95% C . I . ) compared to 8 . 3% ( n = 2/24 ) in wild type ( Figure 7B ) . Taken together , these fragile attachments between homologs suggest that HIM-18 is required for chiasma formation and support the increased chromosome nondisjunction observed in him-18 mutants . Crossovers or crossover precursors trigger chromosome remodeling in late meiotic prophase resulting in mature bivalent formation [46] . Therefore , to investigate whether HIM-18 affects the timing of mature bivalent formation , we assessed the kinetics of events correlated with chromosome remodeling in late prophase such as SC disassembly , the chromosomal dissociation of a CO recombination site marker and Histone H3 serine 10 phosphorylation ( Figure 8 ) . During SC disassembly , which initiates in late pachytene in wild type germlines , SC central region components that were previously localized throughout the full length of chromosomes become progressively restricted to the mid-bivalent by early diakinesis and are mostly gone from chromosomes by late diakinesis ( only 10 . 9% of –2 oocytes carried 3 or more bivalents with residual SYP-1 signal , n = 55 ) ( Figure 8A and 8G and [46] ) . In contrast , although early meiotic events proceed with normal kinetics in him-18 mutants , SC disassembly is delayed in this background , as evidenced by the higher levels of –2 oocytes with chromosome-associated SYP-1 ( 28 . 8% , n = 52; P<0 . 0345 ) ( Figure 8A and 8B ) . Recently , ZHP-3 , a homolog of the budding yeast Zip3 protein , was suggested to mark CO recombination sites starting in late pachytene in C . elegans [47] . Interestingly , despite the reduction in CO frequencies observed in him-18 mutants , we observed approximately six ZHP-3::GFP foci/nucleus in oocytes at early diakinesis ( -5 oocytes ) in both wild type ( n = 25 ) and him-18 mutants ( n = 18 ) ( Figure 8C and 8D ) . This suggests that ZHP-3 may mark a CO precursor instead of the mature CO during late pachytene through diakinesis . Moreover , the timing of dissociation of ZHP-3::GFP from chromosomes was delayed in a similar fashion to that of SC disassembly in him-18 mutants ( Figure 8C and 8D ) . While in wild type , between 5 . 3 to 3 . 7 ZHP-3::GFP foci/nucleus were observed until mid-diakinesis ( –4 and –3 oocytes , n = 29 and 32 , respectively ) and were mostly no longer detected by late diakinesis ( 0 . 2 foci/-2 oocyte , n = 32 ) , in him-18 mutants , in average 2 . 6 ZHP-3::GFP foci ( n = 26 ) were still present in the –2 oocytes ( Figure 8C and 8D ) . Finally , quantitative analysis of Histone H3 phosphorylation ( pH 3 ) , a chromosomal substrate of AIR-2 kinase [45] , [48] , [49] , indicated that the appearance of nuclei with pH 3 positive chromosomes is delayed in him-18 mutants compared to wild type in late diakinesis ( 30% of –3 oocytes in him-18 mutants carried pH 3 positive chromosomes compared to 72% in wild type ) ( Figure 8E and 8F ) . Interestingly , CO-defective spo-11 mutants in which , similar to him-18 mutants , events occurring upon entrance into meiosis such as chromosome pairing and synapsis are normal [50] , showed the most delay in Histone H3 phosphorylation , further implicating mature CO formation as a requirement for proper timing of Histone H3 phosphorylation . Taken together , these data suggest that chromosome remodeling during late prophase is delayed in him-18 mutants due to impaired CO formation .
Several groups reported that the Slx1-Slx4 complex cleaves HJs in vitro , although this cleaving activity is weak and inconsistent with an authentic HJ resolvase activity [7] , [19] , [20] . Although we do not know yet whether C . elegans SLX-1-HIM-18 has authentic HJ cleaving activity in vitro , our genetic and cytological data suggest that some HR intermediates are processed in a HIM-18-dependent manner . We did not identify any known nuclease motifs in HIM-18 . However , we showed that two nucleases , SLX-1 and XPF-1 , interact with HIM-18 . SLX-1 is conserved from yeast to humans and contains an URI nuclease domain and a PHD finger domain . Although it has been reported that XPF-ERCC1 mainly cuts simple-Y , bubble , stem loop and 3′-flap structures in S . cerevisiae and H . sapiens [51] , whether the substrate specificity of XPF-1 is altered from those reported DNA structures to HJs due to the interaction with HIM-18 during HR is unknown . Notably , the SAP motif of S . pombe Cce1 , a mitochondrial HJ resolvase , is required for stable binding to HJs . An attractive hypothesis that builds on this observation is that HIM-18 may bind to the HJs via its SAP motif and promote the nuclease activity of SLX-1 and XPF-1 . In this vein , HIM-18 could serve as a scaffold accommodating different interaction partners ( nucleases ) , thereby facilitating the resolution of HJ intermediates arising in different biological contexts as depicted in our model ( Figure 9 ) . Specifically , we propose that HIM-18 function is required for replication restart after DNA damage and correct CO formation during meiosis to maintain genomic integrity in the germline . We showed that HIM-18 is required for the repair of DNA damage arising during DNA replication in the germline . When a replication fork collides either with a spontaneous or an artificial barrier , single-strand gaps ( SSG ) can be generated at either the lagging or leading strands ( Figure 9A ) . To complete an error-free DNA replication , SSGs must be repaired by HR , and during mitosis , this involves the use of a sister chromatid , instead of a homologous chromosome , as a template for repair . We propose that HIM-18 is required for processing late HR intermediates after the RAD-51-mediated strand exchange and pairing ( Figure 9A-c and 9A-d ) . It is conceivable that MUS-81 also functions in this step as it can cleave substrates mimicking dHJs in biochemical assays [52] and has been implicated in the repair of spontaneous DNA damage [36] and DNA ICLs [53] . Therefore , it will be interesting to test if HIM-18 and MUS-81 have overlapping functions in HJ processing during replication collapse . In BLM-deficient cells , sister chromatid exchange ( SCE ) is increased [54] . As BLM has been implicated in the dissolution of HJs to yield NCOs in multiple organisms [55] , [56] , the increased levels of chromatin bridges with numerous RAD-51 foci observed in him-18; him-6 double mutants may represent an accumulation of repair intermediates during DNA replication stress , which are not processed either via HIM-18 or dissolution by HIM-6 . While dHJ unwinding activity has not been reported for HIM-6 in C . elegans , our data is consistent with distinct roles for HIM-6 and HIM-18 in processing dHJ intermediates ( Figure 9A-e' ) . Apparently , him-18 and xpf-1 mutants are distinct from each other regarding repair of spontaneous DNA damage during mitosis . In contrast to him-18 mutants , xpf-1 mutants do not show an increase in the levels of RAD-51 foci in the premeiotic region of the germline and the RAD-51 staining pattern observed in xpf-1;him-18 double mutants is very similar to that in him-18 single mutants . XPF-1 is required for unwinding or repairing G4 DNA ( G-quadruplex ) structures during DNA replication in mutants of the C . elegans homolog of the FANCJ DNA helicase , dog-1 [57] , [58] . In contrast , we did not detect elevated levels of deletions on polyG/C-tracts in dog-1;him-18 double mutants ( Figure S13 ) . Therefore , HIM-18 does not play a role in the unwinding of G4 DNA during replication . Likewise , whereas XPF-1 is required for NER [59] , fly MUS312 and yeast Slx4 , the HIM-18 orthologs , do not function in NER [16] , [20] . Taken together , these observations suggest that HIM-18 has a distinct function from that of XPF-1 during mitotic proliferation . COs play a critical role in ensuring accurate meiotic chromosome segregation , as exemplified by the alterations in CO number and/or distribution frequently associated with human aneuploidies [60] . Ensuring the formation of at least one CO per homolog pair ( obligate CO ) is vital to the transmission of an intact genome during gametogenesis . In wild type C . elegans , one DSB per pair of homologous chromosomes engages in a CO pathway and is marked by ZHP-3 . In him-18 mutants , CO frequencies are decreased and offloading of ZHP-3 from nascent CO sites is delayed . This delay could be simply explained by dHJs being dissolved instead of resolved in him-18 mutants and the dissolution process perhaps being time consuming ( Figure 9B ) . Occasionally , however , dissolution is not completed in him-18 mutants , because fragile connections ( possibly hemicatenanes ) are still detectable between homologs . This fragile connection persists through diakinesis into prometaphase I possibly leading to nondisjunction ( NDJ ) at anaphase I . This is further supported by our observation of chromosome bridges and lagging chromosomes at the metaphase I to anaphase I transition in him-18 mutants ( n = 9/15oocytes ) , in contrast to wild type where these were never detected ( n = 0/17 ) ( Figure S14 ) . However , the presence of oocytes lacking either chromosome bridges or lagging chromosomes in him-18 mutants suggests that at least a portion of the hemicatenanes may be finally dissolved by TOP-3 just prior to anaphase I . Taken together , our data are consistent with a role for HIM-18 in processing dHJs leading to CO formation during meiosis ( Figure 9B ) . him-18;him-6 double mutants are synthetic lethal . Interestingly , with regard to the numbers of DAPI-stained bodies in oocytes at diakinesis , him-18 suppresses the him-6 mutation ( P<0 . 0001 , by the two tailed Mann-Whitney test; 95% C . I . ) . To explain this meiotic phenotype we propose that HIM-6 may play a role in stabilizing the meiotic D-loop and dHJ intermediates , perhaps via its helicase activity , and specifically promote a CO outcome . In him-18;him-6 mutants , there is no HIM-6-dependent stabilization of D-loops . Some unstable D-loops may be processed via the SDSA pathway . The remaining D-loops may become unstable dHJs , which are not cleaved by HIM-18 and persist during diakinesis . Therefore , the number of DAPI-stained bodies in him-18;him-6 mutants ( 6 . 39 ) is lower than that observed in him-6 mutants ( 7 . 32 ) and higher than in him-18 mutants ( 6 . 04 ) . A requirement for XPF in meiotic CO formation had only been observed , thus far , in D . melanogaster , where MEI-9 is essential for meiotic COs . Our genetic and cytological analyses suggest that XPF-1 seems to function in CO formation during C . elegans meiosis . Interaction of HIM-18 with XPF-1 may be important for altering the substrate-specificity of XPF-1 towards HJs during meiosis . We observed that the decrease in CO frequency on chromosome X in xpf-1 mutants is milder than that in him-18 mutants ( P = 0 . 0120 ) . These data suggest that HIM-18 may define yet another HJ processing activity distinct from XPF-1 . Mus81 , which is also known as a meiotic HJ resolvase , is specialized in interference-independent COs in yeast , plants and mammals [61] . However , in C . elegans , virtually all COs are HIM-14/MSH-4 and MSH-5-dependent , and therefore interference-dependent [62] , [63] . Additionally , mus-81 mutants are largely viable [36] suggesting that MUS-81 may not be required for meiotic COs in C . elegans . Further experiments will address whether SLX-1 , which we demonstrated is a HIM-18 interaction partner , functions during meiotic CO formation . HIM-18 is expressed in the C . elegans germline , being enriched at the premeiotic tip and in late meiotic prophase ( from late pachytene to diakinesis ) . HIM-18 protein levels , as detected by immunostaining , are low between the transition zone and the mid-pachytene stage . In contrast , analysis of mRNA expression by in situ hybridization ( Y . Kohara , personal communication ) suggests a more uniform pattern of expression from transition zone until diakinesis . These data suggest tight regulation of HIM-18 at the protein level , either by translational repression and/or protein degradation between transition zone through the mid-pachytene stage . The translation of a number of mRNAs in the C . elegans germline is repressed by GLD-1 , a member of the STAR KH-domain family of RNA binding proteins [64] . However , sequence analysis revealed that HIM-18 harbors a destruction box ( D-box; RXXL ) , an APC/C recognition motif and an Ubc9 recognition motif ( ψKXE ) . These motifs are usually required for the ubiquitin- and SUMO-dependent proteolytic pathways . In S . cerevisiae Slx4 interacts with the APC/C component Cdc27 , although it remains to be determined whether Slx4 is degraded by APC/C . Further analysis will reveal whether HIM-18 may be a target for a proteolytic pathway during transition zone to mid-pachytene . In summary , we identified HIM-18 as an ortholog of yeast Slx4 , fly MUS312 and mammalian BTBD12 proteins , which plays a critical role in the germline during DSB repair upon replication fork collapse in mitosis and SPO-11-dependent programmed meiotic DSB formation . Our results therefore identified HIM-18 as a new HJ processing factor in C . elegans , which is distinct from the Mus81-Eme1 complex .
C . elegans strains were cultured at 20°C under standard conditions [65] . The N2 Bristol strain was used as the wild-type background . The following mutations and chromosome rearrangements were used in this study: LGI: mus-81 ( tm1937 ) , dog-1 ( gk10 ) , hT2[bli-4 ( e937 ) let- ? ( q782 ) qIs48] ( I; III ) ; LGII: xpf-1 ( e1487 ) , mIn1[dpy-10 ( e128 ) mIs14] ( II ) ; LGIII: him-18 ( tm2181 ) , unc-32 ( e189 ) , sDf121 , qC1[dpy-19 ( e1259 ) glp-1 ( q339 ) qIs26] ( III ) ; LGIV: spo-11 ( ok79 ) , him-6 ( ok412 ) , msh-5 ( me23 ) , nT1[ unc- ? ( n754 ) let- ? qIs50] ( IV; V ) , nT1[qIs51] ( IV; V ) [50] , [62] , [65]–[68] . HIM-18 homology searches were performed using the Ensembl genome browser ( http://www . ensembl . org/index . html ) and Pfam ( http://pfam . sanger . ac . uk/ ) . Although the HIM-18 ortholog in S . cerevisiae was not identified by the Ensembl program , Pfam predicted that the SAP motif of HIM-18 is similar to that in yeast Slx4 . The following motif prediction programs were applied to HIM-18 and its orthologs: COIL and P-SORT II for coiled-coil and leucine zipper predictions , Pfam and HHpred for zinc finger and BTB domain predictions [69]-[72] . Rabbit anti-HIM-18 antibody was produced using a HIS-tagged fusion protein expressed from plasmid pDEST17 ( Invitrogen ) containing coding sequence corresponding to the first 166 amino acids of HIM-18 . 6xHis-HIM-18N was expressed in BL21 E . coli cells and purified with the Ni-NTA Purification System ( Invitrogen ) . Animals were immunized and bled by Sigma-Genosys , The Woodlands , TX . The antisera were affinity-purified against the 6xHis-HIM-18N peptide as described in [73] . Whole mount preparation of dissected gonads , DAPI-staining , immunostaining and analysis of meiotic nuclei were carried out as in [30] and [45] , with the exception of the rabbit anti-HIM-18 antibody , where gonads were fixed with 1% formaldehyde for 5 minutes , then incubated in ice-cold methanol for 1 minute , followed by blocking with 1% BSA for 1 hour . Primary antibodies were used at the following dilutions: mouse α-RAD-51 ( 1∶100 ) , rabbit α-SYP-1 ( 1∶100 ) , guinea pig α-SYP-1 ( 1∶100 ) , rabbit α-HIM-8 ( 1∶100 ) , rabbit α-HIM-18 ( 1∶5000 ) , rabbit α-LAB-1-Cy3 ( 1∶50 ) , rabbit α-pH 3 ( 1∶100 ) , mouse α-REC-8 ( 1∶100 ) , rat α-SMC-3 ( 1∶100 ) and rabbit α-RPA-1 ( 1∶500 ) . FISH was performed as in [74] utilizing a probe to the 5S rDNA locus on chromosome V prepared as in [50] . Quantitative analysis of RAD-51 foci was performed as in [30] except that all seven zones composing the germline were scored . 5–10 germlines were scored for each genotype . The average number of nuclei scored per zone for a given genotype was as follows , ±standard deviation: zone 1 , n = 215±48; zone 2 , n = 259±57; zone 3 , n = 246±61; zone 4 , n = 217±41; zone 5 , n = 190±34; zone 6 , n = 154±24; zone 7 , n = 137±31 . Statistical comparisons between genotypes were performed using the two-tailed Mann-Whitney test , 95% confidence interval ( C . I . ) . Young adult him-18/him-18 animals were picked from the progeny of him-18/qC1 parent animals . To assess ionizing radiation ( IR ) sensitivity , animals were treated with 0 , 10 , 50 or 100 Gy of IR from a Cs137 source at a dose rate of 2 . 16 Gy/min . For nitrogen mustard ( HN2 ) sensitivity , animals were treated with 0 , 50 , 100 or 150 µM of HN2 ( mechlorethamine hydrochloride; Sigma ) in M9 buffer containing E . coli OP50 with slow shaking in the dark for 19 hours . Treatment with camptothecin ( CPT; Sigma ) was similar , but with doses of 0 , 100 or 500 nM . Following treatment with HN2 or CPT , animals were washed twice with M9 containing TritonX100 ( 100 µl/L ) and plated to allow recovery for 3 hours . For hydroxyurea ( HU ) sensitivity , animals were placed on seeded MYOB plates containing 0 or 40 mM HU for 24 hours . HU sensitivity was assessed in 20 animals from 4–22 hours after HU treatment . For all other damage sensitivity experiments , 20 animals were plated 5 per plate and hatching was assessed for the time period 22–26 hours following treatment . Each damage condition was replicated at least twice in independent experiments . 22–24 hour post-L4 hermaphrodites expressing CED-1::GFP ( mammalian MEGF10; [75] ) were mounted under coverslips in 5 µl of a 15 mM sodium azide solution on 1 . 5% agarose pads . Apoptotic cells surrounded by CED-1::GFP signal were observed in the late pachytene region of the germline with a Leica DM5000 B fluorescence microscope . Between 21 and 95 gonads were scored for each genotype . Statistical comparisons between genotypes were performed using the two-tailed Mann-Whitney test , 95% C . I . Meiotic CO frequencies were assayed utilizing single-nucleotide polymorphisms ( SNP ) markers as in [76] , except that +/+ worms were used as a control . PCR and DraI restriction digests of single worm lysates were performed as described in [77] . The following DraI SNP primers were utilized: A ( snp_F56C11 ) , B ( pkP1052 ) , C ( CE1-247 ) , D ( uCE1-1361 ) , E ( snp_Y105E8B ) for chromosome I , and A ( pkP6143 ) , B ( pkP6105 ) , C ( snp_F11A1 ) , D ( pkP6132 ) , E ( uCE6-1554 ) for the X chromosome . Statistical analysis was performed using the two-tailed Fisher's Exact test , 95% C . I . Yeast two-hybrid was performed according to [78] . cDNA of HIM-18 full length , HIM-18N1–166 , HIM-18M165–437 , HIM-18C420–718 , SLX-1 ( open reading frame F56A3 . 2 ) full length , XPF-1 full length , and ERCC-1 ( open reading frame F10G8 . 7 ) full length were cloned into the Gateway donor vector pDONR223 . Each construct was then subcloned into 2 µ Gateway destination vectors pVV213 ( activation domain ( AD ) , LEU2+ ) and pVV212 ( Gal4 DNA binding domain ( DB ) , TRP1+ ) . AD-Y and DB-X fusions were transformed into MATa Y8800 and MATα Y8930 yeast strains , respectively . These yeast strains have three reporter genes: GAL2-ADE2 , met2::GAL7-lacZ and LYS2::GAL1-HIS3 . MATa Y8800 and MATα Y8930 were mated on YPD plates and diploids carrying both plasmids were selected on SC-Leu-Trp plates . Similarly , we made pVV213/pVV212-containing AH109/Y187 and Mav203/Mav103 diploids and pDEST22/pDEST32-containing Y8800/Y8930 diploids ( Figure S12 ) . Pair-wise interactions were tested by scoring for yeast two-hybrid phenotypes ( LacZ , -Ade , -His , -His+either 1 mM or 20 mM 3AT ) at 30°C . | Homologous recombination ( HR ) is a process that provides for the accurate and efficient repair of DNA double-strand breaks ( DSBs ) incurred by cells , thereby maintaining genomic integrity . Proper processing of HR intermediates is critical for biological processes ranging from replication fork restart to the accurate partitioning of chromosomes during meiotic cell divisions . This is further emphasized by the fact that impaired processing of HR intermediates in both mitotic and meiotic cells can result in tumorigenesis and congenital defects . Therefore , the identification of components involved in HR is essential to understand the molecular mechanism of HR . Here , we identify HIM-18/SLX-4 in C . elegans , a protein conserved from yeast to humans that interacts with the nucleases SLX-1 and XPF-1 and is required for DSB repair in the germline . Impaired HIM-18 function results in increased DNA damage sensitivity , the accumulation of recombination intermediates , decreased meiotic crossover frequencies , altered late meiotic chromosome remodeling , the formation of fragile connections between homologs , and an increased chromosome nondisjunction . Finally , HIM-18 is localized to both mitotic and meiotic nuclei in wild-type germlines . We propose that HIM-18 function is required during the processing of late HR intermediates resulting from replication fork collapse and meiotic DSBs . | [
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] | 2009 | Caenorhabditis elegans HIM-18/SLX-4 Interacts with SLX-1 and XPF-1 and Maintains Genomic Integrity in the Germline by Processing Recombination Intermediates |
The impact of pesticides on the health of bee pollinators is determined in part by the capacity of bee detoxification systems to convert these compounds to less toxic forms . For example , recent work has shown that cytochrome P450s of the CYP9Q subfamily are critically important in defining the sensitivity of honey bees and bumblebees to pesticides , including neonicotinoid insecticides . However , it is currently unclear if solitary bees have functional equivalents of these enzymes with potentially serious implications in relation to their capacity to metabolise certain insecticides . To address this question , we sequenced the genome of the red mason bee , Osmia bicornis , the most abundant and economically important solitary bee species in Central Europe . We show that O . bicornis lacks the CYP9Q subfamily of P450s but , despite this , exhibits low acute toxicity to the N-cyanoamidine neonicotinoid thiacloprid . Functional studies revealed that variation in the sensitivity of O . bicornis to N-cyanoamidine and N-nitroguanidine neonicotinoids does not reside in differences in their affinity for the nicotinic acetylcholine receptor or speed of cuticular penetration . Rather , a P450 within the CYP9BU subfamily , with recent shared ancestry to the Apidae CYP9Q subfamily , metabolises thiacloprid in vitro and confers tolerance in vivo . Our data reveal conserved detoxification pathways in model solitary and eusocial bees despite key differences in the evolution of specific pesticide-metabolising enzymes in the two species groups . The discovery that P450 enzymes of solitary bees can act as metabolic defence systems against certain pesticides can be leveraged to avoid negative pesticide impacts on these important pollinators .
Bee pollinators encounter a wide range of natural and synthetic xenobiotics while foraging or in the hive , including phytochemicals , mycotoxins produced by fungi , and pesticides [1] . Understanding the toxicological outcomes of bee exposure to these chemicals , in isolation or combination , is essential to safeguard bee health and the ecosystem services they provide . Like other insects , bees have sophisticated metabolic systems that mediate the conversion of harmful xenobiotics to less toxic forms , and these detoxification pathways can be critically important in defining their sensitivity to xenobiotics including pesticides [2] . In an important recent example of this cytochrome P450 enzymes belonging to the CYP9Q subfamily were shown to play a key role in determining the sensitivity of honey bees and bumblebees to neonicotinoid insecticides [3] . Prior work on honey bees showed that the same P450s also provide protection against the toxic effects of certain insecticides from the pyrethroid and organophosphate classes that are used for the control of parasitic Varroa mites [4] . Taken together these studies suggest CYP9Q P450s may be important generalist detoxification enzymes . To date our understanding of bee biochemical defence systems stems from work on eusocial species , namely honey bees and bumblebees , with much less attention given to solitary species . However , the majority of bee species are solitary , and there is increasing awareness of the importance of solitary bees as pollinators of wild plants and certain crops [5–8] . It is currently unknown to what extent the discoveries on the metabolic systems of honey bees and bumblebees extend to solitary bees , and thus if the use of eusocial species as a proxy for solitary species in ecotoxicological studies is reliable . The red mason bee , Osmia bicornis ( syn . O . rufa ) ( Hymenoptera: Megachilidae ) is the most abundant and economically important solitary bee species in Central Europe [9] . This species pollinates a range of wild plants and is also used for commercial pollination , particularly of fruit crops ( almond , peach , apricot , plum , cherry , apple and pear ) . Understanding O . bicornis-pesticide interactions is particularly important as it has been recommended as a solitary bee model for the registration of pesticides in Europe [10] . However , to date , investigations on this topic have been hampered by a lack of genomic and transcriptomic resources for this species . In this study we addressed this knowledge and resource gap by generating a high quality genome assembly of O . bicornis . We then exploited this genomic resource to compare the complement of P450 genes in O . bicornis with that of other bee species , and identify P450 enzymes that are important determinants of O . bicornis sensitivity to neonicotinoid insecticides .
To generate a high-quality genome assembly of O . bicornis we sequenced genomic DNA extracted from a single haploid male bee using a combination of Illumina paired-end and mate-pair libraries . Additional RNA sequencing ( RNAseq ) of male and female bees was also performed in order to improve the quality of subsequent gene prediction . DNAseq data was assembled to generate an O . bicornis genome of 212 . 9 Mb consistent with genome size estimates derived from k-mer analysis of the raw reads ( S1 Table ) . The final assembly comprised 10 , 223 scaffolds > 1 kb with a scaffold and contig N50 of 604 kb and 303 kb respectively ( S2 Table ) . Structural genome annotation using a workflow incorporating RNAseq data predicted a total of 14 , 858 protein-coding genes encoding 18 , 479 total proteins ( S3 Table ) . The completeness of the gene space in the assembled genome was assessed using the Benchmarking Universal Single-Copy Orthologues ( BUSCO ) pipeline [11] with greater than 99% of Arthropoda and Insecta test genes identified as complete in the assembly ( S4 Table ) . Approximately 78% of the predicted genes could be assigned functional annotation based on BLAST searches against the non-redundant protein database of NCBI ( S1 Fig ) . The gene repertoire of O . bicornis was compared with other colony forming ( Apis mellifera , Apis florea , Bombus terrestris and Bombus impatiens ) and solitary bee species ( Megachile rotundata ) by orthology inference ( Fig 1A ) . The combined gene count of these species was 101 , 561 of which ~90% were assigned to 11 , 184 gene families . Of these 8 , 134 gene families were present in O . bicornis and all other species , and a total of 163 gene families were specific to O . bicornis compared to 21–97 in the other bee species ( Fig 1A ) . Genes encoding cytochrome P450s were identified from orthogroups , and individual bee genomes ( see methods ) , and the complete complement of P450s in each bee genome ( the CYPome ) was curated and named by the P450 nomenclature committee ( S5 Table ) . The genome of O . bicornis contains 52 functional P450s ( Fig 2B and S2 Fig ) , a gene count consistent with the other bee species and reduced in comparison to other insects , even including other hymenoptera [2] . As for other insect species bee P450 genes group into four main clades ( CYP2 , CYP3 , CYP4 and mitochondrial clans ) of which by far the largest ( comprising 33 P450s in O . bicornis ) is the CYP3 clan of CYP6 , CYP9 and CYP336 ( Fig 1A and 1B , S2 Fig ) . Phylogenetic comparison of the CYP9 family within this clade in O . bicornis and 11 other bee species [12] revealed that O . bicornis lacks the CYP9Q subfamily found in eusocial bee species that has been shown to define the sensitivity of honey bees and bumblebees to neonicotinoids ( Fig 1C , S3 Fig ) [3] . The most closely related subfamily in O . bicornis was CYP9BU ( represented by CYP9BU1 and CYP9BU2 ) , a newly described subfamily , that appears to share a relatively recent common ancestor with the CYP9Q subfamily ( Fig 1C , S3 Fig ) . In the absence of the CYP9Q subfamily of P450s it might be expected that O . bicornis would be more sensitive to neonicotinoids ( especially N-cyanoamidine compounds ) than honey bees and bumblebees . To test this we performed acute contact insecticide bioassays using imidacloprid and thiacloprid as representatives of N-nitroguanidine and N-cyanoamidine neonicotinoids respectively . Significant differences were found in the tolerance of O . bicornis to the two compounds with adult female bees >2 , 000-fold more sensitive to imidacloprid ( LD50 of 0 . 046 μg/bee ) than thiacloprid ( LD50 of >100 μg/bee ) ( Fig 2A ) . These values are similar to those reported for honey bees and bumblebees [3 , 13 , 14] with imidacloprid classified as ‘highly toxic’ to O . bicornis according to the categories of the U . S . Environmental Protection Agency , but thiacloprid classified as ‘practically non-toxic’ upon contact exposure ( Fig 2A ) . Thus these results clearly show that , despite the lack of CYP9Q P450s , O . bicornis has high levels of tolerance to the N-cyanoamidine neonicotinoid thiacloprid . The molecular basis of the profound variation in the sensitivity of O . bicornis to imidacloprid and thiacloprid could reside in differences in: a ) their affinity for the target-site , the nicotinic acetylcholine receptor ( nAChR ) , b ) their speed of penetration through the cuticle , or c ) the efficiency of their metabolism . We first examined the affinity of the two compounds for the target-site using radioligand binding assays performed on O . bicornis head membrane preparations , and examined the displacement of tritiated imidacloprid by both unlabelled imidacloprid and thiacloprid . Both compounds bound with nM affinity—IC50 of 8 . 3 nM [95% Cl 4 . 6 , 15 . 1] for imidacloprid and 2 . 4 nM [95% Cl , 1 . 4 , 4 . 1] for thiacloprid ( Fig 2B ) . These values suggest that thiacloprid binds with higher affinity than imidacloprid , however , no significant difference was observed between the slopes of the regression lines of the two compounds ( p = 0 . 3 ) . This finding clearly demonstrates that the tolerance of O . bicornis to thiacloprid relative to imidacloprid is not a consequence of a reduced affinity of the former for the nAChR . To explore the rate of penetration of these two compounds through the cuticle of O . bicornis the uptake of [14C]imidacloprid and [14C]thiacloprid after application to the dorsal thorax was compared . No significant differences were observed in the amount of radiolabelled thiacloprid and imidacloprid recovered from the cuticle or acetone combusted whole bees at any time point post-application ( the final uptake through the cuticle after 24h was 27% of [14C]imidacloprid and 28% of [14C]thiacloprid , Fig 2C ) . Thus , the differential sensitivity of O . bicornis to imidacloprid and thiacloprid is not a result of variation in their speed of penetration through the cuticle . Insecticide synergists that inhibit detoxification enzymes have been used to explore the role of metabolism in the tolerance of honey bees and bumblebees to certain neonicotinoids . Specifically , the use of the P450 inhibitor piperonyl butoxide ( PBO ) provided strong initial evidence that P450s underpin the tolerance of both bee species to N-cyanoamidine neonicotinoids [3 , 15] . We therefore examined the effect of PBO pre-treatment on the sensitivity of O . bicornis to thiacloprid and imidacloprid in insecticide bioassays . No significant difference was observed in the sensitivity of O . bicornis to imidacloprid with or without PBO , however , bees pre-treated with PBO became >7-fold more sensitive to thiacloprid ( Fig 2D ) , suggesting that P450s play an important role in defining the sensitivity of O . bicornis to neonicotinoids . As detailed above , based on phylogeny , CYP9BU1 and CYP9BU2 are clearly the most closely related P450s in O . bicornis to the Apidae CYP9Q subfamily which metabolise thiacloprid in honey bees and bumblebees ( Fig 1C , S3 Fig ) . We therefore examined the capacity of these P450s to metabolise thiacloprid and imidacloprid in vitro by individually coexpressing them with house fly cytochrome P450 reductase ( CPR ) in an insect cell line . Incubation of microsomal preparations containing each P450 and CPR with either thiacloprid or imidacloprid , and analysis of the metabolites produced by liquid chromatography tandem mass spectrometry ( LC-MS/MS ) , revealed that both CYP9BU1 and CYP9BU2 metabolise these compounds to their hydroxylated forms ( 5-hydroxy thiacloprid and 5-hydroxy imidacloprid respectively ) ( Fig 3A ) . Both P450s metabolised thiacloprid with significantly greater efficiency than imidacloprid ( Fig 3A ) consistent with the relative sensitivity of O . bicornis to these compounds . To provide additional evidence that these P450s confer tolerance to N-cyanoamidine neonicotinoids in vivo , we created transgenic lines of Drosophila melanogaster expressing CYP9BU1 , or CYP9BU2 and examined their sensitivity to imidacloprid and thiacloprid . Flies expressing the CYP9BU1 transgene were ~4 times less sensitive to thiacloprid than control flies of the same genetic background without the transgene in insecticide bioassays ( Fig 3B , S6 Table ) . In contrast flies expressing CYP9BU2 showed no significant resistance to thiacloprid . In bioassays using imidacloprid no significant differences in sensitivity were observed between flies with either of the two transgenes and control flies . These results demonstrate that the transcription of CYP9BU1 confers intrinsic tolerance to thiacloprid in vivo . Characterising when and where the neonicotinoid-metabolising P450s identified in this study are expressed is an important step in understanding their capacity to protect O . bicornis in vivo . To investigate this , we 1 ) explored changes in their expression in response to exposure to sublethal doses of imidacloprid and thiacloprid , and 2 ) examined their expression in tissues that are involved in xenobiotic detoxification , or are sites of insecticide action . To investigate if the expression of any genes encoding P450s could be induced by neonicotinoid exposure RNAseq was performed on adult female O . bicornis 24 h after exposure to the LD10 of thiacloprid , imidacloprid or the solvent used to dissolve insecticides alone ( as a control ) . Differentially expressed genes ( corrected p value of <0 . 05 ) between control and treatments were identified and are shown in full in S7 Table and S8 Table . In general , changes in gene expression were modest with just 27 genes significantly upregulated after imidacloprid exposure and 16 genes upregulated after thiacloprid exposure . The function of these differentially expressed genes was either unknown or is unrelated to xenobiotic detoxification , and no P450 showed a significant increase in expression upon exposure to either neonicotinoid ( Fig 4A , S7 Table and S8 Table ) . These findings suggest that constitutive rather than induced expression of the P450s identified in this study is more important in their role in pesticide detoxification . The expression of neonicotinoid-metabolising P450s in the brain , midgut and Malpighian tubules of O . bicornis was assessed by quantitative PCR ( Fig 4B ) . CYP9BU1 was found to be highly expressed in the Malpighian tubules , the functional equivalents of vertebrate kidneys , consistent with a primary role in xenobiotic detoxification . In contrast CYP9BU2 was expressed at equivalent levels in the Malpighian tubules , the midgut and the brain ( Fig 4B ) .
The genomes of all bee species sequenced to date have a considerably reduced complement of cytochrome P450s compared to those of other insect species [12 , 16] . This suggests that , like humans [17] , bees may depend on a relatively small subset of generalist P450s for the detoxification of xenobiotics [2] . An emerging body of work on eusocial bees has provided strong support for this hypothesis , with P450s of the CYP9Q subfamily identified as metabolisers of insecticides from three different classes [3 , 4] , and key determinants of honey bee and bumble bee sensitivity to neonicotinoids [3] . In this study we examined the extent to which these findings apply to solitary bees , using the red mason bee , O . bicornis as a model . Consistent with data from honey bees and bumblebees sequencing of the O . bicornis genome revealed a reduced P450 inventory in comparison to most other insects , however , in contrast to these species no members of the CYP9Q P450 subfamily were present in the curated CYPome . We interrogated the recently published genomes of several other solitary and eusocial bee species [12] and confirmed that the CYP9Q subfamily is ubiquitous in the CYPome of sequenced social bees ( represented by 2–3 genes in most species ) but missing in all solitary bee genomes apart from Habropoda laboriosa , a species in the family Apidae , which has a single CYP9Q gene ( CYP9Q9 ) ( S3 Fig ) . Solitary bees are the ancestral state from which social bees evolved [12] suggesting the CYP9Q subfamily expanded after social bees diverged from solitary bees . A rapid birth–death model of evolution is characteristic of xenobiotic-metabolizing P450s , in contrast to P450s with endogenous functions [18] , and the expansion of the CYP9Q subfamily in social bees may have occurred to allow xenobiotics specifically associated with this life history to be detoxified . In relation to this , recent analysis of the CYPomes of ten bee species has suggested that the expansion of the CYP6AS subfamily in perennial eusocial bees resulted from increased exposure to phytochemcials , as a result of the concentration of nectar into honey , pollen into beebread and plant resins into propolis [19] . The finding that most solitary bees lack the CYP9Q subfamily raises important questions about their capacity to metabolise and , by extension tolerate , certain pesticides . Thus , a key finding from our study is that despite the absence of the CYP9Q subfamily O . bicornis exhibits similar levels of sensitivity to the neonicotinoids imidacloprid and thiacloprid as honey bees and bumblebees , and , like these species , marked tolerance to the latter compound . We show that the observed variation in the sensitivity of O . bicornis to thiacloprid and imidacloprid does not result from differences in their affinity for the nAChR , or speed of cuticular penetration , but rather variation in their speed/efficiency of metabolism by cytochrome P450s . Functional characterisation revealed that , in the absence of the CYP9Q subfamily , O . bicornis employs P450s from the CYP9BU subfamily to detoxify the N-cyanoamidine neonicotinoid thiacloprid . While the CYP9BU subfamily is currently unique to O . bicornis phylogeny shows it is more closely related to the CYP9Q subfamily , with which it appears to share a recent common ancestor , than any other bee P450 subfamily . Given that we show that CYP9BU1 appears to be particularly effective in metabolising N-cyanoamidine neonicotinoids it will be important to explore which P450s other solitary bee species , such as the economically important leafcutter bee , Megachile rotundata , use to detoxify pesticides in the absence of this subfamily ( S3 Fig ) . Work on other insect species has shown that insecticide-metabolising P450s may be constitutively expressed or induced upon exposure to xenobiotic substrates [20] . We found no evidence of induction of any O . bicornis P450s in response to exposure to sublethal concentrations of thiacloprid or imidacloprid suggesting that constitutive expression of these enzymes provides protection against neonicotinoids . Their detoxification capacity may be further enhanced by expression in tissues with specialised roles in metabolism/excretion , and it is notable that CYP9BU1 is expressed at particularly high levels in the Malpighian tubules . The overexpression of CYP9BU1 in these osmoregulatory and detoxifying organs is highly consistent with a primary role in xenobiotic metabolism and parallels the high expression of CYP9Q3 in this tissue—the primary metaboliser of neonicotinoids in honey bees [3] . In summary , we show that the solitary bee O . bicornis is equipped with key biochemical defence enzymes that provide protection against certain insecticides . Together with previous work this demonstrates that while the underlying P450s involved may be different in O . bicornis and eusocial bees , the overarching detoxification pathways used by these species to metabolise neonicotinoids is conserved . Identification of the P450s responsible for the observed tolerance of O . bicornis to N-cyanoamidine neonicotinoids can be used to support ecotoxicological risk assessment and safeguard the health of this important pollinator . For example , the recombinant enzymes developed in our study can be used to screen existing pesticides to identify and avoid synergistic pesticide-pesticide interactions that inhibit these enzymes [21] , and to examine the metabolic liability of future lead compounds as part of efforts to develop pest-selective chemistry . The genomic resources , tools and knowledge generated in this study are particularly timely as O . bicornis has recently been proposed as a representative solitary bee species for inclusion in future risk assessment of plant protection products in Europe [10] .
Genomic DNA was extracted from a single male bee using the E . Z . N . A Insect DNA kit ( Omega Bio-Tek ) following the manufacturer’s protocol . DNA quantity and quality was assessed by spectrophotometry using a NanoDrop ( Thermo Scientific ) , Qubit assay ( ThermoFisher ) and gel electrophoresis . Sufficient DNA from a single male bee was obtained for the preparation of a single PCR-free paired-end library and 5 long mate pair Nextera libraries that were sequenced on an Illumina HiSeq 2500 using a 250bp read metric at Earlham Institute , Norwich , UK . To improve the quality of subsequent gene prediction RNA sequencing was also performed . For this RNA was extracted from female and male O . bicornis 24 h after emergence using the Isolate RNA Mini Kit ( Bioline ) according to the manufacturer’s instructions . The quantity and quality of RNA was checked as described above . RNA was used as a template for the generation of barcoded libraries ( TrueSeq RNA library preparation , Illumina ) and RNA samples sequenced to high coverage on an Illumina HiSeq2500 flowcell ( 100 bp paired-end reads ) . All sequence data have been deposited under NCBI BioProject PRJNA285788 . Reads were assembled using DISCOVAR_de-novo–v 52488 [22] using default parameters . All sequences >500 bp from the initial draft assembly were used in scaffolding with 5 Illumina Nextera mate-pair libraries using Redundans–v 0 . 12a [23] with default parameters . To further increase the contiguity of the draft genome we applied a third scaffolding step , making use of the RNAseq data . Transcriptome contig sequences of O . bicornis and protein sequences of a closely related species Megachile rotundata , were mapped sequentially using L_RNA_scaffolder [24] and PEP_scaffolder [25] . The first round of gene prediction was performed using BRAKER–v 2 . 1 . 0 [26] utilising RNAseq data to improve gene calling . To generate training sets for ab-initio gene modellers AUGUSTUS [27] and SNAP [28] , we searched core eukaryotic and insecta orthologous genes in the O . bicornis assembly using CEGMA–v 2 . 5 . 0 [29] and BUSCO–v 3 . 0 . 0 [11] respectively . BUSCO gene models were used to train AUGUSTUS–v 2 . 5 . 5 , and SNAP ( https://github . com/KorfLab/SNAP ) was trained using the CEGMA gene models . Another set of hidden markov gene models was generated by GeneMark-ES–v 4 . 32 . 0 [30] . In addition , a custom O . bicornis specific repeat library was built from the assembly using RepeatModeler–v 1 . 0 . 4 [31] . To make use of expression data and exploit spliced alignments in genome annotation , expressed transcripts assembled from RNAseq data were further mapped to the O . bicornis genome using PASA–v 2 . 3 . 3 [32] . We initially ran MAKER2 [33] with just the O . bicornis assembly and EST data , collected from NCBI , followed by three consecutive iterations with the draft genome sequence , transcriptome dataset , models from BRAKER , SNAP and GeneMark-ES , the O . bicornis specific repeat library and the Swiss-Prot database ( accessed at May 23 , 2016 ) . Between iterations , the BRAKER and SNAP models were retrained . As BRAKER models are originally predicted from AUGUSTUS , we used AUGUSTUS to train BRAKER models in each successive MAKER2 iteration according to the best-practice MAKER2 workflow . Finally , BRAKER and MAKER2 prediction sets , including PASA alignments , alignment of M . rotundata proteins using exonerate–v 2 . 4 . 0 were combined to generate a non-redundant gene set using EvidenceModeler–v 1 . 1 . 1 [34] . The final annotation set for O . bicornis was compared to other bee genomes to characterize orthology . The proteomes of Apis mellifera , Apis florea , Bombus terrestris , Bombus impatiens , Megachile rotundata , were downloaded from NCBI , and OrthoFinder–v 1 . 1 . 8 [35] was used to define orthologous groups of genes between these peptide sets . P450 sequences were recovered from the bee species using three approaches: 1 ) Text searches of existing annotation , 2 ) mining P450 gene sequences from ortholog data generated above , and 3 ) iterative BLAST searches using A . mellifera curated P450 genes as queries . All obtained sequences were then manually inspected and curated to generate a final list of P450 genes for each species which were named by the P450 nomenclature committee . Accession numbers are provided in S5 Table . For phylogenetic analysis , manually curated protein sequences of cytochrome P450 genes were aligned using MUSCLE v3 . 8 . 31 [36] . FMO2-like ( Protein ID: XP_016772196 . 1 ) and CYP315A1 from A . mellifera were used as an outgroup for phylogenies displayed in Fig 1 and S3 Fig respectively . An initial likelihood phylogenetic tree was created using the R package “phangorn: Phylogenetic Reconstruction and Analysis” v . 2 . 4 . 0 [37] . Parameters including proportion of variable size ( I ) and gamma rate ( G ) were optimized using amino acid substitution matrices JTT for Fig 1 and S3 Fig and LG for S2 Fig based on minimum Bayesian information criterion ( S9 Table ) [37] . Finally rooted ( Fig 1 and S3 Fig ) or unrooted ( S2 Fig ) consensus trees of 1 , 000x bootstrapping using nearest-neighbor interchange were created and visualized using the R package “ggtree” v1 . 12 . 0 [37 , 38] . O . bicornis cocoons were purchased from Dr Schubert Plant Breeding ( Landsberg , Germany ) and stored at 4°C in constant darkness . To trigger emergence cocoons were transferred to an incubator ( 25°C , 55% RH , L16:D8 ) with emerged bees fed ab libitum with Biogluc ( 62% sugar concentration consisting of 37 . 5% fructose , 34 . 5% glucose , 25% sucrose , 2% maltose , and 1% oligosaccharides ) ( Biobest ) , soaked into a piece of cotton wool inside a plastic dish . Males ( which are usually first to emerge ) were removed from cages and discarded to reduce any unnecessary stress to the females used in insecticide bioassays . Acute contact toxicity bioassays on unmated 2 day old female O . bicornis were conducted following the OECD Honey Bee Test guidelines , with modification where necessary [39] . Bees were anaesthetised with CO2 for 5–10 seconds to allow application of insecticide . 1 μL of technical grade imidacloprid was applied to the dorsal thorax of each bee at concentrations of 0 . 0001 , 0 . 001 , 0 . 01 , 0 . 1 , 1 , and 10 μg/μL . No mortality was observed using the same concentrations of thiacloprid so a limit test of 100 μg/bee was performed . Control bees were treated with 1 μL 100% acetone . Three replicates of 10 bees were tested for each concentration . Tested individuals were placed back into cages in the incubator ( 25°C , 55% RH , L16:D8 ) , with five bees per cage . In piperonyl butoxide ( PBO ) synergist bioassays , bees were first treated with the maximum sublethal dose ( in this case 100 μg/μL ) of PBO followed by insecticide one hour later . Synergist bioassays included an additional control group treated only with PBO . Mortality was assessed 48 and 72 hours after application . Probit analysis was used to calculate the LD50 values , slope , and synergism ratio ( where relevant ) for each insecticide ( Genstat v . 18 ( VSNI 2015 ) ) . [3H]imidacloprid ( specific activity 1 . 406 GBq μmol−1 ) displacement studies were conducted using membrane preparations isolated from frozen ( −80°C ) O . bicornis heads , following previously published protocols [13] . Briefly , bee heads weighing 10 g were homogenized in 200 ml ice-cold 0 . 1 M potassium phosphate buffer , pH 7 . 4 containing 95 mM sucrose using a motor-driven Ultra Turrax blender . The homogenate was then centrifuged for 10 min at 1200 g and the resulting supernatant filtered through five layers of cheesecloth with protein concentration determined using Bradford reagent ( Sigma ) and bovine serum albumin ( BSA ) as a reference . Assays were performed in a 96-well microtitre plate with bonded GF/C filter membrane ( Packard UniFilter-96 , GF/C ) and consisted of 200 μl of homogenate ( 0 . 48 mg protein ) , 25 μl of [3H]imidacloprid ( 576 pM ) and 25 μl of competing ligand . Ligand concentrations used ranged from 0 . 001 to 10 000 nM and were tested in triplicate per competition assay . The assay was started by the addition of homogenate and incubated for 60 min at room temperature . Bound [3H]imidacloprid was quantified by filtration into a second 96-well filter plate ( conditioned with ice-cold 100 mM potassium phosphate buffer , pH 7 . 4 ( including BSA 5 g litre−1 ) ) using a commercial cell harvester ( Brandel ) . After three washing steps ( 1 ml each ) with buffer the 96-well filter plates were dried overnight . Each well was then loaded with 25 μl of scintillation cocktail ( Microszint-O-Filtercount , Packard ) and the plate counted in a Topcount scintillation counter ( Packard ) . Non-specific binding was determined using a final concentration of 10 μM unlabelled imidacloprid . All binding experiments were repeated twice using three replicates per tested ligand concentration . Data were analysed using a 4 parameter logistic non-linear fitting routine ( GraphPad Prism version 7 ( GraphPad Software , CA , USA ) ) in order to calculate IC50-values ( concentration of unlabelled ligand displacing 50% of [3H]imidacloprid from its binding site ) . Non-linear regression model fitting and statistical comparison of the slopes obtained was performed in the drc package in R [40] . Bees were anaesthetised with CO2 for 5–10 seconds to allow application of insecticide . 5 , 000 ppm of [14C]imidacloprid or [14C]thiacloprid was applied to the dorsal thorax of each bee using a Hamilton repeating dispenser . Three replicates of five bees were placed into cages and fed a 50% sucrose solution from vertically hanging 2 ml syringes . Control bees were treated with acetone . Radiolabelled insecticide was rinsed off of each group of bees at set time intervals ( 0 , 2 , 4 and 24 hours after application ) with acetonitrile water ( 90:10 ) three times . The acetone-washed bees were then individually combusted at 900°C in an Ox 120c oxidizer ( Harvey Instruments Co . , USA ) followed by liquid scintillation counting of the released 14CO2 in an alkaline scintillation cocktail ( Ultima Gold , PerkinElmer ) using a liquid scintillation analyser ( Perkin Elmer Tri-Carb 2910 TR ) . The levels of excreted [14C]imidacloprid or [14C]thiacloprid , and/or metabolites , were measured by wiping cages with filter papers dipped in acetone and 0 . 5 mL aliquots of cuticular rinse or filter papers were added to 3 mL of scintillation fluid cocktail and the radioactivity was quantified by liquid scintillation analysis as above . An unpaired t-test was used to compare the penetration of the two compounds at each time point . Sequences of O . bicornis candidate genes were verified by PCR as follows: Adult female O . bicornis were flash frozen in liquid nitrogen and stored at -80°C prior to extractions . RNA was extracted from a pool of 3–5 bees using the RNeasy Plus kit ( QIAGEN ) following the manufacturer’s protocol . The quantity and quality of RNA were assessed as described above . First-strand cDNA was synthesised at a concentration of 200 ng/μL by reverse transcription using SuperScript III Reverse Transcriptase ( Invitrogen ) according to the manufacturer’s protocol . 25μL reactions contained 1 . 5U DreamTaq DNA Polymerase ( Thermofisher ) , 10mM of forward and reverse primers ( S10 Table ) and 200 ng of cDNA . PCR reaction temperature cycling conditions were 95°C for 2 minutes , followed by 35 cycles of 95°C for 20 seconds ( denaturation ) , 60°C for 20 seconds ( annealing ) , and 72°C for 7 . 5 minutes ( elongation ) . PCR products were visualised on a 1% agarose gel and purified using QIAquick PCR purification kit ( QIAGEN ) . Samples were sequenced by Eurofins ( Eurofins Scientific group , Belgium ) and analysed using Geneious v8 . 1 . 3 software ( Biomatters Ltd , New Zealand ) . O . bicornis P450 genes and house fly NADPH-dependent cytochrome P450 reductase ( CPR ) ( GenBank accession no . Q07994 ) genes were codon optimised for expression in lepidopteran cell lines , synthesized ( Geneart , CA , USA ) and inserted into the pDEST8 expression vector ( Invitrogen ) . The PFastbac1 vector with no inserted DNA was used to produce a control virus . The recombinant baculovirus DNA was constructed and transfected into Trichoplusia ni ( High five cells , Thermo Fisher ) using the Bac-to-Bac baculovirus expression system ( Invitrogen ) according to the manufacturer’s instructions . The titre of the recombinant virus was determined following protocols of the supplier . High Five cells grown to a density of 2 x 106 cells ml-1 were co-infected with recombinant baculoviruses containing each bee P450 and CPR with a range of MOI ( multiplicity of infection ) ratios to identify the optimal conditions . Control cells were co-infected with the baculovirus containing vector with no insert ( ctrl-virus ) and the recombinant baculovirus expressing CPR using the same MOI ratios . Ferric citrate and δ-aminolevulinic acid hydrochloride were added to a final concentration of 0 . 1 mM at the time of infection and 24 h after infection to compensate the low levels of endogenous heme in the insect cells . After 48 h , cells were harvested , washed with PBS , and microsomes of the membrane fraction prepared according to standard procedures and stored at −80°C [41] . Briefly , pellets were homogenised for 30 s in 0 . 1 M Na/K-phosphate buffer , pH 7 . 4 containing 1 mM EDTA and DTT and 200 mM sucrose using a Fastprep ( MP Biomedicals ) , filtered through miracloth and centrifuged for 10 min at 680g at 4°C . The supernatant was then centrifuged for 1 h at 100 , 000g at 4°C , with the pellet subsequently resuspended in 0 . 1M Na/K-phosphate buffer pH 7 . 6 containing 1 mM EDTA and DTT and 10% glycerol using a Dounce tissue grinder . P450 expression and functionality was estimated by measuring CO-difference spectra in reduced samples using a Specord 200 Plus Spectrophotometer ( Analytik Jena ) and scanning from 500 nm to 400 nm [41] . The protein content of samples was determined using Bradford reagent ( Sigma ) and bovine serum albumin ( BSA ) as a reference . Metabolism of thiacloprid and imidacloprid was assessed by incubating recombinant P450/CPR ( 5 pmol/well ) or control virus/CPR ( 5 pmol/well ) with each insecticide ( 25 μM ) in the presence of an NADPH regeneration system at 30±1°C , shaking , for 1 hour . Three replicates were performed for each data point and the total assay volume was 200 μL . Samples incubated without NADPH served as a control . The reactions were terminated by the addition of ice-cold acetonitrile ( to 80% final concentration ) , centrifuged for 10 min at 3000 g and the supernatant analyzed by tandem mass spectrometry as described previously [42] . LC-MS/MS analysis was performed on a Waters Acquity UPLC coupled to a Sciex API 4000 mass spectrometer and an Agilent Infinity II UHPLC coupled to a Sciex QTRAP 6500 mass spectrometer utilizing electrospray ionization . For the chromatography on a Waters Acquity HSS T3 column ( 2 . 1x50 mm , 1 . 8 μm ) , acetonitrile/water/0 . 1% formic acid was used as the eluent in gradient mode . For detection and quantification in positive ion mode , the MRM transitions 253 > 186 , 269 > 202 ( thiacloprid , OH-thiacloprid ) , and 256 > 175 , 272 > 191 ( imidacloprid , OH-imidacloprid ) were monitored . The peak integrals were calibrated externally against a standard calibration curve . Recovery rates of parent compounds using microsomal fractions without NADPH were normally close to 100% . Substrate turnover was determined using GraphPad Prism version 7 ( GraphPad Software , CA , USA ) . CYP9BU1 and CYP9BU2 were codon optimised for D . melanogaster expression and cloned into the pUASTattB plasmid ( GenBank: EF362409 . 1 ) . These constructs were used to create transgenic fly lines , which were then tested in insecticide bioassays against imidacloprid and thiacloprid , as described previously [3] . To examine if P450 expression in O . bicornis is induced by exposure to sublethal concentrations of neonicotinoids , imidacloprid and thiacloprid were dissolved in acetone to the highest concentration possible , before being diluted to the LD10 of imidacloprid ( 0 . 0001 μg/bee ) and thiacloprid ( 0 . 01 μg/bee ) with 50% sucrose ( w/v ) in order to limit the amount of acetone consumed by bees . Prior to commencing oral bioassays bees underwent a 24 hour ‘training’ period in the Nicot cages to enable them to learn to feed from the syringes . This was followed by a 16h starvation period to encourage subsequent feeding . 15μL of the insecticide/sucrose solution was supplied orally to the bees in disposable plastic syringes . Control bees were fed 15μL of a sucrose solution containing the same volume of acetone used to make up the insecticide/sucrose solutions . When all of the solution had been consumed the bees were fed ab libitum with a 50% sucrose solution for 24 h . After this period for each condition four replicates comprising 5 bees per replicate were snap frozen in liquid nitrogen and RNA extracted from each replicate as above . RNA was used as a template for the generation of barcoded libraries ( TrueSeq RNA library preparation , Illumina ) which were sequenced across two lanes of an Illumina HiSeq2500 flowcell ( 100 bp paired end reads ) . Sequencing was carried out by Earlham Institute , Norwich , UK . To identify genes differentially expressed between control and the treatment the Tuxedo workflow was used to map with TopHat against the annotated reference genome , to estimate gene expression with Cufflinks and test for differential expression with Cuffdiff [43] . To examine the expression of candidate P450 genes in tissues with a known role in detoxification or the site of insecticide action the brain , midgut and Malpighian tubules were extracted from flash frozen adult female O . bicornis . RNAlater-ICE ( Life technologies ) was used to preserve RNA during dissections . RNA was extracted as above and first-strand cDNA synthesised using SuperScript III Reverse Transcriptase ( Invitrogen ) according to the manufacturer’s protocol . Quantitative RT-PCR was carried out using a Rotor Gene 6000 machine with the thermocycling conditions: 3 minutes at 95°C followed by 40 cycles of 95°C for 20 seconds ( denaturation ) , 60°C for 20 seconds ( annealing ) , and 72°C for 7 . 5 minutes ( elongation ) . A final melt-curve step was included to rule out any non-specific amplification . 15μL reactions consisted of 6μL cDNA ( 10ng ) , 7μL of SYBR Green Master Mix ( Thermofisher Scientific ) and 0 . 25μM of the forward and reverse primers . All primers were designed using the Prime3 primer design tool ( http://biotools . umassmed . edu/bioapps/primer3_www . cgi ) and are listed in S10 Table . All primers were designed to amplify a ~200bp region of each target gene with low percentage identity to other target genes . The efficiency of each primer set was examined using a standard curve ( concentrations 100–0 . 01ng of cDNA ) . Elongation Factor α1 and elongation factor γ1 were used as housekeeping genes as these were found to exhibit stable expression between different tissues . Each data point consisted of three technical replicates and four biological replicates . Data were analysed using the ΔΔCT method [44] using the geometric mean of the two housekeeping genes to normalise data . | Bees have evolved sophisticated metabolic systems to detoxify the natural toxins encountered in their environment . Recent work has shown that specific enzymes ( cytochrome P450s ) in these biotransformation pathways can be recruited to protect honey bees and bumblebees against certain synthetic insecticides , including some neonicotinoids . However , it is unclear if solitary bees that carry out important pollination services have equivalent enzymes that play a key role in defining their sensitivity to insecticides . In this study we show that the genome of the solitary bee , Osmia bicornis , lacks the subfamily of cytochrome P450 enzymes that break down certain neonicotinoids in eusocial bees . Despite this , O . bicornis exhibits marked tolerance to the neonicotinoid thiacloprid as a result of efficient metabolism by a P450 enzyme from an alternative subfamily . The discovery that O . bicornis has key detoxification enzymes that determine its sensitivity to neonicotinoids can be leveraged to safeguard the health of this important pollinator . | [
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] | 2019 | Genomic insights into neonicotinoid sensitivity in the solitary bee Osmia bicornis |
Common genetic variants contribute to the observed variation in breast cancer risk for BRCA2 mutation carriers; those known to date have all been found through population-based genome-wide association studies ( GWAS ) . To comprehensively identify breast cancer risk modifying loci for BRCA2 mutation carriers , we conducted a deep replication of an ongoing GWAS discovery study . Using the ranked P-values of the breast cancer associations with the imputed genotype of 1 . 4 M SNPs , 19 , 029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211 , 155 SNPs as part of a multi-consortial project . DNA samples from 3 , 881 breast cancer affected and 4 , 330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis . We replicated previously reported breast cancer susceptibility alleles in these BRCA2 mutation carriers and for several regions ( including FGFR2 , MAP3K1 , CDKN2A/B , and PTHLH ) identified SNPs that have stronger evidence of association than those previously published . We also identified a novel susceptibility allele at 6p24 that was inversely associated with risk in BRCA2 mutation carriers ( rs9348512; per allele HR = 0 . 85 , 95% CI 0 . 80–0 . 90 , P = 3 . 9×10−8 ) . This SNP was not associated with breast cancer risk either in the general population or in BRCA1 mutation carriers . The locus lies within a region containing TFAP2A , which encodes a transcriptional activation protein that interacts with several tumor suppressor genes . This report identifies the first breast cancer risk locus specific to a BRCA2 mutation background . This comprehensive update of novel and previously reported breast cancer susceptibility loci contributes to the establishment of a panel of SNPs that modify breast cancer risk in BRCA2 mutation carriers . This panel may have clinical utility for women with BRCA2 mutations weighing options for medical prevention of breast cancer .
The lifetime risk of breast cancer associated with carrying a BRCA2 mutation varies from 40 to 84% [1] . To determine whether common genetic variants modify breast cancer risk for BRCA2 mutation carriers , we previously conducted a GWAS of BRCA2 mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2 ( CIMBA ) [2] . Using the Affymetrix 6 . 0 platform , the discovery stage results were based on 899 young ( <40 years ) affected and 804 unaffected carriers of European ancestry . In a rapid replication stage wherein 85 discovery stage SNPs with the smallest P-values were genotyped in 2 , 486 additional BRCA2 mutation carriers , only published loci associated with breast cancer risk in the general population , including FGFR2 ( 10q26; rs2981575; P = 1 . 2×10−8 ) , were associated with breast cancer risk at the genome-wide significance level among BRCA2 mutation carriers . Two other loci , in ZNF365 ( rs16917302 ) on 10q21 and a locus on 20q13 ( rs311499 ) , were also associated with breast cancer risk in BRCA2 mutation carriers with P-values<10−4 ( P = 3 . 8×10−5 and 6 . 6×10−5 , respectively ) . A nearby SNP in ZNF365 was also associated with breast cancer risk in a study of unselected cases [3] and in a study of mammographic density [4] . Additional follow-up replicated the findings for rs16917302 , but not rs311499 [5] in a larger set of BRCA2 mutation carriers . To seek additional breast cancer risk modifying loci for BRCA2 mutation carriers , we conducted an extended replication of the GWAS discovery results in a larger set of BRCA2 mutation carriers in CIMBA , which represents the largest , international collection of BRCA2 mutation carriers .
Each of the host institutions ( Table S1 ) recruited under ethically-approved protocols . Written informed consent was obtained from all subjects . The majority of BRCA2 mutation carriers were recruited through cancer genetics clinics and some came from population or community-based studies . Studies contributing DNA samples to these research efforts were members of the Consortium of Investigators of Modifiers of BRCA1/2 ( CIMBA ) with the exception of one study ( NICCC ) . Eligible subjects were women of European descent who carried a pathogenic BRCA2 mutation , had complete phenotype information , and were at least 18 years of age . Harmonized phenotypic data included year of birth , age at cancer diagnosis , age at bilateral prophylactic mastectomy and oophorectomy , age at interview or last follow-up , BRCA2 mutation description , self-reported ethnicity , and breast cancer estrogen receptor status . The associations between genotype and breast cancer risk were analyzed within a retrospective cohort framework with time to breast cancer diagnosis as the outcome [15] . Each BRCA2 carrier was followed until the first event: breast or ovarian cancer diagnosis , bilateral prophylactic mastectomy , or age at last observation . Only those with a breast cancer diagnosis were considered as cases in the analysis . The majority of mutation carriers were recruited through genetic counseling centers where genetic testing is targeted at women diagnosed with breast or ovarian cancer and in particular to those diagnosed with breast cancer at a young age . Therefore , these women are more likely to be sampled compared to unaffected mutation carriers or carriers diagnosed with the disease at older ages . As a consequence , sampling was not random with respect to disease phenotype and standard methods of survival analysis ( such as Cox regression ) may lead to biased estimates of the associations [16] . We therefore conducted the analysis by modelling the retrospective likelihood of the observed genotypes conditional on the disease phenotypes . This has been shown to provide unbiased estimates of the associations [15] . The implementation of the retrospective likelihoods has been described in detail elsewhere [15] , [17] . The associations between genotype and breast cancer risk were assessed using the 1degree of freedom score test statistic based on the retrospective likelihood [15] . In order to account for non-independence between relatives , an adjusted version of the score test was used in which the variance of the score was derived taking into account the correlation between the genotypes [18] . P-values were not adjusted using genomic control because there was little evidence of inflation . Inflation was assessed using the genomic inflation factor , λ . Since this estimate is dependent on sample size , we also calculated λ adjusted to 1000 affected and 1000 unaffected samples . Per-allele and genotype-specific hazard-ratios ( HR ) and 95% confidence intervals ( CI ) were estimated by maximizing the retrospective likelihood . Calendar-year and cohort-specific breast cancer incidences for BRCA2 were used [1] . All analyses were stratified by country of residence . The USA and Canada strata were further subdivided by self-reported Ashkenazi Jewish ancestry . The assumption of proportional hazards was assessed by fitting a model that included a genotype-by-age interaction term . Between-country heterogeneity was assessed by comparing the results of the main analysis to a model with country-specific log-HRs . A possible survival bias due to inclusion of prevalent cases was evaluated by re-fitting the model after excluding affected carriers that were diagnosed ≥5 years prior to study recruitment . The associations between genotypes and tumor subtypes were evaluated using an extension of the retrospective likelihood approach that models the association with two or more subtypes simultaneously [19] . To investigate whether any of the significant SNPs were associated with ovarian cancer risk for BRCA2 mutation carriers and whether the inclusion of ovarian cancer patients as unaffected subjects biased our results , we also analyzed the data within a competing risks framework and estimated HR simultaneously for breast and ovarian cancer using the methods described elsewhere [15] . Analyses were carried out in R using the GenABEL libraries [20] and custom-written software . The retrospective likelihood was modeled in the pedigree-analysis software MENDEL [21] , as described in detail elsewhere [15] .
The genomic inflation factor ( λ ) based on the 18 , 086 BRCA2 GWAS SNPs in the 6 , 724 BRCA2 mutation carriers who were not used in the SNP discovery set was 1 . 034 ( λ adjusted to 1000 affected and 1000 unaffected: 1 . 010 , Figure S3 ) . Multiple variants were associated with breast cancer risk in the combined discovery and replication datasets ( Figure S4 ) . SNPs in three independent regions had P-values<5×10−8; one was a region not previously associated with breast cancer . The most significant associations were observed for known breast cancer susceptibility regions , rs2420946 ( per allele P = 2×10−14 ) in FGFR2 and rs3803662 ( P = 5 . 4×10−11 ) near TOX3 ( Table 1 ) . Breast cancer risk associations with other SNPs reported previously for BRCA2 mutation carriers are summarized in Table 1 . In this larger set of BRCA2 mutation carriers , we also identified novel SNPs in the 12p11 ( PTHLH ) , 5q11 ( MAP3K1 ) , and 9p21 ( CDKN2A/B ) regions with smaller P-values for association than those of previously reported SNPs . These novel SNPs were not correlated with the previously reported SNPs ( r2<0 . 14 ) . For one of the novel SNPs identified in the discovery GWAS [2] , ZNF365 rs16917302 , there was weak evidence of association with breast cancer risk ( P = 0 . 01 ) ; however , an uncorrelated SNP , rs17221319 ( r2<0 . 01 ) , 54 kb upstream of rs16917302 had stronger evidence of association ( P = 6×10−3 ) . One SNP , rs9348512 at 6p24 not known to be associated with breast cancer , had a combined P-value of association of 3 . 9×10−8 amongst all BRCA2 samples ( Table 2 ) , with strong evidence of replication in the set of BRCA2 samples that were not used in the discovery stage ( P = 5 . 2×10−5 ) . The minor allele of rs9348512 ( MAF = 0 . 35 ) was associated with a 15% decreased risk of breast cancer among BRCA2 mutation carriers ( per allele HR = 0 . 85 , 95% CI 0 . 80–0 . 90 ) with no evidence of between-country heterogeneity ( P = 0 . 78 , Figure S5 ) . None of the genotyped ( n = 68 ) or imputed ( n = 3 , 507 ) SNPs in that region showed a stronger association with risk ( Figure 1; Table S3 ) , but there were 40 SNPs with P<10−4 ( pairwise r2>0 . 38 with rs9348512 , with the exception of rs11526201 for which r2 = 0 . 01 , Table S3 ) . The association with rs9348512 did not differ by 6174delT mutation status ( P for difference = 0 . 33 ) , age ( P = 0 . 39 ) , or estrogen receptor ( ER ) status of the breast tumor ( P = 0 . 41 ) . Exclusion of prevalent breast cancer cases ( n = 1 , 752 ) produced results ( HR = 0 . 83 , 95% CI 0 . 77–0 . 89 , P = 3 . 40×10−7 ) consistent with those for all cases . SNPs in two additional regions had P-values<10−5 for breast cancer risk associations for BRCA2 mutation carriers ( Table 2 ) . The magnitude of associations for both SNPs was similar in the discovery and second stage samples . In the combined analysis of all samples , the minor allele of rs619373 , located in FGF13 ( Xq26 . 3 ) , was associated with higher breast cancer risk ( HR = 1 . 30 , 95% CI 1 . 17–1 . 45 , P = 3 . 1×10−6 ) . The minor allele of rs184577 , located in CYP1B1-AS1 ( 2p22–p21 ) , was associated with lower breast cancer risk ( HR = 0 . 85 , 95% CI 0 . 79–0 . 91 , P = 3 . 6×10−6 ) . These findings were consistent across countries ( P for heterogeneity between country strata = 0 . 39 and P = 0 . 30 , respectively; Figure S6 ) . There was no evidence that the HR estimates for rs619373 and rs184577 change with age of the BRCA2 mutation carriers ( P for the genotype-age interaction = 0 . 80 and P = 0 . 40 , respectively ) and no evidence of survival bias for either SNP ( rs619373: HR = 1 . 35 , 95% CI 1 . 20–1 . 53 , P = 1 . 5×10−6 and rs184577: HR = 0 . 86 , 95% CI 0 . 79–0 . 93 , P = 2 . 0×10−4 , after excluding prevalent cases ) . The estimates for risk of ER-negative and ER-positive breast cancer were not significantly different ( P for heterogeneity between tumor subtypes = 0 . 79 and 0 . 67 , respectively ) . When associations were evaluated under a competing risks model , there was no evidence of association with ovarian cancer risk for SNPs rs9348512 at 6p24 , rs619373 in FGF13 or rs184577 at 2p22 and the breast cancer associations were virtually unchanged ( Table S4 ) . Gene set enrichment analysis confirmed that strong associations exist for known breast cancer susceptibility loci and the novel loci identified here ( gene-based P<1×10−5 ) . The pathways most strongly associated with breast cancer risk that contained statistically significant SNPs included those related to ATP binding , organ morphogenesis , and several nucleotide bindings ( pathway-based P<0 . 05 ) . To begin to determine the functional effect of rs9348512 , we examined associations of expression levels of any nearby gene in breast tumors with the minor A allele . Using data from The Cancer Genome Atlas , we found that the A allele of rs9348512 was strongly associated with mRNA levels of GCNT2 in breast tumors ( p = 7 . 3×10−5 ) . The hazard ratios for the percentiles of the combined genotype distribution of loci associated with breast cancer risk in BRCA2 mutation carriers were translated into absolute breast cancer risks under the assumption that SNPs interact multiplicatively . Based on our results for SNPs in FGFR2 , TOX3 , 12p11 , 5q11 , CDKN2A/B , LSP1 , 8q24 , ESR1 , ZNF365 , 3p24 , 12q24 , 5p12 , 11q13 and also the 6p24 locus , the 5% of the BRCA2 mutation carriers at lowest risk were predicted to have breast cancer risks by age 80 in the range of 21–47% compared to 83–100% for the 5% of mutation carriers at highest risk on the basis of the combined SNP profile distribution ( Figure 2 ) . The breast cancer risk by age 50 was predicted to be 4–11% for the 5% of the carriers at lowest risk compared to 29–81% for the 5% at highest risk .
In the largest assemblage of BRCA2 mutation carriers , we identified a novel locus at 6q24 that is associated with breast cancer risk , and noted two potential SNPs of interest at Xq26 and 2p22 . We also replicated associations with known breast cancer susceptibility SNPs previously reported in the general population and in BRCA2 mutation carriers . For the 12p11 ( PTHLH ) , 5q11 ( MAP3K1 ) , and 9p21 ( CDKN2A/B ) , we found uncorrelated SNPs that had stronger associations than the originally identified SNP in the breast cancer susceptibility region that should be replicated in the general population . In BRCA2 mutation carriers , evidence for a breast cancer association with genetic variants in PTHLH has been restricted previously to ER-negative tumors [25]; however , the novel susceptibility variant we reported here was associated with risk of ER+ and ER- breast cancer . The novel SNP rs9348512 ( 6p24 ) is located in a region with no known genes ( Figure 1 ) . C6orf218 , a gene encoding a hypothetical protein LOC221718 , and a possible tumor suppressor gene , TFAP2A , are within 100 kb of rs9348512 . TFAP2A encodes the AP-2α transcription factor that is normally expressed in breast ductal epithelium nuclei , with progressive expression loss from normal , to ductal carcinoma in situ , to invasive cancer [26] , [27] . AP-2α also acts as a tumor suppressor via negative regulation of MYC [28] and augmented p53-dependent transcription [29] . However , the minor allele of rs9348512 was not associated with gene expression changes of TFAP2A in breast cancer tissues in The Cancer Genome Atlas ( TCGA ) data; this analysis might not be informative since expression of TFAP2A in invasive breast tissue is low [26] , [27] . Using the TCGA data and a 1 Mb window , expression changes with genotypes of rs9348512 were observed for GCNT2 , the gene encoding the enzyme for the blood group I antigen glucosaminyl ( N-acetyl ) transferase 2 . GCNT2 , recently found to be overexpressed in highly metastatic breast cancer cell lines [30] and basal-like breast cancer [31] , interacts with TGF-β to promote epithelial-to-mesenchymal transition , enhancing the metastatic potential of breast cancer [31] . An assessment of alterations in expression patterns in normal breast tissue from BRCA2 mutation carriers by genotype are needed to further evaluate the functional implications of rs9348512 in the breast tumorigenesis of BRCA2 mutation carriers . To determine whether the breast cancer association with rs9348512 was limited to BRCA2 mutation carriers , we compared results to those in the general population genotyped by BCAC and to BRCA1 mutation carriers in CIMBA . No evidence of an associations between rs9348512 and breast cancer risk was observed in the general population ( OR = 1 . 00 , 95% CI 0 . 98–1 . 02 , P = 0 . 74 ) [14] , nor in BRCA1 mutation carriers ( HR = 0 . 99 , 95% CI 0 . 94–1 . 04 , P = 0 . 75 ) [13] . Stratifying cases by ER status , there was no association observed with ER-subtypes in either the general population or among BRCA1 mutation carriers ( BCAC: ER positive P = 0 . 89 and ER negative P = 0 . 60; CIMBA BRCA1: P = 0 . 49 and P = 0 . 99 , respectively ) . For the two SNPs associated with breast cancer with P<10−5 , neither rs619373 , located in FGF13 ( Xq26 . 3 ) , nor rs184577 , located in CYP1B1-AS1 ( 2p22-p21 ) , was associated with breast cancer risk in the general population [14] or among BRCA1 mutation carriers [13] . The narrow CIs for the overall associations in the general population and in BRCA1 mutation carriers rule out associations of magnitude similar to those observed for BRCA2 mutation carriers . The consistency of the association in the discovery and replication stages and by country , the strong quality control measures and filters , and the clear cluster plot for rs9348512 suggest that our results constitute the discovery of a novel breast cancer susceptibility locus specific to BRCA2 mutation carriers rather than a false positive finding . Replicating this SNP in an even larger population of BRCA2 mutation carriers would be ideal , but not currently possible because we know of no investigators with appropriate data and germline DNA from BRCA2 mutation carriers who did not contribute their mutation carriers to iCOGS . However , CIMBA studies continue to recruit individuals into the consortium . rs9348512 ( 6p24 ) is the first example of a common susceptibility variant identified through GWAS that modifies breast cancer risk specifically in BRCA2 mutation carriers . Previously reported BRCA2-modifying alleles for breast cancer , including those in FGFR2 , TOX3 , MAP3K1 , LSP1 , 2q35 , SLC4A7 , 5p12 , 1p11 . 2 , ZNF365 , and 19p13 . 1 ( ER-negative only ) [18] , [32] , [33] , are also associated with breast cancer risk in the general population and/or BRCA1 mutation carriers . Knowledge of the 6p24 locus might provide further insights into the biology of breast cancer development in BRCA2 mutation carriers . Additional variants that are specific modifiers of breast cancer risk in BRCA2 carriers may yet be discovered; their detection would require assembling larger samples of BRCA2 mutation carriers in the future . While individually each of the SNPs associated with breast cancer in BRCA2 mutation carriers are unlikely to be used to guide breast cancer screening and risk-reducing management strategies , the combined effect of the general and BRCA2-specific breast cancer susceptibility SNPs might be used to tailor manage subsets of BRCA2 mutation carriers . Taking into account all loci associated with breast cancer risk in BRCA2 mutation carriers from the current analysis , including the 6p24 locus , the 5% of the BRCA2 mutation carriers at lowest risk were predicted to have breast cancer risks by age 80 in the range of 21–47% compared to 83–100% for the 5% of mutation carriers at highest risk on the basis of the combined SNP profile distribution . These results might serve as a stimulus for prospective trials of the clinical utility of such modifier panels . | Women who carry BRCA2 mutations have an increased risk of breast cancer that varies widely . To identify common genetic variants that modify the breast cancer risk associated with BRCA2 mutations , we have built upon our previous work in which we examined genetic variants across the genome in relation to breast cancer risk among BRCA2 mutation carriers . Using a custom genotyping platform with 211 , 155 genetic variants known as single nucleotide polymorphisms ( SNPs ) , we genotyped 3 , 881 women who had breast cancer and 4 , 330 women without breast cancer , which represents the largest possible , international collection of BRCA2 mutation carriers . We identified that a SNP located at 6p24 in the genome was associated with lower risk of breast cancer . Importantly , this SNP was not associated with breast cancer in BRCA1 mutation carriers or in a general population of women , indicating that the breast cancer association with this SNP might be specific to BRCA2 mutation carriers . Combining this BRCA2-specific SNP with 13 other breast cancer risk SNPs also known to modify risk in BRCA2 mutation carriers , we were able to derive a risk prediction model that could be useful in helping women with BRCA2 mutations weigh their risk-reduction strategy options . | [
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] | 2013 | Identification of a BRCA2-Specific Modifier Locus at 6p24 Related to Breast Cancer Risk |
Sodium channels are one of the most intensively studied drug targets . Sodium channel inhibitors ( e . g . , local anesthetics , anticonvulsants , antiarrhythmics and analgesics ) exert their effect by stabilizing an inactivated conformation of the channels . Besides the fast-inactivated conformation , sodium channels have several distinct slow-inactivated conformational states . Stabilization of a slow-inactivated state has been proposed to be advantageous for certain therapeutic applications . Special voltage protocols are used to evoke slow inactivation of sodium channels . It is assumed that efficacy of a drug in these protocols indicates slow-inactivated state preference . We tested this assumption in simulations using four prototypical drug inhibitory mechanisms ( fast or slow-inactivated state preference , with either fast or slow binding kinetics ) and a kinetic model for sodium channels . Unexpectedly , we found that efficacy in these protocols ( e . g . , a shift of the “steady-state slow inactivation curve” ) , was not a reliable indicator of slow-inactivated state preference . Slowly associating fast-inactivated state-preferring drugs were indistinguishable from slow-inactivated state-preferring drugs . On the other hand , fast- and slow-inactivated state-preferring drugs tended to preferentially affect onset and recovery , respectively . The robustness of these observations was verified: i ) by performing a Monte Carlo study on the effects of randomly modifying model parameters , ii ) by testing the same drugs in a fundamentally different model and iii ) by an analysis of the effect of systematically changing drug-specific parameters . In patch clamp electrophysiology experiments we tested five sodium channel inhibitor drugs on native sodium channels of cultured hippocampal neurons . For lidocaine , phenytoin and carbamazepine our data indicate a preference for the fast-inactivated state , while the results for fluoxetine and desipramine are inconclusive . We suggest that conclusions based on voltage protocols that are used to detect slow-inactivated state preference are unreliable and should be re-evaluated .
Sodium channels are the key proteins in action potential firing for most excitable cells . They exhibit a complex , membrane potential-dependent gating behavior [1] . Even minor disturbances in the gating behavior can lead to hyperexcitability , which can be one of the causes of various disorders such as epilepsy , migraine , neuropathic and inflammatory pain , muscle spasms , and chronic neurodegenerative diseases . For several decades , sodium channel inhibitors ( SCIs ) have been successfully used to lower excitability as , for example , local anesthetics , anticonvulsants , antiarrhythmics , analgesics , antispastics and neuroprotective agents . Interestingly , the majority of antidepressants were also found to be potent SCIs . In a recent study [2] the highest incidence of SCI activity was found amongst this therapeutic class . We intend to test if the mechanism of action on sodium channels is similar to that of classic SCIs . Thus far only a single drug binding site is established unequivocally on sodium channels , the “local anesthetic receptor” , located within the inner vestibule , its key residue being the phenylalanine located right below the selectivity filter , on domain 4 segment 6 [3] . However , the contribution of individual residues within the inner vestibule changes from drug to drug [4]–[6] . For certain drugs an alternative binding site have been proposed , which is supposed to be located within the outer pore [7] , [8] , but the exact position of the binding site ( s ) for specific SCIs ( other than local anesthetics ) is currently unsettled . For our case the exact location of the binding site is not relevant , we only need to suppose that the major mechanism of inhibition is preferential affinity to- , and stabilization of a specific inactivated state . The major mechanism of SCIs is stabilization of an inactivated channel conformational state as a result of a preferential affinity for that state . The question of which inactivated state is preferred is under debate for many SCI drugs ( e . g . [9]–[12] , or [13]–[17] ) . Sodium channels are capable of fast inactivation ( complete within a few milliseconds ) , and different forms of slow inactivation ( time constants ranging from ∼100 ms to several minutes ) [18] . Slow-inactivated state preference has been proposed as a therapeutic advantage [19]–[21] . Mutations of sodium channel genes which affect slow inactivation are associated with several diseases [22] . Slow inactivation determines sodium channel availability , and thereby contributes to overall membrane excitability , determining the propensity to generate repetitive firing , and the extent of action potential backpropagation . Slow inactivated state preference has been proposed as a potential therapeutic advantage in specific types of epilepsy , neuropathic pain and certain arrhythmias [19]–[22] . Furthermore , this mechanism of sodium channel inhibition has been proposed to modulate neuronal plasticity [22] . In recent years a number of novel slow-inactivated state-preferring drug candidates have been described , including the recently approved antiepileptic drug lacosamide ( Vimpat ) [19] , [23] . This drug has been found to be effective in a model of treatment-resistant seizures , and of diabetic neuropathic pain , in which tests conventional anticonvulsants were found ineffective [19] . Special voltage protocols are used to evoke and study the slow-inactivated state . Availability of channels is studied after a prolonged depolarization ( to induce slow inactivation ) , followed by a hyperpolarizing gap ( to allow recovery from fast , but not slow inactivation ) . Because availability in such protocols is solely determined by the extent of slow inactivation , a drug that decreases availability is considered to be slow-inactivated state-preferring . However , gating rates ( the rate of inactivation and rate of recovery from inactivation ) are altered by drug binding . A fast-inactivated state-preferring drug stabilizes this state by delaying recovery . A delayed recovery does not necessarily indicate actual modification of the gating rate . For example if the bound drug prevents recovery from inactivation , then recovery will appear to be slowed because the drug needs first to dissociate [24] , [25] . In our current study , however , we chose to use a model according to the modulated receptor hypothesis [26] , [27] , i . e . , the change in affinity equals the actual modification of the gating rates . For this reason in our model increased affinity is synonymous with state stabilization . Altered gating rates have been experimentally demonstrated using gating charge measurements [28] , [29] . Because of the altered gating , the rate of recovery from fast inactivation in the presence of the drug can easily overlap with the rate of recovery from slow-inactivated state . The rate of state-dependent association and dissociation of the drug should also be taken into account . As a result , interpretation of data obtained with these protocols is not straightforward ( e . g . [9] , [30] ) . With the help of simulations , we intended to understand the interactions between binding and gating rates and wanted to test the major prototypical inhibitor mechanisms in commonly used protocols . We wanted to explore what could be deduced from these data , and wanted to find the right protocols that could help to determine the inhibition mechanisms . Our data suggest that conclusions based on conventional protocols are not reliable . For example , the fact that one drug preferentially shifts the “steady-state slow inactivation curve” as compared to another drug does not necessarily mean that the drug prefers the slow-inactivated state . Figure 1 illustrates two ( simulated ) drugs investigated in “steady-state inactivation” protocols ( protocols are discussed below ) . Both drugs shifted the “fast inactivation curve” ( Figure 2 , “FInact_V” ) to the same degree , but Drug 1 caused a larger shift in the “slow inactivation curve” ( Figure 2 , “SInact_V” ) . In this special case , however , Drug 1 was defined to have a higher affinity to fast inactivated state , while Drug 2 had a higher affinity to slow inactivated state . We observed , on the other hand , that fast- and slow-inactivated state-preferring drugs tended to preferentially affect the onset of inactivation and recovery , respectively . Therefore we combined the information from these two protocols by plotting effectiveness in one protocol as a function of effectiveness in the other . We observed that data points for fast- and slow-inactivated state-preferring drugs were confined to definite areas of the effectiveness ( inactivation ) – effectiveness ( recovery ) plane . The two areas were found to overlap; therefore , explicit determination of the mechanism was not possible in all cases . Using patch-clamp experiments , we tested three classic SCIs ( lidocaine , phenytoin and carbamazepine ) and two antidepressants ( fluoxetine and desipramine ) . Properties of inhibition by classic SCIs were consistent with fast-inactivated state preference with fast binding kinetics . Inhibition by antidepressants was distinctly different . Whether the difference was caused by slow binding kinetics or slow-inactivated state preference could not be determined .
For simulations two different kinds of models were used: a phenomenological Hodgkin-Huxley type model and a state model similar to the one published by Kuo and Bean [31] . In both models , however , we introduced slow-inactivated states and drug-bound states with altered gating transition rates . For a detailed description of the models see Methods and Text S1 . The Hodgkin-Huxley type model , which will be referred to as the “tetracube” model because of its topology ( see Methods ) , was used for most simulations . The Kuo-Bean type model , referred to as the “multi-step-activation” ( MSA ) model , was only used for testing the robustness of our observations . In the models , both the degree of alteration of the transition rates and the state preference ( the difference between affinities for different states ) were given by a single factor CF ( for fast-inactivated state-preferring drugs ) or CS ( for slow-inactivated state-preferring drugs ) . The kinetics of association and dissociation to the resting state are defined by the rate constants ka and kd , respectively . Association and dissociation rate constants to other states were calculated as described in Methods . To compare simulated data with experimental results , we used similar voltage protocols in both the simulations and experiments ( Figure 2 ) . Throughout this study we used four protocols: “FInact_V” is a standard “steady-state fast inactivation” protocol in which availability is assessed as a function of pre-pulse membrane potential . The pre-pulse duration was 0 . 1 s when we compared the effects of “FInact_V” and “SInact_V” . In electrophysiology experiments , because drugs with differing mechanisms of action and association kinetics had to be compared , a 2 s pre-pulse duration was used . Note , that although we use the widespread term “steady-state fast inactivation” protocol , the term is incorrect for two reasons . First , it is not necessarily “steady-state” in the sense that the pre-pulse duration may not be long enough for reaching equilibrium of either drug binding or channel gating ( 2 s is enough for the development of some degree of slow inactivation ) . Second , “availability” would be a better term than “inactivation” , because the protocol does not necessarily reflect only inactivation in the presence of a drug , since we cannot separate blocked open and inactivated channels; however , “fast availability” and “slow availability” protocols are improper terms . “SInact_V” is a “steady-state slow inactivation” protocol in which occupancy of the slow-inactivated state is intended to be measured as a function of the membrane potential . It differs from the previous protocol in two respects: pre-pulse duration is longer ( 10 s ) , allowing more complete development of slow inactivation; and this protocol contains a 10 ms hyperpolarizing ( −150 mV ) gap between the pre-pulse and the test pulse . The hyperpolarizing gap serves to separate occupancy of the slow-inactivated state from that of fast-inactivated states: >95% of channels recover from the fast-inactivated state within this period . Despite the name , however , neither “FInact_V” nor “SInact_V” is able to measure drug effects on a pure population of fast or slow-inactivated channels . Fast inactivation practically reaches equilibrium at most membrane potentials within ∼10 ms . With longer durations of pre-pulses in the “FInact_V” protocols the ratio of slow-inactivated channels increases from ∼5% ( 0 . 1 s pre-pulse ) to ∼40% ( 2 s pre-pulse ) . This is accompanied by a minor shift of the curve ( ΔV1/2<4 mV ) . Drug effects can further change this distribution depending on binding kinetics and state preference . In “SInact_V” protocols most unavailable channels are in a slow-inactivated state in the absence of drugs . However , the presence of a drug may alter the distribution of channel states . The unavailable fraction does not consist of slow-inactivated channels only but also is “contaminated” with drug-bound fast-inactivated channels . The conventional name “steady-state” therefore is absolutely untrue for this protocol , as the extent and V1/2 of slow inactivation is strongly dependent on pre-pulse duration . We nevertheless need to use this terminology as we have discussed above . “SInact_t” ( “Slow inactivation onset as a function of time” ) monitors the effect of prolonged depolarizations on sodium channel availability . In the absence of drugs , the onset of slow inactivation is monitored as a function of time ( duration of depolarizing pulses ) . In the presence of a drug , it is not clear whether it reflects pure slow inactivation or a mixture of fast and slow inactivation ( see below for a detailed explanation ) . “Rec_t” ( “Recovery from inactivation as a function of time” ) monitors recovery after a 5 s depolarization to −20 mV as a function of hyperpolarizing gap duration ( the gap is between the 5 s pre-pulse and the test pulse ) . In the absence of drugs , a 5 s depolarization causes both fast and slow inactivation ( approximately 45–55% , respectively ) , and the protocol monitors recovery from both states . The time constants for recovery were 2 . 21 and 58 . 25 ms [32] . In the presence of drugs , measured recovery reflects the combination of dissociation and recovery from both inactivated states . Concentration-response curves were simulated using single depolarizations to 0 mV from holding potentials of −150 , −90 and −60 mV . We plotted the nSOD values of the “Rec_t” protocol as a function of the nSOD values of the “SInact_t” protocol . ( Figure 5 ) . We investigated the effect of changing the following parameters: i ) binding kinetics of drugs , ii ) state preference factors ( CF and CS ) , iii ) drug concentration , iv ) sodium channel model parameters , and v ) hyperpolarizing gap duration in the “SInact_t” protocol . Binding kinetics: We simulated 10 different pairs of rate constants spanning five orders of magnitude from 5*10−4 to 15 µM−1s−1 ( ka ) and from 0 . 1 to 3000 s−1 ( kd ) . The ratio of ka and kd was kept constant ka/kd = 5*10−3 , ensuring that the affinity of the drug toward the resting channel remained constant . State preference factors: CF and CS were given the following values: 2 , 5 , 10 , 20 or 50 . Using the five CF and the five CS values , each with the ten pairs of ka and kd values , we simulated altogether 100 “drugs” in both “SInact_t” and “Rec_t” protocols . To correct for different potencies , the concentration of each drug was scaled: we used the concentration that caused 50% inhibition of single depolarizations at −90 mV holding potential ( Table S5 ) . Figure 5A shows the distribution of “Rec_t” nSOD vs . “SInact_t” nSOD values . As the binding kinetics were accelerated , data points for specific CF/CS values proceeded clockwise along a closed loop . The explanation is that binding kinetics have a range of optimal effectiveness; kinetics that are too slow do not allow for sufficient association during depolarizations , while kinetics that are too fast cause drug molecules to dissociate more during hyperpolarizations . Around the optimum conditions , effectiveness in the “SInact_t” protocol increases with an acceleration in the kinetics in parallel with a decrease of effectiveness in the “Rec_t” protocol . When CF and CS values were changed without concentration correction , the absolute value of the change was proportional to the value of CF and CS , but the characteristic clockwise loop pattern was unchanged ( Figure 5B ) . Drug concentration: The effect of changing concentrations while keeping CF or CS constant ( CF = 10 or CS = 10 ) is shown in Figure 5C . The concentration was decreased and increased tenfold . The effect increased with increasing concentration , while acceleration of the binding kinetics caused the points to move along the clockwise loop as described above . When all simulation results were plotted on the nSOD ( Rec_t ) – nSOD ( SInact_t ) plane , we observed that fast- and slow-inactivated state-stabilizing drugs were confined to limited but overlapping areas of the plane ( Figure 5D ) . Because of the clockwise progression of the points upon acceleration of the binding kinetics , the overlapping area contains mostly “FI_sb” and “SI_fb” type drugs . Sodium channel model parameters: To test the influence of channel parameters , we plotted the results from Monte Carlo simulations of the four prototypical drugs on the nSOD ( Rec_t ) – nSOD ( SInact_t ) plane , and compared those with the areas based on Figure 5D . “FI” drugs were almost exclusively located within the “fast area , ” while “SI” drugs were located within the “slow area , ” practically irrespective of model parameters . The overlapping area was populated mostly by “FI_sb” and SI_fb” drugs , confirming the reliability of “fast” and “slow” areas ( Figure 5E ) . Hyperpolarizing gap duration of the “SInact_t” protocol: In simulations and experiments , we used a 10 ms gap duration , which is enough for a >90% recovery from the fast-inactivated state under control conditions . In the presence of a fast-inactivated state-stabilizing drug , recovery is slowed down . For this reason , in experiments where slow-inactivated state-stabilizing drugs are to be identified , gap duration is often chosen to be of a longer duration ( up to 1 s ) to ensure that the recovery from fast inactivation is complete . Our simulations indicated that “FI_sb” and “SI” type drugs nevertheless overlap in behavior no matter what hyperpolarizing gap duration is chosen ( see Figure 3E ) . We tested the effect of setting the gap duration to 1 s ( Figure 5F ) . “FI” and “SI” type drugs were no better separated with a 1 s than with the 10 ms gap duration . In summary , localization on the nSOD ( Rec_t ) – nSOD ( SInact_t ) plane can reveal the state preference of a drug if it falls on one of the non-overlapping areas . However , many “SI_fb” and “FI_sb” type drugs are expected to fall in the overlapping section and , therefore , their state preference cannot be determined . The following SCI drugs were used: the local anesthetic and antiarrhythmic lidocaine ( 300 µM ) , the anticonvulsants phenytoin ( 300 µM ) and carbamazepine ( 300 µM ) , and the antidepressants fluoxetine ( 30 µM ) and desipramine ( 30 µM ) . The concentrations were chosen to be similarly effective in causing a hyperpolarizing shift ( −10 to −18 mV ) of the “steady-state inactivation” curve ( “FInact_V” – 2 s pre-pulse ) ( Figure 6A ) . In the “SInact_t” protocol ( Figure 6B ) , carbamazepine and phenytoin caused only a small acceleration in the process of inactivation . Fluoxetine and desipramine caused an obvious shift , similar to the one caused by the prototypical drugs “FI_sb , ” “SI_fb” and “SI_sb . ” Lidocaine strongly shifted the curve ( especially in the early phase ) , which is typical of “FI_fb” type drugs . The reason for the small effect of carbamazepine was its fast dissociation kinetics . When the hyperpolarizing gap duration was changed from 10 ms at −150 mV to 5 ms at −120 mV ( similar to the protocol used by Kuo et al . [12] ) , carbamazepine became as effective as lidocaine ( Figure 6B inset ) . In the “Rec_t” protocol ( Figure 6C ) , fluoxetine and desipramine shifted the curve of recovery , similar to the prototypical drugs “FI_sb , ” “SI_sb” and “SI_fb . ” Carbamazepine , phenytoin and lidocaine only altered the early phase of the recovery curve , similar to the drug “FI_fb . ” We created the nSOD ( Rec_t ) – nSOD ( SInact_t ) plots for all five drugs ( Figure 6D ) . The data points for fluoxetine and desipramine were in the overlapping area . The data points for carbamazepine , phenytoin and lidocaine fell into the non-overlapping area of fast inactivation stabilizing drugs .
Slow-inactivated state preference has been proposed to be a therapeutic advantage [19]–[21] , and therefore different drugs have been tested for this property . The question of fast- or slow-inactivated state preference is a complex problem because of the interdependence of binding and gating equilibria . Multiple interconnected equilibria can be relatively easily handled by modeling; therefore , we used this approach to test hypotheses regarding state preference . Our current simulation data suggest that conclusions based on conventional protocols [19]–[21] , [33]–[35] are not reliable . A shift of the “steady-state slow inactivation curve” ( “SInact_V” protocol ) , a shift of the “slow inactivation onset” curve ( “SInact_t” protocol ) and a shift of the recovery curve ( “Rec_t” protocol ) could all be caused by both fast- or slow-inactivated state stabilization . This conclusion was confirmed both by testing whether our observations were true for the entire parameter space and by applying a different type of model . We found that , with all combinations of parameters ( within the reasonable range ) , our observations held true . Furthermore , both the phenomenological tetracube model and the MSA state model gave qualitatively similar results . Nevertheless , the four prototypical mechanisms behaved appreciably differently . For this reason , we investigated the extent to which the two major mechanisms ( “FI” and “SI” ) could be distinguished using the combined information from different voltage protocols . Based on the nSOD ( Rec_t ) – nSOD ( SInact_t ) plots , we concluded that “FI” type drugs can be recognized , provided that their binding kinetics are fast enough . However , “FI” drugs with slower binding kinetics will overlap with “SI” drugs . Determination of the state preference would only be possible if we could measure the binding kinetics of individual drugs . However , distinguishing slow association from association to a slow-inactivated state is not trivial . In order to separate gating kinetics from binding kinetics , a rapid pulse application of the drug is necessary [32] , [36] . Even in this case , association and dissociation rates cannot be correctly determined because the drug binding site on sodium channels is not extracellularly localized . Therefore , the onset rate of a drug effect may be determined by multiple processes: aqueous phase – membrane partitioning , outer to inner leaflet translocation , intramembrane diffusion and association , itself . Any one of these may be the rate limiting step , which obscures the microscopic association rate . We investigated three well-known SCI drugs ( lidocaine , phenytoin and carbamazepine ) and two antidepressants ( fluoxetine and desipramine ) . The uniquely high incidence of SCI activity among antidepressants [2] , as well as their high affinity to sodium channels as compared to classic SCIs , suggests that the inhibition of sodium channels may contribute to their therapeutic effect . The therapeutic profile of antidepressants is different from that of classic SCIs ( anticonvulsants , local anesthetics , antiarrhythmics ) , and we also intended to study whether the mechanism of inhibition was similar to that of classic SCIs . The experimental behavior of the five drugs was remarkably similar to the behavior of prototypical drugs in simulations . We suggest that lidocaine , phenytoin and carbamazepine stabilize the fast-inactivated state , and that they have fast binding kinetics . Their nSOD ( Rec_t ) – nSOD ( SInact_t ) plot clearly fell into the “fast area . ” Furthermore , their effect on the “Rec_t” curve was similar to the effect of “FI_fb . ” Lidocaine behaved similarly to “FI_fb” in the “SInact_t” protocol as well . We hypothesized that the moderate effect of phenytoin and carbamazepine was due to their extra fast dissociation kinetics . This hypothesis was verified in the case of carbamazepine , which produced the characteristic “FI_fb” type effect on “SInact_t” curves upon minor modifications to the protocol . The nSOD ( Rec_t ) – nSOD ( SInact_t ) plots of fluoxetine and desipramine fell into the overlapping area . Thus , their state preference could not be unambiguously determined . However , their properties of inhibition definitely differed from those of classic SCIs .
Patch clamp electrophysiology was done on native sodium channels in cultured hippocampal neurons . Cell culture preparation and electrophysiology were performed as published previously [32] . Cultured hippocampal neurons ( prepared on the 17th day after gestation ) were found to express mostly the Nav1 . 2 and Nav1 . 6 isoform , but Nav1 . 1 , Nav1 . 3 and Nav1 . 7 isoforms were also detected in a some cells [37] . In spite of the differences in expression pattern biophysical properties of sodium currents were remarkably similar [32] , [37] , and potency of individual drugs showed no higher variance than in experiments using Nav1 . 2 expressing HEK 239 cells ( data not shown ) . Error bars on the figures represent SEM , and the number of cells tested ( n ) was between 4 to 10 . All experimental procedures were approved by the Animal Care and Experimentation Committee of the Institute of Experimental Medicine , and as stated by the decision of the Animal Health and Food Control Department of the Ministry of Agriculture and Regional Development , were in accordance with 86/609/EEC/2 Directives of European Community . The simulation was based on a set of differential equations with the occupancy of each state ( i . e . , the fraction of the ion channel population in that specific state ) given by the following equation: ( 1 ) where Si ( t ) is the occupancy of a specific state at time t and Sj ( t ) is the occupancy of a neighboring state . Neighboring states are states where direct transitions are possible . n is the number of neighboring states , and kij and kji are the rate constants of transitions between neighboring states . Differential equations were solved during simulations using a fourth-order Runge-Kutta method . We used either Berkeley Madonna v8 . 0 . 1 ( http://www . berkeleymadonna . com/ ) or a program written in C++ . | Sodium channels are the key proteins for action potential firing in most excitable cells . Inhibitor drugs prevent excitation ( local anesthetics ) , regulate excitability ( antiarrhythmics ) , or prevent overexcitation ( antiepileptic , antispastic and neuroprotective drugs ) by binding to the channel and keeping it in one of the inactivated channel conformations . Sodium channels have one fast- and several slow-inactivated conformations ( states ) . The specific stabilization of slow-inactivated states have been proposed to be advantageous in certain therapeutic applications . The question of whether individual drugs stabilize the fast or the slow-inactivated state is studied using specific voltage protocols . We tested the reliability of conclusions based on these protocols in simulation experiments using a model of sodium channels , and we found that fast- and slow-inactivated state-stabilizing drugs could not be differentiated . We suggested a method by which the state preference of at least a subset of individual drugs could be determined and tried the method in electrophysiology experiments with five individual drugs . Three of the drugs ( lidocaine , phenytoin and carbamazepine ) were classified as fast-inactivated state-stabilizers , while the state preference of fluoxetine and desipramine was found to be undeterminable by this method . | [
"Abstract",
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] | [
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] | 2010 | Fast- or Slow-inactivated State Preference of Na+ Channel Inhibitors: A Simulation and Experimental Study |
The ubiquitin-proteasome system is a post-translational regulatory pathway for controlling protein stability and activity that underlies many fundamental cellular processes , including cell cycle progression . Target proteins are tagged with ubiquitin molecules through the action of an enzymatic cascade composed of E1 ubiquitin activating enzymes , E2 ubiquitin conjugating enzymes , and E3 ubiquitin ligases . One of the E3 ligases known to be responsible for the ubiquitination of cell cycle regulators in eukaryotes is the SKP1-CUL1-F-box complex ( SCFC ) . In this work , we identified and studied the function of homologue proteins of the SCFC in the life cycle of Trypanosoma brucei , the causal agent of the African sleeping sickness . Depletion of trypanosomal SCFC components TbRBX1 , TbSKP1 , and TbCDC34 by RNAi resulted in decreased growth rate and contrasting cell cycle abnormalities for both procyclic ( PCF ) and bloodstream ( BSF ) forms . Depletion of TbRBX1 in PCF cells interfered with kinetoplast replication , whilst depletion of TbSKP1 arrested PCF and BSF cells in the G1/S transition . Silencing of TbCDC34 in BSF cells resulted in a block in cytokinesis and caused rapid clearance of parasites from infected mice . We also show that TbCDC34 is able to conjugate ubiquitin in vitro and in vivo , and that its activity is necessary for T . brucei infection progression in mice . This study reveals that different components of a putative SCFC have contrasting phenotypes once depleted from the cells , and that TbCDC34 is essential for trypanosome replication , making it a potential target for therapeutic intervention .
Transition from one cell cycle stage to the next is achieved in eukaryotes through mechanisms that provide an all-or-none cyclin-dependent kinases ( CDKs ) activation . The activity of these enzymes is regulated by several mechanisms , including association with regulatory subunits ( cyclins ) , phosphorylation and dephosphorylation and interaction with CDK inhibitors ( CKIs ) [1 , 2] . Levels of cyclins ( CYC ) , CKIs and many other cell cycle regulators oscillate during the cell cycle as a result of periodic proteolysis , generating a unidirectional control . These proteins are targeted for degradation by polyubiquitination , i . e . the attachment of multiple copies of ubiquitin . This process is initiated by an ubiquitin-activating enzyme ( E1 ) , which then transfers ubiquitin to an active cysteine residue of an ubiquitin-conjugating enzyme ( E2 ) as a thioester linkage . Although E2s can attach ubiquitin directly to a lysine residue in a substrate , most physiological ubiquitination reactions require an ubiquitin ligase , or E3 [3] . Once the substrate is polyubiquitinated by an E3 , it is then recognized and degraded by the 26S proteasome . The specificity of ubiquitin-dependent proteolysis derives from the many hundreds of E3 ubiquitin ligases that recognize particular substrates through interaction domains [4] . Two E3 enzymes that comprise multiprotein components are of particular importance for cell cycle progression: the SKP1-CULLIN1-F-box complex ( SCFC ) and the anaphase promoting complex or cyclosome ( APC/C ) [5] . The SCFC consists of three invariant components including SKP1 , CULLIN1 , RBX1/ROC1/HRT1 , the E2 ubiquitin conjugating enzyme CDC34 and various F-box proteins [6] . SKP1 and CULLIN1 are required for the structural organization of the SCFC , whereas RBX1 is a RING ( Really Interesting New Gene ) -finger protein that interacts with E2-ubiquitin-conjugating enzymes . F-box proteins play a unique role in SCFC function , as they interact with SKP1 via their F-box motif and simultaneously bind specific substrates to the SCFC [7] . Each F-box is responsible for recruiting individual hyperphosphorylated substrates to various SCFCs that differ in their F-box adapter protein . Once the substrate is recruited , CDC34 transfers ubiquitin molecules onto lysine residues in the substrate , building polyubiquitin chains . Polyubiquitinated proteins are recognised and degraded by the 26S proteasome . The protozoan parasite Trypanosoma brucei is the etiological agent of African trypanosomiasis in humans ( sleeping sickness ) and cattle ( nagana ) . This parasite has a complex life cycle that alternates between the mammalian hosts and the insect vectors of the Glossina genus ( Tsetse flies ) . The dividing procyclic form ( PCF ) in insects and the long slender bloodstream form ( BSF ) in mammals follow sequential G1 , S , G2 , and M phases [8] . A single mitochondrion in each cell contains a DNA complex termed the kinetoplast , which divides co-ordinately with the nucleus [9 , 10] . Previous work on the 26S proteasome of T . brucei suggested a role for the ubiquitin-mediated degradation of trypanosomatid proteins in the control of cell cycle [11 , 12] and the major effect that can be achieved by targeting this enzyme [13] . In order to investigate the role of the ubiquitination machinery in the cell cycle control of T . brucei , we studied the function of trypanosomal homologous genes of the SCFC . Our results reveal that the identified proteins are necessary for the normal proliferation of T . brucei and that knockdown of these genes generate contrasting phenotypes . The apparent differential activities of these proteins may indicate they don´t form a classic SCFC . Besides establishing the importance of these proteins for the growth of T . brucei in vitro , we also demonstrate that the ubiquitin-conjugating enzyme TbCDC34 is able to conjugate ubiquitin , being indispensable for the maintenance of infection in a mammalian host .
Protein sequences of yeast ( Saccharomyces cerevisiae ) and human ( Homo sapiens ) SCFC subunits were obtained from the Entrez Protein database using the NCBI web site ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=protein ) . Each sequence was used as a query to screen the kinetoplastid genome database at GeneDB ( http://www . genedb . org/ ) for potential SCFC candidates . The identified subunit homologues are listed in Table 1 . Sequences were aligned using Multiple Sequence Alignment by CLUSTALW ( http://align . genome . jp ) . Parameters used for these alignments were: gap open penalty = 10; gap extension penalty = 0 . 05 . The procyclic form T . brucei strain 29–13 [14] was cultured in SDM-79 medium at 28°C supplemented with ( v/v ) tetracycline-deficient fetal bovine serum ( BD Biosciences , Franklin Lakes , New Jersey , USA ) and 3 . 5 mg/ml hemin . Bloodstream form cell line 90–13 [14] was grown at 37°C with 5% CO2 supplied in HMI-9 medium containing 10% fetal bovine serum . To maintain the T7 RNA polymerase and tetracycline repressor gene constructs within the cells , 15 μg/ml G418 and 50 μg/ml hygromycin B were added to the SDM79 medium for the 29–13 cell line , whereas 2 . 5 μg/ml G418 and 5 μg/ml hygromycin B were added to the HMI-9 medium for the 90–13 cell line . Primers for amplification of an RNA interference ( RNAi ) target fragment were designed using the RNAit software tool ( http://trypanofan . path . cam . ac . uk/software/RNAit . html ) . RNAi experiments were designed to knockdown the SCFC subunit homologues identified in T . brucei ( S1 Table ) . RNAi target fragments were amplified by PCR using Phusion DNA polymerase ( Finnzymes , Espoo , Finland ) and genomic DNA template from 29–13 PCF cells with the primers listed in S1 Table that amplify: nucleotides 1116–1613 for TbCULLIN1 , nucleotides 16–419 for TbSKP1 , nucleotides 3–307 for TbRBX1 and nucleotides 31–444 for TbCDC34 . The corresponding DNA fragments were ligated into the pZJM vector [15] by replacing the α-tubulin fragment . The resulting RNAi constructs were linearized with NotI enzyme and introduced into T . brucei cells by electroporation . Transfection of the T . brucei procyclic form was performed according to the procedure described in [14] . Transfection of bloodstream form was performed with an Amaxa Nucleofactor electroporator ( Amaxa , Basel , Switzerland ) using program X-001 and human T cell solution , using 2 . 5×107 cells and 10 μg of DNA . After phleomycin selection , single transfected cells were cloned on 24-well plates , cultivated under phleomycin , and induced by tetracycline to synthesize the double-stranded RNA . Growth studies were initiated by diluting logarithmically growing cells to a starting density of 1x105 cells/ml ( BSF ) or 1x106 cells/ml ( PCF ) . Cell density was measured with a Neubauer haemocytometer . To ectopically express HA-tagged proteins , full-length TbCDC34 was amplified by PCR using the primers described in S1 Table from T . brucei 29–13 strain genomic DNA . The fragments were cloned in p2477/p2619 expressing vector [16] and transfected into T . brucei 29–13 or 90–13 strains . To generate double mutants C84S/S86D of TbCDC34 , a degenerate oligonucleotide encoding C84S and S86D was used with the complete gene cloned in TOPO as template for PCR mutagenesis using the Stratagene Quikchange mutagenesis ( La Jolla , California , USA ) kit as instructed by the manufacturer . All constructs were verified by standard sequencing methods ( Macrogene , Seoul , Korea ) prior to introduction into trypanosomes , and expression was further verified by western blotting where appropriate . To generate the endogenous modified version of TbCDC34 , part of the open reading frame of TbCDC34 and the 6HA from p2477-TbCDC34 was amplified , and together with the 3´untranslated region product were inserted into pENT6BTyYFP [16] between the HindIII and XbaI restriction sites , generating a modified version of the plasmid with a 6HA tag . 6His-TbCDC34 and yeast CDC34∆C-His6 lacking the C-terminal 25 amino acids ( provided by R . Deshaies ) [17] were expressed in Escherichia coli BL21 ( DE3 ) +pLysE cells and purified by Ni-NTA ( Qiagen , Venlo , Netherlands ) , according to the manufacturer . Ubiquitinated proteins were isolated using UbiQapture-Q kit ( Enzo Life Sciences , Farmingdale , New York , USA ) according to the manufacturer´s instruction . Ubiquitinated proteins were isolated from the total cell lysates ( 25 μg total protein of parasites overexpressing TbCDC34 wild-type or mutated version ) with 40 μl of UbiQapture-Q matrix by rotating samples for 4 hours at 4°C . After washing four times , captured proteins were eluted with 2X SDS-PAGE loading buffer and analyzed by western blotting using anti-HA antibody ( Sigma-Aldrich , St . Louis , Missouri , USA ) . To analyze TbCDC34 autoubiquitination and the formation of the thioester bond between TbCDC34 and human ubiquitin , the Ubiquitin activating kit ( Enzo Life Sciences , Farmingdale , New York , USA ) was used . 4 μM of purified wild-type or Cys mutant 6His-TbCDC34 or 6His-HsCdc34 were mixed with 100 nM of Uba1 and 2 . 5 μM of human ubiquitin in ubiquitination buffer , ( 50 μl total volume ) , with or without 5 mM Mg-ATP . Reactions were incubated at 37°C for 3 hours with gentle mixing , and were stopped by addition of an equal volume of 2× SDS-PAGE sample buffer and boiled for 5 min . Samples were analyzed by SDS-PAGE and western blotting using an anti-ubiquitin antibody ( Enzo Life Sciences , Farmingdale , New York , USA ) or an anti-6His antibody ( Roche , Indianapolis , IN , USA ) followed by detection with an infrared-coupled anti-mouse secondary antibody ( Li-Cor Biosciences , Cambridge , UK ) . The cell permeable Cdc34 Inhibitor , CC0651 , was purchased from Calbiochem . Trypanosomes were harvested and washed twice in PBS . Pellets ( 1x107 cells ) were lysed in 100 μl of boiling SDS sample buffer ( 10% [v/v] glycerol , 3% [w/v] SDS , 0 . 01% [w/v] bromophenol blue and 50 mM Tris–HCl [pH: 6 . 8] with or without 100 mM dithiothreitol [DTT] ) and resolved by SDS–PAGE on 10% , 12 . 5% or gradient 4–12% SDS–polyacrylamide mini gels . The proteins were electrophoretically transferred onto polyvinylidene fluoride ( PVDF ) membranes using a wet transfer tank ( BioRad , Hercules , California , USA ) . For analysis of the in vitro ubiquitination assay , proteins were transferred to a 0 . 2 μm nitrocellulose membrane with a Trans Blot SD semi-dry transfer system ( BioRad , Hercules , California , USA ) . Non-specific binding was blocked with Tris-buffered saline with Tween-20 ( TBST ) ( 137 mM NaCl , 2 . 7 mM KCl , 25 mM Tris base [pH: 7 . 4] and 0 . 2% Tween-20 ) supplemented with 5% milk . Commercial polyclonal anti-HA antibody was used at 1:1000 ( Sigma-Aldrich , St . Louis , Missouri , USA ) , anti-ubiquitin antibody at 1:500 ( Enzo Life Sciences , Farmingdale , New York , USA ) and anti-6His antibody ( Roche , Indianapolis , IN , USA ) at 1:500 . Incubations with secondary anti-IgG rabbit or anti-IgG mouse horseradish peroxidase conjugates ( Sigma-Aldrich , St . Louis , Missouri , USA ) were performed at 8 , 000-fold dilution in TBST with 1% BSA , while incubations with IRDye 800CW Donkey anti-mouse secondary antibody ( Li-Cor Biosciences , Cambridge , UK ) were performed at 1:20 , 000 dilution in TBST with 1% BSA . Detection was performed by chemiluminescence with ECL ( GE Healthcare , Little Chalfont , Buckinghamshire , UK ) on BioMaxMR film ( Kodak , Rochester , New York , USA ) or by fluorescence scanning using Odyssey CLx Infrared Imaging System and analyzed using Image Studio software ( Li-Cor Biosciences , Cambridge , UK ) . 1x108 cells were harvested at 3450 x g for 10 min at 4°C and washed with ice-cold PBS . Cells were frozen in dry ice for 1 min and total RNA was extracted using Trizol reagent ( Invitrogen , Carlsbad , California , USA ) according to the manufacturer’s instructions . Purified RNAs were quantified by spectroscopy using a Nanodrop ND-1000 spectrophotometer ( Thermo Scientific , Massachusetts , USA ) and RNA integrity was evaluated by agarose gel electrophoresis . RNA samples were treated with RNase-free DNase I ( Promega , Fitchburg , Wisconsin , USA ) and the Superscript III reverse transcriptase kit ( Invitrogen , Carlsbad , California , USA ) was used to generate cDNA according to the manufacturer’s instructions , using 50 pmol of oligo dTs and 5 μg of total RNA . The mRNA abundance of the selected genes was evaluated by quantitative reverse transcription PCR ( RT-qPCR ) using the primers listed in S1 Table . The GPI-anchor transamidase subunit 8 ( GPI8 ) gene ( Tb10 . 61 . 3060 ) of T . brucei was used as an endogenous control for gene normalization . qPCR reactions were performed in a total volume of 20 μl using Power SYBR Green PCR Master Mix ( Applied Biosystems , Foster City , California , USA ) on a Rotor-Gene 6000 instrument ( Corbett Life Science , Australia ) with 500 nM of each specific sense and anti-sense primers . Cycling conditions were 95°C for 10 min followed by 40 cycles of 30 s at 94°C , 30 s at 60°C , 30 s at 72°C . All gene fragments were amplified in triplicate from each biological replicate and the mean values were considered for further calculations using the standard curve method . All data were normalized to the level of the endogenous reference gene GPI8 and to WT expression values . Northern blotting was carried out using standard procedures . RNA was extracted from 108 cells , and 1 μg RNA separated by gel electrophoresis prior to transfer to N-Hybond membrane . Specific probes were labelled with [α-32P] dCTP ( 3000 Ci/mmol ) ( 109 cpm/pmol , NEN ) and signals were detected by scanning them with a PhosphorImager Storm 820 ( Amersham Pharmacia Biotech , Sweden Biosciences ) . Cells were analyzed by flow cytometry for DNA content following induction of RNAi . Cells were collected by centrifugation at 600 x g ( PCF ) or 1500 x g ( BSF ) for 10 min and washed in cold PBS + 2 mM EDTA ( ethylenediaminetetraacetic acid ) . The cell pellets were resuspended in 200 μl PBS + 2 mM EDTA and mixed with 1 . 5 ml of 70% ethanol in PBS and left overnight at 4°C . Cells were washed in PBS and incubated for 30 min at room temperature in 1 ml of PBS containing 10 mg/ml RNase A and 20 mg/ml propidium iodide . Fluorescence analysis was performed with the FACSCalibur flow cytometer ( BD Biosciences , Franklin Lakes , New Jersey , USA ) . Flow cytometry data was fitted to G1 , S , and G2/M curves using Dean-Jett-Fox model of FlowJo software ( Tree Star , Ashland , Oregon , USA ) . To quantify nucleus-kinetoplast configurations , methanol-fixed smears were re-hydrated in PBS for 10 min and stained with DAPI ( 100 ng/ml ) for 5 min . 250 cells were analyzed per slide . Groups of five C3H/He and Balb/c one-month-old mice were inoculated with 1x106 T . brucei 90–13 cells by intraperitoneal injection . Balb/c mice did not raise a significant parasitemia , therefore C3H/He were selected for further studies . Groups of five C3H/He mice were inoculated with 1x106 BSF TbCDC34-RNAi parasites and different doxycycline ( Sigma-Aldrich , St . Louis , Missouri , USA ) drug treatments were performed . One group was administered doxycycline 1 mg/ml in 2 . 5% glucose at the beginning of the infection , orally and ad libitum . A control group only received 2 . 5% glucose . The course of the infection was evaluated by studying parasitemia and survival . Another experiment consisted in providing a group of five mice with 1 mg/ml doxycycline in water to induce TbCDC34 RNAi after 48 hours post-infection , when mice exhibited noticeable parasitemia ( ∼1 . 5x107 cells/ml ) . A control group of five mice received water without doxycycline . Mice survival and parasitemia in peripheral blood obtained from tail bleeds were monitored each day . Experiments were repeated five times . Animal trials in this manuscript were reviewed and approved by the Animal Care Committee of the Instituto Nacional de Parasitología “Dr . Mario Fatala Chaben” , Administración Nacionalde Laboratorios e Institutos de Salud “Dr . Carlos G . Malbrán” ( Buenos Aires , Argentina ) , who evaluated the rationale , a clear scientific purpose and sample size of animals proposed . Animal studies were conducted in accordance with the Guide for the Care and Use of Laboratory Animals , 8th Edition ( 2011 ) and the NIH guidelines under an Institutional Animal Care and Use Committee-approved protocol from the Oregon Health and Sciences University . Statistical significance in parasite’s growth curves was determined by two-way repeated measures ANOVA test followed by Bonferony post-hoc analysis . Statistical significance in RT-qPCRs was determined by t-test analysis . Statistical analysis was performed using GraphPad Prism 5 ( GraphPad Software , La Jolla , CA , USA ) .
The core components of the SCFC of humans and yeast , SKP1 , CULLIN1/CDC53 , RBX1/ROC1 and the E2 enzyme CDC34 , were used to screen the GeneDB databases of T . brucei , T . cruzi and L . major for homologous proteins . The search resulted in the identification of T . brucei candidate TbSKP1 , TbCULLIN1 , TbRBX1 and TbCDC34 proteins , all of which possess characteristic conserved domains ( Table 1 ) . TbRBX1 shares 63% amino acid identity with its human counterpart ( e-value 2 , 8E-38 ) . The RING motif ( aa . 41 to 95 ) is completely conserved in the amino acid sequences deduced for trypanosomatids . This domain is defined by a series of eight Cys , three His , and an Asp that binds zinc ions [18] . The identity of the sequence corresponding to the RING-H2 domain of TbRBX1 is 72% with its human counterpart ( S1 Fig ) . TbSKP1 shows 33% identity ( e-value 3 , 5E-24 ) with its human homologue . The highest amino acid conservation ( 57 . 8% ) is found in the C-terminal region of the protein ( S2 Fig ) . TbCULLIN1 shares 23% identity ( e-value 2 , 3E-43 ) with human CULLIN1 ( S3 Fig ) . The N-terminal domain involved in the interaction with SKP1 is the less conserved region of the protein . The cullin-homology ( CH ) domain , including residues 352 to 583 , recruits the E2 enzyme and the RBX1 protein . This domain contains Arg442 , which is important for the interaction with CDC34 in yeast [19] . The C-terminal region is the most conserved part of the protein ( 40% ) and comprises the Lys residue that is modified by NEDD8 in humans ( K686 in T . brucei ) . Neddylation of CULLIN1 increases ubiquitin ligase activity in vitro by facilitating the recruitment of E2 enzymes [20] . TbCDC34 possesses 20% identity with its human homologue ( e-value 7 , 4E-31 ) . The active site , the ubiquitin-conjugating ( UBC ) domain , contains the catalytic Cys ( C84 ) , the Ser involved in the formation of the ubiquitin chain ( S86 ) and an insertion of 11 amino acids that differentiates CDC34 from other E2 enzymes ( residues 93–104 ) [21 , 22] . It also possesses a C-terminal extension that is essential for mediating the interaction with the SCFC in humans [23] and yeast [24] ( S4 Fig ) . To determine whether the identified T . brucei SCFC homologue genes have a role in the regulation of trypanosome cell cycle , procyclic and bloodstream parasite forms were transfected with constructs for tetracycline-inducible RNAi against TbRBX1 , TbSKP1 , TbCULLIN1 and TbCDC34 . After tetracycline induction , the abundance of targeted gene transcripts was determined and the effects on proliferation and on the cell cycle of T . brucei were examined . While knockdown of TbSKP1 , TbRBX1 and TbCDC34 led to slower population growth rates ( Figs 1–4 ) , downregulation of TbCULLIN1 failed to register any detectable effect on the growth of both life forms of T . brucei ( S5 Fig ) . In S . cerevisiae , the double mutant CDC34-C95S , L99S encodes an inactive ubiquitin-conjugating enzyme that blocks cell growth when overexpressed in wild-type strains [22 , 30] . In order to investigate the effect of overexpressing TbCDC34 in T . brucei , full-length and a double mutant HA-tagged versions of the protein were expressed under the control of the tetracycline repressor in procyclic and bloodstream form cells ( CDC34-6HA and CDC34MUT-6HA ) [16] . Site-directed mutagenesis was used to generate a mutant version of the putative ubiquitin-accepting amino acids Cys84 ( C84S ) and Ser86 ( S86D ) of TbCDC34 . Growth curves of parasites overexpressing TbCDC34 or TbCDC34MUT showed no obvious differences when compared to their non-induced controls , either in PCF or in BSF cells . Protein expression was confirmed by western blot with anti-HA antibody ( Fig 5B ) . Besides the protein corresponding to the tagged version of TbCDC34 , a second higher molecular mass protein was observed only in the extracts of the induced parasites transformed with the wild-type version of the protein , but not in the extracts with the double mutant . This higher molecular mass protein was no longer detected in the presence of dithiothreitol ( DTT ) ( Fig 5B ) . Since the formation of E2~ubiquitin thioester can be monitored as an 8 kDa SDS–PAGE mobility shift under non-reducing conditions , we concluded that this DTT-sensitive band corresponds to TbCDC34 charged with ubiquitin . To confirm this further , we performed immunoprecipitation assays against the HA epitope in T . brucei extracts overexpressing TbCDC34-6HA and TbCDC34MUT-6HA using a matrix that isolates both mono- and poly-ubiquitinated proteins . TbCDC34 proteins were then analyzed by western blot with anti-HA antibody . Flow through analysis showed that the majority of the higher molecular mass species in the wild-type TbCDC34-expressing sample were retained on the matrix ( compare upper and lower panels in Fig 5B ) , while the unmodified wild type and double mutant proteins were not retained . TbCDC34-6HA but not the TbCDC34MUT-6HA version eluted from the matrix ( Fig 5B ) , although the DTT-sensitive conjugate was not detected , likely due to the release of the thioester during elution procedure . To ensure that TbCDC34 can indeed conjugate ubiquitin molecules , we performed an in vitro gel-based ubiquitination assay . This assay employed purified recombinant wild-type His6-TbCDC34 , His6-TbCDC34MUT or human His6-HsCDC34 as a positive control , together with human ubiquitin and the ubiquitin-activating enzyme ( Ube1 ) ( human ubiquitin has only three amino acid differences to T . brucei ubiquitin and can be used as a substrate ) . Western blotting using anti-ubiquitin antibody revealed that the wild-type TbCDC34 , but not the mutant TbCDC34 conjugated human ubiquitin molecules and autoubiquitinated in an ATP-dependent manner . We detected an ubiquitination ‘ladder’ of higher molecular weight protein species corresponding to the ubiquitinated TbCDC34 or free poly-ubiquitin chains in a similar manner than for HsCDC34 , although with a weaker signal intensity ( Fig 5C ) , likely reflecting a lower enzyme specificity to the human ubiquitin and the human E1 enzyme . Moreover , like the human CDC34 protein [31] , TbCDC34 was able to produce di- , tri- and tetra-ubiquitin ( Fig 5C ) . The subcellular localization of TbCDC34 was investigated by immunofluorescence . A procyclic cell line expressing TbCDC34-6HA from the endogenous locus was generated , and expression of TbCDC34-6HA was confirmed by western blot ( Fig 6A ) . Immunostaining of the endogenously expressed TbCDC34-6HA protein in PCF cells indicated that it localized near the kinetoplast , in a specific spot between the nucleus and the kinetoplast ( Fig 6B ) . This specific localization was only seen in cells that were in the G1/S window , as judged by DAPI analysis . When the kinetoplast started duplication , this spot disappeared and the signal became diffuse in the cytoplasm ( Fig 6B ) . In some cells , TbCDC34-6HA was localized in foci at the anterior tip of the cell . Next , we investigated if the levels of the TbCDC34 protein changed during the cell cycle of the parasites . For this purpose , we synchronized with hydroxyurea PCF cells expressing TbCDC34-6HA from the endogenous locus [32] . Parasites were cultured for 12 hours in the presence of 0 . 2 mM hydroxyurea , washed , and the culture was allowed to resume growth in the absence of hydroxyurea . Cell samples were collected once every 2 hours and subjected to flow cytometry analysis and western blot . At the moment when hydroxyurea was removed , most of the cells were synchronized at S phase of the cell cycle; by 2 h they shifted to the G2/M phase . From 4 to 6 h after hydroxyurea removal , there was a constant shift from the G2/M phase to the G1 phase ( Fig 7A ) . Synchronized samples were analyzed by western blot using the anti-HA antibody for quantification of TbCDC34-6HA , in the absence of DTT to analyze the levels of the tagged protein and possible changes in the posttranslational modification of TbCDC34 . The TbCDC34 protein levels remained constant throughout the cell cycle , although a slight increase of the modified version of the protein could be observed at 2 h after hydroxyurea removal ( G2/M ) ( Fig 7B ) . So far , the small-molecule termed CC0651 is the only known selective inhibitor of the human CDC34 and one of very few described E2 enzyme inhibitors . CC0651 inserts into a cryptic binding pocket on hCDC34 , causing a displacement of E2 secondary structural elements of CDC34 [33] . In order to determine if CC0651 has an effect on TbCDC34 and if it could be used to inhibit T . brucei growth , we performed growth-inhibition assays with different concentrations of the compound . CC0651 inhibited cell growth with an IC50 of 21 . 38 μM ( Fig 8A ) , which is similar to the reported IC50 for PC-3 cells [33] . However , in our assay , we measured the growth inhibition at 48 hours , as compared to 5 days for the human cells [33] . Next , we analyzed the effect of the compound on the cell cycle of the parasites . As shown in Fig 8B , there was a decrease of cells in the G1 phase ( 77 . 9% to 46 . 3% ) , an increase of cells in G2/M phases ( 8 . 6% to 24 . 8% ) and an increase in aberrant cells XNXK ( 0 . 4% to 17% ) , with no apparent changes in S phase . These results are similar to the effect observed in the TbCDC34 RNAi experiments , indicating that the compound may act through inhibition of the trypanosomal enzyme . To corroborate that the compound is acting upon TbCDC34 , we performed an in vitro gel-based ubiquitination assay in the presence or absence of CC0651 . As a control , the reaction was also performed with the human CDC34 ( Fig 8C , left panel ) . The addition of CC0651 to the reaction had modest , but detectable effect on TbCDC34 activity: production of free tri-ubiquitin was significantly decreased at later time points ( Fig 8D ) , while the production of di-ubiquitin increased slightly , but not significantly , and no significant effect was observed on the formation of the thioester adduct ( TbCDC34~Ub ) and of the assembly of polyubiquitin chains . These results resemble the mild effects of the compound CC0651 on the human CDC34 [33] and indicate that TbCDC34 activity is modulated by compound CC0651 in vitro and potentially in culture , as a similar phenotype is observed when treating cells with the compound and the RNAi against TbCDC34 .
In this study , we have selectively down-regulated the expression of the core SCFC subunit homologs and the E2 enzyme CDC34 by RNAi in both the procyclic and bloodstream forms of T . brucei . Contrary to what was reported for yeast and human cells ( i . e . necessary for the G1/S transition ) , downregulation of TbSKP1 , TbRBX1 , TbCULLIN1 and TbCDC34 generated different phenotypes that would indicate that they might not be assemble in a stable complex . Downregulation of TbRBX1 affected the parasite’s growth in the procyclic but not in the bloodstream form ( Fig 3A ) . DNA content analysis of TbRBX1-depleted cells showed a rise in the percentage of cells without kinetoplast , with no changes being observed in nuclear DNA ( Fig 3C and 3D ) . When analyzed by flow cytometry , a pronounced decrease in the number of cells at the G1 stage of the cell cycle was observed ( Fig 3B ) . These results might indicate that TbRBX1 functions in the maintenance or replication of the kinetoplast DNA in procyclic cells . Downregulation of TbSKP1 resulted in an increase of cells in the G1/S transition both in the procyclic and the bloodstream form , consistent with what was observed in H . sapiens and S . cerevisiae [5 , 27 , 34 , 35] . In S . cerevisiae , SKP1 mutants arrest in G1 with a 1C DNA content or in G2 , indicating that SKP1 is required for S phase entry and mitosis [27] . It was reported that downregulation of TbSKP1 resulted in a G2/M blockade in the bloodstream form trypanosomes [36] . The different phenotypes observed in our and Benz and Clayton’s work could be attributed to the different RNAi plasmids employed and unavailability of information on remaining protein levels . Our results show a similar RNAi-induced phenotype in both life cycle stages of T . brucei . Nevertheless , we cannot rule out that TbSKP1 could have functions in both G1 and G2/M cell cycle checkpoints , as it has been previously shown in other eukaryotic cells . The SKP1 acts at different cell cycle checkpoints by binding to different F-box proteins and recognizing different phosphorylated substrates [35] . The lack of a detectable phenotype for TbCULLIN1 could be attributable to expression of proteins with redundant function . Indeed , there are several coding genes for the cullin family in the parasite’s genome . We identified at least seven genes with cullin-homology domains in T . brucei ( Tb927 . 8 . 5970 , Tb927 . 11 . 11430: similar to HsCULLIN1; Tb927 . 10 . 7490: similar to HsCULLIN2; Tb927 . 3 . 1290: similar to HsCULLIN4b; Tb927 . 8 . 5210: similar to CULLIN3; and Tb927 . 10 . 6930 and Tb10 . v4 . 0249: with no similarity to any known cullin ) . Taking these data into account , it is possible that another family member could compensate the depletion of TbCULLIN1 . Also we cannot exclude the possibility that Tb927 . 8 . 5970 analyzed in this study is not orthologous gene of CULLIN1 . The strongest and clearer phenotype was observed with TbCDC34 knockdown . The decreased number of cells with a 1N1K configuration and the increase in cells with 2N2K and multinucleated/multikinetoplast ( Fig 1C ) is consistent with a pre-cytokinesis cell cycle arrest . Parasites depleted of TbCDC34 could not complete cytokinesis , but resumed kDNA and nuclear DNA replication , and accumulated as nearly divided 2N2K cells ( Fig 1C–1E ) . Therefore , TbCDC34 activity seems to affect the final stages of daughter cell separation . The endogenous tagged version of TbCDC34 localized near the kinetoplast in cells that had the configuration 1N1K and , when the kinetoplast started to segregate , TbCDC34 was found in different parts of the cell ( Fig 6B ) . In mammalian cells , it was demonstrated that CDC34/UBE2R1 localizes to punctate structures in interphase cells , predominantly in the nucleus , and in mitotic cells it is recruited to the mitotic spindle at the beginning of anaphase [37] . The different localization of TbCDC34 may reflect differences in function with its human counterpart . TbCDC34 is able to form a thioester bond with ubiquitin in vitro and in vivo , and interestingly can accept the activated form of human ubiquitin from the human E1 enzyme , showing the evolutionary conservation of this reaction ( Fig 5C ) . Mutation of Cys84 ( C84S ) and Ser86 ( S86D ) of TbCDC34 disrupted the thioester formation , confirming the active site of the enzyme . Addition of the small inhibitor CC0651 to the in vitro ubiquitination reaction had modest , but detectable effect on TbCDC34 activity ( Fig 8C and 8D ) , and in culture cells was able to inhibit proliferation of parasites , resulting in a similar cytokinesis defect ( Fig 8B ) . The depletion of various subunits of the putative SCFC and CDC34 unexpectedly led to different phenotypes , suggesting that they are either involved in the ubiquitination of a number of different proteins regulating the cell cycle or that they associate into different complexes . A search for the exact composition of these putative complexes using tandem affinity protein tag affinity chromatography , may explain the discrepancies encounter in this work . Importantly , our results revealed the essential function of TbCDC34 for the replication of T . brucei . Effective clearing from infection observed upon knockdown of TbCDC34 , the activity inhibition with the small molecule CC0651 ( Fig 8 ) and structural differences between the human and trypanosomal CDC34 which should facilitate development toward specificity , indicate that TbCDC34 could be considered as a potential novel drug target in the African sleeping sickness . | African sleeping sickness is a neglected tropical disease caused by infection with the protozoan parasite Trypanosoma brucei , which is transmitted to humans by tsetse flies ( Glossina genus ) . Treatment of the disease is complex and relies on limited pharmaceutical options . Understanding how T . brucei regulates cell cycle progression at a molecular level when alternating between the mammalian host and the insect vector could lead to better therapies . In this study , we examined different T . brucei proteins with homology to components of the SKP1-CUL1-F-box ubiquitin ligase complex ( SCFC ) , previously characterized in other eukaryotes as a regulator of cell cycle progression . We found that depletion of the homologues of a putative SCFC cause T . brucei to develop abnormally , generating different phenotypes of the mammalian and insect stages . Interestingly , depletion of the ubiquitin conjugating enzyme TbCDC34 arrest cells in a pre-cytokinesis stage , indicating that this protein is essential for cytokinesis . In addition to improving our fundamental understanding of the molecular regulation underlying the sophisticated life cycle of T . brucei , this work pinpoints a potential target for drug development against trypanosomiasis . | [
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] | 2017 | The ubiquitin-conjugating enzyme CDC34 is essential for cytokinesis in contrast to putative subunits of a SCF complex in Trypanosoma brucei |
Onchocerciasis or river blindness constitutes a major burden to households especially in resource-poor settings , causing a significant reduction in household productivity . There has been renewed interest from policy makers to reduce the burden of Neglected Tropical Diseases ( NTDs ) such as onchocerciasis on individuals and households . This paper provides new information on the patient’s perceptions of onchocerciasis and its economic burden on households in South-eastern Nigeria . The information will be useful to health providers and policy makers for evidence-informed resource allocation decisions . Information was generated from a cross-sectional household survey conducted in Achi community , Oji River Local Government Area ( LGA ) of Enugu State , Southeast Nigeria . A pre-tested interviewer-administered questionnaire was used to collect data . A total of 747 households were visited randomly and data were collected using pre-tested interviewer administered questionnaire from 370 respondents . The respondents’ knowledge of the cause of symptoms of the disease , costs incurred for seeking treatment and productivity losses were elicited . Data were analyzed using tabulations and inferential statistics . A socio-economic status ( SES ) index was used to disaggregate some key variables by SES quintiles for equity analysis . Many people had more than one type of manifestation of onchocerciasis . However , more than half of the respondents ( 57% ) had no knowledge of the cause of their symptoms . Male respondents had significantly more knowledge of the cause of symptoms than females ( P = 0 . 04 ) but knowledge did not differ across SES ( P = 0 . 82 ) . The average monthly treatment cost per respondent was US$ 14 . 0 . Drug cost ( US$10 ) made up about 72% of total treatment cost . The per capita productivity loss among patients was US$16 and it was higher in the poorest ( Q1 ) ( US$20 ) and the third SES quintiles ( Q3 ) ( US$21 ) . The average monthly productivity loss among caregivers was US$3 . 5 . Onchocerciasis still constitutes considerable economic burden on patients due to the high cost of treatment and productivity loss . Prioritizing domestic resource allocation for the treatment of onchocerciasis is important for significant and sustained reduction in the burden of the disease . In addition , focused health promotion interventions such as health education campaigns should be scaled up in onchocerciasis-endemic communities .
Onchocerciasis , otherwise called river blindness is one of the Neglected Tropical Diseases ( NTDs ) that constitute a public health problem [1] . It is commonly a burden to affected households and results to significant reduction in household productivity [2] . The disease is predominantly chronic with low mortality , and occurs largely in settings with low income , low disease awareness and little access to treatment [3] . These important characteristics of the disease contribute to its neglect as a priority public health problem . Onchocerciasis is spread by the bites of small black flies ( Simulium species ) that breed along fast flowing rivers and streams , hence is more widespread in communities sited nearby . The characteristic symptoms of onchocerciasis often associated with long-term exposure to infection are particularly exasperating and disabling [4] . These symptomatic effects are both dermal and ocular and include atrophy of the skin ( lizard skin ) , itching or pruritis , ocular lesion and nodules which are primarily caused by the presence of the microfilariae ( the immature larval forms of the Onchocerca volvulus ) in the subcutaneous tissue . People with heavy infections could have one or more of the three main conditions: dermatitis , eye lesions , and/or subcutaneous nodules [5] . The disease is reportedly the cause of 60% blindness in different parts of Africa , significantly affecting households socio-economic development [6] , though levels of infection in the African Programme for Onchocerciasis ( APOC ) target areas are expected to fall drastically by 2015 [7] . The mainstay of the control of onchocerciasis is through the Community Directed Treatment with Ivermectin ( CDTI ) strategy [8] . This strategy involves delivering the treatment drugs for onchocerciasis to households via community volunteers [9] . The CDTI strategy is an initiative of the APOC , adopted in 1995 in 16 endemic countries , including Nigeria [9 , 10] and have been in existence ever since . However , global attention and resources have shifted to more visible diseases such as malaria and HIV/AIDS , with less attention and resources devoted to the control of onchocerciasis and reduction of its economic burden . Recently , the World Health Organization ( WHO ) advised countries to aim towards the elimination of onchocerciasis by 2025 [11] . Achieving this mandate will require evidence-based resource priority setting towards more focused and sustainable programmatic approaches . There is a dearth of empirical evidence on the economic burden of onchocerciasis on households in Nigeria , especially in terms of costs of treatment-seeking and lost productivity . Also , whilst the sociological and health effects of onchocerciasis have been documented , there is still paucity of information on the people’s knowledge and perceptions of the disease . In Tanzania , affected individuals were found unable to undertake their normal activities for an average duration of 3 . 7 days in a month [12] . In a multi-country study in Nigeria , Ethiopia and Sudan , onchocerciasis was responsible for poor school performance and a higher drop-out rate among infected children ( due to itching , lack of sleep , visual impairment etc ) [13] . Furthermore , low productivity , low income and higher healthcare related costs were found among infected adults [13] . Another study found that patients with ocular lesion reported giving up jobs because of their visual impairment , which led to loss of personal and household economic productivity in many cases [14] . Improved consumer knowledge of disease causation is considered a prerequisite for any disease control efforts . Better knowledge is shown to have a positive effect on prevention , treatment seeking and adherence to treatment , hence facilitates reductions in the economic burden of the disease [15] . Earlier studies report a low level of knowledge about the etiology of onchocercal symptoms which often led to inappropriate treatment seeking and overall disease management [16 , 17] . Reports also suggest clear disconnect between symptoms of the disease and its cause for example , it has been reported that patients attributed their symptoms to the aging process or other blood-related conditions , while others perceived the various ocular and dermal symptoms of onchocerciasis as unrelated diseases , but rarely attributing the disease to black fly bites [14 , 18] . This paper presents new information on households’ economic burden , including productivity losses due to onchocerciasis in Southeast Nigeria . It also explored patient’s knowledge of the cause and symptoms of onchocerciasis . The information is required for evidence-informed decisions on resource allocation towards the control of this neglected tropical disease .
The study was a cross-sectional household survey conducted in Achi community in Oji River Local Government Area ( LGA ) of Enugu State , Southeast Nigeria . The community was purposively chosen in consultation with the onchocerciasis Unit of the Enugu State Ministry of Health because the disease is endemic in this community . Achi is a rural community situated about 20 kilometres East of Enugu the capital of Enugu State . Subsistence farming provides employment for over 85% of the population in the community . Two villages were randomly selected from the community as the study sites . The study involved households with persons living with signs and symptoms of onchocerciasis . Simple random sampling was used to select households to be visited and screened for the presence of any individual with observable manifestations of the disease through physical examination of the skin . Those with observable signs of onchocerciasis manifestations were included in the study . Physical examinations were carried out by selected interviewers extensively trained for over 5 days by the study researchers and a local health worker on the different symptoms and aspects of the disease and how to administer the questionnaire . Most of the interviewers were from the locality and had previously been involved in other onchocerciasis-related community surveys in the area . Images of the different physical manifestations of onchocerciasis were used for the training and in addition , individuals who had different observable signs and symptoms of onchocerciasis , with their consent , were brought to the training , hence the interviewers were well conversant with the different symptoms . A pre-tested interviewer-administered questionnaire was the data collection tool . The questionnaires were administered to those households that met the inclusion criteria . Questionnaires were pre-tested before the survey in a different LGA where the disease is also common . The pre-test was used to refine and modify the questionnaire to improve clarity of questions and context appropriateness . The minimum sample size was calculated , using a power of 80% , 95% confidence level and a prevalence of 67% [19] , which gave a sample size of 334 . This was increased to 370 to control for non-responses . Households were visited in turn until the required sample size was reached . Ethical approval was obtained for the study from the University of Nigeria Ethics Review Board Enugu . Written informed consent was obtained from each respondent before administering the questionnaire .
Table 1 show that most of the respondents were middle-aged ( 46yrs ) and almost two-thirds were females ( 65% ) . More than a third were not educated ( 39% ) . The educated ones spent an average of 5 years in school and the highest level of education attained by many respondents was primary school . Most of the respondents were monogamously married ( 57% ) and almost half of the respondents were farmers ( 45% ) . The average number of household residents was four ( 4 ) . Almost half ( 45 . 4% ) of the respondents had more than one type of symptom but the commonest manifestation was palpable nodule ( 86 . 9% ) , followed by lizard skin ( 4 . 4% ) , itching ( 12 . 7% ) and ocular lesion ( 12 . 5% ) ( Table 2 ) . Less than half of the respondents ( 42 . 6% ) had knowledge of the cause of their symptoms . Male respondents had significantly better knowledge than females ( P = 0 . 04 ) but knowledge did not differ across SES ( P = 0 . 82 ) . Overall , only about 14% of respondents sought treatment in the one month preceding the survey , 16% in the one year preceding survey and more than half ( 60% ) had never sought any treatment for their condition ( Table 3 ) . The table also shows that about half ( 54 . 2% ) of those that sought treatment did so from a private facility . Out of the 48 respondents that reported having sought treatment , the average monthly treatment cost per out-patient visit was US$ 14 . 0 with drug cost ( US$ 10 ) contributing about 72% of total cost ( Table 4 ) . The average transport cost was US$ 0 . 9 , representing about 6 . 4% of total cost . Overall , about 28% patients reported they have had to miss work in the one month prior to the day of interview due to their onchocerciasis-related illness for an average of 3 days ( Table 5 ) . Patients in the poorest ( Q1 ) and the third ( Q3 ) quintile groups missed work for greater number of days ( 4 days ) than the rest of the SES groups . However , fewer number of caregivers had entirely missed work and for an average of 0 . 7 days . Overall , the illness limited an average of 8 days of work for the patients . Patients in the poorest quintile lost more productive days on average ( 9 days ) compared to the richest who lost 7 days in a month . There were no statistically significant differences in the results ( Table 5 ) . The average cost of patient’s productivity loss was $16 . 0 and it was higher among patients in the poorest ( US$20 ) and the third quintiles ( US$21 ) . Similarly , the cost of productivity loss was also highest among caregivers in the poorest quintiles ( US$8 . 5 ) compared to those in the richest quintile ( US$5 ) . Overall , the total cost of lost productivity was US$19 . 5 ( Table 6 ) .
The study assessed the economic burden of onchocerciasis illness and patient’s knowledge of the disease in Southeast Nigeria . The findings show that patients incur considerable economic burden from onchocerciasis from treatment costs and lost productivity . Since treatment was not usually sought for many symptomatic conditions , the brunt of the burden arises from the indirect economic loss represented by loss of productive time of the sufferers and their caregivers . Evaluations of the economic burden of onchocerciasis have often focused on costs to the provider [22] [23] with little attention paid to the potential cost of treatments to patients . There has also been limited empirical estimation of the productivity losses to patients , especially in endemic communities in Nigeria . Earlier studies have shown the negative impact of onchocerciasis on productivity of farmers [2 , 24 , 25] For example , sufferers have been found to have spent additional $8 . 10 and 6 . 75 hours over a period in seeking care [26] . The treatment cost of US$14 reported in this study is an indication that those who seek treatment for onchocerciasis manifestations incur significant cost , which may constitute an important component of the socio-economic burden of the disease in endemic communities . The reported average daily work loss ( represented by total work absence ) found in this study is less than those reported in earlier studies [27] , However , the substantial work-limited-days raises an issue of concern . A study from western Nigeria found that majority of the patients ( 60% ) lost 14 days monthly and others ( 37 . 8% ) lost between 7 and 14 days , respectively , due to illnesses resulting from onchocerciasis [27] . Another study showed that onchocerciasis decreased the daily wage of workers by approximately 16% , those with intermediate manifestations of onchocercial skin diseases were found to earn approximately 10% lower than non-sufferers [28] . Findings from a study in Nigeria reported that farmers with onchocercial skin disease ( OSD ) had an overall lower standard of living , as indicated by their ownership of fewer personal wealth indicators such as motorcycles , radios , iron roofing and cement-plastered houses [29] . Other researchers argue that in blackfly-infested communities , there were generally low levels of productivity by farmers [2 , 19] . These losses are often due to the fact that the ocular and dermal symptoms of onchocerciasis make it difficult for people to work optimally , thus diminishing their output and income generating capacity [30] . The average monthly cost of productivity loss of US$19 . 5 from patients and caregivers could be considered a huge economic burden on the affected individuals and their families , largely subsistence farmers whose consumption depends largely on their productive ability . Our findings also raises some equity concerns in both productivity losses and associated costs , given that lost productivity was greater among the poorest socio-economic . The absence of risk-pooling mechanisms in Nigeria to protect households from financial shocks that could arise from common healthcare expenditures mean that households and more importantly , poorer households are likely to experience a disproportionate burden of the disease . Estimating the level of productivity loss due to onchocerciasis often presents a peculiar challenge as affected individuals are the poor people less likely in paid employment [31] . There may be limitations in using the national minimum wage to estimate productivity loss because it may have overestimated or underrated the burden of the disease , given that the wage per day may not accurately reflect respondent’s earnings , hence the findings of this study should be interpreted with caution . But using this approach allows a uniform way of evaluating lost productivity , given the multiplicity of job types and earnings in rural communities . The proportion of respondents with ocular lesion and lizard skin compares to the reported finding of 7 . 5% of lizard skin and 14% eye lesion by another study in Nigeria[14] . However , an earlier study in Achi , Oji River shows that a majority ( 44 . 3% ) of subjects had nodules , 18% had lizard skin and 20% had leopard skin [19] . The proportion of subjects with nodules was less than found in our study . However , our study shows reductions in the appearance of other skin and ocular conditions which could be an indication of gains in the widespread distribution of ivermectin drug . In the western part of Nigeria , leopard skin was reportedly the commonest manifestation experienced by respondents and the proportions of patients with nodules were also much less ( 10% ) when compared to our study [14] . This could be explained by the geographic locale of the studies and possible differences in transmission patterns of onchocerciasis [7] . Elsewhere , nodules were considered most common and least worrisome feature of the disease [32] . The fact that just about half of the patients had knowledge of the cause of their symptoms shows that there is still a huge gap in knowledge about the cause of onchocerciasis even in endemic areas . Poor knowledge was found in another study in Southwest Nigeria , where study subjects failed to establish the link between the vector and the symptoms of the disease [18] . Other studies in Nigeria and elsewhere have shown similar findings [33 , 34] . Although we did not find any socio-economic differences in knowledge of cause of the disease , females generally had poor knowledge of cause of manifestation even though more women had the disease . This could be that women are less exposed to health messages and awareness campaigns and poor knowledge could result in neglect of protection and early treatment seeking which may exacerbate disease morbidity [35] . A key limitation , of this study is the sole reliance on physical examination of symptoms by non-medical persons which may have lowered the precision of diagnosis . Future costing studies could combine physical examination and precise clinical methods , including biopsies and tests for visual acuity [36] . However , careful training and supervision of examiners used in this study may well have reduced this limitation to a barest minimum . A further limitation is that the sample may not be representative of the entire communities in Southeast Nigeria since it was done in endemic community . It may be necessary in the future , compare findings from highly , low and non-endemic communities . More work is also necessary to strengthen the evidence base of the economic burden for improved resource allocation decisions . An area for future study could also be to conduct large-scale longitudinal studies examining the burden of disease overtime , the level of income depletion and its effects on households of different socioeconomic status . In conclusion , this study has provided important but scarce information on the patient level cost of treatment seeking and productivity due to onchocerciasis . It shows that onchocerciasis still constitutes considerable burden to patients due to the cost of losses in productivity and treatment seeking . This burden is certain to have negative impact on affected household subsistence . Concerted efforts are required to accelerate actions towards the control of onchocerciasis so as to reduce its economic burden . Prioritizing domestic resource allocation for the treatment of onchocerciasis , especially strengthening , scaling-up and sustaining the CDTI strategy over the long run is key to significant and sustained reduction in the burden of onchocerciasis . This will require a strong political will . In addition , health promotion interventions such as health education campaigns should be scaled up in onchocerciasis-endemic communities . | Onchocerciasis is a public health problem in Nigeria , especially among the poor living in endemic communities . There is a dearth of evidence on the burden of onchocerciasis and studies suggest poor knowledge of the cause of onchocerciasis . This information could facilitate evidence-informed decisions on resource allocation towards the control of this neglected tropical disease . A cross-sectional survey was used to assess the knowledge of disease causation among patients , costs incurred for seeking treatment and productivity losses . About 43% had no knowledge of what caused their symptoms . The average monthly treatment cost per respondent was US$ 14 . 0 . Drug cost ( US$10 ) made up 72% of total treatment cost . The per capita productivity loss among patients was $16 and it was higher among the least poor ( Q1 ) ( US$20 ) and the poor SES ( Q3 ) ( US$21 ) . The average cost of lost productivity among caregivers was US $3 . 5 . These findings suggest that onchocerciasis still constitutes considerable economic burden on patients due to the high cost of treatment and lost productivity . This survey provided a measure of patient treatment cost in a setting with paucity of information . It also shows that targeted health education campaigns remain a fundamental policy option . | [
"Abstract",
"Introduction",
"Methods",
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] | [] | 2015 | Exploring Consumer Perceptions and Economic Burden of Onchocerciasis on Households in Enugu State, South-East Nigeria |
Virulence of complex pathogens in mammals is generally determined by multiple components of the pathogen interacting with the functional complexity and multiple layering of the mammalian immune system . It is most unusual for the resistance of a mammalian host to be overcome by the defeat of a single defence mechanism . In this study we uncover and analyse just such a case at the molecular level , involving the widespread intracellular protozoan pathogen Toxoplasma gondii and one of its most important natural hosts , the house mouse ( Mus musculus ) . Natural polymorphism in virulence of Eurasian T . gondii strains for mice has been correlated in genetic screens with the expression of polymorphic rhoptry kinases ( ROP kinases ) secreted into the host cell during infection . We show that the molecular targets of the virulent allelic form of ROP18 kinase are members of a family of cellular GTPases , the interferon-inducible IRG ( immunity-related GTPase ) proteins , known from earlier work to be essential resistance factors in mice against avirulent strains of T . gondii . Virulent T . gondii strain ROP18 kinase phosphorylates several mouse IRG proteins . We show that the parasite kinase phosphorylates host Irga6 at two threonines in the nucleotide-binding domain , biochemically inactivating the GTPase and inhibiting its accumulation and action at the T . gondii parasitophorous vacuole membrane . Our analysis identifies the conformationally active switch I region of the GTP-binding site as an Achilles' heel of the IRG protein pathogen-resistance mechanism . The polymorphism of ROP18 in natural T . gondii populations indicates the existence of a dynamic , rapidly evolving ecological relationship between parasite virulence factors and host resistance factors . This system should be unusually fruitful for analysis at both ecological and molecular levels since both T . gondii and the mouse are widespread and abundant in the wild and are well-established model species with excellent analytical tools available .
