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Human Group IIA secreted phospholipase A2 ( hGIIA ) is an acute phase protein with bactericidal activity against Gram-positive bacteria . Infection models in hGIIA transgenic mice have suggested the importance of hGIIA as an innate defense mechanism against the human pathogens Group A Streptococcus ( GAS ) and Group B Streptococcus ( GBS ) . Compared to other Gram-positive bacteria , GAS is remarkably resistant to hGIIA activity . To identify GAS resistance mechanisms , we exposed a highly saturated GAS M1 transposon library to recombinant hGIIA and compared relative mutant abundance with library input through transposon-sequencing ( Tn-seq ) . Based on transposon prevalence in the output library , we identified nine genes , including dltA and lytR , conferring increased hGIIA susceptibility . In addition , seven genes conferred increased hGIIA resistance , which included two genes , gacH and gacI that are located within the Group A Carbohydrate ( GAC ) gene cluster . Using GAS 5448 wild-type and the isogenic gacI mutant and gacI-complemented strains , we demonstrate that loss of the GAC N-acetylglucosamine ( GlcNAc ) side chain in the ΔgacI mutant increases hGIIA resistance approximately 10-fold , a phenotype that is conserved across different GAS serotypes . Increased resistance is associated with delayed penetration of hGIIA through the cell wall . Correspondingly , loss of the Lancefield Group B Carbohydrate ( GBC ) rendered GBS significantly more resistant to hGIIA-mediated killing . This suggests that the streptococcal Lancefield antigens , which are critical determinants for streptococcal physiology and virulence , are required for the bactericidal enzyme hGIIA to exert its bactericidal function .
Many important human bacterial pathogens are also common colonizers of mucosal barriers . Occasionally , such pathogens penetrate these physical barriers to invade the underlying tissues and cause infections . Antimicrobial molecules , sometimes also referred to as ‘endogenous antibiotics of the host’ , are a critical part of the innate immune response to eradicate these intruders and clear the infection . In humans , one of the most potent bactericidal molecules against Gram-positive bacteria is the secreted enzyme human Group IIA phospholipase A2 ( hGIIA ) [1 , 2] . HGIIA belongs to a family of 11–12 secreted phospholipase A2 enzymes , which are structurally related and hydrolyze various phospholipids [2–5] . In non-inflamed conditions , hGIIA serum levels are low and not sufficient to kill most Gram-positive bacteria [6] . However , sterile inflammation or infection increases hGIIA expression with concentrations reaching up to 1 μg/ml in serum [7] , which is sufficient to kill most Gram-positive pathogens in vitro . A unique feature of hGIIA compared to other secreted phospholipase A2 family members is its high cationic charge , which is required for binding to negatively-charged surface structures and for penetration of the thick peptidoglycan layer surrounding Gram-positive bacteria [2 , 8 , 9] . The potent bactericidal activity of hGIIA against Gram-positive bacteria has been demonstrated in vitro , using recombinant hGIIA , and is suggested by infection experiments that show increased protection from infection using hGIIA transgenic mice [10–16] . To counter the bactericidal effects of hGIIA , pathogens have evolved different resistance mechanisms , for example by suppressing hGIIA expression [17 , 18] or by increasing the net positive charge of surface structures and membrane . The surface modifications include the addition of positively-charged D-alanine moieties to teichoic acid polymers by the highly conserved dlt operon to repulse hGIIA [8] and other cationic antimicrobials [19–22] . In addition , Staphylococcus aureus ( S . aureus ) modifies the charge of its bacterial membrane through the molecule MprF [23 , 24] by adding the cationic amino acid lysine to phosphatidylglycerol ( PG ) , resulting in lysyl-PG [25] . In Group A Streptococcus ( GAS ) , the enzyme sortase A ( SrtA ) , a conserved enzyme in Gram-positive bacteria that recognizes proteins with an LPXTG motif and covalently attaches them to peptidoglycan [26 , 27] , was shown to increase hGIIA resistance [12] . Studies with recombinant hGIIA have highlighted differences in intrinsic hGIIA susceptibility between different Gram-positive species , where Bacillus subtilis is killed in the low ng/ml concentration range [28 , 29] , and GAS is one of the most resistant species known to date [12] . Interestingly , this high resistance is not a common trait of streptococcal pathogens since Group B Streptococcus ( GBS ) is killed by concentrations that are approximately 500 times lower compared to those required to kill GAS [11 , 12] . Streptococci are historically classified by the expression of structurally different Lancefield antigens [30] . Lancefield antigens are cell wall polysaccharides making up approximately 50% of the dry cell wall mass [31] . All GAS serotypes express the Lancefield Group A carbohydrate ( GAC ) , which consists of a polyrhamnose backbone with alternating N-acetylglucosamine ( GlcNAc ) side chains [31] , which are important for virulence [32] . In contrast , all GBS serotypes express the Lancefield Group B carbohydrate ( GBC ) , a multi-antennary structure , containing rhamnose , galactose , GlcNAc , glucitol , and significant amounts of phosphate [33] . Both streptococcal species are important human pathogens as they can cause systemic infections associated with high mortality and morbidity [34–36] . Mouse infection models and ex vivo studies on human serum from infected patients suggest the importance of hGIIA in defense against lethal infections with GAS and GBS [11 , 12] . Given the importance of hGIIA in host defense against streptococci , we set out to identify the molecular mechanisms that confer resistance to hGIIA using a comprehensive and unbiased approach .
A previous study found that GAS strains are among the most resistant Gram-positive bacteria regarding hGIIA-mediated killing [12] . Mutation of srtA in the GAS strain JRS4 , an emm6 serotype , increased hGIIA susceptibility by about 50-fold [12] . GAS M1T1 is a globally-disseminated emm1 clone that is most often responsible for invasive GAS infections in industrialized countries [37 , 38] and was not included previously in hGIIA studies [12] . GAS strain 5448 , a representative M1T1 isolate , showed concentration-dependent killing by recombinant human hGIIA , with an LD50 of 0 . 05 μg/ml ( S1 Fig ) . Also , GAS M1T1 resistance mechanisms against hGIIA at least partially overlap with GAS JRS4 emm6 , since mutation of srtA rendered GAS M1T1 approximately 35-fold more susceptible to hGIIA ( S1 Fig ) [12] . We set out to identify additional genes that affect hGIIA susceptibility of GAS M1T1 using the GAS Krmit transposon mutant library [39] . To ensure complete coverage of the library in our experiment , we optimized our hGIIA killing assay to support an inoculum of 107 CFU , using a final concentration of 0 . 125 μg/ml hGIIA . The Tn-seq experiment with the GAS Krmit transposon mutant library consisted of four non-exposed control samples and four hGIIA-treated samples . Each sample contained on average approximately 30 million reads , of which over 90% of the reads aligned once to the GAS M1T1 5448 reference genome ( S1 Table ) [40] . To quantify the number of transposon insertions per gene , we divided the reference genome into 25 nucleotide windows , resulting in 73 , 182 windows , and mapped each read to a specific window . More than 48% of the windows had at least one read aligned . We identified one gene with an exceptionally high number of transposon insertions at a specific part of the gene ( M5005_Spy_1390 ) , suggesting biased insertion of the transposon ( S2 and S3 Tables and S2 Fig ) . This gene was therefore excluded from further analysis . No other biased transposon insertion sites were observed . We identified 16 genes that contained a significantly different number of transposon insertions after exposure to hGIIA as indicated by P-value of <0 . 05 ( Benjamini-Hochberg ( BH ) corrected; Fig 1A , S3 Fig , and S2–S4 Tables ) . Nine of the 16 genes ( 56% ) showed a decrease in transposon insertions compared to untreated controls , indicating that the products of the disrupted genes provide resistance against hGIIA-mediated GAS killing ( Fig 1A , S3 Fig , S3 Table ) . Three susceptibility genes are located within the dlt operon ( M5005_Spy_1070 , M5005_Spy_1072 , M5005_Spy_1073 ) , which is responsible for D-alanylation of teichoic acids [41] . Consistently , this operon was previously linked to GAS resistance against other cationic antimicrobials , such as LL-37 and hGIIA [8 , 42] . The other six genes with significant fold decrease in transposon insertions are annotated as hypothetical proteins ( M5005_Spy_0918 and M5005_Spy_1794 ) , a lactoylglutathione lyase ( M5005_Spy_0876 ) , LytR ( M5005_Spy_1474 ) of the LytR/CspA/Psr protein family , the transcriptional regulator FabT ( M5005_Spy_1495 ) , and the NAD glycohydrolase inhibitor ( M5005_Spy_0140 ) ( S3 Fig and S2 and S3 Tables ) . Seven genes showed a relative increase in the number of transposon insertions after hGIIA exposure , indicating that the products of these genes are important for hGIIA to exert its bactericidal effect ( Fig 1A , S3 Fig , S4 Table ) . Five of the six genes ( 83% ) mapped to two gene clusters; one gene cluster is annotated as an ABC transporter ( M5005_Spy_0939 , M5005_Spy_0940 , M5005_Spy_0941 ) and the other gene cluster is the previously identified 12-gene cluster responsible for biosynthesis of the Group A carbohydrate ( GAC ) ( Fig 1B ) [32] . Within the GAC gene cluster , gacI and gacH ( M5005_Spy_0609 and M5005_Spy_0610 ) showed significantly increased number of transposon insertions . The small downstream gene gacJ ( M5005_Spy_0611 ) also demonstrated a 3-fold increase , however , the BH corrected P-value is slightly above 0 . 05 . Other genes within the GAC gene cluster are essential or crucial as described previously [39 , 43] . Finally , guaB ( M5005_Spy_1857 ) and the IIC component of a galactose-specific PTS system ( M5005_Spy_1399 ) were identified as their mutation may confer increased resistance to hGIIA ( S3 Fig and S2 and S4 Tables ) . Overall , the transposon library screen identified genes that confer resistance or are important for the mechanisms of action of hGIIA . To validate the Tn-seq findings , we confirmed the involvement of three genes ( dltA , lytR , and gacI ) by comparing hGIIA-mediated killing of WT GAS with previously generated GAS mutants [32 , 42 , 44] . Deletion of dltA and lytR indeed increased GAS susceptibility to hGIIA-mediated killing by 45-fold and 35-fold , respectively ( Fig 2A and 2B ) . The dltA defect could be restored by re-introduction of the gene on a plasmid ( Fig 2A ) . In contrast to dltA and lytR , mutation of gacI , which results in loss of the GAC GlcNAc side chain [45] , increased GAS resistance to hGIIA by approximately 10-fold compared to the parental or gacI-complemented ( gacI* ) strain ( Fig 2C ) . The GAC is conserved in all GAS serotypes . We therefore questioned whether deletion of gacI would have a similar effect on the bactericidal efficacy of hGIIA in four other GAS serotypes ( M2 , M3 , M4 , M28 ) . In all serotypes , deletion of gacI increased resistance of GAS to hGIIA by 5- to 50-fold ( Fig 2D ) , indicating that hGIIA requires the GAC GlcNAc side chain for optimal bactericidal efficacy in all genetic backgrounds tested . To study the activity of hGIIA in a more physiological setting , we spiked pooled normal human serum with different concentrations of recombinant hGIIA . As described previously [32 , 46] , GAS grows in human serum , a trait that is not influenced by the presence of endogenous hGIIA since addition of the hGIIA-specific inhibitor LY311727 [47] did not affect GAS growth in serum ( S4A Fig ) . Addition of recombinant hGIIA to human serum potentiated its bactericidal effect compared to the purified assay as reflected by a 5-fold lower LD50 ( 0 . 01 ug/ml; Fig 3A versus Fig 2 ) . Interestingly , heat-inactivation of serum reduced the ability of hGIIA to kill GAS by 10-fold compared to active serum , indicating that there are heat-labile factors in serum that potentiate hGIIA efficacy ( Fig 3A ) . We determined how the addition of serum would affect the efficacy of hGIIA to kill the mutants with altered hGIIA susceptibility . We first compared bacterial survival of the WT strain and the individual mutants in normal serum ( S4A Fig ) . Interestingly , the lytR and srtA mutant already showed a significant loss of fitness in non-inflamed serum , which is not attributed to the presence of endogenous hGIIA as addition of LY311727 did not restore their survival ( S4A Fig ) . Both ΔsrtA and ΔdltA bacteria remained more susceptible to hGIIA-mediated killing in serum ( Fig 3B and 3C ) , whereas the ΔlytR and ΔgacI mutants were now equally resistant to WT GAS ( Fig 3D and 3E ) . These results reflect the multitude of effects that occur simultaneously in a complex environment such as serum . More specifically , serum likely contains factors that have an opposite effect to hGIIA on lytR and gacI mutants , such that the net survival of these mutants is equal to WT . Finally , we compared the effect of serum heat-inactivation on hGIIA efficacy in the context of individual mutants ( S4B–S4D Fig ) . Similar to WT GAS , heat inactivation of serum reduced the efficacy of hGIIA to kill ΔsrtA , ΔdltA and ΔlytR , suggesting that the hGIIA-potentiating factor ( s ) is required to kill all mutants in our panel . Our observation that GAS ΔgacI is more resistant to hGIIA implies that the GAC GlcNAc moiety is important for the function of hGIIA . To assess whether loss of the GAC GlcNAc side chain affected hGIIA binding to bacteria , we first analyzed binding of hGIIA by fluorescence microscopy using a phospholipase A2-specific antibody ( Fig 4A ) . A visual quantification of hGIIA-stained bacteria indicated reduced binding of hGIIA in the absence of GAC GlcNAc ( Fig 4C ) . In addition , we observed that the localization of hGIIA on the bacterial surface was affected , where hGIIA predominantly localized to the GAS cell poles in WT bacteria ( Fig 4A and 4B ) , but distribution became more disperse upon mutation of gacI ( Fig 4A and 4B ) . Since fluorescence microscopy did not allow for more extensive binding assessments , we also quantified binding of recombinant hGIIA to GAS by flow cytometry . At concentrations up to 1 μg/ml , we did not observe differences in hGIIA binding to the three strains ( Fig 4D ) . Only at concentrations of 5 μg/ml , hGIIA showed reduced interaction with the gacI mutant compared to GAS WT and gacI*-complemented strains ( Fig 4D ) . The contribution of differential hGIIA binding to GAS is therefore only relevant to specific locations such as in tears which contain up to 30 μg/ml hGIIA [28] . Since hGIIA binding is charge-dependent , we analyzed whether reduced binding at high hGIIA concentrations could be caused by difference in surface charge . Using the highly cationic protein cytochrome C , we indeed observed that the gacI mutant has a reduced negative surface charge compared to GAS WT and the gacI*-complemented strain ( S5 Fig ) , which could likely explain the reduced binding of hGIIA to this mutant . Cell wall architecture can significantly affect hGIIA cell wall penetration [2] . To assess how absence of the GAC GlcNAc side chain affected hGIIA cell wall penetration , we measured changes in membrane depolarization over time using the fluorescent voltage-sensitive dye DiOC2 ( 3 ) [48] . In this assay , membrane depolarization results in reduced red fluorescence . HGIIA required at least 5 minutes to penetrate the GAS cell wall since no changes in red fluorescence signal were observed at this time point for any of the strains ( S6A Fig ) . At 30 minutes ( S6B Fig ) , membrane depolarization occurred as visualized by diminished red fluorescence at hGIIA concentrations of 0 . 1 μg/ml in the GAS WT and the gacI*-complemented strain . Compared to these two strains , the gacI mutant exhibited limited effects on membrane potential at all time points and all hGIIA measured ( Fig 4E and S6 Fig ) . These data suggest that hGIIA reaches the membrane faster in the presence of GAC GlcNAc moieties . Membrane depolarization likely precedes more pronounced hGIIA-mediated disruption of the membrane that would allow influx of the fluorescent DNA dye SYTOX green , which can only enter damaged membranes [49] . As expected , hGIIA increased the SYTOX signal in GAS WT and GAS gacI* in both a time and concentration-dependent manner ( Fig 4F and S7A–S7E Fig ) . Importantly , addition of LY311727 completely prevented SYTOX influx ( S7F Fig ) , confirming that our assay indeed reflects hGIIA phospholipase activity on the bacterial membrane . In sharp contrast , SYTOX intensity in GAS ΔgacI increased at a much slower rate and never reached the levels of GAS WT and GAS gacI* after two hours . The observed differences in kinetics and severity of hGIIA on membrane depolarization and SYTOX influx in GAS ΔgacI compared to GAS WT suggest that the GAC GlcNAc side chain is essential for efficient trafficking of hGIIA through the GAS cell wall . A recent study demonstrates that GacI is a membrane protein that is required for the intracellular formation of undecaprenyl-P-GlcNAc [45] . Therefore , loss of GacI could alter membrane composition and fluidity to impact the activity of hGIIA on the membrane . To analyze whether phospholipid hydrolysis is affected in GAS ΔgacI , we performed the SYTOX influx assay on protoplasts [50] . Unlike the previous SYTOX results with intact bacteria , protoplasts from WT , ΔgacI and gacI* strains all became SYTOX positive ( Fig 4G and S8 Fig ) , underlining our conclusion that the presence of the cell wall in the ΔgacI limits access of hGIIA to the streptococcal membrane . Nonetheless , the significantly lower SYTOX in the ΔgacI protoplasts compared to the WT and gacI*-complemented protoplasts ( Fig 4G and S8 Fig ) , suggests that the absence of GacI has a minimal impact on hGIIA degradation . To further reinforce this conclusion , we determined the levels of phosphatidylglycerol ( PG ) in bacteria and protoplasts after treatment with hGIIA ( Fig 4H ) . PG levels were significantly higher in GAS ΔgacI after hGIIA treatment compared to WT , whereas equal PG levels were observed in GAS ΔgacI and WT after hGIIA treatment ( Fig 4H ) . We therefore conclude that cell wall trafficking and not cell membrane differences are the major determinant of susceptibility differences between GAS WT and ΔgacI mutant . We investigated whether the importance of the GAC for hGIIA activity could be extended to other streptococci such as GBS . As previously described , GBS are generally more sensitive to hGIIA compared to GAS [12] . Indeed , killing of GBS strain NEM316 occurred at substantially lower concentrations of hGIIA compared to GAS M1T1 ( compare Figs 5A and 2 ) , also in the presence of serum ( S9 Fig ) . We confirmed that killing depends on the catalytic activity of the enzyme since introduction of an inactivating point mutation ( H48Q; Fig 5B ) or addition of LY311727 abrogated all killing ( Fig 5C ) . Just as the GAC is the molecular signature for GAS , GBS uniquely express another Lancefield antigen , known as the Group B Carbohydrate ( GBC ) . The GBC is a more complex structure compared to the GAC and contains significant amounts of phosphate that introduce a negative charge . Unfortunately , there are currently no GBS mutants available with specific structural variations in the GBC . Instead , we assessed the effect of the complete GBC , through deletion of gbcO [33] , on susceptibility of GBS to hGIIA . Deletion of gbcO rendered GBS at least 100-fold more resistant to hGIIA compared to GBS WT ( Fig 5A–5C ) , and the phenotype is restored upon complementation with gbcO on a plasmid ( Fig 5A ) . We could reproduce the ΔgbcO phenotype by treating WT GBS with tunicamycin , an inhibitor of gbcO-type transferases ( Fig 5D ) [33 , 51] . Finally , as observed in GAS , fluorescence microscopy demonstrated that hGIIA bound to the poles of GBS WT ( Fig 5E and 5F ) . Unlike to GAS , we did not observe that loss of GBC expression reduced binding of hGIIA at higher concentration of hGIIA as assessed by flow cytometry ( S10 Fig ) . In conclusion , these results highlight a key role for streptococcal Lancefield antigens in the bactericidal effect of hGIIA .
Intrinsic resistance to acute phase protein hGIIA varies among Gram-positive bacteria , including among closely-related streptococcal species . GAS , an important cause of lethal infection worldwide , is among the most resistant bacteria , whereas GBS , an important cause of neonatal sepsis and meningitis , is killed by hGIIA at concentrations that are approximately 500-fold lower [12] . For GAS , we confirmed the role of Sortase A and DltA and identified LytR as hGIIA resistance factors . Despite the differences in cell wall composition , i . e . cell wall crosslinking , cell wall associated proteins and membrane physiology , the streptococcal Lancefield antigens are structural requirements for the activity of hGIIA in both GAS and GBS . HGIIA is approximately 10-fold more effective against GAS when spiked into normal serum compared to heat-inactivated serum , and 5-fold more effective compared to our ‘purified’ system . This corresponds to a previous observation where hGIIA activity was approximately 10-fold greater in serum or plasma than in the protein-depleted serum in studies using S . aureus as the target pathogen [52] . This suggests the presence of a heat labile protein in serum that facilitates hGIIA-mediated killing of Gram-positive bacteria . Heat-inactivation of serum is a well-established method to study the influence of the complement system and also abolishes hGIIA activity in acute phase serum [53] . Since the low basal levels of hGIIA in normal human serum are not sufficient to affect GAS viability , the enhancement could indicate a synergistic effect between hGIIA and the complement system . A recent study shows formation of the Membrane Attack Complex ( MAC ) on the GAS surface without affecting bacterial viability [54] . It is therefore tempting to speculate that MAC is deposited on Gram-positive bacteria so that bactericidal enzymes , such as hGIIA , can reach the bacterial membrane more easily . Such a cooperative effect between different innate defense mechanisms would not be surprising , since previous studies have already observed that hGIIA synergizes with neutrophil oxygen-dependent mechanisms to kill S . aureus [55 , 56] . Finally , the concentrations of hGIIA that are measured in human serum are likely underestimating the true availability of this bactericidal enzyme since hGIIA attaches to surfaces of blood vessels due to its hydrophobic nature . We speculate that vessel-attached hGIIA may help prevent bacterial dissemination to other tissues , an effect that has not yet been addressed experimentally . Sortase A , an enzyme that links LPXTG-containing proteins to peptidoglycan , was previously described as a hGIIA resistance factor in GAS serotype M6 [12] . We confirmed that deletion of srtA in a GAS M1T1 background similarly sensitizes GAS to hGIIA both in a ‘purified’ as well as a serum environment . Whether a single or multiple LPXTG proteins confer resistance is an unresolved question . Our study suggests that Sortase A-mediated resistance is not caused by a single LPXTG protein since we did not identify a single LPXTG-encoding gene in the Tn-seq screen ( S5 Table ) . Possibly , the underlying mechanism is similar to the SrtA-dependent resistance of GAS to the antimicrobial peptide cathelicidin [46] , which depends on the accumulation of sorting intermediates at the bacterial membrane . SrtA itself was not identified in the transposon library screen since the mutants are lost in the competitive environment likely due to inherent defects in growth [39] . We identified and confirmed a role for the protein LytR in GAS hGIIA resistance . LytR is a member of the LytR-CpsA-Psr ( LCP ) protein family , a conserved family of cell wall assembly proteins in Gram-positive bacteria [57] . The GAS genome encodes two members of this family , lytR ( M5005_Spy_1474 ) and psr ( M5005_Spy_1099 ) . The fact that we only identified LytR suggests that these proteins have non-redundant , but as yet unidentified , functions . In several Gram-positive pathogens , including Streptococcus pneumoniae , S . aureus and Bacillus anthracis , LCP proteins anchor cell wall glycopolymers such as wall teichoic acid ( WTA ) , lipoteichoic acid ( LTA ) and capsular polysaccharides to the cell envelope and are therefore critical for cell envelope assembly and virulence [57–62] . Additionally , lytR homologues in Bacillus subtilis and Streptococcus mutans contribute to cell wall remodeling by increasing autolysin activity [63 , 64] . Previously , hGIIA activity has been linked to autolysins; autolysin-deficient mutants are more resistant to hGIIA than their parent strain [65] . A suggested mechanism is that hGIIA displaces positively-charged autolysins from negatively-charged WTA and LTA , resulting in localized peptidoglycan digestion and facilitated movement of hGIIA through the cell wall . Currently , the role of LytR either in GAS cell wall assembly or in the regulation of autolysin activity is not known , but LytR-deficient GAS display altered membrane integrity and potential [66] , which could impact hGIIA susceptibility . Moreover , lytR has been linked to GAS virulence in two different studies . In the first study , lytR mutants in two different GAS M1 backgrounds showed a more virulent phenotype in a subcutaneous murine model of infection , which was suggested to be a result of increased SpeB activity [66] . LytR-mediated regulation of SpeB is unlikely to play a role in hGIIA-mediated resistance in our experiments , since we used washed bacteria . In a more recent study , lytR mutants in GAS 5448 M1T1 showed a competitive disadvantage for fitness in vivo upon mixed subcutaneous infection [44] . Unfortunately , there is no information regarding pathology or survival of the mice upon infection with the lytR mutant added alone [44] . We also identified genes that render GAS more susceptible to hGIIA . GacH , gacI , and gacJ are located in the biosynthesis gene cluster of the GAC , which may suggest that the GAC is a target for hGIIA on the GAS surface . Mutation of gacI and gacJ results in loss of the GAC GlcNAc side chain [32 , 45] , whereas mutation of gacH does not affect side chain formation [32] . We therefore hypothesize that the GAC provides hGIIA resistance through two distinct mechanisms . First , a gacI/J-dependent mechanism that works through the GAS GlcNAc side chain as important for binding and penetration of hGIIA to the cell membrane . The second mechanism involves GacH but the underlying molecular aspects remain to be determined . The first mechanism seems to conflict with our previous observations that GlcNAc-deficient GAS have decreased virulence capacity due to increased neutrophil killing and increased susceptibility to antimicrobials in serum including LL-37 [32] . However , hGIIA would not have contributed to in vitro assays since we used non-inflamed serum or plasma where basal hGIIA concentrations are too low to affect GAS viability [32] . The fact that gacI mutants demonstrate reduced survival in vivo suggests that the benefits of expressing the GlcNAc side chain outweigh the increased susceptibility to hGIIA . Since GAS already shows high intrinsic resistance towards hGIIA there is no pressure to lose the GlcNAc side chain . It might even be detrimental since it makes GAS more vulnerable to effects of other antimicrobials or yet unidentified host defenses . In contrast to the GAC [31 , 67] , the GBC is a multi-antennary structure and contains anionic charge due to the presence of phosphate [33] . For GBS , the increased hGIIA resistance in GBC-negative gbcO mutants is therefore likely explained by the loss of negatively charged groups on the surface . This corresponds to previous observations in S . aureus , where loss of the secondary cell wall glycopolymer WTA , increased resistance to several antimicrobial proteins , including hGIIA [10] . Binding of hGIIA to streptococci was reduced when bacteria expressed a modified GAC or lacked complete expression of GBC but these differences were only apparent using high hGIIA concentrations . However , these findings need to be interpreted with caution since possibly only a small portion of the bound hGIIA is required for the bactericidal action of the enzyme . Therefore , even small fluctuations in binding might result in meaningful functional differences . We are currently not able to analyze hGIIA binding at a more sensitive level . Contrary to our expectations , fluorescence microscopy analysis showed that hGIIA bound to the cell poles of both GAS and GBS . However , the observed binding pattern does not correspond to the reported localization of the GAC and GBC , which are distributed over the entire cell wall as shown by early electron microscopy studies [68 , 69] . Binding at the septa of dividing bacteria seems a preferred binding site for bactericidal agents due to a high turnover of peptidoglycan which would make penetration easier [70 , 71] . In addition , the septum is rich in anionic phospholipids [72] , a likely target for cationic hGIIA . Finally , the GAS ExPortal , a unique microdomain in the GAS membrane that is enriched in anionic lipids , would be another favored location of binding for the cationic hGIIA [73] . However , the ExPortal is distributed asymmetrically across the GAS surface and not at the cell poles [73] . The fact that we observe a similar binding pattern to GBS and GAS , may indicate that GAS and GBS express a similar protein that localizes at the cell poles and is used by hGIIA as an initial docking site . Importantly , localization became more disperse upon deletion of gacI in GAS , possibly suggesting a redistribution of hGIIA-interacting structures . Identification of hGIIA susceptible and resistant GBS mutants using a Tn-seq mutant transposon library may help identify such conserved or homologous hGIIA targets in the GAS and GBS cell wall . Lack of the GAC GlcNAc side chain most profoundly affected penetration of hGIIA through the cell wall , a mechanism that depends on charge [2 , 9] . Indeed , membrane depolarization and permeabilization occurs at a much slower rate in the gacI mutant compared to WT and complemented strains . This implies that the GAC GlcNAc side chain facilitates penetration of hGIIA through the cell wall in what is referred to as an ‘anionic ladder process’ [2] . Interestingly , the GAC does not contain any charged structures . Therefore , the underlying mechanism may be linked to the previously mentioned autolysin displacement from interaction with the GAC . In conclusion , we show that the bactericidal agent hGIIA is able to kill GAS in a complex serum environment . However , modification or removal of the Lancefield antigen renders GAS more resistant to the bactericidal activity of hGIIA . Similarly , removing the Lancefield antigen from GBS renders this species also more resistant to the bactericidal activity of hGIIA . The Lancefield antigens , previously thought to be solely involved in physiology , are thus critical cell wall structures for hGIIA to exert its bactericidal effect . The Tn-seq data discussed in this paper provide exciting new insights into the resistance mechanisms of GAS and encourage similar experiments in other streptococci species . Disrupting the resistance mechanisms with therapeutic agents could possibly be sufficient to provide our own immune system the upper hand in clearing invading streptococcal pathogens .
The GAS M1T1 5448 strain was used in this study unless stated otherwise . The 5448ΔgacI knockout and gacI* complemented strain [32] , the 5448ΔlytR [44] and the GAS serotypes M2 , M3 , M4 , and M28 and corresponding ΔgacI knockouts [74] were described previously . Preparation and characterization of the GAS M1T1 5448 transposon library was described previously by Le Breton et al . , 2015 [39] . All GAS strains were grown in Todd-Hewitt broth ( Becton Dickinson ) supplemented with 1% yeast extract ( Oxoid; THY ) as static cultures at 37°C . Kanamycin ( Sigma-Aldrich ) was used at a concentration of 300 μg/ml when appropriate . GBS NEM316 WT , ΔgbcO and the complemented strains ΔgbcO/pTCV were kindly provided by Dr . Mistou [33] . Unless stated otherwise , overnight cultures of GAS were diluted and re-grown to mid-log phase ( OD600nm = 0 . 4 ) , washed and resuspended in HEPES solution ( 20 mM HEPES , 2 mM Ca2+ , 1% BSA [pH 7 . 4] ) solution at OD600nm = 0 . 4 ( ~1x108 CFU/ml ) . For GBS strains , overnight cultures of NEM316 WT , ΔgbcO and the complemented strains ΔgbcO/pTCV were diluted in TH broth and grown to mid-log phase ( OD620nm = 0 . 4 for WT and complemented strains , 0 . 25 for ΔgbcO mutant ) . Bacteria were then diluted in HEPES solution and pushed rapidly through a 27-gauge needle , a process repeated three times , to disrupt bacterial aggregates . Normal human serum and heat-inactivated serum was obtained from healthy volunteers as described previously [54] . Recombinant hGIIA was produced as described previously [75] . The GAS M1T1 Krmit transposon mutant library was grown to mid-log phase in 100 ml THY containing Km and resuspended in HEPES solution to OD600nm = 0 . 4 . Four experimental replicates of 100 μl ( ~ 1x107 CFU ) were subsequently incubated in HEPES solution with or without 125 ng/ml hGIIA for 1 hour at 37°C . After incubation , 3 ml THY was added to all samples and incubated at 37°C until the mid-log phase was reached ( recovery step ) . Cultures were collected by centrifugation and used for isolation of genomic DNA ( gDNA ) . gDNA was isolated by phenol-chloroform extraction . Samples were barcoded and prepared for Tn-seq sequencing as described previously [76] . Tn-seq sequencing was performed on Illumina NextSeq500 ( Sequencing facility University Medical Center , Utrecht , The Netherlands ) . Tn-seq data analysis was performed as previously described [76] . In short , barcodes were split using the Galaxy platform [77] and sequences were mapped to the GAS M1T1 5448 genome [40] using Bowtie 2 [78] . The genome was subsequently divided in 25-bp windows and each alignment was sorted and indexed by IGV [79] . Insertions were counted per window and then summed over the genes . Read counts per gene were adjusted to cover only the first 90% of the gene since transposon insertions in the final 10% potentially do not cause a knock-out phenotype . Then , read counts were normalized to the total number of reads that mapped to the genome in each replicate , by calculating the normalized read-count RKPM ( Reads Per Kilobase per Million input reads; RKPM = ( number of reads mapped to a gene x 106 ) / ( total mapped input reads in the sample x gene length in kbp ) ) . Cyber-T [80] was used to perform statistical analysis on the RKPM values . Genes that contributed to either hGIIA susceptibility or hGIIA resistance were determined when the Benjamini-Hochberg ( BH ) corrected p-value was <0 . 05 . Illumina sequencing reads generated for the Tn-seq analysis were deposited in the European Nucleotide Archive under the accession number PRJEB27626 . Mid-log streptococcal suspensions were diluted 1 , 000 times in HEPES solution and 10 μl was added to sterile round-bottom 96 well plates ( triplicates ) . Recombinant hGIIA or catalytically-deficient hGIIA mutant enzyme H48Q was serially diluted in HEPES solution or human serum and 10 μl aliquots were added to bacteria-containing wells . For hGIIA inhibition experiments , 50 μM LY311727 was added to the HEPES solution or serum . For GAS , samples were incubated for 2 hours at 37°C , without shaking , PBS was added and samples were 10-fold serially diluted and plated on THY agar plates for quantification . For GBS , bacteria were incubated with hGIIA at 37°C for 30 minutes , the samples were diluted in PBS and plated onto blood agar plates . After overnight incubation 37°C , colony forming units ( CFU ) were counted to calculate the survival rate ( Survival ( % of inoculum ) = ( counted CFU * 100 ) / CFU count of original inoculum or Survival ( % ) = ( counted CFU * 100 ) / CFU count at 0 μg/ml hGIIA ) . For pharmacological inhibition of GBC expression , NEM316 WT bacteria were grown to mid-log phase ( OD620nm = 0 . 4 ) in the presence of 0 . 5 mg/ml tunicamycin ( Sigma ) and used in killing assays as described above . Changes in hGIIA-dependent membrane potential were determined using the membrane potential probe DiOC2 ( 3 ) ( PromoKine ) [48 , 81] . Bacterial suspensions ( OD600nm = 0 . 4 ) were diluted 100 times ( ~1x106 CFU/ml ) , 100 μl aliquots were divided into eppendorf tubes and incubated with serial dilutions of hGIIA . After incubation at 37°C , 3 mM DiOC2 ( 3 ) was added and incubated at room temperature for 5 minutes in the dark . Changes in green and red fluorescence emissions were analyzed by flow cytometry . Bacterial staining with the DNA stain SYTOX Green ( Invitrogen ) is a measurement for membrane permeabilization and an indication of bacterial cell death [49] . Serial dilutions of hGIIA in HEPES solutions were added to wells of a sterile flat-bottom 96 well plate . Bacteria were resuspended in HEPES solution containing 1 μM SYTOX green ( OD600nm = 0 . 4 ) and added to hGIIA dilutions in a final volume of 100 μl . For hGIIA inhibition experiments , 500 μM LY311727 was added . Fluorescence over time was recorded using FLUOstar OPTIMA ( green fluorescence 530 nm emission and excitation 488 nm ) at 37°C . Bacterial surface charge was determined as previously described [81] . Briefly , exponential phase bacteria ( OD600nm = 0 . 4 ) were washed twice in 20 mM MOPS buffer [pH 7 . 0] and adjusted to OD600nm = 0 . 7 . After a 10-fold concentration step , 200 μl bacterial aliquots were added to 200 μg cytochrome c ( from Saccharomyces cerevisiae , Sigma-Aldrich ) in a sterile 96-well round-bottom plate . After 10 minutes at room temperature in the dark , the plate was centrifuged , the supernatant was transferred to a sterile 96 well flat-bottom plate and absorbance was recorded at 530 nm . The percentage of bound cytochrome c was calculated using samples containing MOPS buffer only ( 100% binding ) and samples containing MOPS buffer and cytochrome c ( 0% binding ) . To determine hGIIA surface binding , 12 . 5 μl of bacterial cultures in mid-log phase ( OD600nm = 0 . 4 and 0 . 25 for GBS ΔgbcO ) were added to wells of a sterile 96-well round-bottom plate ( triplicates ) . hGIIA was serially diluted in HEPES solution without Ca2+ and added to the bacteria at indicated concentrations . After 30 minutes incubation at 4°C , bacteria were collected by centrifugation and resuspended in HEPES solution without Ca2+ containing 1:300 dilution of anti-phospholipase A2 antibody ( Merck Millipore ) [28] . After incubation at 4°C for 30 minutes , the samples were washed and incubated with a 1:1 , 000 dilution of FITC-labeled goat-anti-mouse IgG ( SouthernBiotech ) or a 1:500 dilution of Alexa Fluor 647 conjugated goat-anti-mouse IgG ( Jackson Immuno Research ) . After washing with HEPES solution without Ca2+ , samples were fixed with 1% paraformaldehyde and fluorescence was recorded by flow cytometry ( FACSVerse , BD Biosciences ) . To analyze hGIIA surface localization by microscopy , bacteria were grown in 10 ml broth to mid-log phase and washed with 0 . 1 M NaHCO3 [pH 9] . For GBS , the bacterial septa were stained by addition of a 1:1 mixture of Vancomycin bodipy FL conjugate ( Invitrogen , V34550 ) and vancomycin ( Sigma ) at a final concentration of 1 . 25 μg/ml , during the last generation time of growth . The surface of GBS was stained with Alexa Fluor 350 Carboxylic acid Succinimidyl ester ( Molecular Probes by Life Technologies , A10168 ) for 1 hour in room temperature . Bacteria were then resuspended in 500 μl HEPES solution and the suspension was divided over two tubes . A final concentration of 10 μg/ml hGIIA H48Q was added to one tube and HEPES solution to the other before a 30 min incubation at room temperature . The samples were washed and resuspended in 200 μl HEPES solution , then again divided to two tubes . A mouse anti-human hGIIA monoclonal antibody ( Clone SCACC353 Cayman Chemical ) or an IgG1 isotype control ( mouse anti human IgA clone 6E2C1 , DAKO ) was added to a final concentration of 10 μg/ml to the bacterial suspensions and incubated at RT . After washing , the samples were incubated with 8 μg/ml of Alexa Flour 594 goat anti-mouse IgG1 ( Molecular Probes by Life Technologies , A21125 ) . After 30 min incubation , the samples were washed in HEPES solution and fixed in 4% paraformaldehyde . Ten μl of bacterial suspension were mounted onto microscopic slides ( VWR ) using MOWIOL ( Sigma ) mounting medium before viewing the samples using Zeiss Axiovert 200M microscope . Pictures were captured using a 63× objective and the AXIOVISION 4 . 8 software . To determine hGIIA efficacy in hydrolyzing membrane phospholipids , the membrane permeabilization assay was modified for protoplasts . Mid-log bacterial suspension were prepared in in protoplast buffer ( 20% sucrose , 20 mM Tris-HCl , 10 mM MgCl2 , 2 mM CaCl2 [pH 7 . 4] ) containing 1 . 4 units/μl mutanolysin ( Sigma-Aldrich ) [50 , 82 , 83] . After incubation for 1 hour at 37°C , protoplasts were collected by centrifugation ( 1 , 200 rpm 15 minutes ) and resuspended in protoplast buffer to an OD600nm = 0 . 4 . Pore formation by hGIIA was monitored using SYTOX Green as described above . Approximately 3*107 CFU from a mid-log bacterial suspension in HEPES solution , or protoplasts in protoplast buffer , were exposed to 2 μg/ml hGIIA for 30 minutes . Afterwards , bacterial suspensions were centrifuged at 140 , 000 rpm for 4 minutes and bacterial pellets were resuspended in MeOH . The protoplast suspensions were mixed with MeOH 1:1 . Bacterial lipids were extracted under acidic conditions in the presence of 10 pmol PG standards ( PG 14:1/14:1 , PG 20:1/20:1 and PG 22:1/22:1 ) as described [84] . Lipid extracts were resuspended in 60 μl methanol and diluted 1:10 in 96 wells plates ( Eppendorf twintec 96 , colorless , Sigma , Z651400-25A ) prior to measurement . Measurements were performed in 10 mM ammonium acetate in methanol . Samples were analyzed on an AB SCIEX QTRAP 6500+ mass spectrometer ( Sciex , Canada ) with chip-based ( HD-D ESI Chip , Advion Biosciences , USA ) electrospray infusion and ionization via a Triversa Nanomate ( Advion Biosciences , Ithaca , USA ) as described [84] . PG species were measured by neutral loss scanning selecting for neutral loss of m/z 189 . Data evaluation was done using LipidView ( ABSciex ) . GraphPad Prism 6 was used to perform statistical analysis . An unpaired two-tailed Student’s t-test was used to compare the means of two groups . A 2-way ANOVA with Bonferroni multiple comparison test was used to compare multiple groups . Data shown are mean ± SD . | The human immune system is capable of killing invading bacteria by secreting antimicrobial proteins . Cationic human Group IIA secreted phospholipase A2 ( hGIIA ) is especially effective against Gram-positive bacteria by degrading the bacterial membrane . HGIIA requires binding to negatively charged surface structures before it can penetrate through the thick peptidoglycan layer and reach the target phospholipid membrane . HGIIA is constitutively expressed at high concentrations at sites of possible bacterial entry , e . g . in tears , skin and small intestine . In serum , normal concentrations are low but can increase up to 1 , 000-fold upon inflammation or infection . In vitro , ex vivo and in vivo experiments suggest an important role for hGIIA in defense against two human pathogens , Group A and Group B Streptococcus ( GAS , GBS ) . We demonstrate that the Lancefield cell wall polysaccharides that are expressed by these bacteria , the Group A Carbohydrate ( GAC ) for GAS and the Group B Carbohydrate ( GBC ) for GBS , are required for optimal hGIIA bactericidal efficacy by facilitating penetration through the peptidoglycan layer . Given the increased hGIIA resistance of antigen-modified or antigen-deficient streptococci , it will be of interest to determine potential regulatory mechanisms regarding expression of streptococcal Lancefield polysaccharides . | [
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] | 2018 | Streptococcal Lancefield polysaccharides are critical cell wall determinants for human Group IIA secreted phospholipase A2 to exert its bactericidal effects |
An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers . It is well known that somatic genome alterations ( SGAs ) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity , in which these SGAs usually do not co-occur in a tumor . With some success , this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway . However , mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways . Here , we propose a novel , signal-oriented framework for identifying driver SGAs . First , we identify the perturbed cellular signals by mining the gene expression data . Next , we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity . Finally , we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach . We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database , and perform systematic evaluations . Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways .
Somatic genome alterations ( SGAs ) such as somatic mutations , somatic copy number alterations and epigenomic alterations are major causes of cancers[1–3] . In general , SGAs in a tumor can be divided into two types: those that affect cellular signaling proteins , perturb the cellular signaling system , and eventually contribute to cancer initiation and progression are called driver SGAs; and those that do not directly contribute to cancer development are designated as passenger SGAs . A fundamental problem of cancer-genome research is to identify signaling pathways that , when perturbed by driver SGAs , lead to cancer development or affect clinical outcomes for patients . Identification of such pathways will not only advance our understanding of the disease mechanisms underlying cancer , but will also provide guidance for the precision treatment of cancer patients . In a cell , signaling pathways detect and transmit cellular signals to maintain cellular homeostasis; often , such signals eventually regulate the transcription of genes in order to initiate certain biological processes . For example , the signal transmitted by a growth factor usually leads to the transcription of genes involved in cell proliferation ( Fig 1 ) . As such , the impact of an SGA affecting a signaling protein in a tumor often manifests as an expression signature embedded in the expression profile of the tumor . For example , a mutation leading to constitutive activation of the epidermal growth factor receptor ( EGFR ) gene may lead to over-expression of its target genes . Thus , the gene expression profile of a cell at a given time reflects the state of its cellular signaling system , although it is a convoluted response to all active signals . Inferring the state of an individual pathway requires the deconvolution of the signals embedded in its gene expression data . The Cancer Genome Atlas ( TCGA ) has collected the most comprehensive genome-scale data to date , including somatic mutations , copy number variations , and gene expression from a large number of different types of cancers . By simultaneously capturing SGAs and gene expression data from each tumor , the TCGA data reflect the cause and readout of perturbed signaling pathways , thus providing a unique opportunity for studying cancer pathways . Identification of perturbed and critical signaling pathways using the TCGA data is challenging in the following ways . First , as a cancer cell usually hosts dozens to hundreds of SGAs , we need to identify the small number of driver SGAs among the large number of passenger SGAs within a given tumor . Current approaches for identifying driver SGAs mainly concentrate on those that occur beyond random chance in a patient population[4–7] . These approaches would fail to find low-prevalence SGAs that affect a specific cancer-driving pathway . The second challenge is caused by the heterogeneity of mutations in tumor cells , in that few tumors have identical SGA patterns . One reason for this phenomenon is that a signal pathway can be perturbed in multiple locations[8] , and different tumors may share a common aberrant pathway but exhibit SGAs affecting distinct proteins on the pathway . Thus , it is a challenge to determine if distinct SGA events in different tumors affect a common pathway . Finally , a cancer results from perturbations in multiple pathways[2] , and distinct combinations of pathway perturbation underlie the heterogeneity of cancers[2] in terms of clinical phenotype . Thus , it is a challenge to determine , among multiple SGAs and multiple aberrant pathways in a tumor , which SGA affects which pathway . Researchers have developed various approaches to search for driving pathways using TCGA by exploiting different properties of tumor cells[9–11] , including mutual exclusivity[12–14] , which is the observed phenomenon that SGA events affecting the proteins within a signaling pathway seldom co-occur in a tumor . A natural explanation for this phenomenon is that , if one mutation is sufficient to perturb the signal of a pathway and leads to the development of cancer , perturbation of other proteins is not required , and therefore co-occurrence of perturbations is seldom observed . This property is observed in different types of cancer cells and pathways[4 , 13 , 15 , 16] . While it is the case that SGAs affecting the genes within a common pathway tend to be mutually exclusive , the reverse may not necessarily be the case , that is , finding a set of mutually exclusive SGAs does not ensures that their corresponding proteins are in the same pathway . Current mutual-exclusivity-based methods[12–14 , 16 , 17] concentrate on finding a set of SGAs of size k , such that the set covers as many tumors as possible while minimizing overlapping cover ( thus maximizing mutual exclusivity ) . As the numbers of tumors and SGAs examined by contemporary genome technology increase , it becomes increasingly easier to find a set of unrelated SGAs that covers a certain number of tumors while exhibiting mutually exclusivity due to the heterogeneity of tumors . To address this shortcoming , Zhao et al[14] further considered the co-expression of SGA-affected genes in order to enhance the search of pathways . The intuition underlying their approach is that proteins within a signaling pathway tend to be co-expressed , so the correlation of the expressions of the candidate genes of a pathway can be used as another objective function to guide pathway search . Both mutual exclusivity and co-expression of SGAs are auxiliary properties of a signaling pathway , but they are not sufficient to indicate whether the SGAs affect a common cellular signal; therefore , they are useful but not the optimal objective function to guide the search for a signaling pathway . In this study , we propose a novel signal-oriented framework for searching cancer pathways by combining gene expression with SGA data . The premise underlying our approach is as follows: since the state of a signaling pathway can be reflected by the expression state of a set of genes it regulates ( i . e . , its signature ) , the task of searching for a pathway can be formulated as a search for a set of SGAs that collectively exhibit strong information with the state of a gene expression signature . Under such a setting , mutual exclusivity and co-expression of SGA-affected genes can further be used as auxiliary objective functions to constrain the search space and to enhance the confidence of the results . This approach addresses the fundamental task of the pathway discovery—finding a set of SGAs perturbing a common signal . We applied this novel framework to the ovarian cancer and glioblastoma data from TCGA , followed by systematically evaluating the impact of the signal-oriented approach on the search for driving pathways and comparing the performance of our exact algorithm with that of heuristic or stochastic algorithms . We show that the signal-oriented approach provides a general framework in which different pathway-searching algorithms can be combined with different signal-oriented objective functions ( beyond those discussed here ) to explore new directions for studying cellular signaling systems .
Data on somatic mutation , copy number alteration , and gene expression from 568 ovarian cancer tumors and 513 glioblastoma tumors , as well as 8 normal control samples from ovarian tissue and 10 cases of normal brain samples were downloaded from the TCGA[4 , 5] . For each tumor , we considered a gene as being differentially expressed if its expression value increased or decreased at least 3-fold in comparison to the median value of the gene in the control samples . We defined a gene as affected by an SGA event if it had a non-synonymous single nucleotide variation in its coding region , and/or an insertion or a deletion; we also labeled a gene as affected by an SGA event if it had copy number alteration ( with the GISTIC[22] score ≥ 2 and z-score ≥ 1 . 64 or GISTIC score ≤ -2 and z-score ≤ -1 . 64 , where the z-score is obtained by the z-transformation of the expressions of the gene across all tumors ) . Hence , only the copy number alterations that affected gene expression ( with a p-value of 0 . 05 ) are included . We removed the genes that exhibit both amplifications and deletions ( with the smaller fraction being over 10% ) in a given set of tumors that are supposed to have a common signal perturbed as inconsistent genes . For example , if a gene X is affected by copy alteration , where 85% of events are amplification and 15% are deletion ( thus smaller fraction is over 10% ) , we would remove this gene from the consideration . This is a relatively conservative consideration of those genes with consistent copy number alteration direction as potential drivers . Finally , if two genes exhibit perfect correlation ( co-amplified or co-deleted whenever altered ) , we treat these genes as one common genomic alteration . These procedures lead to a tumor-by-gene binary matrix recording differentially expressed genes , and a tumor-by-gene binary matrix recording SGA events in tumors . To deconvolute signals embedded in the gene expression data , we hypothesized that if a set of genes performs coherently related functions and tends to be co-regulated in multiple tumors , the genes are likely regulated by a common signaling pathway as a module . To find such modules among the cancer tumors , we employed a knowledge-driven data mining approach , developed in our previous studies[19–21] , which consists of two major procedures: 1 ) identifying functionally coherent gene subsets among the differentially expressed genes in each tumor , such that each gene subset is annotated by a GO term that summarizes the function of the genes; and 2 ) further identifying the gene subsets that are differentially expressed in multiple tumors , which is formulated as the dense bipartite subgraph finding problem . Researchers have used set cover[23 , 24] or the extension of the set cover – module cover [25] model to find gene subsets that are differentially expressed because of pathway perturbation . Our two-step method differs from these in that genes included in each solution subset are both functionally coherent and co-expressed in a considerable number of tumors . We also obtained the tumors that perturb the pathways regulating the expression of each gene subset . To identify functionally coherent gene subsets , we first found a tumor’s differentially expressed genes and grouped them into non-disjoint subsets by mining their annotations [19–21] . This was achieved by representing the hierarchical structure of GO terms as a directed acyclic graph and searching for genes annotated with closely related GO terms . We first associated genes to the GO terms according to annotations of the genes . We then iteratively merged highly specific GO terms and their associated genes to their parent GO terms according to a procedure [19] that strives to minimize the loss of semantic information during the process . In this fashion , we can group genes annotated with closely related terms into a set annotated with a more general GO term that retains the information of the original annotations . We stop the procedure if a further merge leads to a non-coherent gene set , according to a quantitative metric that assesses the statistical significance of functional coherence of the gene set [19 , 21] . This procedure enabled us to partition differentially expressed genes from each tumor into non-disjoint , functionally coherent subsets . Next , we further identified the functionally coherent gene subsets that are affected in multiple tumors . We modeled this problem as a dense bipartite subgraph ( DBSG ) finding problem , of which the detailed algorithm was introduced in our previous work[21] . Briefly , we pooled gene subsets annotated with a common GO concept across all tumors and constructed a bipartite graph , in which the vertices on one side represent the pool of differentially expressed genes sharing the GO annotation , and the vertices on the other side represent the tumors; an edge between a gene and a tumor indicates that the gene is differentially expressed in the tumor . We then searched for a subset of genes that are co-differentially expressed in multiple tumors . We formulated our task as follows: find a maximum dense bipartite subgraph such that each gene must be connected to at least 75% ( a connectivity ratio ) of all tumors in the subgraph and each tumor must be connected to at least 75% of all genes in the subgraph . Thus , each DBSG consists of a set of genes , i . e . , an RM , and a set of tumors in which the RM is perturbed . To search for the candidate members of a signaling pathway regulating an RM , we aimed to find a set of SGA-affected genes that has the following properties: 1 ) the SGA events affecting the genes cover as many as possible of the tumors in which the RM of interest is perturbed; 2 ) the SGA events carry strong information with respect to the expression state of an RM , or , in other words , the SGAs are significantly enriched in tumors in which RMs have been perturbed; and 3 ) each tumor is covered by only one gene in the solution set ( thus mutually exclusive . Note: SGA events are only mutually exclusive among tumors in each DBSG ) . By assigning a weight to each SGA to reflect the amount of information the SGA carries with respect to the state of the RM , we formulate this computational problem as the weighted mutually exclusive maximum set cover problem , a variant of the well-known set cover problem in algorithm theory[26] . To assess the strength of association of an SGA-affected gene ( a genome locus ) with the state of an RM , we apply a hypergeometric test to compute the enrichment of the SGA events of the gene in tumors within which the RM has been perturbed[27] . We then set the log p-value of the SGA enrichment analysis as the weight for the gene; thus , a set of SGAs with a smaller total weight tends to carry more information with respect to the RM when compared to another gene set with a greater total weight . Fig 2C illustrates the problem setting as follows . Of the 16 tumors and 5 genes in a dataset ( Fig 2C ) , 6 tumors are included in a DBSG . We define that a tumor is covered by a gene if an SGA affecting the gene occurs in the tumor; we then represent each gene by the subset of tumors in the DBSG it covers . In our example , g1 = {T1 , T2} , g2 = {T3 , T4 , T5} , g3 = {T1 , T4 , T5} , g4 = {T1 , T3} , and g5 = {T2 , T5 , T6} . For each DBSG , we define the set of all tumors in the DBSG , X , as the ground set; for example , in the figure , X = {T1 , T2 , T3 , T4 , T5 , T6} . We define F as the set of the candidate genes; in our example , F = {g1 , g2 , g3 , g4 , g5} = {{T1 , T2} , {T3 , T4 , T5} , {T1 , T4 , T5} , {T1 , T3} , and {T2 , T5 , T6}} . We define w: F→ ( -∞ , ∞ ) ; the function w gives weight to each gene . Given a subset of genes , F’⊂F , if no two elements in F’ have any common element , i . e . , if no two genes cover the same tumor , we then say that F’ is mutually exclusive; the weight of F’ is ∑S∈F’ w ( s ) . The problem’s goal is to find a mutually exclusive subset of F that covers a maximum number of elements of X ( i . e . , that covers a maximum number of tumors ) . If we find two or more solutions , e . g . , {g1 , g2} and {g4 , g5} , that cover the maximum number of elements , we choose the solution with the minimum weight , {g1 , g2} . This is the formal definition of the weighted mutually exclusive maximum set cover problem . As is the case with the formulations of other studies on mutual exclusivity[12–14 , 16 , 17] , our problem is NP-hard ( see the proof in a separate technical report[28] ) . Previous studies used heuristic or stochastic algorithms [12–14 , 16 , 17] to handle the mutually exclusive set cover problem or its variants , but such algorithms do not guarantee the finding of optimal solutions . In this study , we developed an exact algorithm , called the Weighted Mutually Exclusive Maximum Set Cover algorithm ( or the ME algorithm ) , that guarantees the finding of exact optimal solutions . The algorithm uses parameterized techniques[29] , such that the running time is an exponential function of a parameter that can be bounded by a small number for certain specific applications , instead of the exponential functions of large input sizes that are generally used for solving general NP-hard problems . Because our problem involves about 600 tumors and 30 , 000 genes , an exponential function of any of these input sizes would be intractable . However , using the parameterized technique , we developed an algorithm whose worst time complexity is O* ( 1 . 325m ) , where m , the parameter , is the number of candidate genes when we search driver SGAs affecting a pathway . In fact , the actual running time of the algorithm is much smaller than that of the worst time complexity; the algorithm can finish our computation task on a workstation in 5 to 10 minutes , even when m is 200 , which is sufficiently large candidate size for searching driver SGAs that perturb a signaling pathway . We refer to a set of solution SGAs as perturbation module ( PM ) to reflect that they may perturb a common signaling pathway . Our method adopts a branch-and-bound principle: the algorithm first finds a subset in F , and then branches on it . Due to the mutual exclusivity constraint , if any two subsets in F overlap , then at most only one of them can be chosen into the solution . For example , suppose that the subset S intersects with other d subsets in F ; then , if S is included into the solution , S and other d subsets intersecting with S will be removed from the problem , and if S is excluded from the solution , S will be removed from the problem . We continue this process until the resulting sub-problems can be solved in constant or polynomial time . Let T ( m ) be the number of computations needed when call the algorithm with m subsets in F , then we can obtain the recurrence relation T ( m ) ≤ T ( m− ( d+1 ) ) +T ( m−1 ) . As if d = 0 for all subsets in F , the problem can be solved in polynomial time ( all subsets in F will be included into the solution ) , in the recurrence relation , d ≥ 1 . Therefore , we can obtain T ( m ) ≤ 1 . 619m , which means the problem can be solved in O* ( 1 . 619m ) time ( note: Given the recurrence relation T ( k ) ≤ ∑0 ≤ i ≤ k-1 ciT ( i ) such that all ci are nonnegative real numbers , ∑0 ≤ i ≤ k-1 ci > 0 , and T ( 0 ) represents the leaves , then T ( k ) ≤ rk , where r is the unique positive root of the characteristic equation tk- ∑0 ≤ i ≤ k-1 citi = 0 deduced from the recurrence relation[30] ) . We improved the algorithm’s running time by carefully selecting subsets in F for branching . As the proof of the algorithm is very involved , we present the details in the technical report[28] . We have implemented all algorithms for the paper . Supplement results and codes for algorithms can be found at: http://pitttransmed-tcga . dbmi . pitt . edu/mutuallyExclusive/ .
Using the integrated knowledge-mining and data-mining approaches , we identified 88 dense bipartite subgraphs ( DBSGs ) from the ovarian cancer tumors . Each DBSG includes a response module ( RM ) consisting of at least 10 genes that are differentially expressed in 30 or more tumors . Based on our functional coherence analysis , the genes in an RM were functionally related to each other . To further corroborate these results , we also evaluated the RMs using the Ingenuity Pathway Analysis ( IPA— http://www . ingenuity . com/ ) ; each of our RMs was found to significantly overlap with at least one of the IPA networks . For example , we found 55 RMs , of which more than 90% of their genes overlapped with at least one network from the Ingenuity network database ( results are presented in a supplementary website so that researchers can browse the RMs and their driver SGAs ) . As an example , an RM that consists of 11 genes ( AURKA , CCNB1 , CHEK1 , COL5A1 , EPHB3 , NEK2 , PSRC1 , STMN1 , TACC3 , THBS2 , TWIST1 ) that are up-regulated in 62 tumors . The biological processes in which the genes are involved are summarized by the GO term GO:0051128: Regulation of Cellular Component Organization ( note: because genes in each RM are annotated by a GO term , we use GO term IDs to name RMs and PMs; we also use U_ or D_ to indicate whether genes in the RM are up-regulated or down-regulated , respectively ) . For example , the designation “RM U_GO0051128” means that the genes in the RM are up-regulated and that they are annotated by the GO term GO:0051128; “PM D_GO0009611” is the PM that regulates the RM D_GO0009611 ) . We found that 10 of those 11 genes are in an IPA network ( Fig 3 ) that is labeled as “Cellular Assembly and Organization , Cellular Function and Maintenance , Cell Morphology” . Previous laboratory studies show that 9 of these genes play important roles in tumor initiation and progression in different types of cancers . For example , AURKA was found overexpressed in the early stage ovarian tumors , therefore suggesting that the alteration of AURKA could be an early event of ovarian cancer[31] . High levels of AURKA expression is closely correlated to poor survival of patients with ovarian cancer [32] . The proliferation-related targets AURKA and CCNB1 were overexpressed in clinical ovarian tumor specimens[33] . Our predicted results corroborate with the established roles of AURKA and CCNB1 in cancers . In addition , many references also show that overexpression of seven of the remaining nine genes in the RM are related to cancers ( S1 Table ) . Another example of a cancer-related RM , annotated with the GO term GO:0010564 ( Regulation of Cell Cycle Process ) , includes 10 genes ( BIRC5 , CCNB2 , CDC7 , CDKN2A , CENPE , CENPF , CHEK1 , NEK2 , TIMELESS , UBE2C ) that are up-regulated in 140 tumors . All of those 10 genes are in an IPA network related to “Cell Cycle , DNA Replication , Recombination , and Repair , Cellular Assembly and Organization” ( see Supplement ) . A literature search shows that 9 of 10 genes in the module are related to several types of cancers ( S2 Table ) . Among them , expression of CENPE and CCNB2 correlates with worse clinical outcomes of patients with breast or ovarian cancers [34–36] . The fact that the genes in these RMs are functionally coherently related and co-regulated in multiple tumors from different types of tumors indicates that their aberrant expression is likely regulated by a common signal; thus , expression state of an RM can be utilized as the readout of the state of a hidden signal , allowing the search for the SGA events perturbing the signal . One interesting observation in this RM is that the CDKN2A , a tumor suppressor , is overexpressed . By checking the copy number alteration data , we found that the overexpression of CDKN2A in most of 140 tumors were not caused by the gene amplification . The similar phenomenon was observed in large number of tumors in other tumor types , such as GBM and HNSC , where the CDKN2A was overexpressed in almost all tumors without CDKN2A amplifications . The explanation of this phenomenon needs the further study from cancer biologists . This framework was also applied to TCGA data of glioblastoma multiform ( GBM ) , the most malignant cancer in the brain , and we identified 101 RMs . Comparing RMs in GBM with ones in ovarian cancer , 38 RM pairs were annotated by an identical GO term in both ovarian cancer and GBM , among which 17 modules are significantly overlapped ( p-value and q-value of overlapping < 10–4 , S3 Table ) . For example , the RMs annotated with U_GO0007067 ( mitotic nuclear division ) found from GBM and ovarian cancer have 18 and 17 genes respectively , in which 15 genes are in common , and the union of the two RMs includes 20 genes . As expected , literature studies indicate that almost all the above genes and most of other significant overlapping RMs are related to cancers ( S4 Table ) , including those involved in U_GO0009611 ( response to wounding ) and U_GO0006974 ( cellular response to DNA damage stimulus ) . Thus the approach of searching RMs as reflections of perturbed cellular signals is generalizable to different types of cancers and capable of finding cancer-related RMs . We further investigated if the expression states of RMs are relevant to patients’ clinical outcomes , we dichotomized GBM patients from TCGA according to the expression state of each RM , followed by survival analysis . We found the expression states of 25 RMs are associated with significant differences in patients’ survival ( p-values < 0 . 05 and q-values < 0 . 05 for Kaplan-Meier analysis , see S5 Table and S1 Fig ) . We also applied these methods to the breast cancers from TCGA for searching RMs ( data not shown ) . We used the breast cancer RMs as features for predicting survival of the patients studied by Curtis et al [37] in an open research challenge ( the DREAM 7 Challenge ) , in which RMs were found to be highly predictive of patient survival [38] . Therefore , the expression states of RMs reflect the states of cancer cells , which is determined by cellular signal transduction pathways . We hypothesized that the differential expression of an RM in a tumor is due to the aberrant signal resulting from pathway perturbation by at least one of the SGAs ( somatic genome alterations ) observed in the tumor . Since somatic copy number alterations are common in ovarian cancers ( which may contribute to differential expression of genes ) , we first examined if identified RMs are driven by copy number alterations . For each RM , we treat differential expression of a gene in a tumor as a differential expression event . We also define that it is driven by a copy number alteration event if the gene is copy number altered in the tumor . We then calculated the fraction of copy-number-alteration-driven differential expression events for each module and averaged them across the RMs , which shows that , on average , only 3 . 4% of differential expression events are likely driven by copy number alteration . The results support our hypothesis that the differential expression of an RM is driven by a pathway rather than by direct copy number alterations . As such , a reasonable strategy for identifying the signaling pathway regulating the RM is to pool the tumors in which the RM is differentially expressed and further search for a subset of the SGA events in these tumors that carries the strongest information with respect to the expression state of the RM . We refer to a module of genes affected by such SGAs as a “perturbation module” ( PM ) . When given a DBSG , we first identified all SGA events observed in the tumors within it , and then calculated enrichment of SGAs affecting a gene using a hypergeometric test , assigning the enrichment p-value as the weight of the gene . We applied the ME algorithm to identify an optimal PM for each DBSG , using up to 200 SGAs with the lowest p-values as input , a sufficiently large number when considering that most known biological pathways contain around tens of proteins . The sizes of the returned PMs ranged from 3 to 14 genes , with an average of 7 . 14 . Since our algorithm strives to include SGAs that are specifically enriched in the tumors in a DBSG , the genes in a PM as a whole are highly enriched in the tumors in a DBSG , with enrichment p-values ranging from 5 . 68×10−4 to 7 . 16×10−25 ( median: 8 . 97×10−14 ) . Since SGA events are in effect randomized perturbatons performed by nature , a strong correlation between SGA events and the expression state of an RM suggests that SGAs influence gene expression rather than the reverse direction ( differential gene expression causing SGAs ) . Thus , genes in each PM identified in our study are likely members of the signaling pathway perturbed in tumors that underlie the differential expression of the genes in an RM . Though experimental validation of the results could be conclusive , it is extremely costly . Therefore , we validated our results by comparing them to the existing knowledge using the IPA package , with the understanding that while the knowledge base of the IPA may be incomplete , it is , nonetheless , an accessible approach . Our findings indicated that most PMs were significantly associated with different diseases and/or disorders ( 65 PMs with both p-values and q-values of at most 0 . 001 , with a median of 9 . 21×10−4 ) ; among them , 30 PMs were related to cancers with both p-values and q-values smaller than 0 . 001 . We further investigated whether the identified PMs could be mapped to known signaling pathways , concluding that , indeed , many PMs were enriched in known pathways , including 51 PMs that were enriched in a known pathway with both p-values and q-values of at most 0 . 01 . As an example , we examined the PM corresponding to the RM studied in the previous section , U_GO0051128 , which consists of 6 genes ( CCNE1 , RB1 , FRMD1 , COLIM4 , MAST3 , RNF139 ) . The genes in the PM are enriched in the IPA pathway “Estrogen-mediated S-phase Entry” ( Fig 4A ) , with a p-value of 1 . 83×10–5 . This PM has two genes ( RB1 and CCNE1 ) in the well-characterized RB1 cancer pathway that plays important roles in ovarian cancer tumorigenesis ( Fig 4B ) [5] . Golgi integral membrane protein 4 ( GOLIM4 ) is a type II Golgi-resident protein that involves in processing proteins synthesized in ER and assist in the transport of protein cargo through the Golgi apparatus[39] . These transported proteins include ones shown in Fig 4A . Ring finger protein 139 ( RNF139 ) is a multi-membrane spanning protein with ubiquitin ligase activity . RNF139 interacts with tumor suppressor VHL and JAB1[40] , the latter is responsible for the degradation of tumor suppressor CDKB1B/p27CIP1 in this pathway ( Fig 4A ) . GOLIM4 and RNF139 in this PM were found as potential cancer drivers in various types of cancers [41–43] . Additionally , MAST3 , RB1 , and CCNE1 in this PM are critical in regulating cell cycle [44–46] . As shown in Fig 4C , the mutually exclusive pattern of the SGAs affects genes in this PM identified from the 62 tumors in which this RM was perturbed . It is of highly significance that our algorithm predicts that the amplification of CCNE1 gene conveys the identical information as to the mutation or deletion of RB1 . As shown in Fig 4B , the protein encoded by CCNE1 inhibits that of RB1 ( Fig 4B ) ; both amplification of CCNE1 and mutation/deletion of RB1 have the same effect on a common signal , leading to aberrant regulation of cell cycle entry and thereby causing cancers . Indeed , 6 out of 11 genes in the RM U_GO0051128 are related to cell cycle . When searching the PASTAA ( http://trap . molgen . mpg . de/PASTAA . htm ) database for enriched transcription factor binding sites of genes in this RM , the binding site of E2F1 ( a transcription factor directly downstream of RB1 ) was the most significantly enriched region in the promoters of the genes in this RM ( S6 Table ) . Thus , our algorithm correctly identified a perturbed signaling pathway and its downstream target genes . Fig 4D further shows that proteins encoded by the genes in the PM U_GO0051128 directly interact with other well-known oncogenes , such as TP53 , MDM4 , CCND1 , CCND2 , MYC , E2F3 , E2F5 , BRCA2 , PTEN , MET , and COPS5 ( S7 Table ) . Thus , the results indicate that the signal-oriented approach leads to biologically sensible findings . However , a challenging issue of handling copy number alteration is that a set of genes can be co-amplified ( co-deleted ) within highly overlapping but not perfectly identical copy number alteration fragments in different tumors . Thus , it would be difficult to differentiate the signals of such alterations . For example , RNF139 , TRMT12 , ZNF572 , SQLE , MYC and other genes are often co-amplified but not perfectly correlated in various types of cancers including ovarian cancer ( S2 Fig ) , among which MYC is a known cancer driver gene in numerous types of cancers[47] . When examining other genes in this region , we found that certain amplification events were not associated with corresponding gene expression changes , hence disqualifying these alterations as a potential driving event . Our algorithm returned RNF139 in the solution because the perturbation events of RNF139 ( copy number alteration of a gene with associated expression change ) had strongest information with respect to this RM . While the signal of RNF139 may be convoluted with those of the other genes in the fragment , there is evidence that RNF139 contributes to cancer development by regulating tumor suppressors p27 and VHL[40] that are both critical in cancer development[48 , 49] . Our results indicate that our methods are capable of identifying the copy number alteration fragment that has strong information with respect to an RM as well as finding the most representative gene . The latter is closely related to the capacity of finding the peak of a commonly amplified or deleted region[50] . However , determination of whether this representative gene is an authentic driver requires thorough interrogation of neighboring genes followed by experimental validation . Next , we examined another perturbation module PM D_GO0006915 obtained from TCGA GBM data . The module has five genes MLLT3 , NF1 , MDM2 , LRP1 , and COL24A1 . MLLT3 , NF1 , and COL24A1 are either deleted or mutated while MDM2 is amplified in tumors ( Fig 4E ) . We found that the genomic alterations of these genes suppress the expressions of a set of genes related to apoptotic process of cells . The MDM2 is a well-known oncogene that inhibits tumor suppressor p53 function [51–53] . Inhibition of MDM2 induces cell apoptosis [54 , 55] and reactivates p53 in GBM cells , resulting in inhibition of GBM cell growth in vitro and in GBM xenografts in mice[56] . Thus , it is plausible that amplification of MDM2 in the module PM D_GO0006915 will inhibit cell apoptosis . Inactivation of NF1 by germline mutations are predisposed to the development of benign and malignant tumors of peripheral and central nervous system including GBMs[57 , 58] . Loss of NF1 also renders cancer cell resistant to apoptosis [59] . On the other hand , forced expression of MLLT3 , which is deleted in the module PM D_GO0006915 , significantly increased cell apoptosis [60] . Lastly , low—density receptor protein 1 ( LRP1 ) activates Akt pro-survival pathway thereby inhibit cell apoptosis in neurons[61] . Depletion of LRP1 led to an increase in cell apoptosis [61 , 62] , thus corroborating with our findings that amplification of LRP1 inhibits apoptosis . Since LRP1 mutations have not been reported in cancers , we predict that these mutations exert a similar effect as their gene amplification events . Taken together , our identification of alterations in this PM in GBM validated the established functions of these oncogenic drivers in tumor initiation and progression in GBMs and other types of cancers . Our framework employed two main innovations . First , we revealed the perturbed cell signals from each tumor , and we utilized the information to search PMs in a signal-oriented fashion . Second , we developed an exact algorithm ( the ME algorithm ) that efficiently solves the weighted mutually exclusive maximum set cover problem . In this section , we systematically evaluate the impact of the above approaches on revealing cellular signals and identifying the pathways transmitting them .
The challenge of finding the perturbed signaling pathways that underpin the disease mechanisms and heterogeneity of cancers remains one of the most import areas of cancer genomic research . The future of personalized and precision treatment of cancer patients depends on the ability to identify those pathways and to infer their perturbation states in individual patients for the prescribe targeted treatments[3] . While certain auxiliary properties , such as mutual exclusivity and co-expression of candidate members of a pathway , have been applied in searching signaling pathways with a certain degree of success , these properties are not sufficient for the goal of pathway search , as demonstrated in this study . In this study , we proposed a framework that addresses the crux of the pathway-finding problem: identifying proteins that carry a specific signal . This is achieved by searching for SGAs ( a PM ) that carry strong information with respect to the expression state of an RM , which is a surrogate of the state of a signaling pathway . Since genes in a PM are randomly perturbed by nature , a strong association between genes in a pair of PM and RM usually indicates that perturbation of genes in a PM causes differential expressions of genes in the RM . However , it should be noted that such associations may also result from a selection bias , for example , mutation of one gene and over-expression of another are both enriched in a particular tumor subtype , leading to an apparent association even though they do not have a causal relationship . Therefore , further experiment or detailed causal analysis is needed to validate the potential causal relationship between the PM and RM . Our results indicate that the framework is generalizable in that an unbiased program , such as the Dendrix program , can also benefit from the information of the expression state of RMs to find more informative SGA sets exhibiting mutual exclusivity . Therefore , one main contribution of this work is to demonstrate the utility of framework of deconvoluting cellular signals from molecular phenotypic data , e . g . , gene expression data , and then enabling pathway-searching programs to simultaneously combine multiple objective functions relevant to pathway search , including signal-oriented objective function proposed here , mutual exclusivity of SGAs , and coexpression of SGA-affected genes . In this study , we also proposed an exact algorithm to explicitly take into account the amount of information carried by a set of SGAs with respect to an RM by solving the weighted mutually exclusive maximum set cover problem . While it is possible to use the mutual information [63] between the SGA events of a PM and the expression states of a RM—a quantity that is reversely related to the enrichment p-value—as the goal in searching for a PM , i . e . searching for a maximum information SGA gene set with respect to a given RM , such formulation will lead to a computational problem that is much more difficult to solve . The current algorithm requires that the members in a PM are mutually exclusive , which is a property taken advantage by our algorithm to yield a practical runtime . In practice , such requirement may be too stringent , leading to omission of some solutions . In our future research , we plan to relax this requirement and allow a small degree of overlap into the solution and design an efficient , exact algorithm to address the new problem . | An important goal of studying cancer genomics is to identify critical pathways that , when perturbed by somatic genomic alterations ( SGAs ) such as somatic mutations , copy number alterations and epigenomic alterations , cause cancers and underlie different clinical phenotypes . In this study , we present a framework for discovering perturbed signaling pathways in cancers by integrating genome alteration data and transcriptomic data from the Cancer Genome Atlas ( TCGA ) project . Since gene expression in a cell is regulated by cellular signaling systems , we used transcriptomic changes to reveal perturbed cellular signals in each tumor . We then combined the genomic alteration data to search for SGA events across multiple tumors that affected a common signal , thus identifying the candidate members of cancer pathways . Our results demonstrate the advantage of the signal-oriented pathway approach over previous methods . | [
"Abstract",
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] | [] | 2015 | Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets |
Bacterial pathogens often employ two-component systems ( TCSs ) , typically consisting of a sensor kinase and a response regulator , to control expression of a set of virulence genes in response to changing host environments . In Staphylococcus aureus , the SaeRS TCS is essential for in vivo survival of the bacterium . The intramembrane-sensing histidine kinase SaeS contains , along with a C-terminal kinase domain , a simple N-terminal domain composed of two transmembrane helices and a nine amino acid-long extracytoplasmic linker peptide . As a molecular switch , SaeS maintains low but significant basal kinase activity and increases its kinase activity in response to inducing signals such as human neutrophil peptide 1 ( HNP1 ) . Here we show that the linker peptide of SaeS controls SaeS’s basal kinase activity and that the amino acid sequence of the linker peptide is highly optimized for its function . Without the linker peptide , SaeS displays aberrantly elevated kinase activity even in the absence of the inducing signal , and does not respond to HNP1 . Moreover , SaeS variants with alanine substitution of the linker peptide amino acids exhibit altered basal kinase activity and/or irresponsiveness to HNP1 . Biochemical assays reveal that those SaeS variants have altered autokinase and phosphotransferase activities . Finally , animal experiments demonstrate that the linker peptide-mediated fine tuning of SaeS kinase activity is critical for survival of the pathogen . Our results indicate that the function of the linker peptide in SaeS is a highly evolved feature with very optimized amino acid sequences , and we propose that , in other SaeS-like intramembrane sensing histidine kinases , the extracytoplasmic linker peptides actively fine-control their kinases .
Two-component signal transduction systems ( TCSs ) are a major sensory-regulatory mechanism utilized by most bacteria to monitor and respond to various environmental stimuli such as nutrient concentrations , ionic strength , and antimicrobial substances [1 , 2] . A simple TCS consists of two proteins: a sensor histidine kinase ( HK ) and a response regulator ( RR ) . Upon sensing a cognate ligand , the HK autophosphorylates its conserved histidine residue; then the phosphoryl group is transferred to the aspartate residue of its cognate RR . The phosphorylated RR carries out the adaptive response to the environmental signal , typically by altering gene expression acting as a transcription regulator [3 , 4 , 5] . Although the downstream signaling pathway controlled by the phosphorylated RR is well understood , in most TCSs , the signal sensing step is not clearly defined . Typically , the N-terminus of HKs contains a large extracytoplasmic domain between two transmembrane helices , which is expected to bind cognate signals . However , a subset of HKs , classified as intramembrane-sensing HKs ( IM-HKs ) , lack the extracytoplasmic domain , and their transmembrane helices are connected by a short linker peptide ( <25 amino acids ) , which is too small to function as a signal binding domain [6] . IM-HKs are known to require additional component ( s ) for the signal sensing . For example , BceS and LiaS , the IM-HKs involved in sensing cell wall targeting antimicrobials , need an ABC transporter or a membrane protein to respond to their cognate signals [7 , 8] , indicating that the N-terminal region of IM-HKs is involved in signal transfer , not signal sensing [9] . However , it is not clearly defined how the N-terminal domain transfers the signal to modulate the kinase activity of IM-HKs . In Staphylococcus aureus , an important Gram-positive human pathogen , the SaeRS TCS detects the human neutrophil peptides ( HNPs ) and controls the production of over 20 important virulence factors including alpha-hemolysin ( Hla ) , coagulase ( Coa ) , leukocidins , fibronectin binding proteins ( FnBPs ) , and proteases [10 , 11 , 12 , 13 , 14 , 15] . This TCS consists of an IM-HK SaeS , the response regulator SaeR , and two auxiliary proteins SaeP and SaeQ . The auxiliary proteins SaeP and SaeQ are a lipoprotein and a membrane protein , respectively , and their expression is induced by phosphorylated SaeR ( i . e . , autoinduction ) from the P1 promoter . Upon being induced , the two proteins bind to SaeS and convert SaeS from a kinase to a phosphatase , returning the SaeRS TCS to the ground state [16 , 17] . However , neither protein is involved in sensing HNPs . Two distinct groups of sae targets are known: low affinity ( or class I ) ( e . g . , coagulase [coa] and the P1 promoter of the sae operon ) and high affinity ( or class II ) targets ( e . g . , alpha-hemolysin [hla] ) [18 , 19] . The coa and the P1 promoters have two SaeR binding sites , and their transcription requires the induction of the SaeRS TCS [19] . On the other hand , the hla promoter has one SaeR binding site and is transcribed constitutively regardless of the activation of the SaeRS TCS . In fact , the hla transcription level is not significantly increased upon the HNP1-mediated induction of the SaeRS TCS [16 , 17] . Therefore , as a molecular switch , SaeS requires the following properties: 1 ) Its basal kinase activity should be high enough to support the transcription of the high affinity targets ( e . g . , hla ) but low enough to suppress the expression of low affinity targets ( e . g . , coa and saePQ ) ; 2 ) Its kinase activity should be increased in response to inducing signals . The N-terminal domain of SaeS is predicted to be composed of two membrane helices connected by an extracytoplasmic linker peptide ( hereafter linker peptide ) of nine amino acids ( Fig 1A and S1 Fig ) , although the boundary amino acids of the linker peptide have not been experimentally verified . The transmembrane helices ( TMs ) appear to be critical for controlling the SaeS kinase activity: The L18P mutation in the first TM transforms SaeS into a constitutively active kinase while the I9Q mutation or the double mutation of I9Q/L63Q in the first or the second TM reduces the basal kinase activity [20 , 21] . The linker peptide is also known to be important for the kinase activity . When the linker peptide was shortened by deletion mutagenesis , the resulting mutant SaeS proteins showed a higher ( Δ35–37 ) or a lower ( SaeS Δ34–37 ) basal kinase activity [21] . Flack et al have recently reported that the three conserved amino acids M31 , W32 , and F33 are critical for maintaining the basal kinase activity of SaeS . However , it is still not clear how the N-terminal domain , in particular the linker peptide , controls the kinase activity of SaeS in the absence or presence of the inducing signals such as HNP1 . To investigate the role of the N-terminal domain of SaeS in controlling its kinase activity , we dissected the domain by a comprehensive mutagenesis approach . Our findings suggest that the transmembrane helices and the linker peptide act as a coherent unit and confer responsiveness to specific host signals . In particular , the linker peptide restrains the kinase activity of SaeS and is required for the induction of SaeS’s kinase activity by HNP1 . Moreover , the amino acid sequence of the linker peptide is highly optimized to precisely control the kinase activity of SaeS and staphylococcal virulence in host .
Sequence analysis ( SMART , http://smart . embl-heidelberg . de/ ) predicted that the N-terminal domain of SaeS consists of two transmembrane helices ( a . a . 9–31 , a . a . 41–63 ) connected by a nine amino acid-linker peptide ( a . a . 32–40 in Fig 1A ) [20] . To examine the predicted topology of SaeS , we fused the phoB gene , encoding staphylococcal alkaline phosphatase , to saeS at R6 , G35 , and N71 positions , and assessed the alkaline phosphatase activity . Since alkaline phosphatase is active only in the extracytoplasmic environment , the activity of the enzyme in a fusion protein can reveal the topology of membrane protein [22] . As shown in Fig 1B , the G35 fusion showed significant alkaline phosphatase activity whereas the R6 and N71 fusions did not , demonstrating that G35 of SaeS is exposed to the extracytoplasmic environment , as the topology model suggested . To measure the kinase activity of SaeS , we used a GFP reporter for two promoters: the coagulase promoter ( Pcoa ) and the alpha-hemolysin promoter ( Phla ) . As a low affinity target of the phosphorylated SaeR ( SaeR-P ) , Pcoa can sensitively detect the increase of kinase activity of SaeS; however , it cannot distinguish a small decrease of kinase activity from a large decrease ( S2 Fig ) . On the other hand , as a high affinity target , Phla is rather insensitive to the increase of SaeS’s kinase activity; however , it can detect a large decrease of the kinase activity of SaeS ( S2 Fig ) . In our mutagenesis study , we used a sae deletion mutant complemented with a single copy plasmid where SaeR and SaeS are produced from their native promoter P3 . By using this strain , we eliminated any artifacts from multi-copy plasmids , preserved the stoichiometry of SaeR and SaeS , and avoided complications from the expression of SaeP and SaeQ , which reduce the overall kinase activity of SaeS . First , we replaced either a part of or the entire N-terminal domain of SaeS with the corresponding sequence of GraS , another IM-HK in S . aureus ( Fig 2A ) . When the linker peptide was replaced with that of GraS , neither the protein nor the kinase activity was detected ( SELP in Fig 2B–2D ) , indicating that the hybrid SaeS is unstable . When the regions of the two transmembrane helices or the entire N-terminal domain was replaced with those from GraS , the promoter activity of Pcoa was increased 6–8 times with poor response to HNP1 ( STM and SND in Fig 2C ) . These results suggest that the N-terminal domain is critical in keeping the basal kinase activity low and responding to HNP1 . The SaeS homolog in S . epidermidis ( SaeSepi ) has the same linker peptide sequence as that in SaeS [23] but is predicted to have only one transmembrane helix , whose role in the activation of SaeSepi is not known ( Fig 1A and S1 Fig ) . The SaeSepi protein was not detected by the SaeS antibody ( Fig 2B ) , whereas the FLAG-tagged SaeSepi was detected by anti-FLAG-tag antibody ( S3 Fig ) , indicating a significant antigenicity difference between SaeS and SaeSepi . More importantly , it did not respond to HNP1 ( Sepi in Fig 2C ) . However , when the N-terminal domain of SaeSepi was replaced with that of SaeS , the hybrid SaeS showed a basal kinase activity similar to that of SaeS and responded to HNP1 ( Se-ND in Fig 2C ) , confirming that the N-terminal domain is sufficient to confer a switch function to SaeS . As expected , since their basal kinase activities were not decreased , the promoter activity of Phla was not significantly altered by the hybrid SaeS proteins except for the unstable SaeSELP mutant ( Fig 2D ) . We further investigated the role of each component of the N-terminal domain by deletion mutagenesis . When the first 92 amino acids , which encompasses the entire transmembrane region ( 9–63 a . a . ) , were deleted , the resulting SaeS mutant ( SaeSc ) was locked in the kinase ON state and did not respond to HNP1 ( Sc in Fig 3A–3C ) . When each of the transmembrane helices was deleted , the activity of the reporter promoter Pcoa was increased slightly ( Δ9–31 and Δ41–63 in Fig 3A–3C ) and showed no ( Δ9–31 ) or poor ( Δ41–63 ) response to HNP1 . The poor response to HNP1 was not due to the mislocalization of the proteins because the SaeS mutant proteins were still found in the cell membrane ( S4 Fig ) . When the linker peptide was deleted , despite that no SaeS protein was detected ( Δ32–40 in Fig 3A and 3B ) , Pcoa showed a high promoter activity that did not respond to HNP1 ( Δ32–40 in Fig 3C ) . The other two mutants containing the linker peptide deletion ( i . e . , Δ1–41 and Δ32–92 in Fig 3A ) showed similar results: no or low SaeS protein in Western blot analysis but high basal kinase activity that does not respond to HNP1 ( Δ1–41 and Δ32–92 Fig 3A–3C and S4 Fig ) . No significant change was observed in the Phla activity ( Fig 3D ) , suggesting that no SaeS mutant protein has drastically decreased kinase activity . Taken together , these results indicate that both the transmembrane helices and the linker peptide play a key role in responding to HNP1 and that the linker peptide is critical for restraining the basal kinase activity of SaeS . Since the linker peptide appears critical for the switch function of SaeS ( i . e . , maintaining low basal kinase activity and responding to HNP1 ) , we further examined the role of each amino acid in the linker peptide by alanine scanning analysis . Seven mutant SaeS proteins showed a wild type level of protein expression; however , the N34A and H36A mutants showed either significantly lower ( 40% of wild type level ) or higher expression ( 3 . 3 times wild type level ) , respectively ( Fig 4A ) . In the reporter and coagulase assays , both the uninduced Pcoa transcription and coagulation of rabbit plasma were significantly decreased in the strains carrying W32A , N34A , G35A , or L39A mutant of SaeS , whereas they were greatly increased in those carrying H36A , M37A , or T38A mutant of SaeS ( Fig 4B ) , demonstrating that the basal kinase activity of SaeS can be altered by amino acid changes in the linker peptide . In addition , the Pcoa transcription did not respond to HNP1 in the strains carrying M37A , T38A , or L39A mutant of SaeS , suggesting a critical role of those amino acid residues in responding to the antimicrobial peptide . The Phla promoter activity further confirmed the drastic decrease of basal kinase activity in the W32A , N34A , G35A , and L39A mutants of SaeS ( Fig 4C ) . Both Western blot analysis for Hla and the hemolysis assay on blood agar plates correlated with the Phla promoter activity ( Fig 4C ) . Intriguingly , by an unknown reason , as compared with wild type strain , the F33A mutant strain showed a lower Phla promoter activity despite the fact that Pcoa promoter activity and Hla expression level were rather higher . The sensor kinase SaeS has three enzymatic activities: autokinase , phosphotransferase , and phosphatase . To examine which enzyme activity is affected by the alanine substitutions , we compared those enzymatic activities between the wild type and the following mutant SaeS proteins: SaeS W32A and SaeS G35A ( decreased basal activity and normal induction by HNP1 ) , SaeS T38A ( increased basal activity and no induction by HNP1 ) , and SaeS L39A ( decreased basal activity and no induction by HNP1 ) . To compare the autokinase activity , we mixed an equal amount of maltose binding protein ( MBP ) -SaeS fusion proteins with [γ-32P] ATP and compared the levels of phosphorylated SaeS ( SaeS-P ) at 20 min . As shown , a lower level of phosphorylation was observed with the SaeS mutants that showed lower basal kinase activities in the reporter gene assays ( W32A , G35A , and L39A in Fig 5A ) . Similarly , SaeS T38A , which showed a higher basal kinase activity in the reporter assay ( Fig 4B ) exhibited 2 . 5-fold higher autokinase activity , as compared with the wild type MBP-SaeS fusion protein ( T38A in Fig 5A ) . When the autokinase activity was measured in a time-dependent manner , the SaeS T38A protein autophosphorylated two times faster than did the wild-type SaeS protein , whereas the three mutants with lower autokinase activities ( i . e . , W32A , G35A , and L39A ) displayed a slower rate of autophosphorylation ( Fig 5B and 5C ) . Next , to compare the phosphotransferase activity , we autophosphorylated the MBP-SaeS proteins with [γ-32P] ATP; then , after eliminating the free nucleotide , the phosphoryl transfer was initiated by adding SaeR . As compared with wild type SaeS , the W32A , G35A , and L39A mutants of SaeS exhibited a slower rate of phosphoryl transfer , whereas the SaeS T38A showed a higher phosphoryl transfer rate ( Fig 6D and 6E ) . Taken together , these results indicate that the mutations in the linker peptide directly alter the autokinase and phosphotransferase activities of SaeS . The enzymatic assay results from the purified proteins correlated well with the results of the in vivo reporter assay in Fig 4B . However , since we used purified MBP-SaeS fusion proteins , it was desirable to confirm the results in conditions more closely resembling native conditions . Therefore , we isolated membrane vesicles from S . aureus strains harboring either the wild-type SaeS or the linker mutant SaeS and used the membrane vesicles as the source of SaeS in a phosphotransferase assay . Since the protein expression was different among the SaeS proteins ( Fig 4A ) , the SaeS concentration was normalized by Western blot analysis , and an equal amount of SaeS in the membrane vesicles was mixed with [γ-32P] ATP and SaeR; then the phosphorylation of SaeR was measured in a time-dependent manner . As compared with the membrane vesicle containing the wild type SaeS protein , the vesicles containing W32A , G35A , and L39A mutants of SaeS showed lower phosphotransferase activities whereas the vesicles containing SaeS T38A mutant displayed a higher phosphotransferase activity ( Fig 6 ) , agreeing with the reporter assays ( Fig 4B ) and the phosphotransferase assays with the MBP-SaeS proteins ( Fig 5 ) . These results demonstrate that the linker peptide controls the activity of the SaeRS TCS by altering autokinase and phosphotransferase activity of SaeS . To investigate the precise role of the amino acids in the linker peptide , we replaced the Gly35 with other 19 amino acids and examined their effects on the switch function of SaeS . As can be seen in Fig 7B , substitutions with any polar amino acids abolished both the basal kinase activity and the response to HNP1 . Only G35L and G35F mutants maintained basal kinase activity near wild type level ( L and F in Fig 7B–7D ) . Upon induction by HNP1 , five substitution mutants ( A , L , I , F , and P ) showed a varying degree of responses; however , none of the mutants reached the response level of the wild type ( G ) , suggesting that glycine is the optimal amino acid for SaeS switch function at the position 35 . Results from the Gly35 substitution experiment indicate that the kinase activity of SaeS varies depending on the occupying amino acids . To examine this residue-dependent effect further , we generated a few additional substitutions for other positions and examined their effect on the SaeS switch function . As shown in S5 Fig , SaeS mutant proteins showed disparate basal kinase activities and/or HNP responses depending on the occupying amino acids . Although SaeS F33V showed higher basal kinase activity , the SaeS F33Y mutant showed lower basal kinase activity ( F33 in S5B and S5C Fig ) . The N34Q mutant showed drastically reduced basal kinase activity , whereas N34L showed a wild type level of basal kinase activity ( N34 in S5B and S5C Fig ) . As compared with SaeS L39A , which lost both basal kinase activity and response to HNP1 ( Fig 4B ) , SaeS L39V showed significantly decreased basal kinase activity but normal response to HNP1 ( L39V in S5 Fig ) . These results further confirm that the kinase activity of SaeS sensitively responds to the amino acid changes in the linker peptide . Next , we compared the switch function of the wild type and select mutants of SaeS in various environmental conditions . We subjected the test strains to TSB , RPMI ( Roswell Park Memorial Institute medium ) , human neutrophil , and murine peritoneum; then we measured the Pcoa activity by flow cytometry analysis as an indicator for the SaeS kinase activity . As compared with TSB , in RPMI , the wild type and SaeS T38A mutant showed heightened basal kinase activities , which responded to HNP1 ( Fig 8A ) . On the other hand , the W32A , G35A and L39A mutant SaeS proteins showed almost no basal kinase activity; however , in RPMI , the W32A and G35A mutant SaeS showed a wild type level of induction in response to HNP1 , and even the SaeS L39A , which did not respond to HNP1 in TSB ( Fig 4 ) , did respond to HNP1 in RPMI ( Fig 8A ) . These results suggest that the switch function of SaeS can be altered by growth conditions . When the test strains were subjected to more clinically relevant conditions such as neutrophil and murine peritoneal infection model , the W32A , G35A , and L39A mutant proteins showed significantly lower kinase activity as compared with the wild type and the T38A mutant SaeS ( Fig 8B and 8C ) . Considering the elevated basal kinase activity of the T38A mutant , overall , the wild type SaeS showed the most robust and stable switch function in those environmental conditions , indicating that the amino acid sequence of the linker peptide was highly optimized in SaeS . It should be noted that mice do not produce HNP-like antimicrobial peptides [24] . Therefore , the induction of SaeS kinase activity in murine peritoneum suggests the existence of a novel sae-inducing signal in mice [25] . Finally , we assessed the effect of the linker peptide-mediated alterations of the SaeS kinase activity on S . aureus virulence in a murine model of infection . The strain carrying SaeS T38A , which has elevated basal kinase activity , showed wild type level of virulence while the strains carrying SaeS with lower basal kinase activity ( i . e . , SaeS W32A , G35A , and L39A ) were attenuated ( Fig 9 ) . In parallel , the strain harboring the deletion of the sae operon lost its virulence in infected mice ( Fig 9 ) . These results further confirm the physiological relevance of the linker peptide-mediated control of the SaeS’s kinase activity in the bacterial pathogenesis .
Unlike typical sensor histidine kinases , SaeS lacks a ligand binding domain , and its N-terminal domain is comprised of two transmembrane helices and a linker peptide of nine amino acids . Due to its essential role in staphylococcal virulence and bacterial survival in the host , the sensing mechanism of SaeS has been a focus of extensive research . However , the role of the N-terminal domain , especially the linker peptide , has not been fully understood . In this study , we established that a single amino acid change in the linker peptide can alter the kinase activity of SaeS and cause differential expression of SaeR-regulated genes , resulting in altered virulence of the important human pathogen . In addition , our results suggest that the amino acid sequence of the linker peptide is highly optimized for the proper control of the kinase activity of SaeS . Recently , Flack et al has reported that the three amino acid residues ( i . e . , M31 , W32 , and F33 ) , not the entire linker peptide , are critical for the kinase activity of SaeS [23] . This conclusion is based on the observation that the production of alpha-hemolysin ( Hla ) was greatly reduced in S . aureus strains carrying SaeS with an M31A , W32A , or F33A substitution . However , since the activity of Phla is sensitive only to a large decrease in the kinase activity and insensitive to a moderate decrease or increase of SaeS’s kinase activity [18] ( Figs 2–4 , and S2 Fig ) , the Hla production assay alone cannot detect all changes in the kinase activity of SaeS ( e . g . , the increase of the kinase activity by H36A , M37A , and T38A substitutions . ) In addition , since the authors made only alanine substitutions , the amino acid-dependent changes in the kinase activity of SaeS could not be detected . For example , based on the observation with SaeS M31A , the authors suggested M31 is essential for the kinase activity of SaeS; however , when M31 was substituted with cysteine , the resulting mutant showed constitutively elevated kinase activity ( S5D and S5E Fig ) , demonstrating that M31 itself is not critical for the kinase activity . Intriguingly , sequence alignment of SaeS homologs from Firmicutes revealed that M31 and L41 are highly conserved ( S6A–S6E Fig ) . In addition , sequence alignment of 330 GraS homologs showed the conservation of D35 and Y45 ( S6F Fig ) . We speculate that those conserved amino acid residues play a critical role in hitherto unknown functions such as protein-protein interactions , protein-lipid interactions at the interface , or protein folding . Moreover , the sequence analysis also showed that each bacterial family uses a different set of amino acids for the linker peptide ( S6B–S6E Fig ) , implying that the amino acid sequences of the linker peptide might be differentially optimized for the particular external signals sensed by each bacterial family . The linker peptide is likely to contribute to the switch function of SaeS in two ways . First , it restrains the basal kinase activity of SaeS so that SaeS can function as a molecular switch from the partially ON state to the fully ON state . Without the linker peptide , the kinase activity of the resulting SaeS variant is constantly elevated , even higher than HNP1-induced level , and does not respond to HNP1 ( Δ32–40 in Fig 3B and 3C ) . Second , when S . aureus experiences the host signals , the linker peptide is expected to transduce the external signal input to control the kinase activity of SaeS , possibly via conformational changes . The kinase activity of SaeS sensitively responds to alanine substitutions in the linker peptide ( Fig 4 ) . Since amino acid substitutions with different amino acids resulted in distinct kinase activities ( Fig 7 and S5 Fig ) , it is more likely that the conformational changes caused by the amino acid substitutions , not the biochemical traits of the substituting amino acid itself , altered the enzyme activities of SaeS ( Figs 5 and 6 ) . Therefore , we presume that , in the presence of HNP1 , the linker peptide undergoes conformational changes , and the conformational changes alter the kinase activity of SaeS , possibly via the HAMP ( Histidine kinase , Adenyl cyclase , Methyl-accepting proteins , Phosphatase ) domain ( amino acid 61–114 ) . In this study , we showed that the kinase activity of SaeS can be modulated by amino acid changes in the linker peptide . However , our study does not answer the question of how HNP1 activates the SaeRS TCS . One possibility is that HNP1 activates SaeS by directly binding to the N-terminal domain of SaeS , possibly through interaction with the linker peptide . However , it has been shown that , in certain strains of S . aureus ( e . g . , ISP479R and COL ) , the SaeRS TCS does not respond to HNP1 despite the fact that these strains possesses the wild type SaeS protein [26] , indicating that SaeS alone is not sufficient to respond to HNP1 . In addition , our multiple attempts including co-immunoprecipitation failed to observe any direct interaction between HNP1 and SaeS . Therefore , it is more likely that HNP1 activates SaeS indirectly via a hitherto unidentified receptor molecule ( s ) , and the N-terminal domain of SaeS receives the signal from the receptor molecule as a signal transfer region [9] . Indeed , the BceS/LiaS-like IM-HKs alone cannot perceive stimuli and require additional components such as ABC transporter ( BceS-like IM-HKs ) and membrane protein ( LiaS-like IM-HKs ) for signal sensing [7 , 8 , 27 , 28 , 29 , 30] . Recently , Omae et al showed that the apolipophorin of silkworms represses the kinase activity of SaeS via binding to lipoteichoic acid [21] , indicating that the kinase activity of SaeS can be modulated by interaction with other molecules in the membrane . Although the SaeRS TCS has two auxiliary proteins , SaeP and SaeQ , located in the membrane , they are dispensable for the HPN1 sensing [17] . Therefore , the HNP1-sensing is likely carried out by a receptor molecule ( s ) in the membrane . Upon binding to HNP1 , the HNP1 receptor is expected to induce a conformational change in the N-terminal domain similar to those elicited by H36A , M37A or T38A ( Fig 4B ) by direct protein-protein interaction . Recently T . Mascher postulated that the N-terminal region of IM-HKs is not a signal sensor but a signal transfer region , and that it transduces the external signals to the kinase domain via direct protein-protein interaction with the true sensor molecules [9] . Our results provide indirect evidence that , if such a true sensor exists , the interaction of the N-terminal domain with true sensor molecule ( s ) can modulate the kinase activity of the sensor kinase . We propose that , in the signal transfer process , the entire N-terminal domain of SaeS ( i . e . , two transmembrane helices and the linker peptide ) works as a coherent unit in a manner of a tripwire . In this “tripwire” model , the overall conformation of the entire N-terminal domain is the key determinant in controlling the kinase activity of the sensor kinase . Any stimulus that elicits conformational changes in the N-terminal domain is expected to affect the kinase activity of the sensor kinase either as a repressor or an activator , depending on the nature of the conformational change . In the case of SaeS , if the resulting conformation is similar to that elicited by a W32A , G35A , or L39A substitution , the stimulus will be a repressor while , if the conformation is similar to those elicited by H36A , M37A , or T38A , the stimulus will act as an activator . This tripwire model can also explain how the kinase activity of SaeS is modulated by structurally unrelated molecules such as HNPs , apolipophorin , and beta-lactam antibiotics [21 , 26 , 31] . The receptors for those SaeS modulators are expected to be distinct molecules , possibly interacting with different residues of the N-terminal domain of SaeS . Therefore , it is possible that , by separating the sensing receptor from the signal transfer region , IM-HKs with simple N-terminal domain are able to respond to diverse external signals without losing specificity .
The human subject experiment ( i . e . , purification of human neutrophils ) was approved by the Indiana University Institutional Review Board ( Study number: 1010002390 ) . Before taking blood , informed written consent was obtained from each human subject . The animal experiment was performed by following the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The animal protocol was approved by the Committee on the Ethics of Animal Experiments of the Indiana University School of Medicine-Northwest ( Protocol Number: NW-34 ) . Every effort was made to minimize suffering of the animals . The bacterial strains and plasmids used in this study are listed in S1 Table . Escherichia coli was grown in Luria-Bertani broth ( LB ) , while S . aureus was cultured in tryptic soy broth ( TSB ) or Roswell Park Memorial Institute medium ( RPMI ) . For transduction of plasmids , heart infusion broth ( HIB ) supplemented with 5 mM CaCl2 was used . When necessary , antibiotics were added to the growth media at the following concentrations: ampicillin , 100 μg/ml; kanamycin , 30 μg/ml; erythromycin , 10 μg/ml; and chloramphenicol , 5 μg/ml . Unless stated otherwise , all restriction enzymes and DNA modification enzymes were purchased from New England Biolabs . For PCR amplification , the Phusion DNA polymerase ( New England Biolabs ) was used . Plasmids and genomic DNA were extracted with ZippyTM plasmid miniprep kit ( Zymo Research ) and GenEluteTM Bacterial Genomic DNA kit ( Sigma ) , respectively , according to the manufacturer’s instruction . Plasmid DNA was introduced into E . coli by the method of Hanahan and Meselson [32] and into S . aureus RN4220 by electroporation with a gene pulser ( Bio-Rad ) . Subsequent transduction of the plasmids into target strains of S . aureus was carried out with ϕ85 . To construct NMΔphoB strain , DNA sequences 1 kb upstream and downstream of phoB were PCR-amplified using the primer pairs P2002/2003 and P2004/2005 ( S2 Table ) . The target vector pIMAY was also PCR-amplified with primer pairs P1986/1987 ( S2 Table ) . The PCR products were assembled by the ligation independent cloning method [33] . First , the insert DNA and vector PCR products were treated with T4 DNA polymerase for 30 min at room temperature . Then the DNA fragments were mixed and incubated at 37°C for 30 min and transformed into E . coli . The pIMAY containing the phoB deletion cassette was isolated and electroporated into RN4220 . Subsequently , the plasmid was moved into Newman strain by ϕ85-mediated transduction . The phoB-deletion was carried out by following the procedures previously reported [34] and verified by PCR amplification of the gene locus . To generate PhoB fusions at Arg6 ( R6 ) , Gly35 ( G35 ) and Asn71 ( N71 ) of SaeS , phoB fragments lacking a signal peptide sequence were PCR-amplified with the following primer pairs: P2164/2165 for R6-PhoB , P2107/2108 for G35-PhoB , and P2168/2169 for N71-PhoB ( S2 Table ) . The target vector pCL55-saeRS was PCR-amplified with primer pairs P2162/2163 ( R6-phoB ) , P2103/2104 ( G35-phoB ) , and P2166/2167 ( N71-phoB ) , respectively ( S2 Table ) . All resulting PCR products were treated with T4 DNA polymerase for 30 min at room temperature . The insert phoB fragment and its corresponding vector DNA were mixed and incubated at 37°C for 30 min; then the mixture was transformed first into E . coli , and subsequently into RN4220 and its target strain , NMΔphoB . The test strains were inoculated onto tryptic soy agar plate containing XP ( 5-bromo-4-chloro-3-indolyl phosphate , toluidine salt , 100 μg/ml , Sigma ) [35] . To generate the cytoplasmic domain of SaeS ( SaeSc in Fig 3 ) , the linker peptide deletion mutant of SaeS ( Δ32–40 in Fig 3 ) and the hybrid SaeS with the linker peptide from GraS ( SELP in Fig 2 ) , DNA fragments were amplified from pCL55-saeRS with the phosphorylated primer pairs P1887/1888 , P1958/1960 and P1961/P1962 ( S2 Table ) . The amplified fragments were circularized with T4 ligase and then transformed into E . coli . To generate SaeSND , SaeSepi and SaeSe-ND in Fig 2 , a sequence- and ligation- independent cloning ( SLIC ) method was used [36] . The insert DNA fragments were PCR-amplified with the primer pairs P2061/2062 , P2143/2144 , P2145/2146 , respectively , using genomic DNA of USA300 or S . epidermidis RP62a as a template ( S2 Table ) . On the other hand , the target vectors were PCR-amplified with the following primer pairs: P2059/2060 for SaeSND , P2141/2142 for SaeSepi , and P2147/2142 for SaeSe-ND using pCL55-saeRS as a template . All the resulting PCR products were treated with T4 DNA polymerase for 30 min at room temperature . Then the PCR products and their vector counterparts were mixed , incubated at 37°C for 30 min , and the mixture was transformed into E . coli . To generate SaeSTM ( STM in Fig 2 ) , the phosphorylated primers P2226 and P2227 ( S2 Table ) were used to amply DNA fragment from pCL55-saeRSND ( SND in Fig 2A ) . The amplified fragments were circularized with T4 ligase and then transformed into E . coli directly . Alanine scanning of the extracytoplasmic linker peptide and mutagenesis of Gly35 of the linker peptide were carried out as described by Ho et al[37] . The presence of the mutation was verified by DNA sequencing . To generate a parent plasmid for the promoter-gfp ( green fluorescence protein ) fusions , the gfp fragment was PCR-amplified with primer pair P1969/1970 using pSW4-GFP as a template [38] . The PCR product was digested with KpnI and XhoI and inserted into pYJ335 , resulting pYJ-gfp . To generate gfp fusions for the coagulase promoter ( Pcoa ) and alpha hemolysin promoter ( Phla ) , we used a ligation independent cloning method [33] . First , vector DNA was PCR-amplified from pYJ-gfp using the primers P1969 and P1747 ( S2 Table ) ; then the insert DNA fragment containing the promoter sequence was amplified with primer pairs P1973/1974 for Pcoa , and P1992/1993 for Phla ( S2 Table ) . The PCR products were treated with T4 DNA polymerase in the presence of dCTP ( vector ) or dGTP ( insert DNA ) and mixed together . The DNA mixture was used to transform E . coli DH5α . Once verified , all plasmids were electroporated into S . aureus strain RN4220 and subsequently transduced into the sae-deletion mutant of S . aureus strain Newman ( NMΔsae ) with ϕ85 . Vector DNA was PCR-amplified from pCL55 with primers P1729/P1859 ( S2 Table ) . For insert DNAs , the saeRS region was PCR-amplified from pCL-saeRS with P167/P365 ( for SaeS-FLAG ) , pCL-saeRSELP with P167/P365 ( for SaeSELP-FLAG ) or pCL-saeRSepi with P167/P366 ( for SaeSepi-FLAG ) . The PCR products were treated with T4 DNA polymerase , mixed together , and transformed into E . coli . Overnight cultures of S . aureus strains were diluted 1:100 in fresh TSB and grown at 37°C for 6 h . Cells were collected by centrifugation , suspended in TSM ( 50 mM Tris HCl , 0 . 5 M sucrose , 10 mM MgCl2 , pH 8 . 0 ) containing lysostaphin ( 40 μg/ml ) , and incubated at 37°C for 30 min . After centrifugation ( 4 , 600 ×g , 5 min ) , the protoplast in the pellet was suspended in membrane buffer ( 100 mM Tris HCl , 100 mM NaCl , 10 mM MgCl2 , pH 8 . 0 ) and subjected to sonication . Membrane fractions were recovered by ultracentrifugation ( 120 , 000 ×g ) at 4°C for 30 min and suspended in 1× TKMG buffer ( 50 mM Tris-HCl , 50 mM KCl , 1 mM MgCl2 , 25% glycerol , pH 8 . 0 ) . The supernatant was designated cytoplasmic fraction . All samples were subjected to SDS-PAGE , followed by Western blot analysis . The MBP-SaeS-His6 and SaeR-His6 proteins were overproduced in E . coli BL21 ( DE3 ) harboring plasmids pMCSG19-saeS or pET28a-saeR . Overnight cultures were inoculated into fresh LB broth , and the proteins were expressed by the addition of 1 mM of isopropyl-1-thio-β-D-galactopyranoside ( IPTG ) to the fresh culture . The bacterial culture was further incubated at 16°C for 16 h ( MBP-SaeS-His6 ) or at 37°C for 6 h ( SaeR-His6 ) . The proteins were purified with Ni-column chromatography ( Qiagen ) by following the manufacturer’s recommendations . The purified MBP-SaeS-His6 and SaeR-His6 were dialyzed first in 1× TKM buffer ( 50 mM Tris-Cl , 50 mM KCl , 1 mM MgCl2 , pH 8 . 0 ) and 1× TBS buffer ( 10 mM Tris-HCl , 138 mM NaCl , 2 . 7 mM KCl , pH 7 . 5 ) , respectively , and then in TKM or TBS buffer containing 25% glycerol . The purified proteins were concentrated with Amicon Ultracell-30 ( MW 30 , 000; Millipore ) for MBP-SaeS-His6 or Ultracell-15 ( MW 10 , 000; Millipore ) for SaeR-His6 . Protein concentration was determined by the bicinchoninic acid assay ( Bio-Rad ) , and the purified proteins were stored at -80°C until used . The MBP-SaeS-His6 ( 5 μM ) protein was incubated with 30 μCi of [γ-32P] ATP in 70 μl of TKM buffer . The reaction was started with the addition of ATP to the mixture at room temperature . At various time points , the reaction was stopped by mixing a 7 μl aliquot with 6× SDS sample buffer . The samples were kept on ice until loaded onto a 10% Bis-Tris gel ( Invitrogen ) . After electrophoresis , the gel was autoradiographed , and the degree of phosphorylation was determined with phosphor imaging plate ( GE ) , a Typhoon FLA 7000 imaging system , and Multi Gauge software ( Fuji Film ) . The data were fitted using nonlinear regression to a one-phase exponential association ( Prism 5 , GraphPad ) . Data represent mean values of at least three independent experiments . To phosphorylate MBP-SaeS-His6 , 10 μM of MBP-SaeS-His6 was mixed with 0 . 1 mM of ATP containing 30 μCi [γ-32P] ATP in TKM buffer and incubated at room temperature for 30 min . Excess [γ-32P] ATP was removed with a Micro Bio-Spin 6 Column ( Bio-Rad ) equilibrated with TKM buffer . Seven microliters of the phosphorylated MBP-SaeS-His6 ( MBP-SaeS-His6-P ) were kept as a reference . To start the phosphotransfer reaction , the MBP-SaeS-His6-P protein was mixed with 10 μM of SaeR-His6 in TKM buffer and incubated at room temperature . The reaction was stopped at various time points by mixing a 7 μl aliquot with 6× SDS sample buffer . Samples were kept on ice until SDS-PAGE . After electrophoresis , the gel was autoradiographed , and the degree of phosphorylation was determined as described above . Data represent mean values of at least three independent experiments . S . aureus strains were grown at 37°C to exponential growth phase ( OD600 ≈ 0 . 5 ) at 37°C . Cells were collected , washed once with 10 mM Tris-HCl ( pH 8 . 0 ) , and suspended in TSM buffer ( 20 mM Tris-HCl , 0 . 5 M sucrose , 10 mM MgCl2 , pH 8 . 0 ) containing lysostaphin ( 40 μg/ml ) , followed by incubation at 37°C for 30 min . After centrifugation ( 4 , 600 ×g , 5 min ) , the pellet was suspended in ice-cold membrane buffer ( 10 mM Tris-HCl , 100 mM NaCl , 10 mM MgCl2 , pH 8 . 0 ) and subjected to sonication . Non-ruptured protoplasts were removed by a brief centrifugation at 4 , 600 ×g , and the membrane fraction was recovered after a 45 min centrifugation at 45 , 000 ×g ( Beckman L8-55 ) . The membranes were suspended in 10 mM Tris-HCl ( pH 8 . 0 ) , 2 M KCl and centrifuged for 30 min at 120 , 000 ×g . The supernatant was discarded , and the pellet was suspended in 10 mM Tris-HCl ( pH 8 . 0 ) , 5 mM EDTA . Finally , the membranes were suspended in 1× TKMG buffer ( 50 mM Tris-HCl , 50 mM KCl , 1 mM MgCl2 , 25% glycerol , pH 8 . 0 ) . The protein concentration was determined by the bicinchoninic acid assay ( Bio-Rad ) and immunoblotting with anti-SaeS antibodies . The membranes were stored at -80°C until used . Three hundred microgram of membrane vesicles harboring either the wild-type SaeS or the linker peptide mutant SaeS proteins and 10 μM of the purified SaeR-His6 proteins were incubated with 20 μCi of [γ-32P] ATP ( 3000 Ci/mmol; Perkin Elmer ) in TKM buffer at room temperature . A 7 μl aliquot was mixed with 6× SDS sample buffer at different time points to stop the reaction . The phosphorylated SaeR-His6 proteins were separated by 10% Bis-Tris SDS-PAGE and determined by quantifying the [32P]-labeled species using a Typhoon FLA 7000 imaging system and phosphor imaging plate ( Fuji Film ) followed by quantification with Multi Gauge software ( Fuji Film ) . The data were fitted using nonlinear regression to a one-phase exponential association ( Prism 5 , GraphPad ) . Data correspond to mean values of at least three independent experiments . Peripheral blood neutrophils were isolated from healthy adult blood donors by a method of dextran sedimentation and discontinuous Percoll gradient , as previously described [39] . Remaining red blood cells were removed by hypotonic solution ( eBioscience ) , and neutrophil purity was determined by flow cytometry with anti-CD3 ( OKT3 , eBiosience ) and anti-CD16 ( B73 . 1 , eBioscience ) . Purified neutrophils were maintained in RPMI 1640 medium supplemented with 10% human serum . For preparation of the human serum , non-heparinized human blood was allowed to clot at 37°C for 1 h and centrifuged at 12 , 000 ×g for 15 min . Supernatant serum was collected , filtered through 0 . 22 μm and stored at -80°C . We performed the GFP reporter assays using either microplate reader or flow cytometry . SaeS , SaeR , and a loading control were detected from whole cell lysates harvested during the fluorescence Phla or Pcoa reporter assay . Cell pellets were suspended in 20 mM Tris-HCl buffer ( pH 8 . 0 ) containing a protease inhibitor cocktail ( Complete mini , Roche ) , and cells were lysed using lysostaphin ( 40 μg/ml ) and DNaseI in a 37°C heat block for 30 min . An equal volume of 2× SDS loading buffer was added to the cell lysates , followed by heating for 5 min . After brief centrifugation , 6 μl of each sample was subjected to 12% SDS-PAGE , and proteins were transferred to Protran BA nitrocellulose membranes ( Whatman ) . Membranes were blocked with 10% skim milk ( wt/vol ) in TBST ( 20 mM Tris-HCl , 137 mM NaCl , and 0 . 05% Tween 20 , pH 7 . 6 ) for 1 h . SaeR or SaeS antibody was diluted 1:2000 in TBST containing BSA ( 1 mg/ml ) ( TBST-B ) and incubated with the membranes at room temperature for 1 h . Membranes were washed three times for 5 min each with TBST and then incubated with the secondary antibody , anti-Rabbit IgG-peroxidase ( Sigma ) at a 1:5000 dilution in TBST-B for 1 h . Signals were detected by SuperSignal West Pico chemiluminescent substrate ( Thermo Scientific ) and visualized using a LAS-4000 ( GE Healthcare ) . The densities of the SaeS protein bands were determined by quantification with ImageQuant software TL ( GE Healthcare ) . To visualize equal loading , signals detected from non-specific binding of the SaeS antibody to an unknown cellular protein are shown . All Western blots were repeated at least three times with similar results . To assess blood coagulating activity of the bacterial cells , overnight cultures of S . aureus strains were diluted 1:100 into fresh TSB and grown at 37°C for 4 h . Fifty microliters of bacterial culture were added to 0 . 5 ml of rehydrated BBL coagulase plasma with EDTA ( rabbit , Becton Dickinson ) in a sterile glass test tube and incubated at 37°C until a clot formed in the plasma . Coagulation time was recorded by observing the formation of a clot in the plasma as a function of time . Test strains were grown in TSB to exponential growth phase ( OD600 = 1 . 0 ) . One microliter of the cell suspension was spotted onto tryptic soy agar containing 5% rabbit blood ( Becton Dickinson ) , and the plates were incubated at 37°C for 24 h . The hemolysis zones of colonies were imaged using a Canon ELPH-100HS camera ( Canon ) , and the images were adjusted by Adobe Photoshop CS3 ( Adobe ) . All hemolysin assays were performed in duplicate and repeated three times with similar results . NMΔsae ( pCL55-saeRS ) strains carrying wild-type or mutant SaeS were grown in TSB to the exponential growth phase ( OD600 = 1 . 0 ) . Cells were washed in phosphate-buffered solution and suspended in PBS to OD600 = 0 . 4 . The bacterial suspension ( 107 cfu in 100 μl ) was administered into 10 sex-matched 8 week-old Balb/c mice ( Harlan ) via retro-orbital injection . The infected mice were watched for 20 days . The survival curves were compared by Log-rank ( Mantel-Cox ) test with Prism 5 ( GraphPad ) . Sequence comparison was carried out with the databases and the programs made available by the National Center for Biotechnology Information [40] . SaeS and GraS homologs were identified with PSI-BLAST search with the wild type SaeS and GraS protein sequences . Among the identified sequences , the ones with unusual features such as long linker peptides were removed by visual inspection , leaving 325 SaeS ( 96 from Staphylococcaceae , 136 from Streptococcaceae , 72 from Bacillaceae , 6 from Listeriaceae , 15 from other bacteria ) and 330 GraS homolog sequences ( 116 from Staphylococcaceae , 203 from Bacillaceae , 11 from other bacteria ) . Sequence logos were prepared with WebLogo [41] . saeS ( NCBI-Gene ID:3913143 ) ; saeSepi ( NCBI-Gene ID: 3241744 ) ; saeR ( NCBI-Gene ID: 3913605 ) ; graS ( NCBI-Gene ID: 3913958 ) ; gfpopt ( GenBank: FJ169508 . 1 ) ; phoB ( NCBI-Gene ID: 3914423 ) . | A bacterial pathogen Staphylococcus aureus uses the SaeRS two-component system to control the production of multiple toxins , resulting in a wide range of diseases in human . The sensor kinase SaeS is a member of the intramembrane-sensing histidine kinases ( IM-HKs ) that lacks a sensory domain and harbors a simple N-terminal domain with two transmembrane helices and a short linker peptide . It’s been considered that the linker peptide of IM-HKs transmits the external signals into the cytoplasmic catalytic domain to control the HK’s kinase activity . However , it is unclear how the external signal input propagates through the linker to modulate the kinase activity of HKs . Here we show that the linker peptide of SaeS is critical in maintaining the basal kinase activity and functions as a part of a “tripwire” to jumpstart the activation of the SaeRS system upon exposure to the specific host signals . We establish that a single amino acid substitution of the linker peptide alters SaeS’s kinase activity , resulting in different expression levels of the SaeR-activated genes and alteration of the bacterial virulence in mice . Our study provides new molecular insights into how the pathogenic bacterium utilizes the simple protein domain to control its disease-causing potentials in response to host immune signals . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | The Extracytoplasmic Linker Peptide of the Sensor Protein SaeS Tunes the Kinase Activity Required for Staphylococcal Virulence in Response to Host Signals |
Tick-borne zoonoses in the Order Rickettsiales and Legionellales cause infections that often manifest as undifferentiated fevers that are not easy to distinguish from other causes of acute febrile illnesses clinically . This is partly attributed to difficulty in laboratory confirmation since convalescent sera , specific diagnostic reagents , and the required expertise may not be readily available . As a result , a number of tick-borne zoonoses are underappreciated resulting in unnecessary morbidity , mortality and huge economic loses . In Iran , a significant proportion of human infectious diseases are tick-borne , with anecdotal evidence suggesting that tick-borne zoonoses are widespread but underreported in the country . Epidemiological review is therefore necessary to aid in the effective control and prevention of tick-borne zonooses in Iran . The aim of this review is to provide an in-depth and comprehensive overview of anaplasmosis , ehrlichiosis , spotted fever group rickettsioses and coxiellosis in Iran . Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) guidelines , all relevant publications on tick-borne zoonoses in the Order Rickettsiales and Legionellales in Iran were searched using a number of search terms . The search was confined to authentic resources from repositories of popular data bases among them PubMed , Web of Science , Google Scholar , Science Direct , SpringerLink and SCOPUS . The search items included peer reviewed journals , books and book chapters published between 1996 and 2017 . A total of 1 205 scientific publications and reports were sourced , of which 63 met the search criteria and were reviewed . Of the 63 articles reviewed , 36 ( 57 . 1% ) reported on coxiellosis , 15 ( 23 . 8% ) on anaplasmosis , 11 ( 17 . 5% ) on ehrlichiosis and 1 ( 1 . 6% ) on spotted fever group rickettsiae in a large scale study involving four countries , among them Iran . The existence of tick-borne pathogens in the Order Rickettsiales and Legionellales was confirmed by molecular , serological and microscopic techniques conducted on samples obtained from sheep , cattle , goats , camels , poultry , animal products ( milk and eggs ) , dogs , ticks and even human subjects in different parts of the country; pointing to a countrywide distribution . Based on the review , coxiellosis , anaplasmosis , ehrlichiosis , and SFG rickettsiae can be categorized as emerging tick-borne zoonotic diseases in Iran given the presence of their causiative agents ( C . burnetii , A . phagocytophilum , A . marginale , A . bovis , A . ovis , A . central , E . canis , E . ewingii , E . chaffeensis and R . conorii ) collectively reported in a variety of domestic animals , animal products , arthropods and human beings drawn from 22 provinces in Iran . Given the asymptomatic nature of some of these zoonoses , there is a high likelihood of silent transmission to humans in many parts of the country , which should be considered a public health concern . Presently , information on the transmission intensity of tick-borne zoonoses caused by pathogens in the Order Rickettsiales and Legionellales to humans and its public health impact in Iran is scanty .
Coxiella burnetii is a zoonotic and strictly intracellular Gram-negative bacterium that belongs to the Gammaproteobacteria , and is the agent of Q fever [12] . The main reservoirs of C . burnetii are cattle , sheep , and goats . However , in recent years , an increasing number of animals have been reported to shed the bacterium , including domestic mammals , reptiles , marine mammals , ticks , and birds [13] . While birth products contain the highest concentration of the bacteria , C . burnetii is also found in urine , feces and milk of infected animals [14] . The feces of ticks infected with C . burnetii have particularly high concentrations of viable organisms capable of persisting for relatively longer periods in the environment , and as such , ticks play a crucial role in the circulation of the pathogen [15] . Transmission to human most frequently occurs through inhalation of aerosolized bacteria that are spread in the environment by infected animals after delivery or abortion . Such windborne outbreaks can affect dozens to hundreds of people who may not have direct exposure to the infected animals . Varying incidence levels of Q fever have been reported globally since the disease was first discovered in 1937 in Australia [16] . In the United States , Q fever became a reportable disease in 1999 after which an increase of 250% was reported in the number of human cases between 2000 and 2004 . This was attributed to better recognition of cases [17] . According to CDC [13] twenty seven EU/EEA countries provided information on Q fever in 2014 . A total of 822 cases were reported to The European Surveillance System ( TESSy ) , 782 of which were confirmed ( 95 . 1% ) . Most of the cases were reported in Germany ( 262 , 90 . 1% of which were confirmed ) and France ( 209 , all confirmed ) . Between 2007 and 2010 , more than 4 , 000 human cases ( with 14 deaths ) were reported in Netherlands in the largest Q fever epidemic ever to be reported in that country . Kaplan and Bertagna [18] reported initial cases of Q fever in nine African countries in 1955 . Seroprevalence in humans ranged between 1 and 32% in Chad , Egypt , Coˆte d’Ivoire , Burkina Faso and Dar es Salaam Tanzania [19] . Coxiellosis has been recognized as being endemic in a number of countries in the Middle East region . In Cyprus , coxiellosis was recognized since 1951 [18 , 20 , 21 , 22 , 23] , while in Israel , the first proven case of Q fever ( 75 cases in Haifa area ) was reported in 1949 [24] . Subsequently , Q fever outbreaks have been reported in Iraq , Israel , Turkey , Saudi Arabia and Egypt with prevalence rates ranging between 0% and 90% among different organisms in different countries [25] . In Iran; also within the Middle East , the first clinical case of acute coxiellosis was reported in 1952 , with many more cases being reported between 1970 and 1976 in different countries in the Middle East region [26] . After the 70s , coxiellosis remained neglected in Iran until 2007 when C . burnetii antibodies were reported among febrile patients in Kerman province , southeastern Iran [27] . Since then , many studies have been conducted among different organisms including human subjects and shown that the disease is endemic in Iran [28] . Nevertheless , the information available is scattered , disjointed and largely insufficient to give the overall prevalence of Q fever in Iran , necessitating this systematic review . Anaplasma species of the family Anaplasmataceae , Order Rickettsiales are tick-borne pathogens that can cause a number of diseases in animals and humans . Six species; Anaplasma ovis , A . marginale , A . centrale , A . platys , A . bovis and A . phagocytophilum are well recognized [29 , 30] . These species are obligate intracellular bacteria that parasitize erythrocytes and monocytes of higher vertebrates; mostly ruminants and are particularly important among livestock breeders . Anaplasma platys is mainly pathogenic to canines [31] , while A . ovis mainly affect goats , cattle and sheep . Anaplasma marginale , A . bovis , and A . centrale are well recognized in cattle but also cause sub-clinical or mild conditions in other domestic animals [32] . Anaplasma phagocytophilum is the etiologic agent of Human Granulocytic Anaplasmosis ( HGA ) but also affects a number of animals such as horses , cattle , sheep , goats , dogs and cats . Small rodents also harbor different Anaplasma spp . and thus act as potential reservoirs [33] . Anaplasmosis is widely distributed throughout the world including tropical and sub-tropical areas of South , Central and North America , Australia , Asia and Europe [34] , with a prevalence ranging between 1–100% [35] . A number of anaplasmosis causing pathogens to have been reported in different countries including: United States [36] , Venezuela [37] , Cyprus [38] , China [39] , Spain [40] , among many others . In the United States of America , anaplasmosis has been reported in almost every state; a phenomenon attributed to cross border movement of carrier cattle and subsequent mechanical or biological transmission of the pathogens to susceptible livestock [41] . In all Latin America countries and the Caribbean Islands , anaplasmosis is enzootic with the exception of desert areas and certain mountain ranges ( Andes ) [42] . In Africa , outbreaks of bovine anaplasmosis have been reported in different countries including Kenya , Tanzania , Morocco , Uganda , Ghana among others [43] . In Europe , granulocytic anaplasmosis is the most widespread tick-borne infection in animals [44] and both its geographic distribution and that of its vector ( Ixodes ricinus ) is increasing in latitude and altitude [45 , 46] . In the Middle East , Anaplasma spp . seems to be prevalent in the region; going by the available studies . Records contained by the AU-IBAR , show that anaplasmosis was reported in a number of countries among them Iraq , UAE , Egypt , Qatar , Cyprus , Israel and Jordan as at 2011 [6 , 7 , 8 , 9] . However , the report also revealed that many other countries from the Middle East region did not have any study reporting on anaplasmosis nor did it have any reported cases of Anaplasma spp . Nevertheless , a number of independent studies have reported cases of various Anaplasma spp . in different organisms in the region . For instance , in a study described as the first ever among cattle in Turkey , A . phagocytophilum was reported as the most prevalent ( 30 . 8% ) followed by A . marginale ( 18 . 8% ) , A . centrale ( 18% ) , and A . bovis ( 1/133 , 0 . 7% ) in that order [47] . A . phagocytophilum has also been detected in goats and sheep in Cyprus , and also in ticks in Israel [48] , while Anaplasma platys has been reported among dogs in Cyprus and many other countries [49] . Since its’ first description in Palestine in the early 1920s by Gilbert [50] , A . ovis has been reported among different animals in Turkey [51] , Cyprus [52] , Jordan [53] , Iraq [54] and many other countries in the Middle East . Likewise , A . centrale has also been reported in different countries in the Middle East [55] . In Iran , five species in the genus Anaplasma namely: A . ovis , A . bovis , A . marginale , A . centrale and A . phagocytophilum have been isolated from cattle , sheep , goats , ticks and human serum samples; though most studies on anaplasmosis are limited to a section ( northern part ) of the country . From the foregoing , it is evident that Anaplasma spp . pathogens are circulating in a number of animal reservoirs in Iran making tickborne zoonoses highly endemic in the country . However , studies on the different pathogenic species are patchily distributed among the countries of the Middle East region , making it difficult to establish the actual prevalence of anaplasmosis in the region . Ehrlichiosis comprises of a group of emerging infectious tick-borne zoonoses that are caused by obligate intracellular Gram-negative bacteria in the genus Ehrlichia , family Anaplasmataceae , Order Rickettsiales [56] . Several species of Ehrlichia such as E . chaffeensis , E . muris , E . ewingii , E . canis , E . equi , E . ruminantium and E . risticii are known to infect a number of animals with E . canis being the main species that infects dogs; producing several clinical symptoms [57] . Apart from dogs , Ehrlichia spp . are also known to cause illness not only in human beings but also in other animals like cattle , sheep , goats , horses , dogs , deer , rodents and mice [58 , 59] . Human Monocytic Ehrlichiosis ( HME ) that is caused by Ehrlichia chaffeensis was first described in 1986 , and after about 2 decades , more than 2 , 300 cases had been reported to the CDC [60] . This makes it the most prevalent life-threatening tick borne disease in the US where the disease has been reported in the south-central , southeastern and Mid-Atlantic States [60 , 61] . Apparently , the occurrence of the disease corresponds to regions where the white-tailed deer ( Odocoileus virginianus ) and lone star ticks ( Amblyomma americanum ) exist . Ehrlichia chaffeensis has also been detected in Africa [62] . Ehrlichia ewingii that has recently been associated with human infection [63] is prevalent in North America but has also been detected in South America and Africa in recent times . Only a few human cases have been documented , in the USA; mainly in Tennessee , Missouri and Oklahoma . However , E . ewingii infection in other animals like deer , dogs and ticks has been reported throughout the geographical range of the lone star tick; suggesting that human infection with this pathogen might be more widespread than previously thought [64] . Canine Monocytic Ehrlichiosis ( CME ) that is caused by E . canis has a worldwide distribution with varying seroprevalence rates being reported in different countries . CME has been reported in the United States and in many other countries among them Cameroon with a prevalence of 32% , [65] , Mexico with 44% [66] , Grenada with 24% [67] , South Africa with 3% , Zimbabwe with 52% [68] and Brazil with 2–6% [69] . However , no reports show serological presence of CME in Australia . Ehrlichia muris was first detected in 2009 among 3 symptomatic patients in Wisconsin and 1 in Minnessota USA and has also been found in I . persulcatus complex ticks in Minessota , Wisconsin , Eastern Europe and Japan suggesting that this species may be a potential vector [70 , 71] . Equine Granulocytic Ehrlichiosis ( EGE ) is a disease of horses caused by rickettsial bacteria in the genus Ehrlichia . Two Ehrlichia species that are known to infect horses include E risticii which infects monocytes and E equi that infects granulocytes and is transmitted by the tick Ixoides pacificus [72] . Equine granulocytic ehrlichiosis was first reported among horses in northern California by Gribble in 1969 [73] . Since then , the disease has been reported in Minnesota , Wisconsin , Colorado , Illinois , Florida , Washington , Connecticut , New York and New Jersey in the United States , as well as in Germany , Switzerland , Sweden , and Israel [74 , 75 , 76] . In the Middle East region , outbreaks of canine monocytic ehrlichiosis have been reported in countries such as Saudi Arabia [77] , while limited studies on CME have been conducted in other countries like Turkey . A few reports have also documented seropositivity [78] , clinical case treatment [79] and molecular prevalence [80] of CME in Turkey , while a CME prevalence of 17–26% was reported in Israel [81] . In addition , cases of Equine Granulocytic Ehrlichiosis ( EGE ) have also been reported in Israel [74 , 75 , 76] . Despite being an emerging disease in humans and animals , ehrlichiosis , is not as extensively studied in the Middel East countries as it ought to be [82] . In Iran , Ehrlichia species have been detected circulating among dogs and ticks in 12 provinces , most of them located to the northern part of the country . This points to the presence of Ehrlichia spp . in the country , thus posing a risk of transmission to humans . Spotted Fever Group ( SFG ) Rickettsioses comprises several divergent lineages including: Rickettsia rickettsii group , R . japonica , R . montana , R . massiliae group , R . helvetica , R . felis , and R . akari group [2] . SFG rickettsioses occur worldwide and may cause serious diseases in humans . They are transmitted to people by arthropod vectors , such as ticks , fleas , and lice [2] . A number of spotted fever group rickettsial pathogens have been reported across the world . These include: Rickettsia aeschlimannii , R . africae , R . australis , R . conorii , R . helvetica , R . heilongjiangensis , R . japonica , R . massiliae , R . monacensis , R . raoultii , R . parkeri , R . rickettsia , R . slovaca , R . sibirica subsp . mongolotimonae , R . honei and R . marmionii . Collectively , these rickettsial pathogens were reported in the North , Central and South America , Australia , France , Greece , Spain , Portugal , Switzerland , Argentina , Russia , China , Thailand , Kosovo , Mongolia , India , South Africa , Morocco , Mali , Kenya among other countries [5 , 83 , 84 , 85 , 86 , 86 , 87 , 88] . In the Middle East , a number of known species of the genus Rickettsia have been documented , though some of the studies are questionable [89] . Al-Deeb et al . [90] , reported what they regarded as the first record of a spotted fever group Rickettsia spp . in ticks collected from camels in the United Arab Emirates , while Rickettsia sibrica mongolitimonae was diagnosed in a male patient in Turkey in 2016 [91] . Mediterranean Spotted Fever ( MSF ) caused by Rickettsia conorii subsp . israelensis and regarded as the primary cause of spotted fever group rickettsiosis in Israel has been detected in ticks collected from roe , deer , addax , red fox and wild boars in the country [92] . In addition , Ereqat et al . [93] detected rickettsial DNA in 148 out of 867 ( 17% ) ticks that were tested in Western Bank , Palestinian Territories , while Chochlakis et al . [94] detected rickettsial DNA in 315 ticks ( 8% ) of the 3 , 950 ticks screened , in Cyprus . Idris et al . [95] investigated 347 human sera for rickettsial infections in Dhofar , Oman and established that 59% gave positive reactions . The authors concluded that rickettsial infections were common among the rural population of Dhofar in Oman . Presence of a high variety of ticks which can transmit rickettsiae in several countries including Yemen [96] , Saudi Arabia [97] and Oman [98] , all within the Middle East region makes it highly likely for the disease to occur in the Middle East region . Despite clear evidence of presence of SFG rickettsial pathogens in the Middle East region , no studies have been conducted and reported on the disease within Iran . It is therefore necessary to initiate studies on SFG Rickettsiae to better understand the epidemiology in Iran . Ticks are considered second only to mosquitoes as important vectors of human infectious diseases across the world [99] . The obligate hematophagous arthropods parasitize every class of vertebrates in almost every region of the world [15] . For each bacterial disease , one or several tick vectors and one or several reservoirs may exist [100] . Pathogens that transmit tickborne zoonoses rely on a number of ticks in the family Ixodidae ( the hard ticks ) among them: Amblyomma , Dermacentor , Haemaphysalis , Hyalomma and Rhipicephalus as vectors and vertebrates such as small mammals , sheep or deer as their reservoir hosts [101] . Only Ixodid ticks are regarded as diseases vectors and pathogens are normally maintained in these ticks by transstadial and transovarial transmission [101] . Tickborne pathogens in the Order Rickettsiales and Legionellales normally infect and multiply in almost all organs of ticks , particularly the salivary glands . This enables ease of transmission of the pathogens from the vectors to the vertebrate hosts through bites of infected ticks during feeding . Transmission can also be through inoculation of infectious fluids or feces from the ectoparasites into the skin . Indirect routes of transmission such as contamination of abraded skin or the eyes following crushing of ticks between the fingers also exist . Infectivity of the reservoir hosts , presence of the tick , infestation rate as well as host density are major variables that determine the epidemiology of tick-transmitted diseases [102] . According to Shemshad et al . [103] , a number of Ixodid tick fauna obtained from cattle , sheep , goats , dogs and even shrubbery have been identified in different parts of Iran . The ticks include: Haemaphysalis concinna , H . sulcata , Hyalomma anatolicum , Hy . asiaticum , Hy . detritum , Hy . dromedarii , Hy . marginatum , Hy . schulzei , Rhipicephalus bursa , R . sanguineus , Ixodes ricinus and Dermacentor spp . These ticks have the ability to infest a wide variety of hosts including mammals , birds , reptiles and amphibians [104] . Each tick species has preferred environmental conditions and biotopes that determine its geographic range . As such , tick-borne rickettsioses and coxiellosis infections are geographically localized and mainly occur in foci with optimal conditions for the ticks and other animals involved in the circulation of the bacterial pathogens . The presence of a number of Ixodid ticks in Iran therefore implies that there could be many natural foci of tickborne zoonoses in the country from which they may spread to other areas under changing socio-economic and climatic conditions . Despite this fact , tick-borne zoonoses in the Order Rickettsiales and Legionellales have not received the level of public attention that has been paid to other maladies , largely because their presence or the true magnitude of their occurrence is still under reported . Therefore , like in other countries across the world , tick-borne zoonoses are a public health concern in Iran . In recent years , researchers have shown inceased interest on tick-borne zoonotic diseases in Iran . However , the available studies are fragmented and region-specific making it difficult to ascertain the magnitude of these tick-borne diseases in Iran . The aim of this systematic review is to provide an in-depth and comprehensive overview of tick-borne zoonoses in the Order Rickettsiales and Legionellales in Iran .
The review considered only those publications reporting on tick-borne zoonoses caused by pathogens in the Order Rickettsiales and Legionellales and conducted between 1996 and 2017 in Iran . In this systematic review , the Preferred Reporting Items for Systematic reviews and Meta-Analyses ( PRISMA ) and guidelines proposed by Arksey and O'Malley [105] were used to select publications and reports on tickborne zoonoses in the Order Rickettsiales and Legionellales in Iran . Using the PRISMA guidelines , published literature on Anaplasmosis , Ehrlichiosis , Spotted Fever Group rickettsiae and Coxiellosis in Iran were systematically searched in PubMed , SpringerLink , SCOPUS , Web of Science , WHOLIS , and U . S Center for Disease Control and Prevention ( CDC ) databases . To maximize the search and reduce selection bias , the search was restricted to English articles using a number of key terms such as: “Rickettsioses in Iran”; “Rickettsia”; “Tick-borne Rickettsiales in Iran”; “Anaplasmosis”; “Ehrlichiosis”; “vectors of rickettsioses”; “Q fever in Iran”; “Coxiellosis”; “Tickborne Legionellales in Iran” among others . During the initial search , all articles identified from the indexed databases were selected based on their titles and later screened for eligibility based on the content of their abstracts . A full text review of all articles deemed relevant was then conducted . Data was extracted from the selected publications by filling a table containing six main sections , namely: year of study , disease and study technique used , study region , sample size and organism studied as well as author and year or publication . Another table was used to summarize the data by highlighting the study organism/subject , study area , sample size and author of the article . Disease prevalence was also reported in percentages and their 95% CI provided . The systematic review followed PRISMA guidelines and a PRISMA check list which is provided as a supplementary material ( S1 Checklist ) . The DOI link of the protocol used as a guide in conducting this systematic review can be accessed online through the following link: https://dx . doi . org/10 . 17504/protocols . io . njbdcin . Only studies describing findings on anaplasmosis , ehrlichiosis , spotted fever group rickettsioses and coxiellosis infections affecting humans , animals , animal products and arthropods ( ticks ) in Iran were included in this review . All non-verified sources of information and studies conducted in other parts of the world besides Iran were excluded from this review . This review presents some limitations with regards to missing publications , language bias and publication bias . The combination of terms entered in each individual search aimed at retrieving as many relevant publications as possible but also narrowing down on the amount of results . Hence , it is highly likely that relevant research articles , which did not contain the specified key words in their titles or abstracts , may have been overlooked . In addition , some of the articles retrieved were not written in English and were thus not included in the study , presenting a major bias towards English publications . Furthermore , all the selected publications were obtained through electronic search , thus we acknowledge a bias towards articles published online . Most of the publications included in this review were cross-sectional in design , reporting on anaplasmosis , ehrlichiosis , SFG rickettsioses and coxiellosis infections in a number of organisms at a specific point in time . These types of studies can be subjected to selection and information bias . Another potential source of bias for such studies is the selection of sampled organisms or animals based solely on just their availability ( e . g . domestic animals ) .
Coxiellosis ( Q fever ) studies were reported in a number of domestic animals , animal products ticks and humans . Geographically , a large proportion of the studies were conducted to the southern ( 46% ) and northern ( 41 . 8% ) parts of the country , with a small proportion being conducted in the central ( 4 . 7% ) , western ( 4 . 7% ) and eastern ( 2 . 3% ) parts of Iran . These studies were collectively reported in 19 ( 61 . 3% ) of the 31 provinces of Iran . However , the distribution of the studies in the 19 provinces was uneven as a relatively large proportion of the studies were concentrated in 3 of the 19 provinces i . e . Kerman 11 ( 25 . 6% ) , Sistan and Baluchestan 5 ( 11 . 6% ) and Mazandaran 4 ( 9 . 3% ) provinces , leaving the remaining 16 provinces with between 1 and 3 studies; majority ( 11 out of 16 ) with just a single study each ( Table 1 ) . Domestic animals ( cattle , sheep , goats , camels and dogs ) , animal products ( milk and eggs ) , arthropods ( ticks ) and human subjects ( febrile patients , pregnant women and individuals with high risk occupations like butchers , slaughter house workers , veterinary officers among others ) were the main units of analysis in the various studies that reported on C . burnetii in Iran . However , majority of the studies reported on small ruminants i . e . sheep ( 22 . 6% ) and goats ( 17 . 1% ) , with the dogs , cattle and camels being investigated in 9 . 4% , 7 . 5% and 1 . 9% of the C . burnetii studies , respectively . Coxiella burnetii studies reporting on human subjects accounted for 17 . 0% , while studies on ticks accounted for 15 . 1% of all the studies in Iran . As regards animal products , a total of 5 ( 9 . 4% ) of all the Q fever studies investigated C . burnetii antibodies in bulk milk samples collected from commercial dairy cattle and goats , while one study ( 1 . 9% ) reported on poultry eggs collected from chicken , ducks , goose , quails and ostriches drawn from three provinces ( Isfaham , Gilan and Mazandaran ) in Iran . Among all the domestic ruminants ( cattle , sheep and goats ) studied , the herd prevalence was relatively higher than individual prevalence ( Table 2 ) . In addition , individual pooled prevalence among domestic ruminants was lowest ( 0 . 83–22 . 3% ) in cattle [106 , 121 , 128] and highest ( 22 . 4–65 . 78% ) in goats [106 , 114 , 117 , 121 , 138] . Individual pooled prevalence among sheep was intermediate of the two at 19 . 5–36 . 5% [110 , 112 , 117 , 121 , 126 , 137 , 162 , 163] . In regard to cattle , the highest ( 22 . 3% ) C . burnetii seroprevalence was reported among 246 dairy cattle drawn from 19 commercial herds in Khorasan Razavi province , northeastern Iran [128] , while the lowest ( 0 . 83% ) was reported among 120 dairy cattle in Hamedan province , western Iran [121] . In goats , the highest ( 65 . 78% ) and lowest ( 22 . 4% ) C . burnetii seroprevalence were both reported in the southeastern part of Iran [106 , 114] , while in sheep , the highest ( 33 . 9% ) C . burnetii seroprevalence was reported among sheep drawn from five counties in the southeastern part of Iran , and the lowest ( 19 . 5% ) among sheep with a history of abortion drawn from 4 provinces in the northeast , west , southwest and central parts of Iran [117] . Only one study investigated C . burnetii among one-humped camel population drawn from 11 counties spread across 3 provinces ( North , South and Razavi Khorsan ) in the northeastern part of Iran . From the study , a C . burnetii seroprevalence of 28 . 7% was reported among the camels at the individual animal level [119] . Among dogs , two studies reported on C . burnetii prevalence in Iran , yielding a pooled prevalence of 7 . 7–11% with tick infested dogs that had been referred to the veterinary teaching hospital of Shahid Bahonar University of Kerman having a higher seroprevalence ( 11% ) compared to the 7 . 7% prevalence reported among asymptomatic companion dogs in Fars province , in the southern part of Iran [123 , 137] . Among the animal products , three studies investigated C . burnetii antibodies in bulk milk samples; two from dairy cattle and one from dairy goats . The C . burneti prevalence rate ( 2 . 0% ) reported in bulk milk obtained from 89 dairy goat herds drawn from five provinces ( Fars , Qom , Kerman , Khuzestan and Yazd provinces ) was far much lower than the 45 . 4% reported in bulk milk samples collected randomly from 44 sufficiently large commercial dairy herds in Kerman province , southeast Iran [109 , 133] and the 14% reported in a study of 100 bovine milk samples collected from 5 areas of Qom province [132] . Generally , the pooled prevalence reported on milk samples ( 2 . 0–45 . 4% ) was relatively higher than that reported among eggs ( 1 . 5–7 . 7% ) in a study involving 369 eggs collected from 130 chicken , 104 ducks , 34 goose , 70 quails and 31 ostriches . Out of the 369 eggs tested , only 2 of 130 ( 1 . 5% ) chicken eggs and 8 of 104 ( 7 . 7% ) duck eggs were C . burnetii positive [132] . A total of four studies were conducted among ticks; yielding a C . burnetii pooled prevalence of 4 . 8–13 . 1% . Three of the 4 studies were conducted in the southeastern part of Iran , precisely in Kerman ( 1 study ) and Sistan and Baluchestan ( 2 studies ) provinces , while the remaining study was conducted in Mazandaran province , northern Iran . All the three studies conducted in the southern part of Iran reported presence of C . burnetii among the ticks investigated while all the ticks investigated in the northern part of Iran tested negative for Coxiella burnetii . The investigated ticks were collected from domestic animal’s bodies ( goats , sheep and cattle ) and others from shrubbery . Collectively , the investigated ticks belonged to nine different species , namely: Rhipicephalus sanguineus sensu lato , R . turanicus , R . bursa , Hyalomma anatolicum , H . excavatum , H . asiaticum , H . marginatum , H . dromedarii , H . detritum . Of the nine species , H . anatolicum , H . excavatum , and R . sanguineus sensu lato tested positive for C . burnetii in nested trans-PCR assay [130 , 136 , 137 , 153] . Almost half ( 45 . 4% ) the studies on coxiellosis among human subjects were conducted in the southeastern part of Iran , particularly in Kerman and Sistan & Baluchestan provinces , while 27 . 3% were conducted in the northwestern part of the country . The remaining 18 . 2% and 9 . 1% were carried out in the northern and eastern parts of Iran , respectively ( Table 2 ) . Among human subjects , C . burnetii seroprevalence levels varied between febrile patients , pregnant women and individuals with risky occupations ( hunters , butchers , health workers , veterinary workers and veterinary students ) . Most ( 63 . 6% ) of the coxiellosis studies on human subjects were conducted among febrile patients [107 , 113 , 120 , 124 , 127 , 129 , 131] , while 27 . 3% were conducted among at-risk individuals [111 , 115 , 116 , 118 , 125 , 138] and 9 . 1% among pregnant women [122] . Pooled prevalence among at-risk individuals was relatively higher ( 19 . 8–68% ) , than the pooled prevalence among febrile patients ( 5 . 3–35 . 2% ) . Compared to the other categories of human subjects , a relatively low ( 29 . 3% ) C . burnetii seroprevalence was reported among pregnant women in a single study conducted on 400 random samples collected from pregnant women who had been referred to diagnostic laboratories of Ahvaz and Parsabad in the southwest and northern part of Iran , respectively [122] . A total of 15 studies reported on anaplasmosis among domestic ruminants ( cattle , sheep and goats ) , arthropods ( ticks ) and human beings in Iran . Geographically , most ( 68 . 4% ) of the studies were conducted in the northern part of Iran . Precisely , 5 ( 26 . 3% ) studies were carried out to the northwest , 9 ( 31 . 6% ) to the north , 2 ( 10 . 5% ) to the northeast , 3 ( 15 . 8% ) in central , 2 ( 10 . 5% ) to the southeast and 1 ( 5 . 3% ) to the eastern part of the country . Considering their distribution per province , studies on anaplasmosis were collectively conducted in 14 ( 45 . 2% ) of the 31 provinces in Iran . More than half ( 9 out of 14 ) the provinces where the studies were conducted are located to the northern part of the country . Studies on anaplasmosis were more-less evenly distributed in the 14 provinces with 5 ( 35 . 7% ) provinces having two reported studies each , while the remaining 9 ( 64 . 3% ) provinces had a single study focusing on anaplasmosis each . A total of five species in the genus Anaplasma were isolated from samples obtained from cattle , sheep , goats , ticks and human subjects that were investigated . The five Anaplasma species included Anaplasma ovis , A . bovis , A . marginale , A . centrale and A . phagocytophilum ( Table 3 ) . Of the five species , Anaplasma ovis was the most dominant and was detected in all the study organisms ( cattle , sheep , goats , ticks and human beings ) ; though to varying levels . The pooled prevalence of Anaplasma ovis was highest among sheep ( 5 . 0–87 . 4% ) , followed by goats ( 22 . 3–63 . 7% ) , ticks ( 4 . 7–55 . 5% ) and cattle ( 0 . 0–22 . 2% ) in that order [139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 151] . A . ovis was also reported among human beings in a study conducted in Mazandaran province involving 40 human blood samples of which 25% tested positive [146] . Anaplasma marginale was only reported among sheep and cattle with a pooled prevalence of 0–19 . 37% in cattle and a prevalence of 5% in sheep [132] , while A centrale was only reported among sheep in a study conducted in Mazandaran province , in which 1 of 28 ( 3 . 57% ) sheep tested positive . Anaplasma bovis was reported among cattle ( 2 . 66% ) in the central part of Iran [151] and in ticks ( 59% ) collected from goats and sheep in Mazandaran province , northern Iran [143] . Anaplasma phagocytophilum was reported among cattle and ticks , with a higher prevalence being reported among ticks ( 5 . 1% ) than cattle ( 1 . 33% ) [144 , 151] . A total of 11 studies reported on Ehrlichia spp . among dogs , cattle and ticks extracted from bodies of domestic animals ( cattle and goats ) in Iran . The studies were collectively conducted in 12 ( 38 . 7% ) of the country’s 31 provinces . Over half ( 7 out of 12 , 58 . 3% ) the provinces were concentrated to the northern part of the country , three ( 25% ) to the southern part , one to the Eastern part and one in central Iran . Relatively more studies reported on Ehrlichiosis among dogs ( 58 . 3% ) than ticks ( 41 . 7% ) ( Table 3 ) . In total , three Ehrlichia species were reported in the reviewed studies . The most dominant species; E . canis was reported in 9 ( 81 . 8% ) of the 11 studies while E . chaffeensis and E . ewingii accounted for one study each . E . canis was isolated from both dogs [153 , 156 , 157 , 158 , 159 , 160] and ticks [128 , 149 , 158] while E . chaffeensis was reported among unfed adult Ixodes ricinus ticks in Mazandaran province [153] . Ehrlichia ewingii was reported in dogs’ blood films previously collected at random from different veterinary hospitals in Tehran , the capital of Iran [152] . The pooled prevalence of E . canis was higher among ticks ( 1 . 8–43 . 8% ) compared to dogs ( 0 . 8–16 . 6% ) . In addition , E . ewingii prevalence of 18% reported among dogs’ blood films was relatively higher compared to the E . chaffeensis prevalence of 5 . 1% reported among Ixodes ricinus ticks in Ghaemshahr city in Northern Iran ( Table 3 ) . In the current review , only one article reported on spotted-fever group rickettsioses in a study involving 40 samples of human sera collected from 4 countries among them Iran and examined for presence of antibodies to spotted fever group rickettsiae among other pathogens by Kovacova et al . [161] . Of the 40 human sera samples tested , 45% were positive for SFG rickettsiae by ELISA test and 27 . 5% by IFA test ( 27 . 5% ) .
Geographically , over half ( 57 . 1% ) the studies in this review reported on coxiellosis in 19 of the 31 provinces in Iran . However , the distribution of these studies was not uniform as most studies were concentrated in two provinces ( Kerman and Sistan & Baluchestan ) , both of which are located to the southeastern part of Iran . This skewed distribution excludes 12 provinces which collectively cover a substantial portion of the country . The central , eastern and western parts of Iran were particularly excluded , implying that the findings emanating from the reviewed articles may not reflect the actual epidemiology and prevalence of Q fever in Iran . The ability of Q fever pathogenic agents ( Coxiella burnetii ) to be transmitted from animal reservoirs to humans by inhalation of infected aerosols makes coxiellosis easily transmissible among different organisms over large geographical areas [165] . Indeed , airborne transmission of C . burnetii is a well-documented phenomenon in many regions across the world [166 , 167] . Likewise , livestock movements across regional and national boundaries may also contribute to the spread of coxiellosis from one region to another [168] . Therefore , while studies on coxiellosis may not have covered the entire country , it is highly likely that C . burnetii pathogens exist throughout the country owing to the free movement of animals and people from one region to another . This calls for more studies involving a wider range of domestic and wild animals throughout the entire country to ascertain the actual C . burnetii prevalence in Iran . Coxiella burnetii bacterium has a wide range of hosts including wild and domestic mammals , birds , reptiles and arthropods [169] . However , domestic ruminants ( primarily cattle , sheep and goats ) are the most important and frequent source of human infection of C . burnetii [170] , although transmission from dogs and cats has been documented as well [171] . Once shed , the C . burnetii bacterium may remain infective in the environment for several months [172] , during which time the bacterium survives in arthropod hosts such as ticks from which they spread into ruminants . Based on findings emanating from this review , a majority of Coxiella burnetii studies were conducted on small ruminants i . e . sheep ( 22 . 6% ) and goats ( 17 . 1% ) . Studies on C . burnetii antibodies in goats were conducted in 8 provinces in Iran , with the highest ( 65 . 78% ) seroprevalence recorded in Kerman province and the lowest ( 0% ) in Markazi province . Similar studies conducted among goats elsewhere yielded varied C . burnetii seroprevalence levels; most of which were lower than those reported in Iran . For instance , C . burnetii pooled prevalence of 20–46% was reported in Kenya [173] and 0 . 8–60 . 6% in China [174] . In addition , C . burnetii prevalence of 7 . 8% , 13 . 7% , 9 . 52% , 8 . 8% , 6 . 5% , 8 . 7% and 13% were reported in Netherland , Bulgaria , Bangladesh , Albania , Northern Greece , Spain and Italy , respectively [175 , 176 , 177 , 178 , 179 , 180] . Among sheep , C . burnetii prevalence studies documented in this review were conducted in 10 provinces in Iran , with a herd prevalence of 96 . 1% and an individual pooled prevalence of 19 . 5–36 . 5% being reported . In comparison to the prevalence levels reported among sheep in Iran , relatively lower C . burnetii prevalence rates have been reported in different countries . For instance , a C . burnetii prevalence of 21% was reported in Spain [175] , 13 . 5% and 20% in Turkey [181 , 182] , 18 . 9% in Cyprus [183] , 10% in USA [184] , 1 . 3% in Germany [185] , 2 . 4% in Netherlands [179] , 5% in China [174] , 11 . 6% in Bulgaria [180] , and a pooled prevalence of 11–33% in Africa [19] . The relatively high C . burnetii seroprevalence reported among smaller ruminants in Iran as compared to other animals makes them perfect reservoirs and potential agents of C . burnetii transmission to other animals and humans . According to Maurin and Raoult [172] , massive shedding of C . burnetii during abortions makes sheep and goats the main reservoirs responsible for human infection . Q fever outbreaks resulting from infected ruminants are not new as they have been reported in many parts of the world in the past . Analysis of human Q fever outbreaks in Europe confirmed that the outbreaks were associated with small ruminants and not cattle [186] , with human infections being attributed to inhalation of contaminated aerosols [187] . Between 2012 and 2014 , a Q fever outbreak reported in Australia was attributed to a nearby intensive goat and sheep farming venture with a C . burnetii prevalence of 15% being reported among goats in the farm at the time [188] . Likewise , between 2004 and 2009 , a number of human Q fever outbreaks were reported in Bulgaria , Croatia , France , Germany and Italy; all of which were attributed to sheep farming [189] . These findings attest to the significant role that small ruminants ( mainly goats and sheep ) play in Q fever transmission to humans . Given the high C . burnetii seroprevalence rates reported among goats and sheep in Iran , the possibility for a human outbreak cannot be ruled out; though more studies are required to ascertain such a possibility in future . Among cattle , C . burnetii prevalence was reported in three provinces in Iran , with the highest prevalence reported in Sistan and Baluchestan province and the lowest in Hamedan province . Following this review , the pooled prevalence reported among individual cattle was 0 . 83–22 . 3% , while the herd prevalence was 41 . 1–78 . 9% . These rates were relatively higher compared to the 6 . 2% C . burnetii seroprevalence rates reported among cattle in Northern Ireland [190] , 8 . 5% in Bulgaria [180] , 15% in China [174] , 16 . 0% in Netherlands [191] , 7 . 8% in Germany [185] , 14 . 3% in the Central African Republic [192] and 14 . 5% in Mexico [138] . However , the rates reported among cattle in Cyprus ( 22 . 7% ) and Cameroon ( 30 . 4% ) [193] were higher than those reported in Iran . The varying rate of C . burnetii prevalence among different domestic ruminants in different regions is consistent with findings of high regional variability reported among farm animals in four European countries [194] . The regional variations could be attributed to some confounding factors that may be in operation at the local scale which need to be investigated further . In the current review , only a single study reported on C . burnetii seroprevalence among one-humped camels drawn from North Khorsan , South Khorsan and Razavi Khorsan provinces , in the northeastern part of the country [119] . The C . burnetii prevalence of 28 . 7% reported among camels in Iran was however lower compared to 51 . 6% reported among camels in Saudi Arabia and 80% reported in Chad [195 , 196] . The Food and Agriculture Organization [197] estimates that nearly 150 , 000 dromedary camels live in desert areas ( South and Central ) of Iran , most of which are scattered across 19 of the country’s 31 provinces . The country’s camels account for about 0 . 56% of the world’s camel population and 3 . 8% of the Asian camel population [197] . Despite the large camel population in Iran , only a single study involving 167 camels was conducted in the northern part of Iran . This is seen as insignificant and cannot be relied upon to give a general prevalence of C . burnetii among camels in Iran . Besides , not a single study of C . burnetii prevalence among camels was conducted in the desert areas of the country that lie to the south and central part of the country , where the largest population of camels are found . Given the importance of camels to pastoralists; more so in arid regions of Iran , more studies are needed to establish the true prevalence of C . burnetii among camels in the country . Dogs are well-described reservoirs for C . burnetii [198] . In the current review , only two studies investigated C . burnetii prevalence in Iran; one among asymptomatic companion dogs in Fars province and the other among dogs taken to a veterinary hospital in Kerman province . From the two studies , a pooled prevalence of 7 . 7–11% was reported [123 , 137] . Though Q fever is less prevalent in dogs compared to domestic ruminants , dogs that are exposed to infected wildlife carcasses , sick farm animals and their offspring or livestock environment where the C . burnetii bacterium is present are at higher risk; with the most common mode of transmission being ingestion or inhalation of contaminated aerosols [199 , 200] . Presence of C . burnetii antibodies in asymptomatic companion dogs is of particular importance as they can transmit the disease to humans; thus posing a potential risk of Q fever outbreak [201] . However , the role of dogs in the transmission of C . burnetii to humans remains uncertain in Iran , which necessitates extensive seroprevalence studies of dogs across the country . Over 40 tick species are known to harbor C . burnetii bacterium; thus serving as indicators of its circulation in nature [202] . Ticks play a critical role in the transmission of C . burnetii particularly among wild vertebrates [203] , though direct transmission of this agent to humans from infected ticks is still controversial and not properly documented [204 , 205] . In the current review , four studies investigated C . burnetii prevalence in ticks collected from animal bodies and shrubbery . Coxiella burnetii bacteria were detected in three ( all located to the southeastern part of the country ) of the four studies , with a pooled prevalence 4 . 8–13 . 1% being reported . The overall prevalence of C . burnetii in ticks from Iran was relatively higher than the 0 . 1% reported in Spain [206] , 0% in Europe and Germany [207]; 2 . 5% in Slovakia and Hungary [208] , 0 . 8% in Greece [209] and 2% in Egypt [210] . Tick species are probably the reason for the observed differences in C . burnetii from region to region . In the current review , nine tick species belonging to two genera ( Rhipicephalus and Hyalomma ) were extracted from animal bodies and shrubbery in Iran . Out of these , 3 species: Hyalomma anatolicum , H . excavatum , and Rhipicephalus sanguineus tested positive for C . burnetii . The detection of C . burnetii in 3 tick species is however not surprising since over 40 species have been found to harbor C . burnetii bacterium in many parts of the world [208] . In the current review , only four studies reported on Coxiella burnetii prevalence in animal products; three on bulk milk samples ( 2 from dairy cattle and one from goats ) and one on poultry eggs collected from chicken , goose , quail , ostrich and ducks . The pooled prevalence of C . burnetii was higher in milk ( 2 . 0–45% ) than eggs ( 1 . 5–7 . 7% ) . In addition , C . burnetii prevalence in bulk milk samples from dairy cattle ( 14–45% ) was higher that bulk milk samples from goats ( 2% ) . The current findings are consistent with those of Eldin et al . [211] whose study also reported the prevalence of C . burnetii in dairy products to be significantly higher than products from goats or ewes in France . The C . burnetii prevalence rates reported in bulk milk samples in Iran were however higher than those reported among bovine milk in Switzerland ( 4 . 7% ) [212] , but lower than the 90% reported among dairy herds in the USA on the basis of bulk tank milk testing over a three-year period [213] . According to Cerf and Condron [214] , C . burnetii pathogens can be resistant to physical and chemical factors such as heat , dryness and most disinfectants . This makes it possible for the pathogen to survive for days and weeks in animal products such as milk , cream , butter and cheese , posing a risk of pathogen transmission to humans through consumption of raw animal products like milk and eggs [215] . Most often than not , infected animals shed C . burnetii into the environment through milk , colostrum , eggs , urine , vaginal discharges; especially in birth products [14] . Cases of human Q fever outbreaks associated with consumption of dairy products have been reported in many parts of the world . For instance , Fishbein and Raoult [216] reported a Q fever outbreak in a psychiatric institution in southern France where seropositivity rates for C . burnetii were significantly higher among patients who consumed unpasteurized milk products . Another study conducted in Japan detected Coxiella DNA in commercial chicken eggs and mayonnaise using nested-PCR targeting Coxiella outer membrane protein gene ( com1 ) [217] . Raw milk consumption was identified as a risk factor for C . burnetii seropositivity among dairy cattle farmers in Germany [218] . While C . burnetii prevalence levels reported among animal products in Iran were relatively higher compared to those reported in other studies elsewhere , the number of studies ( 4 ) conducted in Iran were however insufficient to give an accurate overall prevalence of C . burnetii among animal products . This calls for additional studies focusing not only on eggs and milk but also on many other animal products such as cheese , yoghurt and butter . In addition , the studies should also be conducted on milk from other animals such as camels , ewes and buffaloes since they also play an important role in the economy of the country . In the current review , 12 studies focusing on C . burnetii prevalence among human subjects were collectively reported in 12 ( 38 . 7% ) of the 31 provinces in Iran . However most ( 45% ) of the studies were concentrated to the southeastern part of Iran , especially in 2 provinces ( Kerman and Sistan and Baluchestan ) , thus excluding a large portion of the country . Nevertheless , presence of C . burnetii pathogens in a few provinces in Iran could signal a countrywide distribution given the ability of C . burnetii infected aerosols to be transported by wind over long distances coupled with the bacterium’s ability to survive in the environment for long periods of time . Tissot-Dupont [166] concurs and opines that some Q fever outbreaks are related directly to the speed and frequency of wind . In the current review , Q fever seroepidemiological studies on human subjects were generally categorized into three; though disproportionately . The categories included: pregnant women with only 1 of 12 ( 8 . 3% ) studies , individuals with high risk occupations with 5 ( 41 . 7% ) studies and febrile patients with a total of 6 ( 50% ) studies . Coxiella burnetii seroprevalence rates varied among the different categories of study subjects; with individuals considered at risk based on their occupation recording the highest seroprevalence ( 19 . 8–68% ) followed by febrile patients ( 5 . 3–35 . 2% ) . The single study conducted among 400 pregnant women reported a 29 . 3% C . burnetii seroprevalence . As is evident from the findings of this review , Q fever is primarily an occupational hazard with those in close contact with domestic animals and animal products like farmers , veterinarians , slaughterhouse workers , laboratory personnel , health care workers being at a relatively higher risk [167] . According to Parker et al . [219] C . burnetii transmission in humans is dependent on a number of factors among them the nature of work or occupation , frequency of contact with live infected animals , frequency of contact with carcasses and tissues of slaughtered animals , proper use of personal and environmental protective gears as well as individual attitude and level of knowledge among those at risk . Another common cause of human infection with C . burnetii is inhalation of infectious aerosol or contaminated dust containing air-borne bacterium , which is regarded as the major route through which human beings acquire the disease . A single inhaled C . burnetii bacterium has the capacity to produce clinical illness [220] . Other transmission routes of Q fever in human that have been identified include consumption of contaminated animal products , skin or mucosal contact , tick bites , blood transfusion , sexual transmission and embryo transfer [167 , 219] . Researchers acknowledge that Q fever is common among workers in livestock and animal products trade especially those dealing with cattle , sheep and goats [221] . Among febrile patients ( majority with brucellosis like symptoms ) , C . burnetii pooled prevalence of 5 . 3–35 . 2% was reported , with the highest prevalence being among patients admitted to Boo-Ali Hospital in Zahedan County in Sistan and Baluchestan province , southeast Iran [113] . This prevalence rate was relatively higher compared to the 3 . 85% reported among febrile patients in Mali [221] , 5 . 8% in Croatia [222] , 2 . 29% in Denmark [223] and 2 . 07% in France [224] . A study performed on British soldiers with fever of unknown origin in Afghanistan established that 26% of the soldiers had acute Q fever [225] . From the foregoing , it is evident that the prevalence of coxiellosis among febrile patients suspected of having brucellosis was high . The major cause of infections reported in a number of provinces in Iran could be contact with infected livestock and contaminated dairy products . Therefore , necessary health measures for disease prevention targeting the whole country are required . Pregnant women are also at a higher risk of Q fever infection which is potentially dangerous to them as it may cause serious complications for both the foetus and the mother; especially if it occurs in the early stages of pregnancy [226] . In the current review , only one study was conducted among 400 pregnant women drawn from two provinces in the northern and southwestern parts of Iran , from which a C . burnetii seroprevalence of 29 . 3% was reported [88] . This prevalence was , however , much higher compared to the rates reported among pregnant women in other countries such as France ( 2 . 6% ) , Canada ( 4% ) , London ( 4 . 6% ) , Bulgaria ( 7 . 7% ) and Netherlands ( 9 . 1% ) [227 , 228 , 229 , 230] . While only one study reported on C . burnetii seroprevalence among pregnant women in Iran , the prevalence reported was considerably high to be ignored . Infection with coxiellosis is a major cause for concern especially for pregnant mothers and their unborn babies given the adverse health effects that they are bound to trigger such as spontaneous abortion , intrauterine growth retardation , intrauterine fetal death and premature delivery [231] . This systematic review established that the human Q fever studies in Iran were restricted to only three categories of human subjects ( i . e . febrile patients , pregnant mothers and people with risky occupations ) , thus excluding the general public who form a majority of the country’s population . As it is therefore , these findings cannot be generalized to reflect the actual prevalence of coxiellosis in Iran . More studies on Coxiellosis among human subjects are thus needed to ascertain the actual prevalence of the disease in the general population . Future studies should broaden their scope to include both at risk individuals and the general public who may not necessarily be regarded as being at risk . In addition , coverage should extend to all provinces within Iran . Generally , despite surmounting evidence from epidemiological studies of the prevalence of C . burnetii pathogens in Iran , only seven species of domestic animals , different tick species , two animal products ( milk and eggs ) and human subjects have been investigated for prevalence of C . brunetii in the country . The limited number of organisms and animal products excludes many other animals such as cats , pigs , horses , birds , rabbits , fish , rodents , a number of mammals and arthropods that have been reported elsewhere as not only harboring C . burnetii pathogens but also playing a role in transmission [129] . In addition , many studies have also highlighted the presence of C . burnetii in a number of wild animals in other parts of the world . For instance , Webster et al . [232] detected antibodies to C . burnetii in wild brown rats on farms in the United Kingdom; while Madariaga [233] reported a seroprevalence of between 7 and 53% among brown rats in Oxfordshire , UK . Coxiella burnetii antibodies have also been isolated from hares and wild rabbits [234] , coyotes , skunks , foxes , deer , wood rats , squirrels , bush rabbits , wild mice and buffaloes in different parts of the world [235] . While the role of wild animals as reservoirs of C . burnetii is well documented elsewhere , not a single study focused on wildlife in Iran . On the whole , active surveillance and more research studies targeting a broad range of organisms across all provinces of Iran are necessary for successful preventative planning and control of C . burnetii infections in the country . In the current review , 15 studies reported on anaplasmosis in 14 ( 54 . 2% ) of the 31 provinces in Iran . Most of the provinces were located to the northern part of the country [139 , 141 , 144 , 146 , 147 , 150] . This however excludes more than half ( 54 . 8% ) the provinces in the country , leaving a large portion of Iran out . Besides , only 1–2 studies were conducted in each of the 14 provinces , making the study findings too limited to give a reflection of the actual prevalence of anaplasmosis in Iran . Collectively , domestic ruminants ( cattle , sheep and goats ) , ticks and human beings were investigated in the 15 studies , though disproportionately . Of the 15 studies , 36% were conducted on sheep , 24% on cattle , 20% on ticks , 16% on goats and 4% on human subjects . From these , five species in the genus Anaplasma were detected , namely: Anaplasma ovis , A . bovis , A . marginale , A . centrale and A . phagocytophilum . Only two ( A . platys and A . carpa ) of the seven recognized Anaplasma species worldwide were not reported in any of the 15 studies in Iran . In the current review , A . ovis was the most dominant pathogen having been detected collectively in all categories ( human beings , domestic ruminants and ticks ) of studied organisms . Pooled prevalence of A . ovis was highest ( 5 . 0–87 . 4% ) among sheep , followed by goats ( 22 . 3–63 . 7% ) , ticks ( 4 . 7–55 . 5% ) , human ( 25% ) and cattle ( 2 . 66–22 . 2% ) . These findings point to the greater role that small ruminants ( sheep and goats ) play as reservoirs of A . ovis . First described in sheep in 1912 , Anaplasma ovis is widely distributed in Asia , Africa , Europe and USA [236 , 237] , and now infects goats , cattle and some wild ruminants [238] . Moreover , the DNA of A . ovis has been detected in milk samples from goats and sheep in China [239] . Apart from domestic ruminants and animal products , A . ovis has also been detected in different domestic and wild animals with varying prevalence rates among them mongolian gazelle ( 52 . 2% ) [238] , dogs ( 6 . 1% ) [240] , red deer ( 32 . 0% ) and sika deer ( 20 . 0% ) in China [238] . In the current review , A . ovis was also detected in 25% of the 40 human blood samples examined in a study conducted within Mazandaran province , northern Iran . This corroborates the A . ovis variant that was also detected in a patient in Cyprus , indicating the zoonotic potential of the pathogen [241] . Other Anaplasma species were also detected in Iran though to a lesser extent and on a limited number of organisms . For instance , A . marginale was reported among sheep and cattle with a relatively higher prevalence reported among cattle compared to sheep [132] . Studies concur that A . marginale occurs mostly in cattle , but has also been detected in a number of wild animals including deer , bighorn sheep , black wildebeests , pronghorn antelopes , elks , giraffes and bison in other studies conducted elsewhere [242] . A . marginale has also been detected in water buffaloes in Brazil [242] . Unfortunately , despite clear evidence of A . marginale prevalence in many wild animals in other parts of the world , not a single study reported on A . marginale among wild animals in Iran; leaving a large knowledge gap that needs to be filled . In the current review , A . centrale was only reported among sheep drawn from Mazandaran province , in which only 1 out of 28 sheep tested positive . Anaplasma centrale is considered less pathogenic , whose infection causes only mild effects [34] . The pathogen has even been used as a live vaccine for cattle in Israel , South Africa , South America and Australia [243] . The less pathogenic nature of A . centrale may not have aroused the interest of researchers to this particular pathogen in Iran . Nevertheless , more studies are still required to establish the prevalence of A . centrale among different organisms in the country . In the current review , Anaplasma bovis prevalence of 2 . 66% was reported among cattle in the central part of Iran [151] and 59% among ticks extracted from goats and sheep bodies in Mazandaran province , northern Iran [143] . While the major reservoirs of the A . bovis pathogen are known to be cattle and goats [244] , the prevalence rate reported among ticks in the current review was 22 times higher than that reported among cattle . According to Donatien and Lestoquard [245] , A . bovis was first discovered in cattle but has since been detected in many domestic and wild animals in many countries around the world among them Italy , Brazil , South Africa , Korea , China , Tunisia , Spain , Japan and United States of America . Among the reviewed articles , A . phagocytophilum was detected in 5 . 1% of the ticks and 1 . 33% of the cattle that were investigated . The rate of A . phagocytophilum infectivity reported among Ixodes ricinus ticks in northern Iran by Bashiribod et al . [153] , and those reported in a study in Austria ( 5 . 1% ) by Sixl et al . [246] were comparable to the rates reported in the current review , but slightly higher than the rates reported in northwest Poland ( 4 . 5% ) [247] , and Germany ( 4 . 1% ) [248] . Recent investigations show that many species of ticks except I . persulcatus could carry A . phagocytophilum pathogen [249] . Nevertheless , presence of A . phagocytophilum in cattle and hard ticks in Iran is of importance as it portends a possible risk of transmission to humans in different parts of the country . Cross-border movements of persistently infected organisms may contribute to the spread of variants between different geographical areas [250] . Smaller ruminants ( goats and sheep ) are also prone to infection by A . phagocytophilum . Besides , there is evidence that sheep are natural reservoir hosts for A . phagocytophilum in the United Kingdom [251] , while other studies suggest that A . phagocytophilum pathogen is normally persistent in sheep [250] . The pathogen has also been detected in many other domestic animals like horses in a number of countries especially in Great Britain , Denmark , Sweden , Switzerland , France , Germany , Czech Republic and Italy [252] , as well as among dogs , cats and Ilamas [253] . Cases of A . phagocytophilum infection among wild animals have also been reported in different parts of the world . In the USA and Europe for instance , wild rodents such as the white-footed mice [254] and white-tailed deer are well known as natural reservoirs for A . phagocytophilum [255] . Another study in Slovenia revealed that red and roe deer were infected with A . phagocytophilum in about 86% of the cases [256] . A . phagocytophilum strains have also been identified as being potentially virulent to the roe and red deer in Northeast Poland and Slovenia as well as in wild ruminants including Cervidae [257] . Migrating birds are thought to be important dispersal agents of A . phagocytophilum infected I . ricinus in Europe [258] . Despite overwhelming evidence of A . phagocytophilum among wild animals including birds , not a single study focused on wild animals in Iran . Although the reviewed studies on A . phagocytophilum did not focus on human subjects in Iran , serological evidence of human infection with A . phagocytophilum exists in Korea and other parts Asia [259] . Therefore , while conducting studies among domestic and wild animals , attention should also be given to human subjects across the county . The current review confirms the prevalence of Anaplasma spp . infection in parts of Iran as well as the potential role that domestic ruminants and ticks could be playing in the transmission of the pathogens across the country . However , the number of studies reporting on the various Anaplasma spp . was limited to just a few organisms ( cattle , sheep and goats , ticks and human beings ) that were collectively conducted in a small portion of the country . This excludes a whole lot of domestic animals like horses , cats , dogs , donkeys , camels as well as wild animals such as red foxes , wild boars , deer , elk , bison , giraffes , pronghorn antelopes , and non-ruminant wildlife species like rodents , coyotes , fishers , and mountain lions; all of which are susceptible to different strains of Anaplasma spp . [256] . Future studies on anaplasmosis in Iran , should therefore widen their scope to include more domestic and wild animals so as to establish the host range , while ensuring that the studies cover the entire county effectively . In the current review , a total of 11 studies reported on Ehrlichia spp . among dogs , cattle and ticks . The studies were only reported in 12 ( 38 . 7% ) of the 31 provinces in Iran; most of them located to the northern part of the country . Given the limited number of studies and the low spatial coverage , a large portion of the country remains under researched or not researched at all . Collectively , three Ehrlichia species were reported from the 11 studies reviewed . The most dominant species was E . canis that was reported among dogs and ticks in 9 of the 11 studies . The pooled prevalence of E . canis among ticks ( 1 . 8–43 . 8% ) was higher than that reported among dogs ( 0 . 8–16 . 6% ) . Ehrlichia canis initially described in dogs in 1963 [260] , is the primary etiologic agent of canine monocytic ehrlichiosis; a serious and sometimes fatal , globally distributed disease of dogs . The ever increasing importance of dogs as pets following continued population increase makes parasitic diseases such as E . canis a major health concern [261] . E . canis has been detected and reported in dogs from many parts of the world [262 , 263] . In the current review , the highest E . canis prevalence reported among dogs was 16 . 6% . This was relatively lower compared to the 28% overall prevalence reported among dogs in Punjab province , Pakistan [264] , 21% reported in India [265] , 27% in west Indies , and 34% in Costa Rica [266] . However , the rates reported in Iran were higher than the 4 . 9% E . canis prevalence reported among dogs in Turkey [267] . Variation observed in the prevalence of E . canis among the various studies discussed here could be due factors such as the population density , distribution of tick vectors , the sampling methodology and the characteristics of the targeted dog population [268] . Besides ticks and dogs , studies have documented evidence of E . canis in house cats and stray cats [269] . Therefore future studies on E . canis in Iran should not only broaden their geographical range to cover the whole country but also include both house and stray cats and many other animals that may harbour these pathogens in their investigations . In the current study , E . chaffeensis was also detected among hard ticks in Mazandaran province with a prevalence of 5 . 1% being reported , while an 18% E . ewingii prevalence was detected in dog’s blood films obtained randomly from different veterinary hospitals in Tehran , Iran . While studies on E . chaffeensis and E . ewingii were only conducted among ticks and dogs , respectively , in Iran , researchers in other parts of the world suggest that E . chaffeensis and E . ewingii are well known causes of human ehrlichiosis [270] . In addition , a number of domestic and wild animals are also important reservoirs of E . chaffeensis having been reported in different parts of the world . For instance , the white-tailed deer has been singled out as a major reservoir of E . chaffeensis in the United States , though the pathogen has also been detected across the globe in other deer species , such as the spotted deer in Japan and Korea and the marsh deer in Brazil , as well as in many other wild and domesticated animals [271] . Likewise , genetic materials of E . chaffeensis have been detected in coyotes and wild lemurs by PCR , while antibodies of E . chaffeensis have been reported in opossums , raccoons , rabbits and foxes in the USA [272] . From the foregoing , it is clear that studies on ehrlichiosis in Iran are still very limited and hence the need for more research focusing on a wide range of organisms and covering the whole country . In this review , only one study reported on SFG rickettsiae in a joint study conducted in four countries among them Iran [161] . The SFG rickettsiae prevalence of 45 . 0% by ELISA and 27 . 5% by IFA , reported in this review is an indication of the possibility of the presence of spotted fever group rickettsioses in Iran . The exact region or province within Iran where the study was conducted is however not given in the study . Studies conducted in southern Croatia [273] , and Hungary [274] reported presence of antibodies of spotted fever group rickettsiae in domestic animals . Rickettsia rickettsii is regarded as the most serious species of the SFG rickettsiosis particularly in the South Atlantic and South Central census regions of the United States , where it occurs predominantly , with the D . variabilis ticks being the primary vectors in these regions [275] . This review demonstrated a complete lack of studies on SFG rickettsioses in Iran and as a result information about SFG rickettsioses in the country remains very sparse . However , absence of studies or information on SFG rickettsioses in Iran does not imply absence of the pathogen in the population and , therefore , unless studies on SFG rickettsioses are conducted among a wide range of organisms across the country , it would be difficult to establish the host range , zoonotic potential and actual prevalence of SFG rickettsioses in Iran . This is the first review encompassing tick-borne zoonoses in the Orders Rickettsiales and Legionellales in Iran . Most studies reported on coxiellosis as opposed to anaplasmosis , ehrlichiosis and SFG rickettsioses in Iran . A large number of studies on coxiellosis relied on serological techniques ( ELISA test ) for antibody detection as opposed to more accurate molecular techniques . Collectively , only a limited number of organisms ( cattle , sheep , goats , dogs , ticks , milk , eggs and humans ) were studied , thus excluding a wide range of potential organisms particularly the wild animals that have been reported elsewhere as being susceptible to disease pathogens or harboring the various pathogens . Besides , the geographic coverage of most of these studies was very limited with most studies concentrated to the northern and southern parts of Iran . This makes it difficult to generalize the findings to the entire country . Nevertheless , the existence of C . burnetii , Ehrlichia spp . , Anaplasma spp . and Rickettia rickettsi in a number of organisms in 22 out of the 31 provinces in Iran as reported by various studies implies that the diseases caused by these pathogens could be highly prevalent in the country . Given that most tick-borne zoonoses are asymptomatic , there is a likelihood of silent transmission among humans in parts of the country , and thus should be considered a public health concern . Therefore , there is need for more studies involving a wider array of organisms throughout Iran so as to establish the host-range of these tickborne zoonoses , their zoonotic potential and their actual prevalence in Iran . In addition , molecular techniques should be utilized more in the detection of pathogens and identification of the local strains that are in circulation in Iran . Active surveillance of tickborne zoonoses is therefore highly recommended as it would enable researchers to clearly define the epidemiology and public health importance of coxiellosis , anaplasmosis , ehrlichiosis and SFG rickettsioses in Iran . | Tick-borne zoonoses caused by pathogens in the Order Rickettsiales and Legionellales are significant causes of morbidity and mortality in many parts of the world . In recent times , incidences of zoonotic diseases have increased due to the changing climate , cross-border movement of tick-infested animals as well as advancement in molecular techniques that have aided their rapid identification . Tick-borne zoonoses infect and multiply in most organs of ticks , particularly the salivary glands , thus enabling pathogen transmission to a variety of hosts among them mammals , birds , reptiles and amphibians mostly through the bite of an infected tick . These tick-borne zoonoses are therefore geographically localized and occur mostly in foci with optimal conditions for the vectors thus posing a public health concern . Like in many regions across the world , tick-borne zoonoses; caused by pathogens in the Order Rickettsiales and Legionellales , have not received the level of public attention that has been paid to other maladies in Iran . As a result , their presence and actual magnitude is still under-reported in the country; necessitating this systematic review . Sixty three publications were reviewed after the initial evaluation of 1 , 205 articles . Most of the 63 publications reported on coxiellosis in Iran followed by anaplasmosis , ehrlichiosis and SFG rickettsiae in that order . Pathogens causing these diseases and their antibodies were collectively detected in domestic animals ( sheep , cattle , goats , camels and dogs ) , animal products ( milk and eggs ) , arthropods ( ticks ) and human beings in 22 of the 31 provinces in Iran . People in close contact with domestic animals and animal products like farmers , veterinarians , slaughterhouse workers , laboratory personnel and health care workers are at higher risk of infection . Findings emanating from this review suggest that tickborne zoonoses caused by pathogens in the Order Rickettsiales and Legionellales could be prevalent in parts of Iran though the studies are insufficient to give the general epidemiology of the zoonoses in Iran . Besides , the existing studies only covered a limited number of organisms at small spatial scales thus excluding a large portion of the country and a whole range of other potential reservoirs such as horses , pigs , cats , donkeys and wildlife thus creating gaps in knowledge that need to be filled . | [
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] | 2018 | Tick-borne zoonoses in the Order Rickettsiales and Legionellales in Iran: A systematic review |
Reassortment is fundamental to the evolution of influenza viruses and plays a key role in the generation of epidemiologically significant strains . Previous studies indicate that reassortment is restricted by segment mismatch , arising from functional incompatibilities among components of two viruses . Additional factors that dictate the efficiency of reassortment remain poorly characterized . Thus , it is unclear what conditions are favorable for reassortment and therefore under what circumstances novel influenza A viruses might arise in nature . Herein , we describe a system for studying reassortment in the absence of segment mismatch and exploit this system to determine the baseline efficiency of reassortment and the effects of infection dose and timing . Silent mutations were introduced into A/Panama/2007/99 virus such that high-resolution melt analysis could be used to differentiate all eight segments of the wild-type and the silently mutated variant virus . The use of phenotypically identical parent viruses ensured that all progeny were equally fit , allowing reassortment to be measured without selection bias . Using this system , we found that reassortment occurred efficiently ( 88 . 4% ) following high multiplicity infection , suggesting the process is not appreciably limited by intracellular compartmentalization . That co-infection is the major determinant of reassortment efficiency in the absence of segment mismatch was confirmed with the observation that the proportion of viruses with reassortant genotypes increased exponentially with the proportion of cells co-infected . The number of reassortants shed from co-infected guinea pigs was likewise dependent on dose . With 106 PFU inocula , 46%–86% of viruses isolated from guinea pigs were reassortants . The introduction of a delay between infections also had a strong impact on reassortment and allowed definition of time windows during which super-infection led to reassortment in culture and in vivo . Overall , our results indicate that reassortment between two like influenza viruses is efficient but also strongly dependent on dose and timing of the infections .
Reassortment is the process by which viruses carrying segmented genomes exchange gene segments . The reshuffling of genetic material achieved through reassortment supports rapid production of variant viruses that can be markedly different , genotypically and phenotypically , from the parental strains . The more gradual process of genetic drift , resulting from errors in genome replication , and the process of reassortment come together to generate vast genomic diversity among influenza A viruses . It is this diversity that , in turn , permits the rapid evolution of influenza viruses and the generation of novel pandemic and epidemic strains . The contribution of reassortment to the emergence of pandemic influenza viruses is well established: the 1957 and 1968 pandemics arose following reassortment events between avian and human influenza viruses that allowed novel HA subtypes to gain widespread circulation in the human population [1] , [2] , [3] . Reassortment furthermore played a prominent role in the creation of the H5N1 viruses that continue to circulate in poultry of Southeast Asia [4] , and in the H1N1 swine influenza viruses that emerged in humans in April 2009 [5] , [6] . Thus , epidemiological studies indicate that reassortment is an important means of viral diversification and often facilitates inter-species transmission . In addition to its role in pandemic influenza , phylogenetic studies have revealed the importance of reassortment between co-circulating viruses of the same subtype in generating a diverse pool of seasonal influenza viruses [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] . This diversity in turn allows for the selection of variants that escape pre-existing immunity in the population and thereby cause widespread epidemics: evidence suggests that the unusually severe epidemics of 2003 , 1951 and 1947 were each caused by strains generated through intra-subtype reassortment among co-circulating clades [8] , [10] . Previous efforts to study influenza virus reassortment in the lab have been of three main types . First , beginning with the work of Lubeck et al . in 1979 , several research groups have examined the phenomenon of segment mismatch , in which the gene segments of two differing strains are found to assort in a non-random fashion due to functional incompatibilities between the viral proteins or RNA segments [15] , [16] , [17] , [18] . It is clear from this literature that strain differences between parental viruses limit the fitness of many reassortant progeny and thereby restrict the number of different genotypes that arise , or are detected , following co-infection . Thus , segment mismatch is a potent determinant of reassortment efficiency . Second , a number of risk assessment type studies have addressed the potential for variants with increased virulence or transmissibility to arise through reassortment between two strains of epidemiologic importance [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] . Third , since reassortment between circulating strains and the egg-adapted A/Puerto Rico/8/34 virus has been used since 1969 to generate vaccine seed strains that grow well in embryonated chicken's eggs , significant research effort has been put into optimizing this procedure [28] , [29] . Research to date is lacking on the conditions of co-infection that are most favorable for reassortment , and it is therefore unclear under what circumstances we can expect to see novel influenza A viruses arising in nature . In part , this knowledge gap has arisen because , when one studies reassortment between two dissimilar strains , the effects of other parameters are confounded by those of segment mismatch . Herein , we report a novel method for the study of reassortment in the absence of segment mismatch and the application of this method to determine the baseline frequency of reassortment under unbiased conditions , and the impacts of infection dose and timing on this baseline .
In order to obtain data that are not confounded by segment mismatch , we have designed an approach that employs a pair of phenotypically identical but genotypically distinct influenza viruses . Reverse genetics was used to introduce silent mutations into each gene segment of A/Panama/2007/99 ( H3N2 ) [Pan/99] virus such that the segments of the resultant variant , or rPan/99var , virus can be distinguished from those of the rPan/99wt virus using molecular techniques ( described below ) . The mutations introduced were selected carefully such that the rPan/99var viruses were not attenuated in growth relative to the rPan/99wt strain ( Figure 1 ) . As a result , all 256 different progeny that might arise following co-infection with rPan/99wt and rPan/99var viruses are expected to be of equal fitness . Because there are no selective pressures acting differentially on the various progeny strains , co-infection with rPan/99wt and rPan/99var viruses constitutes an unbiased system in which to study reassortment . As shown in Figure 2 , the silent mutations differentiating rPan/99wt and rPan/99var viruses allow the full genotypes of progeny arising from mixed infections to be determined using high resolution melt ( HRM ) analysis [30] . This method exploits the fact that sequence differences between two double stranded DNA ( dsDNA ) molecules confer differences in melting properties . These differences in melting properties can in turn be detected as changes in fluorescence when dsDNAs labelled with a saturating fluorescent dye are heated ( e . g . from 65°C to 95°C ) , since the dye will cease to fluoresce as the DNA melts into single strands . As described in more detail in the methods section , we have applied HRM analysis to clonal virus isolates derived from co-infection in order to identify each gene segment as either wt or var in origin . We have defined the baseline frequency of reassortment as the percentage of progeny viruses with reassortant genotypes that arises after a single cycle of replication , given high levels of co-infection at the cellular level and an absence of segment mismatch or other selection pressures that would promote parental genotypes over reassortant ones . To determine this baseline value , rPan/99wt-HIS and rPan/99var-HA viruses were used to co-infect MDCK cells at a multiplicity of infection of 10 PFU/cell of each virus . Epitope tagged versions of the wt and var strains were used so that the number of cells infected with each virus and the number of cells co-infected could be determined by flow cytometry . Co-infection rates of 99 . 4% were achieved in each of two independent samples . As shown in Figure 3 , the resultant frequencies of reassortment were 87 . 6% and 89 . 2% ( average = 88 . 4% ) . While this result indicates that reassortant viruses arise with high frequency under the unbiased conditions described , the theoretically optimal efficiency of 254/256 , or 99 . 2% , was not achieved: parental progeny viruses were over-represented relative to the expected frequency of 0 . 8% ( p<0 . 001 , exact test ) . This discrepancy could be suggestive of incomplete mixing between genomes in co-infected cells , but also may be the result of a small differences in the input of wt and var viruses into individual cells , due either to a slight skewing of the inoculum or to the Poisson distribution ( with MOIs of 10 , a sizable proportion of cells would be expected to receive , for example , 10 copies of the wt virus but 9 or 11 copies of the var virus ) . Co-infection at the cellular level is a necessary precursor to reassortment . To determine the quantitative relationship between the frequency of co-infected cells and frequency of reassortant progeny viruses , we examined both co-infection and reassortment levels following inoculation of MDCK cells at a range of multiplicities from 10 PFU/cell to 0 . 01 PFU/cell . Twelve hours after infection with rPan/99wt and rPan/99var viruses , supernatant was collected to determine the frequency of reassortant viruses therein . At the same time , cells were harvested and used to determine the numbers of uninfected , singly infected and doubly infected cells by flow cytometry . As shown in Figure 4 , co-infection of MDCK cells was seen with all four MOI conditions but spanned a wide range , with about 13% of infected cells harbouring both wt and var viruses at the lowest MOI to about 90% at the highest MOI . Reassortment was also seen following infection at all four multiplicities and , as predicted , occurred with the highest frequency ( average 78 . 5% ) at the highest MOI and the lowest frequency ( average 9 . 5% ) at the lowest MOI . In particular , reductions in the proportion of reassortant progeny were significant when the MOI was reduced from 10 to 0 . 1 or 0 . 01 PFU/cell ( p<0 . 0001 , Fisher's exact test ) . Rather than a simple linear relationship , however , the data suggest that reassortment levels increased exponentially with the proportion of cells that were co-infected ( Figure 4 ) . The results obtained following co-infection of cultured cells at a range of MOIs indicated that , as expected , reassortment levels are acutely dependent on co-infection rates . In an animal host , one might expect co-infection and therefore reassortment rates to be low since the number of epithelial cells available for viral infection within the respiratory tract is presumably high . To address this hypothesis , guinea pigs were co-infected intranasally with rPan/99wt and rPan/99var viruses at two doses: 103 PFU or 106 PFU of each strain . At 48 h post-infection nasal lavage was collected from each guinea pig and viruses therein were genotyped . As reported in Table 1 , robust reassortment levels were detected under both conditions ( averages of 30% and 59% following infection with 103 PFU and 106 PFU , respectively ) . The difference between the two dosage groups was found to be significant ( p = 0 . 03 , Student's t-test ) , indicating that the level of reassortment in vivo is dependent on inoculum dose . In nature , the probability of an individual being co-infected with two distinct strains ( as opposed to a quasispecies ) of influenza virus simultaneously is expected to be quite low; rather , most co-infections are likely to arise from sequential infection events . We therefore sought to determine the window of time during which a second infection can result in reassortment . To this end , experiments were performed in which inoculation of MDCK cells with rPan/99var virus was followed at a range of time points by inoculation with rPan/99wt virus . MDCK cells were infected with either rPan/99var2-HA virus and rPan/99wt-HIS virus together ( 0 h ) or with rPan/99var2-HA virus alone , followed by sequential infections with rPan/99wt-HIS virus 2 , 4 , 8 , 12 , 16 or 24 h later . Following each addition of virus , a 1 h attachment period at 4°C was used to synchronize infections; since the viral neuraminidase would not be active at this temperature , it is important to note that any stripping of sialic acids from infected cell surfaces would occur up to the point when the second virus was added , but not during the attachment period . At 12 h post-infection with rPan/99wt-HIS virus , supernatant was collected for genotyping of released virus and cells were harvested to determine co-infection rates by flow cytometry . The results show ( Figure 5 ) that a delay of up to 8 h between the primary and secondary infection led to a 5–16% decrease in the proportion of infected cells that were co-infected when compared to a simultaneous infection . These moderate decreases in the number of co-infected cells were accompanied by larger decreases in the amount of reassortment ( 41% reduction in the 8 h group relative to the 0 h group , p = 0 . 003 , Fisher's exact test ) . Nevertheless , up to an 8 h time interval between additions of rPan/99wt and rPan/99var viruses allowed for robust reassortment: 47 . 5% of virus isolates sampled from the 8 h infections carried a mixed genotype . In contrast , a delay of 12 h between primary and secondary infections resulted in just 4 . 75% of isolates showing reassortment . This low level of reassortment with the 12 h interval occurred despite the fact that 43% of infected cells were doubly infected under these conditions . To determine how the introduction of a time interval between two influenza virus infections would impact reassortment in an animal host , we set up a similar series of infections in the guinea pig model . Groups of three guinea pigs were infected intranasally either with rPan/99var and rPan/99wt viruses together ( 0 h group ) or with rPan/99var virus alone , followed by rPan/99wt virus 6 , 12 , 18 or 24 h later . Nasal washings were collected 48 h after the rPan/99wt virus infection . Since the induction of an antiviral response following primary infection has the potential to block secondary infection , we first evaluated whether the rPan/99wt virus infection was productive by quantifying wt and var HA vRNA in each nasal wash sample . In all groups , rPan/99wt virus was found to infect productively ( Table 2 ) ; in the 24 h group , however , Ct values for HAwt were found to be markedly higher than in the other groups , indicating a less robust rPan/99wt virus infection in these animals . In a separate experiment , infection with rPan/99wt virus 48 or 72 h after rPan/99var virus did not result in productive infection ( HAwt Ct>35 in 5/6 guinea pigs ) . Progeny virus isolates obtained from the nasal wash samples were then genotyped and scored as wt , var or reassortant . The results indicate that a delay of up to 12 hours between primary and secondary infections with rPan/99 virus does not reduce reassortment frequency ( Table 2 ) . In fact , higher levels of reassortment were seen in guinea pigs with a 12 h interval between infections than in those infected with both viruses simultaneously ( p = 0 . 02 , Student's t-test; Figure 6 ) . In contrast , secondary infection 18 h after primary infection resulted in a low frequency of reassortant progeny ( 6 . 7% on average ) , whereas no reassortants were detected when the two infections were staggered by 24 h . Thus , while a brief delay between primary and secondary influenza virus infections may actually increase the potential for reassortment , no reassortment was seen with a delay of 24 h or more . We had expected co-infection rates in cell culture and in vivo to decline with increasing time interval between infections . While this was found to be the case at high MOI in MDCK cells , co-infection was increased in guinea pigs with the introduction of a 12 h delay prior to super-infection . To explain this observation , we hypothesized that the delay allows the first virus to undergo one round of replication and then spread , thereby increasing the probability that the second infection will result in doubly infected cells . We tested this hypothesis by performing an MDCK cell based co-infection experiment from a low MOI ( 0 . 01 PFU/cell ) in the presence of trypsin . Under these conditions that allow for viral spread , the proportion of infected cells that were co-infected increased with an 8 or 12 h interval between inoculations ( p<0 . 0001 , chi-squared test ) . A decline in co-infected cells was then seen with 16 and 24 h intervals , indicating that super-infection interference has taken effect at these times after the primary infection ( Figure 7 ) .
We have evaluated , to our knowledge for the first time , the efficiency with which influenza A viruses undergo reassortment in the absence of fitness differences among parental and progeny genotypes . Our results show that reassortment is very efficient: where high rates of co-infection are achieved , high frequencies of reassortant genotypes are seen . These findings demonstrate that compartmentalization within the cell does not prevent extensive mixing of gene segments from two co-infecting viruses . It remains unclear whether this mixing occurs in the cytoplasm upon virus uncoating , in the nucleus during replication , during nuclear export and trafficking to the cell membrane , during the assembly process or throughout the virus life cycle . It is clear , however , that at least one stage of the life cycle allows for unrestricted exchange of gene segments . The efficiency of reassortment was decreased when inoculation with var and wt viruses was staggered by 12 h in cell culture: despite the fact that an appreciable number of cells were co-infected under these conditions , reassortment levels were near the limit of detection . This observation suggests that reassortment is limited when the life cycles of co-infecting viruses are at markedly asynchronous . Given that released virus was sampled 12 h after the second inoculation , however , there was enough time for the second virus to undergo a full cycle of replication [31] , sampling all stages of the life cycle . Thus , perhaps the predominance of progeny carrying full var genotypes was simply due to a predominance of var genomes within the co-infected cells . Higher intracellular levels of var gene segments compared to wt would be expected , given that their replication began several hours before the first wt genomes entered the cell . A system for the study of influenza virus co-infection rates in cell culture was recently described by Bodewes et al . [32] . Two similar recombinant influenza viruses that encoded different fluorescent proteins in the place of neuraminidase ( NA ) were generated . Using these viruses , co-infection of MDCK cells at an MOI of 3 PFU/cell resulted in double infections in about 10% of cells . In contrast , at an MOI of approximately 1 PFU/cell , we observed that about 65% of MDCK cells were co-infected with rPan/99wt and rPan/99var viruses . Variation in experimental design most likely accounts for these differences: the efficiency of infection with the fluorescein encoding viruses may have been decreased by the absence of NA on the virions [33] and also may have differed from that seen with the rPan/99 viruses due to the H5 subtype background that was used . Deviations of our own co-infection rates from those predicted by the Poisson distribution most likely result from antiviral factors present in undiluted virus stocks used for MOI = 10 infections , the contribution of gene segments from non-infectious particles ( especially important at lower MOIs ) , and routine experimental error . The proportion of progeny viruses with reassortant genotypes was found to increase exponentially with the proportion of cells that were co-infected . This relationship may reflect the relative likelihood at each MOI of an individual cell receiving equal doses of wt and var . At low MOI , those cells that are co-infected are highly likely to have only one copy of each genome and therefore a 1∶1 ratio of wt and var . At intermediate MOI , in contrast , the probability of two wt and one var virus ( or vice versa ) entering a given cell is appreciable . At high MOI , virtually all cells are expected to have several copies of each genome so that the wt∶var ratios are likely to be close to 1∶1 ( e . g . under MOI = 10 conditions , 9 wt and 11 var viruses might enter the same cell ) . Thus , the data suggest that discordant doses at the level the individual co-infected cell decrease the efficiency of reassortment . The rates of reassortment observed in co-infected guinea pigs were markedly higher than those seen previously in a ferret model [19] , but comparable to those obtained in a swine based experiment [26] . In a risk assessment study in ferrets , Jackson et al . examined the progeny arising from simultaneous co-infection with 105 . 7 PFU each of an H3N2 seasonal strain ( A/Wyoming/03/03 ) and a highly pathogenic avian H5N1 influenza virus ( A/Thailand/16/06 ) . On average , 8 . 7% of the isolates ( n = 360 ) obtained from ferret nasal washings were reassortants [19] . Similarly , Ma et al . evaluated reassortment following simultaneous co-infection of pigs with 106 PFU each of classical H1N1 and triple-reassortant H3N2 swine influenza viruses . In this case , 84 . 5% of virus isolates ( n = 71 ) carried mixed genomes [26] . The relatively low frequency of reassortant progeny resulting from A/Wyoming/03/03+A/Thailand/16/06 virus co-infection might be accounted for by i ) functional incompatibilites between the gene products ii ) mismatch among packaging sequences [17] , and/or iii ) low co-infection rates resulting from differing receptor binding specificities [34] . Each of these aspects of functional mismatch may have been diminished in the swine experiment since the viruses used were both adapted to this host and the NS , M and NP genes were of classical swine origin in each case [35] . In guinea pigs infected with 106 PFU of each virus , reassortment rates were similar to those obtained in cell cultures infected at individual MOIs of approximately 1 PFU/cell . The two situations should not be compared directly , however , since the MDCK based experiments were limited to a single cycle of replication ( both by the absence of trypsin and the early time point at which samples were collected ) , while multiple rounds of replication occurred in vivo . Amplification of both the input viruses ( leading to more opportunity for co-infection ) as well as the reassortant progeny viruses may have contributed to the high frequency of reassortant genotypes . It should be noted , however , that the reassortant genotypes identified in vivo were , in general , diverse . In other words , the reassortant isolates identified were not members of one or a few clonal populations . Influenza virus infection triggers a number of effects that disfavor super-infection . These include the induction of cellular and host antiviral responses [36] , the stripping of sialic acid receptors from the cell surface by viral neuraminidase [37] , and the destruction of potential target cells through lysis . Since the potency or extent of each of these effects increases over time , we predicted that the opportunity for co-infection and reassortment would decrease over time after a primary infection . In cell culture , under high multiplicity conditions , this hypothesis was found to be correct . In guinea pigs inoculated with 1000 PFU , however , infections staggered by 12 h led to a higher level of reassortment than did simultaneous co-infection . A 12 h delay between first and second infections in vivo may allow for the first virus to undergo one round of replication and then spread , thereby increasing the probability that the second infection will result in doubly infected cells . This hypothesis is supported by our observation that , following low MOI infection in cell culture , time intervals of 8 and 12 h between infections increased the number of co-infected cells compared to simultaneous infection . That fewer co-infected cells and reassortant viruses were produced from infections staggered by 16 h or 18 h in MDCK cells and guinea pigs , respectively , suggests that at these times after primary infection one or more of the mechanisms of super-infection exclusion has begun to take effect . Recent progress in the field on the mechanisms of influenza virus genome packaging have led to the hypothesis that sequence-specific interactions between RNA segments drive their selective incorporation during virion assembly [17] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] . It follows that , if packaging signals vary between strains of influenza A virus , a requirement for such RNA-RNA interactions would limit reassortment between divergent viruses . The regions of each segment thought to be important for packaging [41] , [43] , [46] were avoided in the mutagenesis of rPan/99var virus; thus , rPan/99wt and rPan/99var viruses most likely carry identical packaging signals . For this reason , our data are not expected to , and do not , reveal linkages between the segments at the RNA level . Our results do offer some insight into packaging specificity , however , in that HRM analyses of clonal isolates allowed the typing of each segment as wt or var . We did not see clear examples of isolates that carried both a wt and a var copy of a given segment . This result is in agreement with recent publications by Chou et al . and Inagaki et al . , which showed respectively that eight distinct segments are packaged into one virion [40] and that homologous gene segments compete for incorporation [47] . For the experiments described , we chose to use a seasonal influenza virus representative of the human H3N2 lineage . We would , however , expect influenza viruses of other strains and subtypes to behave similarly to the Pan/99 virus . The reason is that we are essentially studying the reassortment of a virus with itself; any strain should reassort well with itself under the “baseline” conditions described . When conditions are altered from the baseline , however , certain strain specific effects would be expected to arise . Two such effects relevant to the results herein are i ) the role of receptor binding specificity and other host-adaptive traits in determining the efficiency of infection and therefore co-infection in a given host species or target cell type; and ii ) the precise timing with which super-infection interference takes effect . The latter will most likely vary with the rate of viral growth and may hinge on the efficiency of IFN suppression or stripping of cellular sialic acids , depending on the mechanism ( s ) at play . Much remains to be done to gain a comprehensive understanding of influenza virus reassortment and the conditions under which it occurs . In addition to the effects of dose and timing described herein , cell tropism , host species , pre-existing immunity in the host , relative rates of viral growth and a wide range of strain specific factors that contribute to the phenomenon of segment mismatch will each come into play in determining the outcome of mixed influenza virus infections . In turn , the number of virus particles transmitted from a co-infected host to a recipient will be important in determining the epidemiological significance of reassortment events . Comparison of intra- and inter-host genetic diversity in equine and swine influenza suggests that the transmission bottleneck is loose; in other words , the diversity of genotypes transmitted was similar to that present in the initial host [48] , [49] , [50] . Under these conditions , a reassortant virus present even as a minor population would frequently be passed on to additional hosts . In sum , our findings indicate that influenza virus reassortment is an efficient process in a co-infected cell and in a co-infected host , and that the frequency of reassortment both at the level of individual cells as well as that of the animal host is dependent on dose and timing of infection . In establishing a simplified and well-controlled system to examine reassortment , we have laid the groundwork for future studies that will focus on virus and host-specific factors . By systematically varying individual parameters within our system , we hope to quantify the impact of a wide range of factors such that the complexity of influences determining natural reassortment rates can be evaluated for a given situation .
This study was performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Animal husbandry and experimental procedures were approved by the Emory University Institutional Animal Care and Use Committee ( IACUC protocol #2000719 ) . Madin-Darby Canine Kidney ( MDCK ) cells were maintained in minimum essential medium ( MEM ) supplemented with 10% FBS and penicillin-streptomycin . 293T cells were maintained in Dulbecco's MEM supplemented with 10% FBS . Female , Hartley strain , guinea pigs weighing 300–350 g were obtained from Charles River Laboratories . Prior to intranasal inoculation , nasal lavage or CO2 euthanasia , guinea pigs were sedated with a mixture of ketamine and xylazine ( 30 mg/kg and 2 mg/kg , respectively ) . Inoculation and nasal lavage were performed as described previously [51] , with PBS as the diluent/collection fluid in each case . rPan/99wt and variant viruses were recovered by reverse genetics following standard procedures [52] , [53] . Briefly , a 12 plasmid rescue system based on pPOL1 and pCAGGS vectors and co-culture of 293T and MDCK cells were used . Plaque isolates derived from rescue supernatants were amplified in 11-day-old embryonated chicken eggs to generate virus stocks and stock titers were determined by plaque assay on MDCK cells . Four rPan/99 based viruses were used in the research described: rPan/99wt , rPan/99var6 , rPan/99wt-HIS and rPan/99var2-HA . The first is a reverse genetics derived version of the wild-type A/Panama/2007/99 virus . The second , rPan/99var6 , contains the following silent mutations relative to rPan/99wt virus ( nucleotide numbering is from the 5′ end of the cRNA ) : NS C329T , C335T , and A341G; M C413T , C415G and A418C; NA C418G , T421A and A424C; NP C537T , T538A and C539G; HA T308C , C311A , C314T , A464T , C467G and T470A; PA A342G and G333A; PB1 C288T and T297C; and PB2 C354T and C360T . The third , rPan/99wt-HIS , differs from rPan/99wt virus only in that it encodes a HIS tag within the HA open reading frame ( inserted immediately after the sequence encoding the signal peptide [54] ) . The fourth , rPan/99var2-HA , encodes an HA tag within the HA open reading frame ( again , inserted after the signal peptide ) as well as the following silent mutations: NS C329T , C335T , and A341G; M C413T , C415G and A418C; NA C418G , T421A and A424C; NP C537T , T538A and C539G; HA T308C , C311A , C314T , A464T , C467G and T470A; PA G603A , T604A and C605G; PB1 C346T , T348G and A351G; and PB2 T621C , T622A and C623G . The rPan/99wt-HIS and rPan/99var2-HA viruses were used in the MDCK cell base experiments described , whereas the rPan/99wt and rPan/99var6 viruses were used in the guinea pig experiments . Our rationale for the above-described mutagenesis is as follows . The mutations were selected to allow differentiation of wt and var viruses by HRM analysis of ∼100 bp amplicons containing the mutation sites . In addition , we wished to avoid attenuation of the var viruses and therefore used a minimal number of mutations and avoided any known cis-acting signals . Two or three mutations were found to be sufficient to obtain clear HRM results , and C/T and A/G mutations were preferred since they confer the greatest change in melting properties . In addition , a second set of three mutations was introduced into the var HA segment such that the two mutated regions could be used as primer binding sites unique to wt or var so that these segments could be quantified in a standard qPCR assay ( as reported in Table 2 ) . rPan/99wt-HIS and rPan/99var2-HA viruses were mixed in equal proportions and then diluted with PBS to the appropriate titer for inoculation at MOI 10 , 1 , 0 . 1 or 0 . 01 PFU/cell of each virus . For the calculation of MOI , each well of a 6-well dish was assumed to have 1×106 cells . Prior to inoculation of MDCK cells , growth medium was removed , monolayers were washed with PBS three times and the 6-well dish was placed on ice . Each well was inoculated with a 250 ul volume and cells were incubated at 4°C for one hour to allow equal binding of all virus . Unattached virus was removed by aspirating inoculum and washing three times with PBS . After the addition of virus medium ( MEM supplemented with 3% BSA , Penicillin Streptomycin , and 1 ug/ml trypsin where indicated ) , cells were transferred to 33°C . At 12 hours post-infection , supernatant was collected and stored at −80°C for subsequent genotyping of released virus . MDCK-infected cells were harvested and prepared for flow cytometry ( see below ) . To determine the gene constellations of viruses present in the supernatant , 121 ( Figure 3 ) or 21 ( Figure 4 ) clones per sample were isolated by plaque assay of the supernatant and all 8 vRNA segments from each was typed by HRM analysis . Seven 6-well plates of MDCK cells were infected in triplicate for this experiment . For the first plate ( “0 h” time point ) , MDCK cells were infected with rPan/99wt-HIS and rPan/99var2-HA viruses as described above for the simultaneous co-infection at MOI 10 of each virus . The remaining plates were each infected at MOI 10 of rPan/99var2-HA virus alone at time = 0 h and then infected subsequently ( 2 , 4 , 8 , 12 , 16 , or 24 h after the var virus infection ) with rPan/99wt-His virus at MOI 10 . In each case , infections were carried out on ice and with a 1 h attachment period at 4°C , as described above . Supernatant was collected from each plate at 12 h following infection with rPan/99wt-His virus and stored at −80°C . Cells were harvested at this same time point and prepared for flow cytometry ( see below ) . The frequency of reassortant progeny in the supernatant was determined by typing all 8 gene segments of 21 plaque isolates from each sample by HRM analysis . To determine the number of infected and co-infected cells , MDCK cells were harvested 12 h after either wt/var virus co-infection or 12 h after wt virus infection by trypsinizing the monolayer and collecting with serum-supplemented medium . The cells were then washed 3 times with PBS-2% fetal calf serum ( FBS ) and incubated with Penta HIS Alexa Fluor 647 conjugated antibody ( 5 ug/ml; Qiagen ) and Anti-HA-FITC Clone HA-7 ( 7 ug/ml; Sigma Aldrich ) for 45 minutes , on ice . Cells were then washed 2 times with PBS-2% FBS and re-suspended with 200 ul of PBS-2% FBS and 5 ul/sample ( 0 . 25 ug ) of 7-Amino Actinomycin D ( 7-AAD ) , a dead cell excluder ( BD Biosciences ) . Flow cytometry was performed using a FACSVerse flow cytometer ( Becton Dickinson ) and analyzed with FlowJo software . rPan/99wt and rPan/99var6 viruses were mixed in equal proportions and then diluted in PBS to the appropriate titer ( 6 . 6×103 or 6 . 6×106 total PFU/ml for inoculation with 103 or 106 PFU of each virus , respectively ) . A total of five guinea pigs per dose were inoculated intranasally with 300 ul and unused inoculum was stored at −80°C . At 48 h post-infection nasal washings were collected and stored at −80°C . To determine the ratio of wt and var viruses present in the inoculum , 25–30 clones were isolated by plaque assay of the unused inoculum and two vRNA segments from each was typed by HRM analysis . Similarly , the frequency of reassortant progeny shed into nasal washings was determined by typing all eight gene segments of 19–24 plaque isolates derived from each nasal wash sample by HRM analysis . The data shown in Table 1 are the products of three different experiments: animals 1 , 2 , 3 , 6 , 7 , and 8 comprised one experiment; animals 4 and 5 comprised a second; and animals 9 and 10 comprised a third . Five groups of three guinea pigs were used in this experiment . The first group ( the “0 h” group ) was infected with rPan/99wt and rPan/99var6 viruses as described above for the simultaneous co-infection with 103 PFU of each virus . The remaining guinea pigs were each infected with 103 PFU of rPan/99var6 virus alone at time = 0 h and then infected subsequently ( 6 , 12 , 18 or 24 h after the var virus infection ) with 103 PFU of rPan/99wt virus . Nasal washings were collected from each guinea pig 48 h following infection with the rPan/99wt strain and stored at −80°C . The frequency of reassortant progeny shed into nasal washings was determined by typing all eight gene segments of 19–22 plaque isolates derived from each nasal wash sample by HRM analysis . To screen virus released from co-infected cell cultures or guinea pigs , the following steps were performed . 1 ) Plaque isolates were obtained by plaque assay of cell culture supernatants or guinea pig nasal wash fluids in 10 cm cell culture dishes . Agar plugs above well separated ( >0 . 5 cm apart ) plaques were picked using 5 ml serological pipettes , ejected into 160 ul of PBS in 1 . 5 ml tubes , and then stored at −80°C . 2 ) RNA was extracted from the agar plugs using the Qiagen QiaAmp Viral RNA kit , with the following modifications to the manufacturer's protocol: carrier RNA was not used , agar plugs in PBS were heated at 65°C for 5 min prior to mixing with AVL lysis buffer , and 40 ul water was used for the elution step . 3 ) Twelve microliters of RNA was reverse transcribed using Maxima reverse transcriptase ( Fermentas ) according to the manufacturer's instructions . 4 ) cDNA was used as template in qPCR reactions . Four microliters of 1∶4 diluted cDNA were combined with the appropriate primers ( 0 . 4 uM final concentration; see Supplementary Table S1 for primer sequences ) and Precision Melt Supermix ( BioRad ) in wells of a white , thin wall , 384 well plate ( BioRad ) . qPCR and melt analyses were carried out in a CFX384 Real-Time PCR Detection System , as per the instructions provided with the Precision Melt Supermix . Data were analysed using Precision Melt Analysis software ( BioRad ) . Viruses were scored as reassortant if the genome comprised a mixture of wt and var gene segments in any proportion ( e . g . both 7∶1 reassortants and 4∶4 reassortants were treated in the same way ) . Occasionally , one gene segment from a given isolate could not be typed as wt or var with high confidence ( this was the case with approximately 2 . 5% of segments ) ; such isolates were scored as wt or var parental viruses if all other gene segments were wt or var , respectively . If greater than one segment could not be typed , the isolate was excluded from the analysis . The HA segments of rPan/99 wt and rPan/99var6 viruses differ by 6 nucleotides in two clusters: T308C/C311A/C314T and A464T/C467G/T470A . Forward and reverse primers encompassing these mutation clusters were designed: HAwt 295F/HAwt 481R and HAvar 295F/HAvar 481R . These primers specifically amplify the wt or var HA segments , respectively , allowing their quantification by conventional qPCR methods . Thus , RNA extracted directly from nasal lavage fluids was subjected to reverse transcription followed by qPCR using SsoFast Evagreen Supermix ( BioRad ) , according to the manufacturer's instructions . qPCR was performed with a CFX384 Real-Time PCR Detection System and results were analysed using CFX Manager software ( BioRad ) . A one-sided exact test was applied to the data shown in Figure 3 to determine whether the proportion of isolates with parental genotypes was statistically greater than 2/256 , or 0 . 008 ( the expected value if reassortment occurred with full efficiency ) . Two-sided Fisher's exact tests were used to compare proportions of reassortant vs . parental progeny for data shown in Figures 4 and 5 , while chi-squared tests were applied to compare proportions of singly vs . doubly infected cells for flow cytometric data shown in Figures 4 , 5 and 7 . Finally , unpaired , two-sided Student's t-tests were applied to data shown in Figure 6 and Tables 1 and 2 . | Reassortment is the process by which influenza viruses , which carry RNA genomes comprising eight segments , exchange genetic material . Reassortment of the genome segments of two differing influenza strains has the potential to vastly increase the diversity of circulating influenza viruses . Despite its importance to influenza virus evolution , the frequency with which reassortment occurs in a cell or an animal infected with two or more variant viruses is unclear . Toward determining how readily reassortment can occur , we assessed the incidence of reassortment during experimental infection in cultured cells and in guinea pigs . We found that reassortment can occur with high efficiency in both systems , but that that efficiency is dependent on i ) the dose of each virus added to the cells or taken up by the host and ii ) the relative timing with which each virus infects . These results suggest that influenza A virus reassortment may be more prevalent in nature than one might expect based on the results of surveillance studies . | [
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] | 2013 | Influenza Virus Reassortment Occurs with High Frequency in the Absence of Segment Mismatch |
All of our perceptual experiences arise from the activity of neural populations . Here we study the formation of such percepts under the assumption that they emerge from a linear readout , i . e . , a weighted sum of the neurons’ firing rates . We show that this assumption constrains the trial-to-trial covariance structure of neural activities and animal behavior . The predicted covariance structure depends on the readout parameters , and in particular on the temporal integration window w and typical number of neurons K used in the formation of the percept . Using these predictions , we show how to infer the readout parameters from joint measurements of a subject’s behavior and neural activities . We consider three such scenarios: ( 1 ) recordings from the complete neural population , ( 2 ) recordings of neuronal sub-ensembles whose size exceeds K , and ( 3 ) recordings of neuronal sub-ensembles that are smaller than K . Using theoretical arguments and artificially generated data , we show that the first two scenarios allow us to recover the typical spatial and temporal scales of the readout . In the third scenario , we show that the readout parameters can only be recovered by making additional assumptions about the structure of the full population activity . Our work provides the first thorough interpretation of ( feed-forward ) percept formation from a population of sensory neurons . We discuss applications to experimental recordings in classic sensory decision-making tasks , which will hopefully provide new insights into the nature of perceptual integration .
Most cortical neurons are noisy , or at least appear so in experiments . When we record the responses of sensory neurons to well-controlled stimuli , their spike patterns vary from trial to trial . Does this variability reflect the uncertainties of the measurement process , or does it have a direct impact on behavior ? These questions are central to our understanding of percept formation and decision-making in the brain and have been the focus of much previous work [1] . Many studies have sought to address these problems by studying animals that perform simple , perceptual decision-making tasks [2 , 3] . In such tasks , an animal is typically presented with different stimuli s and trained to categorize them through a simple behavioral report . When this perceptual report is monitored simultaneously with the animal’s neural activity , one can try to find a causal link between the two . One particular hypothesis about this link—which we refer to as the “sensory noise” hypothesis—postulates that the accuracy of the animal’s perceptual judgments is primarily limited by noise at the level of sensory neurons [4 , 5] . In terms of signal detection theory , the hypothesis predicts a quantitative match between ( 1 ) the animal’s ability to discriminate nearby stimulus values—known as psychometric sensitivity , and ( 2 ) an ideal observer’s ability to discriminate nearby stimulus values based on the activities of the underlying neural population—known as neurometric sensitivity . Both types of sensitivities can be quantified as signal-to-noise ratios ( SNR ) . With this idea in mind , several studies have compared the neurometric and psychometric sensitivities in various sensory systems and behavioral tasks ( see [6 , 7] for reference ) . However , as was soon realized , any extrapolation from a few recorded cells to the entire population is fraught with implicit assumptions . For example , if neurons in a population behave independently one from another , then the SNR of the population is simply the sum of the individual SNRs . Consequently , any estimate of neurometric sensitivity will grow linearly with the number of recorded neurons K . However , if neurons in a population do not behave independently , the precise growth of neural sensitivity with K is determined by the correlation structure of noise in the population [8–10] . In addition , the neurometric sensitivities also depend on the time scale w that is used to integrate each neuron’s spike train in a given trial [3 , 11–13] . Indeed , the more spikes are incorporated in the readout , the more accurate that readout will be . Adding extra neurons by increasing K , or adding extra spikes by increasing w , are two dual ways of increasing the readout’s overall SNR . As there is no unique way of reading out information from a population of sensory neurons , the sensory noise hypothesis can only be tested if we understand how the organism itself “reads out” the relevant information . In other words , how many sensory neurons K , and what integration time scale w , provide a relevant description of the animal’s percept formation ? Given the “K-w” duality mentioned above , we cannot answer that question based solely on sensitivity ( SNR ) . Another experimental measure should also be included in the analysis . A good candidate for such a measure are choice signals , i . e . , measures of the trial-to-trial correlation between the activity of each recorded neuron and the animal’s ultimate choice on each trial . These signals , weak but often significant , arise from the unknown process by which each neuron’s activity influences—or is influenced by—the animal’s perceptual decision . In two-alternative forced choice ( 2AFC ) discrimination tasks , they have generally been computed in the form of choice probabilities ( CP ) [14 , 15] . The temporal evolution of CPs has been used to find the instants in time when a given population covaries with the animal’s percept [13 , 16] . In a seminal study , Shadlen et al . ( 1996 ) proposed to jointly use sensitivity and choice signals , as two independent constraints characterizing the underlying neural code [17] . They derived a feed-forward model of perceptual integration in visual area MT , and studied numerically how the population’s sensitivity and CPs vary as a function of various model parameters . They acknowledged the existence of a link between CPs and pairwise noise correlations—both measures being ( partial ) reflections of how information is embedded in the neural population as a whole ( see also [12 , 18] ) . However , the quantitative nature of this link was only revealed recently , when Haefner et al . ( 2013 ) derived the analytical expression of CPs in the standard model of perceptual integration [19] ( see Methods ) . In this article , we show that the standard feed-forward model of percept formation gives rise to three characteristic equations that describe analytically the trial-to-trial covariance between neural activities and animal percept . These equations depend on the brain’s readout policy across neurons and time , and hold for any noise correlation structure in the neural population . In accordance with the intuition of Shadlen et al . ( 1996 ) , we show that sensitivity and choice signals correspond to two distinct , characteristic properties of the readout . The equation describing choice signals is equivalent to the one derived by Haefner et al . ( 2013 ) , but stripped from the non-linear complications inherent to the CP formulation . We use a linear formulation instead , which gives us a particularly simple prediction of choice signals at every instant in time . We then show how these equations can be used in order to recover the time window and the number of neurons used in the formation of a percept . A quantitative analysis of choice signals allows us to overcome the “K–w trade-off” inherent to neurometric sensitivity . We specifically focus on situations in which only a finite sample of neurons has been measured from a large , unknown population . We show how to recover the typical number of neurons K , provided that the experimenter could record at least K neurons simultaneously . Finally , we discuss the scope and the limitations of our method , and how it can be applied to real experimental data .
We will study the formation of percepts in the context of perceptual decision-making experiments ( Fig . 1 , see Methods or Tables 1–3 for the corresponding formulas ) . In these experiments , an animal is typically confronted with a stimulus , s , and must then make a behavioral choice , c , according to the rules of the task . A specific example is the classic discrimination task in which the animal’s choice c is binary , and the animal must report whether it perceived s to be higher ( c = 1 ) or lower ( c = 0 ) than a fixed reference s0 ( Fig . 1A , top and middle panels ) . While the animal is performing the task , the neural activity in a given brain area can be monitored ( Fig . 1A , bottom panel ) . Typical examples from the literature include area MT in the context of a motion discrimination task [3] , area MT or V2 in the context of a depth discrimination task [11 , 20] , or area S1 in the context of a tactile discrimination task [21] . For concreteness , we will mostly focus on these discrimination tasks , although the general framework can be applied to arbitrary perceptual decision-making tasks . The animal’s behavior in a discrimination task can be quantified through the psychometric curve ψ ( s ) . This curve measures the animal’s repartition of responses at each stimulus value s ( Fig . 1B ) . If the animal is unbiased , it will choose randomly whenever the stimulus s is equal to the threshold value s0 , so that ψ ( s0 ) = 1/2 . The slope of the psychometric curve at s = s0 determines the animal’s ability to distinguish near-threshold values of the stimulus , i . e . , its psychometric sensitivity . We assess this sensitivity through the just noticeable difference ( JND ) or difference limen , noted Z . The more sensitive the animal , the smaller Z , and the steeper its psychometric curve . We assume that the neural activity within the recorded brain area conveys the stimulus information that the animal uses to make its choice ( Fig . 1A , bottom ) . We describe the activity of this neural population on every trial as a multivariate point process r ( t ) = {ri ( t ) }i = 1…Ntot , where each ri ( t ) is the spike train for neuron i , and Ntot denotes the full population size , a very large and unknown number . ( The number of neurons actually recorded is generally much smaller . ) As is common in electrophysiological recordings , we will quantify the raw spike trains by their first and second order statistics . First , neuron i’s trial-averaged activity in response to each tested stimulus s is given by the peri-stimulus time histogram ( PSTH ) or time-varying firing rate , mi ( t; s ) ( Fig . 1D ) . In so-called “fine” discrimination tasks , the stimuli s display only moderate variations around the central value s0 , so that the PSTH at each instant in time can often be approximated by a linear function of s: m i ( t ; s ) ≃ m i 0 ( t ) + b i ( t ) s . The slope bi ( t ) , defined at every instant in time , summarizes neuron i’s tuning properties ( Fig . 1E ) . Second , we assume that several neurons can be recorded simultaneously , so that we can access samples from the trial-to-trial covariance structure of the population activity ( Fig . 1C ) . For every pair of neurons ( i , j ) and instants in time ( t , u ) , the joint peri-stimulus time histogram ( JPSTH , [22] ) Cij ( t , u ) summarizes the pairwise noise correlations between the two neurons ( eq . 25 ) . For simplicity , we furthermore assume that the JPSTHs do not depend on the exact stimulus value s . Finally , we can measure a choice signal for each neuron , which captures the trial-to-trial covariation of neuron activity ri ( t ) with the animal’s choice ( Fig . 1F ) . Traditionally , this signal is measured in the form of choice probability ( CP ) curves . We consider here a simpler linear equivalent , that we term choice covariance ( CC ) curves [3] . The CC curve for neuron i , denoted by di ( t ) , measures the difference in firing rate ( at each instant in time ) between trials where the animal chose c = 1 and trials where it chose c = 0—all experimental features ( including stimulus value ) being fixed . Unlike many characterizations of neural activity that rely only on spike counts , our framework requires an explicit temporal description of neural activity through PSTHs , JPSTHs , and CC curves . Exact formulas for these statistical measures are provided in the Methods . By keeping track of time , we will be able to predict when , and how long , perceptual integration takes place in an organism . The linear readout model and the animal’s decision policy specify both how the animal’s percepts are formed from its neural activities and how its choices are generated from these percepts . If we had recorded the activities of the entire neural population together with the animal’s behavior , then the parameters of this model could be estimated from the data using any standard regression method . However , this is generally not a realistic experimental situation . Instead , we take here a statistical approach to the problem , which ( 1 ) allows us to deal with incomplete recordings and ( 2 ) relates the estimation problem to the standard experimental measures described above . Equations 7–9 describe the analytical link between measures of neural response to the stimulus ( bi and Cij ) and measures related to the animal’s percept ( Z and di ) , based on the model’s readout parameters ( a , w , tR , and σd ) . This naturally raises the reverse question: can we estimate the parameters of the standard model ( a , w , tR , and σd ) from actual measurements ? From here on , we will denote the true ( and unknown ) values of these parameters , i . e . , the values used in the animal’s actual percept formation , with a star ( a⋆ , w⋆ , t R ⋆ , and σ d ⋆ ) . As mentioned in the introduction , our primary interest concerns the trade-off between the time scale w⋆ of integration , and the size K⋆ of the functional population which conveys the animal’s percept to downstream areas . Thus , we assume that the animal’s percept is constructed from a specific sub-ensemble 𝓔⋆ of neurons , of size K⋆ ( Fig . 3A ) . Neurons inside 𝓔⋆ correspond to nonzero entries in the readout vector a⋆ , while neurons outside 𝓔⋆ have zero entries . Since only a subset of neurons within a cortical area will project to a downstream area , we can generally assume that K⋆ < Ntot . Naturally , all those parameters are not measurable experimentally . For any candidate set of parameters , a , w , tR , and σd , the characteristic equations 7–9 lead to predictions for Z and di ( t ) ( note the absence of star when referring to predictions ) . In turn , the experimenter can measure the animal’s actual choice c⋆ on each trial , from which they can estimate the JND Z⋆ , and the CC curves d i ⋆ ( t ) for all recorded neurons . In the next three sections , we study whether this information is sufficient to retrieve the true readout parameters , depending on the amount of data available . In the ideal scenario where all neurons in the population are recorded simultaneously , N = Ntot , all parameters can be retrieved exactly ( Case 1 ) . In most experimental recordings , however , we only measure the activities of a small subset of that population ( Fig . 3A ) . If this subset is representative of the full population , we may want to retrieve the readout parameters through extrapolation . Unfortunately , any such extrapolation is fraught with additional assumptions—whether implicit or explicit—as it requires to replace the missing data with some form of ( generative ) model . In Case 2 , we impose a generative model for the readout vector a . Coupled with a statistical principle , it allows us to estimate the true size K⋆ of the readout ensemble , provided that the number of neurons recorded simultaneously , N , is larger: N > K⋆ . In Case 3 , we study the scenario in which N ≤ K⋆ . Here , we need to assume a generative model for the neural activities themselves . Since the noise covariance structure assumed by that model exerts a strong influence on the predicted JND and CC curves , a direct inference of the readout scales becomes impossible . If all neurons in the population have been recorded , with a sufficient amount of trials to estimate the complete covariance structure of the population , then the only unknown quantities in eq . 7–9 are the readout parameters w , tR and a , and the decision noise σd . For fixed parameters w and tR , eq . 7 and 9 impose linear constraints on vector a . These constraints are generally over-complete , since a is Ntot-dimensional , while each time t in eq . 9 provides Ntot additional linear constraints . Thus , in general , a solution a will only exist if one has targeted the true parameters w⋆ and t R ⋆ , and it will then be unique . ( If no choice of the readout parameters approximately fulfills the characteristic equations , we would have to conclude that the linear readout model is fundamentally wrong . ) In practice , we can find the best solution to the characteristic equations by simply combining them and then minimizing the following mean-square error: L ( w , t R , a , σ d ) = 1 − b ¯ ⊤ a 2 + λ Z ⋆ 2 − σ d 2 − a ⊤ C ¯ ¯ a 2 + μ ∫ d t d * ( t ) − κ ( Z ⋆ ) C ¯ ( t ) a 2 , ( 11 ) where the parameters λ and μ trade off the importance of the errors in the different characteristic equations . Note that the loss function L depends not only on the readout weights a and the decision noise σd , but also on the parameters w and tR , both of which enter all the time integrations that are denoted by an overhead bar . Once vector a⋆ is estimated , the readout ensemble 𝓔⋆ will correspond to the set of neurons with nonzero readout weights . Unfortunately , measuring the neural activity of a full population is essentially impossible , although optogenetic techniques are coming ever closer to this goal [24–26] . Nevertheless , if the activity patterns of the recorded cells are statistically similar to those of the readout ensemble , and if the number of simultaneously recorded cells exceeds the number of cells in the readout ensemble , we can still retrieve the readout parameters by making specific assumptions about the true readout vector a⋆ . By construction , the method presented in Case 2 can only test ensemble sizes K smaller than N , the number of neurons recorded simultaneously by the experimenter . If N is smaller than the true size K⋆ , the method will provide biased estimates . In current-day experiments , N can range from a few tens to a few hundred neurons . While it is not excluded that typical readout sizes K⋆ be of that magnitude in real neural populations ( as suggested , e . g . , by [8] ) , it is also possible that they are larger . In this case , the only way to estimate the readout parameters is to make specific assumptions about the nature of the full population activity . In turn , the extrapolated results will depend on these assumptions .
The readout model ( eq . 2 ) used to analyze sensitivity and choice signals is an installment of the “standard” , feed-forward model of percept formation [17 , 19] . As such it makes a number of hypotheses which should be understood when applying our methods to real experimental data . First , it assumes that the percept s ̂ is built linearly from the activities of the neurons—a common assumption which greatly simplifies the overall formalism ( but see , e . g . , [33] for a recent example of nonlinear decoding ) . Even if the real percept formation departs from linearity , fitting a linear model will most likely retain meaningful estimates for the coarse information ( temporal scales , number of neurons involved ) that we seek to estimate in our work . More precisely , the model in eq . 1–2 assumes that spikes are integrated using a kernel that is separable across neurons and time , that is Ai ( t ) = ai h ( t/w ) /w . Theory does not prevent us from studying a more general integration , where each neuron i contributes with a different time course Ai ( t ) . The readout’s characteristic equations are derived equally well in that case . Rather , assuming a separable form reflects our intuition that the time scale of integration is somewhat uniform across the population . This time scale , w , is then the one crucial parameter of the integration kernel . Although the shape h ( t ) of the kernel could also be fit from data in theory , it seems more fruitful to assume a simple shape from the start . We assumed a classic square kernel in our applications . Other shapes may be more plausible biologically , such as a decreasing exponential mimicking synaptic integration by downstream neurons . However , given that our goal is to estimate the ( coarse ) time scale of percept formation , our method will likely be robust to various simple choices for h . As a simple example , we tested our method , assuming a square kernel , on data produced by an exponential readout kernel , and still recovered the correct parameters w , tR and K ( data not shown ) . Through the process of integration across time and neurons , each instant in time could be associated to an “ongoing percept” , i . e . , the animal’s estimate of stimulus value at current time . In our model , the animal’s estimate at time tR serves as the basis for its behavioral report ( Fig . 2A ) , and we designate this single number s ̂ as the “percept” . A second strong assumption of our model is that this perceptual readout occurs at the same time tR on every stimulus presentation . In reality , there is indirect evidence that tR could vary from trial to trial , as suggested by the subjects’ varying reaction times ( RT ) when they are allowed to react freely [34 , 35] . In such tasks , we expect the variations in tR to be moderate—because subjects generally react as fast as they can—and we may even try to correct for fluctuations across trials by measuring RTs . On the other hand , when subjects are forced to wait for a long period of time before responding , there is room for ample variations in tR from trial to trial , and the model presented above may become insufficient . As a first step towards addressing this question , we derived a more general version of the characteristic equations 4–6 assuming that tR in eq . 1 is itself a random variable , drawn on each trial following some probability distribution g ( t ) ( supporting S1 Text ) . The main impact of this modification is on CC curves , which become broader and flatter; essentially , the resulting curve resembles a convolution of the deterministic CC curve by g ( t ) ( Fig . 8A ) . This means that if a behavioral task is built such that tR can display strong variations from trial to trial , the methods introduced above will produce biased estimates . In theory , this issue could be resolved by adding an additional parameter in the analysis , to describe g ( t ) ( see supporting S1 Text ) . The linear readout provides a percept s ̂ on every trial . In principle , behavioral experiments could be set up such that the subject directly reports this percept , so that c = s ̂ . Such experiments could be treated completely without a decision model . However , almost all experiments that have been studied in the past involve a more indirect report of the animal’s percept . In these cases , some assumptions about how the percept is transformed into the behavioral report c need to be made . In the choice of a decision model , we have followed the logic of the classic random dot motion discrimination task [3] , in which a monkey observes a set of randomly moving dots whose overall motion is slightly biased towards the left ( s < 0 in our notations ) or towards the right ( s > 0 ) . The monkey must then press either of two buttons depending on its judgment of the overall movement direction . The simplest decision model assumes a fixed integration time window , additive noise on the percept , s ̂ , and an optimal binary decision . A slightly more sophisticated model , the “integration-to-bound” model , assumes that the integration time is not fixed , but rather limited by a desired behavioral accuracy . This model requires variable readout windows , rather than the fixed readout window assumed here , and will require further investigation in the future . In another classic task [2] , the monkey must discriminate the frequencies s1 and s2 of two successive vibrating stimuli on their fingertip . They must press either of two buttons depending on whether they consider that s1 > s2 or not . In this task , the optimal behavioral model would be c = H ( s ̂ 1 − s ̂ 2 ) . In reality , however , the monkey needs to memorize s1 for a few seconds before s2 is presented , so potential effects of memory loss may also come into play ( see e . g . [36] for a study of these problems ) . More generally , behaving animals can display biases , lapses of attention , various exploratory and reward-maximization policies that lead to deviations from the optimal behavioral model . Choosing a relevant behavioral model is a connected problem that cannot be addressed here , and that will vary depending on the task and individual considered . For most tractable behavioral models , the predicted sensitivities and choice signals will ultimately rely on the quantities introduced in this article . Finally , the standard model assumes that percept formation is exclusively feed-forward . The activities ri ( t ) of the sensory neurons are integrated to give rise to the percept s ̂ and the animal’s choice c , yet the formation of this decision does not affect sensory neurons in return . Recent evidence suggests that reality is more complex . By looking at the temporal evolution of CP signals in V2 neurons during a depth discrimination task , Nienborg and Cumming ( 2009 ) evidenced dynamics which are best explained by a top-down signal , biasing the activity of the neurons on each trial after the choice is formed [20] . In our notations , the population spikes ri ( t ) would thus display a choice-dependent signal which kicks in on every trial after time tR , resulting in CC signals that deviate from their prediction in the absence of feedback ( Fig . 8B ) . What descriptive power does our model retain , if such top-down effects are strong ? The answer depends on the nature of the putative feedback . If the feedback depends linearly on percept s ̂ ( and thus , on the spike trains ) , its effects are fully encompassed in our model . Indeed , this feedback signal will then be totally captured by the neurons’ linear covariance structure Cij ( t , u ) , so that our predictions will naturally take it into account . On the other hand , if the feedback depends directly on the choice c—which displays a nonlinear , “all-or-none” dependency on s ̂—then it will not be captured by our model , and lead to possible biases . Even so , our model would still apply if percept and decision were essentially uncoupled before the putative extraction time tR , in which case one could simply compare true and predicted CC signals up to ( candidate ) time tR ( see Fig . 8B ) . In most real-life situations , experimenters only have access to samples from a large , unknown population , so they must resort to a statistical description of readout vector a . Our solution relies on an assumption of restricted optimality , based on Fisher’s linear discriminant formula ( eq . 12 ) . By assuming that readout is made optimally from some unknown neural ensemble 𝓔 , we reformulated the problem of characterizing a in that of characterizing 𝓔 , and could in turn exploit the characteristic equations 4–6 statistically . In real experiments , the true readout profile a may not match this description: most vectors a do not implement optimal readout from a sub-ensemble . This potential discrepancy from the true readout is inescapable , once we start representing a through a statistical model . However , note that our model uses two distinct sources of non-optimality: ( 1 ) the size K of the readout ensemble , which can be much smaller than the full population , and ( 2 ) the decision noise σd , which adds a ‘global’ non-optimality to the readout . Arguably , by combining both factors , our chosen model for a will be flexible enough to provide meaningful estimates when fit to real data . At present , the main limitation is likely to be the size of ensembles of neurons that have been recorded simultaneously . Past work has often shown that small ensembles of neurons are completely sufficient to account for an animal’s behavior [3 , 37] . However , there is an inherent trade-off between the number of neurons and the time scale of integration . One simple explanation for the small sizes of previous readout ensembles is that the true readout time scales used by subjects are much shorter . Unfortunately , as detailed above ( Case 3 ) , extrapolations from a finite-size recording onto the whole population always come at the price of strong additional assumptions . However , as experimental techniques advance , and as the number of simultaneously recorded neurons reaches the number of neurons implied in the readout , we will eventually be able to directly infer the readout parameters from the data . In this case , our method can readily be tested on real data , and hopefully provide new insights into the nature of percept formation from populations of sensory neurons .