Toxoplasma gondii is an intracellular protozoan parasite with a complex life cycle and is distantly related to the malarial genus Plasmodium . The sexual phase occurs only in the true cats ( Felidae ) , while all warm-blooded animals including humans can be intermediate hosts . The infection is transmitted from cats to intermediate hosts via ingestion of oocysts from the faeces of infected cats , and from intermediate hosts back to cats by carnivory . Typically , T . gondii establishes a lifelong chronic infection in intermediate hosts by encysting , after an initial phase of rapid intracellular proliferation and cell–cell spread , in brain and muscle . The life cycle is completed when the infected host is eaten by a cat [1] . However , some T . gondii strains are highly virulent for mice , killing the host as early as ten days after initial infection . Of the three clonal lineages of T . gondii commonly found in Eurasia and North America [2] , [3] , the type I strains are highly virulent for mice [4] . In a genetic cross between a type I virulent and a type III avirulent strain , the serine-threonine kinase secreted from rhoptry organelles , ROP18 [5] , emerged as a major virulence factor [6] . In another genetic cross , ROP16 kinase and the ROP5 pseudokinases were implicated in virulence differences between type II and type III strains [7] . Comparative studies of ROP18 from multiple T . gondii strains , including the major Eurasian types , established that this virulence protein shows extensive polymorphic sequence variation derived from recent episodes of positive selection [8] . In mice the major resistance factors preventing acute death from avirulent T . gondii infection , and thereby allowing T . gondii transmission , are large GTPases of the immunity-related GTPase ( IRG ) family [9] , [10] . These interferon-γ ( IFNγ ) -inducible proteins accumulate on the parasitophorous vacuole membrane ( PVM ) within minutes after infection of a cell by an avirulent T . gondii strain [11] , [12] . The PVM becomes vesiculated then disrupted , and the parasite is killed [11] , [13] , [14] . Encoded by about 15 active genes in the C57BL/6 mouse genome ( [15] and unpublished results ) , the IRG proteins function interactively and nonredundantly in resistance [16] , so that disruption of single members of the family cause highly significant early mortality following infection with avirulent strains [9] . However , during infection by a type I virulent T . gondii strain , few vacuoles accumulate IRG proteins in large amounts or disrupt [12] , [14] , [17] , [18] , and parasite replication in cells is not inhibited by IFNγ [17] . These parallel observations on parasite virulence and host resistance suggested that IRG proteins might be phosphorylated and inactivated by ROP kinases from virulent strain T . gondii . The switch I and switch II loops of the GTP-binding domain of GTPases are sequence elements whose conformational changes during the GTP-binding and hydrolysis cycle largely determine the functional activity of the protein via its interaction with other proteins [19] , [20] . In classical Ras-like small GTPases the switch I loop contains a conserved threonine ( the “G2 threonine” ) interacting with a catalytically important Mg2+ ion . In Irga6 , the only IRG protein for which structural [21] , biochemical ( [22] and Pawlowski et al . , unpublished data ) , and functional ( [11] and Liesenfeld et al . , unpublished data ) information is available , highly conserved threonines are present in the switch I loop , but the typical Ras configuration is not seen and these residues do not coordinate the Mg2+ ion [21] . We show here that the ROP18 kinase from highly virulent strains targets Irga6 , phosphorylating two threonines in the switch I loop . For Irga6 , the switch I loop from residues 100 to 109 is implicated in the formation of a critical dimer interface with a neighbouring Irga6 molecule during the process of GTP-dependent activation and subsequent hydrolysis ( Pawlowski et al . , unpublished data ) . We present evidence that phosphorylation of either of the target threonines inactivates the GTPase and inhibits its normal accumulation on the PVM . Virulent strains also phosphorylate other IRG resistance proteins , and other ROP kinase homologues have been associated with virulence , suggesting that this will prove to be a general mechanism by which T . gondii moderates the IRG system , thus favouring long-term survival of both parasite and host .
We looked directly for phosphorylation of the mouse IRG protein Irga6 , which we immunoprecipitated from T . gondii-infected fibroblasts . IFNγ-induced fibroblasts were labelled in parallel with 33P-phosphoric acid or 35S-methionine/cysteine and infected with the virulent type I RH-YFP strain T . gondii ( Figure 1 ) . A single phosphorylated protein was immunoprecipitated corresponding to the upper band of a doublet metabolically labelled with 35S-methionine/cysteine , and found only in infected cells ( Figure 1A ) . The single phosphorylated band was found only in immunoprecipitates from virulent strain infections ( Figure 1B , type I strains RH-YFP and BK ) . The 33P-labelled band is not due to another protein coprecipitated with Irga6 since it could also be detected by Western blot with monoclonal and polyclonal antibody reagents specific for Irga6 ( Figure 1C and unpublished data ) . Irga6 is a myristoylated protein [23] , and nonmyristoylated Irga6 also runs slightly slower than native myristoylated protein ( [24] and unpublished data ) . However , both Irga6 bands in immunoprecipitates from the virulent strain infections incorporated 3H-myristic acid ( Figure 1D ) , and are therefore myristoylated . Thus , infection by virulent strains of T . gondii , but not by avirulent strains , results in significant phosphorylation of native , myristoylated Irga6 . The phosphorylated sites on Irga6 were identified by mass spectrometry ( MS ) analysis of the phosphorylated Irga6 band immunoprecipitated from IFNγ-induced fibroblasts infected with RH-YFP . Tryptic peptides were found corresponding in mass to phosphorylated derivatives of a region of the switch I loop of the nucleotide binding domain containing two closely-spaced threonines , T102 and T108 ( Figure 2 ) . Putatively phosphorylated peptides containing both target threonines , corresponded in molecular mass exclusively to monophosphorylated derivatives ( Figure 2A ) . To confirm the phosphorylation of the two minimal peptides , the candidates were further resolved by CID ( collision-induced dissociation ) MS/MS ( Figure S1 ) . Among the crystal structures available for Irga6 [21] , T102 and T108 are found in several different configurations ( Figure 2B ) . In one structure ( Protein Data Bank [PDB] 1TQ6 ) , the side chain of T102 is close to the nucleotide , while the side chain of T108 is distant from the nucleotide and largely exposed to solvent ( Figure 2B , left graphic ) . In another ( PDB 1TQ2B ) , T108 is oriented towards the nucleotide , while T102 is rotated away from the nucleotide ( Figure 2B , right graphic ) . The alternation between the two structures correlates with the presence or absence of a short helical turn in the switch I loop . Phosphorylation of one threonine may lock one configuration for the switch I loop , preventing kinase access to the second threonine . Unlike the classical G2 threonine of many small GTPases , neither T102 nor T108 interacts with the Mg2+ ion in any Irga6 structure so far studied [21] . T108 is absolutely conserved among all the IRG proteins , while T102 is highly but not completely conserved ( Figure 2C ) . That T102 and T108 are targets of phosphorylation by virulent T . gondii strains in infected cells was confirmed by Western blot of T . gondii-infected , IFNγ-induced cells with two rabbit antisera raised respectively against peptides of Irga6 containing phospho-T102 and phospho-T108 . After affinity purification both antibodies identified a single band in IFNγ-induced wild-type ( wt ) fibroblasts infected with virulent RH-YFP strain T . gondii . This band was not found in uninduced , infected fibroblasts nor in induced , uninfected fibroblasts . Most significantly , it was also not found in IFNγ-induced fibroblasts infected with the avirulent strains ME49 ( type II ) and CTG ( type III ) ( Figure 3A ) . The same cell lysates were probed with an anti-Irga6 antibody to confirm the expression of Irga6 in the IFNγ-induced samples ( Figure 3B ) . The specificity of the two anti-phosphopeptide reagents for phosphorylated Irga6 was further confirmed by Western blot on IFNγ-induced Irga6-deficient fibroblasts infected with RH-YFP strain parasites , where no signals were detected ( 2A ) . To examine the probable impact of phosphorylation at the two target threonines we assayed the functional properties of two sets of bacterially expressed mutant Irga6 proteins: ( 1 ) negatively charged , phosphomimetic aspartic acid mutants T102D , T108D and the double mutant T102/108D and ( 2 ) neutral alanine mutants T102A , T108A and T102/108A , compared with the wt Irga6 protein [22] . All the T102 and T108 mutations , whether phosphomimetic or neutral , essentially abolished GTP hydrolysis ( Figure 4A and 4B ) and strongly inhibited GTP-dependent oligomerisation ( Figure 4C and 4D ) . The affinities of all the Thr to Ala mutants were in the wt range for both GTP and GDP , while the affinities of the T102D and T102/108D mutants for GDP were about an order of magnitude lower than wt ( Table S1 ) . For further functional analysis , we expressed the six mutant proteins and wt Irga6 , tagged at the C terminus , by transfection in IFNγ-induced mouse embryonic fibroblasts ( MEFs ) infected with the avirulent ME49 strain T . gondii . The proportion of vacuoles detectably loaded with the mutant Irga6 proteins was in all cases lower than the values for the tagged wt Irga6 ( shown for the phosphomimetic Thr to Asp mutants , Figure 5A ) , though in no case eliminated . The amount of the mutant Irga6 proteins accumulated onto individual loaded vacuoles was , however , greatly reduced ( Figure 5B ) . In addition , the mutant proteins had a weak but consistent dominant negative action on the accumulation of wt Irga6 on the PVM , perhaps by interference with homomeric or heteromeric oligomerisation of active Irga6 protein at the PVM ( Figure 5C ) . This could contribute to the biological effect of the kinase even if not all target IRG molecules are phosphorylated . In conclusion , Irga6 mutated at T102 or T108 is inactive biochemically , is impaired in access to the PVM compared with wt protein , and inhibits to some degree the loading of wt Irga6 onto the PVM . The fact that the Thr to Ala mutants were essentially as impaired as the phosphomimetic Thr to Asp mutants ( Figures 4 and 5; unpublished data ) indicates that phosphorylation acts by interfering with the function of two essential threonines in the switch I region of Irga6 , rather than through some active property conferred by the additional negative charge or of the phosphate group itself . The anti-pT102 and anti-pT108 antibodies detected phosphorylated Irga6 by immunofluorescence on the PVM of virulent RH strain T . gondii in IFNγ-induced cells . Irga6 normally accumulates on the majority of virulent strain vacuoles , but the amount of Irga6 accumulated is much lower than on the vacuoles of avirulent strains [12] . Both anti-phosphopeptide antibodies stained the majority of RH vacuoles ( Figure 6A and 6B ) . Significant but much weaker staining of Irga6 ( pT108 ) , but not Irga6 ( pT102 ) , was also present on some vacuoles of the avirulent ME49 strain: the ME49 vacuole stained with anti-pT108 in Figure 6B ( bottom right image ) is a barely visible example . The basis for the weak but significant signals of the anti-pT108 antibodies on ME49 vacuoles is not yet clear . No staining of either anti-pT102 or anti-pT108 antibodies was seen on virulent strain vacuoles in IFNγ-induced cells from Irga6-deficient mice ( Figure S2B ) or on virulent or avirulent strain vacuoles in uninduced cells ( Figure S3 ) . ROP18 is an active T . gondii kinase that occurs in different allelic forms in the three Eurasian and North American clonal lineages [8] . The protein is secreted directly from apical secretory organelles ( rhoptries ) into the cytosol locally at the site of cell invasion , and it accumulates on the cytosolic face of the PVM of the invading organism [5] , [25] . It is thus well-placed to interfere with the accumulation of IRG proteins on the PVM . If ROP18 is responsible for the phosphorylation of Irga6 in vivo , this property should be conferred on a type III avirulent strain ( CTG ) transgenic for expression of active ROP18 from the virulent type I strain , GT-1 , which is identical to RH strain ROP18 in sequence ( [5] and TGGT1_063760 on ToxoDB , http://toxodb . org ) . Indeed this strain has been shown to gain virulence both in vivo [6] and in vitro [26] . We therefore infected IFNγ-induced primary MEFs with the CTG-ROP18 transgenic ( CTG V1 ) , and , as controls , with CTG transgenic for a kinase-dead mutant of ROP18 ( ROP18-D394A; CTG L1 ) and CTG transgenic for the empty vector ( CTG Ble ) [6] . We detected phosphorylation of Irga6 by metabolic labelling with inorganic 33P-phosphate and autoradiography only from cells infected with the CTG strain transgenic for the active kinase ( CTG V1 , Figure 7A ) , and the phosphothreonine-specific antibodies also detected a signal in Western blot only in lysates from the CTG V1 strain–infected cells ( Figure 7B ) . Strong immunofluorescent staining by anti-pT102 and anti-pT108 antibodies was seen on vacuoles from the active ROP18 transgenic ( Figure 8 ) . The great majority of CTG V1 vacuoles were stained ( Figure 8A , red ) , while few vacuoles of the kinase-dead ROP18 ( CTG L1 , green ) and vector only ( CTG Ble , black ) transgenic T . gondii strains were stained . Furthermore , the intensity of staining of the phosphothreonine antibodies was also strikingly higher on the CTG V1 vacuoles ( Figure 8B and 8C ) . No staining of the anti-phosphothreonine antibodies was detected on any of the transgenic CTG strain vacuoles in cells not induced with IFNγ ( Figure S4 ) . However , significant but weak staining with anti-pT108 antibody was seen on the vacuoles of the CTG strains transgenic for the empty vector and for the kinase-dead ROP18 in IFNγ-induced cells ( Figure 8B ) , echoing the weak staining of ME49 vacuoles by anti-pT108 antibody already noted in Figure 6 . That phosphorylation of Irga6 by virulent ROP18 interferes with Irga6 function , already hinted at by the weak dominant negative effect of the Thr to Asp phosphomimetic mutant proteins shown in Figure 5C , is reflected in a drop in the overall intensity of Irga6 on the vacuoles of cells infected with the CTG V1 strain relative to the staining seen with the two control strains especially at low IFNγ concentrations ( Figure 8D ) . We consider it likely that phosphorylation of Irga6 by virulent ROP18 occurs at the vacuole , and that the reduced loading observed is due to destabilization of interactions between IRG molecules and possibly also to conformational changes affecting the positioning of the myristoyl group required for vacuolar association of Irga6 ( [24] and unpublished data ) . The data presented above showed that the presence of virulent strain ROP18 is required for in vivo phosphorylation of T102 and T108 of Irga6 , but did not show a direct interaction between ROP18 and Irga6 . A direct interaction could , however , be shown in a yeast two-hybrid assay between both wt and kinase-dead ROP18 ( Figure S5 ) . To demonstrate that type I ROP18 is in fact an Irga6 kinase with the appropriate specificity , we coincubated bacterially derived purified full-length mature GST-ROP18-Ty fusion protein with bacterially derived purified wt Irga6 protein and the T102A , T108A , and T102/108A mutant proteins in an in vitro kinase assay , followed by autoradiography on the products ( Figure 9A ) . The wt and all three mutant proteins were phosphorylated by ROP18 . However , quantitation of the signal showed a small but consistent reduction for the T102A mutant and highly significant reductions for both the T108A and the double mutant ( Figure 9B ) . Little ROP18 autophosphorylation was detected at the low ROP18 input concentrations used ( indicated in Figure 9A ) . Confirmation that both T102 and T108 are direct targets for phosphorylation by type I ROP18 was provided by strong signals in a Western blot for the anti-pT102 and anti-pT108 antibodies on Irga6 from a parallel in vitro kinase assay performed with a nonradioactive source of phosphate ( Figure 9C ) . No signals were detected in this assay by each reagent on its specific mutant protein , and from neither reagent on the double mutant protein T102/108A , confirming the specificity of the assay system . We conclude that type I ROP18 is indeed a specific and direct kinase of Irga6 T102 and T108 . In vitro , ROP18 clearly also has another minor target of phosphorylation on Irga6 that we have not identified . The IRG system specifies a number of proteins that act cooperatively in organising the destruction of the T . gondii vacuole . It is therefore of interest that a parallel study has identified a second IRG protein , Irgb6 , as a target for virulent ROP18 kinase , with strong indications that threonines of the switch I loop are the targets [27] . In our own studies we can confirm that Irgb6 is also phosphorylated in IFNγ-induced cells infected by the virulent RH-YFP strain , although the level of phosphorylation is considerably lower than that of Irga6 ( Figure S6A ) . Likewise , Irgb10 is phosphorylated by RH-YFP , but in IFNγ-induced cells though very weakly above a significant level of intrinsic , infection-independent phosphorylation ( Figure S6B ) . Strong phosphorylation of Irgb10 dependent on RH-YFP infection was , however , seen in cells transfected with a plasmid expressing Irgb10 ( Figure S6C ) . Some infection-independent phosphorylation of Irgb10 was also apparent in this experiment . Further analysis is required to establish whether the phosphorylation of Irgb10 involves the same target residues as phosphorylation of Irga6 by virulent ROP18 .
Taken together , our results strongly support the hypothesis that the virulence of type I T . gondii strains for mice is due at least in part to their ability to inactivate host IRG resistance proteins by targeted phosphorylation of threonine residues in the switch I loop by ROP18 kinase . Threonine residues form part of the catalytic interface essential for the formation of the GTP-dependent active dimer , and their modification , like that of all the residues in the catalytic interface ( Pawlowski et al . , unpublished data ) , would be expected to inactivate the resistance protein . The threonine corresponding to T108 is conserved throughout the entire family of IRG proteins in the mouse , and in Irga6 appears to be quantitatively the major target of phosphorylation by type I ROP18 ( Figure 9 ) . We and Fentress et al . [27] have shown both Irgb6 and Irgb10 can be phosphorylated and Fentress et al . have shown that Irgb6 can be phosphorylated by ROP18 in the switch I loop , predominantly at the threonine homologous to Irga6 T102 . Polymorphism in ROP18 kinase is evidently not the only factor responsible for reduced IRG protein loading onto the PVM of virulent strains since the effect is robust to IFNγ concentrations over 100 U/ml with native virulent strains ( Figure 6 and [12] , [17] ) but detectable only at IFNγ concentrations below 3 U/ml with the avirulent CTG strain transgenic for ROP18 from a virulent strain ( Figure 8D ) . In earlier studies , we [12] , [17] and another group [18] showed that the loading of IRG proteins onto the virulent T . gondii vacuole was seriously impaired , in vitro in IFNγ-induced fibroblasts , and in vivo in primed peritoneal macrophages . Irga6 loading intensity was substantially reduced , though the number of Irga6-positive vacuoles was not much affected , while the loading of Irgb6 and Irgb10 was completely eliminated on the great majority of vacuoles . At that time , both groups tried to show that virulent-strain ROP18 kinase was responsible for these effects , we by transfection of mature virulent ROP18 from RH strain into cells infected with the avirulent type II strain , ME49 [12] , the other group by infection in vitro with the same avirulent type III CTG strain transgenic for virulent GT-1 strain ROP18 , and the two control strains used in the present report [18] . Neither group saw any diminution in IRG protein loading and consequently concluded that ROP18 was not responsible for the differential loading of IRG proteins . How are these earlier findings to be reconciled with the present data , and with the data of Fentress et al . [27] ? Our present view is that the earlier failures to show an effect of virulent ROP18 were due to the very high level of cytokine-induced activation of the target cells , in our case with 200 U IFNγ/ml , in the case of the other group following an intense in vivo priming regimen [18] . The present experiments show a highly significant effect of the CTG virulent ROP18 transgenic strain ( CTG V1 ) on Irga6 loading only at an IFNγ dose of 0 . 3 U/ml ( Figure 8D ) . Thus virulent ROP18 alone is apparently unable to recapitulate the striking failures of IRG protein loading onto the vacuoles seen even at very high levels of cytokine activation during infection with native virulent type I T . gondii strains . Nevertheless , the transgenic virulent ROP18 has been shown to have a substantial influence on virulence in a mouse mortality assay [6] and on growth and survival of the parasites in IFNγ-induced macrophages [27] . A plausible conclusion is that there are other factors in T . gondii , over and above ROP18 , that can interfere with IRG protein loading onto the vacuoles of type I virulent strains . Future experiments should be directed towards the identification of these factors . For some time now , it has been clear that the striking virulence differential for mice between the three clonal lineages of T . gondii abundant in Eurasia and North America is not reflected in anything comparable in terms of their virulence for humans . There are important clinical differences but it has not been possible to designate these clearly as differences in virulence [26] . Our findings offer an interesting form of explanation for this well-established observation since the molecular targets of ROP18 in mouse cells , IFN-inducible IRG proteins , are not present in humans , having been lost in the primate lineage before the separation of the Old and New World monkeys [15] , [28] . Humans organise their resistance to T . gondii along other lines , perhaps involving IFNγ-induced depletion of the essential amino acid tryptophan [29] , [30] , for which T . gondii is auxotrophic , as well as other mechanisms [31] , [32] . The p65 guanylate binding proteins ( GBPs ) are also plausible candidates for a human resistance mechanism against T . gondii [33] . However , these proteins are not homologous to the IRG proteins and a role in T . gondii resistance remains to be shown . It is possible therefore that the ROP18 kinase has coevolved with the IRG resistance system , so that in humans , in the absence of the IRG system , the ROP18 kinase is nonfunctional and its polymorphism irrelevant . It should be noted that , although the p65 GBPs have a G2 threonine residue in the switch I loop [34] , there is no significant homology with IRG proteins elsewhere in the switch I loop [35] or indeed elsewhere in the entire protein that would suggest they would be targeted by a common kinase . The ROP18 alleles of the three clonal lineages differ strikingly from each other , and there is internal evidence that the protein has been under recent positive selection [8] . The type II allelic product differs from the type I product by 22 amino acids out of 541 . Type II ROP18 RNA is expressed at essentially the same level as the type I ROP18 RNA [8] but our experiments show that type II strain ROP18 protein is apparently relatively incompetent to phosphorylate either Irga6 or Irgb6 in IFNγ-induced cells . The type II ROP18 is , however , an active kinase ( T . Steinfeldt , unpublished results ) , and weak but detectable phosphorylation of Irga6 was detected in IFNγ-induced cells infected with avirulent type II and III strains . It is not yet clear whether this low level of phosphorylation is due to type II or type III ROP18 kinase , respectively . The type III allelic product differs from the type I product at 78 positions , but perhaps more important , its expression level is very greatly reduced by a promoter modification and as a consequence it is considered a null allele [6] , [8] though it is not known whether the encoded protein has kinase activity . If the ROP18 system has been under recent positive selection , it is likely that small rodents have played an important role as intermediate hosts because of the recent overwhelming dominance of the domestic cat in determining the rate of T . gondii evolution . The IRG system is well developed in this taxonomic group , plausibly in response to selection pressure from T . gondii . Thus a functional explanation for the polymorphism in the ROP18 virulence system may be found in a better understanding of the specificity and mode of action of IRG proteins . Recent ( unpublished ) studies from our laboratory have shown that the IRG system of mice is itself highly polymorphic both structurally and functionally , as is susceptibility to virulent strain T . gondii infection . The fact that ROP18 is polymorphic may indicate that different alleles are optimised for different targets , perhaps for other mouse IRG protein sequences or IRG proteins from other evolutionarily significant intermediate host species . These considerations also suggest an explanation for the existence of highly virulent T . gondii strains in the wild , where they may have a selective advantage in confrontations with mice carrying highly resistant IRG alleles capable of providing sterile immunity against less virulent strains . T . gondii has extensively diversified the ROP kinase family since its separation from its distant apicomplexan cousin , Plasmodium , in which these proteins are not found . Other members of the family , some of which have been described as pseudokinases because of destructive modification of the catalytic site , have also been implicated in virulence by genetic studies [7] . Polymorphic ROP16 kinase has recently been shown to act as a tyrosine kinase affecting the host cell inflammatory pathways regulated by STAT3 [36] and STAT6 [37] , probably by direct phosphorylation and activation of these latent transcription factors . Future studies will reveal whether ROP18 is the only member of the ROP kinase family dedicated to the control of the genetically complex and rapidly evolving IRG resistance system , not only in the mouse but also in most mammalian groups outside the higher primates [15] , [28] . The IRG resistance system may be modulated to a greater or lesser extent by ROP kinases and pseudokinases of all T . gondii strains , each seeking in different mammalian hosts a balance between an excess of virulence , resulting in premature death of the host , and too efficient resistance , resulting in clearance of the parasite and sterile immunity . The inactivation of IRG proteins by phosphorylation of essential switch I threonines appears to be a potential Achilles heel for resistance mediated by IFNγ-inducible GTPases . The switch I and II loops of Rho family GTPases have been extensively targeted by bacterial glucosylating , deamidating , and ADP-ribosylating enzymes to favour or inhibit phagocytic uptake [38] . However , the destructive phosphorylation of switch I threonines of IRG proteins mediated by T . gondii ROP18 kinase appears to be a novel virulence mechanism , in this case targeted against a dedicated resistance system rather than a housekeeping protein , and in this sense resembling in principle the “gene-for-gene” virulence-resistance systems [39] characteristic of R-gene–mediated immunity in plants [40] .
Tachyzoites from T . gondii type I virulent strains RH-YFP [41] and RH [42] , BK [43] , type II avirulent strains ME49 [44] , NTE [45] and PRU-YFP [41] , and type III avirulent strain CTG [46] were maintained by serial passage in confluent monolayers of HS27 cells [11] . Tachyzoites from T . gondii transgenic strains CTG Ble ( a type III strain containing a BleR selectable marker as a control ) , CTG L1 ( CTG expressing a kinase-dead mutant form ( ROP18-D394A ) of the type I GT-1 allele of ROP18 [5] , and CTG V1 ( CTG expressing an active form of the type I GT-1 allele of ROP18 ) [6] were passaged in HS27 cells as described for the other T . gondii strains . All tachyzoites were used immediately after harvest for inoculation of untreated , IFNγ-stimulated , and/or transiently transfected fibroblasts at a multiplicity of infection of 5 to 10 [11] . Wt and Irga6-deficient [11] MEFs prepared from 14-d wt C57BL/6 embryos were maintained in DMEM , high glucose ( Invitrogen ) , supplemented as described above but with 10% FCS ( Biochrom ) . L929 mouse fibroblasts were cultured in IMDM ( Invitrogen ) containing 1× MEM nonessential amino acids , 100 U/ml penicillin , 100 µg/ml streptomycin supplemented with 10% FCS as previously described [14] . Cells were induced for 24 h with 200 U/ml IFNγ ( Cell Concepts ) unless indicated otherwise . 1–2×105 MEFs or 2–4×105 L929 cells were seeded to individual wells of a six-well plate , induced with IFNγ or transiently transfected . Cells were harvested by scraping and centrifugation and were lysed in 100 µl per well of 0 . 5% NP-40 , 150 mM NaCl , 20 mM Tris/HCl ( pH 7 . 6 ) , 2 mM MgCl2 supplemented with protease and phosphatase inhibitors ( Complete , Mini , EDTA free and PhosSTOP , Roche ) for 30 min on ice . Postnuclear lysates were subjected to SDS-PAGE and Western blot . Wt and mutant Irga6 proteins were expressed from pGW1H and pGEX-4T-2 vectors in mouse fibroblasts and Escherichia coli BL21 respectively . The wt constructs were pGW1H-Irga6-ctag1 [16] , [24] and pGEX-4T-2-Irga6 [22] , respectively , and the desired mutants were generated in these constructs by site-directed mutagenesis using the Quick-Change protocol ( Stratagene ) . The following mutant Irga6 proteins were generated: Irga6-T102A , -T108A , -T102/108A , -T102D , -T108D and -T102/108D . Ctag1 was used as C-terminal epitope tag [24] . For expression in mouse fibroblasts , pGW1H constructs were transfected using FuGENE6 ( Roche ) according to the manufacturer's instructions . For biochemical analysis , proteins were expressed from pGEX-4T-2 constructs as N-terminal GST fusion protein and purified as described earlier [16] . The C-terminally Ty-tagged mature form of RH-derived ROP18 was cloned via SalI restriction from pGW1H-ROP18-Ty RH-YFP [12] into the bacterial and yeast expression vectors pGEX-4T-2 ( GE Healthcare ) and pGAD-C3/pGBD-C3 [47] , respectively . Generation of pGAD-/pGBD-Irga6 was described previously [16] . The kinase-dead ROP18-D394A mutant ( numbering refers to unprocessed form ) was generated by site-directed mutagenesis . Primers used were ( including reverse complement sequences ) : Irga6: T102A: 5′-gggaatgaagaagaaggtgcagctaaagctggggtggtggaggtaaccatgg-3′; T102D: 5′-gggaatgaagaagaaggtgcagctaaagatggggtggtggaggtaaccatgg-3′; T108A: 5′- gctaaaactggggtggtggaggtagccatggaaagacatccatac-3′; T108D: 5′-gctaaaactggggtggtggaggtagacatggaaagacatccatac-3′; T102/108A: 5′-gaatgaagaagaaggtgcagctaaagctggggtggtggaggtagccatggaaagacatccatacaaacac-3′; T102/108D: 5′-gaatgaagaagaaggtgcagctaaagatggggtggtggaggtagacatggaaagacatccatacaaacac-3′; ROP18-D394A: 5′-gctcagggaattgtgcatacggctatcaaaccggcgaatttcctc-3′ . RH-derived ROP18 was expressed as a N-terminal GST fusion protein from pGEX-4T-2 in E . coli BL21-CodonPlus ( Stratagene ) upon overnight induction with 0 . 1 mM IPTG at 18°C . The cells were lysed in PBS containing 2 mM DTT , 1% Triton X-114 and Complete Mini Protease Inhibitor Cocktail EDTA free ( Roche ) using a French press ( Thermo Scientific ) . Cleared lysates were purified on a GSTrap FF glutathione Sepharose affinity column ( GE Healthcare ) in PBS containing 2 mM DTT and 1% Triton X-114 . The GST-ROP18 fusion protein was eluted with PBS containing 2 mM DTT , 1% Triton X-114 , and 10 mM reduced glutathione . Fractions containing ROP18 were subjected to size exclusion chromatography ( Superdex 200; GE Healthcare ) in PBS and 2 mM DTT . The remaining Triton X-114 was removed by phase separation at 37°C for 2 min and centrifugation , with ROP18 recovered from the aqueous phase . The protein was concentrated with a Vivaspin centrifugal concentrator ( Sartorius ) . Amounts added to assays are cited as volume of the concentrate . The approximate concentration estimated in a Coomassie spot test [48] was 20 ng/µl . Bacterially derived , purified GST-ROP18 was coincubated with wt or T102A , T108A , and T102/108A Irga6 mutants in 25 mM Tris/HCl ( pH 7 . 5 ) and 15 mM MgCl2 for 30 min at 30°C . Each 50 µl reaction contained either 2 µCi of γ32P-ATP ( Hartmann ) for autoradiography or 1 mM unlabelled ATP for Western blot analysis . Samples were immediately boiled in sample buffer ( 80 mM Tris/HCl [pH 6 . 8] , 5 mM EDTA , 4% SDS , 34% sucrose , 40 mM DTT , 0 . 002% bromophenol blue ) for 5 min at 95°C and proteins were resolved by SDS-PAGE . Gels were dried and exposed to Biomax MS film ( Kodak ) or subjected to Western blot analysis . The yeast two-hybrid assay was performed as described previously [16] . Cells were seeded as above onto 6 cm dishes , induced with IFNγ , washed , and preincubated in phosphate- or methionine/cysteine-free medium for 0 . 5 h before labelling with 100 µCi/ml 33P- or 32P-phosphoric acid , 35S-methionine/cysteine ( all Hartmann ) or 200 µCi/ml 3H-myristic acid ( Perkin Elmer ) . After 1 h cells were infected , in the continued presence of label , with the indicated T . gondii strains . After infection for 2 h , cells were washed twice with ice-cold PBS and lysed in 500 µl 0 . 5% NP-40 , 140 mM NaCl , 2 mM Tris/HCl ( pH 7 . 6 ) containing protease and phosphatase inhibitors ( Complete , Mini , EDTA free , and PhosSTOP , Roche ) for 30 min on ice . Postnuclear lysates were incubated in the presence of specific antibodies or antisera overnight followed by 2 h incubation with 50 µl of protein A–Sepharose ( Amersham ) both at 4°C . Beads were washed once with 150 mM NaCl , 10 mM Tris/HCl ( pH 7 . 6 ) and once with 10 mM Tris/HCl ( pH 8 ) and either stored at −80°C or immediately boiled in sample buffer ( 80 mM Tris/HCl [pH 6 . 8] , 5 mM EDTA , 4% SDS , 34% sucrose , 40 mM DTT , 0 . 002% bromophenol blue ) for 5 min at 95°C . Proteins were resolved by SDS-PAGE , and gels were dried and exposed to Biomax MS film ( Kodak ) or imaged using a FLA3000 phosphorimager ( Amersham ) . For analysis of 3H-labelled proteins the gel was preincubated with Autoradiography Enhancer ( Perkin Elmer ) for 1 h at room temperature and intensively washed before autoradiography of the dried gel . Rabbit antisera were raised at Innovagen AB against the following Irga6 phosphopeptides: Irga6 amino acids 97–107 ( NH2- ) C-EGAAK ( pT102 ) GVVEV ( -CONH2 ) , ( serum 87555 ) , and Irga6 amino acids 103–113 ( NH2- ) C-GVVEV ( pT108 ) MERHP ( -CONH2 ) ( serum 87558 ) . In both cases , the N-terminal cysteine was added for conjugation to carrier ( KLH ) . The 5th bleeds of each antiserum were first absorbed on the unphosphorylated peptide , and then affinity-purified on the phosphopeptide . Other immunoreagents used were: 10D7 and 10E7 mouse monoclonal antibodies [24] and 165 rabbit antiserum [23] against Irga6 , anti-Irgb6 mouse monoclonal B34 [49] , anti-Irgb10 rabbit serum 939/4 raised against recombinant full-length Irgb10 , anti-ctag1 2600 rabbit antiserum [11] , and anti-calnexin antiserum ( Calbiochem ) . Second-stage antibodies were: Alexa 488 and Alexa 555 labelled donkey anti-mouse and anti-rabbit sera ( Molecular Probes ) , goat anti-mouse-HRP ( Pierce ) , and donkey anti-rabbit-HRP ( GE Healthcare ) antibodies . For immunofluorescence microscopy , uninduced or IFNγ-induced cells grown on coverslips were infected or not with T . gondii , washed in PBS , fixed in PBS/3% paraformaldehyde for 15 min at room temperature and permeabilised in ice-cold methanol or 0 . 1% saponin at room temperature for 20 min before immunostaining . Vacuoles containing intracellular tachyzoites were identified from the characteristic phase contrast image . Microscopy and image analysis were performed essentially according to Hunn et al . [16] . All slides were counted double-blind , some independently by two observers . 100–500 vacuoles were counted in two to four independent experiments . This precaution is an essential control on observer bias in all nonautomated assessment of fluorescent images . Individual vacuoles for scoring were identified by a systematic scanning procedure in phase contrast and scored positive or negative for specific immunoreagents . Photographs of 20–40 vacuoles were analysed for fluorescence intensity according to Khaminets et al . [12] . Exposure times are indicated in brackets . Statistical analyses were performed as described previously [12] . Oligomerisation of Irga6 was monitored by conventional light scattering as described earlier [16] . The nucleotide hydrolysis was measured by thin layer chromatography and autoradiography as described earlier [16] . The nucleotide binding affinities were determined by equilibrium titration with mant-labelled nucleotides as described earlier [16] . Irga6 proteins immunoprecipitated from IFNγ-stimulated and T . gondii RH-YFP or RH infected cells were separated by SDS-PAGE and subsequently stained overnight in colloidal Coomassie blue solution . After destaining the gel in H2O , phosphorylated and nonmodified Irga6 bands were cut out and immediately subjected to MALDI and Nano-LC ESI- MS/MS mass spectrometry . SDS-PAGE bands of interest were digested as described elsewhere [50] . In brief , bands of interest were cut out and minced . After destaining with 50% 10 mM NH4HCO3/50% acetonitrile ( ACN ) at 55°C and dehydration in 100%ACN , gel pieces were equilibrated with 10 mM NH4HCO3 containing porcine trypsin ( 12 . 5 ng/µl; Promega , Mannheim ) on ice for 2 h . Excess trypsin solution was removed and hydrolysis was performed for 4 h at 37°C in 10 mM NH4HCO3 . Digests were acidified with 5% trifluoroacetic acid ( TFA ) and the gel pieces were extracted twice with 0 . 1% TFA and then with 60% ACN/40% H2O/0 . 1% TFA followed by a two-step treatment using 100% ACN . Extractions were combined , concentrated by vacuum centrifugation , and desalted according to Rappsilber et al . [51] . Experiments were performed with an LTQ Orbitrap Discovery mass spectrometer ( Thermo ) coupled to a split-less Eksigent nano-LC system . Intact peptides were detected in the Orbitrap at 30 , 000 resolution in the mass-to-charge ( m/z ) range 400–2000 using m/z 445 . 120025 as a lock mass . Up to five CID spectra were acquired following each full scan . Peptides were separated on a 10 cm , 75 µm C18 reversed phase column ( Proxeon ) within 140 min at a flow rate of 200 nl/min ( buffer A: 0 . 5% acetic acid , buffer B: 0 . 5% acetic acid , 80% ACN ) . For peptide mass fingerprinting , 2500 spectra in the mass-to-charge ( m/z ) range of 700–4 , 000 were acquired on a 4800 Plus MALDI-TOF/TOF Analyzer ( Applied Biosystems ) in the reflector positive mode using α-cyano-4-hydroxycinnamic acid ( CHCA ) ( 10 mg/ml , 50% ACN in 0 . 1% aqueous TFA ) . Automatic annotation of monoisotopic peptide signals in tryptic digests was performed using internal calibration on trypsin autolysis peaks at m/z 842 and 2211 . Mascot 2 . 2 ( Matrix Science ) was used for protein identification by searching the Swissprot database of Mus musculus [52] . Mass tolerance for intact peptide masses was 20 ppm for MALDI MS data and 10 ppm for Orbitrap data , respectively . Mass tolerance for fragment ions detected in the linear ion trap was 0 . 8 Da . Oxidation of methionine and phosphorylation of serine , threonine , and tyrosine were set as variable modifications . Swiss-PDBViewer [53] and PyMOL v0 . 99 ( DeLano Scientific ) were used for generation of crystal structure images . The multisequence alignment was generated with ClustalW via the EBI server ( http://www . ebi . ac . uk/Tools/msa/clustalw2/ ) using the default settings and edited and shaded with GeneDoc [54] . SigmaPlot v9 ( Systat ) was used for dissociation constant calculation . AIDA Image Analyser v3 ( Raytest ) and ImageQuant TL v7 ( GE Healthcare ) were used for quantification of photostimulated luminescence . MS spectra analysis and peak annotation was performed using the MaxQuant software version 1 . 1 . 1 . 2 . | Many pathogens manipulate the immune system of their hosts to facilitate infection and ensure transmission to subsequent hosts . The intracellular protozoan Toxoplasma gondii , a relative of the malaria parasite , is able to infect and persist in a remarkable variety of warm-blooded hosts . Indeed roughly a third of the human race carry live Toxoplasma cysts in their brains with no overt effects . Toxoplasma infection is kept at bay in many mammals ( but not in humans ) by a resistance system based on a family of proteins known as the immunity-related GTPase ( IRG ) family . IRG proteins accumulate in infected cells on the vacuoles containing the parasite and ultimately destroy them . In this paper , we show that , in the mouse , Toxoplasma can oppose the IRG system by secreting an enzyme called ROP18 into infected cells , which phosphorylates key amino acids on the IRG proteins , rendering them inactive . Not all strains of Toxoplasma can produce an active form of ROP18 , but those strains that do are more virulent . We propose that individual hosts control Toxoplasma with differing efficiency , and the variation we see in ROP18 kinase activity produced by different Toxoplasma strains is an evolutionary response to this . Thus , in different mammalian hosts , each strain seeks a balance between an excess of virulence ( resulting in premature death of the host ) and resistance that is too efficient ( resulting in clearance of the parasite and sterile immunity ) . | [
"Abstract",
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"Results",
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"Methods"
] | [
"evolutionary",
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"ecology",
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] | 2010 | Phosphorylation of Mouse Immunity-Related GTPase (IRG) Resistance Proteins Is an Evasion Strategy for Virulent Toxoplasma gondii |
Functionally autonomous regulatory domains direct the parasegment-specific expression of the Drosophila Bithorax complex ( BX-C ) homeotic genes . Autonomy is conferred by boundary/insulator elements that separate each regulatory domain from its neighbors . For six of the nine parasegment ( PS ) regulatory domains in the complex , at least one boundary is located between the domain and its target homeotic gene . Consequently , BX-C boundaries must not only block adventitious interactions between neighboring regulatory domains , but also be permissive ( bypass ) for regulatory interactions between the domains and their gene targets . To elucidate how the BX-C boundaries combine these two contradictory activities , we have used a boundary replacement strategy . We show that a 337 bp fragment spanning the Fab-8 boundary nuclease hypersensitive site and lacking all but 83 bp of the 625 bp Fab-8 PTS ( promoter targeting sequence ) fully rescues a Fab-7 deletion . It blocks crosstalk between the iab-6 and iab-7 regulatory domains , and has bypass activity that enables the two downstream domains , iab-5 and iab-6 , to regulate Abdominal-B ( Abd-B ) transcription in spite of two intervening boundary elements . Fab-8 has two dCTCF sites and we show that they are necessary both for blocking and bypass activity . However , CTCF sites on their own are not sufficient for bypass . While multimerized dCTCF ( or Su ( Hw ) ) sites have blocking activity , they fail to support bypass . Moreover , this bypass defect is not rescued by the full length PTS . Finally , we show that orientation is critical for the proper functioning the Fab-8 replacement . Though the inverted Fab-8 boundary still blocks crosstalk , it disrupts the topology of the Abd-B regulatory domains and does not support bypass . Importantly , altering the orientation of the Fab-8 dCTCF sites is not sufficient to disrupt bypass , indicating that orientation dependence is conferred by other factors .
Special elements called chromatin boundaries or insulators are thought to subdivide chromosomes in multi-cellular eukaryotes into topologically and genetically autonomous domains [1–10] . Boundaries/insulators have both architectural and genetic functions . The architectural functions depend upon physical interactions between insulators . The first indication that boundary elements interact with each other came from the discovery that insulators can facilitate regulatory interactions between transgenes inserted at distant sites [11–16] . Subsequent work confirmed that the long distance regulatory interactions involved direct physical contacts between boundaries [17 , 18] . Moreover , it was shown that these physical interactions provide the anchors for the formation of topologically independent loops [7 , 9 , 19–21] . In addition to subdividing the chromosome into a series of looped domains , insulators have a number of genetic functions . These functions have been most thoroughly documented using transgene assays and include enhancer/silencer blocking and bypass activities [6 , 22] . In blocking assays , boundaries prevent regulatory interactions when interposed between enhancers or silencers and a reporter gene [23–25] . This insulation activity is position dependent , and boundaries do not block when the enhancers/silencers are located in between the reporter and the boundary . In bypass assays , two boundaries ( instead of one ) are interposed between an enhancer or silencer and a reporter gene [26–28] . When the two boundaries are appropriately matched and correctly oriented , they pair with each other in a manner that brings the enhancers/silencers into contact with the reporter [29 , 30] . While all of the fly boundaries that have been tested have blocking and bypass activities in transgene assays , it is not clear to what extent these activities are important or relevant in their endogenous settings , or how they are related to each other . For example , boundary elements are known to play a central role in the parasegment-specific regulation of the three BX-C Hox genes , Ultrabithorax ( Ubx ) , abdominal-A ( abd-A ) , and Abdominal-B ( Abd-B ) [31 , 32] . However , the functions of the BX-C boundaries in the context of the complex appear , at least on the surface , to be rather different from those detected in transgene assays . The differences are most clearly elaborated for the boundaries associated with the four regulatory domains , iab-5 , iab-6 , iab-7 , and iab-8 , that control Abd-B expression in parasegments PS10 , PS11 , PS12 , and PS13 , respectively ( Fig 1A ) . In order to specify PS identity , each of these regulatory domains must be able to function autonomously . Genetic and molecular studies have shown that boundary elements ( Mcp , Fab-6 , Fab-7 , and Fab-8; see Fig 1A ) bracket each regulatory domain , and that one of their key functions is to ensure autonomous activity [33–44] . The most thoroughly characterized BX-C boundary , Fab-7 , is located between iab-6 and iab-7 ( Fig 1A ) . Fab-7 deletions fuse the iab-6 and iab-7 regulatory domains and they exhibit a complex mixture of gain- ( GOF ) and loss-of-function ( LOF ) phenotypes in PS11 [38 , 40] . The GOF phenotypes arise because iab-6 initiator inappropriately activates iab-7 in PS11 , while the LOF phenotypes arise because repressive elements in iab-7 that are active in PS11 silence iab-6 in that parasegment [45 , 46] . A similar fusion of neighboring regulatory domains and a consequent misregulation of Abd-B is observed when Fab-6 and Fab-8 are deleted [34 , 42] . Though these BX-C boundaries can also block enhancers and silencers from regulating a reporter gene in transgene assays , this type of blocking activity is not directly relevant to the normal biological functions of these elements in the complex [34 , 47–55] . In BX-C , boundaries ensure autonomy by preventing crosstalk between initiation elements , enhancers , and silencers in the adjacent domains , not by blocking these elements from regulating the activity of promoters . As this is a role that may be unique to BX-C , it would be reasonable to think that the mechanisms and factors used to block crosstalk between regulatory elements in adjacent domains might be rather different from those that are needed to prevent enhancers or silencers from influencing RNA Pol II transcription . Several observations have reinforced the idea that BX-C boundaries have properties that distinguish them from boundaries elsewhere in the fly genome and in other eukaryotes . Six of the regulatory domains in BX-C ( including three for Abd-B ) are separated from their target genes by at least one boundary element . Since the tissue-specific regulatory elements in these domains are still able to regulate their respective target genes , the BX-C boundaries must be permissive for interactions between the domains and the transcriptional machinery at the promoters of the three BX-C Hox genes . A plausible mechanism for bypassing BX-C boundaries came from the discovery that Fab-7 and Fab-8 have special promoter targeting sequences that can facilitate enhancer-promoter interactions in transgene assays [56 , 57] . While non-BX-C boundaries can also bring distant enhancers and promoters together in the insulator bypass assay , this activity requires two appropriately matched boundaries and is non-autonomous . By contrast , the PTS elements associated with Fab-7 and Fab-8 appeared to function autonomously in transgene assays . Further evidence that BX-C boundaries are distinct from generic insulators was provided by Fab-7 replacement experiments using su ( Hw ) and scs [58] . While both blocked crosstalk between iab-6 and iab-7 , these two insulators clearly differed from BX-C boundaries in that they also prevented the downstream iab-6 regulatory domain from regulating Abd-B . In the studies reported here , we have asked what boundary functions are actually needed in the context of BX-C . For this purpose , we have replaced Fab-7 with the neighboring boundary , Fab-8 . The Fab-8 replacement we used includes part of the PTS and it fully rescues a Fab-7attP50 deletion . Our subsequent functional dissection indicates that the Fab-8 boundary is able to substitute for Fab-7 because its entirely generic boundary activities ( blocking and bypass ) are appropriately matched to its neighborhood .
In previous studies , Iampietro et al . [59] attempted to rescue a Fab-7 deletion with a 659 bp fragment containing Fab-8 sequences ( Fig 1B ) . While they found that this Fab-8 fragment blocked crosstalk between iab-6 and iab-7 , it was unable to fully support bypass . The 659 bp fragment used by Iampietro et al . lacked a ~100 bp sequence from the centromere proximal side of the Fab-8 nuclease hypersensitive region . There were several reasons to think that this sequence from the hypersensitive region , or even more centromere proximal sequences might be important for Fab-8 function . One came from the characterization of the Fab-8 deletion mutant , iab-7R73 , which removes sequences from the centromere proximal side of the Fab-8 boundary [41] . iab-7R73 has a weak LOF phenotype in PS12 . One explanation for this phenotype is that the deleted sequences are required for bypass activity . Two findings are consistent with this possibility . First , iab-7R73 removes the PTS that in transgene assays can direct enhancer sequences to a promoter [56] . Second , in bypass assays these same PTS sequences are necessary , but not in themselves sufficient to support interactions between Fab-8 and itself , and between Fab-8 and either Fab-7 or an insulator-like element , AB-I , located upstream of the Abd-B promoter [30 , 60] . Finally , the iab-7R73 deletion extends into the proximal half of the nuclease hypersensitive region of Fab-8 , and the region of overlap contains binding sites for two factors known to be involved in the insulator activity of the adjacent Fab-7 boundary , Elba and LBC [34 , 61 , 62] . Since studies on other insulators indicate that critical sequences often map to hypersensitive regions , we re-centered the Fab-8 fragment , F8550 , used for replacement , so that it spanned the entire nuclease hypersensitive region , and included additional centromere proximal sequences that are missing in the iab-7R73 deletion ( Fig 1B ) . As indicated in the Fig 1B , F8550 extends 265 bp beyond the proximal endpoint of the F8659 and includes the minimal PTS ( 290 bp ) tested in transgenic lines [63] . The male and female cuticle preparations in Fig 2 show that this smaller re-centered fragment fully rescues the Fab-7attP50 deletion . Whereas in Fab-7attP50 males , A6 is transformed into A7 ( and thus almost completely disappears ) , the size , pigmentation and also morphology of the A6 cuticle in F8550 males is like that of wild type flies . The same is true for F8550 females . Instead of a duplicate copy of A7 in Fab-7attP50 females ( Fig 2 ) , A6 resembles wild type and its morphology is clearly distinct from the adjacent A7 segment . Thus , like the large F8659 fragment of Iampietro et al . [59] , F8550 blocks cross-talk between iab-6 and iab-7 . However , it differs from F8659 in that it is also able to support regulatory interactions between iab-6 and the Abd-B promoter and the morphology of the sternites and tergites in A6 ( PS11 ) is wild type . Fab-8 has two closely linked binding sites for the conserved insulator protein dCTCF , which are arranged in opposite orientations [64 , 65] . Reporter assays in flies and tissue culture cells indicate that these two dCTCF sites are important in transgene assays for both enhancer blocking and insulator bypass [54 , 60 , 64 , 66–70] . However , it is not known whether the dCTCF sites are required for Fab-8 blocking and/or bypass activities in the context of BX-C . To address this question , we introduced a mutant version of the F8550 fragment , F8550mCTCF , which lacks both dCTCF binding sites , into the Fab-7attP50 landing site . The cuticle phenotype of F8550mCTCF flies points to roles in blocking and bypass . Like the starting Fab-7attP50 platform , the adult F8550mCTCF males lack the A6 segment indicating that PS11 is fully transformed into a copy of PS12 . A similar result is observed in adult females: F8550mCTCF females have two nearly identical copies of an A7-like segment ( Fig 2 ) . These findings indicate that the dCTCF sites are required for blocking activity . A role in bypass is suggested by the patchy pigmentation of the A5 tergite ( PS10 ) in F8550mCTCF males ( Fig 2 ) . Though the severity of this phenotype is clonally restricted and variable , it is fully penetrant . This effect on A5 pigmentation indicates that the F8550mCTCF replacement boundary interferes with or fails to fully support regulation of Abd-B in PS10 by the iab-5 domain . It is quite possible that the dCTCF sites in the Fab-8 replacement are also important for iab-6<->Abd-B regulatory interactions . However , since ectopically activated iab-7 and not iab-6 regulates Abd-B in PS11 ( and PS12 ) , in the F8550mCTCF replacement this possibility cannot be confirmed . While the A6->A7 transformations in F8550mCTCF males and females indicates that the dCTCF sites are required to block crosstalk between iab-6 and iab-7 , it is important to note that phenotypes are different from mutations that remove only the Fab-7 boundary . The Fab-7 boundary deletion mutants display a mixed GOF and LOF transformation of PS11 . Exclusively GOF transformations are only observed in Fab-7 deletions that remove not only the boundary but also the adjacent HS3 iab-7 PRE ( see Fab-7attP50 flies , Fig 2 ) . Remarkably , even though HS3 is included in the F8550mCTCF replacement , there is no evidence of any LOF ( or mixed GOF/LOF ) phenotypes in A6 . This means that the mutations in the dCTCF bindings sites must have effects on Abd-B regulation in PS11 that go beyond a failure to block iab-6<->iab-7 cross talk . The phenotypes evident in F8550mCTCF females support this conclusion . As can be seen in Fig 2 , A6 is completely transformed into a duplicate copy of A7 . However , in both the duplicate A7 and A7 itself , there are some abnormalities that are not evident in A7 in wild type females . One of these is the bristle pattern on the duplicated A7 sternites . In wild type females , the sternite bristles in A7 all point downwards and most are angled slightly towards the center of the sternite . This same bristle pattern is observed in the duplicated A7 tergites of the Fab-7attP50 deletion ( see Fig 2 ) . In contrast , the bristles in the duplicate A7 and the A7 sternites of F8550mCTCF are rotated nearly 90° so that they point inward . Another difference is in the pigmentation of tergites . In wild type , A6 and A7 ( but not A8 ) are pigmented . The same is true in the Fab-7attP50; both the duplicated A7 and A7 itself are pigmented . This is not the case in F8550mCTCF . Neither of these tergites have pigmentation . These findings suggest that Abd-B expression is abnormal in both of these segments in the F8550mCTCF mutant . To explore the effects of mutating the dCTCF sites further , we examined Abd-B expression in the embryonic CNS . Unexpectedly , two different patterns of expression were observed . The first fits with the exclusively GOF transformation of A6 and may also explain the abnormalities evident in duplicated A7 segments in adult F8550mCTCF females . In these embryos , high and nearly equal levels of Abd-B expression are observed in PS11 , PS12 , and PS13 ( Fig 3 ) . In the second , the levels of Abd-B expression are also similar in all three parasegments; however instead of resembling that normally seen in PS13 , the levels of Abd-B expression in the three segments are relatively low and more like that observed in PS11 or PS12 ( S1 Fig ) . In addition , in some embryos , the levels of Abd-B in a subset of PS12 cells is actually higher than that in PS13 cells ( S1 Fig ) . While clearly abnormal , the second pattern does not fit with the adult cuticle phenotypes . It is possible that the regulatory effects of the mutations in the dCTCF sites differ in the two tissues . In this case , there would be a “choice” between two alternative regulatory states in parasegments PS11-13 in the embryonic CNS , either elevated and nearly PS13-like or reduced and PS11/12-like . Alternatively , the expression pattern in the CNS may evolve from low in PS11-PS13 to high in these parasegments as the embryos develop . The phenotypic effects of the F8550mCTCF replacement indicate that the dCTCF sites are required for blocking and bypass activity . We wondered whether dCTCF alone would also be sufficient for these activities . To test this possibility , we generated a replacement Fab-7attP50 transgene that has four copies of the dCTCF binding site , CTCF×4 ( Fig 4 ) . CTCF×4 blocks cross talk between iab-6 and iab-7 and there is an A6-like segment in replacement males . However , the A6 segment is not wild type in males or in females . Unlike more anterior sternites , the A6 sternite in wild type males is devoid of bristles and has a horseshoe shape . In CTCF×4 males , the A6 sternite is covered in bristles and the shape is identical to that in A5 . Similarly , in CTCF×4 females , the pigmentation of the A6 tergite resembles that of A5 in wild type . An A6->A5 ( PS11->PS10 ) transformation is also evident in the dark field images in Fig 4 . In wild type flies , the A6 tergite has two bands of trichomes . One extends along the ventral edge of the tergite , while the other occupies part of the anterior edge . In male and female CTCF×4 flies , the trichomes cover the entire tergite , indicative of an A6->A5 LOF transformation . Though CTCF×4 appears to completely eliminate regulation of Abd-B by iab-6 , the effects on iab-5 activity are considerably less severe . Fig 4 shows that there is some weak depigmentation of the A5 tergite in males; however , though this LOF phenotype is variable much like that observed for the F8550mCTCF mutant boundary . The weak iab-7 LOF phenotype of iab-7R73 would be consistent with the idea that sequences in this 820 bp deletion contribute to insulator bypass . As shown in Fig 1B , the iab-7R73 deletion includes the entire PTS . However , since there is a deletion , Δ330iab-7 , that is slightly smaller than iab-7R73 , which has the same centromere proximal breakpoint as iab-7R73 but is wild type ( Fig 1B ) , it seems likely that most of these PTS sequences are not needed for Fab-8 function . Instead , the critical sequences would be located between the centromere distal endpoint of the Δ330iab-7 deletion and the centromere proximal end of the 659 bp fragment used by Iampietro et al . [41 , 59] . If this is correct , a Fab-8 fragment ( F8337 ) that includes this sequence but not more centromere proximal sequences , should substitute for Fab-7 . The Fab8284 boundary is identical to Fab8337 , except that the distal endpoint is the same as in Iamperio et al . [59] ( Fig 1B ) . Fig 5 shows that this is the case . The morphological features in segments A5-A8 of the adult cuticles of F8337 males and females are those expected in wild type . The same is true for the pattern of Abd-B expression in the embryonic CNS ( Fig 6 ) . The proximal end-point of F8337 extends 53 bp beyond the proximal endpoint of the F8659 fragment used in the experiments of Iampietro et al . ( Fig 1B ) . To test whether this 53 bp sequence is needed for Fab-8 function , we generated a deletion replacement , F8284 . Like F8337 , the smaller F8284 boundary blocks crosstalk between iab-6 and iab-7 , and there is no evidence of GOF transformation in A6 ( PS11 ) . On the other hand , unlike F8337 , the morphological features of F8284 adults are abnormal and there are fully penetrant weak LOF phenotypes in both A5 and A6 ( Fig 5 ) . In males the A5 tergite has small regions that are depigmented , while in females the pigmentation pattern in A6 often resembles that seen in A5 . Normally the A6 sternite in males is devoid of bristles; however , as illustrated in Fig 5 , this not the case in the F8284 replacement . In addition , while the shape of the hard cuticle of the A6 sternite in the male fly shown in the figure resembles wild type , in other males the A6 sternite has a shape much more similar to that in A5 . Finally , in a subset of F8284 male and female flies , we observed small clones of trichomes in the posterior and dorsal regions of the A6 tergite that are normally devoid of trichomes . Even though the dCTCF sites in Fab-8 contribute to both blocking and bypass , multimerized dCTCF binding sites alone have blocking activity but do not in themselves support bypass . In the case of the dCTCF sites in Fab-8 , it seems likely from our deletion analysis that cis-acting elements in the F8337 replacement in addition to the 53 bp sequence from the distal end of the PTS contribute to bypass activity . For this reason , we did not expect this 53 bp PTS sequence to complement the bypass defects of the multimerized dCTCF binding sites . On the other hand , since the full length 625 bp Fab-8 PTS is able , on its own , to mediate enhancer bypass of a heterologous su ( Hw ) insulator in transgene assays , we wondered whether the full PTS element would be able to rescue the bypass defect of the multimerized dCTCF sites . To test this possibility , we combined the 625 bp PTS with the CTCF×4 . Contrary to our expectations , PTS625+CTCF×4 males and females had the same spectrum of LOF phenotypes in A6 and A5 as their CTCF×4 counterparts ( Fig 1B and Fig 4 ) . In previous studies , the bypass activity of the Fab-8 PTS was tested in combination with the gypsy insulator which contains multiple binding sites for the Su ( Hw ) protein [56] . Thus , a plausible explanation for the failure to rescue the bypass defects of the CTCF×4 replacement is that this PTS functions best in conjunction with su ( Hw ) insulators . To test this hypothesis , we asked whether the same Fab-8 PTS fragment facilitates bypass of a multimerized Su ( Hw ) binding sites ( Su×4 ) . Like CTCF×4 , the multimerized Su×4 replacement blocks cross-talk between iab-6 and iab-7 , but fails to support bypass ( Fig 4 ) . Moreover , this bypass defect is not rescued by the Fab-8 PTS ( see PTS625+Su×4 in Fig 4 ) , and the same spectrum of LOF phenotypes are observed in A6 and A5 as those seen with Su×4 alone . Taken together with iab-7R73 deletion and the fact that F8337 has full boundary activity , these findings would suggest that the PTS does not function in the same way in the context of BX-C as it does in transgene assays . In insulator bypass experiments , Fab-8 interactions with itself and with other insulators are orientation dependent [30 , 60] . With only a few exceptions ( Fab-7: see below ) , this is a characteristic property of fly insulators in this transgenic assay . Self-interactions are head-to-head , while heterologous interactions can be either head-to-tail or head-to-head . In the case of the BX-C boundaries that define the Abd-B domain ( Fig 1A ) , heterologous interactions occur head-to-head . For productive regulatory interactions in the transgenic bypass assay , these BX-C insulators are inserted in opposite orientations ( forward<->reverse ) , so that head-to-head pairing interactions generate a stem loop . However , it is not known whether their relative orientation is important for proper insulator function in the context of BX-C . To explore this issue , we tested whether the orientation of Fab-8 in BX-C affects the ability of this insulator to rescue the Fab-7attP50 deletion . For this purpose , we introduced the 337 bp Fab-8 fragment into Fab-7attP50 in the reverse orientation ( F8337R ) . While this 337 bp Fab-8 fragment fully rescues the Fab-7 deletion when it is in the same “forward” orientation as the endogenous Fab-8 insulator , this is not true when its orientation is reversed ( Fig 1B ) . The effects of inverting the insulator on its activity in BX-C are instructive . As can be seen in Fig 5 , F8337R blocks cross talk between iab-6 and iab-7 , and the GOF transformation of PS11->PS12 in male and female Fab-7attP50 adults is completely suppressed . This finding indicates that blocking activity , at least in this particular context , does not depend upon insulator orientation . On the other hand , orientation is critical for insulator bypass , particularly for the iab-6 regulatory domain . The A6 tergites of both sexes are covered in trichomes—a morphological feature that is found in wild type in A5 but not A6 . Also the pigmentation of the A6 tergite in F8337R females is largely restricted to the posterior edge much like that normally seen in A5 . A similar A6->A5 ( PS11->PS10 ) transformation is evident in the A6 sternite of F8337R males . The sternite has bristles and is shaped like the A5 sternite . The effects on iab-5 regulation of Abd-B are less severe . There is a variable depigmentation of A5 indicative of a PS10->PS9 transformation . Consistent with the LOF phenotypes evident in the adult cuticle , the difference in the levels of Abd-B protein accumulation in PS12 and PS11 in the CNS is greater than normal in F8337R embryos ( Fig 6 ) . Also , the level of Abd-B protein in PS12 compared to PS13 appears to be somewhat elevated . Recently , several studies showed that the relative orientation of CTCF sites in mammalian boundary elements is critical for proper insulator function [71–75] . Since the blocking and bypass activity of the Fab-8 boundary requires the two dCTCF sites , an obvious question is whether either of these functions is connected to their relative orientation within the Fab-8 boundary . To test this possibility , we changed orientation of one ( F8CTCF Dir-Dir ) or both of the Fab-8 dCTCF sites ( F8CTCF Dir-Rev ) in the F8337 replacement ( S2 Fig ) . The morphological features in segments A5-A8 of the adult cuticles prepared from F8CTCF Dir-Dir and F8CTCF Dir-Rev adult flies resembles that expected for the wild type ( S3 Fig ) . Thus , the relative orientation of dCTCF sites does not seem to be critical for either blocking or bypass activity of the Fab-8 replacement boundary ( Fig 1B ) . As mentioned above , Fab-7 differs from Fab-8 and most other fly insulators in that its bypass activity in transgene assays is orientation independent [76] . Since reversing the orientation of the Fab-8 insulator disrupted its ability to replace Fab-7 , we wondered whether orientation was important for Fab-7 in its endogenous context . To answer this question , we inserted two different versions of an 858 bp fragment that contains the two major nuclease hypersensitive sites , HS1 and HS2 , that are associated with the Fab-7 boundary , next to the iab-7 PRE hypersensitive site HS3 . In one version , Fab-7858 , the sequences spanning the two major Fab-7 hypersensitive sites , HS1+HS2 , were in the same orientation as they are in the endogenous locus . In the other , Fab-7858R , the HS1+HS2 sequences are in the opposite orientation . The cuticle preps in Fig 7 show that both versions of the 858 bp fragment fully rescue the Fab-7attP50 deletion . Thus , as was observed in the transgene bypass assay , Fab-7 function in BX-C is orientation independent .
In the studies reported here we have used a gene replacement strategy to study the properties of Fab-8 that enable it to function in BX-C ( see summary table in Fig 1B ) . We show that a minimal fragment spanning the Fab-8 nuclease hypersensitive site and including the distal part of PTS sequences fully rescues a Fab-7 deletion . It blocks crosstalk between iab-6 and iab-7 . It is also permissive for interactions between the downstream iab-5 and iab-6 regulatory domains and the Abd-B promoter . The CTCF protein is well known because of its ability to block enhancer-promoter interactions and is found in many insulators from insects to vertebrates [77 , 78] . Transgene experiments have shown that mutations in the two Fab-8 dCTCF binding sites compromise its enhancer blocking activity [63 , 64 , 69 , 73 , 75–77] . The same mutations completely disrupt the ability of the Fab-8 replacement to block crosstalk between the iab-6 and iab-7 regulatory domains . Conversely , when dCTCF sites are multimerized , they are sufficient to prevent crosstalk between iab-6 and iab-7 . On the other hand , the multimerized binding sites do not substitute for Fab-7 , because in this context they lack bypass activity and block the iab-6 ( and to a lesser extent iab-5 ) regulatory domain from regulating Abd-B . This is not the only link between generic boundary functions and the ability to replace Fab-7 . In bypass assays , the dCTCF sites are required for orientation self-pairing between Fab-8 boundaries and for heterologous interactions with other nearby BX-C boundaries . In addition , a sequence at the distal end of the PTS is required for specific interactions with Fab-7 , Fab-8mCTCF , and AB-I [30 , 60] . While self-interactions between Fab-8 boundaries in cis do not occur in wild type flies , in our experimental design , self-interactions between the Fab-8 boundary in its normal location and the Fab-8 replacement are expected . As would be predicted from previous transgene bypass experiments , mutations in the dCTCF binding sites and deletion of the PTS , interfere with Abd-B regulation by the downstream regulatory domains . For the PTS deletion , Abd-B regulation by both iab-5 and iab-6 is partially compromised . In the case of the dCTCF sites , these effects can only be seen for iab-5 ( PS10 ) , because iab-7 , not iab-6 , regulates Abd-B in PS11 . In this context , it is also important to note that the only part of the 625 bp PTS that is needed for full bypass activity is an 83 bp sequence at its very distal end , while the remainder of the PTS is completely dispensable . Moreover , even when all but 30 bp of the 625 bp PTS is deleted ( F8284 ) , the effects on iab-6 and iab-5 regulatory activity are quite modest compared , for example , to that seen for either CTCF×4 or F8337R . This would be consistent with the rather weak LOF phenotypes of the Fab-8R73 deletion , and argues that the PTS by itself , does not have an essential role in the bypass activity of the Fab-8 boundary in the context of BX-C . Since multimerized dCTCF sites lack bypass activity , it seems likely that cis-acting elements contained within the smaller F8284 substitution will turn out to be critical for bypass activity . Of course , though our deletion experiments argue that the PTS makes at most only a minimal contribution to Fab-8 bypass activity , our experimental design does not exclude a scenario in which the PTS is redundant with the bypass elements in F8284 . However , arguing against this scenario is the fact that the full 650 bp PTS fails to complement the bypass defects of not only CTCF×4 but also Su×4 . Since the PTS is able to mediate bypass of a gypsy element ( which contains 12 Su ( Hw ) binding sites ) in a transgene assay , it seems possible that its activity is entirely context dependent—in this case , the specific identity , combination and arrangement of enhancers , insulators , and reporters in the different transgene constructs [56 , 63] . Yet another connection between the bypass activity of BX-C insulators in transgene assays and bypass in BX-C , is orientation dependence . The bypass activity of Fab-8 in transgene assays differs depending on insulator orientation [30 , 60] . The same is true in our replacement experiments . Fab-8 substitutes for Fab-7 when it is inserted in the same relative orientation in BX-C as the endogenous Fab-8 boundary . On the other hand , when the orientation of Fab-8 replacement boundary is reversed , it no longer supports bypass ( though it still blocks crosstalk between iab-6 and iab-7 ) . Instead , it disrupts interactions between iab-6 and the Abd-B gene much like that observed when Fab-7 is replaced by the completely heterologous insulators su ( Hw ) and scs [58] . Notably , however , the effects of su ( Hw ) and scs on Abd-B regulation by iab-5 and iab-6 are orientation independent . Further support for the idea that a bypass type mechanism may be responsible for enabling downstream regulatory domains to skip over one or more boundary elements comes from experiments in which we replaced Fab-7 with a Fab-7 fragment . In transgene experiments , Fab-7 is unusual in that its bypass activity either in combination with itself or with other BX-C insulators is orientation independent [76] . This is also true for the bypass activity of Fab-7 in BX-C . While these similarities argue that some type of bypass mechanism is likely involved in skipping over intervening boundary elements in the Abd-B region of BX-C , there is an important difference between bypass in BX-C and bypass in transgene assays . As illustrated in Fig 8A , BX-C insulators pair with each other head-to-head . When they are in an opposite orientation in the transgene , head-to-head pairing generates a stem-loop structure that brings the enhancer in close proximity to the reporter . By contrast , in transgene assays , head-to-head pairing of insulators that are in the same relative orientation , as they are in BX-C , generates a “circle-loop” and this topological configuration is not favorable for contacts between the enhancer and promoter flanking the paired insulators ( Fig 8B ) . In the BX-C Abd-B domain , all of the insulators are oriented in the same ( by convention “forward” ) direction with respect to each other . They are also predicted to pair with each other head-to-head [60] . If each insulator interacts with its flanking neighbors , the predicted topology of the entire domain , when the Fab-8 ( F8337 ) replacement is in the “forward” orientation ( same as the endogenous Fab-8 ) , would be a series of “circle-loops” linked together at their base by interacting insulators ( Fig 8C ) [29 , 30 , 52 , 79 , 80] . In the illustration in Fig 8C , all of the circle-loops are wound in a clockwise direction , giving a right-handed helix . While the actual in vivo configuration of the loops comprising the Abd-B regulatory domains cannot be determined with techniques currently available , it is clear that this organization will be disrupted when the Fab-8 replacement is in the “reverse” orientation . As illustrated in Fig 8D , the introduction of the Fab-8 boundary in the reverse orientation ( F8337R ) would disrupt the helical arrangement of Abd-B regulatory domains . Head-to-head pairing between Fab-6 and F8337R and between F8337R and Fab-8 generates stem-loops , not circle loops . The first stem-loop corresponds to the iab-6 regulatory domain , while the second corresponds to iab-7 . In this stem-loop configuration , iab-6 and the Abd-B transcription unit are on opposite sides of the insulator complex and contacts between regulatory elements in iab-6 and the Abd-B promoter would be disfavored . This would dovetail with the strong LOF A6->A5 transformation observed in F8337R flies , and the reduced Abd-B expression evident in embryos . Importantly , the two stem-loops formed by head-to-head pairing of F8337R would also disrupt other possible configurations of the circle-loops and interfere with Abd-B regulation by iab-6 . Since the spatial relationship between the iab-5 and Abd-B circles would remain largely the same , one might expect that the effects of the reversed boundary on iab-5 activity would be less severe than those observed for iab-6 . It is interesting to note that equivalent orientation dependent alterations in the regulatory interactions have been observed in mammals when the relative orientation of neighboring CTCF sites is flipped . For this reason , it was somewhat surprising to find that altering the relative orientation of the two dCTCF sites in the Fab-8 has no apparent effect on the activity of the replacement boundary . The replacements still block iab-6⇓◇iab-7 cross talk and fully support bypass . This finding indicates that the orientation dependence of the Fab-8 replacement must be largely dictated by the asymmetric binding of other , unknown factors to the sequences within the minimal F8337 boundary . Thus , though it seems likely that dCTCF sites contribute to the orientation dependence , altering their orientation is not sufficient to override the activity of the other factors . Unlike Fab-8 , Fab-7 pairing interactions with itself and with its neighbors are orientation independent . This means that Fab-7 pairing with neighboring insulators could generate circles , stem-loops or both . As changing the orientation of the Fab-7 insulator had no effect apparent on boundary function , a reasonable speculation at this point is that its pairing interactions are dictated by the orientation dependence of the neighboring boundaries and consequently , that it participates in circle ( Fig 8C ) , not stem-loop formation ( Fig 8D ) . Taken together our model suggests that directional interactions between boundaries in BX-C are essential for the proper spatial organization of the iab enhancers relative to the Abd-B promoter . Likely , this is not in itself sufficient to generate productive regulatory interactions between the appropriate iab enhancers and the Abd-B promoter in each parasegment . Instead , additional elements might be needed . One such element is the promoter tethering element or PTE . Studies by Drewell and colleagues [81–83] have identified a PTE located in between the Abd-B transcription start site and the insulator-like element AB-I . They have shown that PTE can mediate productive contacts between the iab-5 enhancer and the Abd-B promoter in transgene assays . In this case , insulator-insulator interactions would function to organize the iab enhancers into the appropriate three-dimensional loop configuration , while direct contact between the enhancers and the Abd-B promoter would be dependent on PTE-enhancer interactions . In this context , it is interesting to note that PTEs were also found in promoters of several other genes including Scr [84 , 85] , white [86] , yellow [87] , even skiped [88] , and engrailed [89] .
The strategy to create the Fab-7attP50 landing platform is diagrammed in S4 Fig in the Supporting Information and described in detail in [71] . Fragments F8550 ( 64038–64587 ) , F8337 ( 64038–64375 ) , F8284 ( 64038–64322 ) , PTS625 ( 64292–94916 ) and F7858 ( 83647–84504 ) were obtained by PCR amplification and sequenced . The coordinates are given according to the published sequences of the Bithorax complex [90] . The CTCF×4 and Su×4 are described in [29] . Adult abdominal cuticles of homozygous eclosed 3–4 day old flies were prepared essentially as described in Mihaly et al . [40] and mounted in Hoyer's solution . Embryos were stained following standard protocols . Primary antibodies were mouse monoclonal anti-Abd-B at 1:60 dilution ( 1A2E9 , generated by S . Celniker , deposited to the Developmental Studies Hybridoma Bank ) and polyclonal rabbit anti-Engrailed at 1:500 dilution ( kindly provided to us by Judith Kassis ) . Secondary antibodies were goat anti-mouse Alexa Fluor 555 and anti-rabbit Alexa Fluor 647 ( Molecular Probes ) . Stained embryos were mounted in the following solution: 23% glycerol , 10% Mowion 4–88 , 0 . 1M Tris-HCl pH 8 . 3 . Images were acquired on Leica TCS SP-2 confocal microscope and processed using Photoshop , ImageJ , Excell , and Calc ( LibreOffice ) software . | Boundary elements in the Bithorax complex have two seemingly contradictory activities . They must block crosstalk between neighboring regulatory domains , but at the same time be permissive ( insulator bypass ) for regulatory interactions between the domains and the BX-C homeotic genes . We have used a replacement strategy to investigate how they carry out these two functions . We show that a 337 bp fragment spanning the Fab-8 boundary nuclease hypersensitive site is sufficient to fully rescue a Fab-7 boundary deletion . It blocks crosstalk and supports bypass . As has been observed in transgene assays , blocking activity requires the Fab-8 dCTCF sites , while full bypass activity requires the dCTCF sites plus a small part of PTS . In transgene assays , bypass activity typically depends on the orientation of the two insulators relative to each other . A similar orientation dependence is observed for the Fab-8 replacement in BX-C . When the orientation of the Fab-8 boundary is reversed , bypass activity is lost , while blocking is unaffected . Interestingly , unlike what has been observed in mammals , reversing the orientation of only the Fab-8 dCTCF sites does not affect boundary function . This finding indicates that other Fab-8 factors must play a critical role in determining orientation . Taken together , our findings argue that carrying out the paradoxical functions of the BX-C boundaries does not require any unusual or special properties; rather BX-C boundaries utilize generic blocking and insulator bypass activities that are appropriately adapted to their regulatory context . Thus making them a good model for studying the functional properties of boundaries/insulators in their native setting . | [
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] | 2016 | Functional Dissection of the Blocking and Bypass Activities of the Fab-8 Boundary in the Drosophila Bithorax Complex |
Canine mast cell tumours ( CMCT ) are one of the most common skin tumours in dogs with a major impact on canine health . Certain breeds have a higher risk of developing mast cell tumours , suggesting that underlying predisposing germ-line genetic factors play a role in the development of this disease . The genetic risk factors are largely unknown , although somatic mutations in the oncogene C-KIT have been detected in a proportion of CMCT , making CMCT a comparative model for mastocytosis in humans where C-KIT mutations are frequent . We have performed a genome wide association study in golden retrievers from two continents and identified separate regions in the genome associated with risk of CMCT in the two populations . Sequence capture of associated regions and subsequent fine mapping in a larger cohort of dogs identified a SNP associated with development of CMCT in the GNAI2 gene ( p = 2 . 2x10-16 ) , introducing an alternative splice form of this gene resulting in a truncated protein . In addition , disease associated haplotypes harbouring the hyaluronidase genes HYAL1 , HYAL2 and HYAL3 on cfa20 and HYAL4 , SPAM1 and HYALP1 on cfa14 were identified as separate risk factors in European and US golden retrievers , respectively , suggesting that turnover of hyaluronan plays an important role in the development of CMCT .
Mastocytosis is a term that covers a broad range of human conditions involving the uncontrolled proliferation and infiltration of mast cells in tissues . A common characteristic for these diseases is a high frequency of activating mutations in the C-KIT oncogene [1–3] . An intriguing feature of the disease spectrum is its ability to spontaneously resolve despite having a mutation in an oncogene , as seen commonly in the juvenile condition [4] . Mastocytosis in adults can be accompanied by additional haematological abnormalities and a reduced life expectancy [5] . In addition , the disease has major adverse effects on life quality for the affected individuals [6] . The most severe forms of mastocytosis , such as mast cell leukaemia , are considered very malignant and are associated with a poor prognosis due to a lack of treatment options [1 , 2] . CMCT shares many phenotypic and molecular characteristics with mastocytosis , including paraclinical and clinical manifestations , and a high prevalence of activating C-KIT mutations [7 , 8] . CMCT in dogs thus provides a good naturally occurring comparative disease model for studying mastocytosis [9 , 10] . As reported in humans [1 , 11 , 12] , there is evidence for germ-line risk factors in dogs as specific breeds , including golden retrievers , Labrador retrievers , boxers and Chinese shar-pei , have a high frequency of CMCT [13 , 14] . Current treatment options for CMCT encompass radical surgery alone , or in combination with chemotherapy or radiotherapy . The tyrosine kinase inhibitors masitinib and toceranib are licensed for treatment of non-resectable CMCT [9] . Human mastocytosis on the other hand is often not responsive to tyrosine kinase inhibitors , as the common V816D C-KIT mutation makes this receptor resistant to the classical tyrosine kinase inhibitors [3] . The behaviour of mast cell tumours in dogs is difficult to predict and accurate prognostication is challenging despite current classification schemes based on histopathology [15 , 16] . Mastocytosis is commonly seen as a systemic or generalized cutaneous disease whilst CMCT are commonly solitary masses , which are localized in the skin . These spread via the lymphatic system to local lymph nodes and visceral organs such as liver spleen and kidneys [9] . Interestingly haematological spread of CMCT to the lungs has never been reported suggesting that these tumours spread solely via the lymphatic system rather than via a haematogenous route . In humans germline C-KIT mutations have been detected in familial mastocytosis [1] . There is no published research regarding predisposing germline mutations in dogs . Modern dog breeds have been created by extensive selection for certain phenotypic characteristics . As a side effect , unwelcomed traits like diseases have also been enriched in different breeds . The recent bottlenecks during breed creation have given rise to extensive linkage disequilibrium ( LD ) within breeds [17] . Furthermore , as a result of the reduced genetic heterogeneity , the number of genetic risk factors is limited within a breed , thereby reducing the genetic complexity . These characteristics of the dog genome enable efficient disease mapping within a breed , using fewer markers and individuals compared to human studies and reducing the required sample numbers from thousands to hundreds [17 , 18] . The aim of this study was to identify genetic risk factors for CMCT in dogs . We carried out a genome wide association study ( GWAS ) comparing 107 healthy geriatric golden retrievers with 124 golden retrievers affected with CMCT . Samples were collected from Europe and the US , representing two populations from separate continents . This allowed us to identify two different significantly associated loci in the two populations each of which harbours three of the six hyaluronidase genes . Subsequent targeted sequencing and fine mapping was carried out in the associated regions and identified at least one compelling disease-associated variant .
We conducted a case-control GWAS of 273 golden retrievers ( GR ) to find candidate regions associated with CMCT . After quality control and removal of related individuals , the final data set included a total of 124 cases and 107 controls with low levels of relatedness ( genetic relationship matrix value <0 . 25 ) within the two subpopulations , and high genotype call rates ( >90% ) . Two individuals were removed due to low genotyping rate , 40 individuals where removed due to high levels of relatedness . The multidimensional scaling plot ( MDS ) showed that the American and European GRs form two distinct clusters , indicating genetic differentiation between the populations on different continents ( Fig 1A ) . This implies that the CMCT predisposition could have different genetic causes in the two populations . MDS plots for the two groups analysed separately indicated no outliers or substantial stratification within either cohort ( Fig 1B and 1C ) . The two cohorts were first analysed separately , and then together using a mixed model approach . Essentially no genomic inflation was detected in the US and EU analysis , as evidenced by the QQ plots and genomic inflation factor ( λ = 1 . 01 for both EU and US respectively , Fig 2 ) . The Manhattan plots for the two different populations ( Fig 2A and 2B ) showed one major associated locus for each population . However , the two loci were not overlapping , but are on two different chromosomes ( cfa14 and 20 ) , suggesting that different genetic risk factors are influencing the two populations of GRs . The American GR association analysis ( ncases = 59 , ncontrols = 45 ) resulted in one significantly associated region on cfa14 ( nominal significance threshold at -log p>5 . 0 , based on the deviation in the QQ plot , Fig 2A ) . Nine SNPs were found to be significantly associated with CMCT ( Fig 3 ) , with the strongest association ( p = 3 . 2x10-7 , pperm = 0 . 03 ) at CanFam2 . 0 cfa14:14 , 714 , 009 bp conferring a substantial risk effect ( OR = 5 . 3 ) . The risk allele frequency for the most associated SNP was 0 . 86 in cases and 0 . 53 in control GRs , and all cases except for one carry at least one copy of the risk genotype ( S1A Fig ) . However , this case is heterozygous for the European GR risk alleles . The five SNPs with the strongest association are presented in Table 1 , and all significantly associated SNPs are listed in S1 Table . All of the significant SNPs on chromosome 14 show high LD with the most associated SNP ( Fig 3C ) and nine SNPs form a risk haplotype spanning 111 kb ( 14 . 64–14 . 76 Mb ) containing only three genes; SPAM1 , HYAL4 and HYALP1 . Notably , the genes are all hyaluronidase enzymes . The top SNP is located within the 2nd intron of the HYALP1 pseudogene . In the European population ( ncases = 65 , ncontrols = 62 ) , chromosome 20 showed the strongest association , while ten chromosomes showed nominal significance ( -log p>2 . 9 , based on the QQ-plot , Fig 2B ) . The nominal significance determines that there are associated SNPs below the nominal significance threshold , however not all p-values below this level are significant . The strong signal from chromosome 20 suggest that this region has a high probability of being associated , while only some of the less significant regions may be truly associated . On chromosome 20 , 167 SNPs spanning 20 Mb ( 33 . 9Mb–53 . 1Mb ) showed nominal significance . They form two major loci at 42Mb ( most associated SNP p = 1 . 4x10-6 , pperm = 0 . 039 , OR = 6 . 3 , cfa20:42 , 547 , 825 bp ) and 48Mb ( strongest associated SNP p = 4 . 3x10-7 , pperm = 0 . 022 , OR = 4 . 1 , cfa20:48 , 599 , 799 bp ) . Analysis of the LD in the area shows that the top SNPs in each region are in high LD with nearby SNPs , but low LD ( r2<0 . 2 ) with SNPs in the other peak ( Fig 4 ) . The risk allele frequency for the SNP at 42Mb is high , with an allele frequency of 0 . 92 in cases and 0 . 64 in controls . However , the risk allele at 48Mb is less common , with a frequency of 0 . 65 in cases and 0 . 31 in controls . The discrepancy in allele frequencies supports the inference that the associated loci are independent and could harbour separate risk factors for CMCT . The differences in risk haplotype frequencies are also evident from the minor allele frequency plot ( Fig 4B ) . The minor allele frequency is reduced around 42Mb , indicating a reduction in genetic diversity , possibly due to selection in that region . The candidate region contains nearly 500 genes and corresponds to human chromosome 3p21 , a region often affected by chromosomal abnormalities in cancer [19] . The most associated SNP at 48Mb falls between the MYO9B and HAUS8 genes and , interestingly , there is a cluster of hyaluronidase genes ( HYAL1 , HYAL2 and HYAL3 ) positioned within the association locus at 42Mb . As expected the GWAS analysis of the full cohort ( ncases = 124 , ncontrols = 107 ) showed partial overlap with the results from the American and European subsets and resulted in a decrease in the p-values for both cfa 20 and 14 ( Fig 2C ) . Full cohort analysis resulted in a residual genomic inflation after correction ( λ = 1 . 03 ) . Hybrid capture and subsequent Illumina sequencing of the most associated GWAS regions were performed in order to identify all variants in the regions . In total 3 , 357 variants were identified in 0 . 9 Mb on chromosome 14 and 16 , 972 variants were identified in 5 . 5 Mb on chromosome 20 , including both INDELs and SNPs . The 132 SNPs selected for fine mapping were located on cfa 14 in the 14 Mb region ( 30 SNPs ) , on cfa 20 in the 42Mb region ( 38 SNPs ) , and on cfa 20 in the 48 Mb region ( 64 SNPs ) . Fine mapping was performed on DNA from 384 dogs . Twenty-eight SNPs were filtered out due to low genotyping rate ( >0 . 7 ) . This high number was due in part to the presence of repeat elements or duplicated sequences in the proximity of the SNP , allowing primers to align to more than one region in the genome . Six SNP’s were not polymorphic in the sequencing data but were chosen for genotyping because of their interesting location . These SNPs were excluded from further association analysis as all genotyped individuals carried the alternative allele . Five individuals were removed for a low genotyping rate ( <50% ) and 4 control individuals were removed , as they were no longer deemed suitable as controls due to development of a secondary malignancy , these individuals were not included in the original GWAS analysis . After filtering , DNA samples from 375 individuals remained for analysis , comprising 245 American dogs ( 100 cases , 145 controls ) and 130 European dogs ( 65 cases , 65 controls ) . The DNA samples were from dogs included in the GWAS analysis and additional American individuals . Related individuals were not excluded from the analysis . The American GR population showed the strongest fine mapping association to a SNP at cfa14: 14 , 644 , 897 ( piPLEX ( US ) = 6 . 4x10-8 , pperm = 9 . 0 x10-6 ) . This is one of the original GWAS SNPs ( pGWAS ( US ) = 3 . 3x x10-6 ) that formed part of the original GWAS risk haplotype . Six SNPs showed a strong association ( p <10−7 ) and formed a high LD haplotype narrowing the associated risk haplotype to a 60kb region encompassing the HYAL4 and SPAM11 genes ( S2 and S4A Figs ) . Among the most associated SNPs in the HYAL4 gene were three coding SNPs ( cfa14:14 , 685 , 543 , cfa 14:14 , 685 , 602 , cfa14:14 , 685 , 771 ) , of which 14:14 , 685 , 543 was a GWAS SNP . All three mutations in the HYAL4 gene cause amino acid changes , which are predicted as benign ( score < 0 . 2 , PolyPhen-2 ) . A less associated coding SNP in the SPAM1 gene ( cfa14:14 , 653 , 880 ) was also found , which causes an amino acid change , which is predicted to be damaging ( score 0 . 91 , PolyPhen-2 ) . This mutation is more prevalent in cases than controls , although the SNP is not in high LD with the risk haplotype . The most associated GWAS SNP from the American analysis ( pGWAS ( US ) = 3 . 2x10-7 , cfa14:14 , 714 , 009 bp ) , was included in the fine mapping ( pIPLEX ( US ) = 4 . 08x10-6 ) , and was found to be the 7th most associated SNP ( S2 Table and S4A Fig ) . An outstanding causal variant for the cfa14 14Mb association with CMCT in US GRs has yet to be identified . However , the associated haplotype traversing the region could be used as a predictive marker for development of CMCT in US GR dogs . Only a subset of the variants identified from the hybrid capture , were included in the fine mapping . Some coding SNPs in the SPAM1 and HYAL4 genes were not included in the fine mapping due to constraints of the design . The European population showed the strongest fine mapping association to a SNP ( cfa20:42 , 080 , 147 , p = 2 . 0x10-15 , pperm <0 . 00001 ) . This SNP showed an association p = 7 . 0 x10-4 when the US dogs were analysed alone . When the US and European data were analysed together a lower p-value was seen ( cfa20:42 , 080 , 147 , p = 2 . 2x10-16 , pperm<0 . 00001 ) . Interestingly this SNP is not in LD with the surrounding SNPs and appears to be a recent mutation , which is present only on the risk haplotype ( S4B Fig ) . All but two European cases carry a copy of the risk allele ( allele frequencies: cases = 0 . 83 , controls = 0 . 35 ) . However , the allele is rare in both cases and controls in the US population ( allele frequencies: cases = 0 . 07 , controls = 0 . 01 ) ( S2 and S3 Tables and S5A Fig ) . The SNP is a synonymous SNP located at the final position in exon 3 of the Guanine Nucleotide Binding Protein ( G Protein ) Alpha Inhibiting Activity Polypeptide 2 ( GNAI2 ) . This changes the last base from a guanine ( G ) to an adenine ( A ) . A splice site prediction software ( Alternative Splice Site Predictor [20] ) predicted this variant to change the site from a constitutive donor splice site to a suboptimal donor site . The second most associated SNP ( cfa20:42 , 131 , 456 p = 7 . 7x10-6 ) in the European analysis forms a long haplotype with 12 other fine mapping SNPs in high LD , traversing the region across the hyaluronidase genes in the 42MB region ( S4C Fig ) . This SNP is a conserved , coding synonymous SNP located in exon four of the GNAT1 gene ( amino acid D98 ) . The GWAS identified the strongest association to the cfa20 48Mb region in the European population . In the fine mapping the association to the cfa20 48Mb region is less noteworthy than the association to the cfa20 42Mb region . The two most associated SNPs ( cfa20:48 , 599 , 799 and cfa20:49 , 201 , 505 ) from the GWAS were included in the fine mapping . The SNP cfa20:49 , 201 , 505 was found to have the lowest p value of the SNPs located in the 48Mb region piplex_EU = 2 . 1x10-5 . This SNP was found to be the 4th most associated SNP in the European analysis . S3 Table summarises the results of association testing in the European population and the combined European and US population . Phenotypic data such as age of onset , mast cell tumour grade and disease outcome was available for some of the cases . As the samples were collected from multiple institutions and the format of reporting was variable . For the European population the mean age of disease onset varied significantly between dogs which were homozygous versus heterozygous for the GNAI2 risk SNP . Mean age of onset homozygous: 5 . 6 ± 0 . 4 , n = 43 , heterozygous: 7 . 6 ± 0 . 5 , n = 17 , p = 0 . 0073 as determined by unpaired t-test statistics . Only two dogs were homozygous for the protective allele and hence this was too little for statistical analysis . For the United States population age of onset was only available for 15 dogs and hence reliable test statistics could not be performed . The GWAS analysis suggested that the breakdown of hyaluronic acid may play a role in the development of CMCT . We hence wanted to evaluate if hyaluronan formed part of the extracellular matrix of CMCT . Immunohistochemistry was performed on 12 mast cell tumour samples from GRs and on normal control tissues ( skin and pannicular fat ) from an unaffected dog . As seen in S9A and S9B Fig , immunohistochemistry confirmed that the mast cell cytoplasmic membrane does stain intensely positive for hyaluronan confirming that indeed hyaluronan forms part of the mast cell cytoplasmic membrane . Dermal and pannicular collagen directly adjacent to the mast cell tumour is increased and showed more intense staining of the intercellular/extracellular matrix . In comparison , normal dermal and pannicular tissue stained only mildly positive for hyaluronan , except for the basal membranes of the epidermis , which is known to contain hyaluronan , and which stained intensively positive ( S9C and S9D Fig ) . RNA sequencing of a CMCT and a normal cutaneous tissue sample was carried out in order to identify alternative transcript isoforms , and to evaluate which genes are expressed in CMCT . The CMCT was borne by a GR known to be homozygous for the variant SNP at cfa20:42 , 080 , 147 in the GNAI2 gene that is predicted to change the site from a constitutive donor splice site to a suboptimal donor site . An alternative isoform of the GNAI2 gene was identified by visual examination of the TopHat [21] output in IGV . This alternative isoform skips exon 3 , showing that the identified cfa20:42 , 080 , 147 variant does change the splicing at this site . Quantitative PCR was performed on cDNA samples from 9 GRs using splice-specific primers traversing both the normal and the alternative splice site ( S6 and S7 Figs ) . As seen in Fig 5 , PCR products for the alternative splice form , were only detected in the individuals carrying one or more copies of the A allele at the cfa20:42 , 080 , 147 position . On average , the wild-type isoform was expressed at a 6 . 9-fold greater level than the alternatively spliced version as calculated by the difference in CT values between the normal and alternative splice products in homozygous individuals . The alternative splicing , splices out of frame and is predicted to produce a truncated protein , changing the open reading frame from 356 aa to 109 aa . The RNA sequencing data also confirmed that GRs express the hyaluronidase genes . The HYAL1 , HYAL2 , HYAL3 and SPAM1 genes were expressed in both the CMCT and marginal normal tissue , but the HYAL4 and HYALP1 genes showed no evidence of expression in either tissue .
We identified genetic associations between CMCT and three different loci for American and European GR populations . The American population had the strongest association to a locus on chromosome 14 in which the hyaluronidase genes HYAL4 and SPAM1 , and the pseudogene HYALP1 , are located . The European population showed association to two separate regions on chromosome 20 located around 42Mb and 48Mb , of which the 42Mb region harbours the remaining hyaluronidase genes HYAL1 , HYAL2 and HYAL3 . Sequence capture of the associated regions , in a small subset of individuals , identified thousands of variants , of which a large subset in each region followed the GWAS predicted risk haplotype . Fine mapping with additional markers narrowed down the risk haplotype on chromosome 14 from 111kb to a 60kb region , harbouring the SPAM1 and HYAL4 genes . The strongest associated SNP from the fine mapping on cfa14 , was one of the original GWAS SNPs , BIC2G630521696 cfa14:14 , 756 , 089 . Although the majority of candidate variants were included in the fine mapping in this region , including three coding SNPs in the HYAL4 gene and one coding SNP in the SPAM1 gene , there were several candidate variants , which could not be included in the fine mapping . For instance , a non-synonymous coding SNP in the SPAM1 gene and a SNP 227bp downstream of the SPAM1 gene could not be evaluated . Based on this and the strong LD in the region we have identified a predisposing haplotype , but not the causative variant yet . Future work will focus on further restriction of the identified risk haplotype with the aim to pinpoint the causative variant that could potentially be used to predict risk for CMCT development . In the European population , the two associated loci located at 42 and 48Mb on cfa 20 , respectively , were shown to be independent . Low LD was found between the SNPs in the two regions and the allele frequencies were also different , which suggest that they are independent and potentially contain separate risk factors for CMCT . Fine mapping of the relatively large 48Mb region did not narrow down the risk haplotype and the most associated SNP in the region was BICF2P623297 cfa20:49 , 201 , 505 , which was one of the original GWAS SNPs . Fine mapping of the 42Mb region identified the cfa20:42 , 080 , 147 SNP strong association with CMCT in the European population and also mild association in the US GRs where it was rare . This SNP was not in LD with any of the other SNPs in the region although it was located only on the GWAS risk haplotype and hence it is likely to be a recent mutation on the risk haplotype . This SNP causes a change in a splice site in exon 3 of the GNAI2 gene resulting in the production of an alternative transcript isoform through the skipping of the third exon . This alternative splice isoform splices out-of-frame and therefore introduces a stop codon at amino acid position 109 resulting in a truncated GNAI2 protein . Expression analysis of the GNAI2 gene , using splice specific primers , confirmed the presence of alternative splice isoforms in individuals carrying the mutation . As the normal isoform for this gene is still expressed in individuals carrying the risk genotype it is not known what effect the alternatively spliced protein will have . GNAI2 belongs to a group of proteins , which regulate receptor signalling by controlling adenylyl cyclase activity [22] . GNAI2 has frequently been linked to cancer and is also known as the gip2-oncogene [23] . Suppression of GNAI2 has been detected in ovarian cancer [24] and somatic GNAI2 mutations have been identified in diffuse large B-cell lymphoma [25] . GNAI2 is highly expressed in the human mastocytoma cell line HMC-1 ( The Human Protein Atlas [26] ) , as confirmed by both antibody staining and mRNA expression . We also found that GNAI2 was expressed in a GR mast cell tumour , and marginal normal tissue . We have not been able to determine in this study whether the truncated GNAI2 protein has a direct detrimental effect , or whether a loss of function from the truncation results in reduced regulation of adenylyl cyclase and increase activity of certain cellular pathways . This question warrants further study . The coincidence that the two loci identified in the American and European GR populations , each contain three of the known six hyaluronidase genes , has lead us to hypothesize that hyaluronan turnover could play a role in CMCT predisposition . Interestingly , the Chinese shar-pei dog , which has an increase in hyaluronan accumulation in the skin due to a duplication upstream of the HAS2 gene [27] , also has an increased risk of developing CMCT . Furthermore , the naked mole rat has a decreased activity of hyaluronan degrading enzymes , which is believed to contribute to its longevity and resistance to cancer [28] . It is not known whether the GNAI2 variant , located almost 20kb away from the hyaluronidase cluster , also has an effect on the hyaluronidase genes , or if this is a separate risk factor recently acquired by the risk haplotype . It has been shown that the human region ( 3p21 . 31 ) , which is autologous to the canine cfa20 42Mb region , has been under selection in East Asians . This is thought to be due to HYAL2 and its functions in the cellular response to UV-B light exposure [29] . It is possible that the low minor allele frequency in the hyaluronidase gene-containing areas of the genome in the golden retriever is a sign of selection . We speculate that the selection could be related to reproductive fitness , as the hyaluronidase genes play a role in reproduction [30 , 31] . Early studies of mast cells suggested that these cells contain hyaluronan . A correlation between the presence of hyaluronan and mast cells has been documented , and hence it was natural to believe that mast cells were the source of the hyaluronan [32 , 33] . However , later studies show that there is no evidence of mast cells producing hyaluronan in vitro [34] . Hyaluronan is broken down on the cell surface to smaller molecules by hyaluronidase [35] , and the fragmented hyaluronan is then taken into the lysosomes of the cell and there further broken down by intracellular hyaluronidase . We find it plausible that mast cells interact with hyaluronan and play a role in hyaluronan turnover . Concordant with that , our CMCT RNA sequencing demonstrated expression of all the hyaluronidase genes except HYAL4 and HYALP1 . The breakdown products of hyaluronan , known as low molecular hyaluronan , have both pro-inflammatory and pro-oncogenic effects [35] . Studies in rats showed that intravesical injection of hyaluronidase resulted in inflammation and an increase in the number of activated mast cells , suggesting a direct role between hyaluronan break down products and mast cell activation and migration [36 , 37] . In vitro studies have also shown that mast cell proliferation can be inhibited by hyaluronan excreted by co-cultured cells [34] . Furthermore , mast cell secretion products have been shown to regulate hyaluronan secretion from other cells [38] . Mast cells also express the CD44 hyaluronan receptor on their cell surface [39] . Our immunohistochemical staining showed that hyaluronan forms part of the extracellular matrix in mast cell tumours and so likely interacts with the CD44 receptor . The interaction between CD44 and hyaluronan is known to promote both transformation and metastasis of cancer cells . Together these factors suggest that alterations in hyaluronan turnover could play a role in CMCT development . Based on our data it appears possible that alterations in both the GNAI2 and hyaluronidase genes play a role in mast cell tumour development . The association to regions containing hyaluronidase genes on both chromosome 14 and 20 together with the much stronger association to a novel variant in the GNAI2 gene supports both findings . Still more work is required to validate and explore the functional consequences of these candidate genes . Many candidate variants were identified from the sequence capture and only a small subset were included in the fine mapping , which is a major limitation in this study . Many variants need to be studied in more detail to determine their effects . The dog has proven to be a good model for many human disorders . Similarities between CMCT and human mastocytosis suggest that genes and genetic pathways altered in CMCT could also play a role in human mastocytosis . We will continue to evaluate the role of the GNAI2 and the hyaluronidase genes in CMCT and hope that these investigations will help shed a light not only on CMCT , but also on human mastocytosis leading the way to a better understanding of the disease and potential new drug targets .
A total of 127 golden retriever ( GR ) samples were collected in the United States ( 70 cases and 57 controls ) , 113 in the United Kingdom ( 53 cases and 60 controls ) and 33 in the Netherlands ( 18 cases and 15 controls ) . All samples were collected between year 2000 and 2013 . The samples collected in the United States were collected from all over the United States . These samples were all collected by a veterinary professional and were submitted to the BROAD institute either by the veterinarian or by the dog owner . Samples collected in the UK were primarily collected at the Animal Health Trust ( AHT ) . A subset of UK samples were collected by veterinarians or dog owners not affiliated with the AHT . Samples collected in the Netherlands were collected at either Utrecht University clinic of Companion Animals or Veterinary Specialist Center De Wagenrenk . Cases were diagnosed with CMCT by histopathology or cytology . Data was collected when available regarding age of onset , and grading of the mast cell tumour . Control dogs were unaffected by any form of cancer , and were over 7 years old . For the American controls , phoning the owners bi-yearly provided follow up health information . Genomic DNA was extracted from whole blood ( 240 samples ) or buccal swabs ( 33 samples ) using the QIAamp DNA Blood Midi Kit ( QIAGEN ) , the Nucleon® Genomic DNA Extraction Kit ( Tepnel Life Sciences ) , by phenol chloroform extraction , or by salt extraction [40] . Illumina 170K canine HD SNP arrays were used for the genotyping of approximately 174 , 000 SNPs with a mean genomic interval of 13kb . Genotyping of the European samples was performed at the Centre National de Genotypage , France . Genotyping of the American samples was performed at the Broad Institute , USA . The American and European GR cohorts were analysed separately and as a joint dataset . Data quality control was performed using the software package PLINK [41] , removing SNPs and individuals with a call rate below 90% . Markers showing a low level of variability ( MAF<0 . 01 ) were excluded from further association analysis . A total of 1 , 582 SNPs were removed due to platform-related genotyping inconsistencies due to differences in hybridization and calling algorithms used between two different sequencing platforms . Population stratification was estimated and visualized in multidimensional scaling plots ( MDS ) using PLINK to detect outliers and subgroups in the dataset after eliminating SNPs in high LD ( r2>0 . 95 ) . Due to the cryptic relatedness that often exists within a dog breed , the level of relatedness between individuals in each population was calculated using the GCTA software [42] , and a genetic relationship matrix ( grm ) value of 0 . 25 was used as the cut-off threshold to remove highly related dogs ( corresponding to half-sib level of relatedness ) . Regions associated with CMCT were detected by case-control genome-wide association analysis . PLINK and EMMAX software [41 , 43] , were used to calculate association p-values , the latter software corrected for stratification and cryptic relatedness using mixed model statistics [43] . The LD-pruned SNP set was used for MDS , estimation of relatedness in GCTA and within the relationship matrix in EMMAX , whereas the full QC filtered SNP set was used for the association testing . Quantile-quantile ( QQ ) -plots were created in R to assess possible genomic inflation and to establish suggestive significance levels . Permutation testing was performed in PLINK for the PLINK calculated association results , or GenABEL [44] for the mixed model association results . 10 , 000 permutations were performed . Minor allele frequencies and odds ratios , were calculated for each cohort ( cases and controls ) using PLINK . Pair-wise r2–based LD between markers was used to evaluate the size of candidate regions and whether the associated loci were independent . The r2 calculations were performed using the Haploview and PLINK software packages [41 , 45] . Gene annotations were extracted from Ensemble [46] and UCSC [47] . Fifteen dogs ( 7 European ( 3 cases and 4 controls ) , and 8 American ( 5 cases and 3 controls ) ) were selected for sequencing of the associated genomic regions . A custom sequence capture array was designed ( Nimblegen 2 . 1M solid array ) to cover all associated regions . In total the capture array was designed to include 11 . 5 Mb DNA including the top associated regions CanFam2 . 0 cfa 20:41 , 149 , 999–43 , 000 , 000 , cfa 20:46 , 099 , 999–49 , 700 , 000 and cfa14:14 , 599 , 999–15 , 450 , 000 . Sequence capture was performed as previously described [48] . DNA from 15 individuals was individually barcoded and 3 DNA samples hybridized to each of 5 arrays . The DNA captured by each array was used to prepare a sequencing template library , and the libraries were sequenced on four Hi-Seq 2500 lanes . Sequencing data was pre-processed and aligned using BWA [49] , Samtools [50] and Picard to make bam files and to mark duplicate reads . Sequencing data was aligned to the CanFam 2 . 0 reference genome . Coverage of the targeted regions was 7-69x . GATK software [51] was used for data processing and genotype calling as well as filtering of variants . Called variants were annotated using SnpEff [52] and variants were scored according to conservation based on the 29 mammals data [53] using SEQScoring software [54] , producing files which could be visualized graphically in the CanFam 2 . 0 UCSC browser . Bam files were visualized in IGV [55] to evaluate the presence of structural variants . Identified variants were evaluated in the CanFam3 . 1 genome assembly to assure that these variants were not due to faults in the CanFam2 . 0 assembly . SNPs that conformed to the haplotype for the most associated SNP were chosen for fine mapping . Priority was given to SNPs , which were conserved ( as deemed by SEQscoring based on the 29 mammals data , including SNPs up to 6bp from conserved sites ) , coding SNPs , SNPs in UTR regions , SNPs upstream of genes in a predicted promoter region , and SNPs in introns . Additional SNPs , which did not conform to the risk haplotype , were chosen due to their location in interesting regions . SNPs were genotyped using the Sequenom MassArray iPLEX platform . Not all candidate SNPs could be genotyped due to iPLEX ( multiPLEX ) design limitations , or because of limitations in the number of SNPs that could be co-typed . Fine mapping data was analysed using Haploview , and 1 , 000 , 000 permutations were performed . Poly-A selected , strand-specific RNA sequencing was performed on a CMCT surgically excised from a GR . Sequencing libraries were prepared as described [56] . Normal marginal tissue was sampled as control . Samples were sequenced on one Illumina Hi-Seq 2500 lane . Data was analysed and aligned to CanFam3 . 1 using the tuxedo suite [21] . The sequence data was viewed in IGV . Immunohistochemistry was performed in order to visualize if hyaluronan is present in canine mast cell tumours . Slices of archived paraffin-embedded formalin-fixed CMCT tissue were dewaxed . , and endogenous peroxidase blocked by incubation in 1% ( v/v ) H202 in 70% ( v/v ) ethanol for 5 min . Sections were washed sequentially in water and PBS and blocked for non-specific binding by a 30 min incubation in 1% ( w/v ) BSA in PBS . Sections were incubated over night at 4˚C with 2 . 5μg/ml Biotinylated Hyaluronan Binding Protein ( AMS . HKD-BC41 , AMSBIO ) in 1% ( w/v ) BSA in PBS . Sections were washed with PBS and incubated with Vectastain Elite ABC Reagent ( Vectastain Elite ABC Kit , Vector ) for 30 minutes . After an additional wash in PBS the sections were incubated in diaminobenzidine for 7 min . The sections were rinsed in water and counterstained with 10% Mayer’s haematoxylin for 30s . The samples were then washed , dried for two minutes , and mounted in DPX mounting medium . Eight cases of CMCT ( 6 dogs – 2 with multiple tumours ) , on which genome analysis was performed , were selected for IHC . In addition , four cases of CMCT in GRs were selected from AHT recent case submission and similarly stained . As a negative control a portion of haired skin from the lateral chest of a dog with no evidence of skin disease was used . Prior to IHC histopathological evaluation was performed on both test groups to confirm the presence of a CMCT . Blocks containing unaffected tissue margins were as far as possible selected for staining . Medium to dark brown staining in a linear , granular or diffuse staining pattern in the epidermis , dermis , panniculus and in mast cells was considered as positive staining . A normal expected staining pattern as observed in the negative skin control included positive ( linear ) staining of the basement membrane of the epidermis , hair follicles , apocrine gland and blood vessels . Normal positive staining was also visible between dermal and pannicular collagen fibres and between adipocytes . Positive staining in the mast cells was evaluated as nuclear ( granular or diffuse ) , intracytoplasmic ( granular or diffuse ) or cytoplasmic membrane ( linear ) . Positive staining was evaluated as light or intense . RNA was extracted from RNAlater-preserved normal skin and CMCT samples using either TRIzol ( Life Technologies ) or the RNAeasy kit ( QIAGEN ) . RNA integrity was evaluated by microfluidic electrophoresis ( Agilent 2100 Bioanalyser RNA 6000 Nano Kit ) . cDNA was synthesised using the RT-for-PCR kit ( Clontech ) . PCR Primers were designed targeting the exon before and after the alternatively spliced exon . In addition , splice-specific primers were designed traversing the alternative splice site ( see S6 Fig for design ) as well as for the wild-type splice form as a control . Alternative_splice_primer: Forward: CATTGTCAAGCAGATGAAGATG , Reverse: CTGCACACCG TTGTCAGCC Splice_primer_control: Forward: GACCCCTCCCGAGCGGATG Reverse: As for alternative splice Primer_traversing_alternative_spliced_exon Forward: AGAGCACCATTGTCAAGCAG Reverse: TCCGGATGACACAAGACAGATC Quantitative PCR was performed on the 7900HT Fast Real-Time PCR ( Applied Biosystems ) using SyBr Green mastermix ( Applied Biosystems ) . Delta Cq ( Cqnormal_splice−Cqalternative_splice ) was calculated between the splice specific and alternative splice products for each cDNA sample . The PCR products were analysed by agarose gel analysis . This study was approved by the Committee for Animal Care at the Massachusetts Institute of Technology , approval number MIT CAC 0910-074-13 and by the Uppsala Animal ethical board , approval number C2-12 . No experimental animals were used in this research . Blood or buccal swaps were taken with owners consent . Tissue samples consisted of surplus material from surgical resections with owners consent . | Pet dogs develop many of the same diseases as humans . Hence , studying diseases in dogs can be valuable for learning about human conditions . The genetic structure caused by inbreeding within dog breeds has proven to be advantageous to map genetic diseases . Golden retrievers have a very high risk of developing mast cell tumours suggesting that there is a genetic background for this disease . In the present study we investigated genetic risk factors for this disease in golden retrievers . We identified three regions of the genome predisposing to the development of mast cell tumors . A candidate mutation in the GNAI2 gene was found to change the form of this gene . The disease associated regions also harbour multiple hyaluronidase genes ( HYAL1 , HYAL2 and HYAL3 on cfa20 and HYAL4 , SPAM1 and HYALP1 on cfa14 ) suggesting that turnover of hyaluronic acid plays an important role in the development of CMCT . Human mastocytosis shares many characteristics with canine mast cell tumours and we believe our findings can help clarifying the biology behind this disease in humans as well as identifying new therapeutic targets . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Genome-Wide Association Study of Golden Retrievers Identifies Germ-Line Risk Factors Predisposing to Mast Cell Tumours |
Helminthiasis and tuberculosis ( TB ) coincide geographically and there is much interest in exploring how concurrent worm infections might alter immune responses against bacilli and might necessitate altered therapeutic approaches . A DNA vaccine that codifies heat shock protein Hsp65 from M . leprae ( DNAhsp65 ) has been used in therapy during experimental tuberculosis . This study focused on the impact of the co-existence of worms and TB on the therapeutic effects of DNAhsp65 . Mice were infected with Toxocara canis or with Schistosoma mansoni , followed by coinfection with M . tuberculosis and treatment with DNAhsp65 . While T . canis infection did not increase vulnerability to pulmonary TB , S . mansoni enhanced susceptibility to TB as shown by higher numbers of bacteria in the lungs and spleen , which was associated with an increase in Th2 and regulatory cytokines . However , in coinfected mice , the therapeutic effect of DNAhsp65 was not abrogated , as indicated by colony forming units and analysis of histopathological changes . In vitro studies indicated that Hsp65-specific IFN-γ production was correlated with vaccine-induced protection in coinfected mice . Moreover , in S . mansoni-coinfected mice , DNA treatment inhibited in vivo TGF-β and IL-10 production , which could be associated with long-term protection . We have demonstrated that the therapeutic effects of DNAhsp65 in experimental TB infection are persistent in the presence of an unrelated Th2 immune response induced by helminth infections .
Helminth parasites cause significant morbidity worldwide , with estimates indicating that approximately one-third of the almost three billion people living on less than two US dollars per day in developing regions of sub-Saharan Africa , Asia , and the Americas are infected with one or more helminths [1] , [2] . Helminth infections are potent Th2 response inducers in both humans and experimental models , characterized by eosinophilia , high titers of circulating IgE , enhanced Th2 cytokine profile [e . g . , increased secretion of interleukin 4 ( IL-4 ) and IL-5] , and regulatory ( IL-10 , TGF-β ) cytokines and reduced Th1 type cytokines [e . g . , interferon ( IFN ) -γ] [3] . Toxocariasis is an underestimated soil-transmitted helminthiasis that primarily affects people in developing countries [4] , [5] , including Brazil , where prevalence rates reach ∼40% [6] , [7] , while Schistosomiasis causes 14000 deaths per year , with 200–300 million infected people and 10% at risk of infection worldwide , according to the Global Burden of Disease Study [8] . Additionally , tuberculosis remains one of the leading causes of morbidity and mortality in many settings , particularly in the world's poorest countries . It is estimated that of the approximately 8 . 9 million people that developed tuberculosis in 2004 , nearly 1 . 7 million people died from it [9] . Studies in animal models of Th1-inducing pathogens and in pre-clinical trials with certain vaccines showed that infection with helminthic parasites impaired Th1 immune responses [10] , [11] , [12] , [13] , [14] . This decrease in immunogenicity of BCG ( Bacillus Calmete-Guérin , the current vaccine against tuberculosis ) was described in a study conducted in a rural community in Ethiopia where healthy or helminth-infected volunteers received anti-helminthic therapy or placebo , and were subsequently vaccinated with BCG . In that study , the authors showed that helminth infection impaired IFN-γ secretion and increased TGF-β production by peripheral blood mononuclear cells stimulated in vitro with PPD ( purified protein derivative ) after BCG vaccination [15] . These findings highlight potential explanations for the apparent failure of BCG to prevent pulmonary tuberculosis in populations inhabiting tropical regions . Vaccines against tuberculosis ( TB ) under development include attenuated or enhanced live whole-cell , inactivated whole-cell , subunit , virus-vectored , and DNA vaccines followed by several immunization strategies using prime-boost protocols . Several of these candidate vaccines have demonstrated activity in animal models that is equal or superior to that of BCG; trials in human subjects are currently under way [16] . We have previously demonstrated that a DNA vaccine encoding the mycobacterial 65-kDa heat shock protein ( DNAhsp65 ) protected mice and guinea pigs from challenge with a virulent strain of Mycobacterium tuberculosis ( Mtb ) [17] , [18] and , further , cured previously infected mice when administered as naked DNA by intramuscular injection [19] . We have also shown that therapeutic use of DNAhsp65 in combination with anti-mycobacterial drugs shortens the duration of TB treatment , improves treatment of latent TB infection , and is effective against multi-drug resistant TB [20] . However , to improve therapies and vaccination protocols against tuberculosis it is important to investigate the influence of helminth coinfection on the immune response during TB [21] and its effects on treatment and immunization . We previously showed that infecting BALB/c mice with the helminth Toxocara canis elicited a Th2 response but did not alter susceptibility to subsequent infection with M . tuberculosis [22] . In contrast , Schistosoma mansoni infection can strongly enhance susceptibility to TB [13] and impair the protective effects of BCG vaccination [23] . Here , we show that the immune and pathological responses induced by coinfection with T . canis or S . mansoni and TB did not abrogate the therapeutic effect of the DNAhsp65 vaccine . We found that the therapeutic effect was maintained due to Hsp65-specific IFN-γ production as well as an inhibition of TGF-β production in the lung . These results suggest that the DNAhsp65 vaccine may provide an elegant vaccination strategy in tropical countries where infections with multiple pathogens are common and lead to altered immune responses .
Specific pathogen-free female 6-week-old BALB/c mice were obtained from the animal facilities of Faculdade de Ciências Farmacêuticas - Universidade de São Paulo and bred in a SPF facility . All experiments were approved and conducted in accordance with the guidelines of the Animal Care Committee of the University . Infected animals were housed in cages within a laminar flow safety enclosure and kept in a Biosafety Level 3 biohazard animal room . DNAhsp65 vaccine was derived from the pVAX1 vector ( Invitrogen , Carlsbad , CA , USA ) , which had previously been digested with BamHI and Not I ( Invitrogen ) and a 3 . 3-kb fragment ( corresponding to the M . leprae HSP65 gene ) was inserted . The vector pVAX1 was used as control . Plasmids were replicated in DH5α Escherichia coli and purified with Endofree Plasmid Giga kit ( Qiagen , Valencia , CA , USA ) according to the manufacturer's protocol . Endotoxin levels were determined using a QCL-1000 Limulus amebocyte lysate kit ( Cambrex Company , Walkersville , MD , USA ) and were less than 0 . 1 endotoxin units ( EU ) /µg DNA . DNA vaccination was initiated 30 days after TB induction by injection of 50 µg of plasmid DNA in 50 µl of saline into the quadriceps muscle of each hind leg , on four occasions at 10-day intervals . Ten days after the last dose , mice were killed and bacterial growth , lung histology , and cytokine production by lung or spleen cells were accessed ( Figure 1 ) . All figures represent the results from one of three representative experiments: first lungs were removed , using the right cranial lobe and right middle lobe for homogenization , the right caudal lobe for fixation and the left lobe and accessory lobe for CFU . The spleen was also removed from the same mice . Recovery of M . tuberculosis was performed as described previously [31] . Briefly , the number of live bacteria recovered from the lungs was determined as CFU by plating 10-fold serial dilutions of homogenized tissue on Middlebrook 7H11 agar ( Difco ) incubated at 37°C . Colonies were counted after 28 days . Results are expressed as log10 of CFU/g lung . For cytokine measurements , lungs were removed and homogenized in 2 ml RPMI 1640 , centrifuged at 450×g and the supernatant was stored at −70°C until assayed . Commercially available enzyme-linked immunosorbent assay ( ELISA ) antibodies were used to measure IL-4 , IL-5 , IL-10 , TGF-β , IL-12 , and IFN-γ ( OptEIA™ BD-Pharmingen , San Diego , CA ) . Plates were coated with 100 µl/well of the capture antibody ( 1–4 µg/ml ) diluted in Coating Buffer ( 0 . 1 M Sodium Carbonate , pH 9 . 5 ) and incubated overnight at 4°C . Plates were washed 5 times with 300 µl/well Wash Buffer ( PBS with 0 . 05% Tween-20 ) and non-specific binding was blocked by addition of 200 µl/well Assay Diluent ( PBS with 10% FBS , pH 7 . 0 ) , and incubated at room temperature for 1 hour . After washing as above , 100 µl of standards , samples , and controls were added into appropriate wells and incubated for 2 hours at RT . The plates were washed as above and 100 µl of Working Detector [Detection Antibody ( 0 . 5–2 . 0 µg/ml ) +SAv-HRP reagent] was added to each well for 1 hour at room temperature . After 7 additional washes , 100 µl of Substrate Solution ( BD Pharmingen™ TMB SubstrateReagent Set ) was added to each well and incubated for 30 minutes at room temperature in the dark . The reaction was stopped by adding 50 µl of Stop Solution ( 2 N H2SO4 ) to each well . Optical density was measured at 450 nm within 30 minutes of stopping the reaction . Nitric oxide ( NO ) production was assessed by measuring nitrite levels in lung homogenates using the Greiss reagent method [32] . Data are presented as micromoles of NO2− . Spleens from mice killed 10 days after the last DNA dose were aseptically removed and minced and the released cells were washed three times in RPMI 1640 ( Gibco BRL , Grand Island , USA ) . Minced lungs were next treated with 5 ml/lung of collagenase solution ( 0 . 1 g of Type IV collagenase [Sigma Chemical Co . , St . Louis , USA] in 45 ml of RPMI 1640 ) and allowed to shake for 30 minutes at 37°C . The suspension was centrifuged at 450×g for 10 minutes and the pellet was suspended in RPMI supplemented with 10% fetal bovine serum ( Gibco BRL ) , penicillin ( 100 U/ml , Gibco BRL ) , streptomycin ( 100 µg/ml , Gibco BRL ) . Cells from lung or spleen were suspended at 5×106 cells per ml in supplemented RPMI and dispensed into 96-well flat-bottom microtiter plates in a volume of 0 . 1 ml . A total of 10 mg/ml recombinant Hsp65 was added to wells ( 0 . 1 ml ) in triplicate and maintained for 48 h at 37°C . Commercially available ELISA antibodies were used to measure IFN-γ ( OptEIA™ BD-Pharmingen ) in supernatants of cultured cells as above . Data are represented as mean ± SEM , n = 5 ( PBS group ) or n = 6 ( other groups ) and were analyzed with using GraphPad Prism version 4 . 02 for Windows ( GraphPad Software , San Diego , CA ) . Comparisons were performed using unpaired t tests for CFU analyses or one-way ANOVA with Bonferroni's post test for other experiments . Differences were considered significant if P<0 . 05 . Experiments were repeated 2–3 times and similar results were observed in all experiments .
As shown previously [19] , [28] , DNAhsp65-therapy was effective in reducing the number of CFU in Mtb-infected mice ( black bars , Figure 1A–C ) , whereas the treatment with empty vector was not . We have also demonstrated previously that coinfection with T . canis could not increase susceptibility to TB [22] . Here , we confirmed this data ( Figure 1A ) , showing that numbers of bacteria in the lung of T . canis-coinfected mice were quite similar to those in Mtb-infected mice . On the other hand , S . mansoni-coinfection resulted in an increase in CFU counts , demonstrating greater susceptibility to M . tuberculosis bacilli under these conditions , confirming previously published data [13] . To evaluate whether helminth infection could impair DNAhsp65-induced protection against M . tuberculosis , mice were coinfected with worms and treated with DNAhsp65 following the time line presented in Figure S1 . Although T . canis-coinfection did not change bacterial burden in the lungs , we also observed that this helminth did not influence the therapeutic effect of DNAhsp65 ( Figure 1A ) , since we observed a similar reduction in CFU in lungs tissue in both Mtb hsp65 and Tc+Mtb hsp65 groups . Surprisingly , therapy with DNAhsp65 was also highly effective in S . mansoni-coinfected animals , as CFUs were reduced in lungs of this group ( Figure 1B ) . Due to the ability of S . mansoni to alter the immune response in a concomitant Mtb infection [13] , different from T canis that does not influence in bacteria burden [22] , we also evaluated CFU levels in the spleen after treatment with DNAhsp65 and found that the differences in CFU counts suggested that the DNAhsp65-therapeutic effect observed in lungs is maintained systemically ( Figure 1C ) . Histological analysis of the lungs also revealed that cellular accumulation , cellular organization and pulmonary parenchyma commitment were similar in T . canis coinfected and M . tuberculosis infected mice . In contrast , S . mansoni coinfection induced greater lung injury when compared to M . tuberculosis infection itself resulting in increased inflammatory cells numbers and greater tissue congestion . Treatment with empty vector did not alter lung pathology or CFU either in mice infected with M . tuberculosis or coinfected with helminths . In contrast , DNAhsp65 therapy resulted in a significant decrease in lesions in the Mtb group and the coinfected groups . Histological sections of HE-stained lungs from coinfected and M . tuberculosis infected mice were characterized predominantly by the presence of macrophages and lymphocytes that accumulated mainly in perivascular and peribronchial areas . Immunotherapy with DNAhsp65 partially reversed the pathologic effects resulting in a large preserved area in the lung parenchyma ( Data not shown ) . As nitric oxide ( NO ) is an important microbicidal factor , we analyzed its production indirectly within the lung parenchyma through measurement of NO2− . Mtb-infected mice showed increased levels of NO compared to cells from control animals . Cells obtained from coinfected animals showed similar NO production levels when compared to Mtb-infected animals . DNAhsp65 treatment did not alter these high NO levels in any group ( Figure 2C and F ) . Since proinflammatory and Th1 cytokines play important role in the immune response against M . tuberculosis , we sought to investigate whether coinfection could alter cytokine production in lungs after DNAhsp65 treatment . Lung cells were obtained from animals that were either coinfected or singly Mtb-infected and treated with or without DNAhsp65 . In Mtb-infected animals , IL-12 and IFN-γ levels in the lungs were increased when compared to mice receiving PBS or infected only with T . canis ( Figure 2A and B ) or S . mansoni ( Figure 2D and E ) . These cytokines were present at similar levels in coinfected and Mtb-infected animals , even after DNAhsp65 treatment . Besides helminth infection are marked by a Th2 pattern , during coinfection the Th1 pattern kept elevated as seen during infection with M . tuberculosis only . In an attempt to determine those elements of the immune system that respond to DNAhsp65 immunotherapy , we analyzed Th2 and regulatory cytokine profiles in lungs following DNAhsp65 treatment . Infection with T . canis induced elevated levels of IL-4 , IL-5 , and IL-10 compared to control mice ( Figure 3A–C ) . However , in T . canis coinfected mice , regardless of DNAhsp65 therapy , the levels of these cytokines were reduced to control levels . Similar to T . canis infection , S . mansoni caused elevated levels of IL-4 , IL-5 , IL-10 , and TGF-β when compared to PBS and Mtb groups ( Figure 3D–G ) . In the S . mansoni coinfection scenario , these high levels of cytokine expression were maintained . Interestingly , DNAhsp65 therapy markedly reduced Th2 and regulatory cytokine levels induced by S . mansoni , suggesting a pathway for the protection observed in these mice . A Th1 response is a key element during the immune response against TB since it is critical in preventing progression to active disease . The presence of IFN-γ at the site of infection can circumvent the tendency of Mtb to escape phagosome maturation and facilitate control of bacterial burden [33] . To determine whether DNA immunization induced specific T cell stimulation , spleen or lung cells were obtained after coinfection and therapy and cultured in the presence of recombinant HSP65 . The supernatants were collected after 48 h in cell culture and were analyzed for IFN-γ production by ELISA . IFN-γ expression was significantly upregulated in spleen cells from infected mice receiving DNAhsp65 therapy ( Figure 4A ) . This was observed in treated mice infected with Mtb or coinfected with T . canis . All other conditions produced IFN-γ levels that were not significantly different from control values . Thus , high levels of antigen-specific IFN-γ production were correlated with DNAhsp65 treatment and may account , at least in part , for the observed positive therapeutic effects . In the S . mansoni-coinfection model , we analyzed HSP65-specific IFN-γ production by lung cells . As Figure 4B shows , lung cells obtained after Mtb infection produce more IFN-γ when stimulated in vitro with rHSP65 than lung cells from PBS and Sm groups . However , treatment with DNAhsp65 could increase these levels significantly in Mtb-infected and coinfected mice . This elevated production was not observed in untreated or vector treated mice , highlighting the specific therapeutic effects of DNA treatment in Mtb as well in coinfected groups ( Figure 4 ) .
According to the World Health Organization ( WHO ) , Neglected Tropical Diseases , as helminth infections , have a major adverse impact on the health , well-being , and socioeconomic development of poverty-stricken people living in low-income countries [34] , [35] . Because mycobacterial and helminth pathogens are frequently co-endemic and tend to induce opposing immune responses , we investigated the pattern of inflammatory and immune responses in coinfection models featuring pre-exposure to T . canis or S . mansoni and challenge with M . tuberculosis to assess effects of a therapeutic DNA vaccine that has shown efficacy in experimental TB . In our study , we showed that worm coinfection did not abrogate DNAhsp65 therapeutic effects during TB . Coinfected mice had no modulation in Th1 immune response , but present high levels of Th2 and regulatory cytokines , which was related with lung damage ( data not shown ) . When DNA therapy was evaluated , we observed increasing in IFN-γ produced by rHsp65 recalled cells ( Figure 4 ) and downregulation of Th2 and regulatory cytokines in the lungs of coinfected and treated mice ( Figure 3 ) . This immune modulation culminated in preserved therapeutic properties attributed to DNAhsp65 in TB/helminth coinfection models . Our group has focused on the heat shock protein produced by Mycobacterium leprae ( hsp65 ) as a vaccine antigen against several experimental pathologies including TB [20] , [28] , [36] , diabetes [37] , arthritis [38] , leishmaniasis [39] , and cancer in phase I clinical trials [40] , [41] . The success of hsp65 vaccination in these different diseases reflects its effectiveness as an immunodominant antigen and also suggests that it produces hsp-dependent effects , such as those ascribed to other heat shock proteins [42] . The search for new vaccines and therapies against TB is due in part to the widely variable protective efficacy of BCG which ranges from 0 to 80% protection depending on the country [43] , as well as the fact that BCG protects against TB in newborns , but does not prevent latent TB or reactivation in adults [44] , [45] . One hypothesis regarding the variability of BCG efficacy in developing countries includes the presence of Th2-cell and Treg responses , which could be driven by co-existing helminth infections or environmental mycobacterias [46] . In 2005 , Elias and coworkers showed for the first time that a helminth infection could alter the in vivo protective effect conferred by BCG immunization [23] . This pivotal study showed that mice previously immunized with BCG and co-infected with S . mansoni were more susceptible to TB , showing increased bacterial load and lung pathology . Moreover , when spleen cells from these coinfected mice were stimulated in vitro with PPD ( purified protein derivative ) , they produced lower IFN-γ and nitric oxide levels . We demonstrated previously that T . canis infection does not lead to increased susceptibility to pulmonary tuberculosis [22] and in the present work we show that pre-exposure to T . canis infection does not affect the therapeutic effects of DNAhsp65 . Recently , S mansoni infection has been reported to increase susceptibility to TB [13] , which was confirmed in the present work using a different route of TB infection . Mice coinfected with S . mansoni and M . tuberculosis presented increases in lung inflammation and CFU counts , accompanied by augmentation of Th2 cytokine release in lung tissue . In contrast , T . canis coinfection did not increase susceptibility to TB or alter Th1 cytokines production . These observed changes in Th1 and Th2 phenotypes may account for the differences in TB susceptibility between S . mansoni and T . canis coinfection . Despite this , the DNAhsp65 vaccine proved capable of maintaining its therapeutic properties even under TB and S . mansoni coinfection . As we can observe in the Figure 3 , Th2 ( IL-4 and IL-5 ) and regulatory ( IL-10 and TGF-β ) cytokines were downregulated in S . mansoni coinfected mice treated with DNAhsp65 compared to untreated coinfected mice , correlated to CFU counts . Indeed , Th2 lymphocyte subsets have been observed in lung tissue from patients with cavitary tuberculosis , compared with Th1 subsets in non-cavitary disease , suggesting that IL-4 might be an indicator of disease severity [47] and that the Th2 environment can increase immunopathology [48] . These data suggest that the suppression of Th2 and regulatory cytokines induced by DNAhsp65 conferred the protection of the lung parenchyma observed in coinfected hosts . Mutapi and colleagues [49] suggested that Th1/Th2 dichotomy does not sufficiently explain susceptibility or resistance to schistosome infection . Hosts who produce IFN-γ/IL-4/IL-5 in association with IL-10 present high levels of infection , proposing that the regulatory component is strongest to define the severity of pathology . In our results we observed lung damage in coinfected hosts ( data not shown ) , associated with high IFN-γ , IL-4/IL-5 and IL-10/TGF-β , but when mice were treated with DNAhsp65 the regulatory and Th2 components were inhibited and Th1 kept elevated , allowing the preservation of lung integrity . Successful TB control depends on a finely tuned system where IFN-γ provided by effector T cells confers tuberculostatic and tuberculocidal activities to macrophages [50] , [51] . We suggest that the treatment downmodulated the regulatory cytokines in the lung and simultaneously increased the antigen recalled IFN-γ production , allowing to the control of TB in coinfected treated hosts . Helminth infections can activate and expand the T regulatory cell population ( Treg ) in mice as well as in humans . Tregs play an important role in the suppression of Th1 functions during the immune response induced by S . mansoni egg antigens and can also control the Th2 response in chronic infection [52] . Elias and co-workers recently showed that peripheral blood mononuclear cells from BCG-immunized , chronically infected S . mansoni patients showed increased TGF-β production without an enhanced Th2 immune response when stimulated in vitro with PPD , indicating that this phenomenon was related to reduced immunogenicity of BCG [15] . Our data suggest that DNAhsp65 treatment inhibited the soluble factors involved in Treg–mediated immunosuppression , as indicated by the decrease in TGF-β and IL-10 production observed in treated co-infected mice . Moreover , DNAhsp65 transfection induces macrophages to produce IL-6 ( Data not shown ) , a cytokine that decreases suppressive effects of Treg cells and allows the development of effector responses [53] . Therefore , we have shown that DNAhsp65 therapy circumvents several difficulties in coinfected hosts , probably modulating cytokines production that lead to therapeutic efficacy against TB , highlighting its potential to become a valuable tool against TB . In conclusion , this study reiterates importance of evaluating the status of hosts during vaccine development , given the high incidence of TB/helminthes coinfection . | From 14 diseases considered by WHO as Neglected Tropical Diseases , four involve helminth infections , such as schistosomiasis and soil-transmitted helminthiasis . Toxocariasis is a soil-transmitted worm highly prevalent in many developing countries , while schistosomiasis causes an annual mortality of 14 , 000 deaths per year , with 200–300 million infected people and 10% at risk of infection worldwide . Additionally , tuberculosis ( TB ) remains one of the leading causes of morbidity and mortality in many settings , particularly in the world's poorest countries . Mycobacteria and helminths are co-endemic and induce opposing patterns of immune responses in the host , recognized as Th1 and Th2 respectively . These co-existing patterns could be associated with the failure of TB vaccines . In this sense , we investigated the inflammatory and immune response in a coinfection model with T . canis or S . mansoni and M . tuberculosis analyzing the effects of an immunotherapy that has previously shown efficacy in experimental TB . This immunotherapy is based on a DNA vaccine that codifies a mycobacterial heat shock protein ( hsp65 ) , which can prevent TB in a prophylactic and also therapeutic setting . In this work , we show that helminth coinfection does not abrogate the therapeutic effects of DNAhsp65 vaccine against TB . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/bacterial",
"infections",
"infectious",
"diseases/helminth",
"infections",
"immunology/immune",
"response",
"infectious",
"diseases/neglected",
"tropical",
"diseases"
] | 2010 | Helminth Coinfection Does Not Affect Therapeutic Effect of a DNA Vaccine in Mice Harboring Tuberculosis |
Snail intermediate host control is a widely canvassed strategy for schistosomiasis control in endemic countries . While there have been increasing studies on the search for potent molluscicides in the past years , the use of nanoparticulate agents as molluscicides is yet to gain wide attention . The aim of this study was to assess the molluscicidal potential of curcumin-nisin poly lactic acid ( PLA ) entrapped nanoparticle ( CurNisNp ) against Biomphalaria pfeifferi , a snail intermediate host for Schistosoma mansoni . CurNisNp formulated by double emulsion method was tested against the young adults , < 1 week , 1-2-week old juveniles , 1 day ( blastula ) and 7 day-old ( hippo-stage ) egg masses of B . pfeifferi . Mortality in the different stages was determined after 96-h of exposure at varying concentrations ( 350 , 175 , 87 . 5 , 43 . 75 and 21 . 88 ppm ) . The sub-lethal effects of CurNisNp on the hatchability of the 7-day-old egg masses and egg laying capacity of the young adult snails were determined . The CurNisNp diameter , polydispersity index ( PDI ) , zeta potential and drug entrapment efficiency were 284 . 0 ± 17 . 9 nm , 0 . 166 ± 0 . 03 , -16 . 6 ± 2 . 45 mV and 35 . 0% respectively . The < 1 week old juveniles and the 1-day-old egg stage ( blastula ) of B . pfeifferi with LC50 277 . 9 ppm and 4279 . 5 ppm were the most susceptible and resistant stages to the drug respectively . CurNisNp was also observed to cause significant reductions ( P<0 . 05 ) in egg hatchability and egg laying capacity with strong negative correlation between egg laying capacity and concentration ( r = -0 . 928; P<0 . 05 ) . This study showed that CurNisNp has molluscicidal activities on different developmental stages of B . pfeifferi . It is therefore recommended that the formulation be more optimised to give a nanoparticle with a narrow range monodispersed PDI for better drug distribution and eventual greater molluscicidal activities .
Trematodes , the causal agents of schistosomiasis and fascioliasis are important parasites of economic and public health implications in most of sub-Saharan Africa . Schistosomiasis affects over 240 million people worldwide , with up to 700 million individuals living at risk of infection [1] . The disease caused up to 250 , 000 deaths per year in the last decade [2] . Chemotherapy has been the most adopted means of control of schistosomiasis in developing countries . The increase in number of individuals that need to be treated with praziquantel ( PZQ ) necessitates the corresponding increase in PZQ deployment in sub-Saharan Africa thus raising concerns about the emergence and establishment of Schistosoma resistance to PZQ [3] . The control of snail intermediate hosts in an attempt to break the parasite transmission cycle is a widely advocated strategy in schistosomiasis control . Niclosamide , a chemical molluscicide has recorded success in this regard , but its toxicity against non-targeted organisms has been a major set-back to its general adoption [4] . Intensive studies have been conducted in search of agents which are more environmentally friendly to combat the intermediate hosts of Schistosoma . Efforts are directed particularly towards molluscicides of plant origin with several studies reported across the world [5–11] . Nevertheless , there is currently no licensed plant-derived molluscicide despite this myriad of studies . This could be due to inability to standardize these findings for wide scale use . The feasibility of plant-induced toxicity on non-targeted organisms cannot also be ruled out . Nanotechnology has gained increasing interest in biomedicine with the utmost aim of effective delivery of bioactive agents . Particularly it has been widely applied to combat parasitic agents [12 , 13] . While attention is often drawn towards Schistosoma parasites [14 , 15] , little is known about the application of nanomedicine against the snail intermediate hosts of the parasites . Curcumin and nisin are naturally derived non-toxic compounds [16 , 17] with a wide range of activities [18–21] . The two compounds have been shown to have antibacterial , anti-inflammatory and anticancer properties [18 , 19] . Curcumin has been reported to be efficacious against adult Schistosoma mansoni [20 , 21] . The higher bioavailability of these compounds in nanoparticulate forms and improved efficacy against some biological agents [22 , 23] could make their combination into a nanoparticulate formulation a desirable molluscicidal agent . The aim of this study was therefore to evaluate the molluscicidal potential of curcumin-nisin PLA entrapped nanoparticle on Biomphalaria pfeifferi , a snail intermediate host of intestinal schistosomes .
The institutional animal care and use committee in our Nigerian institutes granted waiver since freshwater snails are not among the selected animals that approval is needed . Also , in Nigeria there are no agencies that issue permit for collection of wild freshwater snails . So , no permit was obtained . The test substance ( drug ) : curcumin-nisin poly-lactic acid nanoparticles ( CurNisNp ) , is a yellow biodegradable hygroscopic powder of 35 . 0% composition by mass of the active ingredients . It was prepared by the double emulsion-diffusion-evaporation method at the National Institute of Immunology , New Delhi , India . The formulation was prepared by the double emulsion-diffusion-evaporation method . Curcumin and nisin of equal amount ( 5 mg ) was subjected to dissolution in 200 μL 1% polyvinyl alcohol ( PVA ) . The mixture was dispensed into 50 mg of poly lactic acid containing organic solvents and was sonicated for 1 min to obtain a primary emulsion . The emulsion was added dropwise to 16 mL 2% PVA containing 1% sucrose . The secondary emulsion formed was sonicated at 30 W , 40% duty cycle for 3 mins to form a nanosuspension . This was continuously stirred until all the solvents were evaporated . The nanosuspension was subjected to ultracentrifugation ( 16 , 000 rpm for 15 min ) and then washed . The washing was repeated two times and the formulation was lyophilized with 5% mannitol as cryoprotectant . The physical properties of the formulation including the size , zeta potential and polydispersity index ( PDI ) were measured by dynamic light scattering method using Zetasizer Nano-ZS ( Malvern Instruments , UK ) . The size and PDI of the nanoparticle were determined by dispersing a homogenous solution of the formulation in sizing cuvette and then measured by Zetasizer Nano-ZS . Clear zeta cell was used for zeta potential analysis . Drug encapsulation efficiency was determined by a modified method described by Dauda et al . [13] . Ten milligram ( 10 mg ) of Cur-Nis-NP was dissolved in 10 mL PBS ( 140 mM NaCl , 10 mM phosphate buffer , 3 mM KCl , pH 7 . 4 ) [21] . The homogenous solution was incubated in a rotary shaker at 200 g . The sample was centrifuged at 16 , 000 g for 10 min at specific time after which 1 mL of supernatant was withdrawn and then replaced with 1 mL of fresh PBS [13] . Curcumin and nisin ( 2 . 5 mg each ) was dissolved in 5 mL methanol to form a stock solution ( 100 μg/mL ) . The working standard concentrations ( 5–70 μg/mL ) were prepared from the stock with PBS . The UV-absorbance was measured at 290 nm . The UV-absorbance analysis of supernatant from curcumin-nisin PLA entrapped nanoparticle was carried out at different time intervals . The in vitro drug release from the formulated nanoparticle was estimated from the standard plot obtained from UV-absorbance analysis of free curcumin-nisin . Adults of Biomphalaria pfeifferi were collected from Odo Ona River ( latitude 7°21ʹ-7°22ʹN; longitude 3°50ʹ-3°51ʹE ) in Ibadan , Oyo State , Nigeria . They were properly washed in water and transferred into plastic containers with good ventilation . The snails were brought to the Parasitology Research Laboratory of the Department of Zoology , University of Ibadan for further analysis . Snails were collected blinded of their infection status and were later subjected to cercariae screening through exposure to sunlight for 1–2 h in dechlorinated tap water . Only clean snails were used for the study . Twenty five ( 25 ) adult B . pfeifferi were transferred into a culture jar ( aquarium ) lined with a transparent polythene bag containing dechlorinated tap water . The snails were fed with blanched dried lettuce ( Lactuca sativa ) , and CaCO3 pellets were used as calcium supplements . They were maintained at room temperature ( 26–29°C ) under natural light:dark cycles . The egg masses laid by snails were cut out with a scalpel and transferred into a petri dish containing dechlorinated tap water . Incubation was done as previously described [8 , 24] . The snails hatched within 6-7-days of incubation , and were subsequently transferred and maintained in a larger container to accommodate their growth . The molluscicidal bioassay activity tests were carried out on the snail developmental stages ( <1 week old juveniles , 1–2 weeks old juveniles , and 5–6 weeks old young adults ) in line with the WHO guidelines [25 , 26] . Ten ( n = 10 ) snails were placed in each test container for all the stages tested except the < 1 week old B . pfeifferi juveniles where number of snails exposed was n = 22 . The snails at different developmental stages were placed in 40 mL of varying concentrations ( 350 ppm , 175 ppm , 87 . 5 ppm , 43 . 75 ppm and 21 . 88 ppm diluted with dechlorinated water ) of the nanoparticle formulation and mortality was observed after 96-h exposure . Snails’ avoidance or protective behaviours during exposure were observed . Observation and examination for mortality were done using hand lens or dissecting microscope where necessary . The snails that could move or with an active heart beat ( as observed under the microscope ) were counted as living and vice versa . The percentage mortality was calculated . The ovicidal bioassay activity and egg hatchability tests were carried out on the egg masses of uninfected adult B . pfeifferi using 1 day old blastula stage and 6–7 days old pre-hatched hippo- stage respectively in line with the methods [27 , 28] . Two to three egg masses ( adding up to an average of 26 embryos ) were harvested from the snail cultures and placed in each test container containing different concentrations of the test material . The egg masses were observed every 24 h for one week and afterwards biweekly for four weeks at room temperature and normal diurnal lightening . After every 24 h , the snail egg masses were examined under the microscope for viability and then the percentage mortality calculated . At the pre-hatched stage , the snail embryos were observed under the microscope for movement within their gelatinous egg masses . The pre-hatched eggs were further examined for number of embryos hatched . The egg hatchability was calculated as percentage difference relative to the total number of eggs exposed . The egg laying capacity of young adult B . pfeifferi snails was determined by maintaining and monitoring snails’ oviposition to assess their reproductive viability daily for 5 days post exposure to CurNisNp . This was achieved by counting the number of egg masses laid by the different groups of young adult snails exposed [27 , 29] . The total number of eggs laid by treated and control groups of snails were estimated . All experiments were performed in duplicate with values expressed as mean ±SD . The negative control groups were placed in dechlorinated water . The data were subjected to SPSS version 21 for windows for analysis . Two-way ANOVA was used to test significant differences in snail mortality in different concentrations . Probit regression graphing was used to determine the LC50 and LC90 of the nanoparticulate formulation . Linear Regression analysis and Pearson’s correlation were applied to determine the relationship between snail mortality/egg hatchability/egg laying capacity ( fecundity ) and test concentrations . P <0 . 05 was considered statistically significant .
The CurNisNp diameter , PDI , zeta potential and drug entrapment efficiency were 284 . 0 ± 17 . 9 nm , 0 . 166 ± 0 . 03 , -16 . 6 ± 2 . 45 mV and 35 . 0% respectively . The in vitro release of Cur-Nis is presented in Fig 1 . The protective behaviors of the snails following their introduction into the test concentrations included crawling along the walls of the containers , surfacing behavior and partial retraction of their head-foot . Normal crawling activities resumed after only a few minutes . Mortality in snails was not dependent on concentrations of the formulation ( P>0 . 05 ) . However , mortality was significantly higher in the tested concentrations compared with the negative control ( P<0 . 05 ) . The formulation killed more than half of the young adult snails ( 5–6 week-old ) ( 60 . 0–70 . 0% ) in concentrations 43 . 75 , 87 . 5 and 350 . 0 ppm . The <1 week old juvenile of B . pfeifferi were the most susceptible to CurNisNp with mortality ranging from 82 . 2–100 . 0 ppm ( Table 1 ) . The 1 day exposed egg masses were the least susceptible group with highest mortality of embryos recorded in 175 . 0 ppm of CurNisNp . No embryonic death was recorded in 7-day-old egg masses exposed to 175 . 0 ppm and 43 . 75 ppm of the nano-formulated drug . Half of the 1-2-week-old juveniles ( 50 . 0% ) of B . pfeifferi died at 350 . 0 ppm after 96-h exposure ( Table 1 ) . The <1 week-old juvenile snails had the lowest LC50 ( 277 . 9 ppm ) and LC90 ( 676 . 4 ppm ) while the 1-day-old egg had the highest LC50 ( 4279 . 5 ppm ) and LC90 ( 8184 . 6 ppm ) ( Table 2 ) . The photomicrographs of toxicity effects of CurNisNp on B . pfeifferi embryos are presented in Fig 2A–2E ( A; dead embryo viewed 1 week after exposure , B; dead embryo viewed 4 weeks after exposure , C; empty shell of dead prehatched stage embryo beside a dead blastula stage embryo viewed 4 weeks after exposure , D; deformed embryo , E; normal embryo at prehatched stage ) . The hatchablity of snails was significantly higher in the negative control than in the exposed groups ( P<0 . 05 ) . The embryos hatching from the gelatinous masses significantly increased with time ( P<0 . 05 ) . No snail was hatched out in the nanoparticulate concentrations 350 . 0 , 175 . 0 and 87 . 5 ppm after 24-h exposure . All snails had hatched after 144-h exposure in concentrations 175 . 0 and 43 . 75 ppm ( Table 3 ) . Snail hatchability was independent of nanoparticle concentration ( P>0 . 05 ) ; however , all the hatched snails died within a short period compared with the negative control . The egg laying capacity of the young adult snails exposed to CurNisNp was significantly lower than in the negative control ( P<0 . 05 ) . The fecundity rate was also concentration dependent ( P<0 . 05 ) . The young adult snails exposed to 21 . 88 ppm had the highest egg laying capacity ( 48 . 5 ± 2 . 91 ) while those exposed to 350 . 0 ppm showed the lowest egg laying capacity ( 14 . 5 ± 4 . 23 ) ( Table 4 ) . The Pearson correlation showed significant inverse relationship between CurNisNp concentrations and egg laying capacity of the snails ( r = -0 . 928; P<0 . 05 ) . The average fecundity rate however showed no significant differences with days of exposure of snails ( P>0 . 05 ) .
The curcumin-nisin PLA entrapped polymeric nanoparticle used in this study is novel and could serve as an ideal molluscicide . The PDI of the formulation will facilitate moderate distribution [30] and therefore optimisation of the drug formulation to give a narrow range monodispersed PDI for better drug distribution within the target organism is recommended . This is achievable by varying the concentrations of surfactant , organic and aqueous phase , and drug-polymer ratio [13] . In addition , the origin of the drug , as a formulation from nisin and curcumin , suggests that it will exhibit low to zero toxicity . There is presently no study on toxicity of nisin on non-target organisms , but one study has shown safety of extract from Curcuma longa ( the parent plant from which curcumin is obtained ) on brine shrimps [31] . The snails’ avoidance behaviors following exposure to the test concentrations of the nanoparticulate drug is an indication of possible molluscicidal effects . These observations are in line with those of many Nigerian workers [8 , 9 , 32 , 33] and workers elsewhere [34–36] . The observed crawling out ( distress syndrome ) from the test concentrations and aggregation at the water-air interface by the exposed snails was taken as an escape or avoidance behavior which has been described by the aforementioned workers . This behavior which is as a result of response to loss of water balance [37] helps to increase their chances of survival and as a result hinder the action of molluscicides [10] . Susceptibility of B . pfeifferi to the CurNisNp was dependent on snail developmental stages . This kind of developmental stage-dependent variation in susceptibility to a molluscicidal agent has also been observed in a previous study [8] . The lack of association between snail mortality and nanoformulation concentration contradicts the findings of other studies [8 , 32 , 38–40] . This was particularly observed in those instances where the nanoparticulate drug showed higher activity at low doses when compared with high doses . This contrast with other studies could have been due to the ability of the formulation to penetrate membrane barriers in the organism to reach the target tissues or organ , even at lower doses . The use of nanoparticle formulations may confer an advantage over the use of unbound drug or plant extracts , as employed in the aforementioned studies , as lower doses of nanoparticles could exert similar efficacy . The lower LCs values recorded in the juveniles indicates their higher susceptibility to CurNisNp , but the relatively higher value recorded for the young adult snails could have been due to increase resistance to the formulation as the snails advanced in age . This is reasonable as tolerance to adverse environmental conditions increases with the age-dependent acquisition of better developed organs e . g . mantle and periostracum . Our investigation did not show the concentration-dependent nature of molluscicidal action on embryos reported in earlier studies [8 , 41] . However , our study shared similar morphological alterations such as deformation of gastrula with other studies [8 , 39] . Reduction in snail hatchability in nanoparticle exposed groups suggests an increase in bioavailability which ensured delivery of PLA encapsulated drug to the targeted pre-hatched snails within the protective gelatinous egg masses . Nanoparticles are known for their ability to penetrate host barriers [42] and CurNisNp may be no exception to this . Another possibility is that increase in CurNisNp lipophilicity may facilitate its penetration of the snail eggs , thus leading to greater inhibition of hatching in drug exposed groups , when compared with the negative control . Prolonged time of exposure could however undermine reduction in eggs hatching . The death of all the hatched juvenile snails in nanoparticulate exposed groups compared with the negative control group suggests the cumulative effects of the formulation during exposure . The consistent release of the nanoparticulate drug for more than 5-day ( 120 h ) exposure period could be responsible for this cumulative effect . Although this is the first report on reproductive toxicity of curcumin-nisin nanoparticle in freshwater snails , metallic-nanoparticle induced reproductive toxicity has been observed in molluscs [43] , crustaceans [44 , 45] , and marine invertebrates [46] . The snail fecundity rate reduction observed in the formulation exposed groups in our study was similar to observations in some molluscicides and anti-parasitic agents [29 , 47–49] . The observed reduction in snail egg production might have been due to metabolic changes possibly caused by prolonged exposure of the snail to CurNisNp , which might include destruction of gametogenic cells and damage of hermaphrodite glands possibly resulting from decrease in tissue proteins , DNA damage ( apoptosis ) , or degeneration of cells of these vital organs [29 , 50 , 51] . Such a feature is most desirable in a molluscicide of the kind tested here , which is not strongly toxic against the adult snails , as it could be effective in regulating snail populations without necessarily compromising the functional role of the adult snails within the aquatic ecosystem .
It is clear from this study that CurNisNp is a potential molluscicide . It is active against all the snail stages , but with different dynamics of potency . Although the formulation may not prevent hatching of juvenile snails from the egg masses at the pre-hatched stage , it significantly reduced the number of viable juveniles . The adult snails were relatively resistant to the molluscicide , but the significant reduction in their egg laying capacity makes the formulation a potential desirable molluscicide . It is therefore recommended that the formulation be more optimised to give a nanoparticle with a narrow range monodispersed PDI for better drug distribution and eventual molluscicidal activities . More studies on toxicity , stability and photosensitivity of the nanoparticle should also be considered . | Elimination of snail intermediate host of schistosomiasis has been widely advocated as an arm of integrated control for schistosomiasis . This becomes important as reports now abound on development of praziquantel resistance in schistosomes . Nanotechnology has been applied in control of tropical diseases including schistosomiasis . The few available nanomedicines were targeted against the Schistosoma worms without research efforts on the use of the same technology against the molluscan host of the parasite . This study seeks to assess the molluscicidal potential of CurNisNp on Biomphalaria pfeifferi; a snail intermediate host of Schistosoma mansoni . The formulation was tested on the different developmental stages of the snail . The formulation showed decreasing molluscicidal activity with increase in age of the snail . Impairment of the egg laying capacity of the adult snails was related to nanoparticle concentration . The egg was the most resistant stage . It is evident from the study that the formulated nanoparticle had molluscicidal properties and to further harness this potential , optimisation of the formulation to give a narrow range monodispersed polydispersity index for better drug distribution is recommended . | [
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] | 2017 | Molluscicidal activities of curcumin-nisin polylactic acid nanoparticle on Biomphalaria pfeifferi |
The study of infectious disease has been aided by model organisms , which have helped to elucidate molecular mechanisms and contributed to the development of new treatments; however , the lack of a conceptual framework for unifying findings across models , combined with host variability , has impeded progress and translation . Here , we fill this gap with a simple graphical and mathematical framework to study disease tolerance , the dose response curve relating health to microbe load; this approach helped uncover parameters that were previously overlooked . Using a model experimental system in which we challenged Drosophila melanogaster with the pathogen Listeria monocytogenes , we tested this framework , finding that microbe growth , the immune response , and disease tolerance were all well represented by sigmoid models . As we altered the system by varying host or pathogen genetics , disease tolerance varied , as we would expect if it was indeed governed by parameters controlling the sensitivity of the system ( the number of bacteria required to trigger a response ) and maximal effect size according to a logistic equation . Though either the pathogen or host immune response or both together could theoretically be the proximal cause of pathology that killed the flies , we found that the pathogen , but not the immune response , drove damage in this model . With this new understanding of the circuitry controlling disease tolerance , we can now propose better ways of choosing , combining , and developing treatments .
The clinical goal of treating infectious diseases is to reduce the levels of sickness experienced by infected hosts . One approach to studying this problem is to quantitate illness by correlating the dose response of pathology to pathogen load; graphs like these are called “disease tolerance curves” [1–6] . We argue that the shape of the dose response curves and the underlying mathematical functions producing these curves can teach us how to alter the health-by-microbe relationship . For example , if disease tolerance curves are linear , we need only discover the molecular mechanisms that control the curves’ slopes . If these curves have a more complex shape—for example , a sigmoid shape—then we will need to measure more variables . Since there are not universally agreed upon definitions for many of the words we use to describe the immune response and health , we start by defining the following terms: vigor , resistance , resilience , and tolerance . “Vigor” is the health of an uninfected individual , and “resistance” describes the host’s ability to limit microbe load [1–6] . We use “resilience” to describe the ability of infected hosts to return to their original healthy condition , much in the same way the word is used to describe the way a perturbed ecological system returns to its origin [7 , 8] . For example , a resilient host may suffer from an infection but would easily bounce back to health , whereas infection in a non-resilient host might lead to permanent disability or death . We apply the term resilience to an infected individual because we can follow the path that individual takes from health through sickness and back . “Disease tolerance” is the dose response curve summarizing the pathogen loads that are required to produce a range of host responses . Disease tolerance differs from resilience in that tolerance is an emergent property of populations , while resilience is a property of an individual; disease tolerance measures how resilience changes across a population as pathogen load is varied . Disease tolerance , by definition , cannot be measured in a single individual [9] . The model described in this paper shows how these distinctions between resilience of individuals and tolerance of populations disappear once we understand infection dynamics to the point that we can predict how a population would behave knowing the properties of an individual . To study disease tolerance we used a simple infection system in which we injected a pathogen ( Listeria monocytogenes ) into a fruit fly ( Drosophila melanogaster ) [10–14] . These L . monocytogenes-infected flies suffer from a variety of ailments , including alterations in their circadian rhythm , cold-coma recovery , climbing ability , feeding behavior , metabolism , and death [12 , 15–17] . To determine the disease tolerance curve , we plot the survival rates of the infected flies against microbe loads measured 2 days post infection ( DPI ) . We used this system because it allowed us to measure large numbers of samples and to take advantage of the genetic tools and the understanding of innate immunity available for this model organism . Our immediate goal is to define the mechanisms controlling tolerance . We start with a simple mathematical model representing feedbacks between microbe growth , the immune response , damage , and health , modeling each using logistic equations . We then measured the ground-truth of these models using a D . melanogaster/L . monocytogenes infection that can lead to lethal outcomes . We experimentally measured microbe growth , the immune response , and disease tolerance and found that each of them was well described by sigmoid curves , suggesting that we need at least three variables to describe each . In the case of tolerance , this meant that we had to measure host vigor , the number of bacteria required to injure the host , and the maximum death rate achieved by the fly . In the case of antimicrobial peptide ( AMP ) transcript production , we measured basal levels , the number of bacteria required to induce transcripts to 50% of their maximum value , and the maximum transcript level . We examined how alteration of the values of the parameters in the model changed the output of the model to distinguish between changes that we might see if microbes or the immune response were the principal mediators of damage . Upon testing a variety of D . melanogaster natural variants and mutants along with some L . monocytogenes mutants , we concluded that , in this system , the bacteria and not the immune response is responsible for the damage caused by the infection . When we monitored how disease tolerance changed as we varied host or pathogen properties , we found the tolerance curve changed shape in the manner predicted by the model; for example , the number of bacteria required to damage health changed , while the vigor and maximum death rate remained constant . We demonstrate that this dual approach of measuring and modeling full-length disease tolerance curves can reveal previously unrecognized parameters controlling disease tolerance .