In the following , we generally deal with variables x that assume different values on different trials . An example is the spike count of a single neuron . Trials in turn can be grouped by stimulus s or choice c . We can make this explicit by writing xscq to denote the q-th trial in which the stimulus was s and the subject’s choice was c . Given such a variable , we will write E[x] for its expectation value , i . e . , for the hypothetical value this quantity would take if it could be averaged over infinitely many trials . We will write E[x∣s] for the expectation value conditioned on stimulus s , i . e . , for the expectation value computed over all trials in which the stimulus was s . A similar notation holds when conditioning on choices c . We note that for quantities that are already conditional expectations , for instance , y ( s ) = E[x∣s] , their expectation value E[y ( s ) ] will average out the stimuli according to their relative probabilities , i . e . , E[y ( s ) ] = ∑s p ( s ) y ( s ) . Thereby , each stimulus s contributes to the expectation in proportion to the number of trials associated to it . Then the notations are coherent , since we have E [ E [ x ∣ s ] ] = E [ x ] . Covariances are generically defined as Cov[x , y] = E[xy] − E[x]E[y] , and variances as Var[x] = Cov[x , x] . For vectorial quantities , we assume Cov[x , y] = E[x y⊤] − E[x]E[y⊤] , and introduce the shorthand Cov[x] ≔ Cov[x , x] . Classic measures in decision-making experiments can be interpreted as estimates of the first- and second-order statistics of choice c and recorded spike trains ri ( t ) , across all trials with a fixed stimulus value s: ψ ( s ) ≔ E [ c | s ] , ( 23 ) m i ( t ; s ) ≔ E [ r i ( t ) | s ] , ( 24 ) C i j ( t , u ; s ) ≔ Cov [ r i ( t ) , r j ( u ) | s ] , ( 25 ) d i ( t ; s ) ≔ Cov [ r i ( t ) , c | s ] . ( 26 ) Here , ψ ( s ) is the psychometric curve , mi ( t; s ) is known as the PSTH , and Cij ( t , u; s ) as the JPSTH . The choice covariance ( CC ) curve di ( t; s ) is our proposal for measuring each neuron’s “choice signal” . Theoretically , the temporal signals in eq . 24–26 are well-defined quantities in the framework of continuous-time point processes [38] . In practice , they are estimated by binning spike trains ri ( t ) with a finite temporal precision , depending on the amount of data available . From the psychometric curve , we also derive two simpler quantities: the animal’s just-noticeable difference ( JND ) , Z , and decision bias μd . We obtain them as the best ( MSE ) fit to the following formula: ψ ( s ) = Φ s + μ d − s 0 Z , ( 27 ) where Φ is the standard cumulative normal distribution . Z measures the inverse slope of the psychometric curve ( up to a scaling factor 2 π ) . The decision bias μd , when non-zero , represents a bias towards one button when s = s0 . This formula for the psychometric curve arises naturally when we model the decision task ( see below ) . Throughout the article , we consider the special case of a binary choice c = {0 , 1} . In this case , the variance of the choice conditioned on s is given by σ c 2 ( s ) ≔ Var [ c | s ] = ψ ( s ) ( 1 − ψ ( s ) ) , ( 28 ) and a straightforward computation shows that d i ( t ; s ) = σ c 2 ( s ) E [ r i ( t ) | s , c = 1 ] − E [ r i ( t ) | s , c = 0 ] . ( 29 ) ( These formulas , and all those below , assume that the choice takes values 0 and 1 . Any other binary parametrization should first be reparametrized to {0 , 1} . ) The term in brackets is the difference between the two conditional PSTHs , computed only from trials where the animal took one decision vs . the other ( stimulus s keeping a fixed value ) . This measure is sometimes used as a simpler alternative to choice probabilities [3] . In fact , CC curves and CP curves can be analytically related if one assumes Gaussian statistics: see [19] or supporting S1 Text . The neural statistics in eq . 24–26 are defined conditionally for each stimulus s used in the task . To ease the subsequent analysis , we assume that the activity of each neuron is well approximated by a time-varying , linear dependency on the stimulus s , and that Cij ( t , u; s ) is independent of s . Consequently , m i ( t ; s ) = m i 0 ( t ) + b i ( t ) s , C i j ( t , u ; s ) = C i j ( t , u ) . Since we are modeling a discrimination task , in which stimuli s display only small variations around the central value s0 , the linearity assumption seems reasonable . In turn , we can write b i ( t ) ≔ ∂ s E [ r i ( t ) | s ] . ( 30 ) We will refer to bi ( t ) as the neural tuning . More precisely , it is the slope of the neuron’s tuning curve at each time point . Naturally , actual data ( even from a synthetic simulation ) always somewhat deviate from this idealized situation . In practice , we obtain the best fits for bi ( t ) and Cij ( t , u ) using linear regression , so that b i ( t ) = E [ s m i ( t ; s ) ] − E [ s ] E [ m i ( t ; s ) ] E [ s 2 ] − E [ s ] 2 , ( 31 ) C i j ( t , u ) = E [ C i j ( t , u ; s ) ] . ( 32 ) Similarly , it is convenient to integrate the various CC curves di ( t; s ) ( eq . 26 ) into a single CC curve for each neuron , say di ( t ) . There is no obvious choice for this simplification , because di ( t; s ) has to change with s . For example , the CC signal is non-zero only if stimulus s and threshold s0 are close enough for the animal to make occasional mistakes ( this is reflected in eq . 29 , since σ c 2 ( s ) tends to zero when the animal makes no mistakes ) . In the experimental literature , a common choice is to focus only on the CC curve at threshold , that is di ( t ) = di ( t; s0 ) . In experiments with a limited number of trials , this has the inconvenience of losing the statistical power from nearby stimulus values s that were also tested . We thus propose an alternative definition: d i ( t ) ≔ E [ d i ( t ; s ) ] , ( 33 ) which exploits each stimulus s in proportion to the number of associated trials . In our model , this averaging also limits the influence of the JND Z on the magnitude of CC signals: see eq . 45–46 . The readout defined in eq . 1–2 is linear with respect to the underlying spike trains {ri ( t ) } . To clarify the equations , let us introduce the temporal averaging kernel k ( t | w , t R ) ≔ 1 w h t R − t w , ( 34 ) where parameters w and tR are generally implicit . Then , the integrated spike counts from eq . 1 are simply r ‾ i = ∫ t r i ( t ) k ( t ) d t . Using this notation , eq . 2 becomes s ̂ = a 0 + ∑ i ∫ t a i k ( t ) r i ( t ) d t . Thanks to the linear structure , the two first moments of s ̂ can easily be developed: E [ s ^ | s ] = a 0 + ∑ i a i ∫ t E [ r i ( t ) | s ] k ( t ) d t , Var [ s ^ | s ] = ∑ i j a i a j ∫u ∫t Cov [ r i ( t ) , r j ( u ) | s ] k ( t ) k ( u ) d t d u , Cov [ r i ( t ) , s ^ | s ] = ∑ j a j ∫ u Cov [ r i ( t ) , r j ( u ) | s ] k ( u ) d u . Given our various definitions ( eq . 24–25 ) , and after differentiating the first line with respect to s , see eq . 30 , we obtain: ∂ s E [ s ^ | s ] = ∑ i a i ∫ t b i ( t ) k ( t ) d t = a ⊤ b ¯ , ( 35 ) Var [ s ^ | s ] = ∑ i j a i a j ∫u ∫t C i j ( t , u ) k ( t ) k ( u ) d t d u = a ⊤ C ¯ ¯ a , ( 36 ) Cov [ r i ( t ) , s ^ | s ] = ∑ j a j ∫ u C i j ( t , u ) k ( u ) d u = [ C ¯ ( t ) a ] i . ( 37 ) These are exactly the characteristic equations 4–6 from the main text , after introducing the following vectors and matrices: b ¯ i ≔ ∫ t b i ( t ) k ( t ) d t , ( 38 ) C ¯ i j ( t ) ≔ ∫ u C i j ( t , u ) k ( u ) d u , ( 39 ) C ¯ ¯ i j ≔ ∫ t C ¯ i j ( t ) k ( t ) d t , ( 40 ) d ¯ i ≔ ∫ t d i ( t ) k ( t ) d t , ( 41 ) which simply correspond to the statistics of activity for the integrated spike counts r ‾ i ( eq . 1 ) . Indeed , b ‾ i = ∂ s E [ r ‾ i ∣ s ] ( tuning vector ) , C ‾ ‾ i j = Cov [ r ‾ i , r ‾ j ∣ s ] ( noise covariance matrix ) , and d ‾ i = E [ Cov [ r ‾ i , c ∣ s ] ] ( choice covariance vector ) . Given our assumptions above , the resulting quantities are all independent of the stimulus s . Note though , that all quantities depend on the readout parameters w and tR . Importantly , one can show that the noise covariance matrix C ‾ ‾ scales as w−1 , under mild assumptions ( supporting S1 Text , section 2 ) . To produce a binary choice , the ( continuous ) percept s ̂ is fed into the decision model c = H ( s ̂ − s 0 + ξ d ) , where H is the Heaviside function , s0 is the ( task-imposed ) decision threshold , and ξd ∼ 𝒩 ( μd , σd ) is a Gaussian variable representing additional noise and biases . The mean μd implements a possible bias towards one button when s = s0 . The standard deviation σd implements additional sources of noise in the animal’s decision process . Using this decision model , and mild additional assumptions , we can relate the left-hand sides of eq . 35–37 to experimental data . First , we assume that E [ s ̂ ∣ s ] = s , meaning that s ̂ follows s on average . ( In statistical terminology , s ̂ is an unbiased estimator of s . ) Then , the left-hand side of eq . 35 is simply equal to ∂ s E [ s ^ | s ] = 1 . ( 42 ) Second , we assume that the distribution of r ( t ) ( given s ) is Gaussian . ( In theory , this assumption is violated at small time scales due to the binary nature of ri ( t ) . But in practice this is not an issue , as the spike trains always undergo some form of temporal integration afterwards . ) Then , s ̂ ( given s ) is normally distributed , and eq . 36 ensures that its variance Var [ s ̂ ∣ s ] is independent of s ( see Fig . 2B ) . In these conditions , the predicted formula for the psychometric curve is exactly that of eq . 27 , namely , ψ ( s ) = Φ s + μ d − s 0 Z , and the JND , Z , is given by the following expression: Z 2 = Var [ s ^ | s ] + σ d 2 . ( 43 ) Furthermore , under the same assumptions , we can predict the CC curve for each neuron . We use the following general result: for any bivariate normal variables ( X , Y ) and threshold t , Cov[X , H ( Y − t ) ] = Cov[X , Y]𝒢 ( t; μY , σY ) , where 𝒢 ( ⋅; μ , σ ) is the normal density function . Here , we take X = ri ( t ) , Y = s ̂ + ξ d and t = s0 , to obtain: d i ( t ; s ) = 𝓖 ( s ; s 0 − μ d , Z ) Cov [ r i ( t ) , s ^ | s ] . ( 44 ) With di ( t ) defined as an average CC curve over tested stimuli ( eq . 33 ) , we finally obtain d i ( t ) = κ ( Z ) Cov [ r i ( t ) , s ^ | s ] , ( 45 ) with κ ( Z ) = E 𝓖 ( s ; s 0 − μ d , Z ) . ( 46 ) The final equation for CC signals ( eq . 9 ) is obtained by combining eq . 37 and 45 . In many experimental setups , the averaging over stimuli s will ensure that κ ( Z ) has only a mild dependency on its argument Z . Indeed , note the rough approximation κ ( Z ) ∝ ∫sds𝒢 ( s; s0−μd , Z ) = 1 , valid whenever the tested stimuli s are uniformly distributed over a range of values comparable to Z . This is another practical argument for considering the stimulus-averaged CC signal di ( t ) , from eq . 33 . The just-noticeable difference ( JND ) and the sensitivity can be related to the variances of signal and noise in the population . Here , we briefly review these relations . The variance of any scalar variable x that changes from trial to trial can be decomposed in a signal term σ x2 ≔ Var [ E [ x ∣ s ] ] and a noise term Z x 2 ≔ E [ Var [ x ∣ s ] ] . Then , note that Var [ x ] = σ x 2 + Z x 2 . The noise term Zx defines the minimal level past which fluctuations in x can be attributed to s rather than intrinsic noise—hence the term JND . When a decision is taken on the basis of variable x , the JND governs the inverse slope of the corresponding psychometric curve ( see eq . 27 ) . We also define the sensitivity of variable x as Y x ≔ σ x 2 σ x 2 + Z x 2 , ( 47 ) which is simply the ratio of the signal to the total variance . The sensitivity Yx takes values between 0 and 1 . It thus avoids singularities which may occur when Zx tends to 0 or +∞ . We can also distinguish between signal-related and noise-related variance for the ( time-averaged ) neural activities r ‾ . The signal covariance matrix , Σ , noise covariance matrix , C ‾ ‾ , and total covariance matrix , A , are given by the following relations: Σ ≔ Cov E [ r ¯ | s ] = Cov [ m ¯ ( s ) ] = σ s 2 b ¯ b ¯ ⊤ ( 48 ) C ¯ ¯ ≔ E Cov [ r ¯ | s ] ( 49 ) A ≔ Cov [ r ¯ ] = C ¯ ¯ + σ s 2 b ¯ b ¯ ⊤ . ( 50 ) The last equality is the classic decomposition of total covariance into noise and signal terms . Note that Σ is a rank-1 matrix , owing to the system’s assumed linearity wrt . stimulus s . In turn , these matrices allow to compute the signal- and noise- variances for any weighted sum of the neural activities . For our linear readout ( with added decision noise ξd ) , we have x = a ⊤ r ‾ + ξ d , and thus: σ x 2 = a ⊤ Σ a = σ s 2 ( a ⊤ b ¯ ) 2 , ( 51 ) Z x 2 = a ⊤ C ¯ ¯ a + σ d 2 , ( 52 ) σ x 2 + Z x 2 = a ⊤ A a + σ d 2 . ( 53 ) We now assume that the readout vector a has support only on some neural ensemble 𝓔 . Formally , we introduce the K × Ntot projection matrix H ( 𝓔 ) , such that for i ∈ 𝓔 and every neuron j , Hij ( 𝓔 ) = δij . Then , the restrictions of vectors and matrices in neuron space , such as b ‾ and C ‾ ‾ , to ensemble 𝓔 will be denoted by a subscript r ( for restriction ) , so that b ¯ r ≔ H b ¯ , ( 54 ) C ¯ ¯ r ≔ H C ¯ ¯ H ⊤ . ( 55 ) Our principle of ( restricted ) optimality selects the readout vector a which maximizes the signal-to-noise ratio of the resulting percept s ̂ . Since a ⊤ b ‾ = 1 ( unbiased percept , eq . 35 and 42 ) , the signal variance is imposed to be σ x 2 = σ s 2 ( eq . 51 ) . Under this constraint , optimality is achieved by minimizing the noise variance a ⊤ C ‾ ‾ a ( eq . 52 ) —or equivalently , the total variance a⊤ A a ( eq . 53 ) . The solution , known as Fisher’s Linear Discriminant , is easily found with Lagrange multipliers ( either based on C ‾ ‾ or A ) : a r = ( C ¯ ¯ r ) − 1 b ¯ r b ¯ r ⊤ ( C ¯ ¯ r ) − 1 b ¯ r = ( A r ) − 1 b ¯ r b ¯ r ⊤ ( A r ) − 1 b ¯ r . ( 56 ) The second formulation of ar , based on the total covariance matrix Ar , will prove more useful when we turn to the SVD analysis . It also has the advantage of avoiding the singularity which may occur when vector b ‾ r lies outside the span of matrix C ‾ ‾ r . In that case one simply replaces ( Ar ) −1 by the ( Moore-Penrose ) pseudoinverse ( Ar ) + . When combining the optimal readout in eq . 56 with the equation for the JND ( eq . 52 ) , we obtain the JND predicted by the model: Z 2 = b ¯ r ⊤ ( C ¯ ¯ r ) − 1 b ¯ r − 1 + σ d 2 . ( 57 ) Equivalently , using the formulations based on total variance ( eq . 47 , 53 , 56 ) we obtain the model’s prediction for sensitivity: Y = σ s 2 b ¯ r ⊤ ( A r ) − 1 b ¯ r − 1 + σ d 2 . ( 58 ) When combining the optimal readout in eq . 56 with the characteristic equation for the CC curves ( eq . 9 ) , we obtain the CC curves predicted by the model , d i ( t ) = κ ( Z ) Z 2 − σ d 2 C ¯ i r ( t ) ( C ¯ ¯ r ) − 1 b ¯ r . ( 59 ) Here , di ( t ) is the resulting , predicted CC curve for every neuron i in the population ( not only in ensemble 𝓔 ) . Note that C ‾ i r ( t ) is the restriction of vector C ‾ i ( t ) ( eq . 39 ) to neurons j ∈ 𝓔 , but that i = 1…Ntot still runs over all neurons . Equation 59 can also be expressed in its temporally-integrated form , using the definition ∫ t C ‾ ( t ) k ( t ) d t = C ‾ ‾: d ¯ i = κ ( Z ) Z 2 − σ d 2 C ¯ ¯ i r ( C ¯ ¯ r ) − 1 b ¯ r . ( 60 ) If neuron i belongs to the readout ensemble 𝓔 , matrix C ‾ ‾ r simplifies away from eq . 60 , yielding: d ¯ i ( 𝓔 ) = κ ( Z ) Z 2 − σ d 2 b ¯ i ( 𝓔 ) . ( 61 ) This equation , first shown in [19] , means that choice signals within the readout ensemble are simply proportional to tuning . This is not true , however , for neurons outside the readout ensemble . This has two important implications . First , it proves that choice signals are markedly different for neurons inside or outside the readout ensemble ( an observation made empirically by [12] ) . Second , as we consider readout ensembles 𝓔 larger and larger , eq . 61 will become true for more and more neurons . As a result the statistical indicator V ( eq . 15 ) , which measures the population-wide deviation from linearity between d ‾ i and b ‾ i , is expected to decrease with the readout ensemble’s size K . Finally , under the assumption of ( restricted ) optimality , the time-averaged statistical indicator q ‾ ‾ is always positive . Indeed , averaging over all neurons i in the population is akin to a scalar product: q ‾ ‾ = ⟨ b ‾ i d ‾ i ⟩ i = Ntot − 1 b ‾ ⊤ d ‾ . Using this relation and eq . 60 , we get q ¯ ¯ = N tot − 1 κ ( Z ) Z 2 − σ d 2 b ¯ ⊤ C ¯ ¯ H ⊤ ( C ¯ ¯ r ) − 1 H b ¯ , ( 62 ) which is always positive because both matrices C ‾ ‾ and H ⊤ ( C ‾ ‾ r ) − 1 H are symmetric semi-definite positive . We denote the time-averaged activities of neuron i in the q-th presentation of stimulus s as r ‾ i s q . We interpret these activities as a very large Ntot × Ω matrix , where Ntot refers to the number of neurons and Ω to an idealized , and essentially infinitely large number of trials . Next , we consider the singular value decomposition ( SVD ) of the neural activities . The ( compact ) SVD is a standard decomposition which can be applied to any rectangular matrix R . It is given by R = U Λ V⊤ , where Λ is an M × M diagonal matrix with strictly positive entries λm ( the singular values ) , U is an Ntot × M matrix of orthogonal columns ( meaning U⊤ U = IdM ) , and V is an Ω × M matrix of orthogonal columns ( meaning V⊤ V = IdM ) . Using the indices defined above , the SVD decomposition for the neural activities becomes r ¯ i s q = r ¯ i 0 + ∑ m = 1 M λ m u i m v m s q , ( 63 ) where r ‾ i 0 is the average activity of each cell over all trials and stimuli . The orthogonality of U implies that for all indices m and n , we have ∑ i u i m u i n = δ m n , while the orthogonality of V similarly implies ∑ s q ( v m s q v n s q ) = δ m n . The SVD decomposition ( eq . 63 ) is best interpreted as a change of variables re-expressing neural activities { r ‾ i s q } i = 1 … N tot in terms of mode appearance variables { v m s q } m = 1 … M . As a result , we can define the respective equivalents of all statistical quantities in the space of activity modes . Specifically , we can reinterpret sums over trials in the SVD as expectations , thus emphasizing the statistical interpretation of the SVD . First we note that r ‾ i 0 = E [ r ‾ i s q ] for all neurons i , so that the data for the actual SVD has been “centered” . This centering implies for all modes m that E [ v m s q ] = 0 , ( 64 ) E [ v m s q | s ] = η m s − E [ s ] , ( 65 ) where ηm is the tuning parameter of the m-th mode , just as b ‾ i was the tuning parameter for the i-th neuron . Grouping all mode appearance variables in a vector v , we obtain the signal covariance and total covariance matrices in mode space as Σ v ≔ Cov E [ v | s ] = Cov [ η s ] = σ s 2 η η ⊤ , ( 66 ) A v : = Cov [ v ] = E [ v v ⊤ ] = Id M . ( 67 ) where the last relation follows from the orthogonality of V explained in the previous section . The singular values λm and distribution vectors um then allow us to relate the statistics at the levels of neurons and modes . Using the SVD formula ( eq . 63 ) yields ( in matrix form ) : b ¯ = U Λ η , ( 68 ) A = U Λ 2 U ⊤ . ( 69 ) We now wish to understand which factors govern the sensitivity embedded in a neural sub-ensemble 𝓔 of cardinality K . For simplicity , we will consider the case for which the decision noise is negligible , i . e . , σd → 0 . Then , from eq . 58 , we have Y = σ s 2 b ¯ r ⊤ ( A r ) + b ¯ r . ( 70 ) Here we use explicitly the most general formula , based on the pseudo-inverse of matrix Ar . To re-express this sensitivity of finite sub-ensembles 𝓔 into mode space , we need to find the equivalent , restricted expressions of eq . 68–69 . For that purpose , we introduce the design matrix associated to ensemble 𝓔 in mode space: X ≔ Λ U ⊤ H ⊤ , ( 71 ) where H is the restriction operator from eq . 54 . X is an M × K matrix with elements x i m ≔ λ m u i m . Using this matrix , we obtain from eq . 68–69 that b ‾ r = X ⊤ Ζ and Ar = X⊤ X , so that eq . 70 becomes Y = σ s 2 η ⊤ X ( X ⊤ X ) + X ⊤ η = σ s 2 η ⊤ P η , ( 72 ) where we have defined the M × M matrix P ≔ X ( X ⊤ X ) + X ⊤ . ( 73 ) Note that P is simply the orthogonal projector on Im ( X ) , since P = P2 = P⊤ , and Im ( P ) = Im ( X ) . The projector P = P ( 𝓔 ) spans more and more space as the size K of ensemble 𝓔 increases . In the limiting case , when K is larger than the number of modes M , then necessarily P = IdM , and we obtain Y tot = σ s 2 η ⊤ η = ∑ m = 1 M σ s 2 η m 2 . ( 74 ) In other words , all modes are available experimentally , and sensitivity estimates saturate to their maximum value , independently of ensemble 𝓔 . We can explicitly denote the sensitivity of each mode’s activation variable vm by defining y m ≔ σ s 2 η m 2 . ( 75 ) By solving eq . 68 for η , we obtain η m = ∑ i λ m − 1 u i m b ‾ i , which in turn yields eq . 20 from the main text . Similarly , we can express CC signals in mode space . First , we re-express the CC equation ( eq . 10 ) as a function of the total covariance A ( eq . 50 ) to obtain d ¯ = κ ( Z ) C ¯ ¯ a = κ ( Z ) ( A − σ s 2 b ¯ b ¯ ⊤ ) a . We further recall that a ⊤ b ‾ = 1 ( unbiased percept , see eq . 35 and 42 ) . Hence , up to a scaling and shift , the CC vector d ‾ can be replaced by the total percept covariance vector e ≔ A a = κ ( Z ) − 1 d ¯ + σ s 2 b ¯ . ( 76 ) In the case of an optimal readout , vector a is given by eq . 56 , so that we obtain e = A H ⊤ A r + b ¯ r b ¯ r ⊤ ( A r ) + b ¯ r . ( 77 ) Second , using the corresponding sensitivity Y ( eq . 70 ) , and the SVD expressions for A and b ‾ ( eq . 68–69 ) , and for Ar and b ‾ r as a function of matrix X ( eq . 71 ) , we write: e = σ s 2 Y − 1 A H ⊤ A r + b ¯ r = σ s 2 Y − 1 U Λ X ( X ⊤ X ) + X ⊤ η = σ s 2 Y − 1 U Λ P η . ( 78 ) Here also , the final result can be expressed as a function of P , the projection matrix associated to ensemble 𝓔 in the space of modes ( eq . 73 ) . Note again that e provides the CC signal for every neuron i in the population ( not only in ensemble 𝓔 ) . As 𝓔 tends to the full population , P = P ( 𝓔 ) tends to IdM and we recover e ( ∞ ) = σ s 2 Y tot − 1 b ‾ , the prediction for choice signals in the case of a ( globally ) optimal readout [19] . Using eq . 78 , we can finally compute the analytical predictions for the two CC statistical indicators , q ‾ ‾ and V . Precisely , we compute the following population-wide regression coefficient between e and b ‾: Q ≔ ⟨ e i b ¯ i ⟩ i = N tot − 1 b ¯ ⊤ e = σ s 2 N tot − 1 Y − 1 η ⊤ Λ U ⊤ U Λ P η = σ s 2 N tot − 1 Y − 1 η ⊤ Λ 2 P η . ( 79 ) Again , we made use of the SVD expressions for b ‾ ( eq . 68 ) and e ( eq . 78 ) . Note that , since e is a linear rescaling of d ‾ , Q is a similar rescaling of indicator q ‾ ‾ , as pointed in the main text ( eq . 18 ) . Finally , a very similar computation leads to the expression of indicator V ( eq . 15 ) in the space of modes: V = κ ( Z ) 2 N tot − 2 σ s 4 Y − 2 η ⊤ Λ 2 η η ⊤ P − P η η ⊤ Λ 2 P η . ( 80 ) We are now better armed to understand how sensitivity and CC indicators vary as a function of the readout ensemble 𝓔 . We are mostly interested in averages of these quantities over very large numbers of randomly chosen ensembles 𝓔 of size K; we thus use the generic notation E[x∣K]≔E[x ( 𝓔 ) ∣Card ( 𝓔 ) = K] to denote the expected value of a variable x when averaging over ensembles of size K . Note that this notation is equivalent to the more explicit notation used in the main text , so that E[x∣K] = ⟨x⟩𝓔 ( K ) . From eq . 72 we find: E [ Y ∣ K ] = σ s 2 η ⊤ E [ P ∣ K ] η . To understand the properties of the ( M × M ) matrix E[P∣K] , we view the ( M × K ) design matrix X ( 𝓔 ) ( eq . 71 ) as a collection of K random vectors xi in mode space , viewing neuron identities i as the random variable . Thus , P ( 𝓔 ) is the orthogonal projector on the linear span of the K sample vectors {xi}i ∈ 𝓔 . As a projector , its trace is equal to its rank , so we have Tr ( E [ P ∣ K ] ) = K . Furthermore , since K+1 samples span on average more space than K samples , we are ensured that E[P∣K+1] ≽ E[P∣K] , in the sense of positive semidefinite matrices . Finally , intuition and numerical simulations suggest that E[P∣K] is almost diagonal . Indeed , as the various modes are linearly independent , there is no linear interplay between the different dimensions of xi across samples i . More precisely , the expectation value over neurons is ⟨x i m x i n ⟩ i = N tot − 1 λ m 2 δ m n . This leads to the matrix expression: E [ X X ⊤ | K ] = K N tot − 1 Λ 2 . Let us consider the ( compact ) SVD decomposition X X⊤≔ W D W⊤ , with W⊤ W = Id , and D an invertible diagonal matrix . Then , the projection matrix P is simply equal to W W⊤ . As for the previous equation , it rewrites E [ W D W ⊤ | K ] = K N tot − 1 Λ 2 . Here , both matrices D and Λ are diagonal . So , if we assume a form of independence between W and D , it is reasonable to suppose that E[W W⊤∣K] = E[P∣K] is close to diagonal as well . ( Actually , we postulate that E[P∣K] is exactly diagonal when the random vectors xi follow a normal distribution . In the general case , small or moderate deviations from diagonality can be observed . ) We denote these diagonal terms as ϵ ( K ) ≔ diag ( E [ P | K ] ) . ( 81 ) The properties of E[P∣K] stated above imply that ∑m ϵm ( K ) = K ( trace property ) , and ϵm ( K+1 ) ≥ ϵm ( K ) ( growth property ) . Finally , we can consider the resulting approximations of sensitivity ( eq . 72 ) and CC indicator ( eq . 79 ) : E [ Y | K ] ≃ σ s 2 ∑ m = 1 M ϵ m ( K ) η m 2 , ( 82 ) E [ Y Q | K ] ≃ N tot − 1 σ s 2 ∑ m = 1 M ϵ m ( K ) λ m 2 η m 2 . ( 83 ) In this expression , we recognize the individual mode sensitivities y m = σ s 2 η m 2 . For CC signals , we also make the approximation E[YQ∣K] ≃ E[Y∣K]E[Q∣K] , and recover eq . 21–22 from the main text . Unfortunately , there is no such simple approximation for indicator V , that would lead from eq . 80 to E[V∣K] . In this final part of the Methods , we provide additional information for applying our inference method ( Case 2 ) to experimental data . The neural network used to test our methods is described in detail in supporting S1 Text ( section 3 ) . Briefly , on each trial , 2000 input Poisson neurons fire with rate s , taking one of three possible values 25 , 30 and 35 Hz ( so in our simulation , stimulus units are Hz ) . The encoding population per se consists of 5000 leaky integrate-and-fire ( LIF ) neurons . 1000 of these neurons receive sparse excitatory projections from the input Poisson neurons , which naturally endows them with a positive tuning to stimulus s . Another 1000 neurons receive sparse inhibitory projections from the Poisson neurons , which naturally endows them with negative tuning . The remaining 3000 neurons receive no direct projections from the input . Instead , all neurons in the encoding population are coupled through a sparse connectivity with random delays up to 5 msec . Synaptic weights are random and balanced , leading to a mean firing rate of 21 . 8 Hz in the population . We implemented and simulated the network using Brian , a spiking neural network simulator in Python [39] . The “true” perceptual readout from this network was built from a fixed random set of K⋆ = 80 neurons , with temporal parameters w⋆ = 50 msec and t R ⋆ = 100 msec , and decision noise σ d ⋆ = 1 stimulus units ( Hz ) . The readout vector a⋆ was built optimally given these constraints ( eq . 12 ) . The trials used to learn a⋆ were not used in the subsequent analysis . The resulting JND for the “animal” was Z⋆ ≈ 3 stimulus units ( Hz ) . Then , “experimentally” , neural activity was observed through 15 pools of 170 simultaneously recorded neurons , each pool being recorded on 3 × 180 trials . For the statistical inference method , we assumed a square integration kernel h . We tested all combinations of the following readout parameters ( in matrix notation ) : K = 10:10:150 neurons , w = 10:10:100 msec , tR = 10:10:200 msec , σd = 0:0 . 25:3 stimulus units ( Hz ) . For each tested size K , we picked 2000 random candidate ensembles 𝓔 ( always within one of the 15 simultaneous pools ) to build the predictions . For each ensemble 𝓔 , another ensemble ℐ of 20 neurons , segregated from 𝓔 , were used to predict CC signals outside the readout ensemble ( this was always possible since recording pools had size 170 , and K ≤ 150 ) . The details of these predictions are explained in the following paragraph . Finally , the three terms in the “statistical” loss function ( eq . 16 ) were weighted according to the power of the respective , true measures . That is: λ = Z ⋆ 4 ∫ ∫ d t d u q ⋆ ( u , t ) 2 and μ = Z ⋆ 4 V ⋆ 2 . Here , we detail how to compute the CC indicators q ( u , t ) and V ( eq . 14–15 ) from actual data . For the measured versions q⋆ ( u , t ) and V⋆ ( w , tR ) , this is straightforward . One considers the true , measured CC signals d i ⋆ ( t ) , and computes the population averages in eq . 14–15 over as many neurons i as were recorded . Note however that the final indicators can be corrupted by noise , whenever each measure d i ⋆ ( t ) comes from too few recording trials ( this problem is addressed in the next section ) . Also note that , since the definition of V requires a temporal integration , we actually have to produce a different “true” V⋆ for each tested set of temporal parameters w and tR . Conversely , special care must be taken when it comes to predicted CC indicators . Whenever a candidate ensemble 𝓔 is proposed as the source of the readout , eq . 59 predicts the resulting CC signal di ( t∣𝓔 ) for every neuron i in the population . However , in practice , the noise covariance term C ‾ i r ( t ) is required in the computation , so neuron i and ensemble 𝓔 must have been recorded simultaneously during the same run . This limits the number of neurons i which can participate in the population averages . Furthermore , choice covariances will generally differ between neurons that are part of the readout ensemble and neurons that are not ( see eq . 61 and the associated discussion ) . As a result , the two following averages must be predicted separately: q 𝓔 ( u , t | 𝓔 ) ≔ ⟨ b i ( u ) d i ( t | 𝓔 ) ⟩ i ∈ 𝓔 , ( 84 ) q out ( u , t | 𝓔 ) ≔ ⟨ b i ( u ) d i ( t | 𝓔 ) ⟩ i ∉ 𝓔 , ( 85 ) before one can recombine them in the correct proportions: p ( 𝓔 ) ≔ K N tot , ( 86 ) q ( u , t | 𝓔 ) = p ( 𝓔 ) q 𝓔 ( u , t | 𝓔 ) + 1 − p ( 𝓔 ) q out ( u , t | 𝓔 ) , ( 87 ) and similarly for V ( 𝓔 ) . To compute q out experimentally , each tested candidate ensemble 𝓔 ( of size K ) is associated to a complimentary set of neurons ℐ ( of size I ) , which we use to approximate the average in eq . 85: q 𝓘 ( u , t | 𝓔 ) ≔ ⟨ b i ( u ) d i ( t | 𝓔 ) ⟩ i ∈ 𝓘 . ( 88 ) All neurons in ensembles 𝓔 and ℐ must have been recorded during the same run , which imposes that I+K ≤ N . Hence in our simulations , we chose a size I = 170−150 = 20 neurons . Clearly , 20 neurons is not sufficient for qℐ to be a reliable population average . So in practice , we cannot estimate reliably each prediction q ( u , t ∣𝓔 ) from eq . 87 . Luckily , we are not interested in their value for each individual readout ensemble 𝓔 . We simply need to estimate their means across all tested ensembles 𝓔 of similar size: ⟨ q ( u , t ) ⟩ 𝓔 ≔ q ( u , t | 𝓔 ) 𝓔 with Card ( 𝓔 ) = K ( 89 ) ⟨ V ⟩ 𝓔 ≔ V ( 𝓔 ) 𝓔 with Card ( 𝓔 ) = K ( 90 ) which will be reliable as soon as we test a sufficient amount of candidate ensembles 𝓔 . Note that in the final inference ( eq . 16 ) , a match is sought between the true indicators q⋆ and V⋆—which arise from a single readout ensemble 𝓔⋆ , and the predictions ⟨ q ⟩ 𝓔 and ⟨ V ⟩ 𝓔 —which are average values across all readout ensembles 𝓔 of size K . Thus , a prediction error can occur whenever the true readout ensemble 𝓔⋆ is not a “typical” representative of its size K⋆ . To quantify these potential errors , one should also estimate the indicators’ variance across ensembles 𝓔 of same size . The computations of Z , q and V , as described above , can produce imprecise results when the data are overly limited . Generically , for any quantity X estimated from the data , we can write X noisy = X ideal + ξ , where ξ represents the measurement error on X due to the finite amounts of data . If we could recompute X from a different set of neurons and/or a different set of trials , variable ξ would take a different value—meaning that Var ( ξ ) > 0 . This is an inescapable phenomenon for experimental measures . More problematically , variable ξ can display a systematic bias , meaning that E ( ξ ) ≠ 0 . Since the bias is generally different for the ‘true’ and ‘predicted’ versions , the comparison between the two ( eq . 16 ) will be systematically flawed . To counteract this effect , we applied a number of correction procedures when computing indicators Z , q and V , to ensure that they are globally unbiased . We only provide an overview here , and refer to supporting S1 Text for a detailed description . First , when the optimal vector a is computed with Fisher’s linear discriminant , it systematically underestimates the JND Z ( overestimates the sensitivity Y ) . Essentially , vector ar computed through eq . 12 finds artificial “holes” in matrix C ‾ ‾ r which are only due to its imprecise measurement—a phenomenon known as statistical overfitting . The less recording trials , the more overfitting there will be [40 , 41] . We addressed this problem with a regularization technique , inspired by Bayesian linear regression [42] . We replaced eq . 12 by the following: a r = ( C ¯ ¯ r + λ Id ) − 1 b ¯ r b ¯ r ⊤ ( C ¯ ¯ r + λ Id ) − 1 b ¯ r , where the strength of parameter λ imposes the degree of regularization . We chose λ according to an ‘empirical Bayes’ principle , to maximize the likelihood of the data under a given statistical model ( supporting S1 Text , section 4 ) . It largely mitigated the effects of overfitting , without totally suppressing them—as can be seen in Fig . 5D-E . Second , indicator V ( eq . 15 ) can also display substantial biases ( E ( ξ ) ≠ 0 in the above discussion ) . Indeed , its computation relies on squared quantities—such as d ‾ i 2 or q ‾ ‾ 2—that systematically transform measurement errors into positive biases . The required corrections are very similar to the classic “N/ ( N−1 ) ” correction for the naive variance estimator , with the additional difficulty that V is affected by two sources of noise: the finite number of recording trials , and the finite number of recorded neurons . The exact corrections to ensure an unbiased estimation of V are detailed in supporting S1 Text , section 5 . Third , indicator q ( u , t ) displays little or no measurement bias—because its computation is essentially linear . Yet , it can display an important level of measurement noise ( Var ( ξ ) ≫ 0 in the above discussion ) that may deteriorate the subsequent inference procedure . We mitigated this measurement noise by applying a bi-temporal Gaussian smoothing to q⋆ ( u , t ) and predictions q ( u , t ) , with time constant 10 msec . To estimate the measurement errors due to the finite number of trials , we produced 14 sets of surrogate data by sampling our original trials with replacement ( bootstrap procedure ) . These resamplings were used to derive some of the correction terms for V , and also to derive confidence intervals on our final estimators , as shown in Fig . 6 . This departure from the statistical canon was imposed by the length of the whole inference procedure ( see supporting S1 Text , section 5 , for details ) . In the Supporting Information , we provide a generic implementation of the inference method ( “Case 2” above ) in MATLAB , which can be applied to any data from a 2AFC discrimination task . We also provide the Python code for the network simulation , and MATLAB scripts for the reproduction of the experimental Figures in this article ( Fig . 4–7 ) . | This article deals with the interpretation of neural activities during perceptual decision-making tasks , where animals must assess the value of a sensory stimulus and take a decision on the basis of their percept . A “standard model” for these tasks has progressively emerged , whence the animal’s percept and subsequent choice on each trial are obtained from a linear integration of the activity of sensory neurons . However , up to date , there has been no principled method to estimate the parameters of this model: mainly , the typical number of neurons K from the population involved in conveying the percept , and the typical time scale w during which these neurons’ activities are integrated . In this article , we propose a novel method to estimate these quantities from experimental data , and thus assess the validity of the standard model of percept formation . In the process , we clarify the predictions of the standard model regarding two classic experimental measures in these tasks: sensitivity , which is the animal’s ability to distinguish nearby stimulus values , and choice signals , which assess the amount of correlation between the activity of single neurons and the animal’s ultimate choice on each trial . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | On the Number of Neurons and Time Scale of Integration Underlying the Formation of Percepts in the Brain |
Planaria continue to blossom as a model system for understanding all aspects of regeneration . They provide an opportunity to understand how the replacement of missing tissues from preexisting adult tissue is orchestrated at the molecular level . When amputated along any plane , planaria are capable of regenerating all missing tissue and rescaling all structures to the new size of the animal . Recently , rapid progress has been made in understanding the developmental pathways that control planarian regeneration . In particular Wnt/beta-catenin signaling is central in promoting posterior fates and inhibiting anterior identity . Currently the mechanisms that actively promote anterior identity remain unknown . Here , Smed-prep , encoding a TALE class homeodomain , is described as the first gene necessary for correct anterior fate and patterning during planarian regeneration . Smed-prep is expressed at high levels in the anterior portion of whole animals , and Smed-prep ( RNAi ) leads to loss of the whole brain during anterior regeneration , but not during lateral regeneration or homeostasis in intact worms . Expression of markers of different anterior fated cells are greatly reduced or lost in Smed-prep ( RNAi ) animals . We find that the ectopic anterior structures induced by abrogation of Wnt signaling also require Smed-prep to form . We use double knockdown experiments with the S . mediterranea ortholog of nou-darake ( that when knocked down induces ectopic brain formation ) to show that Smed-prep defines an anterior fated compartment within which stem cells are permitted to assume brain fate , but is not required directly for this differentiation process . Smed-prep is the first gene clearly implicated as being necessary for promoting anterior fate and the first homeobox gene implicated in establishing positional identity during regeneration . Together our results suggest that Smed-prep is required in stem cell progeny as they form the anterior regenerative blastema and is required for specifying anterior cell fates and correct patterning .
Planaria continue to blossom as a model system for understanding all aspects of regeneration [1]–[3] . A sustained and passionate effort by a number of scientists is pushing planaria to the forefront of the regeneration field , both technically [4] , [5] and theoretically [6] , and they are finally starting to be directly informative of phenomena in other systems [7] . They provide an opportunity to understand how the replacement of missing tissues from preexisting adult tissue is orchestrated at the molecular level . When amputated along any plane planaria are capable of regenerating all missing tissue and rescaling all structures to the new size of the animal [8] . Recent work has shown that conserved signaling pathways play a role in axial patterning during both regeneration and homeostatic tissue turnover [9]–[13] . In particular Wnt/beta-catenin signaling is necessary for posterior fate during regeneration , with loss of beta-catenin or Wnt signaling leading to all amputations regenerating anterior structures and a gradual loss of posterior identity during homeostasis [9] , [10] , [12] . Conversely , over activity of Wnt signaling induced by abrogating the expression of negative regulators of the pathways leads to ectopic posterior fate [9] . Further studies have begun to describe the temporal nature of this posterior specification circuit , as well the conserved nature of upstream regulation [14] , [15] . Previously elegantly executed manipulative work has uncovered phenomena that suggest that anterior fated tissue can inhibit the regeneration of anterior fate elsewhere [3] . In addition some headway has been made in understanding the potential signaling systems responsible for this [16] , [17] . In particular the planarian nou-darake ( ndk ) gene , an FGF-like receptor , has been shown to be necessary to restrict the formation of anterior-dorsal brain ganglia/cephalic ganglia ( CG ) to anterior regions [16] . Currently though nothing is known about the instructive signals required to promote anterior fate . We wished to uncover these signals that together must promote anterior fate and correctly pattern the brain as it reforms from stem cell progeny at anterior blastemas . Given the involvement of conserved pathways already uncovered we hypothesized that other genetic circuits employed to specify positional domains in other animals would be responsible for this process during planarian regeneration . One obvious group of genes for this process would be planarian orthologs of the Hox genes and Hox gene co-factors , These are required for anterior-posterior axis specification in the metazoa [18] , [19] . Planarian Hox orthologs have been previously studied , and in some cases are expressed in distinct spatial domains , but have as yet no functions are assigned to them in planaria . This has led us to consider TALE class homeodomain containing genes , a subset of which act as Hox gene cofactors [18] . Collectively , they are known to modulate the activity of Hox proteins by regulating their localization within the cell and by increasing their binding site specificity , but also have many hox independent roles in development [20]–[23] . Here , Smed-prep , encoding a TALE class homeodomain , is described as the first gene that is necessary to instruct anterior fate and patterning during planarian regeneration .
The Smed-prep transcript was identified in an informatics screen for homeodomain proteins in the Schmidtea mediterranea genome . Searching the S . mediterranea genome identifies other TALE class homeodomain proteins [18] , but Smed-prep encodes the only PREP ortholog ( Figure 1A ) . The protein encoded by Smed-prep has high homology to other PREP proteins and contains the conserved features expected of this protein family ( Figure S1 ) . In vertebrates , PREP proteins have been implicated in a number of key developmental processes [23] , including the correct patterning of anterior structures [21] . The function of Hox and Hox co-factors in planaria remains enigmatic . The fact that these two groups of homeodomains act together to pattern tissues in other systems makes them strong candidates for a role in providing positional information in planarians . For this reason we performed a detailed study of Smed-prep . We performed in situ hybridization on whole and regenerating asexual planaria [24] , [25] . We find that Smed-prep is expressed at ubiquitously low levels throughout the parenchyma and at higher levels in the head region . The posterior margin of anterior expression coincides with the most posterior position of cephalic ganglia ( CG ) ( Figure 1C and 1D ) . We also detect low levels of Smed-prep expression in the posterior midline , at higher levels than the broad parenchymal expression , in approximately 50% ( 39/72 ) of animals ( Figure 1B ) . Smed-prep expression is not sensitive to irradiation , indicating that Smed-prep is not expressed in , or dependent on , the ‘neoblast’ stem cells ( data not shown ) . During regeneration induced by pre- and post-pharyngeal amputation ( Figure 1E ) Smed-prep expression is first detected at 24 h and is present in both anterior and posterior blastemas ( Figure 1F ) . New Smed-prep expression is not detected at 6 , 12 or 18 hours of regeneration . Expression in the anterior is bilateral up to 3 days but has expanded across the whole blastema at 5 days ( Figure 1G and 1H ) . At 5 days Smed-prep is expressed throughout the anterior compartment with the notable exception of the eye field . We also detect feint expression in the posterior midline of approximately 50% of trunk fragments at 3 ( 18/41 fragments ) and 5 days ( 23/40 fragments ) of regeneration . We observe this in trunk fragments only ( Figure 1G and 1H ) . This expression is absent later and presumably reappears after regeneration is complete and animals reach a homeostatic state ( see above ) . At 8 days of regeneration , posterior blastema expression is reduced while expression in the anterior continues to be high ( Figure 1I ) . This expression pattern led us to hypothesize a role for Smed-prep in patterning regenerating tissue after amputation . In particular expression in whole worms suggested that Smed-prep might have a role in pattering and/or maintaining anterior structures . We performed RNAi [26] , [27] of Smed-prep to investigate its function during regeneration ( see Figure S2 for summary of injection protocols ) . Smed-prep dsRNA injection before inducing regeneration by amputation ( Figure 1E ) resulted in all worms having either a cyclops phenotype ( Figure 2A ) or no eyes at all ( Figure 2B , Table 1 ) . All animals had correct early blastema formation , normal levels of neoblast proliferation ( data not shown ) and no defects in posterior blastema formation ( Figure 2A , 2B , 2D , and 2E ) . A similar cyclops phenotype has been described for a S . mediterranea slit ortholog [28] . Staining with an anti-arrestin VC-1 antibody specific for planarian photoreceptors and associated neurons [29] we observed that the single eye phenotype appeared to represent a fusion of two eyes ( Figure S2D , S2E ) . We detected no other midline defects in regenerating animals that were described for Smed-slit , and Smed-slit expression itself was normal ( Figure S2F and S2G ) . This suggests , in agreement with the Smed-prep expression pattern , that the cyclops phenotype is due to a defect in anterior patterning and fate rather than any midline defects . Control gfp ( RNAi ) animals had normal eye structure ( Figure S2E ) . We investigated the structure of the planarian ventral nerve cords ( VNCs ) and CG using the anti-SYNORF1 ( 3C11 ) cross-reactive monoclonal antibody [30] . We found that in all Smed-prep ( RNAi ) animals the CG were greatly reduced , with almost no brain at all discernible in the most severely affected RNAi worms ( Figure 2D and 2E ) . In these animals anti-SYNORF1 positive cells do form from differentiating neoblast progeny in the anterior as part of the VNCs . Significantly , anti-SYNORF1 positive cells are present along the whole anteroposterior axis . This suggests , along with correct pharynx and posterior regeneration that Smed-prep ( RNAi ) does not affect the general ability of stem cells to differentiate . All control gfp ( RNAi ) animals were normal ( Figure 2C and 2F ) . We confirmed the loss of CG by looking at the expression of Smed-GluR ( specific for CG ( Figure 2I and 2M ) . This loss of anterior structures suggests a role for Smed-prep in patterning anterior structures and/or a requirement for Smed-prep in allowing neoblasts to differentiate into CG cells . This phenotype is different from that previously described for the S . mediterranea ortholog of adenomatous polypolis coli ( APC ) , a negative regulator of Wnt signaling . Smed-APC-1 ( RNAi ) results in ectopic posterior fate at anterior blastemas [9] . To build a more exact picture of the requirements for Smed-prep we also investigated its role during regeneration more directly . We injected regenerating animals after amputation and then re-amputated ( Figure S2 ) . This approach has previously been used as a proxy to separate regeneration specific effects from homeostatic effects [15] . Control gfp ( RNAi ) worms regenerated normally but Smed-prep ( RNAi ) worms failed to make eyes and CG almost entirely ( Figure 2G and 2H , Table 1 ) . All animals did regenerate normal VNCs within regenerated anterior tissue . This confirms that new Smed-prep expression during regeneration is required to properly replace anterior structures . To investigate whether Smed-prep was required specifically for stem cell progeny to differentiate to CG or instead primarily for global anterior fates we investigated the expression of cintillo [31] and Smed-sFRP-1 [9] , [12] . These genes represent two different anterior markers that are not expressed in CG cells . We find that both cintillo and Smed-sFRP-1 expression are greatly reduced or absent in Smed-prep ( RNAi ) animals at 12 days of regeneration ( Figure 2J and 2K ) . In the case of Smed-sFRP-1 expression we observed a correlation between the strength of the Smed-prep ( RNAi ) phenotype and whether any Smed-sFRP-1 expression was detectable . Those animals that maintained a single eye ( and therefore some CG ) also had some remaining Smed-sFRP-1 expression . Animals with stronger phenotypes ( no eyes ) had no detectable anterior Smed-sFRP-1 expression . All gfp ( RNAi ) animals had normal expression for both these markers ( Figure 2N and 2O ) . Together these data suggest that Smed-prep is required for correct anterior blastema fate patterning during regeneration , rather than solely for CG formation by differentiating neoblasts . This loss of anterior markers led us to consider whether Smed-prep ( RNAi ) leads to a homeotic like posteriorisation of the planarian body plan . We found no evidence for this by looking at the relative position of the regenerating or fully formed pharynx , the expression of a medial marker Smed-Tcen49 [32] , or by looking at the expression of posterior markers such as Smed-HoxD [10] . Thus we infer that Smed-prep ( RNAi ) leads to a reduction in the formation of anterior structures , but neither a change to posterior fate at anterior blastemas nor an expansion in posterior or medial fates in existing tissues ( Figure S2J , S2K , S2L , and S2M ) . We also found that early Smed-sFRP-1 expression at anterior blastemas at 24 hours of regeneration is absent in Smed-prep ( RNAi ) animals . This suggests Smed-prep acts to provide anterior fate and pattern the anterior blastema , after polarity is set ( Figure S2H and S2I ) . The planarian brain and the planarian head have distinct A/P polarity , as is the case in other animals [17] . Smed-prep expression is higher in the anterior and lateral margins of the planarian head ( Figure 1B ) . We wished to know whether this was a reflection of Smed-prep having a role in defining different A/P fates within the anterior blastema itself . In this case any remaining brain fated tissues observed in Smed-prep ( RNAi ) animals ( Table 1 ) would be expected to have posterior brain fate . By investigating the expression of Smed-WntA , a marker of the posterior brain [17] we found that Smed-prep ( RNAi ) animals that regenerated one eye and some CG also maintained antero-posterior identity within their much reduced anterior structures ( Figure 2L and 2P ) . In these animals Smed-WntA still labels a posterior domain of the remaining CG . This suggests that Smed-prep is required to specify an anterior field of cells in which further A/P patterning occurs . We performed long term Smed-prep ( RNAi ) in whole worms , to assess its role during normal homeostasis and tissue turnover . Long-term knockdown did not result in loss or proportional reduction of anterior structures or CG/Brain ( Figure 2R , Table 1 ) . However , Smed-prep ( RNAi ) worms developed a new pair of photoreceptors anterior to the original pair ( Figure 2Q ) . This result suggests that Smed-prep expression in the anterior of whole worms is required for correct positioning of the photoreceptors during homeostasis but not for CG maintenance . Smed-sFRP-1 expression was also affected in these animals , with loss of anterior margin and lateral expression , but maintenance of weaker ventral antero-medial expression ( Figure 2S ) . This provides more evidence to suggest that Smed-sFRP-1 expression is dependent on Smed-prep expression . These data show that Smed-prep has different roles in establishing anterior structures and their subsequent maintenance . The finding that the CG were not reduced in homeostasis led us to consider whether Smed-prep ( RNAi ) would affect the lateral regeneration of anterior structures . We reasoned that if Smed-prep was not required for CG maintenance during homeostasis , then alternative anterior maintenance mechanisms must be active during homeostasis . These alternate mechanisms could also be sufficient to orchestrate lateral regeneration , a scenario where existing anterior structures are left partially intact . We cut Smed-prep ( RNAi ) worms longitudinally ( Figure S2A ) and observed regeneration . We found that Smed-prep ( RNAi ) worms were able to laterally regenerate all structures , with correct scaling , and subsequent normal behavior . While some worms did not regenerate a second eye correctly , all animals regenerated lateral CG . However , on looking at the pattern of the CG structure in more detail we noticed that the bilateral CG fused at the anterior tip ( Figure 2T and 2U ) . In this regenerative scenario Smed-prep ( RNAi ) animals can regenerate antero-laterally but CG structures are not patterned correctly . This indicates that while Smed-prep is specifically required for the replacement of missing anterior structures when they are absent , it is not required to generate missing anterior fated structures during antero-lateral regeneration , i . e . when one side of the brain is still present . Instead , it is only required for the formation of correct pattern during this regenerative scenario . It seems likely that the remaining anterior tissue contains cues , generated downstream of Smed-prep during normal anterior regeneration , that are sufficient to direct neoblast progeny to CG fate . Our experiments thus far suggest that Smed-prep is required for anterior patterning and fate . To formally rule out the possibility that Smed-prep is also directly required during anterior regeneration for stem cell differentiation into CG we utilized the previously described nou-darake ( ndk ) RNAi phenotype [16] . RNAi of this FGF-like receptor gene leads to ectopic posterior expansion of CG during homeostasis and regeneration . We predicted that if Smed-prep was required for anterior patterning but not for neoblast differentiation then double Smed-prep/ndk ( RNAi ) worms would display expanded CG differentiation , but with aberrant anterior patterning and loss of anterior marker expression . Smed-prep/gfp ( RNAi ) and Smed-ndk/gfp ( RNAi ) animals regenerated with reduced and expanded CG respectively compared to gfp ( RNAi ) worms ( Figure 3B and 3C ) . Smed-prep/ndk ( RNAi ) animals had expanded CG but this expansion was patterned incorrectly ( Figure 3D ) . The CG of Smed-prep/Smed-ndk ( RNAi ) animals are fused at the anterior tip , similar to Smed-prep ( RNAi ) laterally regenerated animals ( Figure 3D ) . Both gfp ( RNAi ) and smed-ndk/gfp ( RNAi ) animals have normally patterned bilateral CG ( Figure 3A and 3C ) . To test if this mispatterning was concomitant with the loss of anterior fate we also looked at Smed-sFRP-1 expression . Whereas Smed-sFRP-1 expression was normal in Smed-ndk ( RNAi ) animals after regeneration it was absent or greatly reduced in Smed-prep/ndk ( RNAi ) animals ( Figure 3E–3G ) . This suggests that Smed-prep specifies an anterior domain during regeneration and that stem cell progeny normally differentiate to form CG only within this domain . This restriction requires activity of Smed-ndk , which is also expressed in an anterior domain . In double Smed-prep/ndk ( RNAi ) animals the loss of Smed-ndk removes this restriction on neoblast progeny , allowing them to adopt CG fate without the presence of Smed-prep expression , but does not rescue the defects in anterior patterning . Wnt signaling is central in patterning the antero-posterior axis of planarians by promoting posterior fate [9] , [10] , [12] , [15] . Given the finding that Smed-prep is not required for CG maintenance or formation during homeostasis and lateral regeneration respectively , it remained unclear whether Smed-prep would be required for the ectopic anterior structures observed when Wnt signaling is attenuated . We found that when Smed-beta-catenin-1 ( RNAi ) results in head regeneration at both anterior and posterior blastemas [3]–[5] , ectopic and prolonged expression of Smed-prep in these new heads is observed ( Figure 3H ) . In addition Smed-prep/beta-catenin-1 ( RNAi ) reduced anterior structures at both ends ( Figure 3K ) . As Smed-prep expression is initially present at both posterior and anterior blastemas our data suggest that active Wnt signaling in the posterior blastema suppresses Smed-prep action at posterior blastemas post-transcriptionally . Smed-prep is the first gene clearly implicated as being necessary for promoting anterior fate during regeneration in S . mediterranea . We propose that after initial polarity determination , involving Wnt signals and other as yet unknown mechanisms , Smed-prep expression in neoblast progeny determines an anterior field of cells in which anterior structures differentiate and are patterned . At posterior blastemas Smed-prep activity is inhibited post-transcriptionally by Wnt activity . This now provides the opportunity to discover downstream genes that are required for further fine patterning during anterior regeneration , as some of these are likely transcriptional targets of Smed-prep activity . In other animals the function of PREP TALE class homeodomains remains rather poorly defined compared to those of other TALE class family genes . In the both major invertebrate genetics models , C . elegans and D . melanogaster , a direct ortholog of PREP TALE class homeodomains is absent [18] . Interestingly both worms and flies contain MEIS orthologs ( unc-62 and homothorax respectively ) that have broad roles in specifying fate during development [33] , [34] and other members of the nematode and arthropod phyla do have PREP orthologs [18] . The finding that PREP is involved in zebra fish brain development may suggest that PREP has an evolutionary conserved role in anterior fates . Broader phylogenetic study of its function is required to test this [21] . Here , we show that Smed-prep expression and function delineates the whole anterior domain , including all regions of the brain . Previous studies of Hox and Hox co-factor function have not implicated these two groups of genes in defining the most anterior structures of other vertebrates [35] or arthropods [36] . Significantly , the requirement for Smed-prep is observably different during homeostasis and different regenerative scenarios . This illustrates that the genetic networks available to solve different regenerative scenarios may be diverse and are likely to depend on the informational/signaling capacity of the differentiated portion of starting tissue . In addition it is the first time that homeobox transcription factors have been directly implicated in A/P patterning in planaria . We suspect that other conserved homeodomain proteins will also play core roles in specifying positional information during regeneration .
All experiments were performed with a clonal line originally generated from a single animal of the asexual strain of the planarian S . mediterranea collected in Montjuïc ( provided by Professor Emili Saló i Boix ) maintained at 20°C in tap water treated with activated charcoal and buffered with 0 . 5 ml/L 1 M NaHCO3 . Planarians were fed veal liver and starved for at least one week prior to experiments . To identify planarian homologues of TALE transcription factors we searched a local database of Version 3 . 1 of the S . mediterranea Genome Project for orthologs of mammalian TALE genes ( http://genome . wustl . edu/genomes ) . The contigs 018898 and 020093 containing Smed-prep were analyzed using Vector NTI ( Invitrogen ) and sequence data supplemented by using RACE ( Ambion RLM Race Kit ) . The primers Sm-Prep-Forward with sequence ATTGCTACTAGAGCAATGTGAACAAGC and Sm-Prep-Reverse with sequence ATTCTGCGTCGGGCATTGAT amplify a 810 bp fragment which was used for whole mount ISH hybridization and RNAi knockdown . PREP and TALE proteins sequences were taken from Mukherjee at al [18] and alignments checked with the CLUSTAL [37] . Phylogenetic reconstruction was conducted using MEGA version 4 using the bootstrapped neighbour-joining method [38] . The Smed-prep sequence has been submitted to GenBank with accession number GU290186 . DsRNAs were synthesized as described previously [39] . Control animals were injected with dsRNA of GFP that has no homology in the planarian genome . DsRNA microinjection was performed as described elsewhere [27] . For injection schedules please refer to Figure S2 . For double RNAi experiments concentrations for each gene were maintained at 1 µg/µl after mixing and for GFP controls 2 µg/µl was injected . Whole mount ISH hybridization was carried out as described previously [25] with modifications described in [40] and [24] . The paraformaldehyde solution for the fixation step was prepared fresh and adjusted to pH 9 . 5 using NaOH . For immuno-staining animals were killed in 2% HCl for 5 min on ice and then fixed in Carnoy's solution for 2 h at 4°C . After fixation , samples were processed as described elsewhere [41] , [42] . The following primary antibodies were used: anti-SYNORF1 , a mouse monoclonal antibody specific for synapsin ( Developmental Studies HybridomaBank , dilution of 1∶25 ) and anti-arrestin VC-1 , a mouse monoclonal antibody specific for planarian photosensitive cells ( kindly provided by Hidefumi Orii , used at a dilution of 1∶15 , 000 ) . Goat anti-mouse secondary antibody conjugated to Alexa 488 or Alexa 546 ( Molecular Probes ) was used at a 1∶400 dilution . Brightfield pictures were taken on a Zeiss Discovery V8 from CarlZeiss using an AxioCam MRC from CarlZeiss . Fluorescent pictures were taken on a Leica MZ16F fluorescence stereomicroscope using a Leica DFC 300Fx camera ( Leica Lasertechnik , Heidelberg ) . Confocal laser scanning microscopy was performed with a LeicaSP2 confocal laser scanning microscope ( CLSM ) ( Leica Lasertechnik , Heidelberg ) . | Understanding the genetic basis of tissue regeneration ( remaking ) from adult structures is an important long-term goal for biomedical science . The widespread nature of regenerative phenomena in different animals allows us to study evolution's answers to coordinating this process . We use the relatively simple and experimentally tractable planarian model to study this process . After almost any amputation these animals unerringly replace all missing tissues . This ability has two key components . Firstly , planaria have a population of stem cells capable of rapidly dividing and becoming all the cell types that are missing , such as muscle , gut , and brain cells , after amputation . Secondly , the genetic information in these stem cells and the remaining tissue is able to coordinate the regeneration process so that new structures are the correct size and in the correct place . This allows the production of a fully functional individual at the end of the regeneration process . We are specifically interested in how structures end up in the correct place in new tissue they form . Here we discover and describe the role of a gene , called Smed-prep , particularly central to this process . Smed-prep is required to coordinate the regeneration of the planarian brain , arguably the most exciting part of planarian regeneration . | [
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] | 2010 | The TALE Class Homeobox Gene Smed-prep Defines the Anterior Compartment for Head Regeneration |
Fruits and seeds are the major food source on earth . Both derive from the gynoecium and , therefore , it is crucial to understand the mechanisms that guide the development of this organ of angiosperm species . In Arabidopsis , the gynoecium is composed of two congenitally fused carpels , where two domains: medial and lateral , can be distinguished . The medial domain includes the carpel margin meristem ( CMM ) that is key for the production of the internal tissues involved in fertilization , such as septum , ovules , and transmitting tract . Interestingly , the medial domain shows a high cytokinin signaling output , in contrast to the lateral domain , where it is hardly detected . While it is known that cytokinin provides meristematic properties , understanding on the mechanisms that underlie the cytokinin signaling pattern in the young gynoecium is lacking . Moreover , in other tissues , the cytokinin pathway is often connected to the auxin pathway , but we also lack knowledge about these connections in the young gynoecium . Our results reveal that cytokinin signaling , that can provide meristematic properties required for CMM activity and growth , is enabled by the transcription factor SPATULA ( SPT ) in the medial domain . Meanwhile , cytokinin signaling is confined to the medial domain by the cytokinin response repressor ARABIDOPSIS HISTIDINE PHOSPHOTRANSFERASE 6 ( AHP6 ) , and perhaps by ARR16 ( a type-A ARR ) as well , both present in the lateral domains ( presumptive valves ) of the developing gynoecia . Moreover , SPT and cytokinin , probably together , promote the expression of the auxin biosynthetic gene TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS 1 ( TAA1 ) and the gene encoding the auxin efflux transporter PIN-FORMED 3 ( PIN3 ) , likely creating auxin drainage important for gynoecium growth . This study provides novel insights in the spatiotemporal determination of the cytokinin signaling pattern and its connection to the auxin pathway in the young gynoecium .
Angiosperms ( flowering plants ) are the most successful group of land plants on earth . In these species , flowers are formed , which normally produce a pistil or gynoecium , the female reproductive part of the flower , in their inner floral whorl . The gynoecium is responsible for fruit production and the formation , protection and dispersal of the seeds . Fruit and seeds are a major food source . Therefore , understanding the mechanisms that control gynoecium development in angiosperm species is of crucial importance . In Arabidopsis , the gynoecium is composed of two congenitally fused carpels and from top to bottom we identify the stigma and style , the ovary and the gynophore ( in the apical-basal axis; Fig 1A ) . A tissue with meristematic properties forms along the fused carpel margins ( the so-called medial domain ) , which is called the carpel margin meristem ( CMM ) . The lateral region of the carpel will eventually develop into valves . The CMM gives rise to all medial tissues , including the replum , placenta , ovules , septum and transmitting tract ( Fig 1A ) [1–3] . All these tissues are crucial for the reproductive success of the plant; however , our knowledge on the early events controlling CMM activity and medial tissue formation is fragmentary [1 , 4–6] . We have previously shown that the CMM shows a high transcriptional response to the phytohormone cytokinin [7] , a plant hormone that has been shown to promote cell division and maintain an undifferentiated cell state in aerial meristematic tissues [8 , 9] . Consistent with this , reduced cytokinin levels diminish gynoecium cell proliferation , whereas elevated cytokinin levels promote the proliferation of the medial tissues of the gynoecium [7] . Furthermore , mutations in the cytokinin catabolic genes CYTOKININ OXIDASE/DEHYDROGENASE ( CKX ) , result in larger floral organ size and increased seed yield owing to an increase in meristem size and ovule-forming placenta activity , respectively [10 , 11] . The cytokinin signal is perceived and transduced by a multi-step two-component signaling pathway , where the binding of the hormone causes the autophosphorylation of the membrane-bound cytokinin receptors ARABIDOPSIS HISTIDINE KINASES [AHK2 , AHK3 and AHK4 ( aka CRE1 ) ] , followed by a phosphorelay cascade [12–14] . The phosphoryl group gets relayed from the receptors to the ARABIDOPSIS HISTIDINE PHOSPHOTRANSFERASE proteins ( AHP1-AHP5 ) , with AHP6 competing for the phosphotransfer ( i . e . , interfering with cytokinin signaling ) . The AHP1-AHP5 proteins , which shuttle between the cytosol and the nucleus , phosphorylate the ARABIDOPSIS RESPONSE REGULATOR ( ARR ) proteins in the nucleus . Phosphorylated type-B ARR proteins work as transcription factors activating cytokinin-responsive genes , including the type-A ARR genes , which form a feedback loop negatively regulating cytokinin signaling responses [12–14] . The importance of cytokinin is clear in the shoot apical meristem ( SAM ) , where the gene encoding for the homeodomain transcription factor SHOOT MERISTEMLESS ( STM ) is expressed [15] . STM is required for SAM initiation and maintenance , in part by activating cytokinin biosynthesis ISOPENTENIL TRANSFERASE ( IPT ) genes [16–18] . The cytokinin produced is important for the formation and maintenance of stem cell niches [19–21] . Lack of STM results in SAM abortion whereas increased expression enlarges the meristem producing more organs [18 , 22 , 23] , which also occurs when cytokinin signaling decreases or increases , respectively [18 , 24] . In the young gynoecium , while a high cytokinin signaling output is detected at the medial domain of the ovary , this output is hardly detected at the lateral domain [7] . However , our understanding about the molecular components that contribute to this pattern of cytokinin signaling in specific regions in the young ovary is far from complete . Previous studies have shown the key role that auxin plays during gynoecium and fruit development ( reviewed in: [5 , 6 , 25–27] ) . Altered or impaired auxin signaling responses lead to dramatic gynoecia and fruit apical-basal and medio-lateral patterning defects , incomplete gynoecial apical fusion , altered style and stigma , apical-basal axis gynoecial patterning defects , the block of fruit growth or pod shattering alterations [28–39] . Recently , auxin and cytokinin have been referred as the `yin and yang`of plant development [13] , as they are often regarded as having opposite functions , but act synergistically together producing an output that is more than the sum of each of their independent actions . This is evidenced in meristem development [9 , 40] , root vasculature development [41 , 42] , and in vitro organogenesis [43 , 44] , among others . In this scenario , it is thus expected that cytokinin-auxin interplay actively participates in early gynoecium development [6] . However , we lack knowledge on whether and how the cytokinin signaling pathway is integrated with the auxin pathway in the young ovary . In this work , we investigated molecular elements that contribute to the pattern of cytokinin signaling regions in the young ovary , and the connection of the cytokinin signal to the auxin pathway at the medial domain . Our results support that the competence for cytokinin response in the medial tissue is provided by the bHLH transcription factor SPATULA ( SPT ) , known to be important for early gynoecium development [45–47] . On the other hand , the negative cytokinin signaling regulators AHP6 and ARR16 are expressed at the lateral domain , where cytokinin signaling is barely detected . Furthermore , both cytokinin and SPT activate TAA1 ( an auxin biosynthesis enzyme ) and PIN3 ( an auxin transporter ) .
We previously observed expression of the cytokinin signaling reporter TCS::GFP in the medial tissues , such as CMM , septa primordia , septum , transmitting tract [7] , and in cells where the provasculature will arise ( Fig 1 ) , but could hardly detect expression in the lateral domain of young gynoecia . Our first question was what determined this spatial pattern of cytokinin signaling . To identify possible regulators of cytokinin signaling in gynoecia , we sought for patterning genes important for early gynoecium development and whose expression pattern overlapped with that of TCS::GFP . Strikingly , we found that the expression pattern of the regulatory gene SPATULA ( SPT ) largely mirrored that of TCS::GFP ( Fig 1B–1E; S1 Fig ) [7 , 47 , 48] . SPT encodes a bHLH transcription factor , whose function is key in early gynoecium morphogenesis as it participates in CMM , septum and the transmitting tract development [45–47] . SPT is expressed since early stages in the CMM and its derived structures [47 , 48] . The single spt mutant shows a reduced number of cells in the CMM , absence of the septum and of the transmitting tract , retarded growth of the gynoecial tube and of vasculature , reduced number of ovules , and apical carpel fusion defects , which finally results in poor seed production [45–47] . In this context , we decided to investigate whether SPT was participating in the cytokinin signaling pathway during early gynoecium development . To do this we analyzed the activity of the TCS::GFP transgene in spt mutant gynoecia ( Fig 1F–1I ) . We used confocal laser scanning microscopy to observe fluorescence signal in transversely hand-sectioned gynoecia at stages 7 , 8 , 9 , and 12; stages according to [49] . Remarkably , during early gynoecium development ( stage 7–9 ) , no fluorescence signal was detected in the CMM or septa primordia of spt mutants ( Fig 1F–1H ) . On the other hand , TCS activity was increased when SPT was constitutively expressed ( Fig 1J–1M ) . Interestingly , whereas the fluorescence signal from the TCS::GFP reporter was increased upon 48 hrs of exogenous cytokinin treatment ( Fig 1N–1Q ) , no GFP signal increase was observed in cytokinin treated spt mutant gynoecia ( Fig 1R–1U ) . Note that in the mature spt gynoecia ( stage 12 ) , TCS::GFP fluorescence can be observed at the edges of the defective septa , strongly suggesting that this later signal is non-SPT dependent ( Fig 1I ) . In summary , these results support a positive role for SPT in the cytokinin signaling pathway at the CMM and septa primordia during early gynoecium formation . The cytokinin signaling pathway is necessary for proper gynoecium development Taking into consideration that the lack of SPT function causes severe gynoecial developmental defects [45–47] ( Figs 2I and 3C ) and that , based on our results , it influences cytokinin signaling output ( Fig 1 ) , we expected to observe gynoecium morphological alterations when genes in the cytokinin signaling pathway are mutated . The reporter line TCS::GFP has a synthetic promoter containing type-B ARR binding sites [50] , suggesting that type-B ARRs could be involved in CMM and septum development . We thus analyzed plants with impaired type-B ARR function [13] . Out of the 11 type-B ARR transcription factors present in Arabidopsis , ARR1 , ARR10 , and ARR12 are considered to have the main roles , based on cytokinin response assays , studies on root meristem development , and the severe reduction in cytokinin signaling [51–54] . Unfortunately , largely due to gene redundancy , single or double loss-of-function mutants in type-B ARRs do not show obvious phenotypic alterations [52] . Indeed , we observed no obvious phenotypic differences between wild type plants and the single loss-of-function mutants arr1 , arr10 , and arr12 , nor with the double loss-of-function mutants arr1 arr10 , arr1 arr12 , and arr10 arr12 ( Fig 2 and S2 Fig ) . In contrast , the type-B arr1 arr10 arr12 triple mutant plants are severely affected ( Fig 2 and S2 Fig ) . General plant growth is strongly reduced and flower and fruit production is drastically reduced , suggesting that the meristematic activity is affected in this triple mutant ( S2 Fig ) . We then morphologically characterized the produced gynoecia in the type-B arr triple mutant plants . The arr1 arr10 arr12 background exhibited reproductive defects such as reduced gynoecium and fruit length , reduced replum width , and fewer ovules ( Fig 2A–2F ) , phenotypes not observed in single and double arr mutants ( Fig 2A and 2B; S2 Fig ) . Furthermore , thin sections of arr1 arr10 arr12 triple mutant gynoecia showed septum fusion defects and a reduction of transmitting tract tissue in some gynoecia ( Fig 2H ) , phenotypes that we did not observe in thin sections of gynoecia of single or double arr mutants ( S3 Fig ) , confirming the high level of redundancy among type-B ARR transcription factors . In summary , the analyzed phenotypes provided further evidence supporting the relevance of cytokinin signaling in gynoecium development , and , since some aspects of the triple mutant were reminiscent of the spt single mutant , also support the connection between SPT and the cytokinin signaling pathway during gynoecium development . We next investigated the functional relevance and nature of the relationship between SPT and cytokinin signaling in the gynoecium using a pharmacological assay to evaluate the cytokinin response competence of the gynoecium . The repeated application of cytokinin to wild-type Arabidopsis inflorescences results in tissue overproliferation , causing ectopic outgrowths from the medial domain of the gynoecium; observed three to four weeks after the treatment [7] ( Fig 3A ) . However , this response was affected in the type-B arr mutants and in spt ( Fig 3A ) . Single arr1 and arr12 mutants presented a very reduced response to the exogenous cytokinin treatments , while arr10 presented only a mild reduction ( Fig 3A ) . Some proliferation was observed in the double arr1 arr10 , which resembled the single arr1 mutant . Some proliferation was also observed in the double arr10 arr12 mutant , which was a little less than in the single arr12 mutant . However , no ectopic tissues were produced in the double arr1 arr12 mutant nor the triple arr1 arr10 arr12 mutant ( Fig 3A ) . Interestingly , 14 out of 16 spt gynoecia ( 87 . 5% ) also did not show a cytokinin response in the medial domain ( Fig 3A ) , and only a minor proliferation effect was detected in the other two ( 12 . 5% ) spt gynoecia examined ( S4B Fig ) . It is worth noting that , as recently observed by others [55] , the cytokinin response of the style and stigma of wild-type and spt gynoecia was different to that of the internal ovary . In summary , based on the pharmacological assay , type-B ARR redundancy is observed , with ARR1 and ARR12 playing the major role in the cytokinin response competence of the gynoecium . Moreover , SPT is also a major player in this response . Interestingly , during normal gynoecium development , i . e . , no exogenous cytokinin application , morphological defects only become visible when three type-B ARR genes are not functional anymore ( Fig 3; S2 and S3 Figs ) , demonstrating that the developmental program active during early gynoecium development is robust . However , the pharmacological assay indicates that the full competence of the gynoecium to respond to the artificial high level of cytokinin needs all type-B ARR proteins to be active , because a decreased response is already visible by removing one type-B ARR ( Fig 3A ) . We also asked whether an exogenous cytokinin treatment could rescue the developmental defects observed in the spt mutant . This mutant has two clear fusion defects: at the apex of the gynoecium and in the internal ovary region [45–47] ( Fig 3C ) . Cytokinin was applied to inflorescences during a 48-hour period only . We observed a virtually complete rescue of the apical closure defect in 20 out of 26 spt gynoecia ( 76 . 9% ) , 24 hours after this treatment ( Fig 3B–3E; S4C–S4K Fig ) . However , the spt septum defects were not rescued ( Fig 3E; S4K Fig ) . This suggests that the internal spt fusion defects in the ovary were most likely not due to reduced cytokinin biosynthesis , and that SPT could be acting at a different level of the cytokinin pathway . In conclusion , the data together clearly indicate that SPT is necessary for positive cytokinin signaling output ( both visualized by the TCS reporter , and as the proliferation response to exogenous cytokinin treatments ) in the young gynoecium . Given that the internal fusion defects of spt were not rescued by exogenous cytokinin , together with the resemblances between the spt and the type-B arr1 arr10 arr12 triple mutant phenotypes , and the alteration of cytokinin signaling in the spt mutant , we hypothesized whether one of the ways in which SPT could be connected to the cytokinin pathway could be through the regulation of the type-B ARR genes . To test this possibility , we assayed the transcript levels of ARR1 , ARR10 and ARR12 using quantitative real-time reverse transcriptase-mediated polymerase chain reaction ( qRT-PCR ) from dissected wild-type and spt gynoecia , respectively . Whereas this experiment did not reveal clear changes in the expression level of ARR10 , transcript abundance for ARR1 and ARR12 was reduced in spt when compared to wild-type ( Fig 3F ) , and both showed a higher relative expression than ARR10 in wild type gynoecia ( S5 Fig ) . Note , the gynoecium is a very complex structure with many different tissues . Therefore , we cannot exclude that the changes in expression levels in specific tissues of the gynoecium are not well reflected in this assay . Therefore , we performed an in situ mRNA hybridization on ARR1 in wild-type and spt gynoecia , because ARR1 transcript abundance showed the most conspicuous reduction in dissected spt gynoecia . In wild-type , ARR1 transcripts are present in the CMM , ovule primordia , and in the style region , overlapping with SPT expression pattern ( Fig 3G and 3H; S6 Fig ) . However , in the spt mutant , ARR1 messenger was either not detected or detected at very reduced levels , suggesting that SPT was required for ARR1 expression ( Fig 3I and 3J ) . These results support a role for SPT positively regulating the cytokinin signaling pathway by modulating the expression of at least two type-B ARR genes , ARR1 and ARR12 . On the other hand , since the qRT-PCR experiment did not show a reduction of ARR10 in the spt mutant , we cannot conclude that it is also positively regulated by SPT as ARR1 and ARR12 . Therefore , it is highly likely that SPT affects , besides ARR1 and ARR12 , other components of the cytokinin signaling pathway . The ARR1 promoter fragment contains a G-box , a cis-regulatory motif targeted by bHLH transcription factors ( as SPT ) for gene regulation [39 , 56 , 57] . ARR10 and ARR12 have also bHLH binding motifs in their promoters , but do not have the G-box version . It has been reported that SPT binds only to the G-box version [39 , 57] . Therefore , the positive regulation by SPT on ARR12 expression is most likely indirect or performed by a complex where SPT participates . To determine whether SPT is able to positively regulate ARR1 directly , we performed luciferase transient reporter assays in Nicotiana benthamiana leaves [58 , 59] . We observed that transiently expressed SPT protein was able to activate an ARR1::LUC reporter construct ( Fig 3K ) . To further determine whether this regulation could be due to direct binding to ARR1 regulatory regions in the DNA , we performed chromatin immunoprecipitation assays followed by qPCR ( ChIP-qPCR ) using 35S::SPT-HA and wild-type Arabidopsis inflorescence tissue ( Fig 3L ) . When compared to wild-type , ChIP-qPCR results from the 35S::SPT-HA line showed a significant enrichment of the ARR1 promoter fragment that contains the G-box , reported to be targeted by SPT [39 , 56 , 57] . In summary , all these results together are consistent with SPT being able to activate ARR1 expression in gynoecia , possibly in a direct manner . Furthermore , SPT likely regulates ARR12 as well , but in an indirect manner . Moreover , this regulation would also explain the lack of response to exogenous cytokinin application in spt and in the arr1 arr12 double mutant and , at least partly , the reduction of cytokinin-induced signal response in the CMM and septa primordia in spt gynoecia . However , we cannot discard the possibility of indirect effects of SPT on type-B ARR expression and it is highly likely that SPT affects , besides ARR1 and ARR12 , also other components of the cytokinin signaling pathway . The next question was whether and how the cytokinin signaling pathway interacts with the auxin pathway in gynoecia . It has been previously described that the cytokinin signaling pathway and SPT can interact with several genes in the auxin signaling pathway [13 , 28 , 31 , 38 , 39 , 55 , 60] . We therefore explored whether interactions between them were also taking place during the formation of medial tissues in the gynoecia . Interestingly , TCS::GFP , SPT , and the auxin biosynthesis gene TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS 1 ( TAA1 ) are co-expressed in the CMM ( Fig 4 ) [36 , 61] , and mutant combinations of TAA1 with its paralog TAR2 ( TRYPTOPHAN AMINOTRANSFERASE RELATED 2 ) resulted in plants with severely affected gynoecium development , indicating the importance of local auxin biosynthesis for correct gynoecium morphogenesis [61] . To test whether cytokinin had an effect on TAA1 expression , we applied cytokinin to TAA1:GFP inflorescences and observed a strong increase in the GFP signal in the medial domain of stage 8 and 9 gynoecia , indicating that cytokinin induces TAA1 expression ( Fig 4B ) . Moreover , a microarray data meta-analysis has also identified TAA1 as a cytokinin-responsive gene [62] . We then aimed at exploring the molecular mechanism involved in this induction . Since type-B ARRs are important positive regulators of cytokinin transcriptional response , we investigated whether ARR1 could activate TAA1 . We found that transiently expressed ARR1 is able to activate a TAA1::LUC reporter construct in transient assays in N . benthamiana leaves ( Fig 4E ) . We next performed ChIP-qPCR assays using the dexamethasone ( DEX ) inducible glucocorticoid receptor ( GR ) fusion line 35S::ARR1ΔDDK-GR . In this line , upon DEX induction , ARR1 is constitutively active in the absence of cytokinin [63 , 64] . ChIP-qPCR results from DEX-treated 35S::ARR1ΔDDK-GR inflorescences , when compared to those mock-treated , showed significant enrichment of a TAA1 promoter fragment that contains various type-B ARR binding sites ( Fig 4F ) , consistent with ARR1 directly regulating TAA1 . This strongly suggests that one of the outputs of the cytokinin signaling pathway is to activate the auxin biosynthetic pathway and that ARR1 is a hub connecting the cytokinin signaling and auxin pathway . Our results further substantiate previous reports suggesting the connection between ARR1 and auxin biosynthesis [65] . Given the fact that SPT enables cytokinin responses in the early gynoecium , and that it likely activates ARR1 directly , we evaluated TAA1 response to cytokinin in spt mutants . As expected , when SPT is mutated , gynoecium cytokinin-dependent TAA1::GFP induction is abolished ( Fig 4D ) . We also analyzed TAA1:GFP expression in untreated spt gynoecia , and observed a moderate reduction in GFP signal ( Fig 4C ) , suggesting that SPT is able to activate TAA1 , although additional regulators might contribute to TAA1 expression in developing gynoecia . Interestingly , besides being required for the cytokinin induction of TAA1 expression , based on our luciferase transient reporter assays ( Fig 4G ) and ChIP-qPCR experiments ( Fig 4H ) , SPT seems to directly regulate TAA1 expression by recognizing a cis-motif present within the TAA1 promoter ( Fig 4H ) . In summary , these results indicate that both cytokinin and SPT can activate TAA1 at the medial domain of the ovary , probably in a cooperative fashion , integrating a regulatory node for correct cytokinin signaling and auxin biosynthesis in the medial tissues of the gynoecia . This TAA1 activation might be mediated by phosphorylated type-B ARRs and SPT , possibly as direct regulators , as the ARR1 and the SPT activation of TAA1 and ChIP experiments suggest , though indirect regulation cannot be discarded . Intriguingly , the expression of TAA1 at the medial tissues of the ovary did not coincide with the expression of the auxin reporter DR5rev::GFP , which was not detected in these tissues ( CMM , septa primordia , septum and transmitting tract ) ( S7A–S7D Fig ) . One possible explanation for this discrepancy is that the auxin synthesized by TAA1 is transported outside these tissues by PIN auxin efflux transporters [66] . To determine whether this was the case , we analyzed the expression pattern of different GFP reporters for PIN genes ( PIN1 , 3 , 4 and 7 ) in the medial region of the ovary of wild-type gynoecia and observed that PIN1:GFP and PIN3:GFP were expressed in the medial domain ( S7 and S8 Figs ) . Since pin1 mutants do not produce flowers [67] , we focussed most of our analyses on PIN3 . PIN3:GFP signal was detected in the CMM , septa primordia , septum and transmitting tract ( Fig 5A; S7F and S8K–S8T Figs ) . Given that cytokinin signaling was also detected there , we investigated whether cytokinin was influencing PIN3 expression . Interestingly , when cytokinin was applied , the PIN3:GFP reporter was strongly induced in the medial domain of the ovary , while it seemed to be localized in a non-polar fashion in the cells ( Fig 5B; S9M–S9O Fig ) . This clearly indicated that PIN3 is responsive to cytokinin in these tissues . Accordingly , we observed a similar induction of PIN3 and PIN1 expression by cytokinin in the ectopic outgrowths produced from the medial region of the ovary ( Fig 5C; S9N Fig ) . In these tissues , PIN3:GFP and PIN1:GFP were polarly localized towards the emerging outgrowths ( Fig 5C; S9X Fig ) , which also showed a high DR5rev::GFP signal at their tips [7] . To determine whether PIN3 is relevant for the overproliferation of the medial tissue after cytokinin treatment , we applied cytokinin to pin3 inflorescences ( pin1 could not be tested due to the lack of inflorescences ) . If PIN3 is required , the ectopic outgrowths would not be produced . Indeed , a minor ectopic medial outgrowth was observed in cytokinin-treated pin3 gynoecia ( S10D Fig ) and only apical-basal defects were detected in 78 . 2% of the cases ( i . e . , alterations in the size of the ovary , gynophore and style with respect to each other ) ( n = 330 ) ( Fig 5G–5J and insets ) [32] . This suggests that observed medial tissue responses to exogenously applied cytokinin require a functional PIN3 . To explore the role of PIN3 in medial tissue development , we analyzed thin sections of pin3 untreated gynoecia and observed mild alterations in transmitting tract development ( Fig 5G–5J ) , characterized by reduced blue staining of the cells . A possible explanation for the pin3 mild phenotype in the medial tissue is that the related PIN7 can partially compensate for the PIN3 function , as it has been reported in other developmental programs [68] . Accordingly , the double pin3 pin7 mutant has severe floral defects and none of the gynoecia form correctly ( S10E–S10H Fig ) [66] . However , we were not able to detect PIN7 signal in wild type ovaries , perhaps due to low signal of the reporter line . On the other hand , another explanation is that PIN1 partially compensates for the PIN3 loss , because PIN1 signal is clearly detected in the medial tissues and the reporter line responds to cytokinin ( S8 and S9 Figs ) . To obtain insights about the possible molecular mechanism by which cytokinin activates PIN3 expression at the medial domain , and since type-B ARRs are important effectors of cytokinin signaling , we explored whether type-B ARR activity could be involved , using ARR1 to test this . We found that ARR1 was able to activate PIN3 in luciferase transient reporter assays ( Fig 5K ) . Moreover , ChIP-qPCR data were compatible with the possibility of ARR1 directly activating PIN3 expression in the medial domain of the gynoecia ( Fig 5L ) . The data suggests that ARR1 is able to bind two regions within the PIN3 promoter containing putative cis-regulatory motifs for type-B ARR transcription factors . Next , we investigated whether SPT could also regulate PIN3 expression and whether the PIN3 cytokinin response was SPT-dependent . Expression of PIN3 in the CMM and septa primordia appears to be dependent on SPT given that no PIN3 expression in these medial tissues was observed in a spt mutant background ( Fig 5D; S9E–S9H Fig ) , whereas it increased when SPT was constitutively expressed ( Fig 5E; S9I–S9L Fig ) . qRT-PCRs using dissected gynoecia also showed a decrease in PIN3 expression in a spt background and an increase in a 35S::SPT background ( S9W Fig ) . Moreover , induction of PIN3 expression by cytokinin is also dependent on the presence of SPT , as no PIN3 cytokinin-dependent activation was seen in spt ovaries ( Fig 5F; S9P–S9R Fig ) . Though indirect regulation cannot be discarded , luciferase transient reporter assays ( Fig 5M ) together with ChIP-qPCR data ( Fig 5N ) were consistent with direct regulation of PIN3 by SPT . Specifically , and based on our results , SPT bound to two regions ( `a`and preferentially `b` ) within the PIN3 promoter ( Fig 5N ) . In summary , all these results together support that SPT and the cytokinin-signaling pathway , probably in a cooperative fashion , are connected to auxin biosynthesis and auxin transport at the medial domain of the ovary . Cytokinin signaling repressors are expressed in the lateral domain After finding that the localization of cytokinin signaling in the medial domain depends on SPT , we still wondered whether other factors could be repressing cytokinin signaling in the lateral domain . Besides barely detecting TCS::GFP expression at the lateral domain , we also had observed that exogenous cytokinin treatments could not activate this marker in the lateral domain ( presumptive valves ) . Moreover , TCS::GFP signal could also not be detected in the lateral domain of 35S::SPT gynoecia , not even when cytokinin was added to this line ( S11 Fig ) . Finally , the outgrowths promoted by cytokinin treatments were observed to arise from the medial and not the lateral domain of the gynoecium . Together , these observations indicate that gynoecia lateral tissues ( presumptive valves ) are not responsive to the exogenously applied cytokinin or ectopic SPT expression , and suggest that repression is taking place in these tissues . As mentioned in the Introduction , some components of the cytokinin signaling pathway execute a repressing effect on cytokinin-dependent outputs . We therefore wondered whether these repressors were present in the lateral domain of the gynoecium . The gene AHP6 encodes a histidine phosphotransfer protein that inhibits cytokinin signaling responses [69] and it has been also shown to participate in auxin-cytokinin communication ( i . e . , it is induced by auxin ) [41 , 69 , 70] . In gynoecia , AHP6::GFP reporter activity is strong in the lateral domains of stage 7 , 8 , and 9 gynoecia ( Fig 6A–6D ) , suggesting that AHP6 is negatively regulating cytokinin signaling in the valves . As mentioned above , the TCS::GFP reporter is not active in the lateral domains of gynoecia ( Fig 1 ) . However , this marker was ectopically active in the lateral domains of ahp6 gynoecia ( Fig 6E and 6F ) . Moreover , repeated cytokinin applications to ahp6 inflorescences caused ectopic tissue proliferation in a radial pattern in the apical part of the gynoecium , a morphological effect not seen in wild-type treated gynoecia ( Fig 6G and 6H ) . Interestingly , we also observed that the GUS-reporter construct ( ARR16::GUS ) for the type-A ARR16 gene , which encodes a regulatory protein that negatively regulates cytokinin signaling pathway responses [13] , was active in the lateral domain of stage 7 , 8 , 9 , and 12 gynoecia ( Fig 6I–6L ) . Altogether , these data support a scenario in which the cytokinin signaling pathway is negatively regulated in the lateral domain of the gynoecium by AHP6 , and perhaps by ARR16 as well . This negative regulation can confine cytokinin signaling to the medial domain of the gynoecium ( Fig 7 ) . A dynamic GRN Boolean model during early gynoecium development In this work , we found that SPT enables cytokinin signaling at the medial domain . Moreover , both SPT and cytokinin signaling , probably together , positively regulate auxin biosynthesis and transport genes in this domain . Based on these findings we developed a preliminary gene regulatory network ( GRN; Figs 7 and 8 ) that acts during early gynoecium development . To verify that this network fits the experimental data , we made a GRN Boolean model using the computational tools BioTapestry [71] and GeNeTool [72] ( Fig 8A and 8B ) , and we confirmed that the wiring of this network gives a stable output ( i . e . , fixed steady state for each gene ) ( Fig 8A ) . Note , we modeled TAA1 and PIN3 regulation by SPT and ARR1 in a cooperative manner ( i . e . , meaning that both are necessary ) . The possible cooperative regulation could be through the formation of a protein complex . Support for this is the observation that SPT and ARR1 bind to the same fragments of the TAA1 and PIN3 promoters in ChIP assays . We already explored whether these two transcription factors interact directly . However , we could not detect any protein-protein interaction in yeast two-hybrid ( between SPT and ARR proteins ) nor in a bimolecular fluorescence complementation assays ( between SPT and ARR1 ) ( S12 Fig ) . Nevertheless , SPT and ARR1 could be part of a higher-order complex where both factors are present but do not interact directly . Note , cytokinin should be as well present , to start the phosphorelay cascade that finally leads to phosphorylation of the type-B ARR proteins so that they are functional . Moreover , in silico perturbations of the regulatory links in our model produces the observed phenotypes ( Fig 8C–8F ) . The experimental evidence supports direct regulatory links between the genes , but the value of the identified GRN holds even though if regulatory links would be indirect . The topology of the network presents interesting features . For example , the regulatory interactions between SPT , ARR1 , and TAA1 as well as between SPT , ARR1 , and PIN3 are wired as coherent feed-forward subcircuits [73 , 74] . This type of subcircuit has been detected in other plant developmental processes ( e . g . , [75–77] ) , and can integrate upstream spatial regulatory inputs , cause high expression of the target gene , and temporal delay in switching the target gene on or off [73 , 74] .
In angiosperms , the correct patterning and morphogenesis of the gynoecium is an essential developmental program for the formation of reproductive tissues and , thus , the reproductive success of the plant . Research from many groups led to the identification of key regulatory genes governing gynoecium development ( reviewed in: [1 , 3 , 78–80] ) . Several lines of evidence have also highlighted the importance of hormones during gynoecium development ( reviewed in: [1 , 5 , 6 , 25 , 81 , 82] ) . However , the interaction between the gene regulatory layers and hormonal pathways , the mechanisms that determine different hormone responsive and non-responsive regions , and the interaction between hormonal pathways in the medial tissues of the ovary of the young gynoecium , are topics not explored in depth . In our study we addressed these questions from the cytokinin signaling point of view . Cytokinin signaling is localized in the medial domain of the young gynoecium , and the triple type-B arr mutant phenotypes indicate that proper signaling is required for the correct formation of medial domain structures in the ovary , necessary for normal gynoecium and fruit development . Interestingly , STM , which can activate the cytokinin biosynthesis IPT genes , is expressed at the CMM [15 , 83] , and inducible repression of STM causes carpel fusion defects , reduced CMM development , a reduction in placenta and ovule number , and even a complete absence of the gynoecium [84] . Conversely , increased cytokinin levels in the gynoecium causes a larger placenta and more ovules [11] , and increased replum width [7] , further supporting an important role for cytokinin in early gynoecium development . The bHLH transcription factor SPT is key for early gynoecium development [45–47] . Previous reports showed that members of the bHLH transcription factor family are related to hormone signaling pathways ( e . g . , [85–89] ) . In our study , most importantly , we have identified that SPT enables cytokinin response at the medial domain , thereby stimulating meristematic activity in this domain . The lack of cytokinin signaling observed in spt explains why the CMM and septa primordia of analyzed stage 8 spt gynoecia contain fewer cells , and why , at later stages , less ovules are formed in the mutant than in the wild-type [46] . SPT may act through type-B ARRs , and results support a direct regulation of ARR1 by SPT . Furthermore , results also suggest regulation of ARR12 by SPT , though , this regulation is likely indirect because , though its promoter contains bHLHs motifs , a true G-box bound by SPT was not detected [39 , 57] . On the other hand , since the qRT-PCR experiment did not show a reduction of ARR10 in the spt mutant , we cannot conclude that it is also positively regulated by SPT as ARR1 and ARR12 . If SPT does not regulate ARR10 , it would be most likely regulating additional components of the cytokinin signaling pathway , because , though the arr1 arr12 double mutant has a reduced response to exogenous cytokinins , it does not present a severe mutant gynoecium phenotype . Still , the promoter regions of ARR10 contain bHLH binding motifs , suggesting that it could be regulated by another bHLH transcription factor . Moreover , we identified close links between the cytokinin and auxin pathways at the medial domain of the gynoecium . Both SPT and ARR1 regulate TAA1 and PIN3 , components of the auxin pathway , in the medial domain , probably causing a PIN3-dependent auxin flux away ( auxin drainage ) from the gynoecium centre towards the repla and the valves . The regulation of TAA1 and PIN3 by SPT and ARR1 may be cooperative . Most likely , auxin is directed afterwards to the apical and/or basal part of the gynoecium , where it can flow from the base to the top and back , as recently proposed in the ‘reverse fountain’ model [35] . Auxin drainage would be important for growth and , furthermore , auxin flow in the lateral domains ( presumptive valves ) would prevent them from obtaining medial domain identity [35] . Evidence for the importance of auxin transport comes from the observation of strong defects in medial domain development in the double mutant for the genes REVOLUTA ( REV ) and AINTEGUMENTA ( ANT ) , where auxin transport was altered [36] . The cytokinin signaling repressors AHP6 and ARR16 are found at the presumptive valve tissues ( lateral domain ) and thereby , at least AHP6 , restrict the high cytokinin signaling output that stimulates meristematic activity to the medial domain . This restriction of cytokinin signaling explains why no expansion of TCS signal from medial to lateral domain is observed in exogenous cytokinin treated gynoecia , in 35S::SPT gynoecia , nor in cytokinin treated 35S::SPT gynoecia . The ahp6 mutant gynoecia showed TCS::GFP signal in the valves and appeared to be more sensitive to cytokinin applications compared to wild-type , although the non-treated gynoecia of the mutant appeared normal , suggesting redundancy of this cytokinin restriction function . One thing we noticed is that the TCS::GFP signal in ahp6 gynoecia did not extend to the epidermis of the valves . It has been reported that the epidermis is important for signaling [90] . Perhaps only in double or higher-order mutants for valve expressed cytokinin signaling repressors , gynoecial developmental defects can be observed . Another point of interest is that AHP6 is involved in the hormonal communication between auxin and cytokinin [41] . In vascular pattern formation , the auxin-induced cytokinin signaling repressor AHP6 is involved in the establishment of two mutually inhibitory domains [41] . In the SAM , AHP6 is also involved in establishing inhibitory fields , important for phyllotaxis [70] . It will be interesting in future studies to investigate if AHP6 is involved in establishing inhibitory fields also in the gynoecium . In principle , we already observed separate fields of cytokinin and auxin responses in the young gynoecium . It will also be interesting to investigate if it is the produced auxin induced by cytokinin in the medial domain of the gynoecium that then gets transported by cytokinin-induced PIN3 to the lateral domains to activate AHP6 , or whether AHP6 is under the control of the regulatory genes required for lateral tissue formation . Future experiments might provide further insights into how cytokinin controls the gynoecium and its impact on patterning . Interestingly , a function for the HEC genes and SPT in SAM function was recently reported: HECs were shown to stimulate stem cell proliferation in a tissue-specific and SPT-dependent manner , suggesting that the relative levels of these transcription factors dictate the proliferative potential of stem cells [91] . A reduced SAM size was observed in spt mutant plants [91] , which suggests that SPT function is also likely to be necessary for a positive cytokinin signaling output in the SAM . It would be interesting to explore other elements participating in the regulatory network in early gynoecium development , including the HEC genes , whose triple mutant has similar developmental defects in medial tissues to those observed in the spt mutant [92] . A recent study has already started to explore HEC–SPT function in style and stigma formation , demonstrating positive regulation of auxin biosynthesis and transport , and likely repressing cytokinin signaling in the apical region of the gynoecium [55] . This indicates that the role of SPT in the style and stigma is different from that in the ovary . Some of the effects of cytokinin application resemble phenotypes observed in polarity mutants ( this work ) [7 , 32 , 93 , 94] . It would be very interesting to further explore how the network here described interacts or even overlaps with known polarity cues or regulators such as CRABS CLAW ( CRC ) [46 , 95] , ETTIN ( ETT ) [96] , REVOLUTA ( REV ) [97] , or KANADI ( KAN ) [98–101] . The gynoecium is a key component of the success of the angiosperms [102 , 103] , which comprise over 300 , 000 species on earth . Here we showed , for a Brassicaceae family member , that cytokinin signaling is necessary for its correct development and , therefore , for reproductive competence . Interestingly , the presence of the bHLH transcription factor SPT , cytokinin signaling and auxin biosynthesis genes , and PIN orthologs in basal angiosperms [57 , 104–108] , suggests that these genes already could have a function in gynoecium development in early flowering plants . Future work should shed light on how and when this network emerged .
Seeds were obtained for spt-2 ( CS275 ) , arr1-3 ( CS6971 ) , arr10-5 ( CS39989 ) , arr12-1 ( CS6978 ) , arr1-3 arr10-5 ( CS39990 ) , arr1-3 arr12-1 ( CS6981 ) , arr10-5 arr12-1 ( CS39991 ) , arr1-3 arr10-5 arr12-1 ( CS39992 ) , and DR5rev::GFP ( CS9361 ) from the Arabidopsis Biological Resource Center ( Ohio State University , Columbus ) , TCS::GFP from Bruno Muller , pSPT-6253:GUS from David Smyth , spt-12 , 35S::SPT , and 35S::SPT-HA from Karen Halliday , pin3-4 and pin3 pin7 from Eva Benková , PIN3::PIN3-GFP and TAA1::GFP-TAA1 spt-12 from Lars Østergaard , PIN1::PIN1-GFP and PIN7::PIN7-GFP from Luis Herrera-Estrella , PIN4::PIN4-GFP from Elena Alvarez-Buylla , TAA1::GFP-TAA1 from Anna Stepanova , AHP6::GFP from Ykä Helariutta , TCS::GFP in the ahp6-1 background from Teva Vernoux , 35S::ARR1ΔDDK-GR from Takashi Aoyama , and ARR16::GUS from Takeshi Mizuno . Arabidopsis thaliana , Nicothiana tabacum , and Nicotiana benthamiana were grown in soil under normal greenhouse conditions or in a growth chamber ( ~22°C , long day light regime ) . Inflorescences were treated with cytokinin 6-Benzylaminopurine ( BAP ) as previously described [32] . In summary , one week after bolting , BAP solution drops were placed on the inflorescences once a day for 2 ( 48-hour period ) or 5 to 10 ( repeated applications ) consecutive days . To observe ectopic outgrowths from the medial domain of the pistil , a BAP treatment for five days is given and after three to four weeks observations are made . The BAP solution contains 100 μM 6-benzylaminopurine ( BAP; Duchefa Biochemie ) and 0 . 01% Silwet L-77 ( Lehle Seeds ) in distilled water . Mock treatments contained only 0 . 01% Silwet L-77 in distilled water . All treated plants with their respective controls were cultivated simultaneously under the same growth conditions . For qRT-PCR analysis , stage 8–10 gynoecia or inflorescence with only floral buds were collected and total RNA was extracted using TRIzol ( Invitrogen ) . After DNAse I ( Invitrogen ) treatment , cDNA was prepared using SuperScript III Reverse Transcriptase ( Invitrogen ) according to manufacturer’s instructions and using reverse specific primers for each of the corresponding genes under test ( S1 Table ) . The cDNA was analyzed in an ABI PRISM 7500 sequence detection system ( Applied Biosystems ) with SYBR Green Master Mix ( Applied Biosystems ) according to the manufacturer’s instructions . Three biological replicates and four technical replicates were done for each assay . Data was analyzed using the 2-ΔΔCT method [109] . Target gene expression levels were normalized to ACTIN2/7 . Primer sequences are listed in S1 Table . In situ hybridization was carried out as previously described [110] . The template for the DIG-labeled antisense and sense probe synthesis for ARR1 mRNA was generated by PCR using specific primers ( S1 Table ) and inflorescence wild-type cDNA . The resulting PCR fragment was purified , sequenced and used as template to transcribe the antisense probe with the T7 RNA polymerase ( Promega ) and the sense probe with the SP6 polymerase ( Promega ) . ChIP experiments were performed as previously described [111] . Between 0 . 5 g and 1 g of inflorescences were collected for each experiment . The 35S::SPT-HA homozygous transgenic line [112] and wild-type Ler ( as relative control ) were used for ChIP on SPT . A monoclonal mouse anti-HA ( Sigma; H3663 ) ( 2 μg per sample ) was used to immunoprecipitate SPT-HA complexes . We additionally tested 35S::SPT-HA line ChIP enrichment with no anti-HA antibody as a preliminary test , to ensure specificity of the ChIP reaction . ChIP assays for ARR1 were performed using the DEX ( dexamethasone ) -inducible 35S::ARR1ΔDDK-GR line [64] after DEX induction . Mock treated plants were employed as controls . In short , DEX-treated 35S::ARR1ΔDDK-GR inflorescences were collected 24 hours after two DEX applications ( each one separated by 12 hours ) with 10 μm DEX solution with 0 . 015% Silwet in distilled water , frozen in liquid nitrogen , and stored at -80°C till enough material was collected . Glucocorticoid Receptor alpha polyclonal antibody ( Thermo Scientific; PA1-516 ) ( 2 μg per sample ) was used to immunoprecipitate ARR1ΔDDK-GR complexes . Results from qPCR experiments were analyzed using the 2-ΔΔCT method [109] . Each biological sample was assayed for relative enrichment with respect to its input sample ( fragments were normalized using ACTIN2/7 ) . Binding was concluded if PCR enrichment was detected in at least three out of five independent biological replicates . Primers used for ChIP-qPCR analysis are listed in S1 Table . Promoter regions of PIN3 ( 4 . 3 kb , -4310 to ATG ) , ARR1 ( 2 . 1 kb , -2116 to ATG ) , and TAA1 ( 2 kb , -2047 to ATG ) were amplified from Arabidopsis Col-0 genomic DNA with specific primer pairs ( S1 Table ) , cloned into pGEM-T vector ( Promega ) , digested with SmaI and NcoI restriction enzymes , and ligated into pGREEN-LUC [59] to generate pPIN3::LUC , pARR1::LUC , and TAA1::LUC reporters , respectively , for transient expression assays in N . benthamiana leaves . The 35S::SPT effector construct that was used in the coinfiltrations with the corresponding LUC reporters , was generated by transferring the SPT ORF into the pEARLY100 vector [113] through Gateway reactions , which was previously cloned in the pDONR221 vector ( Invitrogen ) . The 35S::HA-ARR1 has been previously described [114] . The transient Luciferase expression assays were performed by transient transformation of N . benthamiana leaves by Agrobacterium infiltration , which was performed as previously described [115] with minor modifications [58] . At least three plants at the same developmental stage were used for each treatment , and the experiments were repeated at least three times . Tissue preparation and confocal microscopy analysis To observe fluorescence signal , gynoecia were dissected and observed as previously described [116] . In summary , gynoecia were observed longitudinally or cut transversely using a scalpel and mounted in glycerol . Propidium iodide ( PI; Fluka ) , 50 μM PI for 30–60 seconds , was used as counterstain . Imaging was done using a LSM 510 META inverted confocal microscope ( Carl Zeiss ) with either a 20X or 40X air objective . GFP was excited with a 488 nm line of an Argon laser and PI with a 514 laser line . GFP emission was filtered with a BP 500–520 nm filter and PI emission was filtered with a LP 575 nm filter . Scanning electron microscope analysis Fresh tissue samples were visualized in a Zeiss scanning electron microscope EVO40 ( Carl Zeiss ) using the VPSE G3 or the BSD detector with a 15–20 kV beam . Gynoecia were dissected and pre-fixed with cold acetone for 20 min , rinsed , and transferred into GUS substrate solution: 50 mM sodium phosphate pH 7 , 5 mM K3/K4 FeCN , 0 . 1% ( w/v ) Triton X-100 , and 2 mM X-Gluc ( Gold BioTechnology , Inc ) . After application of vacuum for 5 min , SPT::GUS and ARR16::GUS samples were incubated at 37°C for 12 hrs . Tissues were fixed in FAE ( 3 . 7% formaldehyde , 5% glacial acetic acid and 50% ethanol ) with vacuum ( 15 min , 4°C ) and incubated for 60 min at room temperature . The material was rinsed with 70% ethanol and incubated overnight at 4°C , followed by dehydration in a series of alcohol solutions ( 70 , 85 , 95 , and 100% ethanol ) for 60 min each and embedded in Technovit as previously described [117] . Pictures were taken using a Leica DM6000B microscope coupled with a DFC420 C camera ( Leica ) . Transmitting tract analysis Transmitting tract staining was performed as previously described [118] . The SPT cDNA was cloned in the pENTR/D TOPO vector ( Invitrogen ) , verified by sequencing , and introduced into the LexA DNA-binding domain vector ( pBTM116c-D9 ) by Gateway LR recombination . The ARRs fused to the Gal4 activation domain in pACT2 ( Clontech , Mountain View , CA , USA ) are previously described [119] . Yeast transformations were performed as previously described [120] using the L40ccaU strain ( MATa his3D200 trp1-901 leu2-3 112 LYS:: ( lexAop ) 4-HIS3 URA3:: ( lexAop ) 8-lacZ , ADE2:: ( lexAop ) 8- URA3 GAL4 gal80 can1 cyh2 ) [121] . The assay was done on SD-Gluc medium lacking Leucine , Tryptophan , and Histidine complemented with 3 mM 3-Amino-1 , 2 , 4-triazole . Interactions were scored after growing yeast at 25°C for 5 days . SPT and ARR1 coding sequences in Gateway entry vectors were recombined with pYFC43 and pYFN43 to generate C- and N-terminal YFP fusion constructs , respectively [122] . BiFC in young N . tabacum leaves was previously described [117] . YFP signal was assayed 3 days after infiltration using a confocal microscope . The topology of the Gene Regulatory Network ( GRN ) model was visualized using the computational and graphical platform BioTapestry [71] . Regulatory relations among genes are based on the experimental evidence ( this work ) . The two coherent feed-forward subcircuits are formed by starting with SPT regulating ARR1 and both regulate TAA1 , and also both regulate PIN3 . TAA1 also has other positive regulators , but for simplicity only SPT and ARR1 are depicted . ARR1 is depicted , but other type-B ARR genes are part of the network . All regulatory interactions were fed to the computational tool GeNeTool [72] to create the Boolean vector equations and for modeling of the GRN . The two coherent feed-forward subcircuits are both configured as an AND-gate . Boolean output for gene active = 1 and for gene inactive = 0 . Alterations of the topology of the GRN model after perturbations were calculated by GeNeTool and visualized with BioTapestry . | Most of our food comes from fruits and seeds , derived from a fertilized gynoecium . Therefore , understanding the mechanisms that control gynoecium development is of crucial importance . The Arabidopsis gynoecium has two fused carpels , with a medial domain between them , and a lateral domain consisting of the carpel walls . All the tissues that are involved in reproduction arise from the carpel margin meristem in the medial domain . The phytohormone cytokinin provides meristematic activity to cells , and interestingly , in a young gynoecium , the medial , but not the lateral , domain presents strong cytokinin signaling . One question that comes to mind is how this pattern is defined . This work demonstrates that the transcription factor SPATULA enables cytokinin signaling at the medial domain , while cytokinin signaling repressors are present in the lateral domain . A second question is whether and how cytokinin in the medial domain communicates with auxin , an important phytohormone for tissue differentiation . We found that cytokinin and SPT activate auxin biosynthesis and transport genes . The integration of these findings gives the first gene regulatory network acting during early gynoecium development . This network is most likely conserved in flowering plants , and can provide insights of molecular processes that are key for food production . | [
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] | 2017 | The bHLH transcription factor SPATULA enables cytokinin signaling, and both activate auxin biosynthesis and transport genes at the medial domain of the gynoecium |
Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions . However the complexity of these large metabolic networks often hinders their utility in various practical applications . Although reduced models are commonly used for modeling and in integrating experimental data , they are often inconsistent across different studies and laboratories due to different criteria and detail , which can compromise transferability of the findings and also integration of experimental data from different groups . In this study , we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest . The method minimizes the loss of information using an approach that combines graph-based search and optimization methods . The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields , flux and concentration variability and gene essentiality . The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models .
Stoichiometric models have been used to study the physiology of organisms since 1980s [1–3] , and with the accumulation of knowledge , and progressing techniques for genome annotation , these models have evolved into Genome Scale Metabolic Reconstructions ( GEMs ) , which encapsulate all known biochemistry that takes place in the organisms by gene to protein to reaction ( GPRs ) associations [4] . Since the first Genome Scale models developed [5 , 6] , the number of annotated genomes and the corresponding genome scale metabolic reconstruction has increased tremendously [7–9] . With increasing popularity of GEMs , different techniques to analyse these networks have been proposed [10 , 11] . Flux Balance Analysis ( FBA ) , a constraint-based method ( CBM ) enables many forms of analysis based solely on knowledge of network stoichiometry and incorporation of various constraints , such as environmental , physicochemical constraints [12] . FBA has been further expanded by other methods such as Thermodynamics-based Flux Analysis ( TFA ) [13–16] and others [17 , 18] for the integration of available thermodynamics data with GEMs . TFA utilizes information about the properties of reaction thermodynamics and integrates them into FBA . Such properties now can be estimated by Group Contribution Method [19–21] and high-level Quantum Chemical Calculations[22] . Metabolic networks are valuable scaffolds that can also be used to integrate other types of data such as metabolic [23 , 24] , regulatory and signalling [25–27] , that can elucidate the actual state of the metabolic network in vivo . However , both FBA , TFA and other steady-state approaches cannot capture the dynamic response of metabolic networks , which requires integration of detailed enzyme kinetics and regulations [28] . Hatzimanikatis and colleagues have developed a framework that utilizes FBA , TFA and generates kinetic models without sacrificing stoichiometric , thermodynamic and physiological constraints [29–31] . Recently , another approach has been proposed to integrate kinetics into large-scale metabolic networks[32] . As the quality and the size of the models increase with better annotation , the complexity of the mathematical representations of the models also increases . Hatzimanikatis and colleagues [33] observed that majority of the studies and applications using metabolic models have still revolved around the central metabolism and around “reduced” models instead of genome-scale models , indicating that the full potential of GEMs remains largely untapped [34–38] . These reduced models have the advantage of having small sizes as they are built with a top-down manner , but they lack the quality of bottom-up built models since they have been reduced ad hoc , with different criteria and aims , which have not been consistently and explicitly justified [39–41] . An algorithmic approach called NetworkReducer [42] has been recently proposed following a top-down reduction procedure . The main purpose of this approach is to preserve selected so-called “protected” metabolites and reactions , while iteratively deleting the reactions that do not prevent the activity of the selected reactions . This algorithm has been further extended [43] to compute the minimum size of subnetworks that still preserve the selected reactions . In this study , we have developed redGEM , a systematic model reduction framework for constructing core metabolic models from GEMs . Herewith , we propose an approach that focuses on selected metabolic subsystems and yet retains the linkages and knowledge captured in genome-scale reconstructions . redGEM follows a bottom-up approach that allows us to handle the complexity and to yield comprehensive insights in connecting the metabolic model to actual cellular physiology . redGEM can be tailored to generate minimal models with conserved functions . However , our approach is not strictly focused only on the reduction of the stoichiometry for the generation of highly condensed network , but aims also to preserve the constitutive characteristics of metabolic networks . In redGEM , we use as inputs: ( i ) a GEM , ( ii ) metabolic subsystems that are of interest for a physiology under study; ( iii ) information about utilized substrates and medium components; and ( iv ) available physiological data ( Fig 1 ) . After a series of computational procedures , we generate a reduced model that is consistent with the original GEM in terms of flux profiles , essential genes and reactions , thermodynamically feasible ranges of metabolites and ranges of Gibbs free energy of reactions . We applied redGEM on the latest GEM of E . coli iJO1366 [44] under both aerobic and anaerobic conditions with glucose and other possible carbon sources and generated a family of reduced E . coli iJO1366 models .
The wild-type biomass reaction of the iJO1366 model contains 102 biomass building blocks ( BBBs ) . The size and the complexity of the composition makes it necessary to develop techniques to keep the information stored in GEM for the biosynthesis , but yet reduce the size of the network significantly . Methods , such as graph-search algorithms can be used for identification of biosynthetic routes between two metabolites in metabolic networks [45 , 46] . However , these graph theory based approaches cannot be used for our purposes due to two main issues and limitations: i ) they do not make use nor obey mass conservation; hence the pathways they generate are not guaranteed to be able to carry flux in metabolic network or to be elementally balanced , ii ) and they cannot manage pathways that are not linear , such as branched pathways . To overcome these limitations , we used lumpGEM [47] , an in-built tool , which identifies subnetworks that can produce biomass building blocks starting from precursor metabolites . These precursors are provided by redGEM through the systematically generated core network based on degree of connection parameter , D . Each subnetwork is then transformed into a lumped reaction and inserted in the reduced model . lumpGEM forces mass conservation constraints during optimization to identify subnetworks , thus preventing the generation of lumped reactions , which cannot carry flux in the metabolic networks . As an example , for D = 1 , by minimizing the number of non-core reactions In GEM , lumpGEM generated a 17 reactions subnetwork to synthesize histidine from core carbon metabolites ( Fig 3 ) . Histidine is synthesized from ribose-5-phosphate , a precursor from pentose phosphate pathway . The linear pathway from this core metabolite to histidine is composed of 10 steps . However , due to the mass balance constraint , two metabolites , 1- ( 5-Phosphoribosyl ) -5-amino-4-imidazolecarboxamide and L-Glutamine cannot be balanced in a network that is composed of core reactions and the linear pathway from ribose-5-phophate to histidine . These metabolites are balanced in the network by other non-core reactions . Hence , the generated sets of reactions are not linear routes from precursor metabolites to biomass building blocks , but branched , balanced subnetworks ( for formulation of lumpGEM , see Material and Methods ) . Using lumpGEM , we replicated all the biosynthetic pathways in databases such as EcoCyc [48] , either as a part of subnetworks or the exact pathway . In addition , we identified subnetworks that can qualify as alternative biosynthetic pathways . E . coli is well-known to be robust against deletions by having many duplicate genes and alternate pathways[49] . Some of these routes may not be active due to energetics or regulatory constraints but using lumpGEM we can map these possible alternate pathways completely and also derive different biosynthetic lumped reactions . The introduction of such lumped biosynthetic reactions simplifies the core models considerably and allows the use of these models in important computational analysis methods such as dynamic FBA [50] extreme pathway analysis [51 , 52] and elementary flux modes [53 , 54] , as well as for TFA formulations and kinetic modeling . For D = 1 core network , lumpGEM generated 1216 subnetworks and 254 unique lumped reactions for 79 biomass building blocks in total for aerobic and anaerobic case . The remaining BBBs of the total 102 can be produced within the D = 1 core network . For some biomass building blocks , it is possible that all the alternatives for Smin ( the minimal subnetwork size ) subnetworks generated under aerobic conditions are using molecular oxygen , thus cannot carry flux under anaerobic conditions . This necessitates the generation of lumped reactions without any oxygen in the media . For Smin , lumpGEM generated only 4 new lumped reactions for anaerobic case , for 3 metabolites , namely , heme O , lipoate ( protein bound ) and protoheme . All the other lumped reactions generated for anaerobic case are a subset of the 250 lumped reactions ( S2 Table ) for aerobic conditions . In the subsequent studies , we used all lumped reactions in order to allow for studies under different oxygen limitations without changing the model structure . The core model can be found in the supplementary material ( S1 File ) .
Reduced models have been used to understand and investigate cellular physiology for many years . Before the emergence of genome scale models ( GEMs ) , different groups with different aims built reduced models for their studies with a top-down approach . Conversely , GEMs provide the platform to understand all the metabolic capabilities of organisms , since GEMs encapsulate all the known biochemisty that occurs in cells . However the complexity of GEMs make their use impractical for different applications , such as kinetic modeling or elementary flux modes ( EFMs ) . The need to focus on certain parts of these networks without sacrificing detailed stoichiometric information stored in GEMs makes it crucial to develop representative reduced models that can mimic the GEM characteristics . Within this scope , we developed redGEM , an algorithm that uses as inputs genome-scale metabolic model and defined metabolic subsystems , and it derives a set of reduced core metabolic models . These family of core models include all the fluxes across the subsystems of interest that are identified through network expansion , thus capturing the detailed stocihiometric information stored in their bottom-up built parent GEM model . Following the identification of the core , redGEM uses lumpGEM , an algortihm that captures the minimal sized subnetworks that are capable of producing target compounds from a set of defined core metabolites . lumpGEM expands these core networks to the biomass building blocks through elementally balanced lumped reactions . Moreover , redGEM employs lumpGEM to include alternative lumped reactions for the synthesis of biomass building blocks , thus accounting for alternative sytnhesis routes that can be active under different physiological conditions . redGEM builds reduced models rGEMs that are consistent with their parent GEM model in terms of flux and concentration variability and essential genes/reactions . These reduced models can be used in many different areas , such as kinetic modeling , MFA studies , Elementary Flux Modes ( EFM ) and FBA/TFA . redGEM algorithm is applicable on any compartmentalized or non-compartmentalized genome scale model , since its procedure does not depend on any specific organism . As a demonstration , we have applied the redGEM algorithm on different organisms , namely P . putida , S . cerevisiae , Chinese Hamster Ovary cell ( CHO ) and human metabolism . For instance , redGEM algorithm has generated core networks of sizes between 168 metabolites/164 reactions to 360 metabolites/414 reactions for iMM904 [58] GEM reconstucted for S . cerevsiae with degree of connection parameter D varied from 1 to 6 . The generated reduced model irMM904 with D = 1 has the same biomass yield with the parent model GEM as 0 . 29/hr under 10 mmol/gDWhr glucose uptake . Similar to E . coli case , flux and concentration variability , and gene essentiality characteristics of the rGEM are in agreement with the GEM counterparts ( Ataman et al . , manuscript in preparation ) . Moreover , reduced models are promising platforms for the comparison of central carbon ( or any other ) metabolism of different species . This approach can help us to better investigate the metabolic capabilities and limitations of organisms and to identify the sources of physiological differences across different species .
In redGEM , we introduce and use the following definitions: We can also generate the core network from the chosen subsystems using the minimum distance between the chosen subsystems and report the connecting reactions and metabolites . In this case , the degree of connection D is the minimum distance between Si and Sj . redGEM uses the following inputs and parameters: The central workflow of redGEM involves 4 steps: The core carbon network is defined as all the reactions and metabolites in MS , MijD and MiiD ( all i , j pairs ) , RSi , RijD , RiiD ( all i , j pairs ) , RT ( reactions that only cofactor pairs , small metabolites and inorganics participate ) . We used the lumpGEM algorithm to generate pathways for all biomass building blocks ( BBB ) as they are defined in GEM . lumpGEM identifies the smallest subnetwork ( Smin ) that are stoichiometrically balanced and capable of synthesizing a biomass building block from defined core metabolites . Moreover , it identifies alternative subnetworks for the synthesis of the same biomass building block . Finally , lumpGEM generates overall lumped reactions , in where the cost of core metabolites , cofactors , small metabolites and inorganics are determined for the biosynthesis . redGEM defined the core network by the algorithm above , and then we generated all minimum sized subnetwork ( Smin ) for each BBB . Then lumpGEM calculated the unique lumped reactions for all the BBBs , and we used these lumped reactions for further validation and other analysis . lumpGEM takes the following steps to build elementally balanced lumped reactions for the biomass building blocks . In the workflow , lumpGEM Maximize ∑i#ofRnCzrxn , i such that: S . v=0 vBBB , j≥nj , GEM . μmax where , To identify alternative Smin subnetworks for a BBB , lumpGEM further constrains the GEM with the following integer cuts constraint after generating each subnetwork with an iterative manner[59] . The reactions that belong to each subnetwork are denoted as RSmin ∑k#ofRSminzRSmin , k>0 We validate the consistency between rGEM and GEM performing the following consistency checks by comparing: While these are the basic consistency tests , one could define additional checks , which can be specific to the organism and problem under study . We recommend that in all cases one should perform the checks using FBA and TFA , i . e . with and without thermodynamics constraints . The first release of the redGEM toolbox is available upon request to the corresponding author . | Reduced models are used commonly to understand the metabolism of organisms and to integrate experimental data for many different studies such as physiology , fluxomics and metabolomics . Without consistent or clear criteria on how these reduced models are actually developed , it is difficult to ensure that they reflect the detailed knowledge that is kept in genome scale metabolic network models ( GEMs ) . The redGEM algorithm presented here allows us to systematically develop consistently reduced metabolic models from their genome-scale counterparts . We applied redGEM for the construction of a core model for E . coli central carbon metabolism . We constructed the core model irJO1366 based on the latest genome-scale E . coli metabolic reconstruction ( iJO1366 ) . irJO1366 contains the central carbon pathways and other immediate pathways that must be connected to them for consistency with the iJO1366 . irJO1366 can be used to understand metabolism of the organism and also to provide guidance for metabolic engineering purposes . The algorithm is also designed to be modular so that heterologous reactions or pathways can be appended to the core model akin to a “plug-and-play” , synthetic biology approach . The algorithm is applicable to any compartmentalized or non-compartmentalized GEM . | [
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] | 2017 | redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models |
Metazoans display remarkable conservation of gene families , including growth factors , yet somehow these genes are used in different ways to generate tremendous morphological diversity . While variations in the magnitude and spatio-temporal aspects of signaling by a growth factor can generate different body patterns , how these signaling variations are organized and coordinated during development is unclear . Basic body plans are organized by the end of gastrulation and are refined as limbs , organs , and nervous systems co-develop . Despite their proximity to developing tissues , neurons are primarily thought to act after development , on behavior . Here , we show that in Caenorhabditis elegans , the axonal projections of neurons regulate tissue progenitor responses to Wnts so that certain organs develop with the correct morphology at the right axial positions . We find that foreshortening of the posteriorly directed axons of the two canal-associated neurons ( CANs ) disrupts mid-body vulval morphology , and produces ectopic vulval tissue in the posterior epidermis , in a Wnt-dependent manner . We also provide evidence that suggests that the posterior CAN axons modulate the location and strength of Wnt signaling along the anterior–posterior axis by employing a Ror family Wnt receptor to bind posteriorly derived Wnts , and hence , refine their distributions . Surprisingly , despite high levels of Ror expression in many other cells , these cells cannot substitute for the CAN axons in patterning the epidermis , nor can cells expressing a secreted Wnt inhibitor , SFRP-1 . Thus , unmyelinated axon tracts are critical for patterning the C . elegans body . Our findings suggest that the evolution of neurons not only improved metazoans by increasing behavioral complexity , but also by expanding the diversity of developmental patterns generated by growth factors such as Wnts .