To measure a disease tolerance curve , we recorded the response of the host to a broad range of initial pathogen doses . We did this by injecting L . monocytogenes into flies over a range of ten to 100 , 000 bacteria and allowing the infected flies to die . We injected L . monocytogenes into the hemocoel of flies and monitored bacterial numbers 2 DPI to measure the ability of the fly to resist microbe growth when challenged with a range of infection intensities ( Fig 5 ) . We determined the median time to death ( MTD ) for each inoculum and used this time as a measurement of health ( Fig 5B ) . Plotting microbe load versus MTD produced a curve that was readily fit by a four-parameter logistic sigmoid model ( r2 > 0 . 96 ) ( Fig 5C ) . Occasionally , tolerance curves are fit with mathematical functions , but the reason these functions are chosen is that they fit the data and not that they are dissected for further biological insights [5 , 30–32] . More typically , tolerance is visualized as a linear system , which requires just two parameters , vigor and slope [5 , 11] . In contrast , our sigmoid model provides four parameters ( Fig 5D ) . In addition to vigor and slope used to describe a line , the sigmoid model adds the parameters of EC50 and maximum effect . The EC50 of the system is the number of microbes present at day 2 that cause a 50% change in MTD . Maximum effect is defined by the sigmoid’s asymptotic tail at high microbe loads . This defines a maximum death rate , suggesting that there is a previously unrecognized rate-limiting step for death . In the model shown in Fig 1A , depletion of health could be induced either by bacterial damage effectors or indirectly by self-harm caused by the resistance response . We modeled the two possibilities by observing how the shape of the curve changed as we altered the model such that either the resistance response or bacteria were the sole cause of damage . We concentrated on changes in the rate that the immune response was turned on ( τ ) , and the inflection points for the relationships between microbe density and the rates of immune induction and microbe-induced damage induction ( σI and σM ) ( Fig 6A–6D , S4 Table ) . In the case in which resistance mechanisms drove damage ( Fig 6A and 6C ) and bacterial damage was set to zero , changes in τ or the inflection point for microbe-induced immunity caused shifts in both the EC50 and microbe loads . The effect is more extreme when the inoculum is far below the microbial carrying capacity , as this gives the immune response an opportunity to control microbe loads . This results in a reciprocal mechanistic link between resistance and tolerance in which one is always high when the other is low . In the model in which bacteria drive damage , a loss of resistance results in high microbe loads and health pegged at maximum severity ( Fig 6B ) . Shifts in the EC50 of the bacterial damage-driven system , in which resistance-induced damage is set to zero , are caused by changes in the inflection point for bacterial-induced damage , as might be expected for differentially virulent strains of microbes or hosts that are better at neutralizing bacterial toxins ( Fig 6D ) . In this second case , there is no mechanistic reciprocal link between resistance and tolerance , and the two vary independently of each other . To test whether the shape of the disease tolerance curve in this host–pathogen pairing was driven by immunological damage or microbial damage , and to determine how the parameters of a sigmoid disease-tolerance curve varied , we measured the tolerance curves for a collection of Drosophila mutants that we previously showed differ in their resistance and tolerance defenses to L . monocytogenes [10–12] . We also tested natural variant flies from the Drosophila Genetics Reference Panel that were preselected because they showed extreme changes in their response to L . monocytogenes infections [33 , 34] . In addition , we tested L . monocytogenes mutants that altered pathogenesis in the fly [11] . These data supported the model in which bacteria caused damage in the system and did not support the immunological damage model . A mutation in the D . melanogaster gene CG2247 was found previously to reduce the fly’s ability to both survive an infection and control L . monocytogenes growth [11]; here , we found that at 2 DPI , even CG2247 mutant flies injected with just ten bacteria had the same high microbe loads as flies injected with 100 , 000 bacteria , supporting the idea that these mutants alter resistance . This is visible in the tolerance curves , in which all of the points from infected mutant flies clustered on the bottom asymptote of the parental tolerance curve ( Fig 6E–6H , S5 Table ) . An additional example of a fly strain with poor resistance is shown in the supplementary data ( S3 Fig and S5 Table ) . Since flies suffering a loss of resistance showed no change in the maximum death rate of the infection , these results support the model in which bacteria are responsible for damage . RNAseq analysis of CG2247 mutant flies suggests a molecular mechanism for the observed phenotype . We observed that in wild-type flies , the transcripts encoding the enzymes required to generate a reactive oxygen response against L . monocytogenes dropped over the course of the infection but recovered upon antibiotic treatment ( S6 Fig ) [11 , 35] . In contrast , these transcripts dropped to 10-fold lower levels in CG2247 mutants and did not recover to their original levels . We observed a reduction in the characteristic dark spots resulting from the action of this immune response ( S6 Fig ) . We conclude that mutations in this gene disrupt the melanization immune response . The disease-tolerance curve for the natural variant D . melanogaster strain , RAL 359 , showed a reduction in the EC50 of the system without a change in resistance [33] . RAL 359 was as capable as our lab control strain w1118 in controlling L . monocytogenes growth; however , low doses of the microbe had a larger effect on health in this strain than in the control . This shifted the EC50 from 922 to 83 colony-forming units per fly ( Fig 6I–6L and S5 Table ) . The vigor and maximum death rate of these two strains was similar . This shift in EC50 was a common phenotype , as shown in S4 and S5 Figs . Changing the virulence of the L . monocytogenes also altered the EC50 of the system . Mutant L . monocytogenes lacking the virulence factors actin assembly-inducing protein ( ActA ) or listeriolysin O ( LLO ) had previously been shown to kill flies more slowly than wild-type L . monocytogenes ( Fig 5C versus Fig 6O ) [11] . The tolerance curves for flies infected with an ΔactA mutant showed a shift in the EC50 from 922 to 5 , 753 ( Fig 6M–6P and S5 Table ) . We observed a ceiling for the number of L . monocytogenes that can be maintained in a wild-type infected fly ( Figs 2A and 5A ) and hypothesize that this is defined by the number of phagocytes in the fly that provide a niche for L . monocytogenes growth [13] . The ΔactA mutant bacteria approached this limit before they induced maximal pathology and , thus , we did not observe a low asymptotic tail . Δhly mutants produced a similar result to ΔactA mutants , only more extreme ( S4 Fig ) .
The disease space analysis of recovering and dying flies reveals that far more is going on in these sick animals than AMP gene expression , and it lets us organize these events with a simple map . A commonly used time point for gene expression studies in Drosophila is 6 h post infection [44 , 45]; this is a particularly problematic time point because it combines transient expression of heat shock genes with expression of antimicrobials . Flies fated to die have a progressing gene signature in which a set of transcripts is increased without a corresponding increase in microbe load . We anticipate that one will need to measure the course of pathogenic infections to identify these genes , rather than follow microbes that simply elicit an immune response but do not kill wild-type flies ( for example Escherichia coli or Micrococcus luteus [40 , 46] . Just as each pathogen causes a different disease in humans , we expect that there will be a range of pathologies caused by insect pathogens and do not assume that these L . monocytogenes-induced genes are the only morbidity genes in the fly . The nature of this particular death response is unclear , as the identity of the genes does not provide obvious clues . In contrast , recovering flies have a unique gene expression signature that suggests function; for example , enzymes in biosynthesis and energy metabolism are repressed during infection but pop up hysteretically as microbes are removed , suggesting the induction of a recovery response that rebuilds damaged tissues . The shapes of tolerance curves are important because they will define which medications can be used to improve the health of an infected patient depending upon their position on the sigmoid curve . Here is a concrete example: If we think about the dose response curve to sepsis in humans as a linear response , then any drug that limits damage will help every patient . We come to a different conclusion if we consider a sigmoid relationship . Humans suffer from sepsis when relatively small numbers of microbes enter the blood , and very sick patients can have much higher levels of microbes . This suggests the sickest patients will be found on the asymptote of a sigmoid curve , where they will be suffering maximum pathological effects . Drugs that change the basal level of a response , EC50 , or slope will not help these patients; they will only respond to drugs that manipulate maximal response levels . Thus , we need to understand the shape of these tolerance curves and where the patients lie on the curves to select appropriate treatments . The discussion of tolerance in patients raises a tricky point . We’ve shown it is possible to measure a disease tolerance curve in a model system; this demonstrated the nonlinear nature of tolerance and suggested the existence of new dimensions we should follow when measuring disease outcomes . The problem is that this approach requires us to perform many more experiments than we do currently when analyzing phenotypes , and this approach is likely impossible to apply to most human infections for the following two reasons: First , disease tolerance is plotted using summary characteristics of an infection . For example , one might measure the maximum parasite load and minimum health for an infection [5] . This is accessible experimentally , but it is unethical to gather these data from a patient , as you need to treat the patient when they enter the clinic . In one rare case of HIV , infected patients’ tolerance curves have been recorded because these chronically infected patients are followed for years , but that approach isn’t going to be useful for acute infections [47] . Second , disease tolerance is a measurement of the behavior of a population and not an individual . The information we gather about an individual allows us to place a datum on a health by microbe graph , but we don’t know the shape of the curve that should be fit through that individual [9]; thus , we can’t easily determine which parameters need fixing in the sick patient . What we learn from model systems is that there are underlying rules that can be used to explain disease outcomes , and that these rules are nonlinear . By studying these rules in models , we can identify the molecular mechanisms corresponding to the mathematical parameters and then apply this knowledge to human biology by analogy . If we want to measure changes directly in human immune infections , we need to rely on descriptions of disease space , which are described in the accompanying paper [48] .
Flies were maintained on standard dextrose fly media ( 129 . 4 g dextrose , 7 . 4 g agar , 61 . 2 g corn meal , 32 . 4 g yeast , and 2 . 7 g tegosept per liter ) at 25°C with 65% humidity and 12 h light/dark cycles . Shortly after eclosion , adult flies were collected into bottles containing dextrose fly media . At least 24 h post eclosion , adult flies were anesthetized with carbon dioxide , and males were sorted into groups of 20 and placed into vials containing standard dextrose fly media . Experiments were performed on flies 5–7 d post eclosion unless otherwise indicated . CG2247 piggybac allele ( BL18050 ) , Pcmt piggybac allele ( BL18398 ) , kenny piggyback allele ( BL11044 ) , CG7408 piggyback allele ( BL19305 ) , piggybac allele parental strain w1118 ( BL6326 ) , RAL 359 ( BL28179 ) , RAL 787 ( BL28231 ) , RAL 375 ( BL25188 ) , RAL 309 ( BL28166 ) , RAL 73 ( BL28131 ) , RAL 380 ( BL25190 ) , and RAL 821 ( BL28243 ) strains were obtained from the Bloomington stock center . Bacteria were injected into flies essentially as described previously [10 , 12 , 14 , 49] . Flies were anaesthetized with carbon dioxide . A drawn glass needle carrying L . monocytogenes was used to pierce the cuticle on the ventrolateral side of the abdomen . A picospritzer III was used to inject 50 nl of liquid into the fly . Bacteria were delivered at different concentrations to produce injections of approximately 10 , 100 , 1 , 000 , 10 , 000 or 100 , 000 CFU . Infectious doses were determined for each experiment by plating a subset of flies at time zero . Approximately 200–400 flies were used for each dose in the experiment to measure survival and to count colonies . All L . monocytogenes stocks: Wild type/mutant parental strain ( 10403S ) , Δhly ( DP-L2161 ) [50] , and ΔactA ( DP-L3078 ) [51] were stored at -80°C in brain and heart infusion ( BHI ) broth containing 25% glycerol . To prepare L . monocytogenes for injection , bacteria were streaked onto Luria Bertani ( LB ) agar plates containing 100 ug/mL streptomycin and incubated at 37°C overnight . Single colonies of L . monocytogenes from the LB agar plate were used to inoculate 4 mL of brain and heart infusion ( BHI ) broth and incubated overnight at 37°C without shaking . Bacteria were removed from the incubator at log growth phase . Prior to injection , L . monocytogenes cultures were diluted to the desired optical density ( OD ) 600 in phosphate buffered saline ( PBS ) and stored on ice . Single flies were homogenized in PBS using a motorized plastic pestle in 1 . 5 ml tubes . The supernatants were plated using an Autoplate spiral plater and counted using a Qcount automated counter . At least six samples were counted to determine the median number of bacteria for each inoculum . Bacteria were plated onto LB medium and incubated overnight at 37°C before counting . 200–400 flies were injected and checked daily to measure mortality for each inoculum . Flies were housed in vials containing 20–25 flies each . Each set of conditions was repeated at least three times; for example , an experiment for a mutant fly line would be set up independently on three different days to gather microbe load and survival data . Pairs of plating and survival data from these multiple experiments were all plotted on the same tolerance curve . Flies were injected with 50 nl of Listeria ( OD600 = 0 . 001 , or approximately 100 CFUs ) or left manipulated . Following injection , flies were placed in vials containing dextrose fly media and incubated at 29°C . Samples were separated into three groups: Moribund , recovering , and uninfected control . Moribund flies were infected , and samples were collected on the following DPI: 0 . 25 , 1 , 2 , 2 . 25 , 3 , 4 , 5 , and 6 . Recovering flies were infected and flipped onto dextrose fly media containing 1 mg/ml ampicillin 2 DPI . Recovering samples were collected on the following DPI: 2 . 25 , 3 , 4 , 5 , 6 , 7 , 9 , and 16 . Uninfected control flies were not infected but were flipped onto dextrose fly media containing 1 mg/ml ampicillin 2 dpi . Uninfected control samples were collected on the following dpi: 2 . 25 , 3 , and 9 . At each of the indicated time points , groups of 20 flies were homogenized in TRIzol , and RNA was isolated using a standard TRIzol preparation . Additional flies were used to determine CFUs and monitor survival . Biological triplicates were obtained from three independent experiments . Quality of RNA was determined using a BioAnalyzer 2100 . Samples were labeled using the Quick Amp Labeling Kit , One-Color ( Agilent ) , and hybridized to 4x44K ( V2 ) Drosophila Gene Expression Microarray ( Agilent ) using the Agilent Gene Expression Hybridization kit following the manufacturer’s protocol . Microarrays were scanned using an Agilent Technologies Scanner , and processed signal intensities were determined using Agilent’s Feature Extraction software . RNA quality assessment , labeling , hybridization , and microarray feature extraction were performed at the Stanford Functional Genomics Facility . The microarray data were analyzed using Genespring v12 . 1 ( Agilent ) . Microarrays were normalized to the 75th percentile . The median expression level on 0 dpi was set as the baseline . Prior to statistical analysis , low-quality spots were removed based on flag calls . To determine differential gene expression , one-way ANOVA was performed comparing all samples to 0 dpi . P-values were corrected by the Benjamini-Hochberg method . Genes with p-values <0 . 05 at any time point with a fold-change greater than 2 were categorized as differentially expressed . Differentially expressed genes were then clustered using Mfuzz [52] . Only genes that were differentially expressed in moribund and recovering but not in uninfected control were used in the topological data analysis using the Ayasdi 3 . 0 software platform ( ayasdi . com , Ayasdi Inc . , Menlo Park , California ) . Nodes in the network represent clusters of samples of infected flies , and edges connect nodes that contain samples in common . Nodes are colored by the average value of their samples for the variables listed in the figure legends . TDA was used to map the way hosts loop through the disease space in an unsupervised fashion . Two types of parameters are needed to generate a topological analysis . First is a measurement/notion of similarity , called a metric , which measures the distance between two points in some space ( usually between rows in the data ) . Second are lenses , which are real valued functions on the data points . Lenses are used to create overlapping bins in the dataset . Overlapping families of intervals are used to create overlapping bins in the data . Metrics are used with lenses to construct the Ayasdi 3 . 0 output . Multiple lenses can be used in each analysis . There are two parameters used in defining the bins . One is resolution , which determines the number of bins; higher resolution means more bins . The second is gain , which determines the degree of overlap of the intervals . Once the bins are constructed , we perform a clustering step on each bin , using single linkage clustering with a fixed heuristic for the choice of the scale parameter . This heuristic is described in [26] . This gives a family of clusters within the data , which may overlap . We built a network with one node for each such cluster and where we connect two nodes , if the corresponding clusters contain a data point in common . We used two types of lenses . The first type was lenses based on dimension reduction algorithms such as multidimensional scaling and nearest neighbor analyses; these helped analyze the data in an unsupervised manner . The second type was lenses based on the data alone , for example , the levels of antimicrobial gene expression or recovery gene expression . The gene expression markers helped us dissect the graphs using knowledge about the biology of the system . To build our map , we analyzed our dataset with samples as rows and genes as columns . We used the Variance Normalized Euclidean metric . We used the PCA coord1 and PCA coord2 lens as well as two data lenses: CG32373 and Fuca ( Resolution = 19 , Gain = 6 , and equalized was used for all lenses ) . To evaluate the impact of within-host infection dynamics on host health curves , we created a compartmental model consisting of four ordinary differential equations for microbes ( M ) , immune effectors ( I ) , microbial damage effectors ( D ) , and health ( H ) . Microbes grow in a sigmoid fashion at rate r as they deplete the host substrate , and immune effectors can either kill microbes outright at rate ε per effector or inhibit microbial growth at rate η . In these simulations , growth rate limitation was used as the main immune response . Two mechanisms contribute to immune effector production . Reflecting the sigmoid relationship between microbe density and AMP levels , immune effectors can increase in proportion to microbe density up to a maximal rate of τ*kM until effector levels saturate immune pathway machinery ( kI ) . The parameter σI reflects the microbe concentration at the inflection point for immune induction . Immune effectors decay at rate μ . Microbes secrete virulence factors and toxins at a maximal rate φ*kD , modulated by a sigmoidal relationship between damage factor production and microbe density to reflect a process like quorum sensing controls on virulence factor production . As with immunity , there is a carrying capacity imposed on damage effectors . This could reflect either negative feedbacks on further effector production at high effector densities or a bottleneck limiting flux through the system . The parameter σD reflects the microbe density that produces a half-maximal induction rate . Damage effectors degrade at rate ρ . Immunopathology and damage effectors deplete health ( H ) at rates γ and α , respectively , while hosts can recover health according to a sigmoidal relationship with current health ( reflecting the difficulty of achieving recovery at low health due to organ failure and other catastrophe ) at rate z . “Death” is called when health dips below 10% of maximum ( kH ) . All simulations were conducted within a parameter space outlined in S4 Table and run in Matlab ( v . 7 . 11 . 0 ) using the ode45 solver . The sigmoidal curves were fit using the four-parameter method in Prism . We tested several models ( linear , exponential , logistic , and sigmoid ) and picked the model that gave the best adjusted r2 . We used the adjusted r2 to account for overfitting . When the curve-fitting program suggested a clearly erroneous result , such as an extremely high top or low bottom , we fixed the top or bottom at the average level for the vigor or for the average of the last three points for the bottom . Flies were injected with 50 nL of 0 . 01 OD600 ( 1000 CFUs ) of L . monocytogenes resuspended in PBS . Flies were placed in vials containing dextrose fly media and incubated at 29°C . 3 DPI flies were flipped onto dextrose fly media containing 1 mg/mL ampicillin . Groups of 20 flies were homogenized in TRIzol for RNA extraction on the following DPI: 0 , 1 , 2 , 3 , 4 , 6 , 8 , 10 , 12 , 14 , 16 , and 18 . Additional flies were used to determine CFU and monitor survival . We tested w1118 , CG2247 , Pcmt , and RAL359 strains . As a control , additional sets of w1118 were left uninfected and flipped onto dextrose fly media containing 1 mg/mL ampicillin 3 dpi , and samples for RNAseq were collected at the indicated time points . RNA was isolated using standard TRIzol preparation . Library preparation and sequencing was performed by the Duke Center for Genomic and Computational Biology . Briefly , quality of RNA was assessed by Bioanalyzer 2100 . Polyadenylated RNA was enriched from total RNA . Barcoded TruSeq cDNA Libraries were constructed and quality was assessed by Qubit and Agilent Tapestation . 50 bp single end reads were obtained by sequencing the libraries on an Illumina HiSeq 2000 using a full flow cell . Reads were mapped using STAR , and RPKM for each gene was determined by Cufflinks . Groups of 20 female flies were anesthetized with carbon dioxide and injected with 50 nl of 0 . 01 OD600 ( ~1 , 000 CFUs ) of L . monocytogenes resuspended in PBS . After injection , flies were flipped onto dextrose fly media and incubated at 29°C . At 2 dpi , the percent melanized for each group was determined by anesthetizing the flies with carbon dioxide and scoring each abdomen for presence of melanization spots . For each genotype , eight groups of flies were scored . | It is an intuitive assumption that the severity of symptoms suffered during an infection must be linked to pathogen loads . However , the dose–response relationship explaining how health varies with respect to pathogen load is non-linear and can be described as a “disease tolerance curve;” this relationship can vary in response to the genetic properties of the host or pathogen as well as environmental conditions . We studied what changes in the shape of this curve can teach us about the underlying circuitry of the immune response . Using a model system in which we infected fruit flies with the bacterial pathogen Listeria monocytogenes , we observed an S-shaped disease tolerance curve . This type of curve can be described by three or four parameters in a standard manner , which allowed us to develop a simple mathematical model to explain how the curve is expected to change shape as the immune response changes . After observing the variation in curve shape due to host and pathogen genetic variation , we conclude that the damage caused by Listeria infection does not result from an over-exuberant immune response but rather is caused more directly by the pathogen . | [
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] | 2016 | How Many Parameters Does It Take to Describe Disease Tolerance? |
An estimated 2 . 85 billion people live at risk of Plasmodium vivax transmission . In endemic countries vivax malaria causes significant morbidity and its mortality is becoming more widely appreciated , drug-resistant strains are increasing in prevalence , and an increasing number of reports indicate that P . vivax is capable of breaking through the Duffy-negative barrier long considered to confer resistance to blood stage infection . Absence of robust in vitro propagation limits our understanding of fundamental aspects of the parasite's biology , including the determinants of its dormant hypnozoite phase , its virulence and drug susceptibility , and the molecular mechanisms underlying red blood cell invasion . Here , we report results from whole genome sequencing of five P . vivax isolates obtained from Malagasy and Cambodian patients , and of the monkey-adapted Belem strain . We obtained an average 70–400 X coverage of each genome , resulting in more than 93% of the Sal I reference sequence covered by 20 reads or more . Our study identifies more than 80 , 000 SNPs distributed throughout the genome which will allow designing association studies and population surveys . Analysis of the genome-wide genetic diversity in P . vivax also reveals considerable allele sharing among isolates from different continents . This observation could be consistent with a high level of gene flow among parasite strains distributed throughout the world . Our study shows that it is feasible to perform whole genome sequencing of P . vivax field isolates and rigorously characterize the genetic diversity of this parasite . The catalogue of polymorphisms generated here will enable large-scale genotyping studies and contribute to a better understanding of P . vivax traits such as drug resistance or erythrocyte invasion , partially circumventing the lack of laboratory culture that has hampered vivax research for years .
Plasmodium vivax is the most widely distributed human malaria species and causes more illness than P . falciparum in many regions [1] . Its global public health burden is estimated to be US$1 . 4 to 4 . 0 billion [2] . Even in areas of low transmission , up to 20% of the population can have a symptomatic infection each year , with a cumulative experience of 10–30 episodes of malaria during a lifetime [3] . Research on P . vivax is complicated by our inability to propagate the parasite in continuous in vitro cell cultures [4] . This limits our ability to perform genetic crosses , to conduct in vitro functional assays on anti-malarial drug susceptibility or invasion mechanisms , and RNA-based investigations . One alternative to understand phenotypic variations in P . vivax is to rely on purely genetic approaches and to statistically link genetic markers to traits of interests using linkage disequilibrium mapping . A first step for developing genetic studies in P . vivax was achieved in 2008 with the completion of the reference genome sequence [5] generated from the Sal I strain . This strain originated from a patient infected in El Salvador in 1972 and was propagated through infections of Aotus monkeys [5] , [6] . A second milestone was cleared in 2010 with the first P . vivax genome sequenced directly from an infected patient [7] , demonstrating that it was possible to sequence P . vivax field isolates . Currently , both P . vivax genome sequences have been generated from Central/South American parasites [5] , [7] . While this is an important region of endemicity , where P . vivax consistently predominates in prevalence over P . falciparum [8] , these genomes only captured genetic diversity in a subset of the geographical range of P . vivax , and only its most recent expansion [9] . To expand our understanding of genetic diversity in P . vivax , we have sequenced the genome of three field isolates from Cambodia where the parasite diversity is significantly different than in Central/South America and in closer proximity to its geographic origin [9] , [10] . We have also sequenced two field isolates from Madagascar where we have recently identified P . vivax strains capable of infecting Duffy-negative erythrocytes [11] . In addition , we have included another South American parasite , the monkey-adapted Belem strain [12] , and re-sequenced the Sal I strain [5] to rigorously assess the reliability of next generation whole genome sequencing for characterizing DNA polymorphisms . Continuing advances in high-throughput sequencing technologies allowed us to generate high sequence coverage of these genomes , which circumvents most of the problems raised earlier [7] and provides reliable identification of single nucleotide polymorphisms ( SNPs ) .
This study was conducted according to the principles expressed in the Declaration of Helsinki . Patient samples were obtained as part of on-going studies in accordance with human studies protocols IRB N°035-CE/MINSAN ( Comité d'Ethique du Ministère de la Santé de Madagascar , June 30th 2010 ) and IRB N°160 NECHR ( National Ethics Committee for Health Research – Cambodia , October 28th 2010 ) . All patients provided written informed consent for the collection of samples and subsequent analysis . We collected blood samples from two Malagasy ( M08 and M19 ) and three Cambodian patients ( C08 , C15 and C127 ) . For each patient , we processed 5 ml of fresh blood collected in EDTA vacutainers through two consecutive CF11-packed columns to remove leukocytes and platelets . We extracted parasite DNA directly from 200 µl of the remaining red blood cell fraction using DNeasy purification kit ( Qiagen ) . For all samples , we confirmed P . vivax mono-species infection by Plasmodium species PCR-based diagnosis [13] . We also analyzed DNA extracted from the monkey-adapted Belem and Sal I strains of P . vivax ( Text S1 ) . For each sample , we sheared 144–518 ng of DNA into 250–300 bp fragments using a Covaris S2 instrument and used the fragmented DNA molecules to prepare sequencing libraries according to the Illumina protocol for genomic DNA . Briefly , after end repair and A-tailing we ligated Illumina paired-end adapters to the ends of the fragmented DNA molecules and selected fragments of 300 bp ( i . e . P . vivax fragment size of ∼250 bp ) using an E-gel ( Invitrogen ) . We then amplified the final products using 12 cycles of PCR and verified the quality and quantity of the libraries by Agilent BioAnalyzer and qPCR using the Illumina primers . We sequenced each library on one lane of an Illumina HiSeq 2000 and generated between 79 and 230 million paired-end reads of 100 bp . We mapped all reads to the human ( UCSC build hg18 , [14] ) and the P . vivax Sal I strain [5] reference genome sequences . We used the program bwa [15] to independently map each end of all read pairs . We considered as correctly mapped only reads mapped to a unique genomic location with i ) less than 3 mismatches in the first 28 bases , ii ) 5 or less mismatches in the 100 bp sequence and iii ) at most one insertion or deletion . Only read pairs for which both ends fulfilled these criteria were included for further analyses ( Table 1 ) . We identified read pairs that mapped to the exact same positions ( and could represent molecules amplified during the library preparation ) and randomly discarded all but one pair . To identify SNPs , we focused on nucleotide positions covered by at least 20 reads with a quality score greater than 30 . Since SNP identification is complicated in regions of high homology , we excluded from our analysis possible paralogous sequences ( see Text S1 for details ) . In addition , we considered only read pairs that mapped in head-to-head configuration and within 1 , 000 bp of each other . We identified consistent mismatches between reads generated from a given sample and the reference genome sequence using the samtool mpileup [16] with the extended base alignment quality computation . Finally , we only considered a position variable in a given sample if at least 10% of the reads differed at this position from the reference nucleotide ( i . e . Reference Allele Frequency [RAF] <90% ) . We characterized the function of each DNA polymorphism identified using the Sal I gene annotation downloaded from Ensembl . We used perl scripts to annotate whether each polymorphism occurred in an intergenic region or a protein-coding gene and for the latter , whether it resulted in an amino acid change or a premature termination . We reconstructed individual haplotypes ( i . e . the haploid DNA sequence of each P . vivax strain present in a sample ) from the short read sequences mapped at the Duffy Binding Protein ( DBP ) locus . We retrieved , for each sample , all reads mapping to 1 kb upstream and downstream of the DBP gene ( PVX_110810 , chr6:384 , 498–390 , 259 ) and recorded all co-occurrences of consecutive alleles on read pairs: since read pairs are generated from the sequencing of the ends of individual DNA molecules , alleles observed on the same read pair are carried by the same haplotype . For this analysis we focused on common haplotypes and only analyzed polymorphisms with a minor allele frequency of at least 5% in the sample studied ( i . e . only sites variable within a given sample are considered ) . We inferred the haplotype using direct information when available , or allele frequency . The final haplotype sequences were generated by substituting , in the Sal I reference sequence , the variable positions ( i . e . the haplotype polymorphisms inferred as well as the alleles at positions where all strains of a sample differed from the reference sequence , i . e . RAF <5% ) . For the Belem strain none of the mismatches reached an allele frequency of 5% , consistent with a single strain being present in the infected monkey . For five of the samples sequenced in this study , we amplified the Duffy Binding Protein region II from genomic DNA and , after cloning the PCR products , sequenced 12–91 clones per sample by traditional Sanger technology . We reconstructed the haploid DNA sequence of the major strain across the entire genome using , at each nucleotide position , the most frequently observed allele . Analytical and re-sampling approaches ( see Text S1 for details ) showed that this method performs well when one strain represents more than 80% of the parasite DNA ( using a minimum coverage of 20 X ) . We analyzed allele sharing across samples by comparing the haploid DNA sequences of C08 , C127 , M15 , Sal I and Belem . For each annotated protein coding DNA sequence , we calculated the number of nucleotide differences between each pair of samples and determined which haplotypes were closest ( i . e . lowest number of differences ) . We looked for signals of local selection across the entire genome by searching for nucleotide positions where one allele was fixed in one population and the other allele fixed in the other populations . To deal with multiple infections , we considered that all strains in one sample had the same allele if >90% of the reads carried this allele ( we used 90% instead of 100% to account for possible sequencing errors ) .
Studies of malaria parasites obtained from blood samples of infected patients are complicated by the presence of human genomic DNA [7]: due to the difference in genome size , if only one leukocyte is present per 10 parasite cells , more than 95% of the extracted DNA will be of human origin . This is particularly problematic in studying P . vivax as its parasitemia is typically less than 10 , 000 parasites per µl of blood . Here , we analyzed blood samples from five malaria patients ( Table S1 ) , two from Madagascar ( M08 and M19 ) and three from Cambodia ( C08 , C15 and C127 ) . We processed blood samples through cellulose columns to remove leukocytes and platelets [17] , [18] and extracted DNA from the red blood cell fraction . In addition , we analyzed P . vivax DNA from the monkey-adapted Belem strain ( Text S1 ) as well as from the Sal I strain used for generating the P . vivax reference genome sequence [5] . After library preparation , we sequenced each sample on one lane of an Illumina HiSeq 2000 to generate between 79 and 231 million paired-end reads of 100 bp ( Table 1 ) . We mapped all reads to the P . vivax ( Sal I , [5] ) and human [14] reference genome sequences . The Belem strain showed 71 . 09% of the reads mapping to the P . vivax genome ( Table 1 ) , resulting from the extensive effort to remove leukocytes from the blood sample ( Text S1 ) . By contrast , only 1 . 32% of the reads generated from Sal I DNA mapped to the P . vivax genome . This figure can be explained by the absence of leukocyte depletion prior to DNA extraction of the Sal I sample and illustrates the benefits of processing fresh blood samples on cellulose columns to remove host DNA . Despite its relatively low coverage ( 20 X ) , we included Sal I in our analyses since comparison with the reference genome sequence generated from the same strain provided an opportunity to estimate the false positive rate of our SNP calling approach . For the field isolates , a variable proportion of reads ( 4 . 91%–58 . 06% ) could be mapped to the human genome ( Table 1 ) , consistent with incomplete leukocyte depletion from the blood samples . Despite this residual human DNA contamination and stringent quality controls , which eliminated between 27 and 62% of the reads mapped to P . vivax ( Table 1 ) , the amount of DNA sequence generated provided high coverage of the P . vivax genomes ( between 70 X and 407 X , Table 1 and Figure 1 ) . However , the average genome coverage does not accurately represent the quality of the sequencing data . Dharia et al . [7] sequenced the first P . vivax field isolate at an average genome coverage of 30 X . This 30 X coverage translated into 24 . 89% of the Sal I nucleotides being covered by more than 20 high-quality reads , and only 3% of the genes ( 158 out of the 5 , 050 annotated genes in the Sal I genome ) having more than 90% of their coding region sequenced at this coverage . By contrast , owing to continuing advances in massively parallel sequencing , the Malagasy and Cambodian samples analyzed here had at least 93% of their genome sequenced by 20 reads or more , and between 84 and 97% of the genes covered ( Table 1 ) . To determine whether we could use whole genome sequence data to identify DNA polymorphisms , we first compared the reads generated from Sal I DNA to the reference genome previously sequenced from the same strain [5] . Out of ∼11 . 6 million nucleotide positions covered by 20 reads or more , only 121 nucleotides differed from the reference nucleotides in more than 10% of the reads covering those positions , and none were supported by more than 90% of the reads ( see Figure 2A and Text S1 ) . These results highlighted both the high quality of the assembled P . vivax reference genome sequence and the suitability of high coverage genome sequence data for identifying SNPs with low false positive rates . For all further analyses , we focused on positions of the Sal I reference genome that were covered by at least 20 reads in the Belem strain and each field isolate ( i . e . , all samples we sequenced excluding the low coverage Sal I ) . We also excluded from our analyses potentially paralogous sequences that could generate spurious SNP calls ( Text S1 ) . Overall , 19 , 533 , 315 nucleotides ( 86 . 35% of the Sal I reference genome ) were included in the SNP analysis . For each sample , we recorded the percentage of reads differing from the reference nucleotide at each position . For the monkey-adapted P . vivax Belem strain , at any given nucleotide position , all reads carried the reference allele ( i . e . 100% Reference Allele Frequency [RAF] ) or all reads differed from the Sal I reference ( 0% RAF ) ( Figure 2A ) . This is consistent with P . vivax being haploid in the human/monkey host and with the presence of a single strain in the Saimiri monkey . Note that the RAF at some positions differed slightly from 0 or 100% ( typically by less than 5% ) due to sequencing errors . The distribution of RAF was strikingly different for the P . vivax field isolates: in these samples , we consistently observed two alleles at many positions ( Figure 2B ) . This pattern suggested that multiple strains of P . vivax were present in each patient blood sample . For example , a SNP with an RAF of 20% ( as it was frequently observed in C08 ) could occur if two strains of P . vivax were present in the patient blood and the major strain ( accounting for 80% of the parasites ) differed from the reference allele at this position while the minor strain ( making up the remaining 20% of the parasites ) carried the Sal I reference nucleotide . The peaks at 0% RAF in Figure 2B represent positions where all strains present in a sample differed from the Sal I reference allele . We independently validated a subset of the SNPs by cloning and Sanger sequencing the Duffy binding protein region II ( DBPII ) for five of our samples . The Sanger sequencing results were consistent with whole genome sequence findings and validated 17 out of the 17 SNPs identified by Illumina sequencing in this region ( Table S2 ) . In addition , analysis of the cloned sequences revealed distinct haplotypes amplified from a single blood sample , confirming the presence of multiple strains in two of the samples ( Figure S1 ) . Overall , we identified 80 , 657 nucleotide positions where at least 10% of the reads differed from the reference sequence in one or more of the samples . The 80 , 657 SNPs were distributed throughout the genome with an average of 4 . 13 SNPs per kb . However , there was extensive variation in SNP density among genomic regions ( Figure 1 ) . Most notably , the extent of genetic diversity was highly dependent on the gene context: intergenic regions showed a much higher diversity than coding regions ( 6 . 98 vs . 2 . 96 SNPs per kb , respectively ) with intronic sequences harboring an intermediate level of diversity ( 4 . 29 SNPs per kb ) . This observation was similar to the results of a study of 100 kb of contiguous DNA sequence [19] and consistent with purifying selection maintaining the DNA sequence at most genes in the P . vivax genome by removing deleterious mutations . 48 , 224 SNPs occurred in intergenic regions . SNPs in annotated protein coding regions included 13 , 203 synonymous polymorphisms ( sSNPs , 16 . 37% of all SNPs ) , 19 , 191 non-synonymous polymorphisms ( nsSNPs , 23 . 79% ) and 39 substitutions ( 0 . 05% ) introducing an early stop codon . We only observed 1 . 5-fold more nsSNPs than sSNPS , while based on the composition of the P . vivax genome we would expect by chance ∼4-fold more nsSNPs . This observation also suggested that the evolution of most protein-coding sequences in P . vivax genome is driven by purifying selection . We attempted to assign allelic variants observed within a sample to individual P . vivax parasites for the Duffy binding protein locus . For each sample , we recorded the co-occurrence of consecutive alleles on individual read pairs: since read pairs were generated by sequencing the ends of single DNA molecules , alleles observed on the same read pair were carried by the same parasite . Using this procedure , we were able to reconstruct haplotypes for the most prevalent strain for all samples as well as for a second strain for the C15 , C127 and M08 field isolates ( Figure 3 and Figure S2 ) . The inferred haplotypes were identical to the consensus DBPII sequences generated by cloning and Sanger sequencing from the same samples ( Figure S1 ) , validating our haplotype reconstruction approach . The haplotypes inferred from the same patient blood sample were not closely related to each other and represented unrelated P . vivax parasites . It is important to note here that while sequencing data allows identifying genetically distinct parasites , it does not differentiate related parasites derived from parental strains by mutations or recombination ( see e . g . [20] ) . Haplotype reconstruction , when several strains are present in a single sample , depends on several factors , including the SNP density and the extent of linkage disequilibrium . This currently hampers extending our approach to the entire genome . However , the analysis of Duffy binding protein haplotypes was consistent with the distribution of allele frequencies shown on Figure 2 and indicated that , in all field samples , 2–4 strains contributed to more than 95% of the P . vivax DNA ( as opposed to a scenario where dozen of strains would be equally abundant in a patient ) . Two strains were equally abundant in C15 , while for M19 three strains dominated ( with roughly 50% , 25% and 25% frequency ) . In three samples , M15 , C08 and C127 , one single strain largely dominated all others and represented , respectively , 80% , 80% and >90% of the parasites present . Given the high sequence coverage generated here , for these samples , the most frequently observed allele at each nucleotide position was very likely carried by the dominant strain ( see Text S1 for details ) . We therefore inferred for these three samples the haploid sequence of the dominant strain for the entire genome by considering the major allele at each variable position . Combined with the single strain sequences of Belem and Sal I , these sequences provided five haploid genome sequences for P . vivax from three continents . Studies of the global P . vivax genetic diversity have been limited by the lack of informative markers and essentially based on a few loci ( e . g . the mitochondrial DNA [9] , [21] and the Duffy binding protein [22] ) . This has greatly limited our understanding of the P . vivax population structure and history since diversity at these loci may be influenced by natural selection . The five haploid genome sequences generated here provided an opportunity to preliminarily assess the global genetic diversity of P . vivax . Identification of likely neutral sequences in the P . vivax genome is complicated by its gene density: less than half of the genome sequence is intergenic and there are few long stretches ( e . g . ≥10 kb ) of DNA sequences without annotated genes . We therefore focused on the analysis of four-fold degenerate sites ( i . e . , nucleotide positions where substitutions do not change the amino acid sequence ) that are less likely to be directly affected by natural selection . Among 98 , 393 four-fold degenerate sites sequenced , we observed 2 , 193 variable sites , including 1 , 769 sites that differed in only one sample . This represented a significant excess of singletons compared to the number expected under a neutral model of a random mating population of constant size and could indicate that P . vivax population has recently expanded in size , or alternatively , that the parasite population is heterogeneous and composed of many sub-populations . Consistent with previous reports [22] , our analysis of Duffy binding protein sequences showed a star-like phylogeny with no apparent geographic stratification ( Figure S3 ) . This pattern could reflect the actual structure of the P . vivax population or simply indicate the action of natural selection on the DBP gene . To further investigate population structure in P . vivax , we compared the five haploid sequences and determined , for each annotated gene , the geographical origin of the closest haplotype to a given sample ( similar to the nearest neighbor approach described in [23] ) . Our results showed that , while strains from the same location tended to be more similar to each other than to a strain from a different continent , there was considerable allele sharing across continents ( Figure 4 ) . For example , the haplotype sequence for the Cambodian sample C08 was most similar to the Cambodian C127 haplotype for 586 genes but most similar to the Malagasy M15 or South American Belem haplotypes at 463 genes . Consistent with this observation , a tree reconstructed using the total number of nucleotide differences among whole genome haploid sequences showed that strains from a same continent clustered together but with very long external branches ( Figure S4 ) , indicating that most diversity is observed among samples rather than between geographical locations . Previous studies based on mitochondrial DNA [24] and microsatellites [25] have also highlighted that similar haplotypes are often shared across continents ( but see also [26] ) . This observation of extensive allele sharing across continents is unexpected as we may have expected to observe consequences of local adaptation , and therefore greater population differentiation , as P . vivax spread across the world and encountered new environments ( e . g . , different mosquito species , different host's immune response ) . We screened the P . vivax genome for evidence of adaptive selection by looking for SNPs for which one allele was fixed in all parasites from one geographical area while the other allele was fixed in all other parasites . For comparison , this is the situation at the Duffy locus in humans where Duffy-negativity is fixed in Sub-Saharan Africans and absent in non-African populations . Among the 80 , 657 SNPs identified , we only observed 96 SNPs with such dramatic allele frequency differences ( not statistically different from the number expected by chance due to the small number of samples analyzed ) . In addition , while all three Cambodian-specific alleles occurred in close proximity ( within 20 bp from each other ) , the 92 Malagasy-specific alleles were distributed across the 14 chromosomes suggesting that chance rather than natural selection was responsible for these results . This analysis was consistent with our observation of allele sharing across continents and suggested that P . vivax population is not highly differentiated . In conclusion , we showed that continuing advances in sequencing technology allow the robust characterization of genetic diversity in P . vivax genomes . The SNPs identified here will be valuable for vivax malaria research to design population studies ( e . g . studying the diversity of P . vivax in one region ) and to identify the genetic basis of disease-related traits by association studies . In this regard , it is important to note that we identified multiple parasites in each patient blood sample analyzed , which will complicate these studies and will need to be rigorously accounted for . Finally , our analysis of P . vivax genomes from three continents revealed allele sharing across continents and little evidence of local adaptations . While our analysis includes , for the first time , genetic diversity estimates across the entire genome , the number of samples analyzed here is limited . We conducted population genetic analyses using approaches robust to small sample sizes but our results will need to be confirmed as more genome sequences become available for this parasite . One possible explanation for our observations is that the P . vivax population originated recently and dispersed rapidly across the world without major loss of diversity or much influence of natural selection . Alternatively , allele sharing could be due to continuous gene flow in the present P . vivax population: P . vivax is now a cosmopolitan parasite that can be easily spread throughout the world by way of dormant hypnozoites . If this second hypothesis is true , it holds bleak prospects for vivax malaria elimination: with high level of gene flow , genetic polymorphisms conferring drug resistance [27] , [28] or novel invasion mechanisms [11] could spread across the world and further complicate control strategies . | Plasmodium vivax is the most frequently transmitted and widely distributed cause of malaria in the world . Each year P . vivax is responsible for approximately 250 million clinical cases of malaria and its global economic burden , placed largely on the poor , has been estimated to exceed US$1 . 4 billion . In contrast to P . falciparum , P . vivax cannot be propagated in continuous in vitro culture and this limits our understanding of the parasite’s biology . In this study , we sequenced the entire genome of five P . vivax isolates directly from blood samples of infected patients . Our data indicated that each patient was infected with multiple P . vivax strains . We also identified more than 80 , 000 DNA polymorphisms distributed throughout the genome that will enable future studies of the P . vivax population and association mapping studies . Our study illustrates the potential of genomic studies for better understanding P . vivax biology and how the parasite successfully evades malaria elimination efforts worldwide . | [
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] | 2012 | Whole Genome Sequencing of Field Isolates Provides Robust Characterization of Genetic Diversity in Plasmodium vivax |
Our ability to interact with the environment hinges on creating a stable visual world despite the continuous changes in retinal input . To achieve visual stability , the brain must distinguish the retinal image shifts caused by eye movements and shifts due to movements of the visual scene . This process appears not to be flawless: during saccades , we often fail to detect whether visual objects remain stable or move , which is called saccadic suppression of displacement ( SSD ) . How does the brain evaluate the memorized information of the presaccadic scene and the actual visual feedback of the postsaccadic visual scene in the computations for visual stability ? Using a SSD task , we test how participants localize the presaccadic position of the fixation target , the saccade target or a peripheral non-foveated target that was displaced parallel or orthogonal during a horizontal saccade , and subsequently viewed for three different durations . Results showed different localization errors of the three targets , depending on the viewing time of the postsaccadic stimulus and its spatial separation from the presaccadic location . We modeled the data through a Bayesian causal inference mechanism , in which at the trial level an optimal mixing of two possible strategies , integration vs . separation of the presaccadic memory and the postsaccadic sensory signals , is applied . Fits of this model generally outperformed other plausible decision strategies for producing SSD . Our findings suggest that humans exploit a Bayesian inference process with two causal structures to mediate visual stability .
During saccadic eye movements , the image of the world shifts across our retina . Despite these shifts , we perceive targets as having world-stable positions , and have no problem to act upon them whenever necessary . It has been suggested that a combination of predictive and feedback mechanisms subserve this faculty , referred to as spatial constancy [1] . In the literature , spatial constancy has been studied by using motor and perceptual tasks . Using motor tasks , it has been shown that we can look or reach accurately to the remembered position of a target after an intervening saccade ( see [1] for review ) . Using arm movements , Vaziri et al . [2] recently tested the hypothesis that the brain computes the position of a reach target after a saccade based on the optimal integration of predicted and actual sensory feedback . In their paradigm , participants first made a saccade after they briefly foveated a visual target in complete darkness . The brain is known to predict the new retinal position of this target after the saccade by internally remapping its representation relative to gaze [1 , 3 , 4] . Next , the target was postsaccadically viewed for a variable duration , slightly displaced relative to its initial position , before the participant reached at it . Results show that reach endpoints had smaller variance than was possible based on the predicted ( i . e . remapped ) estimate or the actual postsaccadic estimate alone , consistent with integration . The authors further demonstrated that the uncertainty of the postsaccadic target position , which was modulated by varying its viewing time , affected its weight in the integration process . From a perceptual perspective , it has been shown that the sensitivity to perceive the displacement of a visual target severely drops during a saccade . In fact , target displacements up to one third of the saccade amplitude typically go unnoticed , which is known under the term saccadic suppression of displacement ( SSD; e . g . [5] ) . Remarkably , blanking the target briefly after the saccade , before it reappears at a displaced position , significantly improves the sensitivity to the displacement [6] , as does merely changing some characteristic of the saccade target , such as its form or polarity [7 , 8] . This has led to the notion that the visual system a priori assumes that a target will not move or change during the saccade . If this assumption is broken , as with the blank , form change , or with large displacements , it causally regards the postsaccadic target as a new object , and computes the old position using retinal and extraretinal signals . Niemeier et al . [9] formulated the SSD findings from an optimal integration perspective by combining visuomotor signals with a prior that reflects the assumption that targets are not displaced during the saccade . As predicted by their model , behavioral reports show that SSD has a nonlinear relationship with the size of the target displacement . While with small displacements the localization of the initial presaccadic target was strongly contracted to the postsaccadic target , this integration effect was reduced with larger displacements , making localization more veridical . But how does the brain know when to integrate signals and when to process them independently in the computations to obtain spatial constancy ? From a perceptual viewpoint , Vaziri et al . [2] essentially used a blanking paradigm , thereby ignoring the possible assumption that visual targets typically do not move during saccades . Despite the blank , which is assumed to indicate that sources are unrelated , their results show optimal integration of the presaccadic target information and actual postsaccadic target position . Also in the model of Niemeier et al . [9] , the spatial constancy computations are unconditioned to causality: integration always occurs even with large target displacements . In the present study , we test the role of causal inference in the computations to obtain spatial constancy . According to this framework , the brain has to estimate the causal relationship between the presaccadic and postsaccadic signals to establish to what degree they can be integrated or when they should be kept apart , which not only depends on the precision of these signals but also on their spatiotemporal difference [10 , 11] . Based on the presaccadic input , it could be hypothesized that initially foveated representations are less susceptible to SSD than non-foveal representations because their remapped representations are more precise , triggering a segregation strategy . Based on the postsaccadic input , it could be proposed that if the postsaccadic target is presented only briefly , its representation is too weak to infer a target displacement , making the brain rely most heavily on an integration strategy in the later localization of the target . But if the postsaccadic target is viewed longer , displacements may become better detectable , triggering a segregation strategy , especially with large displacements . Here , we test these hypotheses by varying the duration of the postsaccadic display in an SSD task for displacements of the initial fixation target , the saccade target and a non-foveated peripheral target . Because previous studies reported direction-specific SSD , ( e . g . [12 , 13] ) we test for both parallel and orthogonal displacements relative to the direction of the saccade . We show that spatial constancy is not based on the exclusive integration of presaccadic target information and actual postsaccadic sensory feedback nor does it follow from an a-priori assumption that targets do not move during saccades . Our results suggest that spatial constancy naturally follows from the principles of causal inference involving two possible causal structures: one where the pre- and postsaccadic percepts represent the same stable object ( i . e . have a common cause ) , and one where two distinct objects are perceived ( i . e . no common cause ) .
Participants were tested in a saccadic suppression of displacement task in which they had to indicate the presaccadic position of either the fixation target , the saccade target or a peripheral non-foveated target that was displaced parallel or orthogonal during a horizontal saccade ( Fig 1 ) . The displaced target was subsequently viewed for three different durations ( 50 , 300 or ~1000 ms ) . Fig 2 shows the performance of a typical participant , plotting the localization errors ( red dots ) of the three target positions ( rows: FT , ST , NT ) as a function of parallel and orthogonal target displacement , respectively , separately for the three postsaccadic viewing times . Blue shaded areas represent best-fit model predictions , and will be discussed below . Data points should fall along the horizontal dashed line if the participant correctly remembered the presaccadic target location and ignored the target displacement after the saccade . In contrast , if the position of the postsaccadic target ( dashed diagonal line ) interacts with memory for the presaccadic position of the target , the data should diverge from the horizontal line and linearly relate to the size of the target displacement . The localization responses of this participant indicate a mixture of these two patterns . While localization errors become larger with increasing target displacements , beyond a certain target displacement they transition back to smaller errors . Thus , with increasing target displacement , there appears to be a shift in the proportion of responses that are contracted to the postsaccadic target vs . the ones that are unaffected by it . This pattern can be seen in all panels . Fig 3 depicts the localization errors , averaged across participants . The pattern of localization errors is similar to the results of the single participant shown in Fig 2 , particularly the bias toward the postsaccadic target for small displacements and the loss of this contraction for large displacements . Below , this will be interpreted as the outcome of a mixture model balancing integration and segregation processes , but this qualitative structure can already be confirmed by standard statistical analysis . The distinction between small and large displacements is not a sharp one , of course , and could , in a functional sense , depend on target position , viewing time and direction of displacement . Therefore , we took for the following analyses the displacements with absolute value strictly smaller than 2° ( 0 , ±0 . 5 , ±1° ) as “small” and the displacements with absolute values strictly greater than 2° ( ±3 , ±5° ) as “large” . ( Replicating the analyses with the ±2° displacements added to either the “small” or “large” group turned out to yield very similar results . ) An analysis including the three targets ( FT , ST , NT ) , the three viewing times ( 50 , 300 , ~1000 ms ) , the two directions ( parallel , orthogonal ) and the “small” displacements ( 0 , ±0 . 5 , ±1° ) , showed a significant positive linear effect of displacement on localization error ( F ( 1 , 10 ) = 28 . 7 , p < . 001 ) . This effect was present across targets , viewing times and directions , but it was moderated by these factors . For instance , post hoc comparisons revealed that the regression slope of localization error on displacement was less steep for FT trials than for ST and NT trials , the latter two not differing significantly . This is in line with the notion that because FT is initially foveated , it is represented more precisely than ST and NT , and therefore less influenced by its postsaccadic location . As for viewing time , the slope was generally less steep for 50 than for 300 ms , with no significant difference between 300 and ~1000 ms . Overall , parallel displacements produced a steeper slope than orthogonal ones . The moderating effects of viewing time and direction , however , were not present for all targets ( a 2nd order interaction ) . For the FT , slopes were not significantly different across viewing times and directions , although they tended to be steeper for parallel than orthogonal displacements ( p = 0 . 06 ) . For ST trials , there was no moderating effect of time , but a very clear effect of direction ( p < . 001 ) , with a steeper slope for parallel displacements . In contrast , the NT trials showed no moderation of the slope by direction , but they did show a very clear effect of time: here the slope was significantly steeper for ~1000 ms than for 300 ms ( p = . 018 ) , as well as for 300 ms compared to 50 ms ( p = . 025 ) . All in all , this makes for a complicated collection of results , which have in common across all conditions , however , a positive linear effect of small displacements on localization error . This linear relationship between displacement and localization error does not extend to the large displacements . Choosing either the positive large displacements 3° and 5° , or the negative displacements -3° and -5° revealed no effect of displacement on localization error ( p = . 87 and p = . 28 , respectively ) in an analysis including the target , viewing time , and direction factors . To explain these effects , we modeled the role of causal inference in the computations to obtain spatial constancy . Our principal model involves a statistically optimal mixture at the trial level of two possible causal structures on the signals available ( see Methods ) . For each participant , the model was fit to all localization errors simultaneously . For the participant in Fig 2 , the best-fit model is shown by the blue shaded curves . The shade intensity represents the model’s likelihood of localization errors ( p ( s|mv ) ) . The model adequately predicts the positive slope in the errors as observed with small but increasing target displacements . This positive slope reflects the model’s weight on the assumption that the pre- and postsaccadic percepts originate from the same stable target ( i . e . have a common cause ) , so they can be integrated to estimate a more precise but biased response ( in the direction of the postsaccadic target ) . Along the same lines , the model also accounts for the effects of postsaccadic viewing time , the increase of which causes a more precise postsaccadic representation resulting here in a steeper slope in the localization error ( i . e . a stronger contraction or pull to the postsaccadic target ) . Finally , the model infers that for large target displacements , the pre- and postsaccadic percepts likely stem from different causes , for which it is optimal to not integrate but rather disregard the postsaccadic percept . As a result , the probability of a localization response toward the displaced target decreases , which matches with the transition to smaller errors as observed in the data . The continuous lines in Fig 3 depict the best-fit predictions from the model , averaged across participants . As shown , these curves display a good correlation with the localization errors ( R2 = . 65 ± . 06 and R2 = . 85 ± . 03 for the parallel and orthogonal direction , respectively , across participants; see the section Mixture Model for details about the fitting procedure ) . The best-fit parameter values ( see Table 1 ) give insight in the precision with which the target positions are recovered from memory when computing the localization responses ( σm; see Fig 4A ) . A two-way analysis on the σm values revealed significant effects of both target ( F ( 2 , 9 ) = 31 . 9 , p < . 001 ) and displacement direction ( F ( 1 , 10 ) = 5 . 2 , p = . 045 ) , as well as a significant interaction effect ( F ( 2 , 9 ) = 25 . 9 , p < . 001 ) . The interaction is expressed by the finding that this effect is mostly driven by the orthogonal displacements ( see Fig 4A ) . Post hoc comparisons revealed that NT is memorized with a lower precision than FT and ST . Thus , while both ST and NT are viewed in the periphery before the saccade , ST is memorized with higher precision than NT . No significant difference was found between the estimated parameters for FT and ST . Fig 4B depicts the model’s prediction of the precision of the postsaccadic target ( σv ) for the three viewing times . Here the effect of viewing time is significant ( F ( 2 , 9 ) = 7 . 5 , p = . 012 ) and , as expected , post hoc comparisons reveal precision to improve ( lower sigma values ) both from 50 to 300 ms viewing ( p = . 004 ) and from 300 to ~1000 ms viewing ( p = . 008 ) . As the mean data show , there are also errors in the absence of any target displacement . The model explains this by the combined effect of the foveal prior ( σf = 4 . 6° ± 0 . 27° , mean ± SEM ) and the allocentric prior π . The location and precision of the allocentric prior are plotted in Fig 4C , showing that it is centered in between the three target locations , and has a substantial width ( ~12° ) compared to the inferred precision vales of both the remapped , presaccadic target representations ( Fig 4A ) and postsaccadic information ( Fig 4B ) . Finally , in the model , the general degree by which participants’ localization responses were influenced by the displaced target is captured by parameter pc , which represents the prior probability that the target remains stable . Its value was on average 0 . 45 ± 0 . 1 ( mean ± SEM ) , but Table 1 shows that this parameter varied substantially among the 11 participants . This prior in combination with the information of m and v , results in a posterior probability that the target has not moved , p ( C|mv ) , as a function of target displacement . Fig 5 shows that the average p ( C|mv ) is close to one for small displacements , suggesting integration of pre- and postsaccadic targets . For larger target displacements , the curves fall off , suggesting more evidence that pre- and postsaccadic representations stem from different sources . The curves also illustrate the effect of viewing time: when the postsaccadic target is viewed only briefly , inferring causality becomes more difficult , resulting in a more gentle decline of p ( C|mv ) with increasing displacements . The above results follow from fits of a mixture model that assumes a causal inference process that is fully statistically optimal . For comparison , we also fitted two variants of this model , model selection and probability matching ( see Methods ) . The models differ by the response rule applied ( see Methods ) . Across our participants , on average the log-likelihood differences of these models with the mixture model were 344 ± 124 and 125 ± 49 , respectively , indicating that the mixture model ( average log-likelihood -17262 ) outperforms its variants . Since the three models share the same parameters , using an AIC or BIC instead of the log-likelihood criterion in the model comparison would not change this conclusion . For one participant ( number 6 ) no clear difference between the mixture model and model selection was found ( log-likelihood difference < 3 ) ; for two other participants ( number 9 and 11 ) , a probability matching strategy was ranked before the mixture model .
In the current study we modeled and tested the role of causal inference in the computations for spatial constancy across saccades . According to our model , the brain has to estimate whether presaccadic and postsaccadic signals reflect a stable or an unstable visual world , which depends on the spatiotemporal difference between these signals and on their precision . We operationalized the problem experimentally by using the saccadic suppression of displacement paradigm . Participants viewed three targets , with one of them the fixation point , the other the saccade target and the third a peripheral target . After the saccade , one of these three remained for different viewing durations , but often at a slightly displaced position , and participants had to indicate which location it had prior to the saccade . Our results show that: 1 . the integration of the pre- and postsaccadic target positions declines as a function of their spatial separation , 2 . different targets show different strengths of SSD , and 3 . viewing time of the postsaccadic target changes the strength of SSD . Our model could account for all these findings , which will now be discussed in more detail . We replicated the non-linear localization response pattern previously reported by Niemeier and colleagues [9] , but modeled it in a different way . Sensory signals are inherently noisy . This means that even in the case of a completely stable world the pre- and postsaccadic percepts may show some false discrepancy which should be ignored by the brain . In the model of Niemeier and colleagues a spatial window of stability is created by integrating a displacement vector ( i . e . the visual discrepancy ) with a prior centered at zero displacement . This predicts that localization is pulled to the postsaccadic target , irrespective of the size of the displacement . The present model goes a step further , and considers this pulling effect from a causal inference perspective , stating that presaccadic and postsaccadic percepts should be integrated when their discrepancy is relatively small but should be segregated when the displacement increases . More specifically , it infers the probability that a common cause underlies the pre- and postsaccadic percepts . The model dealt with these considerations in an optimal manner , i . e . on any trial it applied a mix of both integration and segregation , each weighted by its respective probability as based on the precision of both percepts , thereby minimizing quadratic error in the long run . Of course , there are alternative forms by which the brain could process the inference about the common cause ( see [10] ) . For example , the brain could also select per trial which causal structure is most likely , and accordingly process the trial in a binary fashion either by integration or by segregation . In most participants , we found that our weighted averaging model better described the data than a model involving binary selection or a model based on the principle of probability matching . In the comparison of the fits of the three models described , Wozny et al . [10] found the last and least optimal variant , probability matching , the clear winner in a multisensory perception experiment . It must be noted , however , that our experimental setting differs principally from that of Wozny et al . and of other applications of the mixture model known to us [11 , 14] . They deal with multisensory perception , where bimodal cues ( typically auditory and visual ) are available to be combined if there is evidence they belong to the same object , even though each unimodal cue is in itself sufficient to solve the task ( e . g . , localize an object ) . Data for either unimodal condition ( just the auditory cue or just the visual cue ) can be obtained without changing the task . In our case , there are two complementary representations in one modality ( vision ) and a division in an experiment with “just the presaccadic remapped memory information” and one with “just the postsaccadic visual information” is not sensible . Consequently , the outcome of the model comparison might well be different for our case . As predicted by our model , we found strong integration when the target displacements were small , characterized by low response variability but large biases toward the postsaccadic target . Increasing the size of the displacement lowers the probability of a common cause ( Fig 5 ) which results in smaller localization errors ( Fig 3 ) . The inferred probability of a common cause can directly be interpreted as the strength of SSD . As shown previously ( e . g . [15] ) , displacements up to one third of the saccade amplitude typically show strong SSD . However , we have found differences in the strength of SSD between targets and displacement directions . We showed that the differences in strength of SSD between targets reflect differences in the precision of the presaccadic target representations upon recall . The regression analysis suggests that FT is represented more precisely than ST and NT , while the model fits showed that both FT and ST were represented more precisely than NT . We lack a clear explanation for this difference , but as shown in Fig 3 , the model generally underestimates the pulling effect of ST and overestimates this for FT . For both FT and ST , localization is better with orthogonal than parallel target displacements , which can be explained by the anisotropy in the precision of their memories . This anisotropy may result from the noisy eye position signals that are used to remap the target representation across saccades [9] . Indeed , our participants showed about twice as much scatter in the saccade end points in the direction of the saccade than orthogonal to it ( 1 . 27 ± 0 . 05° and 0 . 73 ± 0 . 03° , respectively , mean standard deviation ± SEM ) . The estimated parameters of the mixture model indicate that memory precision of FT and ST is also about two times worse parallel than orthogonal ( see Table 1 ) , which suggests that noise sources related to eye position sense play a role in the coding of these representations [9] . The memory of NT , which we found to be less precise than ST and FT , appears to be more variable in the orthogonal than along the saccade direction . Although we cannot explain all the differences in the strength of SSD among the three targets , an important factor may relate to how the brain has coded the visual scene in memory , which we will discuss next . It has been suggested that across saccades the brain stores a structural description of the target display in memory ( e . g . [16] ) . For example , in a task where participants have to remember a pattern of dots , it was shown that the relative positions of the dots could be recalled independent of absolute spatial information [17] . After a saccade , the saccade target could serve as an anchor to which the structural description is related [15 , 18 , 19] . Connecting this finding to the present experiment suggests that participants encoded the equilateral triangle constituted by the three targets . In our experiment , however , the majority of trials had no ST present after the saccade . If the structural description of the target display would then be anchored to the eyes’ landing position instead , it would predict a positive relationship between the saccade landing error and localization error . Indeed , we found a small but significant correlation for ST in almost all participants ( mean r = . 18 ) . In the same vein , this notion could also explain why the ST was recovered with higher precision from memory than NT although both were presaccadically presented at equal eccentricities . If participants indeed stored a structural description as an equilateral triangle , there may be some variability in the size of the triangle from trial to trial . This variability would bear out in more response variability in the orthogonal direction of NT , as we have found . Furthermore , previous work has shown that a group of random static dots are typically remembered closer to each other than they actually were [20 , 21] , like our participants did . Our model explains this observation using an allocentric prior , positioned at about the center of the target display , albeit with some variability among participants This is consistent with current models of efficient coding in visuospatial memory , which propose that people code a display in terms of summary characteristics , such as its center of mass ( e . g . [22 , 23] ) . Despite relative coding accounts , as described above , there is also ample evidence that the brain keeps target representations in a dynamic register ( for a review see [4] ) . These representations , coded in eye-centered coordinates , must be updated when the eyes move . In support , several brain regions have been identified that contain neurons with visual receptive fields ( RFs ) that are normally fixed to one position of the retina but briefly shift in anticipation of a planned saccade to the position the RF will occupy after the saccade ( e . g . [3 , 24 , 25] ) . Although it is currently unknown how the brain transfers object information across shifts of the RFs , it could be an important mechanism in order to achieve space constancy ( e . g . [26] ) . In our experiment , the three target representations would be shifted in the opposite direction of the upcoming saccade . After the saccade a one-to-one comparison can be made between the postsaccadic retinal input and the predicted input to assess visual stability . It could be hypothesized that in anticipation of a saccade a given receptive field shifts in the accurate direction but with a less accurate amplitude . This seems plausible given that saccades to a target typically show more variability in amplitude than direction . While this would be consistent with the observed SSD differences for FT and ST , this is not the case for the NT . VanRullen [27] has argued that while the visual world translates homogeneously during a saccade , its cortical representation does not because the amount of cortex dedicated to a certain sized patch of the retina varies , especially as a function of retinal eccentricity . One possibility is that these non-homogenous shift of RFs introduces noise orthogonal to the saccade in the periphery , which may explain our results for the NT . A precise mapping of shifting RFs would be needed to test this hypothesis . Alternatively , one could speculate that the observed differences between target locations reflect distortions due to RFs that shift not in parallel but towards the ST in anticipation of a saccade [28 , 29] . Although it has been suggested that this anticipatory transient increase in density of receptive fields around the saccade target underlies the boost in attention around the ST area , and thus is beneficial for space constancy for that target , it may be that the encoding of peripheral targets becomes distorted because of these RF shifts . The representation of a target like NT may become stretched or displaced towards the ST , resulting in a compressed memory . Future research should investigate whether these RFs do indeed distort perception . In our experiment , we not only displaced the target but also manipulated the postsaccadic viewing time . In general , longer viewing increases its pulling effect on the localization response . Recently , Zimmerman et al . [30] performed a SSD task in which the viewing time of the presaccadic target was varied . They showed that when the presaccadic target is briefly viewed , i . e . < 0 . 5 s , displacement detection performance is low . Here , we modeled viewing time as a factor that changes the precision of the target representation . Indeed , the longer the target was visible , the higher its precision . In terms of our model , the viewing time manipulation by Zimmerman and colleagues would affect σm which in turn affects the probability of perceiving a common cause p ( C|mv ) . In other words , the system is generally more likely to integrate when the representation of the presaccadic target is noisy , hence displacement detection performance is low . In our experiment , decreasing the viewing time of the postsaccadic target did generally lower the detection performance as well ( i . e . , increase p ( C|mv ) ) . The latter may not be directly obvious from the localization responses which show the strongest pulling effect with the longest viewing duration . The explanation is as follows . Although the integration strategy receives less weight with long viewing , the postsaccadic target representation is more precise , which has an opposite effect and ultimately pulls localization towards it . A final point of discussion relates to model parameter p ( C ) , which represents the a priori probability that the world remains stable . We found a considerable variability among participants for this parameter . In most participants , the p ( C ) estimates can be regarded low , given that in daily life objects rarely jump while we scan the world . We consider it plausible that the experimental context and task instruction , which explicitly mentions the possibility of displacements , alters p ( C ) . For example , if you know beforehand that a certain scene will contain a lot of instability , it seems logical to lower p ( C ) and thus become more skeptical regarding the feasibility to integrate percepts . Taken together , we showed that integration of the pre- and postsaccadic target representations can be modeled using principles of causal inference . When representations follow from spatially close target locations , integration is strong . In contrast , when targets are further apart , integration weakens , depending on precision of involved representations .
The study was part of a research program approved by the local ethics committee of the Social Sciences Faculty of Radboud University ( ECG2012-1304-030 ) . Twelve naïve participants ( eight females , average age 25 . 7 ± 0 . 6 years , mean ± SEM ) participated in the experiment , all with normal or corrected-to-normal vision . Each participant participated in four experimental sessions of approximately 1 h each and informed consent was given beforehand . One participant did not complete all sessions because the eye-tracker helmet felt uncomfortable . We discarded her data . Participants sat in a dimly lit room with their head supported by a chin rest . They operated a two-button computer mouse . Stimuli were controlled using a custom-written program in Delphi ( Embarcadero ) software . Visual stimuli were displayed on a 19 inch CRT monitor ( Philips 109B ) using a vertical refresh rate of 100 Hz and a resolution of 1024 x 768 pixels . The monitor was positioned about 30 cm in front of the participant’s eyes , encompassing 61° x 46° ( HxV ) of the visual field . A photodiode was placed over the bottom-left corner to determine the precise onset and displacement of the visual stimuli with respect to eye movements . Binocular eye position was recorded at 500 Hz using a head-mounted eye tracker ( EyeLink II; SR Research ) . The eye tracker was calibrated using a 9-point grid . A saccade was detected online using a position threshold of 1 . 5° . Participants were allowed to take breaks every 400 trials . After each break the eye tracker was recalibrated and as needed during testing , for example when the program failed to detect a fixation at the start of a trial . We tested participants in an SSD task with three target positions , each of which contained a gray shape ( circle , square , or triangle , all 1° size ) . Fig 1 presents a graphical depiction of a trial . At the start of the trial , the three target shapes appeared 15° apart at equilateral triangular positions against a light-grey background . The shapes designated the fixation target ( FT ) , the saccade target ( ST ) , and a peripheral non-target ( NT ) . The specific shape of each target was held constant for each participant ( e . g . the triangle was always the FT ) , but counterbalanced across participants . The participant was instructed to first foveate the FT , i . e . the triangular target in Fig 1 . After the participant had kept fixation for a random duration of 200–500 ms ( discouraging anticipatory saccades ) , an auditory signal ( 1kHz sine-wave beep , 60 ms ) instructed the participant to saccade to the ST . The saccade was always in horizontal direction , either leftward or rightward in randomized order . The NT appeared midway between the ST and FT , above or below ( randomized ) . The exact position of these targets relative to the screen’s center was varied ( over a range of 27° horizontally and 20 . 6° vertically , flat distributions ) in order to deter learning the exact location of the targets on the monitor . During the saccade , at on average 36 ± 8 . 3 ms ( mean ± std ) after saccade onset , one of the three targets was displaced , while the other two were removed from the display . The target displacement ( -5 , -3 , -2 , -1 , -½ , 0 , ½ , 1 , 2 , 3 , or 5 degrees ) was parallel or orthogonal to the saccade . The displaced target remained visible for 50 ms , 300 ms , or for about 1000 ms until a response was given , the ‘1000 ms’ condition . The time between saccade offset and the response was kept constant such that memory decay of the presaccadic scene was similar for the three viewing conditions . Together , this defined 792 trial types ( i . e . 2 saccade directions , 3 targets , 2 NT locations , 11 displacement sizes , 2 displacement directions ( parallel vs . orthogonal ) , and 3 viewing durations ) . For our first six participants , the 50 ms and 300 ms condition were randomly presented in the first three experimental sessions; the 1000 ms condition was tested in a separate session . For the other group of participants , the three viewing time conditions were fully mixed in all four sessions . No significant differences between both groups were found . Participants gave their response using a mouse cursor ( small crosshair ) indicating the presaccadic position of the displaced target , which they confirmed by clicking the left mouse button . The cursor appeared always 300 ms after the displacement occurred . Participants performed each trial type 4 or 5 times . In case the saccade endpoint deviated more than 5° from the ST location , a red screen was shown for 1000 ms after a response was given . Eye blinks that triggered the target to jump were also followed with a red screen . If the participant did not know about which of the three targets to report , he or she had to shift the cursor to the left border of the display , before clicking the mouse button . Before the actual experiment started the participant completed a series of practice trials until s/he felt comfortable with the task . We performed offline data analyses in Matlab ( The Mathworks , Nattick , MA ) . Trials in which the target displacement did not occur during the saccade ( eye velocity < 50°/s for offline analysis ) were discarded ( 14 . 6 ± 2 . 0%; mean ± SEM ) . Trials in which the postsaccadic target was not perceived ( 2 . 7 ± 0 . 7% ) and trials with localization responses that were closest to a target other than the original position of the postsaccadic target were also discarded ( 3 . 7 ± 1 . 4% ) . We also discarded trials with a red screen ( 2 . 7± 0 . 6% ) . As a result , each participant completed on average 2427 ± 111 correct trials . Across participants , saccade duration was 50 . 7 ± 1 . 1 ms and saccade amplitude 14 . 0 ± 0 . 2° . There was no instruction on saccade reaction time . Average saccade latency , 273 . 7 ± 45 . 4 ms ( mean ± SEM ) , was higher than usual , probably because of the memorization of the presaccadic positions ( cf . [30] ) . The total duration that the targets were displayed before the saccade was on average 1200 ± 60 ms . Data of four experimental configurations , that is a left/rightward saccade and NT above/below , were pooled by transforming them toward the single configuration shown Fig 1A , reducing the number of unique trial types to 198 . Localization error was defined relative to the presaccadic target location , and was signed positive into the horizontal saccade direction and vertically upwards ( see Fig 1A ) . We modeled the role of causal inference in the computations to obtain spatial constancy . The model has to explain the observed responses of each participant . Our principal model involves a statistically optimal mixture at the trial level of two possible causal structures on the signals available . This 2D model is developed here , formulated along the lines proposed in Körding et al . [11] , to which we will frequently refer for further information . In the subsection ‘Alternative Models’ below we will introduce two variants of this model , also considered by Wozny et al . [10] , involving at the trial level not a mixture of , but a choice between the two possible causal structures . By estimating the causal relations between the various sources of information the brain attempts to determine whether two percepts belong together or need to be processed independently . More specifically , on each trial the task of the system is to estimate the presaccadic target position on the screen , denoted s , based on two sources of information , the memory-based remapped presaccadic position percept , denoted m , and the position percept of the postsaccadic visual stimulus , denoted v . Both entities are available with finite precision only ( having some amount of noise ) and are represented by probability distributions , which constitute the input to the causal inference model expounded below . First , we briefly describe how we modeled these probability distributions of the single source percepts m and v . The distributions of both m and v are assumed to be independent 2D Gaussians . It can be expected that the variance of m has several sources , such as retinal noise during target encoding , remapping noise related to target updating , and noise due to memory decay . Some of the noise sources may be anisotropic ( e . g . [9] ) . For simplicity , we do not model these sources but use a combined estimate σm2 for each target position and allow anisotropy . Thus , σm2 is estimated per target position , both for the parallel and orthogonal direction , resulting in 3x2 free parameters for m . For v we assume its variance to be isotropic , primarily determined by encoding noise . Intuitively , the shorter an object is viewed , the more noisy the position percept . Thus , σv2 is estimated per viewing time condition ( irrespective of target ) , resulting in 3 free parameters for v . It has further been suggested that participants localize visual targets towards the fovea ( e . g . [31–33] ) . We modeled this foveal bias by including a prior , specified as an independent isotropic 2D Gaussian with variance σf2 , centered at FT for m and at the saccadic landing point for v ( see Fig 6A ) , and by interpreting the percepts m and v as the results of an optimal Bayesian integration process of accurate sensory signals m~ and v~ , respectively , with this prior . As a consequence , the center of m is not at the true target position , but shifted in the direction of FT by the fraction σm2σf2 of the distance between these points ( see Fig 6B ) . Similarly , the center of v shifts from the true target position in the direction of the saccade landing point by the fraction σv2σf2 of the distance between these two points . These single source distributions play an essential role in the mixture model , in which the evidence for target position s given memory information m and visual information v takes the form of a probability density function p ( s|mv ) . Thus , p ( s|mv ) is the localization response , given estimates m and v . In order to determine this p ( s|mv ) in an optimal way , the system has to process correctly the probabilistic information available in m and v . That is , the system has to acknowledge that , while there is a direct relationship between m and s on each trial , this is not the case for v and s . Depending on the discrepancy between the two sources of information the system may either see no evidence for a displacement and consider the information v as relevant for the presaccadic position s to be reported ( Fig 6C; integration ) , or it may take v to refer to a new visual object without a clear relationship with s ( Fig 6C; segregation ) . In short , the system may distinguish two kinds of trials , requiring different forms of p ( s|mv ) . In this probabilistic setting the optimal procedure for the system is not to choose per trial one of these forms , but to apply on any trial a mix of both , with the weight for each form equal to the estimated probability of it being the correct one given sources of information m and v ( Fig 6D ) . Denoting the situation of a trial where both m and v derive directly from the presaccadic position s by C ( common cause for m and v ) and one where v derives from a different object ( the displacement ) by C- ( no common cause for m and v ) , this leads to a mixture model of the representation of p ( s|mv ) [11]: p ( s|mv ) =p ( s|mvC ) ⋅p ( C|mv ) + p ( s|mvC¯ ) ⋅p ( C¯|mv ) ( 1 ) This model consists of three components: ( i ) p ( s|mvC ) , the distribution of s given m and v when v is the sensory representation of the true position; ( ii ) p ( s|mvC- ) , the distribution of s given m and v when v does not represent the true position , but a displaced version of it; and ( iii ) p ( C|mv ) , the probability that the current m and v are from a trial with common source , with p ( C¯|mv ) = 1−p ( C|mv ) the complementary probability of a trial with m and v referring to different positions . We will now discuss the specification of these three components in turn . The model contains 15 free parameters to fit 2D localization data from 198 different conditions: 3 target positions ( FT , ST , NT ) x 11 displacement sizes ( -5° to 5° ) x 2 displacement directions ( parallel , orthogonal ) x 3 viewing times ( 50 , 300 , 1000 ms ) . Six parameters are used to estimate m; three parameters are used for v ( see Mixure Model ) . The remaining six parameters describe the priors: one for the foveal bias ( σf2 ) , four for the x , y position ( allocentric ) and anisotropic variance of π , and finally one for the general expectation of perceiving a common source ( pc ) . These parameters were fit to all localization responses simultaneously for each participant ( mean: 2589 data points ) using Matlab’s fminsearch with 1000 searches ( random initial parameter values ) per participant . In every iteration of the search process , each condition was simulated 10000 times . These distributions were then compared ( using 0 . 1° bins ) to the actual localization data in order to estimate the likelihood of the data given the model . Across iterations , the parameters were adjusted until an optimal fit was reached , i . e . , the loglikelihood was maximized . The above mixture model assumes a causal inference process that is fully statistically optimal . Of course , it is questionable whether the brain can attain such absolute optimality . To test for this , we additionally fitted two variants of the mixture model , suboptimal in the statistical sense , following proposals by Wozny et al . [10] . These two alternative models use the same ingredients as the mixture model , but differ by the response rule applied . On each trial , given an estimate of p ( C|mv ) , the common-cause probability of the trial , this probability is not used for weighting the common-cause , p ( s|mvC ) , and no-common-cause , p ( s|mvC¯ ) , distributions of the target as in Eq ( 1 ) , but for choosing one of these . While making such a forced choice is not optimal , the choice itself can be made in an optimal way and this constitutes the first alternative model ( referred to as model selection ) : per trial just choose the more likely causal structure , i . e . , if p ( C|mv ) >0 . 5 , choose p ( s|mvC ) , otherwise choose p ( s|mvC¯ ) . The second alternative model ( referred to as probability matching ) amounts to one more step away from optimality: here the choice between the two causal structures is again guided by the common-cause probability of the trial , but now according to the principle of probability matching: with probability equal to p ( C|mv ) choose p ( s|mvC ) and with complementary probability p ( C¯|mv ) choose p ( s|mvC¯ ) . The model fitting procedure for the two alternative models is identical to the one for the mixture model described above ( e . g . same number of free parameters ) and log-likelihoods are compared to determine which model describes the data best for each individual participant . | During saccadic eye movements , the image on our retinas is , contrary to subjective experience , highly unstable . This study examines how the brain distinguishes the image perturbations caused by saccades and those due to changes in the visual scene . We first show that participants made severe errors in judging the presaccadic location of an object that shifts during a saccade . We then show that these observations can be modeled based on causal inference principles , evaluating whether presaccadic and postsaccadic object percepts derive from a single stable object or not . On a single trial level , this evaluation is not “either/or” but a probability that also determines the weight by which pre- and postsaccadic signals are separated and integrated in judging object locations across saccades . | [
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] | 2016 | Causal Inference for Spatial Constancy across Saccades |
Metabolically quiescent pathogens can persist in a viable non-replicating state for months or even years . For certain infectious diseases , such as tuberculosis , cryptococcosis , histoplasmosis , latent infection is a corollary of this dormant state , which has the risk for reactivation and clinical disease . During murine cryptococcosis and macrophage uptake , stress and host immunity induce Cryptococcus neoformans heterogeneity with the generation of a sub-population of yeasts that manifests a phenotype compatible with dormancy ( low stress response , latency of growth ) . In this subpopulation , mitochondrial transcriptional activity is regulated and this phenotype has been considered as a hallmark of quiescence in stem cells . Based on these findings , we worked to reproduce this phenotype in vitro and then standardize the experimental conditions to consistently generate this dormancy in C . neoformans . We found that incubation of stationary phase yeasts ( STAT ) in nutriment limited conditions and hypoxia for 8 days ( 8D-HYPOx ) was able to produced cells that mimic the phenotype obtained in vivo . In these conditions , mortality and/or apoptosis occurred in less than 5% of the yeasts compared to 30–40% of apoptotic or dead yeasts upon incubation in normoxia ( 8D-NORMOx ) . Yeasts in 8D-HYPOx harbored a lower stress response , delayed growth and less that 1% of culturability on agar plates , suggesting that these yeasts are viable but non culturable cells ( VBNC ) . These VBNC were able to reactivate in the presence of pantothenic acid , a vitamin that is known to be involved in quorum sensing and a precursor of acetyl-CoA . Global metabolism of 8D-HYPOx cells showed some specific requirements and was globally shut down compared to 8D-NORMOx and STAT conditions . Mitochondrial analyses showed that the mitochondrial mass increased with mitochondria mostly depolarized in 8D-HYPOx compared to 8D-NORMox , with increased expression of mitochondrial genes . Proteomic and transcriptomic analyses of 8D-HYPOx revealed that the number of secreted proteins and transcripts detected also decreased compared to 8D-NORMOx and STAT , and the proteome , secretome and transcriptome harbored specific profiles that are engaged as soon as four days of incubation . Importantly , acetyl-CoA and the fatty acid pathway involving mitochondria are required for the generation and viability maintenance of VBNC . Altogether , these data show that we were able to generate for the first time VBNC phenotype in C . neoformans . This VBNC state is associated with a specific metabolism that should be further studied to understand dormancy/quiescence in this yeast .
All microorganisms are exposed to fluctuating environments and periodic external stresses , which inhibit their growth . Some microbes may overcome these stress periods by entering into a resistant non-replicative state , assessed by /associated with a decreased culturability [1] . This decrease was first assumed to reflect microbial death as a consequence of a stochastic and induced nutrient deprivation decay [2] . In quantum mechanics , the Schrödinger’s experiment considered a cat simultaneously alive and dead until a measurement was made . A parallel experiment could be done with a dormant microorganism . Answering if it is alive or dead seems trivial but is unquestionably complex [3] . In fact , there are two other alternative explanations . Non-culturable cells could be the resulting state of a genetically programmed death or could be viable but non culturable cells ( VBNC ) that are adapted to living / surviving as dormant cells either by the formation of spores ( bacterial of fungal ) or by modification of the metabolic state of the initial cell [4 , 5] . There is now clear evidence that VBNC are viable cells with an intact cell membrane and a low metabolic activity [6] . Various environmental stressors can induce VBNC , such as starvation , hypoxia , stressful temperature , pH variations [7] . In a finalistic approach , to enable perpetuation of the lineage , VBNC should be capable at some point to exit the dormant state to resume an active and replicating form when environmental conditions improve . This process could be described as reactivation or “resuscitation” . At least 85 species of bacteria [8] and 10 species of fungi [9–11] have been reported capable of adapting a VBNC state . Hence , the VBNC state seems to be an intrinsic capability of many microorganisms including pathogenic ones [12–15] . During infection , the "dormant" pathogens are able to tolerate both immune system attacks and prolonged exposure to antimicrobials [16] . Pathogens are not known to cause disease when present in the VBNC state but virulence is retained and infection can be initiated again following reactivation [6] . Dormancy is an important pathogenic feature for some invasive fungal diseases , such as cryptococcosis [17–19] and histoplasmosis [12] , where dormant organisms are known to reactivate years after primary infection . Cryptococcosis is caused by the ubiquitous environmental basidiomycete yeast Cryptococcus neoformans . It occurs mainly in immunocompromised individuals , and especially those with AIDS , with more than 200 000 new cases of meningoencephalitis and more than 180 , 000 deaths per year worldwide [20] . Latency is an important phase of cryptococcosis pathogenesis . However , the biological features of the primary cryptococcal infection in humans has been rarely investigated [21] , in contrast to tuberculosis . Inhalation of infectious propagules leads to a primary pulmonary infection with a granulomatous immune response [21 , 22] , that either restrains infection or eradicates the yeast . This immune response can fail when immunosuppression occurs leading to reactivation and disease . Recently , evidence was reported for the existence of dormant yeast cells of C . neoformans in models of C . neoformans/host interaction in vivo ( mice ) and in vitro ( macrophages ) [23] . A population of dormant yeast cells harboring characteristics that could be related to dormancy ( low metabolic response , growth latency , increased mitochondrial activity , increased autophagy , decreased neoglucogenesis and reactivation by fetal calf serum ) was described [23] . To characterize these cells , we needed reproducible in vitro conditions that would generate yeast cells with a phenotype identical to that observed in the subpopulations of yeasts upon interaction with hosts ( decreased stress response , delay of growth ) . Hypoxia and nutrient starvation are the two main factors that are known to allow induction of dormancy in various cell types ( including mammalian stem cells , and bacteria ) . Based on previous studies performed on dormancy of skeletal muscle stem cells , it is known that hypoxia is able to induce and maintain dormancy [24] . To induce quiescent M . tuberculosis , two well-known models are used: the Wayne model based on oxygen depletion in nutrient rich medium and the Loebel model based on nutrient deprivation in oxygen rich medium [25] . To induce dormancy in C . neoformans , we developed and standardized an in vitro model using two concomitant stresses , i . e . nutrient starvation and hypoxia . We then analyzed the parameters influencing dormancy and reactivation of dormant yeasts . Using phenotypic microarrays along with proteome , secretome , and transcriptome analyses , we further validated and characterized these yeast cells and the metabolic pathways that are crucial for the generation , maintenance of and exit from dormancy .
Based on our previous identification of dormant cells in a mouse model of cryptococcosis [23] , and on conditions ( nutrient and oxygen limitation ) used to study quiescence in M . tuberculosis [25] , we identified in vitro conditions that induced a phenotype in C . neoformans cells that mimicked the phenotype observed in vivo ( mainly decreased stress response , delay of growth ) . Yeasts grown in liquid YPD medium until stationary phase ( STAT ) which results in a drastic nutrients deprivation , were incubated without change/replenishment of medium ( to avoid any additional nutrient stress ) at 30°C for 8 days in hypoxia ( O2<0 . 1% , these cells will be called 8D-HYPOx from now on ) or normoxia ( 8D-NORMOx ) ( S1 Fig ) . The influence of prolonged incubation time ( 25 days ) , increased ( 37°C ) or decreased ( 4°C ) temperature of incubation , in the presence of hypoxia or normoxia was also tested during preliminary experiments but did not result in an interesting or reliable phenotype compared to the established protocol . We first checked the impact of the selected conditions on the morphology and basic features of C . neoformans cells . Median cell diameters were not different for yeasts in 8D-HYPOx , 8D-NORMOx compared to STAT ( 4 . 5 μm; interquartile range ( IQR ) [3 . 8–5 . 1] , 4 . 6 μm [4 . 1–5 . 0] and 4 . 6 μm [4 . 1–5 . 0] , respectively ) . Median capsule thickness was smaller in 8D-HYPOx ( 0 . 8 μm [0 . 7–1 . 0] and 8D-NORMOx ( 0 . 9 μm [0 . 7–1 . 0] ) compared to STAT ( 1 . 0 μm [0 . 9–1 . 2] ) , ( S2A and S2B Fig , p<0 . 0001 ) . Specific staining revealed no difference in the shape of the nucleus ( Fig 1A ) , nor in the capsule structure based on the anti-glucuronoxylomannan monoclonal antibody ( E1 ) binding ( Fig 1A , S3A Fig ) . Staining with MDY-64 showed an increased quantity of vacuolar membrane ( 3 . 1 ± 0 . 4 increase in the geometric mean fluorescence for 8D-HYPOx/8D-NORMOx compared to STAT ( Fig 1A , S3B Fig ) . Plasma membrane was intact for the majority of cells ( 88% of 8D-HYPOx , 60% of 8D-NORMOx compared to 98% of STAT , Fig 1A , S3C Fig ) . Transmission electron microscopy ( TEM ) revealed a significantly thicker cell wall in 8D-HYPOx compared to STAT ( median 206 nm [170 . 5–245 . 0] vs . 163 . 5 nm [123 . 8–199 . 3] , p<0 . 05 , S2C Fig ) and the presence of large intracytoplasmic vacuoles ( Fig 1B ) . We then verified if the stress response and delay in growth were observed as in vivo [23] . Intracellular levels of glutathione , a well-established marker of stress response in various mammalian cell types [26] , fungi [27] including C . neoformans [23] , were assessed kinetically by using 5-chloromethylfluorescein diacetate ( CMFDA ) staining at day 0 ( STAT ) and up to day 8 ( D4 , D6 , D8 ) . A decrease in stress response was observed over time with an apparently homogenous population harboring a low CMFDA intensity in 8D-HYPOx , as opposed to 8D-NORMOx for which a double population ( low and high CMFDA intensity corresponding to low and high stress response ) was observed ( Fig 1C ) . As a lag phase was observed in "dormant" cells in vivo , we then studied the growth curves upon incubation in rich liquid YPD ( growth curve method ) for 8D-HYPOx , 8D-NORMOx and STAT and compared them after curve fitting using latency ( lag phase λ ) as a major readout ( Fig 1D ) . Median latency was significantly increased for 8D-NORMOx and 8D-HYPOx compared to STAT ( 1103 min [1083–1104] . It increased over time for 8D-HYPOx from 1387 min [1281–1445] at D4 to 1878 min [1800–1898] at D8 , whereas it plateaued for 8D-NORMOx ( 1194 min [1176–1210] at D4 , and 1288 min [960–1351] at D8 ) ( Fig 1E ) . Since latency of growth can be influenced by numerous factors including cell concentration and culture medium ( representing the metabolic potential of the cells influenced by the quantity of nutrients ) , we tested these parameters . Serial 10-fold dilutions of the yeasts ( 3x105 to 3/well ) resulted in an increased latency for both 8D-HYPOx and STAT , in undiluted YPD and minimal medium ( MM ) ( S4A and S4B Fig ) . The latency was higher in MM than in YPD for 8D-HYPOx at all the dilutions studied ( Table 1 ) and for STAT at the lowest dilutions ( <3x103/well ) . Low nutrient medium ( MM ) increased the latency by a differential of 2505 min for STAT and 2656 min for 8D-HYPOx compared to a richer medium ( YPD ) . When extrapolating the linear regression for 1 cell we observed only little difference between latency in 8D-HYPOx and STAT ( Δ8D-HYPOx-STAT ) for YPD ( 1820 min ) or for MM ( 1971 min ) . The slope of the growth curve that represents the rate of cell generation during the exponential phase was comparable for 8D-HYPOx , 8D-NORMOx , and STAT . We then investigated the viability of the yeasts incubated in hypoxia based on nucleic acids stains and apoptosis assays . Cell viability and apoptosis-like phenomenon were measured over time and evolved differently in hypoxia and normoxia ( Fig 2A ) . Viable non-apoptotic cells represented 98 . 7% [98 . 4–99 . 0] in the STAT condition . In hypoxia , the proportion decreased at D2 , D4 and D6 , culminating at 74 . 6% [70 . 5–78 . 8] at D4 and reaching 98 . 9% [98 . 8–99 . 0] in 8D-HYPOx . In normoxia , the proportion decreased and plateaued at D8 at 63 . 6% [62 . 2–65 . 0] . The proportion of apoptotic-like cells , negligible in STAT cells ( 0 . 8% [0 . 5–1 . 6] ) , peaked at D4 in hypoxia ( 21 . 4% [17 . 5–25 . 4] ) while it increased over time to reach a plateau at D6 in normoxia ( 35 . 8% [34 . 9–36 . 7] ) . Dead cells represented less than 3% over time in hypoxia ( 2 . 7% [1 . 7–3 . 7] at D4 ) , while it reached 5% [4 . 2–5 . 9] at D6 in normoxia and was less than 1% in STAT . In parallel , culturability was assessed based on the colony forming unit method ( CFU method ) by plating on solid medium ( YPD agar ) . From 100% in STAT , the culturability decreased in both conditions but significantly more in 8D-HYPOx than in 8D-NORMOx ( 17 . 5% [15 . 4–19 . 7] , vs . 0 . 8% [0 . 5–1 . 0]; p<0 . 01 ) ( Fig 2B ) . Since a small proportion of cells were cultivable by the CFU method , we wondered if this explained the delay in growth observed by the growth curve method ( Fig 1D and 1E ) . Indeed , 8D-HYPOx included a small subpopulation of cells harboring a high CMFDA staining ( high stress response ) ( Fig 2C ) . We thus hypothesized that they were the metabolically active cells responsible for the observed growth , and that they should differ from the other subpopulations . 8D-HYPOx subpopulations were sorted according to their CMFDA intensity ( low , medium and high ) . The latency of the three subpopulations was comparable to that of the unsorted ( bulk ) population and significantly higher than that of the 8D-NORMOx and STAT ( Fig 2C ) . Likewise , culturability assessed by the CFU method was not significantly different in CMFDAlow ( 0 . 19±0 . 20 ) , CMFDAmedium ( 0 . 28±0 . 17 ) , CMFDAhigh ( 0 . 10±0 . 11 ) compared to the unsorted population ( 0 . 53±0 . 41 ) ( p>0 . 01 ) , although significantly lower than for 8D-NORMOx ( 12 . 73% [7 . 62–18 . 62] ) and STAT ( 102 . 5% [93 . 25–111 . 8] ) ( Fig 2C , p<0 . 01 ) . We then assess by a limited dilution method ( plating method ) based on the Poisson law the culturability of the different subpopulations seeded at 3333 cells/well in liquid YPD in 96-well plates . In this assay , the number of positive wells / number seeded allows comparison of the culturability . A decrease in culturability was found for 8D-HYPOx with 10 /18 ( 55 . 5% ) for the CMFDAlow , 16/18 ( 88 . 8% ) for the CMFDAmedium , 14/18 ( 77 . 7% ) for the CMFDAhigh , and 17/18 ( 94 . 4% ) for the unsorted populations whereas all wells ( 100% ) grew for the 8D-NORMOx ( 6/6 ) and STAT ( 3/3 ) ( S4C Fig ) . Overall , using different methods to assess culturability on solid ( CFU method ) and liquid medium ( growth curves and plating method ) , only a small proportion of yeasts incubated in hypoxia and nutrient depletion were cultivable , and they were not distinguishable from the other subpopulations based on stress response . These results suggested that the dormancy induction conditions led to the production of a population of yeasts mostly composed of what is called viable but non-culturable cells ( VBNC ) . A model for the evolution of the cell population phenotypes over time in hypoxia and normoxia is proposed in Fig 2D . One of the major characteristics of VBNC is that they can be reactivated under specific conditions which should translate into a decrease in latency . To prove that the 8D-HYPOx were VBNC , we investigated factors that enabled their reactivation , i . e . switch from non-culturable to culturable cells , taking into account that a small proportion of the cells from the 8D-HYPOx bulk were already culturable . We hypothesized that the latency observed in 8D-HYPOx could be impacted by the high quantity of nutrients available in the medium that would inhibit reactivation of yeasts previously adapted to starvation and thus unable to cope with high nutrient concentration in a new medium . If this was true , then incubation of 8D-HYPOx in diluted medium should have the reverse effect and result in decreased latency . This hypothesis was tested by refeeding 8D-HYPOx in MM diluted in water . MM dilutions resulted in a significant decrease in 8D-HYPOx latency ( Fig 3A ) from 4463 min [3947–5488] in 100%MM to 3722 min [3612–4156] in 20%MM and 3358 min [3240–3664] in 10%MM ( p<0 . 01 , Fig 3A ) . This decrease was also observed for STAT but to a lesser extent ( 1593 min [1397–1720] , 1326 min [1220–1510] and 1245 min [1045–1277] in 100%MM 20%MM and 10%MM , respectively , p <0 . 01 ) . Dilutions of nutrients was thus beneficial for yeasts growth . Then , we tested the effect of conditioned medium ( freeze-dried and rehydrated MM recovered from STAT ) , murine macrophage cell ( J774 cell line ) lysate , and known cofactor of growth in yeasts ( pantothenic acid ( PA , vitamin B5 ) , thiamine ( vitamin B1 ) , sodium pyruvate , and L-DOPA ) on 8D-HYPOx growth . Latency decreased only upon addition of J774 lysate , conditioned medium and PA . As conditioned medium and J774 lysate are each complex media rich in PA , we decided to focus specifically on the effect of PA , based on the fact that PA was known to decrease latency in C . neoformans cells [28] and known as a secreted quorum sensing compound by C . neoformans [29] . Addition of PA to 100%MM showed a dramatic effect on the latency of 8D-HYPOx . For instance , latency was decreased at concentrations of PA above 1 . 25 x 10−2 μM from 3994 min [3797–4332] ( no PA ) and 4111 min [3866–4451] ( 1 . 25 x 10−4 μM ) to 2213 min [1963–2426] ( 1 . 25 x 10−2 μM ) , 2095 min [2007–2170] ( 1 . 25 μM ) and 2133 min [2035–2281] ( 125 μM ) ( Fig 3B , p <0 . 01 ) . In comparison , less effect of PA was observed on STAT cells ( from 1615 min [1532–1637] ( no PA ) to 1433 min [1398–1456] ( 1 . 25 x 10−4μM ) and then 1036 min [1017–1045] ( 1 . 25 x 10−2μM ) , 1048 min [1017–1045] ( 1 . 25 μM ) , 1018 min [982 . 5–1031] ( 125 μM ) ) . Decreased in latency following addition of PA could result from increased growth rate or/and increased number of culturable yeasts and thus reactivation of VBNC . MM dilutions ( 20%MM and 10%MM ) significantly decreased the growth rate ( slope ) for 8D-HYPOx compared to 100%MM but did not alter that of STAT cells ( Fig 3C ) . PA significantly increased growth rate of 8D-HYPOx at concentrations above 1 . 25 μM and that of STAT cells at concentrations above 1 . 25 x 10−2μM ( Fig 3D , p < 0 . 01 ) . We then assessed whether the number of culturable cells increased or whether 8D-HYPOx exhibited improved metabolic adaptation . We used the plating method to calculate the probability for one cell to grow ( S4C Fig ) after addition of PA or MM dilution . Dilution of MM alone ( 10%MM ) did not modify the median proportion of culturable cells , but addition of PA increased it approximately two-fold from 1 . 3 ‰ [0 . 8–1 . 9] to 2 . 5 ‰ [2 . 0–3 . 3] in 100%MM and from 1 . 1 ‰ [0 . 9–1 , 4] to 2 . 5 ‰ [2 . 1–2 . 7] in 10%MM . ( p <0 . 01 , Fig 3E ) . We also wondered whether phagocytosis would change culturability which was not the case with a comparable median proportion of culturable cells ( 1 . 04 ‰ [0 . 78–1 . 48] ) and after 2 h macrophage uptake ( 1 . 21 ‰ [1 . 09–1 . 33] , p = 0 . 35; S4D Fig ) . Overall , when decreasing nutrient availability ( diluting medium ) , latency decreased without change in the number of culturable yeast cells , suggesting that culturable yeasts were better adapted to grow in poor than in richer medium . By contrast , addition of PA allowed VBNC to switch to culturable cells ( cell reactivation ) with a decreased latency reflected an increasing number of culturable cells . However , the 2-fold increase in culturability after addition of PA was not sufficient to explain the extent of the decrease in latency suggesting that PA influenced also growth adaptation ( Fig 3F ) . Altogether , 8D-HYPOx yeast harbored characteristics of VBNC and will be called VBNC from now on . We then explored the characteristics of the VBNC as these cells have never been described in C . neoformans and rarely in other fungi , except for Saccharomyces cerevisiae . We explored the physiologic substrate required by VBNC using the Biolog phenotypic microarray , as well as their mitochondrial status , their secretome , proteome and transcriptome . The physiologic substrates required for the metabolism of VBNC were analyzed by phenotypic microarrays using Biolog substrate utilization tests and compared with that of yeast cells in other conditions . Based on the 761 metabolites tested and excluding 240 compounds positive in the dead cells control , hierarchical clustering showed that VBNC possessed a unique profile ( Fig 4A ) . VBNC had significant lower global metabolic activity as measured by the level of respiration determined from the metabolization of each compound ( median = 124 . 0 [94 . 0–173] and 87 . 0 [65 . 0–118 . 8] ) compared to 8D-NORMOx ( 246 . 0 [106 . 8–260 . 0] and 248 . 5 [123 . 0–262 . 0] for the two biological replicates ) and to STAT ( 262 . 0 [136 . 5–272 . 0] ) ( p<0 . 01 , Fig 4B ) . We identified two compounds ( trimetaphosphate and adenosine-2' , 3' cyclic monophosphate ) that were more metabolized by VBNC than by control yeasts ( S1 Table ) . Since the phenotypic microarrays based on mitochondrial activity measurement showed a decrease in VBNC , we further tested various mitochondrial parameters . Median ROS ( 152 . 3 [148 . 1–156 . 2] vs . 174 . 1 [168 . 9–177 . 8 ) and RNS ( 87 . 1 [78 . 2–101 . 2] vs . 149 . 5 [98 . 7–206 . 5] ) productions were significantly decreased in VBNC compared to STAT , respectively ( p<0 . 01 , S5 Fig ) . Using multispectral flow cytometry , we observed an increase in the mitochondrial mass in VBNC and in 8D-NORMOx compared to STAT and dead yeast cells ( Mitotracker staining ) . Mitochondria in VBNC were mostly depolarized ( low TMRE and high JC-1 stainings ) ( Fig 5A and 5B ) . Expression of the mitochondrial genes CYTb , NADH , mtLSU and COX1 significantly increased in VBNC compared to STAT ( p = 0 . 029 ) ( Fig 5C ) , while DNA concentrations remained similar ( p<0 . 05 ) , suggesting that mitochondria are in high quantity , transcriptionally active , although depolarized . Proteins secreted in the supernatant ( secretome ) and cellular proteins ( proteome ) were compared in STAT , VBNC and 8D-NORMOx using both qualitative and quantitative analysis of the proteins recovered from experiments performed in biological triplicates . We first focused on the differences between those conditions without considering the whole kinetics but only the initial point ( STAT ) and the final points ( VBNC and 8D-NORMOx ) . A total of 1365 proteins were identified within the secretome and 3772 proteins within the proteome . In the secretome , the median number of proteins identified in STAT ( 747 proteins [598–797] ) decreased over time in VBNC ( 411 [406–460] at D8 ) and not in 8D-NORMOx ( 910 [909–928] ) ( S6A Fig , p <0 . 001 ) . The protein concentrations in the secretome remained stable in VBNC and slightly increased in 8D-NORMOx ( S6B Fig , S2 Table ) . In the proteome , the median number of proteins identified in STAT ( 2975 proteins [2951–3029] ) was stable over time , and evaluated at 3057 [3033–3171] in VBNC and 2968 [2960–2976] in 8D-NORMOx ( S6C Fig ) . The protein concentration in the proteome ( 0 . 005 mg/mL [0 . 002–0 . 011] in STAT ) tended to increase in both hypoxia and normoxia reaching 0 . 017 mg/mL [0 . 008–0 . 018] and 0 . 021 mg/mL [0 . 016–0 . 027] in VBNC and 8D-NORMOx , respectively ( S6D Fig ) . We then analyzed the proteins that differed between STAT and VBNC or 8D-NORMOx in the secretome and the proteome . Overall , 17/1365 proteins present in the secretome , and 107/3772 present in the proteome were specific of VBNC ( S7A and S7B Fig and S3 Table ) . When secreted proteins and cellular proteins were pooled , the analysis found 226 proteins present ( Fig 6A ) . A total of 1654 proteins were found exclusively in the proteome common to the 3 conditions , or exclusively in STAT ( n = 150 ) , in 8D-NORMOx/normoxia ( n = 75 ) or in VBNC/hypoxia ( n = 103 ) ( Fig 6A ) . A total of 17 proteins were detected exclusively in the secretome common to the 3 conditions with 6 exclusively in STAT and VBNC ( but different ) , and 16 exclusively in 8D-NORMOx ( Fig 6A , S4 Table ) . For the subsequent analysis , the whole kinetics in hypoxia and normoxia was analyzed [STAT , hypoxia ( D4 , D6 and D8/VBNC ) and normoxia ( D4 , D6 and D8/8D-NORMOx] . GO enrichment analysis of the proteins present in the secretome showed differences in the biological processes , molecular functions and molecular components , whereas similar patterns and distributions were observed for the proteome . Thus , the major enriched biological processes in the secretome were translation and metabolic processing of carbohydrates in hypoxia , while in normoxia they were related to oxidation-reduction mechanisms with no major change over time ( Fig 6B ) . The enriched molecular functions and components were also different ( S8A and S8B Fig and S5 Table ) . In the proteome , in both hypoxia and normoxia , the translation , oxidation-reduction biological processes ( Fig 6C ) , the nucleotide binding transferase activity and the catalytic activity for molecular functions and the cytoplasm , ribosome , ribonucleoprotein and intracellular components were significantly enriched with no change over time ( S8C and S8D Fig and S5 Table ) . Principal coordinate analysis ( PCA ) of the global data revealed that the secretome ( Fig 7A ) and the proteome ( Fig 7C ) were modified in hypoxia and normoxia conditions compared to STAT . A specific pattern was seen as soon as D4 in hypoxia and normoxia . We selected the proteins present in all conditions and analyzed whether and how the level of each protein changed over time . Overall , 85 secreted proteins ( including 49 in normoxia and 36 in hypoxia ) , and 63 cellular proteins ( including 47 significantly more produced in normoxia and 16 in hypoxia ) were differentially produced ( S6 Table ) . Based on these results , hierarchical clustering of the secretome ( Fig 7B ) and the proteome ( Fig 7D ) was performed . STAT clustered together with the normoxia specimens for the secretome , whereas they clustered with the hypoxia specimens for the proteome . Among the 16 cellular proteins overproduced during hypoxia , one was a serine carboxypeptidase ( CNAG_06640 ) ; one was associated with intracellular transport ( CNAG_07846 ) ; six were associated with the processing of DNA / RNA and the cell-cycle ( CNAG_01455 , CNAG_04570 , CNAG_00819 , CNAG_06079 , CNAG_05311 and CNAG_04962 ) , and the 9th protein was linked to the mTOR pathway ( CNAG_01148 , also known as FK506-binding protein ) ( Table 2 ) . The other 7 proteins have no known roles ( hypothetical proteins ) . Cellular proteins overproduced in normoxia were subsequently , down produced in hypoxia . These 47 proteins down produced in hypoxia were mostly proteins from the fatty acid degradation pathway , the glyoxylate cycle: isocitrate lyase ( CNAG_05303 ) , malate synthase ( CNAG_05653 ) and a protein of the neoglucogenesis , the phophoenolpyruvate carboxykinase ( CNAG_04217 ) . STRING analysis was used to visualize the predicted protein-protein interactions for the 63 cellular proteins differentially produced by VBNC ( Fig 8A ) . Several metabolic pathways were identified , but especially the fatty acids metabolic pathways: degradation and metabolism of fatty acids , degradation of α-ketoacids ( valine , leucine and iso-leucine ) , metabolism of alpha-linolenic acid , beta-alanine and elongation of fatty acids as well as peroxisomes . To examine whether VBNC stored fatty acids better than STAT or 8D-Normox , Bodipy 505/515 that stains neutral lipids in lipids droplets was used but no difference was observed ( Fig 8B ) . Indeed , based on a manual count on a large number of cells ( 157 cells in STAT , 642 in VBNC and 392 in 8D-NORMOx ) , the mean dot/cell were respectively 3 , 2 . 91 and 2 . 77 , reaching no statistically significant difference . In addition , two independent flow cytometry experiment measuring the total excitation on 10 000 cells did not show differences in the geometric mean fluorescence intensity between STAT and VBNC . The β-oxidation pathway described in the KEGG database was used to visualize the 63 cellular proteins differentially produced in VBNC and the 8 proteins for fatty acid degradation metabolism ( Fig 8C ) . The fatty acid degradation pathway ( β oxidation ) is well-characterized in Saccharomyces cerevisiae with several genes found also in C . neoformans genome ( Fig 8D ) . Interestingly , the corresponding proteins in C . neoformans were known to be under produced in VBNC . The 9 deletion mutants of proteins identified from the KEGG database and by the orthology with Saccharomyces genome available in the Madhani collection were tested in the model for their ability to generate VBNC . A significant decreased in the proportion of living cell was measured for 04688Δ , 00490Δ , 07747Δ and 00524Δ , all directly involved in fatty acid degradation ( Fig 8E , p<0 . 01 ) , suggesting that the fatty acid pathway is required in VBNC although the level of expression is lower than in STAT cells . We then compared genes expression in the three conditions using transcriptome analysis . To avoid bias related to decreased RNA content and not in gene expression , we performed a specific experiment in which we spiked each sample with the same quantity of S . cerevisiae prior to extraction allowing us to normalize the transcription of C . neoformans to that of S . cerevisiae . The number of transcripts decreased in VBNC , and to a lesser extent in 8D-NORMOx , compared to STAT and LOG conditions that harbored similar level of transcripts ( Fig 9A ) . PCA analysis of the global transcriptomic data from non-spiked samples suggested an evolution from LOG to STAT phases with a clear independent and specific route leading to VBNC or 8D-NORMOx ( Fig 9B ) . GO enrichment analysis of upregulated transcripts showed that the major biological processes involved were: signal transduction , ATP synthesis coupled proton transport and regulation of ARF protein signal transduction in VBNC , oxidation-reduction , protein folding and response to stress in 8D-NORMOx and oxidation-reduction and translation in LOG ( Fig 9C ) . Further analysis revealed 7 clusters that clearly separate the transcriptional profile of the 4 experimental conditions ( LOG , STAT , VBNC , 8D-NORMOx , Fig 9D ) . Specifically , cluster 4 appeared to be composed of genes upregulated in VBNC compared to the other conditions ( underlying pathways summarized in Table 3 and S7 Table ) .
We report the existence of a C . neoformans phenotype whereby dormant cells in vitro are viable but not culturable cells ( VNBC ) . Hence , C . neoformans joins the ranks of microbes where similar phenomena have been described [6 , 8 , 10 , 12–16] with the proviso that for this organism this phenotype has obvious clinical importance given that latency is a common manifestation of human infection . Latency of cryptococcosis that can be linked with dormancy of C . neoformans had been characterized clinically [30 , 31] and epidemiologically [17 , 18] . The dormant yeasts are thought to be located in a pulmonary granuloma but other reservoirs could exist [23 , 32] . Recently , evidence for the existence of dormancy in a subpopulation of C . neoformans upon interaction with the host in vivo ( murine infection ) and with macrophages in vitro have been provided [23] . To obtain an homogenous population of dormant cells in amounts allowing in depth characterization , we set up an in vitro protocol using stresses known to generate quiescence in other cell types , nutrient starvation and hypoxia [33–35] . Indeed , dormant yeasts recovered from murine infection are too scarce ( <30/lungs ) to work with and mixed with other cells requiring sorting that could impact their phenotype [23] . The standardized protocol generated yeasts that exhibited high viability ( >99% ) , low apoptosis ( <1% ) [36] and low culturability ( <0 . 01% ) at D8 which are characteristics of VBNC . This phenotype is associated with dormancy in other microbial species/populations [6–8 , 10 , 37] . Hypoxia turned almost the entire C . neoformans population into a VBNC phenotype ( <15% culturability at D2 ) suggesting that hypoxia triggers the switch towards VBNC rapidly . Hypoxia is an important factor inducing dormancy in mammalian stem cells [24] . In stem cells , hypoxia is required for a strict metabolic regulation to maintain long-term quiescence and self-renewal of populations . Hypoxia is also present to some extend in the granuloma [38–40] . Indeed , hypoxia is also known to influence cell fate ( differentiation , de-differentiation ) within the stem cell niche [41 , 42] . In addition , resistance to apoptosis is known in mammalian cells by the induction of apoptosis inhibitors such as IAP-2 [43 , 44] but opposite results have been found with tumor cells [45] . Nevertheless , by using these in vitro conditions to generate what happened to be VBNC , we acknowledge/are aware that this phenotype could be different from the subpopulation of dormant yeasts identified from the lung of infected mice [23] , although similarities were observed ( low stress response through CMFDA decreased fluorescence , latency of growth , increased mitochondrial transcription ) . One of the major characteristics of cells manifesting the VBNC phenotype is their ability to be reactivated—to divide again—in response to specific stimuli [7 , 46 , 47] . Given that the critical issue in reactivation experiments is to distinguish between more cells being able to grow and faster growth of the culturable cells already present , a method was developed to measure the probability of growth for one yeast cell , inspired by the most probable number method [48] . The determinant experiment to assess the VBNC phenotype was the demonstration that PA addition allowed reactivation ( 1 . 2‰ reactivated VBNCs ) . The effect of PA resulted from both the doubling number of reactivated VBNC and an increase in growth rate , without a clue on which factor was more important . It is highly probable though that resuscitation of VBNC requires other factors that need to be discovered . It is also possible that the majority of the VBNC may not be able to reactivate in the conditions used here as hypoxia may have pushed the yeasts too far into the process of dormancy [49] , with a majority of cells injured while still alive but unable to divide again [50] . We additionally observed that MM dilutions increased growth capacities of VBNC ( decreased latency ) . It did not influence VBNC reactivation but rather increased the growth rate by a faster rewire of the metabolic state of the remaining culturable cells . To explain this counterintuitive observation , we propose that exposure to a high amount of nutrients after long-term starvation was deleterious for VBNC that had to resume normal metabolism , possibly as a result of oxidative damage as metabolism increases . This hypothesis is further based on the fact that death was shown by others to be accelerated in rich substrate [51–53] . Despite the importance of Cryptococcus-protozoan interaction in the explanation of the yeast’s virulence and intracellular pathogenic strategy [54] , we found that macrophage uptake of VBNC had no effect on culturability . This result is different from what was observed for other microorganisms . For example , reactivation of VBNC was possible for the bacterium L . monocytogenes and the protozoans Acanthamoeaba polyphaga [55] and A . castellanii [56] . We first screened the dormancy phenotype through the decrease in metabolic and stress responses [23] , as measured upon staining with the CMFDA fluorescent probe [26 , 57] . Although it allowed us to suspect that hypoxia was an important factor for the generation of VBNC , the equivalent culturability of subpopulations sorted by their fluorescence intensity suggested that CMFDA staining is not a perfect marker of metabolic and stress response in C . neoformans . Decreased metabolic activity in VBNC was demonstrated by several strategies . First , a phenotypic array measurement ( Biolog ) allowed us to show a reduction in the metabolism of 761 compounds in VBNC . Second , the concentration of secreted or intracellular proteins was lower in VBNC compared to 8D-NORMOx as reported during quiescence in other organisms [58–61] . In parallel , the increased number of proteins present in the supernatant of 8D-NORMOx could correspond to increased secretion but more likely to an increased release of proteins due to a high cellular mortality observed in that condition . Third , using a unique strategy of analysis for the transcriptome including the spike of a definite percentage of Log phase S . cerevisiae cells , we demonstrated a global decreased transcriptional activity in VBNC compared to 8D-NORMOx , LOG and STAT conditions , identifying again the lower metabolic activity of VBNC . In VBNC , we found that the mitochondrial phenotype included an increased mitochondrial mass , increased depolarization and an increased expression of mitochondrial genes . This observation could explain why ROS and RNS levels were lower in VBNC compared to STAT . This depolarized state could also reflect activation of autophagy [62] . These results are reminiscent of those obtained in the subpopulation of dormant yeasts observed in mice 7 days after infection and in macrophages , where COX1 and autophagy genes ( ATG9 and VPS13 ) are upregulated compared to other subpopulations of yeasts [23] . We identified and quantified proteins from the secretome ( supernatant ) and proteome ( cell pellet ) over time . Overall , 1365 secreted proteins were identified . This number is greater than previously described [63–65] but the protein extraction methods differed ( gel extraction , TCA / acetone precipitation alone ) , and the identification procedures as well as the culture media used ( YPD or MM ) were different . Nevertheless , of the 191 secreted proteins previously identified in stationary phase in YPD [63–65] , 93 . 7% were found in our corresponding sample ( STAT with 565 proteins identified in all of the 3 replicates ) . In C . neoformans , the secreted molecules can take several pathways to reach the periphery of the cell and the extracellular space . There are conventional and unconventional secretory pathways involving secretory machinery in protein export: Sec4 , Sec6 , Sec14 , the Golgi apparatus , and extracellular exosome vesicles [66–68] . The TCA precipitation step used here to analyze the secreted proteins within the supernatants precipitates soluble proteins and also those in extracellular vesicles [69] . Consequently , we are unable to precisely identify their mechanism of secretion . This opens future directions for studying the relationship between dormancy and extracellular vesicles . A secreted protein , pqp1 peptidase ( CNAG_00150 ) was found overexpressed in hypoxia . This peptidase cleaved the pro-qsp1 quorum sensing peptide into qsp1 ( CNAG_03012 ) , that acts on the whole cell population and regulates virulence [70–72] . However , we did not find qsp1 in the supernatants of the culture media and yet it is present in micromolar amounts in the publication of Homer et al . [70] . It is possible that our extraction protocol was not optimized for small peptides ( 11 amino acids ) or that small peptides might have been eliminated through the various washing steps . However , with our optimized extraction method , we identified more cellular proteins ( n = 3772 ) than previously reported in C . neoformans [64 , 73–75] . From the quantitative proteome analysis of VBNC , we identified the FK506-binding protein which is involved in the mTOR pathway , and known to play a role in quiescence in S . cerevisiae [76] . This protein is able to bind to rapamycin ( Sirolimus ) in both yeasts and mammalian cells promoting quiescence [77 , 78] . In C . neoformans , rapamycin induces cell cycle arrest in the G1 phase [79] . The STRING analysis of the protein-protein interactions provided evidence that the regulation of fatty acids was the major metabolic pathway involved in VBNC . The regulation of fatty acids is a phenomenon that occurs in S . cerevisiae quiescence by participating in energy cellular homeostasis and membrane synthesis [33] . In Vibrio vulnificus , changes in fatty acid membrane composition contribute to maintaining the viability of the VBNC [37] . A common feature among the metabolic fatty acids pathways is the participation of acetyl-CoA [80] . Acetyl-CoA is more than a cofactor in cell metabolism . It is central to the physiology of mammalian cells ( fatty acid metabolism , Krebs cycle , apoptosis , cell cycle , damage- response DNA and epigenetics ) [81] . In C . neoformans , the genes of the three principal production routes of acetyl-CoA in the cytosol are regulated [82]: the β-oxidation of fatty acids by the enzyme Mfe2 , acetate by acetate synthase Acs1 and citrate by Acl1 . Our hypothesis is that maintenance of fatty acid metabolism in hypoxia would allow the cells to maintain membrane integrity and viability , with an increased mortality in deletion mutants in key enzymes of the fatty acid pathway . If these results are in favor of the indispensability of acetyl-CoA for the maintenance of C . neoformans infection , the relationship between acetyl-CoA , PA–a precursor of acetyl-CoA–and dormant cells should be further analyzed . The fact that PA was able to resuscitate VBNC cells , confirms the potential relevance of the link made by the proteomic study between VBNC and metabolic pathways of acetyl-CoA . Qualitative and quantitative changes in fatty composition occurs in C . neoformans depending on the growth phase , with an increase during growth progression [83] . In addition , KEGG pathway mapping of the 63 proteins regulated in hypoxia showed an involvement of 8 proteins involved in fatty acid degradation . The 8 proteins involved were downregulated in hypoxia compared to normoxia . Another reconstruction of the fatty acid degradation pathway using Saccharomyces genome database involved the 4 proteins also downregulated in hypoxia FAA2 ( CNAG_03019 ) , POX1 ( CNAG_07747 ) , FOX2 ( CNAG_05721 ) , POT1 ( CNAG_00490 ) . The mutants pox1Δ , pot1Δ , CNAG_04688Δ and CNAG_00524Δ tested here had a decreased viability in hypoxia compared to the parental strain KN99α strengthening the role of fatty acid degradation in hypoxia and emphasizing the importance of downregulating fatty acid degradation in VBNC phenotype . In Candida albicans macrophages internalization also results in the induction of fatty acid degradation pathways [84] and similar patterns of expression are observed in C . neoformans in the lungs of infected mice [85] and in response to stress within macrophages and amoeba [86] . In summary , we describe conditions that induce C . neoformans to switch to a homogenous population of viable but non culturable cells that can be considered dormant and use those conditions to study the biological characteristics of these yeasts by different approaches . VBNC cells manifest what is potentially an extreme phenotype ( dormancy ) derived from stationary phase ( quiescence ) but demonstrate the production of a very specific secretion pattern critical to the pathobiology observed in the mammalian hosts . Our study identified genes involved in the emergence and maintenance of the VBNC phenotype , which implicate a major role for lipid metabolism . The availability of conditions that reliably induce VBNC provides a new research tool in the field of fungal and specifically C . neoformans biology to study the important features of dormancy and help understand latency in cryptococcal pathogenesis .
Cryptococcus neoformans strains ( S8 Table ) were usually grown in liquid Yeast Peptone Dextrose ( YPD: 1% yeast extract ( BD Difco , Le Pont de Claix , France ) , 2% peptone ( BD Difco ) , 2% D-glucose ( Sigma , Saint Louis , Minnesota , USA ) ) . Minimal medium ( MM , 15mM D-glucose ( Sigma ) , 10 mM MgSO4 ( Sigma ) , 29 . 4mM KH2PO4 ( Sigma ) , 13mM Glycine ( Sigma ) , 3 . 0 μM Thiamine ( Sigma ) , pH5 . 5 ) was used for specific experiments [29] . Cryptococcus neoformans H99O strain stored in 20% glycerol at -80°C was cultured on Sabouraud agar plate at room temperature ( step 1 ) . After 2 to 5 days of culture on agar plate , 107 yeasts ( obtained with a calibrated 1μL loop ) were suspended in 10 mL of YPD in a T25cm3 flask with vented cap disposed up in the incubator and cultured for 22 hours at 30 °C , 150 r/min with lateral shaking until stationary phase ( final concentration≈2×108/mL ) ( step 2 ) . One hundred μL of this first culture was added to 10 mL YPD ( second culture ) and incubated in the same conditions until stationary phase ( Step 3 ) . In stationary phase , the medium is deprived of nutrient ( carbon and nitrogen ) thus preventing yeast growth . Then , the flasks were placed in a GENbag ( Biomérieux ) anaerobic atmosphere generator to generate hypoxia ( < 0 . 1% O2 ) and incubated for 8 days in the dark at 30°C . These yeast cells are called 8D-HYPOx and then VBNC for convenience . Control samples consisted of yeasts in stationary phase ( Step3 , STAT ) and yeasts incubated under the same conditions but in normoxia are called 8D-NORMOx ( Step 4 ) ( S1 Fig ) . Of note , the atmosphere in the bag is enriched in CO2 by the biochemical process of oxygen depletion . Freshly harvested cells from STAT and 8D-HYPOx were fixed overnight at 4°C with 2 . 5% glutaraldehyde in 0 . 1 M sodium cacodylate buffer ( CB ) at pH 7 . 2 . Then samples were washed in 0 . 1 M CB ( pH 7 . 2 ) and post-fixed in a 1% osmium tetroxide in CB at room temperature for 1 h . We pre-embedded the cells in 4% agar type 9: the cells were mixed with agar , spun-down by gentle centrifugation and the pellet stored at 4°C until the agar had solidified . We excised 2 mm3 sections from agar block , and processed them in the automatic MicroWave tissue processor for electron microscopy ( AMW , Leica Microsystems , Vienna , Austria ) as recommended by the manufacturer: samples were washed with water and dehydrated with graded ethanol concentrations ( 25–100% ) , followed by a mixture of graded resin Spurr Agar low viscosity Resin ( Agar scientific , Gometz la ville , France ) concentration in ethanol ( 35–100% ) . The embedded agar blocks were polymerize at 60°C for 48h . Ultrathin sections ( 70 nm ) were performed with an ultramicrotome ‘Ultracut UC7’ ( Leica Microsystems , Vienna , Austria ) , stained with 4% uranyl acetate and Reynold’s lead citrate onto 100 mesh copper grids coated with a thin glow carbon film ( S162-1 , Oxford Scientific , Eynsham , UK ) and then observed under Tecnai SPIRIT ( FEI-Thermofisher Company ) at 120 kV accelerating voltage on a camera EAGLE 4Kx4K ( FEI-Thermofisher Company ) . Unless otherwise stated , yeasts were washed in phosphate buffered saline ( PBS ) twice before and 3 times after staining . Stress response was assessed by measuring glutathione levels using 5-chloromethylfluorescein diacetate ( Cell tracker green CMFDA , Life Technologies ) as already shown in mammalian cells [26 , 87] and yeasts [23 , 27] . Yeasts ( 106 ) were labeled with 3 . 3μM CMFDA in 500μL of PBS for 30 min at 37°C in the dark . Anti-glucuronoxylomannan antibody binding on the capsule was performed with E1 monoclonal antibody , as described previously [88] . Nuclei were stained using 4′ , 6-diamidino-2-phenylindole ( DAPI , Invitrogen , Carlsbad ) . Yeasts ( 107 ) were fixed with 3 . 7% paraformaldehyde for 30 min before staining with 0 . 5μg/mL of DAPI for 5 m . MDY-64 ( Thermofischer Scientific , Waltham ) was used to stain the intracellular vacuolar membranes by incubating 107 yeasts for 5 min with 5μM MDY-64 . Lipid droplets were stained using Bodipy 505/515 ( Invitrogen , Carlsbad ) as already used in Saccharomyces cerevisiae [89] at 1μg/mL with 107 cells for 20 min at room temperature . To assess viability , LIVE/DEAD Fixable Violet Blue ( 416 nm/451 nm ) diluted at 1:1000 and 250 μL was added to 107 yeasts pellets and incubated at 30°C in the dark . Controls were dead yeasts generated upon exposure to 200 mM of oxygen peroxide ( H2O2 ) at 37°C , under 650 r/min agitation or heat killed 1h at 65°C . Yeasts were analyzed by fluorescence microscopy and pictures taken using an AxioCam MRm camera ( Carl Zeiss , Oberkochen ) on interferential contrast microscope ( DMLB2 microscope; Leica , Oberkochen ) with Zeiss Axiovision software . Mitochondrial mass and polarization were evaluated with Mitotracker Green ( Invitrogen ) at 40 nM , JC-1 ( Invitrogen ) at 10 μg/ml and TMRE at 200nM in PBS on 107 yeasts for 30 min at 37°C and then washes 3 times in PBS . Reactive oxygen ( ROS ) nitrogen species ( RNS ) measurements were also performed . Microplate wells were filled with 104 yeasts in 100 μL PBS . Briefly , the probes for ROS ( 2' , 7'-dichlorofluorescein diacetate , Sigma ) and RNS ( dihydrorhodamine 123 , Invitrogen ) were diluted at 100 μM in PBS and methanol , respectively , and 20 μL were added to wells of a microtiter plate containing 104 yeasts in 100 μL of PBS . Hydrogen peroxide ( 4M ) was included as a positive control . The plates were incubated at 37°C in the dark . After 1 hour , fluorescence was measured with a fluorometer ( Fluoroskan ascent fluorometer , Thermos Fischer Scientific , Waltham , MA , USA ) using excitation 485 nm and emission wavelengths of 530 nm [90] . The data were expressed as arbitrary units of fluorescence ±SD . Yeasts ( 107 ) were fixed for 1 h at room temperature in 3 . 2% formaldehyde ( Sigma ) in PBS . After washings in PBS , yeasts were incubated in 1 mL of 5% 2-mercaptoethanol in SPM buffer ( Sorbitol 1 . 2 M ( Sigma ) , potassium phosphate monobasic 50 mM ( Sigma ) , magnesium chloride hexahydrate 1 mM ( Sigma ) , pH 7 . 3 ) for 1 h at 37°C . Yeasts were washed once in SPM buffer and incubated for 1 h at 30°C in 1 mL of a solution containing 2 mg of Zymolyase 20T ( Euromedex ) , 100 mg of lysing enzymes from Trichoderma harzianum ( Sigma ) and 1 mg of BSA ( Sigma ) in spheroplasting buffer ( sorbitol 1 M ( Sigma ) , sodium citrate tribasic dihydrate 100 mM ( Fluka ) , EDTA 10 mM ( Sigma ) , pH 5 . 8 ) . All these incubations were performed in the dark with agitation at 650 r/min in a thermomixer . The spheroplasts were then washed 3 times in SPM buffer permeabilized by incubation in 1 mL of 0 . 1% triton X-100 ( Sigma ) in PBS for 2 minutes on ice and washed twice in PBS . TUNEL-Fluorescein ( In Situ Cell Death Detection Kit , Roche Life Science ) was used to measure apoptosis at the single cell level following manufacturer’s recommendations . Yeasts were resuspended in 50 μL of solution of 10% of TdT and 90% dye-coupled dUTP in PBS , incubated 1 h at 37°C and then washed twice in PBS . Apoptosis control was generated by incubation of 3x107 yeasts in 2 mM H2O2 for 3 h at 37°C . TdT was omitted for the negative control . In each run , yeasts treated with DNAse ( 3 U/mL , Ambion ) were used as a TUNEL positive control . Flow cytometry was used to quantify MDY64 staining , viability and apoptosis assays , yeasts—using the Guava easyCyte 12HT Benchtop Flow Cytometer ( Guava , MERCK , Kenilworth , New Jersey ) . Multispectral flow cytometry ( ImageStreamX , Amnis Corporation ) was used to quantify mitochondrial fluorescence as described before [23 , 88] . For each population of interest , the geometric mean was calculated using the IDEAS software . In specific experiments , yeasts were sorted on a BD FACS aria II flow cytometer . The gating strategy included the exclusion of the aggregates and the doublets . Subsequently , the sorting was carried out according to the intensity of CMFDA in 3 populations ( CMFDAhigh , CMFDAmedium and CMFDAlow ) . Growth curves measurements were done using the Bioscreen apparatus ( Oy Growth Curves Ab Ltd ) . Yeasts suspensions ( 300 μL at 104/mL ) were added to wells of a plate and incubated for 2 to 5 days at 30°C with continuous , high amplitude and fast speed agitation . Optical densities ( OD ) were recorded at 600nm every 20 minutes . The R software with the Grofit plug-in [91] was used and for each replicate and in each condition , we adapted the best mathematical model according to the AIC ( Akaike Information Criterion ) . The two models that best modeled the growth curves were the logistic and Richards modified , where λ represents the length of lag phase or latency of growth , μ the growth rate , A the maximum OD ( concentration ) , ν the shape parameter and t the time of incubation . These formulas were written in Graphpad Prism v6 . 02 to allow curve extrapolation and determination of the latency parameter λ . To assess basic culturability , the suspensions recovered at the end of the assay were enumerated and 3000 cells , counted with the Guava easyCyte 12HT Benchtop Flow Cytometer , were plated in duplicates on YPD agar . The number of colony forming units ( CFUs ) was recorded after 5 days of incubation at 30°C ( CFU method ) . The experiment was performed twice . Results were expressed as mean percent culturability . To assess the ability of 8D-HYPOx cells to grow in liquid medium , we determined the probability for a cell to grow by seeding , for each condition , 576 wells ( six 96-well plates ) with 100 yeasts/well and counting the number of positive wells at the end of the incubation time , taking into account the Poisson’s law ( plate method ) . Plates were incubated 5 days at 30°C in an Infors HT multitron pro at 150 rpm . Two independent experiments were performed for each condition . The probability ( p ) of a cell to grow was calculated using the following formula , where X represents the number of positive wells , Y the number of wells seeded . Interaction with macrophages was performed as described previously [92] using the J774 cell line , the monoclonal anti-capsular polysaccharide antibody E1 as an opsonin [93] , calcofluor-stained yeasts and a yeasts: macrophages ratio of 5:1 . Phagocytosis was assessed by flow cytometry using the Guava easyCyte 12HT Benchtop Flow Cytometer ( Merck ) . Using phenotypic microarray technology ( Biolog , Hayward , CA ) , we analyzed the metabolic capacities of yeasts incubated VBNC/8D-NORMOx ( both with biological duplicates ) , using as controls either STAT or dead cells ( 8 days in hypoxia in a YPD pH = 5 ) ( only 3 conditions , i . e . VBNC , 8D-NORMOx and control could be tested in each series ) . The Biolog apparatus allows quantitative measurement of the metabolism over time based on the physiological state of the yeast cells independently of cell division and based on mitochondrial activity ( proton and electron generation ) [94] . Reduction of tetrazolium dye that absorbed charge produced by cell respiration results in a purple color change that is recorded over time ( Omnilog unit , equivalent of the Optical Density measured at 590nm ) . The optical density measured in the well provide information on the intensity of the corresponding metabolic activity . The effect of carbon , nitrogen , phosphorus , and sulfur sources on the physiological state of the cell and cell respiration was examined in response to various osmotic conditions and to different pHs ( plates PM1 , PM2A , PM3B , PM4A , PM6 , PM7 and PM8 ) . A tetrazolium dye reacting to the redox status of the well turns purple when respiration takes place which has been shown to be a good proxy for the ability of strains to grow on a substrate [95] . Phenotype microarray was done according to instructions from the manufacturer ( Biolog , Hayward , CA ) with the following exceptions . Briefly , ≈7x108 cells were washed 3 times in PBS and then resuspend in 27 mL of PBS . For each condition , the analysis was performed based on the results obtained after 6 days of incubation . Overall , 761 compounds were tested and 240 compounds that were positive in the dead cells condition were excluded from the analysis . The heatmap and clustering of the 521 remaining conditions were performed using R studio v1 . 1 . 456 with ggplot package using the OD values measured by the Biolog normalized between 0 and 1 using the following formulae zi = ( xi−min ( x ) ) / ( max ( x ) −min ( x ) ) . For the endpoint comparison , only positive wells in each condition were compared ( 393 in STAT , 279 in VBNC duplicate 1 , 200 in VBNC duplicate 2 , 454 in 8D-NORMOx duplicate 1 and 380 in 8D-NORMOx duplicate 2 ) . Cells suspensions corresponding to STAT or VBNC ( 1 mL each in biological triplicates ) were centrifuged at 4 000rpm for 5 min at 4°C before a flash freeze step in -80°C ethanol solution and storage at -80°C before extraction . The RNA extraction protocol was adapted from [96] . Briefly , samples were lyophilized during 18h . RNAs were extracted in 3 successive steps: i ) extraction performed in Trizol reagent ( Invitrogen TriReagent ( Sigma-Aldrich , St-Louis MO 63103 USA ) ) , ii ) “separation” by adding chloroform ( Invitrogen ) iii ) overnight isopropanol precipitation at -20°C . The DNA protocol extraction was a manual method based on 3 steps . Briefly , a first stage consisted in lysing the cell pellets by beat beating with MagNA Lyser Instrument ( Roche Diagnostics; 2 rounds at 7 000 r/min for 45 s each ) in a solution of 2% Triton X-100 ( Sigma ) and 1% of SDS ( Biosolve BV Chimie , 5555 CE Valkenswaard , The Netherlands ) , NaCl 100mM; Tris 10mM pH 8 . 0; EDTA 1mM . Then , an extraction step in phenol/chloroform solution ( Phenol:Chloroform:isoamyl alcohol pH 8 . 0 , 1 mM EDA ( Sigma-Aldrich , St-Louis MO 63103 USA ) and a precipitation step in absolute ethanol and 5M of ammonium acetate solution were done . RNAs and DNAs were quantified using the Nanodrop Spectophotometer ( ThermoFisher , Scientific , Inc . ) . All extracts were stored at -80°C before analysis . Briefly , mtLSU primers were designed with Primers . 3 vs 4 . 1 . 0 , while all other specific primers were designed to be compatible with the Human Universal Probe Library set ( 90 probes , octamer , Roche Diagnostics ) ( S9 Table ) [23] . All reverse transcriptase qPCR assays for a given target were performed simultaneously for all samples using the LightCycler Multiplex RNA Virus Master Kit ( Roche diagnostics ) . Each sample was normalized with the geometric mean of the quantification cycle ( Cq ) of the corresponding GAPDH and ACT housekeeping genes expression [97] and fold changes calculated according to Pfaffl after determining the efficiency of each PCR assay [98] . Three biological replicates were performed kinetically upon incubation D4 , D6 and D8 in hypoxia and normoxia starting from step3 ( S1 Fig ) corresponding to stationary phase ( STAT ) . The suspensions were centrifuged at 4000rpm and the supernatants ( secretome ) filtrated through a 0 . 22 μM membrane ( Millex GV , Merck Millipore , Burlington , USA ) . The corresponding cell pellets ( proteome ) were extracted in parallel . For each biological replicate , three technical replicates were pooled to increase the amount of recovered proteins for the supernatants . All samples ( supernatant or pellet ) were lyophilized overnight after flashed freezing in liquid nitrogen . We then adapted a protocol established to purify a sample rich in carbohydrates [99] . The lyophilized samples were resuspended in 0 . 5 mL of sterile milliQ water and the following 3 steps-protocol was done: i ) the samples were precipitated at 4°C by addition of 1 mL of 10% trichloroacetic acid ( TCA ) in acetone ( kept at-20°C ) followed by incubation for 5 m . After centrifugation at 20000g for 10 minutes at -20°C , the pellet was resuspended in 100% acetone with 10mM dithiothreitol ( DTT ) , washed twice in this solution , and air dried; ii ) the pellet was then solubilized in sodium dodecyl sulfate ( SDS ) extraction buffer containing 1% SDS , 0 . 15 M Tris HCl pH = 8 . 8 , 0 . 1M DTT , 1 mM ethylene diamine tetra acetic acid ( EDTA ) , 2 mM phenylmethylsulfonyl fluoride ( PMSF ) , and incubated for 1 h at room temperature and vortexed every 20 m . A 15000g centrifugation for 10 min at room temperature allowed recovery of the supernatant; iii ) Phenol was added at equal volume to the supernatant and the tube was vortexed for 3 m . After centrifugation at 20000 g for 5 min at room temperature , the denser phenolic phase was recovered in the lower part . An equal volume of washing solution ( Tris HCl pH 8 . 0 , 10 mM , 1 mM EDTA , 0 . 7 M sucrose ) was added and the tube vortexed for 3 m . The phenolic phase was then recovered on the upper part and transferred to a new tube . Then , 0 . 1M ammonium acetate in methanol ( -20°C ) was added and the mixture stored at -20 ° C for 30 m . The precipitate was pelleted by 20000g centrifugation for 10 min at -20°C , and washed first with 0 . 1M ammonium acetate in methanol and then with 100% acetone . After a last centrifugation step ( 20000 g for 10 min at -20°C ) , the pellet was air-dried and resuspended in 350 μl of denaturing buffer ( 8M urea , 100 mM TrisHCl pH 8 . 0 ) . Two μL of each sample were then used for dosage with the Biorad DC dosing kit according to the manufacturer’s recommendations . The remaining solution was stored at -80°C until analysis . Venn diagram was generated using Jvenn [109] ( http://jvenn . toulouse . inra . fr/app/index . html ) with proteins only present in the 3 replicates for each condition ( secretome and proteome ) . KEGG pathway database ( https://www . genome . jp/kegg-bin ) was used to map the pathways . It was rendered with the intracellular proteins significantly and differently produced in hypoxia: 63 proteins were transferred from H99 to JEC21 . Nineteen proteins belonged to general metabolic pathway and more precisely 8 to fatty acid degradation ( “map” pathways were not colored , Cryptococcus-specific pathways were colored green , entries were colored red ) . Another fatty acid degradation cycle was reconstructed from yeast genome database cycle ( https://www . yeastgenome . org/ ) and the corresponding proteins were found in Cryptococcus neoformans H99 genome using BLAST ( https://blast . ncbi . nlm . nih . gov/Blast . cgi ) . For transcriptome analysis , yeasts incubated in 8D-HYPOx/8D-NORMOx , as well as STAT and logarithmic ( LOG ) phase yeasts were studied in triplicates for two different sets , one containing spiked Saccharomyces cerevisiae organisms ( w_spike ) and one with C . neoformans cells only ( w/o_spike ) . The former strategy was used to obtain an internal standard of the level of expression of the whole transcripts and adjust the level of expression of each gene to that of the constant expression of S . cerevisiae . In each spiked sample , 15% of LOG phase S . cerevisiae cells were added to C . neoformans cells . Triplicates of yeasts suspensions in the different conditions ( total of 2 . 55x108 C . neoformans cells ) were spiked ( w_spike ) or not ( w/o_spike ) with 4 . 5x107 cells ( representing then 15% of the cells ) of S . cerevisiae cells ( culture in exponential phase ) . All samples were then processed as described above in the Mitochondrial nucleic acid quantification section . We used 1 μg of total RNA to purify polyadenylated mRNAs and to build an RNA library , using TruSeq Stranded mRNA Sample Prep Kit ( Illumina , #RS-122-9004DOC ) as recommended by the manufacturer . Directional library was checked for concentration and quality on RNA chips with the Bioanalyzer Agilent . More precise and accurate quantification was performed with sensitive fluorescent-based quantitation assays ( "Quant-It" assays kit and QuBit fluorometer , Invitrogen ) . Samples were normalized at 2 nM concentrations , multiplexed 4 samples per lane and then denatured at a concentration of 1 nM using 0 . 1 M NaOH at room temperature . Each sample was finally loaded on the flowcell at 9 pM . Sequencing of the 24 samples was performed on the HiSeq 2500 sequencer ( Illumina ) in 65 bases single-end mode . Reads were cleaned of adapter sequences and low-quality sequences using cutadapt version 1 . 11 ( Marcel Martin . Cutadapt removes adapter sequences from high-throughput sequencing reads [111] ) . Only sequences at least 25 nucleotides in length were considered for further analysis . STAR version 2 . 5 . 0a [112] , with default parameters , was used for alignment on the reference genome ( PRJNA411 from NCBI Bioproject ) . Read counts per gene were measured using featureCounts version 1 . 4 . 6-p3 [113] from Subreads package ( parameters: -t gene -s 0 ) . Results are summarized using multiqc version 0 . 7 [114] . Count data were analyzed using R version 3 . 3 . 1 and the Bioconductor package DESeq2 version 1 . 12 . 3 [115] . Normalization and dispersion estimations were performed with DESeq2 using the default parameters but statistical tests for differential expression were performed without applying the independent filtering algorithm . For w/o_spike samples , a generalized linear model was set in order to test for the differential expression between STAT , 8D-HYPOx/8D-NORMOx and LOG conditions . For each pairwise comparison , raw p-values were adjusted for multiple testing according to the Benjamini and Hochberg ( BH ) procedure and genes with an adjusted p-value lower than 0 . 01 were considered differentially expressed . For w_spike samples , mean normalized counts were calculated and the rank of each gene determined for each gene in each condition . High and low rank values corresponded to genes with the highest and lowest expression relative to spiked RNA , respectively . To identify the expression differences across conditions , the read counts were filtered and normalized using the R package “DESeq2” . Genes with read counts below a threshold of 10 in all samples were filtered out . After normalization of read counts , differentially expressed genes for each condition were identified using the generalized linear model , which perform pairwise comparisons among each of the conditions ( STAT , 8D-HYPOx , 8D-NORMOx and LOG ) . Genes passing the threshold , ( FDR <5% ) , were considered significantly differentially expressed and were then considered to be up or down-regulated according to the direction of the fold change . The k-means clustering analyses were performed using R tools , “kmeans” and “heatmap . 2” . To discover the underlying pathways of these differentially expressed genes , we also carried out functional annotation analysis by summarizing the data , including known H99 gene annotation , S . cerevisiae gene ortholog , Gene Ontology ( GO ) , KEGG pathway , and pfam domain , from FungiDB database [116] . GO enrichment and KEGG pathway enrichment for these genes were then assessed by using Fisher’s Exact Test with multiple testing correction of FDR threshold at 5% . Furthermore , conserved domains of each C . neoformans gene were assessed by using the amino acid sequence to search the NCBI Conserved Domain Database ( CDD ) [117] . The presence of signal peptide sites was predicted by SignalP 4 . 1 [118] . The statistical tests used for the comparisons between the different conditions were t-test , Mann Whitney , Wilcoxon tests according to the distribution of the data . All RNA sequence data from this study have been submitted to NCBI ( https://www . ncbi . nlm . nih . gov/geo ) under accession number GSE118549 . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD012570 ( https://cran . r-project . org/ ) . | Quiescence/dormancy in microorganism is a common feature that enables survival at the population level . In fungi , quiescence has been studied in the baker yeast Saccharomyces cerevisiae . In Cryptococcus neoformans , dormancy is of great interest since it is known from the natural history of cryptococcosis that dormancy in yeast can last decades exists before possible reactivation upon immunosuppression . Based on a previous study which identified a subpopulation of dormant yeasts in experimental models of cryptococcosis , we identified here in vitro conditions that enabled the induction of dormancy via the formation of viable but non culturable cells ( VBNC ) . Reactivation of part of these cells was possible through stimulation with vitamin B5 , a quorum sensing molecule . We showed that the global metabolism of the VBNC was down but harbored specific signatures compared to control conditions . We identified mitochondrial metabolism , in particular the fatty acid pathway , as key for the maintenance and viability of VNBC . These findings open the road for research on dormancy . Elucidating the parameters involved will help understand the pathophysiology of the disease including the difficulty in eradication of the yeasts despite therapy , and the possible relapse/recurrence of the infection . | [
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] | 2019 | Cryptococcus neoformans resists to drastic conditions by switching to viable but non-culturable cell phenotype |
With the expansion of offender/arrestee DNA profile databases , genetic forensic identification has become commonplace in the United States criminal justice system . Implementation of familial searching has been proposed to extend forensic identification to family members of individuals with profiles in offender/arrestee DNA databases . In familial searching , a partial genetic profile match between a database entrant and a crime scene sample is used to implicate genetic relatives of the database entrant as potential sources of the crime scene sample . In addition to concerns regarding civil liberties , familial searching poses unanswered statistical questions . In this study , we define confidence intervals on estimated likelihood ratios for familial identification . Using these confidence intervals , we consider familial searching in a structured population . We show that relatives and unrelated individuals from population samples with lower gene diversity over the loci considered are less distinguishable . We also consider cases where the most appropriate population sample for individuals considered is unknown . We find that as a less appropriate population sample , and thus allele frequency distribution , is assumed , relatives and unrelated individuals become more difficult to distinguish . In addition , we show that relationship distinguishability increases with the number of markers considered , but decreases for more distant genetic familial relationships . All of these results indicate that caution is warranted in the application of familial searching in structured populations , such as in the United States .
Forensic identification via exact genetic profile matching has become common practice in the United States [1] . In exact genetic identification , genetic markers found in a crime scene sample are genotyped and exactly matched to a suspect or database entry , suggesting that the sample originates from the matched individual . In some cases , a database search yields no exact genetic profile matches , but does reveal partial matches where some , but not all , alleles match . A partial match could result from a genetic familial relationship between the individual who left the sample and the database entrant . If the database entrant has relatives , they might be investigated to determine if any of their genetic profiles exactly match the sample . Familial searching is now used fairly frequently in the United Kingdom and was instrumental in the identification of suspects of violent crimes for 20 cases lacking other evidence as of 2008 [2] . Its use in the United States has been more limited due to concerns regarding civil liberty infringement , racial bias , and efficacy [3]–[6] . However , in July 2010 , familial searching was used in a highly publicized California case to identify a suspect serial killer ( the “Grim Sleeper” ) [7]–[10] . Despite the increasing use of familial searching in the United States , important questions about the method remain on both social and scientific grounds . In order to understand these concerns , we must appreciate that familial searching is most useful as a database mining method in cases with no suspects . In the United States , the Combined DNA Index System ( CODIS ) is the Federally administered system for National DNA Index System ( NDIS ) , the national offender/arrestee database , which includes entries from State DNA Index Systems [11] . CODIS has standardized the use of genotypes at 13 particular short tandem repeats ( STRs ) ( the CODIS loci ) in forensic identification . The CODIS loci were chosen based on several criteria including reliable multiplexed PCR amplification , availability of commercial genotyping kits , clearly distinguishable alleles , linkage equilibrium , Hardy-Weinberg equilibrium , and high polymorphism in examined population samples [12]–[15] . An NDIS entry contains CODIS loci genotypes and a traceable index number , without other identifying information ( e . g . location , race , or ethnicity ) [16] . In September 2011 , NDIS included over 10 million genotype profiles and continues to grow through new cases and expanded inclusion criteria [1] . These features of the forensic testing landscape matter because , unlike exact DNA identification , a typical database search for familial matches prospectively identifies candidate suspects who , while closesly genetically related to database entrants , are not in themselves in the database , provoking complex privacy concerns [4] , [5] , [9] , [17] , [18] . Additionally , social groups which both share genetic relationships and are over-represented in the database would experience a disproportionate increase in genetic surveillance if familial matching were routinely implemented , further exacerbating their over-representation in these databases [6] , [12] , [17]–[19] . The question of relative inference has been well-studied in other contexts with varying marker types , relationships , and numbers of individuals [20]–[28] . Here we focus on statistical and population genetic assumptions underpinning the familial searching methodology in the forensic context . Specifically , we consider the effects of both uncertainty in allele frequency estimation and population structure . First note that allele frequency estimates calculated within socially-defined population groups ( e . g . African American , European American , Latino ) are used to estimate the probability of an observed partial match , assuming a particular genetic relationship . Match probabilities for some individuals may not be accurately estimated using the available categorical socially-defined population group model and sample allele frequency data , particularly individuals with genetic ancestry outside of typically studied groups or individuals whose socially-defined population group does not inform their genetic ancestry . In exact identification , the probability of observing two individuals with identical specific 13-locus genotypes is astronomically low , with the exception of monozygotic twins . With these extremely low probabilities , differences or inaccuracies in allele frequency estimates are almost inconsequential , possibly changing the probability of an observed genotype a few orders of magnitude , but unlikely to alter the conclusion of the statistical analysis [29] . However , in familial identification , the probability of observing a coincidental partial match is much higher ( e . g . for a parent-offspring relationship exactly one allele is shared by descent per locus ) . With these higher probabilities , population genetic differences in marker informativeness and errors in allele frequency estimation can perturb match probability estimations to such a degree as to affect the interpretation outcome . In this study , we aim to examine some of these concerns by exploring how familial searching techniques behave on populations with varying allele frequency distributions and varying accuracy of allele frequency specification . We formulate and calculate confidence intervals for familial identification likelihood ratio ( LR ) estimates , and investigate how well siblings and unrelated individuals can be distinguished over different population samples with varying allele frequency distributions and under accurately and inaccurately assumed allele frequency distributions . We show that population samples vary in the amount of identifying information encoded in the CODIS loci and , therefore , in relationship distinguishability , even with correctly specified allele frequencies . Since completely accurate allele frequency specification is not guaranteed and the most appropriate population sample may not be known or available , we are also interested in the systematic effects of assuming allele frequencies which are appropriate for one population , but which are not appropriate for the individuals investigated . We show that relationship distinguishability decreases with the accuracy of allele frequency estimates , potentially resulting in high rates of coincidental familial identification for some groups . These results are especially pertinent in the multiple testing context of large database searching . In addition , we explore the relationships between relationship distinguishability , the number and type of markers used for identification , the relationship considered , and the true and assumed coancestry coefficient parameter value .
To determine if a partial genotype match is better explained by a genetic familial relationship or stochasticity , we used the ratio of the likelihood of the observed partial match assuming the individuals share a given genetic familial relationship , to the likelihood of the observed partial match assuming the individuals are unrelated . With the data available , this LR is the most powerful statistic to separate relatives from unrelated individuals [30] . So even though the exact methodology used by forensic agencies for familial forensic identification is not readily publicly available , our use of the LR optimistically assumes the most powerful method using the CODIS loci . In the first part of this analysis , only sibling relationships are evaluated to reduce dimensionality . Other genetic familial relationships were explored and are reported below . Unrelated individuals were simulated in a randomly mating population by independently drawing alleles from allele frequency distributions , similarly to Bieber et al . [31] . Siblings were then simulated by dropping alleles through a pedigree with unrelated parents . We simulated both unrelated individuals and siblings using allele frequency distributions from five socially-defined population samples , Vietnamese , African American , European American , Latino , and Navajo . Using both unrelated individuals and siblings , we calculated the sibling relationship and 95% confidence interval of that estimate , assuming allele frequencies from each population sample . We simulated siblings and unrelated individuals under each of the five allele frequency distributions and calculated and 95% confidence interval of that estimate assuming each of the five allele frequency distributions 10 , 000 times for each pair of population samples . As a result , we have with confidence intervals for sibling relationships between unrelated individuals and siblings simulated from every population sample , assuming allele frequencies from every population sample . In most of the analyses presented here , we focus specifically on the lower 95% confidence limit of ( LCL ) to account for sampling and biological variance in allele frequency estimation and to conservatively identify relationships . We refer to the population sample used to simulate the individuals as the true population sample , as opposed to the assumed population sample used to calculate the LR for their relationship . Figure S1 shows the 95% confidence intervals for 100 simulations of unrelated individuals , where individuals were simulated based on each population sample and confidence intervals were computed assuming the allele frequency distribution of each population sample . Note that across all of these simulations specific parameter values were chosen and kept constant , specifically , sibling relationships , the assumed coancestry coefficient ( probability of two alleles being identical by descent ( IBD ) between two individuals not recently related ) used in calculations of , confidence interval length parameterized by significance level as , and the use of the 13 CODIS STRs . Regardless of the values of these parameters , the relative trends across true and assumed population samples will be maintained , although the scale may vary with parameter value choice . We observed lower distinguishability when the true and assumed allele frequency distributions differ more . The degree of difference between population sample allele frequency distributions at the CODIS alleles is quantified for every population pair using ( Table 2 ) . To account for multiple alleles at multiple loci and varying sample sizes , we estimate with the method of Weir and Cockerham [33] . Note that s reported here were calculated using the only CODIS loci , as is appropriate for an analysis of forensic methods . For a thorough investigation of the population genetics of these samples , more loci would be required , producing different results than those shown here , as reported in other studies [34] , [35] . To explore the relationship between distinguishability and the genetic distance between true and assumed population samples , in Figure 4 , is plotted against for each pair of true and assumed population samples . and are significantly correlated ( ) , supporting the hypothesis that incorrectly assuming allele frequencies leads to low distinguishability and high false positive rates . In particular , we observe low distinguishability when Navajo , or to a lesser extent Vietnamese , is the true population sample , correlating with higher with the other assumed samples . Intuitively , when allele frequencies are misspecified , the most likely error is assuming that common alleles are more rare simply because truly common alleles are more likely to be observed than truly rare alleles . In the same way , rare alleles are assumed to be common , but by definition , rare alleles are less likely to be observed shared between individuals , so overall the misspecification of common alleles as rare dominates . When misspecifying common alleles as rare , observing the same common alleles in multiple individuals seems surprising , so a genetic relationship model is favored over a model of no relationship . That is , the probability of a partial match assuming a relationship is inflated and the probability of a partial match assuming no relationship is deflated . In this way , allele frequency misspecification results in an increase in false positive relative identifications . Although the relationship between distinguishability and allele frequency misspecification has not yet been deeply considered in the context of genetic familial identification ( but see [36] ) , it has been discussed in the forensic literature for exact matching and it is well-known in the linkage analysis community . For exact forensic identification using the 13 CODIS loci , discrepancies between assumed and true allele frequencies affect the computed match probabilities , but seldom change the ultimate outcome [37]–[40] . In linkage analysis , when inaccurate population allele frequencies are used to calculate genotype probabilities , false linkage signals between genotype and phenotype are common [41] , [42] . In the analysis presented thus far , we showed how distinguishability varies over true and assumed population samples with varying allele frequency distributions . To maintain manageable dimensionality , some key parameters likely to vary in forensic analyses were kept constant . Here we explore the relationships between these parameters , particularly different genetic relationships , varying marker data , and varying the true and assumed coancestry coefficients ( and ) . To focus on the relationships between these parameters , in these analyses the correct known allele frequencies were used . Pairs of individuals were simulated taking into account the true coancestry coefficient , , using the genotype probabilities described in the Text S1 , for the following genetic relationships: parent-offspring , sibling , half-sibling , first cousin , second cousin , and unrelated . Note that in contrast with the analyses presented above , here is used to model background relatedness . LRs were computed comparing the probabilities of the simulated data assuming the true relationship and assuming the individuals are unrelated . This analysis was repeated over varying numbers and types markers and a variety of assumed values .