Metazoan body plans display great morphological diversity in their overall organization and detailed patterns . Yet remarkably , these plans are generated from a small number of conserved growth factors that are reused many times during development to refine the plans in distinct ways . Thus , determining how a limited number of growth factors create different body patterns is key to understanding this diversity . It is known that variations in the magnitude and spatio-temporal aspects of signaling by growth factors and their effectors generate different cellular responses and body patterns [1]–[3] . However , the mechanisms by which a developing animal organizes and coordinates the correct amplitudes of signaling at the right times and places are not well understood . Wnts comprise one of the oldest families of growth factors , and have conserved functions in organizing and refining body plans . In many metazoan embryos , Wnts are initially expressed from one pole , with their diffusion generating polarity and early patterning of the primary body axis [4] . During and after gastrulation , Wnt gradients , often derived from the posterior body , continue to establish basic identities in the body plan , and then refine these fates to create specific tissue and organ patterns [4]–[6] . As animals develop , it becomes increasingly challenging to spatially organize Wnt activity into the appropriate high and low signaling domains throughout the body . Furthermore , this organization must be coordinated between the multiple Wnts and other growth factors that pattern tissues and organs . Perhaps as a reflection of these challenges , numerous secreted and membrane-associated inhibitors have been identified that help modulate Wnt gradient activity [7]–[10] . However , our knowledge of what a Wnt gradient must look like to pattern a specific limb or organ is very limited , as is our understanding of the distinct roles of the different antagonists . In Caenorhabditis elegans , the generation of a vulva in the middle of the anterior–posterior axis has become a paradigm for understanding how Wnt and EGF family growth factors generate specific patterns at precise locations ( reviewed in [11] ) ( Figure 1A ) . Vulval organogenesis begins with the mid-body generation of six vulval progenitors from 11 blast cells . These progenitors ( P3 . p–P8 . p ) are specified by two posteriorly derived Wnt gradients ( EGL-20 and CWN-1 [orange and green , respectively , in Figure 1] ) [7] , [12]–[15] , with EGL-20 also polarizing some of the progenitors to face towards the posterior ( e . g . , P5 . p and P7 . p ) [16] ( Figure 1B ) . Later , mid-body-produced Wnts ( LIN-44 and MOM-2 [blue in Figure 1] ) reverse P7 . p polarity so that P5 . p and P7 . p face each other and subsequently divide with mirror image symmetry ( Figure 1C and 1D ) [16] , [17] . With the help of the posterior Wnts ( EGL-20 and CWN-1 ) , centrally produced EGF ( purple in Figure 1 ) instructs P6 . p to adopt a 1° fate , divide three times , and form the vulval lumen that attaches to the uterus ( Figure 1C and 1D ) [7] , [11] , [18]–[20] . In parallel , P6 . p activates Notch signaling in adjacent P5 . p and P7 . p to induce 2° fates , which , after three rounds of division , create the symmetrical sides of the vulva that attach the organ to the epidermis ( Figure 1C and 1D ) . Insufficient signaling alters vulval patterning and reduces the amount of vulval tissue [11] , [21] . Excessive signaling also alters vulval patterning and , if it occurs at certain axial positions , generates ectopic , non-functional vulvae , which can interfere with normal positioning of muscles and neurons that promote egg-laying [19] , [21]–[24] . To understand how growth factors such as Wnts generate specific fates at precise positions , we looked for mutations that affected placement of vulval tissue along the anterior–posterior axis . We were intrigued by mutations in the vab-8 gene , which affect vulval development through an unknown mechanism and are primarily known for disrupting the migration and axon outgrowth of a few neurons [25] , [26] . While nervous systems co-develop with tissues and organs [6] , with only rare exceptions , their importance in refining body plans has been unexplored . In flies , through unknown mechanisms , motor neurons contribute to abdominal and flight muscle patterning [27] , [28] , and in mammals , by secreting VEGF , sensory nerves direct arterial patterning in skin [29] . In addition , we previously discovered that in C . elegans , motor neuron excitation stimulates vulval fate signaling [30] . Thus , we were interested in exploring the possibility that neurons might refine patterning by widely used growth factors such as Wnts . Here we show that C . elegans has evolved a neuronal-based mechanism to refine the amplitude and spatial signaling properties of the posterior-derived Wnt gradients that pattern the epidermis . Two canal-associated neurons ( CANs ) , whose axons span the anterior–posterior axis , ensure that a vulva is generated with the correct morphology and only at the mid-body . When outgrowth of the posterior CAN axon is severely shortened , Wnt signaling is increased along the anterior–posterior axis , especially in the posterior body . This deregulated signaling alters the symmetry of the normal mid-body vulva , and causes ectopic vulval tissue to form in the posterior epidermis . Finally , we provide evidence that although the Ror/CAM-1 Wnt receptor is widely expressed , its expression in the CAN axons is part of a unique Wnt-sequestration mechanism that ultimately directs the locations and strength of Wnt signaling necessary for proper epidermal patterning .
vab-8 encodes a long isoform , VAB-8L , and several short isoforms collectively called VAB-8S [31] . These proteins act in a few neurons to promote their posterior-directed migration and axon outgrowth [31] , [32] . Both isoforms possess C-terminal coiled-coil domains , with VAB-8L having an additional N-terminal kinesin domain . In wild-type animals , only the three central vulval progenitors ( P5 . p–P7 . p ) adopt vulval fates . However , in vab-8 ( gm99 ) and vab-8 ( gm138 ) mutants , which lack both isoforms and have anteriorly displaced neurons , the posterior P8 . p progenitor also acquired a vulval fate ( Figure 2A ) . In the anterior epidermis of wild-type animals , Wnt signaling is limiting in P3 . p because of its distance from the posterior Wnts; therefore , it becomes a progenitor only 50% of the time ( Figure 1B and 2B ) . vab-8 mutations did not cause ectopic vulval fates in anterior P3 . p or P4 . p ( n = 170 ) , but they increased the frequency with which P3 . p became a vulval progenitor ( Figure 2B ) . In the mid-body , at low frequency , vab-8 mutations disrupted the mirror image symmetry of the vulva . Initially , during the generation of the vulval progenitors in wild-type animals , posterior-derived EGL-20/Wnt causes P5 . p and P7 . p to polarize and face towards the posterior ( Figure 1B ) [16] . Later , centrally produced MOM-2 and LIN-44 Wnts maintain P5 . p polarity towards the posterior , but cause P7 . p to reorient and face towards the anterior ( Figures 1C and 2C ) [16] , [17] . vab-8 mutations caused a “posterior-reversed vulval lineage” ( P-Rvl ) phenotype , in which P7 . p remained polarized towards the posterior EGL-20/Wnt signal , so that its subsequent cell divisions caused a second vulval invagination ( Figure 2D ) . To study how VAB-8-regulated cells affect epidermal development , we used mutant backgrounds permitting sensitive quantification of the effects of vab-8 mutations . A loss-of-function egfr/let-23 ( lf ) mutation severely diminishes the 1° fate response in P6 . p such that fewer than the normal three progenitors adopt vulval fates , causing an “underinduced” or “vulvaless” phenotype . This mutant phenotype can be suppressed by activating the EGFR pathway downstream of the receptor ( e . g . , by a loss-of-function mutation in the Ras/LET-60 inhibitor gap-1 [33] ) or by mutations that increase signaling by the parallel Wnt pathway ( e . g . , a loss-of-function mutation in the Wnt pathway inhibitor axin/pry-1 [19] ) ( Table 1 ) . By counting total vulval progeny , the extent to which any progenitor adopts a vulval fate can be quantified . vab-8 ( gm99 ) and vab-8 ( gm138 ) mutations suppressed the underinduced phenotypes of loss-of-function egf/lin-3 and egfr/let-23 alleles and of a dominant-negative ras/let-60 ( dn ) mutation ( Table 1 ) . Since increasing EGFR activity above wild-type levels does not suppress the ras/let-60 ( dn ) mutation [30] , VAB-8-regulated cells must modulate a signal that is distinct from EGF . Based on the posterior bias in the induction of ectopic vulval fates , the P-Rvl phenotype ( which can result from overactive EGL-20 signaling ) , and the increased P3 . p progenitor frequency ( which is normally regulated by EGL-20 and CWN-1 ) , this signal ( s ) could include one of the posteriorly enriched Wnts such as EGL-20 or CWN-1 . In contrast to mutations that disrupt both VAB-8 isoforms , the vab-8 ( ev411 ) mutation , which only affects VAB-8L [31] , did not cause ectopic vulval fates in P8 . p , did not increase the frequency of P3 . p becoming a vulval progenitor , and did not suppress the underinduced phenotypes of egf/lin-3 or egfr/let-23 mutants ( Figure 2A and 2B; Table 1 ) . Thus , VAB-8S is sufficient to inhibit vulval fates . To identify cells whose position might influence epidermal development , we examined promoter activity in the smallest genomic fragment previously shown to encode functional VAB-8S activity [31] . This promoter drove reporter expression in eight head neurons and the pair of CANs ( CANL and CANR ) ( Figure 3A ) . To determine whether any of these neurons regulate epidermal development , we used this promoter to restore VAB-8S ( Pvab-8s::vab-8s ) to all of these cells in egfr/let-23 ( lf ) ; vab-8 ( gm138 ) double mutants . VAB-8S expression in these cells fully restored the egfr/let-23 ( lf ) vulvaless phenotype ( Table S1 ) , suggesting that the positioning of one or more of these cells inhibits vulval fate signaling in P6 . p . We then focused on the CANs , since ( 1 ) of the ten neurons , only the CAN axons span the entire anterior–posterior axis , ( 2 ) vab-8 null mutations severely displace CAN cell bodies and shorten posterior axons [26] , and ( 3 ) the vab-8 ( ev411 ) mutation , which does not affect epidermal development , does not affect CAN cell body position , and only weakly affects CAN axon outgrowth [26] . Since VAB-8 acts cell autonomously in the CANs to promote their posterior migration and axon outgrowth [31] , [32] , we used a previously described CAN-specific enhancer [34] to create an expression vector to specifically restore VAB-8S and proper positioning only to the CANs . This vector drove YFP expression only in the CANs , which was also the only site of co-localization with vab-8s-driven DsRed2 ( Figure 3B and 3C ) , confirming the cell-specificity of this vector . When expressed from the CAN-specific promoter ( PCAN::vab-8s ) , but not the control minimal pes-10 promoter , VAB-8S also restored full inhibition to vulval fate signaling in P6 . p in egfr/let-23 ( lf ) ; vab-8 ( gm138 ) double mutants ( Figure 3D; Table S1 ) . This transgene also fully suppressed the other vab-8 ( gm138 ) epidermal phenotypes , including the ectopic vulval fates at P8 . p ( Figure 3E ) , the increased frequency of P3 . p becoming a vulval progenitor ( Figure 3F ) , and the P-Rvl phenotype ( 0% , n = 64 ) . Thus , VAB-8 acts in the CANs to regulate epidermal development . Since the CANs have been implicated in the control of osmotic balance [35] , [36] , we ruled out the CANs indirectly modulating epidermal development through regulation of animal physiology . Experiments involving direct osmotic stress , disruption of the osmoregulatory system , and mutation of the osmotic-stress-responsive p38 mapk/pmk-1 did not support this model ( Text S1; Tables S2 and S3; Figure S1 ) . Since our genetic and phenotypic analyses suggested the CANs inhibit Wnt activity , we directly examined whether CAN displacement might increase Wnt activity in epidermal progenitors [16] . An mCherry-based Wnt reporter has been described that specifically reflects Wnt signaling in epidermal progenitors beginning after their first division ( Pn . px stage ) and extending through their second division ( Pn . pxx ) ( Figure 1D ) [16] . In axin/pry-1 Wnt inhibitor mutants , both the frequency and intensity of reporter activity was increased in these cells ( Figure S2A–S2C ) . Although vab-8 ( gm99 ) and vab-8 ( gm138 ) mutations did not increase Wnt reporter intensity as dramatically as an axin/pry-1 mutation , they did cause more animals to show reporter activity in P3 . px , P4 . px , P6 . px , and P8 . x progeny ( Figure 4A and 4B ) . These data suggest that the CANs dampen Wnt signaling along much of the anterior–posterior axis , and that deregulation of this signaling might account for the epidermal patterning defects observed in vab-8 mutants . In wild-type animals , mutation or RNA interference ( RNAi ) of individual Wnt genes did not reduce the normal frequency of vulval fates ( Table 2 ) . However , mutation of egl-20/wnt or RNAi of cwn-1/wnt or mom-2/wnt abrogated the ability of a vab-8 ( lf ) mutation to suppress the underinduced phenotype of egfr/let-23 ( lf ) mutants , suggesting one or more of these Wnts is a target of the CANs ( Table 2 ) . Sensitivity to the levels of these Wnts was specific , as mutations in the cwn-2 and lin-44 Wnt genes did not cause comparable effects ( Tables 2 and S4; Text S1 ) . Individual mutations in either egl-20/wnt or cwn-1/wnt also strongly reduced ectopic vulval fates at P8 . p , suggesting that one or more of these Wnts are targeted by the CANs ( Figure 2A ) . During vulval development , egl-20/wnt expression was detected in four rectal cells ( K , F , B , and U ) just posterior to P11 . p ( Figure 4C ) [15] . cwn-1/wnt was strongly expressed in posterior body-wall muscle and a subset of posterior motor neurons ( Figure 4D ) [13] , [37] . These cells were posterior to the P8 . px progeny ( Figure 4D ) , but weakly expressing neurons and muscle extended anteriorly to P2 . p . Strikingly , the CANs also expressed CWN-1/Wnt ( Figure S3 ) [38] , and their posterior axons traveled parallel to the epidermal progenitors and the other CWN-1-producing cells , and terminated at the sources of EGL-20/Wnt ( Figure 4E–4H ) . Thus , the CANs appear well-positioned to regulate epidermal progenitor responses to these Wnts . To further investigate the possibility that the CANs target EGL-20/Wnt , we examined hermaphrodite-specific neuron ( HSN ) migration in vab-8 mutants . The two HSNs are born in the posterior of the embryo , close to the EGL-20/Wnt-producing cells , and migrate to the mid-body position of the prospective vulva [39] ( Figure S4A and S4B ) . egl-20/wnt mutations prevent this migration , while mutations in other Wnt genes have little effect [40] , [41] . Conversely , increased EGL-20/Wnt activity causes anterior overmigration of the HSNs [23] , implying that an EGL-20 gradient drives correct mid-body placement of the HSNs . Consistent with prior reports [26] , [35] , [42] , we found that in vab-8 mutants HSNs overmigrated anteriorly ( Figure S4C ) , suggesting EGL-20/Wnt activity is increased along the anterior–posterior axis . This phenotype was enhanced by increasing EGL-20/Wnt levels with an integrated transgenic array that carries additional copies of the genomic egl-20 locus ( Figure S4D and S4E ) [23] , [40] . Since laser ablation of the CAN precursors also causes HSN overmigration [35] , the effect of vab-8 mutation on HSN migration is likely mediated by the CANs . The integrated egl-20/wnt transgenic array also cooperated with the vab-8 ( gm138 ) mutation to increase the incidence of the P-Rvl phenotype ( Figure 2C and 2D ) , and to cause an embryonic posterior P11 to P12 blast fate transformation , which can occur when either EGF [43] or Wnt signaling activity is abnormally high ( Figure S4F and S4G ) . The P11 fate transformation in vab-8 mutants is also at least partly mediated by the CANs , since CAN-specific restoration of VAB-8S significantly rescued this phenotype ( Figure S4G ) . Since elevated EGL-20/Wnt activity specifically causes a P-Rvl phenotype , and increased activity of either EGL-20 or CWN-1/Wnt could , in principle , account for the increased conversion of P3 . p into a vulval progenitor [7] , [14] , [16] , we asked whether elevation of one of these Wnts could also account for how vab-8 mutations suppress the underinduced phenotype of an egfr/let-23 ( lf ) mutation . When expressed from a heat-shock-regulated transgene ( Phs::egl-20 ) , EGL-20/Wnt suppressed the egfr/let-23 ( lf ) mutation comparably to vab-8 mutations ( Table 2 ) . However , this transgenic array did not induce ectopic vulval fates at P8 . p in wild-type animals ( n = 52 ) . This may be due to a lack of sufficiently high EGL-20/Wnt expression in the posterior epidermis and/or a need to increase signaling by additional Wnts such as CWN-1 . In agreement with the idea that Wnt signaling sufficient for the induction of ectopic vulval fates is still limiting after heat shock of just EGL-20 , mutation of the intracellular Wnt inhibitor axin/pry-1 caused very high Wnt reporter activity in vulval progenitor progeny , fully suppressed the underinduced phenotype of egfr/let-23 mutants , and caused ectopic vulval fates ( Table 1; Figure S2 ) [19] . This high level of Wnt signaling also caused P9 . p and P10 . p to become vulval progenitors , which , along with P8 . p , formed most of the ectopic vulval tissue ( Figure S2D and S2E ) . Together , our combined data suggest that EGL-20/Wnt is one CAN target , and that additional Wnts , including CWN-1 , may also be inhibited by the CANs . Anterior displacement of CAN cell bodies and foreshortening of the posterior axons caused a gradual post-embryonic withering of the posterior half of the animal [42] , which placed some epidermal progenitors closer to the anus , where EGL-20/Wnt is produced ( Figure S5A ) , and reduced posterior body volume ( Figure S6A–S6C ) . We ruled out these physical changes as being the major mechanism by which the CANs regulate epidermal development ( see Text S1 ) . In vab-8 mutants , Wnt reporter activity in vulval progenitors did not strictly correlate with their distance from the anus ( Figure S5B ) ; dpy-17 ( lf ) ; dpy-20 ( lf ) cuticular mutants had normal CAN positioning and axon outgrowth , but even shorter epidermal progenitor distances to the anus and smaller posterior body volumes than vab-8 mutants ( including vab-8 mutants with ectopic vulval fates ) , yet did not show epidermal phenotypes ( Figures S5C–S5F , S6D , and S6E; Table S3 ) ; vab-8 mutations and ablation of the CAN precursors promoted embryonic EGL-20/Wnt-dependent HSN overmigration prior to any tail withering ( Figure S4B–S4E ) [26] , [35] , [42]; and the CANs also prevented overexpressed EGL-20/Wnt from causing embryonic P11 to P12 fate transformations , before tail withering occurred ( Figure S4G ) . While in wild-type animals the median CAN cell body position along the anterior–posterior axis was near the P5 . px progeny , and most posterior CAN axons terminated near P11 . p and the EGL-20/Wnt-producing rectal cells ( Figures 4E , 4G , 5A , and 5B ) , in vab-8 ( gm138 ) mutants , both the CAN cell bodies and posterior axon termini were severely displaced anteriorly ( Figure 5A–5C ) . Given the normal proximity of the posterior CAN axon terminus to the EGL-20/Wnt-producing cells , the axons may secrete a short-range signal that inhibits EGL-20 production . However , severe mutation of vab-8 did not increase egl-20/wnt RNA levels ( Figure S7 ) , and the vab-8 ( ev411 ) mutation , which mildly shifted the posterior CAN axon terminus away from the EGL-20/Wnt-producing cells ( Figure 5B and 5C ) , did not cause epidermal phenotypes ( Figure 2A and 2B; Table 1 ) . We next examined how a ceh-10 homeobox gene mutation that has intermediate effects on posterior CAN axon outgrowth affects epidermal development [35] . Although in ceh-10 mutants the median CAN cell body position was anteriorly shifted to the head , similarly as in vab-8 ( gm138 ) mutants ( Figure 5A ) [35] , the posterior CAN axon extended further than in vab-8 mutants , to a median position of P4 . px rather than P1 . p ( Figure 5B ) . Mutation of ceh-10 did not increase the frequency of P3 . p becoming a vulval progenitor ( Figure 2B ) , and did not suppress the P6 . p-based underinduced phenotypes of egf/lin-3 or egfr/let-23 mutations ( Table 1 ) . However , ceh-10 mutants had a low incidence of ectopic vulval fates in P8 . p ( Figure 5D ) . These data suggest that a rarer aspect of the ceh-10 mutant phenotype , such as severe shortening of the posterior CAN axon rather than displacement of the cell body , may promote vulval fate signaling along the anterior–posterior axis . To determine the relationship between the posterior CAN axon terminus and vulval fate signaling , we examined both parameters in individual ceh-10 mutants . In animals where the furthest posterior CAN axon terminus of the pair of neurons was at or posterior to P8 . p progeny , P8 . p did not adopt a vulval fate ( Figure 5E , e . g . , animal 52 ) . By contrast , in animals where the furthest posterior CAN axon terminus was anterior to P8 . p progeny , P8 . p acquired ectopic vulval fates , with some animals also showing a P-Rvl phenotype in P7 . p ( Figure 5E , e . g . , animal 79 ) . We also identified rare ceh-10 mutants that demonstrated a separation between tail withering and increased vulval fate signaling . In animals 79 , 74 , and 45 , the Wnt-responding P8 . px progenitors that gave rise to ectopic vulvae were further away from the EGL-20/Wnt sources ( >135 µm ) than the non-Wnt-responding P8 . px progenitors in animals not having ectopic vulval induction ( 135 µm ) ( Figure S8 ) . However , in these three animals , the posterior CAN axon never extended beyond P3 . px , demonstrating that productive vulval fate signaling is always correlated with severe posterior axon shortening . To determine whether a similar relationship also exists between the posterior CAN axon terminus and the signaling involved in converting anterior P3 . p into a vulval progenitor , we also examined these two parameters in ceh-10 mutants . Analogous to the study at P8 . p , we found that when the longest of the pair of posterior CAN axons reached only the P3 . p position , the frequency of P3 . p becoming a progenitor increased from the normal ∼50% to 72% ( Figure 5F ) . This increased frequency is similar to that in vab-8 mutants , which have a median furthest posterior axon terminus position of P1 . p . In contrast , when the longest posterior CAN axon terminated past P3 . p , the frequency of P3 . p becoming a progenitor was 41% , which was not statistically different from that of wild-type animals . Together , these data indicate that foreshortening of the posterior CAN axons can cause epidermal progenitors to show evidence of enhanced responses to Wnt signaling . Although anterior and posterior epidermal progenitor responses to Wnts are inversely correlated with the length of the posterior CAN axon , the striking expression of CWN-1/Wnt in the CAN neurons suggests the possibility that when anteriorly displaced , the cell bodies of these neurons might promote Wnt signaling in anterior epidermal cells such as P3 . p . Consistent with this model , we found that the increased frequency of P3 . p becoming a vulval progenitor in vab-8 mutants was more dependent on cwn-1 than egl-20/wnt activity ( Figure S9A ) . If this strong dependence on CWN-1/Wnt was largely due to increased proximity of CWN-1-producing CAN cell bodies to P3 . p , it would be expected that CAN cell bodies would be consistently closer to P3 . p when P3 . p becomes a vulval progenitor . We found 17 ceh-10 mutants to test this hypothesis . In these mutants , at least one of the cell bodies of the pair of CAN neurons was displaced closer to P3 . p , away from its normal median position of P5 . p ( Figure S9B ) . However , regardless of whether P3 . p had or had not become a vulval progenitor , the cell body that was closest to P3 . p ( among the pair of CAN neurons ) was similarly close to P3 . p ( Figure S9C , left panel ) . Also arguing against a positive role for the CAN cell bodies in promoting Wnt signaling in P3 . p , we found that when P3 . p became a vulval progenitor , the cell body that was furthest from P3 . p ( among the pair of CAN neurons ) was significantly further away from P3 . p than in cases where P3 . p did not become a progenitor ( Figure S9C , right panel ) . In fact , in one case , we found an animal where the two CAN cell bodies were directly over P3 . p , yet P3 . p still failed to become a vulval progenitor ( Figure S9B , animal 92 ) . Notably , in this animal , both posterior CAN axons terminated past the P8 . p progeny . The most significant correlation we noticed in animals with at least one of the two CAN cell bodies displaced closer to P3 . p was that when P3 . p became a vulval progenitor , fewer posterior CAN axons reached P3 . p ( Figure S9B ) . Collectively , these results indicate that the key role of the CANs in epidermal development is to inhibit Wnt signaling , and that the posterior CAN axons must extend a certain distance to confer this inhibition . The C . elegans genome encodes one diffusible Wnt antagonist , SFRP-1 [38] . However , SFRP-1 does not mediate the effects of the CANs on epidermal development: its expression is largely restricted to anterior body wall muscle [38] , and unlike CAN-displacing vab-8 mutations , mutation of sfrp-1 did not suppress the underinduced phenotype of egfr/let-23 ( lf ) mutants ( Table 1 ) , and did not cause a P-Rvl phenotype or ectopic vulval fates at P8 . p ( n = 166 ) . However , the only other known extracellularly acting Wnt inhibitor in C . elegans , the transmembrane Wnt-binding Ror/CAM-1 tyrosine kinase , could mediate the effects of the CAN axons on Wnt signaling . Ror/CAM-1 is widely expressed in muscle , the vulval progenitors , and many neurons , including the CANs ( Figure 6A ) [7] , [37] , [44]–[46] . While in certain contexts , such as EGL-20/Wnt-mediated polarization of P7 . p , it transduces Wnt signals [16] , in other contexts such as inhibition of HSN migration , P3 . p vulval progenitor cell specification , and the induction of vulval fates , it appears to antagonize Wnt signaling [7] , [47] . Based on its ability to physically bind Wnts such as EGL-20 and CWN-1 ( potential targets of vab-8 mutations ) , and to interfere with vulval fate signaling in the P6 . p epidermal progenitor when overexpressed in non-epidermal cells , it has been proposed that Ror/CAM-1 inhibits Wnt signaling by sequestering Wnts away from Wnt-responding cells [7] . While this model is plausible , how such a broadly expressed antagonist might refine Wnt gradients to allow specific migratory and tissue patterns is unclear . To explore the possibility that Ror/CAM-1 might largely act from specific neurons such as the CANs to refine the EGL-20 and CWN-1 Wnt gradients that pattern the epidermis , we first examined the extent to which a cam-1 null mutation , gm122 ( Figure 6C ) , phenocopies the effects of vab-8 mutations . Similar to mutation of vab-8 , ror/cam-1 mutation elevated Wnt reporter activity in P6 . px and P8 . px progeny ( Figure 6B ) . Also similar to mutation of vab-8 , ror/cam-1 mutation increased the frequency of P3 . p becoming a vulval progenitor , cooperated with EGL-20/Wnt overexpression to cause embryonic P11 to P12 fate transformations , and suppressed the underinduced P6 . p-based phenotype of an egf/lin-3 mutation ( Figure 6D; Table 2 ) [7] . Notably , mutation of ror/cam-1 did not perturb primary vulval symmetry , induce ectopic vulval fates in P8 . p ( Figure 6E ) , or increase Wnt reporter activity to the same degree as vab-8 mutations . These discrepancies may be due to dual negative and positive functions of Ror/CAM-1 in epidermal development . Within certain epidermal progenitors such as P7 . p , Ror/CAM-1 may transduce Wnt signals , while in the CANs , it may sequester Wnts to refine the posteriorly derived Wnt gradients to which the vulval progenitors respond . Since Ror/CAM-1 mediates the polarizing effect of EGL-20/Wnt on P7 . p [16] , the P-Rvl phenotype , which stems from this polarization , cannot be manifested in ror/cam-1 mutants . Furthermore , we found that Ror/CAM-1 is required for P8 . p to adopt a vulval fate when the posterior CAN axons are foreshortened by vab-8 mutations ( Figure 6E ) . Further consistent with the model that Ror/CAM-1 positively transduces Wnt signals in P8 . p , the increased frequency of Wnt reporter activity in P8 . p progeny was significantly lower in cam-1 mutants than in vab-8 mutants ( Figure 6E ) . The mildly elevated P8 . px Wnt reporter activity in ror/cam-1 mutants must therefore reflect activation of other Wnt receptors that are not sufficient to drive a vulval fate . Since Ror/CAM-1 has been reported to affect CAN cell body positioning and axon outgrowth [35] , we evaluated whether cam-1 mutations might increase posteriorly derived Wnt signaling by affecting the location of the CANs and their axon termini . As has been previously reported , we found that ror/cam-1 mutations caused anterior displacement of the CAN cell bodies that was as severe as that caused by ceh-10 mutation [35] ( Figures 5A and 7A ) . However , since ceh-10 mutants do not have as strong Wnt phenotypes as ror/cam-1 mutants , the CAN cell body displacement in cam-1 mutants cannot explain their increased Wnt signaling . Also , in ror/cam-1 mutants , outgrowth of the posterior CAN axon was only mildly affected , with the median end point being even more posterior than in vab-8 ( ev411 ) and ceh-10 ( lf ) mutants that have no or weaker Wnt phenotypes ( Figures 5B and 7B ) . Thus , Ror/CAM-1 does not inhibit Wnt signaling by regulating posterior CAN axon outgrowth , but could mediate an inhibitory effect of the extended axons . To test whether Ror/CAM-1 expression in the CANs is sufficient to restore some aspect of normal inhibition of Wnt activity in epidermal progenitors , we transgenically expressed CAM-1 only in the CANs of cam-1 ( null ) ; egf/lin-3 ( lf ) double mutants , which have elevated Wnt signaling in the epidermal progenitors . Cell-specificity was confirmed by tagging the cDNA with gfp , which does not interfere with CAM-1 biological activity [47] , and verifying that CAM-1::GFP expression was detected only in the CANs . In general , it was difficult to obtain transgenic lines with detectable and stable levels of CAM-1::GFP expression . However , one transgenic line with CAN-specific expression was selected for further analysis ( Figure 7D ) . In this line , despite similar levels of expression between animals , re-expression of Ror/CAM-1 did not fully restore proper migration to the CANs ( Figure 7A ) . This partial rescue suggests that in these animals , the level of transgenic Ror/CAM-1 expression may be just below physiologic amounts . However , of those animals displaying normal migration of at least one of the two CAN cell bodies , CAN-expressed Ror/CAM-1 completely restored the parental egf/lin-3 ( lf ) underinduced phenotype ( Figure 7C and 7D ) . This result indicates that epidermal progenitors such as P6 . p no longer receive abnormally high Wnt signaling when Ror/CAM-1 is expressed only in the CANs of cam-1 ( null ) mutants . Similar to vab-8 mutants , cam-1 mutants also show an embryonic HSN overmigration phenotype , which has been proposed to be due to overactive EGL-20/Wnt signaling [23] . To evaluate whether Ror/CAM-1 could also be part of the embryonic mechanism by which the CANs regulate Wnt-dependent responses , we examined another transgenic line with CAN-specific expression of Ror/CAM-1::GFP . These animals had a ror/cam-1 mutation and also expressed an HSN GFP marker to visualize HSN migration . Similar to the other transgenic line described above , this line also only weakly rescued the CAN migration defect ( only ∼30% of animals had CANs in their normal mid-body position ) . However , despite this incomplete functional activity , these transgenic animals still showed significant , but not wild-type , rescue of HSN migration ( Figure S10 ) . These data further support a CAN-specific role for Ror/CAM-1 in cell non-autonomously regulating responses to Wnts . To explore the model that Ror/CAM-1 acts from the CANs to sequester Wnts , we examined the requirement of the intracellular kinase domain for inhibition of Wnt signaling in the epidermis . Prior work showed that in conjunction with other Wnt receptor mutations , a ror/cam-1 null mutation or a cam-1 mutation affecting only the extracellular Wnt-binding domain ( sa692 , Figure 6C ) , but not a cam-1 deletion affecting only the kinase domain ( ks52 , Figure 6C ) , can induce ectopic vulval fates [7] . To determine whether similar requirements for the extracellular , but not intracellular , kinase domain extend to the regulation of epidermal progenitor responses assayed in this work , we also examined the effects of these domain-specific mutations . Consistent with the prior work and our model , we found that the cam-1 ( sa692 ) mutation , but not the cam-1 ( ks52 ) mutation , increased the frequency of P3 . p becoming a vulval progenitor and suppressed an egf/lin-3 mutation comparably to the cam-1 null mutation ( Figure 6D; Table 2 ) . To determine whether the extracellular-domain-only requirement specifically extended to CAN-based Ror/CAM-1 inhibition of epidermal progenitor responses to Wnts , we repeated our transgenic rescue experiments with a cam-1 construct that lacked the entire intracellular domain ( ΔIntra , Figure 6C ) . Similar to with the wild-type construct , it was difficult to generate transgenic lines with detectable GFP expression . Our best-expressing transgenic line showed partial rescue of the CAN cell body migration defect , but not as much as the wild-type-expressing line ( Figure 7A ) , and there was no rescue of the CAN axon outgrowth or the mild tail-withering defects ( Figures 7B and S11 ) . Despite its limited activity in promoting proper CAN migration , the ΔIntra construct fully restored inhibition to vulval development in ror/cam-1 ( gm122 ) ; egf/lin-3 ( lf ) mutants ( Figure 7E ) . Also consistent with our model , it has been suggested that the ΔIntra construct may have a higher Wnt-binding capacity than the wild-type receptor [47] , which could explain its seemingly more potent ability to restore Wnt inhibition . To complement our transgenic add-back experiments with a more physiologic assessment of the Ror/CAM-1 site of action in regulating Wnt responses in epidermal progenitors , we used a genetic approach . We scored two quantitative epidermal phenotypes that are equally manifested in ror/cam-1 and vab-8 single mutants . In this strategy , we used a vab-8 mutation to maximally displace the posterior CAN axon terminus away from the epidermal progenitors . If Ror/CAM-1 acts mostly through other neurons and muscle , a cam-1 mutation should still significantly increase Wnt signaling in vab-8 mutants . On the other hand , if Ror/CAM-1 acts mostly through the CANs , a cam-1 mutation should have no further effect on Wnt signaling . Both ror/cam-1 and vab-8 single mutations have the same effect on the frequency with which P3 . p becomes a vulval progenitor , increasing this frequency from 50% to ∼70% ( Figures 2B and 6D ) . However , double mutants showed no further increase in this frequency ( Figure 7F ) . Similarly , both ror/cam-1 and vab-8 mutations suppressed the vulvaless phenotype of egf/lin-3 mutants centered at P6 . p to a similar extent ( Tables 1 and 2 ) , yet combining the two mutations did not further increase this suppression ( Figure 7F ) . Although Ror/CAM-1 is expressed at much higher levels in muscle than the CANs ( Figure 6A ) , our data surprisingly indicate that CAN-expressed CAM-1 plays a critical role in regulating epidermal progenitor responses to Wnts such as EGL-20 . While the EGL-20/Wnt gradient has been visualized in early L1 stage larvae [12] , its potential refinement by specific cell populations like the CANs has not been reported . To investigate whether the EGL-20/Wnt gradient might be affected by the posterior CAN axons , we examined the distribution of EGL-20 in the vicinity of these axons . To accomplish this , we employed two transgenes . One expressed functional EGL-20/Wnt as a fusion to protein A , under the control of its native promoter ( Pegl-20::egl-20::protein A ) [12] . This transgene rescues egl-20/wnt mutant phenotypes , but does not cause gain-of-function phenotypes , and , therefore , expresses EGL-20 at near physiologic levels [12] . The second transgene expressed GFP under the control of the ceh-23 promoter ( Pceh-23::gfp ) , which drives expression in the CANs , as well as a few other neurons [48] . To visualize EGL-20/Wnt in living animals , we injected Cy5-labeled rabbit IgG , which has a high affinity for protein A , into double transgenic animals . This technique has been successfully used to label the extracellular portions of cell surface receptors , including protein-A-tagged receptors [49] . In single Pceh-23::gfp transgene control animals , IgG-Cy5 did not accumulate to a significant amount or label any cell surfaces ( Figure 8A ) . In contrast , when injected into Pegl-20::egl-20::protein A; Pceh-23::gfp double transgenic animals , IgG-Cy5 labeled the surfaces of EGL-20/Wnt-producing rectal cells near the anus ( Figure 8B ) , demonstrating specificity to the IgG-Cy5 signal . Cy5-labeled punctae were also enriched in other parts of the posterior body , but detection was variable , presumably because of variations in labeling between injected animals ( e . g . , Figure S12 ) . However , the posterior enrichment of the Cy5-labeled punctae is consistent with the description of the EGL-20/Wnt gradient in fixed Pegl-20::egl-20::protein A L1 larvae stained with IgG-FITC [12] . Strikingly , Cy5 labeling was also detected along stretches of the posterior CAN axon , suggesting EGL-20/Wnt is bound to the axon ( Figure 8C , three different animals are shown ) . Since our data indicate that only the extracellular domain of Ror/CAM-1 is necessary to inhibit Wnt signaling from the CAN axons , and CAM-1 has been reported to bind Wnts such as EGL-20 in vitro [7] , we tested whether CAM-1 mediates localization of EGL-20 to the posterior CAN axons . First , we observed that when driven by the CAN-specific promoter , Ror/CAM-1::GFP appeared as punctae along the CAN axon , as reported for other cells ( Figure 8D ) [50]–[52] . Notably , the Ror/CAM-1 punctae resembled the size and pattern of the EGL-20/Wnt punctae along the posterior CAN axon , suggesting EGL-20 might be bound to CAM-1 . Next , we quantified the density of EGL-20/Wnt punctae along the posterior CAN axon in wild-type and ror/cam-1 mutants . Mutation of ror/cam-1 reduced the EGL-20/Wnt density along the axon , indicating that CAM-1 mediates some of the EGL-20 enrichment at the axon ( Figure 8E and 8F ) . While in ror/cam-1 mutants the measured EGL-20/Wnt density along the posterior CAN axon was not zero , it may have been lower than what we quantified because it is impossible to be absolutely certain what fraction of the punctae are actually bound to the axon . Nevertheless , these data indicate that EGL-20/Wnt is enriched at , or in very close proximity to , the posterior CAN axon in a Ror/CAM-1-dependent manner , and provide evidence for these axons affecting the distribution of EGL-20 in the extracellular space . These data are in accord with our functional analyses of the properties of the CAN axons , and provide a potential explanation for how the CAN axons exert their unique role in epidermal development .
Despite the identification of so many Wnt antagonists , our understanding of how Wnt gradients are established with precision and integrated with the activities of multiple growth factors is still quite limited . Here , we show that even to create an anatomically simple structure such as the C . elegans vulva , the diffusion of Wnts and their inhibitors does not provide sufficient spatial resolution of signaling . To overcome this problem , C . elegans has incorporated the unique axonal projections of specific neurons to set the exact strengths of Wnt signaling at precise locations . The CAN neurons use their posterior-directed axons and the Ror/CAM-1 Wnt receptor to regionally dampen the activity of posteriorly derived Wnt ( EGL-20 and possibly CWN-1 ) . This dampening prevents ectopic induction of vulval tissue in the posterior epidermis and ensures that other centrally produced Wnts specify proper symmetry in the primary vulva . However , this mechanism still permits the posterior-derived EGL-20 and CWN-1 Wnts to retain sufficient activity in the mid-body and anterior epidermis to specify the vulval progenitors , and cooperate with EGF/LIN-3 in promoting a 1° vulval fate in P6 . p . Surprisingly , this type of regulation of epidermal pattering is unique to the Ror-expressing posterior CAN axons , and is not conferred by the other Ror/CAM-1-expressing cells along the anterior–posterior axis , or the SFRP-1-expressing cells . In fact , the posterior CAN axons are such potent regulators of Wnt signaling that foreshortening their normal length can dramatically alter an epidermal progenitor's response to Wnts . Given the unique role of the posterior CAN axons in regulating epidermal patterning , much of Ror/CAM-1 expression in other cells may be for more subtle control of epidermal patterning that we could not detect in our genetic experiments , or for inhibition of Wnt signaling for cell populations we did not assay , or could reflect other biological functions for CAM-1 . For example , in muscle , Ror/CAM-1 is an important regulator of synapse strength [50]–[52] , and in other cells , it plays important roles in cell polarity , cell migration , axon guidance , and neurite survival [16] , [35] , [37] , [44]–[47] , [53] . Although for technical reasons we could not quantify the EGL-20/Wnt gradient in the vicinity of the epidermal progenitors that respond to Wnt signaling , we could quantify other aspects of EGL-20 distribution in living adult animals . Notably , we detected EGL-20/Wnt punctae along the posterior CAN axon in a pattern similar to how Ror/CAM-1 clusters along the same axon , suggesting EGL-20 may bind to the axon via CAM-1 . In support of this model , mutation of ror/cam-1 reduced EGL-20/Wnt density along the posterior CAN axon . However , Ror/CAM-1 may not be the only factor determining where EGL-20 is distributed within the posterior body . Ror/CAM-1 is expressed at levels similar to the CANs in other neurons , and at much higher levels than the CANs in muscle ( Figure 6 ) . Yet , despite this expression pattern of Ror/CAM-1 , EGL-20/Wnt does not appear to be proportionally concentrated at muscle ( Figure S12 ) , and appears to be somewhat enriched at the posterior CAN axon ( Figure 8 ) . Since Ror/CAM-1 has been reported to act in complexes with other Wnt-binding receptors [45] , [46] , [52] , it is possible that part of the EGL-20-binding properties of the CAN axons arises from co-expression of other specific Wnt receptors or post-translational modifications of CAM-1 that affect its EGL-20-binding properties . Notably , in muscle , CAM-1 functions in a heteromeric complex with LIN-17/Frizzled to transduce CWN-2 , but not EGL-20 , Wnt signaling at the neuromuscular junction [52] . Given the Ror/CAM-1-dependent localization of EGL-20/Wnt to the posterior CAN axon , the ability of CAM-1 to bind EGL-20 in vitro [7] , and the requirement for only its Wnt-binding extracellular domain for CAM-1 to inhibit Wnt signaling from the CANs , it is possible that Wnt sequestration is part of the mechanism by which the CAN axons regulate epidermal patterning ( Figure 9 ) . Our data indicate that if Ror/CAM-1 is absent or if the posterior CAN axon does not grow to a sufficient length , the extracellular distribution of EGL-20/Wnt is altered . In these cases , in a simple model , the EGL-20/Wnt that is displaced from the CAN axons would be available to cause excess EGL-20 signaling in the epidermal progenitors , thereby perturbing normal Wnt patterning . Although only a small fraction of the total EGL-20/Wnt punctae appear to be bound to the posterior CAN axons , there are several possible scenarios in which this pool might be critical in determining the nature and strength of epidermal progenitor Wnt responses . First , many of the EGL-20/Wnt punctae that we detected at other locations might not be truly “free” and able to stimulate epidermal progenitors . They may be bound to other cells or trapped in the extracellular space . Thus , if the EGL-20/Wnt released from the posterior CAN axons is more diffusible than the other EGL-20 , it may have a potent ability to change the pattern of epidermal progenitor responses to Wnts . Alternatively , the rectal cells may produce distinct forms of EGL-20/Wnt , with distinct abilities to bind and signal through different EGL-20 receptor complexes . Perhaps a form of EGL-20/Wnt that is specific for the epidermal progenitors is also preferentially sequestered by the CANs , endowing the CANs with a unique ability to regulate epidermal progenitor responses to EGL-20 . Although our data indicate a critical role during larval epidermal development for the posterior CAN axons in regulating Wnt responses , in certain settings , the CAN cell bodies may also be able to perform this function . Notably , EGL-20/Wnt-dependent HSN migration is completed prior to CAN axon outgrowth [54] , yet embryonic laser ablation of the CAN precursor cells still causes HSN overmigration [35] . Similarly , genetic mutation of ror/cam-1 also causes HSN overmigration [23] , and we can partially rescue this phenotype by re-expressing Ror/CAM-1 specifically in the CANs ( Figure S10 ) . Together , these data suggest that Ror/CAM-1 might also be able to act from the CAN cell bodies to affect EGL-20/Wnt distributions , and hence the migration pattern of the HSNs . Our data raise the possibility that besides their behavioral functions , another core property of neurons may be to act as unique spatial cues to refine developmental patterns created by growth factors such as Wnts . This refinement may be manifested at two levels . First , in a specific tissue in a given organism , a specific fixed pattern of neurons may increase the robustness of inducing a specific pattern . For example , in C . elegans , the CANs ensure that the normal epidermal pattern of a single vulva with mirror image symmetry in the mid-body is invariant . When the posterior CAN axons are severely foreshortened , additional epidermal patterns are observed . However , our data also indicate that the pattern of neuronal positioning and axon outgrowth can also affect the appearance of a tissue pattern . Shortening of the normal length of the posterior CAN axon causes a second vulva to inappropriately form in the posterior body and alters the normal symmetry of mid-body vulval tissue . Based on these data , we also suggest that at a broader level , more phenotypic diversity can be generated among tissues and organisms with neurons than without neurons . In a simplified model of aneural tissue patterning , Wnt gradients established by diffusion direct patterning , with distances between Wnt-producing and -responding cells mostly affecting the pattern . In contrast , in organisms with nervous systems , the layering of different intricate patterns of Wnt-sequestering neurons into developing tissues could expand the diversity of functional Wnt gradients , and hence the diversity of tissue patterns that are possible . The ability of axons to refine Wnt signaling may be conserved , since Wnts are used extensively throughout metazoan development , and Ror kinases and other Wnt receptors are expressed in the nervous systems of diverse species [4] , [5] , [55] , [56] . Furthermore , the close proximity of developing neural circuits and tissues is not unique to C . elegans . In vertebrates , neural crest cells begin to occupy the embryonic gut at the time of organ budding , and axon tracts are found in undifferentiated limb buds [57]–[59] . The ability of neurons to refine spatial domains of Wnt signaling may also be important after development for certain homeostatic functions . In mammals , Wnt signaling must be precisely regulated in specific niches to promote stem cell self-renewal and prevent tumorigenesis [60] , [61] . By expressing the appropriate cognate receptors , neurons may even use the mechanism we have discovered to regulate the extracellular distributions of many other growth factors , thereby expanding the repertoire of biological processes under their control . Although it was almost 200 years ago that Tweedy John Todd reported nerves were important for salamander limbs to regenerate after amputation [62] , neurons have still not been widely demonstrated to be important for non-neuronal development . Most studies of non-behavioral effects of the nervous system have focused on the importance of whole nerves for muscle development and fracture healing [28] , [63]–[67] , and few mechanisms have been described for how the cellular components of nerves may exert non-behavioral effects . Notably , where examined , secretion of neurotransmitters or growth factors has always been part of the mechanism . In the salamander , Schwann cells from the sciatic nerve promote limb regeneration by releasing newt anterior gradient protein ( nAG ) [68] . In mammals , by secreting noradrenaline , muscarinic receptor agonists , and VEGF , nerves have been reported to modulate , respectively , hematopoietic stem cell migration , salivary gland cell proliferation , and endothelial cell differentiation into arteries [29] , [69] , [70] . By contrast , our work provides evidence for a non-secretory function of neurons , where , by binding of one of the oldest conserved metazoan growth factors , neurons can help organize the complex signaling patterns that direct tissue development . The use of neurons to refine patterning by Wnts may have arisen from the ancestral functions of Wnts in establishing and patterning the primary body axis , and their subsequent use in directing neuronal migration , axon guidance , and synaptic strength [4] , [56] . By additionally sequestering Wnts , neurons provide an efficient and unique mechanism to reshape initial Wnt gradients and help generate distinct body patterns . Given that the neuronal function we describe here does not involve the release of canonical neurotransmitters , but rather involves the axon outgrowth properties of neurons , our data raise intriguing questions regarding the earliest functional properties of primordial neurons . In addition to their behavioral importance , the evolution of neurons may also have improved metazoans by refining the plans of the bodies they would ultimately control .
C . elegans were cultured at 20°C [71] . Loss-of-function alleles included the following: LGII: cwn-1 ( ok546 ) , let-23 ( sy1 ) , unc-4 ( e120 ) , cam-1 ( gm122 ) , cam-1 ( sa692 ) , and cam-1 ( ks52 ) ; LGIII: ceh-10 ( gm120 ) and pha-1 ( e2123ts ) ; LGIV: lin-3 ( n378 ) and egl-20 ( n585 ) ; LGV: vab-8 ( gm99 ) , vab-8 ( gm138 ) , vab-8 ( ev411 ) , and him-5 ( e1490 ) . Integrated transgenes included muIs49 , syIs187 , kyIs4 , huIs60 , and cwIs6 ( contains a full-length rescuing genomic Pcam-1::cam-1::gfp construct; a gift from Wayne Forrester ) . The deletion allele cwn-1 ( ok546 ) was generated by the C . elegans Gene Knockout Facility at the Oklahoma Medical Research Foundation . See Text S1 for references . Vulval development was scored during the L4 stage under DIC optics using a Zeiss Axio Imager . Animals were anesthetized with 10 mM sodium azide unless otherwise indicated . The number of vulval nuclei was used to extrapolate how many vulval progenitor cells adopted vulval fates . A vulval progenitor cell generating seven or eight great granddaughters ( Pn . pxxx ) and no hyp7 tissue was scored as 1 . 0 cell induction . A vulval progenitor cell in which one daughter ( Pn . px ) fuses with hyp7 , and the other daughter generates three or four great granddaughters was scored as 0 . 5 cell induction . In wild-type animals , P5 . p , P6 . p , and P7 . p each undergo 1 . 0 cell induction , resulting in a total of 3 . 0 cell induction . Animals with more than 3 . 0 cell induction are overinduced , and animals with less than 3 . 0 cell induction are underinduced . To determine whether P3 . p had become a vulval progenitor , all Pn . p ( P1 . p–P11 . p ) progeny nuclei were counted at the mid-L4 stage and used to extrapolate whether P3 . p had divided . P3 . p division is indicative of having become a vulval progenitor . Transgenic extrachromosomal arrays were generated as described [72] . See Text S1 for details of plasmid constructions and descriptions of transgenic arrays . Wnt activity was visualized using the integrated POPTOP mCherry reporter syIs187 . Larvae were anesthetized with 5 mM levamisole and photographed using epifluorescence and DIC on an Axio Imager Z1 microscope using AxioVision software ( Zeiss ) . In the mCherry channel , animals were photographed with an exposure time of 1 , 200 ms . To set a threshold for reporter detection , fluorescent image files were adjusted to a brightness of −0 . 59 and a contrast setting of 1 . 11 . For reporter scoring in P5 . px–P7 . px progeny , images were adjusted to a brightness of −0 . 50 and contrast of 1 . 22 . HT115 Escherichia coli bacteria containing RNAi clones ( L4440 , empty RNAi vector; K100B4 . 6 , cwn-1; F38E1 . 7 , mom-2 ) were obtained from the Ahringer Lab bacterial feeding library [73] , and RNAi was performed as described [74] . Some images were captured on a Nikon Eclipse T1 confocal microscope , with worms being anesthetized in 500 mM sodium azide ( Figures 3A–3C , 4E–4H , 5C , and 6A ) . Other images were taken with a Zeiss Axio Imager Z1 , with worms being anesthetized with 5 mM levamisole ( Figures 4A , 4C , 4D , 5E , 7D , 7E , and 8 ) . CAN positions were scored on a Zeiss Axio Imager Z1 , with worms being anesthetized in 0 . 1% tricaine/1 . 7 mM levamisole or 5 mM levamisole . To label EGL-20/Wnt::protein A in vivo , adult animals were injected with 10 µg/ml Cy5-rabbit anti-rat IgG ( Jackson ) diluted in 20 mM K3PO4 , 3 mM potassium citrate , 2% PEG6000 ( pH 7 . 4 ) . Animals were scored 6–10 h after injection . | How a limited number of conserved growth factors such as Wnts generate diverse bodies throughout the animal kingdom is a fundamental question in developmental and evolutionary biology . Diversity is thought to arise in part through variations in the strength and location of growth factor signaling . How the signaling properties of growth factors are precisely tuned at specific locations to generate distinct tissue patterns is not well understood . Here , we present evidence that the axons of two specific neurons that span the anterior–posterior axis help pattern the epidermis of the nematode Caenorhabditis elegans . When the posteriorly directed axons of these neurons fail to grow to their normal length , the symmetry of the mid-body vulva is altered , and additional vulval tissue inappropriately forms in the posterior epidermis . We further present evidence that these neurons direct epidermal patterning by binding and sequestering posteriorly derived Wnts , thereby refining the strength and location of Wnt signaling along the anterior–posterior axis . We postulate that the evolution of neurons not only improved animals by endowing them with complex behaviors , but also by helping expand the diversity of body patterns generated by growth factors . | [
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] | 2013 | Neurons Refine the Caenorhabditis elegans Body Plan by Directing Axial Patterning by Wnts |
The virulence of Francisella tularensis , the etiological agent of tularemia , relies on an atypical type VI secretion system ( T6SS ) encoded by a genomic island termed the Francisella Pathogenicity Island ( FPI ) . While the importance of the FPI in F . tularensis virulence is clearly established , the precise role of most of the FPI-encoded proteins remains to be deciphered . In this study , using highly virulent F . tularensis strains and the closely related species F . novicida , IglG was characterized as a protein featuring a unique α-helical N-terminal extension and a domain of unknown function ( DUF4280 ) , present in more than 250 bacterial species . Three dimensional modeling of IglG and of the DUF4280 consensus protein sequence indicates that these proteins adopt a PAAR-like fold , suggesting they could cap the T6SS in a similar way as the recently described PAAR proteins . The newly identified PAAR-like motif is characterized by four conserved cysteine residues , also present in IglG , which may bind a metal atom . We demonstrate that IglG binds metal ions and that each individual cysteine is required for T6SS-dependent secretion of IglG and of the Hcp homologue , IglC and for the F . novicida intracellular life cycle . In contrast , the Francisella-specific N-terminal α-helical extension is not required for IglG secretion , but is critical for F . novicida virulence and for the interaction of IglG with another FPI-encoded protein , IglF . Altogether , our data suggest that IglG is a PAAR-like protein acting as a bi-modal protein that may connect the tip of the Francisella T6SS with a putative T6SS effector , IglF .
Francisella tularensis is a Gram-negative bacterium that causes tularemia [1] . The severity of tularemia is highly variable depending on the route of inoculation of the bacterium and the infecting strain . F . tularensis subspecies tularensis is the most virulent subspecies with a 50% lethal dose estimated to be at most 10 bacteria by the intranasal route for humans [2] . A Live Vaccine Strain ( LVS ) , derived from a F . tularensis subspecies holarctica strain , is widely used to study the pathogenesis of tularemia . F . novicida is another closely related species , which is avirulent for immunocompetent humans but highly virulent in mice . Due to its ability to reproduce the intracellular life cycle of the more virulent subspecies , F . novicida is widely used as a model system to study tularemia [3] . The ability of Francisella strains to cause disease is linked to their ability to replicate within host cells such as macrophages . Upon phagocytosis , Francisella escapes from the vacuole to reach the cytosol , where it replicates rapidly . Escape from the vacuole into the host cytosol is dependent on a genomic island termed the Francisella Pathogenicity Island ( FPI ) [4 , 5] . In addition , the FPI is implicated in the inhibition of macrophage pro-inflammatory response ( e . g . TNF-α secretion ) [6 , 7] . The island is highly conserved between F . tularensis and F . novicida with greater than 97% identity at the nucleotide level [8 , 9] , and contains 17 genes , 8 of which encode for proteins that share homology with proteins from type VI secretion systems ( T6SS ) ( [10] and S1 Fig ) . Two copies of the FPI are present in F . tularensis strains while a single copy is present in F . novicida [8] . In the latter species , another genomic island termed the "Francisella novicida Island ( FNI ) " demonstrates some similarities with the FPI suggesting it might encode another atypical T6SS ( [10] and S1 Fig ) . T6SS are specialized machineries involved in the delivery of toxins and effector proteins to prokaryotic and eukaryotic cells [11–13] . They are functionally related to the bacteriophage contractile tail [14 , 15] . Contraction of an external sheath consisting of TssB and TssC results in the secretion of an inner tube made of stacks of Hcp protein hexamers . The T6SS is tethered to the bacterial envelope by a membrane complex composed of the inner membrane proteins TssL and TssM/IcmF and the outer membrane lipoprotein TssJ [14] . The Hcp tube is surmounted by a complex made of a VgrG trimer capped with the recently identified PAAR protein [16] . PAAR proteins , originally named TagD [17] , are characterized by three proline-alanine-alanine-arginine ( PAAR ) motifs . A zinc atom bound to three histidines and one cysteine is believed to stabilize their three-dimensional structure [16] . This distal VgrG3-PAAR protein complex is thought to act as a membrane-puncturing device allowing the delivery of toxins and effector proteins into the target cell . The mode of secretion may involve physical interactions between the secreted effector and the Hcp , VgrG or PAAR proteins [16 , 18 , 19] . Phylogenetic analysis of T6SSs led to the classification of the F . tularensis T6SS as a unique evolutionary outlier [20] . Indeed , out of the 13 proteins that define the core of prototypical T6SSs , the FPI lacks obvious homologues for at least 5 of them . Furthermore , while the FPI encodes a TssM/IcmF family protein ( PdpB ) , this protein lacks the conserved Walker A box required to bind ATP and to provide energy to the secretion process [9 , 21] . Although Francisella possesses a VgrG protein , it is much smaller than prototypical VgrG proteins [9] . IglC has structural homology with Hcp , the stacking unit of the T6SS inner tube . Yet , it is still unclear whether IglC can also form hexameric rings [22] . Importantly , Clemens et al . recently strengthened the T6SS nature of the FPI proteins by demonstrating that IglA and IglB ( TssB and TssC homologues , respectively ) interact to form the T6SS sheath [23] . While the presence of a functional T6SS encoded by the FPI is now established [9 , 23 , 24] , it is still largely unknown which FPI genes encode for structural component of the T6SS machinery per se and if the FPI proteins demonstrated to be secreted are indeed effector proteins . IglG is an 18-kDa protein of unknown function encoded by the FPI . Intriguingly , a LVS ΔiglG mutant presents delayed kinetics of phagosomal escape compared to parental LVS [7] . Thus , in contrast to vgrG , iglA ( tssB ) , iglB ( tssC ) or iglC ( hcp ) mutants [25 , 26] , the mutant is not fully confined to the vacuole and replicates efficiently in macrophages [7] . Despite efficient replication , the LVS ΔiglG mutant shows delayed cytopathogenicity and impaired inhibition of TNF-α secretion [7 , 27] . Moreover , IglG is required for virulence of LVS in mice , indicating that this protein is central to the pathogenicity of subsp . holarctica [7] . However , it is still unclear whether IglG is a structural component of the T6SS or a T6SS effector . We therefore decided to perform a comparative study using F . novicida and F . tularensis subspecies holarctica and tularensis to identify the nature of IglG . Our results demonstrate that IglG is absolutely required for F . novicida escape into the host cytosol , triggering of the cytosolic innate immune responses and replication within macrophages . In addition , we demonstrate that IglG is a member of the DUF4280 family , which comprises features of the recently described PAAR protein family located at the tip of the T6SS . While the PAAR motifs are poorly conserved within this family , 4 cysteine residues are highly conserved . These residues were shown to be essential for IglG function in the T6SS and in virulence and to contribute to the coordination of a metal ion . This study thus defines the members of the DUF4280 as novel PAAR-like proteins . In addition , we found that IglG is unique among PAAR-like proteins in bearing an N-terminal extension required for virulence . We demonstrate that IglG , in an N-terminal domain-dependent manner , interacts with the FPI-encoded IglF protein , suggesting that IglG may act as a cargo to connect the tip of the Francisella T6SS with other T6SS proteins/effectors . Finally , the importance of IglG for the virulence of F . novicida as well as the highly virulent F . tularensis SCHU S4 strain was demonstrated in vivo .
To gain insight into the function of IglG , we performed a bioinformatic analysis . A BLAST search using the IglG sequence identified a conserved domain of unknown function ( DUF4280; IPR025460; PF14107 ) in the C-terminus of the protein ( residues 59–173 ) ( Fig 1A ) . DUF4280 is a domain present in numerous proteins of unknown function and is found in more than 250 different bacterial species . One of the DUF4280 proteins , Fjoh_3275/Fte1 from Flavobacterium johnsoniae was recently predicted to adopt a fold closely related to the PAAR domain [28] suggesting that other proteins containing the DUF4280 domain might also share these sequence signatures . We thus performed homology modeling using the Phyre [29] and I-Tasser [30] servers with IglG or the DUF4280 consensus sequence as templates . Both servers clearly identified the structures of Escherichia coli or Vibrio cholerae PAAR proteins as suitable templates for modeling and produced high confidence models ( e . g . 85% confidence according to Phyre ) for the DUF4280 consensus sequence and for IglG residues 59–173 ( Fig 1B ) . The models generated by I-Tasser for residues 59–173 of IglG and for the DUF4280 consensus sequence were all very similar to that of a conical β-barrel fold ( Fig 1B ) . The three PAAR motifs were , however , not strictly conserved neither in IglG , nor in the DUF4280 proteins ( Fig 1A ) . Yet , one to one threading [29] with a typical PAAR protein from V . cholerae ( VCA0105 , here denoted VcPAAR ) identified partial sequence conservation for two out of three of these motifs ( Fig 1A ) . Our confidence in the model was strengthened by a molecular dynamics simulation with a 400 ns trajectory on the IglG C-terminus structure presented above . Indeed , the root mean square fluctuation obtained ( RMSF = 0 . 27 +/- 0 . 11 ) was very similar to the RMSF of a well-characterized structural domain ( RMSFSH3 domain = 0 . 24 +/- 0 . 06 ) ( S2 Fig and S1 Movie ) . This simulation thus suggests that the predicted protein fold would be stable over time . We were unable to purify IglG or any of the other DUF4280 proteins tested ( FTN_0054 from F . novicida , PA_2375 from P . aeruginosa and ROD_34101 from Citrobacter rodentium ) in soluble form and in sufficient amount to perform crystallization assays . This observation is in line with the described low solubility of PAAR proteins [16] and highlights the challenge of working on individual T6SS proteins out of the context of the whole machinery . However , His6-tagged IglG could be purified from in E . coli under denaturing conditions and refolded using step-wise dialysis . Importantly , the analysis of the refolded IglG protein by circular dichroism ( CD ) spectroscopy supported our in silico analysis since the proportion of α-helices and β-strands determined with this method ( 25% and 22% , respectively ) was highly similar to the one predicted in our model ( 23% and 22% , respectively ) . Altogether , these data demonstrated that IglG and the members of the DUF4280 family define a novel family of PAAR-like proteins formed by a β-barrel . To investigate the role of IglG for the T6SS , we made a full-length deletion of iglG ( FTN_1314 ) in F . novicida strain U112 . The F . novicida ΔiglG mutant was unable to replicate in J774 macrophages ( Fig 2A ) as well as in primary bone marrow-derived macrophages . This phenotype resembled that of a mutant lacking the whole pathogenicity island ( ΔFPI ) [31] and could be complemented by expression of the IglG protein in trans ( Fig 2A ) . The main function of the FPI during the Francisella intracellular life cycle is to promote the escape of Francisella into the host cytosol [32] . We thus investigated the occurrence of phagosomal rupture by using the β-lactamase/CCF4 assay [33 , 34] . IglG was required for vacuolar escape since we did not observe any phagosomal rupture in ΔiglG mutant-infected macrophages at 2 h post-infection ( Fig 2B ) . At this time point , phagosomal rupture could be detected in more than 25% of the cells infected with the WT strain or the complemented ΔiglG mutant strain . As expected , no phagosomal rupture was detected in cells infected by the ΔFPI mutant or by a Δbla mutant lacking a functional F . novicida β-lactamase [35] . In addition , we did not observe any phagosomal rupture in macrophages infected with the ΔiglG mutant strain even at 18 h ( S3 Fig ) suggesting that in F . novicida , IglG is essential for the escape from the vacuole . Escape of F . novicida into the host cytosol triggers cytosolic innate immune responses , namely secretion of type I IFN [36] and activation of the AIM2 inflammasome [37] . In agreement with the lack of phagosomal escape , the F . novicida ΔiglG mutant failed to induce type I IFN secretion ( Fig 2C ) and inflammasome-mediated death as measured by real-time propidium iodide incorporation ( Fig 2D ) . Similarly , the ΔiglG mutant was not cytopathogenic towards J774 macrophages even at 48 h as determined by measuring the release of LDH into the culture supernatant ( S4 Fig ) . Altogether , these data demonstrate that in F . novicida , deletion of iglG leads to the same phenotype as the deletion of iglC ( the hcp homologue ) or of vgrG , in agreement with a PAAR-like role of IglG in the T6S machinery . Based on its PAAR-like nature , we expected IglG to be secreted as a key component of the Hcp/IglC tube . We first attempted to monitor IglG secretion into host cells by flow cytometry using the β-lactamase reporter [24] . Using this method and in agreement with our previous microscopy-based assay [24] , we could not detect translocation of IglG , VgrG , IglI or IglF , although PdpE and IglC were readily secreted ( S5 Fig , left panel ) . Since all fusion proteins were expressed ( S5 Fig , right panel ) , this suggests that the large tag may have an adverse effect on the secretion of some FPI proteins in F . novicida . We thus switched to a recently described in vitro secretion assay in which addition of KCl triggers T6SS-dependent secretion [23] . Upon KCl addition , IglG was secreted into the culture supernatant ( Fig 3 ) . As expected VgrG was also specifically secreted during this condition [23] , while PdpB , the inner membrane-associated TssM homologue , was not detected in the supernatant ( Fig 3 ) . Importantly , IglG secretion was abolished in a ΔvgrG mutant indicating that a functional T6SS is required for IglG export . Similarly , we could not detect secretion of IglG or VgrG in the absence of KCl indicating that environmental cues associated with T6SS activation are required for their secretion . Deletion of PAAR proteins highly reduces Hcp secretion by V . cholerae and Acinetobacter baylyi [16] . We thus tested whether a deletion of iglG in F . novicida would impair secretion of the Hcp homologue , IglC . Indeed , secretion of IglC was absent in the ΔiglG mutant , although in a few experiments low levels of IglC were still detected in the mutant ( Fig 3 and S6 Fig ) . Similarly , IglC was not detected in the supernatant of the ΔvgrG mutant or in the supernatant of the WT strain if KCl was absent from the growth medium ( Fig 3 ) . Importantly , expression of IglG and VgrG in trans restored IglC secretion by ΔiglG and ΔvgrG mutants , respectively ( Fig 3 ) . Altogether , these data , together with the results from the bioinformatic analysis , are consistent with a PAAR-like role of IglG in the F . novicida T6SS . PAAR proteins bind Zn2+ via 3 histidine and 1 cysteine residues ( Fig 1A and 1B ) . The central Zn2+ ion is thought to be important to stabilize the protein structure [16] . While the cysteine residue is conserved in IglG ( Cys 152 ) and in the PAAR-like proteins , no histidines are present neither in IglG nor in the DUF4280 consensus sequence ( Fig 1A ) . However , both the sequence alignment and the 3D models suggest that this residue , together with 3 other cysteines ( Cys 64 , 105 , 122 in IglG ) could potentially form the metal-binding site of the PAAR-like proteins ( Fig 1A and 1B ) . The strong conservation of these residues in IglG homologues was highlighted by iterative blast searches using the Jackhmmer software ( Fig 1C ) , suggesting that their presence is essential to the function of these PAAR-like proteins . To determine if IglG is able to bind metal ions , we expressed IglG fused to GST and tested whether the purified fusion protein could bind metal by using inductively coupled plasma mass spectrometry ( ICP-MS ) . Although we did observe some variability in metal binding between the different batches of purified proteins ( five independent experiments are shown ) , iron and zinc were specifically associated with GST-IglG . In contrast , we detected only trace amounts of nickel and copper while the other metals tested ( e . g . Mn , Co ) were undetectable ( Fig 4A ) . The observed ratio between iron and zinc binding to IglG ( [Fe]/[Zn] = 1 . 6 ) suggests that , under our conditions ( see discussion ) , iron binding to GST-IglG is slightly favored over zinc binding . Of note , the same experiments performed on a GST-fusion of a typical PAAR protein from Pseudomonas aeruginosa ( PA_0824 ) demonstrated a similar proportion of molecules binding metal ( about 30% of the total purified molecules ) but with a strong binding preference for zinc ( [Fe]/[Zn] = 0 . 3 ) ( S7 Fig ) . Experiments performed in parallel with the GST control protein identified only traces of iron and low concentrations of zinc ( equivalent in molarity to 4 . 2% of the total number of GST molecules ) . The contribution of each IglG cysteine in Fe/Zn binding was evaluated using purified mutant proteins and ICP-MS . Although we could not directly compare the metal-binding capacity of mutant proteins purified on different days , the direct comparison of each mutant IglG protein to the WT IglG protein purified the same day demonstrated a 20 to 40% reduction in metal binding ( Fig 4B ) . These results suggest that the four cysteine residues contribute to the ability of IglG to bind metal ion but that mutating a single cysteine residue is not sufficient to abolish metal binding in vitro . To strengthen our findings , we performed a metal binding assay using circular dichroism ( CD ) spectroscopy [38] . CD spectra of IglG protein purified under denaturing conditions and refolded as described above ( Fig 4C ) were recorded upon addition of different concentrations of Fe2+ . We observed a modification of the CD spectrum indicative of a change of conformation upon addition of 0 . 2 molar equivalent of iron ( i . e . 0 . 2 molecule of iron per molecule of IglG ) ( Fig 4D ) . The CD spectra recorded at increasing iron concentrations intersected at a single point ( isodichroic point ) indicating that addition of increasing concentrations of iron led to a change in the proportion of two unique conformations . Importantly , no further modifications of the CD spectra were observed after addition of one equivalent of iron strongly suggesting that the modifications observed resulted from the binding of one single iron ion per protein . Similar observations were obtained with Zn2+ ( Fig 4D ) , Mg2+ and Co2+ ( S8 Fig ) suggesting , as observed in the ICP-MS experiments presented above , a promiscuous ability of IglG to bind divalent cations . No change of conformation was observed upon addition of up to 50 molar equivalent of TCEP ( Tris ( 2-carboxyethyl ) phosphine ) , a reducing agent suitable for CD application ( Fig 4E ) , which suggests that the cysteine residues are not involved in forming disulfide bonds within the purified IglG protein . Altogether , these experiments demonstrate that IglG binds divalent cations . While this remains to be confirmed with other PAAR-like proteins , it suggests that PAAR and PAAR-like proteins have a conserved ability to bind a metal ion to stabilize this specific protein structure . The nature of the bound metal ion remains to be determined in vivo . To next investigate the role of the cysteines for PAAR-like protein function in the T6SS , we mutated each of these residues in IglG . We first assessed whether the conserved cysteine residues were required for IglG and IglC secretion using the KCl secretion assay . While the wild-type IglG was easily detected in the supernatant in the presence of KCl , we did not detect secretion of any of the four cysteine mutant proteins when expressed in U112 ( Fig 5A ) . Similarly , when each of the mutant proteins was expressed in the ΔiglG mutant , the secretion of the Hcp homologue IglC was strongly decreased and at the same level as in the non-complemented ΔiglG mutant ( S6 Fig ) . Moreover , mutating each individual cysteine completely abolished the ability of the ΔiglG mutant to replicate in macrophages ( Fig 5B ) , to escape from the phagosome ( Fig 5C ) , and to trigger cytosolic innate immune responses ( Fig 5D and 5E ) . To further demonstrate the key roles of the conserved cysteines , we selected the first cysteine mutant ( Cys 64 ) to assess its virulence in mice . Whereas intradermal injection of U112 or of the ΔiglG mutant expressing full-length IglG-2HA killed mice within 4 days , all mice given the ΔiglG mutant , or the ΔiglG mutant complemented with IglGC64G-2HA survived until the end of the experiment ( Fig 5F ) . Altogether , these findings highlight the importance of the four conserved cysteine residues to maintain the T6SS-dependent secretion of IglG and of the Hcp homologue IglC as well as the functional role of IglG in F . novicida virulence . Our data suggest that the role of the conserved cysteines in the PAAR-like proteins may be linked to their metal-binding ability , which is likely to stabilize the PAAR-like fold . Mirroring PAAR proteins [16] , a subset of PAAR-like proteins harbors N- or C-terminal extensions including rearrangement hotspot ( RHS ) repeats [39] , toxin and hydrolase domains ( Fig 6 ) ; suggesting they could act as T6SS effectors . Interestingly , the primary sequence alignment ( Fig 1A ) and the three-dimensional structure modeling ( Fig 1B ) both clearly identified an extension of the PAAR-like domain at the N-terminus of IglG . This extension , encompassing residues 1 to 57 , is not present in the other PAAR-like protein ( FTN_0054 ) encoded in the FNI ( Fig 1A and S1 Fig ) . The specificity of the N-terminal extension is strengthened by the lack of homology to other PAAR-like proteins ( Fig 1A ) or to any polypeptides outside of the genus Francisella ( except for an IglG-like protein in Piscirickettsia salmonis , a species closely related to Francisella [41] , S9 Fig ) . The N-terminal extension of IglG is predicted to contain predominantly α-helices ( Fig 1A and 1B ) . The tertiary structure of this domain was modeled with poor reliability , resulting in one or two helices clearly protruding outside the core PAAR domain albeit with various orientations in the different models ( S10 Fig ) . To experimentally assess the role of this extension in T6SS function and virulence , we engineered a series of N-terminal deletion mutants deleting one or both of the two predicted α-helices ( IglGΔ2–17 and IglGΔ2–39 , respectively ) , the whole N-terminal extension ( IglGΔ2–58 ) or the whole extension together with the first 8 residues of the PAAR-like domain ( IglGΔ2–66 ) . We first analyzed secretion of the IglG proteins in vitro in the presence of 5% KCl , using the strain U112 to overcome any problem with lack of complementation ( Fig 7A ) . Interestingly , the two α-helices of the N-terminal extension were not required for in vitro secretion , since IglGΔ2–17 and IglGΔ2–39 were both efficiently secreted . In contrast , longer deletions ( Δ2–58 or Δ2–66 ) abolished IglG secretion . When tested in the macrophage assays , even the smallest deletion ( Δ2–17 ) failed to complement the ΔiglG mutant for intracellular replication ( Fig 7B ) , escape from the vacuole ( Fig 7C ) , or triggering of the cytosolic innate immune responses ( Fig 7D and 7E ) . Finally , the importance of the N-terminal extension was validated in vivo . Indeed , the ΔiglG mutant expressing IglGΔ2-39-2HA was unable to kill mice , while , as previously mentioned , the ΔiglG mutant expressing IglG-2HA killed 100% of the infected mice within 4 days ( Fig 7F ) . Importantly , these results dissociate for the first time secretion of a FPI protein from its requirement for F . novicida virulence . Furthermore , these results highlight two distinct domains of IglG critical for protein function; a unique α-helical N-terminal extension dispensable for secretion but required for virulence and a conserved PAAR-like C-terminal domain , which is a key component of the T6SS . The IglG proteins are highly conserved between Francisella species with greater than 98% identity ( S11 Fig ) . Therefore , it was puzzling that the phenotypes of F . novicida and LVS ΔiglG mutants ( the latter being deleted of the two iglG copies [7] ) differed so strongly with regard to phagosomal escape , replication and cytopathogenicity . Indeed , the initial defect in phagosomal escape observed for the LVS ΔiglG mutant is only transient , eventually leading to efficient intracellular replication but a delayed cytopathogenic response [9 , 25] . We thus decided to check whether the critical features of the F . novicida IglG protein were also required for IglG function in LVS and in the highly virulent F . tularensis SCHU S4 strain . We first analyzed whether F . novicida-derived IglG could restore the defects of LVS ΔiglG with regard to cytopathogenicity and inhibition of LPS-induced TNF-α secretion [9] . While the wild-type F . novicida protein was equally competent as LVS-derived IglG at restoring the defects , F . novicida derived mutant proteins IglGΔ2–39 , IglGC64G , IglGC105G , IglGC122G and IglGC152G were all unable to restore the defects of LVS ΔiglG . We also confirmed these findings by constructing an IglGΔ2–39 mutation within the LVS-derived IglG protein , and by individually exchanging the cysteines of LVS-derived IglG to either serine or alanine . Again , only the wild-type IglG construct was able to restore the defects of the LVS ΔiglG mutant back to LVS levels , while the phenotype of ΔiglG expressing either of the mutant variants was very similar to that of the non-complemented ΔiglG mutant ( Fig 8A and 8B ) . This was not a consequence of a loss of expression , since all of the mutant forms were efficiently expressed ( S12 Fig ) . We were unable to induce type VI secretion in vitro for LVS or SCHU S4 using high KCl concentrations since the two strains were severely impaired for growth in such a medium . As previously described by a microscopy-based assay [24] , in cellulo secretion of IglC-TEM was clearly detected by flow cytometry when expressed in a LVS ΔiglG mutant ( S13 Fig ) . Similarly , VgrG-TEM was secreted by the LVSΔiglG mutant at a significant level . Still , the number of cells displaying detectable TEM activity was strongly reduced upon infection with the LVSΔiglG mutant compared to WT LVS ( S12 Fig ) suggesting that IglG is required for optimal Type VI secretion in macrophages infected with LVS . The role of IglG in highly virulent F . tularensis had until now not been investigated . We therefore generated a SCHU S4 ΔiglG mutant by deletion of the two iglG gene copies and assessed its ability to grow within macrophages as well as to cause cytotoxicity . In J774 cells , the phenotype was very similar to that of LVS ΔiglG in that the mutant exhibited no detectable growth defect ( Fig 9A ) [25] , and it showed an intermediate cytotoxic effect , although with a slightly faster kinetics compared to that of the LVS ΔiglG mutant ( compare Figs 8A and 9B ) . Thus , the SCHU S4 ΔiglG mutant exhibited an intermediate cytotoxic effect at 24 h , but not at 48 h , which could be restored to wild-type levels by expressing LVS-derived IglG ( Fig 9B ) . The extensive cell death observed in SCHU S4-infected cells was likely responsible for the drop in bacterial counts observed between 24h and 48h . Accordingly , the delayed cytotoxicity kinetics observed upon infection with the ΔiglG mutant correlated with a lower decrease in bacterial counts between 24h and 48 h vs . SCHU S4 ( Fig 9A and 9B ) . As expected , the ΔiglC mutant was severely defective for both growth and cytotoxicity ( Fig 9A and 9B ) . We also confirmed that F . novicida-derived IglG was equally efficient as that of LVS in restoring the defective cytopathogenic response of the SCHU S4 ΔiglG mutant in J774 cells , while the F . novicida mutant variants that either lacked the two α-helices of the N-terminal extension ( IglGΔ2–39 ) or carried a C64G substitution were unable to complement the mutant . We then analyzed the virulence of the SCHU S4 ΔiglG mutant in a mouse model of tularemia . The mutant was avirulent in mice , since it did not cause any mortality even when 107 cfu of the mutant were inoculated intradermally ( Fig 9C ) . As expected , both the WT strain and the complemented ΔiglG mutant killed 100% of the mice at much lower inoculum ( 102 and 103 , respectively ) . Taken together , this comparison validates the role of the two identified IglG domains for F . tularensis T6SS function and virulence . N- and C-terminal extensions of PAAR proteins possess either enzymatic activities or are cargo domains enabling effector secretion [16 , 42] . To test whether the IglG N-terminal extension could act as such a cargo domain , we performed a bacterial two-hybrid ( B2H ) interaction screen for IglG against all other FPI proteins . We identified a specific interaction between IglG and IglF , the latter encoded by the gene immediately upstream of iglG ( Fig 10A upper panel and S1 Fig ) . This interaction was validated by co-immunoprecipitation in F . novicida ( Fig 10B and 10C ) . To identify the IglF-interacting domain within IglG , we generated specific mutations within the latter protein and tested them in the B2H system . The amount of β-galactosidase activity , indicative of a stronger interaction , increased gradually when smaller truncations were introduced in the IglG protein ( Fig 10A , upper panel ) . Strikingly , the interaction was completely abolished when the first 39 residues of IglG were removed ( IglGΔ2–39 ) ( Fig 10A , upper panel ) . This result was validated by co-immunoprecipitation ( Fig 10B ) . In contrast , deletion of the last 39 of the protein ( IglGΔ134–173 ) had only a minor effect on the interaction ( Fig 10A , upper panel ) . In agreement with these results , we did not observe any interaction between IglF and the other F . novicida PAAR-like protein FTN_0054 , which lacks an N-terminal extension . While the N-terminal extension was clearly essential for the interaction , it was not sufficient in this system , since the N-terminal extension alone ( IglGΔ58–173 ) did not interact with IglF ( Fig 10A , upper panel ) . This lack of interaction may be partly due to improper folding of the truncated protein . In support of this hypothesis , a protein consisting of the first 60 residues of IglG fused to the PAAR-like protein FTN_0054 ( starting at the shared alanine at position 10 , see Fig 1A ) established an interaction with IglF , albeit partially reduced ( Fig 10A , upper panel ) . To identify key residues that may participate in the binding , a helical wheel analysis of the IglG N-terminus was performed using the first 56 residues in the analysis . Charged residues , both positively and negatively , at the different sides of the predicted helix were targeted for alanine scanning resulting in single substitutions K10A , R11A , D19A , E20A , D28A and D32A . Strikingly , when tested in the B2H assay , two of these substitions either abolished ( K10A ) or severely diminished ( D32A ) binding to IglF ( Fig 10A , lower panel ) . Interestingly , these residues were predicted to lie close to each other in the same side of the helix . Importantly , individual substitutions of each of the single cysteines to either serine or alanine had no effect on the interaction ( Fig 10A ) further suggesting that the PAAR-like domain per se and the metal-binding ability are not essential for the interaction with IglF . Altogether our results indicate that IglG harbors a PAAR-like domain and an N-terminal extension , which interacts with IglF . The N-terminal extension is required for virulence , possibly leading to the translocation of this putative effector protein into the host cell .
Despite the well-recognized role of the FPI in Francisella virulence in vitro and in vivo , the function of most of the FPI-encoded proteins is still elusive . The homology between FPI proteins and components of a T6SS [44 , 45] was described almost 10 years ago [8] . Yet , in spite of tremendous progress in the understanding of T6SS assembly and function in numerous bacterial species [11 , 14] , the translation of this knowledge to the Francisella FPI has been challenging . This difficulty lies in part in the limited similarity of the Francisella FPI with other T6SS [46] . In this work , we focused on IglG , a protein required for the virulence in mice of F . novicida , F . tularensis LVS and of the highly virulent F . tularensis SCHU S4 strain ( [7] , this work ) . We identified IglG as a protein containing a DUF4280 domain . This work and another recent study [28] identified IglG and two other DUF4280-containing proteins ( FTN_0054 and Fjoh_3275/FteI ) as three proteins likely to adopt a tertiary structure homologous to that of PAAR proteins . In addition , we found that the DUF4280 consensus sequence itself was also predicted to adopt a PAAR-like fold suggesting that the results we obtained on IglG will be valid for most of the DUF4280 proteins present in a large number of bacterial species . We thus propose to assign the term PAAR-like domain to this domain of unknown function , characterized by four highly conserved cysteines . Although we cannot completely rule out that the cysteines might form disulfide bridges , our biochemical analyses and the predicted homology between the zinc-binding PAAR protein and the PAAR-like proteins strongly suggests that the structural fold of PAAR-like proteins is stabilized by the cysteine-mediated metal coordination . Accordingly , we identified IglG as a metal-binding protein . In vitro , IglG had a small preference for iron over zinc in contrast to the typical PAAR protein PA_0824 from P . aeruginosa , which in our experimental conditions , preferentially binds zinc ( S7 Fig ) . Many metalloproteins have the potential to bind different metal ions . The binding of a specific divalent cation to a protein is controlled by the natural order of stability of complexes of bivalent transition metals ( also known as the Irving-Williams series: Mg2+ and Ca2+ ( weakest binding ) < Mn2+ < Fe2+ < Co2+ < Ni2+ < Cu2+ > Zn2+ ) , the relative concentrations of the different cations as well as specific mechanisms , such as the presence of metallochaperones and chelators [47 , 48] . Indeed , we have observed that purified IglG can bind different cations in vitro ( Fig 4C ) . The nature of the metal contained within IglG protein when expressed at physiological level in Francisella within its mammalian host may be influenced by the localization of the protein and the relative concentrations of the different divalent cations in each compartment as well as by specific mechanisms such as the presence of metallochaperones . Furthermore , innate immune mechanisms such as Zn2+ chelation by calprotectin , one of the most abundant protein of the neutrophil cytosol [49] may influence the bioavailability of divalent cations and the nature of the IglG-bound metal . Mutation of single cysteine residues only moderately decreased metal binding , suggesting that three functional side chains are sufficient to bind metal , a feature that has been observed in other proteins [50–52] . However , in vivo , each individual cysteine is required to sustain the role of IglG in F . novicida virulence suggesting that tetravalent coordination of the metal is required for IglG stability and function . Interestingly , the tip of the membrane-attacking complex of bacteriophages P2 and φ92 , which is homologous to the VgrG3-PAAR complex of the T6SS binds iron [53 , 54] . These results highlight the evolutionary conservation of spike proteins and identify PAAR-like proteins as potential evolutionary intermediates between phage spike proteins and bacterial T6SS PAAR proteins . The predicted PAAR-like fold suggests that PAAR-like proteins are located at the tip of the T6SS . Despite its very small size , Francisella VgrG displays a β-structural repeat consistent with an ability to bind PAAR-like proteins [16] although we failed to experimentally demonstrate an interaction between IglG and VgrG by both bacterial two-hybrid and pull-down experiments . Strikingly , while the deletion of the PAAR-like gene iglG confers to F . novicida a phenotype identical to the one observed after the deletion of the hcp homologue iglC , this is not true in F . tularensis LVS and F . tularensis SCHU S4 strain . These results suggest that in F . tularensis , the activity of the T6SS is less stringently dependent on the capping of the T6SS by a PAAR-like protein . Accordingly , using TEM fusion proteins and a β-lactamase/CCF4 flow cytometry assay , we observed a significant ( although strongly reduced compared to WT LVS ) secretion of both IglC and VgrG in the LVS ΔiglG mutant . IglG may thus play an auxiliary role in F . tularensis T6SS function . Yet , it is clearly required for F . tularensis virulence in vivo ( [7] , this work ) highlighting the importance of a fully functional T6SS to trigger disease . As described above , another PAAR-like protein ( FTN_0054 ) is found in the Francisella novicida island ( FNI ) . We could not find evidence of this genomic island playing any major role in the virulence of F . novicida in vitro nor in vivo in a mouse model of tularemia ( S14 Fig ) . It remains to be demonstrated whether this island encodes a functional T6SS involved in bacterial competition and/or in the targeting of specific eukaryotic cells . Interestingly , one FNI protein ( FTN_0052 ) exhibits similarity to proteins from the phosphoesterase family ( pfam04185 ) , which includes both bacterial phospholipase C enzymes and eukaryotic phosphatases making this protein a likely T6SS effector [55] . FTN_0052 has no homologue in the FPI , suggesting that the two genomic islands may encode different arrays of effectors . On the other hand , unique features of the FPI include the N-terminal extension of the PAAR-like protein IglG ( compared to FTN_0054 ) as well as IglF . The comparison of the FPI and the FNI might thus help us to discriminate the proteins involved in the T6SS machinery sensus stricto from specific proteins associated with virulence towards the mammalian host . Interestingly , the iglF gene lies in between the vgrG and the iglG genes . This specific genetic linkage supports our experimental data demonstrating the key role of the IglG N-terminal extension in establishing an interaction with IglF . PAAR-like domains are frequently extended in an N- or C-terminal manner by domains with known T6SS effector functions ( Fig 6 ) , strengthening the functional homology between PAAR [16] and PAAR-like proteins . The N-terminal extension that we identified in IglG is predicted to protrude outside of the PAAR-like core domain ( Fig 1B and S10 Fig ) suggesting it might act as an adaptor domain to connect the tip of the T6SS with other T6SS proteins/effectors . The adaptor nature of the N-terminal extension of IglG is supported by the fact that IglG interacts with IglF in a manner that requires the first 39 residues of IglG . The same region within IglG is dispensable for in vitro secretion of IglG , while the PAAR-like domain is required for IglG secretion . In line with identified PAAR-protein functions [16] , these data suggest that IglG could act as a cargo molecule aiding in the T6SS-mediated delivery of IglF . While deciphering the role of IglF in the T6S machinery or in infected cells will be the goal of future studies , IglF is a secreted protein [24] required for in vitro replication [21] , suggesting that IglF may be a Francisella effector secreted into the host cell in an IglG-dependent manner . Altogether , this work in addition to increasing our understanding of the structure of F . tularensis T6SS ( see model in Fig 11 ) identifies a novel family of PAAR-like proteins with conserved cysteines and metal binding ability , involved in connecting the Type VI secretion machinery to effector activities .
All experiments involving animals were reviewed and approved by the animal ethics committees of the University of Lyon , France under the protocol numbers #ENS_2014_017 and #ENS_2012_061 , by the IRB of the National Research Council , Ottawa , Canada and by the Local Ethical Committee on Laboratory Animals , Umeå , Sweden ( no . A67-14 ) . For testing of SCHU S4 and derivatives , BALB/c mice were purchased from Charles River Laboratories ( St . Constant , Quebec , Canada ) . The mice were maintained and used in accordance with the recommendations of the Canadian Council on Animal Care Guide to the Care and Use of Experimental Animals in a federally licensed , Select Agent-approved , small animal containment level 3 facility , National Research Council , Ottawa , Canada . F . tularensis strains were injected in a volume of 50 μl intradermally in groups of five ( n = 5 ) . The mice were examined daily for signs of infection and were euthanized by CO2 asphyxiation as soon as they displayed signs of irreversible morbidity . For testing of U112 and derivatives , C57BL/6J mice were injected in a volume of 100 μl intradermally in groups of five ( n = 5 ) . Aliquots of the diluted cultures were also plated on GC-agar to determine the number of CFU injected . Actual doses were the following: 830 ( U112 ) , 915 ( ΔiglG ) , 1 , 635 ( ΔiglG/IglG-2HA ) , 1 , 065 ( ΔiglG/IglGΔ2-39-2HA ) and 600 ( ΔiglG/IglGC64G-2HA ) . Previous studies have demonstrated that the LD50 of the U112 strain is approximately 500 CFU [56] . Mice were examined twice daily for signs of severe infection and euthanized by CO2 asphyxiation as soon as they displayed signs of irreversible morbidity . In our experience , such mice were at most 24 h from death , and time to death of these animals was estimated on this premise . Bacterial strains used in this study are listed in S1 Table . E . coli strains were cultured in Luria Bertani broth ( LB ) or on Luria agar plates at 37°C . F . tularensis strains were grown on modified GC-agar base at 37°C . F . novicida strains were grown in tryptic soy broth ( TSB ) supplemented with 0 . 1% ( w/v ) cysteine . When applicable , carbenicillin ( Cb; 100 μg/ml ) , tetracycline ( Tet; 10 μg/ml ) , kanamycin ( Km; 50 μg/ml for E . coli , 10 μg/ml for F . tularensis ) , or chloramphenicol ( Cm; 25 μg/ml for E . coli , 2 . 5 μg/ml for F . tularensis ) were used . F . novicida chromosomal deletion mutants of iglG , vgrG or FTN_0037-FTN_0054 ( FNI ) locus were constructed as previously described [57] by allelic exchange using PCR products followed by Flp-mediated excision of the antibiotic-resistance marker [58] or by using the suicide vector pJEB753 [59] . Primers are presented in S2 Table . To construct the ΔiglG deletion mutant in SCHU S4 , the suicide vector pJEB866 was used . pJEB866 was constructed by lifting the deletion fragment from pJEB753 into pDMK2 [60] using XhoI/SacI digestion . Conjugal mating experiments using S17-1λ pir as the donor strain and sucrose-selection allowed for the allelic exchange of the suicide plasmids within regions of complementary sequence on the chromosome of U112 or SCHU S4 [3] . To remove both copies of the iglG gene in the latter strain , the procedure was repeated . PCR screening and genomic sequencing were used to verify that the anticipated genetic event had occurred . Plasmids used in this study are listed in S1 Table . Primer combinations and restriction sites used to generate the plasmids are listed in S2 Table . All amplified fragments were first cloned into pCR4-TOPO TA cloning vector to facilitate sequencing . PCR or overlap PCR was used to introduce substitution or deletion mutations within iglG . A hybrid gene consisting of IglG and FTN_0054 was constructed using LVS and U112 , respectively as template in the first PCR reaction step . Upon overlap PCR , the resulting hybrid consisted of the first 60 residues of IglG , and where the remaining part was derived from FTN_0054 , starting at alanine at position 10 . 2xHA-tagged IglG was generated by cloning iglG in frame with a single HA tag sequence in the popHA plasmid ( kindly provided by P . Mangeot ) followed by addition of a second HA tag sequence by PCR amplification . Plasmids used for complementation of ΔiglG in trans were constructed by introducing C-terminally 6xHis-tagged or 2xHA-tagged wild-type or mutated versions of iglG into the NdeI/EcoRI sites of pKK289Km [61] or into a cyaA-containing pFNLTP6 derivative [62 , 63] respectively . The latter plasmid was also used to create TEM fusion after removal of the plasmid encoded β-lactamase gene . E . coli TEM-1 amplified from pUC19 ( ThermoFisher Scientific ) was then cloned under the Gro promoter using NheI/BamHI sites . FPI and controls genes were cloned in frame with TEM-1 using EcoRI and NheI sites . Plasmids used for B2H analysis were constructed by introducing NdeI/NotI fragments of mutated iglG into pBRGPω [64] . For GST-IglG expression , the F . novicida iglG gene was PCR-amplified and cloned into the pGEX-6-P3 ( Novagen ) in frame with the GST-encoding gene . Plasmids were transferred into bacteria by chemical transformation or electroporation . E . coli strain KDZif1ΔZ was used as the reporter strain for the bacterial-2-hybrid experiments . It harbors an F9 episome containing the lac promoter-derivative placZif1–61 driving expression of a linked lacZ reporter gene [64] . Cells were grown with aeration at 37°C in LB supplemented with 1 mM IPTG ( Isopropyl β-D-1-thiogalactopyranoside ) . Cells were permeabilized with SDS-CHCl3 and assayed for β-galactosidase ( β-gal ) activity as described previously [65] . Preparation and culture of bone marrow macrophages ( BMDMs ) were performed as previously described [36] . All mice were in the C57BL/6J background ( Charles River , France ) . J774A . 1 macrophage-like cells ( Cellulonet , Lyon , France ) [66] and L929 ISRE-luciferase cells ( obtained from B . Beutler , The Scripps Research Institute ) [67] were cultured in DMEM ( ThermoFisher Scientific ) supplemented with 10% fetal calf serum ( Lonza ) and 1mM glutamine . BMDM were infected as described before [68] at the indicated multiplicity of infection ( MOI ) . For F . novicida intracellular replication assay , macrophages were lysed with 1% ( w/v ) saponin ( Sigma ) in water for 5 min . Dilution , plating on TSA supplemented with 0 . 1% ( w/v ) cysteine and counting was performed using the easySpirale Dilute ( Interscience ) . For LVS and SCHU S4 , cells were infected for 2 h at an MOI of 200 , washed three times , and incubated in the presence of gentamicin ( 5 μg/ml; LVS and 2 μg/ml; SCHU S4 ) for 30 min ( corresponds to time zero ) . At 0 , 24 and 48 h , the macrophage monolayers were lysed in PBS with 0 . 1% deoxycholate , serially diluted in PBS and plated on modified GC-agar base plates for determination of viable counts . Type I interferon secretion was determined by an ISRE-luciferase bioassay [67] . L929 ISRE-luciferase cells were plated the day before at 105 cells per well in a 96 wells-plate . Supernatants from infected BMDMs were added for 4 h onto the ISRE-luciferase cells . Luciferase luminescence was detected using Bright Glo Assay ( Promega ) following the manufacturer’s instructions . TNF-α secretion of J774 cells upon 2 h of LPS stimulation was performed according to our previously established protocols [7] , using the BD OptEIA Mouse TNF-α ELISA Set ( BD Biosciences ) according to the manufacturer’s instructions . Cell death was monitored in BMDMs by monitoring in real time incorporation of propidium iodide ( used at 5 μg/ml ) through measurement of fluorescence emission at 635 nm every 15 min on a microplate reader ( Tecan ) . Quantification of cytopathogenicity in J774 cells was performed by analysis of LDH release in the cell supernatant , using the CytoTox96 LDH kit ( Promega , France ) , following manufacturer’s instructions . Quantification of vacuolar escape using the β-lactamase/CCF4 assay ( Life technologies ) was performed as previously described [33] . Briefly , bone marrow-derived macrophages seeded onto non-treated plates were infected as described above for 2 h , washed and incubated in CCF4 for 1 h at room temperature in the presence of 2 . 5 mM probenicid ( Sigma ) . Propidium iodide negative cells were considered for the quantification of cells containing cytosolic F . novicida using excitation at 405 nm and detection at 450 nm ( cleaved CCF4 ) or 510 nm ( intact CCF4 ) . Identification of conserved domains , of the DUF4280 consensus sequence and of the species presenting DUF4280 proteins were performed using CD database , the Conserved Domain Architecture Retrieval Tool ( CDART ) [69] and the Interpro database . Three dimensional modeling was performed using the default parameters in the I-Tasser and Phyre-2 webservers [30] . Five models were obtained for each template ( DUF4280 and IglG ) with very high scores . Figures of structure were generated with Pymol ( Delano Scientific , https://www . pymol . org ) . Conserved residues were identified using the iterative search algorithm Jackhmmer [70] . The N-terminal α-helix was analysed using EMBOSS::pepwheel software ( http://www . tcdb . org/progs/ ? tool=pepwheel ) . For the molecular dynamics protocol , IglG was modeled with the AMBER99SB force field [71] . The model was ionized and solvated with TIP3P water molecules , setting unit cell dimensions to 61 Å × 57 Å × 73 Å ( residues 53–173 ) . The resulting ~24000 atoms system was minimized and equilibrated locally with ACEMD [72] for 2 ns under NPT conditions , of 1 atm at 300 K , nonbonded cutoff of 9 Å , rigid bonds , and PME electrostatics . A time step of 4 fs was used , in conjunction with a hydrogen mass-repartitioning scheme . During minimization and the first 2 ns of equilibration , the protein’s heavy atoms were restrained by a harmonic potential with k = 1 kcal mol−1 Å−2 . The complete molecular dynamics resulted in a 400ns trajectory where the rmsf was computed by means of the Gromacs subroutines [73] . For protein expression , GST , GST-IglG , His6-IglG encoding vectors were introduced into the E . coli Rosetta strain , which was cultured at 37°C in LB medium supplemented with 100 μg/ml ampicillin to an optical density at 600 nm of 0 . 8–1 . 0 . Protein expression was induced by adding 1 mM IPTG at 20°C for 16 h . Cells were harvested by centrifugation and re-suspended in lysis buffer ( 20 mM Tris pH 8 . 0 , 0 . 5 M NaCl , 10% Glycerol ( V/V ) , 1% triton ( V/V ) supplemented with 1 mg/ml of lysozyme ( Sigma ) , DNAse ( Sigma ) and Ethylenediamine tetra-acetic acid ( EDTA ) -Free protease inhibitors cocktail ( Roche ) ) . Cells were disrupted by sonication and the resulting lysate was cleared by centrifugation ( 20 min at 16 , 000 x g at 4°C ) . For GST and GST-IglG , the supernatant was loaded onto a GST-HiTrap column ( GE Healthcare ) , equilibrated with 15 ml of buffer ( 20 mM Tris pH 8 . 0 , 300 mM NaCl , 5% glycerol ) . The column was washed with 20 ml of buffer containing 20 mM Tris pH 8 . 0 , 300 mM NaCl , 5% glycerol ( V/V ) . The proteins were eluted with 10mM glutathione and concentrated using Amicon 0 . 5 mL 10K ( Millipore ) centrifugal devices . The proteins were dialysed against 20 mM Tris pH 8 . 0 , 300 mM NaCl , 5% Glycerol ( V/V ) at 4°C . To reach more than 95% purity ( as assessed by sodium dodecyl sulphate–polyacrylamide-gel electrophoresis ) , the proteins were further purified by gel filtration using the Superdex 75 column or by the anion exchange monoQ hiTrap Q HP column ( GE Healthcare ) . His6-IglG protein was extracted from the insoluble fraction . Following lysate centrifugation , the pellet was re-suspended in 8M Urea buffer , 20mM Tris pH8 under strong agitation during 20 minutes at room temperature . The resulting solubilized proteins were cleared by centrifugation ( 15 min at 10 , 000 x g at 4°C ) . Supernatant was collected and loaded onto a 5 ml His-trap column ( GE Healthcare ) . The column was washed once with 10mL buffer containing 8M Urea buffer , 20mM Tris pH8 , 1M NaCl , once with buffer containing 25 mM imidazole . The protein was eluted with a linear gradient of imidazole ( 25–500 mM ) in Urea 8M , 20mM Tris pH8 . His6-IglG was refolded by dialysis overnight in buffer containing 20mM Tris pH8 , 250mM arginine , 100mM NaCl , 5% glycerol , 1mM EDTA . Arginine was eliminated by three sequential 2 hours dialysis at 4°C , the first one in buffer containing 20mM arginine , 20mM Tris pH8 , 300mM NaCl , 5% glycerol , 1mM EDTA , the second and the third ones in 20mM Tris pH8 , 300mM NaCl , 5% glycerol , 1mM EDTA . Protein concentrations were determined by measuring the UV light absorbance at 280nm . 100 μl of each sample was diluted to 10 ml in 2% HNO3 with Indium as an internal standard . Zn , Fe , Ni , Cu , Pb and In were measured by ICP-MS ( iCAP Q ThermoFisher Scientific ) with running conditions presented in S3 Table . Far-UV CD spectra ( 190nm-260nm ) of His6-IglG were recorded on a Chirascan Circular Dichroism Spectrometer ( Applied photophysics ) calibrated with ( 1S ) - ( + ) -10-camphorsulfonic acid . Measurements were carried out at room temperature in a 0 . 1cm path length quartz cuvette . Parameters were set as followed: wavelength range 180-260nm , 0 . 2nm increment , bandwith 0 . 5nm; scan speed , 50 nm . min-1; response time 1s . Spectra were corrected by subtracting buffer contributions and protein dilution factors before calculating the mean residue molar ellipticity . Spectra deconvolution was performed using Dichroweb server [74] and the Selcon3 algorithm [75] on His6-IglG refolded in the presence of FeCl2 at 1mM and dialyzed four times in a buffer consisting of NaH2PO4 20mM , NaF 300mM , pH 7 . 5 . To follow metal binding by CD spectrometry , His6-IglG in Tris 20mM pH8 , NaCl 200mM , glycerol 5% was diluted to 10 μM in NaH2PO4 20mM pH 7 . 5 , NaF 300mM , glycerol 5% buffer . Spectra were measured immediately after dilution or following addition of increasing amount of FeSO4 , ZnSO4 , MgSO4 , CoSO4 from 0 . 2 to 2 molar equivalent of protein . TCEP was incubated with His6-IglG at 0 . 5 , 5 and 50 molar equivalent of protein for up to 4 hours on ice before spectra acquisition . Spectra were smoothed using Prism software . F . novicida expressing 2xHA- or TEM1-tagged proteins were subcultured to an O . D . 600 of 1 . 5 and lysed by sonication in Tris 50mM , NaCl 150mM buffer containing an EDTA-free protease inhibitor cocktail ( Roche ) . HA2-tagged proteins expressing lysates ( equivalent to 4 ml of bacterial culture ) or 2% BSA in Tris 50mM , NaCl 50mM as a control were incubated with 10μl of anti-HA agarose antibody ( Sigma ) slurry for 2h at 4°C . Beads were washed four times with the same buffer and incubated for 3h at 4°C with TEM1-tagged proteins expressing lysates ( equivalent to 4 ml of bacterial culture ) under mild agitation . Beads were then washed twice with Tris 50mM , NaCl 50mM , NP40 1% and twice with Tris 50mM , NaCl 50mM before being boiled in Laemmli sample buffer ( Tris-HCl 50mM , SDS 1% , glycerol 5% , Bromophenol blue 0 . 0005% ) with β-mercaptoethanol ( 286 μM ) . Protein lysates for immunoblotting were prepared by using Laemmli sample buffer . Protein lysates corresponding to equal OD600 were loaded on 4–12% Bis/Tris gels ( Invitrogen ) , and run in TGS buffer . Protein transfer was performed with iBlot gel transfer stacks ( Invitrogen ) . Membranes were probed with the following monoclonal antibodies: mouse anti-PdpB , mouse anti-IglC , and mouse anti-IglB ( all provided from BEI Resources , Manassas , VA , USA ) or via commercially available anti-HA ( clone HA-7 , Sigma ) , anti-Penta-His ( Qiagen , MD , USA ) . A secondary horseradish peroxidase ( HRP ) -conjugated goat anti-mouse antibody ( Santa Cruz Biotechnology , CA , USA ) and the Enhanced Chemiluminescence system ( ECL ) ( Amersham Biosciences , Uppsala , Sweden ) were used . Protein secretion during infection was estimated using TEM-β-lactamase fusion proteins and CCF4 substrate as previously described [24] by flow cytometry analysis using a Canto II analyser ( BD ) . The in vitro KCl assay was performed as previously described [23] . Briefly , F . novicida was grown overnight in TSB containing 0 . 1% cysteine . The overnight culture was diluted to OD600 = 0 . 3 in medium with or without 5% KCl . Upon reaching an OD600 = 1 . 5 , the culture was centrifuged at 4 , 700 x g for 20 min at 4°C . The supernatant was filtered-sterilized using a 0 . 22 μm filter and proteins were extracted by methanol-chloroform precipitation . Protein extracts corresponding to 0 . 045 and 1 OD600 of the pellet and the concentrated supernatant fraction , respectively were loaded onto the SDS-PAGE gel and immunoblotted with the indicated antibodies . Statistical data analysis was performed using unpaired t-tests and Prism 5 . 0a software ( GraphPad Software , Inc . ) . Two-tailed P-values are shown . For survival experiments , Log-rank ( Mantel-Cox ) test were performed . The following convention is used: * , P ≤ 0 . 05; ** , P ≤ 0 . 01; *** , P ≤ 0 . 001 . For each strain , when applicable , the discontinued NCBI gene record and the current NCBI locus tag are indicated . For F . tularensis strains , the two FPI loci are indicated . NA: Not applicable . VgrG: U112; FTN_1312; FTN_RS06720; LVS; FTL_0123; FTL_RS00610; FTL_1169; FTL_RS05950; SCHU S4; FTT_1702; FTT_1347 IglC: U112; FTN_1322; FTN_RS06770; LVS; FTL_0113; FTL_RS00560; FTL_1159; FTL_RS05900; SCHU S4; FTT_1712; FTT_1357 IglG: U112; FTN_1314; FTN_RS06730; LVS; FTL_0121; FTL_RS00600; FTL_1167; FTL_RS05940; SCHU S4; FTT_1704; FTT_1349 IglF: U112; FTN_1313; FTN_RS06725; LVS; FTL_0122; FTL_RS00605; FTL_1168; FTL_RS05945; SCHU S4; FTT_1703; FTT_1348 FTN_0054: U112; FTN_0054; FTN_RS00280; LVS; NA; SCHU S4; NA FTN_0052: U112; FTN_0052; FTN_RS00270; LVS; NA; SCHU S4; NA P . aeruginosa ( strain PAO1 ) : PA_0824 | Francisella tularensis is a highly pathogenic bacterium causing tularemia . Its ability to cause disease is linked to its ability to replicate in the macrophage cytosol . The intracellular life cycle of Francisella is controlled by a type VI secretion system ( T6SS ) , which is thought to inject effectors into the host cell to allow bacterial escape into the host cytosol . The molecular mechanisms behind this process are still largely unclear . In this work , we identify IglG as a protein with two important domains , one conserved in proteins from more than 250 bacterial species ( DUF4280 , renamed here as PAAR-like domain ) and one specific for the Francisella genus . Using protein sequence analysis and three-dimensional structure predictions , comparative modeling and biochemistry approaches , our data demonstrate that IglG is a metal-binding protein that based on its PAAR-like domain might cap the VgrG spike of the T6SS and act as a membrane-puncturing protein . Furthermore , we identified that the Francisella-specific domain is directly involved in forming a protein complex with another virulence protein , IglF . This work , in addition to enhancing the molecular understanding of the Francisella T6SS , defines the features of the conserved DUF4280 , a novel PAAR-like domain involved in type VI secretion ( T6S ) of many bacterial species . | [
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] | 2016 | Francisella tularensis IglG Belongs to a Novel Family of PAAR-Like T6SS Proteins and Harbors a Unique N-terminal Extension Required for Virulence |
The RIG-I-like receptor ( RLR ) pathway is essential for detecting cytosolic viral RNA to trigger the production of type I interferons ( IFNα/β ) that initiate an innate antiviral response . Through systematic assessment of a wide variety of genomics data , we discovered 10 molecular signatures of known RLR pathway components that collectively predict novel members . We demonstrate that RLR pathway genes , among others , tend to evolve rapidly , interact with viral proteins , contain a limited set of protein domains , are regulated by specific transcription factors , and form a tightly connected interaction network . Using a Bayesian approach to integrate these signatures , we propose likely novel RLR regulators . RNAi knockdown experiments revealed a high prediction accuracy , identifying 94 genes among 187 candidates tested ( ~50% ) that affected viral RNA-induced production of IFNβ . The discovered antiviral regulators may participate in a wide range of processes that highlight the complexity of antiviral defense ( e . g . MAP3K11 , CDK11B , PSMA3 , TRIM14 , HSPA9B , CDC37 , NUP98 , G3BP1 ) , and include uncharacterized factors ( DDX17 , C6orf58 , C16orf57 , PKN2 , SNW1 ) . Our validated RLR pathway list ( http://rlr . cmbi . umcn . nl/ ) , obtained using a combination of integrative genomics and experiments , is a new resource for innate antiviral immunity research .
Viruses are a major cause of human disease , as highlighted by the pandemics of influenza viruses , HIV–1 , and the current outbreak of the Ebola virus . Pattern recognition receptors ( PRR ) are among the first molecules that detect viruses during infection . The RIG-I-like receptors ( RLRs , one class of PRRs ) are part of the RLR pathway , which forms a crucial innate antiviral defense system [1 , 2] . Two RLRs , RIG-I and MDA5 , reside in the cytosol where they recognize non-self 5’-triphosphate RNA molecules with short double-stranded regions and long double-stranded RNAs ( dsRNA ) , respectively [3] . Activation of the receptors triggers a complex signaling network , key steps of which are the activation of the mitochondrial adapter MAVS , subsequent recruitment of the TBK1 and IKK complexes , phosphorylation/activation of IRF3 and NFκB , and translocation of these transcription factors to the nucleus . These steps ultimately lead to the production of type I interferons ( IFNα/β ) and proinflammatory cytokines , which are crucial for establishing an antiviral state in infected as well as neighboring cells , and also modulate the adaptive immune response [4] The importance of the RLR system is further demonstrated by the observation that viruses of all types employ strategies to interfere with its activation , often at multiple steps [5 , 6] . Better understanding of viral interaction with the pathway has resulted in novel targets for the development of antiviral therapeutics and attenuated live vaccines , for example viruses lacking functional RLR antagonists [7] . Furthermore , mutations in RIG-I , MDA5 , MAVS and other RLR pathway components are associated not only with strong susceptibility to infections , but also IFN-associated autoimmune disorders [8–10] . Previous studies into virus-host interactions and the innate antiviral pathways have used genomics approaches , often generating large data sets describing physical or genetic interactions [11–14] . Other publications have taken a comparative approach based on model organisms [15] or used over-expression screening systems [16 , 17] . Together , these studies have identified numerous genes with antiviral activity , including members of the RLR pathway . However , it remains important to systematically assess the quality of individual data sets as such screens report distinct sets of genes , often with limited overlap between them . Combining the many available genomics data sets in a statistical framework potentially allows for a more systematic discovery and categorization of genes involved in the RLR pathway . Indeed , Bayesian integration of large-scale data that includes weighing individual datasets for their predictive potential has been successful in other cellular systems , for example identifying novel protein interactions [18] , mitochondrial disease genes [19] , and small RNA pathway genes [20] . In this work we systematically exploit the wealth of available ( gen ) omics data , including transcriptomics and proteomics data , genome sequences , protein domain information , and functional genomics , to discover descriptive molecular signatures of the RLR pathway system . Bayesian integration of these data , together with comprehensive computational and experimental validation , confidently identifies novel genes involved in antiviral RIG-I signaling .
To discover molecular signatures that distinguish RLR pathway components from other genes , we explored a wide variety of genome-scale data describing different aspects of the virology and biology of the pathway . Some of these data we used directly , while other data were used as the basis for further calculations ( Table 1 ) . We quantitatively assessed the predictive power of each data set using a literature-curated standard of 49 known RLR pathway components from InnateDB [21] ( ‘RLR genes’ , S1 Fig ) and a set of 5 , 818 ‘non-RLR genes’ that are unlikely to be part of the pathway ( i . e . genes with known functions not directly related to the innate antiviral response , such as development , housekeeping and neurological processes , see Methods ) . Below we describe 10 signatures for predicting novel RLR pathway components . The first five signatures are based on the relationship of RLR genes with viruses , whereas the second set of five signatures are based on properties of the RLR pathway itself . The RLR pathway components published thus far probably constitute only part of the total proteins with a function in this pathway . To prioritize novel high-confidence genes for a role in the RLR pathway , we integrated the 10 identified molecular signatures of RLR genes in a naive Bayesian classifier [18 , 19] ( see Methods ) . This approach weighs data sets based on their predictive value ( i . e . their ability to separate known positives and negatives; Fig 1A , Tables 1 and S1 ) so that ‘better’ data contribute more to the predictions . Each human gene received a posterior probability score ( ‘RLR score’ ) reflecting the likelihood that the gene is part of the RLR pathway based on its behavior in the collected genomics data . A score of zero indicates equal probabilities of a gene being an RLR versus a non-RLR gene . S6 Table presents the genome-wide ranking of RLR scores ( also available at http://rlr . cmbi . umcn . nl/ ) . As expected , known RLR pathway components have the highest RLR scores ( Figs 1B and S5 ) . Two-thirds ( 32/49 ) of these rank within the first 150 genes . The top ranking genes are IRF7 , RIG-I , IKKε , subunits of NFκB , TRADD , TRAF2 , MDA5 , and IKKγ ( NEMO ) ( Fig 1C ) . Other examples of well-described RLR pathway components include IRF3 ( rank 51 ) , ISG15 ( 52 ) , MAVS ( 102 ) , and LGP2 ( 114 ) . Genes that are unlikely to play a role in the pathway ( the set of non-RLR genes ) generally have very low RLR scores , although some of these received high scores as well ( Fig 1B ) . This is not unexpected , as even though this large set of genes was selected from function annotations generally unrelated to the innate antiviral response , this does not preclude that individual genes ( also ) function in the RLR pathway . To gain insight into what kind of genes are present among the RLR predictions , we examined their functions by pathway and gene ontology enrichment analyses . The top 354 genes with the highest RLR scores ( corresponding to high-confidence predictions , see below ) have strong links with other pathways of the innate immune response , such as TLR , NLR , interferon , and cytokine signaling ( S6 and S7 Figs ) . Antiviral defense functions are also among the most frequent and significant terms associated with the high-scoring genes ( S8 Fig and S7 Table ) . Other important biological processes include various apoptosis-related functions , cancer and cell cycle pathways , and regulation of metabolic processes and protein localization . Furthermore , the top predictions include a wide range of protein families , notably proteasome subunits , ubiquitin ( -like ) conjugating enzymes , and genes involved in phosphatidylinositol signaling ( which was recently shown to affect the type I IFN response [14] ) . Finally , 22% of the top predictions are induced in cells treated with interferons ( i . e . they are interferon-stimulated genes , ISGs ) and ~18% are part of the common host transcription response to pathogens ( Table 2 ) . Together , these observations indicate that our framework successfully predicts genes with a likely role in the innate antiviral response and suggests other cellular systems and functions required for this response . We further computationally assessed the reliability of the integrated RLR score by estimating the sensitivity , specificity and false discovery rate ( FDR ) of the predictions using the positive ( RLR genes ) and negative ( non-RLR genes ) standards . Integration of the data sets achieved better sensitivity and specificity than any of the individual data sets ( Fig 1D ) , thereby enriching for RLR genes and depleting false positives ( S5 , S9 and S10 Figs ) . At an RLR rank threshold of 354 ( RLR score -1 . 10 ) , the framework correctly predicts 78% of the known RLR genes with a specificity of 98 . 4% ( Fig 1D ) . At this threshold , only ~57% of the novel predictions are estimated to be false ( S11 Fig , adjusted FDR to match the expected total number of genes involved in the RLR pathway , see Methods ) . This compares to a genome-wide false discovery rate ( i . e . when predicting genes randomly ) of ~99% . Thus , the integrated RLR score increases the probability of correctly identifying novel RLR genes by a factor of 43 compared to random classification . Because we used the same gene sets for calculating the RLR scores and estimating the performance of the resulting predictions ( i . e . without systematic cross-validation ) , there exists a danger of circular reasoning . Therefore , we also carefully validated the quality of the results using various independent and external data sets . First , we examined the high RLR scores for genes that have a known function in innate immunity , but not in the RLR pathway , and therefore were not part of our training set . Components of other PRR signaling pathways ( TLR , CLR , NLR , cytDNA ) have lower scores than RLR genes , but much higher scores than the rest of the genome ( Fig 1B ) . The same is true for genes functioning in other aspects of the innate immune response ( Fig 1B ) . Of the 225 novel predictions ( i . e . those genes that are not part of the training sets ) in the top 354 ( FDR of 57% , see above ) , 142 ( ~63% ) are part of these innate immunity gene lists ( Fig 1C ) . Thus , the majority of high-scoring genes with no known link to the RLR pathway in fact have a function in other PRR pathways or other parts of innate immunity , supporting the relevance of our predictions . Second , we compared our predictions to six recent data sets that are relevant to the innate ( antiviral ) response but that were in no way part of the RLR score calculations . The overlap with the 354 top genes , excluding known RLR genes , is significantly larger than expected by chance for all these data sets ( Table 2 ) . For example , the top predictions include: ( i ) 19 of 45 ( 42% ) interferon-stimulated genes with validated antiviral activity against e . g . HIV–1 , HCV , yellow fever , West Nile or chikungunya virus [16] , ( ii ) 27 proteins from a set of 241 ( 11% ) that interact with the type I IFN protein network during pattern recognition , among which are five confirmed modulators of IFNβ expression and antiviral activity [12] , ( iii ) nine tripartite motif ( TRIM ) family genes , five of which enhance RIG-I-induced activation of IFNβ , NFκB and ISRE ( IFN-stimulated response element ) promoters [17] , and ( iv ) 38 human proteins interacting with innate immune-modulating viral open reading frames ( viORFs ) from 30 viruses [37] . ( v ) Furthermore , the type I IFN response has recently been proposed to play a role in antifungal immunity [38 , 39] and the top RLR predictions are strongly enriched for genes expressed in PBMCs stimulated with the fungal pathogen Candida albicans: almost half ( 39/89 = 44% ) of these occur in our top predictions ( P = 4 . 7 × 10−47 , one-tailed Fisher’s exact test , Table 2 ) . ( vi ) Finally , the overlap between our predictions and a genome-wide screen for regulators of RIG-I-mediated IFNβ production is , at only nine , marginal but significant ( 9/226 genes = 4% , P = 9 . 5 × 10−3 , one-tailed Fisher’s exact test , Table 2 ) [14] . In summary , these diverse and independent experimental data support the validity of our integrated RLR score for predicting genes with a role in the innate antiviral response . To further determine the predictive power of our in silico predictions , we selected 187 candidate RLR genes for experimental validation ( S6 and S8 Tables ) . These include 127 high-confidence candidates from the top 354 , which have not been previously linked to the RLR pathway , supplemented with 60 candidates we selected from the top 1000 predictions , mainly on the basis of limited functional characterization in general ( Fig 2A ) . Importantly , candidates with a known role in RLR signaling , other branches of PRR pathways , or apoptosis were excluded as we were most interested in finding novel components of the RLR pathway . For the selected candidates we performed a medium-throughput RNAi screen ( RNAi screen 1 ) using HeLa cells stably expressing an IFNβ promoter-controlled firefly luciferase reporter ( HeLa-IFNβ-Fluc ) . To activate the RLR pathway and induce Fluc reporter expression we used a known small 5’-ppp-containing RIG-I ligand [40] . This setup led to specific activation of RIG-I , as RIG-I or MAVS siRNA transfection , but not MDA5 or scrambled siRNAs , resulted in loss of reporter activity ( Figs 2B , S12 and S13 ) . All negative controls ( non-transfected , scrambled and MDA5 siRNAs ) scored within 1 . 25 median absolute deviations of the plate normalized IFNβ induction levels ( Z-score cutoff <-1 . 25 or >1 . 25 , Fig 2B ) . At this cutoff , siRNA knockdown of 94 candidates ( 50% of all candidates tested ) affected RIG-I-mediated IFNβ induction ( Figs 2A and S13A–S13D and S8 Table ) . Among these , knockdown of 59 genes decreased RIG-I-mediated IFNβ induction ( down-hits ) and 35 genes increased IFNβ induction ( up-hits ) . It is important to note that the experimental approach only activates the RIG-I branch of the RLR pathway and will not confirm predicted RLR candidates that regulate MDA5 activation and downstream signaling to MAVS . Thus , among the 93 non-confirmed candidates , there might still be novel regulators of the MDA5-mediated IFNβ induction pathway , which should be further investigated . Altogether , the integrated RLR score is clearly a strong and reliable predictor for novel regulators of the RIG-I pathway . From the 94 confirmed hits , we picked the 57 top hits with the largest effect ( stringent Z-score <-2 or >2 ) for a second RNAi screen using a different set of siRNAs ( RNAi screen 2 , Fig 2A ) . In this second RNAi screen , only a single up-hit ( 7% of 15 up-hits tested ) showed a Z-score >1 . 25 . Besides this hit , two negative control wells also had a Z-score >1 . 25 ( Figs 2B , S13E–S13H ) , which suggests that the single confirmed up-hit might be unreliable . The poor reproducibility of the up-hits might be attributed to the screening approach . For instance , we used a large amount of 5’-ppp-containing RIG-I ligand ( see Methods ) , leaving limited room for increased pathway activation . In contrast , the second RNAi screen confirmed 26 down-hits at Z-score <-1 . 25 ( 62% of the 42 down-hits tested ) . Of these , 19 genes ( 45% of tested down-hits ) could be confirmed at a conservative Z-score <-2 ( Fig 2A , 2B and 2C , S8 Table ) . Taken together , the two RNAi screens , guided by the predicted RLR candidates , have substantiated the validity of our approach and have revealed potential novel regulators of the RIG-I receptor pathway . To gain further understanding of how the 19 top hits affect RIG-I-mediated IFNβ promoter activation , another RNAi screen was performed ( RNAi screen 3 ) . In contrast to the first two screens , here we did not use the IFNβ promoter-controlled Fluc reporter translation as readout , but we measured IFNβ mRNA levels using quantitative real-time ( qRT ) -PCR . As expected , knockdown of RIG-I and MAVS abrogated 5’-pppRNA-induced IFNβ mRNA transcription , while MDA5 knockdown [40] and LGP2 knockdown , which regulates only the MDA5-mediated IFNβ mRNA transcription , had no effect ( Fig 2D ) . Of the 19 top hits from the first two RNAi screens , 13 genes ( 68% ) in this third screen again showed a reduction in RIG-I pathway activation . Nine of these showed a significant reduction ( NUP98 , TRIM14 , C16orf57 , PSMA3 , G3BP1 , DDX17 , MAP3K11 , SNW1 , CDK11B; P < 0 . 01 , one-way ANOVA with Dunnett’s post hoc test; Fig 2A and 2D ) , suggesting that these gene products play a so far uncharacterized role in the RIG-I signaling pathway upstream of IFNβ mRNA transcription . In summary , using RNAi-based screening methods we validated more than 50% of the tested candidates . To further assess the predictive power of the in silico integrated RLR score , we ranked the experimentally tested genes based on their RLR score and sequentially calculated the fraction of hits ( either considering all 94 hits from RNAi screen 1 , or only the 57 top hits ) among all tested genes having a certain RLR score or higher ( Fig 2E ) . Higher RLR scores were experimentally confirmed more often , indicating that these indeed correspond to more confident predictions . Further analysis revealed that there is no molecular signature that solely explains the predictions of the validated hits; rather the integrated score of the 10 molecular signatures is important ( S14 Fig ) .
To identify defining signatures of RLR genes in genomics data , we largely depended on current knowledge of the biology of the RLR system and its relationship with viruses . For example , since previous studies had shown that viral antagonism of specific RLR pathway components is prevalent [5 , 6] , one of the first features we investigated , and indeed established , was that human-virus PPIs are a general theme for the RLR pathway as a whole . Similarly , guided by previous observations , we demonstrated that RLR genes conform to the tendency of immunity genes to evolve rapidly and commonly contain innate antiviral TF binding motifs , such as IRF and NFκB , in their promoters . We also included several criteria that are effective for many different biological systems , but were specifically aimed at predicting novel RLR pathway genes in our case , such as the RLR co-expression calculations and RLR protein domain occurrences . We decided not to include associations based on text mining of published literature ( e . g . co-mentioning of gene names in abstracts ) , because such approaches in our hands only enriched for genes already known to be involved in the RLR pathway and therefore compromised our ability to identify novel candidates . Finally , we settled on using a total of 10 molecular signatures that are relevant and predictive for the RLR system . Inclusion of additional data sets , generated for example by future experimental techniques , and substitution of existing data with novel and improved versions , will likely refine this data-driven definition of RLR genes over time and lead to updated Bayesian RLR probabilities that could further improve prediction accuracy . A major challenge in our study arises from the fact that the RLR pathway is highly interconnected with other intracellular pathways , such as other innate PRR pathways ( e . g . TLR and cytosolic DNA sensing ) , the stress response pathway , mitogen-activated protein kinase ( MAPK ) signaling cascades ( e . g . TRAF2 and 6 lead to the p38 MAP kinases ) , and apoptosis ( e . g . via CASP8 and 10 ) ( S1 Fig ) [2 , 12 , 41 , 42] . Although our approach for predicting novel RLR components relied on a well-defined set of genes known to make up the core of RLR signaling , the overlap with other systems was a potential confounding factor . For example , most molecular signatures of RLR genes identified here , especially the virus-based properties such as PPIs with viruses , rapid evolution , and differential expression during infection , could also apply to genes involved in other aspects of antiviral immunity . Nevertheless , combination of the right signatures achieved reasonable specificity for RLR genes ( Fig 1 ) . Thus , we have extended an approach previously used for identifying components of membrane-enclosed organelles such as the mitochondrion [19] and showed that it is also possible to capture the complexity of a diverse and interconnected intracellular signaling pathway . The presented approach for identifying predictive signatures , followed by Bayesian integration , could potentially be applied to any cellular system . Using the sets of known RLR and non-RLR genes , we could systematically assess the relative quality of the individual data sets for predicting novel RLR genes . Indeed the 10 molecular signatures have different predictive values as shown by the likelihood ratio scores ( Fig 1A , Tables 1 and S1 ) , and thus contribute with different weights to the integrated Bayesian RLR score . The data types with the strongest predictive value include NFκB activation mediators , RLR pathway protein domains , and both PPI signatures ( PPIs within the RLR pathway and PPIs between human and viral proteins ) . In contrast to our expectations , antiviral host factors identified in high-throughput RNAi experiments had a relatively small contribution . Besides raw predictive ability , we also considered the coverage of the data sets . For example , there are only few ( <200 ) NFκB activation mediators and antiviral host factors , while the data sets on RLR co-expression , viral miRNA targets , and innate antiviral TF binding motifs identified many more genes ( >4 , 000 ) . Integration of all data sets with their varying coverage and predictive value into a single RLR score resulted in a classifier that is superior to the individual data sets ( Fig 1D ) . This is underscored by the observation that the individual signatures by themselves are unable to explain the predictions for the experimentally validated RLR candidates , and only the integrated RLR score explains all validated genes ( Figs 2E and S14 ) . Aside from our own experimental validation strategies , recent independent studies have confirmed a role for 15 of our predicted RLR candidates in the RLR pathway during viral infection ( Table 3 ) . Most of these publications appeared during the course of our study , and thus were not part of the knowledge or data used for predicting novel RLR genes . For example , TRIM14 ( RLR rank 491 ) has been demonstrated to interact with MAVS leading to activation of IRF3 and NFκB via IKKγ ( NEMO ) [43] . Indeed , our predictions marked TRIM14 as a strong candidate RLR gene and all our RNAi screens confirmed it as a component required for optimal RIG-I signaling ( Fig 2 ) . Two additional high-confidence RLR predictions for which we validated an effect in all three RNAi screens have recently been validated externally as well: G3BP1 [44] and CDC37 [13] . Of the 15 genes recently described in the literature , 11 were part of the candidate RLR genes tested in our RNAi screens ( Table 3 ) . Of these , seven genes affected RIG-I-mediated IFNβ induction in RNAi screen 1 ( Z-score <-1 . 25 or >1 . 25 ) and showed a consistent effect in RNAi screen 2 . Therefore , our experimental screening condition appears to detect these described RIG-I pathway regulators with a sensitivity of ~64% ( 7/11 ) . Furthermore , four out of four down-hits from our experiments ( i . e . genes that decreased IFNβ induction when knocked down , hence positive regulators ) that have been described in the literature were indeed described as positive regulators of RIG-I signaling ( Table 3 ) . Given that our experimental approach detected most , but not all , of the published RIG-I regulators , a substantial number of our predicted RLR candidates not validated by our RNAi screens might still play a role in for example a different cell type , downstream of type I IFN production , or regulate the pathway via MDA5/LGP2 activation . For example , RNF114 ( RLR rank 181 , Z-score RNAi screen 1 = 1 . 68 , Table 3 ) is an ISG and therefore needs to be up-regulated via a positive feedback loop to fully contribute to RLR pathway stimulation [45] . This gene was not confirmed in all RNAi screens , perhaps because the time of RIG-I stimulation in our screens ( 6 hours ) was simply too short . Similar biological reasons could limit the detection of an effect for other genes as well . Therefore we conclude that the hits identified in our RNAi validation experiments may be a conservative estimate of the number of correct RLR predictions . We identified 13 novel RIG-I pathway regulators that reduced IFNβ induction in all three RNAi screens ( Fig 2 ) . These include cell cycle gene CDK11B , heat shock protein HSPA9B , MAP kinase MAP3K11 , proteasome subunit PSMA3 , nucleoporin NUP98 [46] , and the recently identified RLR regulators CDC37 [13] , G3BP1 [44] and TRIM14 [43] ( Table 3 ) . The remaining five genes , DDX17 ( DEAD box helicase 17 ) , C6orf58 , C16orf57 ( USB1 , U6 snRNA biogenesis 1 ) , PKN2 ( serine/threonine protein kinase N2 ) , and SNW1 ( SNW domain containing 1 ) , are overall least characterized . To obtain a first suggestion about how these genes might regulate RLR signaling , we searched for connections with the known human and viral protein interaction networks . Next , we discuss the reported interactions of DDX17 and SNW1 with the RLR pathway . DEAD box RNA helicase DDX17 was recently found to bind Rift Valley fever virus RNA and restrict viral replication in an interferon-independent manner [47] . Our data now suggest a role for DDX17 in RIG-I-mediated IFNβ production as well . DDX17 has reported protein interactions with two other RIG-I regulators identified in our study: CDC37 and CSNK2A1 . Interestingly , DDX17 also interacts with the peptidylprolyl cis/trans isomerase PIN1 [48] , which inhibits RIG-I-mediated IFNβ production by inducing degradation of IRF3 [49] . Furthermore , DDX17 was present among a set of ISG15-modified ( ISGylated ) proteins in HeLa cells treated with IFNβ [50] . Thus , DDX17 could function in IRF3 activation by acting as a negative regulator of PIN1 and might be regulated by ISGylation ( Fig 3 ) . Lastly , DDX17 seems to be a preferred target of viral interference , having reported interactions with six different viruses ( e . g . HIV–1 Rev and influenza virus A NS1 , Fig 3 ) . SNW1 is an intrinsically disordered protein [51 , 52] that interacts with two other newly identified RLR regulators from our study , namely PKN2 [53] and C16orf57 [54] . SNW1 also interacts with the IKBKG ( NEMO ) protein [55] , which is required for NFκB and IRF3 activation [56] . Given that our data shows that knockdown of SNW1 reduces IFNβ induction , SNW1 could be involved in NEMO regulation and thereby contribute to activation of the RLR pathway TFs , NFκB and IRF3 ( Fig 3 ) . The fact that SNW1 was also identified in a siRNA screen for mediators of virus-induced NFκB activation [34] strengthens this hypothesis . Further studies should be conducted to resolve the precise mode-of-action . We have validated the integrated RLR score with various experimental , literature and computational approaches . Our confirmations of a substantial fraction of the predicted RLR genes suggest the value of the prioritized list as a whole . The genome-wide prioritization of RLR pathway components is available in S6 Table and at http://rlr . cmbi . umcn . nl/ , and can be used as a resource in several ways . For example , it can serve in the evaluation of data sets relevant to the innate antiviral and antifungal responses ( Table 2 ) . Many labs routinely consult internal data sets to decide which genes to study further . Comparison of such lists with for example high-scoring RLR candidates could provide insights into the quality of individual data sets for identifying antiviral genes and provide complementary hints about which genes could be important . Finally , the RLR resource could be used for prioritizing genetic variants in patients suffering from severe susceptibility to viral infections or inflammatory disorders caused by inappropriate production of type I interferons . In this work , we have combined integrative genomics with experiments to discover 10 molecular signatures of a cellular signaling system that is central to human infectious disease: the innate antiviral RIG-I-like receptor ( RLR ) pathway . The described signatures span multiple layers of genomics data and provide new insights into the regulation of virus detection and immune signaling . Probabilistic integration of the data resulted in a confident genome-wide ranking of candidate RLR pathway genes . RNAi validation experiments confirmed 94 of 187 novel RLR candidates tested , including 13 novel factors with strong effects on antiviral signaling . These results , together with independent computational and literature-based confirmation , demonstrated the validity and high accuracy of our approach . Our study expands the collection of known antiviral genes , opening up new avenues for research into innate antiviral immunity .