The analysis presented here confirms and quantifies the intuition from population genetics that for particular loci , groups with comparatively low-variance allele frequency distributions have less identifying information encoded in genotypes . Decreased identifying information results in lower relationship distinguishability , even when the correct allele frequency estimates are used ( Figure 2 , Figure S2 ) . This is abundantly apparent for the Native American samples considered in this analysis . With a basic understanding of population genetics , it is clear that socially defined groups , like Navajo , Latino , or European American , have very different underlying population structures reflecting distinct demographic history , degrees of genetic diversity , and admixture . It is hardly surprising that a group which has undergone multiple population size reductions , like the Navajo , has a lower-variance allele frequency distribution than a group with a history of genetic diversity and social inclusion , like African Americans . This is particularly evident at the CODIS loci , which were chosen in part because of their broad allele frequency distributions in a few studied populations , without considering all relevant populations [13]–[15] . These population differences in allele frequency distributions are key when considering a potential source of error: inappropriately assumed allele frequency distributions . When the allele frequency distributions for an inaccurately specified population group are assumed , the probabilities of the observed data under a sibling relationship and under no close genetic relationship become less distinct , so relationship distinguishability decreases . We found that distinguishability decreases with increased distance between assumed and true allele frequency distributions , as measured through . Specifically , both Navajo and Vietnamese samples are more genetically distant to the other three samples considered and show decreased distinguishability when allele frequencies of one of those three samples are assumed . The results of this analysis indicate that when a decision threshold is chosen so that the power to identify siblings is reasonably high , population samples with allele frequencies which differ from those assumed would experience disproportionately higher rates of false positive familial identification ( Figure 3 ) . This could be exacerbated by unknown population-based differences in genotyping which would distort allele frequencies , for example , population-specific mutations in PCR primer binding sites [45]–[51] . More extensive genotyping of genetically diverse populations may make available more appropriate allele frequency distributions . However , it is not clear how or if the most appropriate allele frequency distribution for a pair of samples can be determined . Population-based differential distinguishability will persist , regardless of additional population-specific allele frequency distributions or uniformly applied corrections . One possible correction would be increasing the value of the parameter , however , in Figure S6 we see that even when the true allele frequencies are assumed , increasing decreases distinguishability . If more genetic data were used , particularly markers on the Y chromosome or mitochondrial DNA , as are in some states but not Federally , profile informativeness could be increased to the point where allele frequency approximations made little difference in the ultimate outcome ( Figure S5 ) [10] , [52] . However , additional Y chromosome and mitochondrial markers will only inform matrilinial or patrilinial relationships and any additional markers will be subject to similar population-specific variation , and will be limited by practical genotyping constraints and the need to avoid medically-associated regions . Additionally , it is not clear if more distant relationships ( cousins , second cousins , etc ) would be confidently identified , even with more independent genetic loci ( Figure S5 ) [53] , [54] . As it is , the core 13 CODIS loci , or the minimum 10 loci recommended by the Scientific Working Group on DNA Analysis Methods Ad Hoc Committee on Partial Matches ( SWGDAM ) , seem inadequate to implement sibling matching with low false positive rate and high power in structured populations [52] , [55] . More complex situations , like mixed or low-template DNA samples , require further study and may not be feasible with the 13 CODIS loci [55] , [56] . Motivated by the question of forensic familial searching , in this analysis we focus on distinguishing relatives with a specified relationship and unrelated individuals . In other contexts , it may be more appropriate to distinguish different kinds of relatives ( e . g . siblings and parent-offspring ) or relatives with an unspecified relationship and unrelated individuals . In the former case , the ratio of LRs for the relationships of interest versus unrelated individuals reduces to the LR comparing the two specified relationships . In the later case , models allowing IBD sharing probabilities to vary can be formulated and incorporated into the LR . For example , when comparing a null model with set IBD sharing probabilities for unrelated individuals and an alternative where the likelihood of data is maximized over any IBD sharing probabilities , a LR test can be formulated which follows a distribution under the null hypothesis . This analysis considers familial identification in a forensic context , but is applicable to tests for relatedness applied in the various contexts especially when considering unlinked genetic markers as in paternity investigation , ecological surveys , and conservation biology . When more extensive genotype or sequence data are available , it is appropriate to use more sophisticated tests for relatedness considering linkage or shared haplotype length [28] , [57] , [58] . The population genetic model used in forensic identification is remarkably coarse . In direct identification , the CODIS loci provide ample data to determine identity and non-identity , even with the coarse population genetic model of a small number of discrete homogenous genetic groups corresponding to social racial groups . We have shown that under this model , new concerns arise with familial searching . However , the model itself requires some scrutiny . It is clear that human genetic population structure is complex and humans are not easily split into a small number of discrete homogenous genetic groups [59]–[62] . Even with carefully chosen and defined population samples , it is practically impossible to account for human genetic variation and the discrete population group model fails to account for individuals with mixed ancestry . Additionally , individuals are typically assigned to genetic population groups based on social race . While there is correlation between genetic ancestry and social race , one does not determine the other [63] . As a result , in the discrete population group model , some individuals may not be grouped with the most similar genetic group . Forensic familial searching will most likely be implemented in the context of a large offender/arrestee database , introducing questions of multiple testing over both database entrants , and the number of genetic familial relationships considered . Because forensic methodology practice varies over jurisdictions , it is not clear how these multiple testing issues have been , or will be , addressed . However , it is reasonable to assume that familial searching will result in a list of partial database matches with for genetic familial relationships . The parameter values used in the calculations must be conservative to keep the number of high partial matches manageably short , but the parameters also must allow enough leniency so that a true match will appear in the list considered . Ideally , parameter values used in practice should be tuned using simulations based on real genotype data representing realistic cryptic relatedness and population structure appropriate to the database and relevant population . When tuning parameters , as power increases , false positive rate will as well . Both of these values must be considered in deciding on appropriate parameter values . However across parameter values , some groups may have higher rates of false identification , as we have shown here , raising questions about the practicality of familial searching . Without access to accurate database or population information , or to a clear decision procedure practice , we refrain from making specific recommendations about parameter choice or methodology in this analysis . Individual and population genotype information is necessary to determine the extent to which inaccurately assumed allele frequencies cause high false positive rate in familial matching in practice . For instance , in this study , we considered unrelated individuals , conforming to exactly one of five allele frequency distributions , in completely randomly mating populations . However the use of familial searching rests on the premise that relative groups are in the database and population structure is undeniably present in most databases [64] . Access to suitably secure and encrypted database information would enable analyses with an accurate portrayal of relatedness and population substructure . As recommended by Krane et al . , increased transparency in database makeup , search procedure , and database access are required for rigorous analyses of forensic methodology [65] . If implemented with the core CODIS loci , familial searching may result in low distinguishability and potentially high false positive rates among certain groups , especially if only African American , European American , Southeastern Latino , and Southwestern Latino allele frequency distributions are in assumed LR calculations , as recommended by SWGDAM [55] . Because some of these groups ( Native Americans and some immigrant groups ) are correlated with social groups already over-represented in the criminal justice system , group members would be more likely to have a relative in the database , and that relative would be more likely to have a coincidental partial match with a crime scene sample [3]–[6] , [9] , [17] , [18] , [66]–[68] . Cumulatively , members of these groups are more likely to be investigated as a familial match due to over-represention in the database , and an unusually high false positive familial identification rate .
Our analysis makes use of allele frequency data for the 13 CODIS loci over different population samples socially defined by race . Note that alternate schemes to group individuals will also produce genetic differences between groups [56] , [63] , [69] . Here , we consider genetic differences between socially-determined groups which are relevant to the practice of genetic familial forensic identification . To do so , we used the allele frequencies reported by Budowle and Moretti [29] for samples from ‘Vietnamese , ’ ‘African American , ’ ‘Caucasian , ’ ‘Hispanic , ’ and ‘Navajo’ populations . In this manuscript , these same samples are refered to with the following labels: Vietnamese , African American , European American , Latino , and Navajo . As short hand , we refer samples derived from individuals from each sample as the sample name , for example ‘the Latino sample . ’ The number of individuals genotyped to estimate allele frequencies for each sample varied , with , and individuals sampled for Vietnamese , African American , European American , Latino , and Navajo samples , respectively . The consent and population grouping procedures used in obtaining these data are not clear . In the time since these data were collected , dominant cultural ethics regarding informed consent process have changed considerably , motivated largely by several cases of severe misuse of samples provided by Indigenous communities [70]–[73] . As a result , today it is becoming less acceptable to gather data in the same way [74]–[78] . We use the data because of its public availability , however we look forward to working with data collected using transparent informed consent methodology . LRs are used to compare the probability of observed genotypes for two individuals under two different hypotheses: the individuals are unrelated ( ) and the individuals share a specified genetic familial relationship ( ) [79] . The LR is defined as [79]where is the observed pair of genotypes . When , the observed data are more likely for unrelated individuals and when , the observed data are more likely for individuals with the specified genetic relationship . By assuming independence between all CODIS loci , can be broken down aswhere is the observed genotype for each individual at locus . Relationships between individuals can be described using the identical by descent ( IBD ) sharing probabilities , , and , which are the probabilities that individuals with the specified relationship share 0 , 1 , and 2 alleles IBD , respectively [79] . For example , for a parent/offspring relationship , , and and for a sibling relationship , , and . Using these IBD sharing probabilities , the LR becomeswhere the IBD sharing probabilities in the numerator are specified by the specific genetic relationship considered . The probability of the observed genotype combinations given IBD sharing probabilities depends on the specific combination of alleles observed . The probabilities of all observed genotypes , given IBD sharing probabilities , are defined in Text S1 . These probabilities include a correction for expected background relatedness using the coancestry coefficient . In the first part of this study , we use the value of based on standard methodology in population genetics and as recommended by SWGDAM [55] , [80] . The LR described above provides information about whether the observed data are more likely for unrelated or related individuals . However , the true population allele frequencies ( ) are unknown , so needs to be estimated with the observed allele frequencies . Available sample allele frequencies are subject to sampling variation and variation due to demographic history [81] . Observed allele frequencies follow directly from observed genotype frequencies . Using , the probability of the data is calculated under different IBD sharing schemes , so the estimate of the likelihood ratio ( ) can be computed . By considering the distribution of , we can find the distribution of and calculate confidence intervals on reported values . Sampling variation is inherent in allele frequency estimation since a random sample must be chosen for the estimate . By their nature , different random samples vary in their representation of specific alleles , resulting in different allele frequency estimates . Additionally , random genetic sampling exists in the historical differentiation of populations , resulting in population groups with distinct allele frequencies . Since all present-day human population groups descend from a common ancestral population , the alleles present in each present-day population group reflect a sample of the alleles from the common ancestral population . Under evolutionary equilibrium and a simple model of demographic history , the relationship between population group allele frequencies ( ) can be modeled using a Dirichlet distribution informed by the coancestry coefficient ( ) , accounting for genetic and sampling variation in estimated allele frequencies [81] , [82] . With this model , we define the confidence interval in order to express uncertainty conferred by allele frequency estimate . Using the same approach as Beecham and Weir [81] , we note that the total is the sum of the for each locus . The central limit theorem indicates that , for even as few as 13 independent loci , this sum will be approximately normally distributed [81] . Thus , the confidence interval for is [81]where is the variance of and is the standard normal value for the given , in this study and so . While the typical arbitrary value of is used in this study , the trends explored will be maintained with different values of . Also note that a one-sided confidence interval can be derrived similarly with . This confidence interval is in space , so we can exponentiate the results to get the confidence interval of . The value of ( derived in Text S1 ) depends on the variances of the observed allele frequencies . These , in turn , depend on to accommodate evolutionary variation over populations and this is why numerical techniques such as bootstrapping cannot be used to calculate likelihood ratios , as explained by Beecham and Weir [81] . Using the data provided by Budowle and Moretti [29] , individuals were simulated based on the allele frequencies reported for each of the five population samples . For the population structure analysis , individuals are simulated from a given population sample by independently drawing two alleles from the appropriate allele frequency distribution for every locus . Note that the total independence between drawn alleles implicitly creates a population with a coancestry coefficient of zero ( ) . Independently generated individuals are unrelated . Related individuals are simulated by generating unrelated individuals and randomly dropping alleles through a pedigree to achieve the desired relationship . In this way , we simulate pairs of both unrelated and related individuals from each population sample . The total lack of population structure or cryptic relatedness ( ) in our simulated populations causes unrelated individuals to share fewer alleles than would be expected in a real population . This contrasts with our use of the correction in calculations , conservatively lowering our calculated . This is consistent with forensic applications , where a conservatively high value for is chosen for the anticipated populations . Specifically , and have been suggested for use with populations primarily of European and Native American descent , respectively [43] , [83] . In the second part of this analysis , when we consider the interplay between various parameters , it is necessary to simulate unrelated individuals from a population with a given non-zero coancestry coefficient ( ) . To simulate unrelated and related individuals from a population with , random alleles are drawn using the probabilities of two-individual genotypes , given and a specified relationship , as written in Text S1 . We are interested in comparing LCL distributions generated with different parameters , particularly LCL distributions for truly unrelated individuals and truly related individuals . If the relationship perfectly distinguished relatives and unrelated individuals , these two distributions would be totally separate . The degree of overlap between the related and unrelated distributions roughly indicates the degree of genetic similarity of relatives and unrelated individuals , and so , how well distinguishes the two . To quantify distinguishability , we use an empirical version of the measure proposed by Visscher and Hill [56]where and are the sample means of for the simulations of related and unrelated individuals , respectively , and and are the sample variances of for the simulations of related and unrelated individuals , respectively . Note that is analogous to the non-centrality parameter of the LR test statistic distribution under the alternative hypothesis . Higher indicates greater LR distribution differentiation and more distinguishability , while lower indicates more overlap and less distinguishability . The statistic accurately describes the differentiation in LR distributions , and is particularly appealing because it describes the difference in distributions , so it does not rely on a parameterized decision procedure to discretely determine relationship status . | The forensic identification of criminal suspects through DNA profiling is now common in the United States . Indirect identification by familial DNA profiling is increasingly proposed to extend the utility of DNA databases . In familial searching , a DNA profile from a crime scene partially matches a database profile entry , implicating close relatives of the partial match . While the basic principles behind familial searching methods are simple and elegant , statistical confidence that a partially matched profile belongs to a true genetic relative has not been fully explored . Here , we derive relative identification likelihood ratio statistics and consider how the ability of familial searching to distinguish relatives from unrelated individuals varies over population samples and is affected by inaccurately assumed population background . We observe lower relationship distinguishability for population samples with less identifying information in the genetic loci considered . Additionally , we show that relationship distinguishability decreases with discordance between true and assumed population samples . These results indicate that , if an inappropriate genetic population group is assumed , individuals from certain marginalized groups may be disproportionately more often subject to false familial identification . Our results suggest that care is warranted in the use and interpretation of familial searching forensic techniques . | [
"Abstract",
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"mathematics",
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] | 2012 | Familial Identification: Population Structure and Relationship Distinguishability |
When we perform a cognitive task , multiple brain regions are engaged . Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior . Most research on the interactions between brain regions has focused on the univariate responses in the regions . However , fine grained patterns of response encode important information , as shown by multivariate pattern analysis . In the present article , we introduce and apply multivariate pattern dependence ( MVPD ) : a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses . MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces , and models the multivariate relationship between these trajectories . We applied MVPD to the posterior superior temporal sulcus ( pSTS ) and to the fusiform face area ( FFA ) , using a searchlight approach to reveal interactions between these seed regions and the rest of the brain . Across two different experiments , MVPD identified significant statistical dependence not detected by standard functional connectivity . Additionally , MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels . In the end , MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces , while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity .
Cognitive tasks recruit multiple brain regions [1–4] . How do these regions work together to generate behavior ? A variety of methods have been developed to study connectivity both in terms of the anatomical structure of the brain [5] , and of the relations between timecourses of responses during rest [6] and during specific experimental tasks [7–11] . Functional Magnetic Resonance Imaging ( fMRI ) has proven to be a valuable instrument in this enterprise , offering noninvasive recording with good spatial resolution and whole-brain coverage . In parallel to this literature , multivariate pattern analysis ( MVPA; [12] ) has drastically increased the potential of fMRI for the investigation of representational content , making it possible to detect information at a level of specificity that was unthinkable with previous univariate analyses [13–17] . Despite the success of MVPA , relatively few attempts have been made to transport the potential of multivariate analyses to the domain of dynamics and connectivity . A recent study [18] used trial-by-trial classification accuracy of color and shape in area V4 and in the lateral occipital complex ( LOC ) to predict trial-by-trial accuracy of object classification in the anterior temporal lobe ( ATL ) . Earlier work by the same group [19] used a continuous measure of classification based on correlations , offering a richer description of each brain region’s patterns . These studies are important steps towards exploiting the wealth of information encoded in patterns of BOLD response to study connectivity , but they both characterize the information encoded in a brain region using a single measure ( a given classification ) , rather than in terms of values along multiple dimensions . An additional property of both these methods [18 , 19] is that they use classification along experimenter defined categories . This approach can be useful to probe a specific hypothesis about a given classification . However , it might disregard other information encoded by the regions studied which is orthogonal to the categories chosen by the experimenter . As a consequence , the results depend on the experimenter’s choice of the categories , and on how well the chosen categories capture the functional role of the regions studied . Multivariate pattern dependence ( MVPD ) is a novel method to investigate the ‘connectivity’ between brain regions in terms of multivariate spatial patterns of responses . In keeping with the statistical literature [20] , we will replace the term ‘connectivity’ with the term ‘statistical dependence’ , which we consider more accurate . MVPD is composed of three main stages . In the first stage , the representational space in each brain region is modeled extracting a set of data-driven dimensions ( rather than chosen by the experimenter ) , that correspond to spatial response patterns that ‘best’ characterize that region’s responses over time . In the second stage , the multivariate timecourses of responses in each region are reparametrized as trajectories in the representational spaces defined by these dimensions . In the third stage , the multivariate relations between the trajectories in the representational spaces of different regions are modelled . In a procedure analogous to MVPA , independent data are used to train and test the models . The dimensions and the parameters modelling the relationship between two regions are estimated with all runs but one , and then used to model the relation between those regions in the remaining run . We demonstrate the potential of MVPD in two different experiments , analyzing the statistical dependence between the posterior superior temporal sulcus ( pSTS ) during the recognition of faces and voices , and of the fusiform face area ( FFA ) during the recognition of faces . In both experiments , MVPD identified dependencies between regions not detected by standard functional connectivity , and explained more variance in individual voxels responses than univariate methods . In the end , MVPD revealed different connectivity profiles associated with different dimensions of FFA’s responses .
The volunteers’ consent was obtained according to the Declaration of Helsinki ( BMJ , 1991 , pp . 302 , 1194 ) . The project was approved by the Human Subjects Committees at the University of Trento and Harvard University . The data were collected on a Bruker BioSpin MedSpec 4T at the Center for Mind/Brain Sciences ( CIMeC ) of the University of Trento using a USA Instruments eight-channel phased-array head coil . Before collecting functional data , a high-resolution ( 1 × 1 × 1 mm3 ) T1-weighted MPRAGE sequence was performed ( sagittal slice orientation , centric phase encoding , image matrix = 256 × 224 [Read × Phase] , field of view = 256 × 224 mm2 [Read × Phase] , 176 partitions with 1 mm thickness , GRAPPA acquisition with acceleration factor = 2 , duration = 5 . 36 minutes , repetition time = 2700 , echo time = 4 . 18 , TI = 1020 msec , 7° flip angle ) . Functional data were collected using an echo-planar 2D imaging sequence with phase oversampling ( image matrix = 70 × 64 , repetition time = 2000 msec , echo time = 21 msec , flip angle = 76° , slice thickness = 2 mm , gap = 0 . 30 mm , with 3 × 3 mm in plane resolution ) . Over three runs , 1095 volumes of 43 slices were acquired in the axial plane aligned along the long axis of the temporal lobe . Data were preprocessed with SPM12 ( http://www . fil . ion . ucl . ac . uk/spm/software/spm8/ ) and regions of interest were generated with MARSBAR [25] running on MATLAB 2010a . Subsequent analyses were performed with custom MATLAB software . The first 4 volumes of each run were discarded and all images were corrected for head movement . Slice-acquisition delays were corrected using the middle slice as reference . Images were normalized to the standard SPM12 EPI template and resampled to a 2 mm isotropic voxel size . The BOLD signal was high pass filtered at 128s and prewhitened using an autoregressive model AR ( 1 ) . Outliers were identified with the artifact removal tool ( ART ) , using both the global signal and composite motion . Datapoints exceeding experimenter-defined thresholds were removed from the analysis . An additional noise-removal step was performed with CompCorr [26] . In each individual participant , a control region was defined combining the white matter and cerebrospinal fluid masks obtained with SPM segmentation , and five principal components were extracted . Since the control region does not contain gray matter , its responses are thought to reflect noise . For each run , the timecourses of the components extracted from the control region were regressed out from the timecourses of every voxel in gray matter . For both experiments , the global signal and six motion regressors generated by SPM during motion correction were also included as regressors of no interest . For the FFA seed , data were analyzed both with and without these additional regressors , and results are reported for both analyses . For experiment 1 , we defined a seed region of interest in the right pSTS using the independent functional localizer . Data were modeled with a standard GLM using SPM12 , and the seed ROI was defined in each individual participant as a 6mm radius sphere centered in the pSTS peak for the faces vs houses contrast ( mean MNI coordinates: 54 , -54 , 13 ) . For experiment 2 , we defined a seed region of interest in the right FFA using the independent functional localizer . Data were modeled with a standard GLM using SPM12 , and the seed ROI was defined in each individual participant as a 6mm radius sphere centered in the FFA peak for the faces vs houses contrast ( mean MNI coordinates: 40 , -48 , -20 ) . We defined a gray matter mask by smoothing ( with a 6mm FWHM gaussian kernel ) and averaging the gray matter probabilistic maps obtained during segmentation . The average maps were then thresholded obtaining approximately 130000 gray matter voxels ( 127821 ) . For each voxel in the gray matter mask , we defined a 6mm radius sphere centered in that voxel , and calculated the statistical dependence between the responses in the seed region and the responses in the sphere . Spheres contained 123 voxels . Spheres at the edge of the brain were restricted to the voxels within the gray matter mask . Functional connectivity was calculated low-pass filtering at 0 . 1Hz the mean response in the seed region and the mean response in the searchlight spheres , and calculating Pearson’s correlation between the low-pass filtered responses in the seed and each sphere , thus obtaining a whole-brain functional connectivity map . Statistical significance across participants was assessed with statistical nonparametric mapping [27] using the SnPM extension for SPM ( http://warwick . ac . uk/snpm ) . Let us consider the multivariate timecourses in the seed region: Y1 , … , Ym and in a sphere: X1 , … , Xm , for experimental runs from 1 to m . Each multivariate timecourse Yi is a matrix of size Ti × ny , where ny is the number of voxels in the seed region and Ti is the number of timepoints in run i . Analogously , each multivariate timecourse Xi is a matrix of size Ti × nx , where nx is the number of voxels in the sphere . Data analysis followed a leave-one-run-out procedure: for each choice of an experimental run i , data in the remaining runs were concatenated , obtaining Y t r a i n = ( Y 1 , … , Y i - 1 , Y i + 1 , … , Y m ) ; X t r a i n = ( X 1 , … , X i - 1 , X i + 1 , … , X m ) . Principal component analysis ( PCA ) was applied to Ytrain , and Xtrain: Y t r a i n = U Y S Y V Y T X t r a i n = U X S X V X T Dimensionality reduction was implemented projecting Ytrain and Xtrain on lower dimensional subspaces spanned by the first kY and kX principal components respectively: Y ˜ t r a i n = Y t r a i n V Y [ 1 , … , k Y ] X ˜ t r a i n = X t r a i n V X [ 1 , … , k X ] where V T [ 1 , … , k T ] is the matrix formed by the first kY columns of VY and V X [ 1 , … , k X ] is the matrix formed by the first kX columns of VX . In the first analysis , the number of components kY and kX was chosen for each sphere and iteration using the Bayesian Information Criterion ( BIC ) . In the second analysis , the incremental contribution of each component was tested by comparing the results obtained choosing 1 , 2 and 3 components . We can take a moment to reflect on the interpretation of the procedure we just completed . For each region , each dimension obtained with PCA is a linear combination of the voxels in the region , whose weights define a multivariate pattern of response over voxels . Considering as an example the seed region , the loadings of a dimension j are encoded in the j-th column of Y ˜ t r a i n , and represent the intensity with which the multivariate pattern corresponding to dimension j is activated over time . The mapping f from the dimensionality-reduced timecourses in the sphere X ˜ t r a i n to the dimensionality-reduced timecourses in the seed Y ˜ t r a i n was modeled with multiple linear regression 1: Y ˜ t r a i n = B t r a i n X ˜ t r a i n + E t r a i n ( 1 ) the model parameters were estimated using ordinary least squares ( OLS ) . After having estimated parameters Btrain , predictions for the multivariate responses in the left out run i were generated by 1 ) projecting the sphere data in run i on the sphere dimensions estimated with the other runs , and 2 ) multiplying them by the parameters estimated using data from the other runs . More formally , for each run i , we generated dimensionality reduced responses in the sphere: X ˜ t e s t = X t e s t V X [ 1 , … , k X ] , where VX was calculated using the training data . Then , we calculated the predicted responses in the seed region in run i: Y ^ t e s t = B t r a i n X ˜ t e s t using the parameters Btrain independently estimated with the training runs . In keeping with the use of correlation in standard functional connectivity , we calculated the correlation between the predicted and observed timecourses in each dimension in the seed region . First , we projected the observed voxelwise timecourses in the seed region onto the lower dimensional subspace using V Y [ 1 , … , k Y ]: Y ˜ t e s t = Y t e s t V Y [ 1 , … , k Y ] , ( 2 ) where VY was calculated using the training data . Then , we computed r j = corr ( Y ^ t e s t j , Y ˜ t e s t j ) for each dimension j = 1 , … , kY of the seed region’s subspace . In the end , we generated a single summary measure r ‾ , computing the average of the values rj weighted by the proportion of variance explained by the corresponding dimensions j: w j = S Y ( j , j ) ∑ l = 1 k Y S Y ( l , l ) , r ¯ i = ∑ j = 1 k Y w j r j ( see the relationship between the eigenvalues along the diagonal of S and variance explain in PCA ) . This procedure is motivated by the observation that if a dimension explains more overall variance in the total multivariate response , then explaining variability in that dimension should be weighted more . See Fig 1 for an outline of the method . The values r ‾ i obtained for the different runs i = 1 , … , m were averaged yielding r ‾ . This procedure was repeated for each searchlight sphere , obtaining a whole brain map of r ‾ values for each participant . The significance of r ‾ was tested across participants with statistical nonparametric mapping [27] using the SnPM extension for SPM ( http://warwick . ac . uk/snpm ) . The value r ‾ is a convenient measure of statistical dependence: it reflects how well the prediction generated by MVPD correlates with the observed data . However , in this measure , the target of the prediction is the multivariate timecourse Y ˜ t e s t . Instead , ‘standard’ univariate connectivity based on the mean timecourse aims to predict a different target: mean ( Ytest , 2 ) . This is important because the proportion of variance explained ( cross-validated R-squared ) is given by the amount of variance explained divided by the total variance of the target of the prediction . Univariate connectivity and MVPD could explain the same amount of absolute variance , but still have different proportions of variance explained , because the total variances of the targets of the prediction differ . One way to think about this is that mean-based univariate connectivity ‘gives up’ on predicting variability orthogonal to the mean: if the mean response is predicted perfectly , then the proportion of variance explained will be 100% . In contrast , if MVPD tries to predict the mean as well as other dimensions , it could predict the mean perfectly like univariate connectivity , and still its proportion of variance explained could be less than 100% , because of residuals in the other dimensions . To compare the cross-validated R-squared of univariate connectivity and of MVPD , therefore , we need a measure of their ability to predict a common target . For this reason , for each searchlight sphere we calculated the cross-validated R-squared of mean-based univariate connectivity and of MVPD in the timecourses of individual voxels in the seed region . Predicting the timecourses of all voxels in the seed region is a common target for both univariate connectivity and MVPD , and therefore it makes the cross-validated R-squared of the two methods comparable . To calculate the cross-validated R-squared for both methods , we needed to use a variant of functional connectivity that can perform leave-one-out predictions . The variance explained in functional connectivity is r2 , and it is equal to the variance explained by a linear regression estimated and tested in the same data . We used linear regression estimated in all runs minus one , and tested the variance explained in the left-out run , thus obtaining a leave-one-out variant of mean-based univariate functional connectivity ( that uses the same data-split used in MVPD ) . The linear regression yielded a prediction of the mean response in the seed region . Each voxel’s response was then predicted with the predicted mean response in the seed region . For MVPD , we predicted each voxel’s response projecting the multivariate prediction Y ^ t e s t from its low-dimensional subspace of principal components to voxel space , using the matrix V Y [ 1 , … , k Y ] . Each voxel’s response was reconstructed as the sum of the dimensions’ loadings on the voxels weigthed by the dimensions’ loadings at each timepoint . It can be helpful here to note that this is equivalent to the product Y ˘ t e s t = Y ^ t e s t V Y [ 1 , … , k Y ] T , where Y ˘ t e s t is the voxel-wise prediction ( see 2 and consider that ( V Y [ 1 , … , k Y ] ) T = ( V Y [ 1 , … , k Y ] ) − 1 ) . In the case of the mean-based univariate functional connectivity , the voxelwise prediction can be written as Y ˘ t e s t = Y ^ t e s t 1 T , where Y ^ t e s t is the predicted mean response in the seed region and 1 is a nY × Ti vector of ones , making explicit the common form of the prediction for MVPD and for mean-based univariate connectivity: in the latter the mean is treated as a single dimension with equal loadings for each voxel . For each voxel j in the seed region , variance explained was calculated as v ( j ) = 1 - S S ( Y t e s t ( : , j ) - Y ˘ t e s t ( : , j ) ) S S ( Y t e s t ( : , j ) ) where Y ˘ are the predicted voxelwise timecourses , and the values v ( j ) were averaged to obtain a single measure v ¯ = ∑ j = 1 n Y v ( j ) n Y for each searchlight sphere .
In Experiment 1 , standard functional connectivity identified statistical dependence between the right pSTS and more anterior regions of right STS ( peak MNI: 54 -9 -15 ) and with the left STS ( peak MNI: -52 -27 -6 ) ( Fig 2 , S1 Table ) . MVPD , but not standard functional connectivity , identified statistical dependence with the posterior cingulate ( peak MNI: 0 -71 34 ) , and with larger portions of posterior STS bilaterally ( Fig 2 , S2 Table ) . To evaluate the separate effects of predicting independent data with a leave-one-out approach and of transitioning from univariate to multivariate statistical dependence , we additionally measured univariate statistical dependence with a leave-one-out procedure . As anticipated , predicting independent data reduced the number of significant voxels for univariate dependence ( or ‘connectivity’ ) as compared to standard functional connectivity ( Fig 3A ) , in line with the expectation that predicting independent data is a more stringent test . MVPD , despite predicting independent data , outperformed both variants of univariate dependence ( Fig 3A ) . As a further comparison between univariate dependence and MVPD , we calculated the proportion of variance explained by each model in independent data . Univariate dependence did not explain more than 5% of the variance in any brain region , while MVPD explained more than 20% of the variance in several regions , including the STS bilaterally and posterior cingulate ( Fig 3B ) . As an additional test of the potential of MVPD , we analyzed multivariate dependence between the pSTS seed and the rest of the brain after subtracting the univariate signal ( Fig 4 ) . By doing so , we obtained an analysis procedure which is entirely complementary to standard functional connectivity , which relies entirely on the univariate signal . Even after removing the univariate signal , MVPD detected significant statistical dependence between the right pSTS and posterior cingulate ( peak MNI: 0 -63 28 ) as well as the left STS ( peak MNI: -58 -10 -13 ) . In Experiment 2 , standard functional connectivity identified statistical dependence between the FFA seed and other regions of ventral temporal cortex , as well as with early visual cortex ( peak MNI coordinates: 12 , -90 , -6 ) , the right insula ( peak MNI: 34 , 26 , 1 ) , the thalamus ( peak MNI: -9 , -23 , 11 ) , dorsal visual stream area V7 ( 14 , -70 , 43 ) and intraparietal sulcus ( IPS , peak MNI: 30 , -66 , 32; Fig 5 , in blue , FWE-corrected p < 0 . 05 , S3 Table ) . MVPD additionally identified statistical dependence between the FFA and posterior cingulate ( pCing , peak MNI: 8 , -46 , 38 ) , the right superior temporal sulcus ( rSTS , peak MNI: 51 , -25 , -4 ) , the right anterior temporal lobe ( rATL , peak MNI: 26 6 -33 ) , right dorsomedial prefrontal cortex ( rDMPFC , peak MNI: 8 57 30 ) , and the dorsal visual stream area V3A ( peak MNI 15 , -88 , 31; Fig 5 , in yellow , FWE-corrected p < 0 . 05 , S4 Table ) . MVPD , unlike standard functional connectivity , did not detect significant statistical dependence between FFA and the amygdala ( peak MNI for standard functional connectivity: 22 , 0 , -20 ) . Even after regressing out the global signal and six motion regressors generated by SPM during motion correction ( S2 Fig ) , MVPD detected significant dependence in the posterior cingulate ( peak MNI: -2 -39 40 ) , the dorsal visual stream ( peak MNI: -29 -76 29; 30 -75 32 ) , occipital cortex ( peak MNI: 18 -87 -10; -43 -79 -9 ) . Analysis of voxelwise cross-validated R-squared was performed for mean-based univariate connectivity , and for MVPD with 1 , 2 , and 3 principal components . Increasing the number of principal components led to a corresponding increase in the voxelwise cross-validated R-squared ( Fig 6A for voxels explaining more than 5% of voxelwise variance , ( Fig 6B for voxels explaining more than 10% of voxelwise variance ) . Cross-validated R-squared was also computed after regressing out six motion parameters and the global signal as additional nuisance regressors ( S3 Fig ) . As expected , the greatest voxelwise cross-validated R-squared was observed in the right fusiform gyrus , in the proximity of the seed region’s location . Thanks to the additional contribution of the second and third principal components , variance explained above the 5% threshold was also observed more posteriorly extending towards the occipital face area ( OFA ) , in the left fusiform , and anteriorly extending towards the medial portions of the anterior temporal lobes ( ATL ) . These portions of cortex have been implicated together with FFA in the recognition of faces . [1 , 14 , 15] . The inclusion of dimensions beyond the first PC improved the modeling of statistical dependence between FFA and other regions implicated in face recognition . The voxelwise cross-validated R-squared with univariate dependence remained below 5% in the whole brain . Including additional dimensions beyond the first improved our ability to characterize the statistical dependence between responses in the FFA seed and responses in other brain regions that have been implicated in face processing . As in the case of Experiment 1 , we performed an additional analysis removing the univariate signal , obtaining a fully complementary analysis to standard functional connectivity . This analysis revealed multivariate dependence between the FFA and ventral occipital regions despite the univariate signal was removed ( S4 Fig ) . We then averaged the MVPD-searchlight maps for the first PC and for the second and third PCs , and we studied the spatial distribution of the top 5000 voxels in the brain showing greatest statistical dependence with the first PC ( Fig 7B in yellow ) and the top 5000 voxels in the brain showing greatest statistical dependence with the second and third PCs ( Fig 7B in blue ) . The first PC showed greatest statistical dependence with voxels extending posteriorly towards early stages in the visual processing hierarchy , and dorsally towards regions in the dorsal visual stream . By contrast , the second and third PCs showed a different profile: strongest statistical dependence was found with regions extending anteriorly , towards the medial ATL . MVPD revealed different connectivity profiles for different dimensions of FFA’s representational space , individuating two subspaces showing disproportionate statistical dependence with regions involved in early and late visual processing respectively .
This article introduces multivariate pattern connectivity ( MVPD ) , a new method to investigate the multivariate statistical dependence between brain regions . MVPD is characterized by several key properties . First , the BOLD signal in each brain region is modeled as a set of responses along multiple dimensions , with each dimension corresponding to a function of the voxels in that region . Second , MVPD investigates the statistical dependence between two regions by computing the extent to which the responses in the multiple dimensions characterizing one region can predict the responses in the multiple dimensions characterizing the other region over time . Third , with an analogy to MVPA methods , MVPD uses a cross-validation procedure in which independent data are used for training and testing of the models . A subset of the runs are used as a training set to generate parameters which are then tested assessing their ability to predict responses in a left-out independent run . This leave-one-out approach mitigates the impact of noise , improving on most current methods that do not test the extent to which relationships between regions are sufficiently stable to generalize to independent data . There are two senses in which MVPD is multivariate . First , PCA identifies weighted combinations of multiple voxels that covary over time explaining most of each region’s variance . Therefore , the dimensions that describe the representational space in each region are a combination of multiple dependent variables . Second , in standard functional connectivity , statistical dependence between two regions is measured by correlating two one-dimensional timecourses ( the average responses in each of two regions ) . Instead , in MVPD , statistical dependence is measured by modeling a multiple linear regression that predicts a multi-dimensional timecourse ( the responses along the multiple dimensions in one region ) as a function of another multi-dimensional timecourse ( the responses along the multiple dimensions in the other region ) . In the examples described in the present article , dimensions are obtained with PCA as linear combinations of the voxels that tend to be jointly activated or deactivated over time . From a neuroscientific perspective , we can think of each region as consisting of multiple neural populations with selectivities for different properties of the stimuli that have different distributions over the course of the experiment . Each population has different spatial distributions over voxels . This leads different weighted combinations of voxels to having different timecourses of responses , whose dynamics can provide deeper insights into the interactions between regions than the investigation of average responses . Of course , while different populations with different selectivities and different spatial distributions can lead to dimensions with different time courses , it is unlikely that individual dimensions obtained with PCA correspond in a one-to-one relationship to neural populations with a specific selectivity profile . For example , more than one neural population might be collapsed in a single principal component , or populations might not be assigned to dimensions in a one-to-one mapping because of the orthogonality constraints imposed by PCA . Like standard functional connectivity , MVPD revealed statistical dependence between the FFA and more posterior regions of ventral temporal and occipital cortex , and with regions in early visual cortex . However , MVPD additionally revealed statistical dependence between the FFA and the right ATL , previously implicated in the recognition of face identity [1 , 13–15] . Furthermore , MVPD ( but not standard functional connectivity ) identified statistical dependence between the FFA and the right STS , implicated in the recognition of person identity [21 , 28–30] and of facial expressions [31–33] . Standard functional connectivity , but not MVPD , identified statistical dependence between FFA and the amygdala . This can be due to less stable predictive relationships between responses in the amygdala and FFA dimensions beyond the first PC . Previous studies investigating the functional connectivity of the FFA reported connectivity with the STS in resting state data specifically when the responses in regions selective for other categories were regressed out [34] . MVPD can help to disentangle different kinds of information in the study of statistical dependence: face-specific information might load differentially on different principal components , and the mapping between region can learn to rely specifically on the relevant information . Significant MVPD between FFA and STS might be observed thanks to the potential of the method to rely selectively on a relevant subset of the information encoded in FFA responses . A recent study investigated effective connectivity between FFA and early visual cortex and STS , including participants with developmental prosopagnosia as well as neurotypical controls [35] . Feedforward connections from EVC to FFA and EVC to pSTS showed reduced strength in DP participants . A promising direction for future research consists in applying MVPD to study differences between patient populations and neurotypical controls , to investigate more closely whether neural differences affect specific subsets of the information encoded within a brain region . In the case of developmental prosopagnosia , MVPD could be used to test whether the reduced connectivity from EVC to FFA and pSTS is specific to particular response dimensions within EVC and FFA . MVPD led to important improvements in cross-validated R-squared at the voxel level over mean-based univariate connectivity ( Fig 6 ) . MVPD using a single principal component already improved variance explained over a mean-based univariate approach . Adding a second and a third PC further improved variance explained in ventral temporal cortex as well as the anterior temporal lobes . In the end , MVPD allowed us to separately investigate the connectivity profiles of different dimensions of FFA’s representational space . In particular , different dimensions showed stronger dependence with posterior and anterior regions respectively . Previous connectivity studies found support for the view that posterior ventral stream regions are an entry node in the face recognition network [36] , and previous MVPA studies found evidence of invariant representations of faces in anterior regions [1 , 15] . In this context , the present evidence suggests that different FFA dimensions encode information related to FFA’s inputs and outputs respectively . Future work can investigate the differences in MVPD between different tasks . Whether or not MVPD is sensitive to task differences remains an open question . We consider this a key research direction , in which the greater sensitivity of MVPD can reveal task-dependent changes in the interactions between regions that cannot be detected by standard functional connectivity . In this study , we showed that MVPD can be sensitive to statistical dependence between regions that is not detected by standard functional connectivity . MVPD has the potential to study in even greater detail how statistical dependence is affected by different tasks . For example , in different tasks , the dependence between two regions could remain similar in overall magnitude , but shift from relying on a particular subset of dimensions to a different subset . MVPD could be used to detect this type of task-dependent changes by analyzing not only the overall amount of variance explained , but the matrix of parameters Btrain obtained in different tasks . If some dimensions in one region have a greater influence on responses in another region in one particular task , the parameters in Btrain corresponding to those dimensions will increase in that task . MVPD differs in important respects from previous techniques aimed at studying the dynamic interactions between brain regions in terms of the information they encode . Unlike previous techniques [18 , 37] , MVPD does not rely on discrimination between categories determined by the experimenter , but on dimensions derived in a data-driven fashion . The data-driven dimensions can be related to properties of the stimuli or the task with a subsequent model ( for instance regressing dimensions on conditions , or on stimulus properties using a forward model ) . Another difference between MVPD and the methods introduced by Coutanche and Thompson-Schill [18 , 19] is that the latter characterize each region with a single measure ( how well the pattern in a given timepoint can be assigned to one condition or another ) , while MVPD adopts multiple measures ( the values along the multiple dimensions ) , which can provide a richer characterization of a region’s representation at any given time . An innovative study [37] investigated the relations between brain regions measuring the correlation between representational dissimilarity matrices in different regions . This approach provides a richer characterization of each region’s representational structure by comparing similarity matrices instead of classification accuracies , but it discards trial-by-trial variability . Furthermore , correlations between dissimilarity matrices can only be computed if the same set of conditions are used to generate the dissimilarity matrices in each region . When the conditions correspond to individual stimuli as in [37] this is not problematic , but if stimulus categories are used it raises the question of whether it is appropriate to characterize the representational spaces of very different brain regions in terms of the dissimilarities between the same set of categories . Taking images of objects as an example of stimuli , categorization based on animacy could be most appropriate for some brain regions , while categorization based on color could be more appropriate for other regions . An additional approach has used distance correlation to capture multivariate dependences between regions [38] , finding more robust results than traditional correlations for inhomogeneous regions . MVPD offers as advantages over this approach the ability to test stability of the dependence between two regions in independent data , and to analyze dependence for different representational subspaces ( e . g . Fig 7 ) . This feature of MVPD makes it possible to relate the dimensions characterizing a region’s responses to stimulus properties using forward models , to then investigate what representational content drives statistical dependence between two regions . More generally , methods to model multivariate statistical dependence can be described by the way in which they model the responses of individual regions , and by the way in which they model the dependence between the regions . Some methods ( e . g . [18 , 19] ) use multivariate methods to generate a unidimensional quantity ( e . g . classification accuracy ) , and measure statistical dependence relating these unidimensional quantities between regions ( e . g . with correlation ) . Other methods ( e . g . [37 , 39] ) map directly the responses along multiple voxels in one region onto responses along multiple voxels in another . MVPD combines the two strategy by initially mapping the multi-voxel responses in each region onto a small set of dimensions ( thus reducing the number of parameters that need to be estimated ) , and then modeling the multivariate relationship between these dimensionality-reduced patterns ( e . g . with multiple regression ) . By virtue of modeling the statistical dependence between patterns of responses in different regions , which likely correspond to different processing stages , multivariate measures of dependence are related to some extent to the approach of developing computational models of information processing and using them to predict neural responses [40 , 41] . Two key differences between these approaches are that at present , computational models of information processing have more sophisticated tools to relate neural responses to stimulus properties , but the model parameters are trained independently of neural responses . By contrast , while multivariate dependence does not yet have the same level of sophistication in linking neural responses to stimulus properties , it gives the neural data a more predominant role in shaping the resulting models , by estimating parameters directly using the fMRI measurements . A recent article [42] built a model of visual cortex more closely inspired to the architecture of the brain , making a step in the direction of combining these two strengths . Future work will be necessary to constrain computational models taking full advantage of the wealth of information available in neural measurements while also tying the neural responses to the stimulus content they represent . The most important asset of MVPD is probably its flexibility . The framework of 1 ) modelling representational spaces in individual regions , 2 ) considering multivariate timecourses as trajectories in these representational spaces , and 3 ) fitting models predicting the trajectory in the representational space of one region as a function of the trajectory in the representational space in another offers a wealth of possibilities to build increasingly refined models , both in terms of the characterization of representational spaces and in terms of the models of their interactions . For the characterization of representational spaces , in this article we adopted PCA as a simple example , but other methods such as independent component analysis ( ICA ) and nonlinear dimensionality reduction techniques can also be used . For modelling interactions between regions , we limited the current application to simultaneous , non-directed interactions following an approach similar to functional connectivity , but MVPD makes it possible to model nonlinear maps between representational spaces [43] , and to use models that investigate the directionality of interactions using temporal precedence , along the lines of Granger Causality [8] , Dynamic Causal Modelling [7] , and Dynamic Network Modelling [11] . | Human behavior is supported by systems of brain regions that exchange information to complete a task . This exchange of information between brain regions leads to statistical relationships between their responses over time . Most likely , these relationships do not link only the mean responses in two brain regions , but also their finer spatial patterns . Analyzing finer response patterns has been a key advance in the study of responses within individual regions , and can be leveraged to study between-region interactions . To capture the overall statistical relationship between two brain regions , we need to describe each region’s responses with respect to dimensions that best account for the variation in that region over time . These dimensions can be different from region to region . We introduce an approach in which each region’s responses are characterized in terms of region-specific dimensions that best account for its responses , and the relationships between regions are modeled with multivariate linear models . We demonstrate that this approach provides a better account of the data as compared to standard functional connectivity in two different experiments , and we use it to discover multiple dimensions within the fusiform face area that have different connectivity profiles with the rest of the brain . | [
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] | 2017 | Multivariate pattern dependence |
Understanding the function of important DNA elements in mammalian stem cell genomes would be enhanced by the availability of deletion collections in which segmental haploidies are precisely characterized . Using a modified Cre-loxP–based system , we now report the creation and characterization of a collection of ∼1 , 300 independent embryonic stem cell ( ESC ) clones enriched for nested chromosomal deletions . Mapping experiments indicate that this collection spans over 25% of the mouse genome with good representative coverage of protein-coding genes , regulatory RNAs , and other non-coding sequences . This collection of clones was screened for in vitro defects in differentiation of ESC into embryoid bodies ( EB ) . Several putative novel haploinsufficient regions , critical for EB development , were identified . Functional characterization of one of these regions , through BAC complementation , identified the ribosomal gene Rps14 as a novel haploinsufficient determinant of embryoid body formation . This new library of chromosomal deletions in ESC ( DelES: http://bioinfo . iric . ca/deles ) will serve as a unique resource for elucidation of novel protein-coding and non-coding regulators of ESC activity .
Mouse ESCs , derived from the inner cell mass of the blastocyst [1] , [2] , are a lineage of choice to perform functional genomic studies for several reasons . First , ESCs constitute a sustained source of starting material since they indefinitely self-renew symmetrically in defined culture conditions , generating two functionally identical daughter cells per division [3] . Second , pluripotent ESCs enable the study of most developmental processes in vivo or in vitro , owing to their capacity to make all somatic cell types , including germ cells [4] , [5] . Third , ESCs can model various aspects of tumorigenesis . Undifferentiated ESCs are characterized by the absence of a robust G1/S cell cycle checkpoint [6] , a feature frequently observed in tumor cells [7] . Moreover , ESCs are tumorigenic when ectopically implanted [1] , [2] . Lastly , the ESCs genome is easily modifiable with various mutagenesis techniques . Because ESCs and induced pluripotent stem cells ( iPS ) are valuable resources for modeling human diseases in vitro and in vivo as well as a potential source for cell replacement therapy , major efforts are ongoing to decipher the molecular determinants regulating the cardinal features pertaining to these cells , such as self-renewal , pluripotency , multilineage differentiation and tumorigenic potential . ESCs are capable of being maintained undifferentiated in vitro in the presence of LIF and BMP signaling [8] . Upon removal of self-renewal signals ( e . g . LIF ) , ESCs will differentiate in vitro into aggregated structures called “embryoid bodies” or “EB” . ESC differentiation into EB occurs in an ordered manner , with the generation of derivatives from the 3 germ layers [9] . This feature of in vitro ESC differentiation seems to recapitulate , in a spatiotemporal manner , several of the differentiation processes observed in vivo ( i . e . , normal embryonic development [10] ) . Moreover , ESC differentiation into endoderm , mesoderm , and ectoderm is highly regulated and correlates with expression of a panel of specific markers , which can be used to characterize the extent of the differentiation process at the molecular level [11] . Although several proteins involved in signaling , transcriptional regulation and chromatin modification are implicated in ESC activity , we still do not understand all genetic hierarchies dictating ESC fate [12]–[15] . Recent studies have also documented a function for non-coding RNAs such as microRNAs and lincRNAs in ESC behavior [16] , [17] . Aside from these large classes of determinants , sequence comparison analyses suggest that other elements of the mammalian genome might be regulating biological functions , including ESC behavior . Among these elements are 480 segments of >200 bp termed “ultraconserved elements” , characterized by 100% sequence conservation ( higher degree of conservation than protein-coding regions ) between human , mouse , and rat genome [18] . Of these “ultraconserved” elements , more than 50% show no evidence of transcription , while others overlap with protein coding genes [18] . These sequences are enriched for homeodomain-binding modules , which is intriguing considering the important role of homeodomain transcription factors in ESC pluripotency and developmental processes [19] . Finally , although evolutionary conserved sequences may pinpoint functionally important genomic regions , other crucial elements may lack evolutionary constraints [20] . Several large-scale functional genomics initiatives are currently ongoing to understand the molecular bases of embryonic stem cells . These include single gene inactivation ( or alleles generation ) using diverse strategies such as chemically-induced point mutations [21] , gene/exon trapping ( e . g . , the international gene trap consortium: www . genetrap . org ) and homologous recombination ( The comprehensive knockout mouse project consortium: [22] ) . A repository for KOMP now exists ( www . komp . org ) in which 8500 genes are being targeted ( several in conditional alleles ) within relatively short periods of time . This repository contains several available lines from other initiatives . As a result , in mouse , most protein-coding genes will be deleted and available , many of them as conditional alleles , within the coming years . While these collections represent an outstanding resource for the community , they nonetheless leave a significant proportion of the “functional genome” unexplored . Moreover they fail to examine synthetic interaction between gene neighbours . To complement existing resources that explore functional elements in the mammalian stem cell genomes , we have applied our recently developed retroviral tools to create a collection of ESC with nested chromosomal deletions [23] . Here we document the generation of DelES ( Deletion in ES cells ) library , which contains more than a thousand independent ESC clones highly enriched in chromosomal deletions and representing a large coverage of the mouse genome . Evidence is provided to demonstrate that a large proportion of these clones are competent in functional assays . A complementary method was also optimize to introduce , at high efficiency , a series of selectable marker genes in the backbone of BACs in order to rescue the inability of selected ESC clones to form embryoid bodies in vitro . This first validation allowed the identification of a novel gene essential for EB formation . In addition , a database was created ( http://bioinfo . iric . ca/deles ) to assist in sample management and to compile and interpret all genetic and phenotypic data related to the collection of clones . This database will facilitate the search for genomic regions regulating the ESC activity and the further design of rescue experiments . The library of ESC clones described herein thus has the added potential of deciphering novel determinants involved in ESC activity . On that basis , it is highly complementary to other international functional genomics initiatives .
In order to generate a library of ESC clones containing nested chromosomal deletions ( DelES library ) , we used a retroviral-based method that exploits Cre-loxP technology as described [23] ( summarized in Figure 1A ) . Assisted by robotic cell culture manipulation , we upscaled the previously described procedure to generate the DelES collection ( Figure 1B , see also Text S1 and Table S1 ) . Statistics about the various groups of clones in DelES ( primary , secondary and tertiary clones ) and the types of chromosome rearrangements ( e . g . , nested chromosomal deletions ) are detailed in Figure 1C . A total of 4929 G418R tertiary clones ( i . e . , ESC clones harboring recombination events are selected with geneticin ) originating from 156 anchor sites ( i . e . , families ) were isolated ( Figure 1C and Table S1 ) . Of these , 33 . 8% ( n = 1670 ) were sensitive to puromycin ( puroS ) of which 78 . 3% ( n = 1307 ) were cryopreserved in 96 well plates . Previous work has shown an expected 80% chromosomal deletion rate in puroS clones [23] . When further characterized for proviral integration patterns by Southern blot analyses , we found that these 1307 independent clones harbored in fact 512 distinct chromosomal rearrangements ( referred to as sub-families , not shown ) . Moreover , each family , characterized by a common anchor site , presented an average of 5 . 39 ( range 1 to 20 ) distinct chromosomal rearrangements ( Table S2 ) . So far 423 deletions of which 294 are unique have been mapped by inverse or ligation mediated PCR ( Figure 2A ) , representing 25 . 4% of the mouse genome ( Figure 2B ) . The genomic coverage varies by chromosome , with no identified deletions on chromosomes 19 , X or Y; limited coverage of chromosomes 8 and 13 ( 8 . 7% and 4 . 2% , respectively ) ; and approximately 50% coverage of chromosomes 6 and 18 ( Figure 2B ) . On average , there is approximately 23% genome coverage per autosome . Deletion sizes range from 736 bp to 100 . 79 Mb , with a median of 1 . 61 Mb ( average size: 4 . 95 Mb ) ( Figure 2C ) , and vary according to the chromosome ( Table S3 ) . Chromosomes 1 , 8 and 16 are characterized by many small deletions , while chromosomes 18 and 14 have a few large deletions ( DelES database , http://bioinfo . iric . ca/deles ) . As depicted in Figure 1A and detailed earlier [23] , deletions are typically characterized by G418R clones which have lost the hygromycin and the puromycin genes . Interestingly , we found 29 families in which none of the G418R clones had lost hygromycin and/or puromycin resistance genes . One possibility that could explain this observation is that the anchor virus may have integrated in the vicinity of haplolethal loci . The Table S4 provides a list of genes present in the vicinity of 9 independent anchor loci that are not associated with chromosomal deletions ( e . g . 9 families which had a minimum of 15 G418R tertiary clones but no puroS hygro− clones ) . A literature search was performed to identify candidate genes in these regions which are known or predicted to be haploinsufficient or imprinted ( in red ) . On average , ∼1 haploinsufficient/imprinted candidate gene was identified per 1 . 61 Mbp window ( median deletion size ) starting from each directional anchor site ( Table S4 ) . These candidate genes , alone or in combination with other genes or non-coding elements within these regions , could potentially regulate essential cellular functions or specific characteristics of ESCs and thus cannot be maintained in a heterozygous state . Taken together , these results reveal that close to 300 independent deletions exhibiting a genome-wide distribution have been confirmed in the DelES collection . To evaluate the content of DelES genomic coverage , genes included in currently mapped deletions were classified according to their gene ontology ( GO ) terms . Gene ontology analysis of molecular functions of the 7083 mapped deleted genes revealed similar percentages in each category to that obtained for all annotated MGI genes ( with known functions ) ( data not shown ) . When genes were grouped by molecular function , the most abundant group was genes with signal transduction activity , followed by transcriptional regulation and nucleic acid binding activities ( data not shown ) . Distribution of some key genomic elements covered by mapped deletions , such as protein-coding genes , CpG islands , miRNA , ultraconserved elements , lincRNA , LINE/SINE elements , cancer-related genes and large deletions associated with cancers was evaluated ( Figure 3 , Table S5 ) . For this analysis , elements found in all of DelES's mapped deletions were compared to publicly available genome-wide entries for each category . Percentages represent ratios between the number of observed elements ( found in DelES mapped deletions ) and the number of reported entries ( assuming random distribution of elements ) , based on the current genome coverage of DelES ( 25 . 4% ) . Interestingly , mapped deletions cover close to 100% of each category: genes ( 7083/7348 ) , CpG islands ( 4265/3515 ) , miRNA ( 128/139 ) , lincRNA ( 470/540 ) , ultraconserved elements ( 241/277 ) , LINE/SINE ( 648571/602886 ) , cancer related genes ( 108/104 ) and large cancer-associate deletions ( 5/7 ) . Thus , several categories of protein-coding and non-coding sequences are well represented in deletions that are currently mapped . Moreover , clustering of specific genomic determinants has been reported [18] , [24]–[26] . As expected , several clusters of protein coding and non-coding elements were deleted in DelES clones ( highlighted in red , Table S5 ) . Deletions of entire clusters represent another strength of DelES , as it presents the opportunity to analyze synthetic interactions between family members and to study possible functional redundancies . In order to facilitate access to the DelES collection and all clone-specific information , a database accessible through a web interface offering data mining tools was constructed ( Figure S2 , http://bioinfo . iric . ca/deles ) . A detailed explanation of the content of the interface can be found in Figure S2 and in Text S1 . Taken as a whole , the DelES database allows for the management of biological material ( Plate tab ) and facilitates the search for ESC clones through phenotypic or genetic annotations ( Selection tab ) . The results of the search are directly linked to the complete data sets ( Families tab , Figure S2 ) . Phenotypic information can rapidly be associated to a graphical representation ( Screen tab ) of the mapped deletion including the implicated genomic features and BACs available for complementation studies ( e . g . , family 9 ) . Primary and tertiary clones in the DelES collection are distributed and frozen in 96 well plates . Localization of each clone within plates can be directly visualized online under the plate collection tab ( http://bioinfo . iric . ca/deles ) . Colored well images indicate the presence of suspected undesired chromosomal anomalies and rearrangements ( e . g . , detection of hygromycin gene ) . Master plates containing DelES collection were thawed once to rule out microbial contamination and to determine the proportion of clones which proliferate normally ( e . g . high proportion of Ki67+ cells ) or those which express high levels of alkaline phosphatase activity , typically associated with undifferentiated ESC . Figure 4A shows that nine percent of the puroS hygro− tertiary clones showed low alkaline phosphatase activity ( scores <3; families with clusters of clones with low AP activity are identified as ρ in Figure 4 ) , suggesting that their pluripotent capabilities were impaired . Specific genomic features covered by the nested deletions in these cells may be responsible for maintaining the pluripotent state of ESCs . This possibility will be investigated separately as part of a screen which will include multilineage differentiation assays and additional markers of pluripotency , such as Oct4 . Figure 4B shows that 14% of puroS hygro− tertiary clones presented low levels ( <60% ) of Ki67+ cells . Clusters of clones presenting altered proliferation status ( Ki67+ <60% ) were observed in 17 families ( identified as ψ , Figure 4 ) . The quantification of Ki67 expression was highly correlated with observed cell proliferation rate measured by flow cytometry using calibrated beads ( data not shown ) and batch collection of clones based on cell density or expansion ( i . e . clones collected in first batch “A” expand faster than those in last batch “D” which were the slowest to expand; see Figure S1 and Text S1 ) . This observation was also validated by a cell density assay which estimated ESC colony number one day after plating ( data not shown ) . Unfortunalely , clones with very low proportion of Ki67+ cells are easily lost upon freeze thaw procedures and are difficult to maintain in the collection ( S . F . , personnal observation ) . Overall , the vast majority of clones in the DelES collection express high levels of alkaline phosphatase; they proliferate normally; they support freeze-thaw procedures and they appear free of microbial contaminant . Clones can be recovered individually ( all frozen in 96 well plates and individually ) from several freeze thaw cycles . This suggests that DelES clones might be amenable to different functional screening procedures and that they can be validated separately . However , clones with low Ki67 activity are difficult to maintain . Using control ( parental ) R1 ESCs , we observed a strong correlation between the number of EB generated in culture and the number of ESCs plated ( Figure 5A ) . This value was reliable when cell density was above 5% ( S . F . , Figure 5A and data not shown ) . This observation was exploited to develop a functional screen in which each clone from DelES was individually seeded in two 96 well plates , one with LIF for estimation of ESC colony numbers ( seeding density in Figure 5A ) and the other without LIF for EB differentiation ( Figure 5B ) , aiming to identify minimal deleted regions that cause a block in normal EB development . Three criteria were used to identify clones and families with EB formation anomalies: 1 ) clones were only considered if cell density was above 5% ( 45% of clones ) ; 2 ) EB formation was considered abnormal in a tertiary clone if EB number was below one fifth of that detected in the corresponding primary clone ( 16 . 4% of clones ) ; 3 ) families with EB phenotype were selected only if all clones with deletions exceeding that of the minimal deleted region also show the phenotype ( see Figure 5C for selected vs rejected families ) . The high percentage of clones that were eliminated based on the first criteria reflects the wide distribution of cells ( e . g . , less than 1% to over 10% ) recovered after freeze-thaw process ( see also Methods for methylene blue staining ) . Using criteria described above , 15 . 6% of the families ( n = 14 ) were considered as potentially interesting for the future identification of EB formation determinants ( Figure 5D and http://bioinfo . iric . ca/deles for details of each clones in the selected families . See also Table S6 for primary screen data ) . Several tertiary clones from five randomly selected families ( 9* , 5061* , 5035 , 5214 , 5238; * = families with phenotype ) were tested for in vitro EB formation using standard assays in 60 mm dish in duplicate experiments . Corresponding primary clones were included in these validation experiments . These studies showed a concordance rate of 78% ( 28 out of 36 tested ) between tertiary clones tested in validation experiments and the results obtained in the primary screen . In total , 3 of the 5 tested families were validated including two ( 9 , 5061 ) which show putative phenotype associated with a minimal deletion region , located on chromosome 11 and 18 for family 5061 and 9 , respectively ( see http://bioinfo . iric . ca/deles ) From this assay , it thus appears that the primary screen underestimates the frequency of families which include clones with EB differentiation phenotype . Consistent with this , our 15 . 6% hit rate is below that previously observed in our pilot studies of nine families ( 33% of families showed EB differentiation phenotype [23] . DelES family 9 was chosen as the prototype for complementation studies since clones of this family harboring large deletions are unable to differentiate into embryoid bodies . Of importance for the complementation studies described below , the frequency of EB formation in clones containing large deletions was lower than 1 in 5000 ( i . e . 1 EB for 5000 cell plated ) . The minimal region responsible for the abnormal phenotype ( e . g . , red line in Figure 6A ) was mapped between the breakpoints of tertiary clones 9–35 ( 736 bp deletion , normal in vitro differentiation ) and 9–37 ( 4 . 3 Mbp deletion ) ( Figure 6A ) . This minimal deleted region contains 30 known protein-coding genes and can be covered by 20 contiguous bacterial artificial chromosomes BACs ( Figure 6A ) . These 20 BACs were modified for selection in ESC using a strategy that we adapted from existing recombineering systems to allow for the introduction of selectable marker gene into the chloramphenicol resistance sequence present in the backbone of the different BACs [27] . Details about this method , called SelectaBAC , are provided in Text S1 and in Figure S3 . Two independent tertiary clones with EB formation phenotype , 9–37 and 9–18 , were transfected with each of the 20 BAC constructs separately and assayed for embryoid body formation . Interestingly , none of the BAC but one -RP23-143E19- led to a complete rescue of the differentiation defect of clone 9–37 , and partial rescue of clone 9–18 which contains a larger deletion ( Figure 6B ) . To validate the presence of transfected BAC in complemented ESCs , metaphase fluorescence in-situ hybridization ( FISH ) was conducted using two differentially labeled probes: the first being RP23-143E19 itself and the second a control BAC which maps adjacent to the deleted region ( Figure 6C , lower-left panel ) . Transfected clones were compared with untransfected tertiary controls and R1 ESCs . As expected , normal R1 ESCs had two pairs of closely localized signals , corresponding to the intact mitotic chromosomes 18 ( Figure 6C ) . Haploid deletions were confirmed in tertiary clones 9–37 and 9–18 , which had only one pair of RP23-143E19 signals , closely-localized to one of the two pairs of control BAC signals . When clones 9–37 and 9–18 were transfected with the BAC of interest , 86% and 69% of cells counted displayed a pattern consistent with stable integration of the BAC ( Figure 6C , upper and lower-right panels ) . This pattern corresponds to two pairs of RP23-143E19 signals , one on chromosome 18 identified by the control BAC signals and another on a different chromosome ( not identified by the control BAC signals ) . Twelve percent of transfected 9–37 clones had larger and more intense red signals ( RP23-143E19 ) , which potentially indicates multiple integrations within the same chromosomal region ( data not shown ) . Control primary 9 clone , tertiary clones 9–18 and 9–37 and BAC-complemented tertiary clones 9–18 and 9–37 were injected separately into blastocysts or aggregated with CD1 morulas to evaluate their contribution to developing embryo . Mouse embryos , at E9 . 5 and E14 . 5 were analyzed for the presence of the neomycin gene ( A1 provirus or A1-S1 recombined proviruses ) by PCR , whereas the level of chimerism in newborns was estimated by coat color variation ( Figure 6D ) . As previously reported for the clone 9–18 [23] , the unmodified tertiary ESC clone 9–37 also failed to contribute to tissue chimerism in early embryos or newborn mice ( Figure 6D ) . In contrast , primary clone 9 , used as a positive control , contributed to tissue chimerism of 17 out of 55 mice analyzed ( Figure 6D ) . RP23-143E19-transfected clone 9–37 also contributed to embryogenesis with tissue chimerism in 36% and 18% of E14 . 5 and E9 . 5 embryos , respectively , and in 4 of 12 newborn mice ( Figure 6D ) . RP23-143E19 transfected clone 9–18 also produced chimeric embryos with a frequency of 50% at E14 . 5 . Thus far , all chimeras ( embryos and newborn ) appear phenotypically normal . Confirmation that BAC-transfected ESCs contributed to the chimeric embryos was obtained using Southern blot analyses performed with gDNA extracted from fetal liver cells ( Figure 6E ) . For newborn and adult mice , percentage of tissue chimerism was estimated at 80–95% and 10–35% for derivatives of primary clone 9 and BAC-complemented clone 9–37 , respectively ( Figure 6F ) . A more detailed analysis of BAC RP23-143E19 reveals the presence of four protein-coding genes: Ndst11 , Tcof1 , Rps14 and Cd74 ( Figure 7A ) . Q-RT-PCR analyses were performed to assess the expression level of these genes in family 9 ESCs and EBs , with or without BAC RP23-143E19 complementation . All four genes analyzed are expressed in both control ESCs and EBs ( primary 9 ) with delta CT values ranging between 0 . 5 ( Rps14 , highest expression ) to 14 . 4 ( Cd74 , lowest expression ) ( data not shown ) . Expression levels of all 4 genes in tertiary clone ESCs were about half that observed in primary clone 9 ( compare red bars , tertiary clones to black bars , primary clone in Figure 7B ) . Upon BAC transfection , expression levels of all four genes became either comparable to -or exceeded- that found in the primary clone ( compare blue with black bars for undifferentiated ESCs and pink with green bars for EBs in Figure 7B ) . To gain insight on the contribution of selected elements present on BAC RP23-143E19 to the observed phenotype , seven distinct deletions were generated ( Figure 7A ) . These included deletion of all 4 protein-coding genes separately , the intergenic region between Ndst1 and Rps14 , a distal promoter to Ndst1 and a lincRNA close to Rps14 . Six out of the 7 constructs complemented the EB formation defect observed in clone 9–37 to levels comparable to control primary 9 clone . These 6 constructs complemented clone 9–18 to levels equivalent to those detected with the unmodified BAC ( data not shown ) . Interestingly , the BAC containing the small deletion ( 3 . 89 Kb ) corresponding to the Rps14 gene did not rescue the phenotype observed in clone 9–37 ( Figure 7C ) and clone 9–18 ( data not shown ) , indicating that this genomic region is haploinsufficient for EB formation . Following this observation , Rps14 cDNA expression vector was introduced in 9–37 and 9–18 ESC clones co-transfected with the BAC RP23-143E19 construct no . 5 ( lacking the Rps14 gene , Figure 7A ) , to verify the possibility that a hidden genetic element located within this small region that includes Rps14 was responsible for the EB formation phenotype . Results from these experiments indicated that 3 out of 8 clones isolated from 9–37 doubly transfected cells showed full complementation and 1 out of 4 clones from 9–18 cells was partially rescued ( Figure 7D ) . Expression analyses of these complemented clones revealed that all 4 protein-coding genes ( Ndst1 , Tcof1 , Cd74 and Rps14 ) were expressed at endogenous levels when compared to the primary clone ( data not shown ) . These results thus strongly suggest that Rps14 is haploinsufficient for EB formation . We then transfected Rps14 cDNA alone in 9–18 ( data not shown ) and 9–37 tertiary clones to test if this gene is the sole element responsible for the abnormal phenotype . Interestingly , analyses of several transfected clones showed no complementation with Rps14 cDNA ( Figure 7E ) , raising the possibility that another genetic element is necessary for complementation of DelES family 9 . Nevertheless , these experiments show that Rps14 is not sufficient , but required , to complement the EB formation defect found in DelES family 9 .
In conclusion , DelES is a new resource that offers a library of ESC deletion clones , a BAC complementation system ( SelectaBAC ) and a comprehensive database . DelES benefits from precisely localizable loxP-containing retroviral vectors which accelerate the generation of segmental haploidy , and is complementary to other functional genomics resources . Its usefulness for uncovering ESC fate determinants was demonstrated herein with the identification of Rps14 as a novel haploinsufficient gene for EB formation and early embryonic development . DelES is designed to assess the roles of adjacent coding and non-coding sequences in the mammalian genome , as well as their genetic interactions . Our current efforts are to extend the coverage of mapped deletions in DelES clones and to conduct additional functional screens , such as cell cycle analysis , pluripotency assessment and hematopoietic differentiation , to enrich our publicly available resource .
Viral producer cell lines and infection of target cells were conducted as described [23] . Reagents used for Cre-loxP recombination ( A1 and S1 retroviruses , and pCX-Cre constructs ) were described previously [23] . Briefly , following R1 ESCs [40] infection with anchor virus A1 , approximately 288 puromycin resistant primary clones were isolated . Q-PCR assays were performed on genomic DNA to discard primary clones containing presumptive trisomies . Five million primary clone cells ( one clone at a time ) were infected with the saturation virus S1 . Following hygromycin selection , 107 cells from these secondary populations ( secondary population are derived from a single primary clone ) were electroporated with 25 ug of supercoiled pCX-Cre and selected with G418 , as described previously [23] . Up to 44 G418R tertiary clones were isolated per electroporated secondary population and maintained in 96-well plates ( labeled TER0xxx ) . ESCs maintained in 96-well plates were either dissociated manually or with a Biomek FX robot ( Beckman Coulter ) enclosed in a biosafety cabinet . G418 resistant tertiary clones sensitive to puromycin ( puroS ) were arrayed together in 96-well plates ( labeled CPC0xxx ) . “Normalized” 96-well plates were also generated with puroS clones presenting similar proliferation rate for use in functional assays . ESCs were cryopreserved at each stage of DelES collection generation . A detailed description of the methods used for the generation of DelES collection can be found in Text S1 . Detailed descriptions of the high-throughput assays performed with puroS clones arrayed in normalized plates are provided in Text S1 . The Alkaline Phosphatase Kit ( Chemicon ) was used according to the manufacturer's protocol . ESCs immunostained with a PE-conjugated mouse anti-human Ki67 monoclonal antibody ( dilution 1∶100 , BD Biosciences ) were analyzed by flow cytometry . Cell counts were performed by flow cytometry using TruCOUNT reference beads ( BD Biosciences ) . Cell densities were evaluated by methylene blue staining of ESC colonies . Gelatin-plated clones were seeded in 96 well plates ( Sarstedt ) containing a semi-solid differentiation media and in parallel on a new gelatinized plate ( NUNC ) . EBs were counted following 8 days of differentiation , while colonies on gelatinized plates were stained with methylene blue twenty-four hours after seeding . Automated quantification of the methylene blue stained area was used to evaluate the cell input that produced the corresponding EB number ( Metamorph software ) . Criteria were established to determine families with clones presenting abnormal EB formation phenotypes , i . e . insufficient EB number or disaggregation . The first was to exclude tertiary clones with low cell input values ( methylene blue <5% , n = 722 clones , 55 . 2% ) , which could be the result of a defect in proliferation or cell adhesion , or simply a technical issue . The number of EBs obtained for each tertiary clone was compared to that of the corresponding primary clone . Based on values obtained from larger format experiments , a tertiary clone was called abnormal when it formed less than 20% EBs relative to its primary clone ( n = 96 out of 585 ) . Our final criterion in identifying an abnormal family was to verify a correlation between decreased EB formation with a larger deletion size ( where mapping was available ) . Genomic DNA ( gDNA ) from primary clones was extracted with DNeasy 96 Blood & Tissue Kit ( Qiagen protocol ) and used for Q-PCR screening of presumptive trisomies and mapping of proviral integration sites ( see below and Text S1 ) . Genomic DNA from primary clones , all tertiary clones ( labeled TER0xxx ) , and puroS tertiary clones ( labeled CPC0xxx or MPL0xxx ) were extracted using DNAzol ( Invitrogen ) by centrifugation in V-bottom 96-well plates . Southern blot analyses were performed as previously described [23] . To verify single integration of anchor virus , primary clone gDNA was digested with BglII-BamHI restriction enzymes and detection performed with a neomycin probe . Southern blot analyses with tertiary clone gDNA ( EcoRI restriction digest ) , were either performed with a neomycin probe to asses clonal diversity of rearrangements ( e . g . clone classification into sub-family ) or with a hygromycin probe to confirm the loss of hygromycin resistance gene . The presence/absence of hygromycin gene was also monitored by Q-PCR assays ( Text S1 ) . Integration sites of the anchor virus were mapped in primary clones by I-PCR or LM-PCR . Saturation virus integration sites were mapped in tertiary clones by LM-PCR . The I-PCR approach was previously described [23] . The LM-PCR strategy , which relies on specific oligonucleotides described in Table S7 , was adapted from a published protocol [41] summarized in Text S1 . DNA sequences corresponding to proviral integration sites were mapped using the BLAT alignment tool of the UCSC Genome Browser ( http://genome . ucsc . edu/ , NCBI mouse Build 37 ) [42] . BACs from the RP23 library ( pBACe3 . 6 vector [45] ) were obtained from the BACPAC Resource Center ( Children's Hospital Oakland Research Institute , Oakland , California ) and maintained in their original host strain DH10B in the presence 12 µg/ml chloramphenicol ( unmodified BACs ) or 25 µg/ml kanamycin ( retrofitted BACs ) . SelectaBAC retrofitting strategy , adapted from published protocols [27] , [46] , [47] , is described in the Text S1 . ESCs maintained on a feeder layer in 12-well plates were transfected with 2 ug of circular BAC DNA using Lipofectamine 2000 Reagent ( Invitrogen ) , according to manufacturer's protocol . Selection was started 48 h later , with the following concentration of drugs maintained for at least 5 days: 1 . 5 ug/ml puromycin ( Sigma ) , or 150 ug/ml hygromycin ( Roche ) , or 15 ug/ml blasticidin ( Sigma ) , or 30 ug/ml zeocin ( Invitrogen ) . ESC differentiation in embryoid bodies was performed in a LIF-deprived semi-solid media , as described [11] . Genomic DNA from BAC transfected clones was isolated using DNAzol , according to the manufacturer's instructions ( Invitrogen ) . Southern blot detection of transfected BAC DNA was performed using EcoRV digestion and a probe specific to the neomycin gene , as described [48] . Total cellular RNA was isolated from BAC transfected clones ( undifferentiated ESCs or embryoid bodies ) with Trizol ( Invitrogen ) , according to the manufacturer's instructions . Quantitative RT-PCR assays were performed according to standard protocols described in Text S1 . Mouse chimeras were generated by the transgenic facility of IRIC . ESCs [40] corresponding to primary clone no . 9 , tertiary clones 9–18 and 9–37 ( with in vitro phenotype ) and BAC-transfected 9–18 and 9–37 clones ( rescued in vitro phenotype ) , were injected into C57BL/6 blastocysts or aggregated with CD1 morulas . Tertiary clone 9–18 results showed in Figure 6 are already published [23] . ESCs contribution to chimeric embryos ( at E9 . 5 and E14 . 5 ) was evaluated by PCR using genomic DNA extracted with a standard protocol ( lysis with of 100 mM NaCl , 10 mM Tris pH 8 . 0 , 25 mM EDTA pH 8 . 0 , 0 . 5% SDS , and 2 . 5 ug/ml Proteinase K followed by phenol-chloroform extraction and ethanol precipitation ) or Sigma REDExtract-N-Amp Tissue PCR kit ( primers specific to the neomycin gene are described in the Table S7 ) . Southern blot analysis was performed as previously described [48] with a neomycin probe and EcoRV-digested genomic DNA isolated from E14 . 5 fetal livers using DNAzol ( Invitrogen ) . ESC contribution to adult mice was determined by evaluation of the coat color chimerism . BACs RP23-143E19 and RP23-323M5 were labeled with Spectrum Orange and Green fluorochromes , respectively , via Nick Translation ( Abbott Molecular Cat . No . 32-801300 ) . BACs have been tested both separately and together on mouse control cells from Leukemia Cell Bank of Quebec . A minimum of one hundred interphase nuclei and ten metaphases were evaluated per sample and results are given as signal distribution percentages . | Stem cells have received considerable public attention in part because of their potential application in regenerative therapies . Stem cells can be operationally defined as cells that have the unique property to self-renew , as well as to generate more differentiated progeny ( differentiation ) . However , much remains to be learned about the genes regulating stem cell differentiation and renewal , their relationship to each other , and the signaling pathways that control their expression and/or activity . In this paper , we present a new resource developed in our laboratory , called DelES , for chromosomal deletion in ES cells . By reinserting deleted DNA fragments in a set of ESC clones harboring nested chromosomal deletions , we identified the Rps14 gene as being haploinsufficient for embryoid body formation . We think that our library of more than 1 , 300 clones represents a new resource that should allow the identification of genes and other elements that are essential for stem cell activity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"developmental",
"biology/cell",
"differentiation",
"cell",
"biology",
"genetics",
"and",
"genomics/functional",
"genomics"
] | 2010 | Genome-Wide Interrogation of Mammalian Stem Cell Fate Determinants by Nested Chromosome Deletions |
In recent years , highly detailed characterization of adult bone marrow ( BM ) myeloid progenitors has been achieved and , as a result , the impact of somatic defects on different hematopoietic lineage fate decisions can be precisely determined . Fetal liver ( FL ) hematopoietic progenitor cells ( HPCs ) are poorly characterized in comparison , potentially hindering the study of the impact of genetic alterations on midgestation hematopoiesis . Numerous disorders , for example infant acute leukemias , have in utero origins and their study would therefore benefit from the ability to isolate highly purified progenitor subsets . We previously demonstrated that a Runx1 distal promoter ( P1 ) -GFP::proximal promoter ( P2 ) -hCD4 dual-reporter mouse ( Mus musculus ) model can be used to identify adult BM progenitor subsets with distinct lineage preferences . In this study , we undertook the characterization of the expression of Runx1-P1-GFP and P2-hCD4 in FL . Expression of P2-hCD4 in the FL immunophenotypic Megakaryocyte-Erythroid Progenitor ( MEP ) and Common Myeloid Progenitor ( CMP ) compartments corresponded to increased granulocytic/monocytic/megakaryocytic and decreased erythroid specification . Moreover , Runx1-P2-hCD4 expression correlated with several endogenous cell surface markers’ expression , including CD31 and CD45 , providing a new strategy for prospective identification of highly purified fetal myeloid progenitors in transgenic mouse models . We utilized this methodology to compare the impact of the deletion of either total RUNX1 or RUNX1C alone and to determine the fetal HPCs lineages most substantially affected . This new prospective identification of FL progenitors therefore raises the prospect of identifying the underlying gene networks responsible with greater precision than previously possible .
Definitive hematopoiesis is a complex , multistep process involving increasingly restrictive cell fate decisions by self-renewing , multipotent hematopoietic stem cells ( HSCs ) . Commitment to lymphoid , granulocytic/monocytic ( GM ) , megakaryocytic and erythroid lineages occurs through the differentiation of immature progenitors , and is subject to spatial and temporal control by intrinsic and extrinsic factors [1 , 2] . In recent years , high-resolution characterization of BM progenitor populations has been achieved and this critical advance has allowed detailed interrogation of the gene regulatory networks which govern normal homeostatic and malignant hematopoietic differentiation , both in clinical patient samples and adult transgenic mouse models [3–16] . In particular , various assumptions about the process of myeloid progenitor differentiation have been challenged . For example , the existence of an obligatory intermediate CMP population , as an ancestor of all committed granulocytic/monocytic , megakaryocytic and erythroid progenitors , is now questioned [3 , 7] . Pronk et al proposed the separation of bone marrow CMPs into PreGM and PreMegakaryocytic/Erythroid ( PreMegE ) progenitors , based on CD150/Endoglin expression [5] . Utilizing a Runx1 dual-reporter mouse model ( P1-GFP::P2-hCD4 ) [17] ( which reflects the alternate use of the two Runx1 promoters ) we recently reported that in fact the P2-hCD4- PreMegE fraction comprises pro-erythroid progenitors , whereas P2-hCD4+ PreMegEs are skewed in favor of megakaryocytic output [18] . Examinations of the roles of developmental transcription factors ( TFs ) , particularly Runx factors , in the developing mouse embryo have greatly advanced our understanding of the origins of blood development in utero [19 , 20] . Runx1 expression is observed in the different hematopoietic waves: in the earliest primitive myeloid and erythroid progenitors at embryonic day ( E ) 7 . 5 , in the intermediate erythromyeloid progenitors ( EMPs ) /lymphoid progenitors at E8 . 5 , and in the long-term repopulating HSCs from E10 . 5 [21–27] . Indeed , HSC emergence through endothelial-to-hematopoietic transition ( EHT ) is directed by P2-expressed RUNX1B [17 , 28] . The fetal liver is seeded initially by EMPs , followed by definitive HSCs from E11 . 5 , and forms an ideal niche for hematopoietic stem and progenitor cell ( HSPC ) expansion and maturation [21 , 29 , 30] . By E14 . 5 , a definitive hematopoietic stem and progenitor cell hierarchy is believed to be firmly established in the fetal liver [31 , 32] . Equivalents of previously described BM HPCs , including MEPs , CMPs , Granulocyte/Monocyte Progenitors ( GMPs ) and Megakaryocyte Progenitors ( MkPs ) , have been identified in E14 . 5 fetal liver [31 , 33] . However , the resolution at which these fetal liver hematopoietic progenitor cells can be identified and isolated is far lower than for their bone marrow equivalents [9 , 31 , 34–37] . This hinders the interrogation of this fetal liver hierarchy , particularly in mouse models of hematopoietic disorders with in utero origins . Indeed , higher-resolution purification of myeloid hematopoietic progenitor cells would allow the execution of transcriptomic and clonogenic lineage analysis to investigate the disruption of specific gene regulatory networks and the resultant impact on hematopoietic output . We therefore endeavored to improve our resolution of the specification of fetal liver myeloid hematopoietic progenitor cells . Having initially determined that multilineage cultured colony-forming unit ( CFU-C ) activity is restricted to the CD55+ CD150+ Megakaryocyte-Erythroid Progenitor ( MEP ) and Common Myeloid Progenitor ( CMP ) compartments , we utilized the Runx1-P1-GFP::P2-hCD4 mouse model to further subdivide these populations . Within the MEP , the P2-hCD4+ fraction possessed the entirety of its bipotential megakaryocytic/erythroid ( Mk/Ery ) output . The P2-hCD4+ CMP , meanwhile , had more balanced myeloid output , in particular decreased erythroid specification , than its P2-hCD4- counterpart . Subsequently , we identified CD31 , CD45 and CD48 as candidate markers whose expression correlated with P2-hCD4 . To demonstrate the potential applications of this approach , we characterized perturbations in CD150+ CD31-/+ MEPs and CD150+ CD31low/high CMPs from Runx1-null ( Runx1-flox::Vav1-Cre , Runx1-del[38] ) and Runx1c-null ( Runx1-P1-MRIPV [39] ) mice . Using this approach we revealed fundamental differences between the two mouse models . Whereas , Runx1-del/del CD31low CMPs have impaired erythroid specification and maturation , Runx1-P1-MRIPV/MRIPV CD31high CMPs have impaired megakaryocytic specification but normal megakaryocyte and erythroid maturation . This indicates that the RUNX1B and RUNX1C isoforms fulfill different roles during fetal liver hematopoiesis . Furthermore , it demonstrates the benefits of high specificity fetal liver hematopoietic progenitor isolation for elucidating complex myeloid lineage fate decisions .
The existence of a bipotent MEP has long been posited in mid-gestation fetal liver , in addition to adult bone marrow [3 , 31] . The multipotent CMP is more controversial , however , with various groups reporting that it is actually a heterogeneous population of megakaryocytic/erythroid/granulocytic/monocytic progenitors , capable of varying lineage output [5 , 40] . More recently , CD150 and CD55 expression have been associated with Mk/Ery potential in adult hematopoietic progenitors [41] . Therefore , we analyzed CD55/CD150 expression in the E14 . 5 fetal liver MEP ( Lin- SCA1- C-KIT+ ( LK ) CD16/32low CD34- ) and CMP ( LK CD16/32low CD34+ ) progenitor fractions ( Fig 1A and 1B ) . Additionally , to remove the previously identified CD41+ MkP fraction [33 , 42] , we analyzed solely CD41- MEPs and CMPs . In both the CD41- MEP and CMP fractions , three populations were discernible: CD55- CD150- ( 4% of MEPs , 52% CMPs ) ; CD55+ CD150- ( 69% MEPs , 12% CMPs ) ; and CD55+ CD150+ ( 26% MEPs , 30% CMPs ) . By contrast , we observed the GMP and MkP populations were more homogeneous with respect to both CFU-C activity ( S1A Fig ) and CD150/CD55 expression: 95% of MkPs were CD55+ CD150+ and 91% of GMPs were CD55low CD150low ( S1B–S1F Fig ) . We also confirmed that negligible ( <1% ) CFU-C activity resided in the remaining cKit- fetal liver fraction ( S1A Fig ) . We therefore proceeded to characterize the differentiation potentials of the various CD55/CD150 MEP and CMP subfractions . Interestingly , CD55- CD150- MEPs did not possess any CFU-C activity in semi-solid myeloid MethoCult medium ( Fig 1C ) . Additionally , they did not yield cells expressing either the erythroid marker TER119 , the megakaryocytic marker CD41 , the granulocytic/monocytic markers CD11b/GR1 or the mast/progenitor cell-associated C-KIT following OP9 co-culture in pro-myeloid medium ( Fig 1F and 1G , S1G and S1H Fig ) . By contrast , CD55- CD150- CMPs yielded CFU-Cs at 20% plating efficiency , which solely comprised granulocytic ( CFU-G ) , monocytic/macrophage ( CFU-M ) and GM ( CFU-GM ) colonies ( Fig 1C and 1E ) . Additionally , 98% of OP9 co-cultured CD55- CD150- CMP-derived cells were CD11b+ GR1+ granulocytes/monocytes ( Fig 1H and 1I ) , with just 0 . 01% being TER119+ erythrocytes and 0 . 11% CD41+ megakaryocytes . CD55+ CD150- cells had increased CFU-C activity by comparison ( 25% for MEPs , 40% for CMPs , Fig 1C ) . CD55+ CD150- MEPs solely comprised erythroid colony-forming units ( CFUe , Fig 1D ) and produced 92% TER119+ erythroid cells on OP9 ( Fig 1F and 1G ) . CD55+ CD150- CMPs chiefly yielded CFU-G , M and GM colonies , but also comprised a small number ( approximately 1% ) of erythroid burst-forming units ( BFUe ) , bipotent megakaryocytic/erythroid colony-forming units ( CFU-MkE ) and multipotential granulocytic/erythroid/monocytic/megakaryocytic colony-forming units ( CFU-GEMM , Fig 1E ) . Accordingly 93% CD11b+ GR1+ granulocytic/monocytic and 1 . 8% CD41+ megakaryocytic cells resulted from OP9 co-culture ( Fig 1H ang 1I ) . Finally , CFU-C activity was chiefly observed in the CD55+ CD150+ fractions ( 50% for MEPs , 70% for CMPs , Fig 1C ) . Crucially , the CD55+ CD150+ subpopulation was the only MEP fraction to demonstrate megakaryocytic/erythroid bipotentiality ( yielding 1 . 2% CFU-Mks and 0 . 5% CFU-MkEs , Fig 1D; 79% TER119+ erythrocytes and 14% CD41+ megakaryocytes , Fig 1F and 1G ) . Additionally , the CD55+ CD150+ CMP fraction had the broadest range of CFU-C activity , yielding approximately equal numbers of CFU-M , GM , Mk , MkE , GEMM and BFUe colonies ( Fig 1E ) and all three Mk/Ery/GM cell fractions in OP9 co-culture ( Fig 1H and 1I ) . We therefore demonstrated that inclusion of anti-CD150 and , to a lesser extent , anti-CD55 antibodies in MEP/CMP isolation protocols aids the purification of highly clonogenic multipotent myeloid progenitors . The FL Lin- cKithigh Sca1high ( LSK ) is a highly clonogenic fraction , which can be separated into long-term repopulating HSC and short-term repopulating Multipotent Progenitor ( MPP ) fractions on the basis of CD48 and CD150 expression [32 , 43] ( S1I and S1J Fig ) . To determine whether fetal liver LSK hematopoietic stem and progenitor cells are the likely ancestors of the fetal liver CMPs , MEPs , GMPs and MkPs , we cultured E14 . 5 fetal liver HSCs and LSK CD48-/+ MPPs in pro-myeloid medium for 20 hours ( S1K and S1L Fig ) . The majority of cultured HSCs ( 60% ) retained an LSK immunophenotype , but had upregulated CD48 . Only 6% of cultured HSCs had an LK immunophenotype , but within this fraction CD150+/- CMP , CD150+/- MEP , GMP and MkP fractions were discernible . More convincingly , a large fraction ( 27% ) of cultured LSK CD48+ CD150+ MPPs had acquired an LK immunophenotype , yielding mostly CD150+ MEPs and MkPs . By contrast , 12% of cultured LSK CD48+ CD150- MPPs had an MkP , GMP , CD150- MEP or CD150- CMP immunophenotype . This therefore suggests that fetal liver HSCs are capable of establishing a myeloid progenitor hierarchy , as their LSK CD48+ MPP progeny give rise to the Sca1- cKit+ MkP , GMP , MEP and CMP progenitors . The observation that fetal liver HSCs are capable of establishing a hematopoietic stem and progenitor cell hierarchy does not preclude the possibility that yolk sac erythro-myeloid progenitors ( EMPs ) could also contribute to these cell populations . To determine whether EMPs could directly give rise to a hematopoietic progenitor cell profile comparable to that observed in the fetal liver , we isolated yolk sac cells from E9 . 5 embryos and cultured them in pro-myeloid medium for up to 24 hours ( with or without prior explant culture ( S1M and S1N Fig ) ) . We observed that a small proportion of cells ( up to 15% in the explant cultures ) had the LK immunophenotype . The majority of these cells were either immunophenotypic GMPs or were LK CD16/32- CD150- CD41+ . No CD150+ LK progenitors were produced in these cultures . This therefore suggests that yolk sac EMPs are incapable of directly establishing our observed FL myeloid hematopoietic progenitor hierarchy . We previously used Runx1-P1-GFP::P2-hCD4 dual-reporter mice to demonstrate that Runx1-P2 expression coincides with enhanced megakaryocytic specification in adult bone marrow PreMegEs [18] . Having established that CD150 expression enhances megakaryocyte/erythroid progenitor isolation in fetal liver MEPs , we tried to improve this further by characterizing Runx1-P1-GFP/P2-hCD4-expressing MEPs ( Fig 2A and 2B ) . In E12 . 5 , E13 . 5 and E14 . 5 fetal liver , the CD41- CD150+ MEP fractions were dominated by P1-GFP+ P2-hCD4- cells ( 66% , 72% and 80% respectively ) . P1-GFP- P2-hCD4- populations were also present ( 25% , 25% and 13% respectively ) . However , P1-GFP+ P2-hCD4+ populations were considerably smaller , at 8% , 3% and 7% of CD41- CD150+ MEPs respectively . Importantly , at each timepoint this restricted P2-hCD4+ fraction possessed the vast majority of BFUe , CFU-Mk and CFU-MkE activity ( Fig 2C , S2A and S2B Fig ) . This was particularly marked at E14 . 5 , with this population yielding 20% CD41+ megakaryocytes following OP9 co-culture , compared to <5% in E12 . 5 and E13 . 5 cultures ( Fig 2D and 2E , S2C–S2F Fig ) . To achieve a more accurate estimate of the potentiality of P2-hCD4- and P2-hCD4+ CD41- CD150+ MEPs , we performed single cell OP9 co-culture assays ( Fig 2F ) . Single P2-hCD4- and P2-hCD4+ CD41- CD150+ MEPs displayed plating efficiencies of 33 . 3% and 28 . 5% respectively in these conditions . Alternatively , replating in MethoCult yielded 60 . 4% and 51 . 0% plating efficiencies for P2-hCD4- and P2-hCD4+ CD41- CD150+ MEPs respectively ( Fig 2G ) . Under both culture conditions , approximately 15% of positive wells derived from P2-hCD4+ CD41- CD150+ MEPs yielded both megakaryocytic and erythroid cells . By contrast , no single P2-hCD4- CD41- CD150+ MEPs demonstrated dual lineage specification . We therefore established that MEP bipotentiality is restricted to the CD41- CD150+ P2-hCD4+ MEP subfraction , comprising approximately 1% of immunophenotypic MEPs and fewer than 0 . 05% of total live cells in E14 . 5 fetal liver ( Fig 2A and 2B ) . To fully demonstrate the increased megakaryocytic lineage commitment in E14 . 5 fetal liver P2-hCD4+ CD41- CD150+ MEPs compared to their P2-hCD4- counterparts , we cultured both cell types in pro-myeloid medium for 14 hours ( Fig 2H and 2I ) . P2-hCD4+ CD41- CD150+ MEPs produced more MkPs than the P2-hCD4- population , which yielded greater proportions of erythroid-dominated CD150- MEPs . We therefore demonstrated that the P2-hCD4+ fraction represents a pro-megakaryocyte subpopulation of immunophenotypic MEPs in E14 . 5 FL . Having established that Runx1-P2-hCD4 expression heterogeneity in CD41- CD150+ MEPs is associated with different lineages , we turned to CD41- CD150+ CMPs . E12 . 5 , E13 . 5 and E14 . 5 fetal liver CD41- CD150+ CMPs mostly express Runx1-P1-GFP ( 81% , 86% and 94% respectively , Fig 3A and 3B ) . Additionally , most CD150+ CMPs co-express P2-hCD4 ( 74% in E12 . 5 , 81% in E13 . 5 and 73% in E14 . 5 fetal liver ) . At all three stages , colony-forming output was skewed in favor of CFU-Ms and away from BFUes in P2-hCD4+ CMPs compared to P2-hCD4- CMPs ( Fig 3C , S3A and S3B Fig ) . Moreover , CFU-GM output was enriched at E13 . 5 and E14 . 5 , and multipotent CFU-GEMM activity was enriched in P2-hCD4+ CMPs at E13 . 5 compared to the P2-hCD4- populations ( Fig 3C , S3B Fig ) . OP9 co-culture of the bulk P2-hCD4- and P2-hCD4+ CD41- CD150+ CMP populations revealed changing lineage output in E12 . 5 , E13 . 5 and E14 . 5 fetal liver ( Fig 3D and 3E , S3C–S3F Fig ) . Notably , pro-GM/anti-Erythroid bias was evident in E14 . 5 P2-hCD4+ CMPs . Analysis of single co-cultured CMPs ( Fig 3F and 3G ) confirmed this , as almost 60% of E14 . 5 P2-hCD4- CMPs produced solely erythroid cells , compared to 12 . 5% of P2-hCD4+ CMPs . The granulocyte/monocyte-restricted fraction ( “GM only” ) represented 3% and 16% of the P2-hCD4- and P2-hCD4+ CMPs respectively . Megakaryocytic output was similarly enhanced in P2-hCD4+ CMPs , with 38% possessing Mk-restricted and 9% demonstrating bipotential megakaryocytic/erythroid ( Mk+Ery ) activity , ( compared to 28% and 6% of P2-hCD4- CMPs ) . Importantly , we observed multipotent ( balanced Mk+Ery+GM ) lineage output in P2-hCD4+ CMPs only , albeit in <10% of positive wells . This suggests multilineage activity is highly restricted to Runx1-P2-hCD4+ CMPs in E14 . 5 fetal liver . Following culture in pro-myeloid medium for 14 hours , the majority of P2-hCD4- CMPs had acquired a CD34- MEP phenotype ( 90% of total cells , Fig 3H and 3I ) . Of these , 71% had downregulated CD150 expression , suggesting erythroid commitment . By contrast , >20% of cultured P2-hCD4+ CMPs had acquired an MkP immunophenotype ( upregulating CD41 ) , 10% had a CD41- CD150- MEP immunophenotype , and 10% expressed CD16/32 , thus acquiring a GMP immunophenotype . Runx1-P2-hCD4+ CMPs can therefore efficiently produce immunophenotypic MkPs , MEPs and GMPs , whereas P2-hCD4- CMPs appear committed to the generation of immunophenotypic MEPs . Having established that Runx1-P2-hCD4+ MEPs and CMPs are enriched in multilineage progenitors , we endeavored to identify cell surface markers which correlated with P2-hCD4 expression in E14 . 5 fetal liver . The aim was to achieve the prospective isolation of these populations from wild type ( WT ) or other transgenic mouse lines in the absence of the Runx1-P2-hCD4 reporter . Firstly , we performed Single Cell RNA Sequencing on CD41- CD150+ P2-hCD4- and P2-hCD4+ MEPs and CMPs . By performing a principal component analysis , we observed a clear transition in transcriptomic activity from the P2- MEP to the P2+ CMP ( S4A Fig ) . In particular , erythroid gene expression ( for example , Klf1 ) was highly upregulated in the P2- MEPs being downregulated with the upregulation of P2-hCD4 and also sharing an inverse relationship with the megakaryocytic transcription factor Fli1 and the granulocyte/monocyte transcription factor Spi1 ( Pu . 1 ) . ( Full lists of differentially expressed genes between P2-/+ MEPs and P2-/+ CMPs can be found in S5 Table and S6 Table respectively . ) In order to identify markers which may aid the isolation of P2-hCD4- and P2-hCD4+ MEPs , we analyzed the expression of various cell surface markers , the aim being to identify genes which are upregulated or downregulated in some or all P2-hCD4+ MEPs compared to P2-hCD4- MEPs . Promising candidates included Cd48 , Pecam1 ( Cd31 ) , Ptprc ( Cd45 ) , Eng ( Endoglin ) , Itgb1 ( Cd29 ) and Tek ( Tie2 ) ( S4B Fig ) . We therefore screened antibodies which had been raised against these markers and other heterogeneously-expressed markers in fetal liver ( S4C Fig ) . Promising candidates included the leukocyte common antigen , protein tyrosine phosphatase receptor type C ( PTPRC ) or CD45; CD48 antigen; and platelet/endothelial cell adhesion molecule 1 ( PECAM1 ) or CD31 . All three markers correlated positively with Runx1-P2-hCD4 in LK hematopoietic progenitors ( Fig 4A ) . Immunofluorescence staining , performed on total fetal liver sections from P1-GFP::P2-RFP E14 . 5 embryos , demonstrated that although P2-RFP cells are comparatively rare ( 2 . 89% of total DAPI+ cells , Fig 4B , S4D Fig ) in E14 . 5 FL , a large proportion ( 64 . 4% ) have cell surface CD31 staining . We therefore proceeded to characterize CD31 expression in CD41- CD150+ MEPs and CMPs . In wild type fetal liver , 20% of CD41- CD150+ MEPs expressed CD31 ( Fig 4C ) . By comparison , >90% of CD41- CD150+ CMPs were CD31+ , although CD31low and CD31high populations were discernible . We therefore separated CD41- CD150+ CMPs into the 50% lowest and 50% highest CD31-expressing subfractions ( CD31low and CD31high CMPs respectively ) for further characterization . All MEPs and CMPs expressed CD45 and CD48 ( Fig 4C , S4E Fig ) . Expression of the RUNX1 target genes Gfi1b and Gfi1 in MEPs and CMPs was also assessed , utilizing the Gfi1b-GFP and Gfi1-GFP reporter mouse lines ( S4F and S4G Fig ) . The megakaryocytic/erythroid transcription factor Gfi1b was expressed in all CD41- CD150+ MEPs and CMPs , although the Gfi1b-GFP median fluorescence intensity ( MFI ) was decreased in some CD31high CMPs ( S4F Fig ) . In contrast , granulocyte/monocyte-associated Gfi1-GFP was expressed in only 8% of CD41- CD150+ MEPs and 39% of CD41- CD150+ CMPs , the highest Gfi1-GFP MFI being associated with high CD31 expression ( S4G Fig ) . Assessing the CFU-C activity of bulk and single MEP cells in MethoCult culture ( Fig 4D , S4H Fig ) revealed that all CFU-Mk and CFU-MkE activity resided with CD31+ MEPs; CD31- MEPs possessed CFUe and BFUe activity only . OP9 co-culture confirmed this lineage output ( Fig 4F and 4H; S4I Fig ) , as CD41+ megakaryocyte cells were absent in CD31- MEP co-cultures but present in 7% of positive wells derived from CD31+ MEPs ( S4I Fig ) . This suggested that CD31+ MEPs possess some Mk lineage specificity ( albeit in only 7–20% of cells ) , whereas CD31- MEPs solely constitute erythroid progenitors . Erythroid lineage output was enriched in CD31low CMPs compared to CD31high CMPs ( Fig 4E , 4G and 4I; S4J Fig ) ; 20% of CD31low CMPs produced BFUes and 54% of single co-cultured cells yielded solely TER119+ erythroid cells . CD11b+ Gr1+ granulocyte/monocyte and CD41+ megakaryocyte cell output was enhanced considerably in CD31high CMPs . Interestingly , although CFU-GEMM activity was enriched in CD31high CMPs , the proportion of positive co-cultured wells yielding megakaryocyte/erythroid/granulocyte/monocyte cells was higher for CD31low CMPs . This was probably due to the overall plating efficiencies being higher for CD31high CMPs . Therefore , both CD31low CMPs and CD31high CMP populations are heterogeneous , albeit skewed towards erythroid and GM/megakaryocytic specification respectively . The short-term culture of these populations revealed hierarchical relationships; CD31+ MEPs gave rise to CD31- MEPs and MkPs , whereas CD31- MEPs rapidly downregulated CD150 ( S4K and S4M Fig ) . The CD31low CMPs are skewed to a pro-MEP fate , whereas CD31high CMPs efficiently produce GMPs and CD31+ MEPs ( S4L and S4N Fig ) . CD31low CMPs therefore comprise a more advanced pro-erythroid fraction , but can also produce CD31high CMPs , suggesting some granulocyte/monocyte commitment . As previously indicated , the provenance of the fetal liver myeloid progenitors is not entirely clear . Following initial fetal liver colonization at E11 . 5 , the HSCs expand exponentially and the numbers of repopulating units ( per embryo equivalent ) peak by E16 , as fetal bone marrow colonization is underway ( having begun from E15 ) [29 , 30 , 44] . To add weight to the hypothesis that the CD150+ MEP and CMP populations are fetal liver HSC-derived , we analyzed these fractions in E16 . 5 fetal liver . Firstly , upon analyzing the LK hematopoietic progenitor compartment of the Runx1 P1-GFP::P2-hCD4 E16 . 5 fetal liver , we observed that the frequencies of P2+/- CD41- CD150+ MEPs and CMPs closely resembled that observed in E14 . 5 fetal liver ( S5A and S5B Fig ) . Upon isolating these populations , we observed that the P2+ CD41- CD150+ MEPs and CMPs displayed decreased erythroid and increased megakaryocytic/GM output compared to the P2- fractions ( S5C–S5H Fig ) . We also confirmed that Runx1 P2-hCD4 expression correlated well with CD31 expression in the E16 . 5 fetal liver LK fractions ( S5I-J ) and that wild type E16 . 5 CD31+/- MEPs and CD31low/high CMPs displayed similar lineage specificities , particular concerning erythroid output , compared to their P2+/- equivalents ( S5K-P ) . This therefore suggests that similar MEP and CMP populations can be discerned in E16 . 5 fetal liver as in E14 . 5 fetal liver and therefore that the hematopoietic hierarchy is maintained 5 days after fetal liver colonization , even after the shift to bone marrow colonization has begun . Defining restricted fetal liver hematopoietic progenitor cell compartments should provide the ability to identify the impact of genetic alterations with greater precision . For example , Runx1-flox::Vav1-Cre conditional knockout mice display impaired fetal liver erythroid and megakaryocytic maturation ( S6A–S6H Fig ) . The absence of RUNX1 protein in Runx1-flox/flox::Vav1-Cre ( Runx1-del/del , S6A Fig ) apparently caused a block in the upregulation of TER119 expression . Consequently , there was an accumulation of the immature S0-S2 erythroid lineage subsets , but a decrease in the CD71high TER119high S3 population ( S6B-E ) [45] . Following megakaryocytic culture , Runx1-del/del fetal liver samples produced more CD41high megakaryocytes than their WT and Runx1-del/+ littermates , but the majority of these did not upregulate CD42d , reflecting the previously described megakaryocytic maturation block ( S6F and S6G Fig ) . This was confirmed by the complete absence of morphologically mature megakaryocytes , following staining with May-Grünwald Giemsa reagent ( S6H Fig ) . This megakaryocyte maturation block is reminiscent of Familial Platelet Disorder with Predisposition to Acute Myeloid Leukemia ( FPD/AML ) , almost exclusively the result of germline heterozygous RUNX1 gene mutations/deletions [46 , 47] . To further investigate the impact of the absence of RUNX1 , we performed a detailed characterization of the hematopoietic progenitor composition of Runx1-del/del E14 . 5 fetal liver using our CD31 staining protocol and compared it to wild type and Runx1-del/+ heterozygous littermates ( Fig 5A–5C , S7A and S7B Fig ) . CD41- CD150+ CD31+/- MEP and CD31low/high CMP populations were all expanded in Runx1-del/del fetal liver , as was the pro-GM CD41- CD150- CMP fraction ( Fig 5B–5C , S7B Fig ) . By contrast , pro-erythroid CD41- CD150- MEPs were depleted ( Fig 5A , S7B Fig ) , suggesting the absence of RUNX1 may block erythroid differentiation during fetal liver hematopoiesis . Assessment of CFU-C activities ( S7C–S7F Fig ) uncovered a modest increase in CFUe production by Runx1-del/del CD41- CD150+ CD31+ MEPs but decreased CFU-MkE and CFU-GEMM activity in Runx1-del/del CD41- CD150+ CD31high CMPs . Total CFU-Mk output did not appear to be decreased , but defective megakaryocytic maturation was evident as morphologically mature CFU-Mks were absent from Runx1-del/del hematopoietic progenitor cultures ( S7E and S7F Fig ) . Bulk OP9 co-culture revealed decreased mature TER119+ erythroid cell production by Runx1-del/del CD31low CMPs and CD31-/+ MEPs , but not CD31high CMPs ( Fig 5D and 5E , S7G and S7K Fig ) , confirming an erythroid maturation block . Runx1-del/del CD31low CMPs had increased CD41+ megakaryocytic output , in line with the previously reported increased proliferation/reduced maturation in this lineage [48] . However , this did not answer the question of whether lineage specification was altered by the absence of RUNX1 causing a perturbation of the numbers of pro-erythroid , pro-megakaryocyte and/or pro-granulocyte/monocyte progenitors in hematopoietic progenitor pools . To address this question , we performed single cell OP9 co-cultures and observed similar total plating efficiencies for wild type , Runx1-del/+ and Runx1-del/del populations ( Fig 5F ) . Whereas >50% of wild type and Runx1-del/+ CD31low CMPs solely produced TER119+ Erythroid cells , this was reduced 4-fold in Runx1-del/del CD31low CMPs ( Fig 5G and 5H , S7L Fig ) , with a concurrent increase in pro-GM progenitors . Erythroid cells were more modestly reduced in CD31high CMP cultures ( S7M–S7O Fig ) , but this was not very impactful as the population has a lower pro-erythroid pool in wild type fetal liver compared to its CD31low counterpart . Moreover , the Median Fluorescent Intensity of the TER119+ erythroid cells was decreased in Runx1-del/del CD31low CMP cultures compared to that of wild type littermates ( S7P Fig ) . This confirmed that the proportion of pro-erythroid progenitors residing in the fetal liver CD31low CMP fraction is diminished in Runx1-null E14 . 5 embryos , and that differentiation of these progenitors is impaired . A key advantage of identifying these highly purified CD31low and CD31high CMP fractions in Runx1 null fetal liver is the ability to analyze underlying cell-intrinsic changes driving the lineage specification and maturation defects with greater precision , particularly at a transcriptome level . We therefore analyzed the expression of key HSC and lineage-associated transcriptional regulators in wild type , Runx1-del/+ and Runx1-del/del CD31low and CD31high CMPs ( S7Q Fig ) . One key observation was that several key HSC and megakaryocyte/erythrocyte-associated transcription factors ( Tal1 , Gfi1b and Klf1 ) were downregulated in wild type CD31high CMPs compared to wild type CD31low CMPs , reflecting the megakaryocytic/erythroid lineage commitment which accompanies CD31 downregulation in the CMP compartment and demonstrating that CD31low and CD31high CMPs represent transcriptionally distinct progenitor subsets . Notably , expression of Tal1 , Gfi1b and Klf1 did not differ between wild type , Runx1-del/+ and Runx1-del/del CD31high CMPs . However , they were substantially downregulated in Runx1-del/del CD31low CMPs compared to their wild type and Runx1-del/+ equivalents . In fact , the transcription factors Tal1 , Gfi1b , Klf1 and Gata2 , plus the megakaryocytic/erythroid lineage markers Itga2b , Pf4 and Epor ( S7R Fig ) , were all expressed at comparable levels in Runx1-del/del CD31low CMPs to CD31high CMPs ( of all genotypes ) . This supports the hypothesis that the absence of RUNX1 results in a differentiation block between the CD31high and CD31low CMPs . Additionally , it indicates that the megakaryocytic/erythroid maturation defects are established even at this early stage , with a failure to upregulate key maturation-associated transcripts . Interestingly , unlike the pro-erythroid transcription factor Klf1 , the pro-megakaryocytic Fli1 and the pro-GM Spi1 and Gfi1 were not significantly different in CD31low and CD31high CMPs; nor were they impacted by the absence of RUNX1 . This may indicate that commitment to the erythroid lineage is the default position in the FL CD31high-to-CD31low CMP transition , which was hindered in the absence of RUNX1 . We therefore clearly demonstrate the benefits of isolating highly purified myeloid progenitors to aid in the understanding of the mechanistic basis of congenital hematopoietic defects in the fetal liver . We recently reported that deleting the RUNX1C isoform in adult mice , whilst maintaining total RUNX1 expression , resulted in mild thrombocytopenia , due to impaired megakaryocytic specification but with normal megakaryocyte maturation . To achieve this we utilized the Runx1-P1-MRIPV line , in which the P1-encoded RUNX1C isoform is replaced by the P2-encoded RUNX1B isoform [39] . Given the contrast between this phenotype and that of the Runx1-del/del adult model , we decided to interrogate P1-MRIPV fetal liver hematopoiesis . Having confirmed P1-MRIPV/MRIPV and P1-MRIPV/+ fetal liver cells maintain RUNX1 protein expression at wild type levels ( S8A Fig ) , we examined erythroid and megakaryocytic maturation in these samples ( S8B–S8G Fig ) . Unlike in Runx1-null fetal liver , P1-MRIPV/MRIPV fetal liver displayed only a modest increase in the CD71-/low TER119- S0 fraction and corresponding decrease in the CD71high TER119high S3 fraction . This suggested that the absence of RUNX1C/overexpression of RUNX1B does not significantly impact erythroid maturation ( S8B–S8E Fig ) . Following megakaryocyte culture , we observed that megakaryocytic maturation was also unimpaired , as the proportions of mature CD41high CD42d+ megakaryocytes were similar in wild type , P1-MRIPV/+ and P1-MRIPV/MRIPV cultures ( S8F and S8G Fig ) . We next proceeded to characterize fetal liver myeloid hematopoietic progenitor compartments using our CD31 staining protocol ( Fig 6A–6C , S9A and S9B Fig ) . The impact of deleting RUNX1C alone was far more restricted , the CD41- CD150+ CD31- MEP being the only expanded population identified in P1-MRIPV/MRIPV fetal liver ( Fig 6A–6C ) . As the CD31- MEP is highly erythroid-biased , we investigated whether P1-MRIPV/MRIPV fetal liver progenitors displayed pro-erythroid/anti-megakaryocytic bias . CFU-C assays revealed increased CFUe activity in the P1-MRIPV/MRIPV CD31+ MEP , but decreased CFU-Mk and increased CFU-M activity in the CD31high CMP ( S9C–S9F Fig ) . OP9 co-culture suggested the absence of RUNX1C did not impair the lineage output of CD31- or CD31+ MEPs ( S9G–S9I Fig ) . Whilst the P1-MRIPV/MRIPV CD31low CMP was modestly affected , substantially decreased CD41+ megakaryocyte output and increased CD11b+ GR1+ granulocyte/monocyte output were observed for P1-MRIPV/MRIPV CD31high CMPs ( Fig 6D–6G ) . We therefore hypothesized that the dominant impact of deleting RUNX1C ( or overexpressing RUNX1B ) on megakaryocytic versus granulocyte/monocyte specification occurs in the CD31high CMP fraction . This hypothesis was confirmed through short-term culture of the CMP populations ( S9J–S9M Fig ) , as P1-MRIPV/MRIPV CD31high CMPs produced fewer MkPs and more GMPs than their wild type counterparts , but the lineage specification of CD31low CMPs was largely unaffected . Therefore , in contrast to the impaired erythroid specification of Runx1-del/del CD31low CMPs , P1-MRIPV/MRIPV CD31high CMPs display impaired megakaryocytic specification . This demonstrates that the RUNX1B and RUNX1C isoforms have distinct roles during fetal liver hematopoiesis , akin to adult bone marrow hematopoiesis , which can be uncovered using our enhanced fetal liver HPC purification strategies .
Bone marrow myeloid hematopoietic progenitor cells have been extensively characterized , including the recent demonstration that immunophenotypically similar CMPs can be separated into distinct PU . 1-eYGPhigh GATA1-mCherry- pro-granulocyte/monocyte and PU . 1-eYFPlow GATA1-mCherry+ pro-megakaryocyte/erythroid fractions [40] . This therefore confirms that lineage fate decisions had commenced upstream of the CMPs in ancestral HSPCs . By comparison , the delineation of myeloid lineage-restricted hematopoietic progenitors in fetal liver lags critically behind . We therefore attempted to further compartmentalize the immunophenotypic MEP and CMP fractions to provide a more detailed hematopoietic hierarchy ( summarized in Fig 7A ) . CD55 and CD150 were obvious lead candidates , as CD55 was successfully utilized by Guo et al to subdivide adult CMPs into pro-megakaryocyte/erythroid and pro-granulocyte/monocyte fractions [41]; additionally , CD150 is a well-established pro-megakaryocytic/erythroid and HSC marker [5 , 32 , 43 , 49 , 50] . We observed that the entirety of megakaryocytic/erythroid bipotential and granulocyte/monocyte/megakaryocyte/erythrocyte multipotential CFU-C output resides respectively in the CD55+ CD150+ MEP and CD55+ CD150+ CMP fetal liver fractions . However , the CFU-Mk and CFU-MkE output of CD55+ CD150+ MEPs remained low ( ~5% ) , highlighting the need for further refined hematopoietic progenitor subfractionation . For this , we identified distinct myeloid hematopoietic progenitor subsets on the basis of P2-hCD4 expression in our Runx1 P1-GFP::P2-hCD4 reporter mouse . To broaden the application of this finding , and not rely on the P1-GFP::P2-hCD4 reporter mouse , we searched for cell surface markers which correlated with P2-hCD4 expression . Following a screen of markers associated with heterogeneous fetal liver expression or multipotency/lineage specification in bone marrow hematopoietic progenitors [51] , we identified CD31 , CD45 and CD48 as strong candidates . Downregulation of the pan-leukocyte marker , CD45 [52 , 53] , and the bone marrow hematopoietic progenitor , lymphocyte and macrophage-associated CD48 [49 , 54–56] upon fetal liver erythroid commitment concurs with adult bone marrow erythroid progenitor specification [51] . CD31 , meanwhile , is expressed in endothelial progenitor cells and their mature progeny [57–60] . CD31-null mice are viable and do not exhibit obvious vascular defects; nonetheless CD31 plays significant roles in vascular remodeling and tumor metastatic progression as well as in adhesion , survival , migration and activation of hematopoietic cells [61–70] . In E14 . 5 fetal liver , CD31 is predominantly expressed in cells lining the hepatic vessels , but also in some Runx1+ hematopoietic stem and progenitor cells . We observed CD31 expression was highly enriched in the fetal liver myeloid progenitors with the greatest granulocyte/monocyte and megakaryocytic output . This concurs somewhat with the observation that in fetal liver and bone marrow , the entire multilineage LSK hematopoietic stem and progenitor cell fraction expresses CD31 [57 , 71 , 72] . Contrastingly , the bone marrow LK CD31+ fraction was deficient in granulocyte/monocyte output , possessing chiefly short-term erythroid repopulating cells , whereas we demonstrate here that erythroid lineage commitment in fetal liver coincides with CD31 downregulation . This suggests CD31-expressing short-term progenitors are not equivalent throughout mouse ontogeny . The shift from CD31+ pro-GM/megakaryocytic fetal liver hematopoietic progenitors to bone marrow erythroid progenitors may reflect distinct interactions with their respective niches . CD31-null adult mice have more steady state circulating progenitors , as hematopoietic progenitors fail to migrate across the bone marrow vasculature [68] . This phenotype was observed whether CD31 was deleted in hematopoietic or endothelial cells or both . The retention of the numerous , highly clonogenic CD31- fetal liver erythroid progenitors in their niche may be less crucial than for CD31+ bone marrow erythroid progenitors , which are replaced less frequently by more quiescent precursors . High CD31 expression in fetal liver pro-GM/megakaryocytic progenitors reinforces a phenotypic link between megakaryocytes and endothelial cells , which co-express numerous receptors , transcription factors and other signaling-associated factors [73] . The similarities become even more pronounced when considering hemogenic endothelium , which produces HSCs through EHT , due to the elevated expression of hematopoietic regulators which drive this process [74–76] . Megakaryocytes and endothelial cells are spatially close in hematopoietic vascular niches , their interactions conducted partially by CD31 [77] . Indeed , CD31’s absence impacts multiple aspects of megakaryopoiesis . It would therefore be worthwhile to determine whether CD31 deficiency impacts ancestral HPCs as well as their megakaryocytic progeny . Hematopoietic progenitor cell retention in the fetal liver vascular niche may consequently be severely impaired [68] , potentially adding a new functional dimension to CD31 expression on CMPs and MEPs , as well as an immunophenotyping application . One of the questions raised by our studies was whether the immunophenotypic CMP compartment actually contains single progenitor cells with the ability to produce granulocyte/monocyte , megakaryocyte and erythroid cells , thereby being defined as true Common Myeloid Progenitors . The alternative is that the CMP compartment solely comprises a heterogeneous population of monopotent or bipotent progenitors . Our single cell myeloid OP9 co-culture assays offer evidence that a small minority ( <20% ) of single isolated CMPs yield megakaryocytes , erythrocytes and granulocytes/monocytes , as would be expected for a true CMP . Therefore , we provide evidence that supports the existence of the CMP as a rare population within the fetal liver . The CMP appears to be far scarcer than previously understood and it is likely that the LSK Multipotent Progenitor fractions may dominate in terms of common myeloid ancestry , particularly as they yield greater numbers of CFU-GEMMs than immunophenotypic CMPs . Indeed , we observed that fetal liver LSK HSCs and MPPs appeared to be the ancestors of the LK hematopoietic progenitors , at least in vitro . Contrastingly , we were unable to reproduce a similar myeloid progenitor hierarchy following culture of yolk sac cells . Nonetheless , several studies have suggested that at least some definitive hematopoietic stem and progenitor cells located in the embryo proper do not arise de novo , but instead originate from the yolk sac [75 , 78–80] . Therefore , it is possible that EMPs could be responsible to some extent for the establishment of a fetal liver hematopoietic progenitor hierarchy , including our populations of interest: the CD150+ MEPs and CMPs . Our results would suggest that this is only achieved after fetal liver colonization and differentiation in this supportive niche , yielding fetal liver hematopoietic stem and progenitor cells which , in turn , produce myeloid-restricted progenitors . Our intent in this study was to delineate different fetal liver myeloid progenitor compartments , in order to provide a method to examine homeostatic developmental hematopoiesis and hematopoietic disease models . Indeed chromosomal translocations such as AML1-ETO , PML-RARA and CBFβ-MYH11 , which cause childhood Acute Myeloid Leukemia ( AML ) , frequently arise in utero as demonstrated by the high prevalence of such mutations in neonatal blood samples [34–36] , and may therefore impact fetal hematopoiesis . Furthermore , Ye et al demonstrated that the initiation of AML requires partial myeloid differentiation by transformed CMPs to GMPs , highlighting how an understanding of the myeloid progenitor hierarchy facilitates examination of the origins and progression of malignant hematopoietic disorders [16] . To demonstrate the application of our fetal liver myeloid progenitor scheme , we analyzed the impact of deleting Runx1 ( a mutation which causes a megakaryocytic differentiation block comparable to FPD/AML [38 , 46 , 48] ) on the specification and differentiation of these compartments . Akin to Behrens et al [81] in the adult , we observed deleting Runx1 causes a block in fetal liver erythroid differentiation and pinpointed this block to CD150 downregulation in immunophenotypic MEPs ( Fig 7B ) . We also observed decreased erythroid specification in the Runx1-del/del CD150+ CD31low CMP fraction , and determined that this may be due to a failure to upregulate an erythroid transcriptional network in the transition from CD31high to CD31low CMP . We are therefore able to gather a substantial amount of phenotypic information and also gain mechanistic insights from transcriptome analyses . We also evaluated the impact of removing the dominant RUNX1C isoform on fetal liver myelopoiesis . As we previously described in the adult , RUNX1C-null P1-MRIPV/MRIPV mice do not appear to have impaired megakaryocytic/erythroid differentiation [39] . Nonetheless , the fetal liver CD150+ CD31- MEP fraction was expanded , partially recapitulating the Runx1-null erythroid lineage phenotype . However , P1-MRIPV/MRIPV fetal liver had reduced megakaryocytic specification , with the CD150+ CD31high CMP-to-MkP transition being the most impaired pathway . We did not observe this in Runx1-null fetal liver; in fact megakaryocytic output increased in the absence of total RUNX1 , as observed in adult hematopoiesis [39 , 81] . This difference may explain the apparently contradictory finding by Kuvardina et al [82] that RUNX1 promotes megakaryocytic specification; RUNX1C may have a non-redundant function in megakaryocytic specification , whereas RUNX1B is either necessary or sufficient for megakaryocytic and erythroid maturation . Our myeloid hematopoietic progenitor scheme has allowed us to identify the cells of interest which perpetuate this hematological imbalance . Such a technique could therefore be applied to understanding the cells of origin in familial thrombocytopenia , or a recently described case of RUNX1-deleted Congenital Amegakaryocytic Thrombocytopenia [83] . Moreover , our new protocol for prospective isolation of myeloid hematopoietic progenitors could be applied more broadly to other congenital or somatic genetic disorders which manifest in utero , as well as to analyzing lineage fate decisions in normal fetal liver hematopoiesis .
Runx1-P1-GFP::P2-hCD4 , Runx1-P1-MRIPV , Runx1-flox::Vav1-Cre , Gf1i-GFP and Gfi1b-GFP mice have been described [17 , 38 , 39 , 84 , 85] . Dual-reporter Runx1-P1-GFP::P2-RFP chimeric mouse lines were generated by transfecting P1-GFP embryonic stem cells ( ESCs ) [17] with a P2-RFP targeting construct [86 , 87] , screening for heterozygote knock-in lines targeting the same allele , and injecting correctly targeted ESCs into C57BL6J blastocysts . Runx1 P1-GFP::P2-hCD4 , P1-GFP::P2-RFP , P1-MRIPV , Runx1 flox::Vav1-Cre , Gfi1-GFP and Gfi1b-GFP mice were backcrossed with C57BL/6 mice for at least 10 generations and were housed in specific pathogen free cages with environmental enrichment . To trace Runx1 promoter activity , timed matings were set up between Runx1 P1-GFP::P2-hCD4 or P1-GFP::P2-RFP male mice and wild type ( WT ) ICR female mice . To produce Runx1 P1-MRIPV/MRIPV or Runx1 flox/flox::Vav1-Cre embryos ( plus wild type and heterozygous control littermates ) , timed matings were set up between Runx1 P1-MRIPV/+ mice or between Runx1 flox/+::Vav1-Cre males and Runx1 flox/+ females . To trace Gfi1 and Gfi1b expression , timed matings were set up between Gfi1-GFP or Gfi1b-GFP male mice and WT ICR female mice . Embryos were harvested on embryonic day ( E ) 12 . 5 , 13 . 5 , 14 . 5 or 16 . 5 and the fetal livers dissected for flow cytometric or histological analysis . For yolk sac experiments , embryos were harvested on E9 . 5 and the yolk sacs dissected for flow cytometric analysis , or for explant or pro-myeloid culture ( as described below ) . Approximately 104 cells were kept for genotyping by PCR ( oligonucleotides listed in S1 Table ) . All animal work was performed under regulations governed by UK Home Office Legislation under the Animals ( Scientific Procedures ) Act 1986 and was approved by the Animal Welfare and Ethics Review Body of the Cancer Research UK Manchester Institute . Details of FACS reagents and combinations used for each analysis are listed in S2 and S3 Tables . Prior to flow sorting or analysis of HPCs in E12 . 5 , E13 . 5 , E14 . 5 or E16 . 5 fetal liver , red blood cell lysis was performed as described [18] . Dead cells were excluded using 1μg/ml Hoechst 33258 ( ThermoFisher Scientific ) ; gates were positioned based on Full Minus One controls . Cells were analyzed using a LSR-II or LSR-II Fortessa analyzer ( BD Biosciences ) or a NovoCyte ( ACEA Biosciences Inc . ) . Cells were sorted using a FACSAria-II , FACSAria-III or Influx cell sorter ( BD Biosciences ) . Up to 50 , 000 cultured fetal liver cells were suspended in 150μl PBS and immobilized on twin frosted glass microscope slides ( Fisher Scientific ) by cytospin at 200rpm , low acceleration for 5 minutes in a Shandon Cytospin3 ( Thermo Scientific ) . Air-dried slides were submerged in May-Grünwald Eosin methylene blue Q Path stain ( VWR ) for 3 minutes , rinsed in tap water and submerged in 5% Giemsa’s stain ( VWR ) for 20 minutes . Slides were subsequently air dried , mounted and scanned using the Pannoramic 250 Flash III ( 3DHISTECH ) . Images were acquired with the Pannoramic 250 software and analyzed with the Pannoramic Viewer software ( 3DHISTECH ) . Dissected fetal livers were fixed in 4% Paraformaldehyde ( PFA ) overnight , before they were soaked in 30% sucrose and mounted in OCT compound . 10μm sections were prepared using a cryostat . The sections were incubated in blocking buffer ( PBS with 10% FBS , 0 . 05% Tween20 and 10% goat serum ( DAKO ) ) for 1 hour before the sections were incubated with primary antibodies at 4°C overnight in blocking buffer . Primary antibodies used in this study were rabbit anti-GFP ( 598 , polyclonal , MBL ) ( 1/200 ) ; and purified rat anti-mouse CD31 ( 553370 , MEC13 . 3 , BD Biosciences ) ( 1/100 ) . Sections were washed three times in PBST ( PBS with 0 . 05% Tween20 ) for 15 minutes each and then incubated with fluorochrome-conjugated secondary antibody at room temperature for 1 hour . Secondary antibodies used in this study include Alexa Fluor 488 Goat Anti-Rat IgG ( A11006 , Life Technologies ) ; and Alexa Fluor 647 F ( ab' ) 2 Fragment of Goat Anti-Rabbit IgG ( H+L ) ( A21246 , Life Technologies ) . All secondary antibodies were used at 1/400 dilution . Sections were further washed three times in PBS and mounted using Prolong Gold anti-fade medium with DAPI ( Life Technologies ) . Images ( of Alexa Fluor 488 , Alexa Fluor 647 , DAPI and endogenous RFP ) were taken using a low-light time lapse microscope ( Leica ) using the Metamorph imaging software and processed using ImageJ . Whole cell protein extracts , purified using RIPA lysis buffer , were quantitated by the Bradford assay ( Protein Assay Dye Reagent , Bio-Rad ) on the Glomax Multi Detection System ( Promega ) and loaded alongside the SeeBlue Plus2 Prestained Standard ( ThermoFisher Scientific ) for electrophoretic separation on NuPAGE 4–12% Bis-Tris gels using the Novex Mini Cell system ( ThermoFisher Scientific ) . Protein transfer was performed to nitrocellulose membranes using the iBlot system ( ThermoFisher Scientific ) and membranes were probed with anti-RUNX ( EPR3099 , Abcam ) and anti-beta-actin ( AC-15 , Sigma-Aldrich ) primary antibodies , followed by Horseradish Peroxidase ( HRP ) -conjugated goat anti-rabbit and goat anti-mouse ( ThermoFisher Scientific ) secondary antibodies respectively , in the iBind Western System ( ThermoFisher Scientific ) . HRP activity was detected using Amersham ECL Prime Western Blotting Detection Reagent , imaged using the BioRad ChemiDoc Touch Imaging System and analyzed with BioRad Image Lab Software Version 6 . RNA was extracted using the RNeasy Plus Micro Kit ( QIAGEN ) and complementary DNA was synthesized using the High-Capacity cDNA Reverse Transcription Kit ( ThermoFisher Scientific ) . Quantitative PCR ( qPCR ) was performed using Universal ProbeLibrary assays ( Roche ) ; primers and probes are listed in S4 Table . Expression values were normalized to beta-actin ( Actb ) . E14 . 5 P1-GFP::P2-hCD4/+ fetal livers were prepared for flow sorting as described above . Single P2-hCD4-/+ MEPs and CMPs were sorted into 384-well plates containing lysis buffer and snap frozen . Libraries were prepared using a modified version of the Smart-Seq2 protocol [89] . Briefly , cDNA was prepared using a Mantis platform ( Formulatrix ) and quantified with quantIT picogreen reagent ( Thermo Fisher ) . Dual indexed sequencing libraries were prepared from 0 . 1ng cDNA using an Echo525 automation system ( Labcyte ) in miniaturized reaction volumes . The library pool was quantified by qPCR using a Library Quantification Kit for Illumina sequencing platforms ( Kapa Biosystems ) . Paired-end 75bp sequencing was carried out by clustering 1 . 5pM of the library pool on a NextSeq 500 sequencer ( Illumina ) . Base call files generated from the NextSeq 500 sequencing run were converted to the FASTQ format with the bcl2fast converter ( Illumina ) . All FASTQ files corresponding to the same sample ( derived from separately sequenced lanes ) were then merged into a single FASTQ file ( one per sample ) . Read trimming was performed with trimmomatic ( v0 . 36 ) with the following settings “CROP:75 HEADCROP:5 SLIDINGWINDOW:20:20 MINLEN:36” . The mouse reference genome GRCm38 ( version M12 , Ensembl release 87 ) and the ERCC reference sequence ( Thermo Fisher ) were combined and used as the reference genome for sequencing alignment , performed using STAR ( version 2 . 4 . 2a ) [90] . The expression levels of 49 , 585 features annotated in the GENCODE mouse genome and 92 ERCC features were determined using HTSeq ( version 0 . 6 . 1p1 ) [91] . The following parameters were specified for the HTSeq quantification: ‘—format = bam–stranded = no–type = exon’ . Single cell count data were loaded into the R environment ( R version 3 . 4 . 0 ) as a SCESet object using the Scater package ( version 1 . 4 . 0 ) [92] . Normalized gene expression values were taken from the default normalization performed by scater . Cells with fewer than 250 , 000 sequencing reads , more than 25% unmapped reads , and more than 15% ERCC content were removed . Low abundance genes ( mean count <1 ) were excluded , as were overrepresented genes ( >20% of total sequencing reads ) . Differentially expressed genes were identified using DESeq2 ( version 1 . 16 . 1 , Bioconductor ) . Prior to differential expression analysis , the data were filtered to remove genes with a dropout rate of higher than 75%; differential expression analysis was then performed using the function “DESeqDataSetFromMatrix” and by specifying the contrast of interest . The full scripts for the analysis of these data are available at https://github . com/m-zaki/CRUKMI_github/tree/master/JuliaDraper_PLOSgenetics . The data discussed in this publication have been deposited in the NCBI Gene Expression Omnibus [93 , 94] and are accessible through GEO series accession number GSE107653 . For FACS purified populations ( MEPs , CMPs ) a sample size of n = 1 refers to tissues pooled from embryos from one litter . For total fetal liver analyses ( Western blot , total FL culture ) , a sample size of n = 1 refers to one embryo . Data were evaluated using an Ordinary 2-way ANOVA and expressed as mean ± standard error of the mean ( SEM ) . P<0 . 05 was considered statistically significant . *P<0 . 05 , **P<0 . 01 , ***P<0 . 001 , ****P<0 . 0001 | The production of red blood cells , platelet-producing megakaryocytes , and immune response-directing granulocytes and monocytes is initiated at an early stage in the developing embryo and continues throughout life . The proportion of each cell type varies depending on the specific needs of the organism . We know that in the mouse embryo , specialized blood progenitor cells emerge in the fetal liver and produce mature blood cells in response to different cues . However , it is difficult to distinguish between red blood cell and white blood cell-producing progenitors with sufficiently high accuracy to study these cues . For example , we know that several childhood blood disorders , such as leukemias , are caused by genetic mutations in blood progenitor cells before birth , but studying the effects of these mutations in a mouse disease model is hampered if we don’t know which blood progenitor cells to collect . We have used different genetic markers to help distinguish red blood cell , megakaryocyte and granulocyte/monocyte-producing progenitor cells with a greater precision than was previously possible . Furthermore , to illustrate how this technique can be used to study blood disorders , we demonstrated that mutations affecting the transcription factor Runx1 impair the abilities of different progenitors to produce mature blood cells in different ways . | [
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] | 2018 | A novel prospective isolation of murine fetal liver progenitors to study in utero hematopoietic defects |
Recent developments in cardiovascular modelling allow us to simulate blood flow in an entire human body . Such model can also be used to create databases of virtual subjects , with sizes limited only by computational resources . In this work , we study if it is possible to estimate cardiovascular health indices using machine learning approaches . In particular , we carry out theoretical assessment of estimating aortic pulse wave velocity , diastolic and systolic blood pressure and stroke volume using pulse transit/arrival timings derived from photopletyshmography signals . For predictions , we train Gaussian process regression using a database of virtual subjects generated with a cardiovascular simulator . Simulated results provides theoretical assessment of accuracy for predictions of the health indices . For instance , aortic pulse wave velocity can be estimated with a high accuracy ( r > 0 . 9 ) when photopletyshmography is measured from left carotid artery using a combination of foot-to-foot pulse transmit time and peak location derived for the predictions . Similar accuracy can be reached for diastolic blood pressure , but predictions of systolic blood pressure are less accurate ( r > 0 . 75 ) and the stroke volume predictions are mostly contributed by heart rate .
This paper considers continuous monitoring of cardiac health using computational modelling . Stiffening of the arterial wall , such as aorta , causes reduction in the pulsatile properties in the vascular tree , accelerates the vascular premature ageing and predisposes to the dysfunction of the heart , brain and other organs [1 , 2] . Aortic stiffness can be measured by using invasive methods or medical imaging such as ultrasound [3] and MRI [2] . Another indicator reflecting the cardiac performance is stroke volume ( SV ) , which is typically measured using Doppler ultrasound [4] . However , these imaging modalities typically require special expertise and are only carried out clinically . On the other hand , aortic stiffness is associated with the unfavourable changes in the diastolic and systolic blood pressures ( DBP/SBP ) , which can have several negative consequences in cardiac function and structure [1] . Ambulatory home measurements of DBP and SBP use the techniques based on inflated cuffs , but continuous recording is still cumbersome . It would be helpful to find unobtrusive methods for the long-term monitoring of these cardiac indices during the daily activities and sleep . Arterial stiffness is often assessed by measuring pulse wave velocity ( PWV ) , which is increased in stiffer arteries . The PWV can be estimated by measuring arrivals of pulse waves at two arterial sites: PWV = distancebetweenthesites traveltimebetweenthesites . The travel time is commonly referred as pulse transit time ( PTT ) . Arrival of the pulse wave to distal arterial sites can be easily measured by using a photoplethysmogram ( PPG ) , which is an optical non-invasive sensor that can be placed , for example , in a wearable device [5] . On the other hand , in order to predict aortic stiffness reliably , the first arterial site should be located at the beginning of aorta ( for measurement of aortic valve opening ) . However , a measurement of valve opening can require a device such as phonocardiograph , ultrasound or MRI . To overcome this difficulty , PTT is often approximated using pulse arrival time ( PAT ) which uses the R- wave of electrocardiogram ( ECG ) as a reference timing [6] . However , there exists controversy in the clinical accuracy of using PAT in the predictions due to variations in pre-ejection period ( PEP ) from the R-wave to aortic valve opening [7 , 8] . An alternative approach is to approximate the reference with a measurement from another distal site near aorta . For example , the gold standard for aortic PWV measurement is to measure differences of pulse arrivals to carotid and femoral arteries . The estimation of blood pressure from arrival of pulse waves has also been largely studied; see e . g . [6 , 9 , 10] . Although promising results have been reported , clinical use of these techniques is still limited . Haemodynamic alterations can have significant effects on the accuracy [11] . A common problem with the clinical use of the above methodologies is that the development and validation of the methods typically require a large set of measurements from real human subjects with sufficient variety . Such data collection can be a very difficult and expensive task . A preliminary assessment of the methods without extensive data collection can be carried out using simulators . For example , Willemet et al [12 , 13] proposed approach to use cardiovascular simulator for generation of a database of “virtual subjects” with sizes limited only by computational resources . In their study , the databases were generated using one-dimensional ( 1D ) model of wave propagation in a artery network comprising of largest human arteries [14] . Such 1D models provide computationally efficient way to simulate blood circulation and are also used in several other applications [15] . There are also studies validating 1D simulations against real measurement [16–18] . The virtual database approach was used to assess accuracy of pulse wave velocity measurements for estimation of aortic stiffness [12] and the accuracy of pulse wave analysis algorithms [13] . The aim of our study is to assess theoretical limitations for the prediction of aortic pulse wave velocity ( aPWV ) , blood pressures ( DBP/SBP ) and SV from PTT/PAT measurements . We apply a similar virtual database approach to find correlations between these cardiac indices and PTT/PAT timings measured from different locations . In particular , we train Gaussian process regressor to predict the cardiac indices using different combinations of PTT and PAT measurements . The regressor model is trained using a large set of virtual subjects generated using 1D cardiovascular simulator , and the results are validated using another set of virtual subjects . The result of study can give preliminary implications for the accuracy of such predictions in rather ideal circumstances . Our study is based on the 1D haemodynamic model of entire adult circulations introduced by Mynard and Smolich [19] . It includes heart functions and all larger arteries and veins for both systemic and pulmonary circulation . As heart is included to the model , it can also simulate variations in PEP that are essential in the comparison of PTT and PAT timings . This paper is organized as follows . Cardiovascular model , generation of virtual subjects and prediction methods are described in Methods and Models section . Results section contains numerical experiments . We will finish with Discussion .
The blood circulation model is based on the 1D haemodynamic model described in [19] , which basically extends commonly used 1D wave dynamics model ( see e . g . [14] ) with heart functions and realistic arteria and venous networks including pulmonary and coronary circulations . The components of the model are shortly summarized below , see [19] for more details . The database is created by running the cardiovascular model repeatedly . The model parameters are varied to reflect variations between individual ( virtual ) subjects . In [12 , 13] , the seven parameters were varied: elastic artery PWV , muscular artery PWV , the diameter of elastic arteries , the diameter of muscular arteries , heart rate ( HR ) , SV and peripheral vascular resistance . In their study , the parameters were varied by specifying a few possible values for each parameter and the cardiovascular model was run for all of the resulting 7776 combinations . However , in our study , the cardiovascular model has significantly more model parameters ( e . g . parameters related to heart model and valves , vascular beds , … ) . Such systematic variation of all essential parameters would lead to excessively large number of combinations . In this study , we choose “sampling” approach in which the model parameters are varied randomly . Our aim is to choose random variations that would represent healthy subject and , where applicable , the range of the parameters is of similar range as in [12] . Some choices can be rather subjective due to the limited amount of ( probabilistic ) information from related physiological quantities . Our goal is to choose variations to be wide enough so that “real world” can be considered as a subset of the population covered by the variations . However , if more sufficient information about parameters becomes available , it should be rather straightforward to carry out the analysis with the adjusted distributions . In the following , the superscript ( s ) refers to a virtual subject for which the parameters are specified . The overbar notation ( e . g . L ¯ ) refers to the values used in [19] ( the baseline ) . Unless otherwise mentioned , the variations are chosen to be normally distributed . Furthermore , the statements such as 10% relative variation should be understood in terms of standard deviations instead explicit ranges of the parameter . We use slightly unconventional notation N ( μ , X % ) to denote the Gaussian distribution with mean μ and the standard deviation σ = X/100μ ( i . e . X% variation relative to the mean/baseline ) . The uniform distribution is denoted as U ( a , b ) . We apply Gaussian process regression for the computation of predictors . GPs are widely used , for example , in machine learning , hydrogeology and analysis of computer experiments ( e . g . see [25–27] ) . GPs also provide flexible predictors that can handle non-linear relationship between input data and the response variable as well as uncertainties in the data . However , we note that any other class of regressions capable of nonlinear relationships can also be used for the analysis . For example , similar results can be achieved with multivariate adaptive regression splines [28] . A GP is a stochastic process f ( z ) ( z ∈ R d ) such that f ( z1 ) , … , f ( zn ) is a multivariate Gaussian random variable for all combinations of z1 , … , zn . It can be described by the specifying mean function μ ( z ) = E ( f ( z ) ) and the covariance function k ( z , z′ ) = cov ( f ( z ) , f ( z′ ) ) . For more details , see e . g . [25] . Consider a case in which the inputs z are a vector of PTT or PATs and possibly HR and y is the response variable ( aPWV , DBP , SBP or SV ) . We model the response variables as y ( z ) = h ( z ) T β + f ( z ) + ϵ , ( 34 ) where h ( z ) is a vector of ( deterministic ) basis functions , β is a vector of basis function coefficients , f ( z ) is a GP with zero mean and covariance function k ( z , z′ ) , and ϵ is an Gaussian white noise . The first term represents mean behavior of the GP model . The GP term models non-linear relationship between input data and the response variable as well as correlated uncertainties in the data . Training data comprises of input-output pairs { ( zi , yi ) ; i = 1 , … , N } . We assume that yi’s are output of the above model i . e . yi = y ( z1 ) . Furthermore , let Z ′ = ( z 1 ′ , … , z p ′ ) be inputs for which we want to calculate predictions . Then Y = ( y1 , … , yN ) and Y ′ = ( y ( z 1 ′ ) , … , y ( z p ′ ) ) are both Gaussian and the conditional distribution of Y′ given Y is ( see e . g . [25] , Appendix A . 2] ) , p ( Y ′ | Y ) = N ( μ Y ′ + Σ Y ′ Y Σ Y - 1 ( Y - μ Y ) , Σ Y ′ + Σ Y ′ Y Σ Y - 1 Σ Y Y ′ ) ( 35 ) where μY and ΣY denotes the mean and covariance of Y and ΣYY′ is the cross-covariance of Y and Y′ . The means and covariances can be calculated by pluggin in the model ( 34 ) , which gives μ Y ′ | Y = h ( Z ′ ) T β + k ( Z ′ , Z ) ( k ( Z , Z ) + σ ϵ 2 I ) − 1 ( Y − h ( Z ) T β ) ( 36 ) Σ Y ′ | Y = k ( Z ′ , Z ′ ) − k ( Z ′ , Z ) ( k ( Z , Z ) + σ ϵ 2 I ) − 1 k ( Z , Z ′ ) ( 37 ) where h ( Z′ ) and k ( Z′ , Z ) are shorthand notations for the vector and matrix with the components h ( z i ′ ) and k ( z i ′ , z j ) , respectively . The above conditional mean gives us an prediction of Y′ with a confidence estimate given by the conditional covariance . In this study , the covariance function are chosen to be Matern kernel function with ν = 3/2 with a separate length scales for each input parameter . This kernel function can be written as k ( z , z′ ) =σ2 ( 1+3r ) exp ( −3r ) , r= ( ∑md ( zi−zj ) 2ℓm2 ) 1/2 ( 38 ) where σ2 is the variance and ℓm are the length scales for each input . We note that the choice of the kernel function does not have a large effect to the results as our sample size is large . For example , our experiments show that use of the squared exponential covariance function gives very similar results with differences of the same scale as the prediction uncertainty . The predictors are computed using fitrgp function in MATLAB Machine Learning Toolbox which provides numerically efficient implementation for the GP regression . The basis functions h ( z ) are chosen to be linear . The fitrgp function also estimates hyperparameters θ ( β , σϵ2 , σ2 , ℓ1 , … , ℓd ) by minimizing the negative loglikelihood , L ( θ ) = - log p ( y | Z , θ ) = 1 2 y T Σ θ - 1 y + 1 2 log det Σ θ + n 2 log 2 π ( 39 ) where Σ θ = k ( Z , Z ; θ ) + σ ϵ 2 I . The optimization is carried out using a subset of observations to avoid high computational load . The parameters of fitrgp related to this hyperparameter optimization are chosen to be the default values .
Fig 6 shows predictions of aPWV for a selected set of combinations when the measurement location is LCA . Table A in S1 Appendix summarizes the results for the complete set of combinations . The results show that using PTTff or PTTD as a single input gives moderate accuracy and predictions using either HR , PTTp , or DAT are insufficient . Performance can be improved by combining multiple different timings . For example , the accuracy is significantly improved if both PTTff and PTTp are used for predictions ( r = 0 . 90 ) . Furthermore , including also DAT provides the accuracy of r = 0 . 94 , and adding other timings does not significantly improve accuracy any further . Measurements from RCA provide less accurate predictions ( Table B in S1 Appendix ) : for example , the combination of PTTff , PTTp , PTTD and DAT provides one of highest accuracies for RCA ( r = 0 . 79 ) , but is still only moderate . Such results can be expected as pulse waves travel shorter distance in aorta and also travel through brachiocephalic artery ( see Fig 1 ) inducing additional variations to the ( average ) wave speeds . Performance of wrist measurements ( LRad / RRad ) are even worse ( see Table C in S1 Appendix for LRad; results for RRad are similar ) . For example , the highest accuracy ( r = 0 . 73 ) can be achieved with the combination of PTTff , PTTp , PTTD and DAT . This is also expected as relative large part of the arterial tree to these measurement locations are comprised of brachial and radialis arteries with their own variations to PWV . On the other hand , measurements from lower limb could provide better performance: for right femoral artery , we can achieve r = 0 . 75 using PTTff and r = 0 . 84 using PTTff , PTTp , PTTD and DAT ( Table D in S1 Appendix ) . In this case , pulse travels though the whole aorta to reach these measurement locations . As mentioned above , in practice , the R-peak location in ECG signal is often used as a surrogate to aortic valve opening due to simpler measurement . However , using PATs gives only mediocre accuracy compared to PTT due to the physiological variations in PEP [7 , 8] . Our finding are similar , see for example , Fig A and Table E in S1 Appendix for LCA . The highest accuracy is r = 0 . 79 ( e . g . PATff , PATp , PATD and HR ) which is significantly worse compared to using PTTs . Another approach to avoid measurement of aortic valve opening is to consider differences of pulse arrival times to two distal locations . Such setup also allows us to avoid the influence of PEP variations . Results for measurement between LCA and Fem can be seen in Fig B and Table F in S1 Appendix: difference of PTTff gives r = 0 . 76 which is slightly better than using normal PTTff measurement from Fem , but not as good as normal PTTff measurement from LCA . The highest accuracy ( r = 0 . 87 ) can be obtained , for example , with PTTff , PTTp , PTTD and HR . The predictions of PWV that use the difference between LCA and RCA or the difference between LRad and RRad are less accurate ( r ≈ 0 . 75 − 0 . 78 at best ) ; see Tables G and H in S1 Appendix . Figs 7 and 8 show predictions for DBP and SBP for selected PTT time combinations when measurements are taken from LCA; see also Table A in S1 Appendix for all combinations . For DBP , predictions using PTTff as a single input achieves very low accuracy ( r = 0 . 33 ) . Significantly more accurate predictions can be achieved using HR ( r = 0 . 85 ) or DAT ( r = 0 . 86 ) . For SBP , the performance of PTT based predictions is better but still quite low ( r = 0 . 58 for PTTff and r = 0 . 60 for PTTp ) . Predictions can be improved by adding additional input timings . For DBP , combining PTTff with HR or DAT gives r = 0 . 92 and the highest accuracy r = 0 . 94 is obtained with PTTff , PTTp , PTTD and DAT . Additional input timings also improves performance of SDB predictions: PTTff and HR/DAT results in r = 0 . 735 and the highest accuracy is r = 0 . 75 ( PTTff , PTTp , PTTD and DAT ) . Findings the other measurements locations are similar; see Tables B , C and D in S1 Appendix . We also consider predictions from pulse arrival times ( i . e . using R-peak as a reference timing ) . Compared to PTT times , the results are of mixed accuracy; see Table E for PAT measurements from LCA . For DBP , using PATff as single input yields insufficient predictions ( r = 0 . 19 ) , but PATp gives moderate accuracy ( r = 0 . 67 ) . Combinations of different PAT timings can even achieve higher accuracy than using PTTs: for example , r = 0 . 95 with PATff and DAT and r = 0 . 96 for PATff , PATp , PATD and DAT . For SBP , PATff provides slightly better accuracy compared to PTTff ( r = 0 . 62 ) , but otherwise results are similar . As with aPWV , we consider differences of pulse transit/arrival times measured with two sensor . Measuring between LCA and Fem gives very similar performance to PTT measurements from LCA ( Table F in S1 Appendix ) . However , other considered setups provide less accurate results: see Table G in S1 Appendix for differences between LCA and RCA measurements and Table H for differences between measurements from radialis arteries . Results show that HR has largest contribution to the predictions of SV , meanwhile performance with pulse transit or arrival timings ( without HR information ) can only provide moderate accuracy at best . For example , Fig G and Table A in S1 Appendix show the predictions using measurements from LCA . Predictions with HR as a single input reaches r = 0 . 81 , but predictions using PTTff or PTTD are insufficient estimates ( r < 0 . 25 ) and predictions with PTTp are of moderate accuracy ( r = 0 . 60 ) . SV can be predicted with good accuracy with DAT , but this is due to the strong correlation between HR and DAT as mentioned above . Furthermore , significant improvements will not be achieved by combining several inputs . For example , highest accuracy is r = 0 . 83 which can be obtained , for example , with PTTff , PTTD and HR . Results are similar for all other measurement setups; see Tables B-H in S1 Appendix .