All data sets were calculated for and mapped to a reviewed reference set of 20 , 245 human proteins from UniProtKB/Swiss-Prot , release 2011_11 [57] . This set consists of one manually annotated record for each validated protein-coding gene . Gene/protein identifier mapping was performed using a mapping table from the same UniProt release . Ambiguously mapped identifiers were curated manually . To systematically define RLR pathway components , we mined genome-scale data from a wide variety of sources . The data describe different aspects of the biology of the pathway; from the DNA to the protein level , highlighting evolutionary processes , virus-host interactions , sequence families , etc . We finally settled on 10 data sets that collectively distinguish RLR pathway components from other genes ( see Table 1 for an overview and brief descriptions ) : We assessed the capability of individual data sets to predict novel RLR genes using two ‘gold standard’ training sets: Two additional curated sets of genes were used in our study ( S6 Table ) . The first consists of 153 genes with a known function ( i . e . receptors , signaling components , etc . ) in four PRR signaling pathways; the Toll-like receptor ( TLR ) , C-type lectin receptor ( CLR ) , NOD-like receptor ( NLR ) , and cytosolic DNA sensing ( cytDNA ) pathways , but not in the RLR pathway . TLR , NLR and cytDNA components were obtained from InnateDB ( 27 Mar . 2012 ) . We curated a list of 34 CLR pathway components , based mainly on [78] . The combined PRR pathway gene set was supplemented with several key proteins involved in virus-host interactions . As with the set of RLR genes , cytokines and other secreted proteins were excluded . The second list ( ‘other innate immunity genes’ ) consists of 803 genes with curated annotations from InnateDB ( 12 Jan . 2012 ) for a function in other aspects of the innate immune response , excluding RLR and other PRR signaling pathway genes . Individual ( genomics ) data sets contain important information about the make-up of cellular systems and pathways , but often have limited coverage and introduce data type-specific noise . Combination of multiple heterogeneous types of data , each approaching the characterization of a molecular system from a different angle , therefore has the potential to provide a more complete definition of the system and could have high power for predicting novel components involved . We employed a naive Bayesian framework to facilitate direct comparison and weighing of many data sets describing properties of RIG-I-like receptor pathway components and integrate those data sets that were suitable into a single probabilistic score for each gene . Bayesian integration is well suited to combining evidence from dissimilar types of information and readily accommodates missing data [18–20] . Furthermore , this approach inherently weighs data sets based on their predictive value ( i . e . their ability to separate known positives and negatives , Fig 1A and S1 Table ) so that better data contribute more to the predictions . Indeed , integration enriches for RLR genes and depletes false positive , non-RLR genes ( S5 and S9 Figs ) . Plots , statistics and other calculations were done using custom Perl and SQL scripts , and the R statistical package [83] with additional packages gplots [84] , ROCR [85] and RNAither [86] . One-way ANOVA with Dunnett's post hoc test was performed using GraphPad Prism ( GraphPad Software ) . | Viruses pose a continuous threat to human health , even though our immune systems have evolved to neutralize invading viruses . As part of the innate immune system , the RIG-I-like receptors ( RLRs ) are essential for detecting viruses during infection . Recognition of viral RNA by the RLRs triggers an antiviral response that inhibits viral replication , protects uninfected cells , and attracts specialized immune cells . Better understanding of the innate antiviral response may reveal novel targets for antiviral therapeutics and vaccine development . However , that requires knowledge about which genes and proteins are involved . In the present study , we systematically investigated the wealth of available genomics data ( including gene expression , protein interactions , transcription regulation and genome sequences ) and discovered no less than 10 distinctive properties of genes known to be part of the antiviral RLR pathway . By combining these properties in a statistical framework , we predicted 187 novel RLR pathway components . Our validation experiments showed that ~50% of the predicted candidate genes have a significant effect on antiviral signaling . These results , together with independent computational and literature-based confirmation , demonstrated the validity of our combined bioinformatics and experimental approach . Our study expands the collection of known antiviral genes , opening up new avenues for research into innate antiviral immunity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Integrative Genomics-Based Discovery of Novel Regulators of the Innate Antiviral Response |
Protein subcellular localization is a major determinant of protein function . However , this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments , and therefore it has limited applications in quantitative protein function analyses . Here , we present Protein Localization Analysis and Search Tools ( PLAST ) , an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images . PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments , without requiring manual assignment or supervised learning of these compartments . Using the budding yeast Saccharomyces cerevisiae as a model system , we show that PLAST is more accurate than existing , qualitative protein localization annotations in identifying known co-localized proteins . Furthermore , we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations . First , we identified proteins that have similar localization patterns and participate in closely-related biological processes , but do not necessarily form stable complexes with each other or localize at the same organelles . Second , we found an association between spatial and functional divergences of proteins during evolution . Surprisingly , as proteins with common ancestors evolve , they tend to develop more diverged subcellular localization patterns , but still occupy similar numbers of compartments . This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments . PLAST enables systematic and quantitative analyses of protein localization-function relationships , and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species . A public web interface of PLAST is available at http://plast . bii . a-star . edu . sg .
Proteins have to be localized at the appropriate subcellular compartments to perform their functions . Using high-throughput protein labeling and imaging techniques , several proteome-wide studies have found that protein localization and function are strongly correlated to each other [1]–[4] . However , unlike sequence , structure , expression level , or other protein features , subcellular localization has limited applications in quantitative analyses of protein functions , such as predictions of protein functions [5] and studies of protein function evolution [6] , [7] . One of the main difficulties is that protein subcellular localization is often described or represented in terms of discrete , qualitative categories of subcellular compartments , such as the Gene Ontology ( GO ) categories [8] . Although automated image processing algorithms have been useful in extracting quantitative descriptors for protein localization patterns , the resulting descriptors are often being converted back into these discrete categories using supervised classification or unsupervised clustering methods [9]–[13] . These discrete representations have several limitations . First , they cannot fully describe the continuous and complex spatial distributions of proteins that are localized across multiple compartments [14] and/or distribute non-uniformly within the same compartments [15] . Second , they are often assigned based on manual and/or visual inspections [1] , [3] , [4] , which are prone to bias and imprecision . Third , they only allow simple qualitative comparisons of protein localization patterns , which are often insufficient to distinguish complex or subtle changes . Despite all these limitations , discrete categories of subcellular compartments are still commonly used because they can be easily interpreted by humans . To overcome these limitations , we have developed an automated analysis framework for converting raw image descriptors into quantitative signatures ( or “profiles” ) of protein subcellular localization patterns . We refer to this framework as Protein Localization Analysis and Search Tools ( PLAST ) . First , we measure a large number of unbiased image descriptors that capture different spatial properties of protein localization patterns , and use a support vector machine ( SVM ) algorithm [16] to reduce the contributions of non-informative descriptors . The resulting profiles allow PLAST to maintain continuous representations of protein localization patterns throughout its analysis workflow , and do not require supervised learning of pre- or manually-defined categories of subcellular compartments . Second , PLAST is fully automated and designed to systematically quantify protein localization patterns at the proteome scale . Third , PLAST allows quantitative comparisons of complex protein localization patterns based on standard dissimilarity or distance measurements . Last , PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments . These maps can be thresholded to make “hard” compartment-to-protein assignments at user-desired significance levels . Therefore , PLAST allows researchers to quantify and rank a set of proteins according to their localization dissimilarities to a given protein or organelle , much like searching for proteins with similar sequences in the GenBank or UniProt databases . An important application of PLAST is to study changes in protein localization and function during evolution . Gene duplication is a main source of new genes [6] , [17] . A fundamental question in evolutionary biology is how duplicate genes acquired new or altered biological functions . Change in protein subcellular localization , or “protein relocalization” , is a possible mechanism for duplicate genes to achieve functional divergence [18] . This is supported by observations that protein localization may be easily changed just by single amino-acid substitutions [18] , relocalization is sufficient to alter the functions of some enzymes even in the absence of any mutation in their catalytic sites [19] , and many gene families encode proteins with different subcellular localizations [20] , [21] . Two different models of protein relocalization have been proposed [22] . The “neolocalization” model suggests that duplicates relocalize and adapt to previously unoccupied compartments [23] , whereas the “sublocalization” model suggests that duplicates develop more specific localization patterns over their ancestral compartments [24] . Based on these models , neolocalized duplicates are expected to occupy higher total numbers of compartments than their ancestors; and conversely , sublocalized duplicates are expected to occupy similar total numbers of compartments as their ancestors . However , both neo- and sublocalized duplicates are expected to show more diverged subcellular localization patterns and occupy lower ratios of shared compartments as they evolve . PLAST allows us to compare and test these two models by quantitatively measuring the degree of spatial divergence between duplicates and the number of compartments occupied and shared by them . Here , we describe the key components of PLAST , and show that PLAST is , on average , more accurate than existing , qualitative protein localization annotations in identifying known co-localized proteins . Furthermore , we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations . We found that PLAST can 1 ) identify similarly-localized proteins that participate in closely-related biological processes but do not necessary form stable complexes with each other or localize at the same organelles , and 2 ) reveal an association between spatial and functional divergences of proteins during evolution .
PLAST can be generally applied to microscopy images of proteins labeled with fluorescent protein fusion tags , fluorophore-conjugated antibodies , or other labeling techniques . PLAST has five major steps: cell segmentation , feature extraction , protein localization profile ( “P-profile” ) construction , P-profile dissimilarity computation , and compartment mapping ( Fig . 1A ) . First , we automatically segment cells from microscopy images . To avoid segmentation bias that may be introduced by protein-to-protein variations in expression levels [25] , we do not use fluorescent signals from the labeled proteins . Instead , we have developed a segmentation algorithm based on differential interference contrast ( DIC ) illumination and fluorescent nuclear stains ( Supplementary Fig . S1 ) . Other segmentation algorithms based on fluorescent whole-cell stains [26] may also be used in this step . Second , we extract a large number of quantitative image descriptors ( or “features” ) from the segmented cells . We have designed several new image features based on subcellular regions and local structures of protein distribution patterns ( Local structure features ) . We also extract standard intensity , texture , and moment features [9] , [10] , [27] . Third , we have generalized a SVM-based drug profiling algorithm [27] , which was originally designed to compare the same proteins under perturbed and control conditions , to compare different proteins under the same cellular conditions . For each protein , this method removes the contributions of non-informative features by finding an optimum SVM hyperplane that can separate cells labeled for the protein from a fixed set of reference cells ( P-profileSVM construction and Fig . S2A ) . We used the unit vector orthogonal to the hyperplane as a quantitative profile representing the spatial localization signature of the protein . As an alternative , we also construct profiles by averaging each feature value across all cells . We denote these two profile types as “P-profileSVM” and “P-profilemean” , respectively . Fourth , we measure and store the pairwise dissimilarity scores ( dp ) between the P-profiles of all proteins into a database . Lower dp values correspond to more similar subcellular localization patterns . To compare the localization patterns of a protein to a group of proteins , such as those that constitute a subcellular organelle or compartment , we compute the mean of all the pairwise dp values between the protein and each of the group members ( P-profile dissimilarity score ) . If the protein is part of the group , the dp to itself will be excluded from the calculation . Fifth , we map a comprehensive catalog of major subcellular compartments to each of the proteins in our database . For each protein , we estimate the probability distribution of dp between the protein and the compartments that are not specifically occupied by the protein ( Fig . 1A ) . Then , we standardize the dp values between the protein and all compartments based on this distribution ( Compartment assignment ) . The resulting z-scores ( ) constitute a localization map of all the proteins , and allow us to use the same significance thresholds to assign compartments to proteins . To assess the performance of PLAST , we used the budding yeast Saccharomyces cerevisiae ( S . cerevisiae ) as a model system due to the availability of a genome-wide GFP-fusion-protein image dataset ( Fig . 1B , the “UCSF dataset” ) [1] and a large number of known protein complexes in this organism [28] . The dataset covers ∼75% of the S . cerevisiae proteome . We extracted 623 quantitative features from ∼20 single cells per yeast strain , and constructed P-profiles for all the strains ( Supplementary Fig . S2A and B ) . After quality control ( Quality control ) , we obtained P-profiles for 4066 open reading frames ( ORFs ) ( Fig . 1C ) . To determine the correspondence between P-profiles and compartment categories manually assigned by UCSF via visual inspections ( “UCSF categories” ) , we clustered P-profiles using an affinity propagation algorithm [29] ( Fig . 1D and Supplementary Fig . S3 ) and determined the enrichments of UCSF categories and GO biological processes in each of the 20 identified clusters . We found that PLAST divides most “cytoplasmic” , “nuclear” , or “mitochondrial” proteins into ∼4 to 6 clusters that are enriched in proteins involved in different biological processes ( Supplementary Fig . S4 ) . Thus , PLAST can reveal protein subcellular localization patterns that are not obvious under manual inspections . Previously , two other analysis frameworks were also developed to quantify and classify the same image dataset according to UCSF categories [11] , [13] . We found that P-profiles can achieve higher mean classification accuracy than these two previous frameworks ( Fig . 1E ) . The increase in accuracy mostly came from better detections of categories with small numbers of ORFs , such as “early Golgi” , “bud neck” and “microtubule” , which were completely missed by these two other frameworks ( Supplementary Fig . S2C ) . These improvements were likely due to our better cell segmentation algorithm that does not rely on the fluorescence intensities of the GFP-tagged proteins ( Supplementary Fig . S1 ) , and our SVM-based profiling algorithm that reduces the contributions of non-informative features . Proteins must localize in close proximity to interact physically . We next studied to what extent PLAST can be used to search for physically interacting proteins . We first made use of two high-quality protein-protein interaction datasets obtained from affinity-purification mass spectrometry ( AP-MS ) and yeast two-hybrid ( Y2H ) screening [30] . AP-MS can identify components of larger complexes that may not necessary directly interact with each other , whereas Y2H screening can identify direct and sometimes more transient interactions between components from different complexes or pathways [30] . We found that AP-MS interactors have significantly lower median and mean intrapair dp values than Y2H interactors ( P<0 . 001 , two-sided permutation test; Fig . 2A ) . These results show that PLAST is better in detecting stable protein complexes than transient interactors . To further test the ability of PLAST in searching subunits of stable protein complexes , we used a comprehensive catalog of 197 protein complexes with different subunit numbers and subcellular localization patterns ( Datasets ) . Identifying subunits of complexes that localized at multiple compartments , such as ribosome and proteasome [15] , [31] , is expected to be very challenging . For each complex , we randomly selected a subset of its subunits as query proteins , and ranked all other proteins , except the query proteins , according to their mean dp to the query proteins ( Fig . 2B ) . We systematically measured the precision , recall and F1 scores , which are commonly-used criteria for information retrieval performance [32] , for different numbers of query proteins ( Fig . 2C ) . We also ranked proteins based on their similarities in UCSF or SGD GoSlim localization annotations ( Subunit search based on UCSF or SGD GOSlim annotations ) . These two annotations are not independent from each other , because a large fraction of SGD GoSlim annotations are based on UCSF annotations . We performed paired t-tests between the maximum F1 scores obtained from different profiling/annotation methods for all the protein complexes ( Fig . 2D and E ) . The resulting test statistic is only weakly correlated to protein complex size ( R = −0 . 136 , P = 0 . 056; Fig . S5 ) . Overall , we found that predictions based on P-profilesSVM have significantly higher maximum F1 scores than other methods ( P<3 . 3×10−6 , Bonferroni-adjusted , one-sided paired t-test; Figs . 2C and E ) . Therefore , in the subsequent analyses , we will be using P-profilesSVM to represent protein subcellular localization patterns . Notably , some of the “false positives” selected by PLAST may also interact with the query proteins . For example , RNA polymerase II subunit ( Rpb2 ) and ubiquitin activating enzyme ( Uba1 ) were previously found to be physically associated with proteasome [33] , [34] ( Fig . 2B ) . Thus , our estimated performances of PLAST are conservative . Our results show that , at least for most of the tested protein complexes , P-profiles are more accurate than existing localization annotations in associating subunits of the same protein complexes together . To generate a human-interpretable localization map of the yeast proteome , we used another catalog of cellular compartments as “landmarks” of the subcellular space in a yeast cell . This catalog consists of known protein components of 23 major organelles and 50 large protein complexes ( Catalog of subcellular compartments ) . For each protein , we systematically queried the P-profile database for the dp scores between the protein and all the compartments . We assumed that the probability distribution for these dp scores could be modeled by a mixture of Gaussian distributions , in which the component distribution with the highest mean dp value was the distribution for non-specifically localized compartments . We estimated the mean and standard deviation of this “null” distribution and standardized all the dp scores based on the estimated values ( Compartment assignment and Fig . 3A ) . We found that the null distributions for most proteins are dominant ( Supplementary Fig . S6 ) , indicating most proteins are localized only at small subsets of components in our catalog . The resulting z-scores ( ) constitute the final localization map ( Fig . 3B and S7 , Supplementary Dataset S1 ) , and allow us to assign compartments to different proteins using the same Bonferroni-adjusted P-value thresholds . This standardization step does not change the relative dissimilarities of different compartments to a protein . We identified several general trends from this localization map . First , compartments involved in similar or related biological processes tend to be spatially associated to similar sets of proteins . We performed a hierarchical clustering of all the compartments based on the localization map , and found that functionally-related compartments tend to be grouped into the same clusters ( Fig . 3B and S7 ) . For example , we obtained two clusters of compartments that are related to cellular bud/cytoskeleton and endomembrane system , respectively . These two clusters are also closest to each other , and form a larger supercluster in the dendrogram ( Fig . 3B ) . Similarly , we obtained separate clusters for ribosomal subunits , polysome , and degradation compartments , which together also form a larger supercluster ( Fig . 3B ) . These protein machineries are known to work together to regulate gene expression [35] . The spatial associations of these functionally-related compartments suggest that PLAST , which is based on microscopy images , can identify spatially-associated proteins that participate in closely-related biological processes or pathways but do not necessary form stable complexes with each other or localize at the same organelles . Most affinity-purification-based methods , such as the aforementioned AP-MS method , would have difficulties in relating these proteins together . To perform hard assignments of compartments to proteins , we used a Bonferroni-adjusted threshold of <1 . 0×10−12 . We studied all the compartments that had been assigned to at least one protein , and found that , on average , ∼90% of the proteins assigned to a compartment are not known components of the compartment . We wonder to what extent these “non-components” may perform similar biological functions as other known components of the compartment . For each compartment , we systematically identified significantly enriched GO biological processes among all its known components , and among all its non-components assigned by PLAST . Interestingly , we found 22 compartments whose non- and known components are significantly enriched with at least one common biological process ( <0 . 05 , FDR-adjusted , hypergeometric test; Fig . 3C and Supplementary Dataset S2 ) . For example , several translation factors ( eIF1A , eIF4B , and eIF4G ) and synthases ( glycyl-tRNA and glutamyl-tRNA synthases ) were assigned to cytosolic ribosomes; and several splicing factors ( Prp11 , Prp39 , Prp45 , and Prp5 ) , topoisomerase ( Top2 ) , and transcription initiation factor ( Tfc8 ) were assigned to RNA polymerase II ( Supplementary Dataset S2 ) . Furthermore , we also found that the non-components assigned by PLAST to cytosolic ribosome were also significantly enriched with ORFs that were experimentally found to co-purify with cytosolic ribosome [36] ( P<0 . 001 , hypergeometric test; Fig . S8 ) . Importantly , some of these non-components have uncharacterized functions or not known to be functionally associated with their corresponding compartments . Therefore , our localization map provides a repertoire of potentially novel components of the biological processes or pathways performed at major subcellular compartments in the budding yeast . The second general trend is that most proteins are localized at multiple compartments . To perform a fair comparison between PLAST and UCSF annotations , we used a more relaxed Bonferroni-adjusted threshold of ≤2 . 5×10−4 , which assigns major organelles , namely cytoplasm and nucleus , to similar numbers of proteins as UCSF annotations [1] ( Fig . 4A ) . However , among proteins with assigned compartments , the median and mean numbers of compartments assigned to a protein by PLAST ( ∼13 . 0 to 14 . 5 ) are significantly higher than UCSF ( ∼1 ) and SGD GOSlim annotations ( ∼2 . 0 to 2 . 5 , Fig . 4B ) . We observed the same trends even if we used a reduced set of 22 major compartments for compartment mapping by PLAST ( Fig . 4B ) . Many of the compartments that we used as landmarks are located in the cytoplasm , therefore it is not surprising that PLAST would also assign them to proteins that are localized at the cytoplasm . On average , PLAST assigned 14 . 9 compartments to cytosolic proteins , but only 1 . 9 compartments to non-cytosolic proteins ( Fig . 4C ) . Nevertheless , in all cases , PLAST and SGD GoSlim assign significantly more compartments than UCSF annotations ( P<0 . 001 , Fig . 4C ) . This is likely due to the inefficiency of human scorers to separate complex composite patterns into individual compartments , and therefore analyses of protein localization based on UCSF annotations may be inaccurate . An important application of PLAST is to study spatial and functional divergences of proteins during evolution . Individual cases of neo- and sublocalization ( Introduction ) have been reported in yeasts and hominoids [22]–[24] , and thus both mechanisms were likely to contribute to protein relocalization . Given that the subcellular localization data for ancestral proteins is not available , we use two different approaches to test the prevalence of neo- and sublocalization models . All our subsequent analyses are based on the compartment assignments from the previous section , and also P-profileSVM , which has the best overall benchmark performance ( Fig . 2E ) . In the first approach , we studied duplicates with different divergence times . We assumed that the localization patterns of more recently duplicated gene pairs are more similar to their ancestors . We used a phylogeny of orthologous gene groups estimated for seventeen Ascomycota fungi [37] . We obtained six sets of S . cerevisiae duplicates with different divergence times ( T1 to T6 ) , each of which consists of ∼10–400 duplicate pairs ( Fig . 5A ) . We used PLAST to quantify the spatial divergence levels of all the duplicates . Because protein expression may influence protein localization pattern , we also obtained the protein expression levels of all the duplicates [25] . To treat each divergence time equally , we only used the mean values of all its associated duplicates . A whole genome duplication ( WGD ) was estimated to occur around ∼100 million years ago after the divergence of Kluyveromyces lactis ( K . lactis ) in the phylogeny [6] , [38] ( Fig . 5A ) . We refer to pre-WGD duplicates as “old” duplicates ( T4 to T6 ) , and all other duplicates as “young” duplicates ( T1 to T3 ) . We found that old duplicates have significantly larger intrapair dp values , lower shared compartment ratios , and lower average protein expression levels than young duplicates ( P = 0 . 041 , 0 . 066 , and 0 . 066 , two-sided t-tests; Fig . 5B–D ) . However , we did not observe a significant difference in the total numbers of occupied compartments ( P = 0 . 796 , two-sided t-test; Fig . 5E ) . To test if localization divergence could be predicted based on divergence time or protein expression level , we performed linear regression modeling and found that only divergence time is a significant predictor of both dp and shared compartment ratio ( P<0 . 001 , Fig . 5F ) . Our results are consistent with the expected behaviors of sublocalized duplicates ( Introductions ) , and suggest that localization divergence might be more frequently due to sublocalization . One of the limitations of our analysis is that duplicates with different divergence times may undergo different evolutionary paths . Furthermore , we could only obtain small numbers ( ∼10 ) of duplicates for some of the divergence times , and thus could not perform meaningful enrichment analyses of protein functions on them . In the second approach , we addressed these limitations by studying the large number ( ∼544 ) of duplicates generated from WGD [38] , [39] . We assumed that the localization patterns of duplicates with lower intrapair dp values are more similar to their ancestors . Sixty eight percent of these WGD duplicates were detected in T3 of the phylogeny that we used in the previous section . Based on UCSF annotations , a previous study estimated that ∼24–37% of these WGD duplicates now have diverged localizations and occupy significantly higher numbers of compartments than their ancestors [22] . Thus , the study concluded that localization divergence might be more frequently due to neolocalization . However , UCSF annotations tend to underestimate the number of compartments localized by a protein ( Fig . 4C ) , and we wonder if PLAST can provide additional insights into the localization-function relationships between these duplicate proteins . To quantify the spatial divergence of WGD duplicates , we determined the dp for all 326 duplicate pairs that have P-profiles ( Supplementary Dataset S3 ) , and also the empirical distribution of dp for 10 , 000 randomly selected non-duplicate pairs from the proteome ( Fig . 6A ) . As expected , we found that WGD duplicates as a whole have significantly lower dp values and higher shared compartment ratios than random non-duplicates ( both P<0 . 001 , two-sided permutation test for difference in medians and means; Fig . 6A and B ) . However , we did not find significant difference in the total numbers of occupied compartments ( Fig . 6C ) . We refer to protein pairs with dp less than a certain percentile of the dp distribution for random pairs as “similarly localized” ( SL ) pairs , otherwise as “dissimilarly localized” ( DL ) pairs . We estimated that ∼42–84% of duplicates are now DL pairs ( based on 1st- and 20th-percentile thresholds ) , which are higher than the ∼24–37% estimated based on UCSF annotations [22] . In the following analyses , we will use a 10th-percent threshold to separate SL and DL pairs . Among the ten biological processes with the highest numbers of duplicates , “cytoplasmic translation” and “signaling” have the lowest and highest DL-duplicate ratios , respectively ( Fig . 6D ) . This is consistent with the slower and faster amino acid divergence rates of translational proteins and kinases , respectively , in S . cerevisiae [6] . Some of our automatically identified DL duplicates , such as the sterol esterases Yeh1 and Yeh2 , protein kinases Ypk1 and Ypk2 , and transcription factors Pdr1 and Pdr3 , are known to have diverged localizations [40]–[42] ( Fig . 6E ) . Thus , our results suggest that a large portion of duplicate proteins have diverged localization patterns . Next , we studied if localization and functional divergences are statistically associated events . We considered two different types of functional annotations , namely GO molecular functions and biological processes [8] . Interestingly , we found that protein relocalization in WGD duplicate genes is significantly associated with divergence of biological process ( P = 0 . 0064 ) but not molecular function ( P>0 . 10 , both two-sided Fisher's exact tests; Fig . 7 ) . Few DL and SL duplicates have dissimilar molecular functions ( 18% and 14% respectively , and also see examples in Fig . 6E ) , but ∼45% of DL duplicates are now involved in dissimilar biological processes ( Fig . 7 ) . To the best of our knowledge , this is the first time that such localization-function relationships are demonstrated systematically and quantitatively at the proteome level . Relocalized duplicates maintain similar molecular functions , likely due to their highly conserved sequences; but they also start to involve in different biological processes , likely due to their interactions with different proteins specific to their occupied compartments . Therefore , our results support the hypothesis that protein relocalization may facilitate functional divergence . Finally , we studied neo- and sublocalization using WGD duplicates . Surprisingly , unlike previous analysis based on UCSF annotations [22] , we found that dp is significantly correlated to shared compartment ratio , but not total number of occupied compartments ( P<0 . 001 and = 0 . 286 , respectively; Fig . 8A and B ) . We also found that dp is negatively correlated to protein expression level ( P<0 . 001 , Fig . 8C ) , which is consistent with the slow evolutionary rates of highly expressed proteins [43] . However , using regression modeling , we found that protein expression level is not a significant confounding factor between the association of dp and shared compartment ratio ( Fig . 8D ) . Importantly , these results agree with the results that we obtained from duplicates with different divergence times . All of our results could be explained by multi-compartment localizations of ancestral genes . After duplication , different duplicate copies of a gene evolved to develop more specific localization patterns over ancestral compartments . Therefore the total numbers of occupied compartments remain largely unchanged , while the shared compartment ratios are decreasing . Our results suggest that localization divergence was more frequently due to sublocalization .
Using PLAST , we demonstrate that protein subcellular localization can be quantitatively compared and analyzed to reveal protein localization-function relationships that are not obvious under qualitative representations of protein localization . First , protein localization in multiple subcellular compartments is common . Second , spatially-associated proteins tend to participate in related biological processes , and we have identified a repertoire of potentially novel components of biological processes performed at major subcellular compartments . Third , ∼42–84% of duplicate proteins now have diverged localization patterns . Fourth , most of these proteins still perform the same molecular functions , but close to half of them have started to involve in different biological processes , likely due to adaptations to more specific or new subcellular compartments . Fifth , duplicate proteins occupy similar numbers of compartments irrespective of their divergence times or spatial divergence levels , and therefore protein relocalization might be more frequently due to sublocalization . The sublocalization model is very similar to an extensively studied model of functional evolution called “subfunctionalization” , where duplicate genes evolved to complement each other by jointly retaining the full functions of their ancestral genes [44] . However , the sublocalization model does not preclude further functional evolutions via subfunctionalization or other evolutionary mechanisms [45] . Together , our results underscore the importance and utility of quantitative localization profiles in studying protein localization and function . How can an ancestral gene , or a gene in general , produce proteins localized in multiple compartments ? Several molecular mechanisms are possible . First , the budding yeast Lachancea kluyveri , which diverged from S . cerevisiae before WGD , has a single ortholog to the S . cerevisiae SKI7 and HBS1 genes ( Fig . 6E ) . This pre-WGD ancestral gene is alternatively spliced to generate two different conserved proteins that can perform similar functions as S . cerevisiae Ski7 or Hbs1 , respectively [46] . Second , the filamentous fungus Aspergillus nidulans has a single NADP-dependent isocitrate dehydrogenase ( idpA ) that has two Homo sapiens orthologs ( IDH1 , IDH2 ) and three S . cerevisiae orthologs ( IDP1 , IDP2 , and IDP3 ) [47] . Interestingly , this gene has two alternative transcription start sites that specify mitochondrial or cytoplasmic and peroxisomal localizations; whereas the H . sapiens IDH1 and IDH2 are specifically localized to cytoplasm and peroxisome or mitochondrion , respectively; and the S . cerevisiae IDP1 , IDP2 , and IDP3 are specifically localized each of these three compartments . Third , several core glycolytic enzymes were also recently found to be targeted to both cytoplasm and peroxisome via alternative splicing and stop codon read-through of the same genes in fungi [48] . Similar transcriptional and/or other post-translational mechanisms [14] may also produce proteins with multi-compartment localization patterns . Quantitative localization profiles are therefore essential to analyze the complex localization patterns of these proteins . PLAST has several limitations . First , it only works for proteins that can be fluorescently labeled without affecting their localizations . Second , it does not work well for proteins with very low abundance levels . Third , it has poor detection of yeast two-hybrid interactions , likely due to the more transient nature of these interactions [30] . Nevertheless , it can still achieve better overall performances than current protein localization annotations , especially for protein complexes that localized at multiple subcellular compartments . Recent advances in the developments of brighter fluorescent probes and higher-resolution imaging techniques [49] may further improve the performance of PLAST . As more genome-wide protein localization image datasets become available for different organisms or species [3] , [4] , PLAST can be readily used to compare protein localizations across different levels of a phylogenetic tree . Protein localization maps can be constructed for cells from different organisms or species using a common set of conserved subcellular compartments . Other possible extensions of PLAST include predicting targeting signals or protein-protein interactions based on proteins that have similar subcellular localization patterns . We have implemented the cell segmentation and feature extraction steps of PLAST using a user-friendly and publicly-available bioimage analysis software platform called “cellXpress” [50] , and the P-profile construction and compartment mapping steps using standard R scripts . We have also developed a public web interface ( http://plast . bii . a-star . edu . sg ) that allows researchers to query protein localization maps created using PLAST . These resources enable systematic and quantitative analyses of protein localization-function relationships , and will help researchers to elucidate protein functions and the causes of their changes .
We used the S . cerevisiae GFP image dataset and annotations generated by UCSF [1] , the phylogeny of orthologous gene groups estimated by Wapinski et al . [37] , the WGD duplicate gene list predicted by Gordon et al . [39] , and the AP-MS and Y2H datasets generated by the Dana-Farber Cancer Institute [30] . We retrieved the lists of valid ORFs , GO annotations , GO Slim annotations , GO protein complexes from the Saccharomyces Genome Database ( SGD ) [28] . Further information about these datasets is included in the Supplementary Text S1 . For the UCSF yeast GFP dataset , we have developed an image-processing pipeline to estimate cell boundaries based on DIC illumination and DAPI staining . Our method consists of four major steps: background subtraction , image alignment , cell segmentation , and cell combination ( Supplementary Fig . S9 ) . We minimized non-uniform background intensities in microscopy images using background subtraction . For fluorescence images , we used the rolling ball background subtraction algorithm implemented in ImageJ [51] . We set the rolling ball size to 50 pixels , which is larger than the average diameter of a budding yeast cell in the images . For DIC images , we used ImageJ to convolve the original images with a Gaussian function ( ) and divide the original images with the convolved images . We noticed non-zero lateral offsets between the DIC and fluorescence images ( both GFP and DAPI ) in the UCSF yeast GFP dataset . These offsets might be due to misalignment of image acquisition instruments . To reduce these artifacts , we have developed a three-step image alignment procedure based on cross-correlation ( Supplementary Fig . S10 ) . In the first step , a DIC image was segmented to obtain a binary cell mask ( ) using the Otsu's thresholding method [52] , followed by six successive dilations and eight successive erosions with a 3×3 diamond-shape structuring element . In the second step , we estimated the displacement between the binary cell mask and a GFP image using the standard cross-correlation algorithm:where is the fluorescence intensities of the GFP image at position . In the third step , we translated the DIC image by to align it with the GFP image . The same alignment algorithm was also used to align a DAPI image . After image alignment , we identified individual budding yeast cells from the images ( Supplementary Fig . S9 ) . First , to suppress high-frequency noise in the images , we smoothed the DIC and DAPI images by convolving them with a 9×9 2D Gaussian lowpass filter ( ) . Then , we identified the nuclear regions from the DAPI images using a local structure identification algorithm ( see Local structure identification section below ) . We set the algorithm's window size ( ) to be 35 pixels , and bias constant ( ) to be −60 . Last , to identify cellular regions from the DIC images , we applied the watershed segmentation algorithm implemented in OpenCV to the gradient of the DIC images . The identified nuclear regions were used as initial seeds for the algorithm . We estimated binary local structures using an adaptive thresholding algorithm [53] . Let a 8-bit image of a single budding yeast cell be where and are the x and y coordinates , respectively . We computed an adaptive threshold for each pixel in the image usingwhere is the mean of the intensity values of a window centered at , and is a user-defined bias constant . Then , we estimated binary local structures , , by applying the thresholds to all pixels in the image: To reduce oversegmentation , we combined two attached cellular regions if they satisfy the following two criteria:where and are the areas of the two cellular regions , and are the total DNA intensity of the two cellular regions , is a threshold for maximum cell area , and is a threshold for minimum DNA intensity difference between the two regions . For the UCSF yeast GFP dataset , we used and . We compared cell masks obtained from our segmentation algorithm , a previous graphical-model-based segmentation algorithm [11] , and manual segmentation ( Supplementary Fig . S1 ) . We selected 20 images with sparsely distributed yeast cells and 20 images with densely distributed yeast cells . These two image sets represent easy and difficult conditions for automated cell segmentation . We expect the segmentation errors of the dense images to be lower than the sparse images . To obtain a ground truth for comparing segmentation errors , we manually segmented each images using a pen-based digitizer ( Toshiba M200 laptop ) . The cell masks for the graphical-model-based algorithm were obtained using the Matlab source code downloaded from http://murphylab . web . cmu . edu/software/2007_Bioinformatics_Yeast/ without any modification . We used two different segmentation performance criteria: the boundary and Rand error indices [54] . The boundary error index ( ) measures the averaged distance between the boundaries of cell masks obtained from manual and automated segmentation , respectively . Smaller boundary error index values mean higher automated segmentation accuracy . We define the boundary error index between two sets of boundary pixels ( and ) from a manual segmentation mask ( ) and an automated segmentation mask ( ) , respectively , to be:where and are individual pixels within sets and , respectively; is the cardinality operator; and is the Euclidean norm . We also used the Rand error index [54] , which measures the frequency in which the two segmentation masks disagree over whether a pair of pixels belongs to same or different segmented cellular regions . Let us denote the set of labeled regions in a manual segmentation mask to be and the set of labelled regions in an automated segmentation mask to be , where and are the i-th and j-th connected pixels within the respective masks . Furthermore , we denote to be the number of pixel pairs in the original image that belongs to the same sets in and the same sets in , and to be the number of pixel pairs in the original image that belongs to different sets in and different sets in . Then , the Rand error index is:where N is the total number of pixels in the original image . Based on the cellular and nuclear ( DNA ) regions identified from our segmentation algorithm ( see Cell Segmentation above ) , we defined three additional subcellular regions , namely cytoplasmic ( non-DNA ) , cytoplasmic boundary , and inner cytoplasmic regions ( Supplementary Fig . S11 ) . The cytoplasmic region was computed by subtracting the nuclear regions from the cellular regions . The cytoplasmic boundary region was computed from the cytoplasmic region using a binary erosion operator with a circular structuring element ( radius = 3 pixels ) . Finally , the inner cytoplasmic region was computed by subtracting the cytoplasmic boundary region from the cytoplasmic region . To eliminate the effects of protein-to-protein variations in expression levels and only consider protein spatial localization patterns , we divided the GFP intensity values of all the pixels within the detected cellular regions ( see Identification of subcellular regions above ) of individual yeast cells to their sums . So , the total GFP intensity of each normalized cell became one . All features were extracted only after this normalization step . We extracted 623 features from the normalized GFP images of each budding yeast cell . They include 81 morphology , 45 intensity , 20 intensity ratio , 273 Haralick texture [55] , 18 moment [56] , and 186 local structure features of the five identified subcellular regions . Most of these features were commonly used to describe protein subcellular localization patterns [57] . Local structure features are developed by us for PLAST , and described in more detail below . We have designed a new feature type , called “local structures” , to describe protein distribution patterns in local subcellular regions . Extraction of local structure features consists of three steps: local structure identification , global ratio feature computation , and stepwise ratio feature computation . In the first step , we applied the local structure detection algorithm ( see Local structure identification above ) to identify local structures from the GFP images . A smaller window size ( w ) will extract finer local structures , while a larger window size will extract coarser global structures in the protein localization patterns . For the UCSF yeast GFP dataset , we set and found that the local structures of most yeast cells converged to similar global patterns after w = ∼33 ( Supplementary Fig . S12 ) . Therefore we only used 16 different window sizes ( pixels ) for feature extraction . In the second step , we extracted six features ( where ) based on the identified local structures of window size . These features are total GFP intensity , mean GFP intensity , standard-deviation GFP intensity , binary object number , binary object total area , and binary object mean area . We also extracted the same features ( ) but based on the cellular regions of the same images . Then , we computed global ratio features ( ) of the local structures usingThese features are designed to detect the concentrations of GFP signals in the identified local structures . For the UCSF yeast GFP dataset , this amounted to 6 features per window size×16 window sizes per cell = 96 features per cell . In the third step , we computed the stepwise ratios ( ) of the same six features between local structures of two consecutive window sizes , namelywhere . These ratios are designed to detect changes in the concentrations of GFP signals from finer to coarser local structures ( Supplementary Fig . S12 ) . For the UCSF yeast GFP dataset , this amounted to 90 additional features per cell . Therefore , the total number of local structure features was 96+90 = 186 . To automatically remove badly or wrongly segmented cells , we used two quality control criteria , namely cell area and solidity ( cell area/convex hull area ) . First , we removed segmented cells that have cell area <300 pixels or >2000 pixels . Then , we removed segmented cells that have solidity value >0 . 2 . There were 33 strains with more than one image set , and for each strain we used only one image set . We removed three yeast strains ( YDL125C , YJL026W , YLR109W ) that have abnormal image size ( 58×58 pixels ) . After feature extraction , there were 15 cells with NaN values , and we replaced these values with the medians across all cells in each of the yeast strains . We obtained ∼20 segmented cells per yeast strain on average . Only one of the yeast strains , namely YHR011W , has less than two cells , and thus the whole strain was removed from further analysis . We also removed 55 strains that have mislocalized proteins due to the GFP tags [1] , and 34 strains that have invalid ORFs according to SGD ( see Datasets ) . The final number of yeast strains that we used in our study was 4066 . To construct P-profilesSVM for yeast strains that have been labeled for a protein , we used a SVM [16] with a linear kernel function that has good performance in many real classification problems [58] , [59] . The decision function of the SVM is given bywhere is a normal vector to the decision hyperplane ( Supplementary Fig . S2A ) , is a bias term , is an input sample , is a training sample , is a coefficient determined by the SVM algorithm , yi is the class label of the i-th training sample , is the number of training sample , and is the dot product operator . We trained the SVM to find the optimum W that can separate yeast cells labeled for a protein , and a fixed set of reference cells . Then , we divided W with the sum of all its elements so that it becomes a unit vector . A SVM algorithm with linear kernel function has two main parameters , the cost parameter for constraint violation ( cost ) , and the tolerance of termination criterion ( epsilon ) . We set epsilon to its default value , 0 . 01 . For each yeast strain , a grid search on the values of was performed to determine the cost parameter with the maximum training classification accuracy between the two set of cells . We used the SVM training algorithm implemented in the “LiblinearR” v1 . 80-6 package [60] under the R environment . To generate the reference cell set , we first randomly sampled 9 sets of reference cells ( with 10 , 20 , 30 , … , or 90 cells ) from yeast strains that have been assigned to four of the largest UCSF categories , namely “cytoplasm” , “nucleus” , “mitochondrion” , and “endoplasmic reticulum” . Then , based on each of the reference sets , we constructed a set of P-profiles ( SVM ) for all the yeast strains , and trained a multi-class SVM classifier ( see Supervised classification of UCSF categories ) to classify 2654 ORFs with single UCSF category assignments . The evaluation process was repeated five times with different random selections of reference cell sets , and we chose the set of P-profiles with the highest average accuracy in classifying the 2654 ORFs to represent protein subcellular localization ( Supplementary Fig . S2B ) . We used a multi-class SVM with linear kernel proposed by Crammer and Singer [61] and implemented in the “LiblinearR” v1 . 80-6 package under the R environment . Similar to two previous supervised learning studies of the UCSF yeast GFP datasets [11] , [13] , we used six fold cross validation with six random trials to estimate the classification accuracy for all UCSF categories . In order to make our results comparable to these two previous studies , we performed the supervised classification analysis only on the 2654 yeast strains that had been assigned to single subcellular compartments by UCSF , and without the quality control step as described in the Quality Control section . We computed the dissimilarity score between two P-profiles h and g asTo compare the P-profiles of a protein ( h ) and a group of proteins ( G ) , we took the mean of all the pairwise dissimilarity scores:where . Affinity propagation is an algorithm that identifies representative data points , called “exemplars” , and forms clusters of data points around these exemplars [29] . It starts by considering all data points as potential exemplars , and exchange messages between data points until convergence of a set of exemplars or clusters . Preferences of data points are used to control the number of selected exemplars . Low or high preference values lead to low or high numbers of clusters , respectively . To cluster P-profiles , we used the same preference values for all data points , and varied the values to determine the optimum number of clusters ( Supplementary Fig . S3A ) . We used the affinity propagation clustering algorithm implemented in “apcluster” v1 . 3 . 2 package under the R environment . We used the standard agglomerative hierarchical clustering algorithm implemented in the hclust ( ) function under the R environment . The P-profile dissimilarity scores and Ward agglomeration method were used . The definitions of precision , recall , and F1-scores are:where is the number of true positives , is the number of false positive , and is the number of false negatives . F1-score is the harmonic mean of precision and recall . All of these three criteria are commonly used to measure information retrieval performances [32] . For each profiling/annotation method and each protein complex , we measured the maximum F1-score of the method across all the tested query-protein sizes for the protein complex . To compare the performances of two different methods , we performed a paired t-test between the maximum F-1 scores of the methods obtained from all the protein complexes . The p-values for all pair-wise comparisons of the four methods ( P-profileSVM , P-profilemean , UCSF and SGD Go Slim ) were Bonferroni adjusted . Given a set of known subunits of a protein complex , we identified the UCSF or SGD GOSlim cellular component category associated with the highest number of subunits . Then , we predicted all other ORFs annotated with this category to be the other “subunits” of the complex , and measured the corresponding precision , recall and F1 scores . To construct a localization map , we used a catalog of subcellular compartments that consists of known protein components of 23 major organelles and 50 large protein complexes . The list of protein components for major organelles is based on the manually curated SGD GoSlim cellular component dataset ( see Datasets ) . The original dataset has 25 categories . We removed the “cellular_component” , “microtubule organizing center” , “extracellular region” , “unknown” , and “other” categories , and included the “lipid particle” , “endosome” , and “nuclear envelope” categories . The list of components for large protein complexes is based on the manually curated SGD GO protein complex dataset ( see Datasets ) . The original dataset has 416 protein complexes . We only considered protein complexes with at least 15 subunits , and removed the “microtubule organizing center” complex because it overlaps with the “spindle pole body” complex . For each ORF , we systematically queried the P-profile database for the dp scores between the ORF and all the compartments ( see Catalog of subcellular compartments above ) . Then , we removed outliers by trimming the upper and lower fifth percentiles of the obtained dp scores , and estimated the probability distribution of the retained scores using the kernel density estimation method , density ( ) , implemented in the R environment . We identified the local maximum of the distribution with the largest dp value , and used the dp value as the mean ( μnull ) of the distribution of non-specifically localized compartments . We assumed the non-specific distribution is Gaussian , and estimated its standard deviation ( σnull ) from all dp scores larger than μnull . Then , we standardized all the dp scores using , and also calculated their corresponding P-values based on a normalized non-specific distribution with zero mean and unit variance . Finally , we performed Bonferroni correction on all the obtained P-values , and assigned compartments with adjusted P-values less than a given threshold to the ORF . We obtained a phylogeny of orthologous gene groups estimated for seventeen Ascomycota fungi [37] . We only used S . cerevisiae duplicates that could be traced to their originating ancestors without any loss events . The approximate divergence ages of the phylogenetic tree are based on past estimations [38] , [62] , [63] , and only used for visualization in Fig . 5 . In our analysis , we always divided the duplicates into two groups: “old” ( pre-WGD ) or “young” ( other ) duplicates , and never use the estimated values of these divergence ages . To perform a non-parametric test for the statistical significance of the observed difference in means or medians between two sets of values , we randomly permutated the labels of the datasets for 10 , 000 times . For each permutation , we measured the mean or median difference between the two sets according to the permutated labels . The P-value was estimated to be the fraction of permutations with absolute mean or median difference larger than the observed absolute mean or median difference . We have developed “cellXpress” v1 . 10 ( http://www . cellXpress . org ) to perform all the image processing and feature extraction steps [50] . The software package is general and could also be used to process images of other cell types . The extracted features were then loaded and processed using the R computing environment v3 . 0 . 1 under Gentoo Linux operating system . All the R source code for PLAST and datasets that we used in this study can be downloaded from http://plast . bii . a-star . edu . sg . | Proteins are fundamental building blocks of cells . They perform a variety of biological functions , many of which are essential to the vitality and normal functioning of cells . Proteins have to be located at the appropriate regions inside a cell to perform their functions . Therefore , when proteins change their locations , they may acquire new or different functions . However , the relationships between the locations and functions of proteins are difficult to analyze because protein locations are often represented in distinct and manually-defined categories of subcellular regions . Many proteins have complex or subtle differences in their localization patterns that can be hardly represented by these categories . Here , we present an automated analysis tool for generating quantitative signatures of protein localization patterns without requiring manual or automated assignments of proteins into distinct categories . We show that our tool can identify proteins located at the same subcellular regions more accurately than existing categorization-based methods . Our tool allows comprehensive and more accurate studies of the relationships between protein localizations and functions . By knowing where proteins are located and how their locations were changed , we may discover their functions and better understand how they acquire these functions . | [
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] | 2014 | Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins |
Brucella are facultative intracellular bacteria that chronically infect humans and animals causing brucellosis . Brucella are able to invade and replicate in a broad range of cell lines in vitro , however the cells supporting bacterial growth in vivo are largely unknown . In order to identify these , we used a Brucella melitensis strain stably expressing mCherry fluorescent protein to determine the phenotype of infected cells in spleen and liver , two major sites of B . melitensis growth in mice . In both tissues , the majority of primary infected cells expressed the F4/80 myeloid marker . The peak of infection correlated with granuloma development . These structures were mainly composed of CD11b+ F4/80+ MHC-II+ cells expressing iNOS/NOS2 enzyme . A fraction of these cells also expressed CD11c marker and appeared similar to inflammatory dendritic cells ( DCs ) . Analysis of genetically deficient mice revealed that differentiation of iNOS+ inflammatory DC , granuloma formation and control of bacterial growth were deeply affected by the absence of MyD88 , IL-12p35 and IFN-γ molecules . During chronic phase of infection in susceptible mice , we identified a particular subset of DC expressing both CD11c and CD205 , serving as a reservoir for the bacteria . Taken together , our results describe the cellular nature of immune effectors involved during Brucella infection and reveal a previously unappreciated role for DC subsets , both as effectors and reservoir cells , in the pathogenesis of brucellosis .
Brucella ( α-proteobacteria ) are facultative intracellular Gram-negative coccobacilli that infect humans as well as domestic ( goat , sheep , swine , etc . ) and wild type mammals . Animal infection leads to abortion and infertility with dramatic economic costs . Brucellosis is mainly transmitted to humans through the ingestion of raw milk or non-pasteurized cheese contaminated with Brucella . It is characterized by undulant fever which , if left untreated , can result in chronic disease with serious clinical manifestations , such as orchitis , osteoarthritis , spondylitis , endocarditis , and several neurological disorders [1]–[5] . Human brucellosis remains a significant public health concern in areas of the world where Brucella infections are endemic in food animals . Indeed , brucellosis has been described as being the most common zoonotic disease worldwide with more than 500 , 000 new human cases annually [4] . Brucella are highly infectious via oral and aerosol routes and difficult to treat with antibiotics . No safe or effective vaccine is available to prevent human infection . These characteristics justify the classification of Brucella strains as a category B pathogens , which represent a risk for use as bioweapons [6] , [7] . B . melitensis is the most frequent cause of human brucellosis [1] , [8] . Recently , bioluminescent B . melitensis has been used to visualize the dynamic of bacterial dissemination following intraperitoneal ( i . p . ) inoculation in mice [9] . Results confirmed that this model parallels human infection and identified major sites of bacterial growth , such as the spleen and the liver , during the early chronic phase of infection . However , cells supporting bacterial growth in vivo in these organs and serving as reservoir are unknown . Despite recent progress in mouse models of brucellosis , much remains unknown regarding cellular components of the innate and adaptive immune responses induced by B . melitensis infection . We and others have shown that IFN-γ-producing CD4+ T cells [10]–[14] and inducible Nitric Oxide Synthase ( iNOS/NOS2 ) -producing inflammatory dendritic cells ( iNOS-DC ) [10] are major components of protective immune response against B . melitensis . Activation of these cells involves Toll Like Receptor ( TLR ) 4 and TLR9 coupled to MyD88 adaptor protein [10] . However , there is little understanding on where these cells are localized in situ so they can initiate cellular interactions to control infection . Several experimental models of infection by intracellular bacteria such as Mycobacteria tuberculosis [15] and Listeria monocytogenes [9] , [16] have illustrated the importance of the granulomatous lesion in limiting both tissue damage and bacterial dissemination . The granulomatous lesion is an organized and dynamic structure implicating activated monocytes surrounded by T cells and granulocytes . Its formation involves an orchestrated production of chemokines and cytokines and the upregulation of their cognate receptors along with the expression of addressins , selectins and integrins . Altogether , these elements coordinate the recruitment , migration and retention of cells to and within the granuloma . Chronic granulomatous inflammation has been reported in spleen and liver from natural hosts , human and mice infected by Brucella bacteria [17] . However , the importance of granulomas in the control of Brucella growth , their cellular composition and the signalling pathways implicated in their formation are largely unknown . To address these issues , we developed a B . melitensis strain stably expressing mCherry fluorescent protein ( mCherry-Br ) . This novel tool has allowed us to determine the phenotype of Brucella-targeted cells and characterize the composition and the dynamic of granuloma in the spleens and livers of infected mice . We observed the formation of iNOS+ granuloma structures surrounding infected cells and identified iNOS-DC ( CD11b+ CD11c+ Ly-6G− MHC-II+ iNOS+ ) and activated monocytes ( CD11b+ CD11c− Ly-6G− MHC-II+ iNOS+ ) as the major cell types constituting granulomatous lesions in both tissues . In addition , we observed that alteration of the MyD88/IL-12/IFN-γ axis deeply affects the cellular composition of granulomas , reducing their ability to control bacterial dissemination and leading to replication and persistence of the pathogen in distinct cellular niches .
In order to detect infected cells on tissue sections by fluorescent microscopy , we constructed a constitutively fluorescent strain of B . melitensis . The mCherry protein , a previously described rapidly maturing variant of the red fluorescent protein DsRed [18] , was cloned into the suicide vector pKSoriT-bla-kan downstream of a strong Brucella spp . promoter PsojA [19] , [20] . The final construct was transformed into Escherichia coli strain S17-1 , and introduced into B . melitensis strain by conjugation ( see Material and Method for details ) . Flow cytometry ( Figure S1 . A ) and fluorescent microscopy ( Figure S1 . B ) were used to validate mCherry-expressing Brucella ( mCherry-Br ) strain in vitro . The comparison of wild type and mCherry-Br strains growth curve revealed that mCherry gene expression does not affect ability of B . melitensis to replicate in vitro ( data not shown ) . In addition , following i . p . inoculation of 4×104 CFU , we showed that mCherry-Br was able to infect chronically resistant C57BL/6 mice ( Figure S1 . C ) . At 5 days post infection ( p . i . ) , bacterial burden in spleen from mCherry-Br infected mice was reduced by approximately half-a-log when compared to wild type bacteria-infected mice . However , at 25 and 42 days p . i . , bacterial counts from both groups were similar , suggesting that bacterial virulence is moderately and transiently affected by the insertion of mCherry tracer . In order to characterize the spatio-temporal behavior of Brucella in spleen and liver of infected animals , we used immuno-fluorescence microscopy techniques . At low dose of infection ( 104 to 106 CFU ) in C57BL/6 and BALB/c wild type mice , growth of B . melitensis peaked at 5 days p . i . with modest CFU counts ( between 105–106 CFU/g of spleen [10] ) rendering its visualization on tissue sections very difficult . Our preliminary studies demonstrated that 106 CFU/g of tissue was the limit of sensitivity of our technique , with less than 0 . 1 infected cells by observation surface ( 200×230 µm ) in tissue sections of 5 µm in thickness and using a 63× objective ( data not shown ) . Therefore , in order to maximize our chances of visualizing the dynamic behavior of mCherry-Br in vivo several days after inoculation , we injected 108 CFU of bacteria in wild type mice . With this dose , no increase in mortality of mCherry-Br infected wild type C57BL/6 and BALB/c mice was observed ( data not shown ) . Importantly , when compared to the routinely used low bacterial inoculum , a high infectious dose ( 108 CFU ) showed similar impact on the composition of recruited spleen cell populations involved in innate inflammatory responses to Brucella . Overall , a high bacterial inoculum only shorten the kinetic of cell recruitment ( Figure S2 ) . First analyses revealed that ten minutes after i . p . inoculation , bacteria were massively present in the spleen and the liver , with an average of 106 CFU/g ( Figure S3 . A ) . Both organs maintained a level of 106–108 CFU/g during the 5 first days p . i . ( Figure S3 . A ) , allowing the analysis of mCherry-Br infected cells in situ during this time ( Figure S3 . B ) . Infected susceptible BALB/c mice displayed significantly higher count of bacteria in the spleen at 3 and 5 days p . i . when compared to C57BL/6 mice . However , at day 12 , spleens and livers from wild type mice had less than 105–106 CFU/g and bacteria became undetectable by microscopy analysis ( data not shown ) . Consequently , we limited our analysis to 120 h p . i . in wild type mice . The functions of the spleen are centered on systemic blood circulation . As such , it lacks afferent lymphatic vessels . It is comprised of two functionally and morphologically distinct compartments , namely the red pulp ( r . p . ) and the white pulp ( w . p . ) . The r . p . is a blood filter rich in F4/80+ macrophages that removes foreign material as well as damaged and effete erythrocytes . The spleen is also the largest secondary lymphoid organ of the body that initiates immune responses to blood-borne antigens . This function is usually associated to the w . p . compartment . This latter is centered on the central arteriole within the T cell rich area surrounded by B lymphocyte-associated follicles . At the interface between the r . p . and w . p . , the marginal zone ( m . z . ) is a unique region of the spleen . Considered to be a separate compartment rather than part of the w . p . , it is designed to screen systemic blood circulation for antigens and pathogens and plays an important role in antigen processing . It also contains several populations of specialized macrophages , such as marginal zone-associated metallophilic macrophages that can be identified with anti-MOMA-1 antibody ( for a review see [21] ) . Following i . p . inoculation of 108 CFU in resistant wild type C57BL/6 mice , mCherry-Br was detectable in the spleen by 10 minutes p . i . ( Figure S3 . A ) . Importantly , at this time , our observation showed that mCherry-Br already localized in the r . p . and the m . z . of infected spleens ( data not shown ) . Then , bacteria increased exponentially during the first 24 h ( Figure S3 . A ) and the number of intracellular bacteria peaked at 120 h p . i . with an average of 8–10 bacteria per cell ( Figure 1 . A ) . Importantly , infected cells were mainly located in m . z . and r . p . during the whole kinetic of infection ( Figure 1 . B–C and Figure 2 . A ) while infected cells were rarely observed in splenic w . p . area ( w . p . ) . Further semi-quantitative analyses of infected cell phenotypes in C57BL/6 mice ( Figure 2 . B and Figure 3 . A–B ) showed that first infected cells in spleen were mainly r . p . macrophages with a F4/80+ CD11b− phenotype ( ∼80% ) and MOMA-1+ m . z . metallophilic macrophages ( ∼20% ) . Between 3 and 6 h after inoculation , the frequency of infected MOMA-1+ cells decreased strongly and CD11c+ dendritic cells progressively became infected ( ∼30% of total infected cells at 6 h ) ( Figure 3 . A ) . Using a home-made rabbit polyclonal antibody specific of B . melitensis ( Brucella-Ag ) , we showed that mCherry-Br fluorescence and Brucella-Ag staining were generally overlapping ( Figure S4 ) , although there were some instances where Brucella-Ag staining could be detected in the absence of appreciable mCherry-Br fluorescence , possibly as a consequence of bacterial death or trafficking of antigens away from the bacteria . These results confirm that the majority of Brucella-infected cells can be detected in situ using mCherry-Br . Confocal analysis in Z-stack demonstrated that bacteria localized inside F4/80+ r . p . macrophages ( Figure S5 . A–B ) . Importantly , this was also true for mCherry-expressing VirB-defective Brucella mutant ( Figure S5 . C ) . The VirB mutant displays a strongly reduced ability to infect cells in vitro [22] though it is able to persist several days in vivo and colonize mice similarly to wild type bacteria [23] . Therefore , it has been hypothesized that in vivo persistence could be due to extracellular replication [24] . However , our observations are not in favor of this hypothesis since VirB mutant and wild type bacteria both localized within cells of the same phenotype in infected spleen at least in the first 72 h p . i . ( data not shown ) . As expected , VirB mutant is no longer detectable in situ at 120 h p . i . Our analysis revealed that the phenotype of infected spleen cells presented a drastic evolution between 24 h and 120 h p . i . Indeed , at 24 h p . i . , about 80% of infected cells were CD11b− F4/80+ r . p . macrophages ( Figure 3 . A–B ) , a fraction of which also expressed MHC-II+ molecules ( Figure 3 . B and Figure 4 . A ) . Between 24 h and 72 h , the percentage of CD11b+ Ly-6G−-infected monocytes increased progressively ( Figure 3 . B and Figure S6 ) . At 120 h , these monocytes constituted ∼30–40% of the infected cell population ( Figure 3 . B ) and were mainly found clustered in granulomas ( data not Shown ) . Granulomas were constituted of CD11b+ F4/80+ Ly-6G− MHC-II+ cells surrounded by CD90+ cells ( T cells ) and Ly-6G+ cells ( granulocytes ) ( Figure 3 . A–B , Figure 4 . B , Figure 5 . A ) . A fraction of CD11b+ F4/80+ MHC-II+ cells was also CD11c+ ( Figure 5 . A ) , suggesting that these cells were inflammatory DC . In previous reports , these inflammatory DC were detected by flow cytometry and identified as the main iNOS-producing cells during Brucella infection [10] . In agreement with this finding , we observed in situ that CD11b+ and CD11c+ cells located in granulomas were iNOS+ ( Figure 5 . B ) and co-localized with mCherry-Br signal . Importantly , B cells ( B220+ cells ) , T cells ( CD90 . 2+ cells ) or granulocytes ( Ly-6G+ cells ) seemed very rarely infected during the course of infection in the spleen ( Figure 3 . A–B and data not shown ) . Of note , a weak expression of CD90 . 2 was detected on a fraction of highly infected cells within granulomas ( Figure 3 . A ) . These cells displayed a cellular morphology ( large cells with uncondensed nuclei ) distinct from T lymphocytes and co-localized with CD11b and F4/80 ( data not shown ) , suggesting that they were not lymphocytes but rather myeloid cells . In summary , we concluded that infection seems largely limited to a fraction of the myeloid cell compartment in the spleen . Since the establishment of Brucella infection at day 5 in the spleen correlated with monocytes recruitment and formation of granulomas , we hypothesize that these latter are involved in bacterial containment and/or elimination as these structures contain most of the iNOS-producing cells . Granulomas were also detected in r . p . area of spleen from C57BL/6 mice infected for 5 days with 4×104 CFU ( Figure S7 . A ) . As expected , their numbers were reduced when compared to mice infected with 108 CFU . Surprisingly , at 12 days p . i . with both doses , granulomas were mostly located in w . p . area , specifically in T cells zones ( Figure S7 . A–B ) . Unfortunately , at this time point , we were not able to determine the presence of Brucella since the bacterial load was under our limit of detection . However , the similarity of the phenotype of these structures ( CD11b+ and F4/80+ , Figure S7 . A and S7 . B ) with r . p . granulomas suggests that they may surround rare infected cells . It has been frequently reported that BALB/c mice display higher bacterial loads per organs when compared to C57BL/6 mice during Brucella infection [10] , [25] . The reasons for this susceptibility are unclear but it has been associated with lower frequency of IFN-γ-secreting CD4+ cells and iNOS-producing cells in these mice [10] . In accordance with CFU counts measured in spleens ( Figure S3 . A ) , the average number of bacteria per cell observed by microscopy analysis in BALB/c and C57BL/6 mice was similar at 48 h , though this number was slightly higher at 120 h in BALB/c mice ( Figure 1 . A ) . To determine whether enhanced susceptibility of BALB/c mice could be associated with the persistence of bacteria in distinct cell reservoirs , we further examined the phenotype of infected cells in both mouse strains at that time point . As observed for C57BL/6 mice , the large majority of infected cells were mainly located in splenic r . p . and m . z . in BALB/c mice ( Figure 1 . B–C ) and semi-quantitative analyses of infected cell phenotypes ( Figure 3 . C ) showed a global similarity between C57BL/6 and BALB/c mice . However , the frequency of infected cells located in w . p . was higher in BALB/c mice when compared to C57BL/6 mice ( Figure 1 . B–C ) . Further comparative analysis ( Figure 1 . D–E ) of the spatial distribution of infected spleen cells showed that highly infected cells ( >20 bacteria/cell ) were more frequent in BALB/c infected mice and located mainly in w . p . areas . In conclusion , infected cell phenotypes are relatively similar in both mouse strains but higher colonization of w . p . cells in infected BALB/c mice seems to explain for their higher CFU counts . These cells frequently expressed MOMA-1 or CD11c markers but were negative for CD11b , F4/80 , Ly-6G and CD90 . 2 ( data not shown ) . Brucella have frequently been described as silent or stealthy pathogens , able to actively escape the immune response through their ability to grow furtively within cells [26] . This hypothesis is mainly based on the fact that Brucella's lipopolysaccharide ( LPS ) is a weak activator of macrophages and DC by comparison to conventional LPS from Escherichia coli [27] . In our study , we observed that CD11c+ DC represent 10–30% of spleen cells infected by mCherry-Br during the first 12 h of infection ( Figure 2 . B and Figure 3 . A ) . Therefore , in our experimental conditions ( 108 CFU i . p . ) , we investigated the impact of Brucella infection on DC activation in vivo in C57BL/6 mice . Imaging analysis ( Figure S8 ) of spleen sections revealed that 24 h post inoculation , CD11chigh DCs relocated massively to w . p . T cell area . Flow cytometry analyses demonstrated that DC migration observed in situ at 24 h p . i . was associated with their maturation as shown by cell surface up-regulation of MHC-II and CD86 co-stimulatory molecule expression on CD11chigh spleen cells ( Figure S9 . A ) . Although we observed maturation of all splenic DC subsets ( CD8− and CD8+ ) ( data not shown ) , we also found that direct infection of DC was not required to trigger the phenomenon since DC observed by microscopy in w . p . T cell area at 24 h p . i . were not associated with mCherry-Br signal ( data not shown ) . A lower dose of bacteria ( 4×104 CFU ) was able to induce DC maturation but the peak of maturation was reduced and displaced to 48 h p . i . ( data not shown ) . Importantly , administration of heat-killed ( HK ) Brucella was also able to induce a dose-dependent DC maturation ( Figure S9 . B ) , suggesting that Brucella's pathogen-associated molecular patterns ( PAMPs ) were sufficient to trigger this process . By comparison , HK Escherichia coli induced DC maturation at a ten-fold lower dose ( Figure S9 . B ) . Finally , using genetically deficient C57BL/6 mice , we investigated the role of MyD88 and TRIF adaptor molecules in DC maturation process in vivo and found that MyD88 , but not TRIF , was important to Brucella-induced DC maturation ( Figure S9 . C ) . Since the liver constitutes another important site of bacterial growth , we investigated the phenotype of infected cells in this organ in order to identify the common characteristics of infected cells in immunological and non-immunological sites . At 10 min p . i . , mCherry-Br signal was already detectable in liver sections from infected C57BL/6 and BALB/c wild type mice and co-localized strictly with F4/80+ MHC-II+ Kupffer cells ( data not shown ) . At 120 h post-inoculation , microscopy analyses revealed that , as observed in the spleen , the liver also displayed numerous granulomas composed of F4/80+ MHC-II+ CD11b+ cells ( Figure S10 ) . Granulomas frequently surrounded portal space and harbored the majority of infected cells ( data not shown ) . As it was the case in the spleen , iNOS expression was mainly associated with a fraction of granuloma cells expressing CD11c , suggesting that activated monocytes and inflammatory DC were also major populations of these structures . Importantly , we never observed bacteria inside hepatocytes or Ly-6G+ granulocytes suggesting that , like in the spleen , infection is limited to a specific fraction of the myeloid cell compartment . As granulomas seem to be the main organized cellular structures containing Brucella in the spleen and the liver , we tried to define the role of T and B lymphocytes in their formation . We infected RAG deficient C57BL/6 mice with 108 CFU of mCherry-Br . RAG−/− mice displayed higher CFU counts in the spleen , but not in the liver , at 120 h p . i . ( Figure S11 ) . At the same time , microscopy analysis of tissue sections showed that absence of T and B lymphocytes did not impair F4/80+ CD11b+ CD11c+ iNOS+ granuloma formation in either tissues ( Figure 6 ) . Flow cytometry analyses confirmed that recruitment of iNOS-DC was not compromised in the spleen of RAG−/− infected mice ( Figure S12 ) . However , in RAG−/− infected spleens , granulomas seemed less organized and less dense . In addition , higher numbers of iNOS+ cells and infected cells were observed outside of granulomas and frequency of F4/80− infected cells was higher when compared to wild type mice ( Figure 6 ) . These latter observations could be correlated with the presence of Ly-6G+ infected cells in the spleen of RAG−/− mice ( data not shown ) . In order to gain insight into immune mechanisms controlling bacterial growth in the spleen , we used a genetic approach to investigate the impact of MyD88 , IL-12p35 and IFN-γ deficiencies on the phenotype of infected cells with a particular focus on the ability of the immune response to establish granulomas . These molecules have been described as key elements of Th1-driven immune response controlling Brucella growth in vivo [10] , [14] , [28] , [12] . In agreement with previous published results , all deficient mice displayed higher CFU counts in spleen and liver at 120 h p . i . ( Figure S11 ) . As expected , frequencies of IFN-γ+ cells and iNOS-DC in infected spleen were drastically reduced in all Th1 deficient mice when compared to infected wild type mice ( data not shown and Figure S12 ) . Immunofluorescence analysis of liver and spleen sections from 120 h-infected mice showed that all three deficient mouse strains displayed dense aggregates of CD11b+ cells surrounding infected cells and resembling granulomas ( Figure 7 for spleen and liver from wild type and MyD88−/− mice and data not shown for IL-12−/− and IFN-γ−/− mice ) . Whereas iNOS staining was strongly reduced in all three deficient mouse strains ( Figure 7 and data not shown ) , CD90 . 2+ T cells recruitment around infected cells was normal ( data not shown ) . Moreover , granuloma-like structures in all deficient mouse strains frequently contained higher numbers of infected Ly-6G+ cells but reduced density of F4/80+ and CD11c+ cells ( Figure 8 for liver granulomas and data not shown for spleen granulomas ) . These “unconventional” granulomas , characterized by an inverted ratio of granulocytes versus monocytes/DC , failed to control bacterial growth as demonstrated by an increased mCherry-Br signal in deficient mice , with higher numbers of infected cells outside of granuloma like structures . These “unconventional” granulomas were also detected in infected wild type mice but at a low frequency . One of our initial objectives was to characterize the phenotype of Brucella reservoir cell types in chronically infected mice . Unfortunately , the detection threshold of mCherry-Br signal in situ required approximately 106 CFU/g of tissue ( Figure S3 . A and B ) . Wild type mice initially infected with 108 bacteria displayed CFU counts in spleen and liver inferior to this threshold at 12 days p . i . and MyD88−/− , IL-12−/− or IFN-γ−/− mice showed high mortality rate at the same time point . Thus , in order to increase the lifespan of genetically deficient mice , we tested an inoculum dose of 106 CFU and compared bacterial loads in spleen and liver at 5 , 12 and 30 days p . i . ( Figure S13 ) . Only IL-12p40−/− BALB/c mice displayed CFU counts superior to the detection threshold in spleens at 12 and 30 days p . i . Interestingly , microscopy analysis of tissue sections from spleens from infected IL-12−/− BALB/c mice ( Figure S14 . B ) showed that mCherry-Br signal , initially located in r . p . at 12 h p . i . , progressively relocated in w . p . at 5 , 12 and 30 days p . i . This preferential localization in w . p . was also observed in infected IL-12−/− C57BL/6 at 5 days p . i . ( Figure S14 . C ) , demonstrating that the BALB/c background is dispensable to observe this phenomenon . Surprisingly , the phenotype of Brucella-containing cells located in w . p . of 12 days-infected IL-12−/− BALB/c mice was strikingly distinct from r . p . infected cells previously described in wild type mice ( Figure 9 . A and 9 . B ) . Indeed , these cells were highly infected ( >20 bacteria/cell ) and harbored two distinct cell surface marker combinations . When localized in close proximity of m . z . area , the infected cells expressed MOMA-1 , a specific marker of metallophilic marginal zone macrophages , whereas when located deeply within the w . p . , they expressed CD11c and DEC205/CD205 ( Figure 9 . B ) , both DC-specific molecules . Both types of cells were negative for iNOS , F4/80 , CD11b , CD86 , CD90 . 2 and B220 ( data not shown ) and a fraction of them expressed weakly MHC-II . Although the bacterial load was reduced , the phenotype and location of IL-12−/− infected cells was similar 30 days p . i . , ( data not shown ) . Interestingly , highly infected CD11c+ cells located in the w . p . of wild-type BALB/c mice could also be observed 5 days p . i . but at a very low frequency ( Figure 9 . C and data not shown ) suggesting that those cells are not limited to Il-12−/− BALB/c mice . Given that these heavily infected w . p . cells were not found associated with CD11b+ F4/80+ iNOS+ effector cells , we conclude that they may constitute an important reservoir for B . melitensis under permissive conditions such as absence of Th1 protective response .
Bacteria of the genus Brucella , have been long studied as a relevant experimental model to analyze chronic infections of animals and humans due to the great impact they have on both husbandry practice and human health worldwide [29] . Their virulence and the chronicity of the ensuing disease rely on their ability to modulate both innate and adaptive immune responses and the physiology of the host's cells in which they reside , survive and multiply ( reviewed in [30] ) . The mouse is considered as a useful animal model to investigate the pathogenesis of brucellosis , to identify specific virulence factors of Brucella spp and to characterize the host immune response [29] . If classical studies [31] , [32] , and more recently the use of genetically deficient mice , have been useful to uncover the great avenues of protective immune responses against Brucella infection [33] , this approach appears insufficient to dissect immune effector mechanisms elaborated by the host to fight B . melitensis . Like Mycobacterium tuberculosis [34] , [35] , [36] , and in contrast to other intracellular bacteria such as Listeria monocytogenes [37] or Legionella pneumophila [38] , B . melitensis does not irreversibly affect the health status of the majority of mice genetically deficient for key element of Th1 protective immune response ( such as MyD88 [10] , [39] , IL-12p40 [11] , IFN-γ [12] and iNOS [11] ) during the first four weeks of infection . These observations suggest that immune effector mechanisms against Brucella are multiple and redundant , and necessitate new approaches to be further characterized . This is the perspective from which we decided to use in situ microscopy techniques for studying the local immune response developed around B . melitensis infected cells . Up to now , only gross morphometric and histopathologic analyses have been conducted [40]–[42] on spleen and liver , that are important sites for colonization and replication of Brucella in the mouse model [29] . Both organs develop the so called “histiocytic infiltrates and microgranulomas” [41] , [42] . Of importance , Brucella infection in mice results in lesions mimicking those described in chronic infections in humans which may also develop splenomegaly and hepatomegaly [17] , [43] . To our knowledge , our study and the recent work from Archambaud , C et al . [44] are the first in vivo investigations characterizing the in vivo phenotype of Brucella-infected cells . Archambaud , C et al . have focused their study on Brucella abortus-infected pulmonary tissues following intranasal inoculation . In contrast , we used an i . p . systemic model of infection in order to analyse the phenotype of Brucella melitensis-infected cells in spleen and liver . The i . p . route of infection is generally used to establish a persistent infection in the mouse because it leads to a rapid systemic distribution of Brucella sp . and to high bacterial loads in both spleen and liver [33] , [45] . Brucella has been initially described as an intracellular bacteria able to replicate in professional phagocytes such as macrophages [46] , DC [47] and granulocytes as well as non-professional phagocytes [48] , including epithelial , fibroblastic and trophoblastic cells , in the context of cell line cultures . However , the identity of Brucella reservoir cell types during the course of infection in vivo is largely uncharacterized . If macrophages [40] and trophoblastic cells [49] have been clearly associated with Brucella infection in the natural hosts , little has been demonstrated regarding the contribution of other potential cellular niches for Brucella in vivo . In this study , using a Brucella strain stably expressing mCherry , we demonstrated for the first time that these bacteria present a very restrictive cellular tropism as the majority of infected cells in the spleen and the liver of resistant C57BL/6 wild type mice belongs to a specific fraction of the myeloid lineage . Granulocytes , B cells , T cells , fibroblasts and hepatocytes were never found significantly infected . This result may explain the frequent discrepancies between in vitro and in vivo attenuation of various Brucella mutants . In the physiological cell reservoir and in the complex microenvironment of the host , some of the “virulence factors” identified in vitro may have little or no relevance . A striking example is the mutation affecting the gene virB which encodes the type IV secretion system of Brucella . The virB mutant is unable to grow within host cells in vitro [22] though it can replicate at a similar level as the wild type strain during the first 5 days of infection in vivo [23] . In the present study , virB mutant and wild type bacteria were localized within the same cells in infected spleen at all time-points analyzed during the first three days of infection . This argues in favor of the intracellular localization of virB mutant in vivo and strongly suggests that the type IV secretion system of Brucella is tightly regulated during the infectious process . Since we showed that the nature of the infected cells varied in a time-dependent manner during the first days of infection , it is tempting to speculate that the type IV secretion system depends on the nature of the infected cells and/or its activation status . Our study also brought to light the complexity and the dynamic of the cellular environment of the pathogen during the course of infection . In the spleen , m . z . ( MOMA-1+ ) and r . p . ( F4/80+ ) macrophages are the first infected cells , followed by DCs ( CD11c+ ) located in m . z . and r . p . . We hypothesized that infection-mediated inflammation is responsible of subsequent recruitment of CD11b+ Ly-6G− monocytes . Our data suggest that these cells are rapidly infected and progressively mature to form complex granulomas in r . p . composed of a mix of activated mature monocytes ( CD11b+ CD11c− F4/80+ MHC-II+ ) and inflammatory DC ( CD11b+ CD11c+ F4/80+ MHC-II+ ) and surrounded by T cells ( CD90 . 2+ ) and granulocytes ( CD11b+ Ly-6G+ ) . This scenario yields major differences when compared with the observations by Archambaud et al . [44] in the context of intra-nasal infection with Brucella abortus . Although macrophages are the first infected cells in spleen , liver , as shown in our study , and lungs [44] , DC infection and granulomas formation are not observed in lungs but only in draining lymph node [44] . These discrepancies could be due to differences in dose of infection , route of inoculation and bacterial strain used in both studies . It has been frequently reported that BALB/c mice display higher bacterial loads per organ when compared to C57BL/6 mice during Brucella infection [10] , [25] . The reasons of this susceptibility are unclear . In our experimental model , spleen and liver infected cells displayed a closely similar phenotype and localization . However , higher CFU counts observed in infected BALB/c mice was related to increase colonization of w . p . cells . These latter displayed high number of bacteria and expressed MOMA-1 or CD11c markers but were negative for CD11b , F4/80 , Ly-6G and CD90 . 2 , Transient infection of CD11c+ cells during the early stages of infection in the spleen leads us to characterize the impact of Brucella infection on the maturation of conventional DC . Flow cytometry and in situ staining showed that spleen infection with B . melitensis induced the massive migration of DC in the T cell area of w . p . as well as their maturation during the first 24 h of infection , followed later , by their gradual disappearance . The relationship between Brucella and DCs seems complex and necessitates further experiments to be elucidated . However , our results demonstrate clearly that the course of Brucella infection is associated to DCs activation in spleen . This maturation is dependent of MyD88 adapter molecule and does not require infection as heat killed Brucella induce a similar phenomenon . Importantly , microscopy analysis indicated that none of CD11c+ mature cells migrating to T cell area were infected by B . melitensis ( data not shown ) , suggesting that infection of DCs could impair their migration and maybe their maturation . Again , these results are in agreement with the stealthy strategy [30] attributed to Brucella and with in vitro studies [50] , [51] demonstrating the ability of Brucella to regulate DC maturation . Using genetically deficient mice , we partially clarified some requirements for granuloma formation during Brucella infection . Development of a Th1 response seems a critical step as MyD88−/− , IL-12p35−/− and IFN-γ−/− mice displayed iNOS− altered granulomas , strongly enriched in granulocytes . In contrast , the analysis of RAG−/− infected mice demonstrated that granuloma formation at 5 days p . i . is , for a large part , independent of lymphocytes . We hypothesized , in accordance with the granuloma formation model proposed for Listeria monocytogenes infection [16] , that IFN-γ is needed for maturation of monocytes in iNOS-DC and building of fully functional granuloma . As previously described [10] , IFN-γ was produced by Natural Killer ( NK ) cells and T lymphocytes during Brucella infection . In RAG−/− mice , iNOS-producing DC ( iNOS-DC ) were detectable by flow cytometry and microscopy analysis suggesting that absence of IFN-γ-producing T lymphocyte might be compensated by IFN-γ-producing NK cells . This was confirmed by the absence of iNOS-DC in spleens of RAGγc−/− ( deficient for natural killer cells ) infected mice ( data not shown ) . The presence of phenotypically similar granulomas in the infected spleen and liver support the hypothesis that granulomas constitute the “spearhead” of the immune response against Brucella . Correlation between high susceptibility of MyD88−/− , IL-12−/− and IFN-γ−/− mice and strongly altered granuloma structures in these mice suggested a causal link between the presence of granulomas and Brucella growth control . However , the precise role of granuloma during Brucella infection remains to be clarified . A prominent characteristic of Brucella is its capacity to persist in natural host for life ( reviewed in [30] ) . This property is thought to be related to its capacity to ( i ) locate intracellularly and avoid the fusion of Brucella-containing vacuoles with host cell lysosomes ( ii ) limit or modulate the activation of innate and adaptive immune response ( mainly due to poorly recognized PAMPs of its cell envelope and poorly characterized secreted effectors ) , ( iii ) prolong the lifespan of infected cells . The previously unappreciated restricted specificity of Brucella cell tropism that we observed in vivo allows us to hypothesize that the ability of Brucella to persist could also depend of its capacity to infect cell subsets particularly adapted to sustain its growth and persistence . In absence of Th1 response , granuloma formation was altered and the bacterial burden was significantly higher in spleens of IL-12p40−/− BALB/c mice , making possible microscopy observation in situ and characterization of infected cells during chronic phase of infection ( 12 and 30 days p . i . ) . Our analyses revealed that the main infected spleen cells at these time-points are located in the w . p . and express the following cell surface phenotype: CD11b− CD11c+ CD90− CD205+ F4/80− Ly-6G− MHC-IIlow B220− iNOS− . High expression of CD11c and CD205 suggested that these cells are a particular subset of DC . This phenotype is also partially reminiscent of foamy macrophages that express high levels of CD11c , CD205 and low level of MHC-II [52] and constitute nutrient-rich reservoirs in lung granulomas of M . tuberculosis-infected mice [53] . Interestingly , these cells were also detected , albeit with a low frequency , five days post-infection in the w . p . of wild type BALB/c mice infected with 108 CFU . Previously , we demonstrated the reduced ability of BALB/c mice to mount Th1 [10] response in the context of Brucella melitensis infection . Thus , permissive environments , such as the absence of Th1 response , may drive the differentiation of macrophages toward a long-lived and anti-inflammatory phenotype allowing bacterial persistence . Identification of these cells as potential reservoir for Brucella in chronically infected mice could help to ameliorate therapeutic treatment of brucellosis . In conclusion , this work dissected for the first time the nature of the effectors mechanisms developed in vivo by the immune system after B . melitensis systemic inoculation and described the phenotypic characteristics of infected cells during the initial and chronic steps of the infectious process . These results could help develop new strategies to control Brucella infection .
The animal handling and procedures of this study were in accordance with the current European legislation ( directive 86/609/EEC ) and in agreement with the corresponding Belgian law “Arrêté royal relatif à la protection des animaux d'expérience du 6 avril 2010 publié le 14 mai 2010” . The complete protocol was reviewed and approved by the Animal Welfare Committee of the Facultés Universitaires Notre-Dame de la Paix ( FUNDP , Belgium ) ( Permit Number: 05-558 ) . Genetically deficient mice in C57BL/6 background: MyD88−/− [54] were obtained from Dr . S . Akira ( Osaka University , Japan ) . TRIF−/− mice [55] were a kind gift from Dr . B . Beutler ( The Scripps Research Institute , CA ) , IL-12p35−/− mice [56] from Dr . B . Ryffel ( University of Orleans , France ) , IFN-γ−/− mice [57] from Dr . S . Magez ( Vrije Universiteit Brussel , Belgium ) , RAG1−/− mice [58] from Dr . S . Goriely ( Université Libre de Bruxelles , Belgium ) , RAGγc−/− mice from Dr . Michel Y . Braun ( Université Libre de Bruxelles , Belgium ) . IL-12p40−/− BALB/c mice were obtained from Dr . V . Flamand ( Université Libre de Bruxelles , Belgium ) . Wild type C57BL/6 mice and BALB/c mice purchased from Harlan ( Bicester , UK ) were used as control . All mice used in this study were bred in the animal facility of campus Gosselies from the Free University of Brussels ( ULB , Belgium ) . B . melitensis strain 16M ( Biotype1 , ATCC 23456 ) was isolated from an infected goat and grown in biosafety level III laboratory facility . Overnight culture grown with shaking at 37°C in 2YT media ( Luria-Bertani broth with double quantity of yeast extract ) to stationary phase was washed twice in PBS ( 3500×g , 10 min . ) before use in mice inoculation . The mCherry protein , a previously described rapidly maturing variant of the red fluorescent protein DsRed [18] , was cloned into the suicide vector pKSoriT-bla-kan downstream a strong Brucella spp . promoter [19] , [20] , *nouvelle ref* Köhler S , Infect Immun . 1999 ) previously used to express constitutively the GFP . This promoter was called PsojA and described as controlling the expression of the protein translocase SecE ( Köhler S . ; personal communication ) . The vector was constructed as follows: the mCherry coding sequence was amplified by PCR from pRSET-B-mCherry [18] with the mCherry-up and -down primers and ligated into pGemT-Easy ( Promega ) to generate pGEM-T-mCherry . The mCherry fragment was then excised from pGEM-T-mCherry by HindIII/XbaI double restriction and subsequently cloned into HindIII/XbaI-cut pKSoriT-bla ( pBluescript II KS vector from Stratagene in which RP4 oriT was inserted in order to make this vector mobilizable [59] ) to generate pKSoriT-bla-mCherry . Meanwhile , a 500 bp fragment upstream the coding sequence of secE was amplified from the B . melitensis genome by PCR with the PsojA-up and PsojA-down primers and ligated into pGemT-Easy ( Promega ) to generate pGEM-T-SojA . The PsojA fragment was then excised from pGEM-T-PsojA by NotI/XbaI double restriction and subsequently inserted into NotI/XbaI-cut pKSoriT-bla-mCherry to generate pKSoriT-bla-PsojA-mCherry . Finally , the aphA4 cassette ( a promoterless kanamycin resistance gene [60] was excised from pUC4aphA4 with SalI , and subsequently cloned into the XhoI site of pKSoriT-bla-PsojA-mCherry to generate plasmid pKSoriT-bla-kan-PsojA-mCherry . This final construct was transformed into E . coli strain S17-1 , and introduced into B . melitensis 16M NalR strain by conjugation . Clones that were kanamycin resistant and fluorescent were further checked by PCR , confirming the insertion of the plasmid at the targeted chromosomal PsojA promoter . Primers used in this study ( Sequence ( 5′-3′ ) ) : mCherry-up ( XbaI ) tctagaatggtgagcaagggcgag , mCherry-down ( HindIII ) aagcttttacttgtacagctcgtcca , PsojA-up ( NotI ) gcggccgccttgactatggatgcccgtt , PsojA-down ( XbaI ) tctagactctgtctgatcaggcacaa . Similar protocol has been used to construct mCherry-expressing ΔVirB B . melitenis . Construction of ΔVirB mutant has been previously described [61] . Mice were injected intra-peritoneally ( i . p . ) with indicated dose of B . melitensis in 500 µl of PBS . Control animals were injected with the same volume of PBS . Infectious doses were validated by plating serial dilutions of inoculums . At selected time intervals , mice were sacrificed by cervical dislocation . Immediately after being killed , spleen and liver were collected for bacterial count and flow cytometry and microscopy analyses . For bacterial count , spleens and livers were recovered in PBS/0 . 1% X-100 triton ( Sigma ) . We performed successive serial dilutions in PBS to get the most accurate bacterial count and we plated them onto 2YT media plates . CFU were counted after 3 days of culture at 37°C . Spleen were harvested , cut in very small pieces and incubated with a cocktail of DNAse I fraction IX ( Sigma-Aldrich Chimie SARL , Lyon , France ) ( 100 µg/ml ) and 1 . 6 mg/ml of collagenase ( 400 Mandl U/ml ) at 37°C for 30 min . After washing , spleen cells were filtered and incubated in saturating doses of purified 2 . 4G2 ( anti-mouse Fc receptor , ATCC ) in 200 µl PBS 0 . 5% BSA 0 . 02% NaN3 ( FACS buffer ) for 10 minutes on ice to prevent antibody binding to Fc receptor . 3–5×106 cells were stained on ice with various fluorescent mAbs combinations in FACS buffer and further collected on a FACScalibur cytofluorometer ( Becton Dickinson , BD ) . We purchased the following mAbs from BD Biosciences: Fluoresceine ( FITC ) -coupled 145-2C11 ( anti-CD3ε ) , 53-8 . 7 ( anti-CD8ε ) , M1/70 ( anti-CD11b ) , GL-1 ( anti-CD86 ) , Phycoerythrin ( PE ) -coupled HL3 ( anti-CD11c ) , RM4-5 ( anti-CD4 ) . Allophycocyanin ( APC ) -coupled M5/114 . 15 . 2 ( anti-IA/IE ) . The cells were analyzed on a FACScalibur cytofluorometer . Cells were gated according to size and scatter to eliminate dead cells and debris from analysis . Spleen cells were treated as previously described [62] . Spleen cells were incubated for 4 h in RPMI 1640 5% FCS with 1 µl/ml Golgi Plug ( BD Pharmingen ) at 37°C , 5% CO2 . The cells were washed with FACS buffer and stained for cell surface markers before fixation in PBS/1% PFA for 15–20 min on ice . These cells were then permeabilized for 30 min using a saponin-based buffer ( 1× Perm/Wash , BD Pharmingen in FACS buffer ) and stained with one or a combination of the following intracellular mAbs: allophycocyanin-coupled XMG1 . 2 ( anti-IFN-γ; BD Biosciences ) , purified M-19 ( rabbit polyclonal igG anti-NOS2; Santa Cruz Biotechnology ) stained with Alexa Fluor 647 goat anti-rabbit ( Molecular Probes ) . After final fixation in PBS/1% PFA , cells were analyzed on a FACScalibur cytofluorometer . No signal was detectable with control isotypes . Spleens and livers were fixed for 6 h at 4°C in 2% paraformaldehyde ( pH 7 . 4 ) , washed in PBS , incubated overnight at 4°C in a 20% PBS-sucrose solution under agitation , and washed again in PBS . Tissues were embedded in the Tissue-Tek OCT compound ( Sakura ) , frozen , in liquid nitrogen , and cryostat sections ( 5 µm ) were prepared . Tissues sections were rehydrated in PBS , then incubated successively in a PBS solution containing 1% blocking reagent ( Boeringer ) ( PBS-BR 1% ) and in PBS-BR 1% containing any of the following mAbs or reagents: DAPI nucleic acid stain , Alexa Fluor 350 or 488 phalloidin ( Molecular Probes ) , purified 1A8 ( anti-Ly-6G ) , or rabbit polyclonal antibodies anti-NOS2 ( Calbiochem ) ( note that M-19 anti-NOS2; used for cytofluorometry analysis is not use for immunofluorescence microscopy ) , biotin-coupled HL3 ( anti-CD11c , BD Biosciences ) , NLDC-145 ( anti-DEC205/CD205 , BMA Biomedical AG ) , MOMA-1 ( anti Marginal Zone Macrophages , BMA Biomedicals ) , 53-2 . 1 ( anti-CD90 . 2 , BD Biosciences ) , RA3-6B2 ( anti-CD45R/B220 , BD Biosciences ) , Alexa Fluor 647-coupled BM8 ( anti-F4/80 , Abcam ) M1/70 ( anti-CD11b , BD Biosciences ) , M5/114 . 15 . 2 ( anti-IA/IE , eBiosciences ) . Uncoupled 1A8 mAb and anti-NOS2 polyclonal antibodies were detected using biotin-coupled R67/1 . 30 ( mouse anti-rat IgG2a , BD Biosciences ) and Alexa Fluor 647-coupled goat anti-rabbit IgG ( Molecular Probes ) in PBS-BR 1% , respectively . Biotin-coupled mAbs were amplified using Alexa Fluor 350 or Alexa Fluor 647 Streptavidin ( Molecular Probes ) in PBS-BR 1% . Slides were mounted in Fluoro-Gel medium ( Electron Microscopy Sciences , Hatfield , PA ) . Labeled tissues sections were visualized with an Axiovert M200 inverted microscope ( Zeiss , Iena , Germany ) equipped with high resolution monochrome camera ( AxioCam HR , Zeiss ) . Images , 1384×1036 pixels ( 0 . 16 µm/pixel ) , were acquired sequentially for each fluorochrome with A-Plan 10×/0 . 25 N . A . and LD-Plan-NeoFluar 63×/0 . 75 N . A . dry objectives and recorded as eight bit grey levels * . zvi files . Colocalization between two stainings was analyzed using the AxioVision Colocalization module ( Zeiss ) . Double positive pixels were rendered in white , gray or yellow as indicated in the Figures . Images were exported as TIFF files and figures prepared in Canvas 7 program . For the estimation of the number of bacteria by cells or for the phenotype of infected cells , a minimum of 200 cells by condition were examined . These cells were counted in 6 mice minimum , in two independent experiments . When the number of bacteria by individual cell was too high to be determined , the number of bacteria was assumed to be of 20 or more ( see Figure 1 . E ) . Confocal analysis were performed with LSM510 NLO multiphoton confocal microscope fitted on an Axiovert M200 inverted microscope equipped with C-Apochromat 40×/1 . 2 N . A . water immersion objectives ( Zeiss ) . Optical sections of 1 µm thick , 568×568 pixels ( 0 . 1 µm/pixel ) , were collected sequentially for each fluorochrome and recorded as eight bit grey levels * . lsm files . We have used a ( Wilcoxon- ) Mann-Whitney test provided by GraphPad Prism program to statistically analyze our results . Each group of deficient mice was compared to wild type mice . We also compared each group to each other and displayed the result when it is required . Values of p<0 . 05 were considered to represent a significant difference . * , ** , *** denote p<0 . 05 , p<0 . 01 , p<0 . 001 , respectively . | Brucella are facultative intracellular bacteria chronically infecting humans and animals causing brucellosis , one of the most common zoonotic disease worldwide which can result in infertility and chronic debilitating disease . The cells supporting Brucella growth in vivo remain largely unknown . In order to identify these , we constructed a Brucella melitensis strain expressing a fluorescent protein that allowed us to characterize infected cells by microscopy of the spleen and liver from infected mice . In both tissues , the majority of primary infected cells were cells from the macrophage lineage . The peak of infection correlated with granuloma development . These structures contained the majority of bacteria and were mainly composed of cells expressing CD11b , F4/80 , MHC-II , which are specific of activated monocytes/macrophages . A fraction of granuloma cells also expressed CD11c and were similar to inflammatory dendritic cells ( DCs ) . During the chronic phase of infection in susceptible mice , we identified a particular subset of DC expressing CD205 and serving as a reservoir for the bacteria . Overall , our results describe the nature of immune cells infected by Brucella in vivo and reveal an unappreciated role for DC subsets , both as effectors and reservoir cells . These results could help develop new therapeutic strategies to control Brucella infection . | [
"Abstract",
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] | [
"immunity",
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] | 2012 | In Situ Microscopy Analysis Reveals Local Innate Immune Response Developed around Brucella Infected Cells in Resistant and Susceptible Mice |
Visceral leishmaniasis ( VL ) , with the squeal of Post-kala-azar dermal leishmaniasis ( PKDL ) , is a global threat for health . Studies have shown sodium stibogluconate ( SSG ) resistance in VL patients with chronic arsenic exposure . Here , we assessed the association between arsenic exposure and risk of developing PKDL in treated VL patients . In this retrospective study , PKDL patients ( n = 139 ) , earlier treated with SSG or any other drug during VL , were selected from the study cohort . Trained physicians , unaware of arsenic exposure , interviewed them and collected relevant data in a questionnaire format . All probable water sources were identified around the patient’s house and water was collected for evaluation of arsenic concentration . A GIS-based village-level digital database of PKDL cases and arsenic concentration in groundwater was developed and individual point location of PKDL cases were overlaid on an integrated GIS map . We used multivariate logistic regression analysis to assess odds ratios ( ORs ) for association between arsenic exposure and PKDL development . Out of the 429 water samples tested , 403 had arsenic content of over 10 μg/L , with highest level of 432 μg/L among the seven study villages . Multivariate adjusted ORs for risk of PKDL development in comparison of arsenic concentrations of 10 . 1–200 μg/L and 200 . 1–432 . 0 μg/L were 1 . 85 ( 1 . 13–3 . 03 ) and 2 . 31 ( 1 . 39–3 . 8 ) respectively . Interestingly , similar results were found for daily dose of arsenic and total arsenic concentration in urine sample of the individual . The multivariate-adjusted OR for comparison of high baseline arsenic exposure to low baseline arsenic exposure of the individuals in the study cohort was 1 . 66 ( 95% CI 1 . 02–2 . 7; p = 0 . 04 ) . Our findings indicate the need to consider environmental factors , like long time arsenic exposure , as an additional influence on treated VL patients towards risk of PKDL development in Bihar .
Visceral Leishmaniasis ( VL ) is one of the neglected tropical diseases and a major global threat for health worldwide [1 , 2] . It poses a major health problem in the poverty-ridden state of Bihar , which accounts for nearly 90% of the total cases in India [3] . Post-kala-azar dermal leishmaniasis ( PKDL ) , a dermal sequel of VL , is caused by L . donovani , and is confined to South Asia ( India , Nepal , and Bangladesh ) and East Africa , mainly Sudan [4 , 5] . In Africa , papular or nodular lesions are seen whereas in South Asia , the disease is mainly represented by polymorphic lesions with macules and/or with papulonodules [4] . However , the incidence of PKDL varies from 5–10% in India ( http://www . who . int/leishmaniasis/resources/INDIA . pdf ) and to 50–60% in Sudan [6 , 7] . In many aspects , asian PKDL is much different from the African counterpart [5] . African PKDL may exhibit spontaneous cure , co-occur with the visceral disease and even develop in VL patients undergoing treatment; which is not the case for Indian PKDL . Indian PKDL is characterized by appearance of hypopigmented macular , papular and/or nodular rashes on the skin; often represented by erythema and induration on the face [6 , 7] . PKDL is a perplexing disease and its role in transmission of VL warrants much debate . Risk factors for development of PKDL need extensive study as the control is an inevitable part of the current worldwide VL elimination programme . Groundwater is an essential component of our water resources for drinking , irrigation and industrial purposes . There is growing concern on deterioration of ground water quality due to geogenic and anthropogenic activities . Ground water contains wide varieties of dissolved inorganic chemical constituents in various concentrations due to chemical and biochemical interactions between water and the geological materials through contribution from the atmosphere and surface water bodies . Arsenic ( As ) is a colorless and odorless toxic metalloid element present in airborne particles , water , food and soil; and presents serious human health hazard [8] . Inorganic arsenic tends to be more toxic than organic arsenic . Arsenic exposure can result into chronic arsenic toxicity ( CAT ) , hence , long-term exposure is associated with diabetes , skin disease , various types of cancers , chronic cough , and toxic effects in the liver , kidney , cardiovascular system , and the peripheral and central nervous systems [9] . Importantly , CAT are mainly known to result in skin lesions and various systemic manifestations like chronic lung diseases ( chronic bronchitis , chronic obstructive pulmonary disease and bronchiectasis ) , liver diseases ( non-cirrhotic portal fibrosis ) and other diseases like peripheral vascular disease , polyneuropathy , hypertension and ischeamic heart disease , oedema , diabetes mellitus , weakness and even anaemia [10 , 11] . Among all , dermal effects following the exposure to arsenic are hallmarks of arsenic poisoning [12 , 13] . Chronic arsenic exposure leads to the development of skin lesions , including hyperkeratosis and hyperpigmentation [14] . CAT-induced keratosis emerges as diffuse thickening of palms and soles , alone or in combination with nodules , and are usually symmetrically distributed . Safe drinking water is a fundamental human right and one of the basic needs of an individual . Arsenic contamination of drinking groundwater has majorly impacted environmental health throughout the world including India [15 , 16] . Arsenic is a widely dispersed element in the Earth's crust and principally exists in the environment as sulfides , oxides , and phosphates . There are many possible routes of human exposure to arsenic from both natural and anthropogenic sources [9] . Groundwater contamination by arsenic arises from sources of arsenopyrite , base metal sulfides , realgar ( arsenic-sulfide mineral ) and orpiment , arsenic-rich pyrite , and iron oxyhydroxide . Arsenic is released from minerals by many bio-geochemical processes: oxidation of arsenic-bearing sulfides , desorption from oxides and hydroxides , reductive dissolution , evaporative concentration , leaching from sulfides by carbonate , and microbial mobilization [15 , 16] . Notably , arsenopyrite is a relatively soluble mineral with pH conditions typically found in groundwater ( pH 6 . 5–8 . 5 ) and both in oxidizing and slightly reduced environments ( i . e . , Eh values greater than approximately −0 . 2 V ) [16] . It breaks down to liberate mobile arsenic species , such as arsenite and arsenate , along with iron and sulphur into surface water and groundwater , thus contaminating the water supplies [17] . Rapid industrialization , ineffective water purification and sewage management systems , periodic monsoon and flooding etc are the main players that have exacerbated the problem of arsenic groundwater contamination in India since the past four decades [18 , 19] . Arsenic poisoning is a medical condition caused by elevated levels of sodium arsenite in different parts of the body . In recent years , it has assumed an alarming proportion in different parts of the Gangetic plain ( Uttar Pradesh , Bihar and West Bengal ) , where growing arsenic contamination of drinking water has been reported [18–24] . Notably , the problem of groundwater contamination in 18 out of the 38 districts in Bihar has reached alarming proportions since arsenic in groundwater was first reported in June 2002 in Bhojpur district [22–24] . Arsenic contamination up to 1861 ppb was found in regions of Bihar , against the W . H . O . permissible limit of 10 ppb . Undoubtedly , enhancement of arsenic in cultivated land by irrigation with contaminated water accelerate the uptake level through consumption of agricultural products such as rice , vegetables and other food crops; which have been one of the major cause of the increasing number of health issues in the affected regions [25–27] . Currently , exposure to arsenic has been supposedly linked with development of antimonial resistance among VL patients in Bihar [28 , 29] , as arsenic shares common chemical properties with antimony [28] . Antimonial resistance in VL has also been linked with arsenic exposure and development of cross-resistance has been suggested leading to the enhanced power of the parasites to survive during SSG treatment . Exposure to arsenic toxicity over decades , is thought to be responsible for disrupting pathways of antimony action on the parasite resulting in antimony resistance [29] . Reportedly , development of PKDL has been strongly linked with administration of SSG , for treatment of VL [30] . Epidemiological data and clinical reports have also strongly supported this link [29] . Simultaneously , the prime manifestations of CAT are skin lesions , characterized by pigmentation and keratosis [11] . However , development of PKDL in VL patients treated with other drugs , viz . paromomycin , amphotericin , miltefosine etc , have also been reported [30] . Therefore , we hypothesized of the contributing role for some environmental factors , like chronic exposure of arsenic contaminated groundwater , in development of PKDL . In this study , in light of the risk of CAT-associated dermal manifestations , we hypothesized that the long term exposure to groundwater arsenic contamination acts as a risk factor for development of PKDL in patients treated for VL . This was a retrospective study , as the time period between VL and development of PKDL varied among patients . As previous reports suggest prevalence of groundwater arsenic contamination in the gangetic plains [11 , 22–25] , we sought to test our hypothesis in the Raghopur cohort of Vaishali district in Bihar , which is mostly surrounded by the Ganges from all sides .
This research was conducted with approval of the institutional human Ethical Committee ( IHEC ) and conducted as per the guidelines of the Institutional Review Board of Rajendra Memorial Research Institute of Medical Sciences ( ICMR ) , Patna , India . All PKDL patients provided written informed consent for participation in this research study and subsequent analysis . Groundwater arsenic contamination was reported to UNICEF and the arsenic mitigation team , Public Health and Education Department , Bihar . Being totally intercepted by the Ganges , we selected Raghopur block of the Vaishali District , Bihar , as the target cohort for our retrospective study ( Fig 1 ) . The Raghopur block is located in the southern part of the district , mainly surrounded by some kala-azar endemic blocks namely Hajipur , Bidupur , Shadei Buzurg in north and Mahanar in the eastern side . The block also shares its border with Patna district in the south and Saran district to the west . A total of seven villages , namely Birpur , Jurawanpur Barari , Jurawanpur Karai , Paharpur , Raghopur-north , Raghopur-south and Rampur , were selected in the Raghopur block for this study . These villages are known to be highly endemic for VL with yearly average case incidence of 7 . 81 , 6 . 33 , 11 . 93 , 7 . 40 , 8 . 02 , 4 . 65 and 1 . 46 per 10 , 000 population in Birpur , Jurawanpur Barari , Jurawanpur Karai , Paharpur , Raghopur-north , Raghopur-south and Rampur respectively , during 2012–2014 [Source: District Malaria Office Hajipur ( DMOH ) , Vaishali and State Health Society Bihar ( SHSB ) , Patna] . The area is flat and covered by alluvium of still clay and sand deposited by the Ganges ( Fig 1 ) . Most of the land areas are frequently interrupted by river tributaries and small ground surface water collections . Sparse vegetation canopy existed in the study area that mainly includes grass land and plantations of mainly banana and mango . Cropping pattern is dominated by cereals such as rice , wheat and maize . Climate of the study area experienced with three distinct seasons , summer ( March-June ) , rainy ( July-October ) , and winter ( November–February ) . We designed this retrospective cohort study on the selected villages of the Raghopur block to assess the effect of long term exposure of groundwater arsenic pollution of the people and associated risk of PKDL development . All PKDL patients reporting to Rajendra Memorial Research Institute of Medical Sciences center for treatment during 2009–2014 from the Raghopur block in Vaishali district , were included in this study . After collecting information from the electronic center database , trained physicians ( unaware of the arsenic data ) interviewed the PKDL patients in person in their village and all relevant data were collected retrospectively in a questionnaire format . The information about treatment regimens during VL was cross-checked with the existing center database by interviewing the patients and/or their close relatives . Furthermore , detailed data about their treatment outcome during VL and PKDL , were recorded . The data included information on treatment failure , treatment success , relapse or death . Relevant covariate data was derived from the center database and the baseline interviews . The socio-demographic data included age in years , sex and fundamental education ( in years ) . The height , weight , basal metabolic rate , systolic blood pressure etc data were collected from the center database . Besides socio-demographic , epidemiological and clinical data , we also collected data about their drinking and other utility water sources , and information about the amount of consumption of cooked rice in the family . All probable water sources were identified around the patient’s house and water was collected for evaluation of arsenic concentration . The protocol for arsenic evaluation in hand pumps , using field test kits ( FTK ) , had already been approved by Government of Bihar , Government of Uttar Pradesh , and UNICEF , for detection of arsenic over large areas . Notably , the initial assessment of ground water arsenic contamination was performed by Field Test Kits ( FTK ) designed by National Chemical Laboratory , Pune . Next , all test samples for arsenic results were retested using flow-injection hydride-generation atomic absorption spectrometry ( FI-HG-AAS ) at the Department of Environment and Water Management , A . N . College , Patna . This was followed by recording of the locations of arsenic-affected hand pumps or water bodies , using Global Positioning System ( GPS ) units , was done , followed by mapping of the arsenic occurrences . The other references used were block maps and topographical maps . Arsenic dose ( μg/day ) was calculated as: ( arsenic concentration in water of the primary source , μg/L ) X ( self-reported daily amount of water from that source , L per day ) , n = 139 . Simultaneously , the total arsenic concentration in urine was divided by the concentration of creatinine in the urine to achieve a creatinine-adjusted total arsenic concentration in the urine expressed as μg/g creatinine; as described earlier [31] . For that , total arsenic concentration in urine was assessed by FI-HG-AAS with a detection limit of 2 . 0 μg/L . Creatinine level in the urine was measured with a commercial colorimetric kit [Sigma Aldrich , USA] . Total arsenic concentration in urine and arsenic dose per day was quartiled according to the baseline distribution of the cohort . We also involved dermatological examination of randomly selected individuals living in the study cohort to assess the effect of chronic arsenic exposure of the population . The presence or absence of arsenic-induced skin symptoms , including melanosis , suspicious spotty depigmentation / pigmentation over trunk /limbs , diffuse thickening of soles and palms , pigmentation involving the undersurface of tongue and/or buccal mucosa , leucomelanosis or keratosis , was examined by a clinician with ample experience in diagnosing arsenicosis cases and was blinded to the exposure level . For the study area , survey of India’s ( SOI ) toposheet number 72J of the scale 1:50 , 000 was used for preparation of the base map . The topographic map was geo-referenced with the latitudes and longitudes using the ArcGIS software v9 . 3 ( ESRI , Redlands , CA , USA ) to demarcate the boundary of study villages and interpret the remote sensing data . For delineation of spatial pattern of the river/surface waterlogged areas as well as land uses/land covers of the study villages , screen shot of high resolution satellite imageries were downloaded from Google Earth desktop version-6 . 2 ( USDA Farm Service Agency , 2013 Digital Globe ) . The raw satellite imageries were georeferenced , mosaicked and analyzed using the ERDAS imagines software v9 . 2 ( Hexagon Geospatial , USA; formerly ERDAS , Inc . ) . Features classes were identified based on the visual image interpretation elements ( such as tone , texture , shape , pattern and association ) and results were verified by ground truth collection of earth phenomenon using Global Positioning System ( GPS ) . Online image digitization and overlay technique was used to create the feature layers . A GIS based village level digital database of PKDL cases report and arsenic contamination in ground water was developed . Village wise cases data were short listed using the address given in the registers . All the patient locations were verified in situ by GPS device to geo-coding the case data on the map . Individual point location of PKDL cases were plotted on the arsenic distribution map . Spatial distribution of polygonal inhabitant areas were marked and overlaid on integrated GIS map . Finally , GIS integrated village boundary layer was used to store the quantitative values for mapping , visualization , statistical analysis and represent the results . A stepwise multivariate logistic regression model was used to assess the association of chronic arsenic exposure and risk of PKDL development in treated VL patients . Data on variables were based on prior casual knowledge and was derived from the baseline interview forms . This model was accountable to possible confounding . Each covariate was individually analyzed for the association by logistic regression . The model was primarily adjusted for age ( in years ) and gender . Later , the model was further adjusted in multivariate analysis for presence of potential confounders viz . body-mass index ( BMI in kg/m2 ) , systolic blood pressure ( mm Hg ) , caste status , fundamental education ( in years ) , previous VL treatment with SSG in the family , place of treatment during VL and time to treatment ( in years ) . Confounders with missing or incomplete data were excluded from the analysis . As several participants used the same well or hand pump for water , we included clustering in our analysis using SEs for the hazard model . Odds ratios ( ORs ) were estimated and their 95% CIs were evaluated . All statistical analyses were carried out with SAS 8 . 2 software ( SAS Institute Inc . , USA ) .
The study cohort Raghopur block is located in the southern part of Vaishali district . Using online image digitalization and overlay techniques , environmental features of the cohort were classified into three categories such as river/surface water bodies , settlement/built-up areas ( inhabitants places ) and other land use/land covers ( mixed of agricultural crops , sparse vegetation and grass land ) ( Fig 1 ) . The seven study villages of the cohort were distributed in a geographic area of approximately 11362 . 66 hectare . River stream areas covered 1 . 65% ( 187 . 72 hectare ) of land and were distributed evenly throughout the seven villages . One hundred thirteen settlement clusters ( as per satellite data ) were identified from all seven villages where the population was at risk for kala-azar . Area of these settlement clusters ranged from 0 . 155 to 137 . 21 hectare . Spatial distribution of settlement cluster shows a significant interconnection ( p<0 . 001 ) amongst the study villages . We hypothesized that the long term exposure to groundwater arsenic contamination is an additional risk factor for development of PKDL in patients treated for VL . For that , we identified 157 PKDL patients from the study area ( Fig 1 ) , who were treated at Rajendra Memorial Research Institute of Medical Sciences ( RMRIMS ) , Patna , Bihar during 2009–2014 ( Fig 2 ) . Among them , some were excluded from the study as either history of treatment during VL were not found for eleven patients ( n = 11 ) or were duplicate entries ( n = 3 ) . Therefore , finally , one hundred and forty three ( n = 143 ) PKDL patients were included in the study . After receiving their treatment data during VL episode , we divided them into two subgroups: Group A- patients treated with SSG during VL ( n = 112 ) and Group B- patients treated with other drugs ( eg . Amphotericin B , Miltefosine , Paramomycin , Ambisome etc . ) during VL episode ( n = 31 ) ( Fig 2 ) . After collecting informations from the center database , the PKDL patients were visited in their village and all relevant data were collected retrospectively by clinicians in a questionnaire format . Among the 143 PKDL patients , 92 ( 64 . 3% ) were available for the interviews . Thirty-five ( 24 . 4% ) subjects were living outside the study area during the visit due to migration to other cities for jobs , three subjects could not be located due to misinformation in address and one subject was dead . The relatives of the rest twelve subject were interviewed to gather information . Based on the information , total thirty one ( 21 . 6% ) patients were not treated with SSG , i . e treated with Amphotericin B ( n = 13 ) , Miltefosine ( n = 7 ) , Paramomycin ( n = 8 ) , Ambisome ( n = 3 ) etc . , were also included in the study . Finally , a cohort of one hundred and thirty nine ( n = 139 ) subjects were found as the study population in Raghopur block ( Fig 2 ) . The cohort of 139 patients was aged between 5 to 64 years with males to females 3:2 . The comparative status for history of VL episodeof all PKDL cases in the cohort is shown in Table 1 . No significant difference was observed between the history of VL episode between the two groups of PKDL patients in the study cohort . ( Table 1 ) . The characteristics of confounders of all PKDL cases are shown in Table 2 . The village-wise distribution of PKDL cases is shown in Fig 3 . Most of the patients ( 92% ) were living in this block for the last 10 . 8 to 26 . 4 years . All patients were recruited during 2009–2014 from the outpatient/inpatient departments of Rajendra Memorial Research Institute of Medical Sciences ( ICMR ) , Patna , India . All clinical investigations were ethically approved and performed as per the Declaration of Helsinki . The PKDL patients reported with macular/nodular/mixed polymorphic ( macules with presence of nodules and/or papules ) non-anesthetic lesions on face/forelimbs/shoulder . The previous history of VL and treatment used during the VL episode was recorded along with other information . These suspected PKDL cases were diagnostically confirmed by rK39 strip test followed by LD body detection in Giemsa-stained lesion-biopsy specimens . For smear negative samples ( especially in macular PKDL lesions ) , DNA was isolated from skin lesion samples and PCR was conducted to detect kDNA of the parasite for confirmation of PKDL , as described earlier [32] . All PKDL patients were treated with Amphoterin B ( AmB; 1 mg/kg body weight ) as alternate-day infusions for 40 days for 5 months with two 15-day breaks between the courses . In this study , the spatial distribution of the arsenic contaminated areas was studied by GIS based mapping techniques . GIS based thematic map showed the spatial distribution of arsenic contaminated villages in the focused study cohort at Raghopur block ( Fig 3 ) . The GIS map indicates low , medium and high spatial variability in arsenic concentration through the color scale ( Fig 3 ) . Spatial distribution of polygonal inhabitant areas were marked and overlaid on integrated GIS map . After plotting individual geocoded cohort addresses of PKDL cases on the arsenic distribution map using spatial join functionality , we found that 7 villages in Raghopur block of Vaishali district in Bihar indicate that arsenic concentrations are high in groundwater collected from the youngest alluvial terraces ( Fig 4 ) . Out of the 437 samples tested , 409 samples were confirmed to have arsenic content exceeding the World Health Organization ( WHO ) guideline value ( 10μg/L ) of arsenic . Most samples ( 92% ) tested were having more than 50 ppb . of arsenic content . The wells were considerably less contaminated than the hand pumps in the block . Eighty four ( 60 . 4% ) PKDL patients had at least one highly arsenic contaminated hand pump in their close vicinity and reported of using the water for drinking and other requirements . However , only 12 ( 8 . 3% ) PKDL patient were aware of arsenic contamination in their drinking water . A positive co-relation was established between PKDL cases incidence and the level of arsenic concentration in the ground water . Results denoted that the highest arsenic content is 432 ppb . in a hand pump , collected from Birpur village , east of Raghopur block . This place was located very near to the coast of Ganges . Interestingly , 37 ( 26 . 6% ) PKDL patients reported from Birpur village of the Raghopur block during the study period ( Figs 3 and 4 ) . Simultaneously , in Raghopur-north village , the highest arsenic level detected is 375 ppb . In a hand pump located near the house of a PKDL patient . Next , the highest arsenic value of 321 ppb . was recorded from Ragopur-south village , Raghopur block ( Fig 4A and 4B ) . Notably , forty one ( 29 . 4% ) PKDL cases reported from both Raghopur ( south and north ) villages . Contrastingly , the arsenic load of Paharpur village was medium ( 70–80 ppb ) ( Fig 4A and 4B ) . The highest arsenic level detected was 76 ppb . near the Raghopur-Diyara island area , followed by 72 ppb . in Paharpur . However , in Jurawanpur Barari and Jurawanpur Karai village , the highest arsenic levels read were 74 ppb . and 78 ppb . respectively ( Figs 3 and 4 ) . Both these areas were comparatively less populated in the block . The lowest levels of arsenic content in Rampur village was within 70 ppb . ( Fig 4 ) , making it the least arsenic contaminated village surveyed in the Raghopur block . Interestingly , only two PKDL patient reported from this area . Table 2 shows the distribution of socio-demographic , clinical and exposure characteristics of the PKDL patients in the baseline study cohort . We also used data on potential confounding factors for adjustment of the cohort data . After adjustment for potential confounding , the estimated summary attributable proportion based on arsenic concentration in water for PKDL risk in treated VL patients was 35% . We investigated whether arsenic load has relation with number of cases reported from the village . Our results also denoted that number of cases increased with arsenic load , i . e . more PKDL cases were reported from highly arsenic contaminated areas ( Figs 4 and 5 and Table 3 ) . Arsenic exposure ( in terms of baseline arsenic concentration in the water sources , arsenic dose per day , and total arsenic concentration in the urine samples ) was well associated with the risk of PKDL development ( Table 3 ) . Interestingly , the risk of PKDL development increased with arsenic dose per day and arsenic levels found in the urine samples ( Table 3 ) . The range of Pearson correlation coefficients for the level of arsenic exposures were 0 . 72–0 . 94 , the most strongest factors were arsenic concentration in the water source and arsenic dose per day of the individual for PKDL development . With respect to the data of the ordinal exposure , a one-quartile increase in arsenic concentration of water source or arsenic intake per day or arsenic level in the urine , there was about 12–16% increase in PKDL risk in this multivariate model ( Table 3 ) . The multivariate-adjusted OR for comparison of high baseline arsenic exposure to low baseline arsenic exposure of the individuals in the study cohort was 1 . 66 ( 95% CI: 1 . 02–2 . 7; p = 0 . 04 ) and demonstrated a positive role of arsenic exposure on PKDL development . We also involved dermatological examination of randomly selected individuals living in the study cohort to assess the effect of chronic arsenic exposure of the population . The presence of arsenic-induced skin symptoms like melanosis , spotty depigmentation/ pigmentation over trunk /limbs , diffuse thickening of soles and palms , pigmentation on or below tongue or keratosis , was noted . Severe symptoms like diffuse verrucous lesions of the soles with cracks and fissures were noted in two individuals .