This paper assessed theoretical limitations for the prediction of aortic pulse wave velocity ( aPWV ) , DBP/SBP and SV from pulse transit and arrival time measurements . We applied a virtual database approach proposed by Willemet et al [12 , 13] in which a cardiovascular simulator is used to generate a database of virtual subjects . In this work , we applied one-dimensional haemodynamic model by Mynard and Smolich [19] to construct a simulator for entire adult circulation . This simulator was used to generate a large database of synthetic blood circulations with varied physiological model parameters . The generated database was then used as training data for Gaussian process regressors . Finally , these trained regressors were applied to another synthetic database ( test set ) to assess capability of regressors to predict aPWV , SDB , DBP and SV using different combinations pulse transit/arrival time and HR measurements . The results indicate that aPWV and DBP can be estimated from PPG signal with a high accuracy ( Pearson correlation r > 0 . 9 between true and predicted values for measurement from left carotid artery ) when , in addition to foot-to-foot PTT time , information about the peak and dicrotic notch location is also given as input to the predictor . The predictions of SDB were less accurate ( r = 0 . 75 at best ) . For SV , accurate predictions were mostly based on heart rate , with only a very minor improvement in accuracy when PTT timings were also included as inputs . As this was entirely in silico study , it is not guaranteed that the result can be applicable to the real world as is . However , the aim of the study was to give preliminary results about correlations between the cardiac indices and PTT/PAT timings and the applicability of such predictions . The hope is that the results could to be extended to real clinical applications in future research . The limitations to be addressed in future are the following . First , the cardiovascular model has its limitations . Although previous studies have shown that similar cardiovascular models can be used to simulate human physiology relatively well [16–18] , not all physiological phenomena are fully covered in the Mynard’s model . One example of such phenomenon is respiration . The effect of respiration can be important as the breathing and cardiac cycles are in a close interaction . Several physiological factors , such as the changes in the intrathoracic pressure and the variation in the interbeat intervals modulate the cardiac mechanics and blood outflow from the heart . Even the timing of the shorter cardiac cycles coupled with the longer respiratory cycles has effects on the central circulation . When we considering a healthy heart , the effects of respiration can perhaps be managed by interpreting different virtual subjects to represent inspiratory and expiratory phases of the breathing . Other phenomena that are not covered by the model are , for example , gravity and baroreceptors . Furthermore , lumped parameter models that are used for heart and vascular beds were relatively simple approximations . However , new analytical methods allow us to bridge the models and human bodily functions [30] . The chosen baselines and variations of the model parameters were chosen to represent healthy subject . The choices , however , can be subjective due to the limited amount of ( probabilistic ) information . Our attempt were to produce variations such that the virtual population covered by the chosen parameter variations includes real physiological variations . We , however , emphasize that the presented approach is not limited to the chosen parameters variations and it can be adjusted if more precise information becomes available . Due to the limited phenomena covered by the model , the results may not be reliable when considering subjects with medical conditions . For example , the simplified heart model and variations of related model parameter may not present subjects with heart diseases . In this study , we only considered pulse transit and arrival type of time information as the input to the predictor . Predictions could potentially be improved with other kinds of additional information . For example , aortic PWV predictions could be improved by using information about the distances between aorta and/or measurement points . Information about arterial path lengths could have been easily used in our simulation analysis , but in practice such information would require clinical measurements such as MRI [21 , 22] . On the other hand , the arterial path length are often estimated using the body lengths or measuring distances of certain points in the body [21 , 22] . Such information was not used in this simulation study as precise statistical knowledge of connection between such body measurement and arterial length was not available . Instead , Gaussian process regressors implicitly marginalize predictions over different arterial lengths that are present in the virtual database . Ultimately it would be beneficial to develop approaches that do not need reference measurement ( aortic valve opening/R-peak ) . For example , Choudhury et al [31] presented a machine learning algorithm which uses raise times and pulse widths derived from PPG signal to predict DBP and SBP . Furthermore , deep learning approaches could perhaps be used to infer optimal information from PPG waveform . These are subject of our future research . | Recently there has been a strong trend for self-monitoring of your cardiovascular health and new wearable sport trackers and mobile applications are coming to the market everyday . However , such solutions are mostly taking advantage of heart rate measurement . Other health indices such as blood pressure and pulse wave velocity reflecting to the condition of cardiovascular system would also be of great interest , but such solutions for continuous monitoring are barely existing or are at least unreliable . In this paper , we use computational modelling to assess theoretical capabilities of such measurements . We concentrate on predicting health indices using on pulse transmit time type of measurements . Such measurements could be carried out , for example , with photopletyshmography sensor or an optical sensor already found from several wearable sport trackers . We use cardiovascular modelling to create a database of “virtual subjects” , which is applied with machine learning to construct predictors for health indices . Our findings suggest that aortic pulse wave velocity and diastolic blood pressured could be predicted with a high accuracy , but predictions of systolic blood pressure are less accurate . | [
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] | 2019 | Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data |
Mitochondrial DNA ( mtDNA ) encodes respiratory complex subunits essential to almost all eukaryotes; hence respiratory competence requires faithful duplication of this molecule . However , the mechanism ( s ) of its synthesis remain hotly debated . Here we have developed Caenorhabditis elegans as a convenient animal model for the study of metazoan mtDNA synthesis . We demonstrate that C . elegans mtDNA replicates exclusively by a phage-like mechanism , in which multimeric molecules are synthesized from a circular template . In contrast to previous mammalian studies , we found that mtDNA synthesis in the C . elegans gonad produces branched-circular lariat structures with multimeric DNA tails; we were able to detect multimers up to four mtDNA genome unit lengths . Further , we did not detect elongation from a displacement-loop or analogue of 7S DNA , suggesting a clear difference from human mtDNA in regard to the site ( s ) of replication initiation . We also identified cruciform mtDNA species that are sensitive to cleavage by the resolvase RusA; we suggest these four-way junctions may have a role in concatemer-to-monomer resolution . Overall these results indicate that mtDNA synthesis in C . elegans does not conform to any previously documented metazoan mtDNA replication mechanism , but instead are strongly suggestive of rolling circle replication , as employed by bacteriophages . As several components of the metazoan mitochondrial DNA replisome are likely phage-derived , these findings raise the possibility that the rolling circle mtDNA replication mechanism may be ancestral among metazoans .
Caenorhabditis elegans is a ubiquitous model animal often employed in studies of aging and metabolic disease , processes intimately associated with mitochondrial health . However , comparatively little is known of mtDNA maintenance in this organism [1 , 2] . Early studies of mitochondrial DNA ( mtDNA ) replication in mammalian cultured cells supported a unidirectional strand displacement or ‘asymmetric’ model , producing partially single-stranded-DNA ( ssDNA ) intermediates [3 , 4] . More recently , strand-coupled ‘theta’ replication has been proposed [5] , and support has also amassed for a temporally asynchronous mode of replication involving provisional RNA Incorporation ThroughOut the Lagging Strand ( RITOLS ) , a model in which expanding replication bubbles contain RNA:DNA hybrid tracts [6] . The previously described animal mtDNA replication models share two features . First , initiation of replication relies on elongation from a transcript-primed displacement loop ( D-loop ) . Second , each successful synthesis cycle from the circular template results in only two circular daughter molecules . The previous work on mtDNA synthesis has focused primarily on mammalian species; mtDNA maintenance elsewhere in the animal lineage remains poorly understood . MtDNA is required for nematode development beyond the early larval stages , and perturbations causing mtDNA depletion during embryonic development commonly result in a larval arrest phenotype [7] . The mitochondrial complement in somatic cells of the adult nematode appears largely to result from distribution of the approximately 100 , 000 maternal mtDNA molecules throughout the embryo during development , precluding a need for the mitochondrial polymerase POLG-1 during development [8] . Moreover , mtDNA copynumber tends to fall in ageing worms , suggesting minimal turnover in the somatic tissues [8] . These findings are consistent with mtDNA replication occurring primarily in the adult gonad , with the integrity and quantity of mtDNA produced reflected in the subsequent generation [8] . The confinement of ongoing mtDNA replication to the germline makes C . elegans a convenient model for studies of mitochondrial genome synthesis and mtDNA replication defects [1 , 9] . C . elegans mtDNA harbors two non-coding regions ( NCRs ) , delimiting coding regions of 5 . 5 and 7 . 7 kb respectively ( Fig . 1A; [10] ) . By analogy with the mammalian mtDNA organization , both NCRs have been proposed to play a role in C . elegans mtDNA replication , one as the first-strand origin ( akin to the mammalian D-loop ) and the other serving as a second-strand origin [1 , 7] . To test this assumption , we investigated the mechanism of C . elegans mtDNA replication in vivo and the possible function of the two NCRs therein .
To determine if either NCR could function as a replication origin , we examined mtDNA fragments containing each one of the two NCRs for origin activity using two-dimensional neutral agarose gel electrophoresis ( 2DNAGE ) [11] . Y arcs formed by progressing forks , as well as cruciform structures , were readily apparent ( Fig . 1B , ClaI/ApaI and BsrGI/ClaI ) . Analysis of replication intermediates ( RIs ) derived from restriction fragments lacking both NCRs also revealed full Y arcs and cruciforms , consistent with active replication of the entire mtDNA ( Fig . 1B , ApaI/BsrGI ) . However , a bubble arc indicative of theta-type replication initiation was not detected from any region of the genome , even after long autoradiographic exposures ( S1A–S1E Fig . ) . We next considered that first-strand replication initiation might occur from more than one site in the genome . Such low frequency bubble intermediates may go undetected by fragment 2DNAGE , as only a subset of intermediates are analyzed in each experiment [4 , 6 , 11] . To address this possibility , we performed 2DNAGE analysis after digestion with restriction enzymes cutting only once in the mitochondrial genome . We reasoned that analysis of RIs spanning the complete mtDNA would pool molecules containing D-loop initiation structures along a single arc regardless of initiation site , facilitating detection . Consonant with our 2DNAGE data on sub-genomic fragments , linearization and subsequent 2DNAGE of the full-length genome demonstrated clear Y and X shaped intermediates ( S1F–S1J Fig . ) , yet no bubble arc was observed . We therefore conclude that initiation of C . elegans mtDNA synthesis does not involve the formation of a bubble intermediate at levels detectable by blot-hybridization . To determine if molecular signatures of replication initiation , such as skewed nucleotide composition , are present in the non-coding region of C . elegans mtDNA , we conducted a bioinformatic analysis of cumulative GC skew ( S1K Fig . ; [12] ) . Unlike the D-loop regions of human and mouse mtDNA , our analysis demonstrates that the non-coding region suggested to harbor a D-loop in C . elegans is absent of local minima or maxima , considered features of origin and termination activity respectively . This finding is consistent with the lack of classic initiation ( bubble ) arcs on 2D-NAGE gels of replication intermediates . Prominent initiation intermediates have been described in analyses of mtDNA isolated from dissected human , mouse and chick tissues [5 , 13 , 14] . However , in the worm the vast majority of mtDNA replication is expected to occur specifically in the germline . Therefore , we tested whether a minor fraction of bubble initiation structures from somatic cells would become detectable when germline development was blocked . We isolated mtDNA from synchronized glp-4 mutants which exhibit deficient germline nuclei production at 25°C ( allele glp-4 ( bn2 ) ; [15] ) , and compared nematode cohorts reared at permissive and non-permissive temperature by 2D-NAGE . As expected , based on the work of Bratic et al [8] replication intermediates in glp-4 ( bn2 ) animals cultured at 16°C were comparable in structure and intensity to those detected in wildtype N2 animals . In contrast , at non-permissive temperature , i . e . , in “gonadless” worms , RI levels relative to total mtDNA were dramatically decreased to near the limit of detection by Southern hybridization ( S2A–S2B Fig . ) . Bubble intermediates were not detected on exposures ranging from 1 hour to 14 days , confirming the absence of replication elongation from a D-loop from both germline and post-mitotic cells . The lack of bubble intermediates effectively excludes strand-coupled initiation from a D-loop , i . e . the theta replication mode [4] . We next investigated whether C . elegans mtDNA RI structure was consistent with the asymmetric strand-displacement or RITOLS models of mtDNA replication . Temporally asynchronous replication of the two template strands is predicted to generate partially single-stranded RIs [4 , 16] and references therein] . In 2DNAGE , such ssDNA regions block endonuclease cleavage , producing slow-migrating Y arcs greater than twice the unit length fragment size , and render RIs sensitive to the action of the single-strand specific nuclease S1 [4] . In contrast , RNA:DNA hybrid-containing RIs typical of the RITOLS mode can be detected based on their sensitivity to degradation by RNase H , which exposes ssDNA regions rendered sensitive to S1 nuclease [17 , 18] . These treatments are expected to dramatically alter the electrophoretic migration properties of RITOLS intermediates in 2D gels [4 , 14]; such RNA:DNA hybrid-containing intermediates represent a transient step in replication , preceding synthesis of the definitive lagging strand . We tested for strand-displacement and/or RITOLS intermediates by systematic treatment of mtDNA fragments , collectively representing the complete mitochondrial genome , with S1 nuclease , RNase H , or RNase H followed by S1 nuclease , with subsequent analysis by 2DNAGE . For each replicate , equal amounts of purified mitochondrial nucleic acid were electrophoresed for each treatment condition on the same gel , then transferred and hybridized in parallel with the same preparation of radio-labeled probe . For all mtDNA fragments analyzed , both the Y arc and X arc ( cruciform spike ) persisted after treatment with either S1 or RNase H alone ( Fig . 2A , see also S2C–S2H Fig . ) . The intensity of the Y arc hybridization signal was modestly decreased by treatment with RNase H , yet the majority of fragment RIs remained following subsequent treatment with S1 nuclease ( Fig . 2A; quantification in Fig . 2B ) and , importantly , were not converted to any other structure , demonstrating distinct electrophoretic migration . These experiments indicate that C . elegans RIs lack the extensive ssDNA character expected from strand-displacement synthesis . Furthermore , slow-moving Y arcs were not observed , and depletion of the Y arc signal after treatment with RNase H and S1 was no greater than with RNase H treatment alone ( Fig . 2B ) . Thus RNase H failed to ‘unmask’ substantial ssDNA regions , as would be expected if extensive RNA:DNA hybrid tracts were present . These data are consistent with synchronous ( or very near-synchronous ) replication of the two mtDNA strands independent of a D-loop , and eliminate from further consideration both asymmetric and RITOLS-mode strand-displacement replication . The absence of theta-form , RITOLS and partially ssDNA strand-displacement intermediates led us to consider alternate DNA replication mechanisms . The detection of Y arcs , but not bubble arcs , by 2DNAGE of fragments derived from a circular template is consistent with a rolling circle replication ( RCR ) mechanism [19 , 20] . According to the RCR model , sustained elongation on a circular template produces linear DNA molecules greater than template unit-length that may become resolved to monomers in a variety of ways , or remain concatemeric linear networks [21] . A central prediction of the RCR model is the presence of “lariat” DNA forms in vivo . For C . elegans mtDNA , we hypothesized the occurrence of one-genome unit length circular templates , from which multimeric linear tails would extend . Alternatively , a second replication mode can be envisaged that would involve strand-invasion of a linear template by linear molecules , as occurs in some bacteriophages and the mtDNA of the fungus Candida albicans [22 , 23] . This alternative would predict Y-form RIs in the absence of bubble RIs , but not lariat structures . To determine whether lariat molecules consistent with rolling circle intermediates were present , we directly examined C . elegans mtDNA using transmission electron microscopy ( TEM ) . We observed both circular and branched-circular lariat molecules ( Fig . 3 , S1 Table ) . Most prominent were dsDNA circles with a mean measured length of 13 . 61 kb +/- . 407 kb , consistent with the sequenced mitochondrial genome size of 13 . 794 kb [10] ( Fig . 3A , B ) . Although C . elegans mtDNA has long been thought to be circular [10] , based on its restriction map , to our knowledge this is the first evidence that non-replicating C . elegans mtDNA exists in a topologically circular form . As predicted by our 2DNAGE and bioinformatic analyses , none of these circular mtDNAs contained a visible displacement loop . Lariats with linear tails ranging from < 1 kb to 48 . 2 kb in length , i . e . , more than three genome units , were the next most frequently observed class of molecules ( Fig . 3C , D; S1 Table ) . The mean length of the lariat circular portion measured 13 . 64 kb ( Fig . 3A ) . Fifty-six percent of lariat molecules appeared fully double-stranded at the circle-branch junction ( Fig . 3E , F ) , though ssDNA tracts of less than ∼500 bases are not readily visible under the imaging conditions used . While the X arc intermediates described above are intense on fragment 2DNAGE , their full structure , when undigested by restriction enzymes , is unknown . To further address the structure of the cruciform mtDNA , we isolated X-arc intermediates from a second-dimension gel on which ClaI/ApaI digested C . elegans mtDNA was fractionated . However , when spread for TEM , microfragments of agarose remained bound to the DNA , compromising the visualization of these molecules and precluding their further characterization . In non-fractionated spreads of purified mtDNA such forms would be indistinguishable from cases where two independent molecules are incidentally in contact . Some linear molecules were observed by TEM . However , sub-genomic linear molecules were not detected by Southern blot of C . elegans mtDNA using any of the probes described in this study ( S2 Table ) . We conclude that linear molecules on TEM grids are most likely contaminating nuclear DNA fragments , and therefore excluded them from the analysis summarized in S1 Table . A subset of lariat molecules contained visible interspersed regions of collapsed secondary structure , considered diagnostic for ssDNA ( Fig . 3G ) [24] . Within individual molecules , these regions occurred at several different positions: the junction of the circular and linear tail portions of lariats , further along the linear branch only , or in both locations ( Fig . 3H , I ) . Neither strand-displacement nor theta RIs were observed among the 1262 molecules analyzed by TEM , while lariats made up approximately 4% of mtDNA molecules , in line with previous reports of the proportion of replicating mtDNA in other species , e . g . mammalian cells and Drosophila melanogaster [3 , 25] . The high frequency of non-replicative circular monomers we detected by TEM suggested inter-conversion between the circular monomer and the lariat mtDNA forms . In the course of fragment 2DNAGE , we noted that the hybridization intensity of cruciform structures was most prevalent , relative to the monomer spot , in fragments harboring the 465 bp ‘major’ NCR ( Fig . 1 ) . This observation suggested that the formation of a site-specific cruciform structure could play a role in the maintenance of C . elegans mtDNA and/or the production of monomer circles [21 , 26] . We further addressed cruciform architecture by 2DNAGE following treatment with RusA , an Escherichia coli resolvase highly specific for Holliday junctions substrates in in vitro studies [27 , 28] . This analysis was performed using RusA alone or in combination with S1 nuclease . RusA treatment reduced the hybridization signal of the cruciform spike by 48% relative to the untreated controls , while S1 nuclease alone had no significant effect ( Fig . 4A , B ) . Treatment with S1 after RusA further reduced the cruciform signal , and revealed a subclass of cruciforms resistant to both RusA and S1 that persisted after the combined treatment ( Fig . 4A ) . On 2DNAGE these molecules formed a near-vertical spike ( Fig . 4C ) , a migration pattern typical of hemicatenanes [29] . It has previously been reported that the collapse of adjacent four-way junctions produces resolvase-resistant hemicatenanes [29 , 30] . These findings are reminiscent of the RusA-resistant mtDNA cruciforms we observe; whether these X-junctional molecules are intermediates in the mechanisms of mtDNA RCR or monomer resolution awaits further investigation . of mtDNA RCR or monomer resolution awaits further investigation .
Taken together , our findings indicate that synthesis of C . elegans mtDNA proceeds by rolling circle replication . We propose that multiple replication cycles on single template circles generate lariat structures with multimer tails composed primarily , although not entirely , of dsDNA , which are subsequently resolved to monomer circles . Such conversion could potentially involve the formation and resolution of the cruciform species observed by fragment 2DNAGE ( Fig . 4 ) . In the context of a rolling circle , each initiation event will give rise to multiple genome units , making the identification of a specific start site challenging . Neither the biochemical nor microscopic methods used here revealed a specific site of replication initiation , nor did our bioinformatic analysis identify molecular signatures thereof [12] . The data presented here do not exclude the possibility of replication initiation via site-specific nicking followed by strand invasion , or alternatively by homologous recombination . In such a case , the intensity of the cruciforms from ClaI-ApaI and MfeI-MfeI mtDNA fragment gels would be consistent with the NCR region containing a site-specific origin of such a type . It is worth noting , however , that the C . elegans NCR region contains an approximately 1 kb region of short repetitive elements with a potential to form complex secondary structures necessitating replication fork pausing and restart , or perhaps facilitate intermolecular strand exchange [10] . Our 2DNAGE analysis of mitochondrial nucleic acid isolated from the glp-4 ( bn2 ) mutant strain revealed a marked diminution or absence of both canonical replication intermediates and cruciform species ( S2 Fig . ) . These data further imply that the cruciforms detected in wildtype animals are potentially involved in synthesis and/or resolution of mtDNA in the gonad . The data presented herein do not directly address molecular recombination involving sequence-specific mtDNA strand exchange events in C . elegans . The occurrence of genetic recombination within or between mtDNA molecules in animal mitochondria is highly controversial [30 , 31] , and there is currently no evidence for sequence-specific inter- or intra-molecular recombination of vertebrate mtDNA . Indeed , recent work has demonstrated that recombination is undetectable in the germline of mice segregating neutral mtDNA haplotypes , when tracked over 50 generations [32] . Sequence alterations suggestive of recombination have been detected in heteroplasmic mice carrying a deleterious allele [33] , although the changes were detected at very low frequency and could plausibly have resulted from in vivo template switching during replication . Purified mtDNA both from mice and cultured mouse embryonic fibroblasts has been analyzed extensively by 2DNAGE , without the detection of prominent cruciform species such as those we infer for C . elegans [5 , 34] . In contrast , junctional mtDNA species have been detected by both 2DNAGE and TEM methods in human heart and brain[35 , 36] . Elsewhere within the metazoa , compelling evidence has accrued for recombination between maternal and paternal mtDNA haplotypes in the mussel Mytilus in which inheritance of the mitochondrial genome is doubly uniparental [37] . Moreover , PCR-based methods have also implied novel sequence organizations consistent with intramolecular recombination among short tandem repeat arrays in the mitochondrial genome of the plant parasitic nematode Meloidogyne javanica [38] . The fact that no animal mtDNA resolvases have been identified to date remains a major challenge to the mtDNA recombination concept [39] . We searched the C . elegans genome for plausible mitochondrially targeted homologues of known integrase or resolvase gene families using bioinformatic methods , without success . This raises the possibility that the resolution of rolling-circle replication intermediates to genomic monomers in C . elegans involves known proteins involved in worm mtDNA maintenance [9 , 36 , 37] , which may have adopted novel molecular functions . While some mitochondrial proteins required for the maintenance of mtDNA copy number have been described in C . elegans [40–44] , the functional architecture of the minimal mtDNA replisome remains to be elucidated . Future work describing the effect of manipulation of known factors on mtDNA replication intermediates by 2DNAGE could potentially reveal or exclude roles for conserved mtDNA maintenance factors in rolling circle replication . Several of the known metazoan mtDNA maintenance factors are highly homologous to bacteriophage proteins , including the mtDNA polymerase POLG , RNA polymerase POLRMT , and TWINKLE helicase [3] . Moreover , antiviral drugs are commonly mitotoxic [3] . These observations raise the possibility that the genome of the ancestral endosymbiont may have been replicated by a phage-like RCR mechanism [3] , of which the DNA replication system used in the C . elegans germline is a relic . The analogy with T7 replication is furthered by ( i ) the presence of sporadic ssDNA regions observed along lariat molecules ( Fig . 3H ) , which are consistent with yet-to-be-completed and ligated gaps between lagging-strand synthesis products and ( ii ) looping at the lariat circle-to-tail junction ( Fig . 3I ) that could represent a single replisome engaged in coordinate synthesis of the leading and lagging strands , consistent with the 2DNAGE data presented here ( Fig . 2A ) . If RCR was the ancestral mode of animal mtDNA replication , strand-displacement , RITOLS and other types of theta replication may represent taxon-specific derived mechanisms . They may represent different solutions to the challenge of maintaining genomic fidelity in the oxidative environment of the mitochondrion . Rolling-circle replication of mtDNA has been described in the plant and fungal kingdoms [45–49] . Here we present the first report of RCR in a metazoan , furthering the ubiquity of this mechanism of mtDNA synthesis . Our findings differ from the descriptions of RCR in plants and fungi , in that the C . elegans mtDNA monomer circle remains the most common topology of non-replicative mtDNA . Among the fungi , replication of Saccharomyces cerevisiae mtDNA produces linear molecules , with circles present only transiently during replication; in contrast , mtDNA synthesis in Candida albicans is recombination driven , generating concatameric linear networks [23 , 50] . Intriguingly , the implied similarities between C . elegans and yeasts with respect to mtDNA replication mechanisms and the high proportion of junctional mtDNA intermediates mirror similarities in the patterns and rates of mtDNA mutation observed in these species [51] . These data raise the possibility these two phenomena are linked . Plant mitochondrial genomes are particularly complex , often consisting of a mix of branched , linear and circular topologies that may be many genome multimers in size , rendering monomer circles a rare occurrence [46] . C . elegans mtDNA topology is also distinct in one aspect from models of bacteriophage RCR , due to the apparent absence of linear mtDNA monomers as detected by Southern blot , which in phages may bear distinct ( phage T7 ) or permuted ( phage T4 ) ends [21 , 52] . It has been previously assumed that C . elegans mtDNA adheres to the strand-displacement replication mechanism in which the NCRs contain first- and second-strand origins [7 , 8]; here we demonstrate that this cannot be the case . Neither putative replication origin produces bubble-type intermediates that are clearly observed in multiple mammalian systems including cultured human cells [53] . Rather , junctional mtDNA species were the only detectable and identifiable structures observed specific to either of the NCRs . While the details of concatemer resolution remain to be determined , we suggest that the junctional intermediates identified here may represent termination/resolution structures , in which strand-invasion or branch migration arrest could occur in a site-specific manner , facilitated by the short repeats present in the major NCR [10] . Invasion by the unreplicated ssDNA 3’ end of the lariat tail at the major NCR sequence would create a triple-stranded structure not unlike initial events in DNA strand exchange . Subsequent migration of this junction would provide an opportunity for formation of a second four-way junction . Cleavage and resolution would then generate a gapped circular monomer and lariat tail with 3’ overhang , one genome unit-length shorter . Such a mechanism could also produce rare uni-circular multimers ( see S1 Table ) in which resolution does not occur at an adjacent concatemer . This mode of resolution , while speculative , is consistent with two intriguing aspects of our results . First , a subset of Y arc RIs are sensitive to degradation by RusA ( Fig . 4A ) , indicating that some Y-like forms in fact contain four-way junctions , as predicted by a strand-invasion model . Second , our 2DNAGE analysis of the 5 kb mtDNA region containing the major NCR demonstrated the presence of RusA-resistant cruciforms consistent with hemicatenanes , a DNA species which forms via the convergence of double Holliday junctions , or alternatively by replication fork stalling [54–56] . We did not observe molecules simultaneously involved in elongation and resolution by TEM , which would be consistent with this model . However , we note that if present in vivo , such molecules could possibly exceed 75 kb in size and therefore are likely to be fragile to DNA isolation techniques . The enzymes involved in putative site-specific resolution remain unknown . Other mechanisms can be envisaged which would exploit homologous recombination machinery documented to exist in mitochondria in at least some species[57–59] , for example , involving site-specific DNA binding proteins and/or branch-migration driven by directionally acting helicases . Whether RCR occurs elsewhere in the animal lineage remains to be explored . Unlike previous studies characterizing mtDNA RIs in organisms where both strands of mtDNA contain protein coding information , all protein-coding genes are transcribed from one C . elegans mtDNA strand ( Fig . 1A ) , thus raising the possibility that RCR may be linked to the transcriptional architecture of the mitochondrial genome . Fortunately , the Nematoda are an excellent model system in which to test such hypotheses . The mtDNAs of many nematode species have been sequenced , revealing considerable architectural variation and enabling comparative studies in which the mtDNAs differ by gene amplification or inversion , scrambled gene orders , or translocation of the major NCR in the genome map [60] . As such , the phylum presents a powerful new model system for probing the relationship between mitochondrial genome architecture and replication mode . C . elegans itself offers a compelling model for the study of rolling circle replication in animal cells from both mechanistic and genetic perspectives . MtDNA replication primarily occurs in the C . elegans germline , where high demand during gametogenesis likely requires efficient , yet prolific mtDNA synthesis [1] . Since RCR is the only replication mode that can be detected , it must be sufficient to meet this demand despite the small percentage of molecules replicating at any particular time; we note that mtDNA multimers resulting from RCR can potentially resolve to several copies of the mitochondrial genome ( Fig . 3 ) , in contrast to theta replication , which produces only two daughter mtDNA molecules . We anticipate that the future characterization of factors involved in this process will provide many new insights into animal mtDNA maintenance , the evolution of replication mechanisms , and possibly even the pathological derangement of mtDNA synthesis in humans .
Caenorhabditis elegans strains N2 Bristol and mutant glp-4 ( bn2 ) I were obtained from the Caenorhabditis Genetics Center ( CGC; Minneapolis , USA ) and maintained as described [15 , 61] . For wildtype N2 animals grown in liquid culture , a culture sample was removed every 24 hours for monitoring and the turbidity of the culture tested to ensure ample E . coli OP50 were present . Culture samples were visually also checked for developmentally arrested or dauered larvae that could indicate bacterial depletion—none were observed . N2 and glp-4 ( bn2 ) I animals grown on plates were fed on a lawn of E . coli OP50 as described [62] . Nematodes were actively growing and feeding on E . coli OP50 until immediately prior to mitochondrial isolation . For preparation of intact mitochondria , nematodes were collected , dounce-homogenized and subjected to differential centrifugation to generate a crude mitochondria-enriched fraction; further enrichment was achieved by sucrose-step gradient centrifugation ( 1:1 . 3 M sucrose ) , with all procedures conducted at 4°C [25] . Lysis and DNA extraction protocols for C . elegans were adapted from studies in which fragile mtDNA replication intermediates have previously been successfully isolated intact , for analysis by both 2DNAGE and TEM [18 , 63 , 64] . Briefly , freshly isolated mitochondria were lysed with 1% SDS in the presence of 200 mg Proteinase K and extracted by phenol-chloroform and ethanol precipitation; mtDNA was washed in ethanol and re-suspended in Tris-EDTA pH 7 . 6 for further manipulation . Treatments ( restriction endonucleases , RNases , other nucleases ) were executed according to manufacturer instructions; RusA treatment was as described [28] . 2DNAGE , blot-transfer and probe hybridization were carried out as previously described [25 , 65]; see S2 Table for genomic locations and sequences of mtDNA probes . For all 2DNAGE panels , four-hour exposures are presented . For detailed information on probes see Supplemental Experimental Procedures . Aliquots of RNase I-treated mitochondrial nucleic acid were mounted directly on parlodion-coated copper grids following the Kleinschmidt method and imaged as described [24 , 66] . Molecule lengths were measured in Gatan DigitalMicrograph and calibrated by measurement of a co-spread 3 . 5 kb pglGAP plasmid . Each mtDNA molecule was measured 3 times to obtain mean values as reported in Fig . 3 . Cumulative GC skew analysis was carried out as described [23] using a custom R script . | Defects in the mitochondrial DNA ( mtDNA ) that encodes protein subunits of the respiratory complexes may cause severe metabolic disease in humans . Such defects are often caused by errors during mtDNA synthesis , motivating ongoing studies of this process . The nematode Caenorhabditis elegans has been proposed as a model for the study of mtDNA replication defects . Here we analyze the mechanism of mtDNA synthesis in the C . elegans gonad and demonstrate that it is unique among animals . Nascent worm mtDNA forms branched-circular lariat structures with concatemeric tails that we suggest would ultimately resolve into monomeric circles , the predominant molecular form identified by both transmission electron microscopy and two-dimensional gel electrophoresis . Our discovery that mtDNA replication in C . elegans does not faithfully model that in mammals is significant , because it demonstrates the breadth and evolutionary plasticity of the mechanisms that maintain this critical DNA among animals . Interestingly , the mtDNA replication mechanism within C . elegans is highly similar to that of bacteriophages , from which components of the mitochondrial DNA replisome are thought to be derived . Thus C . elegans may serve as a model for mtDNA synthesis as it occurred within ancient eukaryotes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | A Rolling Circle Replication Mechanism Produces Multimeric Lariats of Mitochondrial DNA in Caenorhabditis elegans |
Aurora B kinase is an essential regulator of chromosome segregation with the action well characterized in eukaryotes . It is also implicated in cytokinesis , but the detailed mechanism remains less clear , partly due to the difficulty in separating the latter from the former function in a growing cell . A chemical genetic approach with an inhibitor of the enzyme added to a synchronized cell population at different stages of the cell cycle would probably solve this problem . In the deeply branched parasitic protozoan Trypanosoma brucei , an Aurora B homolog , TbAUK1 , was found to control both chromosome segregation and cytokinetic initiation by evidence from RNAi and dominant negative mutation . To clearly separate these two functions , VX-680 , an inhibitor of TbAUK1 , was added to a synchronized T . brucei procyclic cell population at different cell cycle stages . The unique trans-localization pattern of the chromosomal passenger complex ( CPC ) , consisting of TbAUK1 and two novel proteins TbCPC1 and TbCPC2 , was monitored during mitosis and cytokinesis by following the migration of the proteins tagged with enhanced yellow fluorescence protein in live cells with time-lapse video microscopy . Inhibition of TbAUK1 function in S-phase , prophase or metaphase invariably arrests the cells in the metaphase , suggesting an action of TbAUK1 in promoting metaphase-anaphase transition . TbAUK1 inhibition in anaphase does not affect mitotic exit , but prevents trans-localization of the CPC from the spindle midzone to the anterior tip of the new flagellum attachment zone for cytokinetic initiation . The CPC in the midzone is dispersed back to the two segregated nuclei , while cytokinesis is inhibited . In and beyond telophase , TbAUK1 inhibition has no effect on the progression of cytokinesis or the subsequent G1 , S and G2 phases until a new metaphase is attained . There are thus two clearly distinct points of TbAUK1 action in T . brucei: the metaphase-anaphase transition and cytokinetic initiation . This is the first time to our knowledge that the dual functions of an Aurora B homolog is dissected and separated into two clearly distinct time frames in a cell cycle .
The Aurora-like kinases are essential mitotic regulators among eukaryotes . In metazoa , there are three such enzymes; Aurora A regulating spindle assembly , Aurora B promoting chromosome segregation and cytokinesis and Aurora C controlling chromosome segregation during male meiosis . But only a single Aurora-like kinase is required in budding and fission yeasts for spindle assembly and chromosome segregation without an apparent involvement in cytokinesis ( for a review , see [1] ) . Aurora B kinase in metazoa forms a chromosomal passenger complex ( CPC ) with three non-enzymatic partners , the inner centromere protein INCENP , Survivin , and Borealin [2]–[4] . In budding yeast , the single Aurora-like kinase Ipl1p also forms a CPC with three homologs of INCENP ( Sli15p ) , Survivin ( Bir1p ) and Borealin ( Nbl1p ) [5] , [6] . The mechanisms of CPC in detecting and correcting aberrant kinetochore-microtubule attachments during mitosis have been well characterized in yeast and metazoa . These involve the phosphorylation of several key kinetochore components by Aurora B [7]–[11] , the activation of spindle checkpoint by targeting the checkpoint components to kinetochores [12] and the promotion of the association of BUBR1 with the anaphase-promoting complex/cyclosome ( APC/C ) [13] . The components of the kinetochore and the spindle checkpoint as well as the regulatory pathways governing kinetochore-microtubule attachments and chromosome segregation are well conserved from yeast to human . The potential role of Aurora B in promoting cytokinesis in metazoa has , however , not yet been clearly delineated or well separated from its regulatory function on mitosis ( for a review , see [1] ) . An Aurora B-mediated phosphorylation of the two subunits in the centraspindlin complex , the Rho GTPase activating protein MgcRacGAP/RacGAP50C/CYK-4 and the kinesin MKLP1/Pavarotti/ZEN-4 , is essential for targeting the centraspindlin complex to the spindle midzone where it binds Ect2 , a guanine nucleotide exchange factor ( GEF ) [14]–[16] . Ect2 then activates the small GTPase RhoA in the equatorial region of the cell membrane to promote the formation of the actomyosin contractile ring that constitutes the initial cleavage furrow . The latter then closes onto the midzone to complete the process of cytokinesis [17] . Recently , a quantitative analysis of Aurora B phosphorylation dynamics indicated the formation of a spatial phosphorylation gradient along the division axis early in the anaphase in HeLa cells [18] . This gradient was postulated to provide the spatial information for positioning the actomyosin ring . But the range of potential substrates of Aurora B and the specific time frame of Aurora B action required for cytokinesis remain unclear . Nor is it well understood whether the Aurora B action following the metaphase-anaphase transition plays an essential role in promoting mitotic exit , cytokinetic initiation , or cytokinetic completion . In Trypanosoma brucei , a parasitc protazoan that causes human sleeping sickness in Sub-Saharan Africa , a single functional Aurora-like kinase , TbAUK1 , is responsible for promoting spindle assembly , chromosome segregation as well as cytokinesis [19] , [20] . Homologs of INCENP , Borealin and Survivin , however , have not been found in the trypanosome genome [21] . Instead , a novel CPC consisting of TbAUK1 and two novel proteins TbCPC1 and TbCPC2 , which bear no structural similarity to those three non-enzymatic proteins , was identified in T . brucei [22] . This CPC displays a typical sub-cellular localization pattern during mitosis similar to that of the metazoan CPC [22] . It associates with the chromosomes during G2 phase , with kinetochores in metaphase , and then moves to the spindle midzone in anaphase . The trypanosomes are known to divide by a pattern totally different from that of metazoa and yeast . They divide longitudinally from the anterior toward the posterior end of the cell [23] . A most unusual pattern of CPC trans-localization has since been observed in trypanosomes toward the end of mitosis and beginning of cytokinesis [22] , [24] . The mid-portion of the elongated spindle bearing the midzone starts to bend toward the dorsal side of the cell , where the flagellum attachment zone ( FAZ ) is aligned . The CPC in the midzone is then transferred to the mid-point of the cellular dorsal side , and then moves to the anterior end of the cell to initiate cytokinesis by moving from the anterior toward the posterior end accompanied with a division of the cell into two [22] , [24] . This unusual mode of cell division in T . brucei , apparently mediated by the CPC , indicates a unique mechanism of cytokinesis that could be shared by all the flagellates that divide longitudinally . One of the burning questions from this observation is whether the TbAUK1 function in the CPC plays an essential role in CPC trans-localization and cytokinesis . A previous RNAi depletion of TbAUK1 from an asynchronous trypanosome population , which was mostly in the G1 phase , arrested the cells in G2/M phase and enriched cells with an enlarged nucleus and two widely separated kinetoplasts [19] , indicating that both mitosis and cytokinetic initiation are blocked . In a separate study , an over-expression of an inactive TbAUK1-K58R mutant in T . brucei exerted a dominant-negative effect resulting in a virtually identical outcome like that from the RNAi experiment [20] . It is thus highly likely that TbAUK1 has also a dual function in T . brucei in regulating metaphase-anaphase transition and cytokinetic initiation . The genetic studies have their limitations in leading the asynchronous cells , which are mostly in the G1 phase , to a phenotype defective in metaphase-anaphase transition thus masking the next potential role of TbAUK1 in controlling cytokinesis [22] . Furthermore , RNAi-mediated silencing of any one of the three CPC subunits led invariably to a disintegration of the complex , making it difficult to study the potential role of TbAUK1 within the CPC complex during cell cycle progression [22] . Here we applied a chemical genetic approach through inhibiting TbAUK1 kinase activity with a small-molecule inhibitor , VX-680 , at different cell cycle stages in a synchronized cell population . It allowed us to dissect the two functions of TbAUK1 at different stages of the cell cycle , which could not be accomplished by genetic manipulations . VX-680 was originally discovered as a selective inhibitor against human Aurora kinases , and has apparent IC50 values of 0 . 6 , 18 and 4 . 6 nM against human Aurora A , B , and C , respectively [25] . It showed greater than 100-fold selectivity for Aurora kinases over 55 other kinases except for Fms-related tyrosine kinase-3 , which has been found missing from T . brucei [26] , [27] . We found in this inhibitor an IC50 value of 190 nM against TbAUK1 . It arrested an unsynchronized trypanosome cell proliferation in the G2/M phase with an enlarged nucleus and two segregated kinetoplasts in each cell as having been observed from an RNAi knockdown of TbAUK1 [19] or an over-expressed TbAUK1-K58R negative mutant [20] . By adding VX-680 to a synchronized trypanosome cell population at different stages of the cell cycle , we were able to uncover what was not found in an RNAi experiment . We demonstrated that TbAUK1 plays essential roles at two distinctive stages of the cell cycle; ( 1 ) the metaphase-anaphase transition; ( 2 ) the trans-localization of the CPC from the spindle midzone to the dorsal anterior of the cell at the beginning of cytokinesis . The dual roles of an Aurora-like kinase have thus been clearly dissected in this model organism ( see Discussion ) .
To investigate the potential inhibitory effect of VX-680 on TbAUK1 kinase activity , recombinant GST ( glutathione-S-transferase ) -tagged TbAUK1 and histone H3 were expressed in transformed Escherichia coli , purified to near homogeneity and used for in vitro assay of the TbAUK1 kinase activity [20] . VX-680 was dissolved in DMSO and added to the assay mixture at a final concentration of 0 , 10 , 50 , 100 , 200 or 400 nM , respectively . TbAUK1 phosphorylated histone H3 with a strong signal in the no drug control , but the activity became increasingly inhibited at higher concentrations of VX-680 ( Fig . 1A ) . The IC50 value of VX-680 against TbAUK1 was calculated to be 190 nM . The potential effect of VX-680 on trypanosome in vitro proliferation was then investigated . Procyclic cells were incubated with VX-680 ranging from 1 to 30 µM , and the cell growth was monitored at different time intervals . The cells grew with a slightly slower rate than the control in the presence of 1 or 5 µM VX-680 , but were significantly slowed down to half of the control rate at 10 µM VX-680 ( Fig . 1B ) . At 20 µM and 30 µM , VX-680 totally inhibited cell growth , which led to an eventual cell death after 2 to 3 days ( Fig . 1B ) . The IC50 value of VX-680 on trypanosome cell growth was estimated to be 10 µM . To test the effect of VX-680 on cell cycle progression , unsynchronized procyclic cells were treated with 10 µM VX-680 and subjected to daily flow cytometry analysis ( Fig . 1C ) and examination for numbers of nuclei and kinetoplasts in each cell ( data not shown ) . The outcome turned out to be similar to that from a TbAUK1 RNAi experiment [19] . There was an enrichment of cells with 4C DNA content ( G2/M cells ) and a corresponding increase of cells with an enlarged nucleus and two segregated kinetoplasts ( 1N*2K ) . Apparently , VX-680 treatment and TbAUK1 RNAi of the procyclic cells result in the same phenotype , suggesting that VX-680 acts by primarily inhibiting TbAUK1 in the current experimental setting . In a separate experiment , the procyclic cells were synchronized by hydroxyurea using a procedure slightly modified from the previously published method [28] and released in late S-phase . VX-680 was then added to the cells at various concentrations ranging from 1 to 30 µM at the time of release , and the cells were analyzed by flow cytometry 2 and 4 hrs thereafter . In the no-drug control , the synchronized cells proceeded from S-phase to G2/M phase within 2 hrs and then progressed to cell division to produce G1 cells after 4 hrs ( Fig . 1D ) . In the presence of 1 µM , 5 µM or 10 µM VX-680 , there was little inhibition of the cell cycle progression within the first 4 hrs ( Fig . 1D ) . But 20 µM VX-680 enhanced the G2/M cells to over 80% of the total population after 4 hrs , whereas 30 µM VX-680 accumulated G2/M cell to over 95% of the total population without cell division after 4 hrs of treatment ( Fig . 1D ) . Thus , within a much shortened time span of 2 hours , comparing with the 24 hr period required in the TbAUK1 RNAi study [19] , an application of VX-680 at a concentration of 30 µM to the synchronized cells in late S-phase resulted in an essentially total arrest of the cells in G2/M phase . This 30 µM concentration of VX-680 was thus chosen for the rest of the experiments , because of its near total arrest of the cells in the G2/M phase within a relatively short time The procyclic cells expressing tagged TbCPC1-EYFP , TbCPC2-EYFP or TbAUK1-EYFP at the apparent endogenous levels [24] were synchronized by hydroxyurea and released in late S-phase . VX-680 was added to the cells at 0 , 1 , 2 , 3 , 4 , 5 , 6 and 7 hrs after the release . Hourly samples after the release were analyzed by flow cytometry and examined by fluorescence microscopy to localize the fluorescent proteins . In the no drug control , the cells progressed synchronously and steadily from S-phase through G2 to mitosis within the first 2 hrs , and began to show the initial sign of cell division after 3 hrs by the emergence of a tiny G1 peak ( with 2C DNA content ) ( Fig . 2 ) . The G1 cells , likely representing the newly divided cells , increased rapidly between the 3rd and the 5th hour accompanied by a corresponding decrease of G2/M cells ( with 4C DNA content ) . This was clearly a period of active cell division . The apparent increase of G1 cell population slowed down subsequently and stopped at the 6th hour with a steadily increasing S-phase cell population , which kept increasing into the 7th and 8th hour accompanied with a steady decrease of G1 cells . The profile of cells at 8 hrs after the release resembled that at 0 hr except for some G1 cells apparently still remaining in the 8th hour sample , suggesting that within a relatively short additional time , the cells would be mostly back to S phase , thus completing a well-synchronized cell cycle progression ( Fig . 2 ) . When the cells were treated with VX-680 immediately after the release , they were able to proceed to the G2/M phase within the first 2 hours at a rate similar to that of the no drug control . It suggests that TbAUK1 plays no role in the S to G2/M transition in trypanosomes . The cells were , however , unable to proceed beyond the G2/M phase to cell-division as there were no G1 cells emerging thereafter . They remained arrested at the G2/M boundary for the rest of the incubation period up to 8 hrs with almost 98% of the cells possessing a 4C DNA content ( Fig . 2 and Fig . S1 ) . Similar results were obtained when VX-680 was added at 1 hr or 2 hrs after the release , suggesting that , after 2 hrs of progression from the late S phase , the cells reached a specific stage requiring TbAUK1 to play a crucial role to move on ( Fig . 2 and Fig . S1 ) . When the drug was added 3 hours after the release , cell division was just initiated at that time ( see the no-drug control in Fig . 2 ) . The drug first slowed down the emergence of G1 cells , then stopped it totally and eventually reduced the number of G1 cells as the S-phase cells started to increase gradually ( Fig . 2 and Fig . S1 ) . This particular 3 hr time point appears to be a crucial moment when TbAUK1 plays an apparently critical function in promoting cytokinetic initiation beyond the G2/M phase , because when VX-680 was added to the cells at 4 , 5 , 6 and 7 hrs after the release , there was little apparent disturbing effect on the subsequent cell cycle progression ( Fig . 2 and Fig . S1 ) . Cytokinesis was thus apparently already initiated among most of the cells after 4 hrs of cell cycle progression from the late S phase . Further progressions through cytokinesis , the next G1 phase , the S phase and the G2 phase apparently do not require the function of TbAUK1 . These synchronized cells , treated with VX-680 at different stages of their cell cycle progression , were also stained with DAPI for the numbers and sizes of nucleus ( N ) and kinetoplasts ( K ) in individual cells . In the no drug control , the cells consisting of one nucleus and one elongated kinetoplast ( 1N1K* ) , which represent cells in S-phase [29] , were in the majority at the beginning ( Fig . 3 ) . They rapidly vanished within the first 5 hrs after release but re-emerged between the 6th to 8th hours . This initial decrease in 1N1K* cells was closely followed by an increase in 1N2K cells and then 2N2K cells and finally the 1N1K cells , which are apparently in the G1 phase . The time course of changes in numbers of nucleus and kinetoplast overlaps well with the anticipated cell cycle progression in trypanosomes [30] . Kinetoplast segregation is known to precede the nuclear division , whereas the transition from 2N2K cells to 1N1K cells represents cell division ( Fig . 3 ) . When VX-680 was added to the synchronized cells at 0 to 2 hrs after the release from the late S-phase , there was a declining 1N1K* population accompanied by a rapidly increasing population of 1N2K cells as observed in the control . But the latter were apparently not further converted to 2N2K or 1N1K cells ( Fig . 3 ) . They are thus likely arrested in the G2/M phase unable to proceed into nuclear division or cytokinesis . This outcome is similar to that observed from knocking down TbAUK1 with RNAi over a longer time span of 24 hours [19] . The slowness in developing a comparable phenotype from an RNAi experiment could be attributed to the time required for degradation of mRNA and protein , which was not encountered in an enzyme inhibition study . After the release from the late S-phase for 3 hrs , the drug addition enhanced the 2N2K cell population ( Fig . 3 ) , but the subsequent increase in 1N1K cells was much reduced , indicating that cytokinesis was inhibited . When VX-680 was added after the cell was released for 4 , 5 , 6 , or 7 hours , however , the 2N2K cells that accumulated during the initial 4 to 5 hours declined and were accompanied with a corresponding increase of 1N1K cells similar to that in the no-drug control ( Fig . 3 ) . The TbAUK1 function is thus apparently no longer required for cell cycle progression once the cells have passed the point of cytokinetic initiation . These experiments provided an important indication that TbAUK1 plays critical roles in two separate stages of the cell cycle in trypanosomes; the completion of mitosis and the initiation of cytokinesis . To investigate the potential effect of TbAUK1 inactivation on the trans-localization of the CPC during cell cycle progression , the synchronized procyclic trypanosome cells expressing tagged TbCPC1-EYFP at the apparent endogenous level were released in the late S-phase and treated with VX-680 at different time points thereafter . The localization of TbCPC1-EYFP was then examined with a fluorescence microscope . In the control cells , TbCPC1-EYFP was dispersed in the nucleus after the release for 1 hr but became concentrated on the metaphase plate after 2 hrs ( Fig . 4; arrow ) . It was then trans-localized to the spindle midzone between the two segregating nuclei 3 hrs after the release and became further enriched in the midzone ( Fig . 4; arrow ) . The protein then moved toward the dorsal mid-point of the cell and migrated toward the anterior end 4 hrs after the release ( Fig . 4; arrow ) , and was eventually and exclusively located at the anterior end of the cell at the 5th hr ( Fig . 4; arrow ) . The migration of TbCPC1-EYFP during the process of cytokinesis was difficult to monitor because it had a very short duration and could be captured only by time-lapse video microscopy ( see below ) . When the cells entered the next round of cell cycle , TbCPC1-EYFP was no longer visible in the G1 phase at the 6th hr of release , but re-emerged in the nucleus when the cell entered S phase again after 7 to 8 hrs ( Fig . 4 ) . This dynamic pattern of trans-localization of the EYFP-tagged TbCPC1 agrees with that of the HA-tagged TbAUK1 , TbCPC1 and TbCPC2 in both the procyclic and bloodstream forms of T . brucei observed previously [22] , [24] . A time-lapse video microscopy was also employed to record this event in the current study ( Video S1 ) . We started the time-lapse experiment when TbCPC1-EYFP was already localized to the spindle midzone . The protein then moved to the dorsal mid-point of the cell between the 11th and 17th min . A portion of the fluorescent spot then quickly appeared at the anterior end of the cell at the 20th min , which was followed by a movement of the entire TbCPC1-EYFP spot to the anterior end within 56 minutes . A dramatic sliding action of TbCPC1-EYFP along the apparent cleavage ingression from the anterior to the posterior end of the cell then occurred from the 104th to the 140th min resulting in dividing the cell ( Video S1 ) . When VX-680 was added to the cells between 0 to 2 hrs after the release , TbCPC1-EYFP became concentrated on the metaphase plate after 2 hrs as in the control cell regardless of the precise time of drug addition ( Fig . 4; arrows ) . TbCPC1-EYFP then remained on the metaphase plate for the rest of the experimental period , indicating that TbAUK1 plays an essential role in promoting the transition from metaphase to anaphase . This observation was further confirmed by data from time-lapse video microscopy ( Video S2 ) . A cell arrested in the metaphase with TbCPC2-EYFP localized to the metaphase plate from a synchronized cell population treated with VX-680 at time 0 of their release was identified after 2 hrs ( Video S2 ) . The cell was monitored continuously for the next 6 hrs . But there was no sign of either progression beyond the metaphase or dissociation of TbCPC2-EYFP from the metaphase plate during this prolonged incubation . Similar results were also obtained with the cells expressing EYFP-tagged TbAUK1 ( data not shown ) . Aurora B kinase in the CPC is known to adjust chromosome bi-orientation for proper kinetochore-microtubule attachments during metaphase in eukaryotes [4] , [31]–[34] . This is achieved through Aurora B-mediated phosphorylation of the kinetochore components and activation of the spindle assembly checkpoint to prevent premature progression into the anaphase ( for a review , see [1] ) . In trypanosomes , the chromatin does not condense and chromosome alignment cannot be clearly distinguished by DAPI staining . However , when VX-680 was added to the cells prior to the onset of metaphase and incubated up to 8 hrs thereafter , essentially all the cells were found arrested in metaphase with TbCPC1-EYFP remaining on the metaphase plate . But the shape of the DAPI-stained nucleus became irregular and enlarged , which could suggest mis-aligned chromosomes ( Fig . 5A; arrowheads ) . This is in contrast to the control metaphase cells in which the DAPI-stained nucleus was in a typical regular diamond shape ( Fig . 5A; see also [35] ) . The irregular and enlarged nucleus was observed among approximately 20% of the cells 1 hr after VX-680 addition and progressed beyond 95% after 8 hrs ( Fig . 5B ) . The TbAUK1 kinase activity could be thus required for proper chromosome alignment on the metaphase plate . To investigate the potential effect of TbAUK1 inactivation on the continuous CPC trans-localization after it has already reached the spindle midzone , we added VX-680 to the synchronized cells 3 hrs after the release , while the majority of cells has entered the anaphase with TbCPC1-EYFP enriched in the spindle midzone ( Fig . 4; arrow ) . TbCPC1-EYFP remained in the midzone for 1 to 2 hrs after the drug treatment without any sign of movement to the dorsal mid-point of the cell , while nuclear division proceeded at a rate similar to that of the no-drug control ( Fig . 4; arrows ) . When nuclear division was completed , TbCPC1-EYFP was found evenly distributed in the two segregated nuclei and the bi-nucleated cells showed no sign of cell division upon prolonged incubation ( Fig . 4; arrows in the panel of +VX-680 at 3 hr ) . Apparently , VX-680 treatment of cells in the anaphase blocked further trans-localization of TbCPC1-EYFP from the spindle midzone and inhibited cytokinetic initiation , but did not hinder the completion of mitosis . TbAUK1 is thus likely required for CPC trans-localization from the spindle midzone to the cell dorsal mid-point to initiate cytokinesis . To further confirm this finding , cells treated with VX-680 3 hrs after release from late S phase were examined with time-lapse video microscopy . TbCPC2-EYFP disappeared from the spindle midzone after 30 min of the drug treatment but re-appeared in the two nuclei immediately thereafter , while no cell division took place ( Video S3 ) . It appears that , in the absence of a functioning TbAUK1 , the CPC becomes dissociated from the spindle midzone and redistributed back into the two newly formed nuclei upon the completion of mitosis , while the cell remains undivided , suggesting that cytokinetic initiation was inhibited by TbAUK1 inactivation . To test a possible involvement of TbAUK1 in driving trans-localization of the CPC from the dorsal mid-point of the cell to the anterior end , VX-680 was added to the cells 4 hrs after the release , when the cells have progressed to the telophase and TbCPC1-EYFP is partly on the dorsal mid-point of the cell and partly at the anterior end ( Fig . 4; arrows ) . TbCPC1-EYFP was found capable of traveling from the dorsal mid-point to the anterior end of the cell followed by conversion of bi-nucleate to single nucleate cells indicating cell division ( Fig . 4 , panel +VX-680 at 4 hr ) . Similar results were also obtained with the cells expressing EYFP-fused TbAUK1 and TbCPC2 ( data not shown ) . Thus , the TbAUK1 kinase function is no longer required once the CPC has been trans-localized to the dorsal mid-point . Cytokinesis can proceed to completion from that point onward without TbAUK1 activity .
In this report , we applied a chemical genetic approach to precisely dissect the specific roles of an Aurora-like kinase TbAUK1 in T . brucei in regulating the cell cycle progression and the trans-localization of the CPC . It enabled us to abolish TbAUK1 function at a specific given moment during the cell cycle progression of a synchronized cell population without disrupting the CPC structure [22] , [24] and monitor the consequences thereafter . The outcome from the present study suggests that the observed drug effect on a synchronized T . brucei population within the initial hours of the cell cycle progression is essentially identical to that from knocking down TbAUK1 with RNAi [19] or from over-expressing TbAUK1-K58R negative mutant in trypanosome cells [20] . The drug effect we have observed in the present study could be thus attributed primarily to the inhibition of TbAUK1 . Recently , Jetton et al . [36] tested another known inhibitor of Aurora B , Hesperadin , on the bloodstream form T . brucei cells and observed blocked nuclear division and cytokinesis but not other aspects of the cell cycle . It corroborates with our current finding . TbAUK1 function is apparently not required for cell cycle progression from late S phase to the onset of metaphase . But continued inhibition of TbAUK1 up to the metaphase prevents the cells from proceeding further into anaphase . This finding agrees with the observed function of Aurora B in metazoa , in which the chromosome segregation defect caused by Aurora B deficiency attributes to defective bipolar spindle assembly [3] , [32] , [37] and improper kinetochore-microtubule attachments [4] , [12] , [31] , [33] , [38] , [39] . These aberrant attachments activate the spindle assembly checkpoint , which in turn inhibits APC/C , resulting in metaphase arrest [40] . TbAUK1 is apparently performing a similar function during this particular phase of mitosis in T . brucei . DAPI-stained DNA patterns suggesting mis-aligned chromosomes are detected around the metaphase plate , which could be likely attributed to improper kinetochore-microtubule attachments when TbAUK1 is inhibited ( Fig . 5 ) . Many well-conserved proteins are involved in regulating kinetochore-microtubule attachment in metazoa and yeast . But the majority of them do not find their structural homologs in the trypanosome genome [21] , which raises an interesting question on how TbAUK1 regulates kinetochore-microtubule attachment and chromosome segregation in the absence of these crucial components . It is possible that the trypanosome kinetochores and spindle checkpoint are composed of structurally distinct proteins that could cooperate with TbAUK1 in fulfilling the roles of promoting faithful chromosome segregation . Future work will be directed to identify these proteins and to investigate their association with TbAUK1 in regulating chromosome segregation . A few of the TbAUK1-associated proteins , other than TbCPC1 and TbCPC2 , have been identified in T . brucei recently [22] , [41] . The Tousled-like kinase TbTLK1 , which is a substrate of TbAUK1 and is capable of auto-phosphorylating , was found co-immunoprecipitated with TbAUK1 but concentrated at the spindle poles during mitosis . It could play a role in regulating spindle assembly [41] . TbAUK1 is also associated with a novel kinesin-like protein TbKIN-A that co-localizes with the CPC on the chromosomes during prophase , and the spindle midzone in anaphase [22] . At the critical moment of nuclear division and cytokinetic initiation , however , TbKIN-A does not trans-localize with the CPC , but , instead , disperses from the spindle midzone and re-distributes back into the two newly formed nuclei [22] . TbKIN-A is thus probably playing roles only during the mitosis . Another kinesin-like protein , TbKIN-B , was found associated with TbAUK1 , TbCPC1 , TbCPC2 , and TbTLK1 [22] , [24] . It trans-localizes in the same pattern as that of TbKIN-A and is thus most likely involved only in regulating mitosis in trypanosomes . The precise mechanism of CPC-mediated initiation of cytokinesis in T . brucei is not known . Since the cell divides from the anterior to the posterior end [23] , a phosphorylation gradient generated by the Aurora B from the midzone as observed in metazoa [18] would thus not provide any useful spatial information for positioning cleavage furrow ingression in T . brucei , which is not constituted by an actomyosin ring in the first place [42] . Our current investigation has clearly indicated that TbAUK1 activity is required for trans-localization of the CPC from the midzone to the dorsal mid-point of the cell in late anaphase , presumably by crossing the nuclear envelope . Once this phase of trans-localization is accomplished , further migration of the CPC to the anterior tip followed by a rapid movement toward the posterior end for completion of cytokinesis are apparently no longer dependent on the activity of TbAUK1 . The latter is thus specifically required only for the initial CPC trans-localization to start cytokinesis . When trypanosome cells have already reached anaphase but have not yet initiated cytokinesis , loss of TbAUK1 activity stops the trans-localization of the CPC , but mitosis proceeds to completion . The CPC is simply re-distributed to the two newly formed nuclei like TbKIN-A and TbKIN-B [22] . Detailed mechanisms in this remarkably unique action of TbAUK1 in initiating CPC trans-localization and cytokinesis will be the subject for much intensive investigation in the future . In summary , we have succeeded in clearly demonstrating two discrete functions of TbAUK1 in T . brucei during its cell cycle progression . The essential role of TbAUK1 in promoting the transition from metaphase to anaphase has been also observed in other Aurora B kinases among other eukaryotes . But the absence from T . brucei of the homologs of many of the other essential proteins required in this transition indicates that the mechanisms of metaphase-anaphase transition in T . brucei may differ significantly from those in other eukaryotes . We also found that once the anaphase is achieved , the function of TbAUK1 is no longer required for further mitotic progression . But it is needed for the unusual trans-localization of the CPC to the cellular mid-dorsal site in telophase to initiate cytokinesis , albeit unrelated to mitosis . This is the first time to our knowledge that the two functions of an Aurora B homolog have been clearly dissected into two discrete cell cycle stages and separated from each other with little interconnection in between . It may constitute a useful model system for more in-depth understanding of the mechanism of cytokinetic initiation .
The procyclic form of T . brucei strain 427 was cultured at 26°C in Cunningham's medium supplemented with 10% fetal bovine serum ( Atlanta Biological ) . Cells were routinely diluted 10-fold whenever the density reached 5×105/ml . TbAUK1 , TbCPC1 and TbCPC2 were each cloned into the pC-EYFP-Neo vector , which was obtained by replacing the PTP module in the pC-PTP-Neo [43] with the enhanced yellow fluorescence protein ( EYFP ) , and transfected into the wild-type 427 cell line . Correct in situ tagging of one of the two alleles was confirmed by PCR and subsequent sequencing . Stable transfectants were selected under 40 µg/ml G418 and cloned by limiting dilution . Procyclic cells expressing endogenously tagged TbCPC1-EYFP , TbCPC2-EYFP or TbAUK1-EYFP were synchronized according to the previously described procedure [28] with minor modifications . Instead of using 0 . 2 mM hydroxyurea to achieve synchronization , 0 . 3 mM hydroxyurea was added to the cell culture and incubated at 26°C for 16 hrs . Hydroxyurea was washed off with fresh medium and the cells were cultured in fresh medium at 26°C for 8 hrs . VX-680 ( 30 µM ) , dissolved in DMSO , was added to the synchronized cells at different time intervals after the release . Time samples of the cell culture were collected by centrifugation , fixed with 4% paraformaldehyde , mounted in the VectaShield mounting medium containing DAPI , and examined for nuclei and kinetoplasts with a fluorescence microscope . The FACS analysis of propidium iodide-stained trypanosome cells was carried out as previously described [20] . Briefly , T . brucei cells were spun down at 320 g for 10 min , washed once in PBS , and re-suspended in 0 . 1 ml PBS . The cells were fixed by adding 0 . 2 ml 10% ethanol , 0 . 2 ml 50% ethanol and 1 . 0 ml 70% ethanol ( all in PBS with 5% glycerol ) and incubated at 4°C . The cells were spun down again at 2 , 900×g for 10 min , washed once with PBS , and re-suspended in PBS . DNase-free RNase ( 10 µg/ml ) and propidium iodide ( 20 µg/ml ) were added before flow cytometry analysis . The DNA content of propidium iodide-stained cells was analyzed with a fluorescence-activated cell sorting scan ( FACScan ) analytical flow cytometer ( BD Biosciences ) . Percentages of cells in each phase of the cell cycle ( G1 , S , and G2/M ) were determined by the ModFit LT V3 . 0 software ( BD Biosciences ) . Full-length coding sequences of TbAUK1 and histone H3 were each cloned into a pGEM-4T-3 vector ( Amersham ) , expressed in E . coli BL21 cells and purified through a column of glutathione Sepharose 4B beads . The purified recombinant proteins were incubated in the kinase buffer ( 10 mM HEPES , pH 7 . 5 , 50 mM NaCl , 10 mM MgCl2 , 1 mM DTT ) containing 1 µCi [γ-32P] ATP ( Perkin Elmer ) at room temperature for 60 min . Reactions were stopped by adding the SDS sampling buffer and boiled for 5 min . Proteins were separated on SDS-PAGE and the dried gel was exposed to Phosphor-Imager . Equal loading of protein samples in the reaction was verified by Coomassie blue staining of a duplicate SDS-PAGE gel . Procyclic cells expressing endogenously tagged TbCPC1-EYFP , TbCPC2-EYFP or TbAUK1-EYFP were harvested by centrifugation at 320×g for 5 min , washed once in PBS , and fixed in 4% paraformaldehyde . The fixed cells were washed with PBS , suspended in PBS and adhered to poly-L-Lysine treated cover-slips . The slides were mounted in VectaShield mounting medium containing DAPI and examined with a fluorescence microscope . To follow the trans-localization of TbCPC1-EYFP or TbCPC2-EYFP with time-lapse imaging , a melted 1% low melting point agarose mixture in 1 . 2 ml of SDM79 medium without phenol red and 5 µl Hoechst 33342 solution ( 1 mg/ml , Invitrogen ) was poured onto the center of a slide-glass and covered with another slide-glass . The top slide-glass was then removed from the agarose pad 10–15 min later and the slurry of the transfected cells was poured onto the pad , covered with a cover-slip , and sealed with paraffin . Time-lapse images of individual cells were acquired with a 6D High Throughput Microscope at the Nikon Imaging Center of UCSF . The images were taken at one point with a fixed time interval ( 1 or 2 min ) . An auto-focusing program using DIC images was installed that produces images with Z-stacks at −1 , 0 and +1 µm from the auto-focused plane . For VX-680 treatment , the drug was added to the culture medium at a final concentration of 30 µM before making the agarose pad . Image acquisition was started about 30 min after pouring the cells on the agarose pad . | The chromosomal passenger complex ( CPC ) is essential for chromosome segregation and cytokinesis in eukaryotes , but the detailed mechanism of cytokinetic regulation remains less clear , partly due to the difficulty in separating the two functions in a growing cell . A chemical genetic approach by adding an inhibitor of the Aurora kinase in the CPC to a synchronized cell population at different cell cycle stages would probably solve this problem . The CPC in Trypanosoma brucei consists of an Aurora-like kinase ( TbAUK1 ) and two novel proteins and bears little resemblance to the CPC in other eukaryotes . It moves from kinetochores to the spindle midzone during metaphase-anaphase transition , and then displays a unique trans-localization to the anterior end of the cell to initiate cytokinesis by moving from the anterior to the posterior end of the cell to separate it into two . To envision the role of TbAUK1 in driving this unusual process , we applied a chemical genetic approach and demonstrated that there are two distinct points of TbAUK1 action in T . brucei: the metaphase to anaphase transition and cytokinetic initiation . This is the first time to our knowledge that the dual functions of an Aurora B homolog is dissected and separated into two clearly distinct time frames in a cell cycle . | [
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] | 2009 | The Aurora Kinase in Trypanosoma brucei Plays Distinctive Roles in Metaphase-Anaphase Transition and Cytokinetic Initiation |
A major question in chronobiology focuses around the “Bünning hypothesis” which implicates the circadian clock in photoperiodic ( day-length ) measurement and is supported in some systems ( e . g . plants ) but disputed in others . Here , we used the seasonally-regulated thermotolerance of Drosophila melanogaster to test the role of various clock genes in day-length measurement . In Drosophila , freezing temperatures induce reversible chill coma , a narcosis-like state . We have corroborated previous observations that wild-type flies developing under short photoperiods ( winter-like ) exhibit significantly shorter chill-coma recovery times ( CCRt ) than flies that were raised under long ( summer-like ) photoperiods . Here , we show that arrhythmic mutant strains , per01 , tim01 and ClkJrk , as well as variants that speed up or slow down the circadian period , disrupt the photoperiodic component of CCRt . Our results support an underlying circadian function mediating seasonal daylength measurement and indicate that clock genes are tightly involved in photo- and thermo-periodic measurements .
Seasonal changes in day-length provide a reliable environmental cue used by many temperate species to adapt to their fluctuating environments . While the available evidence suggests that changes in day-length are monitored by an internal photoperiodic timer [1] , intensive studies of photoperiodicity in animals over the last 80 years have yet to identify an underlying molecular mechanisms [2] ( although significant progress has been made in plants and mammals [3]–[5] ) . This is in marked contrast to the level of understanding of the circadian timer that regulates daily rhythms , where studies in various model organisms , particularly Drosophila , led to the discovery of principles and molecules that are highly conserved in diverse phyla [6] . The Bünning hypothesis [7] invoked a link between the circadian and the photoperiodic mechanisms and suggested that circadian rhythmicity is required for day-length measurement . Bünning's original model assumed that circadian oscillations consist of light ( ‘photophil’ ) and dark ( ‘scotophil’ ) -requiring phases . In short days , ambient light is present only during the photophil phase , and the dark phase is not exposed to light . As days become longer , light coincides with the scotophil phase . The relative size of the photophil and scotophil phases encodes the critical photoperiod ( time of the year ) that induces the seasonal response . A modified version of this model was later named the ‘external coincidence model’ [8] . An alternative hypothesis , the ‘internal coincidence model’ , was also proposed , where light plays only an indirect role , and the critical photoperiod is encoded by unique phase relationships between two internal oscillators . Several experimental protocols have been devised to test the Bünning hypothesis , one of which is the Nanda-Hamner protocol [9] , which employs exotic light-dark cycles of ultra-long periods ( T>72 hr ) . If the seasonal response peaks at 24 hr intervals ( ‘positive Nanda-Hamner’ ) , a link , not necessarily causal , with the circadian system in photoperiodic timing is indicated [9] , [10] . Drosophila melanogaster , which was instrumental in identifying higher eukaryotic circadian clock genes [11] , also exhibits a photoperiodic response [12] , providing an opportunity to test the link between the two timers . This response is manifested as a developmental arrest of the ovaries ( i . e . reproductive diapause ) under short ( autumnal ) days and lower temperatures , presumably enhancing the fly's ability to survive the winter in temperate regions . Although Nanda-Hamner experiments in Drosophila revealed an underlying 24 h oscillation [13] , experiments using the period ( per ) clock mutants [12] suggested that the two systems are independent , as the per null mutants were still capable of discriminating between long and short days ( albeit with a shifted critical day-length ) . Later , natural allelic variation in the timeless ( tim ) locus was associated with diapause in Drosophila [14] , [15] , and in Chymomyza costata [16] . Knockdown of per and the positive circadian regulator , cycle , in the bean bug Riptortus pedestris by RNAi caused simultaneous disruption of both circadian output ( cuticle deposition rhythm ) and photoperiodic diapause [17] . In Drosophila triauraria , genetic variation in tim and cry ( but not in per , Clk or cyc ) was significantly associated with the photoperiodic response [18] . Yet , because the impact of a given clock gene mutation on the photoperiodic response can be interpreted as a pleiotropic effect on diapause , the application of the Bünning hypothesis to these results has been questioned [19] . Given the shallow photoperiodic diapause of D . melanogaster [14] , we have sought an alternative seasonal phenotype in this species that could be used for testing Bünning's hypothesis . Measuring chill comma recovery times ( CCRt ) is an established approach for studying insect thermal adaptation [20] . Here , we build on the earlier observation that flies raised in different photoperiods show differing CCRt [21] , and use this phenotype to test day-length timing in various circadian clock mutants .
The chill-coma recovery times ( CCRt ) of wild-type flies raised at different photoperiods is sexually dimorphic ( Figure 1 ) . Females that developed under short winter-like photoperiod exhibit significantly shorter CCRt than females that were raised under long summer-like photoperiods ( log rank test , χ2 = 11 . 8 , df = 1 , p<0 . 001 ) . However , CCRt did not differ significantly between males raised on long vs . short days ( Figure 1 ) . Females kept in covered vials ( in darkness , DD ) within the same chambers also showed a moderate but significant differential response , driven by the low-amplitude ( ∼2°C ) thermoperiod that was generated by the lighting system ( log-rank test χ2 = 7 . 9 , p<0 . 01 , see Methods ) . In another set of experiments where the heat cycles produced by the two photoperiods were offset by a counteracting temperature cycle , there was no significant difference in the CCRt between long and short photoperiods in females kept in covered vials ( in darkness , DD ) . The difference in median CCRt between long and short days in females exposed to light ( driven by both photoperiod and thermoperiod ) was twice as large as the difference exhibited by the thermoperiod only ( DD ) females ( 14 vs . 7 min ) . This difference in photoperiod was significant after statistically accounting for the temperature effect ( via non-parametric ANCOVA , W = 664 , p<0 . 001 ) confirming the interaction between photoperiod and thermoperiod . We also tested the CCRt of wild-type females over a range of five photoperiods ( Figure S1 ) . For short photoperiods 8 , 10 and 12 hr , the median CCRt was 15 min ( with 95% CI overlapping 13–33 ) . In the 14 hr photoperiod , the median was 13 min ( 12–19 ) and at 16 hr was 24 ( 20–30 ) . Thus , an intermediate photoperiod ( comparable to the critical day length in diapause studies ) would lie within the 14–16 hr interval . We also examined the association between the CCRt and diapause propensity ( Figure S2 ) . Newly emerging females were maintained in diapausing inducing conditions ( Methods ) . After 12 days their CCRt was tested , followed by ovary dissection for determining their reproductive state . The CCRt did not differ significantly between diapausing ( n = 82 ) and non-diapausing ( n = 39 ) females ( χ2 = 0 . 1 , df = 1 , p = 0 . 70 ) . There was however a significant difference in female weight between long vs . short day ( Figure S3 ) : Fresh weight in short days was higher ( F1 , 8 = 9 . 68 , p<0 . 05 ) , due to higher water content ( F1 , 8 = 6 . 57 , p<0 . 05 ) , since dry weight was similar ( F1 , 8 = 4 . 13 , p = 0 . 07 ) . Males also showed weight difference between long and short day , which was significant , both for fresh and dry weight ( Figure S3 ) . Thus , size or water differences cannot fully explain the photoperiodic CCRt response ( which is absent in males ) . In addition , the CCRt of males resembles that of females in short days ( although the size difference between the sexes is substantial ) . The finding that female flies were able to discriminate between long and short days provided us with the opportunity to test the role of circadian clock genes in this response . The CCRt of females from congenic strains carrying per01 , tim01 , and ClkJrk mutations did not show any photo/thermoperiodic effect ( Figure 2 ) . In general , the mutant curves resembled the WT short-day response ( median CCRt 19 min , range 15–25 ) . For example , in long days , the median recovery time for per01 was 14 min ( 13–15 ) , for ClkJrk 19 min ( 18–23 ) and for tim01 23 . 5 min ( 21–35 ) . We did notice however , some differences in cold-tolerance among the mutants , particularly between per and the two other mutants ( note the overlapping CIs ) . To gain insight into the metabolic correlates of the photoperiodic response , we measured glycogen , free fatty acids and protein levels ( Figure S4 ) . In wild-type females , glycogen was significantly higher in long days ( F1 , 46 = 4 . 05 , p = 0 . 05 ) , while four time points taken during the day did not show any significant differences ( F1 , 46 = 2 . 22 , p = 0 . 14 ) . In contrast , in ClkJrk females glycogen levels did not differ between photoperiods ( F1 , 22 = 0 . 85 , p = 0 . 37 ) . For free fatty acids , neither the photoperiod nor the time of the day showed significant differences ( Figure S4 ) in WT or ClkJrk . Similarly , total protein also did not differ between photoperiods , but intriguingly there was a significant photoperiod:Zt interaction in the ClkJrk mutants ( F2 , 21 = 7 . 41 , p<0 . 01 ) . The availability of mutants that exhibit a long or short circadian period provided us with a further opportunity for testing the Bünning hypothesis . We compared long and short mutant alleles of three genes ( Figure 3 ) : perL , perS ( 28 . 8 vs 19 . 3 hr circadian period [22] ) , dbtL , dbtS ( 26 . 8 vs 19 . 3 hr , [23] ) and timUL , timS1 ( 32 . 7 vs 21 . 1 hr , [24] ) . An omnibus ANOVA for analysing the data of all genes simultaneously including experiments at different photo/thermo-periods resulted in a highly significant ‘allele’ factor ( short v long period , F1 , 813 = 28 . 46 , p<0 . 001 ) but not ‘photoperiod’ ( F1 , 813 = 0 . 57 , p = 0 . 45 ) . There was no significant gene:photoperiod interaction ( F2 , 813 = 1 . 39 , p = 0 . 25 ) . In all three genes , the CCRt of the long alleles was consistently shorter ( Figure 3 , particularly evident in per mutants ) , in both photoperiods , suggesting that the long period mutant perceive the day as shorter compared to short period mutants . This result is consistent with Bünning's original model ( Figure 3 ) . We also observed that CCRt does not fluctuate significantly throughout the day in WT , ( χ2 = 1 . 4 , df = 3 , p = 0 . 7; Figure S5 ) so that the differences between the mutants is not due to our sampling of CCRt at different subjective phases . We have also compared the average phase angle of the light-entrained activity of the long and the short-period mutants ( Figure S6 ) . We estimated the phase values using the pooled locomotor activity profile ( 16–35 flies in each experiment ) , averaged over four days . Across genes ( n = 3 , each tested for two alleles , at two photoperiods , giving 12 data points ) , there was a significant difference between the long and the short alleles for both morning peak ( MP; F1 , 9 = 140 . 8 , p<0 . 0001 ) and evening peak ( EP; F1 , 9 = 18 . 3 , p<0 . 01 ) . Both the MP and EP were advanced in short allele flies , but for the MP , the advance was enhanced in short day , resulting in significant allele: photoperiod interaction ( F1 , 9 = 105 , p<0 . 001 ) . We have also explored the role of alternative splicing in the per locus that was previously associated with seasonal adaptation [25]–[27] . Specifically , under low temperatures , as well as short photoperiods , the splicing level of intron 8 in the 3′UTR of per is increased . To test the role of per splicing in CCRt , we used transgenic lines in which the splicing signal is missing and the intronic sequence cannot be spliced ( type A ) , or a construct that does not contain the intron ( type B′ ) [28] . The perA and perB′ transgenes were expressed in per01 flies . For each transgene two independent insertion lines were tested ( see Methods ) and their data were pooled . As shown in Figure 4 , flies carrying the type B′ transgene showed shorter CCRt both under long or short photo/thermoperiod or thermoperiod alone . ANOVA incorporating all the conditions in which the transgenic flies were tested revealed a significant splice type factor ( F1 , 634 = 8 . 53 , p<0 . 01 ) . In contrast to the wild-type Hu strains , the splice variants were not photoperiodic ( Figure 4 ) , presumably due to the different genetic background of the transformant flies ( yw ) . The control lines perG did show a thermoperiodic response ( in DD; χ2 = 10 . 1 , p<0 . 01; N1 , N2 = 128 , 112 ) , but were not photoperiodic ( LD: χ2 = 0 . 1 p = 0 . 77; N1 , N2 = 110 , 121 ) . In general the CCRt of perG resembled the response of perA with relatively longer medians ( LD:SD = 33 , 35 min; in DD , LD:SD = 60 , 39 . 5 min ) . Taken together , the results indicate some effect on cold tolerance for per splicing , further supporting the notion that the circadian clock or signalling to the circadian clock is involved in this seasonal adaptation .
The chill-coma recovery test has been used in various insect species for studying cold tolerance and adaptation [29] . Recent studies in D . montana have demonstrated that the CCRt in this species is under photoperiodic regulation [30] , [31] , consistent with the expectation that the autumnal shortening of the day induces various process , including nutrient regulation and reserve accumulation that allows the flies to survive the winter . Here , we have shown that a similar day-length regulation is present in D . melanogaster , and we exploited this response to study the link between the circadian clock and seasonal timing . While our experiments have not disentangled entirely the thermo- and photo-periodic effects , the difference in CCRt response in LD ( day-length encoded by both photoperiod and thermoperiod ) , and DD ( thermal information only ) would suggest that both cues contribute to the response ( Figure 1 ) . We show that in clock mutant strains per01 , tim01 and ClkJrk the day-length measurement is disrupted . The lack of photoperiodic CCRt in per01 is in apparent contradiction to the previously reported photoperiodic diapause in this mutant [12] . Differences between the CCR and diapause phenotypes may represent two separate photoperiodic circuits that use different genetic networks , a situation which resembles the different circadian locomotor and eclosion circuits [32] , [33] . Interestingly , Helfrich-Förster [34] analysed the bimodal locomotor activity profile of per mutants and suggested that the morning peak is derived from a per-independent circadian component ( see also [35] ) , and that this component might be involved in photoperiodic timing . In addition , a recent study [36] using temperature entrainment suggests that per01 ( and tim01 ) are not entirely clockless . However , it should be noted that the reported photoperiodic response of diapause in per01 ( and per deficiency flies ) is altered because the critical day length ( CDL ) for inducing diapause is several hours shorter than in wild-type [12] , [37] . In our experiments , the per01 mutants mirror their CDL and exhibit the shortest CCRt . This correlation may reflect the situation in the wild , where populations in colder environments ( presumably more cold-adapted flies ) would be expected to show a shorter CDL that will trigger diapause later in the season . The substantial difference in CCRt between long- and short circadian period alleles is particularly informative ( Figure 3 ) . The constitutively ‘short-day’ response of the long-period mutants fits well with the ‘external co-incidence’ model underlying day-length measurements ( Figure 3G ) . In wild-type flies , short days coincide with the photophil phase of the pacemaker , while long days extend to the scotophil phase of the oscillations . In long-period mutants ( where photophil phase is longer ) , various daylengths always coincide with the photophil phase and are interpreted as short days , while in short-period mutants both long and short day-length may overlap with the scotophil phase and be interpreted as long days . The main weakness of this model is the requirement for a uniform waveform of the oscillation under long and short days . In Drosophila however , this model is unlikely to be valid , as the oscillation waveform of overt rhythms ( locomotor activity ) and level of clock proteins change during the seasons [35] , [38] . Furthermore , the model ( Figure 3 ) disregards the entrainment of the mutant oscillation during the seasons [35] . Depending of the phases of the mutants , different outcomes may be predicted ( Figure S7 ) . Indeed , we have observed a consistent phase difference between long and short period alleles ( phase advance in short-period alleles , Figure S6 ) . Interestingly , our data mirrors an early study of the eclosion rhythm of D . pseudoobscura [39] , where wild-type flies kept in T cycles longer than the circadian period ( resembling the short-period mutants in our study , kept under 24 hr cycle ) showed a phase advance , and flies kept in T<τ exhibited phase delays . While the link between the circadian phase and the photoperiodic response is yet not clear , the different photoperiodic phenotypes of the slow and fast clock mutants seems to suggest a causative role for the circadian pacemaker in day-length measurement , but further experiments are required to identify the underlying model ( external- vs . internal-coincidence , or any other model ) . A further analysis of the critical day length of the CCRt ( Figure S1 ) in the long and short clock mutants may provide more insights about the link between the circadian system and the photoperiodic timer , and this will be published elsewhere . Similarly , using the Nanda-Hamner or Bünsow protocols [9] would provide more ways for testing the circadian role in the photoperiodic CCRt . Our results also show that the regulation of per splicing , a process which was previously implicated in the fly's seasonal response [26] , [27] , is also involved in the CCRt , as flies carrying the type B′ transgene exhibited shorter recovery times compared with flies carrying type A ( Figure 4 ) . This fits well with the enhanced splicing at cold conditions in the type B′ transgenic per but contrasts with the observation that the seasonal locomotor activity profile of the two type of transgenic flies is similar [25] . This may suggests that retention or removal of the per 3′ intron is affecting the CCRt , while the splicing process itself is critical for the cold-induced phase advance in the locomotor activity rhythm . Our results reflect the seasonally adaptive nature of per splicing because during the autumnal shortening of the photoperiods ( and decreasing temperatures ) , per splicing will inevitably increase [26] , [35] . This will lead to locomotor changes but also , we suggest , to further physiological changes that may allow the fly to tolerate lower temperatures , as manifested by shorter CCRt ( Figure 4 ) . The circadian clock and the photoperiodic timer appear to act as two modules , each consisting of a group of functionally related genes [40] . Genes interact primarily with genes within the same module , although individual genes may have an effect on the other module . Such pleiotropic effect of individual clock genes on diapause was proposed as an alternative explanation to the “Bünning hypothesis” [10] . In the current work however , the fact that a battery of circadian clock genes are implicated in daily light and temperature measurements strongly indicates that the two modules functionally interact , not simply as isolated pleiotropic effects of one gene on another module . Under pleiotropy , knockdown of different clock genes may results in different outcomes . For example , RNAi targeting either per or cyc in the bean bug Riportus pedestris led to aberrant photoperiodic response , but in opposite directions [17] . In contrast , in our experiments all null mutants exhibit the same trend ( loss of long day response; Figure 2 ) , further suggesting that the Bünning hypothesis provides the most parsimonious explanation . The shortening of CCRt in flies exposed to short photoperiod , which was also recently reported for D . montana [30] , reflects an enhanced cold tolerance acquired during development . This cold acclimation is presumably mediated by cold hardening , a process which involves changes in phospholipid fatty acids composition of cell membranes [41] , [42] , as well as polyols , sugars and other metabolites [43] . The ecological relevance of the improved cold tolerance following cold hardening was previously demonstrated in enhanced D . melanogaster survivorship [44] and reproductive behaviour [45] in flies primed for experiencing low temperatures . Beyond demonstrating the role of the circadian clock in seasonal timing , our results here juxtapose the CCRt phenotype against female reproductive diapause , the classic readout for insect seasonality [46] . While cold hardiness is often associated with diapause our results suggest that the responses are triggered by different mechanisms ( Figure S1 ) . Similarly studies in D . montana shows that the CCRt is not always correlated with diapause and is strain-dependent [30] . For studying seasonal timing , analysing diapause involves extremely laborious dissection of ovaries , and the binary nature of the phenotype ( diapause status ) requires the processing of large sample sizes for detecting appreciable effects . In contrast , the automated CCRt phenotyping allows for high-throughput screening and the protocol requires that flies are maintained at 20°C , which is more conducive to GAL4 misexpression studies than diapause experiments that are usually performed at 12°C . CCRt thus provides a powerful and efficient method for dissecting the genetic and anatomical basis of seasonal timing in D . melanogaster .
The strains per01 , tim01 , ClkJrk were used . All strains were crossed to an isofemale strain originating from a wild Dutch population in Houten [14] . The progeny were screened for individuals carrying the mutation using PCR genotyping as previously described [47] and backcrossed to Hu , a process which was repeated 8 times , resulting in all the mutations inserted in the genomic Hu background ( >99% ) that also carries the ls-tim natural allelic variant [14] . Mutants were made homozygous by further crossing to balancer strains ( also on a Hu background ) . In addition , the strains perL and perS [13] , dbtS , dbtL [23] , timUL and timS1 [48] were used ( the genetic background of these mutants is not Hu ) . The mutant's circadian locomotor activity was verified at 19 . 5°C , which was used for the CCRt experiments ( Figure S6 ) . To investigate the effect of per splicing on CCRt we used congenic transgenic lines that generate either type A ( PERA-18 , PERA-29 ) or type B′ ( PERB′-11 , PERB′-12 ) per RNA and have been used to rescue per01 flies . We have also tested transgenic flies expressing both type A and type B′ ( perG ) . These lines have been described previously [28] . All strains were maintained at 25°C in LD 12∶12 on a standard cornmeal media . Around 100 flies were kept on egg-collection food for 18 hr , and four replicates of 40 eggs each were transferred to new vials . The vials were placed in either long ( 16 hr ) or short ( 8 hr ) day using fluorescent light boxes . Temperature within the light boxes fluctuated during the LD cycle , due to heat produced by the florescent light , from 21°C during the light phase to 19°C during scotophase . Temperature was monitored by data loggers ( Tinytag UK ) . Each experiment was replicated twice with two different light boxes ( total of 8 vials ) . DD samples ( vials covered by aluminum foil , providing constant darkness ) were also included , and were used for analysing the effect of the thermoperiod ( 2°C cycling ) . The flies were developed under these conditions for 20 days , and emerging adults ( age 3–4 days ) were tested for their chill coma recovery as follows: At ZT 3 . 5 ( ZT , Zeitgeber time , hr after lights on ) the flies were anesthetized by ice , sexed and transferred individually to glass activity tubes ( outer diameter = 5 mm , 80 mm ) and cotton plugs were used to place the fly at the middle of the tube . The flies were kept on ice at 4°C for 3 hours . At ZT 6 . 5 , the glass tubes were loaded into the Drosophix locomotor activity monitor ( Padova , Italy ) , which was previously described [27] at 25°C . This infra-red based system uses the same glass tubes used by the Trikinetics system , but the space of the tube was reduced to 2 cm by cotton plugs ( i . e . the fly was approximately 1 cm from the light beam ) . The loading time ( t0 ) for each fly was recorded by the system . A custom written script in “R” [49] was used to calculate the recovery time ( CCRt ) , by subtracting t0 from the time of first movement detected by the system ( consequently , our calculated recovery times are slightly longer , by definition , from previous studies , where recovery time was defined as the time in which the fly was first observed standing ) . Given the exponential distribution of the CCRt , these data are best analysed by survival curves . We used the Survival R library to fit Kaplan-Meier curves , and log-rank tests to compare the different curves , using χ2 statistics with one degree of freedom [50] We used ANOVA to test the contribution of various factors across different experiments ( e . g . day-length , sex , photo- vs . thermoperiod entrainment , etc . ) . For this purpose we used log transformation for variance stabilisation . To compare the effect of photoperiod after correcting for temperature effect , a non-parametric ANCOVA was carried using the Quade procedure [51] . Briefly , the CCRt irrespective of group membership ( photo- or thermo-periodic ) were ranked , and regressed over day-length . The residuals were then compared by the Wilcoxon rank sum test . Male and female flies ( Hu genetic background ) were collected within a six hour post eclosion window and placed under 8∶16LD ( light∶Dark ) at 12 . 2±0 . 2°C . After 12 days , CCRt was measured and immediately followed by dissection of the female ovaries in PBS . Reproductive arrest was determined as previously described [14] . The diapause level in females that were maintained under the same conditions but were not tested for CCRt was not significantly different from females that were exposed to coma inducing temperature ( F1 , 14 = 0 . 44 , p = 0 . 51 ) , indicating that the cold treatment did not contribute to diapause state . Flies developing under LD and SD ( see CCR protocol ) were collected ( 4 days old ) at four time points ( Zt1 , 7 , 13 and 19 ) and immediately frozen in liquid nitrogen and stored at −80C . The fresh weight of 10 individuals was recorded after a 5 min thaw in ice with a precision balance ( Precisa180A ) . Glycogen , proteins and free fatty acid content were measured in these samples and expressed as µg or nmol per fresh weight . Glycogen concentration was obtained from samples that were homogenized in 100 µl of water in ice for 30 sec . After centrifugation at top speed for 5 min ( 4°C ) , the homogenates ( 10 µl were saved for protein assay ) were boiled for 5 min and Hydrolysis buffer was added to final volume of 120 µl ( Sigma-Aldrich , MAK016 ) . Glycogen content was assayed by colorimetric reaction ( 570 nm ) after treatment with Hydrolysis Enzyme and Development Enzyme ( Sigma-Aldrich , MAK016 ) . Glucose background was removed from each sample . 10 µl of homogenates were diluted 10 times in water and proteins quantified spectrophotometrically ( 595 nm ) using Bradford reagent ( 10 µl diluted samples +290 µl reagent; Sigma-Aldrich , B6916 ) . Total proteins were quantified using a BSA ( 10 mg/ml ) standard curve . The free fatty acids were isolated from samples homogenized for 30 sec in ice in 200 ul chloroform-1% Triton X-100 . After 10 min centrifugation the organic phase was isolated and vacuum dried for 30 min to remove the chloroform . The lipids were dissolved in 200 ul of fatty acid buffer ( ABCAM ab65341 ) . Free fatty acid content was assayed by colorimetric reaction ( 570 nm ) after Acyl-CoA synthesis ( ABCAM ab65341 ) . FLUOStar omega plate reader was used for both colorimetric reactions and the Bradford assay . Fresh weight ( FW , g ) was measure from samples collected at Zt 3 . 5 ( LD and SD ) using a precision balance ( Precisa180A ) . Dry weight ( DW , g ) was measured after desiccating the sample at 60°C for 3 days . The difference between FW and DW indicate the water content ( WC , g ) . The locomotor activity of 3–4 days old virgins was measure at 19 . 5±0 . 5°C using the Trikinetics system . The activity of flies was recorded during 4 days entrainment ( either LD or SD ) follow by 5 days of constant darkness ( DD ) . The DD activity was also used to calculate the flies' circadian period of activity using “Cosinor analysis” [52] , which employs the least squares method to fit a sine wave to a time series . Monte Carlo simulations ( n = 100 ) were used to estimate 99% significant level . For phase analysis , the morning and the evening peak were recorded and converted into degrees . Because the data are circular , large angles ( >270° ) were converted to negative values ( subtracting 360° ) . | The circadian clock consists of an extensive genetic network that drives daily rhythms of physiological , biochemical and behavioural processes . The network is evolutionary conserved and has been extensively studied in a broad range of organisms . Another genetic network constitutes the photoperiodic clock and monitors the seasonal change in day-length . Here , we address a major and long-standing question in chronobiology: whether the circadian clock is involved in photoperiodic timing , also known as the Bünning hypothesis . Drosophila , as with many other insects in temperate regions , exhibits a photoperiodic response that allows the insect to anticipate and survive the winter . Here we show that the cold-tolerance of the fly is regulated by the photoperiod . We use this phenotype to test day-length timing in various circadian clock mutants and observe that in null clock mutants , the photoperiodic response is abolished , whereas in mutants that exhibit short or long daily cycles , the photoperiodic response is modified , further supporting a circadian-clock function . Overall , these results provide the first evidence in Drosophila that support for the Bünning hypothesis , and pave the way for the genetic dissection of seasonal timing in Drosophila melanogaster . | [
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] | 2014 | Role for Circadian Clock Genes in Seasonal Timing: Testing the Bünning Hypothesis |
The main adaptive immune response to bacteria is mediated by B cells and CD4+ T-cells . However , some bacterial proteins reach the cytosol of host cells and are exposed to the host CD8+ T-cells response . Both gram-negative and gram-positive bacteria can translocate proteins to the cytosol through type III and IV secretion and ESX-1 systems , respectively . The translocated proteins are often essential for the bacterium survival . Once injected , these proteins can be degraded and presented on MHC-I molecules to CD8+ T-cells . The CD8+ T-cells , in turn , can induce cell death and destroy the bacteria's habitat . In viruses , escape mutations arise to avoid this detection . The accumulation of escape mutations in bacteria has never been systematically studied . We show for the first time that such mutations are systematically present in most bacteria tested . We combine multiple bioinformatic algorithms to compute CD8+ T-cell epitope libraries of bacteria with secretion systems that translocate proteins to the host cytosol . In all bacteria tested , proteins not translocated to the cytosol show no escape mutations in their CD8+ T-cell epitopes . However , proteins translocated to the cytosol show clear escape mutations and have low epitope densities for most tested HLA alleles . The low epitope densities suggest that bacteria , like viruses , are evolutionarily selected to ensure their survival in the presence of CD8+ T-cells . In contrast with most other translocated proteins examined , Pseudomonas aeruginosa's ExoU , which ultimately induces host cell death , was found to have high epitope density . This finding suggests a novel mechanism for the manipulation of CD8+ T-cells by pathogens . The ExoU effector may have evolved to maintain high epitope density enabling it to efficiently induce CD8+ T-cell mediated cell death . These results were tested using multiple epitope prediction algorithms , and were found to be consistent for most proteins tested .
CD8+ T-cells recognize mainly cytosolic epitopes presented on MHC-I molecules . Their response is thus assumed to be directed mainly against viruses [1] , [2] , [3] . Bacterial proteins , on the other hand , are typically expressed outside the cytosol , and as such , induce CD4+ T-cells and B cells responses [4] , [5] , [6] , [7] , [8] , [9] , [10] , and are not expected to induce a CTL response in the classical pathway . For such a response to occur , these proteins must reach MHC-I proteins in the ER . One extensively studied possible mechanism for the presentation of bacterial epitope is "cross presentation" . In general , "cross presentation" refers to the transfer of peptides from the MHC-II presentation pathway to the MHC-I presentation pathway and vice versa [11] , [12] , [13] , [14] . Specifically , peptides of intracellular bacterial proteins derived from endosomal cleavage are presented on MHC-I molecules . This could take place in two ways: either the peptides are translocated to the cytosol , cleaved by the proteasome and delivered to the ER through TAP where they bind to MHC class I molecules , or endosomal peptides bind to MHC-I molecules probably in the endocytic compartment itself ( for a review see [15] ) . Another much more direct mechanism is the translocation of bacterial protein to the cytosol by highly conserved secretion systems . Such systems exist in a variety of bacteria . The secretion system that has been most characterized is the type III secretion system ( T3SS ) in gram-negative bacteria . The T3SS is a complex that allows bacteria to deliver protein effectors across eukaryotic cellular membranes through needle-like structure . In the cytosol , T3SS effectors exert many effects , such as cellular invasion [16] , modulation of host immune response [17] , [18] and apoptosis [19] . Another secretion system is the ESX-1 system in Mycobacterium tuberculosis ( TB ) [20] . Similar systems ( also called ESX/T7S systems ) exist in other gram positive bacteria as well [21] . However , since these systems do not have a needle-like structure , they cannot inject proteins through the plasma membrane . Nevertheless , TB is an intracellular bacterium and its secreted proteins can gain access to the cytosol [22] . The third characterized system that injects cytosolic proteins was studied in the intracellular cytosolic bacterium Listreria monocytogenes that injects the virulence factors Listeriolysin O ( LLO ) and ActA to the host cytosol [23] . These proteins are good candidates for presentation on MHC class I molecules . In similar situations , viruses avoid the presentation of CD8+ T cell epitopes through escape mutations [1] , [24] , [25] , [26] , [27] , [28] . Here we study bacterial sequences to test whether bacteria adopt a similar strategy of epitope removal . Specifically , we systematically compute the epitope density in bacterial effector proteins and show a clear selection against the presentation of epitopes . This selection is highly specific to cytosolic proteins . Evidences for MHC-I presentation in bacteria are limited to specific bacterial proteins , such as the L . monocytogenes proteins listeriolysin O [29] and ActA [30] , the Bordetella pertussis adenylatecyclase toxin [31] , the TB CFP10 antigen [22] and Streptococci protein streptolysin O [32] . A CTL response to extracellular pathogens was also suggested by some studies . Bergman et al . showed that the CTL response has a critical role in eliminating Yersinia infections , and that this response is directed against Yops , the secreted effector proteins of Yersinia [33] . Other studies about the CTL response against extracellular pathogen were carried out by Meissner et al . that demonstrated a vigorous CD8+ T cell influx into the lung in response to Pneumocystis , an extracellular fungal pathogen [34] , and by Mehrzad et al . ( 2008 ) that showed that trafficking of CD8+ T cells during initiation of Escherichia coli mastitis is accelerated when increasing the E . coli inoculum dose [35] . However , none of these studies suggested the existence of escape mutations in bacteria . We here show that such escape mutations are common in most tested effector proteins . The field of immunomics has made a significant leap forward in the last decades . Tools for epitope prediction have been developed for most branches of the immune system . The precision of CD8+ T cell epitopes prediction processing and presentation tools has reached the level that allows a systematic prediction of full organism epitope libraries . CTL epitopes are typically 8-10 amino acid long peptides , bound to MHC-I molecules [36] . These peptides are presented after proteasomal cleavage and transfer from the cytosol via TAP to the ER [37] , [38] , where they bind to MHC-I molecules . We have developed a precise cleavage prediction algorithm [39] and used TAP [40] and MHC binding [41] algorithms , which were found to be precise enough for most MHC-I alleles , to compute presented epitopes densities [24] , [25] , [42] , [43] , [44] . The precision of these densities has been tested in depth [24] , [43] , [44] . In this study , we study the epitope density of proteins in a group of representative bacteria expressing proteins translocated to the cytosol . Three of them , Escherichia coli , Shigella flexneri , and Pseudomonas aureginosa , are gram-negative T3SS-containing bacteria . In parallel , we study cytosol-exposed proteins from the gram-positive Listeria monocytogenes and Mycobacterium tuberculosis . In order to validate the results , we repeat the analysis using three different algorithms to test that the results obtained are not the artifact of the specific MHC binding algorithms used .
We have previously conducted a systematic analysis of the predicted CTL epitope repertoire in human and foreign proteins , and defined the normalized epitope density of a protein or an organism as the Size of Immune Repertoire ( SIR ) score [24] , [25] , [39] , [43] , [45] , [46] , [47] . The number of predicted CTL epitopes from a sequence was computed by applying a sliding window of nine amino acids , and computing for each nine-mer and its two flanking residues whether it is cleaved by the proteasome and whether it binds to TAP channels and to a given MHC-I allele ( Figure 1 ) . The SIR score was defined as the ratio between the computed CTL epitope density ( fraction of nine-mers that were predicted to be epitopes ) and the epitope density expected within the same number of random nine-mers . The choice of the “random” nine-mers will be discussed in the following section . An average SIR score of less than 1 represents an under-presentation of epitopes , whereas an average SIR score of more than 1 represents an over-presentation of epitopes . For example , assume a hypothetical sequence of 1 , 008 amino acids ( 1 , 000 nine-mers ) containing 15 HLA A*0201 predicted epitopes . If the average epitope density of HLA A*0201 in a large number of random proteins was 0 . 01 ( i . e . 10 epitopes in 1 , 000 nine-mers ) , then the SIR score of the sequence for HLA A*0201 would be 1 . 5 ( 15/10 ) . The average SIR score of a protein was defined as the average of the SIR scores for each HLA allele studied , weighted by the allele's frequency in the average human population . These results obviously depend on the definition of a “random protein” . We have thus tested multiple such definitions . An important issue in the analysis of selection is the baseline against which the number of epitopes of a given protein is compared . In previous work on viruses [24] , [25] , [43] , [48] , we have compared human to parallel non-human viruses as a negative control . However , bacteria have a wide range of possible hosts and purely non-human homolog bacteria often do not exist . We thus use three different background distributions to compare with: In the following section , we will use all three baselines to show that selection occurs in cytosolic bacterial proteins . While most viral proteins are expressed in the cytosol and exposed to the MHC-I presentation pathway , bacterial proteins are usually expressed either within the bacteria or within the endosome of a phagocyte , and hence are not exposed to the MHC-I presentation pathway . Thus , we expected that in contrast with viruses [24] , [25] , [42] , [43] , [44] , the SIR score of all bacterial proteins would be distributed around 1 . The epitope density of a protein is affected by two main elements: A ) a direct negative selection of epitopes through the immune response against pathogens expressing proteins carrying many epitopes , B ) inherent features of the protein , determining its amino acid usage , which in turn affects the epitope density . In order to check for the direct effect of negative selection , we compared the SIR score of each protein , not only to 1 , but also to the SIR score of scrambled sequences with identical amino acid distribution ( that we denote as the neutral SIR score ) . When all bacterial proteins are analyzed , the SIR scores distributions of the real and scrambled proteins are similar and are close to 1 ( Figure 2 , T-test , P-value>0 . 15 for all bacteria tested ) . While most bacterial proteins have the expected epitope density ( Figure 2 ) , the epitope density of bacterial proteins that are secreted to the host cytosol may be affected by CTL mediated selection . Such proteins are often present at high concentrations in the cytosol and are exposed to the MHC-I presentation pathway . Five examples of such bacteria are tested in this study: P . aeruginosa , S . flexneri , E . coli , L . monocytogenes and M . tuberculosis . Before examining each bacterium separately , we compared the SIR score in all cytosolic proteins of these bacteria against the SIR score in randomly selected proteins , against scrambled versions of themselves and against 1 . In all three cases , the SIR score of the cytosolic bacterial proteins were significantly lower ( ANOVA P-value<1 . e-11 for all three tests ) . No significant differences were found between the SIR score of randomly selected proteins and their scrambled versions . ( ANOVA P-value = 0 . 9114 ) . These results suggest that bacterial proteins located in the host cytosol have evolved to evade CTL recognition . The most characterized bacterial cytosolic proteins are the effector proteins of secretion systems in gram negative bacteria . We analyzed the SIR score of bacterial proteins in bacteria where we had a clear definition of effector proteins . We first analyzed P . aeruginosa , S . flexneri and E . coli as models for gram negative bacteria with Type III secretion systems . S . flexneri represents intracellular bacteria , P . aeruginosa represents cytopathic extracellular bacteria and E . coli ( entropathogenic ( EPEC ) and enterohemoreagic ( EHEC ) subgroups ) represent extracellular non-cytopathic bacteria . In the following sections , we show that systematically , in most bacteria tested , the epitope density in effector proteins is lower than expected , with one interesting exception . P . aeruginosa is a major cause of health care associated infections . It has only four known effector proteins: ExoS , ExoT , ExoU and ExoY . Almost all Pseudomonas strains contain ExoY and ExoT ( 89% and 96% , respectively ) [52] . However , nearly all strains have either the ExoS or the ExoU gene but not both [53] . ExoS has several adverse effects on the host cell , including actin cytoskeleton disruption ( associated with cell rounding ) and inhibition of DNA synthesis , vesicular trafficking , endocytosis and cell death . ExoS induced stress is characterized by slow death induction of the infected cell . ExoU is a potent phospholipase that is capable of causing rapid cell death in eukaryotic cells . ExoU containing strains of P . aeruginosa are much more cytopathic than their ExoS containing counterparts , which are more invasive .
The absolute number of epitopes or their density might not give the full picture regarding to escape mutations . Such mutations could affect , for example , the quality of the epitopes . We have thus checked if the epitopes still present on T3SS effectors have an affinity similar to epitopes from other proteins . In order for peptides to be presented on MHC-I molecules , they have to pass three stages: Proteasomal cleavage , TAP translocation , and MHC-I binding . We computed the probability to pass these three stages using proteasomal cleavage , TAP binding and MHC-I binding algorithms . An epitope was defined as a peptide with a supra-threshold score at each stage . The averaged proteasomal cleavage , MHC-I binding and TAP binding scores of epitopes derived from random bacterial proteins and from effectors of the three gram-negative bacteria used in this study are represented in Figure 4 . One can clearly see that most effectors have consistently lower scores for cleavage , TAP binding and MHC binding . ( T test 1 . e-10<P-value<0 . 06 ) with two exceptions: ExoU , that , consistent with our previous results , has proteasomal cleavage score and binding score higher than randomly selected proteins , and proteasomal cleavage of E . coli where the differences are not significant ( T test P-value = 0 . 388 ) . Since these scores correspond to the probability that a given peptide will be eventually presented at MHC-I molecule , these results highlight again the efforts made by the bacteria to prevent T3SS-effectors recognition by CD8+ T-cells: not only are there less epitopes in T3SS effectors , but the remaining epitopes have lower probabilities of being presented .
Cytosol localization of bacterial proteins is not unique to T3SS effectors . While Intracellular bacteria are localized within host cells , they usually do not reach the cytosol . Most of the bacteria reside in the phagosome of the host cell . However , some bacterial proteins are exposed to the host cytosol even in Intracellular bacteria . Two examples for such bacteria are Listeria monocytogenes and Mycobacterium tuberculosis .
L . monocytogenes can escape from the phagosome and remain in the cytosol . This escape occurs through the secretion of pore forming toxin- listeriolysin O ( LLO ) [65] that degrades the phagosomal membrane . LLO is a member of cholesterol-dependent cytolysins ( CDCs ) – a large group of pore-forming toxins that depends on membrane cholesterol for their activity . This group consists of about 20 members ( for a review see [66] ) , each produced as a soluble monomeric protein that , in most cases , is secreted by a type II secretion system . LLO is known to reside in the cytosol . However , cytosolic LLO is much less active as a pore-forming toxin . Instead , it is highly degraded due to a PEST-like sequence that promotes its targeting to proteasomal cleavage , preventing the pore forming in the cell membrane and the sequential lysis of its host cell [67] . While in the cytosol , L . monocytogenes secretes another protein , ActA that is used for actin polymerization and horizontal movement within the intestinal epithelial layer [68] . As expected from the results in the previous sections , both LLO and ActA have a lower SIR score than 1 ( T test P-value<7 . 6e-12 ) and both their scrambled versions ( ANOVA P-value = 7 . e-7 ) , and randomly selected proteins ( T test P value<1 . e-12 for LLO and ActA , separately , and ANOVA P-value<1 . e-12 for both proteins together ) . This suggests an immune escape strategy of L . monocytogenes as in gram negative bacteria . As in all previous cases , the average over randomly selected proteins of L . monocytogenes does not show such a decrease in the epitope density ( Figure 5A ) .
Mycobacterium tuberculosis ( TB ) [61] resides in the phagosome of lung macrophages . In MB , the ESAT-6 ( esxA ) and CFP10 ( esxB ) proteins are secreted into the host cell and were proved to reach the cytosol [22] . The access of these ESX-1 proteins to the cytosol might be achieved either by the TB escape from the phagosome or translocation of these proteins to the cytosol through sec61 , or alternatively directly by ESX-1 . Consistent with these last two options , these proteins were shown to induce CD8+ T-cell response regardless of the escape of the bacteria from the cytosol [22] . Besides these two proteins , there is a group of at least 18 ESAT-6 homologues . Very little is known about these proteins , but they show homology to the ESAT-6 protein and are thus suspected to be secreted by the same system [69] .
We tested both ESAT-6/CFP10 proteins and ESAT-6 homologues for their SIR score . Overall SIR scores of ESAT-6 family proteins are lower than 1 ( T test p<1 . e-15 ) and than their scrambled versions ( ANOVA P-value = 3 . 5e-9 ) as well as in comparison to randomly selected tuberculosis proteins ( ANOVA P-value<1 . e-13 ) . Moreover , when checking each protein separately , ESAT-6 , as well as 15 out of 18 of its homologues have shown to have lower SIR scores than both randomly selected proteins and their own scrambles sequences ( T-test P-value<0 . 05 ) . CFP-10 and the ESAT-6 homologues esxC , esxE and esxU have higher SIR scores than their neutral SIR scores ( T-test P-value<0 . 05 ) ( Figure 5B ) . Two of the above proteins , esxE and esxU , are regulated by the same protein , sigM , a member of the extracytoplasmic function subfamily of alternative sigma factors , and were suggested to function in host modulation at later stages of infection but seemed to have no importance in the pathogenesis of acute infection [70] . One could assume that the consistent low SIR score in most of this family members' is due to sequence similarity . We have calculated the edit distance between members of the ESAT-6 family ( divided by the length of the longer among the compared proteins ) . As shown in Figure S2 , most of ESAT-6 family members are very different from each other in their sequences . This result suggests that the selection for immune evasion has occurred in each protein separately . One can thus summarize that CTL epitopes modulation is a mechanism common to practically all cytosolic bacterial proteins .
In contrast with all other confirmed effectors , the SIR score of P . aeruginosa's ExoU was significantly higher than its neutral SIR score ( T test , p <1e-9 ) . Moreover , the candidate epitopes of ExoU have a higher proteasomal cleavage and MHC-I binding scores than other effectors or non-effector proteins ( Figure 4 ) . Thus not only is ExoU not trying to hide , it seems it is making every possible effort to expose itself . Taking into account that ExoU is secreted by cytopathic strains of P . aeruginosa and is known to induce rapid cell death in host-cells , we propose that these P . aeruginosa strains may use the host immune system to induce cell death . Since the goal of ExoU expression is to kill the cell , having the cell recognized by CTLs may be the easiest way to obtain this goal . The utilization of the host's immune response by bacteria was suggested recently by Gagneux et al [71] . In their study on TB , they detected hyper-conserved epitopes in MTBC ( Mycobacterium tuberculosis complex ) proteins , and suggested that the bacteria benefit from T-cell recognition . Similarly , the extremely high epitope density found in the ExoU protein suggests that over-presentation of this protein acts to induce CD8+ T-cell response in the host-cell by the cytopathic strains of P . aeruginosa as part of their mechanism to induce cell death . We are now looking for similar effects in viruses .
In this study we have used the SIR formalism as used in our previous studies . While this formalism was validated for some alleles , its MHC-binding algorithm ( BIMAS ) is relatively old and new algorithms have been introduced since then for some alleles . In order to test that our results are not an artifact of the algorithms used , we have tested the validity of our results using two other algorithms: MLVO and NetMHC ( see method section for a detailed description of these algorithms ) . When using the MLVO , the results were similar to the traditional SIR score ( based on BIMAS ) results , and were often more significant . For most bacteria tested , all effectors were shown to have a lower SIR score than expected from their sequence . The results were significant for most groups of proteins ( Figure 6 , ANOVA p <0 . 03 ) . The exception were again the E . Coli that showed a high variability among strains and proteins , and late effectors of Shigella in which no significant differences were shown ( ANOVA P-value>0 . 5 for both E . coli and late Shigella effectors ) . The main difference between the MLVO and BIMAS results was that in the MLVO formalism , the SIR score of ExoU was lower than its scrambled versions ( T test P-value<0 . 04 ) . Although the accuracy of MLVO is better than most other algorithms for the vast majority of alleles , this algorithm was not systematically tested on other organisms . We thus use the MLVO results at this stage only as a validation of the SIR results . To further validate the results , we have repeated the analysis using NetMHC . In most bacteria tested ( again , with the exception of E . Coli and late effectors of Shigella in which the differences was not significant ( ANOVA P-value>0 . 58 and 0 . 064 , respectively ) ) , the SIR score predicted by the NetMHC of cytosolic proteins was lower than their neutral SIR score ( Figure 7 , ANOVA P-value <5 . e-3 ) . Consistent with MLVO but in contrast with BIMAS formalism , ExoU score was lower than expected ( T-test P-value = 0 . 012 ) . Taken together , in most cases our results using BIMAS algorithm were in agreement with the results of MLVO and NetMHC algorithms , and that the observed reduction in the number of epitopes is not an artifact of a specific algorithm . A summary of the significance of the results in all three algorithms are presented in Supplementary Material ( Table S3 ) .
We have performed a broad analysis of immune-induced selection of CD8+ T-cells escape mutations in cytosolic bacterial proteins . While in general CD8+ T-cell response induces very weak selection , if at all , on bacterial proteins , a strong selection was observed on the T3SS-effectors group of gram-negative bacteria and probably on cytosolic bacterial proteins in general . Furthermore , the strength of the selection on the effectors depends on their time of expression as can be seen in the case of S . flexneri where the early set of effectors was selected more strongly than the late set . These results are in good agreement with our previous studies on herpesviruses [43] , HIV [24] and viruses in general [25] , showing that proteins expressed in phases critical to the fate of infection ( e . g . , early lytic and latent ) evaded immune detection more than others . In order to validate these results , we have repeated the analysis using a recently developed algorithm ( the MLVO ) , as well as the more classical NetMHC with similar results for the vast majority of the proteins . An intriguing possibility is that the direction of selection depends on the function of the effectors . This was demonstrated by the P . aeruginosa cell death mediated effector ExoU that has evolved to have more epitopes , and thus , might induce CTL response . The involvement of ExoU in inducing CTL response is in good agreement with studies of corneal infection by the P . aeruginosa strain which was shown to be dependent on ExoU secretion [72] . Barrett et al . [73] have shown that mouse strains favoring development of a Th1-type response are susceptible to corneal infection , suggesting the involvement of CTL response in this infection . Note that this result was not observed using MLVO , and is thus left as a hypothesis to be checked further . In E . Coli , a very high variability in the epitope density of proteins and strains has been observed , as well as a large difference between the epitope densities in different HLA alleles . We currently have no clear explanation for this variability , except perhaps for a specific adaption of EPEC and EHEC to different populations and thus different epitope densities distributions among HLA alleles . Thus , in contrast with all other bacteria tested here , we cannot safely claim that E . Coli effectors proteins have evolved to avoid detection . Further research is needed to understand the peculiar differences between E . Coli strains . Compared with viruses , bacteria have a relatively low mutation rate of approximately 1 . e-8 ( as compared with approximately 1 . e-5 in viruses ) . Considering the lack of species specificity and the horizontal transfer of many genes , including the members of type III secretion system , bacteria are much less genetically flexible , and therefore , epitope density within a protein might be influenced not only by the immune-induced selection but also by the time when the horizontal transfer took place and the variety of species infected by the bacteria , forcing them to adapt to different HLA alleles and other species-specific constraints . A way to maximize the evolutionary conservation of epitopes ( or the lack of epitopes ) is to directly affect the cleavage mechanism that is common to most mammals . Indeed , when computing the proteasomal cleavage ratio ( number of nine-mers that are the results of proteasomal cleavage divided by the total number of nine-mers ) , effector proteins had a lower ratio than other proteins in all bacteria . These results were significant for P . aeruginosa effectors and late and early S . flexneri effectors ( T-test , P-values = 5 . 7e-5 , 4 . 3e-5 and 6 . 6e-45 , respectively ) , and insignificant for E . coli effectors ( p = 0 . 1 ) ( Figure 8 ) . The current analysis shows that an important part of the immune response against bacteria may be the CTL response against cytosolic bacterial proteins . This response may be a key element in the development of future anti-bacterial therapies .
Pseudomonas aeruginosa , Escherichia coli , Shigella flexneri , Listeria monocytogenes and Mycobacterium tuberculosis gene sequences were used for this analysis . The sequences were obtained from the NCBI ( http://www . ncbi . nlm . nih . gov/ ) database . All sequences are available in the Supplementary Material . For P . aeruginosa , we used 16 ExoU sequences and 18 sequences of the 3 other effectors . For S . flexneri , we used 75 early effectors and 30 late effectors sequences . For E . coli , we used 38 effectors sequences ( 11 Tir , 4 EspF , 4 EspH , 14 EspZ and 5 Map ) . For L . monocytogenes , we used 107 listeriolysin sequences and 483 ActA sequences . For M . tuberculosis , we used 62 Esat-6 proteins sequences . For all bacteria , we took 400 sequences of random non-effectors proteins . For each protein sequence , we produced 50 scrambled sequences as a reference . We have analyzed the ratio between the number of epitopes presented in bacterial proteins and their random counterpart . This ratio was defined as the Size of Immune Repertoire ( SIR ) score . The epitope number was computed using three algorithms: a proteasomal cleavage algorithm [39] , a TAP binding algorithms developed by Peters et al . [40] and the BIMAS MHC binding [74] algorithms . We have computed epitopes for the 33 most common HLA alleles and weighted the results according to the allele frequency in the global human population ( Figure 1 ) . The algorithms' quality was systematically validated vs . epitope databases and was found to induce low FP and FN error rates . The computation of the SIR scores can be performed through our web-server at http://peptibase . cs . biu . ac . il/index . html . The comparison between effectors and their scrambled sequences , as presented in Figures 3–8 , was done on the average of the entire group of proteins . We have also tested the possibility of first averaging each protein separately and then to average the results over all proteins , as we have previously done for some viral proteins [25] , [43] , [44] , [75] . There is no major difference between the results in the two approaches . The results using the latter approach are represented in the Supplementary Material ( Figure S1 ) . Given a peptide with N- and C-terminal flanking regions FN and FC and residues P1 , . Pi , . . Pn , where Pi represents any residue 1 , and n represents C and N positions , the following score was defined:A peptide with a high score , S , has a high probability of being produced , while a low score corresponds to a low probability of production . The appropriate values for to were learned using a simulated annealing process [76] . The algorithm was validated to give a rate of false positives of less than 16% and a rate of false negatives of less than 10% [39] . The probability that a peptide binds the transporter associated with antigen processing ( TAP ) machinery is mainly a function of the residues at the first three N-terminal and the last C-terminal positions . Moreover , this probability can be estimated through a linear combination of the binding energies of the residues . Multiple algorithms for TAP binding frequency were checked . The score computed by Peters et al . [77] gave the best differentiation between presented and random peptides [46] . Each protein was divided into all possible nine-mers by using a sliding window ( e . g . , a 300-amino-acid protein was divided into 292 nine-mers , positions 1 to 9 , positions 2 to 10 , and so on ) . For each nine-mer , we computed the MHC binding energies of 31 different class I human leukocyte antigen ( HLA ) molecules , most of them HLA-A and HLA-B . The affinity of a candidate peptide for each HLA molecule was estimated using the BIMAS software and the binding coefficients predicted by Parker ( [78]; http://www-bimas . cit . nih . gov/molbio/hla_bind/ ) . These matrices estimate the contribution of each amino acid at each position to the total binding strength . While many more modern algorithms exist for MHC binding prediction , we have previously found the BIMAS algorithm to provide trustworthy results in most highly frequents alleles that compose the bulk of the score analyzed here [24] , [33] , [43] . The MLVO algorithm [79] for MHC and TAP binding prediction finds a classifier ( w ) using three label types that are combined into a single constrained optimization problem . The method finds the optimal combination of binary classification of peptides known to bind or not to bind the MHC/TAP molecule , a linear regression based on the measured affinities of peptides with a known IC50 or EC50 binding concentrations and a guess ( often based on information on similar alleles ) . In the current analysis , we have used the MLVO algorithm for MHC binding [79] , as well as for TAP binding . The MHC binding accuracy of the vast majority of MHC-I alleles in the MLVO is over 0 . 95 ( with AUC of over 0 . 98 ) [79] . As in all other cases , the SIR results presented are a weighted average over alleles of the ratio between the computed epitope density and the one expected in a random sequence . The NetMHC algorithm uses an artificial neural network ( ANN ) based method for MHC binding prediction [80] . The ANN is trained by eluted MHC ligands for which binding affinity data is measured . We define an epitope as a peptide that exceeds the threshold of 500 nM ( 'weak binder' ) , and calculated the SIR score accordingly . In order to compare the NetMHC results to the BIMAS and MLVO results , we applied the Ginodi cleavage algorithm [39] and the Peters TAP binding score [40] . Only peptides having a supra-threshold score were tested for MHC binding . Again , the SIR results presented are a weighted average over alleles of the ration between the computed epitope density and the one expected in a random sequence . The different epitope prediction algorithms provide a binding score . In order to produce an epitope list , a cutoff should be applied to these scores . There are two possibilities to use thresholds for the definition of epitopes: a single affinity threshold for all alleles , or an allele dependent threshold . The first attitude is based on the need to bind the MHC molecule for a long enough period to activate T cells . The second attitude is based on the competition for the presentation on a limited number of MHC molecules . For example , an allele such as B*2705 is expected to present a very large number of epitopes from self proteins [81] . Thus a viral protein with a large number of epitopes would have to compete with a similarly high number of epitopes in human proteins . We here use the second option where we have computed an allele specific presentation threshold value that limits the number of predicted presented epitopes from a random sequence ( Supplementary Material , Table S1 ) . While this may lead to the exclusion of some real viral epitopes , it should not affect the ratio between the number of computed epitopes in real and scrambled sequences . Cutoffs for all alleles can be found in the Supplementary Material ( Table S1 ) . The SIR score of various populations was compared to the expected score . A two way nested ANOVA was used to compare the SIR scores of bacterial proteins in real vs . scrambled sequences , as well as the SIR score of effector vs . other proteins in bacteria . The ANOVA analysis was performed using two layers of variables: the main group -effector/non-effector or real/scrambled and the second , nested within the first is the protein identity . A two way T-test with unknown and unequal variance was used in cases where no layers has to be considered ( comparison SIR score of each protein groups to 1 , and comparison of the averaged proteasomal cleavage , tap binding and MHC-I binding scores of epitopes in effectors and non-effector proteins ) . We have designed a CD8+ T cell epitope SQL based library webserver: http://peptibase . cs . biu . ac . il . This website provides detailed CD8+ T cell epitope libraries for the human and mouse genomes as well as for most fully sequenced viruses . It also allows users to upload a file and produce an epitope library . All bacterial proteins in this study were analyzed for their epitope using this webserver . | Bacterial proteins are mainly exposed to B-cells and CD4+ T-cells , while CD8+ T-cells ( CTL ) typically respond to viruses . The limitation of the CTL response to viruses results from processing pathways of epitopes presented to CTLs . These epitopes usually stem from proteins expressed in the cytosol . Such proteins are eventually degraded and presented on MHC-I molecules to CTLs . However bacterial Type III secretion system ( T3SS ) effectors also have an access to the host cytosol and may also be exposed to CTL response . Thus , we can assume that this group of proteins undergoes selection against the presentation of CTL epitopes , as seen in viral proteins . Using multiple epitope prediction algorithms , we show that most T3SS effectors , as well as LLO , and ActA in Listeria monocytogenes and ESAT-6 proteins in Mycobacterium tuberculosis , are systematically selected to reduce the number and quality of their epitopes . The exception in this respect is the Pseudomonas aeruginosa effector ExoU that has high density of high quality epitopes . Since ExoU is known to induce rapid cell death in hosts cells , we assume that P . aeruginosa utilize the immune response to induce such death . The E . coli epitope density is highly variable among strains . | [
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] | 2011 | Bacteria Modulate the CD8+ T Cell Epitope Repertoire of Host Cytosol-Exposed Proteins to Manipulate the Host Immune Response |
Gene co-expression has been widely used to hypothesize gene function through guilt-by association . However , it is not clear to what degree co-expression is informative , whether it can be applied to genes involved in different biological processes , and how the type of dataset impacts inferences about gene functions . Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes . By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation , we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset , annotation quality , gene function , expression similarity measure , and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example . Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better . In addition , to recover the maximum number of known pathways and identify candidate genes with similar functions , it is important to explore rather exhaustively multiple dataset combinations , similarity measures , clustering algorithms and parameters . Finally , we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants . This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets .
With the ease of sequencing , an ever increasing number of genomes from a wide range of species are available . One major challenge is to ascribe functions to genomic features . For example , while ~70% of Arabidopsis thaliana genes have annotated functions [1] , only ~40% of these annotations are supported by experimental evidence such as mutant phenotype or biochemical assays [2] . To increase functional information , transcriptome data have been used to develop hypotheses of gene function based on similarity of expression patterns ( co-expression ) with genes that have known functions [2–4] . The relationship between co-expression and functional correlation was first shown with Saccharomyces cerevisiae and human transcriptome data [5–8] . Subsequently , a large number of plant studies used co-expression analysis to infer gene functions [9–17] . For example , the MYB28 and MYB29 transcription factors are co-expressed with the glucosinolate pathway genes that they regulate [9] . Similarly , the transcription factors CRC and AP1 co-express with 58 fatty acid biosynthesis genes , and crc and ap1 mutants have altered fatty acid compositions [15] . More broadly , methods based on integration of multiple types of omics datasets were developed to account for different levels of regulation and to improve gene functional inferences [18–21] . In these data integration exercises , transcriptome data remain the most abundant and the most effective in capturing gene functional relationships [2 , 18] . Thus , analysis of gene expression results can lead to hypotheses of plant gene functions . Despite its utility , there are known computational and biological limitations in using co-expression for gene functional inference , and these usually are not evaluated in co-expression based studies [2] . First , genes with similar expression profiles may not necessarily have related functions [22] . Second , for those genes that do have related functions , transcription patterns may not be coordinated due to post-transcriptional and other levels of regulation [23] . Third , it is also possible that they do in fact co-express , but that the co-expression criteria need to be optimized . For example , using an expression coherence ( EC ) measure , which is the ratio of the number of co-expressed gene pairs to the total number of gene pairs [24] , only 41% of the Gene Ontology Biological Process ( GO-BP ) terms have higher ECs than expected by chance [25] . The 59% of pathways with low ECs may contain genes that are regulated beyond transcription . Alternatively , a more detailed exploration is required to determine how co-expression should be defined . Consistent with this , in most studies , a fixed threshold of expression similarity is used to identify pairs of co-expressed genes . Depending on the value of this threshold , the degree of co-expression might be over- or underestimated and lead to false positive or negative associations . Therefore , it is necessary to optimize the criteria used to define co-expression to increase the utility of expression data in guilt-by-association studies . One major parameter that impacts co-expression studies is the type of dataset; it is expected that not all expression profiling experiments will be informative for revealing functional relationships between any given gene pair [26] . Most studies combine multiple datasets for gene function inference [9 , 25] . One advantage of this approach is the increased statistical power for establishing correlations . Small number of samples might lead to statistically unreliable connections [27] . However , the inclusion of too many samples can result in the loss of information [28] , and expression datasets that are directly relevant to the underlying biological processes might be more useful in functional inference . For example , to uncover drought response pathway genes , it would be better to use a more specific , drought stress dataset instead of a collection that includes potentially uninformative experiments [13] . Other factors that impact the effectiveness of co-expression studies include the specific samples used ( e . g . stress vs . developmental series ) , method of data transformation ( e . g . fold change vs . absolute expression values ) , and the procedures and parameters used to define co-expression . A comprehensive study evaluating the above is needed and would be highly informative for future studies that use co-expression as a means for functional inference . In addition to inferring functional relationships between two genes , co-expression is useful for uncovering groups of genes with related functions ( referred to as clusters ) . Unsupervised learning methods , particularly various clustering algorithms , are among the most common approaches used to identify co-expression clusters [29] . Once the clusters are identified , functional categories such as GO can be used to evaluate what types of genes are over-represented in each cluster , and gene functions can be hypothesized based on cluster membership [30] . Although clustering and enrichment analyses are straightforward , there is no single best method [31] as there are a large number of clustering algorithms and the cluster memberships ( which genes are in the same cluster ) depend on many clustering variables ( e . g . algorithm , distance measure and number of clusters ) . Because differences in parameter choice strongly influence the types of co-expression clusters obtained , it is important to perform clustering with multiple parameters rather than relying on a single method . In this study , our goal was to maximize the information from co-expression data to improve predictions of functional associations between genes . Specifically , we asked to what extent A . thaliana genes are co-expressed in each metabolic pathway . We also explored the features of high EC pathways . Next , we evaluated the influence of dataset on EC for each metabolic pathway , the best practices in using co-expression to identify novel genes that function in a biological process , and the impact of different commonly-used clustering algorithms and parameters on the ability to identify genes that function in the same pathways . Finally , the biological relevance of cluster membership was validated using an independent phenomics dataset . Overall , we demonstrated that optimizing the use of co-expression based approaches requires consideration of the pathway of interest , expression dataset and clustering algorithm .
A . thaliana metabolic pathways ( AraCyc pathways ) , the genes belonging to these pathways and supporting evidence were obtained from the Plant Metabolic Network ( version 8 , [32] ) . To examine a broader set of gene function in addition to metabolism , A . thaliana Gene Ontology biological processes ( GO-BPs ) annotations were obtained from geneontology . org [33] . Only nuclear genes and pathways/processes with >2 genes were included in further analyses . The metabolic pathway genes were divided into two sets based on supporting evidence . The first set contained all pathway genes regardless of the types of evidence supporting the annotations ( 382 pathways , 5 , 991 genes ) . The second set only contained genes with experimental evidence ( 225 pathways , 934 genes ) . For the GO data , we examined 1 , 710 GO-BP terms covering 23 , 157 genes . To determine if genes of a pathway tend to have a particular subcellular location , subcellular location information was obtained from the SUBcellular Arabidopsis consensus database ( SUBAcon [34] ) , and a contingency table for each pathway and subcellular location was established to calculate the enrichment p-value ( Fisher’s Exact Test ) . The resulting p-values were corrected for multiple testing [35] . To determine whether similar sets of cis-regulatory elements are present among genes in the same pathway , 349 position frequency matrices taken from the Cis-BP database [36] were converted to position weight matrices ( PWMs ) based on the A . thaliana background AT and CG frequencies ( 0 . 33 and 0 . 17 , respectively ) using the Tools for Analysis of MOtifs ( TAMO ) package MotifTools [37] . The PWMs were used to determine the location of motif sites in the 1kb region upstream of the transcriptional start sites of A . thaliana genes with Motility [38] . To assess the impact of post-transcriptional regulation , we used a dataset with associations between miRNAs and their target genes , downloaded from The Arabidopsis Information Resource ( TAIR , [39] ) . Six publicly available Affymetrix ATH1 microarray gene expression datasets used in this study include: Biotic stress: GSE5615-5616 , Light: GSE5617 , Abiotic stress: GSE5620-5628 , Development: GSE5629-5634 , Hormone: GSE39384 , and Diurnal [40–43] . In addition to these , ~700 A . thaliana microarray datasets were downloaded from NASCArrays database [44] . The datasets were downloaded in either normalized form [44 , 45] or as unprocessed data from Gene Expression Omnibus ( GEO ) [46] . For the unprocessed datasets , the CEL files for the AtGenExpress data [40–42] were downloaded from TAIR [39] and quantile normalized using the Bioconductor affy package in R [47] . The Bioconductor LIMMA package [48] was used to calculate fold changes by contrasting treatment and control experiments , and the p–values of significant fold changes were corrected for multiple testing [35] . To generate the null expression correlation distribution , 500 , 000 gene pairs were randomly selected and their Pearson Correlation Coefficients ( PCCs ) were calculated using the SciPy library [49] . The 95th percentile PCC values ( PCC95 ) in the null distributions were used as thresholds for calling the expression patterns of two genes as significantly correlated with a 5% false positive rate . Using the PCC95 values , the expression coherence ( EC ) score was calculated to determine the extent of co-expression among genes in a given pathway [24 , 25] . The EC score of a pathway is the ratio of the number of gene pairs with PCC values higher than PCC95 and the total number of gene pairs in a pathway . Thus , EC values range from 0 ( no gene pair with significant expression correlation ) to 1 ( all gene pairs significantly co-expressed ) . To identify pathways with significantly higher than randomly expected ECs ( high EC pathways ) , pathway-gene associations were randomized 100 times with the sizes of the pathways kept the same . For each dataset , a distribution of randomly expected EC values was established . For a given dataset , a pathway was defined as a high EC pathway if it had an EC score larger than the 95th percentile value of the null EC distribution . The percentile of the pathway ECs in the null EC distribution was referred to as EC percentile . To assess how similar the gene expression profiles among array experiments in a dataset are , the PCC values between the experiments in a dataset were calculated and the median PCC value was used as a measure of homogeneity among the experiments within a dataset . To evaluate the impact of similarity measures , Spearman’s rank coefficient [49] , partial correlation [50] and Mutual Information ( MI ) [51] were used as additional similarity measures to determine pathway EC in the same way as PCC was used . Partial correlations of pathway genes were calculated with two methods: ( 1 ) a Python implementation of partialcorr function in MATLAB , which determines the correlations between residuals of linear regression , and ( 2 ) the R package corpcor that was optimized for genomic datasets [50] . MI was calculated both as normalized and adjusted with the Python scikit-learn package [51] . The adjusted MI measure accounts for impact of sample size ( larger samples might lead to higher MI ) and the normalized MI value was calculated by scaling MI values to between 0 and 1 . Bayesian Networks ( BNs ) were constructed for each pathway using the bnlearn package in R [52] . Hill-climbing algorithm was used to construct BNs with options for continuous data . The transformed p-values ( -log ( p ) ) of arc strengths between nodes ( genes ) in BNs were used as measures of gene association strengths that are used similarly as pairwise similarity . Only the transformed p-values were used because they were nearly perfectly correlated with arc strengths ( r2 = 0 . 9998 ) . BNs were also constructed for randomized pathways to determine threshold p-values for each gene association and the thresholds were then applied to determine how many gene pairs in each pathway have above threshold arc strength p-values to determine pathway EC . To determine the impact of the clustering algorithm on the resulting co-expressed gene clusters , we tested k-means [53] , hierarchical clustering ( hclust ) , c-means [54] and Weighted Gene Co-expression Network Analysis ( WGCNA ) [55] in the R environment and approximate kernel k-means [56] in MATLAB . Clustering parameters tested included the numbers of clusters ( k ) , distance measures , and hierarchical clustering algorithms for relevant methods . Initially , we attempted to obtain the optimal k for clustering the stress expression dataset by obtaining the “elbow plot” . After testing 11 k values ranging from 5 to 2000 , we realized that the selected k was not necessarily the best and the choice of k impacts clustering memberships of genes . For distance-based algorithms , three distance measures ( Euclidean , radial basis function kernel and 1-PCC ) were tested . For hierarchical clustering , we also explored the impact of average , complete and Ward linkage algorithms . For WGCNA , the pickSoftThreshold function was used to determine the ß values based on the scale-free topology model [55] . Commonly used clustering algorithms—such as k-means—are not deterministic , i . e . they may result in a local optimum solution . To evaluate whether multiple runs could result in significantly different results , we ran k-means , approximate kernel k-means and c-means 10 times . We refer to the similarity among 10 runs as consistency between the runs . In contrast , for hierarchical clustering and WGCNA , the co-expression cluster membership was always the same for every run . Fisher’s exact test was used to assess how well memberships within a cluster overlap with those in a pathway . The resulting p-values were corrected for multiple testing [35] . For each clustering algorithm-parameter combination , an “over-representation score” between a cluster and a pathway was defined as the -log ( q ) value where a higher score indicates a more significant degree of overlap between cluster and pathway memberships . An over-representation score ≥1 . 3 ( q <0 . 05 ) was considered to be statistically significant . To account for the possibility that over-representation of some pathways is spurious we asked how often significant over-representation scores arise from randomized expression data . Specifically , the stress expression dataset was permuted to generate 15 random datasets that were used in k-means clustering ( k = 5 to 2000 , 10 independent runs for each k and each random dataset ) . The same approach outlined above was also used to assess how well memberships in a pathway overlap with those in a random cluster . Among 1 , 650 random clusters , none had a significant over-representation score with A . thaliana pathways . To further assess if cluster membership can serve to predict pathway membership , we calculated the F measure ( the harmonic mean of precision and recall ) for each cluster-pathway combination . Precision is the proportion of correct predictions over total predictions; in our case it was the ratio between the number of genes in a cluster that were also found in a pathway and the total number of genes in that cluster . Recall is the proportion of correct predictions over total true positives; in our case it was the ratio between the number of genes in a cluster that were also found in a pathway and the total number of genes in that pathway . F measure was calculated for each pathway-cluster combination with an over-representation score ≥1 . 3 . Here we used the mutant profile data from Chloroplast 2010 , a database consisting of phenotypic screening results for mutants of more than 5 , 000 genes [57 , 58] to confirm the potential functional links between genes found in the same co-expression cluster . This database includes measurements of amino acids and fatty acids as well as chloroplast morphology and photosynthetic parameters . Taking leucine degradation as an example , we expected the leucine content to be more similar between mutants of leucine degradation genes and mutants of genes found in the same co-expression cluster than to wild-type and mutants of random genes . To determine whether this was the case , we retrieved the leucine measurements ( in nmol/g fresh weight ) of 12 leucine annotated degradation genes , genes that were clustered with pathway genes with an over-representation score >1 . 3 ( q <0 . 05 ) , 1000 random genes , where homozygous T-DNA insertions were available , and 184 wild type control plants included in the Chloroplast 2010 database . Significant differences between the leucine levels of mutants and controls , which included randomly selected mutants of genes that are not in the leucine degradation pathway and wild-type plants , were identified with Mann-Whitney tests .
To evaluate the extent to which genes with similar expression patterns have similar functions , we asked whether genes in the same A . thaliana metabolic pathway were co-expressed ( see Methods ) . To address this question , Pearson Correlation Coefficients ( PCCs ) between genes in each of the 382 A . thaliana metabolic pathways in AraCyc were calculated using an expression dataset consisting of 16 different environmental conditions ( referred to as the stress dataset [41] ) ( Fig 1A ) . To broadly examine groups of functionally related genes in addition to metabolic pathways , we also calculated PCCs between genes in each of the 1 , 710 A . thaliana Gene Ontology Biological Process ( GO-BP ) categories . A group of genes in an AraCyc pathway or a GO-BP is referred to as a “functional category” . The median PCC values were <0 . 1 for ~60% of functional categories , suggesting that many genes in the same pathway have dissimilar transcript profiles under stress conditions . To assess statistical significance and control for false positive expression correlation , the PCC values of pairs of genes in the same functional category were compared to PCC values of random gene pairs ( Fig 1A ) . The 95th percentile PCC value of random gene pairs ( referred to as PCC95 ) was 0 . 41 for the stress dataset . In other words , only 5% of random gene pairs have PCC values >0 . 41 . We used PCC95 as the threshold for calling the expression profiles of a gene pair as significantly positively correlated with a 5% false positive rate . Based on this threshold , only 19% of gene pairs within a functional category have significantly correlated expression patterns . To determine whether some functional categories contain more members with highly correlated expression than others , we adopted the expression coherence ( EC ) measure , which ranges from 0 to 1 [25] . Here the "pathway EC" is defined as the proportion of pairs of genes in a pathway or GO category that have significantly correlated transcript profiles . Note that the median ECs in A . thaliana are only 0 . 11 for GO-BPs and 0 . 14 for AraCyc pathways , indicating that 50% of the functional categories have <11–14% gene pairs with significant expression correlation . Consistent with an earlier study [25] , we found that genes in functional categories generally have higher ECs than groups consisting of randomly selected genes ( Mann-Whitney test , p <2 . 2e-16; Fig 1B; S1A Table ) . In particular , 36% of the AraCyc pathways have higher EC values than the 95th percentile of the random EC distribution ( Fig 1B ) ; these are defined as “high EC pathways” . Similarly , 32% of the GO-BPs have higher EC values than the 95th percentile of the random EC distribution ( referred to as “high EC GO-BPs” , S1A Table ) . One explanation for the slightly higher number of high EC pathways than that of high EC GO-BPs may be because metabolism related pathways tend to have more highly coordinated transcriptional regulation compared to other types of functional categories . Consistent with this notion , GO-BP categories related to metabolism , including metabolic pathways ( GO:0008152 ) and its child terms , have higher median ECs ( 0 . 14 and 0 . 13 ) compared to signal transduction ( GO:0007165 , EC = 0 . 11 ) , cell-cycle ( GO:0007049 , EC = 0 . 10 ) and response to stress ( GO:0006950 , EC = 0 . 08 ) categories ( S1 Fig ) . Among metabolic GO-BPs , amino acid metabolism pathways ( GO:0006520 ) have the highest median EC ( 0 . 21 ) among the categories we compared ( S1 Fig ) . Overall , GO-BPs have lower ECs than AraCyc pathways ( Mann-Whitney Test , p = 2 . 41e-03; S1 Fig ) . The ECs for functional categories have a very wide range ( Fig 1B; S1 Fig ) . The differences in ECs may be due to technical issues such as functional annotation quality or methodological issues such as the similarity measure used to assess co-expression . The EC differences can also be due to differences in the biological characteristics of pathways , for example , the role of the pathway , presence of common transcriptional regulatory mechanisms , and regulation at levels beyond transcription . Finally , the dataset used to calculate EC could also be a major factor . In the following sections , we assess the factors influencing ECs and identify ways to maximize ECs for functional categories . Considering false positive annotation can have a significant , negative impact in further analyses , we examined features of high EC categories and the impact of multiple factors on ECs by focusing on AraCyc metabolic pathways in the following sections . Computational predictions of gene function without experimental evidence can lead to false assignments to pathways , resulting in lower pathway EC values . This is particularly important because computational annotations in the Plant Metabolic Network are based on sequence similarity only [59 , 60] . Functional annotations made using sequence similarity based methods are estimated to have an error rate of 49% [61] and high sequence similarity does not necessarily lead to co-expression [62] . To determine whether annotation quality is a major factor influencing pathway EC , we separated pathway genes into those with and without experimental evidence . Consistent with the hypothesis that annotation quality can significantly impact pathway EC , pathways with lower ECs tended to have proportionally fewer genes with experimental evidence ( PCC = 0 . 20 , p = 1 . 53e03; S2A Fig ) . Pathway ECs calculated using genes with experimental evidence were substantially higher ( Mann-Whitney test , p = 5 . 44e-12 , median EC = 0 . 26 ) than those calculated using genes assigned to pathways solely based on computational predictions ( median EC = 0 . 10; Fig 2A ) . This is consistent with the hypothesis that some annotations based solely on computational evidence are incorrect . Although annotation quality influences pathway EC , it explains only ~4% of the variance in the median EC of pathways that include genes assigned based on all evidence ( computational and experimental , EC = 0 . 14 ) and pathways that include genes assigned based on computational evidence ( EC = 0 . 10 ) . The small increase in co-expressed genes pairs when including experimental evidence is potentially due to the small fraction of genes that have experimental evidence ( 5 , 991 genes considering all evidence , 934 genes considering experimental evidence ) . Nonetheless , because annotation quality did have a measurable impact , only genes with experimental evidence were included in further analyses . In addition to gene annotation quality , the similarity measure used to assess gene co-expression could impact pathway EC . Although PCC is among the most widely used similarity measures in co-expression studies , it does not deal with non-linear relationships as well as other similarity measures including Spearman’s rank correlation coefficient and mutual information ( MI ) . Another consideration is that , all three similarity measures above consider only pairwise correlations , thus higher order correlations due to the influence of the other genes in the network are not considered . To assess the influence of higher order correlation , we also evaluated two approaches: ( 1 ) partial correlation , where the correlation between genes is calculated after controlling for the effects of other genes and ( 2 ) a graph model-based approach such as Bayesian Network ( BN ) where the strength of connection of a gene pair is determined by considering all genes in a network . To assess the impact of potential non-linearity and higher order correlations , we first calculated pathway ECs with seven different similarity measures including PCC , Spearman’s rank , two partial correlation methods ( corpcor and partialcorr ) , adjusted and normalized MI , and transformed p-value of arc strength in a pathway BN ( see Methods ) . To assess the statistical significance of EC values and control for false positive ECs , EC values were calculated with randomly chosen gene pairs for each pathway size and for each similarity measure . Thus , for each measure , a random EC distribution is available and used to determine the percentile value of a pathway EC ( referred to as "EC percentile” ) . Thus , a high EC percentile indicates reduced probability that the observed pathway EC is spurious . First we asked if the pathway EC percentiles are correlated among different measures ( Fig 2B ) . For example , EC percentiles calculated with PCC were significantly positively correlated with the EC percentiles calculated with , in order of diminishing degrees of correlations , corpcor ( PCC = 0 . 80 , p = 2 . 35e-50 ) , Spearman’s rank coefficient ( PCC = 0 . 78 , p = 1 . 67e-46 ) , adjusted MI ( PCC = 0 . 53 , p = 5 . 23e-18 ) , partialcorr ( PCC = 0 . 39 , p = 1 . 97e-09 ) , BN ( PCC = 0 . 30 , p = 5 . 35e-06 ) , and normalized MI ( PCC = 0 . 17 , p = 1 . 23e-02 ) . Given the degrees of correlations in EC percentiles differ widely between measures , the similarity measures have significant impact on pathway ECs . Consistent with this notion , the number of pathways with ECs that are significantly higher than randomly expected ( high EC pathways , >95th percentile of the random EC distribution ) vary widely depending on the similarity measure ( Fig 2C ) . Among the measures , corpcor , PCC and Spearman’s rank allowed the highest numbers of high EC pathways to be identified . This finding is consistent with the finding of a recent study examining PCC , Spearman’s rank coefficient , MI , and other similarity measures [27] . Only five of the pathways have high ECs consistently regardless of similarity measures ( Fig 2D ) . Importantly , consistent with the idea that non-linearity and higher order correlations can be important , the ECs of some pathways are only significant if a particular similarity measure is used ( white boxes , Fig 2D ) . Notably , 17 and 10 pathways have high ECs only when the corpcor method and the BN-based measure were used , respectively ( Fig 2D ) , illustrating the importance of higher order correlations . In addition , different methods of calculating partial correlations led to significant differences in high EC pathway recovery . As the corpcor method was optimized for genomic data analysis [50] , our finding is justifiable that the results from corpcor is more informative than the results from partialcorr . For further analyses , we used PCC as the measure of gene co-expression as it is one of the most widely-used similarity measures and , along with Spearman’s rank and corpcor , uncover the highest numbers of high EC pathways . Next we explored biological factors that may influence pathway EC , including pathway size ( the number of genes assigned to the pathway ) , subcellular location , pathway gene function , and evidence of co-regulation . We hypothesized that a pathway with a larger number of genes might have relatively more complicated modes of regulation beyond transcription , leading to low pathway ECs . In addition , gene products with similar functions tend to be co-localized and may be coordinately regulated [63] , as is the case for photosynthesis and other chloroplast-related pathways [64] . However , pathway gene number was not significantly correlated with pathway EC ( PCC = -0 . 03 , p = 0 . 67; Fig 3A ) , and pathway gene product subcellular location was not associated with pathway EC ( Fig 3B ) . To assess whether the general biological functions of a pathway contribute to differences in EC between pathways , we compared ECs between five general pathway categories including activation , generation of precursor metabolites and energy , biosynthesis , degradation and detoxification . However , the significance of enrichment of these general categories was only marginal ( Mann-Whitney Test , p = 0 . 05; Fig 3C ) . Interestingly , although the expression of gene pairs in the general category of generation of precursor metabolites and energy was not always significantly coherent , the specific pathways within—photosynthesis light reactions , chlorophyllide a biosynthesis I and aerobic respiration—had significantly higher ECs compared to random pathways ( 99th percentile of pathway EC distribution ) . This finding suggests that EC , and more generally co-expression , is more relevant to more detailed levels of the functional classification hierarchy . Transcriptional regulation is another major factor that could influence pathway EC . Genes that are co-regulated could have similar transcript profiles , and the differences in the degree of co-regulation may explain differences in pathway EC . To determine the extent of co-regulation , we asked how the presence of cis-regulatory elements differs among pathways . It is expected that pathway genes with similar sets of cis-elements in their promoters would have similar expression patterns and thus contribute to high pathway EC . We mapped 349 transcription factor binding motifs [36] to the promoters of all A . thaliana genes , and identified motifs that were over-represented in the promoters of pathway genes taking each pathway separately and comparing to all other genes . A total of 40 over-represented motifs were found for 17 pathways ( S2 Table ) . However , there was no significant difference in EC between pathways with and without over-represented motif sites ( Mann-Whitney Test , p = 0 . 66; S2B Fig ) . This was surprising given that the 349 motif dataset spans essentially all known A . thaliana transcription factor families , and transcription factors from the same family tend to have similar binding motifs [36] . Thus , the reason why high EC pathway genes do not necessarily have more shared motifs ( S2B Fig ) is not simply due to unknown transcription factor binding sites . This finding can also be due to complex interactions between binding sites , nucleosome positioning and other DNA properties [65] . We also evaluated post-transcriptional regulation by miRNA , but did not find a significant difference in EC between pathways with miRNA target genes and those that did not ( Mann-Whitney Test , p = 0 . 31; S2C Fig ) . Given the dearth of genome-wide post-transcriptional and other levels of regulatory data in plants , it remains to be resolved if post-transcriptional regulation contributes to a lower pathway EC . Among the factors studied—the size of the pathway , subcellular location , functions of pathway genes , and evidence of shared transcription factor binding sites—none significantly impact pathway EC . We next asked whether the expression dataset has a major impact on whether the EC for a pathway is high or low . The analyses described so far were performed using an environmental stress dataset consisting of 112 experiments including biotic and abiotic stress treatments in shoot and root [41] . Low pathway EC values could reflect the fact that pathways are only relevant to one type of stress ( biotic or abiotic ) and a large compiled dataset fails to capture the underlying patterns of co-expression . To address this possibility , we first calculated the random gene pair correlations for three subsets of the environmental stress gene expression dataset: shoot abiotic , shoot biotic , and root abiotic . PCC95 values were higher for subsets ( PCC95 = 0 . 51–0 . 60 ) of the stress dataset than for the entire dataset ( PCC95 = 0 . 41; Fig 3D and 3E ) , indicating that the difference in gene expression between experiments within a dataset , i . e . data heterogeneity , was lower when the samples were divided into biologically relevant subsets . Consistent with this , the average sample correlation within each of the shoot biotic , shoot abiotic , and root abiotic subsets is higher ( 0 . 46 , 0 . 15 , and 0 . 19 respectively ) than the entire environmental stress dataset ( 0 . 13 , Mann-Whitney Test using all pairwise sample PCCs , p = 8 . 73e-26 , 2 . 10e-05 , 1 . 93e-144 respectively ) . Due to the impact of data heterogeneity , fewer high EC pathways tend to be recovered from individual stress datasets compared to combined datasets ( Fig 3F ) . To test whether these findings are specific to the environmental stress data , an additional four expression datasets were analyzed ( development , light , hormone , and diurnal; Fig 3D; S1B Table ) . We found that the threshold PCC95 values of these datasets were significantly negatively correlated with the number of high EC pathways ( PCC = 0 . 97 , p = 1 . 10e-08 ) . Thus , because PCC95 is negatively correlated with data heterogeneity ( as discussed in the previous section ) , higher data heterogeneity likely allows more co-expressed pathway genes to be recovered . Data heterogeneity can be influenced by which datasets are combined and how the expression data are processed and transformed . Combining datasets tends to increase data heterogeneity and thus leads to a better recovery of pathway genes based on co-expression ( Fig 3F ) . Dataset processing also has an effect on data heterogeneity . For example , datasets that were processed to obtain fold change values had a substantially lower PCC95 ( median PCC95 of fold change datasets = 0 . 41; Fig 3E ) than that of the absolute intensity dataset ( median PCC95 of intensity datasets = 0 . 76; Fig 3E ) , although this was not true for the hormone dataset ( Fig 3E ) . Taken together , these results reveal that dataset transformation approaches and nature of the expression dataset impact the threshold for defining significant co-expression and thus significantly shapes pathway EC . A wide range ( 5%-53% ) of pathways have significantly high ECs depending on the dataset used ( Fig 3F ) . This pattern led us to question whether some datasets are more informative than others in recovering specific pathways . To assess this , pathway EC percentiles were calculated for each dataset separately ( S3A Fig ) . Note that for each expression dataset analyzed , we picked half a million pairs of randomly chosen genes from a total of ~22 , 000 to establish background correlations and selected the correlation threshold at the 95th percentile of the random correlation distribution . Because dataset heterogeneity influenced the threshold values used to determine gene co-expression ( Fig 3E ) , we first asked whether larger , combined stress datasets were more informative ( i . e . had higher pathway EC percentiles ) compared to smaller , individual datasets ( Fig 4A; S3B and S3C Fig ) . The combined stress dataset had a higher median EC percentile ( 95 . 6 ) compared to the individual datasets ( 89 . 5–89 . 9 ) ( S1C Table ) . For example , the monoterpene biosynthetic pathway had an EC percentile of 99 . 6 based on the combined stress dataset , but the values ranged from 26 . 3 to 89 . 9 for individual datasets ( S1C Table ) . By contrast , in >14% of the pathways , the EC percentiles determined with the individual datasets were higher than those based on the combined dataset ( Fig 4A; S3B and S3C Fig ) . For example , the lipid dependent phytate biosynthesis I pathway had an EC percentile of 99 . 5 when the root abiotic stress dataset was used compared with EC percentiles<27 for all other individual and combined datasets ( S1C Table ) . Another example is the cuticular wax biosynthetic pathway , which had an EC percentile of 99 . 7 calculated from the shoot abiotic stress data , but had EC percentiles of 26 . 4 and 26 . 6 when root abiotic and shoot biotic stress datasets were used , respectively ( S1C Table ) . This is consistent with the role of cuticular wax in protecting the shoot from drought and other stresses [66 , 67] and the co-regulation of its biosynthetic genes [68] . Similarly , indole-3-acetic acid ( IAA ) degradation genes have EC percentiles of 99 . 9 and 26 . 3 using root and shoot abiotic stress datasets , respectively ( S1C Table ) , consistent with the finding that IAA degradation products have been mainly detected in roots [69 , 70] . These findings lead to the conclusion that EC among genes in the same pathway is strongly influenced by whether individual or combined stress datasets are used , particularly if the pathway in question is biologically relevant to the experimental conditions of the dataset . Thus , it is important to test multiple individual and combined datasets for finding the optimal EC for a pathway . It should be noted that , the small numbers of samples in individual datasets mean less power in detecting co-expression; however , we were able to recover high EC pathways with individual datasets . This is because we have included randomized background information for different sized datasets in calculating threshold pairwise similarities for determining EC and in calculating threshold EC values for identifying pathways with significantly high ECs . A smaller dataset where spurious correlations are expected will have a correspondingly higher threshold because the correlations between randomized gene pairs will be higher . To determine whether the conclusion that EC is strongly influenced by stress ( S ) datasets is generalizable to non-stress ones , we further increased the dataset size by including light ( L ) and developmental series ( D ) . We found that when using dataset L , S , D , and combined ( L+S+D ) datasets , 12 , 46 , 81 , and 96 pathways had significantly higher than expected EC , respectively . Although the combined dataset was the best for uncovering more pathways , the EC percentiles were higher for some pathways when individual datasets were used ( Fig 4B; S3D and S3E Fig ) . Two interesting examples are the trans-zeatin biosynthesis and the iron reduction/absorption pathways . These pathways only had significantly high EC when using the light dataset ( S1C Table ) . Fluctuations in light conditions can alter the expression of trans-zeatin biosynthesis genes [71] . In addition , iron is a central component of chlorophyll . One iron reduction gene , FRO6 , contains multiple light-responsive elements , and another , iron reduction gene FRO7 , has an expression pattern similar to FRO6 [72 , 73] . Consistent with our discussion on the impact of individual and combined datasets in the previous section , these findings indicate that dataset choice impacts the optimal recovery of pathway genes . Next , we asked how data transformation impacts pathway EC percentile . The EC percentiles determined from fold change and absolute intensity were significantly positively correlated for the stress ( PCC = 0 . 38 , p = 4 . 90e-9 ) and hormone ( PCC = 0 . 57 , p = 1 . 17e-20; S3F and S3G Fig ) datasets . Despite these significant correlations , data transformation still resulted in a >50 percentile difference in EC for 27% and 12% of pathways using stress and hormone datasets respectively ( S1C Table ) . Based on our results , it is important to test datasets according to the pathway of interest , but do more expression data samples necessarily lead to better pathway recovery ? To answer this question , we compared pathway EC percentiles across 12 individual and combined datasets ( Fig 4C ) . We found that the stress dataset yielded the highest percentage of high EC pathways ( 53% ) among larger , combined datasets analyzed ( Fig 4C ) . To further assess whether using a much more inclusive , more conditionally independent dataset compared to the 12 datasets we used , would increase the recovery rate of high EC pathways , we analyzed NASCArrays dataset with >700 samples [44] . We found that 24% of the pathways had high EC with the NASCArray dataset . This recovery rate was lower compared to a much smaller dataset such as the stress set , where 53% of pathways had high ECs ( Fig 4D ) . Thus more is not necessarily better . This is because the overlap in within and between pathway expression correlations was larger when the NASCArray dataset was used compared to the stress dataset ( S4A and S4B Fig ) , indicating that it was harder to distinguish within and between pathway gene pairs using the NASCArray data . Next we asked if some pathways have significantly high EC regardless of the dataset used ( i . e . are robust ) . Among pathways , 180 had significantly high EC in >1 datasets ( Fig 4E ) , but photosynthesis light reactions was the only pathway that had significantly high EC in all datasets . This is consistent with earlier findings that light reaction genes are tightly co-regulated [74] . In addition to photosynthesis light reactions , jasmonic acid biosynthesis , aliphatic glucosinolate biosynthesis side chain elongation cycle , fatty acid elongation , palmitate biosynthesis II and chlorophyll a degradation II were also among the most robust pathways in terms of EC . On the other end of the spectrum , 15% of the 179 pathways with significant EC had significantly high EC in only one dataset ( e . g . phenylalanine degradation; Fig 4F ) , further indicating the importance of dataset selection for co-expression associations with unknown genes . In addition , 21% of the pathways ( e . g . ammonia assimilation cycle; Fig 4F ) did not have significant EC regardless of the dataset used; indicating that additional datasets may be required and/or these pathways are mainly regulated at levels beyond transcription . Given that many pathways had significant EC when a particular dataset was used , we asked how many individual datasets are required to recover the 180 pathways with significant ECs . Interestingly , when datasets are included one at a time , the number of pathways with significantly high EC initially increased but appeared to be saturated after the addition of 11 datasets ( S3H Fig ) . Taken together , although genes within pathways can have similar expression patterns , this similarity is best recovered after experimenting with a number of different individual and combinations of datasets as well as with data transformations . In addition , although data heterogeneity increases the number of pathway genes that can be recovered , combining datasets is not necessarily the best approach for all pathways . Comparing to 5–53% high EC pathways that can be discovered when datasets are used individually , combining the analysis results of the individual datasets led to the finding that 80% pathways have high ECs . Clustering genes based on similar expression profiles is commonly performed to find genes that are functionally related [75] . In the best-case scenario , most of the genes in a pathway would be in the same cluster , and the remaining genes in the cluster could be tested to see if they have functions similar to the pathway genes . To evaluate the extent clustering would give us this scenario , we first employed one of the most widely used clustering algorithms , k-means , to group ~22 , 000 genes in the stress gene expression dataset . To determine the optimal k , there are multiple proposed statistical methods including Bayesian Information Criterion ( BIC ) [76] , gap statistic based on the elbow plot [77] , and silhouette score [78] . Although these measures have been successfully implemented in simulated datasets where the grouping is apparent [79] , there is no best method in determining the number of natural groups of the high-throughput genomics data and often researchers have to try multiple k values [80 , 81] . In our initial analysis , we used elbow plot to define k . We computed within cluster sum-of squares for a range of k values starting from 5 clusters and going up to 2000 ( S5A Fig ) . Even though there was no clear elbow point , the decrease in the within sum of squares was apparent when k = 100 which was used for k-means clustering . Once the 100 clusters were obtained , over-representation analysis was used to assess how well pathway and cluster membership coincide and an over-representation score was defined ( see Methods ) . Clusters with significant over-representation scores ( q <0 . 05 ) were analyzed further ( Fig 5 ) . Our expectation for an ideal clustering result was a low q-value ( ~0 ) . Only 30% of the pathways were found to be over-represented in >1 cluster , and 38% pathways had an over-representation score < 2 ( 0 . 01 < q < 0 . 05 ) . As significance alone does not tell us to what extent each cluster is informative in finding additional genes associated with the pathway of interest , we evaluated each clustering result as a prediction problem , where a gene’s membership in a cluster is used to predict its membership in a particular pathway . The performance of the clustering results was evaluated using the F measure , which is the harmonic mean of Precision and Recall . Here precision is the proportion of the number of genes that overlap between a cluster and a pathway to the number of genes in the cluster . Recall is the proportion of the number of genes that overlap between a pathway and a cluster to the number of genes in the pathway . F measures can range from 0 and 1 and higher F measures suggest that both Precision and Recall are high . Precision , Recall and F measures were calculated for every pathway-cluster combination when there was a significant enrichment ( q <0 . 05; Fig 5B and 5C ) . We expected high Precision ( ~1 ) for the most informative clusters , but the highest precision among cluster-pathway combinations was 0 . 11 . In one cluster , 11% of the genes belong to the “glucosinolate biosynthesis from the tryptophan pathway” . The same cluster also yielded the highest F measure ( 0 . 18 ) . This result suggests that there is a need to improve this clustering result , potentially by using different clustering algorithms and parameters . This is explored further in the next section . In the analyses described so far , we used only one clustering algorithm ( k-means ) and fixed parameters ( Euclidean distance , k = 100 ) . Next we assessed how additional clustering algorithms and clustering parameters ( number of clusters defined , distance measure , and number of runs ) impact the identification of co-expressed gene clusters and how this in turn impacts the identification of genes with similar functions . Five algorithms were applied to the stress expression dataset using different parameters including number of clusters ( k ) , consistency among runs , and other algorithm-specific parameters , to obtain 366 different clustering results ( S3 Table ) . Although some of the algorithms ( k-means , approximate kernel k-means , c-means ) often yield local optima instead of an overall best result , clustering runs with the same algorithm and parameters gave very similar results ( average PCC among 10 runs = 0 . 8–1 . 0 ) . Therefore , only the maximum over-representation score from 10 runs is shown ( Fig 5D ) . We found that the choice of k is important; regardless of the algorithm , smaller k values resulted in low over-representation scores ( Fig 5D ) and a smaller number of pathways over-represented among clusters ( S5B Fig ) . This is likely due to the fact that smaller k values lead to larger sized clusters that contain genes from multiple pathways . We also found that the number of members in a cluster that overlap with members of a pathway differs depending on the algorithm used; k-means was the best performing algorithm , followed by approximate kernel k-means and hierarchical clustering with the Ward algorithm ( S5B Fig ) . Overall , with all clustering methods combined , we were able to recover 131 pathways out of 225 ( 64 more pathways than when only k-means , k = 100 was used ) . In contrast , 95 out of 225 pathways were not over represented in any of the clusters , and 22 pathways were only over-represented in one algorithm-parameter combination ( S5C Fig ) . Taken together , the clustering approach is not deterministic; the parameters used influence co-expression associations . Therefore , it is important to evaluate multiple algorithms and parameters to recover pathways of interest . Multiple algorithm-parameter combinations were examined ( e . g . an example combination: k-means , k = 100 ) , to quantitatively assess the degree of improvement in performance measures . First , clusters from 69 algorithm-parameter combinations were generated ( Fig 5D ) . For each pathway , we asked what the maximum over-representation score was among the clusters from all combinations . This maximum score was then compared to the over-representation score of clustering results from our standard method discussed above ( k-means , k = 100; Fig 5E ) . We found that the over-representation scores of the best clusters were increased by an average of 1 . 40 ( 25-fold better q-value ) compared to the score when only one algorithm/parameter was used . We also evaluated clustering performance using F measure ( improved by an average of 0 . 15; Fig 5F ) and Precision ( improved by an average of 0 . 20; Fig 5G ) . These results reinforce the importance of considering multiple algorithms and parameters to maximize pathway-cluster overlap . Furthermore , for algorithms requiring a predefined k , the k value may be different depending on the pathway one would like to recover and it is necessary to try out multiple values for the best results . Thus , selecting a presumably optimal k may yield a more natural grouping of the entire dataset but at the expense of uncovering clusters representing individual pathways . We should emphasize that , although considering multiple clustering parameters allow recovery of 93 pathways , there are still 96 pathways that were not recovered by the five algorithms used in this study ( S6 Fig ) . This may be because genes in these pathways do not have highly coordinated expression patterns and have low pathway ECs . Consistent with this interpretation , more high EC pathways tend to be recovered by clustering compared to low EC ones ( Fishers exact test , p = 4 . 56E-12; S6 Fig ) . We should also emphasize that the scores used to assess the clustering performance ignore the possibility that some genes in the clusters will be novel pathway components . The presence of these genes reduces the over-representation score , precision , and F-measure . These novel pathway component genes are prime candidates for further functional characterization using genetic or biochemical analysis . We established that the degree of gene co-expression in some pathways is influenced by dataset and data transformation and that it is important to use multiple algorithms and parameters when identifying clusters based on co-expression . To demonstrate that novel pathway components can in fact be recovered as a result , we used phenomics data to validate novel gene components of the leucine degradation pathway [57 , 58] . We chose to focus on leucine degradation because it is among the most over-represented pathways in co-expression clusters ( Fig 5 ) , and many components of the leucine degradation network remain to be discovered in plants [82 , 83] . Eighteen novel genes that were not annotated to leucine degradation in the AraCyc database are consistently found in clusters ( ≥10 clustering results; S4 Table ) that are over-represented with 12 annotated leucine degradation genes . Among these genes , AT1G55510 , a branched-chain alpha-keto acid decarboxylase E1 beta subunit , was recently shown to be involved in leucine degradation [82] but has not yet been annotated as such . The fact that AT1G55510 is consistently found in the same clusters as leucine pathway genes prompted us to examine the rest of the genes that cluster with leucine degradation genes ( S4 Table ) for involvement in leucine degradation . We hypothesized that previously unknown associations deduced from co-expression clusters could be verified based on their mutant phenotype data . To test this hypothesis , we used a published phenomics dataset that includes free seed leucine levels for mutants in more than 5 , 000 genes ( Fig 6A ) [58] . The free leucine levels ( nmol/g fresh weight ) of leucine degradation gene mutants are expected to be more similar to genes within the same cluster than to wild type plants or randomly chosen mutants . As expected , the leucine degradation enzyme genes had higher leucine levels than mutants in random genes and wild-type plants ( p = 0 . 05 and 0 . 04 respectively; Fig 6B ) . Next we evaluated the clusters that were over-represented with leucine degradation genes by calculating the log ratio between the proportion of leucine degradation genes in a cluster to the proportion of non-leucine degradation genes in the same cluster . Note that , as k increases , the log ratio tends to increase ( Fig 6C ) . This trend is potentially due to increased statistical power to identify over-representation in smaller sized clusters . Among these clusters , hierarchical clustering with the Ward algorithm ( k = 100 and k = 200 ) and approximate kernel k-means ( k = 50 , k = 400 and k = 500 ) yielded clusters that had genes ( Fig 6D ) whose leucine levels were significantly higher than the wild-type measurements ( p = 0 . 01–0 . 05; Fig 6E; S5 Table ) . Thus , some genes in those co-expressed clusters are likely involved in leucine degradation . Nonetheless , the differences in leucine levels between mutants of genes in the cluster and wild-type plants were small ( Fig 6E ) . This may be due to the fact that some co-expressed genes are false positives . However , some known leucine degradation pathway gene mutants also do not have dramatic differences in leucine level compared to wild-type ( Fig 6B ) and this may also explain the small effect size . We next asked whether a gene that consistently clusters with leucine degradation genes—regardless of the algorithm and parameters used—tends to be a better pathway gene candidate than one that does not . Mutants in genes that were retrieved from three separate clustering results had significantly higher leucine levels than mutants in random genes and wild-type plants ( p = 0 . 03 and 2 . 60e-3 respectively; Fig 6F ) , indicating that consistency may serve as a criterion to increase confidence in candidate genes .
A large number of high-throughput omics data are accumulating . Of these , transcriptome data are the most abundant , covering multiple tissues and conditions , and have been widely used to generate hypotheses about gene functions . Since almost the first microarray studies , researchers have used the guilt-by-association approach to make useful predictions about gene functions . This approach is based on the hypothesis that genes encoding proteins of shared function are more likely to have common features such as gene expression patterns . Here we show that even though this approach is useful , there are many limitations to co-expression-based functional inferences and that these limitations can be potentially overcome through methodological considerations that include pathway gene annotation quality , expression dataset used , clustering algorithms , and the use of an independent dataset such as the mutant phenotype data used here to maximize the utility of co-expression relationships in hypothesizing gene functional relations . By evaluating within-pathway gene expression correlation based on the EC measure , we show that genes encoding proteins involved in the same pathway do not necessarily co-express . For example , only 5% of pathways have significantly high EC using a light treatment dataset . For the remaining 95% of pathways , pathway genes may not be coordinately expressed and/or the light dataset is not informative . For some pathways , co-expression will be ill-suited due to gene sharing among pathways ( thus multiple mode of regulation ) , requirement for condition-specific expression data that are not available , and/or that coordinated regulation of the pathway is at a level beyond transcription . In other situations , several approaches could be taken to improve the recovery of pathways with high EC . By filtering genes based on annotation , it might be possible to obtain a core set of genes that are co-expressed . In addition , using expression datasets of different type ( e . g . treatment and/or tissue types ) , complexity ( e . g . individual or combined ) , and transformation method ( e . g . fold change or absolute intensity value ) could be effective . In this study , we have demonstrated that clustering algorithms and parameters impact the ability to find novel pathway genes . Thus , by relying on a single algorithm and a single parameter—as is most commonly done in published studies—co-expression associations with functional implications might be missed . For any pathway being analyzed it is necessary to find the optimal algorithm and parameters to identify clusters that contain the majority of the known pathway genes . We also demonstrated that using one particular clustering algorithm-parameter combination , in most cases , does not lead to clusters that have optimal overlaps in gene memberships with pathways . Instead , for the best result , we need to consider multiple algorithms and parameters . The methodological considerations we had in this study reflect the multi-parameter nature of co-expression based analyses . Studies that include co-expression based approaches should involve rigorous testing of multiple variables ranging from the pathway of interest to expression dataset and clustering algorithm . | There remain genes with no known function even in the most well studied , model species . One common way to hypothesize gene function is based on the assumption that genes with similar expression profiles tend to have similar functions . However , using datasets and biological pathway information from the model plant Arabidopsis thaliana as an example , we discovered that , although genes in the same pathways are functionally related , genes in only a subset of the pathways have highly similar expression patterns . In addition , our ability to hypothesize gene functions based on expression is significantly impacted by how the dataset is processed and combined as well as the methodology used to identify genes with similar expression . Therefore , multiple datasets and methods should be tested to maximize the functional information that we can get based on similarity in gene expression . | [
"Abstract",
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] | 2016 | Utility and Limitations of Using Gene Expression Data to Identify Functional Associations |
The depletion of cholesterol from membranes , mediated by β-cyclodextrin ( β-CD ) is well known and documented , but the molecular details of this process are largely unknown . Using molecular dynamics simulations , we have been able to study the CD mediated extraction of cholesterol from model membranes , in particular from a pure cholesterol monolayer , at atomic resolution . Our results show that efficient cholesterol extraction depends on the structural distribution of the CDs on the surface of the monolayer . With a suitably oriented dimer , cholesterol is extracted spontaneously on a nanosecond time scale . Additional free energy calculations reveal that the CDs have a strong affinity to bind to the membrane surface , and , by doing so , destabilize the local packing of cholesterol molecules making their extraction favorable . Our results have implications for the interpretation of experimental measurements , and may help in the rational design of efficient CD based nano-carriers .
Among all cyclodextrins ( CDs ) , the most abundant are - , - , and -CDs with six , seven and eight glucopyranose monomers , respectively . They have a rigid conical molecular structure with a hydrophobic interior and a hydrophilic exterior . The internal cavity of these molecules is able to include a wide range of guest molecules , ranging from polar compounds such as alcohols , acids , amines , and small inorganic anions , to non-polar compounds such as aliphatic and aromatic hydrocarbons , while the hydrophilic exterior helps CDs to interact favourably with water . Due to the structural simplicity and small size of CDs , combined with negligible cytotoxicity , they are considered as very suitable nano-delivery vehicles [1]–[4] . They may be combined into larger assemblies such as polymeric networks or nanoparticles , and used for controlled drug-delivery , chemical sensing , or as excipients for a large diversity of compounds [5]–[10] with applications in fields ranging from food technology , pharmacology , and cosmetics to environmental chemistry . Another important application is the use of cyclodextrins to manipulate lipid composition in different cells . Numerous studies have shown that exposing cells or model membranes to CDs results in removal of cellular cholesterol [11]–[17] . The degree of cholesterol depletion is a function of the CD derivative used , its concentration , incubation time , temperature and cell type . In particular -cyclodextrin ( -CD ) has been shown to be the most efficient sterol-acceptor molecule , apparently due to the diameter of its internal cavity that matches the size of these molecules [18] , [19] . The question how CDs are able to remove cholesterol is open to discussion . Originally the idea was proposed that CD remains in the aqueous phase , stabilising the monomer population during the naturally occurring exchange of lipids from the membrane to the aqueous phase [20] . More recently , several authors support the desorption model [2] , [11] , [16] , [21]–[23] , in which cyclodextrins interact directly with membrane embedded-cholesterol . Yancey et al . [16] proposed that cyclodextrin molecules are able to diffuse into the proximity of the plasma membrane , so cholesterol molecules could enter directly into the hydrophobic pocket of the cyclodextrin , without the necessity of completely desorbing through the aqueous phase . Mascetti et al . [14] proposed a model based on polarization modulation infrared absorption spectroscopy ( PMIRRAS ) and Brewster angle microscopy ( BAM ) , supported by ab-initio calculations , in which -CD molecules stack parallel to the plane of the membrane and form perfect channel structures in direct contact with cholesterol monolayers . Despite the substantial amount of experimental effort , the molecular mechanism by which cholesterol is removed remains unclear . The Molecular Dynamics ( MD ) technique provides a suitable tool to investigate this process at atomistic resolution . Previously , CDs have been simulated in aqueous environment , providing structural , dynamic and energetic information of CD aggregates and various inclusion complexes [23]–[27] . The interaction of CDs with membranes has not been addressed thus far in computational studies . Here , we use MD simulations to study the -CD mediated extraction of cholesterol from model membranes , in particular from cholesterol monolayers . Experiments with monolayers have shown that cholesterol can be efficiently removed [16] . Our results show that efficient cholesterol removal requires the presence of -CD dimers , which need to be oriented perpendicular to the membrane surface . Both requirements are favoured at high CD concentration . Based on our results we propose a molecular model for the extraction of cholesterol from membranes , with detailed free energy estimates of the key intermediate steps .
A series of snapshots from a typical cholesterol extraction simulation is depicted in Figure 1 . Our set-up consists of a pure cholesterol monolayer at temperature and pressure conditions which proved to be optimal for cholesterol extraction in-vitro [15] ( see Methods ) . Based on experimental evidence , we assumed that the inclusion complex would consist of a 2∶1 CD∶cholesterol stoichiometry [18] , [28] , [29] . Initially , four -CD dimers were placed close to the surface of the cholesterol monolayer ( Figure 1A–B ) . In the simulation , the -CDs rapidly bind to the cholesterol monolayer interface . During the 200 ns simulation , each of the four dimers extracts a cholesterol molecule from the membrane . The extraction process is illustrated in Figure 1C–F for one of these dimers . Soon after the binding of the -CD dimer to the interface ( Figure 1C ) , in about 10 ns , there is the imminent immersion of cholesterol into the hydrophobic channel ( Figure 1D ) . Within 25 ns , the cholesterol is sucked in further to the point that its hydroxyl head group sticks out at the other side of the nano-channel , into the aqueous phase ( Figure 1E ) . Although the cholesterol is embedded quite deeply within the channel , the dimethyl end of its hydrophobic tail is still in contact with the surface of the monolayer . It is not until the CD/cholesterol complex tilts by that the cholesterol becomes completely desorbed from the monolayer ( Figure 1F ) . The tilting takes place after 100 ns , exposing the less polar part of the -CD ( i . e . the ring of every glucose monomer ) directly to the surface of the monolayer and allowing hydroxyl groups of -CD to hydrogen-bond with the hydroxyl groups of adjacent cholesterol molecules . This conformation remains stable until the end of the simulation time , at 200 ns . The time required to completely extract the cholesterol from the monolayer varied between the individual dimers from 20 to more than 100 ns , with the rate limiting step the tilting of the complex . We set up five independent simulations under the same conditions , obtaining similar behavior for every case . From our simulations we can make a crude estimate of the cholesterol extraction rate . With four cholesterols extracted on a 100 ns time scale from a patch of , we obtain a rate of molecules . This is much faster than the reported desorption rate of pmol [15] , corresponding to molecules . Although part of the discrepancy may be attributed to the difference in concentration ( our simulations were performed at an overall concentration of 0 . 1 M , but the equilibrium concentration in the aqueous sub-phase is unknown ) , we conclude that the experiments probe the actual desorption of the CD/cholesterol complex from the monolayer surface . This requires a much longer time scale , involving a large free energy barrier as will be discussed later . Concentrating for now on the actual formation of the complex , our simulations suggest that cholesterol extraction is favoured by two conditions: ( i ) the stability of the dimer on the monolayer , and ( ii ) the orientation of this dimer with respect to the cholesterol molecules . These conditions are discussed in more detail next . We observed that the successful extraction of cholesterol was linked directly to the stability of the -CD dimer . Simulations in which the dimer stability was decreased , using dimers in a head-tail or tail-tail orientation , resulted in formation of monomers; in their monomeric form cholesterol extraction is not observed on the time scale of our simulations ( see Text S1 for details , Figure S1 and S2 ) . Although the -CD monomers keep interacting with the cholesterol monolayer ( Figure S2 ) , the interaction strength between a single ring and cholesterol is apparently not enough to extract it . The stoichiometry for the -CD-cholesterol complex is still under debate , since it is highly dependent on the conditions used in the experiment ( e . g . dielectric constant of the solvent , salting conditions , concentration of the molecules ) . Ravichandran et al . [18] , based on NMR and UV data , concluded a preferential 2∶1 stoichiometry for -CD and cholesterol . Williams et al . [29] found stoichiometry ratios for the hydroxypropyl -CD ( HP--CD ) /cholesterol complex changing from a 1∶1 to 2∶1 ratio on increasing concentration of the carbohydrate . By a different approach , Tsamaloukas et . al . [28] concluded the preference for a 2∶1 stoichiometry for randomly methylated -CD with cholesterol in the presence of lipid vesicles . Our results are consistent with this preferred 2∶1 stoichiometry , as the 1∶1 complex is not formed spontaneously at least in the presence of a cholesterol monolayer . Further below we will show results from free energy calculations that support the 2∶1 stoichiometry , also under pure aqueous conditions . The ability to uptake cholesterol from the monolayer also depends on the orientation of the dimer with respect to the surface . To explore this more systematically , we performed additional simulations in which single dimers or pairs of dimers were placed on the cholesterol monolayer . When a single dimer is placed in direct contact with the surface of the monolayer , it is unable to keep a straight conformation , tilting immediately by and remaining in this position for the rest of the simulation time ( Figure S2 ) . On the contrary , a pair of dimers is able to keep a straight conformation long enough to allow the cholesterol to enter the hydrophobic cavity of either one or both of the dimers , ending with an effective extraction of cholesterol . With four dimers present , the process is even more efficient as we showed in Figure 1 . Thus , the efficient desorption of cholesterol appears to be a cooperative process between several -CD dimers . To verify the importance of orientation and cooperative effects , we increased the system size to a monolayer of 252 cholesterols interacting with 16 -CD dimers ( Figure 2A ) . To remove the initial bias of having the dimers already close to the interface , here we started with dimers placed further away in solution ( Figure 2B ) . We observe that nearly all of dimers end up binding to the monolayer surface . The time scale of binding varies between 50 and 150 ns , governed by the random diffusion of the monomers . When they approach the surface to within nm , they bind irreversibly . We also note that the dimers are stable and do not dissociate into monomers either in solution or when adsorbed on the monolayer . We further observe that the -CDs aggregate on the monolayer forming stacked barrels ( Figure 2 C , D ) , mostly tilted by with respect to the monolayer surface normal . Some , however , are attached in the correct conformation for the extraction process to occur ( Figure 2 C , E indicated by arrows ) , stabilised in this position by adjacent CDs . The same qualitative behaviour was observed in three independent simulations . The observation of multiple layers of CDs stacked on top of each other seems to be in good agreement with BAM experiments showing -CDs interacting with monolayers and forming bodies of different heights [14] . However , based on ab-initio calculations to interpret their spectroscopic measurements , Mascetti et al . concluded a perpendicular stacking of the CDs . We clearly observe tilted layers ( Video S1 ) , which corroborates with experimental data for the adsorption of CD at the water-air interface with highly concentrated CD solutions [25]–[30] . Our results show that cholesterol extraction is energetically favorable , especially by a CD dimer , but also that tilting and clustering of CDs plays an important role . To understand in more detail the energetics of the whole process , we have considered several sub-processes and calculated the associated free energy changes through calculations of potential of mean force ( PMFs ) along reaction paths ( see the Methods section ) . An overview of the sub-processes and calculated free energies is given in Figure 3 . First , we calculated the dissociation free energy for a CD dimer in water ( Figure 3A ) . The resulting free energy is 2 kJ , implying a clear stabilization of the dimeric conformation , in line with proposed aggregation models based on experimental evidence [31] . Note that the two monomers can bind in three different relative orientations , namely head-head , head-tail and tail-tail . The head-tail as well as the tail-tail orientations were found to be significantly less stable , as these dimers spontaneously dissociate in water ( see Text S1 ) . Next , we looked at the binding free energy of cholesterol inside either a CD monomer or dimer with respect to the aqueous phase ( Figure 3B , C ) . The driving force for the formation of these inclusion complexes is believed to arise from a combination of non-covalent interactions such as van der Waals forces ( hydrophobic interior ) , electronic effects ( probably due to the presence of hydroxyl groups in the glucose rings ) , and steric factors ( the volume size of the hydrophobic cyclodextrin cavity ) [32] . In the case of the monomer , we find that the binding free energy equals 3 kJ , while for the dimer this energy is 5 kJ . The binding of cholesterol to CD is thus favourable in each case , but significantly more so with respect to the dimer . Experimentally it is difficult to distinguish between the 2∶1 and 1∶1 complexes , and different methods predict binding constants varying over orders of magnitude . Keeping these limitations in mind , a comparison of the binding constants calculated from our PMFs predict an order of magnitude comparable to the experimental estimate [33] , [34] for -CD and HP--CD , assuming the experiments probe the 2∶1 stoichiometry . In experiments on DM--CD [35] , the binding affinity could be differentiated between the 2∶1 and 1∶1 stoichiometries; the 2∶1 case showed a much higher affinity in line with our results for -CD ( See Text S1 for details ) The binding of cholesterol to the cholesterol monolayer was also considered ( Figure 3D ) . The energy needed to extract a single cholesterol molecule completely from the monolayer to the water bulk is found to be 2 kJ . This value is similar to the values reported for the binding free energy of cholesterol in membranes of different lipid mixtures [36] , ranging from kJ depending on lipid composition . However , it is more than twice the energy required to extract it from a -CD dimer . This leads to the apparent conclusion that the energy penalty to extract cholesterol from the monolayer ( kJ cost ) cannot be provided by embedding it inside cyclodextrin ( kJ gain ) . Yet we showed that cholesterol is spontaneously extracted from the monolayer when CDs adsorb on the surface ( cf . Figure 1 and 2 ) . To shed further light on this , we also computed the free energy profile for the extraction of cholesterol into a monolayer-adsorbed -CD dimer . The result is shown in Figure 4 . The process is clearly downhill in free energy , with a stabilization of 3 kJ for the formation of the CD/cholesterol complex . During the free energy calculation , we observed spontaneous tilting of the complex as soon as the rigid body of cholesterol was extracted , in line with our previous results . Restraining the complex to an upright orientation , the free energy is increased by 2 kJ , but the overall free energy for extraction is still favorable . These results suggest that it is not only the interaction between the cholesterol and the inner core of -CDs but also the disrupting effect of the carbohydrate on the water-monolayer interface which drives the complexation between cholesterol and cyclodextrin . Put differently , the binding of CD to the monolayer locally disrupts the packing of cholesterol , favouring the uptake of cholesterol . To complete our free energy analysis , we therefore computed the binding free energy of the empty CD dimer to the interface , starting from a tilted conformation ( Figure 3F ) . As expected from our simulations ( cf . Figure 1 and 2 ) binding of cyclodextrin to the monolayer is favorable , with the tilted configuration more favorable than the straight one ( 4 kJ versus 2 kJ ) ( See also Figure S3 ) . The high affinity of sugars for membranes is also exemplified by cryo- and anhydro-protective properties of many sugars , stabilizing membranes in low temperature or dehydrated states [37] . Based on the results of our simulations , combined with the current body of experimental results , we propose a molecular model for the process of cholesterol extraction by -CDs . The model is shown in Figure 5 . When -CDs are in solution ( Figure 5A ) , they have a strong tendency to aggregate; depending on overall concentration , the equilibrium will shift away from monomers to dimers ( Figure 5B ) or higher order aggregates . In the presence of a membrane surface , the cyclodextrins will bind , adopting a tilted conformation ( Figure 5C ) . Tilted structures are not able to extract cholesterol easily , but due to their surface activity the amount of cyclodextrins bound to the membrane accumulates . A high density of CDs increases the probability of having straight conformers in addition to barrel like structures . The straight orientation is optimal for the cholesterol extraction , which is a downhill process at this point ( Figure 5D ) due to the destabilization of cholesterol packing underneath the cyclodextrins . Desorption of the complex from the membrane surface , however , is associated with a large free energy barrier . Occasionally it will occur , leading to the formation of complexes in solution ( Figure 5E ) . Since there is a small energy difference between the 2∶1 and 1∶1 complexes , the relative population will depend on the concentration of -CD . Finally , once desorbed , cholesterol molecules could be transferred to e . g . lipid vesicles or lipoprotein particles ( Figure 5F ) by a simple diffusion mechanism . In Figure 6 we further compare the two possible mechanisms by which the CD/cholesterol complex can be formed: either via desolvation of cholesterol into the aqueous phase ( ‘solvent mediated’ ) or via the desorption from the monolayer directly into cyclodextrin ( ‘surface mediated’ ) . Direct desorption of cholesterol costs 80 kJ ( Figure 5G ) , thus making CD mediated extraction much more efficient ( requiring only 35 kJ to dissociate the dimer from the surface , assuming a tilted configuration ) . The -CD-monolayer interaction decreases the cholesterol-monolayer stability , lowering the energetic barrier for cholesterol desorption ( Figure 6 , blue lines ) . Our energetic analysis is consistent with estimates from experiments on different cell types and model membranes [16] , reported as kJ for CD mediated transfer ( depending on cell type ) and 84 kJ for direct transfer of cholesterol . At this point we discuss the relevance of our model to the interpretation of experiments performed under physiological conditions . Our simulations concern pure cholesterol monolayers only , and a CD concentration around 0 . 1 M . These conditions were chosen because they are optimal for rapid cholesterol extraction in our simulations , allowing the process to be studied on the nanosecond time scale that is accessible to atomistically detailed simulations . Compared to the experimentally measured desorption rate of molecules , the rate of desorption in our simulations is about 9 orders of magnitude faster ( see ‘Cyclodextrins in action’ section ) . We attribute this difference to the different measures of the desorption rate in experiment versus our simulations . Experiments calculate desorption rates by means of the area per lipid change on monolayers ( see Ohvo et al . [15] ) , and are therefore sensitive to the desorption of the complex from the monolayer . In our simulations , however , we measure only the rate of extraction of cholesterol into the CD complex . According to our results , the energy for the complex desorption is about 14 kT; assuming the kinetics of the process scales with , we can already account for 6 orders of magnitude in the difference in the desorption rate . Other energy barriers that might affect the experimental rate of desorption are the tilting/untilting of the complex , or the formation of dimers at the interface . Dissociation of cholesterol from the complex in water could , in principle , also pose another barrier . However , in Ohvo et al . [15] , cholesterol desorption rates were also measured by taking aliquots of the sub phase and measuring the amount of radio-active labelled cholesterol . The same desorption rate was found as with the other approach based on changes in monolayer area , indicating that it is likely that the measured cholesterol in the aqueous sub phase is still complexed to CD . In addition , CD concentration may play an important role in the details and rate of the extraction process . Experimentally , typical concentrations are in the 1–10 mM range . The effective concentration of CDs at the membrane surface is predicted to be orders of magnitude higher , based on the 35 kJ adsorption energy we obtained from our simulations . As we do not observe spontaneous exchange of CDs between the adsorbed and dissolved states in our simulations , we cannot assess the equilibrium concentration of CDs in the aqueous phase , making a direct comparison toward experiment impossible in this respect . A high surface concentration of CDs is likely to facilitate the cholesterol uptake in two manners . First , the propensity to form dimers , which are more efficient in binding cholesterol compared to the monomers increases . Second , uptake of cholesterol from the membrane requires an upright position of CD , which is stabilized at high CD concentration . Based on our results one predicts a cross-over from monomer mediated cholesterol extraction at low CD concentration toward dimer mediated extraction at higher concentrations , and possibly even higher order aggregates at further increase of the CD concentration . The point at which the cross-over takes place will be highly dependent on the composition of the membrane , which may act in multiple ways in affecting the cholesterol extraction process . The most direct way is by stabilizing cholesterol , e . g . inside raft-domains , or destabilizing it in membranes composed of poly-unsaturated lipids . Indirectly , the membrane composition will play a role in the efficiency of the carbohydrates to bind to the membrane surface . Probing the interplay between lipids , cholesterol and cyclodextrins is currently being investigated . Some of the conflicting experimental data indicating either 1∶1 or 2∶1 complexes might result from this non-trivial concentration dependency . In summary , using atomistically detailed simulations , we were able to reveal the molecular mechanism of how cholesterol is extracted by -CDs from a cholesterol monolayer . From our results we conclude that the desorption involves a number of sub-steps: i ) formation of CD dimers , ii ) binding of CDs at the interface , iii ) adsorption of cholesterol into CD , iv ) tilting of CD , v ) desorption of CD/cholesterol complex from the interface . Only the last step involves a substantial energy barrier , the other processes are essentially downhill . However , depending on the overall concentration of CDs the tilting of CD might take place before the cholesterol uptake , leading to a potential second kinetic barrier . With a detailed understanding of the basic molecular mechanism of this process we can begin to rationalize the design of more efficient CDs in numerous applications .
We simulated systems of different size consisting of a pre-equilibrated cholesterol monolayer , -CD dimers and water molecules . The initial coordinates of -CD were taken from the crystal structure [19] . For the small system , the cholesterol monolayer consisted of 52 molecules , with a lateral area of 24 . 7 . Four -CD dimers in head-head , head-tail or tail-tail conformation were placed with the hydroxyl groups in direct contact with the monolayer and solvated by 1 , 800 water molecules ( 2 . 5 nm water layer ) . Periodic boundary conditions were applied in all directions . In the direction perpendicular to the monolayer , a vacuum layer of 3 . 0 nm was added in order to avoid direct interaction between mirror water molecules and the tails of cholesterol in the monolayer . A snapshot of the initial conditions is depicted in Figure 1A . The big system was prepared similarly , and consisted of 252 cholesterol molecules , 16 -CD dimers , and 13 , 400 water molecules . Here , the -CD dimers , were initially placed at a distance of 1 . 0 nm away from the monolayer surface ( cf . Figure 2A , B ) . To avoid the interaction between cholesterol tails and mirror waters , a vacuum slab of 4 . 0 nm was added . The cyclodextrin concentration was 0 . 2 M for the small and 0 . 1 M for the big system . For the free energy calculations , additional systems were set up with only a single cyclodextrin/cholesterol complex in excess water , or a cholesterol monolayer with one cyclodextrin monomer or dimer adsorbed . Simulations were performed using the GROMACS molecular dynamics package [38] . The parameter set for the simulation of -CD was taken from the latest Gromos force field for carbohydrates [39] . The parameters for cholesterol were taken from previous work done by Höltje et al . [40] . The SPC water model [41] was used to model the solvent . A 2 fs time step was used to integrate Newton's equations of motion . The LINCS algorithm [42] was applied to constrain all bond lengths . Non-bonded interactions were handled using a twin-range cut-off scheme [43] . Within a short-range cut-off of 0 . 9 nm , the interactions were evaluated every time step based on a pair list recalculated every 5 time steps . The intermediate range interactions up to a long-range cut-off radius of 1 . 4 nm were evaluated simultaneously with each pair list update , and assumed constant in between . To account for electrostatic interactions beyond the long-range cut-off radius , a reaction field approach [44] was used with a relative dielectric permittivity of 68 . The temperature was maintained at 288 K by weak coupling of the solvent and solute separately to a Berendsen heat bath [45] with relaxation time of 0 . 1 ps . The pressure of the systems was controlled also by weak coupling , with a relaxation time of 1 ps . An anisotropic coupling scheme was used to maintain a constant surface pressure of 33 mN ( See Text S1 for details about the computation of the surface pressure ) . At this pressure the area per lipid equals 48 Å2 in our simulations , in very good agreement with experiments [14] . Notice that the conditions ( temperature as well as surface pressure ) used here have been previously reported to be optimal for cyclodextrin mediated cholesterol desorption from cholesterol monolayers [15] . Before production time , the systems were pre-equilibrated by slow heating up to 288 K . Multiple simulations were performed starting from randomized initial velocities . In total five independent simulations of 200 ns for the small system , and 3 simulations of 200 ns for the big system were performed . An umbrella sampling approach was used to calculate the potential of mean force ( PMF ) for a number of important sub-steps related to the total desorption process , namely i ) the extraction of one single cholesterol molecule from the monolayer , ii ) extraction of cholesterol from a single -CD ring or from a -CD dimer , iii ) dissociation of the -CD dimer , and iv ) desorption of the -CD monomer and dimer from the cholesterol monolayer . For the PMF calculations , we used the umbrella sampling method [46] with 18 window points , spaced by 1 Å , restraining the center of mass of one cholesterol with respect to the center of mass of the monolayer ( i ) , a -CD monomer or dimer ( ii ) , or between the two monomers ( iii ) , or between the cyclodextrin and the monolayer ( iv ) . The restraining potential was harmonic with a force constant of 1 , 000 kJ . 50 ns of simulation was performed for each window , covering a total of 0 . 9 s per system . The PMFs were reconstructed using the weighted histogram analysis method [47] , with 200 bins for each profile . To estimate the convergence in the PMF , each window trajectory was divided in blocks . The statistical error was calculated from the variance between averages over individual blocks , using a block averaging procedure . Blocks were found to be statistically independent over 1–5 ns time intervals . In the case of the complex formation between cholesterol and cyclodextrin , the equilibrium binding constant ( Table S1 ) can be calculated within the framework of classical statistical mechanics using the following expression [24] , [48] , at 1 M standard state: ( 1 ) where is Avogadro's number and is the calculated PMF as a function of the distance between the centers of mass of cholesterol with respect to CD . is the average radius of the cross section of CD to which the cholesterol is confined , and depends on the reaction coordinate . From the expression of , the association or binding free energy may be obtained: ( 2 ) | The ability of certain molecules to capture other molecules forming so-called inclusion complexes has a range of potential important applications in e . g . drug delivery and chemical sensing . Here we study the complexation of cholesterol by small oligosaccharide rings named cyclodextrins ( CDs ) . Cholesterol is an essential lipid in the plasma cell membrane , and the ability of CDs to extract cholesterol is widely used in the biomedical field to control the level of cholesterol in the membrane . The molecular mechanism of this process , however , is still not resolved . Using a detailed computational model of cholesterol and CD , we have succeeded to simulate this extraction process . We observe that the CDs are rapidly binding to the membrane surface in a dimeric form , and , provided that the CD dimers are in a suitable orientation , cholesterol molecules are being extracted spontaneously . The cholesterol/CD inclusion complex remains adsorbed on the surface; our simulations predict that the rate limiting step for the actual transport of cholesterol is the desorption of the complex from the membrane . With a clearer understanding of the basic molecular mechanism of the CD mediated process of cholesterol extraction , we can begin to rationalize the design of more efficient CDs in numerous applications . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] | [
"physics",
"computer",
"science",
"chemistry",
"biology"
] | 2011 | Molecular Mechanism of Cyclodextrin Mediated Cholesterol Extraction |
Legionella pneumophila is a Gram-negative , flagellated bacterium that survives in phagocytes and causes Legionnaires’ disease . Upon infection of mammalian macrophages , cytosolic flagellin triggers the activation of Naip/NLRC4 inflammasome , which culminates in pyroptosis and restriction of bacterial replication . Although NLRC4 and caspase-1 participate in the same inflammasome , Nlrc4-/- mice and their macrophages are more permissive to L . pneumophila replication compared with Casp1/11-/- . This feature supports the existence of a pathway that is NLRC4-dependent and caspase-1/11-independent . Here , we demonstrate that caspase-8 is recruited to the Naip5/NLRC4/ASC inflammasome in response to flagellin-positive bacteria . Accordingly , caspase-8 is activated in Casp1/11-/- macrophages in a process dependent on flagellin , Naip5 , NLRC4 and ASC . Silencing caspase-8 in Casp1/11-/- cells culminated in macrophages that were as susceptible as Nlrc4-/- for the restriction of L . pneumophila replication . Accordingly , macrophages and mice deficient in Asc/Casp1/11-/- were more susceptible than Casp1/11-/- and as susceptible as Nlrc4-/- for the restriction of infection . Mechanistically , we found that caspase-8 activation triggers gasdermin-D-independent pore formation and cell death . Interestingly , caspase-8 is recruited to the Naip5/NLRC4/ASC inflammasome in wild-type macrophages , but it is only activated when caspase-1 or gasdermin-D is inhibited . Our data suggest that caspase-8 activation in the Naip5/NLRC4/ASC inflammasome enable induction of cell death when caspase-1 or gasdermin-D is suppressed .
Legionella pneumophila is the causative agent of Legionnaires’ disease . It was identified for the first time in 1976 , after an atypical pneumonia affected the participants of the American Legion Convention in Philadelphia , United States [1] . After isolation , L . pneumophila were characterized as Gram-negative , flagellated , intracellular facultative bacteria [2 , 3] . The species of Legionella were found mainly in freshwater and soil environments , including lakes and irrigation systems [4] . Infection of humans occurs upon inhalation of water droplets derived from these environments containing Legionella [5] . After inhalation , L . pneumophila can subvert the normal vesicle traffic within alveolar macrophages and form LCV ( Legionella-containing vacuoles ) , a process that is mediated by the injection of hundreds of bacterial effectors through a type IV secretion system called Dot/Icm [6–9] . During its evolution , L . pneumophila were selected based on their replication in protozoa but not in humans , which are accidental hosts [10] . Consequently , L . pneumophila can be recognized by many innate immune receptors in mammalian cells , including proteins from the family of the nucleotide-binding domain and leucine-rich repeat-containing proteins ( NLRs ) . These characteristics make L . pneumophila an excellent model for the study of innate immunity , including intracellular signaling pathways and inflammasomes . The major inflammasome that leads to the restriction of Legionella replication in macrophages is Naip5/NLRC4 . This pathway was discovered in mouse cells upon observations that macrophages from the A/J mouse strain , but not cells from other mice strains , are susceptible to L . pneumophila replication [11] . The resistance was mapped to the Lgn1 locus , which encodes several copies of Naip genes , including Naip5 ( Birc1e ) , which is the gene responsible for resistance [12–16] . Successful lines of investigation culminated in the demonstration that Naip5 recognizes bacterial flagellin and interacts with NLRC4 for caspase-1 activation and the restriction of bacterial replication [17–20] . This platform was named the Naip5/NLRC4 inflammasome and triggers pore formation and pyroptosis , which has been considered one of the most important mechanisms for the restriction of intracellular pathogen replication via inflammasomes [21–24] . Host cell death via pyroptosis eliminates intracellular parasite replication and traps intracellular microbes in pyroptotic cells , facilitating microbial destruction by additional phagocytes [23 , 25–29] . Pyroptosis occurs concomitantly with the secretion of inflammatory cytokines such as IL-1β and IL-18 , a process that requires the adaptor molecule ASC and the formation of NLRC4/ASC puncta [20 , 30] . ASC also functions as an adaptor protein for other inflammasomes , including AIM2 and NLRP3 , which triggers the processing of caspase-1 and caspase-8 [21 , 31–34] . Of note , Naip5/NLRC4 appears to be the only inflammasome required for the restriction of L . pneumophila replication . Macrophages that are deficient in NLRP3 or AIM2 can efficiently restrict L . pneumophila replication [20 , 21 , 23 , 35] . However , the participation of ASC in the resistance of L . pneumophila infection is controversial . In murine macrophages , ASC is dispensable for the induction of pyroptosis and the restriction of bacterial replication [20 , 21] . By contrast , experiments performed with human monocytes indicate that ASC silencing leads to an increase in bacterial replication [36 , 37] . Thus , the role of ASC in the restriction of L . pneumophila replication is still unclear . We have previously demonstrated the existence of a pathway that is dependent on flagellin and NLRC4 but independent of caspase-1 [38] . Here , we used macrophages and Casp1/11-/- mice to systematically assess this pathway . By searching for additional components that operate in the NLRC4 inflammasome independently of caspase-1/11 , we found that caspase-8 interacts with NLRC4 in a process that is dependent on ASC . This pathway effectively accounts for resistance to infection in macrophages and in vivo when caspase-1 is absent . In wild-type cells , caspase-8 is recruited to the Naip5/NLRC4/ASC/caspase-1 inflammasome , but is not activated . Caspase-8 activation in this platform only occurs when caspase-1 or gasdermin-D is inhibited , suggesting that this pathway may be important when pyroptosis is inhibited .
We have previously demonstrated that activation of the flagellin/NLRC4 inflammasome triggers caspase-1-dependent and independent responses to restrict Legionella replication in macrophages and in mouse lungs [38] . However , the caspase-1-independent mechanisms underlying this pathway are unknown . To further characterize this pathway , we performed growth curves using high and very low multiplicity of infections ( MOIs ) in bone marrow-derived macrophages ( BMDMs ) . Macrophages were infected with wild-type L . pneumophila in the JR32 background ( WT Lp ) and the isogenic mutants flaA- and fliI- . FliI is an ATPase that is required for the secretion of flagellin through the flagellar apparatus [39] . Consequently , fliI- mutants express flagellin but are non-motile and non-flagellated , making them an appropriate control for flaA- mutants for investigations related to the role of flagellin . We found that BMDMs from C57BL/6 and Asc-/- mice fully restrict the replication of WT Lp and fliI- bacteria at low and high MOIs . In contrast , Nlrc4-/- cells are permissive and Casp1/11-/- cells are partially restrictive ( S1 Fig ) . Bacterial mutants for flagellin bypass NLRC4-mediated growth restriction and replicate in all macrophages as previously described [17–19 , 40] . These data support previous reports showing that ASC is not required for the restriction of L . pneumophila replication in the presence of caspase-1/11 [20 , 21] . In addition , these data further support our previous assertion that flagellin triggers an uncharacterized pathway that is dependent on NLRC4 and independent of caspase-1 and caspase-11 [38] . We decided to use BMDMs from Casp1/11-/- mice to further investigate this NLRC4-dependent and caspase-1/11-independent pathway . The transduction of BMDMs with a retrovirus encoding NLRC4 fused to GFP allows the visualization of NLRC4 puncta in the cytoplasm of macrophages infected with flagellated bacteria [30] . Here , we used this retroviral system to investigate the formation of the NLRC4 inflammasome in the absence of caspase-1/11 . BMDMs from Casp1/11-/- mice were transduced with NLRC4-GFP and infected with WT Lp , fliI- and flaA- at different MOIs and time points . We found that WT Lp and fliI- triggered the formation of NLRC4 puncta in the absence of caspase-1/11 ( Fig 1A ) . The formation of NLRC4 puncta was influenced by the MOI and significantly diminished in response to flaA- bacteria ( Fig 1A ) . Next , we evaluated the requirement of ASC for the formation of NLRC4 puncta in the absence of caspase-1/11 . We constructed a mouse that was deficient in ASC and caspase-1/11 and found that whereas the formation of NLRC4 puncta occurred in the absence of caspase-1/11 , ASC was essential for formation of the NLRC4 puncta ( Fig 1B ) . These data are in agreement with previous findings indicating that ASC is critical for the nucleation of several inflammasomes , including AIM2 , NLRP3 and NLRC4 [30 , 32 , 33 , 41–46] . Our results confirm that ASC is essential to NLRC4 puncta formation formed in the absence of caspase-1/11 . Next , we used this NLRC4-GFP system to identify additional components of the NLRC4 inflammasome that operates in the absence of caspase-1/11 . Non-inflammatory caspases have been previously shown to participate in the assembly of inflammasomes and to interact with ASC , including caspase-3 , caspase-7 and caspase-8 [32–34 , 37 , 44 , 46–51] . Thus , we transduced Casp1/11-/- macrophages with a retrovirus encoding NLRC4-GFP and evaluated the colocalization of NLRC4 with these caspases . In this experiment , we used the pan-caspase inhibitor Z-VAD to block caspase activation and to visualize puncta formation . We did not detect significant numbers of NLRC4 or ASC puncta containing caspase-3 and caspase-7 ( S2 Fig ) . In contrast , caspase-8 and ASC was present in more than 90% of the NLRC4 puncta ( Fig 1C and S2 Fig ) . These data are in agreement with our findings indicating that ASC is required for NLRC4 puncta formation , accordingly , endogenous ASC colocalizes with NLRC4 and caspase-8 in the same puncta ( Fig 1D ) . To evaluate the participation of caspase-8 in the NLRC4 inflammasome , we transduced BMDMs from Casp1/11-/- mice with a retrovirus encoding ASC fused to GFP ( ASC-GFP ) and analyzed ASC puncta colocalization with caspase-8 . We found that ASC puncta formed readily after the infection and that this process occurred in response to WT Lp and fliI- but not flaA- ( Fig 2A ) . After 8 hours of infection , the formation of ASC puncta was partially dependent on flagellin ( Fig 2A ) . We stained caspase-8 in macrophages transduced with retrovirus encoding ASC-GFP and found that caspase-8 colocalized with ASC puncta in response to infection with flagellated bacteria ( Fig 2B ) . Collectively , these results indicate that flagellin triggers the assembly of an inflammasome composed of NLRC4 and ASC , which colocalizes with caspase-8 . The double-stranded DNA sensor AIM2 is known to recruit ASC to trigger puncta formation in response to infection , leading to caspase-1 activation and IL-1β and IL-18 release [31 , 52–55] . The role of AIM2 inflammasome in the recognition of L . pneumophila has been demonstrated using sdhA- deficient bacteria . In the absence of SdhA , bacteria do not maintain vacuole integrity and localize in the macrophage cytoplasm , triggering activation of the AIM2 inflammasome [56 , 57] . In addition , the AIM2 inflammasome has been shown to trigger caspase-8 activation independently of caspase-1 [33 , 34 , 58] . Thus , we investigated whether AIM2 is present in the NLRC4/ASC/caspase-8 inflammasome that is formed in response to flagellin-positive L . pneumophila . We stained AIM2 in macrophages transduced with retrovirus encoding NLRC4-GFP and found no AIM2 in the NLRC4 puncta ( S3A Fig ) . Moreover , we generated Aim2/Casp1/11-/- mice and found that AIM2 was dispensable for the formation of NLRC4 puncta in response to flagellin-positive bacteria ( S3B Fig ) . Our data are consistent with the hypothesis that caspase-8 is a part of the inflammasome composed of NLRC4 and ASC . Thus , we investigated whether caspase-8 is activated during infection . We found that caspase-8 was strongly activated in Casp1/11-/- BMDMs in response to fliI- but not flaA- bacteria ( Fig 3A ) . In agreement with the requirement of ASC for the assembly of the NLRC4/ASC/caspase-8 inflammasome , we found that caspase-8 activation was abolished in Asc/Casp1/11-/- cells ( Fig 3A ) . Caspase-8 activation occurred normally in Aim2/Casp1/11-/- cells , indicating that AIM2 was not involved in the activation of caspase-8 through the flagellin/NLRC4/ASC inflammasome ( S4 Fig ) . We also evaluated caspase-8 activation by western blot analysis by measuring the cleavage of p55 and the production of p18 isoforms . We found that flagellated bacteria triggered caspase-8 activation in Casp1/11-/- but not in Asc/Casp1/11-/- cells . This phenomenon was evident by the reduction in p55 and increased production of p18 in Casp1/11-/- BMDMs infected with fliI- but not flaA- bacteria ( Fig 3B ) . Next , we evaluated the participation of caspase-8 in caspase-1/11-independent restriction of L . pneumophila replication in macrophages , a process that was dependent on flagellin and NLRC4 . Endogenous caspase-8 was silenced in Casp1/11-/- BMDMs using two independent retrovirus encoding shRNA to target caspase-8 . A non-target sequence was used as a control ( NT ) . By western blotting , we detected reduced caspase-8 expression in Casp1/11-/- transduced cells . The shRNA Casp8 Seq1 was more efficient than Seq2 for silencing caspase-8 as determined by western blot ( Fig 4A and S5 Fig ) and RT-PCR ( Fig 4B ) . Importantly , complete silencing of caspase-8 cannot be achieved because caspase-8 expression is required for macrophage survival [59 , 60] . Nonetheless , using the described silencing conditions , we did not detect signs of cell death or LDH in the supernatant of the transduced macrophages . To evaluate the efficiency of caspase-8 silencing , we quantified caspase-8-containing puncta formation and caspase-8 activation in macrophages infected with flagellated L . pneumophila . We found that the frequency of puncta containing caspase-8 and caspase-8 activation was reduced in caspase-8-silenced cells ( Fig 4C and 4D ) . Next , we evaluated the effect of caspase-8 for the restriction of L . pneumophila replication in Casp1/11-/- BMDMs . We found that silencing caspase-8 culminated in increased replication of fliI- but not flaA- bacteria ( Fig 4E and 4F ) . These data indicated that caspase-8 contributed to the restriction of bacterial replication in a process that was dependent on flagellin , supporting the hypothesis that caspase-8 functionally participates in responses that are NLRC4/ASC-dependent and caspase-1/11-independent . Our data indicate that caspase-8 is part of the NLRC4/ASC inflammasome and that ASC is essential for the assembly of this inflammasome . Thus , we reasoned that in the absence of ASC , the NLRC4/ASC/caspase-8 inflammasome would not be functional . If this hypothesis is correct , Asc/Casp1/11-/- macrophages should be more permissive than Casp1/11-/- and as permissive as Nlrc4-/- . We infected C57BL/6 , Casp1/11-/- , Asc/Casp1/11-/- and Nlrc4-/- BMDMs with fliI- and flaA- , and evaluated bacterial replication after 24 , 48 and 72 hours . Using flagellin-positive bacteria , we confirmed that C57BL/6 BMDMs were restrictive to bacterial growth , Nlrc4-/- were permissive and Casp1/11-/- were partially restrictive ( Fig 5A ) . Importantly , Asc/Casp1/11-/- cells were highly permissive and phenocopied the Nlrc4-/- cells ( Fig 5A ) . As predicted , flagellin mutants bypassed the NLRC4 and replicated in all macrophages evaluated ( Fig 5B ) . These data further confirmed that Casp1/11-/- cells were more restrictive than Nlrc4-/- macrophages , possibly due to the presence of the NLRC4/ASC/caspase-8 inflammasome . We also used non-pneumophila species to compare bacterial replication in Nlrc4-/- and Asc/Casp1/11-/- macrophages . Infections performed with L . gratiana and L . micdadei indicated that Asc/Casp1/11-/- macrophages were as susceptible as Nlrc4-/- , whereas Casp1/11-/- cells were partially restrictive ( Fig 5C and 5D ) . Similar experiments performed with Aim2/Casp1/11-/- macrophages did not support the role of AIM2 in the NLRC4/ASC-dependent growth restriction that occurred in the absence of caspase-1/11 ( S6 Fig ) . These data indicate that flagellated species of Legionellae trigger NLRC4 responses that are independent of caspase-1/11 but dependent on ASC . To further confirm the participation of caspase-8 in this NLRC4/ASC inflammasome , we silenced caspase-8 in Casp1/11-/- and Asc/Casp1/11-/- macrophages . We confirmed the silencing by western blot analysis ( Fig 5E and S7 Fig ) and found that the reduction in caspase-8 expression impaired the restriction of bacterial replication in Casp1/11-/- infected with fliI- ( Fig 5F ) . Inhibition of caspase-8 expression affected neither the replication of fliI- in Asc/Casp1/11-/- cells ( Fig 5G ) nor the replication of flaA- in Casp1/11-/- and in Asc/Casp1/11-/- cells ( Fig 5H and 5I ) . Collectively , these data are consistent with the hypothesis that flagellin activates a response that is dependent on NLRC4 , ASC and caspase-8 and occurs in the absence of caspase-1/11 . Casp8-/- mice are embryonic lethal [59] , and we were not able to generate Casp8/1/11-/- mice . Since the deletion of ASC impairs the assembly of the NLRC4/ASC/caspase-8 inflammasome and caspase-8 activation , we used Asc/Casp1/11-/- mice to assess the role of the NLRC4/ASC/caspase-8 inflammasome in the restriction of Legionella replication in vivo . Using flagellin-positive bacteria such as L . pneumophila , L . gratiana and L . micdadei , we demonstrated that Asc/Casp1/11-/- mice were highly permissive to bacterial replication and phenocopied infection of Nlrc4-/- mice ( Fig 6A–6C ) . Casp1/11-/- mice were more permissive than C57BL/6 , but they were less permissive than Nlrc4-/- mice ( Fig 6A–6C ) . Experiments performed with fliI- and flaA- indicated that Asc/Casp1/11-/- were more permissive than Casp1/11-/- when infected with fliI- but not flaA- ( Fig 6D ) . To determine whether AIM2 accounted for the restriction of bacterial replication in the absence of caspase-1/11 , we compared infection of Aim2/Casp1/11-/- with Casp1/11-/- . We found that Aim2/Casp1/11-/- and Casp1/11-/- supported similar replication levels of fliI- L . pneumophila in the lungs . In contrast , Asc/Casp1/11-/- and Nlrc4-/- mice were significantly more permissive to bacterial replication ( S8 Fig ) . Collectively , these data indicate that AIM2 is dispensable for the functions of the NLRC4/ASC/caspase-8 inflammasome . This molecular platform is assembled in response to flagellin-positive bacteria and operates to restrict bacterial replication in vitro and in vivo in a process that is independent of both caspase-1 and caspase-11 . Activation of caspase-1 inflammasomes induces pyroptosis and contributes to the restriction of infection by flagellated bacteria such as L . pneumophila , Salmonella typhimurium and Burkholderia thailandensis [23] . Accordingly , we have previously demonstrated that L . pneumophila trigger pyroptosis in a process mediated by caspase-1 and caspase-11 , which are activated in response to flagellin and LPS , respectively [20 , 24 , 35 , 61] . Thus , we investigated whether activation of the NLRC4/ASC/caspase-8 inflammasome could trigger cell death in Casp1/11-/- macrophages . We assessed membrane permeabilization fluorometrically in real time via the uptake of propidium iodide . Macrophages were infected with fliI- or flaA- , and pore formation was monitored in real time for 6 hours . C57BL/6 macrophages triggered robust pore formation in response to infection with fliI- and reduced pore formation in response to flaA- ( Fig 7A–7C ) . The pore formation observed in C57BL/6 macrophages infected with flaA- mutants was dependent on caspase-11 [61] and will not be addressed herein . Importantly , despite the absence of both caspase-1 and caspase-11 , we detected significant pore formation in Casp1/11-/- cells infected with fliI- ( Fig 7B ) . This response was not detected in Casp1/11-/- cells infected with flaA- or in Asc/Casp1/11-/- macrophages infected with fliI- or flaA- ( Fig 7B and 7C ) . These data support the hypothesis that NLRC4/ASC/caspase-8 induces pore formation . Experiments performed using Aim2/Casp1/11-/- macrophages corroborate our previous findings , indicating that AIM2 is not required for the activities of the NLRC4/ASC/caspase-8 inflammasome ( Fig 7B and 7C ) . Pore formation induced in response to caspase-1 and caspase-11 activation culminates with the induction of macrophage lysis , a process that can be assessed by the presence of LDH in tissue culture supernatants [24 , 61 , 62] . Thus , we measured LDH release in the supernatants of macrophages infected with fliI- and flaA- for 8 hours . By comparing Casp1/11-/- and Asc/Casp1/11-/- cells , we found that macrophage lysis occurred despite the absence of caspase-1/11 ( Fig 7D ) . Cell death was flagellin-dependent because infections with fliI- but not flaA- induced LDH release ( Fig 7D ) . The participation of caspase-8 in pore formation and cell death induced in caspase-1/11-deficient macrophages was evident using both MOI 5 ( Fig 7 ) and MOI 10 ( S9 Fig ) . Importantly , cell death was not observed in Asc/Casp1/11-/- , a feature that corroborates the pore formation studies and indicates that the NLRC4/ASC/caspase-8 inflammasome triggers pore formation and lysis of infected cells . We also assessed whether the NLRC4/ASC/caspase-8 inflammasome was important for the activation of inflammatory cytokines . We found that whereas C57BL/6 macrophages readily triggered the production of IL-1β after 24 hours of infection with flagellated bacteria , the Casp1/11-/- or Asc/Casp1/11-/—deficient cells do not trigger a IL-1β production ( S10A Fig ) . The production of IL-12p40 by these cells confirmed that all macrophages were primed and could respond to L . pneumophila infection ( S10B Fig ) . To evaluate the participation of caspase-8 in cell death induced by the NLRC4/ASC/caspase-8 inflammasome , we silenced endogenous caspase-8 using shRNA . Macrophages that were transduced with retrovirus encoding shRNA did not exhibit pore formation before infection , indicating that transduction itself did not trigger cell death ( Fig 7E and 7H ) . In contrast , pore formation was evident in Casp1/11-/- but not in Asc/Casp1/11-/- macrophages in response to fliI- infection ( Fig 7F and 7I ) . Pore formation induced in Casp1/11-/- was diminished in caspase-8-silenced cells ( Fig 7F ) . In support of the role of flagellin for triggering these responses , we did not detect pore formation in cells infected with flaA- ( Fig 7G and 7J ) . To further evaluate the participation of caspase-8 in pore formation induced by flagellin , we performed pore formation assays using Z-IETD , a cell permeable peptide that binds irreversibly to the catalytic site of caspase-8 [63–65] . We found that treatment of Casp1/11-/- macrophages with DMSO or Z-IETD did not cause pore formation in uninfected cells ( Fig 7K ) . However , Z-IETD treatment reduced the pore formation induced by fliI- ( Fig 7L ) but not by flaA- ( Fig 7M ) . Collectively , these data indicate that flagellin-positive bacteria trigger pore formation and cell death-independent of caspase-1/11 via a process that requires ASC and caspase-8 . Our data reveal that the NLRC4/ASC/caspase-8 inflammasome is activated in Casp1/11-/- macrophages in response to infection with flagellated Legionella . To evaluate if Naip5 is required for activation of this inflammasome , we used shRNA to silence endogenous Naip5 . In Naip5 silenced Casp1/11-/- macrophages , we detected a reduced activation of caspase-8 in response to WT Lp and fliI- bacteria ( Fig 8A ) . Naip5 silencing was confirmed by RT-PCR ( Fig 8B ) . We also tested if Naip5 is important for pore formation induced via caspase-8 in Casp1/11-/- macrophages . By evaluating pore formation , we found that Naip5 is important for efficient pore formation in response to WT Lp and fliI- bacteria ( Fig 8D–8E ) . As previously reported , no pore formation was detected in response to flaA- bacteria ( Fig 8F ) or in Asc/Casp1/11-/- macrophages ( Fig 8G–8J ) . Finally , we tested if Naip5 is important for restriction of L . pneumophila replication in Casp1/11-/- macrophages . We found that Naip5 is important for restriction of flagellin-positive L . pneumophila replication in Casp1/11-/- macrophages ( Fig 8K ) . Naip5 silencing did not affect the replication of flaA- bacteria in Casp1/11-/- macrophages ( Fig 8L ) . As predicted , Asc/Casp1/11-/- macrophages were permissive to replication of both flaA- and fliI- bacteria and Naip5 did not influenced this process ( Fig 8M and 8N ) . Taken together , these data indicates that Naip5 participate of the NLRC4/ASC inflammasome that trigger caspase-8 activation in the absence of caspase-1/11 . In the experiments shown thus far , we used Casp1/11-/- macrophage as a tool to assess the caspase-8 effects without the interference of caspase-1 . However , because caspase-1 is present in natural conditions , we tested if caspase-8 participates in the NLRC4/ASC inflammasome in the presence of caspase-1 . First , we infected C57BL/6 macrophages with flagellin-positive L . pneumophila to assess if endogenous caspase-8 colocalizes with the Naip5/NLRC4/ASC inflammasome . In uninfected conditions , we detected no significant puncta formation ( Fig 9A ) . However , in response to fliI- bacteria , caspase-8 colocalizes with ASC ( Fig 9B ) and caspase-1 ( Fig 9C ) . We determined that caspase-8 is present in more than 60% puncta containing caspase-1 ( Fig 9C ) . These data indicates that regardless to the presence of caspase-1 , the caspase-8 is recruited to the inflammasome during activation . Next , we evaluated caspase-8 activation in wild-type macrophages . We found that caspase-8 activation does not occur in C57BL/6 macrophages infected with L . pneumophila . As expected caspase-8 is readily activated in Casp1/11-/- , but not in Asc/Casp1/11-/- macrophages infected with fliI- bacteria ( Fig 9D ) . To ensure similar genetic background , we intercrossed a F1 progeny of Casp1/11-/- x C57BL/6 to obtain F2 littermate controls . Infections in macrophages from littermate control mice indicated that caspase-8 activation occur in Casp1/11-/- , but not in Casp1/11+/- and Casp1/11+/+ macrophages ( Fig 9E ) . To further test whether the deficiency in caspase-1 or caspase-11 enable caspase-8 activation , we performed experiments using mice single deficient in caspase-1 or caspase-11 as previously described [35 , 66] . We found that caspase-11 deficiency alone is not sufficient to enable caspase-8 activation in response to L . pneumophila infection ( Fig 9F ) . In contrast , caspase-1-deficient cells expressing caspase-11 as a transgene ( Casp1-/-Casp11tg ) effectively trigger caspase-8 activation in response to fliI- L . pneumophila ( Fig 9F ) . These data indicate that caspase-1 but not caspase-11 is required to prevent caspase-8 activation . Caspase-1 activation via the NLRC4 inflammasome is known to trigger activation of gasdermin-D ( GSDMD ) to induce cell death [67 , 68] . Thus , we tested if inhibition of GSDMD is sufficient to enable caspase-8 activation in the presence of caspase-1 . To achieve this , we inhibited endogenous GSDMD using shRNA and found that despite the presence of caspase-1 , caspase-8 is robustly activated when GSDMD is inhibited ( Fig 9G ) . RT-PCR was used to confirm the silencing of the two different sequences of shRNA used ( Fig 9H ) . To further confirm the GSDMD silencing , we performed pore formation assay in C57BL/6 macrophages infected with L . pneumophila . We found that GSDMD silencing inhibited the caspase-1-mediated pore formation induced by WT Lp and fliI- L . pneumophila ( S11A–S11D Fig ) . However , GSDMD did not participate of pore formation induced via caspase-8 that occurs in Casp1/11-/- macrophages ( S11E–S11H Fig ) . Collectively , these data indicates that despite the presence of caspase-1 , caspase-8 activation occur in the Naip5/NLRC4/ASC inflammasome when GSDMD is inhibited .
The recognition of Legionella flagellin by the Naip5/NLRC4 inflammasome in macrophages is a major mechanism for the restriction of bacterial replication in mouse cells [17–20 , 22 , 69 , 70] . It is well accepted in the field that not all NLRC4 functions require caspase-1 [29 , 38 , 71 , 72] . This conclusion is supported by the observation that Nlrc4-/- mice ( and their macrophages ) are significantly more permissive to L . pneumophila replication than Casp1/11-/- mice [29 , 38] . Here , we unraveled this caspase-1/11-independent pathway and characterized an inflammasome composed of Naip5 , NLRC4 , ASC and caspase-8 , which operates in the absence of caspase-1 and caspase-11 . This inflammasome effectively participates in the mechanisms involved in the restriction of bacterial replication in macrophages and in vivo . Previous biochemistry studies using yeast two-hybrid screening showed that NLRC4 is ubiquitinated by Sug1 , a process that facilitates the activation of caspase-8 [51] . Thus , it is possible that Sug1 is also a component of this NLRC4 inflammasome . In addition , previous studies using the Salmonella enterica serovar Typhimurium have indicated that both caspase-8 and caspase-1 are recruited to the ASC puncta in response to infection . However , caspase-8 is involved in the synthesis of pro-IL-1β and is dispensable for Salmonella-induced cell death [32] . These data contrast with published data using L . pneumophila , which indicate that in the absence of caspase-1/11 , no inflammatory cytokines are produced [20 , 21 , 25 , 30 , 35 , 66] . Accordingly , our data indicates that this Naip5/NLRC4/ASC/Caspase-8 inflammasome is very inefficient to trigger IL-1β maturation when caspase-1 is not present . Importantly , AIM2 is not part of this NLRC4/ASC/caspase-8 inflammasome . AIM2 is well-known to trigger caspase-8 activation via ASC [33 , 34 , 58] . Our data unequivocally demonstrate that AIM2 is neither a component of this inflammasome , nor is it required for inflammasome functions . AIM2 did not colocalize with the NLRC4/ASC/caspase-8 puncta , it was dispensable for the activation of caspase-8 in response to flagellated L . pneumophila and for the induction of cell death and restriction of L . pneumophila replication . The pyrin domain of ASC can bind to the death domain of caspase-8 [46] . Thus , it is possible that the interactions of the ASC pyrin domain with the caspase-8 dead domain are critical for the recruitment of caspase-8 to the complex . Consistent with this hypothesis , our studies unequivocally show that ASC is required for the assembly and function of this NLRC4 inflammasome . Importantly , the characterization of this NLRC4/ASC/caspase-8 inflammasome accounted to clarify a controversy in the field concerning the participation of ASC in the NLRC4 inflammasome . Studies utilizing biochemistry and cells from gene-deficient mice have demonstrated that ASC is dispensable for NLRC4 functions , including pyroptosis and the restriction of L . pneumophila replication [20 , 21 , 29] . However , ASC is essential for caspase-1 cleavage and the processing of inflammatory cytokines in response to flagellated L . pneumophila , a process that is NLRC4-dependent and NLRP3-independent [20 , 21 , 25 , 30] . In addition , ASC participates in the restriction of intracellular replication of L . pneumophila under certain circumstances [36 , 37] . Our studies provide data that help to consolidate these data in a cohesive model . NLRC4 can operate to form an inflammasome in absence of ASC that triggers pore formation and the restriction of bacterial replication [20 , 21 , 25 , 30] . This platform does not form puncta and is ineffective for triggering caspase-1 cleavage and processing inflammatory cytokines . When ASC is present , NLRC4 inflammasome associates with ASC and recruit caspase-1 and caspase-8 to the puncta . This inflammasome is very efficient to cleave caspase-1 and inflammatory cytokines such as IL-1β . Interestingly , our data indicate that caspase-8 is not activated when caspase-1 is present . However , when caspase-1 is missing or when Gasdermin-D is inhibited , we detected a robust caspase-8 activation . These data suggest that activation of caspase-8 in the Naip5/NLRC4/ASC inflammasome functions as a backup strategy to guarantee cell death when key components of the pyroptotic cell death are inhibited . Interestingly , our data and previously published data indicate that gasdermin-D is dispensable for caspase-8-induced cell death [73] . Therefore , when gasdermin-D is inhibited , caspase-8 engages gasdermin-D-independent cell death . It is possible that caspase-8 induces caspase-3 and caspase-7 to targed gasdermin-E ( also known as DFNA5 ) to induce pore formation and cell death independent of gasdermin-D [74 , 75] . This may guarantee appropriate responses to pathogens that inhibit canonical components of pyroptotic cell death such as caspase-1 or gasdermin-D .
The L . pneumophila bacteria used were JR32 and isogenic clean deletion mutants for motility ( fliI- ) and flagellin ( flaA- ) [19 , 38] . L . micdadei ( ATCC 33218 ) and L . gratiana ( ATCC 49413 ) were used to generate streptomycin-resistant strains . RpsL mutants of L . micdadei and L . gratiana were isolated by plating these strains on CYE agar containing 100 μg/ml of streptomycin . All bacteria were grown on buffered charcoal-yeast extract ( CYE ) agar plates [1% yeast extract , 1% MOPS , 3 . 3 mM L-cysteine , 0 . 33 mM Fe ( NO3 ) 3 , 1 . 5% Bacto agar and 0 . 2% activated charcoal , pH 6 . 9] [76] . Bone Marrow derived macrophages ( BMDMs ) were generated from mice as previously described [77] . Briefly , bone marrow cells were harvested from femurs and differentiated with RPMI 1640 ( Gibco ) containing 20% fetal bovine serum ( FBS—Invitrogen ) and 30% L-929 cell-conditioned medium ( LCCM ) , 2 mM L-glutamine ( Sigma-Aldrich ) , 15 mM Hepes ( Gibco ) and 100 U/ml penicillin-streptomycin ( Sigma-Aldrich ) at 37°C with 5% CO2 [77] . BMDMs were seeded at 2 X 105 cells/well in 24-well plates and cultivated in RPMI 1640 medium ( Gibco ) supplemented with 10% FBS , 5% LCCM , 2 mM L-glutamine and 15 mM Hepes . For the in vitro infections , the cultures were infected at a multiplicity of infection of 0 . 015 , 5 or 10 followed by centrifugation for 5 minutes at 300 X g at room temperature and incubation at 37°C in a 5% CO2 atmosphere . In the colony-forming units ( CFU ) experiments , cultures infected at a MOI of 10 were washed two times with PBS , and 1 ml of medium was added to each well . For CFU determination , the cultures were lysed in sterile water , and the cell lysates were combined with the cell culture supernatant from the respective wells . Lysates plus supernatants from each well were diluted in water , plated on CYE agar plates , and incubated for 4 days at 37°C for CFU determination as described previously [28 , 38] . Murine Nlrc4 or Asc were cloned into the pEGFP ( N2 ) vector ( Clontech ) using XhoI and BamHI restriction sites as previously described [30] . NLRC4-GFP or ASC-GFP and GFP were cloned into the pMSCV2 . 2 murine-specific retroviral vector ( Clontech ) . The pCL vector system 51 was used to package the retroviruses in transfected monolayers of Hek Peak cells ( ATCC CRL-2828 ) , which were maintained in RPMI with 10% FBS . The supernatant from the Hek Peak cells containing retrovirus was collected three days after transfection , filtered using a 0 . 45-μm filter and used for BMDM transduction . BMDMs were obtained from Casp1/11-/- , Asc/Casp1/11-/- and Aim2/Casp1/11-/- mice and seeded in differentiation medium . On day 3 of differentiation , the supernatants containing retroviral were added to BMDMs in 20% FBS and 25% LCCM . After differentiation , the BMDMs were seeded at 2 X 105 cells/well in 24-well plates containing 12-mm glass cover slides and cultivated in RPMI 1640 medium supplemented with 10% FBS and 5% LCCM . For the caspase colocalization experiments , the cultures were treated with 20 μm of Z-VAD for 1 hour and infected at a MOI of 1 , 3 or 10 . After infection , the plates were centrifuged for 5 minutes at 300 X g at room temperature and incubated at 37°C in a 5% CO2 atmosphere . At 1 , 2 , 4 and 8 hours after infection , the cells were fixed with 4% paraformaldehyde , permeabilized with 0 . 05% saponin , stained with DAPI and mounted on glass slides using Prolong Gold Antifade Reagent ( Invitrogen ) . For the colocalization assay , the cells were stained with rat anti-caspase-8 ( Enzo– 1G12; 1:50 ) , rabbit anti-cleaved caspase-8 ( Cell signaling- 8592; 1:800 ) , rabbit anti-cleaved caspase-3 ( Cell signaling- 9664; 1:400 ) ; rabbit anti-cleaved caspase-7 ( Cell signaling-8438; 1:400 ) , anti-ASC ( Adipogen- AL177; 1:250 ) , goat anti-ASC ( Santa Cruz–sc33958; 1:50 ) , rabbit anti-caspase-1 ( Santa Cruz- sc514; 1:500 ) or anti-AIM2 ( Cell signaling- 13095; 1:400 ) , followed by Alexa 594-conjugated goat anti-rabbit secondary Ab ( Invitrogen; 1:3000 ) , Alexa 594-conjugated goat anti-rat secondary Ab ( Invitrogen; 1:3000 ) or Alexa 647-conjugated chicken anti-rabbit secondary Ab ( Invitrogen; 1:2000 ) and DAPI and mounted on glass slides using Prolong Gold Antifade Reagent ( Invitrogen ) . The images were processed using LAS AF software ( Leica Microsystems ) and analyzed under fluorescence using a Leica DMI 4000B inverted microscope with a 100X oil objective . The number of NLRC4-GFP or ASC-GFP puncta in the transduced cells and the colocalization were quantified . Bacteria were not stained . Therefore , the whole cell population was scored . Multiphoton microscopy images were acquired using an LSM 780 Zeiss AxioObserver microscope equipped with a 63X oil immersion objective and analyzed using ImageJ software . For retroviral silencing of caspase-8 , Naip5 and GSDMD ( Gasdermin D ) , Hek Peak cells were transfected with lentiviral vectors encoding a small hairpin RNA ( shRNA ) targeting caspase-8 [Sigma- Seq1: TRCN0000231279 ( Sequence- CCGGTCATCTCACAAGAACTATATTCTCGAGAATATAGTTCTTGTGAGATGATTTTTG ) ; Seq2: TRCN0000231281 ( Sequence–CCGGTCCTGACTGGCGTGAACTATGCTCGAGCATAGTTCACGCCAGTCAGGATTTTTG ) ] , Naip5 [Sigma- TRCN0000114742 ( Sequence—CCGGCGCTTGATTATCTTCTGGAAACTCGAGTTTCCAGAAGATAATCAAGCGTTTTTG ) ] , GSDMD [Sigma- TRCN0000219619 ( Sequence—CCGGGATTGATGAGGAGGAATTAATCTCGAGATTAATTCCTCCTCATCAATCTTTTTG ) ; TRCN0000219620 ( Sequence—CCGGCCTAAGGCTGCAGGTAGAATCCTCGAGGATTCTACCTGCAGCCTTAGGTTTTTG ) ] and a negative control vector that included a non-target shRNA sequence ( NT ) . The plates were treated with polyethylenimine ( Sigma-Aldrich ) ( Corning ) . Transduced cells were maintained in RPMI 1640 medium supplemented with 10% FBS at 37°C and 5% CO2 . Lentiviruses expressing shRNAs were collected , filtered using a 0 . 45-μm filter and added to the BMDMs . After selection with puromycin , the resistant cells were seeded at 2 X 105 cells/well in 24-well plates and infected with fliI- or flaA- for CFU determination . The caspase-8 silencing efficiency was measured by immunoblotting: 1 X 106 cells were lysed in RIPA buffer ( 10 mM Tris-HCl , pH 7 . 4 , 1 mM EDTA , 150 mM NaCl , 1% Nonidet P-40 , 1% deoxycholate , and 0 . 1% SDS ) in the presence of a protease inhibitor cocktail ( Roche ) . The lysates were suspended in 4X Laemmli buffer , boiled for 5 minutes , resolved by 15% SDS PAGE and transferred ( Semidry Transfer Cell , Bio-Rad ) to 0 . 22-μm nitrocellulose membranes ( GE Healthcare ) . The membranes were blocked in Tris-buffered saline ( TBS ) with 0 . 01% Tween-20 and 5% non-fat dry milk for 1 hour . Rat anti-caspase-8 p55 ( Enzo– 1G12; 1:500 ) and anti-rat peroxidase-conjugated antibody ( KPL , 1:3000 ) were diluted in blocking buffer for the incubations ( overnight for anti-caspase-8 and 1 hour for the secondary antibody ) . The ECL luminol reagent ( GE Healthcare ) was used for antibody detection . For evaluation of caspase-8 silencing by immunoblots , Image J software were used to estimate the ratio of caspase-8 p55 to α-actin . To assess caspase-8 activation , BMDMs were infected with fliI- or flaA- for 8 hours , and the activity of caspase-8 was measured using the Caspase-8 Glo Assay ( Promega ) according to manufacturer´s recommendations . To evaluate caspase-8 activation by western blot analysis , 1 X 107 cells were infected at a MOI of 10 and lysed 8 hours after infection in RIPA buffer with protease inhibitor , as previously described . The lysates were suspended in 4X Laemmli buffer , boiled for 5 minutes , resolved by 15% SDS PAGE and transferred to 0 . 22-μm nitrocellulose membranes . The membranes were blocked in Tris-buffered saline ( TBS ) with 0 . 01% Tween-20 and 5% non-fat dry milk or for 1 hour . The mouse anti-caspase-8 ( Enzo– 1G12 ) and anti-rabbit peroxidase-conjugated antibody ( KPL; 1:3000 ) were diluted in blocking buffer for the incubations ( overnight for anti-cleaved caspase-8 and 1 hour for the secondary antibody ) . ECL luminol reagent ( GE Healthcare ) was used for antibody detection . Total RNA was extracted from 2 X 106 macrophages using total RNA isolation kit ( illustra RNAspin , GE Healthcare , UK ) , according to manufacturer’s instructions . After extraction , an aliquot of 2 μl was used to determine the RNA concentration in NanoDrop ( Thermo Fisher Scientific ) and 1 μg of the extracted RNA was used for the cDNA conversion using the iScriptTM cDNA Synthesis kit ( BIO-RAD ) in a thermal cycler . The cDNA ( 10 ng ) was used for the quantification of the Caspase 8 gene expression ( TaqMan Assay: Casp 8—Mm01255716_m1 ) by real-time PCR using TaqMan Fast Advanced Master Mix , according to the manufacturer's instruction ( Applied Biosystems ) . Actin beta ( Actb ) gene ( TaqMan Assay: Actb-Mm00607939_s1 ) was used as a control for normalization of expression levels . The quantification of GSDMD and Naip5 were performed using 25 ng of cDNA and 10 μM of each primer , 1X SYBR Green ( Applied Biosystems ) , and was normalized using the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase ( Gapdh ) . The specificity of the PCR products was assessed by melting curve analysis for all samples . The following primers were used: Gapdh , FWD- AGGTCGGTGTGAACGGATTTG , REV–TGTAGACCATGTAGTTGAGGTCA , GSDMD , FWD–TCATGTGTCAACCTGTCAATCAAGGACAT , REV- CATCGACGACATCAGAGACTTTGAAGGA , NAIP5 , FWD- GTTGAGATTGGAGAAGACCTCG , REV-CACACGTGAAAGCAACCATGG . The real-time quantitative reaction was performed in the Viia 7 Real-Time PCR System ( Applied Biosystems ) . The results were analyzed using the 2-ΔΔCT method and are expressed in relation to the reference group . The percentage of silencing knockdown was estimated using the [1- ( 2-ΔΔCT ) x 100] equation [78] . Mice used in this study were breed and maintained in institutional animal facilities . Mice used were C57BL/6 ( Jax 000664 ) , Nlrc4-/- [79] , Casp1/11-/- [80] , Asc-/- [81] , Casp11-/- and Casp1-/-Casp11tg [66] were . Double-deficient mice were generated by intercrossing a F1 progeny of the parental strains . All mice were matched by sex and age ( all were at least 8 weeks old at the time of infection ) and were in a C57BL/6 mouse genetic background . For the in vivo experiments , approximately 5–7 mice per group were used , as indicated in the figures . For in vivo infections , the mice were anesthetized with ketamine and xylazine ( 50 mg/kg and 10 mg/kg , respectively ) by intraperitoneal injection followed by intranasal inoculation with 40 μl of phosphate-buffered saline ( PBS ) containing 1 X 105 bacteria per mouse . For CFU determination , the lungs were harvested and homogenized in 5 ml of sterile water for 30 seconds using a tissue homogenizer ( Power Gen 125; Thermo Scientific ) . Lung homogenates were diluted in sterile water and plated on CYE agar plates for CFU determination as previously described [28 , 38] . Pore formation in BMDMs was quantified based on the permeability to propidium iodide ( PI ) in damaged cells as previously described [61] . BMDMs were seeded in a black , clear-bottom 96-well plate ( 1 X 105 cells/well ) . Before infection , the medium was replaced with 10% RPMI without phenol red , 0 . 038 g/ml NaHCO3 , 6 μl/ml PI and anti-L . pneumophila ( 1:1000 ) . Infected BMDMs were maintained at 37 °C , and PI was excited at 538 nm . The fluorescence emission was read at 617 nm every 5 minutes using a plate fluorometer ( SpectraMax i3x , Molecular Devices ) . Total pore formation was determined by lysing cells with Triton X-100 . BMDMs were seeded in 24-well plates ( 5 X 105 cells/well ) . Infections were performed in RPMI1640 medium without phenol red , 15 mM HEPES and 2 g/l NaHCO3 supplemented with 10% FBS . After 8 hours of infection , the supernatants were collected for analysis of lactate dehydrogenase ( LDH ) release . Total LDH was determined by lysing the cultures with Triton X-100 . LDH was quantified using the CytoTox 96 LDH-release kit ( Promega ) . For cytokine determination , enzyme-linked immunosorbent assay ( ELISA ) were used . BMDMs were seeded into 24-well plates ( 5 X 105 cells/well ) and infected with WT Lp , fliI- and flaA- ( MOI 10 ) for 24 hours . BMDMs supernatant was assessed using ELISA kits according to manufacturer´s recommendations ( BD Biosciences ) . The care of the mice was in compliance with the institutional guidelines on ethics in animal experiments; approved by CETEA ( Comissão de Ética em Experimentação Animal da Faculdade de Medicina de Ribeirão Preto , approved protocol number 218/2014 ) . CETEA follow the Brazilian national guidelines recommended by CONCEA ( Conselho Nacional de Controle em Experimentação o Animal ) . For euthanasia , the mice were treated with ketamine and xylazine ( 50 mg/kg and 10 mg/kg , respectively ) by intravenous injection . The data were plotted and analyzed using GraphPad Prism 5 . 0 software . The statistical significance was calculated using the Student’s t-test or analysis of variance ( ANOVA ) . Nonparametric test Mann–Whitney U test were used for analysis of in vivo experiments . Differences were considered statistically significant when P was <0 . 05 , as indicated by an asterisk in the figures . | Legionella pneumophila is the causative agent of Legionnaires’ disease , an atypical pneumophila that affects people worldwide . Besides the clinical importance , L . pneumophila is a very useful model of pathogenic bacteria for investigation of the interactions of innate immune cells with bacterial pathogens . Studies using L . pneumophila demonstrated that Naip5 and NLRC4 activate caspase-1 and this inflammasome is activated by bacterial flagellin . However , macrophages and mice deficient in NLRC4 are more susceptible for L . pneumophila replication than those deficient in caspase-1 , indicating that the flagellin/Naip5/NLRC4 inflammasome triggers responses that are independent on caspase-1 . Here , we used L . pneumophila to investigate this novel pathway and found that caspase-8 interacts with NLRC4 in a process that is dependent on ASC and independent of caspase-1 and caspase-11 . Although caspase-8 is recruited to the Naip5/NLRC4/ASC inflammasome , it is only activated when caspase-1 or gasdermin-D is inhibited . Our data suggest that caspase-8 activation in the Naip5/NLRC4/ASC inflammasome may favor host responses during infections against pathogens that inhibit components of the pyroptotic cell death including caspase-1 and gasdermin-D . | [
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] | 2017 | Inhibition of caspase-1 or gasdermin-D enable caspase-8 activation in the Naip5/NLRC4/ASC inflammasome |
Vector-borne diseases are emerging and re-emerging in urban environments throughout the world , presenting an increasing challenge to human health and a major obstacle to development . Currently , more than half of the global population is concentrated in urban environments , which are highly heterogeneous in the extent , degree , and distribution of environmental modifications . Because the prevalence of vector-borne pathogens is so closely coupled to the ecologies of vector and host species , this heterogeneity has the potential to significantly alter the dynamical systems through which pathogens propagate , and also thereby affect the epidemiological patterns of disease at multiple spatial scales . One such pattern is the speed of spread . Whereas standard models hold that pathogens spread as waves with constant or increasing speed , we hypothesized that heterogeneity in urban environments would cause decelerating travelling waves in incipient epidemics . To test this hypothesis , we analysed data on the spread of West Nile virus ( WNV ) in New York City ( NYC ) , the 1999 epicentre of the North American pandemic , during annual epizootics from 2000–2008 . These data show evidence of deceleration in all years studied , consistent with our hypothesis . To further explain these patterns , we developed a spatial model for vector-borne disease transmission in a heterogeneous environment . An emergent property of this model is that deceleration occurs only in the vicinity of a critical point . Geostatistical analysis suggests that NYC may be on the edge of this criticality . Together , these analyses provide the first evidence for the endogenous generation of decelerating travelling waves in an emerging infectious disease . Since the reported deceleration results from the heterogeneity of the environment through which the pathogen percolates , our findings suggest that targeting control at key sites could efficiently prevent pathogen spread to remote susceptible areas or even halt epidemics .
Urbanization , due to both population growth in cities and immigration from rural communities , now concentrates almost half of the global human population ( 3 . 3 out of 6 . 8 billion people ) into urban centres where crowding promotes the spread of infectious diseases [1] . Transmission of vector-borne diseases in urban environments , particularly dengue fever and dengue haemorrhagic fever [2] , malaria [3] , yellow fever [4] , [5] , and West Nile virus ( WNV ) fever [6] , is an increasingly important global health concern [7] , [8] . Not all of the causes of recent increases in urban vector-borne disease are clear . The modified environments of cities have a direct effect on populations of arthropod vectors through environmental drivers such as temperature and water retention [9] . More subtly , urbanization may have structural effects on disease transmission systems . Specifically , urban environments are highly heterogeneous in the extent , degree , and distribution of environmental modifications . While this heterogeneity directly translates into varying levels of risk for the inhabitants of different areas , it may also affect the dynamical transmission systems through which the pathogen propagates [10] , just as heterogeneity in ecological systems gives rise to novel patterns of diversity and persistence [11] . We hypothesized that environmental heterogeneity in urban environments gives rise to decelerating waves of infection due to the inhibition of local propagation in locations unfavourable for disease transmission . We emphasize that the decelerating waves we hypothesize are not due to environmental or temporal gradients in transmission , as has been described previously for a fungal pathogen infecting plants [12] , but solely to endogenous dynamics influenced by spatial heterogeneity . By analogy to the theory of percolation in disordered media [13] , we conjecture for such systems the existence of a critical fraction of sites which must be “transmission-promoting” for an introduced pathogen to propagate . These predictions differ qualitatively from the asymptotically constant and accelerating waves predicted by the theory of spread in homogeneous environments [14]–[16] and observed in other systems [17]–[19] . To test this hypothesis , we studied the spread of WNV in the region of its epicentre in New York City over the period since its emergence in North America [20] , [21] . WNV is a single-stranded positive sense RNA virus belonging to the genus Flavivirus , family Flaviviridae , and can cause fatal meningitis and encephalitis in humans [22] , [23] . Persistence of the virus is maintained by an enzootic cycle primarily involving ornithophilic mosquitoes of the genus Culex and passerine birds [24] . Humans and other mammals ( e . g . , horses ) are dead-end hosts which get infected by the bite of infectious mosquitoes ( also predominantly from the genus Culex ) . Transmission risk is the highest towards the end of each WNV season , typically in late summer and early fall . WNV is currently the most widespread arbovirus in the world and is now the most prevalent vector-borne disease of humans in North America . Then , to better understand the effect of habitat heterogeneity on epidemic spread , we developed a percolation model for WNV transmission . Percolation theory , which has been used previously to study the spread of pathogens on contact networks [25]–[27] , concerns the distribution of connected clusters in a random graph as a representation of liquid transport in a heterogeneous medium [13] . The porosity of the idealized medium is characterized by a global parameter p , the proportion of open sites . An important theoretical property of heterogeneous media is the existence in a lattice of infinite extension of a critical point , pc , which must be exceeded for an infinite cluster of adjacent sites to exist [13] . In practice , in finite lattices of even very small extent , pc is the threshold that must be exceeded for connectivity , i . e . , the frequency of open sites required for the system to “percolate” . Such open and closed sites of percolating media are analogous to the environmental properties that impede and promote the transmission of pathogens in heterogeneous landscapes . It follows that there will exist a critical point in the fraction of transmission promoting habitats for the propagation of pathogens in heterogeneous environments [28] , [29] .
Estimates of the speed at which waves of WNV spread across New York City during the years 2000–2008 ranged from 0 . 6 meters day−1 to 12 km day−1 using a method based on subsequent differences in the square root of the convex hull of observed infections ( Fig . 1 , Figs . S1 , S2 and S3 in Text S1 ) , 0 . 6 meters day−1 to 37 km day−1 using a maximum distance method , and 0 . 0000884 meters day−1 to 3 . 724 km day−1 using a boundary displacement method ( Fig . 1 ) ( see Materials and Methods ) . Changes in the estimated spread rate showed the hypothesized deceleration ( negative correlation with time ) in one or more analyses for all years ( Table 1 ) . In 2008 , the virus appears to have originated from two separate locations giving rise to independent and converging wave fronts , compromising the detectability of deceleration using the convex hull and maximum distance methods . Thus , in contrast to the asymptotically constant wave-speeds predicted by theory for spread in a homogeneous environment [30] , and the accelerating spread due to occasional long distance dispersal at the continental scale [31] , the spread of WNV in New York City nearly always decelerated . These patterns are well illustrated by our model ( Fig . 2a ) . The basic reproductive number for the local dynamics given by our model was obtained using the “spectral radius method” , and is given by the expressionwhere and are the probability of transmission from an infectious vector to a reservoir host , and from an infectious reservoir host to a vector , respectively; is the biting rate of vectors; and are the length of the incubation period in the vectors and in the reservoir hosts , respectively; and are the mortality rates of the vectors and reservoir hosts; is the recovery rate of infectious reservoir hosts; is the excess mortality rate of infectious reservoir hosts; and are the total population size of vectors and reservoir hosts , respectively . Based on empirical measurements of these rates , our model predicts a local R0 for WNV between 1 . 4 and 4 . 4 for a vector-to-host ratio of 1 and 10 , respectively ( Fig . 3a ) . The expression for the basic reproductive number we present above is the square root of the “next generation” reproduction number , which assumes that the pathogen must pass through both the vector and the host . Although field-based estimates of the basic reproduction number are not available for West Nile Virus , our predictions are consistent with values estimated for other members of the Flaviviridae family [e . g . , 32] . Modelled patterns of spatial spread quantitatively elaborate on the qualitative pattern we predicted . Particularly , in a constant lattice with all sites promoting transmission , spread occurred according to a travelling wave with asymptotically constant speed following a transient increase and constant wave form , recovering the well known behaviour of spread in a homogeneous environment as a limiting case [14] ( Fig . 2b ) . In heterogeneous habitats , however , as the fraction of transmission-promoting sites decreased , spatial spread of the pathogen was increasingly inhibited . One effect of heterogeneity was to diminish the eventual wave speed ultimately achieved relative to the homogeneous lattice ( Fig . 2c ) . In the vicinity of the percolation threshold ( pc = 0 . 5927… for the von Neumann lattice used here [12] ) , another effect emerged: time series of observed spread rates were erratic , segmenting into periods of temporary acceleration and deceleration ( troughs and peaks in Fig . 2c ) , due to alternating confinement of spread to narrow corridors and expansion in self-organized clusters of transmission-promoting sites . Finally , the aggregation of these accelerating and decelerating episodes resulted in a third effect ( our main hypothesis ) : overall decelerating spread as the proportion of transmission-promoting sites decreased toward the critical point in its vicinity ( 0 . 52<p<0 . 6 ) ( Fig . 2d ) . To better understand the robustness of these patterns we further studied the sensitivity of wave speed to a variety of assumptions . First , we investigated the effect of variation in the vectors-to-host ratio ( Fig . 3a ) . Necessarily , at or below the critical ratio of vectors to hosts , the pathogen did not spread in the lattice and immediately above this critical ratio , the wave-speed was too small to be measurable . However , further increasing the ratio of vectors to hosts lead to measurable wave speeds that increased with increasing vector-to-host ratio . Most importantly , the wave decelerated for a large range of vector-to-host ratios with no major differences between the average ratio of final and median wave-speed , a summary measure of deceleration , or the frequency of realizations with deceleration overall , showing that the phenomenon is robust to a wide range of ecological conditions ( Fig . 3b ) . We also analysed the sensitivity of wave speed to dispersal rate ( Fig . 3c , d ) on a heterogeneous lattice close to the percolation threshold ( p = 0 . 6 ) . While increasing dispersal rate unsurprisingly increased wave speed , there was no threshold dispersal value below which the wave speed could not be measured , while the predicted deceleration was always present . A final concern was that the preceding theoretical results were obtained under the assumption that dispersal of the pathogen was contained within the local neighbourhood of an infected site . Previous results in analogous systems have shown that the inclusion of long-distance connections reduces pc compared to exclusively local dispersal [33] . As a diagnostic for the predominance of local dispersal in the WNV data , we tested for a correlation between time elapsed since the annual index case and the Euclidean distance between each observed infection and the presumptive origin of the annual outbreak , i . e . , its displacement from the epicentre . Because purely local dispersal leads to a propagating wave-front , distance from the outbreak origin and time elapsed must be positively correlated . No such correlation occurs in the case of global dispersal alone , while in the mixed case the wave front only remains intact when local dispersal dominates spatial spread ( Fig . 4 ) . Applying this test to the WNV data provided strong evidence that spread of WNV was indeed dominated by local dispersal in 2000–2002 and 2004–2007 , but not 2003 or 2008 ( see Table S1 in Text S1 ) . Notably , there was no evidence for dominance of local dispersal in 2008 , the year in which we suspect WNV emerged at multiple locations . Finally , we simulated “mixed dispersal” scenarios in which local dispersal was combined with global dispersal , either through occasional dispersal to a random transmission-promoting site , or in the form of a small world network [34] . In these simulations , distance from the outbreak origin and time elapsed retained their positive correlation as long as local dispersal dominated and the outbreak origin was correctly identified . In conclusion , we found that decelerating travelling waves were robust to a wide range of potentially confounding factors as long as the heterogeneity in the environment was in the vicinity of the critical point . To investigate percolation conditions in New York City , we tested for association between prevalence of WNV in birds and land cover type on a 50 m×50 m grid , the territory size of American Robin ( Turdus migratorius ) , a dominant amplifying reservoir in this system ( [35]; Table 2 ) . Five land cover types ( Open-Space , Low-Intensity Developed , Evergreen Forest , Herbaceous , and Woody Wetland ) , all of which are characterized by <50% impervious surface , were significantly associated with prevalence at the Bonferroni corrected significance level ( αBonferroni = 0 . 005 ) , suggesting that these land cover types promote the transmission of WNV . Areas of high intensity developed land cover , characterized by 80%–100% impervious surfaces and comprising 40 . 1% of the land surface of NYC , were significantly negatively associated with prevalence , suggesting that this land cover type is indeed an impediment to the spread of WNV in New York City . Importantly , this developed high-intensity land cover type is widely distributed throughout New York City ( Fig . 5 ) so that transmission-promoting land cover types are scattered within a larger inhospitable matrix . Notably , the proportion of transmission-promoting land cover types was 0 . 599 ( 95% CI [0 . 598–0 . 600] from the binomial distribution ) , practically indistinguishable from the percolation threshold , pc = 0 . 5927… for a Bernoulli site percolation on a von Neumann lattice . This agreement suggests that transmission promoting habitats in New York City are indeed in the vicinity of the critical point , though the near perfect equivalence should be interpreted with caution , since the characteristic scale of transmission and the geometry and spatial correlation of the transmission-promoting sites remain unknown . That is , our assumption of Bernoulli site percolation on a von Neumann lattice is an idealization . The idealization is justified by the biological basis of the 50 m×50 m granularity ( territory size of American Robin ) and indirect evidence obtained above that transmission is primarily local . To the extent that this idealization fails to capture the geometry of the environment as perceived by both vectors and hosts and/or relevant correlations , the analytic critical point ( pc = 0 . 5927… ) only approximates the true unknown critical value . While percolation thresholds are known to vary between 0 . 4 and 0 . 8 for different site geometries , [12] , the extreme values in this range are associated with rather exotic scenarios and the majority of ecologically plausible geometries give values between 0 . 5 and 0 . 6 . It is therefore probable that transmission is unstable throughout the city , even if our assumptions about network geometry should prove overly simplistic . An alternative explanation for the observed deceleration is that spread rate merely tracks an exogenous seasonal variable . Most plausible such possibilities were excluded by further analysis . Two candidate variables are temperature , which strongly modulates the development of the larval stage of the mosquito vector , and therefore the growth and abundance of vector populations ( see Fig . 1 in [36] ) , and precipitation , which limits available breeding habitat for the primary vector species in NYC ( Culex pipiens , Cx . restuans , and Cx . salinarius ) . Inspection of average daily temperature ( obtained from reports at JFK Airport , La Guardia Airport and Central Park NOAA weather stations ) and mosquito abundance overlaid on spread rate , however , show that the decline in wave speed typically precedes seasonal declines in temperature and mosquito abundance ( see Fig . 1 and Figs . S1 , S2 and S3 in Text S1 ) . Further , correlations between estimated wave-speed and degree day ( 11°C base temperature ) , precipitation , total mosquito abundance and the abundance of Culex sp . , were not statistically significant at the α = 0 . 05 level ( using Holm-Bonferroni corrections for multiple tests ) , with the exception of total mosquito catch per unit effort , which was correlated with wave-speed measured in mosquitoes in 2003 using the convex hull method; and mosquito catch per unit effort for Culex species , which was correlated with wave-speed measured in birds and in the combined dataset in 2000 , using the boundary displacement method ( see Tables S2 , S3 and S4 in Text S1 , respectively ) . Given that these variables are the key determinants of vector population dynamics , it is implausible that either separately or collectively they are responsible for the decelerating spread we observed . However , we acknowledge that the pattern of decelerating wave-speed found for WNV in NYC might be explained by other alternative factors that we were unable to explore , e . g . the intensity of dispersal between neighbouring areas of NYC or intensity of local transmission . Our results demonstrate that spatial heterogeneity alone is sufficient to produce the decelerating pattern , and in the absence of support for alternative explanations , we propose it as the mechanism underlying the observed pattern found in New York City . A further alternative hypothesis to explain the pattern we observed is simple stochastic fadeout , where the pathogen goes locally extinct due to a decreasing frequency of transmission events in a finite system of hosts and vectors such that the accumulation of local extinctions is manifest as a decline in spread rate . If the correct system size was known ( i . e . the absolute rather than relative number of hosts and vectors occupying cells of the spatial model ) , it would be possible to investigate this hypothesis rigorously . We believe that such an analysis exceeds current capabilities , however , because choosing a sufficiently small system size will undoubtedly and artefactually lead to stochastic fadeout . Due to this ambiguity , we believe that a stochastic formulation of the model we present here would be unhelpful . In contrast , two counter-arguments suggest that the observed pattern of deceleration is unlikely the result of stochastic fadeout: ( 1 ) In all years ( 2000–2008 ) studied , WNV successfully spreads from one end of New York City ( Staten Island or Queens ) to the other end , whereas a stochastic fadeout would generally lead to the arrest of the pathogen in the part of the city in which it first appeared; ( 2 ) We found evidence of deceleration consistently in all years studied . If the decelerating pattern was due to stochastic fadeout , we would expect to find deceleration in a smaller subset of the annual epizootics studied , since stochastic fadeout depends by definition on random events that break the transmission chain of the pathogen . In contrast , the underlying structure of the habitat in terms of transmission-promoting and transmission-inhibiting land-cover types is constant , supporting a consistent pattern of spread . While stochastic processes undoubtedly take place during the spatial spread of WNV in NYC , we suspect that the decelerating wave pattern we found is better explained by habitat heterogeneity . To conclude our study , we noted that this finding can be deployed to improve control . Unstable transmission implies that the spread of infection might be delayed or even halted by identifying and closing corridors of transmission that link remote susceptible areas and outbreak epicentres . Accordingly , in a final set of analyses we compared five potential control strategies according to their effectiveness in limiting the spread of WNV on a lattice with environmental heterogeneity close to the percolation threshold ( Fig . 6 ) . Of the tested strategies , the most effective was to treat transmission-promoting site locations in the immediate neighbourhood of sites at which infection exceeded a detection threshold ( >5% ) , despite that this resulted in only modest increase in the total number of sites treated . This strategy was also the most effective when simulations were run on a lattice where all sites were transmission-promoting , although the number of sites that required treatment was considerably larger than for other control methods ( Fig . S6 in Text S1 ) . This finding that selectively blocking the propagation of WNV from highly infected sites to transmission-promoting sites in their neighbourhood is a highly effective strategy is consistent with models for other disease systems [37] , [38] , but has not yet been incorporated formally into vector control guidelines [39] . We hereby propose that such strategies be given consideration . Understanding the emergence and spread of vector-borne pathogens in cities remains an important problem for the ecology of infectious diseases . We have shown here that one ubiquitous property of cities , spatial heterogeneity , gives rise to endogenously decelerating waves , a phenomenon that is not known to occur elsewhere . We detected such waves in annual outbreaks of WNV in New York City between 2000 and 2008 and confirmed three important conditions for the observed deceleration to be driven by heterogeneity: ( 1 ) predominance of local dispersal , ( 2 ) association between WNV prevalence and environmental heterogeneity , in this case infection-promoting land cover types , and ( 3 ) prevalence of infection-promoting land cover types in the vicinity of the critical threshold . Our results suggest that towards the end of annual epizootics , when transmission risk to humans is the highest , the extent of the area infected is unlikely to expand considerably . To our knowledge , this is the first study to provide evidence of decelerating waves of infection due to environmental heterogeneity in the absence of a gradient , a result which supports selective treatment of transmission-promoting areas in the vicinity of infected sites as a strategy to delay or even halt disease spread .
The data reported here were collected by the New York Department of Health and Mental Hygiene ( NYCDOHMH ) between 2000 and 2008 . Between 2000 and 2007 , dead birds were voluntarily reported by the public to the Department by phone or in person and then collected by NYCDOHMH personnel . If the condition of the carcass allowed , it was identified to species , and tested by both PCR and ELISA for live WNV as well as for antibodies against WNV . Dead birds were designated positive if both tests showed a positive response . Between 2000 and 2008 , mosquitoes were collected weekly in CDC light and Reiter's gravid traps . Trap catch was separated in the lab to species , and grouped into pools of up to 50 individuals from the same species , on the same date and collected from the same trap . These pools were than tested using PCR for WNV . Geographically coded records were converted to the NAD 1983 State Plane New York Long Island FIPS 3104 coordinate system for mapping and calculation of infected area . Mapping and geostatistical analysis were performed using ESRI ArcGIS and R ( ESRI ArcMap 9 . 2 , R project [40] ) , using R packages PBSmapping , maptools , splancs and spatstat . Mosquito abundance was measured as daily catch-per-unit-effort ( CPUE ) , i . e . , the average number of mosquitoes collected per trap night . Because collections did not occur every day and there was substantial variation in CPUE on subsequent days , we smoothed estimated CPUE using local polynomial regression . We estimated the wave-speed at which WNV spread in NYC using three methods , a convex hull method , a boundary displacement method , and a maximum distance method , as recommended by [41] . The convex hull method consisted of estimating the infected area for every day during annual epizootics as the area of the convex hull encompassing all locations at which WNV was presently or previously detected and calculating daily change in the square root of this area . This method has been shown to introduce a bias if disease spread is anisotropic [31] , as in our case . We corrected for this bias by measuring wave-speed as the average daily increase in the length of transects originating from the epicentre at 22 . 5° increments as those intersect the boundaries of the infected area on subsequent days ( boundary displacement method ) . The maximum distance method consisted of determining the maximum displacement of locations at which WNV was detected with respect to the initial case during each annual epizootic and taking subsequent differences in this quantity . Wave-speed was estimated to be zero on days when WNV was not detected or when it was detected inside the previously estimated infected area ( convex hull and boundary displacement methods ) or closer to the initial case than the prior maximum extent ( maximum distance method ) . When the infected area/distance increased , we normalized the wave-speed by dividing the calculated wave-speed by the number of days since the last observed expansion . To model the spread of WNV in NYC we used a deterministic coupled map lattice with local dynamics given by an extended Ross-MacDonald model [42] , [43] ( Fig . 2a ) . The transmission portion of the model combines an SIR model for reservoir hosts and an SEI model for vector mosquitoes . These equations , which are derived on biological grounds , are similar , but not identical , to previously published models of WNV transmission [44] . Non-biting transmission modes of infection ( host-to-host transmission through cohabitation and scavenging , as well as vector-to-vector transmission through co-feeding [45] ) were initially considered , but later omitted as they affected only R0 and not the pattern of spread ( Fig . S4 in Text S1 ) . State variables and parameters are listed in Table 2 . Host and vector populations were kept constant . No seasonal forcing was included to show that observed patterns of deceleration were endogenously generated by spatial heterogeneity . The transmission model is given by the following equations , and the basic reproductive number [46] was obtained using the spectral radius method [47] . To model dispersal , cells in the first order von Neumann neighbourhood were coupled by allowing a proportion ( 1% ) of the reservoir bird population to disperse in each direction with reflecting boundary conditions at each time step ( i . e . , site percolation ) . As for the analysis of land cover types , cell size is envisioned to represent the typical territory size of birds that are hosts of WNV , i . e . , 50 m ( 50 ( m , corresponding to the territory size of American Robin ( Turdus migratorius ) [35] , a dominant amplifying host in this system . All rate parameters were defined in units per day . In simulations , sites were randomly assigned to transmission-promoting and uninhabitable categories with probability p ( value depending on simulation ) , and uninhabitable sites were constrained to contain no mosquito or bird populations . We initialized each iteration with a single infectious host at the origin of the lattice . Wave speed of WNV in the spatial model was estimated as the rate of change in the estimated infected area encompassing all sites in which >1% of birds became infectious ( 100 ( 100 lattice , 6 , 000 time-steps ) . Numerically erratic behaviour induced by the discrete lattice was smoothed by a moving average with a bandwidth of 500 days . Simulated wave-speed trend was calculated as . Final wave-speed was measured when the first site at the edge of the lattice reached 1% bird prevalence , or at the end of the simulation if the infection failed to reach an edge . We took as evidence of a decelerating wave; for visualization was averaged over 100 realizations for each set of parameters to describe the average wave-speed trend in time ( Fig . 2d ) . In a small subset of realizations infection failed to propagate due to the lack of hospitable sites in the neighbourhood of its origin . In these cases . These realizations were nonetheless included in the calculation of average wave-speed based on the argument that many such failed attempts of spread occur in nature and are integrated into the pattern of spread for WNV in a season , such as we describe in New York City . Since one might alternatively argue that the increasing proportion of failed realizations with decreasing proportion of hospitable sites will bias the wave-speed trend , and could itself lead to an overall decelerating wave-speed close to the percolation threshold , we also calculated the conditional average wave-speed excluding failed realizations . The conditional wave-speed also showed deceleration in the vicinity of the percolation threshold ( Fig . S5 in Text S1 ) , however , in a smaller range than the unconditional wave-speed ( 0 . 54<p<0 . 58 vs . 0 . 52<p<0 . 6 ) . We conclude that decelerating waves are not an artefact of the increasing number of failed realizations as p declines to the critical value . To determine the sensitivity of wave speed to the assumption of local dispersal , in another set of simulations we allowed a proportion of hosts from each transmission-promoting site to disperse to a randomly chosen transmission-promoting site . In the case of global dispersal exclusively , wave speed is undefined and as the system is well-mixed . When global dispersal occurs in conjunction with local dispersal , sites that receive global dispersers initiate local spread in their vicinity if R0>1 . Such long-distance connections have been shown to reduce compared to the case of exclusively local dispersal in analogous systems [33] . We also incorporated long-distance dispersal using a small world-type model , where we rewired 5% of the local connections between sites following standard methods [34] . Simulations using this model were qualitatively similar to simulations with a mixture of global and local dispersal . An alternative characteristic of local dispersal is that the Euclidean distance of the wave-front from the origin ( “displacement” ) increases significantly with time since the start of the outbreak . We investigated how the addition of global dispersal affects this positive correlation by measuring displacement at each time step in simulations . We labelled all sites with >1% infectious hosts in the current time step . To mimic the effect of under-reporting , each labelled site was selected with probability 0 . 17 , the estimated reporting rate for bird decoys in urban environments [48] . Assuming strictly local dispersal , displacement indeed increased with time ( Fig . 4a ) , while there was no correlation between displacement and time when only global dispersal was assumed ( Fig . 4b ) . When local dispersal was supplemented by global dispersal , the positive correlation between displacement and time was retained if at least half of all dispersers spread locally ( Fig . 4c ) . It follows that the significant positive correlation of distance to the origin and time is an indicator of the presence of an intact wave-front and therefore the dominance of local dispersal . The presence of multiple origins did not qualitatively change this pattern when distance was calculated to any of the multiple origins , as the local dispersal around any origin ensures the positive correlation . However , when distance was calculated to a putative origin that was in fact far from the true origin , distance to this false origin could be negatively correlated with time ( Fig . 4d ) . This second criterion was therefore used to reject putative origins of the WNV epizootic in NYC . Prevalence was estimated from the ratio of WNV-positive dead birds to all reported dead birds averaged over 2001–2007 . We obtained a comprehensive land cover map for NYC using the land cover classification from the National Land Cover Dataset 2001 ( http://www . mrlc . gov/nlcd_multizone_map . php ) . We assigned each recovered bird carcass to the unique land cover type in which it was found and performed pair-wise χ2 tests on the number of WNV-positive and total dead birds found in each land-cover type versus all other land-cover types to test the hypothesis of homogeneity ( Table 3 ) . There is strong evidence in the literature that detection and reporting rates of birds differ across land-cover types [48] . However , there is no evidence that the detection and reporting rates of WNV-positive and negative dead birds is significantly different . Since we estimate the WNV prevalence across land-cover types by the ratio of WNV-positive to all dead birds reported , we assume only that the detection and reporting rates of WNV-positive and negative dead birds are the same . In this case , differences in detection and reporting rates of both WNV-positive and all dead birds across land-cover types cancel out in the calculation of WNV prevalence . | Current theory of the spatial spread of pathogens predicts travelling waves at constant or increasing speed in homogeneous environments . However , in urban environments , increasing and often unregulated development produces a highly heterogeneous landscape . Such heterogeneity affects pathogens spread by insect vectors particularly , which typically have short dispersal distances . We hypothesized that high levels of heterogeneity can slow the spread of such pathogens , resulting in decelerating epidemic waves . We analysed the annual spread of West Nile virus ( WNV ) in New York City ( NYC ) , using a dataset containing >1 , 000 , 000 records since the origin of the North American pandemic in 1999 . Our analysis provides the first evidence of endogenous decelerating travelling waves in an emerging infectious disease . We found that WNV spread with decreasing speed in each season and rejected four alternative hypotheses to explain this deceleration . A mathematical model shows that high levels of heterogeneity can lead to such decelerating travelling waves . Interestingly , the level of heterogeneity in land-cover types associated with WNV-positive dead birds in NYC is of the order of magnitude required to produce decelerating travelling waves in the model . Consequently , we propose that control strategies targeting key sites may be effective at slowing WNV spread in NYC . | [
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] | 2011 | Decelerating Spread of West Nile Virus by Percolation in a Heterogeneous Urban Landscape |
Humans often make decisions based on uncertain sensory information . Signal detection theory ( SDT ) describes detection and discrimination decisions as a comparison of stimulus “strength” to a fixed decision criterion . However , recent research suggests that current responses depend on the recent history of stimuli and previous responses , suggesting that the decision criterion is updated trial-by-trial . The mechanisms underpinning criterion setting remain unknown . Here , we examine how observers learn to set a decision criterion in an orientation-discrimination task under both static and dynamic conditions . To investigate mechanisms underlying trial-by-trial criterion placement , we introduce a novel task in which participants explicitly set the criterion , and compare it to a more traditional discrimination task , allowing us to model this explicit indication of criterion dynamics . In each task , stimuli were ellipses with principal orientations drawn from two categories: Gaussian distributions with different means and equal variance . In the covert-criterion task , observers categorized a displayed ellipse . In the overt-criterion task , observers adjusted the orientation of a line that served as the discrimination criterion for a subsequently presented ellipse . We compared performance to the ideal Bayesian learner and several suboptimal models that varied in both computational and memory demands . Under static and dynamic conditions , we found that , in both tasks , observers used suboptimal learning rules . In most conditions , a model in which the recent history of past samples determines a belief about category means fit the data best for most observers and on average . Our results reveal dynamic adjustment of discrimination criterion , even after prolonged training , and indicate how decision criteria are updated over time .
Understanding how humans make decisions based on uncertain sensory information is crucial to understanding how humans interpret and act on the world . For over 60 years , signal detection theory has been used to analyze detection and discrimination tasks [1] . Typically , sensory data are assumed to be Gaussian with equal variances but different means for signal-absent and signal-present trials . To decide , the observer compares the noisy sensory data to a fixed decision criterion . Performance is summarized by d′ ( discriminability ) and c ( decision criterion ) based on measured hit and false-alarm rates . Standard analysis assumes stable performance ( all parameters fixed ) and observer knowledge of the means , variance , prior probabilities and payoff matrix [1 , 2 , 3 , 4 , 5 , 6 , 7] . The assumption of stable performance is problematic for two reasons . ( 1 ) Observers may learn about the environment and use that information to set the decision criterion . ( 2 ) The environment may not be stable or the observer may not believe that the environment is stable . To circumvent these problems , researchers include training sessions , fix the environmental parameters ( e . g . , priors , payoffs ) within blocks , and treat learning effects as additional noise ( i . e . , its “variance” can simply be added to those of internal and/or external noise in the experiment ) . However , research investigating history effects in psychophysical tasks has shown that an observer’s current decision is affected by multiple aspects of the stimulus history ( e . g . , recent decisions , stimulus intervals , trial type , etc . ) . These effects occur even when the environment is stable , the stimulus presentation is random , and observers are well trained [8 , 9 , 10 , 11 , 12 , 13] . Observers behave as if the environment is dynamic and , as a result , measures of discriminability and sensitivity are biased and the confidence intervals computed for the best fitting parameters of the psychometric function are too narrow [14] . While assuming instability in a static world is suboptimal , in a world that is constantly changing a fixed criterion makes little sense . To optimize decisions in dynamic environments , observers must update decision criteria in response to changes in the world by adapting to the value and uncertainty of sensory information . Humans respond appropriately to changes in visual and motor uncertainty [15 , 16 , 17 , 18 , 19 , 20] . Observers adjust the decision criterion when uncertainty is varied randomly from trial to trial [18] . If the location of visual feedback for a reach is perturbed dynamically over trials , participants track this random walk near-optimally [15] . Landy and colleagues [17] demonstrated that participants tracked discrete changes in the variance of a visual perturbation . Summerfield and colleagues [19] investigated a visual discrimination task in which participants categorized gratings with orientations drawn from two overlapping distributions . Means and variances were updated randomly with different levels of volatility . Participants’ performance changed as a function of volatility . While the above studies examine the dynamics of decision-making , they only provide indirect evidence of criterion shifts . Many of these studies observed changes in decisions and response time , but few studies have examined how trial history specifically affects decision criteria and what is the underlying mechanism responsible for learning and updating the decision criterion . Lages and Treisman [13] describe the dynamics of criterion setting and updates of priors based on previous stimulus samples and responses applied to tasks with no experimenter feedback , so that the criterion drifts to the mean of previously experienced stimuli . Summerfield and colleagues [19] consider a discrimination task with feedback in which the categories and their associated uncertainty can change several times per block of trials . They compare several suboptimal models , all of which predict the choice probability by probability matching . Here , we investigate how humans learn to set and update criteria for perceptual decisions in both static and dynamic environments . To examine the underlying mechanisms of criterion learning , we take a quantitative approach and compare models of how a decision criterion is set as a function of recently experienced stimuli and feedback . Observers completed two different experimental tasks . One task was the typical discrimination task , in which the observer’s criterion is unobservable . We introduce a novel overt-criterion task , in which the decision criterion is set explicitly by the observer . This allows us to measure and model the setting of the decision criterion directly . We used the overt-criterion task , which has greater statistical power due to the richer dataset , to develop and test models of how the criterion is updated in standard discrimination experiments under uncertainty . In contrast to the models investigated by Summerfield and colleagues [19] , we directly measure the criterion , and include parameters for sensory noise and predict a specific response based on the noisy stimulus information and a model of criterion update . While observers converged to the optimal criterion over many trials when conditions were static and followed dynamic changes in the category means , we found that , in both tasks , the majority of observers used suboptimal learning rules . Our results reveal dynamic adjustment of a discrimination criterion , even after prolonged training in a static environment .
All observers completed three tasks: ( 1 ) An orientation-discrimination task in which discrimination thresholds were measured and used to equate the difficulty of the covert- and overt-criterion tasks across observers ( Fig 1A ) , ( 2 ) A covert-criterion task in which observers categorized an ellipse as belonging to category A or B ( Fig 1C ) , and ( 3 ) An overt-criterion task in which observers explicitly indicated their criterion on each trial prior to the presentation of a category A or B ellipse ( Fig 1D ) . Additionally , 8 out of 10 observers completed an orientation-matching task in which adjustment noise was measured ( Fig 1B ) . Categories in the covert- and overt-criterion tasks were Gaussian distributions with different mean orientations and equal variance ( Fig 1E; see Methods ) . In Expt . 2 , observers completed the same tasks in Expt . 1 . However , the environment was dynamic and only 3 out of 10 observers completed the orientation-matching task . Category distributions were Gaussian distributions with different mean orientations and equal variance , but means of the category distributions changed gradually over time via a random walk ( see Methods ) .
The present study examined the strategies observers used to learn and update their decision criterion in an orientation-discrimination task under both static and dynamic uncertainty . Under static conditions in which category means were constant , we showed that while observers converged to the optimal criterion over many trials , their trial-by-trial behavior was better described by suboptimal learning rules than by the optimal rule . Thus , even though conditions were static , the criterion continued to systematically drift with changes in stimulus statistics throughout the experiment . Under dynamic conditions in which category means changed slowly over time , observers followed changes in the means of the category distributions closely with a 1–4 trial lag . Specifically , we found that at the group level a model in which the recent history of past samples determines a belief about category means , the exponentially weighted moving-average rule , was more likely than the alternative models across most tasks and conditions with the exception of the overt-criterion task under dynamic conditions in which the reinforcement learning model was more likely . Our results suggest that the decision criterion is not fixed , but is dynamic , even after prolonged training . Finally , we provided a novel technique , the overt-criterion task , which can be used to explicitly measure criterion placement and a computational framework for decision-making under uncertainty in both static and dynamic environments . Based on findings in the visuo-motor and reinforcement-learning literature , in which feedback is gradually or discretely updated [15 , 16 , 17 , 20 , 21] , we would expect a model in which recent samples are given more weight to better explain performance under dynamic conditions . However , this is a suboptimal strategy under static conditions . Nevertheless , research on history effects in psychophysical tasks suggests that observers behave as if the environment is dynamic , which is consistent with our results [8 , 9 , 10 , 11 , 12 , 13 , 14] . Furthermore , the regression analysis we performed in Expt . 1 revealed beta weights for the covert- and overt-criterion tasks that suggest an exponentially weighted average of the previous stimuli . Overall , our analysis provides additional evidence against the ideal-observer model and the assumption of a stable criterion , even in static environments . Intuitively , in a world that is constantly changing , it makes sense to continually update your decision criterion , weighting your most recent experiences more heavily . The previous study that is closest in spirit to the current work is that of Summerfield and colleagues [19] . Their experiment was similar to ours; in their case category means changed suddenly at every 10 or 20 trials , and category variances could also change . However , they used a traditional discrimination task without explicit measurement of the criterion , and their primary analysis used the predictions of each of three models in a decidedly suboptimal manner: probability matching . They found two extremely different models , a limited-memory model and a Bayesian model ( that uses probability matching rather than the optimal decision ) both accounted for significant amounts of variance in their data . In our analysis , we are interested in the entire sequence of computations from estimating the stimulus parameter of interest ( orientation , perturbed by sensory noise ) through the binary category decision , and compare a wider array of models that include the ideal observer . We found that suboptimal models that use the recent history of past samples best accounted for both covert and explicit criterion setting [19] . While a dynamic decision criterion might be useful in the real-world , by using such a criterion ( especially in Expt . 1 ) in our experiments , observers are making suboptimal inferences about the category membership of an ellipse . These results are consistent with the idea that suboptimal inference is more than just internal noise [22] . This is also consistent with the overestimates of the noise parameters that we find in our model fits , which suggests that there is additional noise beyond sensory and adjustment . Acuña and Schrater [23] suggest that seemingly suboptimal decisions in sequential decision-making tasks can be accounted for by uncertainty in learning the structure of the task . Uncertainty about the structure of the environment could affect observers’ criterion placement ( i . e . , observers might be uncertain as to whether the category parameters are changing and/or the rate of change ) . In novel situations , one must learn the task structure and the parameters of the environment to perform optimally . For the purpose of increasing statistical power for our model comparison , we introduced a novel task , in which we made the decision criterion explicit . Previous research suggests that participants change strategies when implicit tasks are made explicit [24 , 25 , 26] . Specifically , participants who perform optimally during an implicit task are not optimal when the task is made explicit . This is thought to be a result of higher-level strategic adjustments interfering with lower-level processing . While strategies were fairly consistent under static conditions , we found a clear difference in preferred strategy under dynamic conditions . Specifically , we found the exponentially weighted moving-average model fit best in the covert-criterion task and the reinforcement learning model fit best in the overt-criterion task . Additionally , we observed a difference in the exponentially weighted moving-average model’s decay rate and the reinforcement learning model’s learning rate . Across experiments , the decay and learning rates under static conditions were slower than decay and learning rates under dynamic conditions . However , there was also a difference across tasks . In both experiments , the decay rate was slower in the overt- than the covert-criterion task and the learning rate was faster in the overt- than covert-criterion task . Since a slower decay rate is beneficial under static conditions but disadvantageous under dynamic conditions and a faster learning rate is beneficial under dynamic conditions but disadvantageous under static conditions , the parameter differences we observed might explain the differences we see in the preferred strategies used across tasks . In particular , this may explain why the reinforcement learning model performed better than the exponentially weight moving-average model in the overt-criterion task under dynamic conditions . The differences in decay and learning rate between the covert- and overt-criterion tasks might be due to a difference in time-scale that results from the temporal dynamics of the two tasks ( the overt-criterion task took twice as long to complete the same number of trials ) or due to the different levels of processing ( e . g . , sensory vs . motor ) required for each task . In the future , it might be interesting to see how the decay and learning rates trade off as a function of the rate of change ( i . e . , the random-walk variance ) in the experiment . Previous research shows that participants update the decision criterion when changes to the prior probabilities and payoff matrix occur [1 , 2 , 3 , 4 , 5] . There is a systematic bias in these shifts: Humans exhibit conservatism , that is , a bias towards the neutral criterion when the optimal criterion is shifted away from neutral . While several hypotheses have been proposed as to why conservatism occurs , most recently Ackermann and Landy [2] have suggested that conservatism can be explained by distorted probability and utility functions . Our results do not explain this bias , but it is likely that conservatism is present and contributes to the dynamics of trial-by-trial criterion shifts under the conditions of Expt . 2 . To provide a better understanding of this bias , further research should aim to examine criterion learning in situations in which conservatism is known to exist . Finally , psychophysical studies rely heavily on accurate estimates of d′ . By calculating d′ from hit rates and false alarms in the usual way , a fixed criterion is assumed . However , if the observer’s criterion varies over trials , performance will be a mixture of multiple points on the ROC curve , resulting in a biased ( too-low ) estimate of d′ . We have shown here that decision criteria are adjusted dynamically . Examining the dynamics of trial-by-trial criterion placement provides us with a richer understanding of participants’ behavior when making decisions in the presence of uncertainty . Our results suggest that typical estimates of d′ are biased , and that by using a model that accounts for a dynamic criterion we can compute a more accurate measure of discriminability and in turn , obtain a more comprehensive understanding of discrimination under uncertainty .
This research involved the participation of human subjects . The Institutional Review Board at New York University approved the experimental procedure and observers gave informed consent prior to participation . Ten observers participated in Expt . 1 ( mean age 25 . 4 , range 20–33 , 5 females ) and Expt . 2 ( mean age 23 . 4 , range 20–28 , 4 females ) . Five observers provided data for both experiments , three of whom completed Expt . 1 prior to completing Expt . 2 . All observers had normal or corrected-to-normal vision . One of the observers ( EHN ) was also an author . Stimuli were presented on a gamma-corrected Dell Trinitron P780 CRT monitor with a 31 . 3 x 23 . 8 deg display , a resolution of 1024 x 768 pixels , a refresh rate of 85 Hz , and a mean luminance of 40 cd/m2 . Observers viewed the display from a distance of 54 . 6 cm . The experiment was programmed in MATLAB ( MathWorks ) using Psychophysics Toolbox [27 , 28] . Stimuli were 10 x 2° ellipses presented at the center of the display on a mid-gray background ( Fig 1 ) . In the orientation-matching and overt-criterion tasks , a yellow line was presented at the center of the display ( 10 x . 35° ) . In all tasks except the overt-criterion task , trials began with a central yellow fixation cross ( 1 . 2° ) . Ten observers participated in one , 1 . 5-hour session consisting of an orientation-discrimination task ( ~10 min ) , a covert- and an overt-criterion practice block ( ~5 min combined ) , one block of the covert-criterion task ( ~20 min ) , and one block of the overt-criterion task ( ~40 minutes ) . The order of the covert- and overt-criterion tasks was randomized across subjects . Eight out of ten observers returned for a second session in which they completed an orientation-matching task ( ~20 minutes ) . Before starting the experiment observers were given detailed instructions regarding the tasks they would be asked to complete . The two short ( 20 trial ) practice blocks were used to ensure that observers understood the experimental tasks . Before each block , a condensed version of the instructions and the name of the task were shown to remind observers of the procedure and inform them of the task they would be completing on that block . Expt . 2 was similar to Expt . 1 except that observers did not complete the orientation-matching task and the category distribution means in the covert- and overt-criterion tasks were not constant throughout the block . Rather , category means were updated on every trial following a random walk . The category A mean on trial n+1 was μA , n+1 = μA , n+ε , where ε ~N ( 0 , σrandom ) and σrandom = 5° . The relative position ( μA < μB ) and the distance between the means remained constant . In the covert-criterion task , the statistical structure of the task involves three variables: category C , stimulus orientation S , and measurement X . On each trial , C is drawn randomly and determines whether S is drawn from category A ( N ( μA , σ ) ) or category B ( N ( μB , σ ) ) . We assume that on each trial , the true orientation is corrupted by sensory noise ( with standard deviation σv ) to give rise to the observer’s measurement of orientation ( X~N ( S , σv ) ) . The observer uses this measurement to infer the category . In the overt-criterion task , the statistical structure of the task involves five variables: criterion orientation θc , criterion placement z , category C , stimulus orientation S , and measurement X . On each trial , criterion orientation is inferred from the previous trials . We assume that criterion orientation is corrupted by adjustment noise ( z~N ( θc , σa ) ) . After the criterion is set , C is drawn randomly and determines whether S is drawn from category A ( N ( μA , σ ) ) or category B ( N ( μB , σ ) ) . As in the covert-criterion task , we assume the true orientation of the stimulus is corrupted by sensory noise ( X~N ( S , σv ) ) . Finally , the observer uses this measurement and the feedback about its category membership to update the criterion orientation for the next trial . We found that model fits for the overt case could not discriminate adjustment noise ( σa ) from sensory noise ( σv ) , and so for this case , sensory noise was fixed and only an adjustment noise parameter was fit . Sensory noise was set to each observer’s measured sensory uncertainty . Below we describe both optimal and suboptimal models of criterion learning that vary in computational and memory demands . The selection of the following models was partially inspired by the models used in Summerfield and colleagues’ research [19] investigating perceptual classification strategies in rapidly changing environments , in which they compared a Bayesian observer model to a Q-learning model and a heuristic model that is similar to our limited-memory model . In their models , sensory noise is omitted , and in its place , a fixed degree of trial-trial choice variability is introduced by a probability-matching rule . In contrast , we compare a more extensive set of models that include parameters controlling the level of sensory noise and predict a specific response based on the noisy stimulus measurement and a model of criterion update . | Understanding how humans make decisions based on uncertain sensory information is crucial to understanding how humans interpret and act on the world . Signal detection theory models discrimination and detection decisions as a comparison of “stimulus strength” to a fixed criterion . In a world that is constantly changing a static criterion makes little sense . We investigate this as a problem of learning: How is the decision criterion set when various aspects of the context are unknown ( e . g . , category means and variances ) ? We examine criterion learning in both static and dynamic environments . In addition to a more traditional discrimination task in which the criterion is a theoretical construct and unobservable , we use a novel task in which participants must explicitly set the criterion before being shown the stimulus . We show that independent of environment and task , observers dynamically update the decision criterion , even after prolonged training in a static environment . Our results provide evidence against an assumption of stability and have implications for how psychophysical data are analyzed and interpreted and how humans make discrimination decisions under uncertainty . | [
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] | 2017 | Suboptimal Criterion Learning in Static and Dynamic Environments |
Trypanosomatid parasites of the genus Leishmania are the causative agents of leishmaniasis , a neglected tropical disease with several clinical manifestations . Leishmania major is the causative agent of cutaneous leishmaniasis ( CL ) , which is largely characterized by ulcerative lesions appearing on the skin . Current treatments of leishmaniasis include pentavalent antimonials and amphotericin B , however , the toxic side effects of these drugs and difficulty with distribution makes these options less than ideal . Miltefosine ( MIL ) is the first oral treatment available for leishmaniasis . Originally developed for cancer chemotherapy , the mechanism of action of MIL in Leishmania spp . is largely unknown . While treatment with MIL has proven effective , higher tolerance to the drug has been observed , and resistance is easily developed in an in vitro environment . Utilizing stepwise selection we generated MIL-resistant cultures of L . major and characterized the fitness of MIL-resistant L . major . Resistant parasites proliferate at a comparable rate to the wild-type ( WT ) and exhibit similar apoptotic responses . As expected , MIL-resistant parasites demonstrate decreased susceptibility to MIL , which reduces after the drug is withdrawn from culture . Our data demonstrate metacyclogenesis is elevated in MIL-resistant L . major , albeit these parasites display attenuated in vitro and in vivo virulence and standard survival rates in the natural sandfly vector , indicating that development of experimental resistance to miltefosine does not lead to an increased competitive fitness in L . major .
Leishmaniasis is caused by protozoan parasites of the genus Leishmania , and presents as a variety of clinical manifestations ranging from lesions on the skin to disseminated visceral infections [1] . Cutaneous leishmaniasis ( CL ) often results in self-resolving lesions , whereas visceral leishmaniasis ( VL ) is habitually fatal when left untreated . With an annual incidence of 2 million cases and a prevalence of more than 12 million , leishmaniasis is responsible for 70 , 000 deaths annually [2] . 88 countries have reported infection , resulting in 350 million individuals at risk for infection and an estimated 2 . 4 million disability-adjusted life years ( DALYs ) [2] . These statistics are grossly underestimated due to misdiagnosis and insufficient disease surveillance systems . Leishmania species have a digenetic life cycle including both extracellular promastigote and obligate intracellular amastigote forms . Extracellular flagellated promastigotes reside in the midgut of the phlebotomine sandfly vector . Following infection in the mammalian host , promastigotes are engulfed by macrophages where they differentiate into non-motile amastigotes in the phagolysosome . This differentiation is triggered by environmental cues , mainly pH and temperature [3] . Current antileishmanial drugs include pentavalent antimony , amphotericin B , paromomycin , pentamidine , and miltefosine; most are toxic and expensive . To date , no successful vaccine exists , and the few antileishmanial drugs mentioned either risk becoming ineffective due to emerging resistance , or are limited in their use due to cost and parental administration [4 , 5] . Miltefosine ( MIL ) is an alkylphosphocholine drug with demonstrated activity against various parasite species and cancer cells , as well some pathogenic bacteria and fungi [6] . Since its registration in 2002 , miltefosine remains the only oral agent used for the treatment of all types of leishmaniasis . The U . S . Food and Drug Administration ( FDA ) recently ( March 2014 ) approved Impavido ( miltefosine ) for the treatment of cutaneous , visceral and muco-cutaneous leishmaniasis . While the mechanism of action of MIL is not understood in its entirety , several studies have pointed at alterations in phospholipid metabolism , impairment of bioenergetic metabolism , and ultimately the induction of apoptosis as potential modes of actions [7–10] . Knowledge of experimental MIL resistance in Leishmania is limited to defects in drug internalization ( defective inward translocation of MIL ) and increased drug efflux [11] . Previous investigations in L . donovani have revealed the presence of several key point mutations in the P-type ATPase dubbed the LdMT ( L . donovani miltefosine transporter ) [12] . However , subsequent studies demonstrated that the LdMT alone was not sufficient to facilitate translocation , leading to the identification of the β-subunit LdRos3 and its importance to the function of the LdMT [13] . Mutations in the LdMT and Ros3 contribute to the MIL-resistant phenotype by significantly decreasing MIL uptake . Specifically , T420N and L856P mutations in the LdMT contributed to significantly decreased MIL uptake [12] . Other mutations identified in MIL-resistant L . donovani include W210 ( LdMT ) and M1 ( LdRos3 ) [14] . Sequencing of the entire miltefosine transporter was performed in both L . major and L . infantum , and all identified sequence mutations differed from those previously detailed in L . donovani ( L856P , T420N , W210 , and M1 ) [15] . In the same study , no mutations were observed in the β-subunit Ros3 in any of the MIL-resistant populations . Widespread clinical resistance has not yet been demonstrated , nonetheless two L . infantum isolates from HIV co-infected patients have been reported to exhibit MIL resistance [16 , 17] . The analysis of clinical isolates from patients infected with L . donovani that had relapsed to standard MIL therapeutic regimes demonstrated that the recovered parasites were significantly more tolerant to MIL [14] . None of the resistance markers i . e . point mutations aforementioned were found in the isolates . In the absence of a definitive mechanism of miltefosine resistance , the concept of fitness or “proficiency” of drug resistant pathogens is becoming more relevant and how the acquisition of resistance may impact the life cycle of the parasite , particularly its capacity to survive both in the insect and mammalian hosts and thus its ability to compete with wild type ( sensitive ) parasites [18–20] . Most of these studies are focused on antimony resistance in L . donovani and more recently , drug combinations [21] . Here we present the characterization and fitness of clonal lines of L . major that have experimentally acquired resistance to miltefosine , with relevance to survival in the mammalian host and phlebotomine vector .
All studies using vertebrate animals were conducted in accordance with the U . S . Public Health Service Policy on Humane Care and Use of Laboratory Animals and followed the standards as described in the Guide for the Care and Use of Laboratory Animals . Per these standards , all vertebrate animal studies were conducted following review by the University of Notre Dame Institutional Animal Care and Use Committee under protocol #15–047 ( approved October 16 , 2012 ) . The University of Notre Dame is credited through the Animal Welfare Assurance #A3093-01 . Leishmania major strain Friedlin V1 ( MHOM/JL/80/Friedlin ) promastigotes were cultured at 27°C in M199 medium ( medium 199 ( CellGro ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) , 20 mM HEPES , 10 mM adenine , penicillin/streptomycin , hemin , biotin , L-glutamine , and 7 . 5% NaHCO3 ) and passaged every 3–4 days . Macrophages ( RAW264 . 7 cell line ) were cultured at 37°C with 5% CO2 in RPMI supplemented with 10% heat-inactivated FBS , penicillin/streptomycin , and L-glutamine , and passaged every 2–3 days . MIL-resistant cultures of L . major were generated using step-wise selection . Cultures were passaged every 3–4 days at an initial concentration of 5x105 promastigotes/mL . Increasing concentrations of MIL ( Sigma ) were introduced to the cultures beginning with 2 . 5 μM MIL and successively to 5 , 8 , 10 , 15 , 20 , 30 , and 40 μM MIL . Cultures were exposed to an increased concentration of MIL when growth rates were equivalent to the growth rate of the wild-type ( WT ) . To account for clonal variation , 2 clones of each resistant line were generating by plating in M199 plates as previously described [22] . Clones 1 and 2 were simultaneously maintained . Growth rates were measured for each set of resistant populations and compared with the WT strain . Parasites were counted at an initial concentration of 5x105 parasites/mL and growth was measured daily using a Neubauer chamber until the population reached stationary phase . To further assess stability and fitness , two fluorescent FACS-based apoptotic markers were used to evaluate MIL-selection . Membrane permeability was assessed using the kit YO-PRO1 ( Invitrogen ) according to manufacturer’s recommendations . Briefly , samples were pelleted and washed in 1X M199 complete media . Following the wash , samples were resuspended in 1X M199 complete media and YO-PRO ( Invitrogen ) and Propidium Iodide ( Invitrogen ) were added and incubated for 20 minutes . Exposure of phosphatidylserine ( PS ) residues was investigated with Annexin-V-FITC ( Miltenyi Biotec ) following manufacturer’s instructions . Analyses were performed in a Beckman Coulter FC500 Flow Cytometer . In order to assess the MIL-resistance achieved , the half-maximal effective concentration , EC50 , was performed using the resazurin-based CellTiter-Blue ( Promega ) method as previously described [23] . Cultures were counted using a Neubauer chamber . 1x106 parasites/mL were incubated for 48 hours at 27°C in M199 medium ( CellGro ) and appropriate concentrations of MIL ( Sigma ) , pentamidine isethionate ( Sigma ) , amphotericin B ( Sigma ) , potassium antimony ( III ) tartrate hydrate ( Sigma ) and paromomycin sulfate salt ( Sigma ) , were used in order to accurately evaluate the resistance . Solvent ( DMSO ) controls were used where appropriate . Hundred μL from each well were incubated at 37°C at 5% CO2 for 4 hours with 20 μL Cell Titer Blue ( Promega ) . Fifty μL of 10% SDS were added to each well , and fluorescence was measured ( 555 nm λexc/580 nm λem ) using a Typhoon FLA-9500 laser scanner ( GE Healthcare ) and analyzed with ImageQuant TL software ( GE Healthcare ) . EC50 values were calculated by non-linear regression analysis using SigmaPlot ( v 11 . 0 ) . All experiments were done in triplicate with appropriate controls in each case . Both WT and MIL-resistant cultures were sequenced for previously described point mutations in the L . donovani MT ( T421N , L856P , W210* ) and Ros3 subunit ( M1 ) [14] and in L . major ( G852D , M547del ) [15] . DNA was amplified with primers outlined in S1 Table . PCR product sizes ranging from 149–277 bp were purified using the GeneJET Gel Extraction Kit ( Thermo ) and sent to the Genomics Core Facility at the University of Notre Dame for sequencing . Sequences were analyzed using ClustalX [24] . Total RNA was isolated from logarithmic and stationary phase promastigotes using Trizol Reagent ( Invitrogen ) , reverse transcribed with SuperScript II Reverse Transcriptase ( Invitrogen ) after deoxyribonuclease I treatment with TURBO DNA-free Kit ( Ambion , Invitrogen ) . All qRT-PCR reactions were performed in triplicate using SYBR Green ( Invitrogen ) fluorescence for quantification in a 7500 Fast Real-Time PCR System ( Applied Biosystems ) . The ΔΔCƬ method was used to determine relative changes in gene expression [25] with data presented as fold change in the target gene expression in L . major MIL-resistant cultures normalized to internal control genes GAPDH and SOD , using L . major WT as a reference strain . Standard PCR conditions were: 95°C for 10 min , followed by 40 cycles of 94°C for 1 min , 60°C for 1 min , and 72°C for 2 min . Primer design was based on nucleotide sequences of L . infantum genes coding for the L . donovani MT , L . donovani Ros3 , SHERP , GAPDH and SOD genes . All experiments were performed in triplicate with appropriate controls included in each case . Two different methods were utilized to assess metacyclogenesis as described previously [26] . Briefly , a Ficoll ( Sigma ) gradient was set-up using 4 mL of 20% Ficoll overlaid with 4 mL 10% Ficoll in M199 medium without FBS and 4 mL of 5-day stationary-phase culture in M199 medium laid on top . The step gradients were centrifuged at room temperature for 10 min at 1300 x g without braking or acceleration to separate out the layers . The top two layers of the gradient were recovered and the percentage of metacyclic parasites was determined by counting in a Neubauer chamber before and after the enrichment procedure . For agglutination analysis , 5-day stationary-phase cultures were pelleted and resuspended in 1 mL M199 medium ( CellGro ) and 10 μL peanut agglutinin ( 50 μg/mL ) ( Sigma ) was added . After 30 minutes of room temperature incubation , samples were centrifuged at 200g for 10 minutes . The supernatant was recovered and the percentage of metacyclic parasites was determined by counting in a Neubauer chamber before and after the enrichment procedure . All experiments were done in triplicate . RAW264 . 7 murine macrophage cells were counted using Trypan Blue ( Amresco ) and plated at 5x105 cells/well in 12-well plates . Infections were performed with metacyclic parasites isolated as described above . Infections were carried out at a multiplicity of infection ( MOI ) of 10 parasites per macrophage . Free parasites were removed by one wash with RPMI without FCS 6 h post-infection and samples collected at 6 , 12 , 24 and 48 h post-infection by DiffQuick staining of cytospin whole-cell preparations and visualized with light microscopy . All infections were done in triplicate and at least two independent experiments were performed . Phlebotomus papatasi ( Origin: Turkey , PPTK ) was reared in the Department of Biological Sciences , University of Notre Dame , according to conditions previously described [27] . For the experiment , three-to-five day old female sandflies were used . Two groups , one experimental and one control , each containing 50 female and 10 male sandflies were placed in a 500 mL plastic container ( ø = 6 . 3 cm , height = 6 . 5 cm ) ( Thermo-Nalgene ) covered with a piece of nylon mesh ( 0 . 5mm ) . Blood feeding was performed through a young chicken skin membrane attached to a feeding device . Prior to sandfly feeding , fresh mouse blood was heat inactivated for 30 min at 56°C . Infection of sandflies with L . major FVI strain promastigotes was done by addition of 1×107 logarithmic parasites/mL into the blood meal . Sixteen to twenty four hours after blood feeding , the presence or absence of blood in the sandfly digestive tract was verified by anesthetizing flies with CO2 and observing the midgut distension under a stereomicroscope ( Carl Zeiss ) . One week post-blood meal , midguts of blood-fed sandflies were individually dissected and thoroughly homogenized in 30 μl PBS buffer ( pH 7 . 4 ) using a hand held tissue homogenizer and pestle . Parasites were counted in a Neubauer chamber . 5x105 metacyclic parasites isolated by peanut agglutinin ( see above ) from stationary cultures of L . major FVI were injected subcutaneously in the left hind footpad of Balb/c mice , as previously described [26] . Lesion development was monitored by measuring weekly the thickness of the footpad using a Vernier caliper . Number of parasites at lesion site were enumerated by limiting dilution assay [28] . Cell lines were passaged at least once through mice before performing in vivo virulence studies to minimize the loss of virulence after prolonged in vitro culture . Significance was determined by p-values calculated from a two-tailed student’s T-test in GraphPad Prism 6 . 0 unless otherwise stated . L . donovani MT: GenBank accession number AY321397 . 1; L . donovani Ros3 GenBank accession number DQ205096 . 1; SHERP: GenBank accession number XM_001683391; GAPDH: GenBank accession number XP_001684904 , and SOD: GenBank accession number XP_001685502 .
L . major FVI MIL-resistant parasites were generated using step-wise selection up to 40 μM MIL . Parasites were unable to proliferate in higher MIL concentrations , likely due to reaching the critical micellar concentration of MIL leading to degradation of the membrane due to the detergent effects of MIL [29] . FVI WT promastigotes were plated in solid M199 media and two random clones were used for MIL selection in flasks . In order to assess the degree of MIL-resistance in our lab populations of L . major we measured EC50 values using the resazurin-based CellTiter-Blue ( Promega ) assay . MIL-resistant cultures exposed to the highest concentrations of MIL ( 30 μM , 40 μM ) , and labeled R30 and R40 herein , have accordingly higher EC50 values than R10 and R20 ( Fig 1 ) . MIL-resistant cultures growing in the absence of MIL exhibited lower EC50 values than their counterparts under constant MIL-selection . However , it is important to note that this decreased EC50 value of MIL-resistant L . major is still higher than the EC50 of WT L . major cultures ( Fig 1 , dotted line ) after at least 95 passages ( 2 passages per week , ca 11 months ) . This suggests that once any degree of resistance is accrued MIL-resistant cultures do not revert back to WT phenotype , despite the removal of MIL selective pressure ( Fig 1 ) . It is worth noting that a different resistant phenotype may be obtained if drug selection is performed in axenic promastigotes or intracellular amastigotes , as shown for paromomycin selection in antimony-resistant L . donovani [17 , 30] . We next determined any difference in growth patterns between the sensitive ( WT ) , resistant ( R30 ) and resistant grown in the absence of MIL ( R30no ) L . major populations . Growth curves showed that MIL-resistant L . major proliferation is similar to L . major WT and cured lines ( Fig 2 ) , indicating that increased MIL exposure has no effect on proliferation in L . major . We used a FACS-based approach to detect two different apoptotic markers i ) membrane permeability and ii ) PS exposure to determine the response of parasite to stress after MIL selection . L . major R30 cell lines exhibit minimal stress and are comparable to WT populations judging the histogram levels corresponding to Annexin V and YO-PRO as analyzed by flow cytometry ( S1 Fig ) . Experimental MIL-resistance in L . donovani has previously been attributed to identified point mutations in the MT and Ros3 subunit ( T421N , L856P , W210 , and M1 ) [31] . We sequenced the regions of the transporter and subunit in two independent clones of the R40 line ( highest concentration; R40 . 1 and R40 . 2 ) that had been under drug selection for at least 75 passages . As shown in Table 1 , these mutations were not found in our lab populations . These results are in accordance with previous characterization of MT in MIL-resistant L . major [15] . Two genuine mutations identified in the L . major MT were pinpointed for this study: a three-nucleotide deletion ( M547del ) and a transition mutation ( G852D ) [15] . As seen in Table 1 , our lab populations displayed identical sequences to WT . Although our data do not eliminate the possibility of other unidentified genetic mutations having a role in MIL-resistance in L . major , it is interesting to observe that even at higher concentrations ( R40 ) and after long-term exposure to MIL ( at least 75 passages ) none of the reported mutations were found . We investigated the possibility of any conferred resistance to alternative antileishmanial treatments by measuring EC50 values as described in Material and Methods . No cross-resistance was found in any of the R30 clones or cured lines to amphotericin B , antimony ( III ) and paromomycin ( Table 2 ) . Interestingly , miltefosine resistance significantly increases the sensitivity of the parasite to treatment with pentamidine 3-fold lower than WT ( Table 2 ) . When MIL has been withdrawn , the sensitivity of the parasite to this particular treatment is restored to levels comparable with the wild-type ( Table 2 ) , suggesting a potential synergistic mechanism . A similar synergy has been reported for sitamaquine/pentamidine combinations in L . donovani [32] , although the use of a combined therapy of miltefosine and pentamidine is hindered by the high toxicity of pentamidine [33] . Lastly , treatment of R30 MIL-resistant cultures with paromomycin had a significant effect on the sensitivity ( ranging from 2–4 fold lower than WT ) of one of the clones ( R30 . 2 ) , indicative of potential clonal variability . Procyclic L . major promastigotes differentiate into highly virulent metacyclic promastigotes during metacyclogenesis [34] . This process occurs in the midgut of sandflies and can be mimicked in vitro when acidification occurs in the medium . Due to the lack of phenotypic differences in our clonal lines we performed the following in vitro and in vivo experiments with the R40 . 2 line . We enriched metacyclic promastigotes by Ficoll 400 step gradient and peanut agglutination , as described in Material and Methods . Analyses of metacyclogenesis showed that L . major R40 had higher percentages ( 2-fold ) of metacyclics than L . major WT ( Fig 3 , right panel ) . qRT-PCR was used to amplify SHERP gene , which is almost exclusively and highly expressed in infective and non-replicative stages of the parasite [35] . SHERP expression was significantly elevated in R40 parasites ( Fig 3 , left panel ) , confirming our metacyclic enrichment approaches . Increased metacyclogenesis has been reported in antimony-resistant L . donovani clinical isolates [36] , and metacyclogenesis is regarded as a major contributor to the fitness of the parasite . In New World cutaneous species , L . mexicana resistant to Glibenclamide , an ATP-binding-cassette ( ABC ) -transporter blocker exhibited a reduced expression of the Meta-1 protein [37] . The stationary phase-specific differences of R40 primed us to study their capacity to infect RAW264 . 7 murine macrophage cells . We routinely passage our L . major cell lines through Balb/c mice to compensate for the loss of virulence due to in vitro culture . 5-day stationary cultures were subjected to peanut agglutination , and R40 and WT lines were incubated with RAW264 . 7 cells at a multiplicity of infection of 10 metacyclics per host cell . Intracellular parasite burden was determined by nuclear staining and microscopy at 6 , 12 , 24 , and 48 h postinfection . Initial levels of R40 infections are comparable to the control ( Fig 4A ) . A significant difference in R40 infectivity was apparent 48 hours post infection . This was further corroborated by decreased intracellular proliferation of R40 cells 48 hours post infection by over 20% ( Fig 4B ) . Pentamidine-resistant L . mexicana showed no differences in the in vitro infectivity in resident mouse macrophages when compared with the wild-type clone [38] . In contrast , higher metacyclogenesis levels in clinical isolates of L . donovani resistant to antimony translated into higher in vitro infection levels [36] . We next investigated the virulence of WT and R40 using an established experimental mouse infection [39] . Control and R40 were normalized for virulence through one passage in Balb/c mice [40] . 105 WT and R40 metacyclic parasites were inoculated into the hind footpad of groups of five-six female Balb/c mice . A Vernier caliper was used to monitor lesion formation by measuring the increase in footpad size weekly . Control parasites attained a lesion size of ca . 4 mm , 5 weeks after inoculation and resulted in necrotic lesions ( Fig 5 ) . Interestingly , R40 were highly attenuated and lesions were only apparent 4 weeks after infection . Our observations in vitro with R40 cells showing a decreased infectivity and intracellular proliferation seem to have extended well to an in vivo mouse model . Amphotericin-resistant L . mexicana parasites were able to infect Balb/c mice , but the resulting lesion growth was slower than that after infection with susceptible parasites [41] . In contrast , several clinical isolates of L . donovani resistant to pentavalent antimonials showed a greater virulence in a mouse model of visceral leishmaniasis [42] . Importantly , our data suggest that metacyclogenesis alone is not a reliable marker of fitness , at least in MIL-resistant L . major , and in vitro and in vivo studies are necessary to further assess its competitive fitness . In this scenario , the L . major / MIL combination resembles the reduction in fitness widely observed in Plasmodium falciparum populations resistant to chloroquine [43] . Fitness of Leishmania parasites is linked to transmission success in the natural insect vector , therefore we tested whether MIL resistance would impact the capacity of Leishmania to survive in the natural sandfly vector . Three-to-five day old female Phlebotomus papatasi ( Origin: Turkey , PPTK ) sandflies were infected with 1×107 logarithmic parasites/mL as described in Material and Methods . 24h post-blood meal , the presence or absence of blood in the sandfly digestive tract was verified and one week post-blood meal , 9 midguts of blood-fed sandflies infected with WT and 14 midguts from the R40 group were individually dissected . Parasite load per individual midgut was assessed . No significant differences were observed between the two groups ( Fig 6 ) suggesting that MIL resistance does not affect the survival capacity of L . major in the natural vector . In summary , as shown for L . donovani [44] , the generation of experimental resistance to MIL is easily achieved by step-wise selection in L . major . Axenic resistant promastigotes proliferate as control cells , and the phenotype is stable . As suggested by our data , metacyclogenesis is an important process in the life cycle of the parasite , but should be carefully interpreted as a fitness marker . A combination of in vitro , in vivo and vector studies are necessary to fully assess the competitive fitness of MIL-resistant L . major , and studies would be further strengthened with the use of recent clinical isolates of both MIL-sensitive and MIL-resistant L . major parasites . Further studies will attempt to understand the impaired ability of MIL-resistant L . major to survive in the mammalian host at the molecular level . Overall , our findings are relevant for current and future antileishmanial chemotherapy strategies . | Cutaneous Leishmaniasis ( CL ) is characterized by the appearance of ulcerative lesions on the skin , and results from infection with trypanosomatid parasites such as Leishmania major . Current treatments for CL are expensive and have a wide range of toxic side effects of variable severity . Miltefosine , a recently introduced treatment option , is the first oral drug for leishmaniasis treatment . Although widespread clinical resistance has not yet been established , miltefosine-resistant parasite populations are easily created in a laboratory environment . Through step-wise selection , we have created populations of L . major resistant to miltefosine . These resistant parasites grow at a similar rate to miltefosine-sensitive parasites and exhibit similar stress responses . Accordingly , miltefosine-resistant parasites display a decrease in tolerance when selective pressure of MIL is withdrawn from the population . There is no conferred resistance to treatment with other antileishmanial agents , though increased sensitivity to alternative treatments is observed in some instances . Leishmania undergoes a complex life cycle including the differentiation to highly infective forms , in a process termed metacyclogenesis . Experimental resistance to miltefosine increases metacyclogenesis in L . major , however resistant parasites display a lower fitness than their sensitive counterparts , as judged by their attenuated virulence in vitro and in vivo . | [
"Abstract",
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] | [] | 2015 | Fitness and Phenotypic Characterization of Miltefosine-Resistant Leishmania major |
In eukaryotes , type 1A topoisomerases ( topos ) act with RecQ-like helicases to maintain the stability of the genome . Despite having been the first type 1A enzymes to be discovered , much less is known about the involvement of the E . coli topo I ( topA ) and III ( topB ) enzymes in genome maintenance . These enzymes are thought to have distinct cellular functions: topo I regulates supercoiling and R-loop formation , and topo III is involved in chromosome segregation . To better characterize their roles in genome maintenance , we have used genetic approaches including suppressor screens , combined with microscopy for the examination of cell morphology and nucleoid shape . We show that topA mutants can suffer from growth-inhibitory and supercoiling-dependent chromosome segregation defects . These problems are corrected by deleting recA or recQ but not by deleting recJ or recO , indicating that the RecF pathway is not involved . Rather , our data suggest that RecQ acts with a type 1A topo on RecA-generated recombination intermediates because: 1-topo III overproduction corrects the defects and 2-recQ deletion and topo IIII overproduction are epistatic to recA deletion . The segregation defects are also linked to over-replication , as they are significantly alleviated by an oriC::aph suppressor mutation which is oriC-competent in topA null but not in isogenic topA+ cells . When both topo I and topo III are missing , excess supercoiling triggers growth inhibition that correlates with the formation of extremely long filaments fully packed with unsegregated and diffuse DNA . These phenotypes are likely related to replication from R-loops as they are corrected by overproducing RNase HI or by genetic suppressors of double topA rnhA mutants affecting constitutive stable DNA replication , dnaT::aph and rne::aph , which initiates from R-loops . Thus , bacterial type 1A topos maintain the stability of the genome ( i ) by preventing over-replication originating from oriC ( topo I alone ) and R-loops and ( ii ) by acting with RecQ .