The findings of our current study suggest significant effect of arsenic exposure through groundwater contamination on risk of PKDL development in VL patients , treated with SSG or other drugs during the VL episode . As contained in water , daily intake of arsenic creates a medical condition by elevated levels of sodium arsenite in different parts of the body . Recently , Perry et al . demonstrated that long-term arsenic exposure and subsequent adaptation of L . donovani to sublethal levels of arsenic in their human hosts may have led to cross-resistance to antimonials and the arsenic hotspots in Bihar coincided with areas with SSG treatment failure [28 , 33 , 34] . The dermal effects following the exposure to arsenic are hallmarks of arsenic toxicity , where hyperkeratosis and hyperpigmentation are the commonest examples [14 , 35] . Evidences also suggest that SSG directly or indirectly influences the incidence of PKDL [30] . However , it is important to note that about 27% of PKDL also develops in VL patients treated with other drugs , like amphotericin B , ambisome , miltefosine , miltefosine—amphotericin B , or paromomycin in Bihar [30 , 36 , 37] . It is plausible that greater number of PKDL cases from SSG-treated VL cases can also be due to massive use of SSG in Bihar during the 80s , when no other option was readily available for VL treatment in this area . Therefore , besides the risk factor of ineffective treatment of SSG , the environmental factors prevailing in the area may play additional risk for PKDL development and further studies are required to strengthen this point . In the study cohort , all probable water sources were identified around the patient’s house and high levels of arsenic concentration was found . Interestingly , human exposure to inorganic arsenic is associated with an increased risk of dermal malignancies and acts as a cofactor in the development of skin tumors in combination with ultraviolet ( UV ) irradiation [38] . Furthermore , arsenic hazard studies report close link between the clothing habits of individuals and health risk development; potentiating the dermatological effect of arsenic in the presence of UV-ray exposure through sunlight [39 , 40] . Interestingly , PKDL presents with a spectra of dermal manifestations , especially in the sun-exposed areas of the body , relating lesional patterns with the clothing habits of individuals and with exposure to UV radiation [30 , 41 , 42] . Sun exposure has been reported to induce rapid immunological changes in skin and peripheral blood [43] , and also to help in immunosupression through reduction in dermal DC subset populations in psoriasis patients [44] . The VL patients , residing in the study cohort , are vulnerable to both arsenic exposure and to over-exposure to sunlight as their main occupation is farming . Another study reports that occupational exposure of arsenic among workers in a glass plant , with blood arsenic levels five times higher compared to the control group , leads to increased DNA damage in leukocytes [45] . As leukocytes play a major role in cure of VL , over-exposure of arsenic may restrict leishmanicidal functions of leukocytes in VL patients during treatment , leading to escape of parasites from the killing mechanism . Therefore , arsenic groundwater contamination may act as an additive risk factor for PKDL development in Bihar . Interestingly , reactive oxygen species ( ROS ) -mediated oxidative damage is a common denominator in arsenic pathogenesis [46] . In addition , arsenic also induces severe morphological changes in mitochondrial integrity leading to oxidant-induced DNA damage [46] . Free radical formation from the superoxide radical , combined with glutathione-depleting agents , increase the sensitivity of cells to arsenic toxicity [46] . Therefore , individuals exposed to arsenic , have an increased formation of ROS/RNS , including superoxide radical , singlet oxygen , hydroxyl radical ( OH• ) and hydrogen peroxide . The parasites of VL generally reside in the liver and spleen tissue microenvironments and influence qualitative and quantitative aspects of the host immunity [47] . Oxidative and nitrosative stress components have serious adverse effects on the host during VL and PKDL infection . Furthermore , non-restoration of normal activities of peroxisomal catalase and superoxide dismutase in the host has been found responsible for unsuccessful clearance of Leishmania parasites from liver and spleen [48] . The prolonged exposure to groundwater arsenic contamination probably adds up to the increased oxidative stress and peroxisomal dysfunction in the host . It can be suggested that long time arsenic exposure may exert its influence on keratinocytes and lymphocytes , leading to modulation of cytokines that may promote development of PKDL in treated VL patients , which may also have influence on the incidence of VL in Bihar . The findings of this study suggested a positive co-relation between the incidence of PKDL cases and the level of arsenic concentration in the ground water of the cohort . However , it is difficult to interpret the exact role of arsenic on number of PKDL cases reporting from a contaminated area as background incidence of VL may have some impact . For example , the low number of PKDL cases reporting from Rampur village could also be due to low VL incidence in that area . Our findings also suggested that there were no apparent differences between history of VL episode between the two groups of PKDL cases in the cohort ( Table 1 ) . This also indicates a probable role of environmental factors , like arsenic exposure , in PKDL development . Interestingly , the immune system has been reported to be a sensitive target for arsenic exposures that may be associated with decreased host resistance to infectious agents [49 , 50] . Arsenic causes significant changes in T-cell secreted cytokine levels with altered T-cell activation status leading to immunosuppression favoring opportunistic infections in exposed individuals . Notably , arsenite also suppresses the activation of Th1 ( T bet ) cells , and alters the percentages of Th17 ( RORγt ) and T-reg ( FoxP3 ) population [51] . As T-cells are crucial deciders for the fate of VL infection , chronic arsenic exposure could also have contributed for incidence of VL . Notably , exposure to arsenic is associated with an increased prevalence of malnutrition [52] , leading to susceptibility to skin lesions [53] . Malnutrition also induces immunosuppression . There is prevalence of mass malnutrition among VL patients and their family members in Bihar . Therefore , it is plausible that long term arsenic exposure could also have contributed for incidence of VL in the affected areas , finally also co-affecting PKDL incidence . However , no definite conclusions can be drawn without further studies on this aspect . Reportedly , PKDL-causing Leishmania donovani strains express higher levels of certain surface proteins that are associated with dermatotropism of the parasite [54] . Besides possible effect of ineffective treatment of VL or other risk factors , exposure to arsenic may additionally contribute to the emergence of PKDL in Bihar , through its pro-dermatotropic effects on the parasite surface . Further work is underway to study the parasite protein expression profile during chronic arsenic exposure that would further enlighten the mechanism of parasite dermatotropism . The current work highlights the need to consider environmental factors like arsenic exposure as an additional risk factor for PKDL development in India . However , the role of arsenic exposure on occurrence of VL cannot be ruled out . Further extensive mechanistic and epidemiological studies are required to assess the real role of arsenic exposure on PKDL development . | Post-kala-azar dermal leishmaniasis ( PKDL ) is a sequela of visceral leishmaniasis ( VL ) that appears after patients have apparently been cured of visceral leishmaniasis; even been reported in patients without a history of VL . Previous clinical and epidemiological data ascertains the main risk factor associated with the development of PKDL is previous treatment for VL with antimonials ( SSG ) ; however , PKDL also occurs after treatment with other drugs like paromomycin , miltefosine etc . Here , in light of the risk of arsenic-associated dermal manifestations , we hypothesized that the long term exposure to groundwater arsenic acts as an additional risk factor for development of PKDL in patients treated for VL with SSG or other drugs . Using a cohort , we retrospectively assessed the risk of arsenic in development of PKDL in treated VL patients . Our findings support a significant association and prompts parasites might persist successfully in individuals over-exposed to arsenic and may exhibit features of dermatotropism leading to development of PKDL after treatment for VL . Further research is needed to dissect the mechanistic role of arsenic on VL , as well as PKDL development . | [
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] | 2016 | Chronic Arsenic Exposure and Risk of Post Kala-azar Dermal Leishmaniasis Development in India: A Retrospective Cohort Study |
Bone morphogenetic protein ( BMP ) gradients provide positional information to direct cell fate specification , such as patterning of the vertebrate ectoderm into neural , neural crest , and epidermal tissues , with precise borders segregating these domains . However , little is known about how BMP activity is regulated spatially and temporally during vertebrate development to contribute to embryonic patterning , and more specifically to neural crest formation . Through a large-scale in vivo functional screen in Xenopus for neural crest fate , we identified an essential regulator of BMP activity , SNW1 . SNW1 is a nuclear protein known to regulate gene expression . Using antisense morpholinos to deplete SNW1 protein in both Xenopus and zebrafish embryos , we demonstrate that dorsally expressed SNW1 is required for neural crest specification , and this is independent of mesoderm formation and gastrulation morphogenetic movements . By exploiting a combination of immunostaining for phosphorylated Smad1 in Xenopus embryos and a BMP-dependent reporter transgenic zebrafish line , we show that SNW1 regulates a specific domain of BMP activity in the dorsal ectoderm at the neural plate border at post-gastrula stages . We use double in situ hybridizations and immunofluorescence to show how this domain of BMP activity is spatially positioned relative to the neural crest domain and that of SNW1 expression . Further in vivo and in vitro assays using cell culture and tissue explants allow us to conclude that SNW1 acts upstream of the BMP receptors . Finally , we show that the requirement of SNW1 for neural crest specification is through its ability to regulate BMP activity , as we demonstrate that targeted overexpression of BMP to the neural plate border is sufficient to restore neural crest formation in Xenopus SNW1 morphants . We conclude that through its ability to regulate a specific domain of BMP activity in the vertebrate embryo , SNW1 is a critical regulator of neural plate border formation and thus neural crest specification .
The development of all metazoans involves the sequential specification of restricted cell fates from pluripotent progenitors . Understanding the spatial and temporal dynamics of cell differentiation is of fundamental importance to developmental biology research , in particular , how sharp borders and domains are delineated in a field of progenitor cells . For example , the neural crest , a vertebrate-specific embryonic cell type that gives rise to many different cell types ( e . g . , cartilage and bones of the face , melanocytes , neurons and glia of the peripheral nervous system , and smooth muscle of the heart ) , is induced at a very specific domain in the developing ectoderm between the neural plate and the epidermis [1] ( Figure 1A ) . Patterning of the vertebrate ectoderm across its dorsal-ventral axis is known to be dependent on a gradient of bone morphogenetic protein ( BMP ) activity . BMP ligands are capable of functioning as morphogens , providing positional information to a field of cells depending on the strength and duration of signaling activity [2] . In Xenopus and zebrafish embryos , a high ventral to low dorsal gradient of BMP activity is thought to contribute to embryonic patterning . The classical model proposes that in the ectoderm , high BMP activity is required for epidermal fate , a moderate level of activity is necessary for neural plate border fates , including competency to become neural crest , and BMP inhibition is a prerequisite for neural fate [3] ( Figure 1A ) . Analogous to the observation that gastrulating cells become competent to respond to BMP signals in a temporal fashion along the anterior-posterior axis [4] , neural and neural crest fates require BMP signaling at temporally distinct periods [5] , [6] , in addition to the requirement for different levels of BMP activity . However , whilst these studies have demonstrated a particular requirement in time and space for BMP activity to specify certain fates , they have provided little insight into how the BMP activity may change spatially and temporally to provide the means for these developmental events to occur , or how this is regulated . Indeed , while the BMP activity gradient has been visualized in gastrulating Xenopus and zebrafish embryos [4] , [7] , no comprehensive visualization of BMP activity domains at post-gastrula stages has been reported in Xenopus or zebrafish . The ability to directly observe how BMP activity is remodeled at different stages will help determine how this contributes to the specification of particular cell fates and the formation of sharp tissue boundaries , as well as clarifying which BMP ligands are likely to contribute to BMP activity in specific tissues . Here , we report the identification of a novel regulator of BMP activity in the vertebrate embryo , SNW1 , in a large-scale functional screen in Xenopus embryos for neural crest fate . The expression of SNW1 is enriched in dorsal tissues during neurula stages in both Xenopus and zebrafish embryos . Depletion of SNW1 in both organisms leads to a loss of neural crest , concomitant with an expansion of the neural plate and loss of the sharp neural plate border . We have established that dorsally expressed SNW1 is critical for neural crest formation and that the effects of SNW1 occur independently of mesoderm formation . In addition to its effect on neural crest specification , we have shown that SNW1 is necessary for BMP activity during neurula stages . More specifically , in the absence of SNW1 , a specific domain of BMP activity is lost in the ectoderm , adjacent to the neural plate . Experiments in mammalian cell culture and Xenopus embryo explants indicate that SNW1 does not directly affect the core BMP signaling pathway , suggesting that SNW1 must function upstream of the BMP receptors . Finally the proximity/overlap of the SNW1 expression domain , neural crest cells , and the BMP activity domain prompted us to test whether SNW1 affects neural crest formation via its ability to regulate BMP activity . Indeed , targeted overexpression of BMP2b was able to rescue the neural crest phenotype of Xenopus SNW1 morphants . Thus , SNW1 is a critical factor that is required for a specific BMP activity domain in the ectoderm in order to specify neural crest fate during neurulation .
To discover how a new cell population can be specified at a defined location in a developing vertebrate embryo we performed a large-scale in vivo functional screen in Xenopus laevis for neural crest fate , which occurs at the neural plate border ( Figure 1A and 1B ) . A Xenopus tropicalis full-length cDNA library was used as a template for transcribing mRNA in vitro . The mRNAs were then pooled and injected into Xenopus embryos for overexpression . Embryos were fixed at the early neurula stage 14 and subjected to whole-mount in situ hybridization ( WISH ) for the neural crest marker Slug . Positive pools were then deconvoluted by injection of subpools and again assessed by WISH for Slug until single clones were isolated . We found a total of 14 proteins whose overexpression altered the levels of the neural crest marker Slug , and the screen was validated by the fact that some of the positive hits were known modulators of signaling pathways required for neural crest fate , or factors previously identified as neural crest regulators . Examples were c-Myc , whose overexpression resulted in increased Slug staining [8] , and the Wnt antagonist Dickkopf1 ( Dkk-1 ) and the BMP antagonist Noggin2 [1] , [9] , [10] , both of which repressed neural crest fate ( Figure 1C ) . One unexpected hit was SNW1 , which substantially inhibited Slug staining when overexpressed ( Figure 1C ) . SNW1 , which is a nuclear protein , has been variously implicated in transcription regulation in response to several different signals [11]–[13] , and in transcriptional elongation through its ability to interact with pTEFb [14] , and in yeast it is thought to be involved in pre-mRNA splicing [15] . It is extremely well conserved at the amino acid sequence level throughout its entire sequence from Caenorhabditis elegans to humans ( Figure S1 ) . Semi-quantitative reverse transcription PCR ( RT-PCR ) indicated that SNW1 is expressed at all stages of Xenopus development examined ( Figure S2A ) , and the presence of SNW1 mRNA in unfertilized eggs suggests that it is maternally deposited during oogenesis . Quantitative PCR ( qPCR ) on dorsal and ventral halves of Xenopus embryos confirms that SNW1 transcripts are present in both halves of the embryo ( Figure S2B ) , but WISH for SNW1 mRNA at two neurula stages ( stage 14 and 17 ) reveals that SNW1 expression is specifically enriched on the dorsal side , in the neural plate region ( Figure 1D ) . Western blotting demonstrated that SNW1 protein levels were relatively low up to the end of gastrulation ( stage 12 ) , at which point the levels increased and continued to do so through the rest of development to stage 32 ( Figure 1E ) . The 61-kDa SNW1 band disappears when SNW1 is knocked down by injection of a translation-blocking antisense morpholino ( MO ) , MoSNW1a ( Figures 1E and S3A ) . Interestingly , this MO efficiently reduced SNW1 protein levels from stage 12 onwards ( Figure 1E ) , suggesting that the protein detected prior to this stage is maternally deposited and thus resistant to the MO , whereas the protein detectable after stage 12 is zygotically produced . To investigate whether SNW1 expression was conserved in another vertebrate species , we cloned zebrafish SNW1 and examined its expression pattern . WISH on the naturally translucent zebrafish embryos provided higher resolution in terms of identifying the tissues where SNW1 mRNA is enriched . During gastrula stages , SNW1 is expressed ubiquitously . By 11 h post-fertilization ( hpf ) , SNW1 is detected ubiquitously in the embryo , except in the yolk-associated tissue . It appears to be enriched anteriorly on the dorsal side and is expressed in both the ectoderm and mesoderm ( Figure 1F ) . The same expression pattern persists until 13 hpf . Consistent with what we observed in Xenopus embryos , the SNW1 protein is deposited maternally in zebrafish embryos and increases in levels from 6 hpf ( Figure 1G ) . SNW1 was isolated as a neural crest repressor in the overexpression screen , so we expected that depletion of SNW1 in embryos would induce more neural crest . Surprisingly , injection of MoSNW1a into one cell of a two-cell Xenopus embryo resulted in a dramatic loss of the neural crest markers Snail , Slug , and Sox9 [16] , [17] on the injected side ( Figure 2A ) . To check that the loss of Snail , Slug , and Sox9 staining reflected an inhibition of neural crest fate and not merely a delay , we investigated the effect of SNW1 knockdown later in development , when the neural crest cells have migrated and invaded the branchial arches [1] , using Snail and Twist as markers [18] . At stage 28 the amount of Twist-positive neural crest cells in morphant embryos was severely reduced on the injected side compared with the uninjected side and both sides of control embryos ( Figure 2B ) . Similarly , a dramatic loss of Snail-stained neural crest cells was observed in MoSNW1a-injected embryos , and the neural crest cells that were induced did not migrate normally ( Figure S3D ) . This was also evident when a different MO was used ( MoSNW1b ) ( Figure S3A , S3C , and S3D ) . We also examined neural crest derivatives at later stages of development . Cranial neural crest cells differentiate into cartilage and bones of the face , among other cell types [19] . Examination of branchial cartilage formation using alcian blue staining of MoSNW1a-injected embryos at the tadpole stages indicated that where SNW1 was depleted in half the embryo , the cartilage of the face did not form correctly ( Figure 2C ) . Closer examination of the tadpoles demonstrated that non-neural-crest derivatives of the face formed correctly , such as the eyes ( Figure S3E ) and the blood vessels ( data not shown ) . Given that both overexpression and depletion of SNW1 had the same inhibitory effect on neural crest induction in Xenopus , it was important to determine whether X . tropicalis SNW1 mRNA could rescue X . laevis SNW1 depletion . The five mismatches between the MoSNW1a sequence and the X . tropicalis SNW1 sequence ( Figure S3A ) are sufficient to protect the X . tropicalis mRNA from silencing by the MO ( Figure S3B ) . Xenopus embryos injected with the MO alone or with X . tropicalis SNW1 mRNA alone showed reduced expression of Slug and Sox9 . However , injection of the MO with the X . tropicalis SNW1 mRNA restored both Slug and Sox9 expression ( Figure S4A ) , confirming the specificity of both the overexpression and knockdown phenotypes . Interestingly , opposite effects of knockdown and overexpression on tailbud stage morphology were observed ( Figure S4A , bottom row ) . Depletion of SNW1 resulted in dorsalized embryos that curled downwards because of more tissue on the dorsal side , whereas overexpression of SNW1 resulted in slightly ventralized embryos with smaller heads and anterior structures . This suggests that distinct mechanisms account for the repression of neural crest when SNW1 is overexpressed versus when it is depleted , which is discussed in more detail below . To investigate whether the requirement of SNW1 for neural crest fate was conserved in zebrafish we designed an antisense MO , MoZFSNW1 , to block splicing ( Figure S3A ) . Despite being injected within the first hour post-fertilization , this MO is effective only after 8 hpf , suggesting it inhibits only zygotic transcripts , analogous to the situation with the Xenopus SNW1 MOs . Knockdown of SNW1 in zebrafish using MoZFSNW1 inhibited neural crest fate as demonstrated by Sox10 and FoxD3 staining ( Figure 2D ) . A dorsalized-like phenotype was also observed when SNW1 was depleted in zebrafish embryos with MoZFSNW1 ( Figure S4B ) . This phenotype , which was characterized by loss of ventral tail fin tissues , and loss of posterior structures including the most posterior somites , was partially rescued by overexpression of X . laevis SNW1 ( Figure S4C ) . Thus , the requirement for SNW1 for neural crest fate is conserved in zebrafish . As demonstrated by qPCR on dorsal and ventral halves of Xenopus embryos , SNW1 is expressed in both of these domains ( Figure S2B ) . However , in situ staining in Xenopus and zebrafish embryos revealed that SNW1 mRNA is enriched in the dorsal ectoderm and mesoderm ( Figure 1D and 1F ) . Specifically , in both organisms the enriched domain of expression in the ectoderm overlaps the neural plate as well as the neural crest ( Figure 3A ) . To determine whether it was the dorsally expressed SNW1 that was required for neural crest specification we targeted the injection of MoSNW1a to either a dorsal or ventral quadrant of Xenopus embryos at the four-cell stage . Only depletion of SNW1 on the dorsal side , where its expression is enriched , affected neural crest fate ( Figure 3B ) . Since SNW1 is also enriched in dorsal mesoderm , it was important to establish that the effects of SNW1 depletion on neural crest fate were direct and not the result of perturbing earlier mesoderm induction or gastrulation movements , both of which could influence neural crest specification [1] . Depletion of SNW1 had no effect on the mesoderm marker Xbra , nor on the dorsal and ventral mesoderm markers Goosecoid ( Gsc ) and Sizzled ( Szl ) , respectively , as examined by WISH at stage 12 ( Figure S5A ) . Knockdown of SNW1 also had no effect on mesoderm induction or convergence-extension movements in an animal cap assay [20] ( Figure S5B ) . Moreover , in zebrafish , depletion of SNW1 had no effect on the mesoderm markers No tail a , Even-skipped-like 1 , Cdx4 ( Figure S5C ) , or MyoD ( Figure 2D ) . To demonstrate that the loss of neural crest fate upon depletion of SNW1 is a direct effect on ectodermal patterning , we inhibited mesoderm induction by overexpressing Cerberus short ( CerS ) in Xenopus embryos . Full-length Cerberus inhibits Nodal , Wnt , and BMP signaling , whereas the N-terminally truncated form , named CerS , inhibits only Nodal signaling and hence mesoderm formation [21] . The expression of the mesoderm markers Xbra and Gsc was completely repressed by CerS overexpression , and the embryos develop without gastrulation movements , as evidenced by the lack of a blastopore ( Figure 3C ) . Despite a lack of mesoderm formation , the neural plate and neural crest cells are induced , as seen by robust staining for Sox3 and Slug , respectively , in embryos overexpressing CerS ( Figure 3C , lower two rows ) . Importantly , injection of MoSNW1a expands the expression domain of Sox3 , consistent with the dorsalized morphology observed previously ( Figure S4A ) , and represses the expression of Slug in both wild-type and CerS-overexpressing embryos ( Figure 3C ) . These results indicate first that , contrary to what was previously thought [19] , neural crest fate appears to be induced in Xenopus embryos in the absence of mesoderm , and second that the effect of SNW1 depletion on neural crest fate occurs entirely by perturbing ectoderm patterning . Our observation that SNW1 depletion leads to an expansion in the expression domain of Sox3 , as well as loss of neural crest , prompted us to assess how overall dorsal/ventral patterning was affected when SNW1 was knocked down . At the neurula stages , dorsal midline/notochord markers such as Goosecoid , Chordin , Xnot , and Noggin were strongly expressed in morphant embryos , although the notochord was visibly shortened and wider ( Figure 4A ) , consistent with a shortening of the anterior-posterior axis seen at later stages ( Figure S4A ) . This could be an effect on convergence-extension movements , but we do not believe that this contributes to the effect on the neural crest , since in the absence of morphogenetic movements when CerS is overexpressed , we still observed the specific effect of depleting SNW1 on the neural crest marker Slug ( Figure 3C ) . Expression of Xnot and Noggin at the anterior neural plate border was also absent in the morphant embryos ( Figure 4A , arrows ) . The paraxial mesodermal expression of Wnt8 was lost , although the ventral mesodermal expression near the blastopore was maintained ( Figure 4A , arrows ) , and the somitic mesodermal marker MyoD was not affected ( Figure 4A ) , similar to what was seen in the fish ( Figure 2D ) . Next , we examined a panel of ectodermal markers , starting with the neural plate marker Sox3 . As previously shown ( Figure 3C ) , the domain of Sox3 expression was expanded in SNW1 morphants , and the border of the neural plate was very diffuse ( Figure 4A and 4B ) . Consistent with this , the domain of Epidermal keratin ( EpiKer ) staining was reduced in SNW1-depleted embryos , and the sharp border between neural and non-neural tissue was lost . Examination of the anterior neural marker Otx2 revealed little difference , except a slight expansion laterally , consistent with the observation that the neural plate is wider and that the embryos exhibit a dorsalized morphology later in development . The same was seen for Otx2 staining in fish embryos ( Figure S5C ) . Interestingly , in Xenopus the neural expression of Zic3 parallels what was observed for Otx2 , whereas the expression normally found at the neural plate border was fainter and more diffuse . The same was true for BMP4 , which is normally expressed at the neural plate border but is more diffuse in SNW1 morphants ( Figure 4A ) . Most importantly , neural plate border markers ( Msx1 ) , including neural crest markers ( Sox9 , Snail , Slug , and FoxD3 ) , were completely absent in morphant embryos ( Figure 4A ) . Thus , neural plate border specification is completely lost when SNW1 is depleted . Careful examination of Sox3-stained Xenopus morphant embryos , especially from a lateral view , reveals that the neural plate border is consistently diffuse and expanded towards the ventral side ( Figure 4B ) . There is a clear loss of the sharp border of the neural plate . We see a similar expansion of the neural plate in zebrafish morphants and loss of neural crest as seen by N-cadherin and Sox10 double in situ hybridizations ( Figure 4C ) . Furthermore , in Xenopus embryos the neural plate and epidermal ectoderm appear to merge together without a distinct border between the two tissue types ( Figure 4D ) . Without neural plate border specification and the expression of Msx1 , neural crest cells cannot be induced [22] , [23] . Among factors known to be important for neural plate border specification are the transforming growth factor β ( TGF-β ) superfamily ligands of the BMP family [1] . The loss of neural plate border markers in the SNW1 morphant embryos and the clear disruption of the sharp neural/non-neural border seen in embryos stained for Sox2/Sox3 and Epidermal keratin ( Figure 4B and 4D ) raised the exciting possibility that SNW1 might directly affect the BMP activity in vivo that is required for dorsal/ventral patterning of the ectoderm and neural crest specification . BMP signaling leads to phosphorylation of the intracellular signal transducer Smad1 [2] and thus phosphorylated Smad1 ( p-Smad1 ) can be used as a readout of BMP activity . BMP activity is first detected in developing Xenopus embryos at stage 9 , and its activity increases through gastrulation and early neurulation as indicated by the level of p-Smad1 detected in whole embryo lysates ( Figure 5A ) . At stage 13 ( early neurula ) , SNW1-depleted embryos exhibited lower p-Smad1 compared to control embryos , and SNW1 overexpression resulted in higher levels of p-Smad1 compared to control embryos ( Figure 5A ) . This would account for the opposite phenotypes seen when SNW1 is knocked down versus overexpressed ( Figure S4A ) . The significant stage-specific decrease in p-Smad1 levels in SNW1-morphant Xenopus embryos prompted us to examine how BMP activity is affected spatially . When stage 14 Xenopus embryos were bisected into dorsal and ventral halves , the majority of p-Smad1 detected in whole embryo lysates was derived from the ventral halves of embryos ( Figure 5B ) . This ventral BMP signaling was substantially decreased in the morphant embryos , but slightly increased when SNW1 was overexpressed ( Figure 5B ) . Similarly , knockdown of SNW1 in zebrafish embryos led to a dose-dependent decrease in p-Smad1 levels from 12 hpf ( 4–5 somites ) ( Figure 5C ) . To visualize the spatial differences in BMP activity detected by Western blotting ( Figure 5B ) and determine how they are affected by SNW1 depletion , we initially used immunostaining for p-Smad1 in fixed Xenopus embryo halves to directly observe BMP activity in vivo . Consistent with the fact that MoSNW1a does not deplete SNW1 protein prior to stage 12 ( Figure 1E ) , there was no effect on p-Smad1 staining in stage 10 embryos that were bisected sagittally . Thus , the initial ventral to dorsal gradient formed normally in morphant embryos . However , at stage 13 , when the BMP activity gradient has changed spatially , a clear loss of the nuclear p-Smad1 staining was evident on the ventral side of morphant embryos and in the anterior ( Figure 5D ) , consistent with the Western blotting data . We have already demonstrated that SNW1 expression is enriched on the dorsal side of the embryo and that the enhanced expression on this side of the embryo is responsible for its effects on neural crest fate ( Figures 1D and 3B ) . Therefore , the reduction of p-Smad1 on the ventral side cannot explain the effects on neural crest fate . This suggested that there must be a more localized domain of BMP activity that specifically influences neural crest specification . To gain better resolution when examining BMP activity in vivo , we generated a zebrafish reporter transgenic line for BMP signaling . This line contains a stably integrated monomeric red fluorescent protein ( mRFP ) reporter driven by BMP responsive elements ( BRE ) . An initial characterization of this BRE-mRFP line is shown in Figure S6 . WISH for mRFP in control embryos at 11–13 hpf reveals known domains of BMP activity , such as the tailbud ( Figure 5E; see dorsal-posterior views ) , where high p-Smad1 staining has been reported [24] , [25] . In the anterior we detect a novel horseshoe-shaped domain of BMP activity at the border region between neural and non-neural ectoderm . This domain progressively sharpens from 11 hpf to 13 hpf ( see red arrows , Figure 5E ) . In SNW1 morphant embryos , mRFP levels in this domain are significantly reduced . Very interestingly , mRFP is most strongly reduced at the lateral edges of the horseshoe-shaped domain in SNW1 morphants , whereas the tailbud and the most anterior neural plate border activities are not significantly affected ( Figures 5E and S7B ) . We examined which BMP ligands may be responsible for this localized activity at the neural plate border by carrying out in situ staining for BMP2b , BMP4 , and BMP7 in zebrafish embryos . BMP2b in zebrafish , which is functionally equivalent to BMP4 in Xenopus [26] , is specifically expressed at this region , whereas zebrafish BMP4 is mainly expressed in the anterior prechordal plate and tailbud , and zebrafish BMP7 is predominantly expressed in the endoderm ( Figure S7 ) . Importantly , BMP2b transcripts were unaltered in SNW1 morphants , indicating that SNW1 must act downstream of BMP2b transcription ( Figure S7B ) . The BRE-mRFP zebrafish line provided us with increased resolution when observing BMP activity in vivo . This prompted us to use immunostaining of p-Smad1 on transversely bisected Xenopus embryos to see whether , indeed , BMP activity at the neural plate border is conserved and whether this localized BMP activity requires SNW1 . Fluorescein dextran ( Fdx ) with or without MoSNW1a was injected into one cell at the two-cell stage ( Figure S8 ) . In embryos injected with Fdx alone , p-Smad1 could be detected at the region of the embryo that corresponds to the neural plate border on both the injected and uninjected sides . In embryos injected with Fdx and MoSNW1a , p-Smad1 could be detected only on the uninjected side of the embryo ( see enlarged images , Figure S8 ) . Thus , the requirement for SNW1 for BMP activity at the end of gastrulation is conserved in the two species examined . Furthermore , SNW1 is specifically required for the localized BMP activity found at the lateral edges of the neural plate . We have demonstrated that SNW1 is required for neural crest fate , and have characterized the essential role of SNW1 in regulating BMP activity post-gastrulation . To find the link between these two activities of SNW1 , we further exploited the BRE-mRFP fish line to visualize how SNW1 expression , neural crest cells , and the neural plate border BMP activity relate to each other spatially over time . Double in situ hybridization was carried out on BRE-mRFP embryos at 11 and 13 hpf . At 11 hpf , Sox10-positive neural crest cells and the enriched SNW1 expression domain in the neural plate overlap ( Figure 6A and 6B ) . Neural crest cells also overlap with the broad horseshoe domain of BMP activity , as detected by in situ staining for mRFP ( Figure 6A and 6B ) . Double in situ staining for SNW1 and mRFP demonstrates that at 11 hpf , there is no detectable separation between these domains ( Figure 6A and 6B ) . Thus , neural crest cells appear to form where SNW1 expression and BMP activity meet . By 13 hpf , when the broad domain of BMP activity has sharpened , the embryos have undergone elongation , and the neural tube has started forming , there is clear separation between the SNW1 expression domain , including neural crest cells , and the domain of BMP activity ( Figure 6A and 6B ) . We carried out a similar analysis in Xenopus embryos by transversely bisecting embryos and carrying out WISH for Slug on one half , and immunostaining for p-Smad1 on the other half . Overlay of the p-Smad1 staining on the Slug staining reveals that the domain of BMP activity at the neural plate border overlaps with the neural crest cells ( Figure 6C ) . Since two studies have previously suggested a role for SNW1 in BMP- and TGF-β-dependent transcription [11] , [27] , and mammalian SNW1 was originally discovered in a two-hybrid screen for proteins that interact with the transcriptional repressor of TGF-β superfamily signaling pathways , Ski [28] , [29] , we investigated whether SNW1 might elicit its effects on neural crest fate by functioning intracellularly in TGF-β superfamily signal transduction pathways . We decided to use MDA-MB-231 cells for these assays as they express the necessary receptors and were previously shown to have a robust response to both TGF-β and BMPs at the levels of Smad phosphorylation and transcriptional output [30] . Small interfering RNA ( siRNA ) depletion of SNW1 in MDA-MB-231 cells , however , had no effect on the levels of phosphorylated Smad1/5 or Smad2 in response to TGF-β or phosphorylated Smad1/5 in response to BMP4 , indicating that SNW1 does not influence the BMP or TGF-β signaling pathways at the level of Smad activation in these cells ( Figure 7A ) . Furthermore , SNW1 depletion using either the siRNA pool or individual siRNAs had no effect on the induction of a BMP-dependent reporter , BRE-Luciferase ( Figure 7B ) , or a TGF-β-dependent reporter , CAGA12-Luciferase ( Figure S9A ) , although depletion of Smad4 reduced the activity of both of these reporters as expected [31] ( Figures 7B and S9A ) . Moreover , despite reports of SNW1 interacting with Smad2 , Smad3 , and Ski when the proteins were overexpressed [11] , [32] , we found no evidence for such interactions for the endogenous proteins ( Figure S9B ) , although we could confirm the previously demonstrated interaction between Ski and Smad proteins [28] in the same experiment ( Figure S9B ) . These results argue against SNW1 functioning in either the core BMP or TGF-β pathways . The experiments described above were performed in tissue culture cells using purified ligand . To determine whether the results hold true in Xenopus , we compared the effect of SNW1 knockdown on p-Smad1 levels in whole embryos at stage 14 with that in animal caps isolated at stage 8 . 5 then cultured to stage 14 . In both of these cases the inducing ligands are endogenously produced . In whole embryos , SNW1 depletion resulted in a reduction of p-Smad1 levels equivalent to that caused by overexpression of the BMP antagonist Noggin [33] ( Figure 7C ) . In contrast , in animal caps , depletion of SNW1 had no effect on p-Smad1 levels , whereas Noggin overexpression substantially reduced p-Smad1 ( Figure 7C ) . SNW1 therefore affects BMP signaling only in the context of the whole embryo , and not in an isolated tissue or in cell culture , further indicating that SNW1 is not a core component of the intracellular BMP pathway . Taking these results together with the results in the previous two sections , we conclude that SNW1 is required for BMP activity in vivo , that it plays an important role in demarcating the boundary of BMP activity at the neural plate border , and that it must act downstream of ligand transcription , but at the level of , or upstream of , the BMP receptors . Having shown that the major effect of SNW1 depletion or overexpression is a change in BMP activity , we asked whether this was sufficient to explain the inhibitory effects of SNW1 depletion on neural crest fate and neural plate border formation . We therefore devised an alternative method of specifically lowering BMP activity after the mid-blastula transition to determine whether this would mimic the effect of SNW1 knockdown . We targeted a plasmid expressing the secreted diffusible BMP antagonist Noggin to the dorsal ectoderm , where BMP signaling is normally very low . Noggin produced in this region from mid-blastula transition onwards diffuses into neighboring tissue , thus inhibiting BMP activity . This was sufficient to lower BMP activity in the embryo , equivalent to the effect seen when SNW1 is depleted ( Figures 5 , 8A , and 8B ) . Most importantly , we observed a loss of Slug staining , disruption of the neural plate border , expansion of the neural plate as judged by Sox2 staining , and a dorsalized phenotype similar to what we observed with SNW1 depletion ( Figure 8C ) . This strongly supports the idea that BMP activity between stages 12 and 14 is required for neural border sharpening/formation . Since we know that zebrafish BMP2b/Xenopus BMP4 is responsible for the BMP activity at the neural plate border , we carried out an epistasis experiment to determine whether targeted expression of zebrafish BMP2b post-zygotically on the dorsal side of Xenopus embryos could rescue the neural crest phenotype in SNW1 morphant embryos . We therefore injected a plasmid expressing zebrafish BMP2b into dorsal blastomeres that would give rise to cells in or at the neural plate border . As expected , neural crest cells are very sensitive to the level of BMP activity , and the higher dose attempted ( 16 pg ) resulted in the loss of Slug staining on the injected side of control embryos ( Figure 8D ) , suggesting that this dose was too high . Similarly , this dose was unable to rescue neural crest fate in SNW1 morphant embryos . However , when we decreased the dose by half , we were able to maintain or slightly increase Slug staining in control embryos . Very importantly , at this dose , we managed to rescue Slug expression on the injected side of a significant number of SNW1 morphant embryos ( Figure 8D ) . This experiment unequivocally links the effects of SNW1 depletion on BMP activity and the requirement of SNW1 for neural crest fate . Our work has therefore demonstrated that the effect of SNW1 on BMP activity post-gastrulation is critical for neural plate border formation and neural crest fate .
We identified SNW1 in a large-scale in vivo functional screen in Xenopus for neural crest fate . Our characterization of SNW1 has revealed that it is a key novel regulator of in vivo BMP activity in vertebrate embryos . Specifically , SNW1 is absolutely essential for the regulation of BMP activity at the end of gastrulation and affects critical domains of BMP activity . Using MOs to study the loss-of-function effects of SNW1 in Xenopus and zebrafish embryos , we depleted zygotic SNW1 , thereby targeting our analysis to post-gastrulation stages of development , and showed that this reduced all readouts of particular domains of BMP activity at these stages of development . Since BMP activity is crucial for dorsal/ventral patterning during gastrulation , it is possible that maternal SNW1 may play important roles in regulating BMP activity and tissue patterning during these earlier developmental stages . In wild-type embryos , immunostaining for p-Smad1 and analysis of the BRE-mRFP fish revealed that BMP activity is spatially and temporally dynamic during the stages of development examined . We have identified a novel horseshoe-shaped domain of BMP activity in 11- to 13-hpf zebrafish embryos at the border between the neural and non-neural ectoderm , and we have evidence that an analogous domain exists in Xenopus . Detailed spatial analysis further demonstrated that depletion of SNW1 results in a specific loss of BMP activity in this domain , at the lateral neural plate border ( Figure 9 ) , and also on the ventral side of the embryo , but morphant embryos maintain most of the BMP activity found at the tailbud and at the most anterior neural plate . SNW1 is enriched dorsally during post-gastrulation stages , and we further isolated the effects of SNW1 depletion on neural crest fate to the dorsal side of the embryo . These results suggest that the BMP activity at the neural plate border , which overlaps with neural crest cells and the enriched domain of SNW1 expression during the early neurula stage , is responsible for the loss of neural crest fate ( Figure 9 ) . Indeed , zygotic expression of the zebrafish BMP2b near or at the neural plate border is sufficient to rescue the loss of neural crest fate in SNW1 morphant embryos . We did observe additional effects of SNW1 depletion , such as the loss of ventral/posterior tissues in zebrafish ( Figure S4B and S4C ) , and the loss of some ventral BMP activity when SNW1 is depleted in whole embryos may be responsible for this phenotype . In summary , we have found that disruption of the BMP activity at this particular stage of development ( the end of gastrulation ) and at a particular location in the embryo ( the neural plate border ) through the depletion of SNW1 results in the disruption of the sharp neural plate border and virtually complete loss of neural crest fate . This is an ectoderm-specific effect since SNW1 depletion has exactly the same effect when embryos fail to induce mesoderm or undergo gastrulation , as occurs when CerS is overexpressed . We have thus characterized how the BMP activity changes during the neurula stages of development , found a factor that is required for particular domains of BMP activity in the embryo during this time , and demonstrated that this factor plays an essential role in ectodermal patterning . Consistent with previous reports that the ectoderm responds to signals before or during gastrulation to specify neural tissue [5] , [6] , we found that Sox2 and Sox3 expression is induced in the expected region of the embryo when SNW1 is depleted . However , the expression of these markers is expanded more ventrally , consistent with the dorsalized phenotype of the morphant embryos seen at the tailbud stages . Unlike previous studies where expansion of the neural plate merely shifts the neural plate border , with the neural crest and other border fates still induced [16] , we observed a complete loss of border fates . Included in the markers examined , Noggin and Wnt8 are induced in post-gastrulation embryos at the neural plate border region , and this specific domain of expression is lost in SNW1 morphant embryos ( Figure 4A ) . Since both Noggin and Wnt8 are secreted molecules involved in signaling required for neural crest fate [1] , [10] , it is possible that this loss of expression contributes to the robust loss of neural crest cells in morphant embryos . However , we think that the loss of their expression at the neural plate border is more likely a consequence of the fact that the border does not form in SNW1 morphants , because of the loss of BMP signaling in this region , rather than that they are direct targets of SNW1 , since expression of Noggin and Wnt8 in the notochord and posterior mesoderm , respectively , is not affected by SNW1 knockdown ( Figure 4A ) . The two tissues that normally form on either side of the neural crest , neuroectoderm and epidermis , are both specified in SNW1 morphant embryos , although there is no distinct border between them , as judged by the expression of Sox2 and EpiKer , respectively ( Figure 4D ) . We have demonstrated that SNW1 is a fundamental regulator of BMP activity . To confirm it is SNW1's effects on BMP activity that account for its effects on neural plate border formation at the end of gastrulation , we mimicked the effect of the SNW1 MO by targeting the zygotic overexpression of a diffusible BMP antagonist , Noggin , to the dorsal ectoderm using DNA injection . Injection of mRNA for overexpression or the non-targeted overexpression of Noggin results in a gross dorsalization of the embryo , with a majority of the ectoderm being respecified as neural ( data not shown ) [16] , [34] . In contrast , localized expression of Noggin after the mid-blastula transition has a milder phenotype , phenocopying the SNW1 morphant , demonstrating that late alteration of the BMP activity in whole embryos affects just border formation and not neural induction per se . We also performed the reverse experiment , expression of a BMP ligand via injected exogenous cDNA at the correct stage of development , at the correct locale , and at a correct dose . We showed that this is sufficient to rescue neural crest fate in SNW1 morphant embryos . If BMP activity however is increased earlier in development through injection of BMP mRNA for expression , the embryos develop with a severely ventralized phenotype , with complete loss of neural and head structures ( data not shown ) . Thus , SNW1 is a key regulator of BMP activity at the end of gastrulation , and , at this stage , BMP activity at the neural plate border is absolutely required for the formation of a sharp neural plate boundary and the specification of border fates . During embryonic development , the same pathways are redeployed at different times and in different contexts to elicit distinct responses . We have demonstrated , to our knowledge for the first time , that neural induction , which is dependent on regulation of BMP activity during gastrulation , is uncoupled temporally from neural border sharpening and border fate specification , which require BMP activity , albeit at a later stage . In overexpression studies , SNW1 was previously reported to function with receptor-activated Smad proteins to regulate the transcription of TGF-β superfamily target genes [11] , [27] , placing it downstream of the receptors . However , we found that SNW1 in a model tissue culture system is not required for either TGF-β- or BMP-dependent transcription , and endogenous SNW1 did not co-immunoprecipitate with any endogenous Smad proteins or Ski . Together , these results argue against SNW1 being a component of the core BMP signal transduction pathway . Similar to our observations in cell culture experiments , we found that depleting SNW1 in animal caps has absolutely no effect on p-Smad1 levels , whereas intact morphant embryos have substantially lower levels of p-Smad1 compared to control embryos . The animal cap is an isolated , mostly homogeneous tissue , cultured ex vivo . The fact that depletion of SNW1 does not affect p-Smad1 levels in this context further confirms that SNW1 is not a direct component of the BMP pathway downstream of the receptors . Furthermore , we can rule out any effect of SNW1 on the core pathway components specifically in the dorsal ectoderm , as targeted expression of a low dose of BMP2b is sufficient to rescue the effects of SNW1 depletion . This indicates that the BMP receptors and downstream signaling pathway must be completely functional in these cells , and SNW1 must thus act upstream . Since SNW1 is a nuclear factor that has been implicated in transcriptional regulation [11]–[13] , we performed a microarray analysis comparing the expression profiles of control versus morphant embryos in an attempt to identify direct targets of SNW1 . However , we did not detect any significant changes in the mRNA levels of known modulators , ligands , or components of the BMP signaling pathway ( unpublished data ) . SNW1 has also been reported to be involved in transcriptional elongation and pre-mRNA splicing [14] , [15] . We therefore favor the possibility that SNW1 might regulate gene expression , at this level , of components required for BMP receptor activation , which could include the ligands themselves , proteins involved in their activation , extracellular matrix components , or co-receptors . Since the function of SNW1 is conserved between Xenopus and zebrafish and its protein sequence is highly conserved from C . elegans to human , its function may also be conserved in other organisms . Indeed , we have evidence that SNW1/Bx42 RNA interference expression in the Drosophila wing disc perturbs P-Mad ( Drosophila phosphorylated Smad1 ) levels ( unpublished data ) , consistent with reported effects of SNW1/Bx42 RNA interference reducing the expression of Dpp target genes , Spalt and Optomotor Blind in the same system [35] . In sum , we have identified a novel role for SNW1 in neural plate border sharpening and neural crest fate . Since the neural crest is a vertebrate-specific embryonic tissue type , it is not surprising that this functional effect is a consequence of a more fundamental and likely conserved role that SNW1 plays in regulating BMP activity .
In vitro fertilization of X . laevis embryos and their culture , staging , manipulation , injection , and dissection was carried out as previously described [36] , with the exception that animal caps were cultured in Danilchik's blastocoel buffer [37] . Zebrafish were maintained and staged as previously described [38] , [39] . Synthetic mRNA for injection was prepared as previously described [36] . X . tropicalis SNW1 mRNA for injection was synthesized using cDNA template from clone TEgg090l09 from the X . tropicalis full-length cDNA library ( Source BioScience; [40] ) , and X . laevis SNW1 mRNA was synthesized from I . M . A . G . E . Consortium clone ID 5083691 . Noggin DNA injections were carried out using pCS2-Noggin [34] , and BMP2b DNA injections used pCS2-zBMP2b . pCS2-zBMP2b was made by amplifying the coding region of BMP2b from zebrafish total gastrula cDNA and cloning the appropriately digested PCR product into pCS2+ . The insert was checked by sequencing . Green fluorescent protein ( GFP ) mRNA was synthesized from pCS2-GFP ( a gift from P . Blader ) , and CerS mRNA from pCS2-CerS [21] . Antisense MOs were designed and obtained from GeneTools and are listed below . In Xenopus , MOs were injected at the one- to 32-cell stages as indicated , whilst in zebrafish they were injected at the one- to eight-cell stage . The following MOs were used: MoControl ( against human β-globin ) , CCTCTTACCTCAGTTACAATTTATA; MoSNW1a ( against X . laevis SNW1 ) , CTAGCGCCATTTTCTCTGTCGATC; MoSNW1b ( against X . laevis SNW1 ) , GGCTATGGAGGAAGTGACCTAAGAG; and MoZFSNW1 ( against zebrafish SNW1 ) , ACAGCTTCTCTGCGTCTTACCTTGT ) . The X . tropicalis full-length expressed sequence tag library was obtained from Source BioScience ( http://www . lifesciences . sourcebioscience . com/clone-products/cdna-and-orf-resources-/xenopus-tropicalis-est-clones . aspx ) . At the time of ordering , the collection comprised just over 6 , 900 unique clones . Pools of 48 individual clones ( ∼7 . 5 µg of DNA ) were linearized using AscI and used as templates for in vitro transcription reactions with SP6 RNA polymerase to generate 5′ capped mRNA for injection into one-cell X . laevis embryos . Embryos were fixed at stage 14 and stained for Slug expression . Pools showing a strong effect on Slug staining were then deconvoluted . Individual clones isolated after deconvolution include the following: Dkk-1 ( TGas131c10 ) , c-Myc ( TEgg042l13 ) , Noggin2 ( TNeu122a14 ) , and SNW1 ( TEgg090l09 ) . Zebrafish SNW1 cDNA including 5′ UTR was amplified from total cDNA ( somitogenesis stage ) using the following primers: 5′-TCCAAGATGTCGCTTACAAG-3′ and 5′-CAGAGAAAGTGTCTCACTCC-3′ . The PCR product was cloned into pGEM-T ( Promega ) and sequence verified . A detailed report of the transgenic BRE-mRFP line will be published elsewhere . Briefly , the construct used to generate pGL3-BRE-mRFP was the pGL3-BRE-Luciferase plasmid [41] , in which we replaced the luciferase gene with mRFP . The BRE-mRFP sequence was further subcloned into the TOL2 vector to generate a stable transgenic line as described [42]–[44] . An mRFP antisense in situ probe was generated by cloning a full-length mRFP cDNA into pGEM-T ( Promega ) . WISH was performed in Xenopus and zebrafish essentially as previously described [45] , [46] . The probes are described below . The chromogenic reaction was carried out using BM purple alkaline phosphatase substrate ( Roche ) or NBT/BCIP ( Sigma ) . Fdx ( Molecular Probes ) was visualized with an anti-fluorescein antibody ( 11426338910; Roche ) . The double in situ protocol in zebrafish was modified from [47] using digoxigenin- and fluorescein-labeled antisense probes . The chromogenic substrates used were NBT/BCIP ( Sigma ) in combination with BCIP ( Roche ) or INT/BCIP ( Roche ) . The use of BCIP as the first chromogenic substrate results in turquoise staining . However , when used after NBT/BCIP , BCIP gives rise to grey-purple staining . INT/BCIP was used as the second chromogenic substrate for Hgg1 detection . For double in situ hybridization in Xenopus , embryos were incubated with two differentially labeled probes during hybridization . The first probe was developed as described for single in situ hybridizations . For the second probe , embryos were washed three times in methanol , then rehydrated in decreasing concentrations of ethanol until 100% PBS-Tween was reached , before repeating the antibody incubation and staining steps . The second chromogenic substrate used was either BCIP alone or Magenta Phos ( Sigma ) . Immunostaining of Xenopus embryos was carried out as previously described [48] using a 1∶200 dilution of p-Smad1 antibody ( 9511; Cell Signaling Technology ) . Immunostaining of zebrafish embryos was as previously described [4] . Digoxigenin-UTP ( Roche ) –labeled antisense RNA probes were synthesized using cDNA templates encoding Snail [49] , Slug [16] , Sox9 [17] , Twist [18] , FoxD3 [50] , Sox2 [16] , Sox3 [51] , Wnt8 [52] , Noggin [34] , Xbra [53] , Xnot [54] , Chordin [55] , Goosecoid [56] , BMP4 [57] , Sizzled [58] , Zic3 [59] , Otx2 [60] , MyoD [61] , Epidermal keratin [62] , Sox10 [63] , Foxd3 ( zebrafish; [64] ) , MyoD ( zebrafish; [65] ) , N-cadherin [66] , Otx2 ( zebrafish; [67] ) , Cdx4 [68] , Even-skipped-like 1 [69] , No tail a [70] , BMP2b [71] , BMP7 [72] , and Hgg1 [73] . The antisense probe against Msx1 was generated from the plasmid XMsx1 in pGEM4Z and protects the region of Msx1 encoding amino acids 40–129 . The antisense probe against zebrafish BMP4 was generated from the plasmid pCS2P+-zBMP4 ( a gift from Arne Lekven ) and protects full-length BMP4 . The probe against Xenopus SNW1 was generated from pGEM-T-XlSNW1 and protected the region of SNW1 encoding amino acids 102–300 . The probe against zebrafish SNW1 was generated from pGEM-T-zSNW1 and protects full-length SNW1 . Alcian blue staining of cartilage was carried out as previously described [8] . Xenopus embryos or animal caps were snap frozen at the required stage , and extracts were prepared as previously described [74] . Mammalian cell extracts were prepared as described in [31] . For zebrafish protein extracts , typically ten embryos ( including chorion ) were snap frozen and subsequently processed using the same protocol as for Xenopus embryos , except that the embryos were lysed using a plastic pestle and a pellet pestle motor ( Kontes ) . Western blotting was performed using standard techniques and the following antibodies: from Santa Cruz Biotechnology , anti-SNW1 ( H-300 ) , anti-Smad4 ( B8 ) , anti-MCM6 ( sc-30139 ) , anti-Ski ( H-329 ) ; from Abcam , anti-SNW1 ( ab67715; for Figure 7A ) , anti-Tubulin ( ab6160 ) ; from Cell Signaling Technology , anti-p-Smad1 ( 9511 ) ; from Millipore , anti-p-Smad2 ( clone A5S ) ; from Zymed , anti-Smad1 ( 38-5400 ) ; and from BD Biosciences , anti-Smad2/3 . Each lane on the Western blots is an average of 10–15 embryos , with 1–1 . 5 embryo equivalents of extract loaded . Extraction of total RNA from Xenopus embryos was carried out using Trizol reagent ( Invitrogen ) followed by a cleanup and on-column DNase treatment using the RNeasy Mini Kit ( Qiagen ) . The AffinityScript QPCR cDNA Synthesis Kit ( Stratagene ) was used for cDNA synthesis following the manufacturer's protocol . Semi-quantitative PCR from cDNA templates was carried out using GoTaq ( Promega ) on a GeneAmp PCR system 9700 ( Applied Biosystems ) , and qPCR was performed using an ABI 7500 Fast system ( Applied Biosystems ) with SYBR Green Master Mix ( Applied Biosystems ) . The primers were as follows: SNW1 , 5′-TGATGCTATTGCTCGACAGG-3′ and 5′-CTTCTGGGACACGGATTTGT-3′; Chordin , 5′-CATGCTCTTTCGAAGGTCAA-3′ and 5′-GATCACAAATCACGGTACGC-3′ [75]; Sizzled , 5′-TGCCGTAGTATGTGTGTAGCTG-3′ and 5′-ACTCTTTGCTGAGAGTGTCCAA-3′ [75]; Slug , 5′-CACGTTACCCTGCGTCTGTA-3′ and 5′-GCAGGTGGGCTCTTAAGTTG-3′; and ODC , 5′-ACAAAGAAACCCAAACCAGA-3′ and 5′-CAAACAACATCCAGTCTCCAA-3′ [75] . MDA-MB-231 , MDA-MB-231 CAGA12-Luc/TK-Renilla and MDA-MB-231 BRE-Luc/TK-Renilla cell lines were cultured in DMEM supplemented with 10% FBS . siRNA transfection was carried out using INTERFERin siRNA transfection reagent ( PolyPlus Transfection ) with 1 nM of siRNA . Cells were incubated after transfection for 72 h before further processing . siRNAs were purchased from Dharmacon . RISC-Free siControl ( D-001220-01-05 ) ; Human Smad4 SMARTpool ( M-003902-01 ) ; Human SNW1 SMARTpool ( L-012446-00 ) ; Human SNW1-5 ( J-012446-05 ) ; Human SNW1-6 ( J-012446-06 ) ; Human SNW1-7 ( J-012446-07 ) ; Human SNW1-8 ( J-012446-08 ) ; and ON-TARGETplus Control Non-targeting siRNA 1 ( D-001810-01 ) . MDA-MB-231 cells stably expressing Renilla from the Herpes Simplex Virus Thymidine Kinase ( HSVtk ) promoter ( Promega ) and Luciferase from either the TGF-β-dependent promoter CAGA12 [76] or the BMP-dependent promoter BRE [41] were used to study signal-induced transcription . Luciferase and Renilla were assayed as previously described [31] . Co-immunoprecipitations ( IPs ) were carried out as previously described [31] . The TNT Coupled Reticulocyte Lysate System ( Promega ) was used for direct translation of in vitro transcribed mRNA , and translation was carried out according to the manufacturer's protocol . 35S-labeled methionine and unlabeled amino acids were added to a 20 µl of reticulocyte lysate reaction along with 4 µg of mRNA template ( same mRNA:volume ratio as injected into embryos ) . For reactions where MOs were added to block translation , 400 ng of MO was added ( same MO:volume ratio as MOs injected into embryos ) . Samples were separated on a 15% SDS gel , which was Coomassie stained before being dried and exposed to Hyperfilm ( Amersham ) to detect protein bands positive for 35S-Met . | A subset of cells in the ectoderm of vertebrate embryos becomes the neural crest , which contributes to the bones and cartilage of the adult face . The neural crest arises in a location between the epidermis , which becomes the future skin , and the neural plate , which becomes the future central nervous system . Through our studies in both frog and fish embryos , we have discovered that the protein SNW-domain containing protein 1 ( SNW1 ) is absolutely essential for defining the edge of the neural plate , where neural crest forms . SNW-domain containing proteins have been implicated in a variety of nuclear activities ranging from transcriptional regulation and elongation to RNA splicing . We show that SNW1 functions upstream of bone morphogenetic protein ( BMP ) receptors to regulate BMP activity , and is necessary for the activity of the BMP signaling pathway at the neural plate border where the neural crest is specified . In the absence of SNW1 , BMP activity is reduced in this region and neural crest cells are lost . Given that SNW1 family proteins are highly conserved from nematodes to humans , SNW1's BMP regulatory function is likely conserved in other animals . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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"Methods"
] | [
"developmental",
"biology/embryology",
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] | 2011 | SNW1 Is a Critical Regulator of Spatial BMP Activity, Neural Plate Border Formation, and Neural Crest Specification in Vertebrate Embryos |
The neglected tropical diseases ( NTDs ) cause significant morbidity and mortality worldwide . Due to the growth in international travel and immigration , NTDs may be diagnosed in countries of the western world , but there has been no specific focus in the literature on imported NTDs . Retrospective study of a cohort of immigrants and travelers diagnosed with one of the 13 core NTDs at a Tropical Medicine Referral Unit in Spain during the period April 1989-December 2007 . Area of origin or travel was recorded and analyzed . There were 6168 patients ( 2634 immigrants , 3277 travelers and 257 VFR travelers ) in the cohort . NTDs occurred more frequently in immigrants , followed by VFR travelers and then by other travelers ( p<0 . 001 for trend ) . The main NTDs diagnosed in immigrants were onchocerciasis ( n = 240 , 9 . 1% ) acquired mainly in sub-Saharan Africa , Chagas disease ( n = 95 , 3 . 6% ) in immigrants from South America , and ascariasis ( n = 86 , 3 . 3% ) found mainly in immigrants from sub-Saharan Africa . Most frequent NTDs in travelers were: schistosomiasis ( n = 43 , 1 . 3% ) , onchocerciasis ( n = 17 , 0 . 5% ) and ascariasis ( n = 16 , 0 . 5% ) , and all were mainly acquired in sub-Saharan Africa . The main NTDs diagnosed in VFR travelers were onchocerciasis ( n = 14 , 5 . 4% ) , and schistosomiasis ( n = 2 , 0 . 8% ) . The concept of imported NTDs is emerging as these infections acquire a more public profile . Specific issues such as the possibility of non-vectorial transmission outside endemic areas and how some eradication programmes in endemic countries may have an impact even in non-tropical western countries are addressed . Recognising NTDs even outside tropical settings would allow specific prevention and control measures to be implemented and may create unique opportunities for research in future .
The neglected tropical diseases ( NTDs ) are a group of mainly chronic illnesses identified as causing considerable morbidity and mortality typically amongst the world's poorest populations and which have also been shown to promote poverty [1] . The effect of the NTDs as a group is such that they have been considered to be part of a group of four , together with HIV/AIDS , tuberculosis ( TB ) and malaria . NTDs cause over 500 , 000 deaths annually and have been estimated to result in a greater number of disability-adjusted life years lost than malaria and tuberculosis [2] , [3] . Although in this sense many infections could be included , 13 core NTDs have been specifically targeted due to their prevalence and the burden of disease they cause globally and they include helminths , protozoa and tropical bacteria [1] , [2] . Experts have argued other infections , such as those caused by arboviruses like dengue and yellow fever [4] , and those caused by helminths like Strongyloides sp [5] , should also be included and this will occur as efforts to control these diseases as a means to target poverty continue to develop . In the year 2000 , the Millennium Declaration established goals to eliminate extreme poverty , hunger and disease and specifically addressed the health and economic impact of infectious diseases [1] . However , the NTDs remain relatively unknown to the general public , partly as a consequence of the decreased media coverage of these infections [6] . There has also been decreased investment by the pharmaceutical companies in new treatments for these conditions [7] . Recent increased efforts and interest at the international level have focused on the diseases mainly in endemic areas . The growth in international travel and immigration which is taking place currently is a well-known phenomenon , and NTDs are no longer geographically-restricted as both immigrants and travelers ( including immigrants who travel to visit friends and relatives , VFRs ) in the western world may present with these infections . This study focuses on imported NTDs from the perspective of a Tropical Medicine Referral Unit in a European country which , with the exception of leishmaniasis , is not endemic for these infections . To date , there has been no significant focus in the literature on the subject of NTDs as a group in immigrants and travelers , and no specific studies on the emerging phenomenon of imported NTDs .
This was a retrospective analysis of data obtained over a 19-year period , data were analyzed anonymously and written informed consent was not obtained from individual participants . The research was approved by the Ramón y Cajal Hospital's Ethics Committee ( Comité Etico de Investigación Clínica , CEIC , Hospital Ramón y Cajal ) . A retrospective study of a cohort of 6168 patients ( 2634 immigrants , 3277 travelers and 257 VFRs- travelers visiting friends and relatives ) seen at a Tropical Medicine Referral Unit in Madrid , Spain during the period April 1989 to December 2007 was performed . The unit is part of the Infectious Diseases department of a large ( >1200-bed ) tertiary referral teaching hospital and sees a large proportion of sub-Saharan immigrants referred from a non-profit organization for African immigrants based in Madrid as well as a large subset of Latin American immigrants . In Spain , basic medical assistance is given to all the population , national or foreign , and to both legal and illegal immigrants . Patients need only to be in possession of a health card to receive medical care . If there are difficulties in accessing this health card ( cultural/linguistic barriers ) , patients may be referred from non-profit organizations . A traveler was defined as any person who had crossed an international border within 10 years before presentation and an immigrant was defined as any person arriving in a country different from their own with the objective of settling in the new country . A VFR traveler was defined as any traveler ( or their children , even if they were born in Spain ) who returned to their country of origin for the purpose of visiting friends and relatives . Screening for asymptomatic patients comprised blood count , biochemistry , basic urinalysis , HIV , hepatitis B virus and hepatitis C virus serology , rapid plasma reagin for syphilis , tuberculin skin test , stool parasites , PCR for malaria in sub-Saharan Africans ( since 2005 ) , and Trypanosoma cruzi serology ( both immunofluorescent antibody test and ELISA ) in Latin Americans . Other tests were ordered according to specific symptoms and signs . Cases were identified from a database of patients if they had one or more of the 13 core NTDs ( ascariasis , trichuriasis , hookworm infection , schistosomiasis , lymphatic filariasis , trachoma , onchocerciasis , leishmaniasis , Chagas disease , leprosy , human African trypanosomiasis , dracunculiasis , Buruli ulcer ) [1] . Gender , age and country of origin/travel were recorded . Standard diagnostic methods for each infection were used . A patient was defined as having NTD polyparasitism if more than one NTD was diagnosed . Positive epidemiological risk factors were considered in immigrants from endemic areas for the infection and if a traveler had visited an endemic area and had history of exposure . Cases of schistosomiasis , onchocerciasis and lymphatic filariasis were classified as definite or probable cases based on the evidence available as outlined below . Diagnoses based on finding typical parasite eggs on stool examination ( formol ether concentration method ) . Definite schistosomiasis in an immigrant was diagnosed after identifying the parasite eggs in specimens ( urine , stool or biopsy specimens ) . Probable schistosomiasis in an immigrant was diagnosed on finding positive serology in a patient with clinical symptoms/signs of schistosomiasis ( hematuria and/or eosinophilia ) or if there was documented response to treatment with praziquantel ( resolution of symptoms/signs and normal eosinophil count ) in a patient with clinical symptoms/signs of schistosomiasis ( hematuria and/or eosinophilia ) . Definite schistosomiasis in a traveler was defined after identifying the parasite eggs in specimens ( urine , stool or biopsy specimens ) or if history of exposure and positive serology . Probable schistosomiasis in a traveler was diagnosed if history of exposure and suggestive clinical signs/symptoms ( swimmer's itch , Katayama fever or hematuria and/or eosinophilia ) or history of exposure , suggestive clinical symptoms/signs and response to praziquantel ( resolution of signs/symptoms and normal eosinophil count ) . Diagnosis established on finding microfilariae after lysis-centrifugation of night-time blood specimens . Probable cases of lymphatic filariasis based on epidemiological risk factors , compatible clinical symptoms/signs and response to treatment . Diagnosis of definite onchocerciasis in immigrants was based on identification of Onchocerca volvulus microfilariae in “skin snips” or clinical signs/symptoms of onchocerciasis ( pruritus and/or skin lesions suggestive of onchocerciasis ) and a positive Mazzotti provocation test with diethyl carbamazine ( DEC ) ( symptoms appearing after a single oral dose of 25–50 mg of DEC , performed in patients with negative skin snips and no evidence of ocular involvement ) . Probable onchocerciasis in an immigrant was diagnosed if clinical signs/symptoms and response to treatment with ivermectin ( symptom resolution ) . Onchocerciasis ( definite ) was diagnosed in a traveler when microfilariae were identified in skin snips or if positive serology in a patient with clinical symptoms/signs of onchocerciasis ( pruritus/skin lesions suggestive of onchocerciasis ) . Slit lamp examinations were performed in patients with ocular symptoms or prior to the Mazzotti provocation test . Diagnosis based on identification of amastigotes in skin or bone marrow biopsies , and/or positive culture results ( Novy-McNeal-Nicolle medium ) and/or positive PCR results ( Leishmania nested PCR , LnPCR , of blood/bone marrow/skin biopsies ) [8] . T . cruzi infection was defined by positive serology using two different techniques ( ELISA and IFAT ) . For all patients with Chagas disease , an ECG and echocardiogram were requested , and upper/lower gastrointestinal barium studies or esophageal manometry were requested in patients with symptoms . Cases were diagnosed on obtaining positive skin smears ( presence of Ziehl-Nielsen acid fast bacilli ) in a patient with clinical symptoms/signs of leprosy . An SPSS 12 . 0 for Windows package ( SPSS Inc . , Chicago , USA ) was used to analyze the data .
In total , 103 patients were diagnosed with ascariasis ( 86 immigrants , 16 travelers , and 1 VFR ) . Amongst the immigrants there were 54 females and 32 males ( mean age 24 years , range: 1–74 yrs ) . Most patients were from Central Africa [Equatorial Guinea ( 68 ) , Angola ( 3 ) , Cameroon ( 1 ) , and Central African Republic ( 1 ) ] , followed by South America [Ecuador ( 6 ) , Colombia ( 3 ) ] , and West Africa [Liberia ( 2 ) , Nigeria ( 2 ) ] . There were eight male and eight female travelers ( mean age 32 years , range 23–80 yrs ) . The majority had traveled to sub-Saharan Africa: Equatorial Guinea ( 3 ) , and one each to Congo D . R . , Mozambique , Senegal , and Cameroon . Other countries visited included: India ( 2 ) , Ecuador ( 2 ) , Morocco ( 2 ) , Algeria , Mexico , and Guatemala . One female VFR traveler to E . Guinea was diagnosed with ascariasis . All the patients diagnosed with trichuriasis were immigrants ( 47 females and 27 males , mean age 26 years , range: 1–73 yrs ) , the majority from Central Africa [Equatorial Guinea ( 65 ) , Angola ( 2 ) , Cameroon ( 1 ) ] followed by Ecuador ( 2 ) and one each from Brazil , Dominican Republic , Guatemala , and Liberia . Out of 11 hookworm infections , nine occurred in immigrants ( five females , four males , mean age: 35 years , range: 9–69 yrs ) and two in travelers ( both male , both aged 29 yrs ) . All the immigrants were originally from Equatorial Guinea , and the travelers had visited Senegal/Guinea Bissau and Thailand . There were 28 immigrants diagnosed with schistosomiasis ( 13 definite and 15 probable cases ) , 26 males and two females ( mean age 25 years , range: 16–55 yrs ) . Out of the 13 definite cases , 8 had S . haematobium and 5 had S . mansoni . The majority ( 26 ) of patients were from sub-Saharan Africa . Patient distribution according to country of origin was as follows: Equatorial Guinea ( 5 ) , Mali ( 5 ) , Cameroon ( 3 ) , Nigeria ( 3 ) , Sierra Leone ( 2 ) , Angola ( 2 ) , Ghana ( 2 ) , Liberia ( 2 ) , Niger ( 1 ) , Republic of Guinea ( 1 ) , Egypt ( 1 ) , and Ecuador ( 1 ) . With respect to the main symptoms/signs: no patients presented with acute symptoms , 18 ( 64 . 3% ) had eosinophilia , and 13 ( 46 . 4% ) had hematuria . Out of 43 cases of schistosomiasis diagnosed in travelers , 19 were considered definite and 24 were probable cases , 23 were male and 20 female ( mean age 34 years , range 23–55 yrs ) . Out of 19 definite cases , 5 had S . haematobium and 14 were classified as Schistosoma sp . ( positive serology and history of exposure ) . For all travelers , risk factors for acquisition of schistosomiasis were identified ( freshwater exposure in endemic countries ) , and a large proportion of patients ( 19/43 , 44 . 2% ) had traveled to Mali . With respect to main symptoms/signs: 7 patients ( 16 . 3% ) referred symptoms suggestive of acute schistosomiasis , 32 patients ( 74 . 4% ) had eosinophilia , and 17 ( 39 . 5% ) had hematuria . Only two cases were diagnosed amongst VFRs: one 53 year-old female ( Schistosoma sp . , traveled to E . Guinea ) and one35 year-old male ( S . haematobium , traveled to Cameroon ) were diagnosed with schistosomiasis . Two probable cases of lymphatic filariasis were identified ( microfilariae were not obtained from specimens but they had suggestive clinical signs/symptoms and responded to treatment ) . A 60 year-old male immigrant from India presented with eosinophilia , bilateral testicular hydrocele and prominent chyluria . A 52 year-old Spanish male who had worked in Equatorial Guinea for 2 . 5 years had presented with eosinophilia , bilateral lower limb edema and a left testicular hydrocele . A total of 240 cases of onchocerciasis were identified in immigrants . Of these , 169 cases were definite cases and 71 were probable cases . There were 158 females and 82 males ( mean age 33 years , range: 5–76 yrs ) . All but two of the cases of onchocerciasis in immigrants occurred in African patients . The majority of patients were from Equatorial Guinea ( 213/240 , 88 . 8% ) , followed by Cameroon ( 6/240 , 2 . 5% ) , Nigeria ( 4/240 , 1 . 7% ) , Angola ( 4/240 , 1 . 7% ) , Zaire ( 3/240 , 1 . 3% ) and one each from Republic of Guinea , Mali , Togo , D . R . Congo , Ghana , Sierra Leone , Sao Tome , Ivory Coast , Colombia , and Ecuador ( 1/240 , 0 . 4% ) . A linear regression analysis was performed taking into account the number of new diagnoses of onchocerciasis ( excluding two patients from South America ) and the number of new African immigrants seen per year . A statistically significant decrease was found in the number of cases of onchocerciasis diagnosed each year at the unit ( p<0 . 001; β −1 . 84 , 95% C . I . −2 . 46 to −1 . 22 ) ( See figure 1 ) . There were 17 travelers ( nine females and eight males , mean age 37 years , range: 21–59 yrs ) diagnosed with definite onchocerciasis . Of these , 16 patients were long-term travelers , ( trip duration >3 months , range: 3–336 months ) and 1 patient had traveled for 1 month . All patients had traveled to sub-Saharan Africa and countries visited included: Equatorial Guinea , Angola , Cameroon , DR of Congo , Ivory Coast , Burkina Faso , Central African Republic and Mali . Some patients had visited more than one country during their travel . Amongst VFR travelers , 14 diagnoses of onchocerciasis were made ( 5 males and 9 females , mean age 31 years , range: 6–53 yrs ) , 12 had traveled to E . Guinea and 2 had traveled to Cameroon . Amongst the group of immigrants , there were three males and three females ( mean age: 34 years , range: 17–50 yrs ) . There were three cases of cutaneous leishmaniasis ( CL ) ( patients were from Brazil , Algeria and Panama ) , two cases of mucocutaneous leishmaniasis ( ML ) ( from Bolivia and Ecuador ) and one visceral leishmaniasis ( VL ) ( from Cameroon but had stayed in Northern Africa for two years ) . There were seven male and two female travelers ( mean age: 37 years , range 28–55 years ) . CL was diagnosed in six ( these travelers had visited Costa Rica ( n = 2 ) , Panama , French Guyana , Colombia and India ) , ML in two ( from Ecuador and Bolivia/Peru ) and VL in one patient ( Peru ) . In total , 95 immigrants were diagnosed with Chagas disease: 62 females and 33 males , ( mean age 36 years , range: 16–69 years ) . The majority of patients were from Bolivia ( 90/95 , 94 . 7% ) and one each from Brazil , Chile , Ecuador , Paraguay , and Honduras . With regards to risk factors for acquisition of Chagas disease in their countries of origin , 79 patients were from rural areas , 76 patients recalled having seen the vector in their homes in their countries of origin , 15 patients had received a blood transfusion in endemic countries and for 7 patients vertical transmission was a possibility ( mother with known positive T . cruzi serology ) . Not all patients had completed their full work-up to ascertain degree of visceral involvement during the study period: 16 patients were found to have abnormalities on ECG ( out of 68 tests performed ) and 10 had abnormalities on echocardiogram suggestive of Chagas disease ( out of 58 tests performed ) . Out of 36 esophageal manometries and 44 barium enemas performed 4 in each group , showed abnormalities suggestive of Chagas disease . Nine immigrants were diagnosed with leprosy , five males and four females ( mean age 42 years , range: 24–64 yrs ) . Two patients were from the Dominican Republic , two from Colombia , two from Equatorial Guinea and one each from Mauritania , Brazil , and the Philippines . Four patients had lepromatous leprosy , two patients , tuberculoid leprosy , one patient , borderline-tuberculoid leprosy , one had borderline-lepromatous leprosy and one had borderline leprosy . The one traveler diagnosed with leprosy had lived for nearly 60 years in Cuba ( expatriate ) and was diagnosed with borderline-lepromatous leprosy . There were no cases of trichuriasis , hookworm infection , lymphatic filariasis , leishmaniasis , Chagas disease , or leprosy diagnosed in VFR travelers . No cases were diagnosed in this series . Some patients were diagnosed with more than one NTD ( with a maximum of 2 NTDs per patient ) : 72 immigrants , 4 travelers and 2 VFR travelers . In the group of immigrants , 2 were diagnosed with hookworm infection and ascariasis ( both from E . Guinea ) , 3 were diagnosed with hookworm and trichuriasis ( all from E . Guinea ) , 8 were diagnosed with ascariasis and onchocerciasis ( all from E . Guinea ) , 49 were diagnosed with ascariasis and trichuriasis ( 44 from E . Guinea , 2 from Ecuador and one each from Liberia , Angola and Cameroon ) , 1 patient was diagnosed with CL and trichuriasis ( from Brazil ) , 2 with leprosy and onchocerciasis ( from E . Guinea ) and 7 with onchocerciasis and trichuriasis ( all from E . Guinea ) . In the group of travelers , 1 patient was diagnosed with hookworm and ascariasis ( travel to Brazil for 25 days ) , 1 patient with ascariasis and onchocerciasis ( travel to Cameroon for 6 months ) , and 2 patients were diagnosed with onchocerciasis and schistosomiasis ( travel to Central African Republic for 5months and the other patient had traveled to Burkina Faso and Ivory Coast for 1 month ) . In the group of VFR travelers , 2 patients were diagnosed with schistosomiasis and onchocerciasis . These patients had traveled to Cameroon and Equatorial Guinea for 15 days and 2 months , respectively . NTD polyparasitism was most frequent amongst immigrants , followed by VFR travelers and then by other travelers , respectively ( p<0 . 001 for trend ) .
NTDs are acquiring a more public profile , and in parallel the concept of imported NTDs , is also emerging . As demonstrated , NTDs are not restricted to the tropics , and may be diagnosed in countries of the developed world in travelers , immigrants and VFRs . Certain limitations of the study , such as the under-representation of patients from Asia , Oceania and North America , may not allow extrapolation of all results to other groups of travelers and immigrants . Some patients were lost to follow-up reflecting the mobile nature and difficulty of access to health services of both immigrants ( illegal residents were also included in the study ) and travelers . This may have led to an underestimation of the frequency of NTDs in this group , especially if loss of follow-up implied a diagnosis was not reached . However , the large sample size and the length of the study period add strength to the study , and highlight some important issues . NTDs manifesting in endemic areas and imported NTDs will share certain features but differences may also be expected . The spectrum and frequency of diseases diagnosed in endemic and non-endemic areas may differ . Disease burden may be lower when the infections occur outside endemic areas . Although the number of patients traveling with pre-existing medical conditions is increasing [9] , a large proportion of travelers are healthy and in the same way an important proportion of immigrants who seek work opportunities and who are able to travel to do so , would generally be expected to be in good health . The occurrence of an NTD in a healthy person should not have the same devastating effects as those observed in endemic areas , where infections and re-infection with multiple NTDs are not infrequent and worsen the burden of disease . Populations in endemic regions can be infected with multiple neglected tropical diseases [10] and this was also observed with imported NTDs . As would be expected due to the greater risk and length of exposure , NTD polyparasitism was most frequent amongst immigrants , followed by VFR travelers and other travelers , respectively , and this difference was statistically significant for trend . The most frequent cause of polyparasitism in this series was due to coinfection with one of the geohelminths ( ascariasis , trichuriasis , and hookworm ) , reflecting the large burden of disease caused by these parasites worldwide [1] . There were few cases of NTDs amongst VFR travelers , who as a group accounted for only a small proportion of the study population ( approximately 4% ) . Risk factors for acquisition of some of the NTDs ( often chronic diseases ) in VFRs may be due to exposure during more recent travel or may be related to exposure years previously before migration took place . Acquisition of other NTDs ( such as T . cruzi infection which is very rarely diagnosed in travelers ) is more likely to have occurred before migration than during VFR travel . VFRs have been shown to have an increased risk of acquiring infections during travel [11] , and in this study NTDs were found to be significantly more frequent in VFR travelers compared to other travelers ( but less frequent than in immigrants ) . However , further research on NTDs in VFR travelers would be of value and comparisons should be interpreted with caution . An important difference was the large number of immigrants infected with O . volvulus , which was diagnosed infrequently in travelers . Most of the patients diagnosed with onchocerciasis were from Equatorial Guinea , which achieved independence from Spain in the 1960s . These historical links between both countries explain the large number of patients from E . Guinea , an area highly endemic for onchocerciasis , and therefore the high number of O . volvulus infections . Differences may arise due to the length/type of exposure necessary for acquisition of some infections , as these conditions are achieved more readily by the local population in endemic areas . However , with the recent changes in travel patterns , not only quantity but also quality of travel becomes an issue , as more people visit remote and exotic destinations . As occurred for some cases in this study , several case reports describe only short durations of exposure for filarial-infected travelers and risks may be linked to the lack of preventive measures and specific exposure in vector habitats [12] , [13] . In future , more travelers may present to health centers in areas of the western world with filariasis . On the other hand , most cases of schistosomiasis were diagnosed in travelers and this may be partly due to the presence of semi-immunity in adult immigrants from endemic areas . In communities where Schistosoma species are endemic , prevalence and intensity of schistosomiasis have been found to be higher in children whereas some adults may generate certain protective responses [14] , [15] . These protective mechanisms following exposure would not be expected to have developed in travelers , especially following only short-term exposure . Other NTDs , such as dracunculiasis , Buruli ulcer and African and American trypanosomiasis are very infrequent in travelers , with only very few cases reported in the literature [16]–[19] , and these diseases were not diagnosed in this cohort either . The absence of NTDs such as dracunculiasis even in the cohort of immigrants may reflect the decrease in incidence world wide which may be attributed to the progress of the global dracunculiasis eradication program [20] . The Global Program to Eliminate Lymphatic Filariasis has also had an important impact in communities in Africa and Asia [21] . An example of the success of another of these programs could be illustrated by the findings in this study , which showed a significant decrease in cases of onchocerciasis in recent years , despite the increasing number of patients attended at the unit . The Onchocerciasis Control Program in West Africa focusing on vector control and the Ivermectin donation program established in 1987 , have increased general interest in health-related public-private partnerships [22] , [23] . The results presented in this analysis may reflect how the implementation of such programs in an endemic developing country may have more widespread effects by changing the presentation and epidemiology of the disease in a non-endemic country . Once NTDs are imported into non-endemic areas , the possibility of transmission and the resulting impact should be considered . In general , the epidemiology of infectious diseases will be influenced by the interactions between pathogen , host ( human , animal or vector ) and the environment [24] , and most of these infections will have only limited transmission as the required vector may be absent and the environmental conditions unfavorable . However , there have been rising concerns regarding the emergence of some pathogens due to infectious agents being imported into novel non-endemic areas and the possibility of accidental spread of disease vectors between areas which may act as drivers for the emergence of infections [24] . Local vectors may become infected with imported infectious agents resulting in local cases as occurred in the first outbreak of chikungunya virus infection in a temperate country which was registered in Italy , where the vector , Aedes albopictus is already established [25] . Similar outbreaks could theoretically occur in other non-tropical countries and involving other vectors and other imported infectious pathogens . The prospect of possible future spread of some of the NTDs outside their usual geographical areas should therefore not be dismissed . Global changes in climate and temperatures may affect the distribution of vectors and trigger disease outbreaks and the possibility of non-vectorial transmission also emerges [24] , [26] . Chagas disease , paradoxically , is an NTD with a reported decreasing health and economic impact in endemic countries due to the success of multi-national control programs aimed principally at the interruption of vectorial and transfusional transmission [27] , but the disease now appears to be emerging outside these areas [28] . In Europe , and especially in Spain , cases of Chagas disease have been increasing due to the recent increase in immigration from Latin America and the disease may become an important cause of cardiomyopathy in the near future [28] . Tainted donor blood or organ grafts and vertical transmission would be the main modes of transmission of Trypanosoma cruzi imported by immigrants in countries where vectorial transmission does not occur . This growth in immigration has had sufficient impact to warrant changes in national legislation with respect to the screening of blood donations [29] , and yet pregnant Latin American women are not always screened for Trypanosoma cruzi infection . Specific preventive strategies would need to be developed and implemented at a national level in order to control non-vectorial transmission outside endemic areas . Health professionals attending immigrants and travelers from tropical countries should consider protocols for screening and prevention of NTDs in their everyday practice . Regarding pre-travel advice , for travelers and especially VFRs , adherence to a few basic precautions , ( safe consumption of food/water , protection against arthropod bites and avoiding swimming in fresh water/walking barefoot ) could help prevent the majority of NTDs . Early detection of cases may permit prevention of secondary transmission . The control of NTDs in endemic areas with low-cost effective interventions such as rapid-impact packages , may lead to long-term economic growth given the reported high return rates on investments in these diseases [1] , [21] , [30] , [31] . Recognising that certain NTDs may also have an impact in areas of the western world should help create unique opportunities for research and control measures in countries with greater means . Studies in non-endemic areas may provide valuable data as patients will not ordinarily be re-infected unless they travel to endemic countries again . Hopefully the problem of the neglected tropical diseases “spilling over” into more developed countries will be linked to even greater combined international efforts to control these infections . | Neglected Tropical Diseases ( NTDs ) have been targeted due to their prevalence and the burden of disease they cause globally , but there has been no significant focus in the literature on the subject of NTDs as a group in immigrants and travelers , and no specific studies on the emerging phenomenon of imported NTDs . We present the experience of a Tropical Medicine Unit in a major European city , over a 19-year period , describing and comparing NTDs diagnosed amongst immigrants , travelers and travelers visiting friends and relatives ( VFRs ) . NTDs were diagnosed outside tropical areas and occurred more frequently in immigrants , followed by VFR travelers and then by other travelers . The main NTDs diagnosed in immigrants were onchocerciasis , Chagas disease and ascariasis; most frequent NTDs in travelers were schistosomiasis , onchocerciasis and ascariasis , and onchocerciasis and schistosomiasis in VFRs . Issues focusing on modes of transmission outside endemic areas and how eradication programs for some NTDs in endemic countries may have an impact in non-tropical Western countries by decreasing disease burden in immigrants , are addressed . Adherence to basic precautions such as safe consumption of food/water and protection against arthropod bites could help prevent many NTDs in travelers . | [
"Abstract",
"Introduction",
"Methods",
"Results",
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] | 2010 | Neglected Tropical Diseases outside the Tropics |
The energy efficiency of neural signal transmission is important not only as a limiting factor in brain architecture , but it also influences the interpretation of functional brain imaging signals . Action potential generation in mammalian , versus invertebrate , axons is remarkably energy efficient . Here we demonstrate that this increase in energy efficiency is due largely to a warmer body temperature . Increases in temperature result in an exponential increase in energy efficiency for single action potentials by increasing the rate of Na+ channel inactivation , resulting in a marked reduction in overlap of the inward Na+ , and outward K+ , currents and a shortening of action potential duration . This increase in single spike efficiency is , however , counterbalanced by a temperature-dependent decrease in the amplitude and duration of the spike afterhyperpolarization , resulting in a nonlinear increase in the spike firing rate , particularly at temperatures above approximately 35°C . Interestingly , the total energy cost , as measured by the multiplication of total Na+ entry per spike and average firing rate in response to a constant input , reaches a global minimum between 37–42°C . Our results indicate that increases in temperature result in an unexpected increase in energy efficiency , especially near normal body temperature , thus allowing the brain to utilize an energy efficient neural code .
Brain signaling is metabolically expensive . Energy expenditure not only constrains the size and architecture of the brain , which limits its computational power , but is critical to the interpretation of functional brain imaging signals through related metabolic mechanisms ( e . g . oxygen consumption and blood flow ) [1] . Comprising only about 2% of the body's mass , the mammalian brain consumes about 20% of its energy [2] , [3] , [4] . Another unique feature of mammals is their warm body temperature ( about 35–39°C ) . How a warm body temperature affects signaling and energy budget in the brain is largely unknown . Here we address this critical and interesting question through simple Hodgkin-Huxley models as well as recordings from cortical neurons during changes in temperature . Operating neurons is expensive , in part owing to the need to maintain a significantly higher concentration of Na+ ions outside , versus inside , nerve cells [5] , [6] , [7] , [8] . Na+ entry into neurons , which must be returned to its extracellular location through the operation of the Na+/K+ ion pump by the expenditure of energy via hydrolysis of ATP , occurs through generation of action potentials ( particularly along long intracortical , unmyelinated axons ) and synaptic potentials during active signaling . These influxes of Na+ into neurons occur in addition to a background leak of Na+ ions through the neuronal membrane . Thus , to understand the energy costs of neuronal signaling in the cortex , it is essential to understand the entry of Na+ into neurons and neuronal processes during neuronal activity . A maximally energy efficient action potential would entail no overlap of the inward Na+ current that generates the upstroke and the outward K+ current that facilitates the downstroke . Any overlap of these two opposing currents would merely result in an electrically neutral exchange of positive ions . Classic investigations by Hodgkin of squid giant axon revealed an excess entry of approximately 4 times as much Na+ as minimally required to generate the action potential [9] . This value of 4 times excess Na+ entry has figured prominently in estimates of the distribution of the sources of energy consumption in the mammalian brain [1] , [5] , [10] , [11] , as well as in the calculation of the average firing rate of cortical neurons [6] . For example , one classic modeling study of energy consumption in mammalian brains stated “A realistic estimate of the Na+ entry needed is obtained [for action potential generation] by quadrupling [the minimal Na+ entry] to take account of simultaneous activation of Na+ and K+ channels” [5] . Calculations such as these have predicted that up to 50% or more of the energy consumption in mammalian brains is devoted to the reversal of ion exchanges owing to Na+ entry ( and K+ exit ) during action potentials [5] , [10] . Based upon this calculation , it has been proposed that the brain can support an average firing rate of less than 0 . 2 spikes/second , suggesting that the nervous system operates through a very sparse code [6] . Reconstruction of the inward Na+ and outward K+ currents occurring during action potential generation in mammalian cortical axons revealed , in contrast to the results predicted , an excess ratio of Na+ entry of only 1 . 3 [12] , [13] , indicating that axons in the mammalian brain are far more energy efficient than previously appreciated . This high energy efficiency in action potential generation is achieved through a relatively complete Na+ channel inactivation prior to substantial activation of the outward K+ current [12] , [13] . These results prompted studies based on the hypothesis that the kinetics of ion channels may be optimized through evolution from invertebrates ( e . g . squid giant axon ) to mammals ( e . g . rodent cortical axons ) [12] , [13] . This increased efficiency of mammalian axons has important implications not only for the average firing rate of cortical neurons , but also the practical limitations on the size and morphology of the brain . Here , we reexamine this issue through a set of computational models and experimental study . We hypothesize that the achievement of the highly energy efficient action potentials in cortical neurons is achieved in large part through the development of a warm body temperature . Prior recording and modeling studies have demonstrated that the efficiency of action potential generation is highly sensitive to the kinetics of underlying ionic currents [13] , [14] , [15] . Indeed , changing temperature has a strong influence on the kinetics of the Na+ and K+ currents underlying action potential generation [16] , [17] , [18] . Homeothermic animals ( e . g . mammals and birds ) have the ability to maintain a relatively constant brain temperature in the range of 34–42°C [19] , [20] , [21] , [22] , [23] , [24] , while poikilothermic animals experience much wider variations in body temperature . The species of squid most studied ( Loligo ) lives in an ocean environment varying in temperature from approximately 10–23°C , which is far colder than naturally occurring in most mammals . Since temperature is a strong determinant of ion channel kinetics , which in turn can dramatically change action potential efficiency , we explored here the possibility that mammalian neurons may generate action potentials with maximal energy efficiency at normal body temperatures .
The examination of spike efficiency through simulation studies of squid giant axon action potential generation by Hodgkin [9] were performed with a temperature of 18°C , while the recordings of mammalian axons were performed at 37°C [12] . We explored whether or not these variations in temperature may help to explain the marked difference in excess Na+ entry between these two species by performing simple Hodgkin-Huxley style simulations of action potentials in either the traditional HH single compartment model or in a simple uniform cylindrical model of a cortical axon [25] and varying temperature ( Figure 1A ) . We used simple , single compartment models with only INa , IK , and ILeak so as to clearly demonstrate the principles of the effects of temperature on excess Na+ entry during action potentials . Simulations with more complete computational models of layer 5 pyramidal neurons , including back propagating action potentials from the axon [25] , yielded similar results as those reported here . For the present simulations , we assumed a Q10 for both Na+ and K+ currents of 2 . 3 [17] , [18] , [26] , [27] and varied reversal potential with temperature according to the Nernst equation . Varying the Q10 used from 1 . 5 to 3 yielded qualitatively similar results as those shown in Figure 1 . Using either the traditional HH or cortical axon single compartment models , increases in temperature caused a marked decrease in the excess Na+ entry occurring during action potential generation , such that at 18°C a value of approximately 4X excess is obtained , while at 37°C , a value of 1 . 41 is observed for our model of cortical axons ( Figure 1A ) . The traditional HH model of the squid axon fails to generate action potentials at temperatures above approximately 28°C . At this temperature , the excess Na+ ratio is 2 . 5 ( Figure 1A ) . In our model of a cortical axon spike , examination of action potentials at 18°C reveal an inward Na+ current that exhibits a prominent inward shoulder during the repolarizing phase of the spike , resulting in a strong overlap of inward Na+ and outward K+ currents ( Figure 1B ) , as in squid giant axons at this temperature ( Figure 1C; [9] ) . The same simulation , but at 37°C , however reveals an inward Na+ current that overlaps very little with the outward K+ current , as seen in mammalian axons at 37°C ( Figure 1B; [12] , [13] ) . For the squid axon HH model , the overlap of inward Na+ and outward K+ also decreases when temperature increases ( Figure 1C ) . In addition , we also notice a dramatic decrease in spike duration as a function of temperature for both models ( see Figure 1D ) . There is a nearly linear correlation between spike duration and excess Na+ entry ratio ( see Figure 1D , inset ) . This relationship in our models arises from the fact that lowering temperature results in kinetically slower ionic currents , resulting in both an increase in overlap of the inward Na+ and outward K+ currents and a longer duration action potential ( see Figure 1B , D ) . Examining the relationship between action potential duration and excess Na+ entry ratio ( inset in Figure 1D ) may lead one to hypothesize that it is the shortening of the duration of the action potential that is the primary effect in the reduction of Na+ entry with each spike at higher temperatures ( e . g . Figure 1A , B ) . To test this hypothesis , we fixed the action potential waveform to either that occurring in the cortical axon model at 18°C , or to that occurring in the model at 37°C ( Figure 2 ) . We then “injected” this waveform into the model and examined the amplitude –time course of the resulting Na+ and K+ currents , when their kinetics were set to temperatures varying from 6 to 37°C ( Figure 2; supplemental Figure S1 ) . When fixing the action potential waveform to that obtained at 18°C , we found that making the kinetics faster ( i . e . raising temperature ) dramatically reduced the overlap between the Na+ and K+ currents ( Figure 2A–D ) and reduced the excess Na+ entry ratio ( supplemental Figure S1 ) . Interestingly , fixing the action potential waveform at that occurring at 37°C , but using the ion channel kinetics occurring at 18°C , resulted in a large increase in Na+ entry and Na+/K+ current overlap , despite the fact that the action potential was much shorter in duration than that which normally occurs at 18°C ( cf . Figure 2B , F ) . As with the long duration action potential ( Figure 2A–D ) , making the kinetics of the underlying Na+/K+ currents faster ( e . g . increasing temperature ) while injecting the fixed 37°C action potential waveform , resulted in a marked decrease of the total Na+ current and overlap of Na+ and K+ currents ( Figure 2E–H ) . Our analysis of these results reveals that the faster channel kinetics associated with increased temperature result in a marked decrease in the total Na+ current and Na+/K+ current overlap . This is primarily the result of an increase in Na+ channel inactivation , especially during the falling phase of the action potential , when the driving force on Na+ is especially large ( see below ) . Thus , increasing temperature does not result in a decrease in total Na+ current or a decrease in Na+/K+ current overlap by decreasing the action potential duration . Rather , the increase in temperature results in both a decrease in spike duration as well as a decrease in excess Na+ entry owing largely to the increased rate of Na+ channel inactivation ( see below ) . Changes in the overlap of inward Na+ and outward K+ currents results in systematic changes in dV/dt of the action potential , and the ratio of the maximal falling to rising dV/dt values , with temperature ( Figure 3 ) . We define γ as ( IdV/dtImin ) / ( IdV/dtImax ) . This variable is strongly influenced by the level of separation of the inward Na+ and outward K+ currents , and by other factors such as the peak amplitude of INa and IK . We include it here because it is a readily measureable variable in real neurons , and therefore useful for comparison with results of our model . Figure 3B shows that the value of γ increases nonlinearly with an increase in temperature , for both the classical Hodgkin-Huxley model as well as our simple model of a cortical action potential . Plotting the excess Na+ entry ratio as a function of γ revealed that as γ increases ( representing in part decreased overlap of INa and IK ) , the excess Na+ entry ratio decreases ( Figure 3C ) . The higher values of γ for the traditional HH model reveals a higher rate of spike repolarization ( relative to spike depolarization ) than is present in our simple cortical model . We reasoned that the marked changes in overlap of inward Na+ and outward K+ currents during action potential generation with increases in temperature were due to changes in the kinetics of Na+ activation and inactivation , and K+ activation . Plots of the peak values of the time constants for activation of INa ( τm ) and IK ( τn ) and inactivation ( τh ) of INa , revealed an exponential and strong decrease in all three with increases in temperature ( Figure 4A–C ) . Phase plots of the INa activation ( m ) and inactivation ( h ) and IK activation ( n ) variables versus membrane potential during generation of an action potential at 18 and 36°C revealed significant and important effects of temperature ( Figure 4D–F ) . Since the currents vary over a very wide range of values , a logarithmic scale was used to monitor the smaller values during spike repolarization ( Figure 4D–F ) . Interestingly increases in temperature from 18 to 36°C resulted in an increase in Na+ channel inactivation during nearly all phases of the action potential , with peak inactivation increasing from 0 . 5% of channels available at 18°C to only 0 . 1% available at 36°C ( Figure 4E ) . In addition , increasing temperature from 18 to 36°C also results in a significant reduction in IK activation during the rising phase of the action potential ( Figure 4F ) . Even though increasing temperature increases ionic current kinetics substantially , the plot of m , h , and n versus membrane potential during action potential generation were substantially different from the steady state values of the currents ( m∞; h∞ , n∞; supplement Figure S2 ) . Comparison of the results in Figures 1B and 4D–F suggest that the main contribution to the increase in efficiency of spike generation with increasing temperature is the strong increase in Na+ channel inactivation ( Figure 4E ) , especially during the first half of spike repolarization , when Na+ activation is still high ( Figure 4D ) , resulting in little excess Na+ entry during the falling phase of the action potential , as well as a decrease in spike duration . However , changes in IK activation with increases in temperature may also contribute , by decreasing spike duration . These changes in INa activation/inactivation and IK activation result in an exponential decrease in the total amount of Na+ that enters during each action potential ( Figure 5B , black circles ) , thus decreasing the metabolic demand of spiking . The critical role of temperature dependent increases in the rate of INa inactivation was confirmed by keeping this rate constant ( τh ) constant while allowing τm and τn to vary ( Figure 5A , red circles ) . In this circumstance , the strong decrease in Na+ entry per action potential with increases in temperature was reversed , such that the Na+ entry ratio actually increased with temperature ( Figures 5B , D , 6E ) . Keeping either INa activation rate ( τm ) or IK activation rate ( τn ) constant individually did not abolish the strong decrease in Na+ entry/spike with temperature ( Figures 5B , 6E ) . The decrease in spike duration with increase in temperature still occurred during constant τh , τn or τm , although this effect was greatly reduced when Na+ channel inactivation ( τh ) was invariant ( Figure 5C ) . This result indicates that the decreases in spike duration and excess Na+ entry ratio with temperature ( effects that are inter-related; see discussion ) result largely from changes in the kinetics of Na+ channel inactivation . One complicating factor is that temperature affects the intrinsic excitability and spiking rate of neuronal elements [19] , [21] , [28] , [29] , [30] , [31] , [32] , [33] , [34] . Indeed , in our HH cortical model , increases in temperature result in an increase in firing rate in response to constant current pulses ( 0 . 005–0 . 02 pA/µm2 , 500 ms ) . Interestingly , at higher temperatures ( approximately 38–40°C ) , the firing rate in our models increases rapidly with increases in temperature ( Figures 6A ) . Thus , for a constant current input , increases in temperature result in a marked decrease in the amount of Na+ that enters with each action potential ( Figure 6B ) , but an increase in the number of action potentials generated in response to a constant input such as a square pulse of current . The total Na+ load ( the number of spikes generated times the Na+ entry per spike ) to a constant square pulse input decreases with temperature to a minimum at approximately 38–40°C . At temperatures above this minimum , the rapid increase in firing rate results in an increasing Na+ load on the neuronal process ( Figure 6C ) . We suspected that this non-linear increase in firing rate with temperature in the simple HH model ( Figure 6A ) may result from a non-linear effect on spike afterhyperpolarization ( AHP ) since the amplitude and duration of the AHP largely determines neuronal discharge rate [31] , [33] and it is known that increases in temperature decrease the duration of single spike afterhyperpolarizations in cortical neurons [31] . Plotting the duration of the model AHP ( measured as the time to return to baseline following the generation of an action potential ) as a function of temperature revealed a highly non-linear relationship , with AHP duration decreasing rapidly with increases in temperature above approximately 37°C ( Figure 7A ) . Plotting the amplitude-time course of IK ( Figure 7C ) as well as the ratio of the K+ current to Na+ current ( Figure 7B ) indicated that even though the K+ currents during the late phases of the AHP are small , lowering temperature results in a significant increase in their amplitude during the late phases of spike repolarization . At cold temperatures ( e . g . 18°C ) , IK became especially large even 25–60 msec after the spike . These changes resulted in a larger , more prolonged spike AHP ( Figure 7 ) . In confirmation of the important role of changes in IK on these effects on the HH model , we found that keeping the activation rate ( τn ) of IK constant , while allowing the activation ( τm ) and inactivate rates ( τh ) of INa to vary with temperature , nearly abolished the ability of changes in temperature to cause non-linear changes in discharge rate ( Figure 6A , D ) as well as the non-linear decrease in AHP duration with increases in temperature ( Figure 7D ) . Keeping either the activation rate ( τm ) or inactivation rate ( τh ) of INa constant , while allowing the other kinetic time constants to vary with temperature , did not change the presence of this non-linear relationship , although it did alter the range of temperatures over which it occurred ( Figures 6A , D; 7D ) . As shown above , keeping the inactivation rate ( τh ) of INa invariant inverted the relationship between the total Na+/spike and temperature , while keeping τm or τn invariant does not fundamentally alter this relationship ( see Figure 6E ) . Consequently , the relationship between total Na+ entry per direct current pulse ( Na+/signal ) is strongly affected by keeping INa inactivate rate ( τh ) invariant ( Figure 6F ) . Keeping the activation rate of INa ( τm ) constant has relatively little effect , while keeping the activation rate of IK ( τn ) constant exaggerates the decrease in total Na+/current pulse ( Figure 6F ) . Our HH-style simulation results suggest that increases in temperature may result in several important changes in neuronal action potential generation: 1 ) action potentials will become shorter in duration and smaller in amplitude , with a marked decrease in overlap of the inward Na+ and outward K+ currents , resulting in a marked reduction in Na+ load/spike; 2 ) The firing rate of the neuronal process to a constant increases will increase non-linearly with changes in temperature , particularly at temperatures above approximately 37°C . Next we tested whether or not these predictions would be confirmed in somatosensory layer 5 and entorhinal layer 2/3 cortical pyramidal cells recorded in vitro in response to constant current pulse injection ( Figures 8 , 9 ) , or during the spontaneous generation of the cortical slow oscillation ( supplemental Figure S3 ) . As predicted by HH simulations , increases in temperature resulted in a significant decrease in spike duration ( Figure 8A , B ) , spike height ( Figure 8C ) , and a steady increase in firing rate in layer 5 somatosensory cortical pyramidal cells between approximately 20 and 35°C in response to the intrasomatic injection of a constant current pulse ( 500 msec , 100 pA; Figure 8D; n = 6 cells ) . At temperatures above approximately 35°C , the firing rate increased markedly to the constant current pulse , such that the slope of the frequency-temperature ( f-T ) relationship increased dramatically ( Figure 8D ) . Interestingly , in layer 2/3 cortical pyramidal neurons spontaneously generating the recurrent network-driven slow oscillation [35] , increases in temperature also resulted in a marked increase in neuronal spiking during Up states ( supplemental figure S3; n = 10 cells ) , as reported previously [36] . Similarly , layer 2/3 cortical pyramidal cells also increased their responsiveness to the intracellular injection of a current pulse ( 150 pA; 500 msec ) with increases in temperature from 23 to 42°C ( Figure 9A–C , E; n = 10 cells ) . Increases in temperature resulted in a small depolarization of layer 2/3 entorhinal cortical pyramidal cells ( −76 . 7+/−3 . 2 mV 23°C; −74 . 9+/−3 . 1 mV 36°C; −68 . 9+/−3 . 7 mV 42°C; p<0 . 01 , t-test between Vm at 23 and 42°C ) ( Figure 9D ) . The increase in responsiveness to a constant current pulse was only partially due to the small depolarization of the resting membrane potential with increases in temperature . Compensation for the change in membrane potential with the intracellular injection of current did not abolish the increase in responsiveness with temperature ( Figure 9E , compare orange and green bars; n = 10 ) . Since the number of action potentials and the discharge rate changed with temperature , we were unable to measure the effects of temperature on single spike or spike train induced afterhyperpolarizations . Previous results have demonstrated that decreases in temperature slow the kinetics of fast , medium , and slow afterhyperpolarizations , although to a differential degree , presumably owing to the properties of intracellular Ca2+ signaling [31] . In both layer 5 and layer 2/3 pyramidal cells , as predicted by the HH model , the action potential duration ( as measured at half amplitude ) decreased exponentially with increases in temperature ( Figures 8B , 9F ) , action potential amplitude decreased with temperature ( Figures 8A , B; 9A–C ) , and the ratio of the minimum dV/dt to maximum dV/dt during the spike increased with temperature ( Figures 8E , 9G ) . These results confirm the validity of this model as a basic representation of the effects of temperature on cortical action potential generation . Increases in temperature from 23 to 42°C also resulted in a significant decrease in apparent input resistance in both layer 5 ( 105+/−15 MOhms 23°C; 88+/−11 MOhms 42°C; p<0 . 01 ) and layer 2/3 ( 246+/−25 MOhms 23°C; 164+/−31 MOhms 42°C; p<0 . 01 ) pyramidal neurons . Increases in temperature from 23 to 37°C also resulted in a small decrease ( −1 . 6+/−1 . 7 mV; n = 10; p<0 . 05 ) in spike threshold for layer 2/3 pyramidal neurons ( −47 . 2+/−3 . 4 mV 23°C; −48 . 8+/−2 . 9 mV at 37°C ) . Simulation of changes in apparent input resistance and resting membrane potential reveal that , as expected , both decreasing input resistance and hyperpolarization decrease firing rate at nearly all temperatures , without changing strongly the shape of the f-T relationship ( supplemental Figure S4A ) or the relationship between temperature and total Na+ entry per current pulse ( supplemental Figure S4B ) . These results suggest that decreases in apparent input resistance with temperature will at least partially offset the depolarization of resting membrane potential ( Figure 9D ) . Simulations also indicate that decreases in apparent input resistance and membrane time constant with increases in temperature will result in only small increases in excess Na+ entry per action potential ( see supplement Figure S5 ) , although these may increase the energetic costs of the network as a whole , owing to the requirement for more action potentials per unit of time in the presynaptic neuronal network in order to reach firing threshold in the postsynaptic cell . One possible confounding factor in our in vitro recordings is that increasing temperature indirectly increased neuronal excitability through decreasing the oxygen content of the bathing solution . To examine this possibility , we measured the oxygen content of the ACSF at the upper interface of layer 2/3 of the entorhinal cortical slice and the bath solution while varying temperature ( Figure 10A ) . We then independently reduced the oxygen content of the ACSF while maintaining a constant temperature ( Figure 10; n = 8 ) . Increasing temperature from 30 to 41°C resulted in a marked decrease in oxygen content of the bathing solution , from an average of 96 . 5 ( +/−3 . 4; n = 5 ) to 15 . 4 ( +/−4 . 5 ) mm Hg ( Figure 10A; n = 5 ) . As observed previously , increasing temperature resulted in an increase in pyramidal cell action potential response rate to a constant current pulse ( Figure 10B , C ) . In contrast to these effects , acutely decreasing ACSF oxygen content from 98 . 5 ( +/−3 . 2 ) down to 6 . 4 ( +/−4 . 3 ) mm Hg did not significantly affect action potential duration ( Figure 9D ) , and resulted in a small , but statistically significant ( p<0 . 01; t-test between lowest and highest mm Hg ) decrease in action potential response rate ( from 35 . 1+/1 . 4 Hz at 98 . 5 mm Hg to 33 . 4+/−0 . 95 Hz at 6 . 4 mm Hg ) to the intracellular injection of a depolarizing current pulse ( Figure 10E , F ) . These results indicate that decreases in ACSF oxygen content do not explain the increase in neuronal excitability associated with increases in temperature , and if anything , result in the under-estimation of the magnitude of this effect .
Increases in temperature have marked and strong effects on action potential generation , and these in turn impact upon the energy efficiency of neuronal activity . Here we demonstrate that increases in temperature result in a large increase in the energy efficiency of single action potential generation owing to the increased rate of Na+ channel inactivation and subsequent decreased spike amplitude , duration and overlap between inward Na+ and outward K+ currents . Thus , increases in temperature naturally result in a marked decrease in excess Na+ entry during spike generation , reducing the need for activation of the Na+/K+ ion pump ( ATPase ) , and thus reducing energy expenditure . This result suggests that the higher body temperature of endotherms such as mammals ( versus ectotherms such as squid ) has the advantage of resulting in a marked increase in energy efficiency of single action potential generation . However , increases in temperature also resulted in an increase in spike discharge rate to a constant amplitude input or during spontaneous network activity , owing in part to decreases in the amplitude and duration of K+ currents initiated by action potentials [31] , [33] . Interestingly , Hodgkin-Huxley style models , and whole cell recordings from cortical pyramidal cells , reveal increases in firing rate to be particularly pronounced at temperatures above approximately 37°C . Thus , even though individual spike efficiency increases with increasing temperature , the enhanced firing rate raises energy requirements . Maximal overall energy efficiency in neuronal responsiveness is observed near 37°C . Energy expenditure in the brain is divided among requirements for action potentials , synaptic potentials , maintenance of resting membrane potential , axonal and dendritic transport , and other metabolic functions [1] , [5] , [6] , [7] , [10] , [11] , [21] , [37] . Estimates of the relative contribution of energy demands related to action potential generation to the overall energy needs of the mammalian brain have varied from approximately 25 to more than 50% [5] , [10] . The estimates of high energy demands related to action potentials have led to the speculation that average firing rates in the brain may be very low ( <0 . 2 Hz ) [6] , a hypothesis that has some experimental support [38] . However , the estimates of unusually high energy demands of action potential generation have been based largely upon the observation by Hodgkin [9] of four times excess Na+ entry during spike generation in the squid giant axon [5] , [13] . This observation and calculation was performed at 18°C , and the results must be corrected to 36–39°C in order to be applied to the mammalian brain . Unfortunately , this correction has not been systematically applied . Our calculations predict that at 37°C , there should be relatively little excess Na+ entry during action potential generation ( excess ratio of around 1 . 3 ) ( Figure 1A ) . Recent observations in cortical pyramidal cells and axons at 37°C confirm the high energy efficiency of action potential generation in these neurons , owing in part to a markedly reduced overlap in inward Na+ and outward K+ currents [12] . Taking these observations into account suggests that the energy load of action potential generation in endothermic animals may be as much as three times lower than previously calculated , allowing for a significantly higher average discharge rate . Increases in temperature increase the kinetics of conformational state changes in all ionic channel types involved in action potential generation , with a Q10 ranging from 1 . 5 to 4 [17] , [18] , [26] , [27] . Particularly important for action potential energy efficiency is the temperature dependent increase in rate of Na+ channel inactivation [13] , [18] , which markedly reduces the duration of action potentials , with a smaller effect on action potential amplitude , as well as decreasing the overlap between inward Na+ and outward K+ currents owing to nearly complete Na+ channel inactivation during the falling phase of the spike at 37°C ( Figures 1–3 ) . Normal mammalian brain temperature varies from approximately 36–39°C , depending upon state of the animal ( e . g . resting , exercise ) and location within the brain , although some mammals ( and some species of birds ) can exhibit brain temperatures as high as 45°C [19] , [20] , [21] , [22] , [23] , [24] . Under stress , such as during infection or environmental conditions that reduce the effectiveness of body cooling mechanisms ( e . g . high humidity and temperature ) , human brain temperature can reach levels in excess of 40°C [21] . Rapid rises in temperature to high levels , especially in children and adolescents , can result in the initiation of a febrile seizure [39] , suggesting that the operational balance of excitation and inhibition is temperature dependent . Even small changes ( e . g . 1–2°C ) in brain temperature can have significant effects on network function . Prior investigations of the effect of increased temperature on neurons reveal consistent changes in action potentials including decreased duration and amplitude , increased rate of rise and fall , and decreased spike afterhyperpolarization , as partially predicted by HH equations for changes in the kinetics of the underlying ionic channels [19] , [21] , [28] , [29] , [30] , [31] , [32] , [33] , [34] . In addition , increases in temperature typically result in a decrease in membrane input resistance , and can , in some cell types , depolarize the membrane potential through the activation of TRPV channels which conduct cations and have a reversal potential well above rest [32] , [40] , [41] , [42] . Interestingly , even without changes in membrane potential , simple HH equations predict that increases in temperature will result in enhanced neuronal responses to a constant current input , owing to increases in discharge frequency . This effect of temperature results largely from strong temperature dependent decreases in the spike afterhyperpolarization owing to decreased amplitude and duration of outward K+ currents activated by action potentials . These decreases in K+ current amplitude and duration may result from decreases in activation owing to the shortened and smaller action potential ( which would allow , for example , less Ca2+ to enter per action potential ) , changes in the kinetics of second messenger events within the neuron following the action potential , or changes in the kinetics of the K+ channels themselves [31] . In our recordings , the firing rate of cortical pyramidal cells to a constant input was particularly enhanced at temperatures above approximately 36°C ( Figure 6A , 8D , 9E ) . Although this increase in firing rate was similar to that predicted by a simple HH model , it occurred at a lower temperature than predicted ( Figure 8D ) , presumably owing to the presence of a wide variety of complex ionic currents , such as Ca2+ activated K+ currents , in real neurons that were not included in our simple simulation [16] , [31] . Different neuronal subtypes in the brain vary in their energy efficiency of action potential generation owing in part to differences in overlap of action potential-related Na+ and K+ currents [15] , [43] . In a subtype of cortical interneuron , the fast spiking cell , as well as cerebellar Purkinje neurons , the generation of short duration action potentials which have a short relative refractory period extends the dynamic range of the neuron , allowing for the generation of actions potentials from low to high ( hundreds of Hz ) frequencies . The short duration of these action potentials is achieved through the presence of rapidly activating K+ currents responsible for spike repolarization [44] . However , this mechanism of short spike generation , as opposed to rapid Na+ channel inactivation , significantly increases the energy demands on the neuron , owing to significant overlap of the inward Na+ and outward K+ currents [14] , [43] . A subset of cortical pyramidal cells also exhibit unusually short duration action potentials , owing to rapid action potential repolarization [45] . Presumably these short duration action potentials are generated at the cost of energy efficiency . Short duration action potentials , however , do not always imply a high energetic cost . The action potentials generated in the axon of cortical pyramidal neurons are shorter in duration than those in the soma , although the axonal spikes exhibit relatively little overlap in Na+ and K+ currents , resulting in high energy efficiency [12] , [13] . Dual somatic/axonal recordings from these neurons demonstrate a marked difference in K+ channel properties between the soma and axon , with Kv1 . 1/1 . 2 channels being prevalent in the axon initial segment [46] , [47] . In addition , somatic and axonal recordings from dentate granule cells reveal that the kinetics of activation and inactivation of voltage-dependent Na+ channels in the axon initial segment are approximately 2X faster than those in the soma [13] . This rapid inactivation kinetics of axonal Na+ channels may result from the presence of 1–3 auxiliary subunits in axonal locations [48] , [49] , [50] . Thus , while temperature has a large effect on energy efficiency of action potential generation , alterations in types and properties of the ionic channels underlying the generation of spikes also contributes significantly to inter- and intra-cellular variations . Indeed , it is well known that animals can alter their neuronal and action potential properties to adapt to varying environmental temperatures [51] , although it is not yet known how these changes affect the energy efficiency of spiking . Sparse neuronal discharge , in which the impact of each action potential is maximized in the task of the network in which it is embedded , is a powerful means to reduce overall energy expenditure [38] . In behaving animals , different types of cortical neurons , varying by cell type and layer , exhibit differing levels of neuronal activity , from very sparse ( e . g . layer 2/3 and 6 pyramidal cells ) , to moderate ( e . g . layer 5 pyramidal cells ) to highly active ( e . g . fast spiking inhibitory interneurons ) [52] , [53] . We presume that the electrophysiological properties of each neuronal subtype ( and even sub-portions of each neuron ) are adjusted to optimize their unique roles in the network , while simultaneously working within the limits of energy availability and capacity to dissipate heat . Our observation that total Na+ load ( spike rate×Na+/spike ) on a neuron reached its minimum at about 37°C suggests that neurons may have optimized their energy use for this temperature range , as has been observed for a number of cellular and organ functions such as enzymatic function and the balance of heat production/loss in an ambient external environment of 25°C [21] . Our results demonstrate that there is no need to hypothesize highly specialized changes in the mechanisms of action potential generation in the evolution from invertebrates to mammals , or from poikliotherms to homeotherms – simply the increase in body temperature , an energy expensive commodity , results in a large and significant decrease in the cost of spike generation and propagation , thus allowing for larger and more complex brains . | Conserving energy is essential to life . The brain , while only 2% of the body mass , uses an astounding 20% of its energy . It has long been assumed that this large energy consumption was due to the need to generate the electrical signals through which brain cells communicate: the action potentials . However , recent results reveal that the wires of the mammalian brain – the axons – are remarkably energy efficient . How is this energy efficiency obtained ? Here we addressed this question by performing recordings and computational models of mammalian brain cells . We found that the increase in body temperature associated with the evolution of warm-blooded animals had an energetic benefit . The action potentials of warm-blooded animals became remarkably energy efficient , owing simply to the increase in body temperature . These results indicate that mammalian brains , although requiring a great deal of energy to operate , are actually more efficient than expected . | [
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] | 2012 | Warm Body Temperature Facilitates Energy Efficient Cortical Action Potentials |
Muscle fiber size is activity-dependent and clinically important in ageing , bed-rest , and cachexia , where muscle weakening leads to disability , prolonged recovery times , and increased costs . Inactivity causes muscle wasting by triggering protein degradation and may simultaneously prevent protein synthesis . During development , muscle tissue grows by several mechanisms , including hypertrophy of existing fibers . As in other tissues , the TOR pathway plays a key role in promoting muscle protein synthesis by inhibition of eIF4EBPs ( eukaryotic Initiation Factor 4E Binding Proteins ) , regulators of the translational initiation . Here , we tested the role of TOR-eIF4EBP in a novel zebrafish muscle inactivity model . Inactivity triggered up-regulation of eIF4EBP3L ( a zebrafish homolog of eIF4EBP3 ) and diminished myosin and actin content , myofibrilogenesis , and fiber growth . The changes were accompanied by preferential reduction of the muscle transcription factor Mef2c , relative to Myod and Vinculin . Polysomal fractionation showed that Mef2c decrease was due to reduced translation of mef2ca mRNA . Loss of Mef2ca function reduced normal muscle growth and diminished the reduction in growth caused by inactivity . We identify eIF4EBP3L as a key regulator of Mef2c translation and protein level following inactivity; blocking eIF4EBP3L function increased Mef2ca translation . Such blockade also prevented the decline in mef2ca translation and level of Mef2c and slow myosin heavy chain proteins caused by inactivity . Conversely , overexpression of active eIF4EBP3L mimicked inactivity by decreasing the proportion of mef2ca mRNA in polysomes , the levels of Mef2c and slow myosin heavy chain , and myofibril content . Inhibiting the TOR pathway without the increase in eIF4EBP3L had a lesser effect on myofibrilogenesis and muscle size . These findings identify eIF4EBP3L as a key TOR-dependent regulator of muscle fiber size in response to activity . We suggest that by selectively inhibiting translational initiation of mef2ca and other mRNAs , eIF4EBP3L reprograms the translational profile of muscle , enabling it to adjust to new environmental conditions .
Control of gene function at the level of mRNA translation is emerging as a major regulator of cell and developmental biology , with medical relevance in cancer and elsewhere [1]–[5] . Several broadly acting signaling pathways appear to control translational modules in cultured cells , whereby thousands of mRNAs are coordinately regulated through specific recognition of motifs in the mRNA that remain to be fully defined [6] , [7] . One major regulator of such translational modules is the TOR ( Target of Rapamycin ) pathway [8] , [9] . In organisms from yeast to man , TOR regulates protein synthesis , cell size , and general metabolism through several distinct mutliprotein complexes [10]–[12] . A major function of these complexes is to target TOR's protein kinase activity to particular substrates , among them the proteins p70 S6 kinase ( p70S6K ) and eukaryotic Initiation Factor 4E binding proteins ( eIF4EBPs ) , which regulate protein synthesis [13] , [14] . But how TOR achieves specific regulation of diverse cell behaviors in so many different cell types is unclear . Here we define a mechanism that sensitizes specific mRNAs to TORC1 activity in skeletal muscle . Muscle has advantages for the study of TOR signaling and its role in cell size control . Muscle fibers are among the few cell types that undergo dramatic and reversible changes in size during the normal life of an organism . Moreover , as postmitotic cells , muscle fibers are unaffected by changes in cell cycle that complicate analysis of cell growth and size in proliferative cells [15] . A major regulator of muscle fiber size is exercise , known experimentally as “activity” [16]–[18] . For example , 14 days of mouse hind limb suspension , which decreases electrical activity and the force it elicits , led to reduction of 25%–55% in muscle mass [19] , [20] . In humans , reduction of activity leads rapidly to muscle wasting with huge societal and health implications in hospitalized patients and the ageing population . Electrical inactivity has been shown to promote protein breakdown through activation of atrogenes , some of which encode components of both the proteasomal and autophagic degradation pathways [21]–[23] . Inactivity also reduces TORC1 signaling in muscle , which has been suggested to reduce protein synthesis [24] , [25] and lead to atrophy . Zebrafish muscle provides a particularly good system in which to study the effects of activity on muscle growth and in neuromuscular disease [26]–[28] . Two types of muscle fibers are formed in each segmented myotome during the first day postfertilization , a superficial layer of mononucleate slow fibers and a larger number of underlying multinucleate fast fibers [29]–[33] . Over ensuing days , both fiber types undergo significant growth , but the slow fibers remain mononucleate . Zebrafish first move at 17 hours postfertilization ( hfp ) and undergo repeated contractions during the embryonic and larval stages [34] . Such contractions can readily be blocked without preventing development [35]–[39] , providing an opportunity to examine the effect of activity on muscle growth in the developmental context , which is hard to achieve in other vertebrate models . Here we show that inactivity prevents translation of a specific set of muscle mRNAs , including that encoding Mef2ca , a transcription factor essential for normal muscle growth . Inactivity acts in two ways , first by promoting accumulation of the inhibitor of protein synthesis initiation eIF4EBP3L and second by reducing TORC1 signaling and thus permitting eIF4EBP activity . Active eIF4EBP3L blocks translational initiation of Mef2ca , preventing normal myofibrilogenesis and muscle growth .
To test the role of activity in zebrafish muscle growth , we examined the reduction in myofiber size in two inactive conditions: the immotile mutant chrndsb13/sb13 , which lacks the acetylcholine receptor δ subunit ( Figure S1 ) , and after exposure to tricaine mesylate ( MS222 ) , a zebrafish anesthetic drug . Both treatments block electrical activity and therefore contraction in muscle . Three parameters were assayed , the width of myofibrillar bundles within slow myofibers , myosin heavy chain ( MyHC ) immunofluorescence detected with F59 antibody , and the content of various myofibrillar proteins by Western analysis . In the absence of activity , myofibrillar bundle width diminished by 30% in chrndsb13/sb13 mutants and 22% after MS222 treatment , compared to their respective controls ( Figure 1A , B ) . Similarly , there were decreases in slow MyHC immunofluorescence of 61% and 36% and reductions in slow MyHC by Western of 12% and 52% , respectively ( Figure 1A , C , D , F ) . To investigate whether the decrease in MyHC immunoreactivity was unique to slow fibers or whether it also occurred in fast fibers , we assessed total somite MyHC . MyHC immunoreactivity was decreased by 44% in chrndsb13/sb13 relative to its siblings ( Figure 1E ) . The fast muscle protein myosin light chain , recognized by F310 , was reduced by 42% , as were general muscle proteins such as Actin ( 40% ) and MyBPC ( 35% ) ( Figures 1D , F and 2A ) . Thus , in the zebrafish , as in other models , inactivity reduces myofibril content , permitting the use of zebrafish to investigate in vivo the mechanisms that regulate muscle size following disuse . We have previously shown that lack of Mef2 activity leads to poor myofibril assembly and that lack of Myod reduces early muscle growth [40] , [41] . We therefore tested whether inactivity reduced production of these proteins in parallel with the reduction in muscle growth . MS222 treatment for just 17 h led to a dramatic loss of Mef2 protein without any appreciable change in Myod relative to other control muscle proteins such as Actin and Vinculin ( Figure 2A , B ) . Several Mef2 genes are expressed in differentiated zebrafish muscle , particularly mef2ca , mef2cb , mef2d and mef2aa [42]–[44] . Of these , mef2ca and mef2d are the most abundant at 48 hpf ( Figure S2 ) . Mef2C has pleiotropic anabolic effects on murine myogenesis [45] , [46] . Consistent with this observation , we found that Mef2c protein was reduced by MS222 treatment and in chrndsb13/sb13 mutants ( Figure 2C ) . Moreover , whole-mount immunofluorescence confirmed a decrease of Mef2c in myotome nuclei following MS222 treatment ( Figure 2D ) . To test whether a decrease in Mef2c contributes to the reduction in myofiber width , mutant and sibling embryos from a mef2catn213/+ incross were compared . A 20% decrease in myofibril bundle width and in somite volume was observed in 48 hpf ( Figure 2E , G ) and 5 dpf mutants ( Figure 2F , H ) . These findings show that Mef2ca activity is required for normal muscle growth . We next asked whether Mef2c mediates the effect of muscle activity on myofiber width . MS222 causes an even greater reduction of myofibril bundle width than a complete loss of Mef2ca , indicating that muscle activity does more than promote Mef2ca activity ( Figure 2E , G ) . Nevertheless , in the absence of activity , loss of Mef2ca has no effect on myofibril bundle width ( Figure 2E , G ) , suggesting that the Mef2ca pathway is inactive when muscle itself is inactive . These results argue that Mef2ca contributes to muscle activity-induced growth , but is not the sole mechanism . The preferential reduction in Mef2c protein in inactive muscle could stem from either increased proteolysis or reduced synthesis . In adult muscle , inactivity potently activates both the proteasomal and autophagy-lysosomal proteolysis pathways [47] , [48] . To test the role of proteasomal degradation in Mef2 regulation , muscle activity was abolished in the presence of MG132 , an inhibitor of the proteasome . MG132 treatment of fish embryos up-regulated known targets of the proteasome , p53 and Sqstm1 [49] , [50] , providing positive controls for MG132 efficacy ( Figures 3A and S3A ) . Inactivity led to decrease in Mef2 levels even in the presence of MG132 ( Figure 3A , B ) , indicating that Mef2c decline is not due to proteasomal degradation . Congruently , rather than increasing Mef2c , MG132 alone decreased Mef2c , suggesting that proteasomal degradation is not a significant Mef2c turnover pathway in embryonic muscle ( Figure 3A , B ) . Autophagy was then assessed by the level of Sqstm1 , which is a target to autophagic degradation and marker of autophagic flux [51] . The TORC1 inhibitor rapamycin , a known and potent inducer of autophagy , reduced zebrafish full-length 50 kd Sqstm1 , serving as a positive control by showing that autophagy reduces Sqstm1 in zebrafish ( Figure 3A , B ) . The level of zebrafish Sqstm1 was not decreased by muscle inactivity ( Figure 3A , B ) . As a further control , we found no increase in sqstm1 mRNA that could have compensated for a loss of Sqstm1 protein ( [52]; Figure S3B ) . Therefore , muscle inactivity did not induce autophagy in embryonic zebrafish . Moreover , inhibition of autophagy with the lysosomal inhibitor chloroquine did not block the reduction in Mef2c protein caused by inactivity ( Figure S3C ) . Furthermore , Mef2c level appeared unchanged under rapamycin-induced autophagy , indicating that Mef2c is not a target for autophagic degradation ( Figure 3A , B ) . Thus , the reduction of Mef2c in inactive muscle was apparently not due to enhanced degradation by the ubiquitin-proteasome or autophagy pathways , which suggested that its synthesis is regulated . To determine whether the reduction in Mef2 was due to a reduction in mRNA synthesis , we used qPCR . Upon MS222 exposure , no decrease in mRNA level for any of the four Mef2 genes was detected ( Figure 3C ) . Thus , reduced mRNA levels does not account for loss of Mef2 in inactive muscle . The lack of demonstrable change in Mef2 mRNAs or Mef2c proteolysis in inactive embryonic muscle prompted us to examine changes in protein synthesis , particularly because muscle activity is known to promote protein synthesis [53] , [54] . We therefore developed a method to quantify RNA associated with polysomes in fish embryos . Polysomes are complexes that contain actively translating mRNA associated with two or more ribosomes and represent the densest RNA-containing fraction of cytoplasm . Embryo cytoplasmic lysate was subjected to sucrose density gradient separation and the location of the zebrafish polysome-containing fraction determined ( Figures 3D , E and S4 ) . As controls , polysomes were diminished by addition of EDTA to the extract , which dissociates polysomes , and enhanced by addition of cycloheximide , which stabilizes polysomes by inhibiting translational elongation , thereby stalling ribosomes on the mRNA ( Figure S4 ) . The effect of activity on polysomes was then determined . Following 17 h MS222 exposure , the amount of nucleic acid in the polysome fraction was reduced compared to that in the 80S monosome and 60S and 40S ribosomal subunit peaks ( Figure 3E ) . Polysome-associated RNA decreased by ∼10% in MS222 compared to active controls . This finding shows that muscle inactivity reduced protein synthesis in zebrafish , providing a potential explanation for the reduction in Mef2c in inactive muscle . To determine whether specific mRNAs such as those encoding Mef2 are subject to translational regulation , the proportion of mef2ca mRNA in polysomes was assessed . mRNA from the polysome ( heavy fraction; P ) was purified and the change in mRNAs following MS222 treatment determined by qPCR ( Figure 3F ) . Translation of mef2ca reduced by 55% in the absence of activity , whereas translation of various controls , such as dmd ( dystrophin ) and actn3b ( α-actinin 3b ) , differed significantly , showing no reduction ( Figure 3F ) . We did not detect significant reduction in translation of other mRNAs tested by qPCR . As expected , relative reduction of mRNAs in the polysomal fraction was complemented by a relative increase in the subpolysomal fraction ( Table S2 ) . These findings show that there is preferential loss of specific mRNAs from polysomes in inactive muscle , and that Mef2ca protein translation is specifically promoted by muscle activity . TORC1 was previously shown to differentially regulate translation [1] , [2] , [55] , [56] , and is a major player in activity-related muscle growth [18] , [19] . We therefore asked whether muscle activity affects TORC1 activity in the embryonic zebrafish . TORC1 pathway activity was assessed by examining the phosphorylation of eIF4EBP and ribosomal protein S6 , known downstream targets of the TORC1 complex involved in translational regulation [57] . When zebrafish embryos were exposed to MS222 , Western analysis revealed that phosphorylation of S6S240/244 and eIF4EBPT37/46 was decreased by 40% and 30% , respectively , compared to untreated controls ( Figure 4A ) . As these proteins are widely expressed , immunostaining of phopho-S6 ( pS6 ) and total S6 was used to assess changes occurring in muscle tissue . MS222 caused a decrease in muscle pS6 compared to controls ( Figure 4B ) . The levels of S6 itself also appeared somewhat down-regulated , although no loss of ribosomal 18S or 28S rRNA was detected in inactive embryos ( Figure S5 ) . Thus , muscle activity activates the TORC1 pathway , making it a good candidate as a regulator of the translational response to activity . TORC1 is inhibited by rapamycin , which is known to reduce muscle growth [18] , [19] . Rapamycin treatment of zebrafish embryos reduced muscle growth , but to a lesser extent than inactivity ( Figure S6A–C ) . Strikingly , although rapamycin reduced muscle growth , it was much less effective than inactivity at reducing Mef2c ( Figure 3A , B; see also [58] ) or Myosin and Actin protein accumulation ( Figure S6D ) . Congruently , rapamycin was more effective at reducing S6 phosphorylation than eIF4EBP phosphorylation ( Figure S7 ) , whereas inactivity appeared equally effective on each TORC1 target ( Figure 4A ) . Thus , the greater effectiveness of inactivity compared to rapamycin on reducing muscle growth correlates with the ability of inactivity to down-regulate Mef2c translation . TORC1 can affect both global translation by promoting rRNA synthesis via S6K [59] , [60] , and differential translation by blocking eIF4EBPs [1] , [2] . eIF4EBPs prevent translational initiation by binding eIF4E , inhibiting its interaction with eIF4G , and thereby preventing recruitment of the 40S ribosomal subunit to the mRNA [61] , [62] . To determine which TOR-dependent translational regulation operates in inactive muscle , we first looked at rRNA . We tested the levels of rS28 and rS18; after 16 h MS222 they were not reduced ( Figure S5 ) . On the other hand , reduction in protein level following inactivity occurred rapidly and varied from protein to protein ( Figures 1 , 2 , and 3 ) , which implied that TORC1-dependent differential translational regulation occurs , potentially via eIF4EBP . Active TORC1 phosphorylates and inhibits eIF4EBP , thereby promoting translational initiation of specific mRNAs [1] , [2] , [63] , [64] . The vertebrate eIF4EBP protein family consists of three members—eIF4EBP1 , eIF4EBP2 , and eIF4EBP3—that share >55% amino acid identity ( Figure S8A ) . Each harbors a conserved eIF4E binding site , YDRKFLL , two canonical TOR phosphorylation sites , TPGGT , and several transregulatory phosphorylation sites ( Figure S8C ) . Zebrafish have four eIF4EBPs , with an eIF4EBP3 duplicate named eIF4EBP3-like ( eIF4EBP3L; Figure S8B ) . eIF4EBP3L has greater homology ( 78% identity ) to human eIF4EBP3 than between any other zebrafish∶human eIF4EBP pair ( Figure S8B ) . Two eIF4EBP3s are found in many teleosts ( www . ensembl . org ) , suggesting they arose from the early teleost genome duplication . The zebrafish eIF4EBP genes have distinct tissue expression: eif4ebp1 and eif4ebp2 widely and highly in head and neural tissue , eif4ebp3 only abundant in pancreas , and eif4ebp3l in the somite , eye , and branchial arch region [65] . Thus , eif4ebp3l appeared to have a particular role in muscle tissue . The effect of muscle activity on eIF4EBP gene expression was assessed . Upon exposure to MS222 , the level of eif4ebp3l mRNA increased 2-fold , particularly in muscle , but did not otherwise change tissue mRNA distribution . MS222 also induced eif4ebp1 mRNA 1 . 9-fold ( Figures 4C and S8D ) . To rule out effects of MS222 on neural activity and to examine the effect of activity specifically in muscle tissue , we assessed mRNA levels in the chrndsb13 mutant , in which muscle alone is inactive , and found a 2 . 5-fold increase in eif4ebp3l mRNA but without change in eif4ebp1 mRNA ( Figure 4D ) . These data indicate a specific role for eif4ebp3l , and not eif4ebp1 , in the response to muscle inactivity . Separately , we analyzed the effect of inactivity on so-called atrogenes , genes known to be induced in muscle atrophy caused by malnutrition , denervation , sepsis , or ageing in adult mammalian muscle [66] , [67] . We found , as in the case of eif4ebp3l , a 2-fold increase in atrogin-1 , murf2 , and trim32 mRNAs induced by MS222 ( Figure 4E ) and similar induction in chrndsb13 mutants ( Figure S9 ) . These results show that inactivity up-regulates a suite of mRNAs regulating protein turnover in muscle , including eif4ebp3l . When translation of eif4ebp3l mRNA was analyzed by polysomal fractionation , a significantly increased fraction was in polysomes ( Figure 3E ) . These increases in both eif4ebp3l mRNA level and its translation rate suggest that eIF4EBP3L protein will increase in inactive muscle . Lacking an eIF4EBP3L-specific antibody , we could not show such up-regulation directly , but total eIF4EBP protein did appear higher in inactive embryos ( Figure 4A ) . We hypothesized that increased eIF4EBP3L might cause certain mRNAs to become more sensitive to TORC1-regulation in inactive muscle . Consistent with this idea , more significant reduction in myofibrilogenesis was caused by MS222 than by inhibiting TORC1 with rapamycin alone ( Figure S6 ) . Thus , muscle inactivity both increased eIF4EBP level and reduced eIF4EBP phosphorylation , raising the possibility that eIF4EBP3L drives the differential translational repression in inactive muscle . As inactivity increased active ( unphosphorylated ) eIF4EBP3L and reduced translation of mef2ca mRNA and myogenesis , we tested whether Mef2ca protein is down-regulated when eIF4EBP3L is active . Embryos were injected with mRNA encoding eIF4EBP3L to achieve a 2-fold increase , comparable to that induced by inactivity ( Figure S10A ) . First , the intensity of Mef2c immunofluorescence was measured in confocal stacks of skeletal muscle nuclei ( Figure 5A ) . Overexpression of eIF4EBP3L in active control muscle did not significantly affect Mef2c levels , in contrast to the reduction observed with MS222 ( Figure 5A , B ) . However , overexpression of eIF4EBP3L in inactive MS222-treated embryos , a condition in which eIF4EBP is hypophosphorylated ( Figure 4A ) , significantly decreased Mef2ca protein levels by a further 20% below that caused by MS222 alone ( Figure 5B ) . We conclude that eIF4EBP3L can inhibit Mef2c accumulation in muscle , but that muscle activity normally suppresses eIF4EBP3L activity . To determine whether inhibition of TORC1 activity can mimic the effect of MS222 on eIF4EBP3L , rapamycin was applied to embryos injected with eif4ebp3l mRNA and controls . Rapamycin suppressed TORC1 activity in zebrafish embryos , reducing both phosphoS6 and phospho4EBP ( Figure S7 ) . As in Figure 3A , rapamycin had no effect on Mef2c level in mock-injected control active muscle ( Figure 5A , C ) , presumably because the low endogenous eIF4EBP3L level was insufficient to cause a significant reduction in initiation of Mef2ca translation . However , when eIF4BP3L was overexpressed , rapamycin caused a 40% reduction in Mef2c level ( Figure 5C ) , comparable to that triggered by MS222 ( Figure 5B ) . Thus , TORC1 activity suppresses eIF4EBP3L function in active skeletal muscle . To test the role of endogenous eIF4EBP3L , we knocked down eIF4EBP3L by injecting 1–2 cell stage embryos with morpholino antisense oligonucleotides ( MO ) targeting the exon1-intron1 splicing site ( BP3LMO1 ) and against the start codon ( BP3LMO2 ) of eif4ebp3l . In the absence of specific antibody against eIF4EBP3L , we verified the efficacy of BP3LMO1 by qPCR and found a decrease of 80% in eif4ebp3l mRNA level , whereas mef2ca mRNA was not affected , relative to actin ( Figure 5D ) . In embryos injected with control MO , inactivity led to a 40% decrease in Mef2c protein . This decrease was prevented when eIF4EBP3L was knocked down ( Figure 5E ) . Thus , eIF4EBP3L is necessary for the reduction in Mef2c in inactive muscle . The involvement of eIF4EBP3L in Mef2c reduction caused by inactivity is most simply explained by altered translational initiation . To test this hypothesis , embryos injected with eif4ebp3l RNA were subjected to polysomal fractionation . Overexpression of eIF4EBP3L led to a 40% decrease in mef2ca mRNA in the polysome fraction , whereas dystrophin mRNA , a negative control that was unaffected by activity ( Figure 3F ) , showed no change ( Figure 5F ) . Moreover , addition of rapamycin to inhibit TORC1 enhanced the effect of eIF4EBP3L overexpression ( Figure 5F ) , and decreased Mef2c protein ( Figure 5C ) . Whereas either eif4ebp3l RNA injection or 6 h rapamycin treatment alone reduced mef2ca mRNA in polysomes , neither was sufficient to lower Mef2c protein ( Figure 5A , C , F ) . This finding suggested that , in order for the translation inhibition to lead to reduction of Mef2c protein level , such inhibition must exceed a threshold , as occurs when both eIF4EBP3L is up-regulated and TORC1 is inhibited . To confirm that endogenous eIF4EBP3L specifically regulates mef2ca translation , we reduced eIF4EBP3L level with morpholino and performed polysome fractionation on active and inactive muscle . In the presence of control MO , muscle inactivity reduced mef2ca translation by 37% ( p = 0 . 0035 , while having no effect on dmd mRNA; Figure S11 ) . In contrast , in the presence of eIF4EBP3L MO , muscle inactivity had no significant effect on mef2ca translation ( Figure S11 ) . In addition , knockdown of eIF4EBP3L in both active and inactive muscle led to an increase in mef2ca mRNA in polysomes , but did not affect dmd mRNA ( Figure S11 ) . Nevertheless , Mef2ca protein did not accumulate upon eIF4EBP3L knockdown ( Figure 5E ) . These data suggest that eIF4EBP3L is required for the reduction in mef2ca translation in inactive muscle and retains the ability to target specific mRNAs in active muscle . As Mef2ca regulates myofibrilogenesis and fiber growth ( Figure 2E , F ) , we asked whether eIF4EBP3L also does so . In active control muscle , eif4ebp3l morphant myofibrilogenesis appeared similar to that in controls ( Figure 6A ) . Inactivity caused a 75% decrease in slow MyHC signal in embryos injected with control MO . However , this decline was prevented when eIF4EBP3L was knocked down with either BP3L MO ( Figure 6A ) . We conclude that eIF4EBP3L prevents normal myogenesis under inactive conditions . Inactive muscle has both increased eif4ebp3l mRNA and decreased phosphorylation of TORC1 targets , both of which cooperate to specifically suppress translation ( Figures 4 and 5 ) . To test whether eIF4EBP3L can influence myofibrilogenesis independent of TORC1 activity , we generated constitutively active eIF4EBP3L by mutating the five potential threonine/serine phosphorylation sites to alanine , thereby generating 5A3L ( Figure S8C ) . However , injecting 5A3L RNA led to early embryonic death , possibly due to widespread translational repression . To overcome the early lethality , we cloned 5A3L into a zebrafish heat-shock–inducible expression vector also containing a GFP marker to achieve controlled mosaic expression in muscle . Slow myofibrilogenesis was assayed at 54 hpf by comparing GFP-expressing fibers to unmarked neighboring fibers within the same somite . Overexpression of 5A3L led to a 36% decrease in myofibril bundle width ( Figure 6B ) , whereas controls expressing GFP alone showed no significant change . Moreover , a variety of other cell types in epidermis , notochord , and neural tube showed no detectable morphological change upon 5A3L overexpression ( Figure S10B ) . Thus , active eIF4EBP3L reduces slow myofibrilogenesis .
By applying polysomal mRNA fractionation to whole zebrafish embryos , we have developed a method to analyze changes in their translational control . We find that muscle activity promotes translation of a specific mRNA that is normally expressed in muscle and encodes an important muscle regulator , namely Mef2ca ( Figure 7 ) . The translational changes we highlight occur in relation both to total RNA content of the embryo and to other muscle-specific mRNAs , such as Dystrophin and α-Actinin-3b . The reduced translation triggered by inactivity leads to rapid loss of Mef2 protein . Muscle electrical activity controls many aspects of adult muscle character , including contractile and metabolic properties and fiber size . In developing amniote primary muscle , however , the major effect of reduced activity is reduced growth rate [68] , [69] ( reviewed in [70] ) . We show here that growth is also reduced in electrically inactive zebrafish muscle , consistent with previous comments [39] . Without activity , initial differentiation of slow and fast muscle fibers appears normal , but accumulation of myofibrillar protein is diminished . In the mononucleate slow fibers , which form in normal numbers , myosin fails to assemble into myofibrils at the normal rate , leading to a reduced width of myofibril bundles . The pathways that mediate the effects of activity on muscle growth are many and fall into two broad categories , catabolic pathways triggered by inactivity and anabolic pathways promoted by activity . The relative contribution of proteolysis or protein synthesis changes to muscle mass decrease in the inactive condition is a controversial topic . Although catabolism is a major driver , altered protein synthesis may also contribute [54] , [71] , [72] . Our data reveal eIF4EBP3L as a new regulator of muscle growth that is up-regulated in inactive muscle and inhibits translational initiation through targeting a specific subset of mRNAs . A high-throughput approach will be needed to define the full set of mRNAs showing altered translation in inactive muscle . We also found that muscle inactivity causes a 2-fold increase in E3 ligase atrogenes , known targets of FoxO , that trigger proteasomal degradation of muscle constituents [73] , [74] . Thus , our data suggest that both decreased protein synthesis and increased catabolism are triggered in response to inactivity in developing muscle . How does inactivity up-regulate eif4ebp3l expression ? In other systems , eIF4EBP mRNAs are up-regulated by the FoxO pathway [75]–[79] . Considering the increase in transcription of both eif4ebp3l and known atrogenes in inactive muscle , we hypothesize that FoxOs are activity-dependent regulators of transcription of both eif4ebp3l and the E3 ligases in growing zebrafish muscle . We show that loss of eIF4EBP3L function prevents atrophic changes caused by inactivity during growth . If a human eIF4EBP functions similarly in adults , it could constitute a novel therapeutic target for prevention of acute muscle wasting . Changes in other eIF4EBPs , such as eIF4EBP1 , have been observed in muscle atrophy caused by fasting , and have been suggested to regulate general translation rate [80] . Interestingly , we observed significant increase in eif4ebp1 mRNA in our zebrafish inactivity model only when whole body electrical activity , not just muscle activity , was blocked . As eif4ebp1 is highly expressed in the central nervous system , these data suggest that eIF4EBP1 may respond to activity by mediating growth-related changes , such as synaptic elaboration , during neural development [81] , [82] . eIF4EBPs block translation by binding to eIF4E and preventing eIF4G attachment to the 5′ cap-dependent mRNA initiation complex . TORC1 pathway activity phosphorylates eIF4EBP , inhibiting its interaction with eIF4E and thereby promoting translational initiation . The TORC1-4EBP pathway was previously thought of as a general regulator of 5′ cap-dependent translation [61] , [63] , [83] . However , recent large-scale screens revealed differential mRNA translation by TORC1-4EBP in cultured cells . Specific sequences within an mRNA , known as TOP-like or Pyrimidine-Rich Translational Elements ( PRTEs ) , correlate with sensitivity to translational regulation [1] , [2] . We find similar differential control by TORC1-4EBP3L in zebrafish muscle in vivo . The mef2ca mRNA that is translationally repressed by eIF4EBP3L has a PRTE in the 5′ UTR , as does mef2d ( Table S3 ) . However , as eif4ebp3l itself also has a PRTE , defining an mRNA sequence consensus for eIF4EBP3L translational repression in muscle will require further analyses . Muscle inactivity reduced phosphorylation of eIF4EBP and ribosomal protein S6 , suggesting that loss of TORC1 pathway activity triggered the translational and growth reduction . However , in zebrafish , inactivity reduces Mef2c levels and muscle growth more effectively than the TORC1 inhibitor rapamycin , as previously observed in mammals [19] . We found that up-regulation of eIF4EBP3L in inactive muscle helps explain why inactivity is more effective than rapamcyin alone . In active muscle , eIF4EBP3L levels are low and the activity of eIF4EBP3L is further suppressed by phosphorylation by TORC1 . In contrast , when eIF4EBP3L is induced in inactive muscle , translation becomes more sensitive to TORC1 activity . In this situation , the inactivity also lowers eIF4EBP3L phosphorylation , leading to a high level of active eIF4EBP3L , which specifically inhibits translation of certain mRNA targets , such as mef2ca . We confirm this hypothesis by showing that when eIF4EBP3L is overexpressed , Mef2c level becomes more sensitive to rapamycin . Thus , the level of eIF4EBP3L acts as a gatekeeper , controlling the sensitivity of muscle to TORC1 activity . In contrast to eIF4EBP1 and eIF4EBP2 , the function of eIF4EBP3 has been unclear [84] , [85] . We show that eIF4EBP3L blocks translational initiation of specific mRNAs and simultaneously sensitizes the inactive muscle to TORC1 . One advantage of such control is that , when activity returns , protein synthesis can rapidly resume as soon as TORC1 activity increases and phosphorylates eIF4EBP3L . Thus , if the pathway we have revealed in developing muscle were to also operate in inactive adult muscle , eIF4EBP3L might constitute a fast switch for recovery of atrophic muscle . Other eIF4EBPs also appear to function in the reaction of muscle to distinct stresses . Stress induces Drosophila eif4ebp as a metabolic brake [86] , [87] . Likewise , either eIF4EBP1−/− or eIF4EBP1−/−;eIF4EBP2−/− mice show defects in fat and muscle metabolism under stress conditions , but not under normal conditions [86] , [88] , [89] . Our data suggest that eIF4EBP3L acts as a metabolic brake preventing anabolism in inactive muscle , and also regulates cell growth by specific translational control in response to TORC1 . The presence of high levels eIF4EBP3L sensitize muscle to TORC1 , in agreement with the recent finding that eIF4E/eIF4EBP ratio is a key determinant of TORC1 action [90] . Activity , nutritional status , and other factors influencing TORC1 activity would be expected to have stronger effects when eIF4EBPs are up-regulated . Strikingly , many manipulations that trigger either muscle growth or atrophy alter expression of an eIF4EBP [78] , [80] , [91] , [92] . In the brain , eIF4EBP2 appears to act downstream of TORC1 signaling to control the translation of specific mRNAs involved in synaptogenesis and linked to autism [5] . As there are at least three eIF4EBP genes in all vertebrates examined , it is possible that each becomes a gatekeeper of TORC1 in response to distinct stresses , and could then select which particular mRNAs become TORC1 targets . The specific regulation of mef2ca mRNA translation begs the question of the role of Mef2 in growing muscle . Myofibril assembly is critically dependent upon Mef2 activity , which seems to be particularly important for thick filament biogenesis [41] , [46] . We show that Mef2ca by itself is essential for normal fiber growth . Mef2ca mRNA first accumulates as muscle undergoes terminal differentiation [41] , and we show that myosin is reduced and myofibrilogenesis is inefficient in mef2ca mutants . The additional presence of Mef2d , Mef2cb , and Mef2aa in skeletal muscle may contribute to overall Mef2 activity , permitting lower but significant rates of myofibrilogenesis in mef2ca mutants [44] . Up-regulation of Mef2 level by electrical activity will contribute positively to myofibrilogenesis . Loss of Mef2ca alone has less effect upon myofibrilogenesis than loss of activity . It is likely that other translational and/or transcriptional targets of activity , possibly including Mef2d , contribute to this difference . The Mef2 protein is itself a transcription factor activated by muscle electrical activity via calcineurin and inhibited in cancer-induced muscle wasting [93] , [94] . Our discovery of a TORC1-4EBP-Mef2 pathway that regulates muscle mass reveals an additional level of activity-dependent regulation of Mef2 . The TORC1-4EBP-Mef2 pathway might be involved in other muscle-wasting conditions that affect TOR activity , such as fasting , ageing , and cachexia . By showing that Mef2c regulates muscle fiber growth regardless of any earlier role in myoblast terminal differentiation , our data support the view of Mef2 as a muscle homeostatic regulator [95] , [96] . It will be interesting to determine whether translational regulation of human MEF2C is also important in the nervous system , where MEF2C is associated with autism and synaptic regulation [97] . In the heart , Mef2c is required for cardiomyocyte differentiation and expression persists in differentiated cardiomyocytes [44] , [98] . Our finding of activity-dependent accumulation of Mef2 in skeletal muscle suggests a mechanism that could regulate heart muscle cell size and/or function . At later developmental stages , Mef2a becomes a major Mef2 required for normal heart development and prevention of heart attack in mouse [99] and zebrafish [42] , [44] , [100] . The precise role of Mef2a and other Mef2s in the adult heart and skeletal muscle is unclear , but our data suggest a role in controlling the balance between anabolism and catabolism . Strikingly , both murine Mef2c and zebrafish mef2ca mRNAs accumulate at muscle fiber ends [41] , [101] . We speculate that this mRNA localization may facilitate their translational regulation by activity-dependent signals , including , but not necessarily restricted to , TORC1-4EBP . Such regulation would provide a novel paradigm in regulation of muscle gene expression , whereby changing translation sends a signal back to the nucleus to regulate transcription . In this scenario , the gatekeeper function of eIF4EBP3L could control the sensitivity of localized mRNAs to local signals generated at the muscle fiber end . For example , mechanical force , transmitted from muscle to its attachments at the fiber end , could trigger signals mediating the effects of activity on translation . Such regulation via TORC1 may contribute to the potent hypertrophic effect of high-force stimuli on muscle [102] , [103] . Regulated translation of transcription factors could mediate mechanical or other signals elsewhere in biology . There is great societal importance to determining the mechanisms by which physical activity enhances well-being both in the elderly and throughout life . Our findings show that one effect of activity is to control translation of specific muscle proteins that themselves influence muscle growth . As a major metabolic tissue constituting almost half our body mass , skeletal muscle and its energy balance are increasingly understood to be a significant endocrine regulator of whole body physiology . It will be important to determine whether and at what life stages the pathway we have revealed regulates the response of human muscle to activity .
All work described was performed under licenses conforming to UK Animals ( Scientific Procedures ) Act 1986 . D . rerio wild-type and mef2catn213 [104] , chrndsb13 [39] mutants were maintained on Tübingen background and reared according to [105] . 0 . 016% MS222 ( 3-amino benzoic acid ethyl ester , 0 . 64 mM ) was added to embryo medium for 17–24 h . Embryos were injected at 1–2 cell stage with 1–2 ng of MOs against eif4ebp3l; exon1-intron1 junction ( BP3L MO1 ) and start codon ( BP3L MO2 ) or with standard control MO ( Gene Tools ) or with 100 pg eif4ebp3l RNA made using Ambion Megascript kit from CloneJET plasmid containing full-length zebrafish eif4ebp3l . The plasmids pCS2-ZF-HSP70-5A3L-IRES-GFP or empty vector were injected at 1 cell stage . Heat shock of 39°C for 2 h was applied at 30 hpf and 48 hpf , and myofiber width was measured at 54 hpf . Cell Trace BODIPY ( #C34556 Invitrogen ) was added during the second heat shock , washed , and imaged live 4 h later . Dechorionated 2 dpf embryos were treated for 6–8 h with 1–5 µM rapamycin or 100 nM MG132 ( Cayman #13697 ) . eIf4ebp3l cDNA was PCR cloned into CloneJET pJET1 . 2 ( Thermo Scientific ) using primers listed in Table S1 . The 5A3L mutant was generated by substituting T33 , T42 , T46 , S61 , T66 to alanine using QuikChange II Site-Directed Mutagenesis Kit #200523 ( Agilent Technologies ) with primers listed in Table S1 . The 5A3L mutant eIf4ebp3l was cloned into XbaI/SalI digested hsp70-4-MCS-IRES-mGFP6 , kindly donated by S . Gerety and D . Wilkinson [41] . Immunodetection in whole embryos was as previously described [41] . Confocal stacks were collected on a Zeiss LSMExciter confocal microscope and signal intensity was calculated using Volocity software . All embryos are shown in lateral view , anterior to left , dorsal to top . Myofibrillar bundle width was measured on slow MyHC in 10 fibers in somite 17 by assessing transverse dorsoventral width of three well-bundled regions in anterior , middle , and posterior of each fiber from maximal intensity projections . To ensure fair fiber size comparison in mosaic analyses , the myofibrils of GFP+ fibers were compared with the four immediately surrounding GFP− slow fibers . For Westerns , protein extracts were made from 20–50 embryos using SDS loading buffer . Samples were incubated for 5 min at 100°C , sonicated , and stored at −20°C . Proteins from ∼5 embryo-equivalents were separated by PAGE , blotted to PVDF membrane which was blocked with 5% low fat milk for 1 h RT , incubated overnight with primary antibody and for 1 h RT with secondary antibody , developed with ECL Super Signal® #34080 ( Thermo Scientific ) , and assessed on ImageJ software . Primary antibodies used were: pS6 #5364 , S6 #2317 , p4EBPT37 #2855 , 4EBPT46 #4923 ( Cell Signalling ) , Vinculin #V9131 , Actin #A2066 ( Sigma ) , Mef2c #55913 ( Anaspec ) , Mef2 C-21 #SC313 , MyoD C-20 #SC302 ( Santa Cruz ) , SQSTM1 #ab56416 , Tropomyosin #ab7786 ( Abcam ) , general MyHC ( A4 . 1025 ) [106] , slow MyHC ( F59 ) [31] , MyLC #F310 ( DSHB ) , and Myosin binding protein C ( MyBPC , a kind gift of E . Ehler ) . In situ RNA hybridization was performed as previously described [41] . Probes for eif4ebp1 , eif4ebp2 , eif4ebp3 , and ei4ebp3l were prepared by T7/T3 RNA polymerase with primers listed in Table S1 . Other probes were as described: mef2ca , mef2d [42] , mef2aa , mef2cb [44] , mef2ab ( NCBI AL918279 ) , and mef2b ( Exelixis 3573227 , NCBI JX292158 ) . qPCR analysis used SYBR green MESA Blue ( RT-SY2X-03 WOUB , Eurogentec ) with specific primers listed in Table S1 and were performed in triplicate to ensure reproducibility . cDNA was generated from 20 embryos using Invitrogen SuperScript III . We adjusted the protocol of [107] to zebrafish . Briefly , dechorionated embryos were incubated with 400 µg/ml cycloheximide for 10 min , snap frozen in liquid nitrogen , pulverized by pestle and mortar , and stored at −80°C . Powder was resuspended in 500 µl lysis buffer/70 embryos and the sample pipetted 10 times . Nuclei were removed by centrifugation ( 12 , 000× g , 10 s , 4°C ) . Supernatant was supplemented with 250 µl of extraction buffer and centrifuged ( 12 , 000× g , 5 min , 4°C ) to remove mitochondria and membranous debris . 75 µl ( 1/10th ) of the supernatant was stored at −80°C for total RNA . Remaining supernatant was layered onto a 10 ml linear 10%–50% sucrose gradient and ultracentrifuged for 2 h in Beckman SW40Ti at 38 , 000 rpm at 4°C , brake off . Samples were collected passing a UV 254 nm detector ( Gilson UV/VIS-155 ) to detect the polysome profile and fractionated , generally into three ∼4 ml fractions . Samples were incubated with 200 µl/ml proteinase K , 1% SDS , 10 mM EDTA pH 8 , for 30 min at 37°C . RNA was recovered by 1∶1 phenol∶chloroform extraction followed by ethanol precipitation with glycogen . RNA pellet was washed overnight with 2 M LiCl , and resuspended in 500 µl RNAse free water , treated with DNAse I for 30 min , ethanol precipitated , and 200 ng RNA was used per reverse transcription reaction . | Most genes are transcribed into mRNA and then translated into proteins that function in various cellular processes . Initiation of mRNA translation is thus a fundamental control point in gene expression . Working in a zebrafish model , we have found that muscle activity ( or inactivity ) can differentially regulate the translation of specific mRNAs and thereby control the growth of skeletal muscle . Emerging evidence suggests that control of translational initiation of particular mRNAs by an intracellular signaling pathway acting through TORC1 is a major regulator of cell growth and function . We show here that muscle activity both activates the TORC1 pathway and suppresses the expression of a downstream TORC1 target—the translational inhibitor eIF4EBP3L . This removes a brake on translation of certain mRNAs . Conversely , we show that muscle inactivity can up-regulate this translational inhibitor , thereby causing reduced translation of these mRNAs . One of the mRNAs targeted in this manner by eIF4EBP3L is Mef2ca , which encodes a transcription factor that promotes assembly of muscle contractile apparatus . Our work thus reveals a mechanism by which muscle growth can be differentially influenced depending on the context of muscle activity ( or lack thereof ) . If this pathway operates in people , it may help explain how exercise regulates muscle growth and performance . | [
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] | [] | 2013 | eIF4EBP3L Acts as a Gatekeeper of TORC1 In Activity-Dependent Muscle Growth by Specifically Regulating Mef2ca Translational Initiation |
In the context of recent arbovirus epidemics , questions about the frequency of simultaneous infection of patients with different arbovirus species have been raised . In 2014 , a major Chikungunya virus ( CHIKV ) epidemic impacted the Caribbean and South America . As part of ongoing screening of schoolchildren presenting with acute undifferentiated febrile illness in rural Haiti , we used RT-PCR to identify CHIKV infections in 82 of 100 children with this diagnosis during May—August 2014 . Among these , eight were infected with a second arbovirus: six with Zika virus ( ZIKV ) , one with Dengue virus serotype 2 , and one with Mayaro virus ( MAYV ) . These dual infections were only detected following culture of the specimen , suggesting low viral loads of the co-infecting species . Phylogenetic analyses indicated that the ZIKV and MAYV strains differ from those detected later in 2014 and 2015 , respectively . Moreover , CHIKV and ZIKV strains from co-infected patients clustered monophyletically in their respective phylogeny , and clock calibration traced back the common ancestor of each clade to an overlapping timeframe of introduction of these arboviruses onto the island .
Chikungunya virus ( CHIKV ) ( family Togaviridae , genus Alphavirus ) , is the causative agent of chikungunya fever . After first isolation of CHIKV in 1952 in present-day Tanzania , outbreaks and epidemics were limited to regions of Asia , Africa , and the Pacific Islands . In 2013 , CHIKV emerged for the first time in the Americas , with sustained autochthonous transmission and rapid spread through the region [1–3] . The acute symptoms of CHIKV infection are similar to those of infection with other arbovirus species , including Dengue virus ( DENV ) , Zika virus ( ZIKV ) , and Mayaro virus ( MAYV ) , each presenting with a constellation of symptoms including fever , headache , and myalgias/arthralgias . Long-term , CHIKV infections have been linked with persistent arthralgias in a subset of cases[3]; it has also been reported that upwards of 90% of CHIKV-infected individuals are symptomatic , in contrast to findings with ZIKV , where it is estimated that only 20% of infected persons are symptomatic [4 , 5] . The similarity of the clinical presentation of acute-phase arbovirus infections is further complicated by the potential for simultaneous infection with multiple arboviruses . In a recent literature review , co-infections with CHIKV and DENV ranged from 1% to 34% of patients [6] . However , virtually no data are available on frequency of co-infection with CHIKV and arboviruses other than DENV . Even where good laboratory diagnostic facilities are available , identification of co-infections often does not occur , as there is a tendency to cease investigation once an initial pathogen has been identified , and/or identification of a second pathogen may require facilities for virus isolation , which may not be available . As part of ongoing studies of acute undifferentiated febrile illness in a cohort of school children in rural Haiti , we identified 82 children with RT-PCR-confirmed CHIKV infections during May-August 2014 , corresponding with the time period when the Caribbean CHIKV epidemic was moving through Haiti . Specimens were also simultaneously screened by RT-PCR for DENV1-4 , then additionally for ZIKV . Aliquots of the plasma specimens were then inoculated onto cell cultures for the isolation of additional pathogens of potential interest [6] . We report here results of these studies , focusing on rates of arbovirus co-infection in our patient cohort and potential sources of origin of the co-infecting strains .
Blood specimens were collected from schoolchildren with acute febrile illness seen at the Christianville School clinic in Gressier , Haiti , beginning in May 2014 . This clinic serves as the primary source of healthcare for approximately 1 , 250 children at four school campuses ( the main LaSalle campus [campus A] and three small satellite elementary school campuses [campuses B , C , and D] ) operated by the Christianville Foundation in the Gressier/Leogane region; schools are located within a radius of approximately 10 miles . The clinic has facilities for short-term stays of a few hours for sick children , with those requiring hospitalization referred to the local community hospital . As previously described , acute undifferentiated febrile illness was defined as occurrence of fever in a child with no obvious source of infection ( i . e . , no respiratory symptoms , symptoms of UTI , or evidence of malaria or typhoid ) [7] . We have previously reported isolation of ZIKV [8] , DENV [9] , Human coronavirus NL63 [10] , and Enterovirus D68 [11] from children in this school cohort; however , the cases/outbreaks in these prior publications did not include CHIKV , or cases within the May-August , 2014 , time frame of the current study . Clinical features of these cases are reported elsewhere [12] . Whole blood ( ca . 0 . 5–2 ml ) was collected in K2EDTA tubes ( BD Vacutainer , Becton , Dickinson and Company , Franklin Lakes , NJ ) . As part of the initial diagnostic evaluation , plasma was screened for CHIKV viral RNA ( vRNA ) by molecular methods [13 , 14] . All virus isolations and RNA extractions on CHIKV-positive specimens were performed in a BSL-3 facility at the University of Florida ( UF ) Emerging Pathogens Institute in Gainesville , Florida . The UF IRB and the Haitian National IRB approved all protocols , and written informed consent was obtained from parents or guardians of all study participants . To assess the sensitivity of RT-PCR tests and for confirmation purposes , CHIKV isolations from plasma specimens were attempted in epithelioid cells derived from African Green Monkey kidneys ( Vero E6 , ATCC CRL-1586 ) . The Vero E6 cells used for virus isolation were maintained in cell culture medium comprised of aDMEM ( advanced Dulbecco’s modified essential medium ) supplemented with 10% low antibody , heat-inactivated , gamma-irradiated fetal bovine serum ( FBS , Hyclone , GE Healthcare Life Sciences , Pittsburgh , PA ) , L-alanine and L-glutamine supplement ( GlutaMAX , Invitrogen , Carlsbad , CA ) , and 50 μg/ml penicillin , 50μg/ml streptomycin , 100μg/ml neomycin ( PSN antibiotics , Invitrogen ) with incubation at 37°C in 5% CO2 . Confluent cell cultures were split into 25cm2 flasks 24 hours prior to inoculation to attain 60% confluent cell monolayers the following day . Prior to inoculation , the culture medium was removed and inoculum containing 100μl of plasma that had been filtered through a sterile 0 . 45 filter and mixed with 400μL aDMEM with 3% FBS , GlutaMAX , and PSN antibiotics was added to the monolayer . The inoculated monolayer was incubated at 37°C in 5% CO2 , and rocked every 15 minutes for 1 hour . A negative control ( mock-infected ) cell culture was inoculated with 500ul of DMEM without virus or plasma and handled in parallel with the other cultures . After allowing for virus adsorption for 1 hr , the inocula were removed and replaced with 3ml of aDMEM with 3% FBS , GlutaMAX , and PSN antibiotics , and thereafter incubated at 37°C in 5% CO2 . Cell cultures were refed every 3 days by the replacement of 1 . 5ml of spent media with aDMEM with 3% FBS . The cultures were observed for up to 21 days’ post-inoculation , or until CHIKV-induced cytopathic effects ( CPE ) were observed in the cell monolayers using an inverted microscope . When CPE were observed throughout 50% of the monolayer , a final collection of 2ml spent media , and 1ml of scraped cells in spent media , were cryopreserved at -80°C for future tests . Total RNA was extracted from both plasma specimens and spent media , and tested by real-time RT-PCR ( rtRT-PCR ) following published protocols for CHIKV vRNA [13] for confirmation of previous tests performed in Haiti . RNA extracted from plasma specimens were then screened for DENV serotypes 1–4 [15] and ZIKV [16] vRNAS by rtRT-PCR . Cycle threshold ( Ct ) -values under 38 were considered positive . Viral genomic RNA that was extracted from CHIKV , DENV1-4 , and ZIKV strains that were obtained from the Biodefense and Emerging Infections Research Resource Repository ( BEI Resources , Manassas , VA ) were used as positive control materials for rtRT-PCR . Cell cultures were also tested for the presence of DENV and ZIKV vRNAs by rtRT-PCR , even if the corresponding plasma specimen tested negative . Additionally , spent media from cultures displaying non-CHIKV CPE , that were DENV and ZIKV negative by rtRT-PCR , were screened with a duplex RT-PCR for the vRNAs of other alphaviruses ( Venezuelan equine encephalitis - , Eastern equine encephalitis - , Western equine encephalitis - , Aura—and Mayaro viruses ) and flaviviruses ( Yellow fever - , Saint Louis encephalitis - , Bussaquara - , Ilheus - , and Rocio viruses ) [17] . The DENV-1 strain from BEI and the 2015 MAYV sample from our laboratory [18] were used as the flavivirus and alphavirus positive controls , respectively , in the duplex RT-PCR protocol . Whole genome sequence data from 10 of the CHIKV samples ( 7 from children with co-infections , 3 from selected randomly mono-infections ) were obtained by Sanger sequencing and a primer-walking approach , as previously described [14 , 19] . Similarly , we designed sequencing primers for MAYV and ZIKV that also amplify approximately 800bp overlapping segments , and used a primer walking method for whole genome sequencing of those viruses [8 , 18] . For the sequencing of DENV , primers described by Christenbury et al were utilized [20] . Amplification of each segment was performed using an Accuscript high-fidelity first-strand cDNA synthesis kit ( Agilent Technologies , Santa Clara , CA ) followed by PCR with Phusion polymerase ( New England Biolabs , Ipswich , MA ) . The 5’ and 3’ ends of the viral genomes were obtained using RNA-ligase mediated ( RLM ) systems for 5’ and 3’ Rapid Amplification of cDNA Ends ( RACE ) per the manufacturer’s protocols ( Life Technologies , Carlsbad , CA ) . Amplicons were purified , sequenced bi-directionally , then the sequences assembled with the use of Sequencher DNA sequence analysis software v2 . 1 ( Gene Codes , Ann Arbor , MI ) , and subsequently analyzed in comparison to DENV , MAYV , and ZIKV sequences available in GenBank for nucleotide sequence comparisons . The vRNA sequences we obtained differed from those of the corresponding viruses in our repository , confirming the newly analyzed sequences did not arise from laboratory contamination . Alignments for each virus-specific dataset ( CHIKV , DENV-2 , MAYV , and ZIKV ) , including full genome sequences selected to represent the major clades described so far for each virus were obtained using the MUSCLE algorithm implemented in MEGA7 ( http://www . megasoftware . net/ ) [21–23] . GenBank accession numbers ( AC ) , geographical location , and year associated with isolation of each strain are reported in S1 Table . Based on previous evidence of recombination reported for MAYV , potential presence of recombination was also investigated for the new 2014 MAYV isolate ( KY985361 ) [24] with algorithms implemented in the RDP4 [25] software ( http://web . cbio . uct . ac . za/~darren/rdp . html ) , as previously described [24] . The phylogenetic signal in each virus-specific data set was evaluated by likelihood mapping using TreePuzzle ( http://www . tree-puzzle . de/ ) [26] , and substitution saturation plots using DAMBE6 ( ( http://dambe . bio . uottawa . ca/DAMBE/dambe . aspx ) [27] . Maximum likelihood ( ML ) trees were inferred by IQ-TREE using the best-fitting nucleotide substitution model chosen according to Bayesian Information Criterion ( BIC ) ( S2 Fig ) [28 , 29] . Statistical robustness for internal branching order in each phylogeny was assessed by UFBoot—Ultrafast Bootstrap ( BB ) Approximation ( 2 , 000 replicates ) implemented in IQ-TREE [30] , and strong statistical support along the branches was defined as BB>90% . UFBoot was eployed as it accelerates computing and reduces overestimating branch support [30] . Alignments files are available from the authors upon request . The temporal signal for each dataset was assessed using ML trees with TempEst v1 . 5 ( http://tree . bio . ed . ac . uk/software/tempest/ ) [31] . Bayesian inference of time-scaled phylogenies were carried out with BEAST v1 . 8 . 4 ( http://beast . bio . ed . ac . uk/ ) [32 , 33] by enforcing either a strict or relaxed molecular clock [34] and the SDR06 substitution model with empirical base frequencies and gamma distribution of site-specific rate heterogeneity . Two demographic priors were also tested for each analysis: constant population size or non-parametric Bayesian Skyline Plot ( BSP ) . Bayesian Markov Chain Monte Carlo ( MCMC ) were run for 50–200 million generations ( sampled at fixed intervals to obtain posterior distributions with 10 , 000 data points ) , depending on the data set , in order to assure proper mixing of the MCMC , which was assessed on the basis of the effective sampling size ( ESS ) of each parameter estimate , accepting only ESS values >200 . The best clock/demographic model for each virus-specific data set was chosen by comparing marginal likelihood estimates ( MLE ) [35] , obtained using path sampling ( PS ) and stepping-stone sampling ( SS ) methods [33 , 36] with Bayes Factor ( BF ) ( S2 Table ) . In practice , the BF natural logarithm was used for comparison with lnBF<2 indicating no evidence against the null hypothesis ( i . e . less parameter-rich model ) , 2–6—weak evidence , 6–10—strong evidence , and >10 very strong evidence [37] . For each data set , the maximum clade credibility ( MCC ) tree ( tree with the largest product of posterior clade probabilities ) was selected from the posterior tree distribution of the best fitting clock/demographic model , after appropriate burn-in , using TreeAnnotator v1 . 8 . 4 implemented in the BEAST software package . The final trees were manipulated in FigTree v1 . 4 . 3 for display purposes ( http://tree . bio . ed . ac . uk/software/figtree/ ) . XML files for the BEAST runs are available from the authors upon request .
Of the 82 rtRT-PCR CHIKV-positive specimens , attempts were made to isolate virus from 62 specimens , with the remainder not inoculated onto cell cultures due to insufficient specimen volume . Typical CHIKV-induced CPE , consisting of cell membrane blebbing , cell lysis , and apoptosis , were observed in cultures from 43 of the 62 samples on average 5 days post inoculation ( dpi ) , with some specimens displaying advanced CPE as early as 2 dpi and others not until 20 dpi ( Fig 1 ) ; target vRNA’s were positive by rtRT-PCR for CHIKV but negative for DENV and ZIKV [15–17] . All negative control cell cultures lacked CPE and tested negative by rtRT-PCR for the target vRNAs of this work . Eleven ( 15% ) of the 62 samples cultured did not display any CPE during the period of culture and tested negative for target vRNAs in these studies , despite initial positive CHIKV results by rtRT-PCR . Eight cultures displayed CPE inconsistent with those expected for CHIKV ( Fig 2 ) . The mixed CPE in these latter cultures was associated with co-infection with CHIKV together with another virus , as determined by molecular tests of the cultured specimens: co-infecting arboviruses included ZIKV ( n = 6 ) , DENV type 2 ( n = 1 ) , and MAYV virus ( n = 1 ) . Sex and age of patients were comparable for those with CHIKV mono-inections and the subset of CHIKV cases with co-infections . Seven of the 8 co-infections were from Campus A of the school; one co-infection ( CHIKV and ZIKV ) was in a student from Campus B . CHIKV , ZIKV , MAYV and DENV-2 alignments displayed strong phylogenetic signal ( S1 Fig ) , as well as temporal signal sufficient for the calibration of an accurate molecular clock ( S2 Fig ) . ML trees with branch lengths scaled in nucleotide substitutions per site ( S3 Fig ) , as well as time-scaled Bayesian phylogenies ( Fig 3 ) using the best fitting molecular clock/demographic model ( S2 Table ) , were inferred for each data set . Bayesian and ML reconstructions displayed very similar topologies and suggested three independent phylogenetic lineages of CHIKV in Haiti , possibly the result of three separate introductions ( Figs 3A and S3A ) . The CHIKV strains from the current study were in a well-supported monophyletic clade which was most closely related to a strain from El Salvador ( Fig 3A ) . Within this clade , six CHIKV strains were from patients co-infected with ZIKV and one with DENV-2 . Molecular clock median estimate of the time of the most recent common ancestor ( tMRCA ) for the clade was August 2013 , with 95% highest posterior density ( 95%HPD ) intervals between April 2013 and January 2014 ( Fig 3A ) . As shown in Fig 3 , two other 2014 CHIKV isolates , not collected as part of this study but previously reported by Lanciotti ( KR559476 and KR559478 ) from mono-infected Haitian patients , clustered within a second distinct clade related to different Central and South American strains , but separate from our Gressier strains . One of the 10 sequenced CHIKV strains from our cohort , from a randomly selected mono-infection in a child from Campus B , was in yet a third clade , which clustered most closely with a strain from the Dominican Republic . The six ZIKV sequences obtained in June 2014 from the CHIKV co-infected patients were highly similar ( 99 . 9% ) to each other and also cluster within a well-supported monophyletic clade , which , interestingly , includes a divergent strain isolated in 2016 from Guadaloupe ( Figs 3B and S3B ) . The primary clade of co-infecting ZIKV strains is closely related to another ZIKV clade including isolates from the 2016 USA Florida outbreak [38] . In contrast , previously reported [8] Haitian strains from this same school cohort in December of 2014 ( KU509998 ) , and Haitian strains from early 2016 ( KX051563 [39] and KX269878 [40] ) belonged to distinct phylogenetic lineages , consistent with multiple independent introductions of ZIKV to Haiti . The median origin of the Haitian ZIKV co-infection strains was estimated by molecular clock in April 2014 with a 95%HPD between January and May 2014 ( Fig 3B ) , a timeframe overlapping with the estimates of the corresponding CHIKV co-infections clade . The specimen from the CHIKV and DENV-2 co-infection was obtained on June 11 , 2014 . The DENV-2 isolate was closely related to a previously reported strain isolated in early 2016 ( KX702404 ) from a US traveler returning from Haiti [39] and they both cluster within a well-supported clade including Brazilian and Peruvian subclades ( Figs 3C and S3C ) . According to the time-scaled phylogeny ( Fig 3C ) , the tMRCA of the DENV-2 isolate from the CHIKV co-infected patient traced back to February 2008 with 95%HPD between May 2007 and October 2008 . Finally , the MAYV strain from a CHIKV co-infected patient seen on June 4 , 2014 is the earliest documented case of MAYV in Haiti to date . Its genome is highly similar ( 99 . 4% ) to a strain from Brazil isolated in 1960 , and phylogenetic analysis cluster both strains in a well-supported monophyletic clade ( Figs 3D and S3D ) , within genotype L [24 , 40] , with a tMRCA dating back to 1958 ( 95%HPD interval of 1949–1960 ) . It should be noted , however , that since we only have one MAYV strain clustering with the Brazilian 1960 strain , the tMRCA is unlikely to represent the date of introduction of MAYV in Haiti . Another , previously reported Haitian strain ( KX496990 ) [18 , 24] , clusters in a different well-supported clade within genotype L , a scenario once again consistent with multiple independent introductions of the virus in the Caribbean .
Despite increasing recognition of the frequency with which simultaneous co-infection with multiple arbovirus species occurs [19 , 39 , 41–43] , co-infection dynamics are not well understood , and the clinical , pathologic , and immunologic significance of such co-infections remains to be determined . As a starting point in unraveling these dynamics , we were interested in better quantifying the frequency of such infections , and exploring possible origins of co-infecting strains . To optimize our ability to identify co-infections ( and following standard practices in our laboratory [8 , 17 , 19 , 39] ) , we continued diagnostic studies even after initial identification of CHIKV or another pathogen in a patient sample . Interestingly , while we identified multiple co-infecting viruses in cell culture , the original patient rtRT-PCR in this study was , in each instance , negative for the second pathogen , suggesting that viral loads for the co-infecting species were low . We also had instances where viral cultures were negative in the setting of a positive rtRT-PCR , possibly reflecting the presence of non-viable virus in the sample , and/or strains that have mutated or which do not grow well in Vero cells . If we are to better understand the role of co-infections in disease occurrence , the diagnostic significance of both rtRT-PCR negative/culture positive and rtRT-PCR positive/culture negative samples will require further study . Co-infections with CHIKV and DENV are well described , with reported rates of co-infection ranging from 1% to 34% [6]; the highest reported co-infection rates were from Madagascar ( 26% ) and Nigeria ( 34% ) . Another study from the Colombian-Venezuelan border reports a CHIKV/DENV co-infection prevalence of 7 . 6% [43] . In our study , in contrast , the co-infection rate was only 1% , with only two additional dengue cases ( both DENV-1 ) identified among CHIKV-negative patients; this may reflect a low level of circulating DENV in the community during the CHIKV epidemic , and/or a high existing level of population immunity to the virus . We also identified one CHIKV/MAYV co-infection . We have previously reported a mixed MAYV and DENV-1 infection that occurred outside of the time frame of the CHIKV epidemic ( January , 2015 ) in the same school cohort [18] . The high genomic similarity of this Haitian MAYV isolate with a Brazilian strain isolated in 1960 corroborates our hypothesis that MAYV has been criptically circulating in the human population at a sub-epidemic level , most likely in Brazil , for years and that it was introduced just recently onto Hispaniola [24] . What was unexpected in our data set was the relatively high frequency of ZIKV/CHIKV co-infections . This occurred in a setting in which we were only screening plasma samples . As we have previously reported [39] , urine can be positive for ZIKV for a longer period of time than plasma; if we had also screened urine , there is the possibility that we would have identified additional ZIKV-positive patients . We , and others , have previously reported cases of ZIKV/CHIKV co-infection [19 , 42 , 44 , 45] , so finding the two together is not surprising . The ZIKV case cluster in the current study would appear to have been relatively widespread within the community ( i . e . , not a point source at the school ) , as one of the six co-infections was from a child at a different school campus , ca . 5 miles from the main campus . Interestingly , our molecular clock analysis indicates that the strains of CHIKV and ZIKV found in our patients were both introduced in Haiti within the same short time frame , each one giving rise to a well-supported monophyletic clade , one including all CHIKVs from CHIKV/ZIKV co-infected subjects , the other one including all ZIKVs from the same co-infected subjects . The simultaneous emergence of these two clades in the Haitian population is compatible with a simultaneous co-infection scenario or indicate , at the very least , the sequential infection of a patient with two arboviruses within a relatively short period of time . In light of recent reports that mosquito co-infection with ZIKV and CHIKV allows simultaneous transmission without affecting vector competence [46] , it is plausible that the two viruses were co-transmitted by the same mosquito . Given that these are among the earliest , if not the earliest , documented ZIKV infections in the hemisphere , the observation that they appeared together with epidemic CHIKV raises interesting questions about the possible role of CHIKV in the initiation of the ZIKV epidemic in the Americas . Because our work draws on student populations from four schools located within a radius of about 10 miles , we have some feel for disease prevalence within the immediate study area; however , generalizability of these data to larger regions of Haiti may be limited . Nonetheless , our findings are consistent with the idea that there were multiple introductions of CHIKV into Haiti between 2013 and 2014 , with identification of three distinct phylogenetic lineages , each clustering with strains from different parts of the Caribbean and/or South America . This was also the case for ZIKV: the ZIKV co-infecting strains did not cluster with the ZIKV strains previously obtained in Haiti in 2014 and 2016 , consistent with multiple introductions or re-introductions of this arbovirus in Haiti between 2014 and 2016 . Although for DENV-2 and MAYV the data are more limited , the tMRCAs for DENV-2 suggested that the introduction in Haiti happened between 2008 and 2014 , a time interval again overlapping with the estimated introduction of both CHIKV and ZIKV co-infecting strains circulating in Haiti . Clustering of both the ZIKV and DENV-2 strains together with ZIKV and DENV-2 strains obtained in Florida in 2016 [38 , 47] , as well as the multiple independent introductions of CHIKV , ZIKV , and MAYV to Haiti inferred from the phylogenies , demonstrate the potential role of the Caribbean as a node for arbovirus infections connecting South , Central , and North America . | Chikungunya virus ( CHIKV ) was the cause of a major epidemic in Haiti from May through August , 2014 , as the disease swept through the Caribbean and South and Central America . During the time-period of the epidemic , our group was monitoring a cohort of children in semi-rural Haiti , using molecular and virology methods to assess the etiology of acute undifferentiated febrile illness . We identified CHIKV infections in 82 of 100 children presenting with fever during this time-period; six of the children infected with CHIKV were found to be co-infected with Zika virus ( ZIKV ) , one with Dengue virus serotype 2 , and one with Mayaro virus . For the CHIKV and ZIKV infections , a molecular clock calibrated framework revealed results suggestive of simultaneous co-infection . Our data highlight the frequency of dual infections in arbovirus disease cases ( with the possibility of simultaneous co-infection ) , and document the frequent movement of virus strains within the Caribbean and the American continents . | [
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] | 2018 | Detection and phylogenetic characterization of arbovirus dual-infections among persons during a chikungunya fever outbreak, Haiti 2014 |
Mokola virus ( MOKV ) appears to be exclusive to Africa . Although the first isolates were from Nigeria and other Congo basin countries , all reports over the past 20 years have been from southern Africa . Previous phylogenetic studies analyzed few isolates or used partial gene sequence for analysis since limited sequence information is available for MOKV and the isolates were distributed among various laboratories . The complete nucleoprotein , phosphoprotein , matrix and glycoprotein genes of 18 MOKV isolates in various laboratories were sequenced either using partial or full genome sequencing using pyrosequencing and a phylogenetic analysis was undertaken . The results indicated that MOKV isolates from the Republic of South Africa , Zimbabwe , Central African Republic and Nigeria clustered according to geographic origin irrespective of the genes used for phylogenetic analysis , similar to that observed with Lagos bat virus . A Bayesian Markov-Chain-Monte-Carlo- ( MCMC ) analysis revealed the age of the most recent common ancestor ( MRCA ) of MOKV to be between 279 and 2034 years depending on the genes used . Generally , all MOKV isolates showed a similar pattern at the amino acid sites considered influential for viral properties .
The lyssavirus genus consists of twelve species recognized by ICTV [1] of which five [ ( Rabies virus ( RABV ) , Lagos bat virus ( LBV ) , Mokola virus ( MOKV ) , Duvenhage virus ( DUVV ) , and Shimoni bat virus ( SHIBV ) ] have been isolated in Africa [2] . LBV , MOKV , DUVV and SHIBV occur exclusively in Africa . SHIBV was recently isolated from Hipposideros vittatus ( formerly known as H . commersoni ) [3] . Another proposed lyssavirus species is Ikoma lyssavirus ( IKOV ) isolated from an African civet in Tanzania [4] . The first isolations of MOKV were made in 1968 and 1969 from organ pools of shrews ( Crocidura flavescens manni ) in Ibadan , Nigeria [5] , [6] , [7] . The only isolations from humans were in 1968 and 1971 from two girls from Nigeria [6] , [8] , [9] . However , there were no classical signs of rabies in either of these cases . Whilst the 1968 isolation was made from the cerebrospinal fluid of a girl who presented with fever and convulsions but fully recovered with no neurological damage , the 1971 isolate was from the brain of a girl who died of a poliomyelitis-like encephalitic disease . A further isolation was made in 1974 from a shrew ( Crocidura spp . ) in Yaounde , Cameroon [10] . The only isolation from a rodent ( Lophuromys sikapusi ) was in 1981 , from Bangui , Central African Republic [11] . MOKV was also isolated from other animal species including companion animals . A survey on lyssaviruses undertaken in Zimbabwe between 1981 and 1984 revealed six isolations of MOKV from domestic animals , namely a dog and cats that had been previously vaccinated against rabies and unvaccinated cats [12] , [13] . In 1989 MOKV was isolated from a cat in Addis Ababa , Ethiopia [14] . No further isolation of MOKV was made in Zimbabwe until 1993 , when the virus was again isolated from a domestic cat [15] . In the Republic of South Africa , the first isolation was made in 1970 from a domestic cat in Umhlanga Rocks , Kwa-Zulu Natal Province ( KZN ) [16] . At the time the isolate was assumed to be RABV and the isolate was only identified retrospectively using antigenic typing with monoclonal antibodies during the discovery of MOKV in Zimbabwe in the 1980s [17] . Twenty five years later , in 1995 , MOKV was isolated from a domestic cat in South Africa , this time from Mdantsane in the Eastern Cape Province ( EC ) [17] . Two more isolations followed in 1996 , one each in KZN and EC and both from domestic cats of which one was vaccinated against rabies [18] , [19] . In 1997 and 1998 three more isolations were made from rabies-vaccinated cats in KZN [18] , [19] . Following several years in which MOKV was not encountered , two isolations were from rabies-vaccinated domestic cats in 2006 and 2008 from the EC province and these are the most recent known isolations of this virus [20] , [21] . From South Africa all isolations of MOKV were from a domestic cat . Viral RNA was detected by PCR from a domestic dog ( in 2005 ) from Mpumalanga Province of South Africa , virus isolation was unsuccessful in this case [20] . A summary of all MOKV isolates and the approximate geographic location of their origin are presented in Table 1 and Fig . 1 . Generally , MOKV infected domestic animals were not observed to be particularly aggressive , but displayed other rabies-like signs that included dehydration , unusual behavior , hypersensitivity , neurological disturbance and salivation [19] . Despite MOKV being isolated from a variety of mammal species , this species is the only lyssavirus never to have been isolated from bats . Cross protection of WHO and OIE recommended rabies vaccines against various rabies-related lyssavirus species have been reported in a number of studies [22] , [23] , [24] , [25] , [26] . However , no rabies vaccine provided complete protection against MOKV [27] , [28] , [29] , [30] , [31] , [32] . More evidence that RABV derived vaccines do not protect against MOKV infection is shown by circumstantial evidence of the fatal infections of numerous domestic animals that had been vaccinated against RABV [13] , [18] , [19] , [20] , [21] . Given this scenario and the apparent obscurity of MOKV , we argue that much more information is needed to improve our scant understanding of the epidemiology , disease dynamics and the ecology of this virus . Some phylogenetic studies have been undertaken on MOKV [18] , [20] , [21] , [33] , [34] , but these studies were invariably performed on a smaller number of isolates and limited to partial gene sequences . Despite these limitations , these studies provided some evidence of the existence of different virus clusters , delineated according to geographical incidence . Generally , the genetic variance was shown to be inversely related to the spatial distribution of isolates . For example , South African MOKV isolates were shown to be closely related , but distinguishable based on province and as a cluster more distant from those made in a neighboring country , Zimbabwe [21] , [33] , [34] . Such patterns of genetic diversity may indicate extended periods of isolated evolution , as have been reported for terrestrial rabies virus variants [35] . An exception appeared to be a grouping that included one isolate from Cameroon and one from Ethiopia . The study reported here involved a multi-disciplinary collaborative effort amongst various laboratories in order to generate for the first time a comprehensive dataset of all the known MOKV isolates available . We have shown that most , but not all of the viruses mentioned in literature could be tracked and that some contamination or misnaming occurred . Given a final cohort of eighteen MOKV isolates , the objective of the study was to sequence full nucleoprotein ( N ) , phosphoprotein ( P ) , matrix ( M ) and glycoprotein ( G ) genes . The estimation of viral lineage divergence times and subsequent application of a molecular clock is dependent on an accurate estimation of the rate of nucleotide substitution . Bayesian techniques using the Markov Chain Monte Carlo ( MCMC ) methods have been successfully applied to estimate the evolutionary rate and divergence times from dated sequences of RABVs [36] , [37] , [38] , [39] , [40] . This study applied a relaxed molecular clock to N- , M- , P- and G-gene datasets to obtain estimates of the time to the most recent common ancestor ( MRCA ) and rate of evolution for MOKV . The subsequent analysis allowed for study of the phylogeny and diversity within this African lyssavirus species .
MOKV included in this study were comprised of archived isolates . Information on the geographic location , year of isolation , species origin and references of those MOKV isolates is presented in Table 1 . The isolates were either passaged several times ( passage number unknown ) in suckling mice or in tissue culture , or both . Total RNA was extracted from the samples using the TRIzol® method ( Invitrogen ) according to the manufacturer's instructions . The N , P , M and G genes were sequenced using different primer combinations and cycling conditions available from the authors upon request . All PCR products were analyzed by agarose gel electrophoresis and subsequently purified ( Wizard PCR Preps DNA Purification System; Promega ) . The purified PCR products were sequenced with BigDye Termination Cycle Sequencing Ready Reaction Kit 3 . 1 ( Applied Biosystems ) according to the manufacturer's protocol and analyzed on an ABI Prism 3100 DNA sequencer ( Applied Biosystems ) . Within the duration of this project next generation sequencing technology became available and was applied on a selection of samples . Complete genome sequence was obtained directly from brain tissue for four MOKV isolates ( RV4 , RV1017 , RV1021 and RV1035 ) ( Marston , unpublished ) . Briefly , TRIzol ( Invitrogen ) extracted viral RNA was depleted of host genomic DNA using RNase-free DNAse ( Qiagen , UK ) and host ribosomal RNA was depleted using Terminator 5′-Phosphate-Dependent Exonuclease ( Epicentre Biotechnologies ) . The RNA was fragmented , a random-primed cDNA library was made and run using the Roche 454 GS FLX System . The sequencing data were assembled in the GS de novo assembly software ( Roche ) . The de novo assembled contigs for each isolate were individually aligned using Seqman ( DNAStar ) using reference sequence EU293117 and/or specific isolate sequences where available . The resulting consensus sequences were used in GS Reference Mapper ( Roche ) to obtain further sequence reads from the original raw data for each isolate . All four complete genome sequences were obtained , apart from the extremities of the genome ( UTRs ) . The UTRs were inferred from the previously determined MOKV UTR sequences by using RT-PCR primers situated at the beginning and end of the genome ( Marston , unpublished ) . Nucleotide sequences were assembled and edited using Vector NTI 9 . 1 . 0 ( Invitrogen ) . Multiple sequence alignments were generated using ClustalX and exported in FASTA format . Phylogenetic and evolutionary analyses were conducted using Mega 5 . 05 [41] for a variety of data sets , i . e . the N , P , M and G gene nucleotide sequences as well as the concatenated sequence . The p-distances between MOKV N gene nucleotide and amino acids sequences were also calculated . The Maximum Clade Credibility ( MCC ) phylogenetic tree , estimates of the rate of molecular evolution ( substitutions per site per year ) and the most recent common ancestor ( MRCA ) for MOKV were inferred using a Bayesian Markov Chain Monte Carlo ( MCMC ) method in the BEAST package ( BEAST and associated programmes are available via http://beast . bio . ed . ac . uk/ ) [42] . For this analysis , an input file for BEAST was generated using the BEAUti programme . For MCMC analysis of the concatenated gene sequence dataset ( N , P , M and G genes ) partitioning into genes was implemented The analysis utilized the general time reversible model with gamma distribution and proportion of invariable sites ( GTR+G+I ) with site heterogeneity [43] and population histories were constructed using the Bayesian skyline plot [44] . The relaxed ( uncorrelated lognormal ) molecular clock was chosen as demographic model . The statistical uncertainty in the data for each parameter estimate is reflected by the value of the 95% highest posterior density ( HPD ) . For each estimate , duplicate BEAST runs were performed to test the reproducibility of the analysis . The BEAST output was assessed using the TRACER programme . For each analysis , a chain length of >30 million steps resulted in an effective sampling size ( ESS>200 unless noted ) , with 10% burn-in removed . Trees and parameters were recorded every 10 000 steps . The trees obtained from BEAST were used as input for the TREEANNOTATOR programme to find the MCC tree . Phylogenetic trees were edited for publication using FigTree ( version 1 . 3 . 1; http://tree . bio . ed . ac . uk/software/figtree/ ) [42] . Posterior probability values represent the degree of support for each node on the tree .
We have generated a comprehensive dataset of all available isolates of MOKV , however , we were unable to trace some , as indicated in Table 1 . Of the 24 reported detections of MOKV over the past 50 years , only 18 isolates could be included in this study . We were unable to track virus isolates for four cases reported in literature . Three of these were historical cases from Nigeria , the existence of which are now uncertain viz . two human isolates and a further isolate from a shrew [6] , [8] , [9] . The fourth was a dog-associated case reported in recent times from South Africa [20] , for which an isolate was never produced . Isolates RV39 ( Cameroon , 1974 ) and RV610 ( Ethiopia , 1990 ) were excluded from the MCC phylogenetic analyses as sequence data indicated that these isolates , in our hands , were likely to be the same virus ( Fig . 2 ) . A small number of nucleotide differences on some genes were believed to be due to mutations introduced from multiple cell culture passages over years of laboratory maintenance . A number of domains on the lyssavirus genome have been implicated in the varying degrees of virulence between virus isolates of a lyssavirus species , as well as between virus isolates of different species of the Lyssavirus genus . A comparison of these amino acid positions is provided in Table 2 . A similar pattern of amino acid substitutions on these positions was observed for the majority of MOKV isolates with specific differences observed on AA 144 ( P-gene ) , AA 81 ( M-gene ) and AA 194 , 198 , 268 , 352 and 330 ( G-gene ) . The genetic relationships between the different MOKV isolates was determined by construction of a MCC tree using the concatenated full coding regions of the N , P , M and G gene sequences as well as the individual genes ( supplementary material ) . The MOKV isolates analyzed in this study formed a cluster supported by bootstrap values >70% when nucleotide sequences from the concatenated N , P , M and G genes ( Fig . 3 ) , N ( supplementary material , Fig . S1 ) , P ( supplementary material , Fig . S2 ) , M ( supplementary material , Fig . S3 ) and G gene ( supplementary material , Fig . S4 ) were used . The same tree topology was observed for both nucleotide and amino acids sequence analysis ( data not shown ) . The MCC trees indicated that isolates grouped according to geographic location . Phylogenetic analysis of MOKV isolates from South Africa and Zimbabwe demonstrated geographic clustering consistent with previous findings [18] , [34] . The South African isolates formed two clusters consisting of KZN and EC provinces respectively . The Zimbabwean isolates from the 1980s ( all from Bulawayo ) formed a single cluster , distinct from the single 1993 isolate ( from Selous ) . The same grouping was demonstrated for these isolates ( South African and Zimbabwean ) irrespective of the gene used for phylogenetic analysis . The Central African Republic and Nigeria isolates formed independent clusters irrespective of the gene used for analyses . The P-distance comparison between different MOKV isolates was performed using the N gene nucleotide and amino acid sequences ( Table 3 ) . Comparison of the nucleotide sequences indicated the difference between the MOKV isolates to be between 0 and 15% ( 85% nt seq identity ) , with the highest value ( 15% ) being between U22843 ( Zimbabwe ) and RV4 ( Nigeria ) . The highest nucleotide difference between South African isolates was 5 . 7% ( 226/08 and 229/97 ) while for Zimbabwean isolates it was 12 . 3% ( between U22843 and RV1017/21846 ) . Collectively , the nucleotide difference between South African isolates and Zimbabwean isolates was 14 . 4% ( U22843 and 226/08 ) . When comparing amino acid differences between MOKV isolates the same trend was observed , with MOKV isolates displaying an overall intragenotypic amino acid variation of 6 . 4% . In order to investigate the evolutionary relationship of MOKV , a MCMC analysis was used to estimate the rate of nucleotide substitution calculated in substitutions/site/year as well as the time of the most recent common ancestor ( MRCA ) of MOKV ( Table 4 ) . When analyzing the N and G gene datasets , the mean nucleotide substitution rate was estimated to be 2 . 172×10−4 ( N ) and 2 . 123×10−4 ( G ) . This is in agreement with previously published nucleotide substitution rate estimates ( N gene: 1 . 1×10−4 to 3 . 8×10−4 substitutions per site per year , G gene: 5 . 56×10−4 to 1 . 286×10−3 substitutions per site per year ) [40] , [45] , [46] , [47] , [48] . The age of the MRCA of MOKV was estimated to be 591 years old ( 95% HPD 294–1005 years ) or 657 years old ( 95%HPD 279–1174 years ) , respectively . Analyses of the M-gene and P-gene yielded less robust estimates ( 1883 years and 1703 years respectively ) ( supplementary material , Fig . S2 and S3 ) with 95% HPD ranges much wider that the estimates for the N and G gene datasets . This is possibly due to the more variable nature of the M- and P-genes . Estimates based on the concatenated sequences ( N , P , M and G-gene coding regions ) yielded estimates within the ranges of the other genes ( 1157 years old , 95%HPD 413–2034 years ) ( Fig . 3 ) . The successful use of NGS on four of the isolates ( RV4 , RV1017 , RV1021 and RV1035 ) enabled full genome consensus sequences to be obtained without the use of specific primers . In comparison to the work involved to obtain gene specific sequences for N , P , M and G on each of the MOKV isolates this approach was relatively simple and time efficient . Of the total number of reads obtained from the brain RNA preparations , between 0 . 25 and 1% were viral equating to between 294 and 1006 reads .
This study was aimed at producing further insights into the phylogeny and diversity within a unique African lyssavirus species , MOKV . It was our objective to include all MOKV's encountered in history , but the existence or identity of several reported viruses and/or isolates could not be corroborated ( Table 1 ) . These included 3 virus isolates that were reported from Nigeria , the existence of which is now doubtful [6] , [8] , [9] and an isolate reported recently from South Africa [20] . It was also unfortunate that isolates from Cameroon and Ethiopia had to be excluded from this study , as these viruses , in our hands , were likely of the same original stock . Nevertheless , a panel of 18 MOKV isolated over a period of nearly 50 years ( Table 1 ) could be included and thus represents the most comprehensive phylogenetic analysis of full N , P , M and G genes of the MOKV species . The monophyletic grouping of isolates from the KZN province of South Africa , isolated over a period of 28 years , indicates the continual presence and stability of the same viral lineage in this geographical domain . This KZN group could be distinguished from the other South African MOKV group , from the EC province , but the time point of divergence is rather recent with the MCRA for these two MOKV groups in the order of 150 years . The sequence diversity observed also seems to determine biological properties of the isolates . Parallel experimental infection studies in mice showed that the pathogenicity of MOKV ( isolates 12341 , 252/97 , Table 1 ) had been underestimated , although specific markers could not be determined [49] . Our analysis using a more comprehensive set of sequences corroborated these results ( Table 2 ) , but the relevance need to be confirmed by further studies . Previously , when lyssaviruses were still classified according to genotypes , it was proposed that a new genotype is defined by >80% nucleotide differences and >92–93% amino acid differences [50] , [51] . Although this classification is no longer used , the p-distance analysis in this study indicated the MOKV isolates fall within the defined ranges . The MOKV isolates also displayed less sequence divergence than that seen among LBV isolates ( 20 . 9% nucleotide sequence difference and 6 . 7% amino acid sequence difference between LBV isolates ) . However , the delineation between LBV and MOKV using maximum clade credibility appears not as robust as when using M-gene where LBV isolates rather cluster with MOKV ( supplementary material , Fig . S3 ) . MRCA estimates of MOKV utilizing different genes ranged widely from 591 to 1883 years ( HPD 214–4318 years ) . Although these dates for the MOKV MRCA correspond with the timeframe estimated by Bourhy et al . [47] for the emergence of RABV associated with non-flying mammals ( 749 years ago , 95% HPD 363–1215 years ) , it must be noted that the small sample size of MOKV could also influence the robustness of the results . Also , purifying selection can mask the ancient age of viruses that appear to have recent origins as shown for other RNA-viruses [52] , [53] , thus making it difficult to objectively model the evolutionary history of MOKV . Use of NGS technologies to obtain four of the MOKV genomes directly from RNA preparations without amplification using specific primers was highly successful . Unlike the approach taken for the other isolates where often primers had to be designed for each specific isolate due to the high divergence seen in the sequences between the MOKV viruses , for the NGS approach the same methodology , i . e . random priming was applied to all isolates , regardless of their divergence . Although a high percentage of non-target sequences were produced , full coding sequences were obtained from all four MOKV isolates which was then put forward to the individual gene analyses . Given the inherent accuracy of 99 . 9% of Roche 454 and a sequence depth between 294 and 1006 reads per position the inclusion of sequencing errors are highly unlikely . Twenty three MOKV isolations and one PCR-based identification have been made to date from six African countries in a period of more than 40 years . Since these African countries are from regions in Africa that are far apart , it is likely that MOKV is present in many others African countries and spread over vast portions of the continent . Moreover , over the past almost 20 years MOKV has only been isolated from South Africa ( Fig . 1 ) . Since it is known that MOKV is not only limited to South Africa , the lack of isolation from elsewhere is reflective of the non-existence of appropriate surveillance , including for rabies virus , across Africa . Limited diagnostic capabilities ( e . g . typing or sequencing of rabies cases/specimens/isolates ) across the continent , remains a key factor . Such enhanced surveillance would likely result in the discovery of more isolates and therefore , a higher diversity of MOKV and would thus improve our understanding of MOKV incidence and circulation . Since rabies vaccines do not offer protection against MOKV , a case can be made for the relative importance of a better understanding of the ecology of MOKV [32] . One of the limiting factors in studying MOKV is the fact that the reservoir species for this virus is not known . Although shrews have been implicated , it remains speculative . Lyssaviruses have a strong association with bats and it seems peculiar that MOKV may be the only exception in this regard - among all the other members of the genus . Indeed , virus neutralizing antibodies ( VNA ) , neutralizing both LBV and MOKV have been detected in sera from frugivorous bats ( Rousettus aegyptiacus and Eidolon helvum ) [54] , [55] , [56] . However , belonging to the same phylogroup II , LBV and MOKV have been reported to cross react in serological assays [7] , [24] , [45] , [57] . Since there have been repeated reports of LBV isolations from fruit bats [58] , [59] , the neutralizing activity of bat sera to MOKV apparently does not confirm the circulation of MOKV in those bat species . However , it cannot be excluded that other yet unidentified African bats may act as reservoir for MOKV . On the other hand , consistent encounters of MOKV in domestic cats and small mammalian species invite speculation along the lines of a prey-to-predator transmission pathway . For MOKV , the estimated MRCA from our study coincides and provides support for the timeframe suggested for the emergence of terrestrial rabies [47] . It is possible that MOKV remained stable in an extant African host environment , while RABV evolution was vastly accelerated given a plethora of host opportunities and global distribution . | According to the World Health Organization , rabies is considered both a neglected zoonotic and tropical disease . Among all the lyssavirus species known to exist today , Mokola virus is unique and appears to be exclusive to Africa . In contrast to all other known virus species in the genus Lyssavirus of the family Rhabdoviridae , its reservoir host has not been identified yet . As only limited sequence information is available , this study significantly contributes to the understanding of the genetic diversity and relatedness of Mokola viruses . In a collective approach , the complete nucleoprotein , phosphoprotein , matrix , and glycoprotein genes of all Mokola viruses isolated to date were sequenced in various rabies laboratories across the world . A phylogenetic analysis was undertaken and the most recent common ancestor was determined . Subsequently , results were linked to epidemiological background data . We also conducted a comparative study of distinct antigenic sites considered influential for viral properties within those genes . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Diversity and Epidemiology of Mokola Virus |
Epidermal keratinocytes form a highly organized stratified epithelium and sustain a competent barrier function together with dermal and hematopoietic cells . The Notch signaling pathway is a critical regulator of epidermal integrity . Here , we show that keratinocyte-specific deletion of total Notch signaling triggered a severe systemic B-lymphoproliferative disorder , causing death . RBP-j is the DNA binding partner of Notch , but both RBP-j–dependent and independent Notch signaling were necessary for proper epidermal differentiation and lipid deposition . Loss of both pathways caused a persistent defect in skin differentiation/barrier formation . In response , high levels of thymic stromal lymphopoietin ( TSLP ) were released into systemic circulation by Notch-deficient keratinocytes that failed to differentiate , starting in utero . Exposure to high TSLP levels during neonatal hematopoiesis resulted in drastic expansion of peripheral pre- and immature B-lymphocytes , causing B-lymphoproliferative disorder associated with major organ infiltration and subsequent death , a previously unappreciated systemic effect of TSLP . These observations demonstrate that local skin perturbations can drive a lethal systemic disease and have important implications for a wide range of humoral and autoimmune diseases with skin manifestations .
The vertebrate skin is an organ in which keratinocytes , underlying mesenchymal cells , and circulating hematopoietic cells engage in reciprocal communication as they monitor organ integrity [1] . Therefore , skin is an ideal system in which to study how complex , multicompartmental networks function . Epidermal keratinocytes are organized in several distinct layers with the innermost ( basal ) layer containing the stem cells and transiently amplifying cells [2] . The next layer contains spinous cells that , under normal conditions , begin a terminal differentiation program , giving rise to granular and cornified layers [3 , 4] . However , after injury , spinous cells proliferate to contribute to the restoration of an intact integument and produce cytokines that trigger an inflammatory response [1] . Defects in execution of this terminal differentiation program can lead to what are collectively known as skin-barrier defects [5] . Notch activation contributes to spinous cell differentiation . Loss of canonical Notch signaling , induced by deletion of the RBPSUH gene ( coding for RBP-j , the DNA binding partner of Notch ) , causes severe epidermal barrier and differentiation defects highlighted by reduced spinous , granular , and cornified layer cells [6] . However , overexpression of activated Notch1 exclusively in basal cells under the K14 promoter triggers their premature differentiation into spinous cells , concomitant with loss of proliferative capacity [6] . The pattern of Notch1 activation in epidermal keratinocytes is consistent with its proposed role in suppressing basal cell proliferation and promoting spinous cell differentiation via cell autonomous modulation of targets [6–11] . However , overexpression of activated Notch1 in the differentiated spinous cells ( where it is normally present ) triggers basal cell hyperproliferation and formation of acanthotic epidermis with a thickened granular layer [12] , indicating that the Notch signaling pathway has a complex role by not only promoting differentiation and exit from the basal layer but also by contributing in a non-cell autonomous fashion to skin homeostasis [12 , 13] . How Notch performs its functions within spinous cells is a matter of some controversy , and multiple autonomous mechanisms have been proposed , including both canonical and noncanonical pathways [6–11] . To study the role of Notch signaling in skin homeostasis and barrier formation , we used the Msx2-Cre line to delete components of the Notch signaling pathway in skin keratinocytes [14] . A burst of Msx2-Cre expression on embryonic day 9 . 5 ( E9 . 5 ) creates chimeric skin with dorsal and ventral patches ( clones ) lacking floxed alleles , confining the consequences of gene loss only to a fraction of the surface area ( for a detailed analysis of Msx2-Cre expression in skin , see [14 , 15] ) . This allows animals lacking total Notch signaling to survive through birth ( Figure 1A ) . With this system , we have shown previously that Notch loss also involves non-cell autonomous alteration in transforming growth factor ß and insulin-like growth factor signaling [15] . Removal of both Notch1 and Notch2 proteins or both presenilin-1 ( PS1 ) and presenilin-2 ( PS2 ) proteins ( the catalytic subunits of γ-secretase ) within epidermal clones is sufficient to cause early postnatal lethality [14] . In the current study , we investigated the mechanistic basis for this early demise . The progressive loss of Notch alleles in skin keratinocytes generated a dose-dependent increase in thymic stromal lymphopoietin ( TSLP ) expression by suprabasal keratinocytes in direct response to defective skin differentiation/barrier formation in utero . TSLP is a recently discovered , epidermally derived cytokine implicated in the pathogenesis of atopic dermatitis and asthma [16] . Like interleukin 7 ( IL-7 ) , TSLP can support B cell development; however , the in vivo effects of high TSLP levels on B-lymphopoiesis are not fully understood [17] . A perinatal increase in TSLP levels produced a dose-dependent expansion of pre- and immature B cells in the periphery , causing a B-lymphoproliferative disorder ( B-LPD ) , a previously unappreciated effect of TSLP . In its extreme form , B-LPD complications , including B cell infiltration into vital organs , culminated in death . Therefore , this study provides the first physiological confirmation that skin perturbation can cause a lethal systemic disease .
Removal of total Notch signaling from the skin using Msx2-Cre led to death at weaning ( Figure 1B; see [14] ) . To identify the cause of death in Msx2-Cre/+; N1flox/flox; N2flox/flox ( N1N2CKO ) and Msx2-Cre/+; PS1flox/flox; PS2–/– ( PSDCKO ) mice , a comprehensive necropsy was performed on the moribund animals . Surprisingly , we found that mice from both genotypes had extremely high white blood cell ( WBC ) counts ( >150 , 000 cells/μl ) at around the time of death ( Figure 1C ) . Importantly , analysis of an extensive Notch allelic series revealed that the severity of the leukocytosis during the first few weeks of life was correlated inversely with Notch dosage ( Figure 1C ) . To characterize the cells causing leukocytosis , we analyzed various hematopoietic parameters in Notch-deficient mice . Peripheral blood analysis showed that the leukocytosis in both N1N2CKO and PSDCKO animals was of a lymphoblastic/lymphocytic nature; hence the mice displayed a severe case of lymphoproliferative disorder ( LPD; Figure 2A ) . This LPD was accompanied by the failure of bone marrow ( BM ) to participate in normal hematopoiesis , as demonstrated by reduced red blood cells and platelets ( normocytic anemia and thrombocytopenia; Figure 2A ) . Because the blood phenotypes of N1N2CKO and PSDCKO mice were similar , we use the term “mutant” to describe the common LPD features of both genotypes and “wild type” for all genetic combinations that either lack Cre or carry Msx2-Cre and a wild-type allele of PS1 . Macroscopic examination showed that mutant mice were smaller than their wild-type littermates and had enlarged spleen and lymph nodes but smaller than normal thymus , suggesting that the expanding lymphocytes are of B cell origin ( Figure 2B and Table S1 ) . Flow cytometry ( FC ) analysis on blood from mutant animals confirmed that the expanding population contributing to LPD was of the B cell lineage ( B-LPD; Figure 2C ) . These observations suggested that extreme B-LPD could be the cause of death in PSDCKO and N1N2CKO animals . Unexpectedly , Msx2-Cre/+; RBP-jflox/flox ( RBP-jCKO ) mice lived significantly longer ( 95 days on average ) and showed lower WBC counts ( ∼70 , 000 cells/μl ) compared to mice lacking Notch receptors or γ-secretase ( Figure 1B and 1C ) . Although we could not exclude the possibility that Cre-mediated deletion of RBPSUH is marginally less efficient than that of PS1 or Notch1 and Notch2 , examination of RBP-j protein in RBP-jCKO animals confirmed its loss in keratinocytes ( Figure S1 ) . If RBP-j could actively repress some target genes in the absence of Notch signaling and this repression would be lost upon RBP-j deletion , then the milder RBP-j phenotype could be explained by de-repression of some targets that ameliorated the phenotype [18] . Alternatively , the Notch pathway may have bifurcated; RBP-j-independent ( yet γ-secretase-dependent ) targets of Notch in skin [10 , 19] may contribute to the severity of the phenotypes reported here . To distinguish between these possibilities , mice that lack both γ-secretase and RBP-j in the skin were generated ( Msx2-Cre/+; PS1flox/flox; PS2–/–; RBP-jflox/flox; PSDRBP-jCKO ) . In this genetic experiment , de-repressed targets should suppress the PSDCKO phenotype ( i . e . , PSDRBP-jCKO would display the RBP-jCKO phenotype [18] ) . However , if RBP-j-independent targets of Notch contribute to the life span and leukocytosis , then the γ-secretase mutation should be epistatic to RBP-j ( i . e . , the animal will have the PSDCKO phenotype ) . A combination of both would be expected to produce an intermediate phenotype . The PSDRBP-jCKO mice lost RBP-j yet had the same life expectancy as PSDCKO mice ( Figure 1B ) and had WBC counts comparable to those of PSDCKO ( Figure 1C ) , a result inconsistent with target de-repression . To confirm that these outcomes were Notch-dependent but RBP-j-independent , we also created the triple mutant Msx2-Cre/+; N1flox/flox; N2flox/flox; RBP-jflox/flox ( N1N2RBP-jCKO ) . As with PSDRBP-jCKO animals , the life span of N1N2RBP-jCKO animals was not prolonged by removal of RBP-j ( Figure 1B and 1C ) , indicating that a Notch-dependent activity contributes to the phenotype . This suggested that canonical Notch signaling could not be the sole determinant of the phenotypes described and instead supports the alternative assertion that RBP-j-independent effects of Notch in the skin may contribute to leukocytosis [10 , 20] . This result provides the first genetic evidence in a vertebrate to support the existence of a bifurcation in Notch signaling downstream of γ-secretase . To understand B-LPD progression better , the WBC count from mutant animals was measured over their life span ( Figure 2D ) . The WBC counts increased exponentially over the first 2 weeks starting at around P4 but plateaued during the third week of life within the same time window when most mutant animals expired . However , to our surprise , the longest surviving mice showed a trend toward normalization of their WBC counts ( Figure 2D ) . A reduction in peripheral B cell number ( and thus normalization of WBC count ) was more evident in animals with partial loss of Notch signaling and in the longer living RBP-jCKO mice ( unpublished data ) . Accompanying the surge in peripheral WBC count , B220+ B cells expanded the spleen ( splenomegaly ) and infiltrated several vital organs including lung and liver ( Figure 2E , 2F , and Figure S2A ) . To characterize the identity of the expanding cells in B-LPD further , two additional markers , CD43 and IgM , were applied to segregate pro- , pre- , and immature B cell populations by FC ( Figure 3A ) . The FC analysis showed that pre-B cells ( B220+CD43–IgM– ) constituted the majority of the expanding population in both BM and periphery ( Figure 3B , red ovals ) . Immature B cells also expanded , as would be expected if differentiation of pre- to immature B cells persisted in the mutant animals ( Figure 3B ) . The ability of pre-B cells to differentiate and the high WBC counts reached at an early age are consistent with a polyclonal origin of this B-LPD [21] . Large expansion of immature B cells was associated with elevated IgM levels , which precipitated at low temperature , a condition known as cryoglobulinemia ( Figure S2B ) . Overall , despite the short duration of B-LPD in Notch-deficient mice , we hypothesized that extremely high WBC count , leukostasis , cryoglobulinemia , anemia , and infiltration of B cells into vital organs conspired with the skin phenotype to produce cachexic mice that failed to thrive during the early stages of postnatal development . To determine whether B-LPD was an important contributor to early death , we performed an allogeneic bone marrow transplantation ( BMT ) experiment with mutant animals as the recipients . Lethally irradiated mutant mice transplanted with BM derived from their wild-type littermates around postnatal day 10 ( P10 ) lived significantly longer than their untransplanted counterparts ( Figure 4A ) . However , the transplanted mutants still died within a few weeks after transplantation , this time because of severe skin phenotypes including exfoliation , bleeding , inflammation , and infection ( Figure S2C ) . When treated with systemic antibiotics , the life span of transplanted N1N2CKO animals was extended further and became comparable to that of RBP-jCKO mice ( Figures 4A and 1B ) . However , antibiotic treatment of PSDCKO mice did not extend further their life span due to the greater severity of skin disease in these animals ( Figure S2C ) . The WBC counts and FC analyses showed no reoccurrence of B-LPD in the transplanted mutants ( Figure 4B and 4C ) . However , a significant expansion of granulocytes/monocytes was observed in the peripheral blood of transplanted mutant animals during the final days of life with WBC counts reaching ∼20 , 000 cells/μl ( Figure 4B , 4C , and Figure S3 ) . Similar granulocytosis accompanied by elevated peripheral T-lymphocyte percentage was observed in RBP-jCKO mice of the same age ( Figure S3 ) . The observations detailed above suggested that if B-LPD was controlled , then life expectancy of the mutant animals could be increased significantly . Indeed , either a sublethal dose of total body irradiation ( ∼450 cGy ) or focal irradiation of the mutant animals extended their life span . In both cases , life expansion correlated with a delay in B-LPD surge ( Figure S4 ) . Taken together , these experiments strongly demonstrated that B-LPD , acquired by animals with Notch- or γ-secretase-deficient skin , was a critical mediator of early lethality . However , the short life expectancy of transplanted mutants and RBP-jCKO was related to their skin disease , infection , and granulocyte/monocyte expansion , which again occurred in a Notch dose-dependent manner and in all adult animals lacking canonical Notch signaling ( unpublished data and Dr . Freddy Radtke , personal communication ) . Considering the importance of Notch signaling at various stages of lymphopoiesis [22 , 23] and the potential for ectopic expression of Msx2-Cre in a hematopoietic organ , we asked whether deletion of Notch in BM or any hematopoietic organ might have caused B-LPD . To map the sites of Msx2-Cre activity thoroughly , we applied the following approaches . First , using a PCR protocol designed to detect the deleted allele of PS1 ( PS1Δ ) , we confirmed that neither hematopoietic cells nor any hematopoiesis-related organ experienced Cre-mediated deletion of PS1 in PSDCKO mice ( Figure 4D ) . Of note , we collected peripheral blood from the mutant animals at peak WBC counts , which thus was composed mostly ( >90% ) of expanding B cells , but still found no evidence of PS1 deletion . To rule out the possibility that loss of Notch signaling in a small subset of BM stromal cells could drive B cell expansion in the mutant animals , we re-analyzed Prx1-Cre; PS1flox/flox; PS2–/– mice in which Cre is active in BM stroma , the osteoblasts [24 , 25] , and the osteoclasts [26] . The WBC analysis in all these mice showed no sign of B cell expansion ( unpublished data ) , indicating that another organ provided the trigger for B-LPD . Next , we analyzed two reporter lines , Msx2-Cre; ZEG/+ and Msx2-Cre; Rosa26R/+ . Only the skin was marked by these reporters ( unpublished data ) with no detectable reporter staining in any hematopoietic lineage or organ . Together , these findings led us to hypothesize that B-LPD was driven non-autonomously in wild-type B cells as a consequence of reduced Notch signaling in the skin . To test this hypothesis , we applied the allogeneic BMT paradigm to ask whether hematopoietic stem cells isolated from the mutant animals could propagate B-LPD in normal recipients . The BM derived from mutant or wild-type littermates was equally competent in its ability to engraft in lethally irradiated wild-type littermate hosts and reconstitute a complete hematopoietic system that sustained a normal WBC count in the recipient animals over several months of follow-up ( Figure 4E ) . The PCR analysis confirmed the complete repopulation of the recipients' hematopoietic system by donor-derived BM ( Figure S5B ) . In addition , BM transplanted from mutant or wild-type animals into sublethally irradiated nonobese diabetic/severe combined immunodeficiency ( NOD/SCID ) mice were indistinguishable in their ability to rescue the recipients ( Figure S6 ) . Collectively , these findings confirmed the non-autonomous nature of B-LPD and implicated the skin as the primary organ responsible for the disease in Notch/γ-secretase-deficient animals . Having identified skin keratinocytes as the only cells in which Cre-mediated deletion of Notch signaling was occurring , we hypothesized the existence of ( an ) epidermally derived cytokine ( s ) capable of driving B cell expansion that accumulated to high systemic levels in inverse correlation with Notch dose . To search for such factor ( s ) , we performed microarray analysis of mutant and wild-type total-skin RNA samples collected at P9 . Given the dose–response observed with life expectancy and B-LPD , we performed a modified trend analysis asking for transcripts that were modestly elevated in Notch1-deficient ( N1CKO ) skin but substantially elevated in N1N2CKO or PSDCKO skin . A small subset of altered transcripts , highly enriched for chemokines and chemoattractants , displayed the desired trend ( Figures 5A and S7 and Table S2 ) . Among them , TSLP was the second most abundant transcript and the only epidermally derived cytokine capable of driving fetal B cell proliferation in mouse [17 , 27] . Quantitative reverse transcription PCR ( qRT-PCR ) on epidermal mRNA samples confirmed a ∼20-fold increase of TSLP mRNA in mutants ( Figure 5A and Table S2 ) , and immunohistochemical analysis on skin sections identified suprabasal keratinocytes as the source of TSLP ( Figure 5B and Figure S8 ) . ELISA measurements detected TSLP levels >5000-fold above the normal levels in sera from mutant mice ( ∼50 ng/ml versus <10 pg/ml ) that were already detectable at birth ( unpublished data ) . A comprehensive serum analysis failed to detect differences in any other cytokine or autoimmune signature that could provide an alternative mechanism for B-LPD in the mutant mice ( Table S3 ) , including IL-7 , the main cytokine implicated in B cell development . Interleukin 6 ( IL-6 ) , the only other cytokine implicated in B-lymphopoiesis , which showed moderate yet significant up-regulation in the skin microarray trend analysis , was elevated <2-fold in serum ( Figure 5C ) . Examination of the sera from entire allelic series of Notch-deficient mice by ELISA confirmed and extended our trend analysis: The TSLP levels showed a strong inverse correlation with the dose of Notch signaling and life expectancy ( Figure 5D ) and a direct correlation with WBC counts of the mutant animals ( Figures 1B , 1C , 5D , and Figure S9 ) . Collectively , RNA and protein analyses created a consistent picture pointing to TSLP as the likely candidate satisfying most of the criteria for the B-LPD-inducing agent: sensitivity to reduction in Notch dose , systemic availability , and the ability to promote B cell development [17 , 27] . However , such a proliferative role for TSLP has not been demonstrated previously . To satisfy Koch's postulate for disease causation , we injected recombinant mouse TSLP into wild-type mice . Daily injection of the animals with TSLP starting at birth and continuing for 7 days led to a dose-dependent elevation of WBC count ( Figure 6A ) . The FC analysis on peripheral blood from the mice injected with TSLP identified the expanding cell population as B220+ B-lymphocytes , not seen in mice receiving carrier alone ( Figure 6B ) . Further analysis of peripheral blood from wild-type mice receiving 1 μg of TSLP identified the expanding B cell population as pre- and immature B cells ( Figure 6C ) . Injecting wild-type animals with 1 μg of TSLP daily resulted in steady-state serum TSLP levels of ∼250 pg/ml , comparable to that in N1CKO animals ( Figures 6A , 5D , and Figure S9 ) . Likewise , WBC counts in these animals were also indistinguishable from those seen in N1CKO mice at P8 ( Figures 6A , 1C , and Figure S9 ) . Thus , elevated TSLP levels were sufficient to cause mild B-LPD in an otherwise normal newborn mouse . Significantly and in agreement with published reports , no surge in WBC count was detected when the treatment regimen was initiated at P14 or later ( Figure 6A; [28 , 29] ) . To demonstrate a correlation between endogenous levels of TSLP and high WBC counts , we analyzed K14-TSLPtg transgenic mice [30] . This analysis revealed high serum TSLP levels of ∼3 ng/ml in K14-TSLPtg newborns and , as predicted by our hypothesis , neonatal B-LPD similar to that observed in RBP-j-deficient animals ( Figure 6D–G and S9 ) . Importantly , B-LPD in K14-TSLPtg newborns developed in the absence of any overt skin morphology ( unpublished data ) , indicating that elevated TSLP is not impeding epidermal differentiation . Consistent with the findings above , K14-TSLPtg animals also developed B-LPD only during the neonatal period , which disappeared later in life despite elevated serum TSLP levels ( Figure 6D and 6E ) . This experiment confirmed that TSLP overexpression after the neonatal period did not sustain B-LPD in mice . To ask if B-LPD represented the confluence of high TSLP with a responding , fetal pre-B cell population , we performed fetal liver transplantation ( FLT ) into lethally irradiated mutant animals . Surprisingly , FLT reconstituted normal adult hematopoiesis in the recipients , suggesting that fetal pre-B cells in an adult niche microenvironment lost their ability to respond to high levels of TSLP ( Figure S10 ) . To ask if continuous exposure to high TSLP levels sustained a responding pre-B cell population , we performed a third allogeneic BMT experiment in which BM from the mutant animals was transplanted into their lethally irradiated mutant littermates ( which continued to produce high TSLP levels , unpublished data ) . Again , the donor-derived BM reconstituted normal adult hematopoiesis in the recipients , curing their B-LPD , consistent with the limited temporal window in which B-LPD can develop ( Figure 4A ) . These results confirmed that B-LPD developed as a result of exposure to high TSLP levels in the perinatal period . Loss of Notch signaling in skin keratinocytes leads to elevated epidermal TSLP production and high serum TSLP levels , reaching a maximum only when all Notch proteins or γ-secretase are removed ( Figure 5 ) . The TSLP overexpression thus may be a general response of epidermal keratinocytes to differentiation ( and skin-barrier ) defect [5] . Notch-deficient epidermis was defective in epidermal differentiation , leading to incomplete formation of upper spinous and granular layers , as reported in RBP-j-deficient animals ( Figure 7A; [6] ) . This was accompanied by global down-regulation of skin lipid biosynthetic enzymes and reduced epidermal lipid content ( Figure 7B and 7C and Table S4 ) . Consequently , a defect in skin-barrier function was detectable directly by using the dye exclusion assay; this procedure stains areas with defective barrier , and indeed , only the stereotypical pattern of Cre-expression was stained in the mutant animals , consistent with a barrier defect in γ-secretase-deficient keratinocytes ( Figure 7D; [6] ) . To further ascertain if TSLP overexpression is a consequence of Notch loss or a consequence of defective differentiation/barrier formation , we isolated keratinocytes from mutant and wild-type pups and measured TSLP in the culture medium of cells grown on plastic . Both mutant and wild-type keratinocytes fail to form a fully differentiated epidermis under these conditions , and both secreted similar and significant levels of TSLP , as would be expected if TSLP overproduction was a general consequence of abnormal keratinocyte differentiation and not a specific consequence for loss of Notch signaling ( Figure 7E ) . Because NFκB signaling , a potent inducer of differentiation , can activate TSLP [31 , 32] , we asked if this pathway was responsible for TSLP expression in cultured keratinocytes . Addition of an inhibitor of NFκB signaling , BAY 11–7082 [33] , abrogated TSLP production in a dose-dependent manner in both wild-type and γ-secretase-deficient keratinocytes ( Figure 7E ) . To solidify the conclusion that TSLP overproduction is a readout of failed differentiation and not a specific repressed target of Notch signaling , we analyzed wrfr–/– mice that lack fatty acid transport protein 4 ( FATP4 ) and die at birth because of severe epidermal differentiation and skin-barrier defects [34] . As in Notch-deficient mice , B-LPD was not yet detectable in wrfr–/– mice at birth . These mice , however , did show a substantial surge in skin TSLP transcript levels around the time of stratification and barrier formation ( E15 . 5–17 . 5 ) , which led to elevated serum TSLP levels at birth ( Figure 7F ) . Because FATP4 expression is not altered in Notch-deficient skin ( unpublished data ) and Notch pathway targets are not altered in wrfr–/– skin ( Figure 7F ) , this observation provided an independent confirmation that up-regulation of TSLP expression is a common readout of differentiation/barrier formation defects and not a repressed target of Notch signaling .
This report details the mechanism by which a local perturbation to the skin induced a lethal systemic disease . We demonstrate that progressive loss of Notch signaling in the embryonic ectoderm caused a dose-dependent impairment of epidermal differentiation and reduced lipid biogenesis , stimulating keratinocytes to secrete excess TSLP into systemic circulation . The pathophysiological consequence of persistent differentiation/barrier defects and the subsequent accumulation of TSLP had a potent proliferative effect on fetal/newborn B-lymphopoiesis , causing an exceptional expansion of pre- and immature B cell populations in newborn animals ( i . e . , neonatal B-LPD; Figure 8 ) . A severe B-LPD with its systemic complications including infiltration of B cells into various vital organs , anemia , and cryoglobulinemia explained the early death of the animals lacking total ( canonical and noncanonical ) Notch signaling in the skin . Indeed , we were able to extend the life span of the mutant newborn animals simply by suppressing their B-LPD through either BMT or sublethal or focal irradiation administered before the onset of full-blown B-LPD in the second week of life . The ameliorating effects of these treatments also reflect the fact that pre-B cells emerging in the adult BM niche are refractory to high TSLP levels ( see below ) . A growing body of work has provided extensive evidence that TSLP overexpression in skin or lung epithelia can cause local allergic inflammation and subsequent development of atopic dermatitis and asthma , respectively [16 , 35 , 36] . However , the systemic consequences of TSLP overexpression were not clear [29 , 37] . In this report , we describe the mice in which defects in skin differentiation drive endogenous TSLP expression , beginning before birth . We find that TSLP levels are correlated directly with the degree of disruption in differentiation . This enabled us to recognize a novel consequence for TSLP elevation unique to fetal/newborn hematopoiesis and to ascribe TSLP production to failure in skin differentiation rather than its cause , as high levels of TSLP did not alter skin differentiation in newborn K14-TSLPtg mice . It has been recognized that TSLP has a differential effect on fetal versus adult B-lymphopoiesis [27 , 38] , despite the presence of fully functional TSLP receptors on both fetal and adult B cells [27 , 39] . In addition , in vitro results show that a liver-derived fetal pre-B cell line , NAG8/7 , but not a BM-derived adult pre-B cell line , IxN/2B , proliferate in response to TSLP [40] . Consistent with these findings , we demonstrate that endogenously overexpressed or exogenously supplied TSLP , delivered during the perinatal period , lead to B-LPD and the appearance of a substantial number of pre-B cells ( B220+CD43–IgM– ) in the periphery in a dose-dependent manner ( Figure S9 ) . Transition from fetal/newborn to adult hematopoiesis occurs during the second week of life in the mouse BM [41] . We find that persistently high levels of TSLP do not produce B-LPD after this transition , explaining why B-LPD has not been detected in previous studies [29 , 37] . The narrow window in which TSLP can drive pre-B cell expansion explains why B-LPD in Notch-deficient and K14-TSLPtg animals is ( 1 ) confined to the first few weeks of life , ( 2 ) disappears in animals that experience a less severe disease , and ( 3 ) does not recur in the mutant mice after BMT , because adult-type pre-B cells that are reconstituted from the donor BM do not proliferate in response to high TSLP levels [38] . In addition , the fact that B-LPD does not recur in the mutant mice after FLT is consistent with the hypothesis that the niche is instrumental in regulating the response of pre-B cells to TSLP . This is reminiscent of the critical role the hematopoietic stem cell niche plays in defining the fetal versus adult characteristics of hematopoietic stem cells [42] . N1N2CKO or PSDCKO mutant animals transplanted with either wild-type or mutant BM do not enjoy a normal life span , dying before 3 months of age . Mice lacking some Notch alleles or lacking RBP-j do not develop lethal B-LPD but instead die prematurely from compromised skin integrity , exfoliation , inflammation , and systemic infection ( unpublished data ) . Although antibiotic treatment delays the death of the mutant animals by controlling their infection , we find persistent granulocytosis even in antibiotic-treated mice , suggesting that systemic infection and the resulting inflammatory response are severe and incurable . Infection is the most likely cause of granulocytosis in the mutant animals; however , it is intriguing to speculate that persistently high levels of TSLP also contribute to this blood disease ( [37] and Dr . Freddy Radtke , personal communication ) . Notably , we observe a tight correlation between systemic TSLP levels , WBC counts , and degree of Notch loss in the neonatal skin . All Notch paralogs contribute to skin homeostasis , and stepwise reduction of Notch dosage leads to progressive skin perturbation resembling atopic dermatitis ( unpublished data ) . However , we find no evidence arguing for a specific molecular connection between Notch loss and TSLP . Instead , the tight reverse correlation between serum TSLP levels and Notch dosage in the skin highlights the fact that TSLP levels reflect , with great precision , the magnitude of the differentiation defects . Thus , TSLP may be a direct readout of defects in differentiation , a systemic signal that many insults , including Notch loss , activate . To confirm this , we demonstrate that wild-type and mutant keratinocytes release significant , but similar , levels of TSLP when placed in culture in the absence of any exogenous stimulus ( e . g . , tumor necrosis factor α ) . In addition , wrfr–/– embryos show a substantial increase in epidermal TSLP transcripts in utero around the time of barrier formation . Therefore , TSLP is a keratinocyte “quality control” response to defective differentiation/barrier formation and not a secondary product of functional barrier failure after birth . One model for how the TSLP “sensor” works may be through the compensatory activation of NFκB . However , precisely how Notch loss in vivo leads to NFκB activation remains an important unanswered question that falls beyond the scope of this paper . A striking finding is that RBP-jCKO mice show lower TSLP levels , a sublethal B-LPD , and improved epidermal morphology when compared to mice lacking total Notch signaling ( Figure S11 ) . To differentiate between de-repression of canonical targets and loss of noncanonical target expression , we performed a genetic analysis demonstrating unequivocally that RBP-j-independent Notch activity is of significance in skin homeostasis . This points to the importance of RBP-j-independent effects of Notch , including non-cell autonomous effects on ligand ( Delta and Jagged ) presenting cells , and only when all these arms of Notch signaling are lost , extreme TSLP levels are reached , causing lethal B-LPD ( Figure 8 ) . Identification of the relevant noncanonical targets also falls beyond the scope of this paper . In this report , we identified canonical and noncanonical Notch signaling as essential regulators of epidermal differentiation/barrier formation , TSLP as a faithful reporter of keratinocyte differentiation/barrier defects , and B-LPD as the pathological consequence of chronic , perinatal TSLP elevation . Our analysis was done in a physiologically relevant setting: chronic skin-barrier formation defect caused by localized reduction in Notch signaling in a temporal and dose-dependent manner . How keratinocytes directly sense the degree of differentiation/barrier defect and translate such a stimulus into TSLP output remains an important , unsolved question . Nonetheless , the demonstration that defective skin differentiation can drive a lethal systemic B-LPD in mice through TSLP overexpression and the observation that human B cells also respond to TSLP [29] bring up a therapeutically important possibility that chronic high levels of TSLP may be an initiating factor in loss of B cell tolerance and/or B-lymphocytosis , a leukemia-like disease in humans . More important , this raises the possibility that skin has a central role in driving a wide variety of inflammatory and humoral diseases in which skin complications are also present .
Compound strains of mice were engineered as described [14] . All animals were maintained in mixed genetic backgrounds; however , littermates were compared whenever possible . All mice were kept in the animal facility under Washington University animal care regulations . In studies related to longevity , mice were monitored regularly for sign of cachexia and failure to thrive; care was taken to reduce competition for affected pups by removal of wild-type littermates unnecessary for the study . Severely affected individuals were left with their dams for their entire life span . Morphological details of the cutaneous phenotypes will be published elsewhere . The following cohort of animals was analyzed: Msx2-Cre/+; N1flox/flox ( N1CKO ) , Msx2-Cre/+; N1flox/flox; N2flox/+ ( N1N2hCKO ) , Msx2-Cre/+; N1flox/flox; N2flox/+; N3–/– ( N1N2hN3CKO ) , N1N2CKO , PSDCKO , RBP-jCKO , PSDRBP-jCKO , N1N2RBP-jCKO , Prx1-Cre/+; PS1flox/flox; PS2–/– , Msx2-Cre/+; Rosa26R/+ , Msx2-Cre/+; ZEG/+ , CD4-Cre/+; ZEG/+ , K14-TSLPtg . Wild-type cohorts in this study included Cre-negative littermates , Msx2-Cre/+; PS1flox/+; PS2–/– , Msx2-Cre/+; RBP-jflox/+ , and Msx2-Cre/+; Notch1flox/+ . For hematoxylin-and-eosin staining , tissue samples were fixed in 4% paraformaldehyde in phosphate-buffered saline ( PBS ) , dehydrated in ethanol , and paraffin-embedded . Tissues blocks were sectioned at 5 μm; Lac-Z staining on E15 . 5 embryo was performed as previously described [14] . Immunostaining on paraffin-embedded tissue samples was performed with the following biotinylated antibodies: anti-B220 ( clone RA3-6B2 , BD Pharmingen ) , anti-F4/80 ( Abcam ) , anti-CD3e ( clone 145–2C11 , BD Pharmingen ) , and anti-TSLP ( R&D Systems ) . Horseradish-peroxidase-conjugated streptavidin and DAB substrate kit ( Pierce ) were used to visualize the signal . For RBP-j staining , anti-RBP-j antibody ( clone T6709 , Institute of Immunology ) was used together with biotinylated anti-rat secondary antibody . Sections were counterstained with hematoxylin . Serum TSLP , IL-6 , and IL-7 levels were measured according to the manufacturer's instructions in the Quantikine mouse TSLP , IL-6 , and IL-7 ELISA kits ( R&D Systems ) . Single cell suspensions from peripheral blood , BM , and spleen were prepared for FC analysis as described [43] . The following antibodies were used: anti-B220 ( RA3-6B2 ) conjugated to fluorescein ( FITC ) , phycoerythrin ( PE ) , or peridinin chlorophyll-a protein-cyanin 5 . 5 ( PerCP-Cy5 . 5 ) , anti-CD45 ( 30-F11 ) conjugated to PerCP-Cy5 . 5 , anti-Ly-6G ( 1A8 ) , anti-TER-119 , anti-Thy-1 ( 5E10 ) , anti-CD3e ( 500A2 ) , and anti-CD43 ( S7 ) conjugated to PE , anti-IgM ( R6–60 . 2 ) conjugated to FITC and PE ( all from BD Pharmingen ) . Stained cells were studied using a BD FACScan Flowcytometer ( Cytek Development ) , and the data were analyzed using FlowJo software . For allogeneic BMT , the immunocompetent recipient mice were lethally irradiated with 950 cGy at ∼P10 and transplanted with freshly harvested , unfractionated BM cells from their littermates as previously described [43] . However , NOD/SCID mice received BMT after a sublethal dose of irradiation ( 300 cGy ) . In each case , 2 × 106 cells in 100 μl of PBS + 2% fetal bovine serum were injected into the retro-orbital sinus of the irradiated recipient animal . A cohort of transplanted N1N2CKO mice were treated orally with 50 μl of 200 mg/ml cephalexin ( Ranbaxy Pharmaceuticaks ) twice daily . To study disease progression and monitor disease occurrence/recurrence , all mutant and irradiated/transplanted mice were monitored closely over their life spans for signs of weakness , weight loss , and morbidity . Blood samples were collected from the mandibular vein . Hematological analysis ( Hemavet 950 analyzer , Drew Scientific ) comprised complete blood count including WBCs , platelets , red blood cells , white cell differential counts , and hemoglobin measurements [44] . White cell differential counts were confirmed on blood smears . In addition , blood samples were collected for FC analysis . Moribund mice were euthanized , and peripheral blood , BM , lymph nodes , thymus , lung , liver , and spleen were collected for a comprehensive pathological analysis as described previously [45] . Wild-type mice were injected intravascularly with carrier alone ( 50 μl PBS ) or with carrier containing 0 . 5 , 1 , or 1 . 5 μg of recombinant mouse TSLP ( R&D Systems ) daily for 7 d starting on P0 , P7 , or P14 . Mice were euthanized 12 h after the last injection , and tissues were collected for analysis . An ELISA assay of serum measured the systemic TSLP level . Complete blood count and FC analyses on BM , blood , and spleen were performed to check for any sign of B-LPD . To test barrier function defect , a dye penetration assay was performed as previously outlined [46] . Briefly , intact E18 . 5 embryos were stained in X-gal ( pH 4 . 5 ) for 12 h at 37 °C . After X-gal staining and three rounds of PBS wash , the embryos were photographed with a digital camera . Primary keratinocytes were isolated from the dorsal midline skin of newborn mutant and wild-type littermates . The cells were maintained in 60-mm2 dishes with a medium of low calcium concentration as previously described [47] . Keratinocytes were plated at 40% confluence and allowed to double . Confluent plates ( 80% ) were treated with 2 . 5 , 5 , or 10 μM NFκB inhibitor ( BAY 11–7082 ) or carrier alone ( DMSO ) . After 24 h , cells were re-fed with inhibitor/DMSO containing fresh medium with or without 2 mM CaCl2 to induce differentiation . After 24 h , cells and their media were harvested for analysis . Keratinocyte lysates in SDS were immunoblotted to check for PS1 ( H-70 , Santa Cruz Biotechnology ) and α-tubulin ( B-5–2 , Sigma-Aldrich ) as described [15] . Frozen skin sections , 7 μm , were stained with 0 . 15 mg/ml Nile Red in 75% glycerol for 2 min and counterstained with DAPI [48] . Conventional PCR for PS1 alleles was done on genomic DNA of most tissues . For blood , fresh or frozen blood samples were used directly as template with KlenTaq10 ( DNA Polymerase Technology ) supplemented with 1 . 3 M final concentration of betaine . qRT-PCR was performed as described [15] . The primer sequences are provided in the Text S1 . Detailed description of microarray analyses on skin or epidermal mRNA samples from P9 Notch-deficient animals and skin mRNA samples from wrfr–/– embryos are provided in the Supporting Information ( Text S1 ) . The bar graphs present the mean and standard deviation of each measured parameter . Student's t-test is applied as the test of significance unless otherwise specified .
Accession numbers for genes mentioned in this paper from the National Center for Biotechnology Information ( http://www . ncbi . nlm . nih . gov ) are FATP4 ( AJ276492 ) , NFκB ( AY521463 ) , Notch1 ( NM_008714 ) , Notch2 ( NM_010928 ) , Notch3 ( NM_008716 ) , PS1 ( NM_008943 ) , PS2 ( NM_011183 ) , RBP-j , ( NM_009035 ) , and TSLP ( NM_021367 ) . | Skin is the largest organ of the body , forming an elaborate barrier that prevents water loss and protects the internal environment from outside invaders . When this barrier is compromised , keratinocytes , keratin-producing epidermal cells , alert and recruit the immune cells to the site of the breach as part of an adaptive defense mechanism . However , chronic activation of such an “alarm” could have undesired consequences . Using genetic engineering to progressively remove components of Notch signaling from mouse skin in utero resulted in chronic skin-barrier defects , mimicking a form of human skin disease called atopic dermatitis . Surprisingly , we discovered that a persistent alarm signal in newborns triggered a systemic B-lymphoproliferative disorder , which precisely mirrored the degree of skin defect and was lethal in its extreme form . This alarm signal , in the form of a cytokine called thymic stromal lymphopoietin , was produced by Notch-deficient keratinocytes that failed to form a competent skin barrier . Therefore , we uncovered a long-range proliferative effect on fetal pre-B cells in vivo that is induced by injured skin and mediated by thymic stromal lymphopoietin . These findings highlight the central role that skin-derived factors can play in initiating systemic diseases with skin involvement . | [
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] | 2008 | Notch-Deficient Skin Induces a Lethal Systemic B-Lymphoproliferative Disorder by Secreting TSLP, a Sentinel for Epidermal Integrity |
The Onchocerca ochengi adult implant and Brugia malayi microfilariemic Severe-Combined Immunodeficient ( SCID ) mouse models are validated screens to measure macrofilaricidal and microfilaricidal activities of candidate onchocerciasis drugs . The purpose of this study was to assess whether 5 daily sub-cutaneous ( s . c . ) injections of standard flubendazole ( FBZ ) suspension ( 10mg/kg ) , a single s . c . injection ( 10mg/kg ) or 5 daily repeated oral doses of FBZ amorphous solid dispersion ( ASD ) formulation ( 0 . 2 , 1 . 5 or 15mg/kg ) mediated macrofilaricidal efficacy against O . ochengi male worms implanted into SCID mice . The direct microfilaricidal activity against circulating B . malayi microfilariae of single dose FBZ ASD formulation ( 2 or 40 mg/kg ) was also evaluated and compared against the standard microfilaricide , ivermectin ( IVM ) . Systemic exposures of FBZ/FBZ metabolites achieved following dosing were measured by pharmacokinetic ( PK ) bioanalysis . At necropsy , five weeks following start of FBZ SC injections , there were significant reductions in burdens of motile O . ochengi worms following multiple injections ( 93% ) or single injection ( 82% ) . Further , significant proportions of mice dosed following multiple injections ( 5/6; 83% ) or single injection ( 6/10; 60% ) were infection negative ( drug-cured ) . In comparison , no significant reduction in recovery of motile adult O . ochengi adult worms was obtained in any multiple-oral dosage group . Single oral-dosed FBZ did not mediate any significant microfilaricidal activity against circulating B . malayi mf at 2 or 7 days compared with >80% efficacy of single dose IVM . In conclusion , multiple oral FBZ formulation doses , whilst achieving substantial bioavailability , do not emulate the efficacy delivered by the parenteral route in vivo against adult O . ochengi . PK analysis determined FBZ efficacy was related to sustained systemic drug levels rather than achievable Cmax . PK modelling predicted that oral FBZ would have to be given at low dose for up to 5 weeks in the mouse model to achieve a matching efficacious exposure profile .
Onchocerciasis remains a severe public health problem despite the sustained efforts of mass drug administration ( MDA ) programs aimed at eliminating this vector-borne , parasitic neglected tropical disease [1–3] . Elicited by the filarial nematode Onchocerca volvulus and transmitted by black flies of the genus Simulium , it is endemic in much of Sub-Saharan Africa , as well as more limited foci in Brazil , Venezuela , and The Yemen with 37 million people infected [4 , 5] . The pathology associated with onchocerciasis ranges from troublesome skin itching , skin disease ( onchodermatitis ) , to blindness , which is caused by a sclerosing ocular keratitis that affects 0 . 8 million individuals and is the second cause of infectious blindness after trachoma [5–7] . Onchocercal disease is induced by death of migratory larval microfilariae ( mf ) , causing liberation of inflammatory stimuli including the endosymbiont Wolbachia and inflammatory recruitment of granulocytes [8 , 9] . The debilitating symptoms of onchodermatitis and river blindness also cause great economic losses in endemic areas [10] . Onchocerciasis is targeted for elimination as a public health problem [11 , 12] . The African Program for Onchocerciasis Control ( APOC ) and , latterly , The Extended Special Project for Elimination of NTDs ( ESPEN ) were created to this end [13] . The current strategy involves the use of the microfilaricidal drug , ivermectin ( IVM , Mectizan ) . Ivermectin targets mf produced by mating adult filariae , and is deployed using an MDA strategy [2 , 3 , 14 , 15] . Because IVM is microfilaricidal yet lacks significant macrofilaricidal activity , it must be administered repetitively and with very high population coverage over an extended period of time in order to break transmission . This is predicted as at least twelve annual treatment rounds [2 , 3 , 16] . The undeniable success of MDA programs can be seen in certain country settings where a decline in prevalence has been recorded as well as a reduction in disease burden [17] . However , in other countries , the limitation of MDA programs for onchocerciasis within Africa is evident and several persistent areas of infection remain , possibly due to emerging resistance [18–21] . Also , in certain regions , poor adherence to IVM treatment is apparent and associated with sustained skin infection prevalence . Where onchocerciasis MDA overlaps with health districts endemic for the related filaria , Loa loa , low adherence to treatment is linked to the perceived risk of IVM induced severe neurological adverse reactions [22 , 23] . Therefore , if onchocerciasis elimination targets within the ambitious 2030 United Nations Sustainable Development Goal timeframes are to be achieved , there is an urgent need to implement alternative strategies . For these reasons , a safe , chemotherapeutic agent that will selectively kill adult O . volvulus worms without mediating rapid ‘ivermectin-like’ microflaricidal activity , is urgently needed , preferably with a short treatment period of less than 7 days . Typically , the most practical and safest administration of a field agent is through oral dosage [24] . Flubendazole ( FBZ ) , is a benzimidazole anthelmintic that acts through interfering with the equilibrium among tubulin subunits and elicits macrofilaricidal effects by preferentially binding to nematode tubulin [25] . FBZ was initially developed for use against gastrointestinal parasitic nematodes in livestock [26] , but was later approved for use in humans in the treatment of soil-transmitted helminths with high efficacy [27–29] . The drug has also been shown to be highly efficacious in experimental models of lymphatic filariasis when administered subcutaneously [24 , 30 , 31] as well as in a human trial treating onchocerciasis [32] . Onchocerca ochengi , a cattle parasite , is the closest phylogenetic relative to the target human parasite , O . volvulus . Recently , multiple subcutaneous injections of FBZ has mediated >90% efficacy against O . ochengi in severe-combined immunodeficient ( SCID ) mice [33] . Unfortunately , the approved formulation has a very limited bioavailability when orally administered . When administered parenterally , severe reactions around the subcutaneous injection site were reported in the clinical trial [32] . Efforts have been made to develop a re-formulation of FBZ that would enable oral dosing [24 , 34 , 35] . The purpose of this study was to assess the oral efficacy of a new formulation of FBZ ( Janssen Pharmaceutica ) in SCID mice implanted with adult male O . ochengi parasites . Single versus multiple subcutaneous doses of standard FBZ suspension were also compared . In addition , the selective toxicity of oral FBZ against adult Onchocerca versus bloodborne mf in circulation was tested in a Brugia malayi SCID mouse infusion model . The primary goal was to identify regimens that cause minimally 90% reduction in adult Onchocerca parasite numbers in treated animals compared to untreated controls .