Type 1A topoisomerases ( topos ) are essential and ubiquitous enzymes found in bacteria , archaea and eukarya [1] , [2] . They all require single-stranded DNA ( ssDNA ) regions for activity . Such substrates can already be present , for example , in negatively supercoiled DNA , at the replication fork and in R-loops , or can be generated by the action of proteins , such as helicases or RNA polymerases . E . coli topo I , the first topo to be discovered [3] , is the prototype enzyme of this family . This enzyme binds to ssDNA close to double-stranded DNA ( dsDNA ) regions [4] and is therefore well suited to relax the excess negative supercoiling generated behind RNA polymerase molecules during transcription [5] , or introduced by DNA gyrase , the enzyme that negatively supercoils DNA in bacteria [6] . The best evidence for a major role of topo I in the regulation of supercoiling came from the observation that topA null mutants accumulate compensatory mutations in gyrA or gyrB genes allowing them to grow [7] . These mutations decrease the supercoiling activity of gyrase which leads to a reduction in the global chromosome supercoiling level below that of wild-type cells [8] . The role of topo I in transcription is supported by the finding that it physically interacts with RNA polymerase [9] . One major consequence of excess negative supercoiling is R-loop formation [10] . This is supported by the observation that the growth defect of topA null mutant can be partially compensated by RNase HI overproduction [11] . Evidence for extensive R-loop formation in the absence of topo I has been provided both in vitro and in vivo [12]–[15] . It is believed that topo I prevents R-loop formation mainly by relaxing transcription-induced negative supercoiling [15] . After a temperature downshift to reactivate gyrase in a topA null mutant carrying a gyrB ( Ts ) allele , RNase HI overproduction was shown to prevent a transient growth arrest that correlated with the accumulation of excess negative supercoiling ( hypernegative supercoiling ) and extensive RNA degradation [16] . RNase HI overproduction was found both to reduce the accumulation of excess negative supercoils and to promote their rapid removal by topo IV [16] , [17] , the other enzyme that can relax negative supercoiling in E . coli [18] . Moreover , evidence for R-loops impeding transcription of ribosomal RNA genes ( rrn operons ) in topA null mutants has been reported [19] . Interestingly , R-loop-dependent gene expression inhibition related to RNA polymerase arrest and RNA degradation has also been reported for yeast cells lacking topo I , a type 1B topo [20] . Thus , R-loop-mediated impairment of gene expression appears to be a major mechanism by which excess negative supercoiling inhibits growth . E . coli topo I is a relatively abundant protein being in the top 25% of the most abundant proteins in E . coli ( 134 ppm ) [21] . The topA gene is under the control of promoters recognized by different sigma factors , σ32 , σS and σ70 and its expression is important for E . coli's response to various stresses including heat and oxidative shock [22] . RNase HI overproduction was shown to partially restore the expression of σ32-regulated genes required for the heat shock response [23] . Although studies of topo I mostly focused on its role in supercoiling regulation and its effect on gene expression , evidence for the involvement of this enzyme in other DNA transactions such as chromosome segregation and replication initiation has been provided [17] , [24]–[27] . Interestingly , one of the first functions to be proposed for topo I was a role as a specificity factor to inhibit replication initiation outside oriC , such as initiation from R-loops , that could occur in an in vitro reconstituted system for oriC-dependent replication [28] . However , there is no experimental evidence for such a role of topo I in vivo . E . coli topo III , the second type 1A topo to be discovered , has a much higher preference for ssDNA than E . coli topo I [29] . As a consequence , topo III is very inefficient in relaxing DNA with a physiological supercoiling density and , in fact , this enzyme plays no role in supercoiling regulation [18] , [30] . However , topo III was shown to be a very potent decatenase during replication in vitro provided that a ssDNA region was present on the DNA substrate for the binding of the enzyme [29] . The presence of a unique amino-acid sequence in the topo III protein named the “decatenation loop” ( absent in eukaryotic type 1A enzymes ) , was found to be essential for the decatenation of replication intermediates [31] . Unlike topo I , topo III is a protein of low abundance ( 9 . 4 ppm ) [21] . Moreover , as opposed to topA null mutants , cells lacking topo III activity display no growth defects [32] . Recently , it has been shown that topo III plays a role in chromosome segregation in vivo that is likely related to replication , as this function was shown to be mostly required when the activity of topo IV [33] , the main cellular decatenase , or gyrase [34] was severely impaired . Topo III physically interacts with SSB protein and this interaction presumably allows topo III to act at the replication fork in the cell [35] . Similar to eukaryotic type 1A topos ( see below ) , topo III activity was shown to be stimulated by RecQ helicase in vitro [35]–[37] , but these two proteins do not physically interact . Evidence for RecQ acting with topo III in E . coli cells has been reported [30] . However , because some important properties of the strains used in this work could not be observed in an independent study , the conclusion that RecQ acts with topo III has been questioned [33] . Saccharomyces cerevisiae type 1A topo was the first enzyme of this family to be discovered in eukaryotic cells [38] . Being the third topo identified in this organism , it was named Top3 . The existence of this topo was revealed following the isolation of a mutation , in top3 , that stimulated recombination between repeated sequences [38] . Interestingly , phenotypes of top3 mutants including slow growth and sporulation deficiency were suppressed to different extents by inactivating SGS1 , encoding the RecQ homolog of S . cerevisiae , or by overproducing E . coli topo I [38]–[40] . Moreover , deleting RAD51 , encoding the RecA homolog of S . cerevisiae , was shown to rescue the slow growth phenotype of top3 mutants [41] . Altogether , these data suggested that Sgs1 processed recombination intermediates to generate structures that could only be resolved by a type 1A topo , such as Top3 or E . coli topo I . Physical interactions between type 1A topos ( named topo III in higher eukaryotes ) and their RecQ-like partner from eukaryotic organisms ( e . g BLM in humans and in Drosophila ) have been demonstrated [1] , [39] , [42] , [43] . It is now well established that these complexes can efficiently resolve homologous recombination intermediates ( Double Holliday Junctions; DHJs ) without genetic exchange [1] , [44]–[46] . Reactions of BLM with topo III are often stimulated by the presence of RPA , the SSB homolog of eukaryotes that presumably stabilizes the BLM-generated ssDNA region , the substrate for topo III [44] , [46] . Eukaryotic topo III enzymes have a higher requirement for ssDNA than E . coli topo I and , in fact , they are generally considered to be more closely related to E . coli topo III than topo I [1] . In E . coli , an interplay between topo I and III has been reported in two instances . In the first one , the topB gene was isolated as a multicopy suppressor of a topA null mutant [47] . Despite the significant correction of the growth defect of the topA null mutant by overproducing topo III , relaxation of the excess negative supercoiling introduced by gyrase was barely detected . This is consistent with our observation that topo III overproduction , unlike RNase HI overproduction , is unable to prevent the supercoiling-dependent transient growth arrest of a topA gyrB ( Ts ) strain , following a temperature downshift ( [16]; Baaklini and Drolet , unpublished ) . These results might have suggested that topo III overproduction complemented a yet unknown function of topo I that was not directly related to excess supercoiling . Indeed , here we present genetic evidence for an important role of topo I acting with RecQ to resolve RecA-dependent recombination intermediates that otherwise inhibit chromosome segregation . Moreover , our data suggest that the requirement for this activity is related to over-replication mostly from oriC that takes place in the absence of topA , presumably due to excess negative supercoiling . In the second instance , deleting topB from a topA null mutant carrying a gyrA or gyrB compensatory mutation , led to the formation of very long filaments with unsegregated nucleoids having abnormal structures and , eventually , to growth arrest [48] . Here , our data suggest that these phenotypes are exacerbated by excess negative supercoiling and are mostly related to over-replication from R-loops . Overall , our data demonstrate that bacterial type 1A topos maintain the stability of the genome by preventing unregulated replication and at least one of its consequences , namely the inhibition of chromosome segregation .
To look for chromosome segregation defects in a topA null mutant , cells of a ΔtopA gyrB ( Ts ) strain were stained with DAPI and prepared for microscopy such that both cell morphology and DNA content could be examined . By growing the cells at 30°C , the permissive temperature for gyrase , we could test the true effect of losing topA on nucleoid shape . As can be seen in Figure 1A , whereas nucleoids of gyrB ( Ts ) cells were well separated and compact , those of isogenic gyrB ( Ts ) ΔtopA cells were less compact and clearly not separated , thus showing chromosome segregation defects . To verify if these problems were related to excess negative supercoiling , topA null cells were grown at 37°C so that gyrase activity was reduced . At this temperature the chromosome supercoiling level decreases below that of wild-type cells and , as a result , topA null cells can grow robustly [11] , [49] . At 37°C , chromosome segregation in the gyrB ( Ts ) ΔtopA strain was significantly improved , as many cells had well separated and more compact nucleoids as compared to cells grown at 30°C ( Figure 1A , gyrB ( Ts ) ΔtopA , 37°C vs 30°C ) . We tested the effect of RNase HI overproduction on chromosome segregation in the gyrB ( Ts ) ΔtopA strain grown at 30°C . It did not correct the chromosome segregation defect ( gyrB ( Ts ) ΔtopA/pSK760 ) . Thus , we conclude that topA null cells suffer from supercoiling-dependent chromosome segregation defects that are unrelated to R-loops . RNase HI overproduction did not correct the chromosome segregation problem whereas it clearly stimulated the growth of gyrB ( Ts ) ΔtopA cells at 30°C ( Figure 1B , gyrB ( Ts ) ΔtopA vs gyrB ( Ts ) ΔtopA/pSK760 ) . Therefore , at this temperature the defect was not strong enough to offset the positive effect of overproducing RNase HI . We have previously shown that RNase HI overproduction could not complement the growth defect of topA null mutants at lower temperatures . In fact , it had a negative effect [47] , [50] . The cold sensitivity of topA null mutants was found to be , at least in part , related to the inability of topo IV to efficiently relax negative supercoiling at low temperatures [16] , [17] . As a result , hypernegative supercoiling accumulated . We found that the chromosome segregation defect of our gyrB ( Ts ) ΔtopA strain was exacerbated at 24°C since the cells were generally longer and the DNA more diffuse as compared to cells grown at 30°C ( Figure 1A , gyrB ( Ts ) ΔtopA , 30°C vs 24°C ) . Overproducing RNase HI further stimulated cell filamentation and produced cells with large DNA-free regions ( Figure 1A , gyrB ( Ts ) ΔtopA/pSK760 , 24°C ) . Growth of gyrB ( Ts ) ΔtopA cells on solid LB medium at 24°C was very poor whether RNase HI was overproduced or not ( Figure 1B , 24°C ) . Thus , the cold sensitivity of topA null cells triggered by excessive hypernegative supercoiling correlates with a strong chromosome segregation defect that seems to be exacerbated by RNase HI overproduction . Topo III overproduction was previously shown to correct the growth defect of topA null mutants at low temperatures [47] . In fact , unlike RNase HI , topo III overproduction was able to correct the growth defect of gyrB ( Ts ) ΔtopA cells at 21°C [47] . Since topo III is a potent decatenase and because the growth defect of gyrB ( Ts ) ΔtopA cells at 24°C correlates with a strong chromosome segregation problem ( Figure 1A ) , topo III overproduction may have complemented by correcting this segregation defect . This was confirmed by the observation that overproducing topo III almost fully , at 30°C , or partially , at 24°C , corrected the chromosome segregation defect of gyrB ( Ts ) ΔtopA cells ( gyrB ( Ts ) ΔtopA/pPH1243; Figure 2A , at 30°C the nucleoids are well separated and compact; at 24°C some nucleoids separated , shorter cells and the DNA is more compact as compared to cells not overproducing topo III ) . As expected , topo III overproduction also promoted growth on solid media at these temperatures ( Figure 2B ) . Thus , topo III overproduction corrects the growth defect of topA null mutants , at least in part , by facilitating chromosome segregation . The next series of experiments were performed to test the hypothesis that , as is the case in eukaryotic cells , E . coli type 1A topos can act with RecQ to resolve RecA-generated recombination intermediates . We first tested the effect of deleting recQ on growth and chromosome segregation in topA null cells . We found that deleting recQ was as good as overproducing topo III in correcting the growth defect of our gyrB ( Ts ) ΔtopA strain at both 30 and 24°C ( Figure 2B; gyrB ( Ts ) ΔtopA ΔrecQ; western blot experiments showed that topo IV was not overproduced in the topA null strain lacking recQ; Figure S3 ) . Deleting recQ also partially corrected the chromosome segregation defect at these temperatures ( Figure 2A ) . Thus , our results suggest that recQ and topB act in a pathway that is related to chromosome segregation in the absence of topA . We next tested the effect of deleting recA on growth and chromosome segregation in topA null cells . The deletion of recA partially corrected the growth defect of our gyrB ( Ts ) ΔtopA strain at both 30 and 24°C ( Figure 2B; gyrB ( Ts ) ΔtopA ΔrecA ) , though the effect was not as good as the one conferred by deleting recQ or overproducing topo III ( Figure 2B ) . In fact , the positive effect of deleting recA on the growth of topA null cells at 24°C was more readily observed after three days of incubation ( Figure 2B , 72 h ) . Deleting recA also partially alleviated the chromosome segregation defects of topA null cells at both temperatures ( Figure 2A ) . These results demonstrate that the chromosome segregation defects of topA null mutants are largely RecA-dependent and therefore support the involvement of homologous recombination . In E . coli , positive effects of deleting recQ on growth and chromosome segregation are often attributed to unnecessary RecA-mediated recombination via the RecFOR pathway at arrested replication forks [51] , [52] . In this pathway , RecQ helicase acts with RecJ , a 5′-3′ exonuclease , to provide ssDNA regions on which RecF , O and R facilitate RecA nucleoprotein filament assembly . We found that deleting recJ , recO or recR had no effect on growth and chromosome segregation in our gyrB ( Ts ) ΔtopA strain ( Figure 2 , gyrB ( Ts ) ΔtopA ΔrecO; data not shown for recJ and recR ) . This indicated that the RecFOR pathway was not involved and therefore may suggest that RecQ and type 1A topos act together in a RecA-dependent recombination pathway . If indeed RecQ acts on RecA-generated recombination intermediates to generate substrates for type 1A topos , neither topo III overproduction nor recQ deletion should improve the growth of gyrB ( Ts ) ΔtopA cells lacking recA . Moreover , overproducing topo III should also have no effects on the growth of gyrB ( Ts ) ΔtopA ΔrecQ cells . To test these predictions , the appropriate strains were constructed and spot assays were performed . As predicted , combinations of recQ and recA deletions or of topo III overproduction and recA mutation resulted in the same growth phenotype as the recA mutation alone ( Figure 3A , compare gyrB ( Ts ) ΔtopA ΔrecQ , gyrB ( Ts ) ΔtopA ΔrecA and gyrB ( Ts ) ΔtopA ΔrecQ ΔrecA; Figure 3B , compare gyrB ( Ts ) ΔtopA/pPH1243 , gyrB ( Ts ) ΔtopA ΔrecA and gyrB ( Ts ) ΔtopA ΔrecA/pPH1243 ) . Furthermore , topo III overproduction did not improve the growth of gyrB ( Ts ) ΔtopA ΔrecQ cells ( Figure 3C ) . These results are consistent with RecQ processing RecA-generated recombination intermediates in such a way that they can only be resolved by a type 1A topo . Since topo III needs to be overproduced , we believe that the much more abundant topo I enzyme is normally involved in the resolution of these intermediates ( see Discussion ) . Our microarray results indicated that the SOS response was constitutively expressed in our gyrB ( Ts ) ΔtopA strain and therefore that RecA was overproduced ( not shown ) . The lexA3 allele was used to test the effect of the SOS response on the growth of the gyrB ( Ts ) ΔtopA strain . This allele makes the SOS response non-inducible and therefore considerably reduces the amount of RecA proteins produced . The lexA3 allele was found to be slightly better than deleting recA to improve the growth of the gyrB ( Ts ) ΔtopA strain ( Figure 3D , compare gyrB ( Ts ) ΔtopA , gyrB ( Ts ) ΔtopA lexA3 and gyrB ( Ts ) ΔtopA ΔrecA; several lexA3 transductants were tested and were found to behave the same way ) . This result may suggest that the major effect of the recA mutation on the growth of the gyrB ( Ts ) ΔtopA strain was not related to the silencing of the SOS response but rather to the inactivation of the recombination function of RecA . If this is true , the lexA3 allele should behave differently from the recA mutation when combined with the recQ mutation in the gyrB ( Ts ) ΔtopA strain . Figure 3E shows that it was indeed the case . Whereas the recA growth phenotype was dominant over the recQ one ( Figure 3A ) , the reverse was observed for the lexA3 allele i . e . , the gyrB ( Ts ) ΔtopA ΔrecQ lexA3 strain grew like the gyrB ( Ts ) ΔtopA ΔrecQ one ( Figure 3E ) . Thus , these results indicate that the lexA3 allele improved the growth of the gyrB ( Ts ) ΔtopA strain mostly by reducing the amount of RecA proteins , but at the same time that a minimal level of RecA-dependent recombination was required for the optimal growth of the gyrB ( Ts ) ΔtopA strain . Our results showed that the RecF pathway for the loading of RecA on ssDNA was not involved in the RecA effects in the gyrB ( Ts ) ΔtopA strain ( Figure 2 ) . The RecBCD pathway is the other one involved in the loading of RecA on ssDNA in E . coli . The introduction of a recB::Tn10 mutation in our gyrB ( Ts ) ΔtopA strain resulted in a strain that grew very poorly . Growth inhibition was clearly observed at 37°C , a temperature normally fully permissive for the growth of the gyrB ( Ts ) ΔtopA strain ( Figure 3F , compare gyrB ( Ts ) ΔtopA and gyrB ( Ts ) ΔtopA recB ) . At 30°C , growth was barely detected ( Figure 3F ) and the strain did not show grow at 24°C even after 6 days of incubation . These results indicate that the RecA effects in the gyrB ( Ts ) ΔtopA strain are most likely mediated through the RecBCD pathway and , more importantly , that a RecA-independent RecB function is required for the survival of the gyrB ( Ts ) ΔtopA strain . Such a RecB function has been linked to replication fork regression that can occur when forks are stalled [53] , [54] . Thus , these results may suggest that replication is problematic in the gyrB ( Ts ) ΔtopA strain . This is supported by the results presented below . Our data suggested that hypernegative supercoiling in topA null mutants triggered RecA-dependent recombination that led to the accumulation of RecQ-processed intermediates . Without a sufficient amount of type 1A topo activity to resolve these intermediates , chromosome segregation could not occur . However , how excess negative supercoiling stimulated RecA-dependent recombination to a level that caused chromosome segregation defects is unclear . We have recently used a Tn5 mutagenesis system to isolate genetic suppressors of the growth defect of a gyrB ( Ts ) ΔtopA rnhA strain ( Materials and Methods; Usongo and Drolet , manuscript in preparation ) . The growth defect of this strain was previously shown to be related to chromosome segregation problems that could be corrected by overproducing topo III [17] . Improving gyrase activity also suppressed the chromosome segregation defects [17] . Moreover , our study of replication in this mutant led us to speculate that unregulated replication either from oriC or R-loops , or from both , could contribute to the segregation defects [34] . In agreement with this hypothesis , insertion mutants were found in loci involved in replication . In one mutant , the kanr cassette was found to be inserted within the oriC region , close to the middle ( Figure 4A , aph ) . It was possible that the suppressed strain could survive without an active oriC region , as replication could occur from R-loops due to the absence of the rnhA gene ( constitutive stable DNA replication , cSDR ) [55] . Therefore , to verify if this oriC15::aph mutation was still competent for replication initiation , we tried to introduce it in wild-type ( RFM443 ) , gyrB ( Ts ) ( RFM445 ) and gyrB ( Ts ) ΔtopA ( RFM475 ) isogenic strains . Kanamycin resistant transductants were readily obtained for the gyrB ( Ts ) ΔtopA strain . Southern blot analysis confirmed that the gyrB ( Ts ) ΔtopA transductants carried the mutated but not the wild-type oriC region ( Figure S4 , RFM475 oriC15::aph ) . The few kanamycin resistant transductants of the wild-type and gyrB ( Ts ) strains were found to be false-positives as they kept the wild-type oriC region ( Figure S4 , a false positive RFM443 kanr is shown ) . Repeated transduction experiments yielded similar results . Therefore , we concluded that the oriC15::aph mutation was viable only when the topA gene was absent . Our finding that overproducing RNase HI had no effect on the growth of the gyrB ( Ts ) ΔtopA strain carrying the oriC15::aph mutation ( at 37 and 41°C , not shown ) , indicated that this strain does not replicate its chromosome via cSDR . This is in agreement with a previous report showing that , as opposed to an rnhA null mutant , a topA null mutant could not survive without a functional oriC/DnaA system [27] . Therefore , our topA null mutant most likely uses the oriC15::aph allele to initiate the replication of its chromosome . However , we can predict that this allele would be less active than a wild-type one and therefore should be able to complement the growth defect of a strain in which excess replication from oriC is growth inhibitory . The dnaAcos mutant , isolated as an intragenic suppressor of a dnaA46 mutant , fails to grow at 30°C and below , because of excessive replication initiation from oriC [56] . A dnaAcos strain carrying the oriC15::aph mutation showed good growth at both 36 and 30°C , whereas an isogenic strain with a wild-type oriC region did not ( Figure S5 , dnaAcos oriC15::aph vs dnaAcos ) . Thus , this result confirmed that ( i ) the oriC15::aph mutation is functional in replication initiation and ( ii ) it is less active than a wild-type oriC region . Our results with the oriC15::aph mutation suggested that topo I may play an important role in regulating replication initiation from oriC . In a previous study , the left-half of the oriC region was shown to be essential for oriC function in vivo [57] . This section carries the DUE ( DNA unwinding element , AT-rich ) region from which oriC duplex melting is initiated ( Figure 4A ) [58] . The smallest oriC fragment found to be functional in vivo was a fragment encompassing nucleotide 1 to 163 of the oriC region ( Figure 4A , oriC231 ) . However , a wild-type strain carrying this fragment was sensitive to rich media ( LB ) . It was concluded that the right-half of oriC was essential for multi-forked replication that is required to support high growth rates in rich media [57] . Therefore , the fact that the kanr cassette was inserted at position 142 in the oriC sequence ( Figure 4A ) , likely explains why our oriC15::aph mutation was not functional in a wild-type strain . However , not only was the mutation oriC-competent in our topA null mutant , it apparently allowed multi-forked replication , since our gyrB ( Ts ) ΔtopA strain was able to grow robustly in rich media . Therefore , these results suggest that topo I plays an important regulatory role at oriC . Flow cytometry was used in rifampicin run-out experiments with cells grown in M9 medium at 37°C to investigate the regulation of replication initiation in our strains . As recently shown [34] , both wild-type and gyrB ( Ts ) cells contained 2n chromosome , thus indicating that replication initiation was well regulated in these strains ( Figure 4B ) . Cells of the gyrB ( Ts ) ΔtopA strain had a near perfect 2n chromosomal pattern with one small additional peak , showing some asynchrony ( Figure 4B ) . However , flow cytometry analysis revealed that replication initiation was not well regulated in the topA null mutant carrying the oriC15::aph mutation , as peaks reflecting 1 , 2 , 3 , or 4 chromosomes were clearly observed ( Figure 4B , gyrB ( Ts ) ΔtopA oriC ) . Highly asynchronous replication was also previously detected in a wild-type strain carrying the oriC231 mutation [57] . Flow cytometry analysis also revealed that the DNA/mass ratio was higher by roughly 40% in the gyrB ( Ts ) ΔtopA strain as compared to wild-type and gyrB ( Ts ) strains ( Figure 4C ) . Introducing the oriC15::aph mutation into the topA null strain restored the DNA/mass ratio to the level seen in wild-type and gyrB ( Ts ) strains ( Figure 4C , gyrB ( Ts ) ΔtopA oriC ) . Thus , the oriC15::aph mutation caused replication initiation to be less efficient in the gyrB ( Ts ) ΔtopA strain as shown by the loss of regulation and the lower DNA/mass ratio . The oriC15::aph mutation was very effective in correcting the growth defect of our gyrB ( Ts ) ΔtopA strain at both 30 and 24°C ( Figure 5B , compare gyrB ( Ts ) ΔtopA and gyrB ( Ts ) ΔtopA oriC ) . This mutation also significantly corrected the chromosome segregation defects of the topA null strain at both temperatures ( Figure 5A , gyrB ( Ts ) ΔtopA oriC ) . Therefore , the recA-dependent chromosome segregation defects in the topA null mutant are likely related to excess replication from oriC . We conclude that one major role of E . coli topo I in genome maintenance is to prevent over-replication originating from oriC . We have recently shown that deleting topA could complement the growth defect of our gyrB ( Ts ) strain at non-permissive temperatures ( 40 to 42°C ) by partially correcting its replication initiation and chromosome segregation defects [34] . However , we found that the topB gene was required for chromosome segregation and overproducing topo IV , the main cellular decatenase , could not substitute for topB . These results , and others , allowed us to conclude that topo III plays a role in replication that becomes essential when gyrase activity is defective . Here , we have confirmed that recombination was not involved by showing that deleting recA or recQ did not correct the growth and chromosome segregation defects of the gyrB ( Ts ) ΔtopA ΔtopB strain at a non-permissive temperature ( 40°C , Figure S6 ) . Moreover , RNase HI overproduction had no effect . Thus , at non-permissive temperatures for the gyrB ( Ts ) allele , the growth and chromosome segregation defects of the gyrB ( Ts ) ΔtopA ΔtopB strain [34] are unrelated to recombination and R-loops . It was observed that the optimal temperature for the growth of the gyrB ( Ts ) ΔtopA ΔtopB strain was 37°C . Indeed , at 30°C the growth defect was found to be exacerbated ( Figure S7a , compare 37 and 30°C for gyrB ( Ts ) ΔtopA ΔtopB/pSK762c ) . This strain also generated a higher proportion of longer cells at 30 than 37°C ( Figure S7b , gyrB ( Ts ) ΔtopA ΔtopB , 37 vs 30°C ) . Since gyrase was re-activated at 30°C , we considered the possibility that deleting topB exacerbated topA phenotypes at this temperature . If this was true , overproducing RNase HI should have a positive effect on growth and chromosome segregation in our triple mutant . Indeed , this turned out to be true as the spot assay revealed that growth was better , by at least two logs , when RNase HI was overproduced ( Figure 6b , compare gyrB ( Ts ) ΔtopA ΔtopB/pSK760 , RNase HI overproduced and gyrB ( Ts ) ΔtopA ΔtopB/pSK762c , RNase HI not overproduced ) . Moreover , the strong chromosome segregation defects illustrated by the formation of very long filaments fully packed with diffuse DNA , were significantly corrected by overproducing RNase H . In this case , cells were shorter and the DNA was more compact ( Figure 6A ) . Thus , R-loops-related problems of a topA null mutant were exacerbated by deleting topB and were mostly expressed as chromosome segregation defects . The deletion of recA significantly improved the growth of the gyrB ( Ts ) ΔtopA ΔtopB strain at 30°C , though this was not as effective as overproducing RNase HI ( Figure 6B , compare gyrB ( Ts ) ΔtopA ΔtopB ΔrecA/pSK762c and gyrB ( Ts ) ΔtopA ΔtopB/pSK760 ) . However , deleting recA was at least as effective as overproducing RNase HI in correcting the chromosome segregation defects of the gyrB ( Ts ) ΔtopA ΔtopB strain ( Figure 6A , compare gyrB ( Ts ) ΔtopA ΔtopB ΔrecA/pSK762c and gyrB ( Ts ) ΔtopA ΔtopB/pSK760 ) . Furthermore , overproducing RNase HI had no effects on growth and chromosome segregation when recA was deleted ( Figure 6A , compare gyrB ( Ts ) ΔtopA ΔtopB ΔrecA/pSK760 and gyrB ( Ts ) ΔtopA ΔtopB ΔrecA/pSK762c ) . These results demonstrate that the R-loop-dependent chromosome segregation defects in cells lacking type 1A topos , are also dependent on RecA . Unlike inactivating recA , the deletion of recQ did not correct the phenotypes of the gyrB ( Ts ) ΔtopA ΔtopB strain ( Figure 6 , compare gyrB ( Ts ) ΔtopA ΔtopB ΔrecQ/pSK762c and gyrB ( Ts ) ΔtopA ΔtopB/pSK762c ) . However , RNase HI overproduction was still able to correct these phenotypes when recQ was absent ( compare gyrB ( Ts ) ΔtopA ΔtopB ΔrecQ/pSK760 and gyrB ( Ts ) ΔtopA ΔtopB ΔrecQ/pSK762c ) . Thus , the RecA- and R-loop-dependent growth and chromosome segregation defects of the gyrB ( Ts ) ΔtopA ΔtopB strain are not caused by the accumulation of RecQ-processed recombination intermediates that are substrates for type 1A topos . As RecA was previously shown to be required for cSDR that initiates from R-loops [55] , over-replication could possibly be the triggering event for the growth and chromosome segregation defects of cells lacking type 1A topos . This is supported by the genetic evidence presented below . One of the best suppressors of the growth defect of the gyrB ( Ts ) ΔtopA rnhA strain that displays cell filamentation and chromosome segregation phenotypes similar to our gyrB ( Ts ) ΔtopA ΔtopB strain , had the kanr cassette inserted within the promoter region of the dnaT gene ( Figure S8A ) . DnaT is one of the various proteins that constitute the primosome ( including PriA [59] ) . This protein complex allows the assembly of a replisome outside of oriC . Interestingly , the first mutation found to inhibit SDR mapped within dnaT [60] . The SOS-dependent form of stable DNA replication ( iSDR ) was shown to be inhibited in this case [55] . However , the involvement of dnaT in the R-loop-dependent form of SDR ( cSDR ) is still unknown [61] . To test this , the dnaT18::aph mutation was introduced in a dnaA46 ( Ts ) strain also carrying an rnhA null mutation . The absence of RNase HI allows the dnaA46 ( Ts ) strain to grow at 42°C as it can replicate its chromosome from R-loops ( Figure 7A and B ) . Therefore , the fact that the dnaT18::aph allele inhibited the growth of the dnaA46 ( Ts ) rnhA strain at 42°C , indicated that the dnaT gene was required for cSDR ( Figure 7C , 42°C , compare rnhA dnaA46 and rnhA dnaA46 dnaT ) . The dnaT18::aph mutation was also found to partially correct the chromosome segregation defects of the gyrB ( Ts ) ΔtopA rnhA strain ( Figure S9 ) . This suggested that replication from R-loops could , at least in part , be responsible for these problems . We therefore tested the ability of the dnaT18::aph mutation to correct similar defects in cells lacking type 1A topos . For this purpose , a different null allele of topA , the topA20::Tn10 allele that was previously shown to behave similarly to the ΔtopA allele used in the present study , was chosen [11] . A ΔtopB gyrB ( Ts ) strain was used in which the topA20::Tn10 allele was either immediately introduced to obtain the ΔtopB gyrB ( Ts ) topA20::Tn10 control strain , or introduced after the dnaT18::aph allele to obtain the ΔtopB gyrB ( Ts ) dnaT18::aph topA20::Tn10 strain . The chromosome segregation defects were found to be more severe in our new ΔtopB gyrB ( Ts ) topA20::Tn10 strain as compared to the other one carrying the ΔtopA allele ( compare Figure 6A , gyrB ( Ts ) ΔtopA ΔtopB/pSK762c and Figure 8A , ΔtopB gyrB ( Ts ) topA20::Tn10 and data not shown ) . Indeed , the ΔtopB gyrB ( Ts ) topA20::Tn10 strain at 30°C produced almost exclusively extremely long filaments that were fully packed with diffuse DNA . This could be related to our previous observation that R-loop-related problems in the absence of topo I were more severe in strains carrying the topA20::Tn10 allele instead of the ΔtopA one [62] . RNase HI overproduction also significantly corrected both the growth and chromosome segregation defects of our ΔtopB gyrB ( Ts ) topA20::Tn10 strain ( Figure 8A and B , compare ΔtopB gyrB ( Ts ) topA20::Tn10 and ΔtopB gyrB ( Ts ) topA20::Tn10/pSK760 ) . However , at 24°C , RNase HI overproduction had no effect ( Figure 8C , compare ΔtopB gyrB ( Ts ) topA20::Tn10/pSK760 and ΔtopB gyrB ( Ts ) topA20::Tn10/pSK762c ) . This was expected , as the cold-sensitivity of cells lacking topo I is not corrected by RNase HI overproduction ( see above ) . It was found that the dnaT18::aph mutation was at least as effective as RNase HI overproduction in correcting the chromosome segregation defects of the ΔtopB gyrB ( Ts ) topA20::Tn10 strain ( Figure 8A , compare ΔtopB gyrB ( Ts ) dnaT topA20::Tn10 , ΔtopB gyrB ( Ts ) topA20::Tn10/pSK762c and ΔtopB gyrB ( Ts ) topA20::Tn10/pSK760 ) . However , RNase HI overproduction was slightly better than the dnaT18::aph mutation in correcting the growth defect ( Figure 8B , compare ΔtopB gyrB ( Ts ) topA20::Tn10/pSK760 and ΔtopB gyrB ( Ts ) dnaT topA20::Tn10 ) . The dnaT18::aph also had a negative effect on the growth of the ΔtopB gyrB ( Ts ) topA20::Tn10 strain at 37°C ( Figure 8D , compare ΔtopB gyrB ( Ts ) dnaT topA20::Tn10 and ΔtopB gyrB ( Ts ) topA20::Tn10 ) . This could be due to the presence of the gyrB ( Ts ) allele that was previously shown , at this semi-permissive temperature , to be incompatible with a mutation ( priA null ) inactivating the primosome [63] . Thus , our results support the hypothesis that the R-loop and RecA-dependent chromosome segregation defects in cells lacking type 1A topos are , at least in part , related to over-replication initiated from R-loops . The fact that the dnaT18::aph mutation slightly promoted the growth of our topA null mutant ( Figure 5B , 30 and 24°C , compare gyrB ( Ts ) ΔtopA dnaT vs gyrB ( Ts ) ΔtopA ) , suggests that cSDR is primarily a problem for topA null cells that is exacerbated by deleting topB . This would be consistent with the assumption that topo I is the primary type 1A topo involved in the inhibition of R-loop formation [47] . Seven different kanr insertion mutations in the C-terminal region of RNase E , the main endoribonuclease in E . coli ( Usongo and Drolet , manuscript in preparation ) , were found to suppress the growth defect of our topA rnhA gyrB ( Ts ) strain . Interestingly , experimental evidence for an interplay between RNase HI and RNase E in RNA degradation has been reported [64] , [65] . One of these rne mutations ( rne59::aph , Figure S8C ) was introduced in a dnaA46 ( Ts ) rnhA strain to test its effect on cSDR . The presence of the rne59::aph mutation significantly reduced the ability of the dnaA46 ( Ts ) rnhA strain to grow at 42°C ( by 2 to 3 logs; Figure 7D , 42°C , rnhA dnaA46 vs rnhA dnaA46 rne ) . This result shows that the mutated RNase E inhibited cSDR . A topA topB gyrB ( Ts ) strain was constructed , with the topA20::Tn10 allele as described above , that carried the rne59::aph mutation . The rne59::aph mutation was found to be slightly better than RNAse HI overproduction to correct the growth defect of cells lacking type 1A topos ( Figure 8B , compare ΔtopB gyrB ( Ts ) rne topA20::Tn10 and ΔtopB gyrB ( Ts ) topA20::Tn10/pSK760 ) . Furthermore , it was at least as effective as RNase HI overproduction and the dnaT18::aph mutation to correct the chromosome segregation defects in these cells ( Figure 8A ) . Thus , our results with the rne59::aph mutation lend further support to the hypothesis that cells lacking type 1A topos suffer from excess replication originating from R-loops . The origins of replication for cSDR ( oriKs ) in rnhA null mutants are mostly found within or close to the ter region where bi-directional replication initiated at oriC normally terminates [55] . Thus , the origin to terminus ( oriC/ter ) ratio , is expected to be lowered by the occurrence of cSDR . This is indeed what was found for the rnhA null mutant ( Figure S10 , RFM443 vs RFM430 rnhA::cam ) . The ori/ter ratio was also similarly reduced in the topA null mutant , thus supporting the occurrence of cSDR in the absence of topo I ( Figure S10 , RFM475 ) . Several of our kanr insertion mutants were found to reduce the expression of the holC gene ( Usongo and Drolet , manuscript in preparation ) . In a previous study , kanr insertion mutants that reduced the expression of the holC gene were also found to suppress the growth defect of a dnaAcos strain [66] . The holC gene encodes the χ subunit of DNA pol III , the replicative polymerase in E . coli [67] . The χ subunit interacts with SSB and this interaction was recently shown to play an important role in replisome establishment and maintenance [68] . The holC2::aph mutation was tested for its ability to suppress phenotypes of cells lacking type 1A topos . For this purpose , a topA topB gyrB ( Ts ) holC2::aph strain , carrying the topA20::Tn10 allele , was constructed . The holC2::aph mutation was shown to slightly correct the growth defect of cells lacking type 1A topos activity ( Figure 8B and C , 30 and 24°C respectively , ΔtopB gyrB ( Ts ) holC topA20::Tn10 vs ΔtopB gyrB ( Ts ) topA20::Tn10 ) . Both cell length and the amount of DNA were also slightly reduced ( Figure 8A ) . The fact that holC mutations by themselves can cause filamentation and chromosome segregation defects [68] , may explain why the holC2::aph mutation only partially corrected the phenotypes of the ΔtopB gyrB ( Ts ) topA20::Tn10 strain . The holC2::aph mutation also partially corrected the growth defect of our topA null mutant ( Figure 5B , 24°C , 48 h; compare gyrB ( Ts ) ΔtopA and gyrB ( Ts ) ΔtopA holC ) . Moreover , in rifampicin run-out experiments , replication did not appear to be well regulated in the topA null mutant carrying the holC2::aph mutation , as peaks reflecting 1 , 2 , 3 , or 4 chromosomes were clearly observed ( Figure S11 , compare gyrB ( Ts ) ΔtopA and gyrB ( Ts ) ΔtopA holC ) . This result supports the hypothesis that the χ subunit of pol III plays a role in replication initiation [68] and therefore suggests that initiation from oriC could also be problematic in cells lacking both type 1A topos .
As stated in the introduction , the strand passage activity of E . coli topo III , but not topo I , was shown to be strongly stimulated by RecQ in vitro [35]–[37] . This would suggest that E . coli topo III and RecQ can act together to maintain the stability of the genome , as shown in eukaryotic cells [69] . However , no clear evidence for such a role of topo III has been reported in E . coli . Recent experimental evidence points to a role for topo III in chromosome segregation related to replication and independent of RecQ ( [33] , [34]; this work ) . In fact , the data presented here suggest that topo I , not topo III , is the primary type 1A topo acting with RecQ in E . coli . Indeed , the strong chromosome segregation and growth defects of topA null cells at low temperatures were shown to be partially corrected by deleting recQ or recA , independent of the RecFOR pathway and by overproducing topo III , a protein that is normally of very low abundance . Moreover , both deleting recQ and overproducing topo III were found to be epistatic to recA in correcting the growth problems . This is consistent with RecQ processing RecA-dependent recombination intermediates in such a way that they can only be resolved by a type 1A topo , as is the case in eukaryotic cells . In this context , topo III overproduction would substitute for topo I and perform the resolution , thus meaning that topo III can also perform this reaction in vivo . Alternatively , in the absence of topA , DNA substrates for topo I may accumulate and some of them could be processed by topo III , thus leading to the depletion of this very low abundant protein . This situation would lead to the accumulation of RecQ-processed recombination intermediates , if topo III normally resolves them . However , we think that this is unlikely because while a topA recQ strain grow very well , deleting topB make this strain very sick with phenotypes identical to those of topA topB null cells . If recQ was acting with topB , then deleting topB should have had no effect on the growth of the topA recQ strain . Altogether , our results are more consistent with topo I being the primary type 1A topo working with RecQ in E . coli . Despite the previously observed lack of stimulation of topo I activity by RecQ in vitro , we still believe that these two proteins can functionally interact . Indeed , it may be that the optimal experimental conditions and/or the appropriate substrate for their functional interaction have not yet been well defined . Alternatively or additionally , the much higher abundance of topo I in vivo as compared to topo III may compensate for its lower level of activity with RecQ . In fact , the finding that either E . coli topo I expression or a SGS1 mutation could compensate for the absence of Top3 in S . cerevisiae [38]–[40] , supports the assumption that E . coli topo I can act with RecQ in vivo . Moreover , in an in vitro system for DHJs resolution by BLM helicase with a type 1A topo , E . coli topo I was shown to efficiently substitute for human topo IIIα [70] . Hsieh and co-workers have recently obtained experimental evidence for their “unravel and unlink” model whereby BLM first melts a DNA region to which RPA protein binds and topo IIIα acts to resolve a DHJ [1] , [43] . Indeed , a topo IIIα mutant unable to physically interact with BLM was shown to partially resolve a DHJ in the presence of RPA , thus suggesting that the functions of the two proteins may be separated [43] . A similar model might also be proposed for RecQ acting with topo I , the activity of which can be stimulated by SSB [71] , as the two proteins do not physically interact . Interestingly , whereas topo I is present in all bacteria , topo III is present only in a few bacteria [72] . Therefore , if a collaboration between a type 1A topo and a RecQ-like helicase is also required in bacteria , it is not surprising that topo I performs this function . However , why such a function would be required in bacteria is currently unknown . In diploid organisms RecQ-like helicases act in concert with topo III to prevent the exchange of genetic material between DNA molecules involved in recombination ( DHJ dissolution; [73] ) . In the present study , it was found that inactivating recB almost completely inhibited the growth of our gyrB ( Ts ) ΔtopA mutant , while deleting recA improved its fitness . This indicated that a RecA-independent RecB function was required for the survival of the gyrB ( Ts ) ΔtopA strain . Such a RecB function has been linked to replication forks regression that can occur when forks are stalled [53] , [54] . In the cell , RecB is present in the RecBCD complex that has both a recombination ( RecA loading at χ sites ) and a dsDNA degradation ( exonuclease V ) function . It has been hypothesized that upon fork reversal , a HJ forms and degradation of the dsDNA end is initiated by RecBCD . Following the encounter of a χ site and in the presence of RecA , homologous recombination can take place , leading to the formation of a second HJ . The involvement of homologous recombination is supported by the observation that , as opposed to recB mutants , recD mutants , that lack the dsDNA degradation activity of RecBCD , can survive under conditions that promote extensive replication forks reversal if the recA gene is present [74] , [75] . Next , the two HJs can be resolved by RuvABC . Alternatively , as is the case during the process of genetic exchange in diploid organisms , we propose that RecQ can act on the two HJs to promote convergent branch migration . This would lead to the formation of a hemicatenane that must be unlinked by a type 1A topo to allow chromosome segregation . In the absence of RecA , the second HJ does not form and the dsDNA is instead degraded by the RecBCD complex up to the first HJ to produce a fork structure that is used to restart replication . In this context , RecQ does not promote the formation of hemicatenanes and , as a result , chromosomes segregation is not impeded by the lack of type 1A topos activity . Experimental evidence for replication forks reversal has been reported in E . coli cells carrying defective DNA helicases involved in replication ( DnaB and Rep; [53] , [74] ) and , more recently , following replication-transcription collisions [75] . We speculate that the high level of negative supercoiling in the gyrB ( Ts ) ΔtopA strain at low temperatures promotes the formation of alternative non-B DNA structures that may cause the stalling of replication forks and their reversal . Alternatively , such non-B DNA structures may first block transcription and the arrested RNA polymerases may , in turn , stop the progression of replication forks to cause their reversal . Furthermore , over-replication that occurs in this strain likely exacerbates the problem and makes the cell unable to adequately deal with the reversed forks . Clearly , more work will be required to fully characterize the role ( s ) of type 1A topos acting with RecQ in bacteria and to find out under which circumstances this activity would be required in various DNA transactions . In E . coli , replication initiated at oriC is tightly regulated so that it occurs once and only once per cell cycle [58] . This process is synchronized with the “initiation mass” . DNA supercoiling is among the many elements , including DnaA that are required for replication initiation at oriC . Indeed , in vitro replication initiation necessitates that the oriC plasmid be negatively supercoiled [76] . In vivo , deleting topA was found to correct the thermo-sensitive growth of a dnaA ( Ts ) mutant [27] and altering gyrase supercoiling activity inhibited replication initiation from oriC [77] . Moreover , we have recently shown that a topA deletion could correct the replication initiation defect of a strain defective for gyrase supercoiling activity [34] . Interestingly , in a screen to isolate DnaA inhibitors a compound was recently found to rescue a dnaAcos mutant from lethal hyperinitiation by targeting gyrase [78] . Thus , in vitro and in vivo data demonstrate that negative DNA supercoiling is required for replication initiation from oriC . The recent determination of the crystal structure of a truncated DnaA ortholog in complex with ssDNA supports a model whereby DnaA opens the oriC region by a direct ATP-dependent stretching mechanism [79] . This work provides the strongest evidence to date for a direct participation of DnaA in DNA melting at oriC , and is fully compatible with other elements , such as DNA supercoiling , also playing a role in this process . In a recent biochemical study , DNA fragments containing at least the left portion of oriC up to I1 or I2 ( Figure 4a ) were shown to be required for DnaA-ATP binding to ssDUE in the absence of torsional stress [80] . This result is totally consistent with our finding that an oriC region lacking these I1 and I2 sequences ( oriC15::aph ) is functional in a topA null mutant , where the negative supercoiling level is elevated , but not functional in an isogenic topA+ strain . Thus , our results , together with those reported in the two studies mentioned above , suggest that DNA supercoiling plays an important regulatory role at oriC . When topB was deleted from a topA null mutant , a new growth inhibitory phenotype , again related to replication , appeared at temperatures where the oriC-related phenotype was attenuated . Our data suggest that this major phenotype in the absence of type 1A topos is related to replication from R-loops ( cSDR ) . This is consistent with a major role of topo I in the inhibition of R-loop formation and with the identification of topo I , like RNase HI [81] , as a specificity factor to inhibit replication initiation at sites other than oriC ( e . g . R-loops ) , in an in vitro system [28] . Thus , although the strong phenotype expressed such as extensive cell filamentation , unsegregated nucleoids and growth inhibition , is triggered by deleting topB , cSDR is probably also activated in our single topA mutant . This is supported by the fact that the dnaT18::aph mutation improved the growth of our topA mutant and by the finding that , as was the case in an rnhA null mutant , the ori/ter ratio was lower in this topA mutant as compared to a wild-type strain . However , even if cSDR is activated in topA null mutants , the oriC/DnaA system is still required in these cells to replicate the chromosome . A similar situation has been described for recG mutants , in which cSDR is also activated but cannot support replication of the whole chromosome [82] , [83] . As the strong phenotype is due to the simultaneous absence of both type 1A topos , it is likely related to similar functions performed by the two enzymes . We have previously shown that an R-loop was a hot-spot for topo III activity in vitro [47] . By acting on an R-looped plasmid , topo III was shown to destabilize the R-loop . As topo III can travel with the replication fork [35] , it could possibly act by destabilizing R-loops blocking the progression of the replication forks . Interestingly , topo III was recently shown to prevent R-loop accumulation during transcription in mammalian cells [84] . Thus , inhibition of R-loop formation might be another important function of type 1A topos that has been conserved throughout evolution . The absence of a type 1A topo activity for decatenation ( e . g . RecQ with topo I ) also likely contributes to the strong chromosome segregation defects seen in cells lacking both topo I and III .
Bacterial strains used in this study are all derivatives of E . coli K12 and are listed in Table S1 . Details on their constructions as well as the list of plasmids used in this study are also given in Table S1 . Transductions with P1vir were performed as described previously [34] . PCR was used to confirm that the expected gene transfer occurred in the selected transductants . Insertional mutagenesis with pRL27 was performed in a topA rnhA gyrB ( Ts ) strain and will be described in details elsewhere ( Usongo and Drolet , manuscript in preparation ) . Briefly , pRL27 carries a hyperactive Tn5 transposase gene under the control of the tetA promoter , and an insertional cassette with a kanamycin resistance gene ( aph ) and a pir-dependent origin ( oriR6K ) bracketed by Tn5 inverted repeats [85] . Following electroporation of pRL27 in a pir- background , the kanr cassette inserts randomly into the chromosome . A topA rnhA gyrB ( Ts ) /pBAD18rnhA strain was electroporated with pRL27 and plated on LB containing 25 µg/ml kanamycin at 40°C , to select for suppressors that grew in the absence of arabinose ( no RNase HI produced ) . At this temperature , the strain does not grow because of extensive inhibition of the supercoiling activity of gyrase [17] , combined with over-replication ( [34] and see below ) . P1vir was grown on the kanr clones that re-grew at 40°C and each phage lysate was used to infect a topA rnhA gyrB ( Ts ) /pPH1243 strain , that normally grows only in the presence of IPTG , to overproduce topo III from pPH1243 [17] . Transductants were selected on LB plates containing IPTG and kanamycin ( 50 µg/ml ) at 37°C . Transductants that re-grew in the absence of IPTG were selected for further characterization . Four of the insertion mutants , described in Figure 4 and S8 , were used in the present study . Cells from glycerol stocks were resuspended in LB to obtain an OD600 of 0 . 6 . Five µl of 10-fold serial dilutions were then spotted on LB plates that were incubated at the indicated temperatures . The experiments were performed with cells from glycerol stocks to minimize the chance of selecting cells with compensatory mutations . However , we eventually found that similar results were obtained whether the cells were from glycerol stocks or from overnight liquid cultures ( not shown ) . Cells were grown overnight at 37°C in liquid LB medium supplemented with the appropriate antibiotics . Overnight cultures were diluted in LB medium to obtain an OD600 of 0 . 01 and grown at the indicated temperature to an OD600 of 0 . 8 . The cells were recovered and prepared for microscopy as previously described [17] . Pictures ( fluorescence ( DAPI ) and DIC ) were randomly taken with a LSM 510 Meta confocal microscope from Zeiss . The images were processed using Adobe Photoshop . Representative images are shown both in the Results and Supporting Information sections . The procedure for flow cytometry in rifampicin run-out experiments with cells grown in M9 medium has been described [34] . The DNA/mass ratio was calculated has previously reported [34] . | DNA topoisomerases are ubiquitous enzymes that solve the topological problems associated with replication , transcription and recombination . Eukaryotic enzymes of the type 1A family work with RecQ-like helicases such as BLM and Sgs1 and are involved in genome maintenance . Interestingly , E . coli topo I , a type 1A enzyme and the first topoisomerase to be discovered , appears to have distinct cellular functions that are related to supercoiling regulation and to the inhibition of R-loop formation . Here we present data strongly suggesting that these cellular functions are required to inhibit inappropriate replication originating from either oriC , the normal origin of replication , or R-loops that can otherwise lead to severe chromosome segregation defects . Avoiding such inappropriate replication appears to be a key cellular function for genome maintenance , since the other E . coli type 1A topo , topo III , is also involved . Furthermore , our data suggest that bacterial type 1A topos , like their eukaryotic counterparts , can act with RecQ in genome maintenance . Altogether , our data provide new insight into the role of type 1A topos in genome maintenance and reveal an interplay between these enzymes and R-loops , structures that can also significantly affect the stability of the genome as recently shown in numerous studies . | [
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] | 2014 | Roles of Type 1A Topoisomerases in Genome Maintenance in Escherichia coli |
Metagenomics is revolutionizing our understanding of microbial communities , showing that their structure and composition have profound effects on the ecosystem and in a variety of health and disease conditions . Despite the flourishing of new analysis methods , current approaches based on statistical comparisons between high-level taxonomic classes often fail to identify the microbial taxa that are differentially distributed between sets of samples , since in many cases the taxonomic schema do not allow an adequate description of the structure of the microbiota . This constitutes a severe limitation to the use of metagenomic data in therapeutic and diagnostic applications . To provide a more robust statistical framework , we introduce a class of feature-weighting algorithms that discriminate the taxa responsible for the classification of metagenomic samples . The method unambiguously groups the relevant taxa into clades without relying on pre-defined taxonomic categories , thus including in the analysis also those sequences for which a taxonomic classification is difficult . The phylogenetic clades are weighted and ranked according to their abundance measuring their contribution to the differentiation of the classes of samples , and a criterion is provided to define a reduced set of most relevant clades . Applying the method to public datasets , we show that the data-driven definition of relevant phylogenetic clades accomplished by our ranking strategy identifies features in the samples that are lost if phylogenetic relationships are not considered , improving our ability to mine metagenomic datasets . Comparison with supervised classification methods currently used in metagenomic data analysis highlights the advantages of using phylogenetic information .
Thanks to the possibility to characterize microbial communities through next generation sequencing , microbial ecology has become a central topic in many environmental and therapeutic applications . Extensive explorative studies of the microbiota colonizing several districts of the human body have been conducted , highlighting the large variability from site to site , as well as the interpersonal differences in the same body site [1] . The more extensively studied district is the human gastrointestinal tract ( GI ) , whose metagenomics composition appears to be influenced by several factors [2] , including age [3 , 4] , geography [5] , diet [6] , and lifestyle [7] . In addition , a correlation between imbalances or abnormal composition of the gut microbiota and a number of pathologic conditions has been proposed . These alterations might be due to therapeutic interventions , like antibiotic treatment [8] , or different lifestyle [9] . The growing body of evidence of the importance of the gut microbiota for the self-sustainability of health of the “holobiont” is opening the debate on the design of therapeutic intervention strategies . Fecal transplantation has shown its effectiveness and safety in the treatment of recurrent Clostridium difficile infections [10] , which are known to correlate with altered microbiomes following antibiotic treatment [11] . Alternatives for bioremediation of microbiota alterations is the supplementation of pro- or prebiotics , while it has been suggested that antibiotic treatment and vaccination can be used to guide the structure of the gut microbiota towards a status that is compatible with health [12 , 13] . Most of these intervention strategies would greatly increase their efficacy using a precise definition of the microbial species that are differentially distributed in health and disease conditions . This task faces several difficulties . On one hand , most of the microorganisms composing the human and environmental microbiota are poorly characterized , difficult to cultivate , and lack a precise taxonomic classification . On the other hand , methods to unambiguously define the microbial taxa that are responsible for these differences are still lacking , and their identification usually relies on a small number of arbitrarily chosen association tests with high-level taxonomic classes , or on statistical learning methods , both evaluating only taxa for which a taxonomic classification is possible [14] . In addition , the low abundance of most microbial taxa in metagenomic samples poses additional challenges only recently tackled with statistical methods [15] . In amplicon metagenomics , the composition in term of microbial genera of a sample is inferred from the high throughput sequencing of a small number of diagnostic genomic loci , the most popular being the V1–V6 variable regions of the 16S rDNA gene for bacteria [16] and the ITS spacer for fungi [17] , selectively amplified using broadly conserved PCR primers . As a proxy for species , Operational Taxonomic Units ( OTUs ) are determined by the clustering of the sequences up to a given level of similarity , usually 97% . Using the OTUs abundances , the differentiation between samples or classes of samples is accomplished by measuring their β-diversity , i . e . the variations in community membership across the different groups [18] . Given that the sequences of marker genes are available , phylogenetic measures of diversity such as UniFrac [19 , 20] have proven to be able to identify subtle differences in the structures of microbial communities by weighting species abundances with the phylogenetic relationships amongst taxa . Here we present PhyloRelief , a ranking strategy to identify the taxa significantly contributing to the differentiation of groups of amplicon metagenomic samples . By integrating the phylogenetic relationships amongst taxa into the framework of statistical learning , the method is able to unambiguously group the taxa into clades without relying on a precompiled taxonomy , and accomplishes a ranking of the clades according to their contribution to the sample differentiation . We applied the method to a meta-analysis of two recent datasets of comparative studies of the gut microbiota of European , USA , African and South American healthy individuals , identifying bacterial taxa that are differentially distributed with geography and age . Comparison of the performances of the method to popular feature selection and classification algorithms shows that or strategy is effective in identifying microbial clades associated to the different sample groups , providing a novel analysis method for targeted metagenomic datasets .
Given a partitioning of the samples into two or more classes ( {C1} , {C2} , … ) , PhyloRelief ranks the internal branches in the OTU tree by assigning them a score w that reflects their importance in the differentiation of the classes . In its simplest form the procedure is as follows . First , one sample S is randomly chosen and its nearest hit H ( i . e . the nearest sample of the same class ) and miss M ( i . e . the nearest sample of a different class ) are individuated ( Fig . 1 ) . Next , the score w of each clade is increased by an amount proportional to the contribution of the clade to the distance between S and M , and decreased by an amount proportional to its contribution to the distance between S and H . In this way , the score of those clades that support the fact that S is more distant from M than from H is increased , while the score of those that support the contrary is decreased . A detailed description of the update rules is given in the Methods section . After that the procedure has been repeated over all possible choices of S , each clade has a score w that is high if the clade supports the partitioning of the samples into classes , {C1} , {C2} , and low if it does not ( Fig . 1 ) . The critical step of the procedure is the choice of the update function , for which different definitions are possible . Here we define ( see Methods ) : a ) an unweighted update function , that , for each clade , is proportional to the fraction of the clade that is unique to one of the classes , i . e . the fraction of the phylogenetic tree from which descend only OTUs belonging to one of the classes; b ) a weighted update function , in which each branch of the tree is weighted by a quantity proportional to its unbalance between the classes , i . e . the difference between the number of sequences in samples from one class and from the other . Analogously to the Relief-F extension of the Relief algorithm , PhyloRelief can be applied to multi-class problems and can use k-nearest neighbors in the score computation , becoming robust in the case of noisy or unbalanced data sets [21] . The peculiar nature of the features that we are ranking ( i . e . subtrees in a tree ) introduces a correlation that needs to be taken into account when analyzing the data , and that can be exploited to define a set of independent clades ranking them according to their relevance . If a given branch is heavier , due to unbalanced OTUs distribution between the different classes , its weight will propagate to the parent branches , where it is either reinforced by coalescing with branches sharing a similar unbalance , or diluted if the coalescing branches have contrasting or no unbalances . Exploiting this property , individual lineages can be clustered into taxonomic clades by inspecting the profile of the weights along the tree and identifying the branch where this has a local maximum . This rule , exemplified in S1 Fig , ( see Methods ) naturally defines a set of independent taxonomic clades and ranks them according to their contribution to the diversification between the classes . Using this ranking , the minimal set of clades necessary to describe the classes to a certain level of accuracy is determined by running non-parametric tests of class diversification , such as PERMANOVA[23] and ANOSIM[24] , as a function of the number of clades . In order to illustrate the potentialities of the method , we analyzed two recent datasets , one including 528 samples from healthy individuals of different ages from the United States , from Guhaibo Amerindians living in two villages in Venezuela , and from four rural communities in Malawi [5] , and the other including samples from 14 healthy children from the Mossi ethnic group living in a rural setting in Burkina Faso and 15 healthy children living in Florence ( Italy ) [6] . To allow joint analysis of these two datasets , OTUs were picked using a reference database ( see Materials and Methods ) and the OTU tables were merged and rarefied to the same number of reads . A PCoA analysis of the weighted UniFrac distances ( Fig . 2A ) shows that the samples segregate by geographical origin , with the USA and Italian samples clearly distinct from the African ( Malawi and Burkina Faso ) samples , and the Venezuelan occupying an intermediate position between the two groups . Previous meta-analyses of these data have shown differences in microbiota composition correlating to the “Western” ( USA and Italy ) or “non-Western” ( Malawi , Burkina Faso and Venezuela ) origin of the samples , and it has been suggested that these differences are related to the different balance between protein-rich and fiber-rich diet in these communities [2 , 5 , 6] . Stratifying the data by age of the subjects shows ( S2 Fig ) that the age is also an important factor in the variability of the human gut microbiota , and that this variability seems to be highest at younger age . To identify the taxonomic groups that associate with the geographical origin and that might be correlated to the different diets of the five different populations , we partitioned the samples into two classes , one including the Western subjects ( from Italy and the USA ) , and the other including the non-Western ( Malawi , Burkina Faso and Venezuela ) subjects , and applied the PhyloRelief algorithm to these two classes . To identify the number of clades that were more relevant to differentiate the two classes , we performed ANOSIM and PERMANOVA analysis with increasing number of clades ranked according to the PhyloRelief weights ( Table 1 ) . This procedure showed that both ANOSIM and PERMANOVA had a maximum comprised between 20 and 30 clades , indicating that using this number of clades the separation between the groups is largest . In Fig . 2B we show a phylogenetic tree of the OTUs present in the samples , with those included in the 30 most relevant clades identified by PhyloRelief highlighted ( in red OTUs more prevalent in Malawi , Burkina Faso and Venezuela , in green OTUs more prevalent in the USA and Italy ) . It is worth noting that most of these clades were specifically more represented in the non-Western samples , while only few were specific of the Western individuals , and that much of the differences were confined within the order Bacteroidales . In particular , the Malawi , Burkina Faso and Venezuelan samples were rich in Prevotellaceae , while the Western samples were rich in Ruminococcaceae . In Fig . 2C the PCoA of the weighted UniFrac distances computed on the 30 most relevant clades is shown . Although the Western samples were distinct from the rest , they showed a large degree of variability , with a small fraction of samples from the USA closely related to the Malawi , Burkina Faso and Venezuelan samples . In addition , age was still a major factor , being closely associated to the second component of the PCoA ( S3 Fig ) . To further investigate the individual distribution of the 30 most relevant clades , we show in Fig . 2D a heatmap of the logarithm of the prevalences of the OTUs within these clades . These data confirmed that there was a group of individuals from the USA that were closely related to the non-Western individuals , sharing three clades of Prevotellaceae with most of the Malawi , Burkina Faso and Venezuelan subjects . Stratifying the subjects by age , we found that in both classes young subjects ( below 2 years of age ) were clearly distinct from older subjects ( Fig . 2D , upper panel ) . In addition , while we found clear separation between Western and non-Western adult subjects , some of the Western young subjects were classified by hierarchical clustering together with the non-Western young subjects and vice-versa , suggesting that at young age cultural or geographical differences are less important in determining the structure of the gut microbiota probably related to the instability of the gut microbiota , a phenomenon typical of childhood[5] . To highlight the role of age , and to identify the age for which the differences between young and older individual was highest , we partitioned the samples into two groups using as variable the age threshold , performing a PERMANOVA analysis of the weighted UniFrac distances between the groups as a function of this threshold . We found ( S4 Fig and S1 table ) that the differentiation between young and older subjects was largest when the age threshold was set to two years , and that above 14 years of age , there was no difference between the microbiome of young and adult subjects . However , running the PhyloRelief analysis on the complete dataset , we could not unambiguously identify a minimal set of bacterial clades associated to this differentiation ( S2 Table ) . This result was likely due to the different gut microbiota of Western and non-Western adult subjects . For this reason , we repeated the analysis separately for Western and non-Western samples . ANOSIM and PERMANOVA showed that the maximum differentiation between individuals below age of 2 and above age of 2 for the Western and for the non-Western samples was achieved using 90 ( Table 2 ) and 30 clades ( Table 3 ) , respectively , where both PERMANOVA R2 and ANOSIM R have a maximum . The differentiation between young and adults was much sharper in Western subjects , with a prominent role played by Lachnospiraceae and Ruminococcaceae in the adults ( Figs . 3A and S5 ) . In non-Western subjects ( Figs . 3B and S6 ) , there was also a contribution of the presence of five clades of Prevotellaceae to the differentiation of the adult gut microbiota . In both Western and non-Western samples the younger subjects have higher abundance of Bifidobacteriaceae ( Fig . 3 ) , probably due to breast-feeding in infants [5] . Bifidobacteriaceae were present at lower prevalence in most adult subjects , except for the adults from Burkina Faso probably due to the absence of dairy food in adult age in this African population [25] . The main goal of supervised classification is to build a model from a set of labeled samples to classify new , uncategorized data in high dimensional datasets in the presence of complex relationships between the variables . Identifying a ranking strategy to reduce the dimensionality of the dataset can improve the effectiveness of classification algorithms in metagenomic datasets , where correlations between the variables are introduced both by the phylogenetic relationships between the clades and by the fact that relative abundances are measured . The Random Forest ( RF ) classifier was recently proven to be the most effective in this class of problems [26 , 27] , both for feature selection and classification . Although the main goal of this work is to define a phylogeny-based OTUs ranking method , it is interesting to assess the predictive power of the ranked taxa for the classification of samples into predefined categories in comparison to other state of the art algorithms . For this purpose , we selected four publicly available datasets , including data from four body sites ( forehead vs . external nose and volar forearm vs . popliteal fossa ) [1] , from fecal samples ( IBD vs . healthy ) [28] and skin [29] ( using both subject identification—3 classes—and subject/hand identification—6 classes—as target ) that have recently been used as benchmark in comparative evaluations of classification methods for metagenomic data [26 , 27 , 28] . We compared the performance of PhyloRelief coupled with the RF classifier to LEfSe [30] , an algorithm that uses statistical tests for biomarker discovery , to MetaPhyl , a recent phylogeny-based method for the classification of microbial communities [31] and to Random Forest , used both as classifier and feature selection method . The performances were assessed in terms of average predictive accuracy using the K-category correlation coefficient ( KCCC ) , a multiclass extension of the Matthews Correlation Coefficient ( MCC ) [32] ( see Materials and Methods for details on the procedure ) . The results are reported in Table 4 . We found that while in one case ( in the volar forearm vs . popliteal fossa sample ) the OTUs identified by LEfSe had a higher predictive value , in all other cases PhyloRelief coupled to RF performed equivalently to the most efficient alternative algorithm ( FH vs . EN: Phylorelief 0 . 220+/-0 . 073—MetaPhyl 0 . 170+/-0 . 106; IBD: Phylorelief 0 . 213+/-0 . 074—LEfSe 0 . 238+/-0 . 065; FS subject: PhyloRelief 1 . 0—LEfSe 1 . 0; FS subject/hand: PhyloRelief 0 . 684+/-0 . 026—RF 0 . 670+/-0 . 026 ) , suggesting that taxa identified using phylogenetic information have high predictive power in classification problems .
High throughput sequencing applied to the study of microbial communities is revolutionizing the way we understand the role of microorganisms in the environment and in health and disease conditions . The relatively low cost of sequencing has triggered an exponential increase in the amount of data generated , that have highlighted correlations between the structure of the microbiota and important human pathologies for which conventional intervention strategies were not effective . This suggests that a precise definition of the structure of the healthy vs . disease microbiota could allow early diagnosis and the definition of effective intervention strategies in a number of pathologies . To become a viable diagnostic and therapeutic tool , the evolution of sequencing technologies needs to be paralleled by progress in computational tools enabling to significantly correlate phenotypes to the smallest possible number of microbial taxa . This would allow , on one hand , to develop relatively cheap and easy to use diagnostic tools , and on the other hand to design focused and personalized intervention strategies . PhyloRelief is an algorithm that resolves the problem of relevant taxa identification by applying the Relief strategy of feature ranking in a phylogenetic context . The improvement of this method over existing ones consists in its ability to accomplish a ranking of the microbial clades , defined on the basis of the taxa distribution amongst the samples weighted by phylogenetic information , discovering those that contribute to the differentiation between two or more classes of samples . Importantly , this result is obtained without relying on a predefined set of taxonomic categories that are often hard pressed to describe the complexity of the evolutionary relationships between microorganisms . We applied the algorithm to case control studies derived from the literature , in all cases identifying taxa that are significantly differentially distributed . Of particular interest were the results obtained when comparing infants vs . adults in the different geographies , showing that age has a much greater influence in the USA and Italy than in the African and South American samples , with a much larger fraction of the OTU differentially distributed between young children and adults in the former than in the latter . Comparing the performances of the algorithm to LEfSe , MetaPhyl and to Random Forest in a classical supervised classification schema using cross validation , we found that the taxa ranked by PhyloRelief had also a high predictive value , performing as well as—and in some cases outperforming—current gold standard methods . The algorithm is general and does not rely on any specific sequencing technology , as long as a phylogenetic tree of the OTUs and the distribution of the OTUs in the different samples are available . The method presented here is technology agnostic since it can be used to interpret data generated by the targeted amplification of marker genomic loci , such as the variable regions of the 16S rDNA gene for bacteria , or the ITS sequences for fungi as well as complete metagenome sequencing data , such as those obtained with Illumina technologies . In addition , the algorithm can readily be extended to regression problems to include cases where a continuous variable differentiate the individual samples using the RReliefF extension of Relief [21 , 22] . The PhyloRelief class of algorithms fills a significant gap in the growing array of computational methods that are currently used for the analysis of metagenomic data , and will impact importantly on the application of metagenomics to the development of novel diagnostic markers , leading the application of these approaches from the bench to the bedside .
We will assume that a phylogenetic tree T of the OTUs is given , and that a distance matrix DS between the samples S has been computed according to some measure of β -diversity . Given the availability of phylogenetic data , β -diversity measures incorporating phylogenetic information , such as weighted and unweighted UniFrac [19 , 20] have become popular in the context of metagenomic research , but other measures , such as Bray-Curtis dissimilarity index could also be used . Let us define a partitioning of S into sample class {C1} and {C2} . Usually , this partitioning is obtained either by exploratory analysis of the distance matrix DS , or by the study design ( e . g . according to the origin of the samples , health status or age of the donor in the case of human samples , etc . ) . The purpose of the PhyloRelief algorithm is to rank the OTUs according to their relevance in the partitioning of S into {C1} and {C2} . To accomplish this result , we developed a modified version of the Relief-F procedure that takes into account the phylogenetic information contained in the tree T . To this purpose , the algorithm does not work directly with the OTUs , but with the clades ( or sub-trees ) Ti of the tree T . Below we report the two main steps of the algorithm , i . e . i ) the scoring scheme ranking the sub-trees of the tree T , and ii ) the merging step that identifies the independent clades . We compared the predictive performance of PhyloRelief with the Random Forest classifier ( PhyloRelief +RF ) to LEfSe + RF , MetaPhyl ( without feature selection ) and Random Forest used as both classifier and feature selection method ( RF + RF ) . To assess the prediction performance of the weighted features we implemented a predictive pipeline based on a stratified 10x random subsampling cross validation ( CV ) . Data are partitioned into a training set and a testing set ( 75% and 25% of the samples respectively ) . In order to avoid overfitting and selection bias effects , the feature selection procedure was included in the cross validation loop [40 , 41] . For each training set , the number of ranked features n0 that provides the smallest average KCCC is found by a nested 10x random subsampling CV . Later , the features are ranked using the entire training set and the model is trained using the top ranked n0 features . The model is finally tested on the independent testing set and a KCCC is computed . In the case of LEfSe + RF , LEfSe was treated as feature selection method using the common p-value threshold of 0 . 05 . For MetaPhyl , no feature selection was performed and the nested CV was used to find the optimal model parameters ( parameters grid: λ = {100000 , 1000 , 100 , 10 , 1 , 0 . 1 , 0 . 01 , 0 . 001 , 0 . 0001} and w = {0 , 0 . 2 , 0 . 4 , 0 . 6 , 0 . 8 , 1} ) . For the Random Forest classifier , the number of trees was set to 500 and the weights were computed as in [42] . In PhyloRelief OTUs were ranked using the weights computed on the related clades . The pipeline was developed in Python using the scikit-learn module ( http://scikit-learn . org ) . | In metagenomics , the composition of complex microbial communities is characterized using Next Generation Sequencing technologies . Thanks to the decreasing cost of sequencing , large amounts of data have been generated for environmental samples and for a variety of health-associated conditions . In parallel there has been a flourishing of statistical methods to analyze metagenomic datasets , concentrating mainly on the problem of assessing the existence of significant differences between microbial communities in different conditions . However , for a large number of therapeutic and diagnostic applications it would be essential to identify and rank the microbial taxa that are most relevant in these comparisons . Here we present PhyloRelief , a novel feature-ranking algorithm that fills this gap by integrating the phylogenetic relationships amongst the taxa into a statistical feature weighting procedure . Without relying on a precompiled taxonomy , PhyloRelief determines the lineages most relevant to the diversification of the samples guided by the data . As such , PhyloRelief can be applied both to cases in which sequences can be classified according to a known taxonomy , and to cases in which this is not feasible , a common occurrence in metagenomic data analysis given the increasing number of new and uncultivable taxa that are discovered using these technologies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Explaining Diversity in Metagenomic Datasets by Phylogenetic-Based Feature Weighting |
Arboviral diseases including dengue are increasingly spreading in the tropical/subtropical world including Africa . Updated knowledge on the distribution and abundance of the major vectors Aedes aegypti and Aedes albopictus constitutes crucial surveillance action to prepare African countries such as Cameroon for potential arbovirus outbreaks . Here , we present a nationwide survey in Cameroon to assess the current geographical distribution and prevalence of both vectors including a genetic diversity profiling of Ae . albopictus ( invasive species ) using mitochondrial DNA . Immature stages of Aedes were collected between March and August 2017 in 29 localities across Cameroon following north-south and east-west transects . Larvae and pupae were collected from several containers in each location , reared to adult and morphologically identified . Genetic diversity of Ae . albopictus from 16 locations were analysed using Cytochrome Oxidase I gene ( COI ) . In total , 30 , 381 immature stages of Aedes with an average of 646 . 40±414 . 21 per location were identified across the country comprising 69 . 3% of Ae . albopictus and 30 . 7% of Ae . aegypti . Analysis revealed that Ae . aegypti is still distributed nation widely whereas Ae . albopictus is limited to the southern part , around 6°4’N . However , Ae . albopictus is the most prevalent species in all southern locations where both species are sympatric except in Douala where Ae . aegypti is predominant . This suggests that factors such as climate , vegetation , and building density impact the distribution of both species in Cameroon . Mitochondrial DNA analysis revealed a low genetic diversity in Ae . albopictus populations with a major common haplotype resulting in low haplotype diversity ranging from 0 . 13 to 0 . 65 and 0 . 35 for the total sample . Similarly , low nucleotide diversity was also reported varying from 0 . 0000 to 0 . 0017 with an overall index of 0 . 0008 . This low genetic polymorphism is consistent with the recent introduction of Ae . albopictus in Cameroon . This updated distribution of arbovirus vectors across Cameroon will help in planning vector control programme against possible outbreak of arbovirus related diseases in the country .
Aedes-borne arboviral diseases such as dengue , Zika , and chikungunya are increasing global public health problems due to their rapid geographical spread and rising disease burden [1 , 2] . The viruses causing these infections are transmitted to vertebrates , including human , mainly by bites of infected mosquito belonging to the Aedes genus . However , transmission by blood transfusion and/or by sexual contacts have also been reported in Zika virus [3] . Two main epidemic vectors of these arboviral diseases , Aedes aegypti Linnaeus 1762 and Aedes albopictus ( Skuse ) 1894 , have different origins . Aedes aegypti , originating from African forests , is currently found in most tropical and subtropical regions worldwide [4 , 5] . While Ae . albopictus originating from South Asia forest has invaded all the five continents during the past 30–40 years [6 , 7] . This rapid spread of Ae . albopictus has been mainly facilitated by international commercial exchanges notably international trade of used tires as previously demonstrated [8] , and strong ecological and physiological plasticity allowing it to thrive in a wide range of climates and habitats [9] . This invading species was recorded for the first time in central Africa in Cameroon in the early 2000s [10] and became the dominant species over the native species Ae . aegypti in sympatric areas [11] . Interestingly , dengue , Zika , and chikungunya , which were considered to be scarce in central Africa , are emerging in several urban foci simultaneously with the introduction of Ae . albopictus [12–14] suggesting a modification of epidemiology of arboviral diseases . During the last two decades , the circulation of arboviral diseases has been well documented in Cameroon [14–20] , but nationwide studies have not been undertaken to get an accurate picture of distribution of these viruses across the country . Meanwhile , the seroprevalence of dengue in general population was assessed in three main cities of Cameroon located in different ecological settings in 2006/2007 . This study revealed that 61 . 4% of people tested had immunoglobulin ( Ig ) G and 0 . 3% IgM in Douala , 24 . 2% IgG and 0 . 1% IgM in Garoua and 9 . 8% IgG and no IgM in Yaoundé [21] . More recently , another study in blood donors in population from six cities across Cameroon revealed that the overall seroprevalence of Zika was low around 5% , peaking at 10% and 7 . 7% in Douala and Bertoua respectively and only 2% in Ngaoundere and Maroua [22] . Indeed , Ae . albopictus has been detected as the main vector in Gabon during concurrent dengue/chikungunya outbreak in 2007 [23 , 24] . Both Ae . aegypti and Ae . albopictus have been found infected by chikungunya virus during a large outbreak that occurred in the Republic of the Congo in 2011 [25] . The coexistence of both Ae . aegypti and Ae . albopictus has been well documented in several regions throughout the world . In central Africa , both species often share the same larval habitats , although Ae . albopictus preferred man-made containers such as used tires and discarded tanks surrounded by the presence of vegetation whereas Ae . aegypti preferred larval habitats located in a neighborhood with a high building density [26 , 27] . Competitive displacement between both species has also been well studied in South America and South East Asia revealing that the invasive species often have the competitive advantage over the native species . For example , in Asia , the overall advantage of Ae . aegypti over the resident species Ae . albopictus has been reported [28 , 29] . In contrast , replacement of Ae . aegypti by invasive species Ae . albopictus was reported in south-eastern USA and Brazil [30–32] and was suspected in Réunion [33] and Mayotte [34] . In central Africa , the dominance of the invading species Ae . albopictus over the native species Ae . aegypti was also reported in the locations where both species were found together [24 , 35] . The last study conducted in Cameroon on the geographical distribution of Ae . aegypti and Ae . albopictus date more than 10 years . The results of the study revealed that Ae . aegypti was present across the country while the distribution of Ae . albopictus was limited to the south , under 6°N [36 , 37] . Climatic limitations and the dynamics of invasion have been used to explain the absence of Ae . albopictus beyond 6°N but no temporal study has been performed to assess the dynamic of this distribution and establish whether this species could spread further northwards increasing the risk of arbovirus transmission . Ae . albopictus being more competent to transmit dengue , chikungunya and probably Zika in central Africa [11] , it is important to properly define the vector composition in this region to adequately prepare for future outbreaks . This requires updating the data on the geographical distribution and prevalence of Ae . aegypti and Ae . albopictus country-wide . Furthermore , analysis of the genetic diversity of the invasive species is also needed to characterize the population demographic history of this species in Cameroon since its introduction . Previous studies analysing the genetic diversity of Ae . albopictus in Central Africa using the cytochrome oxidase subunit I ( COI ) gene had revealed a low polymorphism and showed that Cameroonians’ population are related to tropical rather than temperate or subtropical out-groups [9 , 35] . It remains to establish how this genetic diversity has evolved in the past decade and whether the population of this species has experienced demographic events such as expansion , new colonisation or bottleneck . Here , we present an extensive and nationwide analysis of the current geographical distribution and prevalence of Ae . aegypti and Ae . albopictus in Cameroon as well as the genetic diversity of the invading Ae . albopictus species to improve the entomological surveillance of these vectors .
This study was approved by the Cameroonian national ethics committee for human health research N°2017/05/911/CE/CNERSH/SP . Oral consent to inspect the potential breeding sites was obtained in the field in household or garage owners . Mosquitoes were collected in 28 locations across Cameroon , a central African country located between 1°40–13°05N and 8°30–16°10E ( Table 1 and Fig 1 ) , following north-south and east-west transects . Cameroon is characterized by a broad range of biotopes varying from the sudano-sahelian climate in the far north to the equatorial guinean forest climate in the south with strong local climate heterogeneities due to huge variations in altitude ( 0 to 4 , 000 m above sea level on Mount Cameroon ) [36] . The annual precipitation rate vary from 400 mm in the arid areas to 10 , 000 mm at the foot of mount-Cameroon ( >4 , 000m above sea level ) . Annual temperatures and relative humidity vary between 18°C to 28°C and 85% to 45% , respectively [38] ( Table 1 ) . Mosquito sampling focused mainly on urban settlements that spread along the trade routes throughout the country . This is because invading Aedes species has been directly linked to human activities [39] and important outbreaks usually occur in urban settings . Immature stages of mosquitoes were collected from mid-March to August 2017 corresponding to the rainy season . In most locations located under 6°N where both Ae . aegypti and Ae . albopictus were previously detected [36 , 37] , collections in each town were performed in both the downtown and suburb to assess habitat segregation of both species according to building density and vegetation as demonstrated previously [27 , 35] . For collection located above 6°N , investigations were performed across the city and pooled together . In each selected location , all the containers with water ( potential breeding sites ) were inspected and container with at least one larva and/or pupa suspected to belong to Aedes genus ( positive breeding sites ) was recorded . The number of potential and positive containers were reported . The immature stages were collected , transported to the insectary and pooled together according to the environment ( downtown vs . suburban ) and location . They were maintained in the insectary until adult emergence . The emerged adults were identified under a binocular magnifying glass by the morphological criteria previously described [40–42] , numbered , pooled in a breeding cage according to species and location , and further reared in the controlled conditions ( 27°C +/- 2°C; relative humidity 80% +/-10% ) until generation 1 ( G1 ) for further analysis . G0 adults were stored at -20°C for further molecular and genetic analyses . The prevalence of Ae . aegypti and Ae . albopictus was compared per environment and per location and statistically analysed using the Chi-square test . The Cameroon data Shape files were downloaded from the Global Administrative Areas ( GADM ) version 2 . 8 web site and the global positioning system coordinates were projected according to the WGS 84-EPSG 4326 system and the proportion of each Aedes species was generated with the QGIS version 3 . 4 . 1-Madeira software . The total DNA was extracted from 20 G0 individuals of Ae . albopictus collected in 17 sites ( including samples from Yaoundé ) using the Livak method as previously described [43] . DNA extracts from each locality were used as templates to amplify a 700-bp fragment of MtCOI gene . The sequences of the primers used are albCOIF 5’-TTTCAACAAATCATAAAGATATTGG-3’ and albCOIR 5’- TAAACTTCTGGA TGACCAAAAAATCA-3’ [44] . Polymerase chain reaction ( PCR ) amplification was performed using a Gene Touch thermal cycler ( Bulldog Bio , Portsmouth , USA ) as described previously [44] . Amplicons from the PCR were analysed by agarose gel electrophoresis stained with Midori green and visualized under UV light . Fifteen PCR products from each locality were purified using Exo-SAP protocol according to manufacturer recommendations and sequenced directly . Sequences were visualized and corrected manually when necessary using BioEdit software version 7 . 0 . 5 . 3 and aligned using Clustal W [45] . Sequences were numbered based on the reference sequence downloaded from GenBank ( Accession number KU738429 . 1 ) that originated from China . The number of haplotypes ( h ) , the number of polymorphism sites ( S ) , haplotype diversity ( Hd ) , nucleotide diversity ( π ) , were computed with DnaSP 5 . 10 [46] . The statistical tests of Tajima [47] , Fu and Li [48] were estimated with DnaSP in order to establish non-neutral evolution and deviation from mutation-drift equilibrium . Different haplotypes detected were compared to previous COI region sequences published in GenBank from populations that originated from China , USA , Singapore , Thailand , Italy , Japan and Congo [49 , 50] . These COI sequences were used to construct the maximum likelihood phylogenetic tree using MEGA 7 . 0 [51] . Genealogical relationship between haplotype detected across Cameroon was assessed using TCS [52] and tcsBU [53] software .
A total of 4 , 054 potential breeding sites was inspected in 28 locations across Cameroon , out of which 1 , 103 ( 27 . 20% ) were found containing immature stages of Aedes ( positive breeding sites ) . Detected breeding sites were grouped in six categories: used tires , discarded tanks; miscellaneous; water storage tanks; natural and recycled tires commonly used by the locals to protect wells ( Table 2 ) . Used tires were the most potential breeding sites discovered at 87 . 53% ( 3 , 545/4 , 050 ) and mostly infested ( 85 . 31% , 941/1 , 103 ) by Aedes larvae . The prevalence of other breeding sites was very low with 0 . 45% ( 5/1 , 103 ) in natural breeding sites , 3 . 0% ( 33/1 , 103 ) in tires covering water wells , 1 . 45% ( 16/1 , 103 ) in miscellaneous and 5 . 07% ( 56/1 , 103 ) in discarded tanks ( Table 2 ) . 30 , 381 immature stages of Ae . albopictus and Ae . aegypti species were collected in 29 localities between mid-March and August 2017 in Cameroon ( Table 3 ) . Several other species were found in association with Ae . aegypti and Ae . albopictus . These include Aedes simpsoni Theobald 1905 , Aedes vittatus Bigot 1861 , Anopheles gambiae s . l . Giles 1902 , Culex tigripes De Grandpré and De Charmoy 1900 , Culex pipiens quinquefasciatus Say , 1823 , Culex perfuscus Edwards 1914 , Culex duttoni Theobald 1901 , Culex antennatus Beker 1903 , Culex sp Linnaeus 1758 , Eretmapodites brevipalpis Ingram and De Meillon 1927 , and Toxorhynchites brevipalpis Ribeiro 1991 . Aedes aegypti was found across the country in all the sites investigated whereas Ae . albopictus distribution was limited to the southern part of the country under 6°4’N ( Fig 1 and Table 3 ) . Overall , Ae albopictus was more prevalent ( 69 . 28% ) than Ae . aegypti ( 30 . 72% ) ( Table 3 ) . In all the locations in which both species were found together Ae . albopictus was found to be more abundant except in Douala where Ae . aegypti was predominant in downtown and suburban . When analyses were done according to the environment ( suburban vs downtown ) in each location , Ae . albopictus was found to be the dominant species in the suburban and downtown in all the sympatric areas except in Garoua-Boulai , Douala , Limbe , and Edea where Ae . aegypti was predominant in downtown ( Table 3 ) . Analysis also revealed that in some locations such as Bertoua , Kribi , Sangmelima , Ebolowa and Bafoussam , Ae . albopictus is highly prevalent in all areas and nearly excluding the native species which sometimes represents less than 3% . In total , 226 individuals of Ae . albopictus from 17 localities throughout Cameroon were analysed with the mtCOI gene ( Table 4 ) . Sequences analysed based on 636 nucleotides revealed a low polymorphism , with four substitution sites defining five haplotypes resulting in low haplotype diversity ( hd ) ranging from 0 . 13 to 0 . 65 with overall hd of 0 . 32 . Similarly , low nucleotide diversity ( π ) index was recorded varying from 0 . 0000 to 0 . 0017 with 0 . 00075 for total sample . The predominant haplotype H1 ( 79 . 7% in total sample ) was recorded in all the locations with frequency ranging from 35 . 7% to 100% ( Fig 2 , S1 Table ) . This haplotype was also the most prevalent in almost all locations excepted in Bafia and Kribi where it is rather H2 ( 57 . 1% ) and H3 ( 54 . 5% ) haplotypes , respectively ( S1 Table ) . The predominant H1 haplotype matches perfectly with the reference sequence downloaded from GenBank originating from China ( KU738429 . 1 ) and corresponds to the predominant haplotype detected recently in a neighbouring country , the Republic of the Congo [50] . All the three haplotypes isolated previously in the Republic of the Congo were also detected with the same primer sets in Cameroon . Analysis of haplotype network revealed that each haplotype is isolated from the others by one mutational step . Overall , all the Tajima statistics estimated were negatives ( D = -0 . 43 , D* = -0 . 43 , Fs = -0 . 93 , and F* = -040 ) , but not statistically significant ( Table 4 ) . Phylogenetic analysis of the 636bp fragment showed that Cameroonian and Congolese Ae . albopictus populations have the same origin as they cluster together on the Maximum likelihood tree ( Fig 3 ) . Nucleotide sequences of five haplotypes detected across Cameroon have been deposited in the GenBank database ( accession numbers: MH921568 , MH921569 , MH921570 , MH921571 and MH921572 ) .
This study presents an extensive profiling of the prevalence and geographical distribution of Ae . albopictus and Ae . aegypti in Cameroon updating data generated more than 10 years ago . This current report reveals that Ae . albopictus distribution continues to be restricted to the southern part of the country , around 6°N latitude while Ae . aegypti is found throughout the country . The predominance of the invading species ( Ae . albopictus ) over the native species ( Ae . aegypti ) is also reported in almost all locations where both species are sympatric . The current distribution of Ae . aegypti and Ae . albopictus is similar to the previous distribution reported in Cameroon in 2003 [36] and 2007 [37] and in the Central African Republic in 2012 [35] where the distribution of Ae . albopictus was shown to be restricted to the South around 6°N . This boundary has been suggested to be due to the unfavourable climatic conditions for the establishment of this species in the northern part of central Africa above 6°5’N rather than the dynamics of invasion process that is still ongoing as suggested previously [36] . Indeed , mean annual temperatures in Cameroon vary between 20 to 28° C and increase from the south towards the north . Although , both Ae . aegypti and Ae . albopictus have desiccant-resistant eggs , previous studies showed that Ae . aegypti eggs are more tolerant to high temperatures than those of Ae . albopictus [54] . Consequently , the climatic conditions in the southern part of Cameroon ( i . e . , equatorial climate , average annual temperature <26 . 5°C ) are favourable to the development of Ae . albopictus . A higher prevalence of Ae . albopictus was observed in some sympatric areas in both suburban and downtown environment , which is in contrast to the previous results in central Africa . Ae . aegypti has often been the prevalent species in downtown with high building density whereas Ae . albopictus was predominant in suburban area surrounded by vegetation [27 , 35] . In some southern locations such as Limbe and Edea , Ae . albopictus was found to be more prevalent in suburb whereas it is Ae . aegypti that was found more in downtown . Ae . aegypti was also the prevalent species in both suburb and downtown sites in Douala located in the coastal region in the southern part of Cameroon . This observation is consistent with a previous report in Douala in 2006 suggesting that the prevailing climate in Douala is not favourable for the propagation of the invading species [37] . All these observations suggest that the differences in the proportions of both species found in different locations in southern Cameroon may probably reflect differences in environmental factors such as climate , vegetation and building density as suggested previously [37] . Due to the fact that both species exploit the same ecological niches and resources ( larval habitat , blood source ) , Ae . aegypti and Ae . albopictus have competitive interactions [30 , 32 , 55] that may result in the replacement of one species by another in a given environment . Indeed , several studies carried out around the world have shown the changes of the range and abundance of the native species after the introduction of Ae . albopictus [31 , 33 , 34] . In Cameroon , where both species exploit the same types of resources , it is possible that such competitive phenomena are in progress . The mechanisms for the competition are not well known , but many authors believe that it could occur at the pre-imaginal phase and that several factors such as temperature , precipitation , response to symbionts , parasites , predators , and chemical interferences delaying growth could be the main driving forces [30 , 32] . In addition , other studies demonstrated that mating interference in favour to Ae . albopictus , called satyrization , is one of the probable cause of the competitive displacement of resident Ae . aegypti by the invasive Ae . albopictus where they co-exist [56 , 57] . On the other hand , the coexistence of Ae . aegypti and Ae . albopictus was reported in certain locations in Florida ( USA ) two decades after competitive displacement [58] . Used tires were the containers mostly found and mostly positive as breeding sites across the country . This is consistent with previous studies in central Africa demonstrating that used tires are the main productive for both Ae . aegypti and Ae . albopictus [11 , 35–37] . The propensity of Ae . aegypti and Ae . albopictus to colonize used tires may be due to the fact that these species are native to the forest and breed mainly in natural tree holes , which share the characteristics of tires , as the dark colour and the dark interior provide attractive resting or oviposition site for Aedes spp . as previously suggested [35] . Nevertheless , in this study , sampling was targeted mainly at garages and used tire shops to increase the chances of finding the immature stages of Aedes spp . It is important to highlight the remarkable presence of tires as they are used to protect water wells in certain locations such as Garoua Boulai , Bertoua , Bankim and Tibati . This important observation could help raise the awareness in the populations of inadvertently providing larval habitats for arbovirus vectors due to this habit potentially helping to fight against the arboviral diseases . The presence and higher prevalence of Ae . albopictus in southern part of Cameroon can have a significant impact on the epidemiology of mosquito-borne arboviral diseases since Ae . albopictus has been found competent to transmit about 22 arboviruses [59] . Interestingly , the emergence of dengue and chikungunya viruses in domesticated environments in central Africa coincides with the introduction of Ae . albopictus in this area [11 , 12 , 24] . In addition , Ae . albopictus was found infected by zika virus in natural conditions in Gabon in Central Africa [60] . It was also demonstrated that Ae . albopictus from Bangui in the Central African Republic is able to transmit enzootic chikungunya virus strain [61] . This suggests Ae . albopictus can serve as bridge to transfer viruses from sylvan area to urban in central Africa whether this species become dominant in wild settings . Further studies assessing the spread of the invading species Ae . albopictus in sylvan and rural environments are also needed . The discrepancy observed in the distribution of Ae . albopictus and Ae . aegypti across the country notably the restriction of Ae . albopictus in the southern part suggest that the implementing of vector control programme should take into account the specificity of each area . However , data collected across the country show that a good system of waste management in the domesticated environment including the destroying of the used tires could contribute to reduce the density of both Ae . aegypti and Ae . albopictus in Cameroon and indirectly reduce the risk of transmission of diseases transmitted by these mosquitoes . MtDNA analysis using the COI gene in Ae . albopictus populations from Cameroon revealed a low polymorphism with only five haplotypes detected across the country . Among these haplotypes , three ( H1 , H2 and H3 ) of them have been detected previously in the Republic of the Congo with the same primers [50] . This low polymorphism reported in Cameroon and Republic of the Congo is in accordance with the previous studies using another portion of the COI gene in areas newly colonised by Ae . albopictus including Central African countries [9 , 35 , 62] . It was previously suggested that this low polymorphism is mainly due to the recent introduction of Ae . albopictus from a founder population [35] . Indeed , Ae . albopictus was reported for the first time in Cameroon in 1999 . Phylogenetic analysis showed that the haplotype sequences from Cameroon are very close to other sequences isolated to the populations originating from China and Congo , suggesting Cameroonian’s and Congolese populations could have the same origin . Meanwhile , the fact that more haplotypes are found in Cameroon suggests that this country could have been the entrance point of Ae . albopictus in Central Africa potentially through the Port of Douala which is the major one in Central Africa . Primers used in this current study were not the same as those used in the previous study in Cameroon , Central African Republic , and Sao Tome Island . Thus , it was not possible to compare the haplotypes detected in this study with the previous ones detected in Central Africa . Nevertheless , the current results support previous findings suggesting that it is likely that the invading population which colonized Central Africa originated mainly from other tropical regions of the world [9 , 35 , 62] . Further studies , including samples from all central African region using other markers such as double digest RAD sequencing , are required to assess the genetic structure and the level of the gene flow between central African Ae . albopictus populations . This study shows that for the past 10 years the distribution of Ae . albopictus is still restricted to southern Cameroon below 6°5’N latitude while Ae . aegypti is present across the country . This suggests that the prevailing climate in the northern part of Cameroon is not conducive to the invading species Ae . albopictus in this part of the country . However , the invading species is more prevalent in almost all locations in sympatric with the native species suggesting replacement of native species Ae . aegypti is ongoing in some locations . | Aedes albopictus and Ae . aegypti are the most important arbovirus vectors worldwide . Ae . albopictus , native of Asia , was recorded for the first time in early 2000s in Cameroon , central Africa . Previous studies performed a decade ago in Cameroon showed that Ae . albopictus has a geographical distribution limited to the south under 6°N . Whereas the native species Ae . aegypti was present across the country . To update our knowledge in this regards , a nationwide survey was performed in Cameroon to assess the current geographical distribution and prevalence of both vectors including a genetic diversity profiling of Ae . albopictus ( invasive species ) using mitochondrial DNA . Analysis revealed that Ae . aegypti is still distributed nation widely whereas Ae . albopictus is limited to the southern part , around 6°4’N . However , Ae . albopictus is the most prevalent species in all southern locations where both species are sympatric except in Douala where Ae . aegypti is predominant . This suggests that factors such as climate , vegetation and building density impact the distribution of both species in Cameroon . Mitochondrial DNA analysis revealed a low genetic diversity in Ae . albopictus populations with a major common haplotype detected in almost all locations . This study provides the relevant data that can be helpful to establish the vector surveillance of epidemic arbovirus vectors across the country . | [
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] | 2019 | Update on the geographical distribution and prevalence of Aedes aegypti and Aedes albopictus (Diptera: Culicidae), two major arbovirus vectors in Cameroon |
In the Drosophila germline , transposable elements ( TEs ) are silenced by PIWI-interacting RNA ( piRNA ) that originate from distinct genomic regions termed piRNA clusters and are processed by PIWI-subfamily Argonaute proteins . Here , we explore the variation in the ability to restrain an alien TE in different Drosophila strains . The I-element is a retrotransposon involved in the phenomenon of I-R hybrid dysgenesis in Drosophila melanogaster . Genomes of R strains do not contain active I-elements , but harbour remnants of ancestral I-related elements . The permissivity to I-element activity of R females , called reactivity , varies considerably in natural R populations , indicating the existence of a strong natural polymorphism in defense systems targeting transposons . To reveal the nature of such polymorphisms , we compared ovarian small RNAs between R strains with low and high reactivity and show that reactivity negatively correlates with the ancestral I-element-specific piRNA content . Analysis of piRNA clusters containing remnants of I-elements shows increased expression of the piRNA precursors and enrichment by the Heterochromatin Protein 1 homolog , Rhino , in weak R strains , which is in accordance with stronger piRNA expression by these regions . To explore the nature of the differences in piRNA production , we focused on two R strains , weak and strong , and showed that the efficiency of maternal inheritance of piRNAs as well as the I-element copy number are very similar in both strains . At the same time , germline and somatic uni-strand piRNA clusters generate more piRNAs in strains with low reactivity , suggesting the relationship between the efficiency of primary piRNA production and variable response to TE invasions . The strength of adaptive genome defense is likely driven by naturally occurring polymorphisms in the rapidly evolving piRNA pathway proteins . We hypothesize that hyper-efficient piRNA production is contributing to elimination of a telomeric retrotransposon HeT-A , which we have observed in one particular transposon-resistant R strain .
The main function of the PIWI-interacting RNA ( piRNA ) system in Drosophila is suppression of transposon activity in the germline . piRNAs are processed from long transcripts , piRNA-precursors , encoded by distinct genomic regions enriched in TE remnants , termed piRNA clusters [1] . piRNAs recognize complementary targets , exerting RNA silencing at post-transcriptional and transcriptional levels [2] . In some cases , piRNAs cause transformation of a target locus into a novel piRNA cluster that amplifies piRNA response [3 , 4 , 5] . piRNAs generated in ovarian nurse cells are transmitted into the oocyte to launch the processing of piRNA cluster transcripts in the germline of the progeny through an epigenetic mechanism [6] . Thus , the maternal pool of piRNAs silences those TEs in the progeny that are present in the maternal genome . Invasion of alien TEs through paternal inheritance triggers a sterility syndrome , termed hybrid dysgenesis . This occurs due to the absence of maternally transmitted piRNAs complementary to the TE inherited with the paternal genome . However , through several generations , TE silencing is established as a result of the generation of corresponding piRNAs by paternal TE copies or by de novo TE insertions within endogenous piRNA clusters [6 , 7] . The phenomenon of I-R hybrid dysgenesis accompanied by female sterility is caused by mobilization of the non-LTR ( long terminal repeat ) retrotransposon I-element in crosses between reactive ( R ) and inducer ( I ) D . melanogaster strains [8] . R strains are natural D . melanogaster strains collected before the 1950s , the genomes of which do not contain active I-elements but do contain remnants of ancestral I-related elements from previous invasions [9 , 10] . The active I-element has reinvaded natural populations of D . melanogaster in the middle of 20th century . All modern natural populations of D . melanogaster carry active I-elements and are therefore inducers [11] . In a cross of an R female with an I male , hybrid dysgenesis is observed in the F1 progeny because of the very low amount of maternally inherited piRNAs derived from the ancient I-element fragments [6] . The permissivity to I-element activity is measured as a percentage of non-hatching embryos laid by dysgenic females; this ratio is called reactivity [10] . Previously , it was observed that transgenes that contain a transcribed fragment of an I-element cause suppression of dysgenic syndrome in I-R crosses [12] . We have shown that these transgenes produce I-specific small RNAs , which reduce the reactivity of the transgenic lines [4] . Moreover , I-transgenes inserted in euchromatin become de novo piRNA clusters . In this case , suppression of hybrid dysgenesis was achieved by artificially introduced transgenic constructs acting as an additional source of transposon-specific piRNAs . Another well-known genetic system of hybrid dysgenesis caused by the paternal contribution of a P-element is also at least partially related to the lack of maternally deposited P-element-specific piRNAs [6] . Reactivity of natural R strains varies considerably [13] , but the nature of this variability is unknown . Heterochromatic I-element-related copies were proposed to play a role in suppression of the hybrid dysgenic syndrome in low reactive R strains [13] . There is also strong evidence that piRNAs from pericentric clusters play a role in the protection against dysgenesis syndrome in D . virilis [14 , 15] . The effect of R female age on their reactivity was shown to be mediated by the accumulation and maternal transmission of secondary piRNAs derived from the I-element-related copies in ovaries of aged parents [16] . Thus , defective I-element copies and piRNAs produced by them play a critical role in the protection against active I-element invasion . However , the molecular basis and role of the piRNA system in the natural variation of reactivity of R strains has never been explored . To uncover the natural mechanisms of different efficiencies of TE suppression , we performed a systematic study of a set of D . melanogaster R strains collected in France in the middle of 20th century , and revealed the crucial role of the piRNA system in variable response to TE invasion . We show that the reactivity of natural R strains negatively correlates with the ancestral I-element-specific piRNA content . Exploring the reasons for high level of I-specific piRNAs in one of the most I-element-resistant R strain , we discovered that natural variation in the efficiency of primary piRNA production in the germline and somatic follicular cells is the most likely reason for enhanced production of ancestral I-element piRNAs . Our data suggest that the efficiency of the primary piRNA production varies among natural populations , which can dramatically influence the content of different TEs . piRNA proteins have been shown by several groups to be evolving rapidly under adaptive evolution [17 , 18 , 19 , 20 , 21] . Our data are the first to demonstrate an important phenotype that might be caused by this kind of variation .
R strains used in the study are of wild origin , collected in France before the 1950`s and were maintained in the collection of Institut de Genetique Humaine ( CNRS ) , Montpellier . The reactivity of chosen R strains varies significantly , as shown in Fig 1A . I-element expression in the ovaries of dysgenic females is greater in a cross with the strong R strain , Misy , compared to the weak R strain , Paris , and is comparable with I-element activation in piRNA pathway gene spn-E mutant ( Fig 1B ) . In order to understand why R strains differ so much in their ability to protect against transposon invasion , we first estimated the amount of piRNAs specific to I-element in the ovaries of R strains . By Northern analysis , we show that I-specific small RNAs are significantly more abundant in ovaries of the weak R strain Paris than in strong R strain Misy; their level in Paris is comparable with I-specific piRNA content in I strains ( Fig 1C , S1 Fig ) . The differences in the I-element-specific small RNA content were verified by sequencing of small RNAs extracted from the ovaries of four R strains . We found that weak-intermediate R strains Paris , Zola , and cn bw; e contain much more I-specific small RNAs than Misy and an earlier characterized strong R strain , wK ( Fig 1D ) . Most of the I-specific small RNAs are 24–29 nt long and show the characteristic nucleotide bias of piRNA species ( 1U ) . Reactivity negatively correlates with the I-element-specific piRNA content ( Spearman test , r = -0 . 9 , P-value <0 . 1; Fig 1E ) . To verify that the difference in I-element piRNA abundance between strong and weak R-strains is statistically significant , we have performed the differential expression analysis of piRNAs . We processed the pairs of small RNA-seq from strong ( Misy and wK ) and weak ( Paris and Zola ) R strains as pseudo-replicates . Indeed , we found that I-element piRNAs are significantly more abundant in weak R strains than in strong ones ( log2 fold change = 2 . 33 , P-value = 9 . 8e-4 ) ( S1 Table ) . piRNA clusters produce the majority of TE-specific piRNAs in Drosophila ovaries . Distinct piRNA pathways function in the germline and ovarian somatic cells . In follicular cells , primary piRNAs are generated from the single strand precursors mainly transcribed from uni-strand flamenco piRNA cluster to silence retroviral elements such as gypsy , ZAM and Idefix [1 , 22 , 23] . Dual-strand piRNA clusters that generate piRNAs corresponding to both genomic strands suppress a broad spectrum of TEs in the germline [1 , 24] . In the germline , primary piRNAs produced by the piRNA clusters are amplified through the ping-pong amplification loop [1 , 25] . Ancestral I-element fragments reside in the dual-strand piRNA clusters [6] . Therefore , we tested whether differences in the level of cluster-derived I-specific piRNAs in R strains correlated with different reactivity . To address this , we performed a genome-wide comparison of the content of single-mapped small RNAs corresponding to I-element fragments that map to different piRNA clusters [1] , between a strong R strain , Misy , and a weak R strain , Paris . We found that the amount of such small RNAs is much higher in the Paris strain than in Misy ( Fig 2A ) . At first , we looked specifically at the I-element fragments within 42AB , which is the strongest piRNA cluster in D . melanogaster [6] . A normalized amount of such single-mapped piRNAs was higher in weak R strains ( Fig 2B ) . Reactivity negatively correlates with the content of single-mapped I-element-specific piRNAs from 42AB ( Spearman test , r = -0 . 9 , P-value <0 . 1; S2A Fig ) . Similarly to cluster 42AB , we found that the content of single-mapped small RNAs corresponding to the I-element fragments within piRNA clusters 75 , 76 , and 134 was higher in the weak R strain than in the strong R strain ( S3A Fig ) . PCR of genomic DNA followed by sequencing confirmed that in all strains the I-element fragments were intact within piRNA clusters ( S2B and S3B Figs ) . BS2 and Rt1b insertions within I-element fragments were identified in 42AB and #134 piRNA clusters , respectively , in some R strains ( S2B and S3B Figs ) . However , these insertions do not cause global changes in the number of small RNAs mapping to these loci ( Fig 2B , S3A Fig ) . In summary , we found the negative correlation between reactivity of R strains and the amount of ancestral I-element specific piRNAs . Therefore , we set out to explore the nature of these differences in piRNA production . To compare the expression of piRNA precursors generated by piRNA clusters in different R strains , we chose a region of the 42AB cluster that contains a fragment of an I-element . We designed a 42AB-I probe corresponding to this region and performed in situ RNA hybridization on the ovaries of R strains . In weak R strains , signals were stronger and were detected in the nuclei of nurse cells at the early stages of oogenesis , in contrast to strong R strains , where staining was observed only at later stages ( Fig 2C ) . The probe has 78% identity ( 442 bp , e-value = 5e-109 ) with a canonical I-element and could detect not only 42AB transcripts , but also some other ancestral I-element transcripts . Thus , this result suggests that expression of I-element remnants including those localized in the 42AB cluster is stronger in weak R strains . To confirm this observation , we studied the expression of this region by RT-qPCR of total ovarian RNA from R strains . Primers used for RT-PCR and ChIP-qPCR analyses of 42AB-I-element were designed using sequences of all studied R strains ( S2B Fig ) . To avoid aging effects that considerably affect reactivity and expression of I-related heterochromatic copies [16 , 26] , 3-day-old females obtained from young parents that were also obtained from young parents ( and so on , for at least seven generations ) were used for analysis . The steady-state level of the piRNA precursor transcripts from this region was significantly higher in weak R strains ( Fig 2D ) . A similar result was obtained when 3-day-old females from parents of mixed ages were used ( S4A Fig ) . These data show that the expression level of the I-element-specific piRNA precursors is an intrinsic characteristic of different R strains with different reactivity . We did not detect a difference in the expression level of two other regions of the 42AB cluster , harboring fragments of active TEs ( S4B Fig ) . It is important to note that Gypsy12 and Cr1 TE fragments next to the I-element within 42AB and #134 piRNA clusters , respectively ( Fig 2B , S3A Fig ) , also produce very few single-mapped piRNAs in the Misy strain , which allowed us to suggest that these TE fragments are likely part of the same precursor transcripts as the I-element remnants . One may suggest that such transcripts would be targeted by low abundant I-element piRNAs , resulting in a low level of 3’-directed Zucchini-dependent piRNA production [27 , 28] . The germline-specific HP1 homolog , Rhino ( Rhi ) , is essential for piRNA production by dual-strand piRNA clusters , suggesting its putative role in piRNA precursor transcription [29 , 30 , 31] . We performed Rhi ChIP-qPCR and observed that the region of the 42AB cluster containing the I-element fragments shows significantly higher Rhi occupancy in the weak R strains ( Fig 2E ) . Equally high enrichments of Rhi were observed at 42AB regions devoid of I-element fragments in the strong R strain Misy and weak R strain Paris ( Fig 2E ) . Thus , I-element remnants located within piRNA clusters produce more piRNAs in the weak R strains than in the strong R strains , which is in accordance with a high level of piRNA precursor transcription as well as Rhi binding to these regions . It is believed that piRNA cluster transcripts are processed into primary piRNAs , which further increase the abundance and diversity of piRNAs by engaging in an amplification process with the transposon and piRNA cluster transcripts , or Zucchini endonuclease dependent , phased piRNA production [1 , 16 , 25 , 27 , 28] . Biogenesis of piRNAs represents a classic feedback regulatory mechanism in which changes in piRNA production at any stage can be amplified or suppressed at subsequent steps . We tested several models that could explain the difference in the content of I-element piRNAs in R strains . If a higher level of I-element piRNAs in weak R strains are I-element-specific , it would be easily explained by the presence of extra I-element fragments within piRNA clusters in these strains . Alternatively , general inter-strain differences in the efficiency of piRNA processing or maternal transmission of piRNAs could potentially affect I-element and other TE piRNA abundance . We suggest that in natural R strains different causes , or combinations thereof , may lead to low reactivity . For further in-depth analysis of the natural variations in the efficiency of anti-transposon response , we chose two R strains from the opposite sides of the reactivity spectrum: a strong R strain , Misy , and the Paris strain , which demonstrates the lowest reactivity to the I-element ( Fig 1A ) . It was shown previously that piRNA abundance correlates with TE copy number both genome-wide and within piRNA clusters [32] . It is possible that differences in the abundance of I-element-specific piRNAs in R strains can be explained by the fact that the genomes of weak R strains accumulate more I-element remnants located within endogenous piRNA clusters and produce more I-element-specific piRNAs overall . To test this hypothesis , we sequenced the genomes of both Paris and Misy strains . At first , we looked specifically at genomic sequences corresponding to I-elements and found them to be very similar ( Fig 3A ) . The analysis of TE copy abundance using DNA-seq data did not reveal a significant difference in I-element occupancy between Paris and Misy genomes ( P-value = 0 . 74 , two-sided Wilcoxon rank sum test ) . At the same time , genomes of both Paris and Misy strains have much fewer I-elements compared to the wK strain ( P-value < 1 . 0e-15 , two-sided Wilcoxon rank sum test ) . Moreover , the I-element remnant landscape is similar in the genomes of Paris and Misy strains ( S2 Table ) which is in accordance with previously reported data relating to the fixation of older TE insertions [33] , and may indicate the common origin of these strains . A few strain-specific I-element insertions predicted in Paris and Misy genomes locate at the regions producing a negligible number of unique small RNAs in both strains ( S5 Fig ) . Many of the I-element insertions in the genome of the strong R strain wK were absent from the reference I strain iso-1 as well as other R strains ( S2 Table ) . Thus , strong differences in reactivity and piRNA production cannot be explained by variation in the number of ancestral I-element remnants present in different R strains . Similar to our observation , non-significant relationship between the incidence of hybrid dysgenesis and paternal P-element dosage was reported [34] . Nevertheless , we cannot completely exclude the possibility that some actively transcribed I-element fragments hidden in the unassembled heterochromatin could still contribute to the piRNA production in weak R strains . The maternal pool of piRNAs launches processing of the master locus transcripts into piRNAs in progeny [6] . It is possible that transmission of maternal piRNAs might be more efficient in the R strain Paris , that could lead to the enhanced production of piRNAs in general in this strain . To test this , we analyzed the repertoire of piRNAs in 0-2-hour-old embryos , before zygotic activation of transcription ( small RNA-seq from embryos ) , and compared the piRNA ratio in ovaries to that in early embryos for two R strains , Misy and Paris , normalized to the TE copy number ( Fig 3B ) . We did not observe a higher level of maternal transmission for piRNAs corresponding to an I-element or any other TEs expressed in the germline of strain Paris compared to Misy ( Fig 3B , pink and green dots ) . However , accumulation of piRNAs corresponding to TEs with predominant expression in follicular somatic cells ( Fig 3B , blue dots ) is observed in the ovaries of the Paris strain , indicating that these transposons produce more piRNAs . Comparison of the content of TE-specific piRNAs between Misy and Paris 0-2-hour-old embryos shows that I-element small RNAs are more abundant in embryos of Paris strain than in Misy , which is in agreement with ovarian small RNA data ( S6 Fig ) . Thus , the strong suppression of the I-element in the Paris R strain could not be explained by either the accumulation of I-element remnants nor enhanced maternal deposition of piRNAs in the Paris strain compared to Misy . Instead , it is likely caused by the enhanced production of piRNAs from the same I-element remnants scattered along piRNA clusters . Two R strains with dramatically different reactivity have the same I-element fragment copy number , but these copies generate more piRNAs in the weak R strain Paris than in the strong R strain , Misy . We hypothesized that in Paris production of primary piRNAs is enhanced in general and is not restricted to any particular TE , including the I-element . However , with the exception of I-element fragments , it is difficult to examine primary processing in the germline: the amount of primary piRNAs corresponding to active transposons is confounded by the presence of secondary piRNAs derived from the ping-pong piRNA amplification loop [1 , 25] . A characteristic of the ping-pong mechanism is the number of complementary piRNA pairs with a 10-nucleotide overlap between their 5’ ends [1 , 25] . Comparison of the total piRNA content between Misy and Paris strains did not detect global differences ( S7A Fig ) . Ping-pong piRNA profiles of TEs expressed in the germline are also highly similar in R strains ( S7B Fig ) . This indicates that a general limitation of the germline primary piRNA biogenesis in the strong R strain , if any , can be compensated by the ping-pong amplification for most of the active TEs . Accordingly , we did not observe variations in the expression of piRNA precursors , or Rhi enrichment at the regions of the 42AB locus harboring fragments of TEs active in either strain ( Fig 2E , S4B Fig ) . Next , we decided to look at the variation in the content of piRNAs derived from uni-strand piRNA clusters , where piRNAs originate from one genomic strand and are not subjected to secondary amplification . Comparison of the abundance of TE-specific piRNAs normalized to copy number between Misy and Paris shows that TEs expressed predominantly in follicular cells [24] produce more piRNAs in Paris ( Fig 4A ) . A ping-pong-independent piRNA pathway operates in Drosophila follicular cells [24] . Therefore , we decided to determine differences in primary piRNA processing between Drosophila strains by analyzing follicular piRNAs . To compare cluster-derived piRNAs , we focused on the piRNAs single-mapped to the TEs from the major uni-strand somatic piRNA cluster flamenco ( cluster #8 ) . We detected more piRNAs corresponding to such TEs ( normalized to copy number ) in Paris compared to Misy ( Fig 4B ) . piRNA cluster #2 located at 20A region of X chromosome is the only known uni-strand cluster that is expressed in the germline and produces primary piRNAs [30] . We compared piRNA production by cluster 20A in Misy and Paris . Since we failed to assemble the genomic sequence of the entire 20A cluster using DNA-seq data , we analyzed the regions enriched by single-mapped piRNAs , comprised of highly damaged fragments of mdg1 and roo TEs . Comparison of single-mapped piRNAs generated by distinct regions of 20A ( normalized to copy number ) revealed their increased abundance in ovaries of Paris compared to Misy ( Fig 4B ) . We also observed decreased content of single-mapped piRNAs ( normalized to the TE copy number ) generated by TE fragments located in flamenco and 20A uni-strand piRNA clusters in ovaries of the strong R strain wK , compared to the Paris strain ( S8 Fig ) . Next , we compared piRNA production by genic 3’UTR ( untranslated region ) piRNA clusters [35] between Paris , Misy and wK and observed that the majority of the 3’ genic piRNA clusters including traffic jam ( tj ) , which is the strongest genic piRNA cluster in follicular cells [36] , produce more piRNAs in Paris ( Fig 5A and 5B ) . We confirmed this by Northern blotting of tj small RNAs ( S9A Fig ) . A comparison of genome sequences revealed no significant differences in the regions encompassing major genic piRNA clusters in Paris , Misy and wK strains ( S9B and S9C Fig ) . Cluster analysis of piRNA production by 3’UTR piRNA clusters in R strains and in 16 RAL strains from the Drosophila melanogaster Genetic Reference Panel ( DGRP ) [37 , 38] shows strong inter-strain differences in the efficiency of genic piRNA processing . Paris strain belongs to the group producing most abundant 3’UTR piRNAs ( S10 Fig ) . We discovered a natural variability in genic piRNA abundance and decided to explore the influence of this variability on the level of mRNAs that serve as the piRNA precursors . Stronger piRNA processing of tj mRNA in Paris does not significantly affect the steady state level of tj mRNA ( Fig 5C and 5D ) . In addition , we discovered a potent 3’ genic piRNA cluster in the egalitarian ( egl ) 3’UTR that originated as a result of a pogoN1 transposon insertion in Paris , leading to the production of abundant piRNAs collinear to the egl mRNA ( Fig 5E , S9A Fig ) . Egl is an RNA-binding protein essential for the localization of transcripts during oogenesis and early development [39] . Expression analysis of the egl gene in different strains has shown a decrease in the steady-state level of egl mRNA as well as Egl protein in ovaries of strain Paris ( Fig 5F and 5G ) , which is likely a result of mRNA degradation by piRNA processing . Thus , we demonstrate that piRNA production per se in some cases can affect mRNA stability and cause moderate effects on the expression of genes harboring 3’UTR piRNA clusters . We conclude that production of the primary piRNAs in the germline and follicular cells is more efficient in Paris . We suggest that low reactivity of the Paris strain likely results from high efficiency of primary piRNA production in general rather than only I-element-specific piRNAs . What could be the reason for natural variability in primary piRNA production ? piRNA factors are rapidly evolving proteins characterized by an increased rate of amino-acid evolution [17 , 18 , 19 , 20 , 21] . Allelic polymorphism of piRNA biogenesis factors could influence piRNA biogenesis . Taking into account the greater abundance of primary piRNAs both in the germline ( I-element and 20A uni-strand piRNA cluster ) and in follicular cells ( flamenco and 3’UTR genic clusters ) in Paris , one may suggest that these effects are mediated by a mutation of a piRNA factor/factors common for both ovarian tissues . High intra-specific variability in the level of the piRNA pathway gene transcripts was revealed in D . simulans transcriptome studies [17 , 40] . Using western-blotting , we did not detect differences in the protein expression of the main small RNA pathway factors in ovarian extracts from Misy and Paris strains ( S11 Fig ) . However , this leaves the possibility that studied Drosophila strains contain allelic variants of some piRNA biogenesis factors characterized by different biochemical activity that affect primary processing . We compared the genomes of Misy , Paris and 16 fruit fly RAL strains from the DGRP project [37] to look for possible allelic polymorphisms in genes encoding piRNA biogenesis factors . As expected , all strains display numerous polymorphic sites in the piRNA factors , including functionally significant amino acid substitutions; some of them are specific for Paris ( S3 Table ) . We believe that some allelic variant or a combination of several variants is responsible for an unusually high efficiency of primary piRNA production in this strain . Our findings provide a strong basis for further in-depth analysis of the naturally occurring polymorphism of the piRNA system and its role in adaptive genome defense . During the analysis of TE-specific small RNA in R strains , we noticed an extremely low read number of telomeric retrotransposon HeT-A piRNAs in Paris ( Fig 4A ) . The telomeres of D . melanogaster consist of the specialized telomeric retrotransposons HeT-A , TART and TAHRE [41 , 42 , 43 , 44] . HeT-A is a main structural component of Drosophila telomeres represented by about 30 complete elements per diploid genome [42 , 45] while TART and TAHRE are represented by several copies , but are not found in every telomere [41] . Ovarian small RNA mapping to the canonical HeT-A sequence ( Fig 6A ) and Northern analysis of small RNAs ( Fig 6B ) confirmed that the number of HeT-A-specific small RNAs is dramatically reduced in Paris . Genomic sequence coverage shows that the HeT-A copy number in Paris is substantially lower than in Misy and wK ( Fig 6C and 6D ) . The analysis of TE copy abundance using DNA-seq data revealed a significant difference of HeT-A occupancy between Paris and Misy or wK genomes ( Fig 6D ) . To evaluate relative HeT-A copy number , we performed PCR on genomic DNA of different strains ( S12 Fig ) and confirmed that in the genomes of two independent Paris substrains , the HeT-A copy number is dramatically lower than in Misy and other studied strains , while the HeT-A copy number did not correlate with reactivity ( S12 Fig ) . A relationship between the abundance of HeT-A piRNA , as detected by Northern blot ( Fig 6B ) and HeT-A copy number ( S12 Fig ) , was observed . In the strain Gaiano , which is characterized by increased HeT-A copy number [46 , 47] , abundant HeT-A-specific piRNAs were detected by Northern blotting ( Fig 6B ) . The strain Paris possesses a lower HeT-A copy number compared to the D . melanogaster strain RAL-852 , that was previously shown to have the lowest HeT-A copy number in the DGRP collection [48] . We also failed to detect HeT-A in Paris using DNA FISH on polytene chromosomes of salivary glands ( S13 Fig ) . A comparison of genome sequence data obtained in 2013 and 2016 ( Fig 6D ) revealed a stable heritable low HeT-A copy number in Paris . The analysis of TE copy number and genomic sequence coverage on other telomeric retrotransposons , TART and TAHRE , did not show a dramatic difference between the Paris and Misy strains , although slightly increased copy number of these elements may indicate that they can partially compensate for HeT-A loss in Paris ( Fig 6D , S14A Fig ) . Abundant TART-specific small RNAs are revealed in ovaries of Misy and Paris ( S14B Fig ) . Evolutionary conserved cooperation of autonomous and non-autonomous telomere-specific retrotransposons may be explained by distribution of different roles among elements [49] . Most likely , TART provides reverse transcriptase activity for non-autonomous HeT-A that serves as a main structural component of Drosophila telomeres [50] . Indeed , HeT-A and TART are characterized by different copy number , structure and patterns of transcription [42 , 45 , 50 , 51] . The strain Paris is an excellent model to study the roles of different telomeric elements and particularly HeT-A in genome stability . We speculate that elimination of HeT-A in Paris may be the direct consequence of enhanced primary piRNA production in this strain ( see Discussion ) .
In this study , we found that natural variations in the level of TE-specific piRNAs can lead to dramatic differences in the protection against I-element transpositions in I-R dysgenic crosses . It is the first report of age-independent strain-specific variation in the general efficiency of piRNA production; the previously published examples described distinct piRNA cluster-specific variations [3 , 52] . A correlation was discovered between the hybrid dysgenic abnormality rate and the level of piRNAs corresponding to the ancestral I-element fragments in natural D . melanogaster R strains lacking an active I-element . Additionally , a negative relationship between reactivity and transcription of the piRNA precursors as well as Rhi binding to the piRNA cluster regions containing I-element fragments was observed . An estimation of the level of piRNA cluster transcripts upon Rhi depletion led to conflicting results [29 , 31] , leaving a question about the exact role of Rhi in the piRNA cluster activity . From our data we can conclude that primary piRNA production , Rhi enrichment , and transcription of the piRNA-producing locus are closely related to each other in the germline-specific dual-strand piRNA clusters . This correlation was observed only for the ancestral I-element fragments in R strains lacking active I-elements . The effect was noticeable because the primary I-element piRNAs were not masked by the abundant secondary piRNAs . The transposition rate of TEs is controlled by distinct mechanisms , including regulation of promoter activity , chromatin structure , splicing , and small RNA pathways [2 , 51 , 53 , 54 , 55 , 56] . In some cases , the factors capable of inducing loss of transposition control are unclear . For example , a high level of hobo and I-element transpositions in the genome of the reference D . melanogaster strain iso-1 is observed despite normal production of hobo- and I-element-specific piRNAs in the ovaries of this strain [5 , 57 , 58] . Molecular mechanisms underlying the accumulation of actively transposed copia retrotransposon in the genomes of inbred D . melanogaster strains also remain unknown [59 , 60] . In dysgenic crosses , maternally transmitted pools of piRNAs play a critical role in the suppression of paternally inherited TEs [6] . However , the nature of the different responses to transposon invasion in different natural strains is poorly understood . Here , we have shown that variation in the ancestral I-element-specific piRNA content is responsible for the different manifestations of I-R hybrid dysgenesis mediated by I-element mobilization in natural populations . I-R hybrid syndrome represents a simple model of intraspecific hybrid dysgenesis , in which a maternal pool of piRNAs targets the paternally inherited TEs . However , I-element mobilization per se does not explain the occurrence of the sterility of dysgenic females . The important role of the systemic perturbation in the production of piRNAs including genic piRNAs in the dysgenic gonads has been discussed [14 , 15] . Our data demonstrating the variation in the abundance of distinct types of piRNAs including ovarian somatic species in natural strains , involved in hybrid dysgenic crosses , agree well with this idea . The cause of a higher production of I-element-specific piRNAs in weak R strains remains unclear; most likely , different mechanisms affect the level of I-specific piRNAs in different R strains , which requires deep genome-wide analysis of each strain . Using two R strains , a strong and a weak one , we investigated the putative factors responsible for this phenomenon . We were only able to link strong suppression of I-element to enhanced production of primary piRNAs in the weak R strain , Paris . We did not detect extra I-element fragments capable of producing abundant piRNAs in the genome of Paris , however , we do not exclude the possibility that some actively transcribed I-element fragments hidden in the heterochromatin could contribute to the piRNA production in Paris or in other weak R strains . The hypothesis about stronger transmission of maternal piRNAs in the Paris strain was also rejected since we did not reveal any differences between weak and strong R strains . Instead , we detected inter-strain differences in the levels of primary piRNA production and piRNA precursor expression under controlled aging and temperature conditions , suggesting that natural variation in piRNA production efficiency is responsible for the observed differences . We speculate that production of primary piRNAs may be more efficient in Paris in general due to systemic changes in the piRNA pathway that provide a high level of the ancestral I-element piRNA production as well as enhanced production of other primary piRNAs in germ and somatic ovarian cells . We did not reveal quantitative differences in the expression of several piRNA factors or any evidence for alteration in their activity . Certain allelic variants or combinations thereof may be responsible for the higher level of primary piRNAs found in Paris . Variation in TE diversity and their copy number requires adaptive evolution of the piRNA machinery . Indeed , strong evidences of rapid positive selection within a core set of piRNA genes within Drosophila species were reported by different groups indicating an arms race between the piRNA pathway and TEs [18 , 19 , 20 , 21] . piRNA pathway genes also exhibited large variation in transcript levels in wild-type strains of D . simulans [17] . Moreover , TE transcript level was shown to be negatively correlated with piRNA pathway gene expression [40] . However , no relationship between TE abundance and the increasing rate of amino-acid evolution of the piRNA pathway factors was revealed in the Drosophila genus [61] . Instead , improved codon usage increasing the translational efficiency of the piRNA machinery was correlated with TE abundance in Drosophila [61] . In all these studies the authors examine the genomes that have stable set of TEs . It was proposed that constraint on the efficiency of the piRNA machinery may be greater only at the first steps of a new TE invasion [17 , 21] . R strains with different reactivity serve as an excellent model to simulate such scenario and to explore the role of the piRNA factor polymorphism in the adaptive response to a new TE invasion . Indeed , variation in the production of primary piRNAs in R strains becomes apparent in the initial response to the I-element invasion . This does not affect active transposons because the mechanism of piRNA amplification , with participation of TE mRNAs , masks the differences in the amount of primary piRNAs ( Fig 7 ) . It is believed that TE insertions in the master loci provide adaptive immunity [6 , 7] . We develop this idea and suggest that such insertion could provide prompt and effective defense only if the efficiency of the primary piRNA production is sufficient . Even though numerous genic piRNAs collinear to the cellular mRNAs are produced in different organisms , the influence of the piRNA production on the stability of mRNAs that serve as the piRNA precursors has not been explored . One hypothesis is that genic piRNAs production is a side-effect of the recognition of erroneous piRNA targets [28] . Nevertheless , the TJ protein encoded by the gene tj which harbors one of the major 3’ genic piRNA cluster , was shown to be upregulated in piwi mutant follicular cell clones , of late , but not of early egg chambers [35] . We describe two examples of the natural genic piRNA cluster polymorphisms and their influence on the level of mRNA that serve as the piRNA precursors . Different abundance of the tj 3‘UTR piRNAs in Misy and Paris strains does not significantly affect tj mRNA level whereas formation of the potent piRNA cluster within 3’UTR of gene egl leads to the decrease of the corresponding mRNA and protein . Steady-state mRNA level of genes whose 3’UTR generate piRNAs likely depends on the ratio between transcribed and piRNA-processed mRNA pools . Thus , production of 3’ genic piRNAs can slightly shift the abundance of corresponding mRNAs and proteins that may be evolutionarily significant . The question arises: Is it advantageous or not for the genome to have strong primary piRNA expression ? On the one hand , it provides efficient protection against I-element expansion , which might be considered a beneficial feature . Efficient production of 3’ genic piRNAs can alter the level of numerous gene transcripts and proteins which in the end could affect gene regulation network that may be evolutionarily significant . At the same time , a limited selective advantage for host transposon repression has been previously suggested [62] . Analysis of piRNA-mediated silencing has also revealed limits to the optimization of piRNA-mediated defense against active TEs [32] . We speculate that the necessity of telomere elongation by transpositions of telomeric retrotransposons explains this limitation; because , while enhanced production of piRNA can be advantageous for eradication of harmful TEs , it can also cause damage by targeting domesticated elements such as the telomeric retroelement HeT-A . Unexpectedly , genome-wide analysis revealed that Paris , which is resistant to I-element invasion , is also characterized by an extremely low copy number of the major telomeric retrotransposon HeT-A . In the Drosophila germline , telomeric retrotransposon transcription and the rate of their transpositions onto the chromosome end are regulated by the piRNA pathway: The piRNA system suppresses excessive retrotransposon activity to maintain sufficient RNA levels to provide telomere elongation [51 , 63] . Telomeres represent an unusual sort of piRNA clusters , because they produce both piRNAs and their target mRNAs , which serve as templates for telomere elongation . HeT-A is particularly sensitive to piRNA pathway disruption , and demonstrates strong derepression in contrast to a modest response of TART [51 , 64] . We suggest that HeT-A might be also more sensitive than TART to the enhanced production of primary piRNAs . Only for Paris , we could connect strong suppression of the I-element to enhanced production of primary piRNAs in general . We speculate that exceptionally effective production of primary piRNAs in Paris could lead to a greater level of HeT-A piRNAs , which in the end would lead to elimination of HeT-A mRNA that are essential for telomere elongation ( S15 Fig ) .
R strains wK , Misy , Paris , cn bw;e , cn;e , and Zola were obtained from the collection of Institut de Genetique Humaine ( CNRS ) , Montpellier , France . Reactivity was evaluated by measuring the percentage of non-hatching embryos laid by the progeny resulting from the cross of R females with w1118 males containing functional I-elements , according to a previously described procedure [12] . Strains bearing spindle-E ( spn-E ) mutations were ru1 st1 spn-E1 e1 ca1/TM3 , Sb1 es and ru1 st1 spn-Ehls3987 e1 ca1/TM3 , Sb1 es; iso-1 ( y1; cn1 bw1 sp1 ) is an isogenic strain used for whole-genome sequencing by the Drosophila Genome Project . The Gaiano III ( GIII ) strain carries the third chromosome with the Tel locus mutation affecting telomere length [46] . RAL-852 strain used in the analysis of HeT-A copy number was obtained from laboratory of Dr . T . Mackay , NCSU , Raleigh , NC . Northern analysis of small RNAs was performed as previously described [4] . The I-element probe contained a fragment of I-element corresponding to nucleotides 2109–2481 of the GenBank entry M14954 . The HeT-A antisense probe contained a fragment of the ORF containing nucleotides 4330–4690 of GenBank sequence DMU06920 . A PCR fragment amplified using primers 5’-gatcatttccaagagcttttcct-3’ and 5’-taatacgactcactatagggagagggaataaatatcaacag-3’ was used for traffic jam ( tj ) antisense riboprobe preparation . egl probe was synthesized using a PCR fragment amplified with primers 5’-tacaattaatacgactcactataggcgacaaagatattaggaaacc-3 and 5’-cctaacaaacaaagcgcagacac-3’ . Hybridization with P32 5’-end-labelled oligonucleotide 5’-actcgtcaaaatggctgtgata-3’ complementary to miRNA-13b-1 was used as a loading control . The blots were visualized with the phosphorimager Typhoon FLA-9500 ( Amersham ) . In situ RNA analysis was carried out according to the previously described procedure [65] using a digoxigenin ( DIG ) -labeled strand-specific riboprobe and alkaline phosphatase ( AP ) -conjugated ( diluted at 1/2000 ) anti-DIG antibodies ( Roche ) . The 42AB-I probe corresponds to the genomic fragment chr2R:6261222–6261811 ( BDGP assembly R6 ) . The I-element probe contains a fragment of ORF2 corresponding to nucleotides 2109–2481 of the GenBank entry M14954 . Fluorescence in situ hybridization ( FISH ) with polytene chromosomes was performed as described [66] . The HeT-A probe contains a fragment of HeT-A ORF , corresponding to 1746 to 4421 nucleotides in the GenBank sequence DMU 06920 . The DNA probe was labeled using a DIG DNA labeling kit ( Roche ) . RNA was isolated from the ovaries of 3-day-old females obtained from young parents ( F7 ) or from parents of mixed ages . cDNA was synthesized using random hexamer and SuperScriptII reverse transcriptase ( Life Technologies ) . cDNA samples were analyzed by real-time quantitative PCR using SYTO-13 dye on LightCycler96 ( Roche ) . Values were averaged and normalized to the expression level of the ribosomal protein gene rp49 . Standard error of mean ( SEM ) for three independent RNA samples was calculated . The primers used are listed in S4 Table . ~150 pairs of 3-day-old female ovaries obtained from young parents ( F7 ) were dissected for every IP experiment . ChIP was performed according to the published procedure [67] . 2 . 5 μg and 50 ng of chromatin were taken for each ChIP and input probe , respectively . Protein G Agarose/Salmon sperm DNA ( Millipore ) was used without preincubation with chromatin . Chromatin was immunoprecipitated with rat Rhi antiserum [68] . Quantitative PCR was conducted with Lightcycler96 ( Roche ) . Percent input was calculated using the formula: % input = ( 2^ ( Ct input–Ct IP ) ) × Fd × 100 , where % input is the ChIP efficiency expressed in percent when compared with total DNA; Ct IP and Ct input are threshold cycles for ChIP and input samples , respectively; Fd is the dilution factor . SEM of triplicate PCR measurements for two biological replicates was calculated . Small RNAs 19–29 nt in size from total ovarian RNA isolated from cn bw;e , Paris , Misy , and Zola strains and from 0-2-hour-old embryos of Paris and Misy strains were cloned as previously described [69] . Libraries were barcoded according to the Illumina TrueSeq Small RNA sample prep kit manual and sequenced using the Illumina HiSeq-2000 system . After clipping the Illumina 3’-adapter sequence , small RNA reads that passed a quality control and minimal length filter ( >18nt ) were mapped ( allowing 0 mismatches ) to the Drosophila melanogaster genome ( Apr . 2006 , BDGP assembly R5/dm3 ) using bowtie [70] . In order to identify piRNAs , the sequenced small RNAs were mapped to the canonical sequences of transposable elements ( http://www . fruitfly . org/p_disrupt/TE . html ) with the allowance of up to 3 mismatches . Small RNA libraries were normalized to library depth . The plotting of size distributions , read coverage , and nucleotide biases were performed as described previously [4] . Ovarian and embryonic small RNA-seq data for cn bw;e , Zola , Paris , and Misy strains were deposited at Gene Expression Omnibus ( GEO ) , accession number GSE83316 . Small RNA-seq data from ovaries of wK ( GSM1024091 ) and iso-1 ( GSM1123781 ) strains were described previously [4 , 5] . Small RNA-seq data from ovaries of 16 RAL strains from the DGRP project [37] were obtained from NCBI SRA , SRP019948 [38] . Paired-end ( ~250 bp fragment size ) and mate-pair ( ~8 kb fragment size ) libraries from Paris and Misy strain genomic DNA were prepared according to the Illumina standard protocols and sequenced on the Illumina HiSeq 2000 . The genomic deep sequencing data were deposited in the NCBI SRA Database , SRP076499 . DNA-seq data of wK ( SRA accession number SRP021106 ) were described previously [5] . Insertion sites of I-elements in Paris , Misy , and wK genomes were identified by using the mcclintock meta-pipeline ( https://github . com/bergmanlab/mcclintock ) with the canonical sequences of TEs ( http://www . fruitfly . org/p_disrupt/TE . html ) and the annotated TE insertion sites in the reference iso-1 strain from FlyBase ( BDGP r . 6 ) . Only the non-redundant unambiguous TE insertions were taken into account . | Transposon activity in the germline is suppressed by the PIWI-interacting RNA ( piRNA ) pathway . The resistance of natural Drosophila strains to transposon invasion varies considerably , but the nature of this variability is unknown . We discovered that natural variation in the efficiency of primary piRNA production in the germline causes dramatic differences in the susceptibility to expansion of a newly invaded transposon . A high level of piRNA production in the germline is achieved by increased expression of piRNA precursors . In one of the most transposon-resistant strains , increased content of primary piRNA is observed in both the germline and ovarian somatic cells . We suggest that polymorphisms in piRNA pathway factors are responsible for increased piRNA production . piRNA pathway proteins have been shown to be evolving rapidly under selective pressure . Our data are the first to describe a phenotype that might be caused by this kind of polymorphism . We also demonstrate a likely explanation as to why an overly active piRNA pathway can cause more harm than good in Drosophila: Highly efficient piRNA processing leads to elimination of domesticated telomeric retrotransposons essential for telomere elongation , an effect which has been observed in a natural strain that is extremely resistant to transposon invasion . | [
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] | 2017 | Natural variation of piRNA expression affects immunity to transposable elements |
Pavlovian predictions of future aversive outcomes lead to behavioral inhibition , suppression , and withdrawal . There is considerable evidence for the involvement of serotonin in both the learning of these predictions and the inhibitory consequences that ensue , although less for a causal relationship between the two . In the context of a highly simplified model of chains of affectively charged thoughts , we interpret the combined effects of serotonin in terms of pruning a tree of possible decisions , ( i . e . , eliminating those choices that have low or negative expected outcomes ) . We show how a drop in behavioral inhibition , putatively resulting from an experimentally or psychiatrically influenced drop in serotonin , could result in unexpectedly large negative prediction errors and a significant aversive shift in reinforcement statistics . We suggest an interpretation of this finding that helps dissolve the apparent contradiction between the fact that inhibition of serotonin reuptake is the first-line treatment of depression , although serotonin itself is most strongly linked with aversive rather than appetitive outcomes and predictions .
Serotonin ( 5-hydroxytryptamine [5-HT] ) is a neuromodulator that appears to play a critical role in a wealth of psychiatric conditions , including depression , anxiety , panic , and obsessive compulsions . However , despite the importance of serotonergic pharmacotherapies , notably selective serotonin reuptake inhibitors ( SSRIs ) , the roles that serotonin plays in normal and abnormal function are still mysterious . We start from three particular findings . First , 5-HT is involved in the prediction of aversive events , possibly as a form of opponent [1–3] to dopamine [4–11] . Second , 5-HT is involved in behavioral inhibition [12–14] , preventing or curtailing ongoing actions in light of predictions of aversive outcomes . The third finding is the collection of psychopharmacological data implicating 5-HT in animal models of depression and anxiety [15–17] , together with the fact that depleting 5-HT ( by dietary depletion of its precursor , tryptophan ) in human subjects who have recovered from depression , can reinstate an acute , at times fulminant , re-experience of subjective symptoms of the disease , as assessed by various rating scales [18–21] . Furthermore , while SSRIs are used in the treatment of depression , genetically induced , constitutive decreases in the efficiency of 5-HT reuptake are a risk factor for depression [22–24] . These findings are hard to connect: the second fact seems orthogonal to the first and third , which are themselves in apparent contradiction . If 5-HT is really involved in predicting aversive outcomes , then depleting it should surely have positive rather than negative affective consequences . We suggest that the missing link comes from considering the interactions between Pavlovian predictions and ongoing action selection . The interaction is seen in conditioned suppression [25] , a standard workhorse test for aversive predictions . Animals are trained to emit appetitive instrumental actions ( such as pressing a lever for reward ) , and to associate ( by classical conditioning ) a light with a shock . Presentation of the light during instrumental performance reduces the rate at which animals emit those responses . Neither the theoretical nor the neurobiological status of this interaction is completely resolved , though there is some evidence of the involvement of 5-HT in the nucleus accumbens in its realization [26–28] . Here , we treat a subset of the inhibitory processes associated with Gray's behavioral inhibition system ( BIS ) [7 , 13 , 29 , 30] in terms of what might be called a preparatory Pavlovian response . Consummatory Pavlovian responses are ( evolutionarily ) pre-programmed reactions to the presence of affectively significant outcomes such as food , water , or threats . Preparatory Pavlovian responses are similarly pre-programmed responses to predictions of those outcomes . Even though the predictions are learned , the responses are not , and may therefore be behaviorally inappropriate in certain circumstances [31 , 32] . For our purposes , and as long noted by Deakin and Graeff [7] , the most important preparatory Pavlovian response to a prediction of a ( sufficiently distant ) threat [30] is inhibition , in the form of withdrawal or disengagement . This explicitly links the first two findings discussed above , as the inhibition is directly associated with aversive predictions . To explore the consequences of reflexive , direct inhibition of action for learning in affective settings , together with the repercussions when 5-HT is compromised , we built a highly simplified model that sought to isolate these effects from more general learning effects . More specifically , we built a model of trains of thoughts . In our treatment , we considered thoughts as actions that lead from one belief state to the next . Trains of thought gained value through their connections with a group of terminal states that were preassigned either positive or negative affective values . 5-HT directly inhibited chains of thought predicted to lead toward negative terminal states . Our model can be seen in terms of 5-HT's pruning of a decision tree of outcome states and choices [33 , 34] . We argue that the results on tryptophan depletion ( TrD ) above now emerge when considering the consequences of this reflexive behavioral inhibition on ongoing learning about the world , and on subsequent action choice and predictions . The most notable effect in the model is a critical bias toward optimistic valuation . That is , states and actions with potentially negative consequences are under-explored and incorrectly ( over ) -valued because of the reflexive inhibition . When inhibition fails , though , which is the last of the three issues mentioned above , there are two adverse consequences . First , the inhibition is no longer a crutch for instrumental action choice , so subjects have to learn to avoid potentially bad situations rather than being able to rely on this reflexive mechanism . Second , due to a mismatch between policy and value function , characteristic inconsistencies between the predicted and actual values arise , with the actual values encountered being more negative than predicted , though also actually more realistic . This mismatch between policy and value function also leads to an overall reduction in rewards obtained . Boosting 5-HT in the model again restores the status quo . Of course , this highly simplified model cannot possibly , by itself , accommodate all the diverse and confusing roles of 5-HT . Nevertheless , it replicates some prominent behavioral and pharmacological facets of depression and anxiety in humans and animal models , which we return to in the Discussion . The next section defines the model of trains of thought more formally . The Results section considers normal ( hence biased ) learning , and the consequences of impairments to 5-HT processing . We save for the Discussion a broader discussion of data and theories pertaining to 5-HT .
Figure 1 illustrates our underlying model of trains of thought . It is intended to emphasize a role for 5-HT in behavioral inhibition , and is therefore couched at an abstract level . Throughout , we will equate thoughts with actions , and revisit the more general action setting later . We initially focus on the effect of one inhibitory reflexive action in the context of otherwise fixed actions ( a fixed policy ) . A train of thoughts starts at one of a set of internal belief states ( , ) , may proceed through more such states , and ends in one of set of terminal outcome states ( , ) . The connectivity between belief states is sparse , with states leading preferentially to other states in and outcome states with positive values; and states leading preferentially to other states in and outcome states with negative values ( red arrows ) , though each could also lead to states of opposite “sign” ( black arrows in Figure 1 ) . In addition , trains of thought can be inhibited by 5-HT ( see below ) . In this simple model , the value of an internal state is the average value of the terminal states to which it ultimately leads . More formally , the model is a form of Markov decision process ( see [35] ) , with four sets of sparsely interconnected states ( , ) . Two sets , and ( each with 100 elements in the simulation ) are associated respectively with positive ( r ( s ) ≥ 0 , s ∈ ) and negative affective values ( r ( s ) ≤ 0 , s ∈ ) ; both are drawn from suitably truncated 0-mean , unit variance , Gaussian distributions ( see inset histograms in Figure 1 ) and are terminal states . The other sets , and ( each with 400 elements ) , contain internal states and are not associated with immediate affective values ( r ( s ) = 0 , ∀s ∈ ∪ ) . A policy is a ( probabilistic ) mapping from states to actions a ← π ( s ) and defines the transition matrix between the states in the model . For simplicity , we consider a fixed , basic , policy π0 . In this , each element of effectively has eight outgoing connections: three to other ( randomly chosen ) elements in ; three to randomly chosen elements in ; and one each to randomly chosen elements in and . Similarly , each element of has eight outgoing connections: three to other ( randomly chosen ) elements in ; three to randomly chosen elements in ; and one each to randomly chosen elements in and . Thoughts are modelled as actions a following these connections , labelled by the identities of the states to which they lead . Text S1 gives details of a more complex environment in which we explicitly explore effects of impulsivity . To isolate the effect of 5-HT in inhibiting actions in aversive situations , we consider the highly simplified proposal that serotonin stochastically terminates trains of thoughts when these reach aversive states . More specifically , under serotonergic influence the transition probabilities are modified in a manner that depends on states' values . We let the probability of continuing a train of thought ( of continuing along the fixed policy π0 ) be dependent ( and inversely related to ) the value V ( s ) of a state: where α5HT is a multiplicative factor that scales the impact of 5-HT ( see Figure 2 ) . When thoughts are not continued ( inhibited ) , they stop and restart in a randomly chosen state ( though see below for relaxations of this ) . The more disastrous the potential sequelæ of state s , the more negative Vπ ( s ) , and so the less likely the chain was to be continued . On the other hand , even slightly positive values would essentially veto any termination . This introduces an asymmetry into the model defined by the simple base policy . Other possibilities for the information reported by 5-HT and for the dynamic interaction between 5-HT and dopamine are considered in the discussion , and the fixed base policy π0 is relaxed below . The value of each state represents the expected reward obtainable from that state when following a particular policy . Under the fixed policy π0 , dynamic programming techniques [35] allow the value function Vπ ( s ) over states s to be written , and solved for , concisely as: Vπ ( s ) = r ( s ) , s ∈ , and where γ is a discount factor ( γ = 0 . 9 in our simulations ) . Dynamic programming also uses a function [36] over states and thoughts defined for those actions that exist by Optimal values V* ( s ) and are those value functions associated with any policy that maximizes the long-run affective outcomes of the train . While it is not possible to use these techniques directly to evaluate the value function under serotonergic influence ( the inhibition depends on the value function itself and thus represents a nonlinear interaction ) , the temporal difference learning rule [35] can be used to acquire estimates of the values of states under serotonergically modified policies . The temporal difference learning rule specifies an online learning rule for which the change in the estimated value based on taking action a at state s and therefore arriving deterministically at s′ = a ( s ) is: where the learning rate ε = 0 . 05 . A slightly simpler alternative rule suggests that learning of is itself prevented by termination: That does not change under this rule given termination implies that learning is only slowed for these states , rather than being biased toward zero . We generally report results from this variant . In the sequel , we show values after substantial learning ( 20 , 000 trains ) , plus the consequences of manipulating serotonin ( by manipulating α5HT ) once the values are already acquired .
By construction , the environment in Figure 1 is symmetric with respect to rewards and punishments , and so the overall statistics of the values of states are balanced about zero . Indeed , Figure 3A shows that for the base policy , 20 , 000 learning steps are ample to acquire a reasonable value Vest ( s ) for the states ( the remaining discrepancies from Vtrue ( s ) , here defined for α5HT = 0 , arise from the stochasticity in the choice of action together with the fixed learning rate ) . Critically , there is no bias in either Vest ( s ) or Vtrue ( s ) . By contrast , Figure 3D shows the substantial bias in consequent on setting a large value of α5HT = 20 . In this case , low-valued states are much less well visited and explored . The bias comes despite the use of learning rule [5] , which only slows down learning for low-value states rather than also distorting it . Of course , in this case , the extent of the bias depends on the initial values for the states ( all of which are set to zero in the simulation ) . Figure 3E shows how frequently each of the outcome states was reached in a run ( as a function of its outcome r ( s ) ) . Since behavioral inhibition terminates trains on their way to potential disaster , aversive terminal states are sampled less ( shown by the red regression line ) , which is consistent with the bias of the estimated value . Figures 3C and 3F show these effects as a function of α5HT . The greater the inhibition , the worse estimated the values are ( Figure 3C ) , particularly for aversive states; however , the more benign is the exploration ( Figure 3F ) . Learning with greater inhibition leads to a more optimistic set of values; however , this is coupled with a more aggressive rejection of all actions even mildly associated with negative outcomes . Reducing the value of α5HT after learning a value function under its influence can be expected to have various consequences , as it introduces a mismatch between policy and value function . The most obvious one is a more negative average affective outcome ( the average value of trains of thought ) in the model . This is because choices are less biased against actions that are predicted to have aversive consequences , and so the latter occur more frequently . A second consequence is that there will be substantial adverse surprises associated with transitions that previously were inhibited . The surprise at reaching an actual outcome can be measured using the prediction error for the last transition of a chain from state to a state . We may expect negative prediction errors to be of special importance , because of substantial evidence that aversive outcomes whose magnitudes and timing are expected so they can be prepared for , have substantially less disutility than outcomes that are more aversive than expected ( at least for physiological pains; see [37] ) . Figure 4 shows the consequences of learning under full inhibition and then wandering through state space with reduced inhibition . The change in the average terminal affective value as a percentage of the case during learning that α5HT = 20 is shown in Figure 4A . As was already apparent in Figure 3F ( which averages over the whole course of learning ) , large costs are incurred for large reductions in inhibition . For α5HT = 0 , the average reward is actually negative , which is why the curve dips below −100% . This value is relevant , since the internal environment is approximately symmetric in terms of the appetitive and aversive outcomes it affords . Subjects normally experience an optimistic or rosy view of it , by terminating any unfortunate trains of thought ( indeed , 55% of their state occupancy is in compared with ) . Under reduced 5-HT , subjects see it more the way it really is ( the ratio becomes 50% ) . Figure 4B and 4C show comparative scatter plots of the terminal prediction errors . Here , we consider just the last transition from an internal state to an outcome state . Prediction errors here that are large and negative , with substantially more aversive outcomes than expected , may be particularly damaging . Figure 4C compares the average terminal prediction errors for all transitions into states in with no serotonergic inhibition α5HT = 0 , to those for the value α5HT = 20 that were used during learning . For the case that α5HT = 20 , the negative prediction errors are on average very small ( partly since the probability of receiving one is very low ) . With reduced inhibition , the errors become dramatically larger , potentially leading to enhanced global aversion . By comparison , as one might expect , the positive prediction errors resulting from transitions into are not greatly affected by the inhibition ( Figure 4B ) . Two additional effects enrich this partial picture . One , which plays a particularly important role in the cognitive behavioral therapy literature , is that depressed patients have a tendency to prefer to recall aversive states or memories [38 , 39] . Figure 5A shows the consequence of doing this according to a simple softmax ( see Methods ) . These curves , as in Figure 4A , show the percentage average utility compared with α5HT = 20 , β = 0 across values of α5HT , and for β = −10 , −9 , … , 10 . As might be expected , biasing the starting point to , and , even worse , to those particular states in that are most deleterious , has a big negative impact on average utility . For α5HT = 0; β = −10 , occupancy of relative to became a paltry 27% as subjects ruminate [40 , 41] negatively . The second factor is our restriction to just inhibition of trains of thought rather than a more fine-scale manipulation of the relative probabilities of different thoughts . We now relax this and explore the effect of additionally allowing preferential transitions toward certain states . In Equation 6 , for positive values , the parameter θ biases action choice toward actions leading to positively valued states , whereas for negative values it does the opposite ( i . e . , subjects prefer to transition to negatively valued states ) . Figure 5B shows the effects of θ . It is apparent that rather extreme values of θ can both significantly aggravate or suppress the effect of α5HT . For the highest positive values of θ the curves reverse shape , showing that it can be beneficial not to inhibit trains of thought . This arises since the model of Figure 1 was chosen to have the extreme property that there is always the possibility of avoidance ( in that all the states in admit at least one action that leads to ) , and inhibiting trains of thought removes this outcome . A different , and rather counterintuitive , interaction between inhibition and reward seeking obtains in environments where rewards are hidden behind punishments ( see Text S1 and Figure S1 ) .
We suggested that this form of behavioral inhibition arises through predictions of aversive outcomes , tied to serotonin's putative role in reporting aversive prediction errors as an opponent to dopamine . This comes directly from the original notions of behavioral inhibition and serotonergic effects from Gray , Deakin , Graeff , and their colleagues [6 , 7 , 13 , 29 , 30]; however , it is perhaps best seen as a subset of the current version of Gray's BIS [29] . One salient difference is that BIS is suggested as being primarily engaged by conflict , rather than ongoing predictions of future aversive outcomes . Of course , a main source of conflict is that between approach and avoidance , with the latter coming from these aversive predictions . An interesting consequence of dividing the prediction of the value of future outcomes between two separate opponent systems is that it is indeed possible to have simultaneous appetitive and aversive expectations , as opposed to just one combined net prediction . Although we used the net prediction to control inhibition , it would be interesting to explore other possibilities associated with the BIS view , such as that any aversive prediction could arrest ongoing action , even if outweighed by appetitive predictions . Further , rather than have the aversive predictive values of states lead to termination of trains of thought , it is possible that the negative prediction error ( δ− from Equation 8 ) , which Daw et al . [10] suggested is being reported by phasic serotonin , could be responsible instead . Alternatively , in the mirror reflection of the proposal that a tonic dopaminergic signal reports average reward ( and controllable/avoidable punishment ) and energises behavior [46 , 47] , it could be that a more tonic serotonergic signal , averaging aversion over longer time horizons and favoring quiescence , could be responsible . Another difference between our account and the full BIS is that , in the latter , although actions are indeed inhibited in the face of conflict , the BIS is then suggested as initiating a set of behaviors ( such as exploration or risk assessment ) to resolve that conflict . The set of preparatory Pavlovian actions associated with aversive predictions appears to be more refined than that associated with appetitive predictions ( mostly just approach ) , with a wide range of different defensive possibilities being selected between according to the nature and proximity of the threat [30 , 48] . One class of these is even laid out along columns of the dorsal periacqueductal gray ( PAG [49] ) . Nevertheless , any of these defensive manoeuvres would interrupt the ongoing chain of actions , and this is what we modelled . Risk assessment and exploration are of most obvious use in the face of uncertainty and ignorance , whereas conditioned suppression , and thus the sort of inhibition that we consider , remains even after substantial learning . It would certainly be worth going one stage further , modelling the interruption in terms of a switch between different Markov decision problems , with new information changing the transition and payoff structures . One of our central results is the effect of an acute reduction in α5HT after learning with elevated α5HT has taken place . In our model , this leads to a decrease in behavioral inhibition of actions leading to negative states . Although specific effects might arise from local manipulations of 5-HT concentrations or receptor responsivity , key data come from the systemic manipulation associated with acute TrD [50] , in which plasma levels of tryptophan and , at least in animals , central nervous system levels of serotonin , are drastically reduced ( by up to 90% ) . Although the particular chains of thoughts analysed here have not been the subject of experimental scrutiny , there is by now a considerable body of literature on the effects of TrD on normal human functioning . In broad agreement with our results , various effects have been related to decreased reward processing [39 , 51 , 52] , decreased behavioral inhibition [44 , 53–57] , rumination [21] , facial fear recognition [58] , and , more indirectly , increased aggressiveness [54 , 59 , 60] . Perhaps of most direct relevance to our implementation are the results of a recent study which decoupled rewards from correct performance of an action from the outcomes of the actions [61] . This study actually involved a sophisticated assessment of the effects of TrD on reversal learning . However , one way of viewing a portion of the results stems from an abstract representation of the task . Subjects had to press one of two buttons ( A or B ) in response to one of two stimuli ( also called A and B ) , with presses associated with A leading to a symbolic reward and presses associated with B leading to a symbolic punishment . Critically , these outcomes were independent of the rectitude of the subjects' responses , so they couldn't avoid the punishment by making errors . In this case , subjects more often failed to press button B correctly than button A , and this difference disappeared after TrD . This is directly consistent with the present interpretation of serotoninergic inhibition of actions that lead to aversive outcomes . Famously , TrD does not have a uniform effect on all subjects . There is an important genetic polymorphism in the 5-HT reuptake mechanism , with subjects having the less efficient version generally showing greater effects [52 , 57 , 62–66] . For this to be consistent with our formulation , the difference in functional 5-HT levels before and after TrD has to be greater in the subjects with less efficient reuptake . This in turn might most simply be due to increased levels of 5-HT ( and behavioral inhibition ) throughout development in carriers of the short 5HTTLPR allele . Perhaps related to this is the finding that TrD produces a dose-dependent relapse of depressive symptomatology in formerly depressed patients [18–20 , 41] , or in patients with risk factors such as a family history of depression [63] ( although the three-way interaction between TrD , 5HTTLPR , and past depression is hard to fit into this framework [67] ) . There is a significant body of work on the effects of serotonergic manipulations on affective processing , particularly on processing of facial expressions [58 , 68–70] . It is difficult to interpret this work in our context for several reasons: first , there have often been effects on recognition of specific aversive facial expressions ( e . g . , fear ) but not others ( e . g . , disgust ) . Our model does not speak to these distinctions . Second , in these tasks , subjects identify stimuli by pressing a button . Thus , there is a Pavlovian association between certain buttons and the aversive stimuli , and , interpreting these tasks in the same framework as we interpreted the work of Cools et al . [61] , one might predict that TrD would increase rather than decrease accuracy . The precise effect , however , would depend on the relative strength of the instructed and the reflexive Pavlovian response , and on the antagonism between the responses . Indeed , both aspects have been found: acute manipulation of serotonin increased recognition accuracy of fearful faces with increasing serotonin [58 , 68 , 70] , whereas a more chronic increase in 5-HT ( via SSRIs ) yields a decrease in recollection of negative memories [69] . Furthermore , while the exact relationship between behavioral inhibition and amygdala activation still needs clarification , it is additionally possible that increased amygdala activation may relate to perceptual mood congruency effects [38]: after disinhibition , thoughts often visit negative states , and it is possible that this may affect prior expectations about stimulus which in turn could speed up processing of negatively valenced information . TrD ( or indeed SSRIs ) have not previously been used in tasks like the Markov decision problem of the type we discussed . A direct prediction of the model is that subjects trained under TrD would explore states less when tested in a normal regime , while those trained under SSRIs would do so more ( assuming that SSRIs indeed elevate 5-HT levels ) . Similar predictions hold for subjects with short or long alleles of the 5-HT reuptake mechanism on these tasks . This would essentially represent a generalisation of the findings by Cools et al . [61] to the domain of sequential decisions . The tasks could use external , observable actions; more directly , it would also be useful to monitor the execution of affective trains of thought , and study the perturbation of this under serotonergic manipulations . In designing such studies , it is important to bear in mind the potentially opponent instrumental and Pavlovian effects , in just the same way that boosting dopamine and monitoring the effects on negative automaintenance may be confusing . Note that although there are various important datasets as to the effects of TrD on simple probabilistic and delay-discounting tasks [51 , 52 , 56 , 71–75] , these studies do not encompass the sorts of behavioral chains that we propose 5-HT to be able to halt . One of the backdrops for the present theory was the extensive modeling of phasic dopamine as a prediction error for future reward , and the results that ( 1 ) the baseline firing rates of dopamine cells are insufficient to report prediction errors for negative rewards ( i . e . , punishments ) ; ( 2 ) the ample psychological evidence for the existence of a pair of systems , one associated with appetitive outcomes and the other with aversive outcomes; and ( 3 ) the evidence that at least some aspects of 5-HT and dopamine are in mutual opposition . Indeed , based on these data and the theories of Deakin and Graeff [6 , 7] , Daw et al . [10] suggested that serotonin rather than dopamine reports negative prediction errors based on an antagonism between serotonin and dopamine at both a behavioral and pharmacological level . For example , in rodents , 5-HT antagonises the general excitatory effects of dopamine [4] , the self-administration of amphetamine and intracranial self-stimulation [76 , 77] , the effects of dopamine on appetitive learning [8] , and the potentiation of appetitive learning by amphetamine [78] . However , pure opponency is far too simple . For instance , there is by now extensive evidence that 5-HT modulates dopaminergic activity both through receptors in the ventral tegmental area and by modulation of distal release sites , and that this modulation can occur in both inhibitory and excitatory directions [4 , 77 , 79–89] . Even the rise in 5-HT due to SSRIs has overall pro-dopaminergic effects , both at behavioral and physiological levels [81 , 90–92] , and there is one report that DA antagonists reverse the antidepressant effect of SSRIs [93] . Further , there is evidence that DA itself is released in many aversive circumstances [94 , 95] , and is involved in aversively motivated behaviors like avoidance [96–98] . In our terms , apart from the aspects of the interaction of dopamine and 5-HT that were explored in Figures 5B and S2 , there are a couple of other effects . First , inhibition in our model has the consequence of increasing the average expected reward . As such , tonic dopamine , which has been suggested to report such a quantity [46 , 47 , 99–101] , would be increased when 5-HT is boosted , and potentially vice versa [88 , 92] . This would compete with the more direct effect that 5-HT inhibits actions , and particularly inhibits actions supported by dopaminergic predictions or rewards [8 , 78 , 102] , and thus high levels of 5-HT might also depress levels of tonic dopamine , more in line with accounts that stress the opponent role of dopamine and serotonin [4 , 10 , 103] . The second complexity ( Boureau , personal communication ) is that active defense ( such as active avoidance ) requires energizing , and indeed appears to be controlled by the ( presumably dopaminergically reported ) appetitive outcome of reaching a state of safety rather than the ( presumably serotonergically mediated ) outcome of leaving a state of fear . That is , it appears that dopamine reports the rewards reaped from avoiding or controlling aversive outcomes [15 , 94 , 104] . We mentioned the mirror notion that the relationship between 5-HT and inhibition arising through aversive predictions is parallel to the obverse relationship for dopamine and engagement/approach through appetitive predictions [32] . In this case , appetitively directed chains of thoughts would be favored . Indeed , Smith et al . [105 , 106] , in their work on the conditioned avoidance model of schizophrenia , suggested something rather like this . In their account , dopamine controls the extent of search through a forward model , although they did not couple this to dopamine's involvement in appetitive prediction . In all , disentangling and elucidating these varied relations between dopamine and serotonin is a pressing task . It would be reasonable to argue that the present model is more relevant to anxiety than depression . There is at best a somewhat fuzzy distinction between the two in terms of risk factors [107] and pharmacology [108] , and they are extraordinarily co-morbid [109] . There is also no complete definition of either disease in terms of the sort of reinforcement mechanisms that we have been considering . While depressive ( but not anxious ) symptoms can be reinduced by TrD in a subpopulation of patients , TrD it is not the only such manipulation , and it is not effective in all patients . Patients who are responsive to seratonin–norepinephrine reuptake inhibitors ( SNRIs ) are more sensitive to catecholamine depletion by α-methyl-tyrosine [110 , 111] than TrD , and a recent report with a DA antagonist successfully re-induced depressive symptoms in formerly depressed people [93] . The latter authors suggest that DA may be a “final common path” for depression , and may relate more to the depressive state than serotonin , which in turn may be more important in defining a trait [15 , 112 , 113] . In addition , only 50% of formerly depressed subjects do respond to TrD [41 , 114] , and a pooled analysis of 71 formerly depressed subjects found that previous response to SSRIs had less predictive power for TrD response than chronicity of the depressive disorder and sex [114] . As mentioned , resolving the actual relative contribution of serotoninergic inhibition will be tricky . In sum , the findings in this study argue for an involvement of the serotonin reuptake mechanism in mood disorders such as anxiety and depression in the following manner: due to a decreased efficiency of the transporter , increased behavioral inhibition results in acquisition of overly optimistic values . Such value functions are adaptive , but only in conjunction with strong behavioral inhibition . On the other hand , they do render the individual highly sensitive to large decreases in average experienced rewards when serotonin function is reduced . This might underlie a ( controversially ) larger sensitivity to TrD and SSRIs of persons with the short 5HTTLPR allele ( see [115] ) . Returning to the sequential decision-making tasks suggested above , this study would predict that the short 5HTTLPR allele would be associated with more reflexive avoidance of states predictive of punishment , and it may be possible to assess this with differential effects of TrD on carriers with the short and long 5HTTLPR allele . A further , more involved conjecture , which returns to the fact that serotonin is not the sole causative agent in depression , is that it is the effects of reduced 5-HT on affective experience that leads to the various symptoms of depression , acting via the otherwise normative operation of the multiple systems involved in behavioral control . For instance , we have argued that the consequences of 5-HT reduction include unexpected punishments , large negative prediction errors , and a drop in average reward . These changes in the statistics of reward demand explanation , for example in terms of a shift in the characteristics of the environment , and should cause normative behavioral responses . In particular , the unsignalled aversion that comes independent of the subject's actions can be seen as a form of uncontrollable punishment . Uncontrollability lies at the heart of an important characterization of depression centred around learned helplessness [104 , 116] . We concentrated on the effects of reduced 5-HT rather than on the reasons for this reduction . The obvious option is that it is a pathological result from processes operating at a purely cellular level . However , it could also arise as a normative meta-adaptation to the statistics of experienced punishments and rewards . Formalizing this fully would require a more general theory of inhibition—what level of inhibition is optimal ? Tools for the characterisation of the trade-off between accurate knowledge about a state's value and the cost incurred in learning about it are already in existence [34 , 117 , 118] and might be applicable to aspects of the present case . | Serotonin is an evolutionarily ancient neuromodulator probably best known for its role in psychiatric disorders . However , that role has long appeared contradictory to its role in normal function , and indeed its various roles in normal affective behaviors have been hard to reconcile . Here , we model two predominant functions of normal serotonin function in a highly simplified reinforcement learning model and show how these may explain some of its complex roles in depression and anxiety . | [
"Abstract",
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] | [
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"neurological",
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"neuroscience"
] | 2008 | Serotonin, Inhibition, and Negative Mood |
Recent advances in modeling oxygen supply to cortical brain tissue have begun to elucidate the functional mechanisms of neurovascular coupling . While the principal mechanisms of blood flow regulation after neuronal firing are generally known , mechanistic hemodynamic simulations cannot yet pinpoint the exact spatial and temporal coordination between the network of arteries , arterioles , capillaries and veins for the entire brain . Because of the potential significance of blood flow and oxygen supply simulations for illuminating spatiotemporal regulation inside the cortical microanatomy , there is a need to create mathematical models of the entire cerebral circulation with realistic anatomical detail . Our hypothesis is that an anatomically accurate reconstruction of the cerebrocirculatory architecture will inform about possible regulatory mechanisms of the neurovascular interface . In this article , we introduce large-scale networks of the murine cerebral circulation spanning the Circle of Willis , main cerebral arteries connected to the pial network down to the microcirculation in the capillary bed . Several multiscale models were generated from state-of-the-art neuroimaging data . Using a vascular network construction algorithm , the entire circulation of the middle cerebral artery was synthesized . Blood flow simulations indicate a consistent trend of higher hematocrit in deeper cortical layers , while surface layers with shorter vascular path lengths seem to carry comparatively lower red blood cell ( RBC ) concentrations . Moreover , the variability of RBC flux decreases with cortical depth . These results support the notion that plasma skimming serves a self-regulating function for maintaining uniform oxygen perfusion to neurons irrespective of their location in the blood supply hierarchy . Our computations also demonstrate the practicality of simulating blood flow for large portions of the mouse brain with existing computer resources . The efficient simulation of blood flow throughout the entire middle cerebral artery ( MCA ) territory is a promising milestone towards the final aim of predicting blood flow patterns for the entire brain .
There is agreement that the neurovascular unit locally controls the cerebral blood flow response . Yet , oxygen supply exceeds the metabolic demand of neuronal activation for reasons that still remain uncertain [5] . Because of the massive size of the mammalian brain with its immense number of neurons and capillaries , the precise temporal and spatial coordination among cellular components still eludes exact physiological description . For example , studies suggest that functional hyperemia causes local neuronal metabolism increase of 5% , which in turn augments local blood flow by 30% up to almost 130% of base line perfusion [6] . However , the exact timing , regulation , and extent of dilation in individual spatially distributed vascular compartments during functional hyperemia are still being investigated [3 , 7–9] . The cerebral circulation also exercises a second blood flow control mechanism known as cerebral autoregulation [10–17] . Clinical observation [11] suggests that the total cerebral blood flow ( CBF ) remains constant over a wide range in perfusion pressure ( ±50 mmHg , ±6666 Pa ) . Many excellent contributions [18–20] correctly attribute the constancy of cerebral blood supply to global resistance adjustments . Yet , the involvement of specific vascular compartments , speed and spatial coverage of local vasodilatory/vasoconstrictive districts remains elusive [11 , 18–20] . Moreover , quantification of network effects and control principles among vascular compartments requires an anatomically accurate mathematical model of the cerebral circulation . Propelled by the advances in neuroimaging , several groups have begun to integrate medical image data with large-scale computer models [3 , 4 , 9 , 21–23] . Generally , these efforts fall into two types . One type adopts a reductionist approach using simplified networks to highlight global blood flow distribution patterns [7 , 24–28] . The second type follows a bottom-up strategy which aims at replicating relevant microcirculatory components down to the level of the cellular ensemble . Noteworthy examples include quantifying the neurovascular coupling in functional hyperemia [3] , analysis of pressure drop dependence on cortical depth [22] , predictions of blood flow control by intra-cortical arterioles [9] , and cortical oxygen distribution [29 , 30] . The ultimate goal of bottom-up models is a hemodynamic simulation of the entire brain , yet virtual circulation models of the whole brain have been perceived as intractable due to size and nonlinearity of the mathematical coupling between blood flow and oxygen kinetics [24] . This manuscript will introduce a computational procedure that integrates multimodal neuroimage data covering different length scales into a unified virtual representation of the murine cortical circulation . Two-photon imaging provides data for the reconstruction of capillary networks . High resolution micro computed tomography ( μCT ) imaging is used to capture the connectivity between main arterial branches and pial blood vessels . The morphometrics of the micro , meso- and macro-scale vascular models have been statistically analyzed in order to synthesize virtual blood flow networks with anatomically equivalent statistics , but without being confined to the limited field-of-view or resolution of imaging modalities . The aim of this paper was to quantify network effects of uneven red blood cell distribution in the cerebral circulation . Although uneven red blood cell distribution also known as plasma skimming can be observed in single bifurcations , neuroimaging of the entire cerebral circulation has so far not been accomplished . To overcome this shortcoming , we integrated physiological data from several neuroimaging modalities covering three different lengths scales . Massive computer simulations of large microcirculatory networks of the murine primary cortex revealed a trend of depth-dependent hematocrit , which is a significant finding indicating that the intricate architecture of the cortical microcirculation serves a self-regulating function to maintain uniform oxygen perfusion .
We first assessed morphometrics of experimental data obtained from murine primary somatosensory cortex samples ( N = 4 , E1 . 1-E4 . 1 ) . The indexing and naming scheme for the data sets is listed in Table 1 . The total microvascular segment count was 24 , 669±9 , 594 splines . An important property is that all four original two-photon laser scanning microscopy ( 2PLSM ) data sets contained blood vessels that divide into more than two daughter branches ( multifurcations ) . Specifically , the four data sets contained 654 , 725 , 1686 , 1440 multifurcations , respectively . Statistics on cumulative metrics including vascular surface area , path length and luminal volume are compiled in Table 1 . Although the data originate from the same cortical region , there are subject-specific variations between different specimens . There was higher variability in the low end of the vascular diameter spectra , because unavoidable uncertainty affects the thinnest vessels close to image resolution threshold as observed previously [31] . We also estimated the surface area to tissue volume ratio of the blood-brain-barrier ( BBB ) of the microvascular network as 8 . 8±1 . 1 mm2 vasculature/mm3 tissue . This number was obtained by summing the ( endothelial ) surface area of the capillary bed; this estimate compares to experimental values of the BBB surface of about 10-17 mm2/mm3 in humans [32–34] . A modified constructive growth algorithm ( mCCO [30] ) was used to create 60 synthetic data sets ( S1 . 1-S4 . 15 ) of the murine primary sensory cortex . For each of the four experimental data sets , 15 clones with statistics matching closely their experimental original were created , so that the S1 . 1–15 series matched the original E1 . 1 , and S4 . 1–15 matched data set E4 . 1 . Artificial networks smoothly connect arterial vessels through the capillary bed to the veins without gaps or the need to insert artificial segments as observed with other methods [27] . In addition , since blood vessels are not exactly straight , realistic tortuosity values were imposed by a Bezier spline-based technique described previously [30] . Moreover , at the boundaries of the synthetic data sets neither pial surface vessels , nor deeper laying arterioles , capillaries , or venules were severed or had to be pruned thanks to the precise geometric control of the vasogenic growth algorithm . Artificial network growth took less than five minutes for each dataset on a personal computer . We also compared morphometrics of experimental ( N = 4 ) against synthetic vibrissa primary somatosensory cortex data sets ( N = 60 , S1 . 1-S4 . 15 ) . No discernible feature differences can be inferred from visual inspection as shown for three experimental ( E2 . 1 , E3 . 1 , E4 . 1 ) and six synthetic data sets ( S2 . 5 , S3 . 3 , S4 . 5 , S2 . 3 , S3 . 4 , S4 . 8 ) in Fig 1A . Total count amounted to 24 , 679 ± 8389 spline segments and 16 , 451 bifurcations . Spline segments were defined as tubular connections ( splines ) between branching points ( bifurcations or multifurcations ) . This counting method ensured that the final tally is independent of image grid resolution or number of segment sub-partitions . The comparison of cumulative properties and probability density functions shows excellent agreement between the experimental and synthetic networks as seen in the plots Fig 1B and 1C . The synthetic networks are different realizations , but statistically equivalent replica ( clones ) of the original image samples . The nonlinear biphasic blood flow , pressure and hematocrit equations for all four experimental networks and all sixty synthetic networks converged within five minutes [29] . Results were visualized with 3D rendering software Walk-In Brain developed at our institution [36 , 37] . Path analysis was conducted based on flow trajectories traversing the network along streamlines . Biphasic blood flow and network effects determining blood pressure and hematocrit distribution through large experimental ( N = 4 ) and synthetic ( N = 60 ) networks perfusing a large portion of the cortex were studied . Typical pressure distributions along the microcirculatory network hierarchy are shown in Fig 2 . Pressure drop trajectories through the microcirculation showed patterns consistent with experimental data [38–40] . Results of the path analysis in Fig 2 also depict the wide variations of hemodynamic states when blood traverses the dense microcirculatory network from the pial surface vessels through penetrating arterioles into the capillary bed and finally back to the collecting veins . The trajectories of individual paths ( green , blue , magenta and yellow ) display wide variability of hemodynamic states along the flow direction . Flow analysis reflected that a perfusion pressure drop in the microcirculatory networks from 120 to 5 mmHg ( 15 , 999-667 Pa ) resulted in a mean tissue perfusion of 68 . 9 ml/100g/min ( 11 ∙ 10−6 m3/kg/s ) which is within experimentally observed ranges [41 , 42] . We further inspected the RBC flux distribution as a function of network hierarchy ( = vascular ) and position inside the cortical hierarchy ( = neuronal ) . The results were acquired for both empirical and synthetic data sets . Two representative specimens are highlighted in Fig 3A and 3B; eight more examples are displayed in Fig 3C . Typical paths belonging to different cortical layers are color coded in Fig 3 . Flow paths were generated by tracing the flow from arterial inlet nodes downstream through the capillary bed until reaching a venous outlet . Paths were sorted according to their tissue supply function as follows: a path depth label equal to the cortical depth of the deepest segment was assigned to each flow path . Thus , all paths were uniquely ordered within a spectrum of shallow to deep reaching paths according to the neuronal layer ( I-VI ) hierarchy in agreement with previously reported values [35 , 43 , 44] . Fig 3 depicts hematocrit values along representative paths in shallow ( layer I-green ) and deeply penetrating paths ( layer V/VI-yellow ) . Along each path and between different paths there is high variability along the flow direction . For example , discharge hematocrit in data set S1 . 1 reaches values as high as hmax~0 . 7 , and as low as hmin~0 . 18 . However , there is an overall trend of higher hematocrit being carried to lower cortical levels ( layer-V/VI paths ) . The trend of relatively higher hematocrit , h , conveyed to deeper tissue layers ( p-value<0 . 01 , using one-way ANOVA test in MatLab ) was observed consistently in all experimental and synthetic data sets . The bulk flow , Q , showed the opposite trend; it was reduced in segments of deeper layers which are connected by longer paths as is summarized in Fig 4 . In contrast to bulk flow and hematocrit , the RBC flux ( = volumetric flow rate of the RBC phase ) exhibited weak layer dependency , it was almost constant irrespective of the cortical depth . We also observed that the variance of capillary RBC fluxes decreased with cortical depth , thus RBC fluxes in deeper layers show lower variability than paths on the surface . Taken together , biphasic blood rheology and network effects seem to induce depth dependent hematocrit supply to the cerebral cortex which leads to more homogenized RBC fluxes in deeper layers ( = lower variance in RBC fluxes ) . Further analysis of diameter dependence on hematocrit confirmed the high degree of hematocrit variability across the diameter spectra as previously observed [4] ( S1 Supplement ) . The agreement between the simulation results obtained for experimental and synthetic data confirms that the synthetic networks are hemodynamically equivalent to the experimental networks . The satisfactory match in morphometrics and hemodynamics between experimental and synthetic data justifies the extension of network synthesis to large anatomical regions as described next . Vascular networks covering the circulation of the entire MCA territory were generated with the help of our modified CCO ( mCCO ) algorithm as described in Gould et . al [30] . The mCCO algorithm was launched with the MCA M1 as the first segment . The location of the MCA territory within the context of the mouse cortex is shown in Fig 5 top-row . Sequentially , more segments were added at the cortical surface depicted in Fig 5 top-row , while minimizing the vascular tree volume subject to blood flow constraints . Thus , gradually the algorithm generated all arterial branches of the pial network on the cortical surface . Then , it was directed to proceed with penetrating arterioles and microcirculatory growth to a depth of approximately 1 mm below the pial surface , until a preset vessel density was reached . At each step of the segment generation , connectivity and bifurcation position were optimized to obtain minimum tree volume . The diameters of the network branches were recursively recomputed in accordance with hemodynamically-inspired principles [45] . The total number of splined segments in the artificial MCA territory was 993 , 185 . This was roughly 60 times the number of segments in the cortical samples . The topology of the synthetic MCA territory resembled maps available in mouse atlases [46 , 47] . Branching density and pattern of the pial arteries as well as the number of penetrating arterioles was within ranges of the reconstructed sets of μCT images as listed in Table 2 . Detailed views in Fig 5 show pial , microcirculatory and individual capillary scales illustrating different aspects of the massive network model covering three length scales ranging from the MCA M1 segment with a diameter [48] of 142 µm down to the capillary bed [35] , d<6 µm . Morphometrics of the synthetic MCA networks are summarized in Table 2 . Fig 5A–5C depicts the pressure , flow and hematocrit field from the outflow of the Circle of Willis ( MCA M1 ) , down to the smallest capillaries in the microcirculation . The anatomical detail and branching pattern is depicted for the highly irregular , tortuous microcirculatory network . The simulation of the entire MCA territory included the compartments of pial arteries , penetrating arterioles , pre-capillaries , capillaries , post-capillaries , ascending venules and pial veins . To complete the MCA circulation , the venous tree including venules was synthesized in reverse and connected to the capillary bed as described previously [30] . Fig 6 depicts the distribution of pressure , flow and hematocrit throughout the MCA territory . Fig 6A shows comprehensive three-dimensional maps of the anatomical hierarchy , pressure distribution , blood flow in the MCA territory , and uneven biphasic hematocrit . Fig 6B–6E highlights the anatomical grouping , pressure , flow , and hematocrit distribution throughout individual compartments . In these views , explosion diagrams separating the anatomical groups ( pial arteries , penetrating arterioles , pre-capillaries , capillaries , post-capillary venules , venules and pial veins ) were used to better delineate the hemodynamic states in each group . Visual inspection of the microcirculatory compartments ( pre-capillaries , capillaries , and post-capillaries ) depicted in Fig 6E reveal higher hematocrit levels in deeper cortical layers than on the surface . Simulations conducted for the entire circulation on the MCA territory required boundary conditions at only two points; MCA M1 arterial blood pressure ( p = 120 mmHg , 15 , 999 Pa ) , hematocrit level ( h = 0 . 35 ) , and venous outlet pressure ( p = 5 mmHg , 667 Pa ) . The solution encompassed blood pressure , flow and hematocrit for 5452 pial vessels , 27 , 374 segments perpendicular to the pial surface , and 960 , 359 capillaries of the entire center MCA territory , for a total of 993 , 185 . In total , the proposed iterative method succeeded in bringing to convergence a total of 2 , 648 , 853 equations for biphasic blood flow . The predicted perfusion rate for the MCA territory was 50 ml/100g/min ( = 8 . 3 ∙ 10−6 m3/kg/s ) which is in agreement to literature ranges [41 , 42] of 40–163 ml/100g/min ( = 6 . 7–27 . 2 ∙ 10−6 m3/kg/s ) . The trend of higher hematocrit levels in deeper cortical layers seen in the smaller cortical samples was also confirmed in the massive simulations for the MCA territory as shown in Fig 7 . It should be noted that the simulations showed virtually no boundary effects in the center of the MCA territory where the primary sensory cortical samples were located . The suppression of boundary effects that can be achieved by large-scale simulation is extremely important for simulating hemodynamic blood flow control such as it occurs in functional hyperemia or under autoregulatory control . A full simulation of the entire MCA territory ( arterial and venous side ) required 65 iterations and ~2 hours on multicore workstations .
We performed multiscale morphometric analysis of the cerebral circulation in mouse over three length scales . On both the macro and the mesoscale , statistical data for the Circle of Willis , the middle cerebral artery and its pial arterial network were extracted from high quality micro-CT ( µCT ) data [56] . Microcirculatory morphometrics were acquired by two-photon imaging ( 2PLSM ) delineating the micro-angioarchitecture down to the level of individual capillaries for sizable sections ( ~1x1x1 mm3 ) of the vibrissa primary sensory cortex . There were statistical differences between the 2PLSM microcirculatory data sets especially in the diameter information as can be expected from a high resolution analysis of cortical microcirculatory networks . However , these variations did not significantly alter hemodynamic flow patterns . The morphometrics ( arterial , capillary and venous segment number , connectivity and branching patterns , probability density functions for length , diameter and surface area spectra ) informed a synthetic vascular growth algorithm . Because the statistics ( e . g . segment numbers ) could directly be input into the mCCO algorithm , we were able to create 15 synthetic replica for each of the four data sets . In total , we synthesized artificial vascular networks ( N = 60 ) with morphometrics and blood perfusion patterns that are statistically equivalent to the experimental data . The wealth of experimental and synthetic data used in this study provided a testbed for hemodynamic analysis of biphasic blood flow through the cortical microcirculation . Hemodynamic simulations were performed using computer algorithms described and tested extensively [29] . We performed biphasic blood flow simulations on both experimental ( N = 4 ) and synthetic microcirculatory networks ( N = 60 ) . Simulation results predicted patterns of blood flow , pressure and hematocrit within ranges currently known from experiments . Even though our blood flow computations are deterministic [4 , 29] , computed hemodynamics states varied widely within the labyrinth of paths traversing the microcirculation . We pinpointed randomness of the angioarchitecture as the origin of the wide range of predicted hemodynamic states . The finding of variability in hemodynamic states due to network architecture is significant , because it suggests that there are no characteristic properties ( e . g . average hematocrit , mean capillary pressure ) that would justifiably represent a typical physicochemical state of a microvascular compartment ( arterioles , capillary bed , venules ) . It also explains why idealized trees such as binary ordered hierarchical graphs [26] are unsuitable surrogates for microcirculatory flow networks , because their regular and symmetric branching patterns lack the randomness in network topology seen in the murine anatomy . Specifically , ordered trees have equal states in all branches of a given hierarchy , which leads to even hematocrit splits due to symmetry in daughter branching diameters . Variability in hemodynamic states reported previously [4] has implications for neuroimaging research . Specifically , even exact measurements at an individual point within the limited neuroimaging field of view ( e . g . ~1 mm2 surface in two-photon images ) would be prone to exhibit wide variations . The patchiness ( variability ) obtained by image acquisition at a single point cannot be overcome by more accurate imaging . Instead , an effective response to counteract variability due to network randomness is to adopt imaging protocols that emphasize spatially distributed samples over point measurements . In other words , measurements intended to infer global trends necessitate spatially distributed samples . Specifically , point observations acquired for single blood vessels can be expected to exhibit wide variations due to network effects , even if measurements are precise . Our large-scale computer simulations suggest a depth dependent hematocrit gradient in the cortical blood supply as summarized conceptually in Fig 8 . Detailed analysis of the spectrum of individual microcirculatory blood flow paths illuminated a clear trend; namely that deeply penetrating microvessels convey more red blood cells than paths running closer to the pial surface . The observation of higher hematocrit in deeper paths was observed in all simulation experiments for the primary sensory sets ( experimental data sets , N = 4; synthetic microcirculatory networks , N = 60 as seen in Fig 4 ) as well as for the large-scale blood flow simulations covering the entire MCA territory shown in Fig 7 . The predicted homogenization effect results in more uniform RBC fluxes , because shorter superficial paths tend to have higher bulk flow , Q , but carry less hematocrit , h . On the other hand , longer deeper penetrating paths have to overcome higher resistance leading to lower flows , but enjoy increased hematocrit as summarized in Fig 4 and Fig 7 . As a consequence , this phenomenon also suggests that shorter surface paths which tap into fresh arterial oxygen supply have fewer RBCs , while deeper paths have higher concentrations of RBCs which on average carry lower O2 saturation . Another effect of hematocrit gradient is that net oxygen fluxes conveyed to different cortical layers are more evenly balanced than would be the case if RBCs distributed uniformly ( no plasma skimming ) . We also noticed that the variance of RBC fluxes decreased with cortical depth . Accordingly , the distribution of RBC fluxes in deeper layers is more homogeneous than in surface layers . Random network architecture together with non-uniform hematocrit distribution due to the complex biphasic blood rheology seems to be two synergetic factors for ensuring homogenous oxygen supply irrespective of the cortical tissue depth . Since this homogenization effect needs no external feedback , it is plausible to infer that layer dependency of hematocrit and reduction of RBC flux variance serves a self-regulatory mechanism to balance oxygen supply to all cortical layers . The plasma skimming effect describes a phenomenon seen in microvascular bifurcations ( d<300 µm ) [57 , 58] in which thinner side branches syphon disproportionately large amounts of plasma from the parent segment than thicker daughter branches . Our mechanistic simulations illustrate how plasma skimming phenomena apply over thousands of bifurcations and multifurcations in a tortuous vessel network , effectively overcoming the geometrical unavoidability of path length differences as shown in Fig 8 . Our recently developed kinetic plasma splitting model ( KPSM ) was our choice for computing large-scale network effects in this study . The main critical reasons include: ( i ) the KPSM split rule is able to handle multifurcations that occur in the murine microcirculatory anatomy ( 7 . 1% , 5 . 9% , 8 . 9% , 6 . 7% of all segments had multifurcations in experimental data sets ) , ( ii ) its predictions fall within physiologically meaningful property ranges . Specifically , it does not lead to predictions of zero or excessive hematocrit , and ( iii ) its linear and differentiable mathematical properties guarantee convergence of massive network computations . A full account documenting the KPSM model can be found in S3 Supplement . The previously introduced network synthesis used a modified constrained constructive optimization ( mCCO ) [30] algorithm . The mCCO algorithm originally conceived by Schreiner [45] deploys two very simple principles: ( i ) minimization of vascular volume , and ( ii ) hemodynamic flow principle constraints which enforce that the total blood flow entering the network discharges in exactly equal amounts through the terminal outflow segments . Remarkably , this approach builds network structures whose topology resembles vascular network anatomy observed in vivo . One major task consisted of testing whether realistic network representations with arterial-capillary-venous closures could be synthesized with morphometric and hemodynamic properties matching networks acquired with neuroimaging modalities . The results showed that synthetic data ( N = 60 ) created with a modified mCCO algorithm were statistically and hemodynamically equivalent to experimental cortical data sets ( N = 4 ) . The hemodynamically inspired vascular growth procedure enabled the construction of realistic representations of the cortical blood supply of the entire MCA territory spanning multiple length scales from the large arteries ( mm range ) to the smallest capillaries ( µm range ) , and draining through the pial veins ( mm range ) or three orders of magnitude in length scales . It allowed us to seamlessly integrate state-of-the-art topological data acquired from two entirely different imaging modalities ( µCT and 2PLSM ) into a single , coherent multiscale representation of the entire MCA territory with unprecedented anatomical detail that includes both the arterial and the venous side of the cerebrocirculation . Because simple , blood flow inspired construction principles are applied at all length scales , the resulting MCA circulation has no discontinuities or gaps between the main cerebral arteries , the pial arterial network , or the microcirculation . Morphometrics , anatomical details such as the shape of the cortical surface and hemodynamic principles , are incorporated at each stage of the growth algorithm . Thus , our proposed methodology may serve as an alternative to the practice of merely stitching together data from different locations or length scales . The application of biphasic blood flow simulations for the entire MCA territory shows that large-scale blood flow and hematocrit simulations are feasible with existing computer resources . The large-scale simulations confirmed the trend of hematocrit layer dependence predicted for the smaller cortical samples . The massive simulations also elucidate the spatiotemporal coordination between different vascular compartments at different length scales ( arteries vs arterioles vs capillary bed ) . The anatomical detail achieved with the MCA model may serve as a starting point for dynamic simulations that elucidate the involvement of different vascular components in regulating functional hyperemia , autoregulation or collateral blood supply in stroke . Because the network extended over a sizable portion of the mouse cortex , predictions for the center of the primary sensory cortex were free of boundary effects . The synthetic MCA circulatory network also has the critical advantage that boundary conditions , which have been reported to hamper simulations on thin data sets [9] , are applied very far away from the area of investigation . For example , Fig 5 displays typical subsections comparable in size to the 2PLSM data sets which are located far away from the MCA boundaries ( MCA M1 segment and veins of the superior sagittal sinus ) . Thus , in samples situated at the center of the MCA territory , boundary conditions have negligible impact on hemodynamic predictions . The blood flow simulation for the entire MCA territory required only the arterial inlet pressure at the M1 segment and the blood pressure at the venous side . We point out three additional reasons why the ability to synthesize morphologically and hemodynamically equivalent data sets is significant . ( i ) Artificial networks continuously connect the arterial side and the venous side without gaps . In 3D neuroimages assembled from two-dimensional image stacks , it is easy to miss segment connections or segments running between two slices . ( ii ) No segments are severed nor is there a need to prune dangling segments at domain bounds ( this cleanup is unavoidable in image reconstructions [3 , 23] ) . In particular , fragmentation to pial arteries and many microcirculatory segments running perpendicular to the pial surface lead to boundary effects that can substantially affect predictions [9] . ( iii ) The most important benefit is the ability to expand the scope of data acquired by neuroimages without being confined to the bounded field-of-view or limited resolution of the imaging modality . The ability to conduct brain-wide simulations would free the modeler from the burden of making uncertain assumptions at the boundaries of the artificial domain ( edge of the image or simulation domain boundary ) . Because our algorithm succeeded in converging blood flow computations with hematocrit split for the entire MCA circulation in about two hours of CPU time , our group is confident that the proposed computational approach will enable blood flow simulations and oxygen transport on a brain-wide level in the near future . Despite the evidence for trends such as depth dependent hematocrit , it should be emphasized that individual flow paths may experience substantially weaker or even reverted trends , as can be expected from the inherent randomness of the microcirculatory network architecture . The 2PLSM technique provided a very detailed inventory of the cortical microcirculation . The four data sets did not include information about the subcortical blood supply to the white matter . White matter subcortical circulation is physiologically separated from the cortical blood supply . Accordingly , we assumed that the white matter supply is hydraulically separated from the cortical blood supply . However , certainty about this point would require a model of both the cortical and the subcortical networks ( white matter blood supply ) . This task is intriguing , but is currently beyond the reach of 2PLSM , which is limited to ~1 mm depth . This is clearly a point for future research , but is currently outside the scope of this paper . The main finding of depth dependency of hematocrit supply to the cortical layers is the result of a model prediction whose basis rests on experimental observations about plasma skimming and uneven hematocrit splits observed in capillaries outside the brain [59–61] . Therefore , the next logical step is to experimentally verify layer dependent hematocrit with deep imaging such as adaptive optics ( AO ) two-photon imaging [62] . If experiments confirm depth dependence and homogenization of RBC flux distribution , it would constitute a remarkable mathematical modeling contribution , which actually predicted , instead of merely explained , cortical blood supply . In the adverse case , the model would have prompted the need to revise our understanding of biphasic blood flow rheology as it relates to the cortical microcirculation ( = diameter and hematocrit dependent viscosity laws , and hematocrit split rules ) , since so far it has been assumed that plasma skimming is active in capillaries throughout the entire circulatory system including the brain . The conclusions about oxygen supply also need to be verified experimentally and computationally . The methods presented previously might be a first step in this direction [4] . However , oxygen predictions require discretization of the extracellular space which can be done in principle using the methods presented in Gould et . al [29] , but is beyond the scope of this paper . We predicted uneven depth dependent hematocrit distribution due to the complex biphasic blood rheology . Because our simulation did not include external factors such as gravity , we conclude that the result of depth dependent hematocrit arises from the combination of structural and hemodynamic properties of the network . Our findings suggest that network effects due to biphasic blood rheology and randomness of the network architecture are a controlling factor for ensuring adequate oxygen supply irrespective of the cortical depth . Since the observed homogenization of RBC variability requires no feedback , depth dependent hematocrit gradient may serve an important self-regulatory mechanism to balance oxygen supply to all cortical layers . Uneven distribution of hemodynamic states in the microcirculation as well as the notion of layer-dependent hematocrit also have implications on the interpretation of the fMRI BOLD signal where it is usually assumed that hemodynamic states and hematocrit are homogeneous and evenly distributed throughout the microcirculation . The predictions in this work suggest that focal analysis of the fMRI BOLD signal would be more relevant than assuming global constants for the entire cortex . We demonstrated that the modified constrained constructive optimization algorithm ( mCCO ) is successful in synthesizing artificial microcirculatory networks with topological and hemodynamic properties that are statistically equivalent to experimental data sets from different imaging modalities and length scales . Simulations of the entire MCA circulation , which until recently would have to be considered intractable , are now becoming accessible to rigorous numerical analysis due to stable , efficient and physiologically consistent plasma skimming algorithms implemented on existing computer hardware . The synthesis of anatomically faithful cerebrocirculatory networks with desired topology closes the gap between large-scale blood flow simulations performed on image-derived data sets on one hand , and simulations on purely synthetic data sets on the other . The successful synthesis of the entire MCA territory with biphasic blood flow simulation constitutes a step towards the ultimate goal of first principle simulations of cerebrocirculatory blood and oxygen distribution patterns for the entire brain .
Nine female C57BL/6 mice were imaged for pial vascular network structures following intravascular injection of a lead pigment contrast agent as described elsewhere [56 , 63–65] . The mice were perfusion fixed prior to micro computed tomography ( µCT ) imaging with 7–20 µm isotropic resolution of the cerebral angioarchitecture . The resulting 3D images were filtered and the vascular lumen reconstructed as previously described [66–68] . Fig 9A shows raw µCT samples of the mouse vasculature from a 20 µm resolution image . The pial network statistics such as penetrating arteriole density and vessel diameter were compiled with results summarized in Table 2 . Four volumes ( N = 4 ) that encompassed the murine vibrissa primary sensory cortex [35] were imaged using two-photon laser scanning microscopy ( 2PLSM ) and are shown in Fig 9C . 2PLSM was employed to extract the spatial arrangement , length and orientation of blood vessels in the vibrissa primary sensory cortex [31 , 35 , 69] . Blood vessels in four data sets ( ~1x1x1 mm3 ) were labeled as pial arteries , penetrating arterioles , capillaries , ascending venules , or pial veins . Categorization was based on size and branching level according to Strahler order rather than physiological markers . No effort was made to differentiate pre-capillary arterioles from post-arteriole capillaries because it requires differential labeling of smooth muscle and pericytes . Capillaries were differentiated from ascending venules by a diameter cutoff of 6 µm and penetrating venules were differentiated from pial veins for vessels within a depth of 100 µm below the pia and a diameter less than 12 µm . Diameter information was also derived from images . The network information was stored using sparse connectivity matrices . Length , diameter , and tortuosity spectra are depicted in Fig 1 . More details on image acquisition [31 , 35 , 69] , image reconstruction [70] , as well as the formulation of the network equations [29] can be found elsewhere . Artificial microvascular networks ( N = 60 ) for large sections of the cortex ( ~1x1x1 mm3 ) were synthesized using a previously described vascular growth algorithm [30] . Four examples are displayed in Fig 9D . The algorithm preserved dimensions of the experimentally acquired cortical samples , pattern and dimension of pial arteries , number , orientation and connectivity of penetrating arterioles , and morphometrics of the capillary bed , draining venules and pial veins , as listed in Table 1 . Statistics and morphometric comparisons of experimental and synthetic data sets are displayed in Fig 1 . The arterial network of the entire MCA territory spanning three orders of magnitude in length from large arteries ( ~1 mm range ) down to the entire capillary bed ( ~1 µm ) was synthesized based on morphometric statistics of source data from multimodal images ( µCT and 2PLSM ) . Microcirculatory blood flow was modeled as a biphasic suspension comprised of red blood cells and plasma . Bulk blood flow was described by Poiseuille law relating volumetric flow to pressure drop as a function of resistance which in turn depends on diameter , d , and hematocrit-dependent viscosity [71] . In addition , a kinetic plasma skimming model ( KPSM ) presented previously [29] accounted for the uneven RBC distribution , known as plasma skimming . This model has only one adjustable parameter , the skimming coefficient , m . It was set to value of m = 8 in all microcirculatory models , although this parameter could be refined as shown recently [72–74] . The nonlinear systems of conservation balances in system ( 1 ) were solved iteratively to calculate blood pressures , p , flow , Q , and hematocrit , h . Here , R is the resistance matrix , C1 and C2 are fundamental connectivity matrices [75] and C3 is the advection flux matrix . Boundary conditions are summarized in Table 3 . More details on the mathematical background are given in S2 Supplement; implementation details are discussed elsewhere [29] . | The brain’s astonishing cognitive capacity depends on the coordination between neurons and the cerebral circulation , a system known as the neurovascular unit . The spatial and temporal coupling between these two networks is the object of intense research . However , the concise anatomical description of the cerebral circulation has so far been intractable . This paper introduces a methodology for the in silico creation of realistic models for the entire cerebral circulation . This innovation incorporates topological data from several neuroimaging modalities covering three lengths scales as input into a computer algorithm , which assembles anatomically accurate circulatory networks . When simulating blood flow as red blood cells suspended in plasma for experimental and synthetic cortical network models , we discovered that red blood cells tend to be more concentrated in deeper layers of the cortex compared to the surface . RBC fluxes are more homogenous in deeper layers . The phenomenon of depth dependent red blood cell supply supports the notion that the intricate architecture of the cortical microcirculation serves a self-regulating function to maintain uniform oxygen perfusion to neurons . We also demonstrate the practicality of predicting blood flow patterns for the entire brain with existing computer power . | [
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] | 2018 | Simulations of blood as a suspension predicts a depth dependent hematocrit in the circulation throughout the cerebral cortex |
Babesia bovis is an apicomplexan tick-transmitted pathogen of cattle imposing a global risk and severe constraints to livestock health and economic development . The complete genome sequence was undertaken to facilitate vaccine antigen discovery , and to allow for comparative analysis with the related apicomplexan hemoprotozoa Theileria parva and Plasmodium falciparum . At 8 . 2 Mbp , the B . bovis genome is similar in size to that of Theileria spp . Structural features of the B . bovis and T . parva genomes are remarkably similar , and extensive synteny is present despite several chromosomal rearrangements . In contrast , B . bovis and P . falciparum , which have similar clinical and pathological features , have major differences in genome size , chromosome number , and gene complement . Chromosomal synteny with P . falciparum is limited to microregions . The B . bovis genome sequence has allowed wide scale analyses of the polymorphic variant erythrocyte surface antigen protein ( ves1 gene ) family that , similar to the P . falciparum var genes , is postulated to play a role in cytoadhesion , sequestration , and immune evasion . The ∼150 ves1 genes are found in clusters that are distributed throughout each chromosome , with an increased concentration adjacent to a physical gap on chromosome 1 that contains multiple ves1-like sequences . ves1 clusters are frequently linked to a novel family of variant genes termed smorfs that may themselves contribute to immune evasion , may play a role in variant erythrocyte surface antigen protein biology , or both . Initial expression analysis of ves1 and smorf genes indicates coincident transcription of multiple variants . B . bovis displays a limited metabolic potential , with numerous missing pathways , including two pathways previously described for the P . falciparum apicoplast . This reduced metabolic potential is reflected in the B . bovis apicoplast , which appears to have fewer nuclear genes targeted to it than other apicoplast containing organisms . Finally , comparative analyses have identified several novel vaccine candidates including a positional homolog of p67 and SPAG-1 , Theileria sporozoite antigens targeted for vaccine development . The genome sequence provides a greater understanding of B . bovis metabolism and potential avenues for drug therapies and vaccine development .
Babesiosis is a tick-borne , hemoprotozoan disease enzootic in ruminants in most sub-temperate and tropical areas of the world ( reviewed in [1] ) . It is recognized as an emerging zoonotic disease of humans , particularly in immunocompromised individuals [2] , and is of historical significance as the first protozoan agent recognized to be arthropod transmitted [3] . With no widely available vaccine and a nearly global distribution , babesiosis is one of the most important arthropod-transmitted diseases of cattle , with over half of the world's cattle population at risk [4] . Live attenuated vaccines are used for the control of babesiosis in many parts of the world , but rely on region-specific attenuated strains for which vaccine breakthrough is not uncommon ( reviewed in [5] ) . Due to the blood-based production of these attenuated vaccines and the possibility of reversion to virulence with tick passage , they are not licensed in the US . The consequences of a disease outbreak in a naïve cattle population with no available vaccine would be catastrophic . Babesia , the causative agent of babesiosis , is in the order Piroplasmida within the phylum Apicomplexa [6] . Similar to other members of this phylum , such as the phylogenetically closely positioned Theileria and its distant cousin , Plasmodium , Babesia undergoes a complex life cycle that involves both vector and mammalian hosts . In contrast to Plasmodium , for which Anopheles mosquitoes vector transmission , Theileria and Babesia are transmitted via tick vectors . For all three hemoprotozoans , sporozoites are injected into the blood stream of the mammalian host and it is at this stage where the life cycle of Babesia differs from that of Theileria and Plasmodium . For Theileria , infection leads first to lymphocytic stages followed after schizogony by intraerythrocytic development [7] . In plasmodial infection , the sporozoite first infects hepatocytes in which the stage infecting the erythrocytes is produced . In contrast , babesial infection with sporozoites leads directly to infection of erythrocytes . Once inside an erythrocyte , both Theileria and Babesia are found in the cytoplasm while Plasmodium resides in a parasitophorous vacuole . In spite of the differences in the mammalian cell types that the parasites invade , the hallmarks of a B . bovis–induced clinical syndrome in cattle , including severe anemia , capillary sequestration of infected erythrocytes , abortion , and a neurologic syndrome , are remarkably similar to human malaria caused by Plasmodium falciparum [8 , 9] . Whether the mechanisms leading to these clinical features are unique or are shared between these two related hemoprotozoans is unknown . Complete apicomplexan genome sequences for T . parva , T . annulata , and P . falciparum have been reported [7 , 10 , 11] . Comparisons of these genomes revealed that only approximately 30%–38% of the predicted proteins could be assigned a function , suggesting that the majority of the proteins for these organisms are novel [10 , 11] . Data from the genome sequences demonstrate many differences between Plasmodium and Theileria , such as the number of rRNA units and their developmental regulation , the lack of key enzymes in certain metabolic pathways , lengths of intergenic regions , gene density , and intron distribution . The genome sequence of the virulent , tick-transmissible Texas T2Bo isolate of B . bovis , reported here , will allow for an even more comprehensive , genome-wide comparison of this triad of important vector-borne apicomplexan hemoprotozoa , and can be used to identify genes that play common and species-specific roles in apicomplexan biology . Furthermore , insight from such comparisons may improve our ability to design potential prophylactic and therapeutic drug targets .
Assembly of whole genome shotgun sequence data of the Texas T2Bo isolate of B . bovis indicates that the parasite contains four chromosomes , confirming previous results from pulse field gel electrophoresis [12 , 13] . Chromosome 1 , the smallest of the four chromosomes , contains a large physical gap flanked by two large contigs ( 821 , 816 bp and 285 , 379 bp in length ) . The gap is estimated to be 150 kbp by pulse field gel electrophoresis ( unpublished data ) and contains five contigs that vary in size from 12 kbp to 28 kbp , with the order of the contigs in the gap unknown . Chromosomes 2 and 3 were fully sequenced and are 1 , 729 , 419 and 2 , 593 , 321 bp in length , respectively . Chromosome 4 contains an assembly gap that has not been unambiguously resolved; a 1 , 149 bp contig separates two contigs of 827 , 912 bp and 1 , 794 , 700 bp . Thus , the nuclear genome of B . bovis consists of four chromosomes of 2 . 62 , 2 . 59 , 1 . 73 , and ∼1 . 25 Mbp in length . At 8 . 2 Mbp in size , the genome of B . bovis is similar in size to that of T . parva ( 8 . 3 Mbp ) [10] and T . annulata ( 8 . 35 Mbp ) [7] , the smallest apicomplexan genomes sequenced to date ( Table 1 ) . Each B . bovis chromosome contains an A+T-rich region ∼3 kbp in length presumed to be the centromere ( Figure 1 ) based on features similar to those described for the putative centromere on P . falciparum chromosome 3 [14] . Three of the chromosomes are acrocentric , while chromosome 4 is submetacentric . The organization of telomeres and sub-telomeric regions resembles that seen in Theileria [7 , 10] , as protein coding genes are found within 2–3 kbp of the end of CCCTA3–4 telomeric repeat sequences . The B . bovis genome contains three rRNA operons , two on chromosome 3 and one on chromosome 4 , and 44 tRNA genes distributed across all four chromosomes . A total of 3 , 671 nuclear protein coding genes are predicted in the B . bovis assembled sequence data . In addition to the nuclear genome , the parasite contains two A+T-rich extra-chromosomal genomes: a circular 33 kbp apicoplast genome and a linear ∼6 kbp mitochondrial genome ( Table 1 ) , described below . A series of in silico metabolic pathways for B . bovis were reconstructed from 248 proteins assigned an EC number , including glycolysis , the tricarboxylic acid cycle and oxidative phosphorylation , de novo pyrimidine biosynthesis , glycerolipid and glycerophospholipid metabolism , the pentose phosphate pathway , and nucleotide interconversion ( Figure 2 ) . Notably , a number of major pathways appear to be lacking in the parasite , including gluconeogenesis , shikimic acid synthesis , fatty acid oxidation , the urea cycle , purine base salvage and folate , polyamine , type II fatty acid , and de novo purine , heme , and amino acid biosyntheses . Although heme biosynthesis activity present in P . falciparum is predicted to be absent in B . bovis , it does encode delta-aminolevulinic acid dehydratase ( BBOV_II001120 ) , which catalyzes the second step in heme biosynthesis . The predicted metabolic profile of B . bovis is more similar to that of Theileria [7 , 10] than to that of P . falciparum [11] . Like Theileria , B . bovis does not appear to encode pyruvate dehydrogenase . Thus , there is no classical link between glycolysis and the tricarboxylic acid cycle . Interestingly , massively parallel signature sequencing has demonstrated that lactate dehydrogenase is the third most highly transcribed gene in T . parva schizonts [15] , suggesting that in these organisms lactate may be the primary end product of glycolysis . This could be true for B . bovis as well . The enzymes adenine phosphoribosyltransferase and hypoxanthine-guanine phosphoribosyl-transferase ( HGPRT ) involved in salvage of purine bases appear to be lacking in B . bovis . HGPRT is present in P . falciparum ( PF10_0121 ) [11] , but absent from T . parva and T . annulata . Interestingly , although the purine salvage pathway is incomplete , B . bovis may be able to salvage purine nucleosides [16] . A recent analysis of B . bovis expressed sequence tags ( ESTs ) identified two adenosine kinases [17] , a finding corroborated by the genome sequence data , which also revealed the presence of adenosine deaminase . These enzymes are absent in T . parva , while P . falciparum encodes adenosine deaminase . While we cannot exclude that HGPRT is present in the chromosome 1 gap , the apparent absence of HGPRT in B . bovis is in contrast to previous studies demonstrating the incorporation of radio-labeled hypoxanthine in parasite erythrocyte cultures [16 , 18] . Although several enzymes involved in purine salvage are present , there appears to be no direct path to the production of inosine monophosphate , and it is possible that the necessary enzymes are present but are not similar to known enzymes . Unlike P . falciparum and the Theileria spp . , B . bovis does not appear to encode dihydrofolate synthase , which converts dihydropteroate to dihydrofolate . However , this deficiency could be compensated through importation via a folate/biopterin transporter ( BBOV_IV002460 ) and increased dihydrofolate reductase–thymidylate synthase ( DHFR-TS ) activity . Consistent with a previous study using the Israel strain of B . bovis [19] , the T2Bo DHFR-TS contains three of the four amino acid substitutions found in a mutant P . falciparum DHFR-TS with strong resistance to pyrimethamine , a DHFR inhibitor . An additional single point mutation is linked with the ability of B . bovis to develop strong resistance to pyrimethamine [19] . Babesia bovis has the smallest number of predicted membrane transporters [20] among the sequenced apicomplexan species ( Table S1 ) , but encodes more members of some families ( for example , glucose-6-phosphate/phosphate and phosphate/phosphoenolpyruvate translocators , members of the drug/metabolite transporter superfamily ) . It encodes fewer members of the ABC efflux protein family than T . parva but has more transporters for inorganic cations , including a cation diffusion facilitator family protein that is absent in T . parva and other apicomplexans . Both B . bovis and T . parva lack aquaporins , the calcium:cation antiporters , and amino acid permeases that are present in the genome of P . falciparum . Orthologs of the different types of amino acid transporters cannot be identified in B . bovis , including the dicarboxylate/amino acid:cation ( Na+ or H+ ) symporter family amino acid:cation symporter that is present in T . parva [10] . Most members of the phylum Apicomplexa harbor a semi-autonomous plastid-like organelle termed the apicoplast , which was derived via a secondary endosymbiotic event [21] . The B . bovis apicoplast genome is 33 kbp and unidirectionally encodes 32 putative protein coding genes , a complete set of tRNA genes ( 25 ) , and a small and large subunit rRNA gene ( Figure S1 ) . The B . bovis apicoplast genome displays similarities in size , gene content , and order to those of Eimeria tenella , P . falciparum , T . parva , and Toxoplasma gondii ( Table S2; [22–24] ) . As observed with other apicoplast genomes , the B . bovis apicoplast genome is extremely A+T rich ( 78 . 2% ) , in contrast to the nuclear genome ( 58 . 2% ) . In addition to the apicoplast genome encoded proteins , it has been demonstrated in P . falciparum that proteins encoded by nuclear genes are imported into the apicoplast ( reviewed in [25] ) to carry out a variety of metabolic processes , including heme biosynthesis [26] , fatty acid biosynthesis [27] , and isoprenoid precursor synthesis via the methylerythrose phosphate pathway [28] . Nuclear encoded proteins targeted to the apicoplast of P . falciparum have a bipartite targeting sequence consisting of a signal peptide that directs the protein to the secretory pathway and an apicoplast transit peptide that redirects the protein from the default secretory pathway into the lumen of the apicoplast [29 , 30] . Analysis of the metabolic functions ascribed to the apicoplast in P . falciparum reveals that only the enzymes for isoprenoid biosynthesis are found in B . bovis . To detect additional apicoplast-targeted proteins , PlasmoAP , a program developed to predict apicoplast targeting for P . falciparum [31] , was used and revealed only 14 additional candidate proteins . This result is , perhaps , not unexpected , as the program was trained with P . falciparum sequences and likely works well only for P . falciparum because of skewed codon usage resulting from the low G+C content of P . falciparum . A third approach included visual inspection of BLAST search outputs of the entire B . bovis proteome against the nr database ( National Center for Biotechnology Information ) for potential amino-terminal extensions . This search resulted in 25 potential apicoplast-targeted sequences that had non-apicomplexan homologs with significant E values and bona fide amino terminal extensions . In total , 47 proteins ( the eight involved in the methylerythrose phosphate pathway , 14 SignalP sequences identified with PlasmoAP , and 25 proteins identified through BLAST and visual inspection for amino terminal extentions ) are predicted to be targeted to the B . bovis apicoplast ( Table S3 ) , by far the fewest of any organism for which this type of analysis has been done . P . falciparum and T . parva are predicted to have 466 and 345 apicoplast-targeted proteins , respectively [10 , 32] . The paucity of proteins predicted to be targeted to the B . bovis apicoplast may partially reflect the biology of the organism , with fewer functions attributed to the B . bovis apicoplast compared to P . falciparum , but is more likely a reflection of the lack of appropriate prediction algorithms . The apicoplast has been an attractive target for development of parasiticidal drug therapies as the biosynthetic pathways represented therein are of cyanobacterial origin and differ substantially from corresponding pathways in the mammalian host [21 , 33] . A recent study of the apicomplexan T . gondii demonstrated that fatty acid synthesis in the apicoplast is necessary for apicoplast biogenesis and maintenance , and indicates that this pathway would be an ideal target for drug design [34] . Thus , the reduced metabolic potential of B . bovis has important ramifications for drug design , suggesting that drugs targeting fatty acid synthesis would not be effective against babesiosis due to the absence of this pathway . B . bovis contains a 6 kbp linear mitochondrial genome ( Figure S2 ) . It encodes three putative protein coding genes , including cytochrome c oxidase subunit I , III , and cytochrome b . These are membrane-bound proteins that form part of the enzyme complexes involved in the mitochondrial respiratory chain . Cytochrome b and c subunit III are encoded on the same strand , while cytochrome c subunit I is encoded on the opposite strand . This coding arrangement is identical to that of Theileria spp . but different from that of P . falciparum [7 , 11 , 35] . Each of the encoded proteins employs the universal ATG as the start codon , in contrast to the T . parva cytochrome c subunit I , which has an AGT start codon [35] . In addition to the three protein coding genes , the B . bovis mitochondrial genome includes at least five partial rRNA gene sequences ranging in size from 34 to 301 bp . All five rRNA sequences are homologous to parts of the large ribosomal subunit of rRNA . They are encoded on both strands of the mitochondrial genome with rRNA 1 and 5 on the same strand and 2 , 3 , and 4 on the opposite strand . A terminal inverted repeat was identified from position 11–180 and 6005–5836 . The B . bovis proteome was used to construct protein families using Tribe-MCL , a sequence similarity matrix-based Markov clustering method , and a method based on a combination of hidden Markov model domain composition and sequence similarity [36] . In addition to housekeeping gene families found in most eukaryotes , the pathogen contains only two large gene families . One of these families , encoding the variant erythrocyte surface antigen ( VESA ) , has been previously defined [37] . The second , which we have termed SmORF ( small open reading frame ) , is novel . Smaller notable families encode a 225 kD protein , known as spherical body protein 2 ( SBP2 ) [38] , and the variable merozoite surface antigen ( VMSA ) family [39] . The 8 . 2 Mbp genome of B . bovis consists of four nuclear chromosomes , and two small extra-nuclear chromosomes for the apicoplast and mitochondria . B . bovis appears to have one of the smallest apicomplexan genomes sequenced to date . Consistent with the small genome size , analysis of enzyme pathways reveals a reduced metabolic potential , and provides a better understanding of B . bovis metabolism and potential avenues for drug therapies . Using several different approaches , identification of proteins predicted to be targeted to the apicoplast reveals far fewer proteins than for related organisms . This may be due in part to the lack of appropriate detection algorithms . However , the conservative approach used to identify the genes encoding these proteins provides a solid base from which to extend these analyses . A foundation for the elucidation of antigenic variation and immune evasion has been established with genome-wide characterization of the ves1 gene family , and discovery of the novel smorf gene family . ves1 and smorf genes are co-distributed throughout the chromosome , with the majority located away from telomeres and centromeres . As many as 33 potential loci of ves1 transcription have been identified , and cDNA analysis suggests that this transcription is more broad-based than with other hemoprotozoa . Comparative analysis indicates that many stage-specific and immunologically important genes from P . falciparum are absent in B . bovis . However , through both COG analysis and synteny , additional B . bovis vaccine candidates , including homologs of P . falciparum p36 , Pf12 , T . parva p67 , and four of six T . parva proteins targeted by CD8+ cytotoxic T cells , have been identified .
R . microplus adults were allowed to feed on calf C-912 inoculated with the T2Bo strain that was one passage ( splenectomized calf ) removed from a field isolate and frozen as a liquid nitrogen stabilate [76] . Progeny larvae were placed on calf C-936 , blood was collected 7 d post tick infestation , and microaerophilous stationary phase culture was established according to [77] with modifications as described in [18] . Parasite genomic DNA from parasites in culture for 34–39 weeks was extracted using standard methods [78] . Small ( 2–3 kbp ) and medium ( 12–15 kbp ) insert libraries were constructed by nebulization and cloning into pHOS2 . A large insert library ( 100–145 kbp ) was constructed in pECBAC1 ( Amplicon Express ) and consisted of clones resulting from HindIII or MboI partially digested DNA . A total of 103 , 478 high quality sequence reads ( average read length = 870 ) were generated ( 58 , 251 reads from the small insert library and 45 , 227 reads from the medium insert library ) and assembled using Celera Assembler ( http://sourceforge . net/projects/wgs-assembler/ ) . The sequence data fell into 50 scaffolds consisting of 88 contigs . The bacterial artificial chromosome library was end sequenced to generate an additional 2 , 874 reads that were used to confirm the assembly and for targeted sequencing in the closure phase . Gaps in the assembly were closed by a combination of primer walking and transposon based or shotgun sequencing of medium insert clones , bacterial artificial chromosome clones , or PCR products . This genome project has been deposited at DDBJ/EMBL/GenBank under accession number AAXT00000000 . The version described in this paper is the first version , AAXT01000000 . Chromosomal gene models were predicted using Phat [79] , GlimmerHMM , TigrScan [80] , and Unveil [81] after training each gene finding algorithm on 499 partial and full-length B . bovis genes totaling ∼453 kbp . The training data were manually constructed after inspection of the alignment of highly conserved protein sequences from nraa using the AAT package [82] and PASA to align a collection of ∼11 , 000 B . bovis ESTs [17] to the genome sequence . Jigsaw was used to derive consensus gene models [83] from the outputs of the gene finding programs and protein alignments . The consensus gene models were visually inspected and obvious errors such as split or chimeric gene models were corrected based on either EST or protein alignment evidence using the Neomorphic Annotation Station [84] before promotion to working gene models . Genes encoding tRNAs were identified using tRNAscan-SE [85] . BLAST [86] was used to search nraa using the predicted B . bovis protein sequences , and protein domains were assigned using the InterPro database [87] . The presence of secretory signals and transmembrane domains were detected using SignalP [88] and TMHMM [49] , respectively . Functional gene assignments were assigned based on the BLAST data , and a Web-based tool called Manatee ( http://manatee . sourceforge . net/ ) was used to manually curate and annotate the data . Proteins were annotated as hypothetical proteins if there was less than 35% sequence identity to known proteins , and as conserved hypothetical proteins if there was greater than 35% sequence identity to other proteins in the database that were unnamed . If a protein was predicted to have a signal peptide and at least one transmembrane domain , but was otherwise considered as a hypothetical or conserved hypothetical protein , it was annotated as a membrane protein , putative . If there was greater than 35% sequence identity for 70% of the sequence length , the protein product would be assigned a name only when a publication record could verify the authenticity of the named product . In the absence of published evidence , the named product was listed as putative . The mitochondrial and apicoplast genomes were manually annotated , and apicoplast-targeted proteins were analyzed using PlasmoAP ( http://v4-4 . plasmodb . org/restricted/PlasmoAPcgi . shtml ) [31] . PASA [89] was used to align ∼86% of the B . bovis ESTs to the genome sequence data and provided evidence for transcription of 1 , 633 genes . Sybil ( http://sybil . sourceforge . net/ ) was used to create an all-versus-all BLASTP search using the proteomes of B . bovis , T . parva , and P . falciparum . These outputs were subjected to Jaccard clustering [10] , placing proteins into distinct clusters for each proteome . Clusters from different proteomes were linked based on best bidirectional BLASTP hits between them to provide Jf-COGs . A minimum block size of five with one gap was allowed in the analyses . Analysis of ves1 transcription utilized total RNA isolated from microaerophilous stationary phase culture culture using TRIzol ( BRL ) treated three times with RNase-free DNase ( Ambion ) for 30 min at 37 °C . RNA was reverse transcribed with a Superscript ( Invitrogen ) reverse transcription kit using random hexamers according to the manufacturer's instructions . Universal primer sequences that would anneal to the two specific subunit types could not be found . Therefore , in the first RT-PCR experiment , primers were designed to amplify as many of the genes as possible . The following primers were used for ves1β cDNA: beta2For: 5′ GGA CTA CAG AAG TGG GTT GGG TGG and beta4Rev: 5′ ATA GCC CAT GGC CGC CAT GAA TGA; ves1α cDNA: alpha3For: 5′ CAG GTA CTC AGT GCA CTC GTT GGG TGG AG and alpha6Rev: 5′ CCC TAA TGT AGT GNA CCA CCT GGT TGT ATG C . Due to the high degree of sequence similarity ( >99% ) of the published ves1 loci in cosmid 53 and 54 ( accession numbers AY279553 and AY279554 , respectively ) to the genome sequence , a second RT-PCR experiment used primers designed to amplify the published LAT [37] . This experiment used primers LATbetaF1: 5′ GCA ACC GCA CGA CAG and LATbetaR2: 5′ CGC TGA CAC GCT AGT for the ves1β gene . A final cDNA cloning experiment was designed to elucidate the transcriptional profile for ves1 by targeting ves1α and ves1β genes associated with Rep sequence clusters [37] . Primers were as follows: ves1β: 00789F1: 5′ AGA CTG TGA ATC TCG GCT CA and 00789R: 5′ CAG CGG CAC CAC TAC CTT T; ves1α: 00792F2: 5′ TGC CCA GGA CAG TTA TG and 00792R2: 5′ TGA TGC CCT CTT CAA TAG TT . Whenever possible , ves1 primers were designed such that they would flank introns , providing an additional assurance that the amplicon obtained was not from contaminating genomic DNA; however , this was only possible for ves1β experiments .
The B . bovis T2Bo genome is deposited at DDBJ/EMBL/GenBank under accession number AAXT00000000 . | Vector-transmitted blood parasites cause some of the most widely distributed , serious , and poorly controlled diseases globally , including the most severe form of human malaria caused by Plasmodium falciparum . In livestock , tick-transmitted blood parasites include the protozoa Theileria parva , the cause of East Coast fever and Babesia bovis , the cause of tick fever , to which well over half of the world's cattle population are at risk . There is a critical need to better understand the mechanisms by which these parasites are transmitted , persist , and cause disease in order to optimize methods for control , including development of vaccines . This manuscript presents the genome sequence of B . bovis , and provides a whole genome comparative analysis with P . falciparum and T . parva . Genome-wide characterization of the B . bovis antigenically variable ves1 family reveals interesting differences in organization and expression from the related P . falciparum var genes . The second largest gene family ( smorf ) in B . bovis was newly discovered and may itself be involved in persistence , highlighting the utility of this approach in gene discovery . Organization and structure of the B . bovis genome is most similar to that of Theileria , and despite common features in clinical outcome is limited to microregional similarity with P . falciparum . Comparative gene analysis identifies several previously unknown proteins as homologs of vaccine candidates in one or more of these parasites , and candidate genes whose expression might account for unique properties such as the ability of Theileria to reversibly transform leukocytes . | [
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] | 2007 | Genome Sequence of Babesia bovis and Comparative Analysis of Apicomplexan Hemoprotozoa |
Numerous lines of evidence point to a genetic basis for facial morphology in humans , yet little is known about how specific genetic variants relate to the phenotypic expression of many common facial features . We conducted genome-wide association meta-analyses of 20 quantitative facial measurements derived from the 3D surface images of 3118 healthy individuals of European ancestry belonging to two US cohorts . Analyses were performed on just under one million genotyped SNPs ( Illumina OmniExpress+Exome v1 . 2 array ) imputed to the 1000 Genomes reference panel ( Phase 3 ) . We observed genome-wide significant associations ( p < 5 x 10−8 ) for cranial base width at 14q21 . 1 and 20q12 , intercanthal width at 1p13 . 3 and Xq13 . 2 , nasal width at 20p11 . 22 , nasal ala length at 14q11 . 2 , and upper facial depth at 11q22 . 1 . Several genes in the associated regions are known to play roles in craniofacial development or in syndromes affecting the face: MAFB , PAX9 , MIPOL1 , ALX3 , HDAC8 , and PAX1 . We also tested genotype-phenotype associations reported in two previous genome-wide studies and found evidence of replication for nasal ala length and SNPs in CACNA2D3 and PRDM16 . These results provide further evidence that common variants in regions harboring genes of known craniofacial function contribute to normal variation in human facial features . Improved understanding of the genes associated with facial morphology in healthy individuals can provide insights into the pathways and mechanisms controlling normal and abnormal facial morphogenesis .
Numerous lines of converging evidence indicate that variation in facial morphology has a strong genetic basis . These include the results of human heritability studies using twin and parent-offspring designs [1–5] , Mendelian craniofacial syndromes [6] , transgenic animal models with distinctive craniofacial phenotypes [7–9] , and studies mapping QTLs for craniofacial shape in several mammalian models [10–14] . However , we still have little understanding of how genetic variation relates to the diversity of normal facial traits commonly observed in humans . Understanding the genetic basis for normal facial variation has important implications for human health . Many genetic syndromes with dysmorphic facies are characterized by relatively subtle morphological changes , often involving quantitative traits with continuous distributions [6] . The range of variation for any given facial trait often displays substantial overlap between affected and healthy individuals . Thus , understanding the genetic factors that contribute to normal facial trait variation may provide valuable insights into the causes of craniofacial dysmorphology , including common craniofacial birth defects such as orofacial clefts [15 , 16] . To date , only a few studies have explicitly tested for associations between aspects of normal human facial morphology and common genetic variants . Among these , two genome-wide association ( GWA ) studies have been carried out on healthy individuals of European ancestry using 3D facial imaging and a combination of traditional and more advanced morphometric methods to derive phenotypes [17 , 18] . Between these two studies , a handful of intriguing genome-wide significant signals were reported , although they were largely non-overlapping . Notably , both studies reported an association between PAX3 variants and anatomical changes in interorbital region , an intriguing finding given that mutations in PAX3 cause Waardenburg Syndrome type 1 which is characterized by hypertelorism among other morphological abnormalities . Both studies also reported significant associations with measures of nasal projection in their discovery cohorts , although different genomic regions were implicated . In addition , several more focused candidate gene studies of loci implicated in craniofacial syndromes or in developmental pathways involved in craniofacial development have connected one or more craniofacial dimensions or aspects of shape with a small number of common genetic variants [19–28] . At least three candidate gene studies [20 , 25 , 28] have reported modest associations between common variants in FGFR1 and normal variation in craniofacial morphology , but in each case a different constellation of traits was involved . It is notable that none of the genes from these studies , including FGFR1 , were identified in the two previous GWA studies of facial morphology . Thus , while prior studies have detected a handful of biologically plausible genes associated with variation in craniofacial features , it is clear that these efforts are just scratching the surface and the potential for additional discovery is great . In the current study , we performed GWA analyses on a set of 20 craniofacial measurements commonly used in clinical assessment ( Fig 1 ) derived from 3D surface images in two well-characterized samples of unrelated White individuals of European ancestry from the USA: a sample comprised of 2447 individuals collected through the University of Pittsburgh ( i . e . , the Pittsburgh sample ) and an independent sample of 671 individuals collected under the direction of the University of Colorado ( i . e . , the Denver sample ) . All participants were genotyped using the same SNP array ( Illumina OmniExpress+Exome v1 . 2 ) , which included just under one million SNPs , and were imputed to the 1000 Genomes reference panel ( Phase 3 ) . We conducted association tests in each sample separately and combined the results using a meta-analytic approach .
In total , we observed seven associations in five traits that exceeded the conventional threshold for genome-wide significance ( p < 5 x 10−8 , Table 1; Figs 2–5 ) . One of the associations also exceeded our study-wide significance threshold of p < 5 x 10−9 , calculated based on 10 independent traits ( see Methods for details ) . Due to the large number of traits , we will limit our presentation of results to genome-wide significant signals . The entire list of meta-analysis associations with p-values < 5 x 10−7 is available in S1 Table . Manhattan plots showing the meta-analysis results , as well as the results for each sample , are available in supplemental figures S1–S20 Figs . We observed two significant associations for cranial base width: one at 14q21 . 1 ( top SNP rs79272428 , p = 1 . 01 x 10−8 , Fig 2A ) and the other at 20q12 ( top SNP rs6129564 , p = 1 . 65 x 10−9 , Fig 2B ) . Notably , the chromosome 20 association exceeded our strict threshold for study-wide statistical significance . For intercanthal width , we observed two significant associations: one at 1p13 . 3 ( top SNP rs619686 , p = 4 . 70 x 10−8 , Fig 3A ) and the other at Xq13 . 2 ( top SNP rs11093404 , 4 . 16 x 10−8 , Fig 3B ) . There were also significant associations with nasal width at 20p11 . 22 ( rs2424399 , p = 2 . 62 x 10−8 , Fig 4A ) and nasal ala length at 14q11 . 2 ( top SNP rs8007643 , p = 3 . 36 x 10−8 , Fig 4B ) . We observed a second independent peak on chromosome 20 for nasal width located 371kb upstream of the main peak . The second peak remained ( top SNP rs80186620 , p = 5 . 32x10-6 , S21 Fig ) after conditional association analysis adjusting for the effects of rs2424399 on nasal width . Finally we observed a significant association with upper facial depth at 11q22 . 1 ( top SNP rs12786942 , p = 4 . 59 x 10−8 , Fig 5 ) . For all of the above associations , the results were driven primarily by the larger Pittsburgh dataset . The cranial base width ( 14q21 . 1 ) , intercanthal width ( 1p13 . 3 ) and upper facial depth associations were at least nominally significant ( p < 0 . 05 ) in both datasets . Sample-specific association results for all SNPs with p-values less than 5 x 10−7 are listed in S2 Table for the Pittsburgh sample and S3 Table for the Denver sample . In an attempt to replicate the main findings from the prior two GWA studies in Europeans , we tested previously implicated SNPs against traits from our Pittsburgh dataset that capture similar aspects of morphology . This was not possible for every prior genotype-phenotype association given differences in the measurements available . With that limitation in mind , the Pittsburgh dataset was chosen for comparison because it was the larger of our two datasets and the phenotyping protocol was most similar to prior GWA studies . For Paternoster et al . [17] , we attempted to test three of their four genome-wide significant associations , two of which involved nasal ala length ( Table 2 ) . In our data , nasal ala length showed a nominally significant association ( p = 0 . 018 ) with rs1982862 , an intronic variant in CACNA2D3 . Conversely , we found no evidence of association between this measure and rs11738462 , an intronic variant in C5orf64 . The previously observed association between PAX3 and the position of nasion relative to the orbits could not be tested directly . However , we found no evidence of association between the implicated SNP rs7559271 and intercanthal width , which captures aspects of interorbital septum morphology . As a further exploratory analysis we also looked at the association between rs7559271 and several vertical or projective measurements involving nasion , but no significant associations were found for any of these traits . For Liu et al . [18] , we attempted to test each of their six previously reported genome-wide significant associations ( Table 2 ) . We observed a strong association ( p = 1 . 70 x 10−5 ) between nasal ala length and rs4648379 , an intronic variant in PRDM16 . We also observed an association between rs6555969 , a SNP near C5orf50 and upper facial depth ( p = 0 . 005 ) , which is a reasonable approximation of the zygion-nasion distance reported by Liu et al . [18] . To test the association between interorbital distance and rs17447439 , an intronic variant in TP63 , we used measures of intercanthal and outercanthal width; however , we did not observe an association with either measure . Finally , Liu et al . [18] reported associations between SNPs in PAX3 , C5orf50 and COL17A1 and the position of nasion relative to the orbits . We tested these three SNPs in our dataset against intercanthal width , a trait involving roughly similar anatomical components . Notably , we found associations between rs974448 ( PAX3 , p = 0 . 002 ) and rs6555969 ( C5orf50 , p = 0 . 049 ) and intercanthal width .
Based on meta-analysis , we observed seven associated loci for five facial traits: cranial base width ( Fig 1A ) , intercanthal width ( Fig 1H ) , nasal width ( Fig 1K ) , nasal ala length ( Fig 1N ) , and upper facial depth ( Fig 1B ) . The most significant of these , meeting the strict study-wide threshold for significance ( i . e . , p < 5 x 10−9 ) , was the association of cranial base width at 20q12 410kb downstream of MAFB , a transcription factor previously implicated in orofacial clefts [29] and facial characteristics in cleft families [30] . However , the MAFB SNP associated with clefting was 250kb away and not in LD with the SNP observed here . In addition to orofacial clefting , mutations in MAFB cause multicentric carpotarsal osteolysis syndrome , which includes mild facial anomalies . These phenotypes are consistent with the developmental role of MAFB in regulating the migration of cranial neural crest cells during the patterning of skeletomuscular features of the head [31] . Altogether , these lines of evidence suggest a possible role for MAFB in normal facial variation . Another association for cranial base width was observed at 14q21 . 1 in the vicinity of PAX9 , SLC25A2 , MIPOL1 , and FOXA1 . The homeodomain protein-coding PAX9 is important for craniofacial development in mice [32 , 33] and dental development in humans [34] . Using in situ hybridization , Peters et al . [32] showed that Pax9 is expressed throughout the developing cranial base in mice at E13 . 5 . Biological evidence for other genes in this region also suggests possible roles in facial variation including MIPOL1 , which has been observed to be affected by chromosomal aberrations in patients with craniofacial phenotypes , including holoprosencephaly [35] . Because holoprosencephaly involves alteration in the horizontal spacing of facial structures , variants in genes associated with this condition may also influence measures of craniofacial width in healthy subjects . Taken together , these previous observations point to the plausibility of genetic variants in this region influencing normal facial variation . Two genetic associations were observed for intercanthal width . One of these was at 1p13 . 3 within the gene GSTM2 , which codes an enzyme involved in detoxification of compounds . Among the genes within 250kb of the peak signal are two potentially relevant candidate genes , GNAI3 and ALX3 . Mutations in GNAI3 , which encodes a G protein subunit involved in pharyngeal arch patterning , cause auriculocondylar syndrome , a rare craniofacial disorder [36 , 37] , although hyper- or hypotelorism have not specifically been described . ALX3 is a homeobox gene essential for head and face development . Mutations in ALX3 result in frontonasal dysplasia 1 [38] in humans and nasal clefts in mice [39] . Ocular hypertelorism is a prominent feature of frontonasal dysplasia and is believed to result from disruptions of the Hedgehog signaling pathway [40 , 41] . The second association with intercanthal width was observed for a broad 900kb LD block on Xq13 . 2 . The peak of the diffuse association signal is over PABP1C1L2A , which encodes an uncharacterized poly-A binding protein . However , at the edge of the LD block , roughly 500kb centromeric to the peak signal , is HDAC8 , which encodes a histone deacetylase involved in epigenetic gene silencing during craniofacial development [42] . Mutations in HDAC8 cause Cornelia de Lange syndrome [43 , 44] , a developmental disorder characterized by facial dysmorphology including hypertelorism . A mutation in HDAC8 has also been described in a family with an X-linked intellectual disability syndrome with distinctive facial features , which included hypertelorism [45] . A number of other genetic associations with facial traits were observed at loci harboring genes with relevant biological roles . For example , an association with nasal width was observed at 20p11 . 22 near the PAX1 gene . Mutations in PAX1 cause otofaciocervical syndrome [46] , characterized by facial dysmorphology , including specific nasal features such as a sunken nasal root and excessive narrowing . PAX1 plays a role in chondrocyte differentiation [47] , which may explain its association with nasal width , a measure of the distance between the left and right cartilaginous nasal alae . Nevertheless , a study of Pax1 expression in mice showed expression in the pharyngeal arches at E11 . 5 , but not in the developing olfactory placodes [48] , so it is unclear how this gene influences nasal development . An association with nasal ala length was observed at 14q11 . 2 in a region containing an RNase gene cluster plus at least 25 other genes ( within about 400kb of the association peak ) . Among the many genes in region are ZNF219 , which encodes a transcriptional partner of Sox9 essential for chondrogenesis in mice [49] , and CHD8 , mutations in which are associated with autism spectrum disorder in conjunction with macrocephaly and distinct facial features including a broad nose [50] . A similar story pertains to the association between SNPs on 11q22 . 1 and upper facial depth . The peak signal occurs within TRPC6 , which encodes a cation channel subunit mutated in hereditary renal disease [51] . TRPC6 has no known connection to craniofacial development , but other genes in the region have reported craniofacial expression , including YAP1 [52] . In aggregate , we observed a number of genetic associations near genes with biologically plausible roles in facial variation . A common theme was that associated loci harbored genes involved in syndromes with craniofacial phenotypes . This result fits with a long-standing hypothesis about the relationship between Mendelian syndromes and complex traits in which common variants near genes causing Mendelian syndromes are involved in related common , complex diseases and traits , including normal phenotypic variation [53] . That being said , for any of the observed associations , it is not clear which variant might be functional , though we hypothesize that the functional variants underlying the statistical signal will be regulatory . Moreover , it is not clear which genes they regulate . Thus , references to implicated genes should always be treated with appropriate caution . While none of our genome-wide or suggestive ( p < 5 x 10−7 ) signals included SNPs implicated in either of the previous two European-focused GWA studies [17 , 18] , we nevertheless found evidence of association when we tested the top SNPs from these studies against comparable phenotypes from our data . Strongest among these was nasal ala length , a lateral projective measure of the nose extending from alar cartilage to the nasal tip , previously associated with 1p36 . 32 ( rs4648379 , PRDM16 ) [18] or 3p14 . 3 ( rs1982862 , CACNA2D3 [17] . We found at least nominal associations with both of these regions in our data , with one ( rs4648379 , PRDM16 ) showing evidence at p = 1 . 70 x 10−5 . Both prior GWA studies reported an association between SNPs at 2q35 ( PAX3 ) and morphology of the interorbital septum . We tested these SNPs and found an association between rs974448 and intercanthal width ( p = 0 . 002 ) , lending some additional support to the claim that common variants in PAX3 might influence aspects of normal facial morphology . Our ability to test previously reported genetic associations was limited by a lack of directly comparable phenotypes , which is related to differences in data collection methods and the type and number of measurements available . In addition , the prior two European GWA studies each used imaging modalities different from the kind used here . Similar factors may also explain some of the discrepancies in association results observed between our two study cohorts . Although care was taken to generate the same set of distance measures in both cohorts , the different 3D cameras and landmarking protocols used could result in different patterns of association . Despite these differences , the measurements from both cohorts were found to be very similar in their overall distributions . Alternative phenotypes , such as multivariate measures of facial shape , can also be used in these types of studies . However , prior attempts to extract shape variation from landmark and surface data in similarly sized samples ( e . g . , using Procrustes–based methods ) have not yielded statistically significant associations [17 , 18] . One reason for this may be that the effect of any single gene is diluted because the resulting phenotypes represent such a complicated mix of local and global shape features . The problem is highly complex and there is presently little consensus on the most prudent approach to complex facial phenotyping [54] . Fortunately , several promising approaches are on the horizon , such as the BRIM methods being developed by Claes et al . [28] . It is likely that samples an order of magnitude larger than anything available at the moment will be required before we can begin to exploit the richness contained in human 3D facial datasets . Despite these limitations , we have found evidence of genetic association between chromosomal regions containing genes with important roles in facial development and quantitative traits that characterize key features of the normal human craniofacial complex . In addition to improving our general knowledge of the factors that underlie the diversity of facial forms we see in humans , these genotype-phenotype associations may help us better understand the wide range of phenotypic expression and severity seen in some rare genetic syndromes . Common variants in a number of different genes or regulatory elements may contribute to the expression of dysmorphic phenotypes present in these conditions . Moreover , such associations in healthy individuals may aid the search for clues to the etiology of much more common craniofacial anomalies . For example , three of the traits reported here ( cranial base width , nasal width and intercanthal width ) have all been previously implicated as potential phenotypic risk factors for orofacial clefting , the most common craniofacial birth defect in humans [55] . Weinberg et al . [15 , 56] have shown that the unaffected , but genetically at-risk , relatives of cleft-affected individuals exhibit increased breadth through the middle and upper face . The identification of the genes that influence these traits may help us identify important risk-modifiers for clefting [16] . Testing the SNPs implicated here for associations in our cleft families is a future goal of our research group .
Institutional ethics ( IRB ) approval was obtained at each recruitment site and all subjects gave their written informed consent prior to participation ( University of Pittsburgh Institutional Review Board #PRO09060553 and #RB0405013; UT Health Committee for the Protection of Human Subjects #HSC-DB-09-0508; Seattle Children’s Institutional Review Board #12107; University of Iowa Human Subjects Office/Institutional Review Board #200912764 and #200710721; Colorado Multiple Institutional Review Board #09–0731; UCSF Human Research Protection Program Committee on Human Research #10–00565 ) . Our study included two independent samples , each comprised of unrelated self-described White individuals of European ancestry from the United States . The Pittsburgh sample included 2447 unrelated individuals ranging in age from three to 49 years . The majority of these participants ( n = 2272 ) were recruited at research centers in Pittsburgh , Seattle , Houston and Iowa City as part of the FaceBase Consortium’s 3D Facial Norms Dataset , described in detail by Weinberg et al . [57] . The remaining subjects were recruited as healthy controls for a separate study at Pittsburgh on orofacial cleft genetics . The Denver sample included 671 unrelated individuals ranging in age from three to 12 years . These participants were recruited from Denver and San Francisco as part of a separate FaceBase Consortium study of facial shape genetics in multiple ethnic populations [58] . The basic demographic features of these samples are provided in S4 Table . In both samples , subjects were excluded if they had a personal history of facial trauma , a personal history of facial reconstructive or plastic surgery , a personal history of orthognathic/jaw surgery or jaw advancement , a personal history of any facial prosthetics or implants , a personal history of any palsy , stroke or neurologic condition affecting the face , a personal or family history of any facial anomaly or birth defect , and/or a personal or family history of any syndrome or congenital condition known to affect the head or face . 3D facial surfaces were captured using digital stereophotogrammetry , a standard imaging method resulting in high-density , geometrically accurate point clouds representing the surface contours of the human body [59] . Facial surfaces in the Pittsburgh sample were collected with 3dMD imaging systems ( 3dMD , Atlanta , GA ) . Facial surfaces in the Denver sample were imaged using the Creaform Gemini camera system ( Quebec , Canada ) . A common set of 24 standard facial soft-tissue landmarks [60] was collected on each 3D facial surface and the xyz coordinate locations recorded ( S22 Fig ) . Landmarks were collected manually in the Pittsburgh sample as described in Weinberg et al . [57] . An automated landmark collection method was used in the Denver sample . From these landmarks , we calculated 20 linear distances that correspond to craniofacial measurements commonly used in clinical assessment [61] . These measurements are shown in Fig 1 and listed in S5 Table . For bilateral measurements , values were summed across the left and right sides in order to minimize the number of traits tested . Trait values were inspected for outliers by computing age- and sex-specific z-scores . For each study sample , genotyping was performed by the Center for Inherited Disease Research ( CIDR ) . Saliva samples were used to genotype 3 , 186 participants for 964 , 193 SNPs on the Illumina ( San Diego , CA ) OmniExpress+Exome v1 . 2 array plus 4 , 322 custom SNPs chosen in regions of interest based on previous studies of the genetics of facial variation . In addition , 70 duplicates and 72 HapMap control samples were genotyped for quality assurance purposes . Data cleaning was performed by the University of Washington Genetics Coordinating Center ( UWGCC ) using standard analysis pipelines implemented in the R Environment for Statistical Computing , as previously described [62] . These analyses include interrogating samples for genetic sex , chromosomal anomalies , relatedness among participants , missing call rate , and batch effects , and interrogating SNPs for missing call rate , discordance between duplicate samples , Mendelian errors ( as measured in HapMap control parent-offspring trios ) , Hardy-Weinberg equilibrium , and differences in allele frequency and heterozygosity between sexes ( for autosomal and pseudo-autosomal SNPs ) . Supplemental S6 Table shows the number of SNPs omitted and retained for each quality filter . Imputation was performed to capture information on unobserved SNPs as well as sporadically missing genotypes among genotyped SNPs , using haplotypes from the 1000 Genomes Project [63] Phase 3 reference panel of 2 , 504 samples from 26 worldwide populations . First , pre-phasing was performed in SHAPEIT2 [64] , and then imputation of 34 , 985 , 077 variants was performed in IMPUTE2 [65 , 66] . Masked variant analysis–that is , imputation of genotyped SNPs as though they were unobserved in order to assess imputation quality–showed high concordance between imputed and observed genotypes ( 0 . 998 for SNPs with MAF < 0 . 05 and 0 . 982 for SNPs with MAF ≥ 0 . 05 ) indicating high quality imputation . Imputed SNPs were included in analyses if the minimum genotype probability for a given variant was greater than 50% . Principal component analysis using 96 , 700 autosomal SNPs pruned from the total panel based on call rate ( > 95% ) , MAF ( > 0 . 05 ) , and LD ( pairwise r2 < 0 . 1 in a sliding window of 10 Mb ) , was used to assess population structure . Supplemental S23 Fig depicts the observed genetic structure of the population across the first two principal components of ancestry ( i . e . , eigenvectors from the PCA ) . Linear regression was used to test the association between each PC , as the dependent variable , and each SNP in the genome . These analyses confirmed that none of the first 20 principal components were due to local variation in specific genomic regions . Prior to genetic analysis , each of the 20 linear distance measures was adjusted for the effects of sex , age , age2 , height , weight , and facial size ( calculated as the geometric mean of the linear distance measures ) using linear regression in order to generate 20 adjusted phenotypes ( i . e . , residuals ) . The inclusion of age and age2 as covariates was done in an effort to adjust for both linear and non-linear aspects of age on the phenotypes . After model fitting different sets of covariates , including more complicated spline functions , we settled on a combination of age and age2 as the most reasonable approach based on akaike information criterion values calculated across age-adjustment models . Linear models were then used to test genetic association between each phenotype and each SNP , under the additive genetic model , while simultaneously adjusting for the first four principal components of ancestry . For SNPs on the X chromosome we coded hemizygous males as 0/2 so they are on the same scale as 0/1/2 females . Analyses were performed separately for the Pittsburgh and Denver cohorts , and combined via inverse variance-weighted meta-analysis . To appropriately model SNP effects , we required that the minor allele be present in at least 30 participants , corresponding to a MAF threshold of 0 . 6% in the Pittsburgh cohort and 2% in the Denver cohort . SNPs meeting the minor allele count criterion in both Pittsburgh and Denver cohorts were included in the meta-analysis . The final number of genotyped SNPs available for analysis after minor allele filtering was 659 , 955 for the Pittsburgh sample , 638 , 772 for the Denver sample , and 637 , 391 for the meta-analysis . The number of imputed and total ( genotyped plus imputed ) SNPs is available in S7 Table . Given the large number of tests , we used the conventional threshold of p < 5 x 10−8 ( i . e . , Bonferroni correction for 1 million tests ) for genome-wide statistical significance . Because we expect many of our traits to be correlated , we used the eigenvalue method described by Li and Ji [67] to determine that the effective number of independent traits was 10 . Thus , we set the threshold for study-wide statistical significance at p < 5 x 10−9 ( i . e . p < 5 x 10−8 divided by 10 ) . Because these thresholds are very conservative , we also reported “suggestive” evidence of association of p < 5 x 10−7 in S1–S3 Tables . Phenotypes were generated using the R Environment for Statistical Computing , and genetic association was performed using PLINK [68] . All of the phenotypic measures and genotypic markers used here are available to the research community through the dbGaP controlled access repository ( http://www . ncbi . nlm . nih . gov/gap ) at accession number: phs000949 . v1 . p1 . The raw source data for the phenotypes–the 3D facial surface models–are available for the 3D Facial Norms dataset through the FaceBase Consortium ( www . facebase . org ) . Finally , searchable results datasets of the p-values from the studies reported here are available through the FaceBase Human Genomics Analysis Interface ( facebase . sdmgenetics . pitt . edu ) . | There is a great deal of evidence that genes influence facial appearance . This is perhaps most apparent when we look at our own families , since we are more likely to share facial features in common with our close relatives than with unrelated individuals . Nevertheless , little is known about how variation in specific regions of the genome relates to the kinds of distinguishing facial characteristics that give us our unique identities , e . g . , the size and shape of our nose or how far apart our eyes are spaced . In this paper , we investigate this question by examining the association between genetic variants across the whole genome and a set of measurements designed to capture key aspects of facial form . We found evidence of genetic associations involving measures of eye , nose , and facial breadth . In several cases , implicated regions contained genes known to play roles in embryonic face formation or in syndromes in which the face is affected . Our ability to connect specific genetic variants to ubiquitous facial traits can inform our understanding of normal and abnormal craniofacial development , provide potential predictive models of evolutionary changes in human facial features , and improve our ability to create forensic facial reconstructions from DNA . | [
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] | 2016 | Genome-Wide Association Study Reveals Multiple Loci Influencing Normal Human Facial Morphology |
Peptidoglycan recognition proteins ( PGRPs ) and commensal microbes mediate pathogen infection outcomes in insect disease vectors . Although PGRP-LD is retained in multiple vectors , its role in host defense remains elusive . Here we report that Anopheles stephensi PGRP-LD protects the vector from malaria parasite infection by regulating gut homeostasis . Specifically , knock down of PGRP-LD ( dsLD ) increased susceptibility to Plasmodium berghei infection , decreased the abundance of gut microbiota and changed their spatial distribution . This outcome resulted from a change in the structural integrity of the peritrophic matrix ( PM ) , which is a chitinous and proteinaceous barrier that lines the midgut lumen . Reduction of microbiota in dsLD mosquitoes due to the upregulation of immune effectors led to dysregulation of PM genes and PM fragmentation . Elimination of gut microbiota in antibiotic treated mosquitoes ( Abx ) led to PM loss and increased vectorial competence . Recolonization of Abx mosquitoes with indigenous Enterobacter sp . restored PM integrity and decreased mosquito vectorial capacity . Silencing PGRP-LD in mosquitoes without PM didn’t influence their vector competence . Our results indicate that PGPR-LD protects the gut microbiota by preventing hyper-immunity , which in turn promotes PM structurally integrity . The intact PM plays a key role in limiting P . berghei infection .
Malaria is caused by parasites from the genus Plasmodium . The disease kills over 500 , 000 people annually , most of which are children under the age of 5 [1] . In order to transmit between humans , Plasmodium must overcome several obstacles to complete its development in Anopheles mosquitoes [2–4] . The peritrophic matrix ( PM ) , and immuno-competent midgut epithelial cells , are two barriers that interfere with parasite transmission through their mosquito vector . The PM is non-cellular and composed of chitin fibrils and chitin-binding proteins . The structure lines the midgut lumen and wraps the food bolus within the endoperitrophic space , thus protecting the epithelium from abrasive food particles and enteric pathogens [5] . The tight junctions between midgut epithelial cells form another contiguous barrier against parasite invasion [6] . Midgut epithelial cells invaded by Plasmodium undergo apoptosis and are replaced by new cells . This rapid turnover not only maintains the integrity of the epithelium , but also clears invading parasites [7] . In addition to overcoming physical barriers present in the mosquito midgut , epithelial cells in this environment also present robust cellular and humoral immunity [3] . This activity includes the synthesis of antimicrobial peptides ( AMPs ) , reactive oxygen species ( ROS ) and nitric oxide ( NO ) , all of which contribute to parasite clearance [4] . Anopheles mosquitoes have 3 types of hemocytes: granulocytes , oenocytoids and prohemocytes [3] . These cells eliminate pathogens via phagocytosis and encapsulation . Hemocytes are also important in Plasmodium-mediated immune memory , which enhances the mosquito’s ability to clear parasites upon reinfection [8] . In addition , complement like protein TEP1 by forming TEP1/LRIM1/APL1C complex , is another key systemic antiplasmodial immune mechanism that recognizes and eliminates Plasmodium ookinetes in the midgut [3] . Three major immune signaling pathways , Toll , IMD ( Immune Deficiency ) and JAK/STAT , are critical mediators of malaria infection dynamics in Anopheles mosquitoes [3] . Peptidoglycan recognition proteins ( PGRP ) are pattern recognition molecules that function as receptors and regulators of the Toll and IMD signaling pathways [9] . Anopheles has 7 PGRP genes , 4 in the Long subfamily ( including PGRP-LA , -LB , -LC and -LD ) and 3 in the short subfamily ( PGRP-S1 , -S2 and –S3 ) [10] . Anopheles PGRP-LC is a receptor of the Immune Deficiency ( Imd ) pathway that is responsible for triggering synthesis of downstream effector molecules [11] . Knock down of PGRP-LC results in increasing susceptibility to Plasmodium infection . PGRP-LA , another receptor of the Imd pathway , protects A . coluzzii from Plasmodium infection in a manner similar to that of PGRP-LC [12] . PGRP-LB , a negative regulator of the Imd pathway , has a dual role in Anopheles mosquitoes , facilitating parasite infection and protecting natural gut bacteria [12 , 13] . However , mechanisms of other PGRPs in response to parasite infection are still inadequate . The gut microbiota is another important factor that strongly influences vector competence [14] . Interactions between enteric bacteria and the mosquito immune system help to maintain gut homeostasis and protect mosquitoes from pathogens infection [13 , 15–17] . In the absence of gut microbes , Anopheles become highly susceptible to Plasmodium infection . Co-feeding parasites with bacteria restores resistance to parasite infection in mosquitoes previously treated with antibiotics to remove their indigenous microbiota . Gut microbes also induce expression of several immune molecules , including antimicrobial peptides and pattern recognition receptors [13] , and enhance vector refractoriness by promoting hemocyte differentiation [8] . Some residential bacteria , including Enterobacter and Chromobacterium isolated from field mosquitoes , directly inhibit parasite infection by secreting secondary metabolites such as reactive oxygen species [15 , 18] . In this study , we examined the function of PGRP-LD in A . stephensi and found that this receptor protects the mosquito against Plasmodium infection . PGRP-LD helped maintain homeostasis of the mosquito gut microbiota by negatively regulating innate immune responses . The healthy microbiota in turn contributed to the integrity of PM , and the intact PM enhanced Anopheles resistance to malaria parasites . Our results suggest that a finely tuned balance between the immune system , gut microbes and the PM is key to determining the capacity of mosquitoes to transmit malaria .
The putative Anopheles stephensi PGRP-LD is 42 kD transmembrane protein with 77% identity to Anopheles gambiae PGRP-LD . Sequence analysis indicates that it has a peptidoglycan-binding domain . However , the putative protein lacks most of the residues essential for PGN binding and catalytic activity , which are well characterized domains of Drosophila PGRPs ( S1 Fig ) . To investigate the role of PGRP-LD in parasites defense , we knocked down its expression in vivo via microinjection of gene-specific double stranded RNA and then analyzed the susceptibility of treated mosquitoes to infection with P . berghei . The level of pgrp-ld was reduced by approximately 67% 2-days post dsRNA treatment compared to dsGFP controls ( Fig 1A ) , and we observed no significant cross reactivity with other long PGRPs , including PGRP-LA , -LB , -LC ( Fig 1B ) . Knock down of pgrp-ld didn’t influence the survival rate of mosquitoes ( S2 Fig ) . However , reduced PGRP-LD ( dsLD ) resulted in a significant increase in the number of oocysts from 0 in dsGFP to 31 in dsLD mosquitoes ( Fig 1C ) . As PGRPs play important roles in activation and regulation of immune responses , we hypothesized that increased susceptibility of dsLD mosquitoes to parasites infection might resulted from the dysregulation of innate immune responses [9] . To address this question we next analyzed expression of 8 immune genes in dsLD and dsGFP treated mosquitoes 26hr post parasite challenge . The genes we investigated encoded 3 antimicrobial peptides ( Cecropin , Gambicin and Defensin ) , 1 negative regulator of IMD signaling pathway ( Caudal ) and 4 proteins related to cellular and epithelial immune responses ( TEP1 , PPO , NOS and DUOX ) [3 , 19] . Interestingly , most of the effector encoding genes , including cecropin , defensin , tep1 , ppo and duox , were significantly upregulated in response to parasite challenge ( Fig 1D ) . However , these induced effectors did not control parasite infection outcomes . This finding suggests a discrepancy exists between increased susceptibility to parasites and enhanced expression of immune genes in the absence of PGRP-LD . We next examined if pgrp-ld similarly regulated immune responses in mosquitoes prior to blood meal . The same 8 genes were expressed in mosquitoes fed only on sugar . As expected , 4 of these genes ( cecropin , gambicin and defensin , and duox ) were upregulated in dsLD treated mosquitoes , while tep1 , ppo , nos and caudal expression remained unchanged ( Fig 2A ) . As both antimicrobial peptides and ROS present bactericidal activities , we next examined if over-activated immune responses exerted an influence on microbiota homeostasis [20 , 21] . Bacterial load of both culturable and unculturable bacteria were measured in dsLD mosquitoes before consumption of a blood meal . In agreement with our hypothesis , knock down of pgrp-ld resulted in an ~500 times reduction of culturable microbes such that dsGFP individuals housed average 1 . 7X104 CFU/midgut , while dsLD individuals housed average 3 . 3X101 CFU/midgut ( Fig 2B ) . Similarly , the 16s rRNA gene copy number was significantly lower in dsLD compared to dsGFP mosquitoes ( Fig 2C ) . We next analyzed if community structure of the gut microbiota was influenced in the absence of PGRP-LD . Midguts of dsRNA treated mosquitoes were dissected and bacterial community structure was determined by 16S rRNA next generation sequencing . No significant difference in taxonomic structure was observed between microbial communities in dsGFP and dsLD mosquitoes ( S3 Fig ) . These results indicate that over-activated immune responses in the presence of reduced pgrp-ld expression leads to a reduction in the number of residential bacteria , without influencing the taxonomic composition of the gut microbial community . Thus , PGRP-LD helps to protect commensal bacteria by preventing the overactivation of host immune responses . In addition to investigating bacterial abundance and taxonomic composition , we also examined the spatial distribution of residential bacteria in dsLD mosquito midguts . Localization of residential bacteria in A . stephensi midguts was examined 48hr post blood meal , which is when cumulative population reaches its maximum density as determined by fluorescent in situ hybridization ( FISH ) using a universal 16s ribosomal RNA ( rRNA ) gene probe [22] . We observed a clear physical separation of gut microbiota and epithelium in dsGFP controls ( Fig 2D1–2D3 ) . However , dsLD treated mosquitoes exhibited a defect in spatial segregation , with increasing bacteria coming into direct contact with the gut epithelium , and even penetrating epithelial cells ( Fig 2D4–2D6 ) . Taken together , these results suggest that PGRP-LD helps to maintain the spatial homeostasis of gut microbes . The PM , which is composed of chitin fibrils and glycoproteins , is a sheath like structure that lines the digestive tract of most insect midguts and prevents luminal contents from coming into direct contact with midgut epithelial cells [23 , 24] . Mosquitoes have type I PMs , the formation of which is triggered by ingestion of a blood meal [5] . We hypothesized that the microbial diffusion we observed in dsLD midguts may occur because these mosquitoes present a structurally compromised PM . We thus analyzed PM structure in dsLD and dsGFP mosquitoes by hematoxylin and eosin ( H&E ) and Periodic Acid Schiff ( PAS ) staining . A fully formed PM was visualized in dsGFP controls 48 hr post blood meal ( Figs 3A1 and 3A2 and S4A1 ) . Conversely , the PM of dsLD mosquitoes appeared fragmented ( Figs 3A3 and 3A4 and S4A2 ) . To further confirm the impaired PM structure in dsLD mosquitoes , dsRNA treated individuals were fed a blood meal supplemented with FITC-labelled dextran molecules ( 500 kDa ) . We observed dextran beads were restrained within the endoperitrophic space in dsGFP mosquitoes 48 hr post feeding ( Fig 3B1 ) . In contrast , we observed beads penetrating gut epithelial cells in dsLD mosquitoes , indicating that PM structure was compromised when pgrp-ld expression was experimentally reduced ( Fig 3B2 ) . We next examined if impaired PM structure was due to the dys-regulation of PM genes . We monitored expression of 2 peritrophin genes ( peritrophin1 and 14 ) , and 2 chitinases ( chitinaseA and chitinaseB ) , all of which are involved in the PM formation and degeneration , in dsRNA treated mosquitoes 24hr and 48hr post blood feeding [25 , 26] . When pgrp-ld expression was knocked down , the 2 chitinases were upregulated 24 hr post blood meal , followed by a significant downregulation 48hr post blood meal comparing to dsGFP controls . Expression of peritophin 1 was lower at both time points , with a significant reduction 48hr post blood meal ( Fig 3C ) . These data reinforce our hypothesis that the compromised PM in dsLD mosquitoes is due to the dysregulation of PM associated genes . Taken together , these results suggest that PGRP-LD plays a role in maintaining PM structural integrity in the gut of A . stephensi . Gut microbes promote PM structural integrity [27–29] . Because gut microbe abundance was significantly reduced in dsLD mosquitoes , impaired PM structure in these mosquitoes may be due to gut dysbiosis . We next analyzed if resident microbes impact PM structure in A . stephensi . We again examined the structure of the PM in both normal and antibiotic treated mosquitoes ( Abx ) 48-hour post blood meal by H&E staining . Antibiotic treatment cleared the majority of native gut bacteria ( S5 Fig ) . Furthermore , an intact PM was observed in guts of normal mosquitoes , which contained the blood bolus within the endoperitrophic space ( Fig 4A and 4B ) . In contrast , when the gut microbiota was removed , no PM was observed and blood was dispersed within the entire gut lumen ( Fig 4C and 4D ) . We again analyzed expression of the same 4 PM genes and found similar expression profiles as in dsRNA treated mosquitoes , with a decrease in the expression of peritrophin1 and 14 and an initial increase of PM digesting chitinases 24 hr post blood meal in antibiotic treated mosquitoes ( Fig 4E ) . Thus , gut microbes may play a role in regulating expression of PM genes , thereby maintaining PM structural integrity . To further analyze the functional association between gut microbes and PM structure , we colonized guts of antibiotic treated mosquitoes with Enterobacter sp . ( three different doses , 1X105/ml , 106/ml and 107/ml 1 . 5% sugar solution ) prior to administering a blood meal . As Enterobacter cloacae is able to inhibit Plasmodium infection in A . stephensi [30] , we then examined if Enterobacter sp . isolated from our mosquito colony were able to inhibit parasite colonization . Two days post-inoculation , each concentration reached an average density of 7 . 2X104/ midgut , 1 . 3X104/midgut and 2 . 2X104/midgut , respectively , which is comparable to that found indigenously in normal mosquitoes ( 1 . 5X104 CFU/midgut ) ( Fig 5A ) . We next examined the infection rate in these mosquitoes and found that increasing susceptibility to P . berghei infection was rescued to normal levels when Abx treated mosquitoes were re-colonized with all three Enterobacter concentrations ( Fig 5B ) . Because no difference in infection rate was observed in the three inoculation concentrations , the PM of mosquitoes recolonized with 1X105/ml Enterobacter sp . was stained with H&E and PAS 2-day post blood meal . Clear PM structures were observed in both normal mosquitoes ( Figs 5C1 and 5C4 and S4B1 ) and mosquitoes supplemented with Enterobacter sp . ( Figs 5C3 and 5C6 and S4B3 ) . Conversely , no PM was observed in antibiotic treated individuals ( Figs 5C2 and 5C5 and S4B2 ) . These results suggest that the presence of gut microbes is essential to maintain the structural integrity of the PM during blood feeding . The PM functions as a physical barrier in mosquito that limits Plasmodium infection [31 , 32] . To further analyze whether the increasing susceptibility in dsLD mosquitoes was due to a compromised PM , we next silenced PGRP-LD in antibiotic treated mosquitoes that lacked a PM and then monitored their susceptibility to parasite infection . In agreement with our previous results , silencing PGRP-LD led to an 8 fold increase in oocyst numbers in dsLD mosquitoes comparing to dsGFP controls ( Fig 6A ) . However , no detectable difference of oocysts number was observed in antibiotic treated mosquitoes injected with dsRNAs ( Fig 6B ) . This result further confirms that enhanced susceptibility to Plasmodium infection in dsLD mosquitoes results from the comprised PM . Together , these results indicate that PGRP-LD helps to maintain homeostasis of the gut microbiota by negatively regulating immune responses . The healthy gut microbes promotes the structural integrity of PM . The intact PM functions as a physical barrier that reduces the capacity of parasites to establish infection in mosquitoes .
In both invertebrates and vertebrates PGRPs play important roles in regulating interactions with pathogens and commensal bacteria [9] . In this study , we show that PGRP-LD protects A . stephensi from parasite infection by regulating homeostasis of the mosquito’s gut microbiota ( Fig 7 ) . Reduced pgrp-ld activates the host immune system , which depletes the abundance of gut microbes in this niche . This impairs PM structure and increases susceptibility to parasite infection . PGRP family members were first identified because they share a conserved PGRP domain that is able to detect peptidoglycan ( PGN ) present on the cell wall of both Gram+ and Gram- bacteria [9 , 33] . Recent studies using disease vectors show that PGRPs also play important roles in parasite defense [9 , 12 , 34–36] . The function of PGRP-LC is well characterized in Anopheles mosquitoes and the tsetse fly , where the protein is responsible for initiating synthesis of downstream effectors in response to both native microbes and invading pathogens [9 , 11 , 37] . PGRP-LA participates in defense against parasite infection by functioning similarly to PGRP-LC [12] . PGRP-LB acts as a negative regulator of Imd signaling pathway through its amidase activity [12 , 38–40] . In tsetse , PGRP-LB has evolved to exhibit bactericidal and anti-parasitic activity [41] . Unlike the above-mentioned PGRPs , little is known about the mechanistic role of PGRP-LD in pathogen defense in either Drosophila or other insects , except that it protects Armigeres mosquitoes from E . coli infection by modulating expression of downstream antimicrobial peptides [42] . We show here that A . stephesi PGRP-LD promotes host defense against P . berghei . Experimental knock down of pgrp-ld expression induces the expression of downstream effectors both in the presence or absence of parasite challenge . Based on its structure , A . stephensi PGRP-LD lacks conserved residues essential for either PGN binding or amidase activity , which has been identified in Drosophila PGRPs [43–45] . This is in contrast with most of PGRPs , which function as negative regulators that prevent over activation of immune signaling pathways by catabolizing immunostimulatory peptidoglycan [46 , 47] . One explanation is that PGRP-LD may use less well conserved residues to bind peptidoglycan . Alternatively , PGRP-LD may interfere the signal transduction of immune pathways , as does Drosophila PGRP-LF that dampens Imd signaling strength by interfering with PGRP-LC-peptidoglycan binding activity [48] . Further investigations are required to determine how PGRP-LD regulates immune system function . The gut microbiota enhances host intestinal barrier function and pathogen tolerance in both vertebrates and invertebrates [47 , 49] . In A . stephensi , pgrp-ld knockdown elevates immune activity that eliminates the majority of gut microbes but fails to eliminate P . berghei . In these mosquitoes , the spatial structure of remaining bacteria was altered . Gut microbes that are usually restrained within the endoperitrophic space localize in close contact with midgut epithelium . Our results indicate that PM structure is compromised in dsLD treated mosquitoes . We then observe PM structure is impaired and expression of PM genes varies significantly . These results suggest that the defect of PM structure results from the dysregulation of PM genes . In addition , we also find that the PM of A . stephensi is absent 48 hr post blood meal in antibiotic treated mosquitoes in which most enteric microbes are cleared . This defect is also associated with decreasing peritrophin and increasing chitinase expression . Thus PM structural integrity is associated with the homeostasis of gut bacteria in A . stephensi , similarly as in many disease vectors [27–29] . When mosquitoes treated with antibiotics are re-colonized by Enterobacter sp , both PM structural integrity and vector competence are restored . In agreement with the finding in An . coluzzii that PM is induced by gut microbiota [29] , our results further confirm that gut microbiota of Anopheles mosquitoes is essential for PM integrity . However , we are currently unable to say at what abundance and how gut microbiota are able to maintain intact PM structure . The PM serves as a physical barrier to parasite infection establishment in multiple disease transmitting vectors , including tsetse flies , sand flies and ticks [27 , 32 , 50–56] . Our study shows that in antibiotic treated mosquitoes that present a compromised PM , knockdown of pgrp-ld expression does not change infection prevalence compared to controls . This result reinforces that increasing susceptibility of dsLD mosquitoes to P . berghei infection is due to the comprised PM as opposed to reduced levels as of PGRP-LD directly . In agreement with most vectors , our results show that PM is a major physical barrier that prevents P . berghei infection establishment in A . stephensi . In summary , our data demonstrate that a complex interplay exists between the host immune system , gut microbes and the PM , and this interplay determines parasite infection outcomes in A . stephensi . PGRP-LD , functioning as a key mediator , helps to maintain this balance . Detailed studies on the regulation of PGRP-LD on immune signaling pathways , and the influence of gut microbiota on PM formation , are currently under way and may provide new insights into interactions between immune system , gut microbiota and parasites .
All animals were handled according to the guidelines for the Care and Use of Laboratory Animals of the National Institutes of Health and the Office of Laboratory Animal Welfare . The research protocol was approved by the Institutional animal care and use committee , Department of Laboratory Animal Science , Fudan University ( IACUC 20161784A359 ) . The Anopheles stephensi mosquito ( strain Hor ) was reared at 28°C , 80% relative humidity and at a 12h light/dark cycle . Adults were maintained on 10% sucrose and BALB/c mice . Newly eclosed mosquitoes were administrated with fresh filtered 10% sucrose supplemented with 10 U/ml penicillin , 10 μg/ml streptomycin and 15 μg/ml gentamicin daily , for up to 5 days [13] . PCR amplicons tailed with T7 promoter sequences were used to synthesize dsRNAs using MEGAscript RNA kit ( Ambion , Invitrogen ) . The cDNA clones Astepgrp-ld ( ASTE010245 ) , and plasmid eGFP ( BD Biosciences ) served as templates for amplification using gene specific primers ( S1 Table ) . Five to 6-day-old females received a total 69 nl dsRNAs ( 4μg/μl ) injected intra-thoracically using nanoject II microinjector ( Drummond ) . Injected mosquitoes were allowed to recover for 5 days prior to infection [57] . Survival rate was recorded daily for 5 days post dsRNA treatments and compared to that of dsGFP controls . Silencing efficiency was verified by qPCR 2-day post dsRNA treatment with primers listed in S1 Table . RNA was extracted from flash frozen mosquitoes utilizing the standard TRI reagent ( Sigma-Aldrich , China ) protocol . cDNA was prepared from total RNA using the 5XAll-in-One MasterMix ( with AccuRT Genomic DNA Removal Kit ) ( ABM , China ) . Levels of target genes were determined by Roche LightCycler 96 Real Time PCR Detection System with SYBR Green qPCR Master Mix ( Biomake , China ) using the following conditions: 95°C for 5 min , 40 cycles of 15 sec at 95°C , 30 sec at 60°C , and 15 sec at 72°C . Fluorescence readings were taken at 72°C after each cycle . Melting curves ( 60°C–95°C ) were performed to confirm the identity of the PCR product . The data were processed and analyzed with LightCycler 96 software . Expression of cecropin , gambicin , defensin , tep1 , prophenoloxidase , nos , duox and caudal were analyzed 5 days post dsRNA administration with primers listed in S1 Table . Ribosomal gene S7 widely used in studies of Anopheles gene expression was used as the internal reference [58–61] . PCR efficiency of each primer set was determined by standard curve . Relative quantitation results were normalized with S7 and analyzed by the 2–ΔΔCt method [62] . Gene expression of dsLD treated group was normalized to dsGFP controls . The normality of data sets was determined by Shapiro-Wilk test before t test analysis . Values are represented as the mean ( ±SEM ) , and statistical significance was determined using a Student’s t test and Excel software . A . stephensi were starved overnight and then fed on P . berghei ( ANKA ) infected BALB/c with parasitemia of 6–7% using standard protocols [63] . Mosquitoes were starved for 24 hr before blood feeding . After imbibing a blood meal , mosquitoes were maintained at 21°C . Un-engorged mosquitoes were removed 24hr post blood meal . Midguts were dissected and infection intensity were determined microscopically 8-day post infection . The oocyst data were not normally distributed as determined by Shapiro-Wilk test . Thus , significance was determined using the Mann-Whitney test . Mosquitoes were collected 5 day after dsRNA treatment or antibiotic treatment and surface sterilized with 70% ethanol twice and 0 . 9% NaCl twice . Midguts were dissected and homogenized in 0 . 9% NaCl . Homogenates were serially diluted and plated on LB agar plates . CFUs were counted 2 days after incubation at 28°C . Total DNA was extracted by the method of Holmes and Bonner as described [64] . Bacterial density was quantified by qPCR using universal 16S rRNA primers [28] ( S1 Table ) . Ribosomal gene S7 was used as the internal reference . Significance was determined using the Student’s t-test . The composition of the gut microbiota in dsRNA treated mosquitoes was analyzed by pyrosequencing that targeted the V3-V4 region of bacterial 16S rRNA [65] . 10 midguts of dsRNA treated mosquitoes were pooled for 1 biological replicate . DNA of 3 biological replicates of each treatment were prepared for further sequencing analysis ( S1 Text ) . For fluorescent in situ hybridization ( FISH ) , abdomens of dsLD treated females 2 day post blood meal were fixed and sectioned as described [66] . Slides were hybridized with 10ng/μl universal 16S ribosomal RNA probe ( 5’-GCTGCCTCCCGTAGGAGT-3’ ) labeled with Alexa Fluor 555 ( Life technology ) . Tissues were visualized using Nikon ECLIPSE IVi microscope connected to a Nikon DIGITAL SIGHT DS-U3 digital camera . Forty-eight hour post blood meal mosquito abdomens were fixed and sectioned as described above [66] . Samples were sectioned at 5 μm , stained with hematoxylin and eosin ( H&E ) ( Huntz Enterprises Inc . , China ) and Periodic Acid Schiff ( PAS ) ( Sigma-Aldrich , China ) according to the manufacturer’s protocol . Slides were hard mounted using Canada balsam ( ChemsWorth ) . Slides were viewed using bright field illumination on a Nikon ECLIPSE IVi microscope connected to a Nikon DIGITAL SIGHT DS-U3 digital camera . Four days post dsRNA treatment A . stephensi were fed with blood meal supplemented with 500 kDa FITC-labeled dextran molecules ( 2 . 5mg/ml blood ) ( Sigma ) which were filtered using PD MiniTrap Sephadex G10 columns ( GE Healthcare ) as described [27] . Forty eight hours post-feeding , midguts were dissected and FITC signal observed using a Zeiss , LSM710 confocal microscope connected to a Nikon DIGITAL SIGHT DS-U3 digital camera . Expression of 4 PM genes was analyzed 24 hr and 48 hr post blood meal using primers targeting peritrophin1 ( ASTE010406 ) , peritrophin14 ( ASTE009456 ) , 2 chitinases , herein named chitinaseA ( ASTE005630 ) and chitinaseB ( ASTE000328 ) ( S1 Table ) . The administration of mosquito commensal bacteria was performed as described [67] . Briefly , an overnight culture of Enterobacter sp . was washed 2 times in phosphate-buffered saline ( PBS ) and introduced to mosquitoes via a sugar meal . A final concentration of 1X105~1X107 /ml bacteria was added to 1 . 5% sterile sugar . All mosquitoes were starved for 24hr hours before being offered a blood meal . Mosquitoes were given a blood meal 2-day post bacteria treatment . Age matched wild-type and antibiotic treated mosquitoes were used as controls . | Malaria parasites must overcome several obstacles to complete their development in mosquito . Understanding the interactions between parasites and mosquitoes will provide potential targets to control malaria transmission . PGRP-LD is a peptidoglycan recognition protein , of which limit information is available in insects . Here we show that A . stephensi PGRP-LD mediates malaria parasite infection outcomes by influencing homeostasis of the gut microbiota . Reduction of the gut microbiota density , resulting from upregulation of immune activities in PGRP-LD knock down mosquitoes , changes expression of PM genes and causes PM fragmentation . The compromised PM leads to increasing susceptibility to parasite infection . We also discovered that the PM is lost in mosquitoes in which the gut microbiota is removed by antibiotic treatment . Knock down of PGRP-LD in these mosquitoes doesn’t increase their vector competence . Altogether , these results indicate that capacity of Anopheles mosquito to transmit parasites is determined by a finely tuned balance between host immunity , gut microbiota and peritrophic matrix . PGRP-LD is a key mediator in regulating this balance . Our results expand knowledge on interactions between immune system , gut microbiota and Plasmodium , and will shed light on equivalent processes in other disease transmitting vectors . | [
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] | 2018 | PGRP-LD mediates A. stephensi vector competency by regulating homeostasis of microbiota-induced peritrophic matrix synthesis |
Morphogenesis and pattern formation are vital processes in any organism , whether unicellular or multicellular . But in contrast to the developmental biology of plants and animals , the principles of morphogenesis and pattern formation in single cells remain largely unknown . Although all cells develop patterns , they are most obvious in ciliates; hence , we have turned to a classical unicellular model system , the giant ciliate Stentor coeruleus . Here we show that the RNA interference ( RNAi ) machinery is conserved in Stentor . Using RNAi , we identify the kinase coactivator Mob1—with conserved functions in cell division and morphogenesis from plants to humans—as an asymmetrically localized patterning protein required for global patterning during development and regeneration in Stentor . Our studies reopen the door for Stentor as a model regeneration system .
The ability to develop and regenerate complex morphologies from a simpler starting point is among the properties that set living organisms apart from inanimate matter . Although these processes are most often considered in the context of embryos and multicellular organisms , even individual cells need to develop and regenerate after injury . Metazoan development is conceptually straightforward , in that organisms rely on the existence of numerous individual cells that differentiate into various cell types with specialized functions , thereby creating the complex architecture of the larger organism . However , it is less clear how similar levels of complexity can exist in an individual cell that cannot rely on the differentiation of its subunits . The morphogenesis of individual cells represents a key process in cell and developmental biology , but its mechanisms are almost completely unknown [1] , [2] . To understand the fundamental features of complex morphogenesis , we need a model where it can be induced in the context of a single cell . In some cases , the process of regeneration mimics that of morphogenesis , so a single-cell model for regeneration could be a very powerful tool . For this reason , we turned to the large ciliate Stentor coeruleus ( ∼1 mm long ) . Stentor was first described in 1744 by Abraham Trembley and has a long history as a classical system for studying regeneration in single cells ( Figure 1A ) [3] . The large size of Stentor cells made them amenable for surgical manipulations such as cutting and grafting , allowing experimental approaches comparable to those of experimental embryology to be applied to the study of single cells . Stentor coeruleus , like other ciliate organisms , is covered in cilia that are used for locomotion . Stentor is a filter feeder , which uses its oral apparatus ( OA ) , a dense band of cilia around the anterior of the cell , to sweep other living cells into its mouth . It is known to feed on bacteria , algae , and even other ciliates [4] . At the posterior , Stentor possesses an anchoring structure known as the holdfast , or foot , which is used to transiently attach to surfaces . The OA and holdfast , along with ciliated stripes that run the length of the organism , define the cell cortex and set up the anterior–posterior , dorsal–ventral , and left–right axes , which are maintained throughout division . Stentor thus displays complex patterning and axiation comparable to what is seen in embryos . Perhaps the most striking property of Stentor is that it has the ability to regenerate an entire normal organism from only a fraction of the original cell . Its large size , complex architecture , ease of surgical manipulations , and ability to regenerate give Stentor significant advantages over other ciliate models and even made it the focus of some early embryologists . Thomas Hunt Morgan showed that surgically produced cell fragments could regenerate into properly proportioned cells ( Figure 1B ) , arguing that regeneration in Stentor was a strictly controlled morphological process [5] . The study of Stentor reached its apotheosis in the work of Vance Tartar , who made extensive use of microsurgery to understand the basic principles of morphogenesis in Stentor [4] . Tartar showcased the robust nature of Stentor's regenerative ability in minceration experiments that disrupted the polarity of the cortex but did not prevent the cells from reestablishing normal polarity [6] . He also grafted parts of cells to one another to show that a single region of the cell known as the locus of stripe contrast could control the formation of a new body axis [7] . The ability to induce the regeneration of specific cellular structures is a major advantage of Stentor as a model for morphogenesis [8]–[10] . But Stentor was never developed as a molecular model system , and thus , despite ongoing fascination with the question of how a cell can develop such complexity , the molecular basis of pattern formation and regeneration in Stentor remains unknown . Here we demonstrate that RNA interference ( RNAi ) technology is highly effective in Stentor , thus enabling us to study molecular mechanisms of Stentor development . For our initial attempt to use RNAi to identify a molecular determinant of morphogenesis in Stentor , we noted that the sequence of morphological events that take place when the cell regenerates a new OA is virtually identical to those observed when a cell forms a second OA during normal division [11] . We therefore used a candidate-based approach to delineate potential regulators of Stentor morphogenesis by focusing on conserved components of both cell division and polarization/morphogenesis . One potential candidate is the conserved eukaryotic kinase-regulator Mob1 . Mob1p was first identified in budding yeast [12] and is part of a highly conserved family of which yeast has two members , Mob1p and Mob2p . Mob1p is involved in the mitotic exit network and is required for proper cytokinesis , whereas Mob2p is involved in the regulation of Ace2p and polarized morphogenesis network and is required for proper cell morphology [13] . Mob1 is a highly conserved kinase co-activator that binds to NDR/LATS kinases and stimulates their activity . Mob1 has been implicated in the Hippo signaling pathway in Drosophila [14] and plays a role in a variety of processes including apoptosis , mitosis , morphogenesis , and proliferation [15] . Recent work on the only member of the MOB family in Tetrahymena thermophila suggests that Mob1 function is conserved in ciliates and that Mob1 is required for proper cytokinesis , but it is unclear whether Mob1 functions in ciliate morphogenesis [16] . Here we show that Mob1 is conserved in Stentor and is asymmetrically localized in the cell . Using RNAi , we discovered that Mob1 is a global patterning protein that is required for proper development and regeneration .
The Stentor genome is currently being assembled and annotated . In order to determine if the RNAi machinery is conserved in Stentor coeruleus prior to completion of the Stentor genome , we obtained a genomic sequence using short Illumina reads that were assembled using the targeted assembly algorithm PRICE [17] . Using reads with homology to Tetrahymena proteins as seed sequences , we specifically assembled sequences with homology to known RNAi machinery such as Argonaute , Dicer , and RNA-dependent RNA polymerases . We were able to assemble a number of homologs for each of the RNAi machinery components ( Table S1 ) . Using a recently reported functional analysis of the Argonaute homologs in Paramecium [18] as a reference point , we performed a neighbor-joining phylogenetic analysis of Stentor Argonaute homologs . Like those of Paramecium and other ciliates , all Stentor Argonaute proteins cluster in the PIWI subfamily ( Figure 1C ) ; hence , we use the term Sciwi for Stentor coeruleus PIWI . All of the Sciwi proteins contain the conserved “DDH” motif ( Figure S1 ) , which has been shown to be necessary for the slicer activity of PIWI proteins [19] . Based on the high sequence conservation of the RNAi machinery , we asked whether gene expression could be perturbed by RNAi in Stentor . RNAi has been performed in two other ciliates , Paramecium tetraurelia and Blepharisma japonicum , using the method of feeding with bacteria expressing double-stranded RNA [20] , [21] . However , in other ciliates , such as Tetrahymena thermophila , RNAi by feeding does not work . To test whether RNAi by feeding is effective in Stentor , we performed a knockdown of α- and β-tubulin—key components of the cortical structures in Stentor—by feeding bacteria containing an expression plasmid encoding dsRNA directed against α- or β-tubulin . There were eight α-tubulin and six β-tubulin homologs identified from the PRICE assembly and at least one shared ≥20 mer among all of the sequences . We hypothesized that since tubulin is a key component of the cell structure , its knockdown would display a clear phenotype as a proof of principle for RNAi . We found that RNAi resulted in a significant knockdown at the level of the transcript ( Figure 2A ) . Targeting either α- or β-tubulin with RNAi vectors caused cells to take on a rounded shape not seen in untreated cells after 5 d of feeding ( Figures 2B , C and S2 ) . Identical results were obtained targeting either tubulin gene or either half of the tubulin genes individually , arguing the result was not an off-target effect ( Figure S2C ) . Using antibodies against α-tubulin to highlight the cortical rows , we observed that tubulin knockdown resulted in the disorganization of cortical rows ( Figure 2D , E ) . We also noted that the macronucleus in tubulin knockdown cells often collapsed into two large nodes , one located near the anterior and one at the posterior pole of the cell ( Figure S2B ) . This failure to maintain an elongated macronucleus is consistent with a previous observation that microtubules are involved with elongation [22] . Cells depleted of tubulin appear to sustain cortical damage such as breaks and discontinuities of the cortical rows , which they are unable to repair properly ( Figure 2E , arrows ) . Tartar found that cortical discontinuities induced by surgery often resulted in transient protrusions , resembling posterior poles , extending from the cell [6] . Consistent with that observation , we found that some tubulin knockdown cells formed ectopic posterior poles , suggesting a role for an organized cortex in the maintenance of cell polarity ( Figure S2D ) . To demonstrate that the morphological defects seen in Figure 2 are specific for tubulin RNAi , we performed RNAi using a gene whose function is predicted to be unrelated to cortical row organization , namely the ciliary length regulating kinase LF4 [23] , [24] . When LF4 was knocked down via RNAi in Stentor , the cilia increased in length , but tubulin staining of cortical rows as well as cell shape and patterning were unaffected ( Figures 2D and S3A–D ) . This result rules out the possibility that activation of the RNAi machinery causes nonspecific changes in cell morphology . Additionally , RNAi using sequences targeted to planarian ODF2 and unc22 , genes not present in ciliates , resulted in normally shaped cells ( Figure S3E , F ) . These data show that RNAi constitutes a powerful tool for studying the molecular mechanisms of regeneration and morphogenesis in Stentor . Having established the efficacy of RNAi , we set out to use this method to test the function of a candidate morphological determinant , Mob1 , based on the reasoning outlined in the introduction . From the targeted PRICE assembly , we discovered a total of six genes with high homology to Mob1 ( Figure 3A ) . A seventh sequence was identified with homology to Phocein , a protein that shares the MOB/Phocein domain that defines the family ( Figure 3A ) . All six putative Mob1 homologs were 99% identical to each other at the protein level ( Figure 3B ) and shared 52% identity with Mob1 versus only 38% identity with Mob2 protein sequences from S . pombe and we refer to them as Mob1 . To determine the localization of Mob1 in Stentor , we generated a polyclonal antibody against a Stentor Mob1 peptide sequence shared between all six identified proteins . On Western blots of Mob1 immunoprecipitated from Stentor lysates , the affinity-purified Stentor Mob1 antibody recognized a single band of the appropriate size at 26 kDa ( Figure 3C ) . When used for immunofluorescence , the antibody clearly labeled the posterior and appeared to label the region around the OA , although this staining was less clear ( Figure 3D ) . This localization pattern was blocked by pre-incubating the primary antibody with the immunizing peptide , which suggested that it is specific to the Mob1 family and not the result of a nonspecific antibody binding ( Figure S4 ) . This dual localization pattern was similar to the pattern seen in Tetrahymena [16] . Interestingly , unlike in Tetrahymena , the antibody did not appear to exclusively label basal bodies in Stentor , but rather diffusely labeled the cortical rows ( Figure 3E ) . To get a better idea of Mob1 localization throughout the cell cycle , we followed dividing cells and fixed them at different stages of division . Division proceeds through a series of eight morphologically defined stages [4] . During stage 1 the oral primordium begins to form as a clearing of rows along the locus of stripe contrast at the midline of the cell , which expands during stage 2 . In stages 3 and 4 , this clearing is filled by the synthesis of new basal bodies , which are then ciliated as the oral primordium increases in length . In stage 5 , the cell elongates as the oral primordium further develops and the macronucleus begins to condense . By stage 6 , the macronucleus collapses into a single large node , and cortical partitions between the anterior and posterior daughter cells become visible . Finally , during stages 7 and 8 , the macronucleus extends back to its normal shape and is divided between the two daughters as the oral primordium is positioned at the presumptive anterior of the posterior daughter and the posterior of the anterior daughter is constricted to form a new holdfast and the cells are finally separated . Because there are no described methods to synchronize Stentor cells , we observed vegetatively growing cultures and isolated cells that presented visible evidence of cell division . Although the earliest stages of division are difficult to identify within a culture , we were able to isolate cells from stage 2 all the way through stage 8 ( Figure 4 ) . From these data , we were able to determine that Mob1 expands its posterior localization by stage 3 or 4 . By stage 5 that expansion begins to focus into a discrete band around the midline , and by stage 6 this band spread around the cell , anterior of the oral primordium and is positioned near the presumptive posterior pole of the anterior daughter cell . During stages 7 and 8 , this band clearly defined the constriction of the newly forming posterior and there was a clear break between the two halves of the dividing cell . Thus , Mob1 appears to localize at the posterior end of the anterior daughter cell prior to completion of cell division . To determine the function of Mob1 in Stentor cells , we created an RNAi vector targeting Mob1 sequence . Because of the high sequence similarity among the Stentor Mob1 homologs , 85%–95% identity at the nucleotide level ( Figure S5 ) , we expect that any long dsRNA Mob1 construct would target all six Mob1 genes , although we specifically used Mob1a for this study . When aligned pairwise with all other Stentor Mob1 homologs , Mob1a shared at least one ≥20 mer between the sequences for all possible pairs and so it is possible that this single construct would be sufficient for the knockdown of all Mob1 genes . Additionally , RNAi constructs were made specifically targeting Mob1b , c , and d as well and all gave identical results ( unpublished data ) . Because the MOB family of proteins has conserved functions in both cell division and morphogenesis , we expected phenotypes that would affect cytokinesis and cell polarity [15] . RNAi knockdown of Mob1 in Stentor was extremely effective and resulted in a 30-fold reduction of Mob1a transcript levels compared to the GAPDH control after 4 d of feeding ( Figure 5A ) . This treatment caused dramatic defects in Stentor morphology , which progressively worsened as feeding of the RNAi vector continued . After 24–48 h of RNAi , we observed cells with altered cell proportionality; cells had lost their characteristic “wine-glass” shape and became more cylindrical ( Figure 5B , C and S6 ) . Mob1 thus appears to play a key role in the regulation of proportional cell shape , the phenomenon first characterized by Morgan in his landmark 1901 paper [5] . Between 48 and 96 h of Mob1 knockdown , cells displayed further morphological abnormalities that could be separated into two categories . The first consisted of cells that were highly elongated and curved , apparently a result of a deformed cortex , which caused the cells to twist ( Figure 5D ) . The other class of defects consisted of multipolar ( medusoid ) cells with multiple OA regeneration bands and ectopic tails , growing from the cell body , that were often functional posteriors ( Figure 5E ) . These morphological effects were not observed with RNAi targeting any other genes we tested , suggesting they are specific to the Mob1 knockdown . Identical phenotypes were observed when either half of the gene was targeted separately ( Figure S7 and Movies S1–S3 ) . In addition to these morphological defects , Mob1 knockdown cells show clear defects in cytokinesis ( Figure S8 ) , comparable to those observed in Tetrahymena , although cell division was rare and seen in less than 5% of Mob1 ( RNAi ) cells over a 5-d period , which is typical for Stentor in our growth conditions [16] . Some of the more severely affected medusoid cells were so abnormally shaped that it was impossible to define what had happened to the cells from only a single time-point , raising the possibility that multiple failed attempts at cell division might have played a role in development of the phenotype . To obtain a clear idea of the development of these phenotypes , we imaged individual cells every 2 h after feeding them the RNAi vector for 48 h . We observed that all cells went through similar stages of aberrant morphogenesis ( Figures 5F and S9 ) , initially losing their canonical wine-glass proportions and elongating slightly relatively early in the time course ( Figure 5F , 16 h ) , and eventually converting to the medusoid form . During the evolution of the Mob1 ( RNAi ) phenotype , cells underwent a round of spontaneous regeneration of the OA . This is a normal process in Stentor and does not normally result in aberrant morphogenesis , but in Mob1 ( RNAi ) cells , spontaneous OA regeneration was immediately followed by off-axis growth—that is , the extension of a new posterior pole along an axis different from the previously existing anterior–posterior axis , indicating that this process might trigger the development of further defects ( Figure 5F , 32 h ) . The cell cycle of Stentor is between 96 and 120 h in our growth conditions , and consistent with this long duration , we found that no cells initiated cell division during the 52-h observation period , making it unlikely that the observed morphological defects could be products of failed cytokineses ( n = 20 ) . These data suggest that Mob1 is required for OA localization and for the proper regulation of posterior structures; and in the absence of Mob1 , posterior growth becomes unregulated . However , our results also imply that regeneration of the OA might be triggering the development of more severe defects and a switch from disproportioned and elongated bipolar cells to multipolar cells . Interestingly , when we localized residual Mob1 protein at different stages in Mob1 ( RNAi ) cells ( Figure 6 ) , we noted that Mob1 protein is first lost from more anterior regions ( Figure 6B ) , and only by the medusoid stage is Mob1 staining almost completely absent ( Figure 6C ) . This raises the possibility that differentially localized Mob1 is performing different functions in Stentor , and its loss in these specific locations triggers the development of different phenotypes . We next hypothesized that if different populations of Mob1 perform different functions in the cell , we would be able to determine these functional differences using microsurgery to remove specific regions of the cell containing Mob1 . In the case of a simple bisection , the anterior fragment of the cell would lack the posterior population of Mob1 and need to regenerate posterior structures , whereas the posterior fragment would lack the anterior population of Mob1 and need to regenerate a new OA and anterior ( Figure 1B ) . Morphologically normal cells , taken after 72 h of feeding the Mob1 RNAi vector , were bisected and those fragments were observed every 2 h . Compared to control cells ( Figure 7A ) , Mob1 ( RNAi ) cell fragments grow ectopic tails resembling normal posterior structures ( Figure 7B ) . Anterior fragments maintained the original OA and only grew ectopic tails adjacent to the previous posterior structures , which would suggest that the OA has some control over posterior growth . Conversely , posterior fragments failed to properly localize their regenerating OA , which remained on the dorsal side of the cell , and resulted in cells that were able to grow new posterior structures at the anterior end . These results show that Mob1 is not required to initiate regeneration , although once initiated neither the anterior nor posterior halves properly regenerated the OA or the holdfast . Furthermore , this suggests that Mob1 plays a key role in defining polarity and regulating polarized cell growth during normal development as well as regeneration . Interestingly , 10% of cells were only mildly affected and successfully regenerated their missing structures ( holdfast and OA ) . However , they still lost normal cell proportions , indicating that the RNAi had occurred in these cells ( n = 20 , Figure 7C ) . We hypothesize that these cells represent incomplete knockdown of Mob1 and that cell proportionality is more sensitive to Mob1 depletion than OA and posterior pole formation . The fact that proportionality defects can occur without inducing regeneration suggests that these two phenotypes are functionally separable . A challenge for using RNAi to study development is that phenotypes can take time to fully develop because protein turnover takes a longer time than transcript knockdown . Such a lag between message depletion and protein depletion is a universal feature of RNAi in all organisms and simply reflects the greater stability of protein compared to mRNA . In the case of Stentor , Mob1 knockdown cells observed 48 h into the RNAi time course still showed normal morphology and were able to fully regenerate after bisection , to an extent comparable to control ( RNAi ) cells ( Figure 8A ) , despite the fact that mRNA levels were dramatically reduced relative to controls . This phenotypic lag relative to the timing of mRNA knockdown along with immunofluorescence data that clearly show the presence of Mob1 protein in the posterior even in elongated cells ( Figure 6B ) suggested that there could still be a sufficient amount of Mob1 protein to function during regeneration . In most systems , there is no way to bypass this phenotypic lag and one must simply accept it as a caveat for RNAi experiments , but in our case the ease of Stentor manipulation provides a way to speed up the development of an RNAi phenotype by physically removing the parts of a cell where the target protein resides . To this end , we surgically removed the head and the tail , which are the portions of the cell where the majority of Mob1 protein is localized , after inducing Mob1 knockdown by RNAi . If the phenotypic lag was due to retained protein in these regions , this surgical operation should reduce the lag between mRNA knockdown and development of morphological phenotypes . In Control ( RNAi ) cells , removal of both the head and tail structures yielded morphologically normal cells after 24 h ( Figure 8B , top ) , with Mob1 signal returning as early as 3 h postsurgery as observed by immunofluorescence ( Figure S10 ) . However , when both the heads and tails were removed from morphologically normal Mob1 ( RNAi ) cells at an early stage of knockdown when cells still showed normal morphology , they developed phenotypes similar to those seen at much later stages of Mob1 knockdown ( Figure 8B , bottom ) . The result that surgically accelerated removal of Mob1 proteins reduces the lag between gene knockdown and development of morphological phenotypes supports the idea that Mob1 protein functions globally in establishing both anterior and posterior polarity in Stentor .
The ability to perform RNAi in Stentor to manipulate genes of interest , such as we have done with Mob1 , will pave the way for many future studies to unravel the mechanism of single-celled pattern formation and regeneration . Although the standard drawbacks of RNAi still apply to Stentor—namely , the cell-to-cell variability in the level of knockdown and phenotypic lag due to target protein stability—Stentor provides unique methods for addressing these issues because manipulating individual cells is trivial and surgical removal of the protein pool is possible , at least when the target protein is concentrated in a specific region of the cell . These results , to our knowledge , represent the first molecular analysis of regeneration in Stentor to be reported and build on observations of proportional regeneration first made by Morgan over 100 years ago . The kinase co-activator Mob1 is clearly localized to the posterior in vegetative cells . At distinct stages during cell division , Mob1 is found to first expand toward the anterior , where it is later focused into a discrete band around the midline of the cell . Toward the end of division , it creates a clear boundary between the daughter cells , where it localizes to the newly forming posterior of the anterior daughter cell . Localization of Mob1 to the midline of dividing cells is not unique to Stentor and is comparable to observations of Mob1 in a variety of other organisms , including Tetrahymena [16] , Alfalfa [25] , and budding yeast [26] , although it is interesting to note that Mob1 is clearly asymmetrically localized to the anterior daughter at the midline of both Stentor and Tetrahymena during division . Loss of Mob1 due to RNAi knockdown results in a loss of normal proportions , apparent uncontrolled cell growth , and cytokinesis defects . When considering these data alongside the data from Tetrahymena , it certainly suggests that the single ciliate MOB family member might share the more specialized functions of the multiple MOB family members in other organisms , which has also been suggested by Tavares et al . [16] . Although it is still unknown if any of the functional interactions of the MOB family are also conserved in ciliates , such as specific interactions with NDR kinases and STE-like kinases , we hope to address these questions in the future with the advent of a more complete Stentor genome . From these data we can conclude that Mob1 is essential for maintenance and regeneration of cell polarity and proper cell proportions . We also show that RNAi by feeding can now be used as a routine tool to study morphogenesis and regeneration at the level of single cells in Stentor . There are likely to be many localized pattern regulatory proteins in addition to Mob1 that control development in Stentor , and Mob1 will serve as a model for their study . With this remarkable single-cell system , we have opened the doors to studying the molecular mechanisms of regeneration at a resolution impossible to attain in other regenerative models . Moving forward we hope to develop more ways to manipulate Stentor and further investigate the role of Mob1 , and its associated pathways , in order to expand our knowledge of cell polarity , regeneration , and morphogenesis .
Stentor coeruleus cells were obtained commercially ( Carolina Biological Supply , Burlington , NC ) but subsequently maintained in culture within the lab by growing in the dark at 20 °C in Modified Stentor Medium ( MSM ) , 0 . 75 mM Na2CO3 , 0 . 15 mM KHCO3 , 0 . 15 mM NaNO3 , 0 . 15 mM KH2PO4 , 0 . 15 mM MgSO4 , 0 . 5 mM CaCl2 , and 1 . 47 mM NaCl modified from the original recipes described by Tartar [4] and De Terra [27] . This medium provides no nutrients and must be supplemented with living prey . In order to provide prey with a known genome , we use Chlamydomonas reinhardtii grown separately in TAP medium [28] and washed in MSM before feeding . The 300 mL Stentor cultures are given 3×107 Chlamydomonas cells two or three times per week . Homologs were identified by best-reciprocal BLAST starting with Paramecium tetraurelia proteins ( Table S1 ) . Target gene sequences were obtained by PCR amplification from genomic DNA and cloned into pPR-T4P ( kind gift from J . Rink ) , a modified pDONR-dT7 in which a ligation-independent cloning site was added [29] . Cloning was performed by either the ligation-independent method or cohesive-end ligation . Additional information about the RNAi constructs used in this study is included in Table S3 . Multiple sequence alignments were made using ClustalW2 with default settings . The list of Argonaute proteins used in the analysis is included in Table S2 . The un-rooted neighbor-joining tree was made with 1 , 000 bootstrap replicates using the MEGA v5 . 1 program [30] . FigTree v1 . 4 was used to visualize the tree data . RNAi was performed by transforming HT115 E . coli with each plasmid to allow for dsRNA expression of the target gene . Transformed bacteria were grown to log phase and then induced with 1 mM IPTG for 3 h at 37 °C . After induction , bacteria were washed and resuspended in MSM , then fed to Stentor that had been previously starved for 24–48 h . Induction and feeding of bacteria was then repeated for 2–5 d . Negative controls used for RNAi experiments were either pPR-Sciwi03 or pPR-LF4 . RNA was extracted from 50 cells per sample using PureLink RNA mini kit ( Life Technologies , Grand Island , NY ) . After purification , RNA was treated with DNaseI ( New England Biolabs , Ipswitch , MA ) , repurified , and then primed with oligo-dT and reverse transcribed using the SuperScript III kit ( Life Technologies , Grand Island , NY ) . Samples were diluted as necessary , and 5 µL were used in each qRT-PCR reaction . Reactions were run on a C1000 ThermoCycler ( Bio-Rad , Hercules , CA ) with an annealing temperature of 54 °C . Primer sets were designed for α- and β- tubulin , GAPDH , and Mob1 ( Table S4 ) . Each qRT-PCR run was finished with a melt curve to determine the homogeneity of the amplified product . Starting quantity was calculated using a standard curve and a genomic DNA control for each primer pair . Three technical replicates were performed for each of 1–3 biological replicates . Error bars represent standard deviation for biological replicates . For samples with one biological replicate , standard deviation of technical replicates is shown with uncapped error bars . Mouse monoclonal anti-acetylated tubulin ( clone 6-11B-1 ) was used at a 1∶500 dilution ( Sigma , St . Louis , MO ) . MOB1 antibody was generated in rabbits whose pre-immune bleeds had been screened before immunization using the synthetic peptide N-CFIDRFKLVDQKELAPLAELI-C ( Covance , Denver , PA ) and affinity purified using a SulfoLink Immobilization Kit for Peptides ( Pierce Biotechnology , Rockford , IL ) . Purified Mob1 antibody was used at a concentration of 3 µg/mL . Alexa-488 goat–anti-mouse and Alexa-488 goat–anti-rabbit secondary antibodies ( Life Technologies , Grand Island , NY ) were used for immunofluorescence , and IRDye 800CW goat–anti-mouse and IRDye 680RD goat–anti-rabbit secondary antibodies ( LI-COR Biosciences , Lincoln , NE ) were used for Western blotting . Cells were isolated from culture and washed in fresh MSM . Cells were then isolated in minimal volume in a 1 . 5 mL tube for fixation and staining in suspension . Cells were fixed in ice-cold methanol for 10 min at −20 °C , rehydrated at room temperature in a 1∶1 MeOH:PBS mixture for 5 min , and 1× PBS for 10 min . Cells were blocked in 1× PBS , 2% BSA , and 0 . 1% Triton-X-100 for 1 h at room temperature . In order to avoid centrifugation , cells were allowed to settle to the bottom of the tube between steps . A total of 1 , 500 Stentor cells were washed 3× in MSM and 1× in ice-cold MSM and lysed in 50 mM Tris-HCl pH 8 . 0 , 125 mM NaCl , 1% NP-40 containing complete protease inhibitor cocktail tablets ( Roche Diagnostics Corp . , Indianapolis , IN ) , mixed by pipetting and incubated for 30 min while rotating at 4 °C . Lysates were centrifuged at 10 , 000×g for 15 min at 4 °C , and the supernatant was incubated with anti-Mob1 antibody for 2 h while mixing at 4 °C . Samples were then incubated with Protein A Anti-Rabbit IgG beads overnight while mixing at 4 °C ( Rockland Immunochemicals Inc . , Boyertown , PA ) . Sample buffer was added and boiled for 10 min before running on a 10% SDS-PAGE gel and transferred onto a nitrocellulose membrane . Blots were probed with anti-Mob1 primary antibody ( 1∶500 ) and Rabbit IgG TrueBlot secondary antibody ( 1∶1 , 000 ) ( Rockland Immunochemicals Inc . , Boyertown , PA ) , developed using Chemiluminescent HRP substrate , and exposed to film . Brightfield images were collected on a Stemi 2000C and an Axio Zoom V16 equipped with a 1× and 2 . 3× objective ( Carl Zeiss MicroImaging , Thornwood , NY ) . Images were captured using an AxioCam MRc digital microscope camera ( Carl Zeiss MicroImaging , Thornwood , NY ) or a Rebel T3i digital SLR camera ( Canon U . S . A . , Inc . , Melville , NY ) . DIC images were captured on an Axiovert 200M microscope ( Carl Zeiss MicroImaging , Thornwood , NY ) equipped with 10× 0 . 22 NA and 40×0 . 75 NA objectives with an AxioCam MRm digital microscope camera ( Carl Zeiss MicroImaging , Thornwood , NY ) . Fluorescence images were collected on a Deltavision deconvolution microscope ( Applied Precision , Issaquah , WA ) equipped with 10×0 . 4 NA , 20×0 . 5 NA , and 100× oil 1 . 4 NA objectives using a CoolSnap HQ ( Photometrics , Tucson , AZ ) digital microscope camera . Immunofluorescence images are Z-stacks taken with 2 µm step sizes for 20× images and 0 . 2 µm step sizes for 100× images . Images of cells that were too large to fit into a single image were manually stitched using the cortical rows to align the two images , and the seam is indicated with a yellow dashed line . Brightfield images of live , fully extended cells were first binarized using ImageJ v1 . 46 . A custom MATLAB program was then used to create an outline and midline of the binarized cell image ( Figure S3; code available upon request from the authors ) . Perpendicular lines were computed every 10 pixels along the length of the midline and their intersections with the cell outline calculated to define the cell width . Cell lengths were then normalized , and cell width versus cell length was plotted for all cells . The plot shown is a trendline of data from all cells , with a sliding average of 2n the number of samples collected . In order to compare cell shapes between control and RNAi cells using this analysis , we define a shape factor that summarizes the shape of each cell as follows: for each cell , the widest point of the cell outline is assumed to represent the OA , whereas the point farthest away from the OA is assumed to represent the tail . The area of the cell contained between those two extremes is then calculated by numerical integration using the trapezoidal rule . For comparison , the area of the right trapezoid was constructed by drawing a straight line from the tail point to the OA outline point . We then define the shape factor as the ratio of the actual area to the area of the right trapezoid . This unit-less parameter will have a value of 1 if the cell is a perfect right cone—that is , if its sides are perfectly straight ( a cylinder would be a special case of this ) . Thus , the more conical or cylindrical the cell is , the closer the shape factor gets to 1 . If the cell has a taper like a wine glass or a wild-type Stentor cell , it will have a shape factor of less than 1 . The change in cell shape from tapering to cylindrical seen in Mob1 RNAi is thus reflected by an increase in shape factor . Surgery was performed following methods reported by Tartar [7] . Cells were isolated from culture and washed in fresh MSM . Cells were transferred to 1–2% Methylcellulose ( Sigma , St . Louis , MO ) in MSM , mounted on a slide in a 1 cm × 1 cm well , and visualized on a Olympus Stemi-2000c stereoscope . Microsurgery was performed using glass-stirring rods ( Fisher , Pittsburgh , PA ) after hand pulling glass needles from the tips of the rods using a butane torch . | Cells have the ability to develop complex morphologies , but the mechanisms that determine these varied shapes are not well understood . Cell shape determination can be challenging to study in multicellular organisms because it can be difficult to know whether shape changes are determined internally within an individual cell or externally , driven by input from neighboring cells or by both . The giant unicellular ciliate , Stentor coeruleus , provides an ideal single-cell model in which to study morphogenesis due to its large size and reproducible , complex patterning . Although Stentor was a popular experimental organism around 100 years ago , molecular tools were not subsequently developed to sustain its use as a model system today . Here we demonstrate that RNA interference ( RNAi ) “by feeding” is effective in Stentor and demonstrate its utility for studying morphogenesis and cell polarity patterning in this organism . We show that the conserved Mob1 kinase regulator protein is asymmetrically localized to the posterior end of Stentor and is positioned at the newly forming posterior pole during cell division , suggesting that it may have a role in morphogenesis . Using RNAi , we show that depletion of Mob1 results in Stentor cells with marked defects in morphogenesis . Our findings suggest that Stentor coeruleus can be a powerful model system studying morphogenesis and regeneration at the single-cell level and that Mob1 is a patterning protein required for its normal development and regeneration . | [
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] | 2014 | The Kinase Regulator Mob1 Acts as a Patterning Protein for Stentor Morphogenesis |
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