Male CB . 17 ( BALB/c congenic ) SCID mice , 5–6 weeks of age , were purchased from Charles River Europe . Mice were shipped to either The Research Foundation for Tropical Diseases and the Environment ( REFOTDE ) , Buea , Cameroon ( for O . ochengi studies ) or University of Liverpool Biological Services Unit ( UoL BSU ) , UK ( for the B . malayi study ) in filter topped boxes . Mice were maintained in individually ventilated caging ( IVC ) with HEPA filtered air system ( Tecniplast , UK ) with autoclaved bedding and fed / watered ad libitum with UV sterilised appropriate certified rodent diet ( irradiated chow ) / autoclaved water . Following a 7 days acclimatization period , SCID mice were 7–8 weeks of age at initiation of studies . Mongolian gerbils were originally purchased from Charles River and a colony bred at UoL BSU under specific-pathogen-free ( SPF ) conditions . All experiments carried out in Cameroon were approved by the Animal Care Committee , REFOTDE . All experiments undertaken in UK were approved by institutional Animal Welfare Ethics Review Boards of University of Liverpool and Liverpool School of Tropical Medicine and were undertaken in accordance with national legislation ( Home Office Project Licence 30/2974 ) . O . ochengi positive cattle from Adamawa Province , Cameroon , were selected by nodule palpation in the umbilical region . The O . ochengi positive cattle were transported to the South West Province and maintained in pastureland local to REFOTDE , Buea . Individual infected cattle were slaughtered at a local abattoir . For each cow , the umbilical region was excised and transported to REFOTDE within 2 hours after slaughter . Skin samples were washed and rinsed in sterile PBS , the hair removed by shaving and onchocercomata ( nodules ) were excised from the dermal surface using sterile scalpels and forceps . Nodules were immediately placed in complete sterile RPMI medium ( 10% foetal calf serum , 100U/ml penicillin , 0 . 1mg/ml streptomycin and 2 . 5μg/ml amphotericin B ) , the capsular surface of the nodule incised and gentle pressure applied to liberate adult stage O . ochengi parasites . Nodules were incubated in petri dishes in complete medium for 4h , 37°C , 5% CO2 to allow adult male parasites to migrate from nodule tissue . Intact , motile male O . ochengi parasites were washed in sterile complete RPMI and incubated overnight 37°C , 5% CO2 . SCID mice were weighed and ear marked , anaesthetised by a combination of ketamine and medetomidine ( 1/10μg/kg subcutaneous ( SC ) ) , administered with prophylactic antibiotic penicillin G , SC , shaved on the upper left abdomen and swabbed with iodine . Anaesthetised mice were arranged on sterile drapes on top of heat pads , and a small incision made through both skin and abdominal wall with sterile surgical instruments . Fifteen motile O . ochengi male worms were picked from cultures using sterile forceps and implanted into the peritoneal cavity with 0 . 5ml RPMI . Efficiency of implantation was confirmed by verifying absence of male worms in the dish and on forceps by washing with medium . Mice were sutured through the abdomen and skin and iodine re-applied , administered with an α2-antagonist ( atipamezole ) and placed on heat pads until recovered . Post-surgery and recovery from anaesthesia , mice were housed in original family groups in IVCs , monitored regularly . B . malayi microfilariae ( Bmmf ) were isolated from the peritoneal cavity of experimentally infected Mongolian gerbils by peritoneal washing in RPMI medium containing 100U/ml penicillin , 0 . 1mg/ml streptomycin ( RPMI+PS ) using a sterile catheter drain , under anaesthesia , as previously described [33] . Motile Bmmf were purified from host cells by PD-10 desalting sephadex column ( GE Healthcare ) at room temperature ( rt ) , washed 3x in RPMI+P/S rt , resuspended in known volume of RPMI+PS , maintained at 37°C , 5%CO2 whilst density was enumerated by microscopy . Motile Bmmf were adjusted to 1 . 25x106/ml in RPMI+PS 37°C . Subsequently , 200μl aliquots were loaded into 1ml low dead space syringes with 29 gauge needles and maintained at 37°C . Mice were placed in small animal thermal cages at 37°C for 5 minutes to dilate the tail vein . Motile Bmmf were infused via the lateral tail vein with manual restraint in a maximum volume of 200μl . Efficiency of inoculation was confirmed by needle washout and enumeration of mf retained in the needle / syringe . Efficiency of inoculation ( ( inoculation number—needle washout number ) / inoculation number x100 ) was >96% in all animals infused . All compounds and vehicles were provided by Janssen and the formulations were prepared on site at REFOTDE , Buea or shipped prepared to Liverpool . The vehicle for FBZ Bend 1/9 oral ( OR ) suspensions ( JNJ-161941-AAA/HPMC AS-HG 1/9 ) was 0 . 5% w/v Methocel A4M ( Premium ) in demineralized water . The vehicle for the FBZ SC suspension was 0 . 5% w/v HEC ( Hydroxyethylcellulose ) in demineralized water + 0 . 1% Tween80 . Briefly , oral FBZ suspensions were prepared from the supplied Spray Dried Drug and first homogenised in demineralised water using a Polytron disperser then made up to volume with Methocel , a different formulation was prepared for each treatment group to keep the dosing volume constant . The subcutaneous suspension was first homogenised in demineralised water + 0 . 1% Tween80 using a Polytron disperser then made up to volume with HEC . Following surgical implantation with O . ochengi or infusion with Bmmf , mice were ear-notched and individuals involved in dosing assigned mice a unique identification code and randomly assigned mice to test or control groups . A sample size of n = 11 for test groups was determined as minimally adequate to detect a ≥90% average reduction in adult male O . ochengi worm burden with >80% power , based on prior variation in worm burden recovery rate in untreated or vehicle dosed SCID mice ( mean % recovery = 29 . 07 , s . d . = 20 . 1 ) [33 , 36] . A group size of n = 5 for test groups was determined as minimally adequate to detect ≥70% average reduction in circulating Bmmf 2 days following treatment based on prior variation in microfilaraemias per millilitre of tail vein blood in vehicle dosed SCID mice ( mean mf/ml = 242 , s . d . = 225 ) [33] . O . ochengi implanted mice from an individual donor source ( each individual cow ) were distributed evenly within each treatment / control group to minimize bias due to inter-donor variation and/or experimental variation in daily isolations potentially affecting long-term adult O . ochengi viability . For O . ochengi testing , two experiments ( A and B ) were conducted . In experiment A there was one untreated ( negative ) control group ( n = 11 ) and a positive control group was included in which FBZ was administered SC by injection at 10 mg/kg once per day ( QD ) for 5 consecutive days ( n = 6 mice ) . In experiment B , there was one vehicle ( negative ) control group ( n = 11 ) . For B . malayi testing , one vehicle ( negative ) control group and one positive control group was included where IVM was administered OR at 0 . 2 mg/kg once ( both n = 5 ) . Dosing for all experiments is listed in Table 1 . Individuals involved in randomisation were also involved in dosing and were thus unblinded to treatment . Individuals responsible for evaluating primary and secondary efficacy readouts were blinded as to treatment group . For all treated groups blood was collected for pharmacokinetic analysis . A plasma profile was sampled on the final day of dosing ( or on the day of dosing for single dose SC groups ) . SC groups were continuously sampled after dosing by one sample per week until necropsy . For each time point , 3 animals per group were sampled , using a micro-sampling technique . Each animal was sampled a maximum of twice per day . The exact time of sampling was recorded for each sample . Approximately 20μl blood was collected per animal from the tail vein , using EDTA-coated Microvette® CB 300 K2E tubes ( Sarstedt ) . Blood was placed immediately on ice prior to centrifugation . After centrifugation ( 1900*g for 10 min ) 10μl plasma was transferred into a 0 . 5 ml Eppendorf tube and placed immediately at -20°C . All plasma samples were shipped on dry ice to Janssen Beerse via Liverpool School of Tropical Medicine in a single batch at the end of the study . Analysis of the plasma samples was done at PD&S-PDM regulated bioanalysis department ( J&J PRD , Beerse ) . Plasma samples were analysed for JNJ-161941 ( FBZ ) and the metabolites JNJ-1809600 ( R-FBZ ) and JNJ-114699 ( H-FBZ ) using a qualified LC-MS/MS method . The following pharmacokinetic parameters were calculated: Cmax , Tmax , and AUC values . Dose-proportionality was evaluated . The plasma curves were prepared in the same matrix as the plasma study samples . For each analytical run QC samples were analysed together with the study samples and calibration curve . All analytical batches were accepted based on calibration curve and QC acceptance criteria in line with the current FDA guidelines . A limited pharmacokinetic analysis on the mean plasma concentrations was performed using Phoenix™ Professional ( Version 6 . 2 . 1 ) . A non-compartmental analysis using the lin/log trapezoidal rule with lin/log interpolation was used for all data . For simulating PK profiles of different dosing regimes , the PK data was fitted using a two compartment model for the SC formulations and a one compartment model for OR preparations . PK parameters were generated for each experimental arm using ADAPT 5 ( Biomedical Simulations Resource–University of California ) . The PK parameters were then then used to generate simulations for different dosing scenarios using the ADAPT 5 simulator . For PK analysis SC and ORl data were fitted using the following equations . Where X1 represents the drug mass in dosing compartment , X2 represents drug mass in the systemic circulation and X3 represents the drug mass peripheral compartment . ka represents the rate of absorption ( h-1 ) , ke the rate of drug elimination ( h-1 ) , kcp and kpc the rates of transfer between the central and peripheral compartments ( h-1 ) , V represents the volume of distribution ( mL ) and C is the concentration of drug at any given time ( mg/L ) . For the OR preparations , a one compartment model was sufficient to fit the data as evidenced by AIC and BIC parameters as well as the intercompartmental transfer rates being negligible . For these reasons , the parameters kcp and kpc were fixed to zero to fit the oral data into a one compartment model . Mice were euthanized by UK Home Office approved schedule 1 method at 5 weeks post-first dose administration . The primary efficacy parameter was the worm burden of live male worms recovered after necropsy from the abdominal cavity , including visceral connective tissues . As secondary readout , cure rate ( number of mice without active infection ) was also evaluated . As a further secondary readout , motility scoring of recovered adult male O . ochengi parasites was determined by visual inspection after 15 minutes in culture in 37°C RPMI post-recovery , including motility in response to gentle prodding with a blunt pipette . A semi-quantitative score for worm motility was applied . Worms that showed no motility were counted as ‘0’ = immotile , ‘1’ = only anterior or posterior twitching motility ‘2’ = reduced sigmoidal motility ‘3’ = full sigmoidal motility . As an additional secondary readout , male O . ochengi recovered at necropsy were washed in PBS and individually placed in a solution of MTT in PBS , final concentration 0 . 5 mg/ml , incubated for 2 hours at 37°C with 5% CO2 . After washing in PBS , the O . ochengi were incubated in 100% DMSO for 1 hour at 37°C with 5% CO2 to dissolve and release the blue formazan product . The samples were read at OD 490 nm . For primary efficacy readout the number of circulating mf were enumerated by tail vein scratch sampling method and collection of 2x20μl fresh blood aspirated by Gilson pipette at baseline and 2 days post-treatment . For secondary readout , mice were euthanized by UK Home Office approved schedule 1 method at 7 days post-treatment . A 40μl blood volume was collected by cardiac puncture immediately after cullling . Immediately after blood collections , volumes were spread onto uncoated glass slides to make air-dried thick smear preparations . Briefly blood was collected either from the tail vein ( 2x20μL , as for peripheral blood microfilaremia ) or from the heart ( 2x30μL at necropsy using a 25G 1mL syringe , as for cardiopulmonary microfilaremia ) and transferred onto a glass slide and then processed for a thick smear through a scratch method . Resulting smear was left to dry then slides were incubated in distilled water for 4min to lyse erythrocytes , fixed in methanol for 1min and finally stained with 40% Giemsa for 40min followed by a wash in distilled water until clear . Total numbers of Bmmf were counted per two replicate slides by microscopy and adjusted ( x25 ) to obtain a parasitaemia per millilitre of blood . Of the 120 thick blood smears evaluated , 10% ( n = 12 ) were independently re-assessed by a scientist not directly involved in the study and percentage variance was determined as <20% for all samples ( mean = 10 . 3% , +/-5 . 59% S . D . ) . For primary readout analysis of O . ochengi worm recovery , a dose/schedule providing a reduction in average worm burden of ≥90% versus the control group was considered as efficacious . The percent effectiveness was calculated using geometric mean ( GM ) as follows: %efficacy=GMuntreatedgroup−GMdosegroupGMuntreatedgroupX100 , where GM is a geometric mean of the number of male O . ochengi recovered at 5 weeks post-treatment for each group . Since the number of recovered male O . ochengi at 5 weeks post-treatment was zero for some animals , a value of 0 . 1 was added to each data for the calculation of the GM . Number of male O . ochengi recovered at 5 weeks post-treatment ( +0 . 1 ) for both experiments were examined for normality , using the Shapiro-Wilk test [37] . The data failed to pass this test . Therefore , data was log10 transformed and the transformed data was tested for normality as before . The log10 transformed data did not satisfy the Shapiro-Wilk test for normality . Therefore , Kruskal-Wallis non-parametric test was used to compare the groups in experiment A and followed by post-hoc Dunn’s test [38] to compare each dose group to the untreated group . The Mann Whitney non-parametric test was used on the original raw data to compare the two groups in experiment B . For primary readout analysis of Bmmf in the peripheral circulation , a dose/schedule providing a reduction in average worm burden of ≥70% versus the vehicle control group 2 days after dosing was considered as efficacious as a rapid microfilaricide . The efficacy of a test group was calculated as following: %effectiveness=100 ( GMmicrofilariaemiabaseline–GMmicrofilariaemia+48h ) GMmicrofilariaemiabaseline For changes in peripheral microfilaraemias , change was calculated as follows: deltaperipheralmf/ml=mf/mlat+2daysor+7days–mf/mlbaseline . Delta peripheral mf/ml at +2 day and cardiopulmonary circulating mf/ml +7 days post-treatment continuous variable distributions were examined for normality by the Shapiro-Wilk test . Data that was not normally distributed was then log10 transformed before being re-tested for normality . All raw data or log10-transformed data did not deviate from a normal distribution pattern . Therefore , 1 Way ANOVA with Dunnett’s tests were applied to examine significant differences between vehicle and drug groups in delta peripheral mf/ml at +2 days and Log10 cardiac mf/ml at +7 days post-treatment . All tests were performed at a significance level of 5% . Additional efficacy parameters analysed in both O . ochengi experiment A and B were: 1 . frequency of animals with zero worm count / group ( cure rate ) 2 . the frequency of mice with normal vs reduced motile male O . ochengi present at necropsy 3 . the metabolic activity of motile male O . ochengi retrieved at necropsy . The proportions of mice with or without infection were expressed as percentages of the total group number . Raw data ( numbers of infected and uninfected mice ) were compared for each group in a 5x2 contingency table and tested for significance by Chi-square Test . Post-hoc tests were undertaken comparing control against specific treatment groups by 2x2 contingency tables and two-tailed Fisher’s Exact Tests . For O . ochengi metabolic activity , formazan optical densities from worm extractions were averaged ( mean ) per mouse in each group , measured by MTT-formazan reduction colourmetric assay . Mean O . ochengi metabolic activity data was checked for normality , using the Shapiro-Wilk test . Subsequently , 1 Way ANOVA with post-hoc Dunnett’s Tests were used to determine significance between control and treated groups of mice . All tests were performed at a significance level of 5% .
Raw data for numbers of O . ochengi recovered per mouse / treatment are plotted in Fig 1 for both experiment A and B . Table 2 details the geometric means ( 95% confidence intervals ) of raw data +0 . 1 and the derived calculated percentage efficacy for both experiments . Multiple injected FBZ ( 10mg/kg ) mediated a high level of efficacy against male O . ochengi ( 92 . 9% ) and single injection ( 10mg/kg ) also mediated substantial efficacy ( 82% ) . Oral dosing of FBZ did not emulate the efficacy of injections ( 0% , 19% and 29 . 9% for 0 . 2 , 1 . 5 and 15mg/kg , respectively ) ( Table 2 ) . Subsequently , the statistical variation between numbers of male O . ochengi recovered at 5 weeks post-dosing was scrutinised ( Fig 1 ) . Multiple injected FBZ ( 10mg/kg ) significantly reduced worm burden compared with untreated controls ( Fig 1A ) . Variation in worm burdens in all other groups were not significantly different than untreated controls ( Fig 1A & 1B ) . Table 3 details the proportions of mice in each dosing group that did not contain viable male O . ochengi at necropsy ( cure rate ) for both experiments . Table 4 details the frequencies of normal or reduced motile worms five weeks post-dosing . In experiment A , by 2x5 Chi-square analysis , a significant difference in frequency in fully motile vs reduced motile O . ochengi was apparent between the groups . However , it was determined by post-hoc Fisher’s Tests , that none of the treatment groups displayed a significantly different frequency motility level when compared with the untreated controls . In experiment B , the FBZ 15mg/kg QD 5x OR regimen group displayed a significantly lower frequency of fully motile O . ochengi males compared with the vehicle control ( Fisher’s Exact Test P<0 . 0001 ) . Fig 2 details the metabolic activity of male O . ochengi in mice that contained viable , motile worms five weeks post-dosing for both experiments . Motile worms isolated from single injected FBZ treated mice displayed similar metabolic activity compared with untreated controls . Further , motile O . ochengi recovered from multiple dosed oral FBZ mice ( at 0 . 2 or 1 . 5mg/kg ) did not show a significant reduction in metabolic activity compared with untreated controls . However , in adult motile O . ochengi derived from mice treated with FBZ orally at 15mg/kg QD x5 , the metabolic activity was significantly reduced compared with the matching vehicle controls ( unpaired T test , P = 0 . 0496 ) . Table 5 details the change in peripheral circulating microfilaraemias initially recorded at baseline and at two days following single oral dose with the positive control IVM at 0 . 2mg/kg compared with FBZ at 2 or 40mg/kg . Only IVM mediated a significant , 80 . 9% rapid reduction in circulating levels of Bmmf . In comparison , the change in peripheral circulating mf following low or high dose FBZ were not significantly different to reductions evident in the vehicle control group ( 11 . 8% and 49% efficacy , 2 and 40mg/kg , respectively , vs 37 . 5% , vehicle ) . After seven days following treatment , the level of B . malayi mf in cardiac blood was assessed following necropsy ( Fig 3 ) . The geometric mean level of microfilaraemia in vehicle treated animals was 8236mf/ml ( 985–19486 95% C . I . ) . Single dose IVM-treated animals had a significantly reduced burden of microfilaraemia ( 84 . 7% efficacy , 1way ANOVA F = 4 . 6 , P = 0 . 019 , P<0 . 05 , vehicle vs IVM , Dunnett’s post-hoc test ) . In comparison the levels of parasitaemias in low or high single doses of FBZ were not significantly reduced compared with vehicle . For all experiments , the plasma concentrations and pharmacokinetics parameters of FBZ , H-FBZ and R-FBZ are depicted in Table 6 . After single subcutaneous administration of FBZ to male SCID mice at 10mg/kg , peak plasma concentrations were observed 1hr after dosing . After multiple oral administration of FBZ to male SCID mice for 5 days , peak plasma concentrations at day 5 were observed at 0 . 5hr after dosing at 0 . 2 and 1 . 5mg/kg , and at 1hr after dosing at 15mg/kg , suggesting a rapid absorption . Cmax and AUClast values increased dose proportionally . In experiment A , the R-FBZ/FBZ ratio ranged from 0 . 1 to 0 . 4 and from 0 . 3 to 0 . 7 for H-FBZ/FBZ across all FBZ dosed groups . In experiment B , the R-FBZ/FBZ ratio was 0 . 4 , the H-FBZ/FBZ ratio was 0 . 55 . After single administration of FBZ to male SCID mice at 2 and 40 mg/kg , peak plasma concentrations were observed at 0 . 5hr after dosing , suggesting a rapid absorption . Cmax and AUCinf values increased less than dose proportionally to the dose between 2mg/kg/day up to 40mg/kg . In this latter study the R-FBZ/FBZ ratio ranged from 0 . 4 to 0 . 6 and from 0 . 4 to 0 . 5 for H-FBZ/FBZ across both FBZ dosed groups . The PK exposures of parental FBZ and its major metabolites FBZ-R and FBZ-H at the highest oral dose ( 15mg/kg QD 5x ) or the injected dose ( 10mg/kg QD 5x ) were simulated and compared with one another using a PK modelling approach ( Fig 4 & S1 Data ) . From these simulations , an oral dose regimen that more closely emulated the exposure profile of multiple injected FBZ was determined as 0 . 2 mg/kg QID for 35 days .
FBZ has been proposed as a relatively ‘low hanging fruit’ to reformulate and repurpose as an oral Onchocerca macrofilaricide [24 , 39] . Delivered as a multiple injection , FBZ is highly potent in mediating rapid death of O . ochengi [33] and O . volvulus in vivo [24 , 32] . Additionally , as with other members of the benzimidazole ( BZ ) class , FBZ is more efficacious at targeting adult Onchocerca rather than Onchocerca mf [24 , 40] , making it an attractive option for an indication where rapid microfilaricidal activity would want to be avoided ( e . g . areas of L . loa co-endemicity ) . Selective toxicity stems from BZ binding to nematode β tubulin with approximately 10-fold greater affinity than mammalian tubulin , principally due to polymorphisms around amino acid position 200 . Efficacy appears to be most readily manifest in embryotoxic effects on female worms , whereby prevention of microtubule elongation interferes with chromosome segregation and mitotic cell division , leading to defective embryogenesis . A fecund female intra-nodular Onchocerca small animal model is currently not available to scrutinise this specific impact of oral FBZ , which may be potentially delinked from macrofilaricidal activity ( i . e . sterilising activity only ) . However , surrogate filarial models of onchocerciasis ( Brugia pahangi and Litomosoides sigmodontis gerbil infection models ) allow for scrutiny of embryogenesis post-drugging . Efficacy testing with oral FBZ at matching doses has been undertaken in these lymphatic filarial infection models by independent laboratories and their findings will be published elsewhere . Beyond targeting the female germline , other rapidly dividing filarial cells would also presumably be sensitive to the β tubulin capping and subsequent mitosis blocking effects of FBZ . Indeed , histopathological evidence suggests intestinal and hypodermal tissue abnormalities are rapidly manifest after brief in vitro exposure of adult filariae to FBZ [41] . Furthermore , multiple injections target male worms in vivo [33] . Three concerns have been raised over the development of FBZ as a macrofilaricide: First , the parenteral route of administration is not compatible with a field-based NTD indication and intra-muscular injections cause undesirable inflammatory adverse reactions at the injection site . Second , FBZ is poorly bioavailable when given orally . The third concern is that FBZ has a narrow safety margin before deleterious effects on mammalian cell division , which may lead to carcinogenic toxicity , are detected in preclinical toxicological assays . Addressing the first two caveats , we have been successful in developing an orally-bioavailable formulation of FBZ . Sparse PK measurements taken in SCID mice dosed during our infection experiments demonstrate that rapid and dose-proportional absorption occurred after oral dosing with the FBZ Bend 1/9 formulation , confirming previous rich PK measurements . The PK profile of the multiple oral administered FBZ displayed a distinct profile compared with that of subcutaneous injections . Whilst oral dosing mediated upwards of >50 fold higher Cmax of the active FBZ-R than achievable with injections , the injected FBZ gave a sustained chronic exposure of FBZ-R over 35 days of exposure . We hypothesized that the high Cmax obtained following oral dosing would mitigate against the much-reduced systemic half-life of the active FBZ-R metabolite in mediating macrofilaricidal activity . However , initial in vivo testing with 0 . 2 or 1 . 5 mg/kg dosing for five days did not achieve any notable adulticidal activity against implanted O . ochengi male worms ( 0 & 19% efficacy , respectively ) nor was there any significant indication of reduced viability of O . ochengi retrieved from SCID mice 35 days after dosing . When elevating the dose to 15 mg/kg , a sub-optimal 30% efficacy was recorded with partial yet significant reductions in viability assays of the retrieved O . ochengi parasites at +35 days . Because filariae with reduced viability following short high dose FBZ exposures ( 24 hour , 10μM ) can recover after washout in vivo , the partially reduced metabolic activity detected following 15mg/kg oral treatment may reflect a temporary and reversible drug effect [41] . This was in marked contrast to parenterally-delivered FBZ which achieved 82% macrofilaricidal efficacy after a single injection . Confirming our previous observations [33] , multiple injections were still required to deliver >90% efficacy against adult O . ochengi . Further oral dose elevations beyond 15mg/kg for 5 days were ruled out in this preclinical model due to toxicological findings indicating a negative safety window at or beyond this dose level . Using PK modelling we show that to maintain FBZ levels consistent to those observed in efficacious SC dosing , a 4 time daily dosing of 0 . 2mg/kg of the oral formulation over 35 days is needed . Such a dosing regime would maintain FBZ levels above those obtained from 10mg/kg QD 5x SC for at least 80% of the whole duration of treatment . The need for multiple dosing per day is due to the oral formulation’s short half-life ( ~2h ) . The most likely reason for the longer exposure achieved by SC administration of FBZ is its slow absorption across the subcutaneous barrier which creates a depot effect allowing for the drug to be released steadily over a period of >35 days . We coincidently tested whether oral FBZ and its metabolites impacted on circulating B . malayi mf in the blood ( as a surrogate bloodborne microfilarial model of L . loa ) . The rationale for this was to evaluate any potential safety risk of oral FBZ mediating rapid ‘IVM-like” microfilaricidal activity . Despite the increased peak plasma concentration of the oral formulation , we did not identify any evidence that FBZ was directly microfilaricidal and conclude that elevated exposures of this BZ drug are not likely to mediate substantial rapid activity against bloodborne mf . In conclusion , oral dosing with the FBZ Bend 1/9 formulation achieves bioavailability of FBZ and its active metabolite but does not confer significant macrofilaricidal activity against adult O . ochengi nor significant microfilaricidal activity against bloodborne mf in the pan-filarial SCID mouse in vivo models utilised . Efficacy is not driven by Cmax but rather by sustained drug levels over long periods of time as indicated by the discrepancy between the terminal half-life of FBZ when administered subcutaneously ( apparent terminal t1/2 up to 648h ) and when administered orally ( apparent terminal t1/2 0 . 5h-4h ) . This discrepancy can be further appreciated by comparing the simulated exposure profiles of FBZ when administered orally and subcutaneously at similar doses . Evidently , the markedly higher initial levels of drug in the oral formulations ( ~70-fold higher Cmax ) are redundant in terms of producing superior pharmacological activity . A prolonged exposure lasting for ~35 days however , even at peak concentrations that are ~70-fold lower than what is observed in current oral profiles could achieve better macrofilaricidal efficacy . Either a sustained release formulation or prolonged oral dosing durations would be necessary to achieve matching efficacious exposure to injection routes but caution in this approach would be necessary given the low safety window determined for this drug . | Onchocerciasis , caused by the filarial parasitic worm , Onchocerca volvulus , is a major cause of infection-related blindness and skin disease and is targeted for elimination . Current drugs are not optimal in all patient populations and a short-course cure would accelerate elimination . Flubendazole is used to treat intestinal worms in humans . The current oral formulation is not well absorbed into the blood . Flubendazole treatment has mediated curative efficacy in onchocerciasis patients when given as an injection but also causes adverse reactions at the injection site . In this study , we tested whether a new oral formulation of flubendazole with improved absorption could selectively kill Onchocerca adult parasites compared with circulating larval filarial parasites , using immunodeficient mouse models . Whilst injections of flubendazole mediated high levels of efficacy , five-day oral treatment , at a range of doses , did not emulate this curative effect . Oral flubendazole also did not significantly affect levels of microfilariae in the blood . After comparing levels of flubendazole in the blood following injection versus oral treatment , we conclude that a long duration , low dose of the oral formulation , for 5 weeks , would be needed to match the exposure of the drug following injection . | [
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] | 2019 | Short-course, oral flubendazole does not mediate significant efficacy against Onchocerca adult male worms or Brugia microfilariae in murine infection models |
With the global distribution , morbidity , and mortality associated with tick and louse-borne relapsing fever spirochetes , it is important to understand the dynamics of vector colonization by the bacteria and transmission to the host . Tick-borne relapsing fever spirochetes are blood-borne pathogens transmitted through the saliva of soft ticks , yet little is known about the transmission capability of these pathogens during the relatively short bloodmeal . This study was therefore initiated to understand the transmission dynamics of the relapsing fever spirochete Borrelia turicatae from the vector Ornithodoros turicata , and the subsequent dissemination of the bacteria upon entry into murine blood . To determine the minimum number of ticks required to transmit spirochetes , one to three infected O . turicata were allowed to feed to repletion on individual mice . Murine infection and dissemination of the spirochetes was evaluated by dark field microscopy of blood , quantitative PCR , and immunoblotting against B . turicatae protein lysates and a recombinant antigen , the Borrelia immunogenic protein A . Transmission frequencies were also determined by interrupting the bloodmeal 15 seconds after tick attachment . Scanning electron microscopy ( SEM ) was performed on infected salivary glands to detect spirochetes within acini lumen and excretory ducts . Furthermore , spirochete colonization and dissemination from the bite site was investigated by feeding infected O . turicata on the ears of mice , removing the attachment site after engorment , and evaluating murine infection . Our findings demonstrated that three ticks provided a sufficient infectious dose to infect nearly all animals , and B . turicatae was transmitted within seconds of tick attachment . Spirochetes were also detected in acini lumen of salivary glands by SEM . Upon host entry , B . turicatae did not require colonization of the bite site to establish murine infection . These results suggest that once B . turicatae colonizes the salivary glands the spirochetes are preadapted for rapid entry into the mammal .
Vector competency for a microbial agent is defined as the acquisition , maintenance , and subsequent transmission of the pathogen , and is dependent on host seeking behavior , duration of attachment , transstadial passage , and transovarial transmission [1] . Pathogens must attain sufficient densities within the host to promote uptake by the vector then quickly adapt to a new environment , given the physiological and immunological differences between mammal and vector . Transstadial and transovarial passage is essential with pathogens utilizing one or both routes [1] , and subsequent transmission is dependent on the feeding behavior of the vector and the microbes ability to efficiently infect the host . Understanding the interplay between mammalian host , pathogen , and vector is essential toward disease control . Two families of ticks , Argasidae ( soft ticks ) and Ixodidae ( hard ticks ) , have evolved different feeding behaviors . Argasid ticks generally engorge within an hour while ixodids take several days . Most vector competency and transmission studies of tick-borne pathogens have focused on microbes transmitted by ixodid ticks [2]–[8] . Less is known concerning the transmission process of soft tick species within the genus Ornithodoros , which display a different feeding behavior , yet also transmit pathogens of public health relevance , including tick-borne relapsing fever spirochetes . Variations in the feeding behaviors between argasid and ixodid ticks may account for the observed differences in transmission between their respective pathogens . For instance , optimal transmission of Borrelia burgdorferi and Anaplasma phagocytophilum occurs after 48 hours , and the five to seven day bloodmeal of Ixodes spp . ensures transmission and the continued life cycle of the pathogens [3] , [6] , [9] , [10] . Borrelia turicatae , a species of tick-borne relapsing fever spirochete , is transmitted by Ornithodoros turicata , which depending on the developmental stage completes the bloodmeal within 5–60 minutes [11] . The feeding behavior of the O . turicata suggests that the adaptation of B . turicatae to the vector is much different than pathogens transmitted by ixodid ticks . The kinetics of B . turicatae entry into the host are largely unknown . Using a Swiss Webster animal model , we characterized B . turicatae transmission from the arthropod vector . The number of infected third stage nymphs required to successfully transmit spirochetes was determined . We also compared murine infection when ticks fed to repletion or were interrupted within 15 seconds of attachment . Given our results , scanning electron microscopy ( SEM ) was performed to localize B . turicatae within tick salivary glands , and B . turicatae colonization and dissemination from the bite site was also evaluated . Our findings demonstrate that three ticks provide a sufficient infectious dose to infect 80–100% of mice , and that transmission and dissemination of B . turicatae are rapid events .
Animal work and husbandry was conducted in adherence to the United States Public Health Service Policy on Humane Care and Use of Laboratory Animals and the Guide for the Care and Use of Laboratory Animals . Animal husbandry was provided under the Association for Assessment and Accreditation of Laboratory Animal Care and Office of Laboratory Animal Welfare assured program at Mississippi State University . The studies were approved by the Mississippi State University Institutional Animal Care and Use Committee ( IACUC protocol #11-091 ) . O . turicata ticks used in this study originated from an uninfected colony maintained at the Rocky Mountain Laboratories , NIAID , NIH , and subsequently Mississippi State University . All ticks were housed at 25°C and 85% relative humidity [12] . To obtain an infected cohort of ticks , two 4 week old Swiss-Webster mice ( Harlan Laboratories Inc . , Tampa , FL , USA ) were needle inoculated with B . turicatae 91E135 , and 75 uninfected second stage nymphal ticks were fed on each animal when spirochetes were detected in the blood ( 1×106 spirochetes/ml ) . After molting , a 3–4 week process , ticks were subsequently fed to evaluate B . turicatae transmission . In all experiments , mice were sedated with 25 mg/ml Ketamine and 7 . 6 mg/ml Xylazine at 0 . 05 ml/25 g body weight during the bloodmeal . Interrupted feedings and full bloodmeals were performed using 1–3 infected third stage nymphal ticks . Five to 12 mice were fed upon by one to three ticks , and after attachment the animals were individually housed . Ticks that fed to repletion were allowed to attach simultaneously , and the time required for engorgement was recorded . To evaluate rapid transmission , an individual tick was placed on the shaved abdomen of an animal and a timer was started once the tick inserted their mouth parts , as determined by the inability to move the tick with a paintbrush . After 15 sec the tick was removed . When two or three ticks were fed , they were allowed to individually attach for 15 sec , removed , and a subsequent tick was placed on the animal . The total time a given tick was on an animal was also recorded . Dark field microscopy , quantitative PCR ( qPCR ) , and seroconversion were used to determine infection frequencies after tick bite . For 10 consecutive days after tick bite , a drop of blood ( approximately 2 µl ) was collected by tail nick onto a slide and examined using a Zeiss Axiovert ( Thornwood , NY , USA ) dark field microscope . Thirty fields were scanned for spirochetes . An additional 2 . 5 µl of blood was collected from each animal by tail nick and pipetted into 47 . 5 µl of Lysis-Stabilization Buffer ( Agilent , Santa Clara , CA , USA ) , and the qPCR was performed as previously described with minor modifications [13] . Primers and probe were designed to B . turicatae flaB ( forward primer: CCAGCATCATTAGCTGGATCAC , reverse primer: GTTGTGCACCTTCCTGAGC , and probe: YAK–TGCAGGTGAAGGTGCGCAGGTT–BBQ ) . B . turicatae cultured in mBSK medium [14] , [15] was used to generate a standard curve from 1×104 to 1×108 spirochetes/ml . qPCR assays were performed using the ABI 96-well Step-One Plus Instrument ( Life Technologies , Foster City , USA ) in triplicate with 20 µl reactions containing 2× Brilliant qPCR Master Mix ( Agilent Technologies , Waldbroon , Germany ) . To detect spirochetes in the salivary glands from ticks that fed on mice that failed to become infected , immunofluorescent assays ( IFA ) were performed as previously described using chicken serum generated against B . turicatae recombinant flagellin ( rFlaB ) [16] . The cuticle and midgut were removed and the inner cavity of the O . turicata was rinsed with PBS - 5 mM MgCl2 . Salivary glands were removed , crushed , adhered to a cover slip by heat , and fixed in acetone for 30 min . The chicken anti-rFlaB was diluted 1∶20 in PBS - 0 . 75% BSA , and the secondary antibody was an Alexa Fluor 568 Goat Anti-Chicken IgG ( Life Technologies ) diluted 1∶200 . Spirochetes were visualized using a Zeiss Axioskop 2 Plus ( Carl Zeiss Microscopy , Munich , Germany ) . Four weeks after tick feedings , blood was collected from mice and immunoblotting was performed as previously described [17] . Protein lysates from 1×107 spirochetes and 1 µg of recombinant Borrelia immunogenic protein A ( rBipA ) were analyzed using Mini-PROTEAN TGX precast gels ( Bio-RAD , Hercules , CA ) . Gels were transferred to Immobilon PVDF membrane ( Millipore , Billerica , MA , USA ) . Immunoblots were probed for one hour with mouse sera diluted at 1∶500 and the secondary molecule HRP-rec-Protein A ( Life Technologies ) was applied at a 1∶4 , 000 dilution for one hour . Serological reactivity was determined by chemiluminescence using the Amersham ECL Western Blotting Detection Reagents ( GE Healthcare Bio-Sciences Corp . , Piscataway , NJ ) . To perform SEM on intact salivary glands within the inner cavity of O . turicata , the cuticle and midgut were removed , discarded , and the tick was washed with PBS , and placed in Karnovsky's fixative . Individual salivary glands were also excised and placed in Karnovsky's fixative . Samples were treated with 2% Osmium tetraoxide for 2 hours then dehydrated by immersion in increasing concentrations of ethyl alcohol , with the final concentration of 100% ethyl alcohol . Cryofracturing was performed on excised salivary glands in liquid nitrogen . Samples were dried using a E300 Critical Point Dryer ( Polaron Equipment . Watford , United Kingdom ) and viewed under a JEOL JSM-6500F Field Emission scanning electron microscope ( JEOL USA , Inc . , Peabody , MA , USA ) . To evaluate dissemination within murine blood , three infected O . turicata were allowed to feed to repletion on the ear pinna of sedated Swiss Webster mice , as stated above . Following detachment the ears were rubbed with 7 . 5% povidone-iodine and rinsed with 70% ethanol . Bite sites were removed using a sterile 2 mm tissue biopsy punch immediately after detachment . To assess colonization of the bite site , tissue biopsies were placed into 5 ml mBSK medium [14] , [15] , incubated at 35°C for two weeks , and 10 µl of medium was analyzed by dark field microscopy Zeiss Axiovert ( Thornwood , NY , USA ) . Spirochete dissemination in the blood was evaluated by dark field microscopy for 10 consecutive days after tick feeding and immunoblotting using serum samples collected four weeks after feeding ticks as described above . Data analysis of transmission frequencies was performed using Microsoft Excel ( Microsoft , Redmond , WA , USA ) , and comparisons of spirochete densities within the blood were performed with the Statistical Package for Social Sciences ( SPSS ) software ( IBM Corp . , Armonk , NY ) . Confidence intervals for transmission frequencies were constructed with binomial proportions to compare interrupted feeding and full blood meal treatments . To compare B . turicatae blood densities between groups of mice in which ticks were allowed to fully feed or were interrupted 15 sec after attachment , bacterial densities were converted to spirochetes per ml of blood from qPCR Ct values as previously described [13] . Confidence intervals were calculated when spirochetes were detected in three or more mice , when murine sample size was sufficient .
The lack of available information regarding transmission efficiencies by O . turicata initiated a comparative study feeding one to three third stage nymphal ticks on a given mouse . All ticks engorged within 10–30 minutes . Comparing microscopy , qPCR , and serological responses indicated that immunoblotting and qPCR results were similar and more consistent in assessing murine infection than dark-field microscopy ( Table 1 ) . For example , when spirochetes were detected in the blood by microscopy or qPCR , mice seroconverted to B . turicatae protein lysates and rBipA ( Figure 1 A and B ) , an antigen for relapsing fever spirochetes [18] , [19] . Animals in which spirochetes were not detected in the blood by qPCR failed to seroconvert ( Figure 1 C and D ) . Serological responses from the four mice ( Figure 1 A–D ) were representative of remaining animals that were fed upon by infected ticks . Murine infection rates after one to three O . turicata fed indicated that three ticks provided an infectious dose to a minimum of 80% of mice ( Table 1 ) . Also , IFA enabled B . turicatae visualization in the salivary glands from 9 of 10 ticks that were individually fed on mice ( Figure 2 A–C ) , suggesting that while most ticks were colonized with spirochetes , they failed to deliver a sufficient infectious dose . Evaluating the timing of transmission was performed using O . turicata nymphs that originated from the same cohort as the ticks that fed to repletion . Detecting spirochetes after allowing ticks to attach for 15 seconds indicated that transmission was a rapid event ( Table 1 ) , and the total time an individual tick was in contact with an animal was less than 40 seconds . Interestingly , transmission frequencies were identical when one or two ticks fed to repletion or were removed 15 seconds after attachment ( Table 1 ) . When three ticks were fed on a given animal , differences in infection rates after interrupted and uninterrupted bloodmeals were not statistically significant ( Table 1 ) . Also , detection of infection in most mice was determined by the fifth day when three ticks fed to repletion or were interrupted during the bloodmeal ( Figure 3 ) . When three ticks engorged or were removed shortly after attachment , the density of the spirochetes within the blood were similar over a 10 day period as determined by qPCR ( Figure 4 ) . Spirochetes were detected on the fourth day after tick bite and B . turicatae quantities in the blood were comparable regardless whether ticks fed to repletion or were interrupted within 15 seconds of attachment . The progression of infection was cyclic , with B . turicatae relapsing in the blood beginning on the seventh day after tick bite , and by the ninth day all animals were once again spirochetemic . Also , spirochete kinetics in the blood when one or two ticks fed on an animal were identical ( data not shown ) . These results suggest that the complete infectious dose delivered by O . turicata occurs within 15 seconds of attachment . Salivary gland acini and excretory ducts from unfed ticks were evaluated for the presence of spirochetes by SEM because colonization of these sites would enable rapid transmission . B . turicatae grown in vitro provided a reference for spirochete size and morphology ( Figure 5 A ) . Intact salivary glands visualized by SEM indicated the presence excretory ducts and acini ( Figure 5 B and C ) . Analyzing cryofractured salivary glands identified distinguishable spirochetes in a portion of acini lumen ( Figure 5 D–F ) , while bacteria were undetected within cryofractured excretory ducts ( data not shown ) . We also failed to detect spirochete-like organisms in salivary glands of uninfected ticks ( data not shown ) , indicating the identified bacteria were B . turicatae . B . turicatae colonization of the bite site was evaluated after a bloodmeal . Feeding three third stage nymphs to repletion on the ears of five mice and removing the bite site immediately after detachment ( Figure S1 ) indicated that spirochetes disseminated from the bite site during tick feeding ( Table 2 ) . All mice became infected on the fourth day after tick bite as determined by dark field microscopy and subsequently by immunoblotting using serum samples collected from the animals four weeks after tick bite ( data not shown ) . Similar results were observed in animals in which the bite site was left intact ( Table 2 ) . B . turicatae was also cultivated from the ear biopsies , indicating that spirochetes remained viable at the bite site after the bloodmeal . Collectively these results suggest that populations of B . turicatae colonize the bite site while others disseminate in the blood and are sufficient to establish an infection in mice .
Previous transmission studies of B . turicatae were conducted over 70 years ago using five to 270 ticks that fed on animals , including mice and nonhuman primates , and a single O . turicata larvae was found to deliver a sufficient infectious dose to man [11] , [20] , [21] . The studies were often performed using field collected O . turicata and the numbers of infected ticks within a cohort was unclear [11] , [20] , [21] . Additionally , transmission was evaluated by visualizing spirochetes in the blood , but densities of B . turicatae were not quantified . In this present report , successful transmission from one to three ticks was determined by comparing dark field microscopy , molecular detection with qPCR , and serological responses . We found that qPCR was more reliable at detecting B . turicatae in murine blood than dark field microscopy . Failure to seroconvert suggested that infections were not occurring in mice that were negative by qPCR and dark field microscopy . Characterizing transmission of B . turicatae in mice identified interesting differences compared to the Ornithodoros hermsi-Borrelia hermsii model of tick-borne relapsing fever spirochetes . In our study , uninfected second stage O . turicata nymphs initially engorged when mice were infected with approximately 1 , 000 spirochetes/µl of blood . During the subsequent bloodmeal , three ticks were required to successfully infect 80–100% of Swiss Webster mice with B . turicatae , while one and two ticks provided an infectious dose in 20% of the animals ( Table 1 and 2 ) . When the initial acquisition bloodmeal of second stage nymphal O . hermsi occurred with nearly 300 spirochetes/µl in murine blood , subsequent successful transmission frequencies from single ticks to Swiss Webster mice were 96% [22] . Furthermore , upon transmission , the average densities of B . turicatae in mice were nearly a log lower than observed with B . hermsii , which attain over 1×107 spirochetes/ml [23] . B . hermsii is naturally maintained in mice , squirrels , and chipmunks [24] , and successful transmission in rodents ensures the continued life-cycle of the spirochetes . The maintenance of B . turicatae in nature is less understood . The ecological niche of the tick vector overlaps that of feral swine ( unpublished observations ) , wild canids , bats , and the gopher tortoise [25]–[28] , suggesting B . turicatae may be adapted to infect these vertebrates more efficiently than mice . The rapidity of B . turicatae transmission by tick bite was initially suggested by Dr . Gordon Davis . He reported that male O . turicata fed to repletion within 6 to 23 minutes , and transiently mentioned that transmission could occur within a minute [11] . We observed similarities in spirochete densities between animals in which cohorts of ticks were allowed to engorge or were interrupted during the bloodmeal , indicating the infectious dose was delivered during initial attachment . It was also unlikely that an interrupted bloodmeal resulted in the mouthparts and salivary gland components remaining at the bite site , causing an erroneous inoculum . Unlike ixodid ticks , Ornithodoros spp . fail to produce attachment cement [11] , [29] , and are relatively easy to remove . The cohort of ticks used in this study was also subsequently fed , indicating their mouthparts remained intact . An interrupted bloodmeal may naturally occur , and salivary gland colonization is essential for B . turicatae transmission . Full engorgement by O . turicata was reported to be partially dependent on the attachment site and host activity [11] , and both vector and likely mammalian hosts are active nocturnal feeders [25]–[27] . The transmission kinetics of B . turicatae suggested that spirochetes were localized within regions of salivary glands that would promote rapid transmission , for example , excretory ducts and the lumen of saliva producing acini [29] . Visualization of spirochetes by SEM indicated that acini lumen was a colonization site for B . turicatae , and while spirochetes may also localize in excretory ducts we did not visualize the bacteria within the ducts . Colonization of the acini lumen enables transmission through the saliva and increases the likelihood of continuing the spirochetes life-cycle when feeding on an active host . Our findings and studies in the B . hermsii-O . hermsi model of relapsing fever spirochetes indicate that transmission and dissemination of the bacteria within the tick and mammal is unique [30] . For example , B . burgdorferi , the causative agent of Lyme borreliosis , initially colonizes the midgut of Ixodes spp . During the subsequent bloodmeal , spirochetes replicate in the midgut , migrate to the salivary glands , and transmit to the host , which takes approximately 48 hours [9] , [31] . Upon entering mice , B . burgdorferi colonizes the attachment site for 48 hours prior to disseminating , as demonstrated by removing the bite site to prevent infection [32] . As B . turicatae enters the midgut , the spirochetes colonize the salivary glands during the following weeks given that O . turicata can subsequently transmit bacteria upon molting . Our findings suggest that the model of vector transmission and early mammalian infection of B . turicatae includes persistent colonization of salivary gland acini , and during the bloodmeal the bacteria are deposited at the bite site shortly after attachment . Within the host , B . turicatae disseminates into the blood during the time required for tick engorgement . The ability of B . turicatae to remain infectious in a vector that can endure five years between feedings [21] indicates the importance of understanding the molecular mechanisms utilized by the spirochetes within the salivary glands and during the bloodmeal . Tick-transmitted bacteria adapt genetically when transiting between vector and mammal , with B . burgdorferi being the most characterized of these pathogens [2] , [33] , [34] . Studies have demonstrated the interplay between B . burgdorferi surface proteins with vector produced proteins . The tick receptor for OspA ( TROSPA ) is produced in the midgut and enables B . burgdorferi adhesion and colonization of the tissue [35] . B . burgdorferi also induces the up-regulation of tick genes including salp15 , which encodes a 15 kDa salivary gland protein [36] . Salp15 acts as an immune suppressor and shields B . burgdorferi from antibody-mediated killing [36] , [37] . Less is known regarding the mechanisms of vector adaptation and transmission by relapsing fever spirochetes . Mans and colleagues conducted a comparative study of salivary gland proteins between hard and soft ticks , reporting that the major protein families were conserved between Ixodidae and Argasidae [38] . However , little is known regarding the interplay between relapsing fever spirochetes and soft tick salivary proteins , and whether the saliva aids in bacterial infection of the mammal . While the molecular mechanisms involved with relapsing fever spirochete transmission are still poorly understood , this study suggests that upon salivary gland colonization , B . turicatae is preadapted to infect mammalian blood . Future studies will focus on identification of gene products expressed by both ticks and spirochetes that enable salivary gland colonization and host transmission . Additionally , the bacterial dose delivered during the bloodmeal and the role of O . turicata saliva in enabling spirochete infection of the mammal will be determined . Collectively , these studies will further enhance our understanding of the mechanisms occurring during the tick-mammalian infectious cycle . | Relapsing fever spirochetes cause recurrent febrile episodes , rigors , nausea , vomiting , malaise , and pregnancy complications , and are a leading cause of hospital admissions in regions of Africa . Routes of pathogen transmission include crushed human body lice and feces , or through bites by Ornithodoros spp . of ticks . The life cycle of Ornithodoros turicatae , the vector of Borrelia turicatae , includes over six nymphal stages , upwards of a ten year life span , and a bloodmeal that is completed within an hour . We investigated B . turicatae transmission from the tick vector and assessed the rapidity of spirochete entry into the mammal and dissemination in the blood . Salivary glands from infected ticks were also evaluated to visualize B . turicatae within the tissues to determine spirochete localization . We conclude that given the transmission dynamics of B . turicatae , it may be important to target conserved surface proteins that relapsing fever spirochetes produce in the salivary glands in order to develop preventative measures against the pathogens . | [
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] | 2014 | Transmission Dynamics of Borrelia turicatae from the Arthropod Vector |
HIV-prevalence , as well as incidence of zoonotic parasitic diseases like cystic echinococcosis , has increased in the Kyrgyz Republic due to fundamental socio-economic changes after the breakdown of the Soviet Union . The possible impact on morbidity and mortality caused by Toxoplasma gondii infection in congenital toxoplasmosis or as an opportunistic infection in the emerging AIDS pandemic has not been reported from Kyrgyzstan . We screened 1 , 061 rural and 899 urban people to determine the seroprevalence of T . gondii infection in 2 representative but epidemiologically distinct populations in Kyrgyzstan . The rural population was from a typical agricultural district where sheep husbandry is a major occupation . The urban population was selected in collaboration with several diagnostic laboratories in Bishkek , the largest city in Kyrgyzstan . We designed a questionnaire that was used on all rural subjects so a risk-factor analysis could be undertaken . The samples from the urban population were anonymous and only data with regard to age and gender was available . Estimates of putative cases of congenital and AIDS-related toxoplasmosis in the whole country were made from the results of the serology . Specific antibodies ( IgG ) against Triton X-100 extracted antigens of T . gondii tachyzoites from in vitro cultures were determined by ELISA . Overall seroprevalence of infection with T . gondii in people living in rural vs . urban areas was 6 . 2% ( 95%CI: 4 . 8–7 . 8 ) ( adjusted seroprevalence based on census figures 5 . 1% , 95% CI 3 . 9–6 . 5 ) , and 19 . 0% ( 95%CI: 16 . 5–21 . 7 ) ( adjusted 16 . 4% , 95% CI 14 . 1–19 . 3 ) , respectively , without significant gender-specific differences . The seroprevalence increased with age . Independently low social status increased the risk of Toxoplasma seropositivity while increasing numbers of sheep owned decreased the risk of seropositivity . Water supply , consumption of unpasteurized milk products or undercooked meat , as well as cat ownership , had no significant influence on the risk for seropositivity . We present a first seroprevalence analysis for human T . gondii infection in the Kyrgyz Republic . Based on these data we estimate that 173 ( 95% CI 136–216 ) Kyrgyz children will be born annually to mothers who seroconverted to toxoplasmosis during pregnancy . In addition , between 350 and 1 , 000 HIV-infected persons are currently estimated to be seropositive for toxoplasmosis . Taken together , this suggests a substantial impact of congenital and AIDS-related symptomatic toxoplasmosis on morbidity and mortality in Kyrgyzstan .
As the Central Asian countries became independent after the breakdown of the Soviet Union in 1991 fundamental socio-economic changes took place . This led , inter alia , to a deterioration of public infrastructure especially in the veterinary and public health sector . There is growing concern about the emergence of several neglected tropical diseases with a potentially high disease burden in these newly independent states [1] . Whilst epidemiological data show a rising incidence of some zoonotic diseases such as echinococcosis during the last twenty years [2] , there is currently no information about the prevalence of others , including toxoplasmosis . In general , there is a paucity of published literature on Toxoplasma seroprevalence in countries of the former Soviet Union both during and after the Soviet era . One study from the Russian city of Omsk suggests that the seroprevalence of toxoplasmosis has increased in post Soviet Russia [3] . In the Kyrgyz Republic there has been considerable migration to urban centres ( mainly Bishkek and Osh ) since 1991 . Nevertheless , 66% of the population lives in rural areas ( 3 . 5/5 . 3 million people ) and approximately 25% of the current urban population has recently moved in from rural areas ( source: National Statistical Office of the Kyrgyz Republic ( www . stat . kg ) ) . In rural areas small subsistence type farming rather than large scale collectivised farming systems ( Kolkhozes ) has become common , which led to a considerable lowering of living standards for many people [4] , [5] . Whereas risk factors for infection with T . gondii in urban centres are likely to be comparable to other urban areas around the world , in the rural areas life according to older pre- or early-Soviet era pastoralist traditions has emerged , representing a special epidemiological risk environment . In the neighbouring country of Uzbekistan , the seroprevalence ranged from 14 . 6% to 24 . 6% in individuals aged 15–40 years [6] . In comparison , seroprevalence of T . gondii infection in Europe ranges from 5–10% in the northern parts of Scandinavia [7] to 54% in Southern Europe . In the United States , data from a nationwide survey of subjects sampled between 1999 and 2004 suggested that the overall age adjusted T . gondii seroprevalence among persons of age 6–49 years was 10 . 8% [8] . In a region of Kazakhstan , socio-culturally similar to rural Kyrgyzstan the seroprevalence was determined at 16 . 1% [5] . The number of HIV-infected patients in the Kyrgyz Republic is growing rapidly: the estimated prevalence has risen tenfold from 0 . 02% in 2001 to 0 . 2% in 2009 [9] . Moreover , coverage with the highly active antiretroviral treatment ( HAART ) is low: approximately 1’900 patients ( range: <1’000–2’700 ) are estimated to be in need of HAART , but only 231 are reported to receive this treatment ( a coverage of 12% ) [9] , which puts the considerable proportion of dually infected patients at risk for developing AIDS-related toxoplasmosis . Vertical transmission of the parasite in the case of a primary infection during pregnancy can lead to congenital toxoplasmosis with severe consequences and even to abortion [10] . Congenital toxoplasmosis is associated with a variety of syndromes including chorioretinitis and neurological problems such as hydrocephalus . The aims of the present study were to measure the seroprevalence of Toxoplasma infection in two populations in Kyrgyzstan ( rural and urban ) as a basis for estimating the overall burden of toxoplasmosis in the Kyrgyz Republic . This includes calculating the incidence of congenital toxoplasmosis and estimating morbidity rates due to HIV-associated toxoplasmosis . In addition , a risk factor analysis was carried out in the rural study population .
This cross-sectional study has received prior ethical approval by the Ministry of Health of the Kyrgyz Republic ( Nr . 2008/258 and 2009/268 ) and was undertaken in the Kochkor district of Naryn Oblast in central Kyrgyzstan . Rural study area: The Kochkor district is located in a mountainous region in the central part of Kyrgyzstan in Naryn Oblast ( province ) , and has a population of approximately 60’000 . Livestock husbandry is the principal occupation of the local inhabitants ( source: national statistical office of the Kyrgyz Republic ( www . stat . kg ) ) . The district comprises 11 counties with 31 villages . The study was conducted in 8 villages located in this district . This area was believed to be typical of many of the rural areas of Kyrgyzstan that have problems with echinococcosis . A concomitant surveillance study of echinococcosis was planned and this provided an opportunity to investigate toxoplamosis in a rural Kyrgyz population using the same study resources . Urban study area: Bishkek is the capital and largest city of the Kyrgyz Republic with a population of approximately 900’000 , and is surrounded by farmland ( mainly cultivation of vegetables ) . Subjects were invited to participate in the rural study . Out of a total population of 9’914 in the Kochkor population , 1’065 volunteered to participate representing 10 . 7% of the target population . Blood samples were taken and all volunteers were asked to complete a questionnaire . Of the 1065 participants , four failed to provide the necessary data and were excluded . A number of diagnostic laboratories were asked if they could supply anonymous random serological samples from blood that had been referred for routine diagnostic testing from city residents greater than 1 year of age . A total of 899 sera were supplied whose patient origin was Bishkek . Otherwise only the age and gender of the patients was known . This represented approximately 0 . 1% of the total city population . The questionnaire included general information about the person , village of residence , name , age , sex , family size , nationality , occupation , and living standard , questions on livestock raised and specific facts which might constitute risk factors . These include the source of drinking water , cat ownership and number of cats owned , as well as eating habits: consumption of shashlyk , a Kyrgyz speciality made mainly from sheep meat , as the most likely source of undercooked meat , and consumption of home-made sour cream as the most likely source of unpasteurized milk , respectively . All questionnaires had unique numerical identifiers . To ensure the cultural appropriateness of the questions and to guarantee that each question was fully understood , the questionnaire was designed and tested for its comprehensibility by several of the authors with language skills in Russian , Kyrgyz and English . We developed and used a new test system rather than using the available commercial test kit partly for reasons of cost but more importantly as part of a technology transfer programme between Switzerland and Kyrgyzstan . The detailed methodology for this test is described in the Supplementary information S1 . Data was collated in Excel ( Microsoft Co . , Redmond , WA ) and imported into R ( www . r-project . org/ ) for analysis . A mixed logistic modelling approach was implemented , with the various risk factors entered as fixed effects and the village of origin as a mixed effect . Risk factors with a p>0 . 15 value were sequentially removed from the model . In addition , the final random effects model was compared to a fixed effect model by quasi-likelihood to identify the most parsimonious model . The linearity of the increase in age-specific prevalence was tested using a Generalized Additive Model ( GAM ) . For statistical evaluation of binomial data , the χ-square test with 95% confidence intervals according to Clopper and Pearson were used . The data we obtained in this study enabled us to estimate the likely incidence of congenital and AIDS related toxoplasmosis in Kyrgyzstan . The details of the methodology we used is given in Supplementary information S2 and S3 .
Of the 1’061 participants from the rural Kochkor district and of the 899 patient sera from laboratories and hospitals in Bishkek , 728 ( 69% ) and 611 ( 68% ) , respectively , were from women . The median age at the time of sampling in the Kochkor area was 34 years ( interquartile range: 22–47 ) in women , and 27 years ( interquartile range 12–45 ) in men , whereas in the urban population the median age at time of sampling was 30 years ( interquartile range: 20–47 ) and 25 years ( interquartile range: 7–44 ) , respectively . The specifics of both populations participating in the study are summarised in table 1 . The overall crude seroprevalence in the rural population was 6 . 2% ( 95%CI: 4 . 8–7 . 8 ) compared with 19 . 0% ( 95%CI: 16 . 5–21 . 7 ) in the urban population of Bishkek . When adjusted for the differences in age and gender profile of the sampled population compared to the census figures this indicated that the actual seroprevalence was 5 . 1% ( 95% CI 3 . 9–6 . 5 ) in the rural areas and 16 . 4% ( 95% CI 14 . 1–19 . 3 ) in the urban areas . The proportions of seropositive men from rural and urban areas were 5 . 7% ( 95%CI: 3 . 5–8 . 7 ) ( adjusted 5 . 5% , 95% CI 3 . 0–7 . 8 ) and 18 . 4% ( 95%CI: 14 . 1–23 . 4 ) , ( adjusted 15 . 8% 95%CI , 12 . 0–20 . 8% ) and of seropositive women 6 . 5% ( 95%CI: 4 . 8–8 . 5 ) ( adjusted 4 . 6% , CI 3 . 5%–5 . 5% ) and 19 . 3% ( 95%CI: 16 . 3–22 . 7 ) ( adjusted 16 . 7% , 95% CI = 13 . 0–19 . 4 ) , respectively . The age- specific seroprevalences in both study groups are shown in figure 1 . Neither significant gender differences in age-specific T . gondii seroprevalence , nor significant deviations from linear increase of the age-specific incidences were found . The calculated odds ratio for seropositivity per year of age in the Kochkor district was 0 . 16 ( 95%CI: 0 . 10–0 . 22 ) for women , 0 . 19 ( 95%CI: 0 . 10–0 . 28 ) for men , and overall 0 . 185 ( 95%CI: 0 . 13–0 . 24 ) ; in urban Bishkek the corresponding calculated odds ratios were 0 . 59 ( 95%CI: 0 . 42–0 . 75 ) , 0 . 88 ( 95%CI: 0 . 44–1 . 31 ) , and overall 0 . 64 ( 95%CI: 0 . 49–0 . 79 ) . The particulars of the rural study population with respect to putative risk factors are summarized in table 2 . Of those , only age , poor living standard and number of sheep owned were found to be statistically significant independent risk factors . The results of the risk-factor analysis are shown in table 3 . In 2008 , 127 , 332 live births were recorded in Kyrgyzstan ( latest available figures ) . Assuming that the toxoplasmosis prevalences in women from Bishkek and Kochkor measured in this study are representative of both urban and rural districts , we calculate that in 2008 approximately 175 ( 95% CI 136–216 ) children were born to mothers who seroconverted during pregnancy . This results in approximately 15 cases of chorioretinitis ( 7 present at birth , 8 with later onset ) , 6 intracranial calcifications , 1 hydrocephalus , 2 other CNS abnormalities and 2 neonatal or late foetal deaths . Of a total of 3434 HIV-positive patients according to official government figures , 342 are estimated to be co-infected with T . gondii , of which 127 are at risk to develop AIDS related toxoplasmosis in the Kyrgyz Republic within the next 10 years . According to the estimate UNAIDS [9] the same numbers would amount to 9’700 , 967 and 357 , respectively . Estimated cases for the major two cities of Bishkek and Osh , as well as for the different oblasts are shown in Supporting information table S1 .
Although we believe that the present study includes a representative sample of a mainly rural community of pastoralists and the urban population in Bishkek , there are important agrarian populations of Kyrgyz , Uzbek and Tajik ethnicity in the Fergana valley area of Southern Kyrgyzstan , which may have a different seroprevalence . Thus , the seroprevalence found in our study may not be representative for the entire country . Thus , the case numbers of clinically relevant toxoplasmosis have to be seen as an estimate representing mainly pastoralist communities and larger cities . Nevertheless , the magnitude of future putative clinical cases of toxoplasmosis can be derived from these estimates . The sampling strategy was also not random as there were some departures from the normal population profile in Kyrgyzstan . In particular there is an over representation of women in our sample . This might produce some bias with regards to the factors associated with toxoplasmosis seropositivity in the rural population . However , the sampling strategy should neither affect the estimates of the incidence of congenital toxoplasmosis nor the incidence of complications of HIV infection as these were calculated from the age specific prevalences . This study design in which people were invited to participate clearly resulted in a disproportionate number of women entering the study and this may be because many men were unavailable due to work . In the rural population there were similar numbers of boys and girls in the study under 10 years of age which would be consistent with this hypothesis . The urban samples were supplied by diagnostic laboratories and it is not clear why these samples were over represented by women . An additional limitation is the assumption that infection pressure or exposure has not changed with time . For example older people may have had greater exposure when they were younger and this could inflate the increase in age as a risk and overestimate the numbers of infants born with congenital infections . However changes in infection pressure would be expected to give non-linear increases in the age stratified sero prevalence . We tested this hypothesis by using generalised additive models which should have detected any significant deviations in the linear increase in the log of the odds ratio . Whilst this does not prove that infection pressure has not changed over time it does provide additional evidence that our assumptions are valid . On the contrary , the migration of individuals from rural areas with a low infection pressure to the cities with a higher infection pressure might mean that that this cross sectional study has underestimated the infection pressure and hence under estimated the number of cases of congenital toxoplasmosis . The diagnostic test we developed and used in this study may have resulted in a small underestimate of the prevalence of toxoplasmosis . The Platelia Toxo IgG assay against which we evaluated our test system has a reported sensitivity and specificity approaching 100% [8] . Our assay was positive in 49 of 50 samples positive with the commercial test . This would indicate a sensitivity of 98% ( CIs 89 . 35–99 . 95% ) assuming that the Platelia Toxo IgG assay is indeed such a perfect gold standard . Therefore it is possible we have underestimated the prevalence of toxoplasmosis in these populations by approximately 2% . | A serological study on toxoplasmosis was undertaken in a rural and urban population in Kyrgyzstan . The observed seroprevalence was adjusted because of differences between age and gender stratifications in the study group compared to population census figures . This gave an estimated seroprevalence in rural and urban populations of 5 . 1% and 16 . 4% respectively . In our analysis we determined the risk-factors for infection in the rural population to be age , low social-status and low number of sheep owned . While the seroprevalence in this rural population was relatively low , the seroprevalence found in the urban population of Bishkek correlated better with international data . Extrapolating from our data , about 173 seroconversions during pregnancy may be expected annually in Kyrgyzstan . In addition , considering a prevalence of HIV-Toxoplasma-co-infection between 7/100 , 000 ( official HIV-prevalence data ) and 19 . 4/100 , 000 ( UNAIDS-estimates ) , 350–1 , 000 people are at risk for AIDS-related toxoplasmosis . Therefore , in the face of the rising prevalence of HIV infection education of medical personnel on treatment and prevention of toxoplasmosis is recommended . | [
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] | 2013 | Toxoplasma gondii Infection in Kyrgyzstan: Seroprevalence, Risk Factor Analysis, and Estimate of Congenital and AIDS-Related Toxoplasmosis |
Schistosomiasis ( bilharzia ) is a chronic and potentially deadly parasitic disease that affects millions of people in ( sub ) tropical areas . An important partial immunity to Schistosoma infections does develop in disease endemic areas , but this takes many years of exposure and maturation of the immune system . Therefore , children are far more susceptible to re-infection after treatment than older children and adults . This age-dependent immunity or susceptibility to re-infection has been shown to be associated with specific antibody and T cell responses . Many antibodies generated during Schistosoma infection are directed against the numerous glycans expressed by Schistosoma . The nature of glycan epitopes recognized by antibodies in natural schistosomiasis infection serum is largely unknown . The binding of serum antibodies to glycans can be analyzed efficiently and quantitatively using glycan microarray approaches . Very small amounts of a large number of glycans are presented on a solid surface allowing binding properties of various glycan binding proteins to be tested . We have generated a so-called shotgun glycan microarray containing natural N-glycan and lipid-glycan fractions derived from 4 different life stages of S . mansoni and applied this array to the analysis of IgG and IgM antibodies in sera from children and adults living in an endemic area . This resulted in the identification of differential glycan recognition profiles characteristic for the two different age groups , possibly reflecting differences in age or differences in length of exposure or infection . Using the shotgun glycan microarray approach to study antibody response profiles against schistosome-derived glycan elements , we have defined groups of infected individuals as well as glycan element clusters to which antibody responses are directed in S . mansoni infections . These findings are significant for further exploration of Schistosoma glycan antigens in relation to immunity .
Schistosomiasis ( bilharzia ) is a chronic and potentially deadly parasitic disease , and a major public health burden in ( sub ) tropical areas . An estimated 207 million people are affected and 779 million people are at risk of being infected with schistosomes [1] , [2] . Schistosomiasis is caused by members of the helminth genus Schistosoma ( S . ) with S . haematobium , S . mansoni , and S . japonicum being the most widespread . Schistosomes have a complex life-cycle with larval , adult worm , and egg stages interacting with the human host , each playing a role in immunology , immunopathology and maintenance of infection . Schistosoma infection is commonly treated with Praziquantel ( PZQ ) [3] , [4] . Although PZQ has proven to be very effective , concern has been raised about development of drug resistance upon the currently ongoing mass treatments in endemic areas [5] , [6] and the need for an alternative anti-schistosomal drug is regularly emphasized [7] . Furthermore , drug treatment does not prevent reinfection and repeated treatments are essential for people living in endemic areas , resulting in high costs and requirements to infrastructure . Therefore it is of great importance that a vaccine inducing protection against schistosomiasis is developed . Multiple longitudinal studies have shown that infected individuals do acquire significant levels of immunity after prolonged exposure to Schistosoma . The acquisition of immunity is age-dependent in human populations living in schistosomiasis endemic areas with children being far more susceptible to re-infection than older children and adults [8]–[12] indicating that it takes many years of exposure , multiple infections and treatments , and maturation of the immune system to acquire this type of immunity . Several immunological parameters , including specific antibody and T cell responses , are predictive of the age-dependent immunity or susceptibility to re-infection after treatment [8] , [13] , [14] . Especially high levels of IgE against adult worm antigens [15]–[20] , but also IgG1 , IgG3 and IgA [8] , [14] levels have been associated with increased resistance to infection after treatment . IgM , IgG2 and IgG4 , on the other hand , are blocking antibodies with possible detrimental consequences for the expression of protective immunity [14] , [21] . IgM can block eosinophil-dependent killing mediated by IgG antibodies from the same or other sera [22] , [23] . IgM was found to be more highly expressed in children than in adults and is therefore higher in the non-immune group compared to the more resistant people [24] , [25] . Antibody responses in schistosomiasis have been mainly studied using soluble worm antigen ( SWA ) and soluble egg antigen ( SEA ) , each consisting of complex mixtures of antigenic ( glyco- ) proteins , or using specific recombinant protein antigens . Most antibodies generated during Schistosoma infection are however directed against parasite glycans [26]–[30] . This is not surprising considering the fact that glycans are abundant in schistosomal secretions , decorate the outer surface of all Schistosoma stages , and are highly immunogenic [31] , [32] . Schistosoma life stages each express a different glycan repertoire [31] , [33] , [34] . Elaborate studies on the glycome of the different Schistosoma life stages have indicated that hundreds of different glycan structures are present within the N- and O-linked glycans and the glycolipids [31] . So far , serum antibodies to only a small set of schistosome-related glycans have been determined in a limited number of studies [25] , [29] , [30] . The large gap in our knowledge about the contribution of anti-glycan antibodies to immunity to schistosomes may be overcome using a shotgun glycan array approach which allows the detection of serum antibodies to a large number of parasite-derived glycans simultaneously . In this glycan array technology , natural glycans isolated directly from relevant cells or organisms are presented on a surface to quantitatively measure the binding to complementary molecules at the whole natural glycome level thus including unique and unusual ( e . g . pathogen-specific ) glycans [1] , [35]–[40] . We have generated such a shotgun glycan microarray containing natural N-glycan and lipid-glycan fractions derived from 4 different life stages of S . mansoni ( male adult worm , female adult worm , cercariae , and eggs ) , and applied this array to the analysis of IgG and IgM serum antibodies in a selection of sera from an S . mansoni natural infection cohort . This resulted in the identification of antigenic glycans as well as differential glycan recognition profiles characteristic for different age groups and shows that the shotgun schistosome glycan microarray approach has discriminative properties to define groups of infected individuals .
Ethical approval for the Piida study was obtained from the Uganda National Council for Science and Technology ( UNCST ) and cleared by the Office of the President . The study was also supported by the Cambridge Local Research Ethics Committee . Prior to enrolment , the study was explained to each selected adult or parent/guardian of each selected child for the study and verbal consent obtained . Verbal informed consent was sought because of the high level of illiteracy in Piida and because Lougungu , the predominant language , is not a written language . This method was approved by the ethical review committee of the UNCST . Verbal consent was documented by recording the name of each individual providing consent . S . mansoni adult worms , cercariae and eggs were obtained as reported previously ( Robijn et al , 2005 ) . BSA- and NH2-linked synthetic oligosaccharide conjugates were synthesized as described [35] , [41]–[44] . Cy3 conjugated goat anti-human IgG ( Fc-specific ) , BSA and ethanolamine were from Sigma ( Zwijndrecht , the Netherlands ) . Alexa fluor 647 conjugated goat anti-human IgM ( μ chain specific ) was from Invitrogen ( Breda , The Netherlands ) . Human sera were obtained from S . mansoni infected individuals living in the Piida community , Butiaba , which is situated on the shore of Lake Albert in Uganda where S . mansoni is endemic with 72% prevalence [24] , [45] , [46] . The detection of S . mansoni eggs in the feces was used as an indicator of infection with S . mansoni . The study design , epidemiology , and sample collection have been described in detail previously [24] . In the current study , anti-glycan antibody responses were determined among two separate age-groups , 21 children aged 5–11 years ( mean age: 9 ) and 20 adults aged 20–46 years ( mean age: 29 ) , non-randomly selected from the original Piida study cohort based on intensity of infection and sex . All subjects had patent S . mansoni infection and intensity of infection did not differ significantly between the two groups [P = 0 . 51 , geometric mean ( GM ) infection intensity ( epg ) was 478 . 33 ( CI95%: 260 . 90 , 868 . 37 ) among children and 665 . 80 ( CI95%: 278 . 39 , 1592 . 36 ) among adults] . The two groups were comparable with respect to sex , with roughly 3 females: 2 males in both age-groups . Anti-SEA-IgG4 and -IgE and anti-SWA-IgG4 responses were comparable in the two age groups ( P>0 . 20 ) ; anti-SEA-IgG1 responses were significantly greater among the children ( P<0 . 001 ) , whilst anti-SWA-IgG1 and -IgE were significantly greater among the adults ( P≤0 . 01 ) . S . mansoni male and female worms , cercariae and eggs were homogenized in water ( 4 ml per g wet weight ) and sequentially methanol and chloroform were added ( 7 and 13 volumes , respectively ) . The upper phase contains the glycolipids and the pellet the ( glyco ) proteins . Glycans were released from the different preparations of S . mansoni glycolipids and glycoproteins by ceramidase and PNGase F treatment , respectively . Released glycans were subsequently purified , labeled with 2-aminobenzoic acid ( 2-AA ) , and fractionated by hydrophobic interaction liquid chromatography with fluorescence detection , as described previously [35] , [47] . Glycan fractions , ( synthetic ) glycoconjugates , and proteins were dissolved in 20 µl of 1× spotting buffer ( Nexterion Spot , Schott Nexterion ) with 10% DMSO in 384-wells V-bottom plates ( Genetix , New Milton , UK ) . A total number of 1143 samples ( 192 from male worms , 192 from female worms , 384 from cercarial lipid glycans , 192 from cercarial N-glycans , 102 from egg N-glycans , and 81 ( synthetic ) glycoconjugates ) were printed in triplicate on epoxysilane-coated glass slides ( Slide E , Schott , Nexterion ) by contact printing using the Omnigrid 100 microarrayer ( Genomic Solutions , Ann Arbor , MI ) equipped with SMP3 pins with uptake channels that deposit 0 . 7 nl at each contact . Each array was printed three times on each glass slide . Dot spacing was 290 µm ( X ) and 245 µm ( Y ) , and array spacing was 6000 µm . Printed slides were incubated overnight at room temperature at sufficient humidity to prevent drying of the spots and to allow covalent binding of printed 2-AA-labeled glycans and glycoconjugates to the epoxysilane via reaction with primary or secondary amines [35] . Microarray slides were covered with a hand-cut silicone gasket creating barriers to separate the three printed arrays and to hold wash and incubation solutions within the individual array areas . To remove unbound compounds , the arrays were rinsed with 1 ml PBS . Remaining active epoxysilane groups were blocked with 2% BSA , 50 mM ethanolamine in PBS for 60 minutes at room temperature while shaking . Subsequently , the slides were rinsed with PBS . Each microarray was incubated with serum ( diluted 1∶100 in PBS-0 . 01% Tween20 with 1% BSA ) for 60 min at room temperature while shaking . After washing the slides with successive rinses of PBS-0 . 05% Tween20 and PBS , the slides were incubated with Cy3-labeled anti-human IgG and Alexa Fluor 647-labeled anti-human IgM ( diluted 1∶1 , 000 in PBS-0 . 01 Tween20 ) for 30 minutes at room temperature while shaking and protected from exposure to light . After a final rinse with PBS-0 . 05% Tween20 , PBS and water the slides were dried and kept in the dark until scanning . A G2565BA scanner ( Agilent Technologies , Santa Clara , CA ) was used to scan the slides for fluorescence at 10 µm resolution using 2 lasers ( 532 nm and 633 nm ) . At these wavelengths the 2-AA label does not fluoresce . Data and image analysis was performed with GenePix Pro 6 . 0 software ( Molecular Devices , Sunnyvale , CA ) . Spots were aligned and re-sized using round features with no CPI threshold . Background-subtracted median intensities were averaged and processed as described by Oyelaran et al . [48] and median values of negative controls included on each array were subtracted . Datasets were log2 transformed to remove the basic trends of variance and plotted against the sample numbers . Hierarchical clustering analysis ( HCA , complete linkage clustering using Euclidean distance ) and Principal component analysis ( PCA ) were performed to define associated groups of sera and glycan fractions using MultiExperiment Viewer v4 . 5 and Simca-P+ 12 . 0 ( Umetrics ) , respectively . For HCA , non-parametric testing was used for comparisons and a p value <0 . 01 was used to identify glycan fractions that were differentially recognized by serum antibodies [49] . Glycan samples of interest were analyzed by matrix-assisted laser desorption ionization time of flight mass spectrometry ( MALDI-TOF-MS ) with an Ultraflex II mass spectrometer ( Bruker Daltonics , Bremen , Germany ) in the negative ion reflectron mode using 2 , 5-dihydroxybenzoic acid ( DHB , Bruker Daltonics ) ( 20 mg/ml in 30% ACN ) as matrix . Glycopeakfinder ( http://www . glyco-peakfinder . org ) was used to define glycan composition .
Using the shotgun glycan microarray , anti-glycan IgG and IgM responses in sera from S . mansoni infected individuals were determined . First , the IgG and IgM responses against a set of BSA-conjugated synthetic glycan structures that were included in the glycan microarray were compared between the two age groups ( <12 years vs >20 years ) . Overall , the IgG response was higher in the group of children compared to adults with significant differences between the groups in response to Fuc ( α1–3 ) GalNAc ( β1–4 ) GlcNAc ( F-LDN ) and Fuc ( α1–3 ) GalNAc ( β1–4 ) [Fuc ( α1–3 ) ]GlcNAc ( F-LDN-F ) ( Figure 1 ) . Also the IgM response was higher in children and differed significantly from that in adults for Gal ( β1–4 ) [Fuc ( α1–3 ) ]GlcNAc ( Lewis X , LeX ) and F-LDN ( Figure 1 ) . When comparing IgG and IgM responses , IgG responses against F-LDN-F and GalNAc ( β1–4 ) GlcNAc ( LDN ) were significantly higher than IgM in both age groups , while responses against LeX were dominated by IgM ( Figure 1 ) . With respect to the numerous printed glycans isolated directly from the Schistosoma life stages , Figure 2 shows that overall the IgG and IgM response patterns against the different glycan fractions are similar between the two age groups , but with a higher anti-glycan response intensity in the age group <12 years . Examining the responses against individual glycan fractions printed , statistical analysis using a Mann Whitney U rank order test ( p<0 . 01 ) revealed a significant difference between the two age groups for 14 . 5% and 13 . 4% of all glycan fractions present on the array for IgG and IgM respectively with all responses being higher in children than in adults . For IgG , this group of differentially recognized glycans mainly consisted of cercarial glycolipid glycans ( n = 54 ) , cercarial ( n = 32 ) and egg N-glycans ( n = 33 ) , while for IgM the differentiating fractions contained glycans isolated from cercariae ( N-glycans followed by lipid glycans , n = 78 and 32 respectively ) . Since the number of glycan fractions printed on the array was not equal for all sources , the numbers of differentially recognized glycan fractions were plotted as percentages of the total number of glycan fractions from each source ( Figure 3E ) . This showed that almost one third ( 32 . 4% ) of the total number of egg-derived N glycans were differentially recognized by IgG when comparing the responses between children and adults , while for IgM this was highest for the cercarial N-glycans ( 40 . 6% ) . To explore which glycan structures were differentially recognized between children and adults , the top 10 of glycan fractions with the biggest difference in response were analyzed by MALDI-ToF-MS ( Tables S1 ( IgG ) and S2 ( IgM ) ) . Most of these fractions contained mixtures of glycans , and of potential antigenic glycan elements . The glycan fractions that were differentially recognized by IgM and were higher in children than in adults contained glycans with short fucosylated and/or xylosylated ( truncated ) core structures and a few more complex structures which contain both core fucose and xylose and LeX elements in the antennae . For IgG , the proposed glycan structures are more complex and may contain other types of glycan elements such as LeX-LeX ( di-LeX ) and GlcNAc-LeX ( extended LeX ) . HCA and PCA of the subset of differentially recognized glycan fractions between the two age groups showed three clusters for IgG ( high ( red ) , intermediate ( white ) , and low response ( blue ) ) ( Figures 3A and 3C ) . For the group <12 years , 11 individuals ( 52 . 4% ) clustered together in the high response cluster , 4 children clustered in the intermediate ( 19% ) and 6 ( 28 . 6% ) in the low response group ( Figure 3D ) . In contrast , only 3 adults showed a high or intermediate IgG response while the majority shows a low ( 85% ) IgG mediated response ( Figure 3D ) . Three clusters were observed for IgM ( high , intermediate and low response ) ( Figures 3B and 3C ) . Most of the children ( 81% ) clustered in the high response cluster while the majority of adults clustered in the low ( 45% ) and intermediate ( 45% ) response clusters ( Figure 3D ) . These data indicate that IgG and IgM responses can be different for a selection of individuals since some of the high IgM responders did not show a high IgG mediated response . All of the children clustering in the high IgG response cluster also showed a high IgM response . The results described above show that , although the responses are significantly different between the two age groups , the individuals do not cluster precisely according to the age groups . Especially the intermediate response clusters contain individuals from both age groups , indicating that factors other than age are responsible for the differential anti-glycan IgG and IgM responses . To explore this possibility further , we performed a non-supervised HCA to define other possible individual and glycan clusters . Non-supervised HCA and PCA of IgG responses showed two main clusters of individuals with difference in anti-glycan responses ( Figure 4A and 4B ) . The high response cluster 1 contains 14 individuals ( 12 children and 2 adults ) of which 13 were also found in the high response cluster in the supervised age comparisons . The responses for the 27 individuals ( 9 children and 18 adults ) in the other cluster are much lower ( Figures 4A , 4B and 4C ) . With respect to the IgM responses , HCA and PCA also identify a high ( red ) and a low ( blue ) response cluster ( Figures 4A and 4B ) . The high response cluster contains 19 individuals ( 17 children and 2 adults ) and 22 individuals fall into the low response group ( 6 children and 16 adults ) ( Figure 4D ) . Although the high response cluster mainly contained children and the low response cluster mainly adults , the non-supervised clustering was different from the supervised clustering on age-dependent differentially expressed glycan fractions indicating that factors other than age play a role in IgM response clustering ( data not shown ) . From this non-supervised IgG and IgM response analysis for the entire array , four groups of individuals can be defined: group 1 with high IgG and high IgM responses , group 2 with high IgG and low IgM responses ( mixed ) , group 3 with low IgG and high IgM responses ( mixed ) , and group 4 with low IgG and low IgM responses . Group 1 consists of 10 children and 1 adult , while group 4 contains 4 children and 15 adults ( Table 1 ) . Interestingly , group 2 ( 2 children , 1 adult ) and group 3 ( 5 children , 3 adults ) do not seem to be biased in terms of age and show intermediate egg counts after treatment . The grouping of individuals in the non-supervised HCA and PCA described above was mainly due to glycan clusters C1 and C3 ( Figures 4A and 4B ) together forming the majority of the glycans present on the shotgun glycan microarray . However , for both IgG and IgM an additional smaller glycan cluster ( Figure 4 , glycan clusters C2 and C4 ) was observed for which the grouping of individuals is different . For IgG , glycan cluster C2 mainly consisted of egg ( n = 37 ) and worm N-glycans ( n = 35 ) , while IgM glycan cluster C4 mainly contained glycans isolated from cercarial N-glycans ( n = 86 ) . When plotting these numbers as percentages of the total number of glycan fractions from each source it was shown that more than one third ( 36 . 3% ) of the total number of egg-derived N glycans were present in glycan cluster C2 and 44 . 8% of cercarial N-glycans are present in glycan cluster C4 ( Figure 4D ) . HCA on glycan cluster C2 ( Figure 5A ) revealed that all individuals from the original high response cluster also belong to the high response group when exploring responses to glycans in cluster C2 only ( C1highC2high ) . Interestingly , a group of 10 individuals that belonged to the original low response cluster clustered differently from the rest with lower IgG responses against the subset of glycans in glycan cluster C2 ( C1lowC2low ) and thus differ from the other 17 present in this group of individuals that show an overall low response but show a high response for this selection of glycans in glycan cluster C2 ( C1lowC2high ) . When comparing additional information for these subgroups of individuals from the original low response cluster it became clear that there were no differences in age , but egg counts post treatment ( epg5 ) were lower for those individuals with the lowest IgG responses for glycan cluster C2 ( Figure 5B ) . Strikingly , nine out of ten in the low response cluster were females . The response against the subset of synthetic glycan structures showed that the IgG response in the C1lowC2low is lower than for the C1lowC2high group for all glycan structures tested , but significantly lower for F-LDN and F-LDN-F only ( Figure 5C ) . Also IgM glycan cluster C4 showed a different grouping of individuals than for the complete glycan microarray ( Figure 5D ) . As for IgG , all individuals from the original high response cluster also belong to the high response group when exploring responses to glycans in cluster C4 only ( C3highC4high ) . However , eight individuals from the original low response cluster show a higher IgM response ( C3lowC4high ) than the other 14 individuals for the glycans in cluster C4 ( C3lowC4low ) . For this group of 8 individuals the egg counts at 9 months post treatment ( epg5 ) were higher than for the C3lowC4low group but this was not statistically significant ( Figure 5E ) . In contrast to the clusters of individuals defined by anti-glycan IgG , no differences were observed for the IgM response when comparing C3lowC4low and C3lowC4high clusters ( Figure 5F ) . In particular for IgM responses against the glycans that make up cluster C4 it is clearly visible that the sera fall into three separate groups ( Figure 5D ) , whereas only two groups are observed for cluster C3 . This provides an important indication that different subsets of glycans give rise to antibodies which are discriminative for different groups of individuals .
To achieve more insight into the human immune response against Schistosoma-derived glycans we analyzed sera of infected individuals for antibody reactivity using a shotgun glycan microarray approach . In this study we selected sera from infected individuals from a larger study in Piida [24] to give two distinct age groups to be compared . In the larger study , S . mansoni was found to be highly endemic with an overall prevalence of 72% and with a peak in infection prevalence and intensity in children aged 10–14 years [24] , [45] , [46] . The selected sera that were chosen allow the exploration of differences in anti-glycan antibody responses between children and adults . In highly schistosomiasis endemic areas like Piida , young children are immunologically , and perhaps physiologically , more susceptible to reinfection after treatment than adults [11] , [50] and immunological parameters , including specific antibody and T cell responses , are predictive of the age-dependent immunity or susceptibility to re-infection after treatment [8] , [13] , [15] , [23] . First , we explored the IgG and IgM response to a limited set of synthetic glycoconjugates ( Figure 1 ) to which antibody response profiles have been analyzed previously . In accordance with literature , schistosomiasis induced IgM responses to LeX were higher compared to IgG responses [25] , [29] , [51] . For GalNAc ( β1-4 ) [Fuc ( α1-3 ) ]GlcNAc ( LDN-F ) high IgM and moderately high IgG responses have been reported [29] . In our glycan microarray analysis this was not the case for children , but when looking at adults only , the relative response to LDN-F is indeed slightly higher for IgM than for IgG ( Figure 1 ) . A study on chimpanzees experimentally infected with S . mansoni showed that responses to F-LDN and F-LDN-F are similar , and dominated by IgG [51] . Also in our glycan microarray analysis of naturally infected humans , the anti F-LDN-F response is clearly dominated by IgG , but with the response against F-LDN being higher than against F-LDN-F . Specific for the current group comparison of children and adults , significant differences were observed for F-LDN ( IgG ) , F-LDN-F ( IgG ) and LeX ( IgM ) , with in each case responses being higher in children . One previous study also indicated higher IgG response against F-LDN in children compared to adults living in an endemic area [30] . Just like in the glycan microarray in this study the IgM response against LDN-F was alike in children and adults [30] . Another study showed that median values for the IgM response against LeX were higher in children , but only slightly [25] , While the analysis of antibody responses to the limited set of synthetic conjugates yields some useful insights , the complete glycan microarray includes glycan fractions isolated directly from the schistosome providing the possibility to study numerous additional glycans . Focusing on these glycans , the IgG and IgM responses were also higher in children than adults for most fractions , similar to the observations for the synthetic glycoconjugates . The stronger antibody response against glycans in children and increased susceptibility to reinfection in this age group suggests that there is an inverse correlation between anti-glycan antibody titers and immunity . This would be in line with the smoke screen theory which reasons that high antibody responses towards glycans are beneficial for the parasite rather than the host by subverting the immune system away from epitopes that could provoke protective immune responses [26] , [27] . However , anti-glycan antibodies responses cannot be generalized as there are hundreds of different , defined glycan antigens of schistosomes and it could also be hypothesized that while many are subversive , other antibody isotypes or responses to specific subsets of glycan elements may be linked to protective immunity . For example , it has been shown that IgM and IgG2 antibodies that reacted with schistosomula and egg carbohydrate epitopes are negatively associated [14] while IgE directed against glycolipids has been suggested to be positively associated with resistance to reinfection [52] . The glycan clusters in the unsupervised HCA ( Figure 4A ) also provided an important indication that different subsets of glycans give rise to antibodies which can be discriminative for different groups of individuals and clearly suggested that not all glycans show a similar antibody response . In the currently used shotgun array , the glycan fractions together contain many different glycan elements expressed by one or more schistosome life stages . While some glycan fractions contain only a single glycan antigen , most fractions are formed of mixtures of glycans that were not separated by the chromatographic procedure used , or they contain glycans which display more than one antigenic glycan element , e . g . in different branches of a di-antennary N-glycan . Therefore , it would be too early to speculate which specific responses to each glycan element occur in the different groups of the cohort . To this end the fractions first need further sub-fractionation and structural analyses to improve separation and definition of the antigenic glycan elements present . What can already be learned from the stage-specific glycan fractions as a group is that for IgG most differentially recognized fractions were derived from cercarial lipid glycans , while IgM responses were clearly most dominant against cercarial N-glycans . Both cercarial lipid and N-glycan fractions contain glycans with LeX elements , however pseudo-Lewis Y elements are unique for cercarial lipid glycans while core β2-xylose occurs in cercarial N-glycans [31] , [53] , [54] possibly giving rise to differences in dominant responses observed for IgG and IgM . When analyzing the glycan structures in the top 10 of glycan fractions that were differentially recognized between children and adults differences were indeed observed for IgM and IgG . Differential IgM responses between children and adults seem to be mainly against fractions with short fucosylated and/or xylosylated ( truncated ) core structures and mono LeX elements while differential IgG responses were against more complex structures containing LeX-LeX ( dimeric LeX ) and LeX- ( F- ) GlcNAc elements . The differences in anti-glycan antibody responses between children and adults for this selection of sera may reflect differences in age or differences in length of exposure or infection in an endemic area . The non-supervised HCA showed that the individuals did not cluster precisely according to age suggesting that other factors play a role in anti-glycan antibody response profiles . Also within one cluster of individuals the anti-glycan antibody responses varied for different glycan clusters as was shown for glycan clusters C2 and C4 ( Figures 4A and 4B ) . One single glycan antigen is clearly not representative for the whole group and this stresses the need for screening antibody responses against multiple glycans and glycan elements . Shotgun glycan microarrays are valuable tools in this type of screening allowing the definition of groups of individuals as well as glycan element clusters to which similar antibody responses are generated in individuals . Having shown that the shotgun Schistosoma glycan microarray has discriminative power for studying differences in anti-glycan immune responses in different groups of individuals , this technique can now be applied to a randomly-selected epidemiological cohort to address whether anti-glycan antibody responses reflect differences in age , infection intensity or other factors that have not been explored yet . | Schistosomes are parasitic worms that cause chronic and potentially deadly disease in millions of people in ( sub ) tropical areas . An important partial immunity to infection does develop but this takes many years of exposure and multiple infections . Therefore , children are far more susceptible to re-infection after treatment than adults . This immunological protection is associated with specific antibody and T cell responses . Many antibodies generated during Schistosoma infection are directed against carbohydrate chains ( glycans ) expressed by the parasite . The nature of the glycan epitopes recognized by antibodies in natural schistosomiasis infection serum is largely unknown . We have used a so-called shotgun glycan microarray approach to study differences in anti-glycan antibody responses between S . mansoni-infected children and adults . This resulted in the identification of differential glycan recognition profiles characteristic for the two different age groups that may reflect differences in age or differences in length of exposure or infection in people living in an endemic area . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"immunology",
"biology",
"microbiology"
] | 2012 | Differential Anti-Glycan Antibody Responses in Schistosoma mansoni-Infected Children and Adults Studied by Shotgun Glycan Microarray |
We have demonstrated that vaccination with pDNA encoding cysteine proteinase Type II ( CPA ) and Type I ( CPB ) with its unusual C-terminal extension ( CTE ) can partially protect BALB/c mice against cutaneous leishmanial infection . Unfortunately , this protection is insufficient to completely control infection without booster injection . Furthermore , in developing vaccines for leishmaniasis , it is necessary to consider a proper adjuvant and/or delivery system to promote an antigen specific immune response . Solid lipid nanoparticles have found their way in drug delivery system development against intracellular infections and cancer , but not Leishmania DNA vaccination . Therefore , undefined effect of cationic solid lipid nanoparticles ( cSLN ) as an adjuvant in enhancing the immune response toward leishmanial antigens led us to refocus our vaccine development projects . Three pDNAs encoding L . major cysteine proteinase type I and II ( with or without CTE ) were formulated by cSLN . BALB/c mice were immunized twice by 3-week interval , with cSLN-pcDNA-cpa/b , pcDNA-cpa/b , cSLN-pcDNA-cpa/b-CTE , pcDNA-cpa/b-CTE , cSLN , cSLN-pcDNA and PBS . Mice vaccinated with cSLN-pcDNA-cpa/b-CTE showed significantly higher levels of parasite inhibition related to protection with specific Th1 immune response development , compared to other groups . Parasite inhibition was determined by different techniques currently available in exploration vacciation efficacy , i . e . , flowcytometry on footpad and lymph node , footpad caliper based measurements and imaging as well as lymph node microtitration assay . Among these techniques , lymph node flowcytometry was found to be the most rapid , sensitive and easily reproducible method for discrimination between the efficacy of vaccination strategies . This report demonstrates cSLN's ability to boost immune response magnitude of cpa/cpb-CTE cocktail vaccination against leishmaniasis so that the average parasite inhibition percent could be increased significantly . Hence , cSLNs can be considered as suitable adjuvant and/or delivery systems for designing third generation cocktail vaccines .
Leishmaniasis is one of the most important vector borne infections that can cause a spectrum of diseases , ranging from a clinically silent process to a fatal progressive disease in human . It is a major public health crisis in many countries including Iran resulting in an estimated of 12 million new cases occurrence , each year ( World Health Organization website , http://www . who . int/vaccine_research/diseases/soa_parasitic/en/index3 . html ) . This parasitic disease is diagnosed as three clinical forms named cutaneous , mucocutaneous and visceral leishmaniasis . Clinical manifestation of the disease depends on both the species involved and the host . The tissue lesion in cutaneous form , can last for months or years before healing . An interesting feature is that despite the disappearance of the lesion and resistance to reinfection , residual parasites remain in the host , probably for a very long time , if not forever [1] . Current curative therapies for cutaneous leishmaniasis are costly , often poorly tolerated and not always effective . This disease is one the few parasitic diseases likely to be controllable with vaccination [2] . Generally; vaccination is largely protein-based and requires direct administration of dead or attenuated parasite , recombinant proteins , or virus-like particles . For targets resembling intracellular pathogens like Leishmania species , such vaccines generate incomplete immune responses and fail to induce protective affects as usually generate only antibody-mediated ( humoral ) immune responses and often require periodic booster injections [2] , [3] . However , cell-mediated immune responses are required for clearance of such parasite and generation of cytotoxic T-lymphocyte ( CTL ) cells that kill infected cells . Currently , only attenuated live organism vaccines generate significant cell-mediated immune responses , but these are associated with certain safety concerns and can be difficult to manufacture consistently [2] , [3] . DNA vaccination offers an attractive alternative to traditional non replicating vaccine strategies . Intracellular production of antigens from delivered DNAs can result in both humoral and cellular immune responses . DNA-based vaccines also offer practical advantages as well , mostly because of the capability of developing countries to cheaply and rapidly produce pDNA from bacteria . Furthermore , it is possible to formulate several antigens from different stages of the parasite life cycle or different subspecies as one shot of vaccine [4] . However , despite decades of research , safe and efficient delivery of pDNA to initiate proper immune responses remains one of the major drawbacks in bringing DNA vaccination into clinical trials . Synthetic particle carrier systems are known as one of the important tools for improvement of current performed DNA vaccines . In such approaches , pDNA is encapsulated into , or complexed via electrostatic interaction with a synthetic carrier , resulting in fabrication of particles with a size ranging from nanometers ( nm ) to few micrometers . The pDNA release from these particles has proven to be a very effective delivery strategy as the passive targeting to antigen presenting cells ( APCs ) by size exclusion mechanism , protection from nuclease degradation , cellular uptake enhancement , antigen depot formation at the injection site and controlling the release rates of pDNA that might be important for timing immune responses , are all likely to be occurred [5] . Lipid-based delivery systems represent one of the most advanced drug delivery technologies to date . Different lipid-based adjuvants are introduced and evaluated for pDNA formulation , e . g . Solid lipid nanoparticles ( SLN ) [based on class I of lipids] , liposomes , Transfersomes , niosomes and virosomes [based on class II of lipids] and Micelles , emulsions [based on class III of lipids] [6] . In general , the common ground of these systems for transfection is the need for cationic lipids such as 1 , 2-dioleoyl-3-trimethylammonium ( DOTAP ) to facilitate pDNA binding . A neutral helper lipid such as L-alpha-dioleoyl phosphatidylethanolamine ( DOPE ) or cholesterol is also required to increase the transfection properties of the pDNA/lipid complexes . Several authors have administered liposomes , niosomes and other lipid based systems for pDNA delivery [6] . But despite offering a number of technological advantages over other existing transfection reagents , SLN utility for DNA vaccination has not been conveniently investigated in vivo . As SLN can be manufactured in large scale and under favorable technological parameters without the need for organic solvents and have an acceptable stability that facilitates their manipulation for different processes such as lyophilization and steam sterilization [7] , , they may become a potentially valuable addition and promising alternative to the well-established dossier of non-viral transfection agents leading by cationic liposomes . Leishmania express large quantities of cysteine proteinases ( CP ) which are members of the papain superfamily [9] . In Leishmania major ( L . major ) , two most important CPs have been described . CPA is a type II cysteine proteinase which is expressed at higher level in amastigote stage and stationary phase promastigote . CPB is a type I cysteine proteinase which present maximally at the amastigote developmental stage and is encoded with an unusual C-terminal extension [10] . The presence of this highly variable CTE differentiates Leishmanial rCPB from the other CPs in the papain superfamily [10] , [11] , [12] . CTE can be glycosylated and partially removed by proteolytic cleavage during processing of the enzyme to its mature form [11] . Hence , CTE fragment is not crucial for enzyme activity and intracellular trafficking , although it is highly immunogenic and responsible for immune evasion and play a role in the diversion of the host immune response [11] , [12] . We have reported that antigenic rCTE of L . infantum elicitated a predominant IgG2 response in asymptomatic dogs and in vitro proliferation of PBMCs . Immunization with CTE also displayed both type 1 and 2 immune signatures in experimental murine model of L . infantum infection and therefore is not protective as a vaccine candidate [13] . Furthermore , we had demonstrated that the cpa/cpb cocktail is more protective against cutaneous leishmanial infection than the separate forms [14] . Therefore , there was still a need to study the effect of CTE deletion in this cocktail vaccine against L . major . Despite the proven antigenicity and immugenicity of these DNA vaccine candidates , the largest drawback of this kind of vaccination is the obscurity in intracellular delivery of pDNA that causes low levels of gene expression ( transfection ) which in turn limits the resulting immune responses [15] . Therefore , pDNA must use with a proper and safe formulation in order to coordinate innate and adaptive immune responses and generate strong immunity . Recently , several immunoadjuvants like BCG , G-CSF and CpG-ODN and also various delivery systems like PLGA microspheres and liposomes have been used to potentiate the immune responses against Leishmania antigens [16] . Herein , we used cationic solid-lipid nanoparticle ( cSLN ) as a non viral transfection agent for delivery of the cocktail DNA vaccine . In our previous studies , cSLN formulation as a delivery system have revealed comparable efficiency/cytotoxicity ratio to linear PEI-25 kDa-pDNAs polyplexes , protected cpa , cpb-CTE and cpb genes from extracellular enzymatic degradation and also exhibited considerable low cytotoxicity [17] . In this study , these characterized formulations of cocktail vaccine candidates were evaluated for their immune induction potential in BALB/c mice as sucesptible animal model .
All solutions were prepared using MilliQ™ ultrapure ( Milli-Q-System , Millipore , Molsheim , France ) and apyrogenic water to avoid surface-active impurities . Cetyl palmitate , tween 80 and cholesterol were purchased from Merck ( Darmstadt , Germany ) . N-[1- ( 2 , 3-Dioleoyloxy ) propyl]-N , N , N trimethylammonium chloride ( DOTAP ) , Sodium dodecyl sulfate ( SDS ) were purchased from Sigma–Aldrich ( Deisenhofen , Germany ) The materials applied for PCR , enzymatic digestion and agarose gel electrophoresis were acquired from Roche Applied Sciences ( Mannheim , Germany ) . Cell culture reagents including Fetal Calf Sera ( FCS ) , M199 medium , HEPES , L-glutamine , adenosine , hemin , gentamicin , and RPMI were sourced from GIBCO ( Gibco , Life Technologies GmbH , Karlsruhe , Germany ) and Sigma ( Germany ) respectively . The cSLN suspension was produced by a technique previously described by Doroud et al . [17] . Briefly , desired amount of DOTAP ( 0 . 4% w/v ) was dissolved in hot aqueous phase which was then added to the melted cetyl palmitate and cholesterol ( 5 . 1% w/v ) phase containing tween 80 as a nonionic surfactant at 3 . 2:1 molar ratio . Emulsification was carried out by stirring the mixture at 2000 rpm by a mechanical stirrer ( IKA® , Germany ) for 10 min at 90°C . Samples were then homogenized using a high shear homogenizer ( IKA® , Germany ) at 18 , 000 g for 15 min . cSLN dispersion was obtained by direct cooling of hot O/W microemulsion on an ice-bath while stirring at 1000 rpm . cSLNs were washed by centrifugation ( 6000 g , 10 min , three times ) using 100 kDa Amicon® Ultra centrifugal filters ( Millipore , Schwalbach/Ts , Germany ) to purify the suspension from the excess amounts of surfactant . Endotoxin concentration in the cSLN formulation was determined by limulus amoebocyte lysate ( LAL ) assay ( LAL Kit , Charles Riever Endosafe , T2092 CTK7 , and USA ) . The physicochemical stability of the formulations were evaluated at 4±1°C , 25±1°C at dark for 1 month at regular time intervals via observation of any changes in suspension clarity , particle size and zeta potential assessments . pGEM-cpa , pGEM-cpb and pGEM-cpb-CTE were available from our previous studies [17] and each of antigenic fragments were subcloned into pcDNA 3 . 1 ( − ) vector ( Invitrogen ) . Plasmid DNAs were transformed into the DH5α E . coli strain and purified by alkaline lysis method ( QIAGEN Endofree Plasmid Giga Kit ) and then confirmed by PCR and digestion ( data not shown ) . In all three constructs ( pcDNA -cpa , pcDNA -cpb , and pcDNA –cpb-CTE ) , the cpa , cpb , and cpb-CTE open reading frames were under control of the CMV promoter , inserted downstream of a Kozak consensus sequence and in frame with an initiation codon . The total concentration and purity of pDNAs were determined by NanoDrop® ND-1000 spectrophotometer ( Labtech , UK ) . Construct corresponding to pQE-cpa and pQE-cpb were produced in fusion form with an N-terminal histidine ( 6XHis-tag ) for expression and purification of rCPA and rCPB , as previously described [14] . The cpb-CTE gene was subcloned into the cloning site of the bacterial expression vector pET-23a expression plasmid , downstream of the T7 promoter . The E . coli strain BL21 ( DE3 ) was transformed with pET-cpb-CTE and grown at 37°C in 100 ml LB medium supplemented with 100 µg/ml ampicillin and 25 µg/ml chloramphenicol . The culture was induced with 1 mM IPTG at an OD600 of 0 . 8 and grown for a further 4 . 5 h at 37°C . Cells were centrifuged at 8000 rpm for 20 min . Bacterial pellets were dissolved in lysis buffer [50 mM Tris–HCl ( pH = 8 ) , 100 mM NaCl and 1 mM EDTA] and frozen overnight at −20°C . After centrifugation ( 10 , 000 g , 15 min at 4°C ) pellets were washed extensively with washing buffer [20 mM Tris–HCl ( pH 8 ) , 20 mM NaCl and 1 mM EDTA] . Inclusion Bodies ( IB ) were purified by imidazole-SDS-Zn reverse staining method . The purified recombinant protein was concentrated by ultrafiltration using Amicon Filter ( MWCO: 10 kDa ) and dialysed against PBS . Protein concentration was determined with BCA assay kit ( Pierce , Rockford , USA ) . Purified recombinant proteins were analyzed by SDS-PAGE and Coomassie blue staining to assess the integrity and purity of proteins . These proteins were recognized by previously prepared rabbit anti-CPB antisera using western blot technique [12] . cSLN–pDNA complexes were prepared by adding volumes corresponded to 650 µg of each purified pDNA ( pcDNA-cpa , pcDNA-cpb , pcDNA-cpb-CTE ) to cSLN suspension at a DOTAP:pDNA ratio of 6∶1 ( w/w ) and 60 min incubation at room temperature separately , as described before [17] . The final formulations were named as Spa , Spb and Spb-CTE respectively . Complete condensation of pDNAs , complexation with cSLN and the ability of the formulation to protect pDNAs from DNase I digestion were analyzed as previously demonstrated by agarose gel electrophoresis [17] . Statistics were performed using Graph-Pad Prism 5 . 0 for Windows ( Graphpad Software Inc 2007 , San Diego , Calif . , USA ) . All the data analyzed with one way ANOVA ( Multiple comparisons Tukey post test ) when required , with the exception of size and zeta potential measurements , which were analyzed with a Student's t-test . A p-value of ≤0 . 05 was considered as significant difference between the groups . “n” represents number of mice per group or samples per assay .
cSLN were produced by using the modified microemulsion and high shear homogenization method , cetyl palmitate and cholesterol as matrix lipid , DOTAP as charge carrier and Tween 80 as surfactant . Obtained nanoparticles were approximately 257±23 nm in size and positively charged with a zeta potential of +52±8 mV in milli Q water and size distribution of 0 . 34±0 . 08 . This suspension was stable for 30 days ( p<0 . 05 ) . The SLN-pcDNA stable complexes ( Spa , Spb and Spb-CTE ) were prepared by pDNA adsorption on the surface of cSLNs via direct complexation with pcDNA-cpa , pcDNA-cpb and pcDNA- cpb-CTE , respectively . These formulations were also characterized according to their size , zeta potential and poly dispersity index ( PDI , Table 1 ) . The results indicated that Spa , Spb and Spb-CTE cationic formulations had an average size of 244±12 , 250±15 and 237±12 nm , respectively . There was no significant difference in the size of different preparations ( p>0 . 05 ) . Spa , Spb and Spb-CTE had a mono disperse formulation as the PDI value was about 0 . 2 for all of them . The observed zeta potential revealed all the formulations were cationic ( ζ potential = 22 to 27 mV ) that is suitable for interaction with the negatively charged cell surface and the cell entry . The agarose gel electrophoresis analysis was used to test Spa , Spb and Spb-CTE formulations for their ability to condense pDNA through electrostatic interactions after preparation ( data not shown ) . Gel retardation assay for SLN-pDNAs confirmed complete complexation between pDNA and cSLN at a DOTAP:pDNA ratio of 6∶1 . Cationic SLN were able to protect pDNA against DNase I digestion as previously reported [17] . Spa , Spb and Spb-CTE formulations were stable at refrigerated temperature ( 4±1°C ) over one month storage . Endotoxin concentration in the cSLN formulations was 0 . 215 EU/50 ug . In our previous in vitro studies on COS-7 cells , we demonstrated a very low degree of toxicity of both cSLN and cSLN–pDNA complexes . Furthermore , flow cytometry analysis confirmed SLN-pDNA complexes were able to promote transfection of COS-7 cells at least for 72 hrs after treatment of these cells without a significant reduction in cell viability and in vitro CPs expression capacity [17] . The efficacy of cocktail pDNA vaccines containing pDNAs encoding CPA/CPB , CPA/CPB-CTE , and the same cocktail pDNA vaccines formulated with cSLN ( Spa/b and Spa/b-CTE ) were evaluated by their capability to induce protection against Leishmania infection in the BALB/c mice model . It is worth to mentin that , no apparent sign of local intolerance such as redness , swelling , bruising , pain observed at the site of injection 1 and 24 hr after vaccine administration . Parasite inhibition in different groups was assessed on both FP and LN of animals . For this purpose , dynamic measurement with a metric caliper , parasite load determination by flowcytometry and imaging techniques were done on the infected mice FP . In parallel , the parasite load was assessed in the LN via microtitration and flowcytometry methods . As shown in Figure 1 , FP swelling of seven different groups of mice n = 10 ) was measured after a challenge inoculation with EGFP-transfected stationary phase promastigotes of L . major . All the mice which have received the empty cSLNs ( G5 ) , pDNA vector without any insert formulated with cSLN ( G6 ) or PBS ( G7 ) ; showed significant lesions by 7 weeks post-challenge . There was a significant ( p<0 . 05 ) difference between animals received formulation Spa/b and Spa/b-CTE ( G1 and 3 ) and animals immunized with non-formulated cocktail vaccines ( G2 and 4 ) at 9th week post challenge . The latter groups did not show any significant difference with the control groups . There was also a significant ( p<0 . 05 difference between group 1 and 3 , confirming that the higher level of protection observed in group 3 . However , this difference between the cocktail vaccine compositions was not significant in group 2 and 4 and this difference could not be simply described due to the presence of CTE in the texture of the Spa/b cocktail vaccine . It seems that utilizing cSLNs for the formulation of pDNAs could potentiate the manifestation of this difference between the vaccine compositions . Vaccination with cSLN-pDNA encoding cpa , cpb ( G 1 ) or cSLN-pDNA encoding cpa , cpb-CTE ( G3 ) delayed FP swelling when compared to immunization with cSLN and PBS ( G5 and 7 ) , at this time point . For a certain time , immunization with the pcDNA-cpa/b and pcDNA-cpa/b-CTE cocktail vaccines had a significant effect in delaying FP swelling . However , this effect was not long lasting and 9 weeks after challenge , there was a significant difference ( p<0 . 05 ) in protective effect between groups 3 and all of the other groups including group 1 . Furthermore , the mean FP lesion size in group 1 , was still significantly ( p<0 . 05 ) lower than that in the control groups . Totally , vaccination with a cocktail of cpa , cpb-CTE formulated with cSLNs ( G3 ) resulted in control of the infection progress compare to the control groups as all of the animals in this group developed significantly smaller FP lesions ( 2 . 02±0 . 7 mm ) at 9th week after challenge . To assess the reliability of this conventional and time consuming experiment at 9th week post challenge , we also detected EGFP-expressing L . major in the FPs of mice using fluorescence imaging system that is supposed to give a precise two-dimensional image from the extent of infection , independent of the inflammatory responses at the FP injection site . This experiment enabled us to clearly detect the parasite load in the infected FPs ( Fig . 2 ) . As shown in this Figure , the GFP fluorescence in the control group of animals was not localized to the site of the inoculation and the parasites spread to the whole FP . We also observed that the increasing thickness of the infected FPs was not correlated with the intensity enhancement of the detected GFPs in the tested animals ( Fig . 2 ) . The sum green intensity ( pixel ) from the imaging studies were higher in infected FP of the control groups . Furthermore , only group 3 of the tested animals revealed significantly ( p<0 . 05 , n = 4 ) lower GFP intensity compare to the other groups ( Fig . 2 ) . EGFP expression in L . major amastigotes resident in the FPs was also monitored by flow cytometry technique . High expression levels of EGFP were observed in PBS treated group ( G 7 , Fig . 3 ) . Fluorescence activated cell sorting ( FACS ) analysis indicated a clear quantitative separation between parasite transfected and normal cells . The frequency of EGFP positive cells of the FPs determined by FACS analysis using the appropriate gating are shown in Fig . 3 . Group 3 of the vaccinated animals had the lowest infection rate ( 12 . 77%±0 . 62 ) compare to the other groups . The percentages of GFP-expressing parasites were significantly lower in the FP cells of the group 1 and 3 of the vaccinated animals . The infection progression was also followed by determining the total parasite load in the LNs of challenged mice at 9 weeks post-infection by the microtitration method ( Fig . 4A ) and flowcytometry ( Fig . 4B ) . According to microtitration method and compare to control groups , parasite load in the LNs of vaccinated groups with Spa/b-CTE ( G3 ) decreased significantly ( p<0 . 05 , n = 3 ) ( Fig . 4A ) . The expression of EGFP in the LNs was readily evident from the intense green fluorescence of the parasites ( Fig . 4B ) . The parasites reside inside the LN cells were quantified by monitoring EGFP expression via flowcytometry analysis . As shown in Fig . 4B , Group 1 and 3 both expressed significantly ( p<0 . 05 , n = 3 ) lower percent of EGFP that is correlated with the least amastigote parasite existence in the lymphatic cells . On the other hand , parasite burden in the group 3 was significantly ( p<0 . 05 ) lower than group 1 . It is noteworthy that , the rate of infection in all groups was in concordance with the delay in the appearance of lesions , the thickening peaked at 9 weeks post-infection and the parasite load in the LNs and FPs determined by microtitration , flow cytometry and imaging methods . Mice vaccinated with formulated cocktail plasmids i . e . Spa/b and Spa/b-CTE ( G1 and G3 ) showed a significant decrease in the FP tissues parasite load as well as LN , compared to the control groups . The ability of the vaccines to inhibit the infection progress and parasite replication [parasite inhibition ( PI ) %] was predicted according to the assessment of the FPs and LNs parasite load via imaging and flow cytometric method in terms of the decrease in intensity of green fluorescence observed in vaccinated animals , as well as parasite burden of the FPs and LNs by flowctometry and microtitration method . As shown in Table 2 , the given rates were calculated by comparing the precentage of fluorescence intensity or parasite burden in FPs and LNs of vaccinated mice to the lowest and highest intensity of fluorescence in the non vaccinated groups . Average of PI% rates were more expressive , when FP imaging and LN flow cytometric techniques were used . The average PI% in G3 was significantly higher than other vaccinated groups when determined by different methods , executed on FPs and LNs . The mean average of parasite reduction in this group was 87 . 11% ( IC95% , 85 . 9–88 . 33 ) by FP imaging , 65 . 64% ( IC95% , 61 . 29–69 . 98 ) by FP flow cytometry , 41 . 43% ( IC95% , 35 . 05–47 . 81 ) by LN microtitration and 86 . 77% ( IC95% , 87 . 78–85 . 75 ) by LN flow cytometry ( Table 2 ) . The profile of the alteration in the PI% results in all of the vaccinated groups were correlated with the results of the LN parasite burden via microtitration assay and footpad swelling . Neither imaging nor flow cytometry assays could discriminate between the parasite inhibition results of G1 and G3 of the vaccinated animals , when manipulating the animals' footpads ( Table 2 ) . In order to compare the induced immune responses by different DNA cocktail vaccination strategies and explore the protective effects against L . major challenge in BALB/c mice , IFN- γ and IL-5 production were assessed after in vitro stimulation of LN cells with both Leishmania soluble antigen ( SLA ) and recombinant CPs , pre and post challenge . According to IFN-γ production , pooled cells from three mice of groups 1 , 2 , 3 produced significantly ( p<0 . 05 ) higher levels compared to control groups before infection in response to the rCPs ( Fig 5A ) . Although group 3 produced higher amounts of IFN-γ ( 522 . 98±11 . 99 pg/ml ) , but was not significantly higher than the other vaccinated groups . Low IFN-γ production was detected in supernatants of LN cells of all three control groups in response to rCPs , before challenge . At 9th weeks post challenge , the rprotein specific IFN-γ production level increased only in group 1 and 3 of the animal vaccinated with Spa/b and Spa/b-CTE formulations , respectively . Although , the difference in IFN-γ production level was only significant ( p<0 . 05 ) , in group 3 ( 773 . 29±16 . 78 pg/ml ) of animals compared to control groups ( Fig . 5B ) . No IL-5 was detectable in the supernatant of cells from all groups after stimulation with rCPs before challenge ( data not shown ) . In contrast , significant levels of IL-5 were detected in the supernatant of cells from group 5 , 6 and 7 , in response to rCPs SLA at 9th week after infection , compared to all the vaccinated groups of animals ( G1 , 2 , 3 and 4 , Fig . 5C ) . There were no significatnt difference in Con A-induced cytokine production , among the tested groups . Further analysis of the induced cytokines profile by means of IFN-γ/IL-5 ratio revealed that only formulated cocktail DNA vaccines ( Spa/b and Spa/b-CTE ) could clearly induced strong Th1 immune responses ( Fig . 6 ) . As shown in this Figure , this ratio was significantly ( p<0 . 05 ) higher in group 3 of vaccinated mice compared to the other groups suggesting a higher level of protective immunity . To compare IgG isotypes in protected and non protected vaccinated groups , sera were collected before and 9 weeks after challenge and assessed for IgG1 and IgG2a . To determine the antibody specificity , all sera of vaccinated and control mice were pooled and assayed by ELISA method before ( Fig . 7A , B and C ) and after ( Fig . 7D , E and F ) challenge with L . major promastigotes . As it is shown in these Figures , the group of mice that developed an effective protective response ( e . g . , group 3 ) had substantially higher levels of CPA-specific IgG2a antibody compared to unprotected mice , both before ( n = 13 ) and after ( n = 10 ) challenge ( Fig . 7A , C ) . This is consistent with the results obtained above that SLN formulated cocktail DNA vaccine encoding cpa and cpb-CTE ( Spa/b-CTE ) preferentially induced a Th1 response . According to the Fig . 7D , higher levels of CPB-specific IgG2a was produced in G1 and 2 of the vaccinated mice . This might be described by the immunogenic nature of CTE fragment . The productions of IgG1 in the control groups were significantly higher than IgG2a after challenge , when stimulated by SLA ( Fig . 7E ) . The ratio of IgG2a/IgG1 in sera of mice immunized with Spa/b-CTE formulation was higher than the other groups when titrated against rCPA ( Fig . 8 ) . This ratio was also higher in sera of mice immunized with the pcDNA-cpa/bCTE formulation than all the other vaccinated groups . Challenged with GFP expressing L . major promastigotes induced high level of IgG1 antibody titers against SLA in all groups ( Fig . 7E ) . The sera of mice immunized with Spa/b-CTE formulation showed higher levels of specific IgG2a antibody compared to the IgG1 , when titrated against SLA and higher IgG2a/IgG1 ratio was only observed in this group of vaccinated animals .
Although vaccination in the endemic populations is the most cost-effective tool to diminish the burden of Leishmaniasis , an effective vaccine to control this disease is not commercially available yet [2] . It is unlikely that an effective anti-Leishmania vaccine based on the use of a single antigen will be achieved . This might be due to the complex and biphasic life cycle of Leishmania parasite . Therefore , a rational approach toward developing an effective cocktail vaccine should be the use of extracellular and intracellular parasite antigens resulting in a valuable cumulative immune response [3] , [14] . In this regard , ease of combining different pDNA vaccine candidates has made genetic vaccination an attractive platform for vaccination strategies [3] , . However in most cases , even multivalent or cocktail DNA vaccines have failed to achieve the required level of protection possibly due to the lack of an appropriate delivery system and/or adjuvant [3] , [16] . Therefore , there is still an urgent need for development of new , safe and improved adjuvant and/or delivery systems to enhance the immunogenicity of the available vaccine candidates . There are versatile conflicting reports about DNA vaccination effectiveness against leishmaniasis . Most of DNA vaccine candidates have been tested as single vaccine regimens , but there are also some reports about using combination of genes [1] , [3] . Up to date despite of the heterogeneity of vaccination protocols , mean average of parasite load reduction was determined to be 59 . 24% ( IC95% , 47 . 75–70 . 73 ) [3] . This might be due to the reason that adjuvants and delivery systems were rarely added to the formulations containing candidates of the mentioned third-generation vaccines [3] . Approved adjuvants for human vaccines are poor inducers of antigen-specific Th1 responses that are necessary for an intra cellular parasite like Leishmania [21] . Therefore , several strategies , including live vectors; saponins; Freund's and montanide ISA 720 water-in-oil emulsions; oil-in-water emulsions ( MF59 ) ; dendritic cells ( DC ) and liposomes have been utilized in different studies and trials to deliver antigens and redirect the immune responses towards desired Th1 pathway [22] . However , most of these failed to provide both long-term immunity and safety for human vaccines . Therefore , it is still crucial to develop a potentiated delivery system with Th1 stimulating , safe and cost-effective properties for such a promising vaccination technology . Nanoscale vehicles are able to boost the quality and magnitude of an immune response in a predictable , designable trend that can be applied for wide-spread use of genetic vaccination , for developing vaccines for diseases such as cutaneous leishmaniasis , which is currently managed only through relatively ineffectual therapeutic regimens . Nanoparticles as vaccine delivery systems , promote bioactive vaccine candidate protection against extracellular degradation and modulate cellular and humoral immune responses via targeting antigens to APCs such as DCs and therefore would be potentially useful as effective vaccine adjuvants [15] . Cationic lipid-based systems could be formulated as emulsions , liposomes or cationic solid lipid nanoparticles ( cSLN ) . These Lipid-based delivery systems are able to protect the nucleic acid payload and significantly reduce its degradation and extend its activity , improve the pDNA pharmacokinetic characteristics and thus induce more potent immune responses due to a depot effect in which persistence of pDNA at the site of delivery allows uptake by local immune cells , enhance intracellular uptake and delivery to target APCs [15] . Amongst these , SLNs have offered a number of technicopractical advantages including proper storage stability , easy production procedure , steam sterilization and lyophilization possibility and acceptable safety profile [7] , [8] . In regards to vaccination studies , significant enhancement was reported when using cSLN-pDNA formulation for cholera toxin and lipid A delivery so that well tolerated particles with sizes greater than 100 nm exhibited higher adjuvant activity enhancing both T-helper types of immune responses compared to the FIA [8] . Hence , in this study we considered SLN for formulating cpa and cpb or cpb-CTE genes . In our previous study , these genes as DNA prime boost vaccination regimen have induced partial protective responses in susceptible BALB/c mice [14] . Heterologous prim-boost immunization approaches have complex logistics and high costs associated with manufacturing a second vaccine platform . Therefore , utilizing a suitable delivery system might be an important strategy to eliminate the need for boosting with the recombinant proteins and enhance third generation vaccine's potency by preventing rapid elimination of the administered pDNA from the circulation . In this regard , cSLN formulations were prepared and characterized regarding their size , zeta potential , nuclease protection , in vitro transfection efficiency , and cell viability , in our earlier report [17] . To assess the cSLN utility prospect as a leishmanial vaccine delivery system , we exploited GFP-transfected Leishmania for generating experimental cutaneous leishmaniasis in BALB/c mice . This model was reported as a novel dynamic immunopathogenic tool allowing visualization and correlation of fluorescence intensity with parasite burden [23] , [24] . Some concerns might be raised according to an anti-GFP immune response which could be induced against expressed GFP by the parasites . Several studies indicated that no immune responses have been detected in animals immunized with recombinant EGFP . In other words , recombinant EGFP is not able to stimulate APCs , nor do it induce a significant T-cell response or anti-EGFP antibody production [25] . In our investigations for precise judgment about the feasibility of this delivery system , we looked for a more rapid , sensitive and easily reproducible method to predict average parasite inhibition ( PI ) in the FP and LN of the vaccinated animals . Therefore , different techniques were used in this regards and the outcomes were correlated to the conventional standard caliper-based method and microtitration parasite burden as well as cytokine and antibody responses to choose the most sensitive , precise and less time-consuming technique for following Leishmania infection , in mice model . As illustrated in Table 2 , to evaluate the protection rate; FP swelling ( Fig . 1 ) , imaging ( Fig . 2 ) and flowcytometric analysis on the FP ( Fig . 3 ) and LN ( Fig . 4B ) were assessed in immunized mice and the results were compared to each other and the control groups . The LN parasite burden was also determined by microtitration conventional method ( Fig . 4A ) , at the same time point . The results demonstrated that the size of lesion in mice immunized with Spa/b and Spa/b-CTE at week 9 post challenge were significantly ( p<0 . 05 ) smaller compared to control groups . Interestingly , the mice immunized with cpa/cpb and cpa/cpb-CTE had also revealed significant difference in FP swelling compared with the group of mice immunized with the formulated cocktail of pDNA-cps ( p<0 . 05 ) . However , there was no significant difference between the groups of mice received non-formulated cocktail vaccines . The same results were obtained through LN analysis by microtitration and flowcytometry . The mentioned methods disclosed the significant PI discrepancy between Spa/b and Spa/b-CTE formulations , while FP analysis couldn't discriminate the effective vaccination strategy between these formulations . However , although FP caliper and LN microtitration based procedures were accurate , precise and capable of differentiation between effective formulations in terms of tissue prasitism , they were time-consuming , rather difficult and not absolutely reproducible as the risk of operator errors are more probable in these experiments . Therefore according to the presented data , it seems that parasite inhibition can be directly estimated by flowcytometry analysis performed on LN cells; as a more rapid , sensitive , and easily reproducible method for screening anti Leishmania vaccine candidates and delivery systems . The number of viable L . major was quantitated in the FPs and LNs of vaccinated groups of mice after challenge and compared to the control groups , and used as an indication of the protection rate or average parasite inhibition percent ( PI% ) . The significantly ( p<0 . 05 ) higher mean PI% was seen in group 3 ( 73 . 36±15 . 94% ) which was immunized with Spa/b-CTE formulation . This result further revealed the adjuvant effect of the cSLN for potentiating the immunogenicity of this genetic vaccination strategy ( Fig . 1 , 2 , 3 , 4A and B ) and ( Table 2 ) . It seems that a/b-CTE cocktail vaccine induced lower levels of the protection and needs a suitable delivery system to maintain and enhance its immunoprotective activity ( 44 . 38±12 . 59% ) . This obtained protection rate is in accordance with the percents of reduction of parasite load , reported to obtain with DNA vaccination , without a booster injection ( 59 . 24% ) [3] . However , despite using cSLN , Spa/b formulation couldn't provoke the same PI% . as did Spa/b-CTE ( 58 . 31±17 . 61% ) . This difference could be better described by the antigenic nature of the pDNAs used in this experiment and further confirms our previous results that CTE is highly immunogenic but not protective and more favorable to direct the immune system responces towards Th2 type . Based on the presented data , cSLN formulation conferred immunoprotecting activity to a/b-CTE genes which were non-immunoprotecting in their free form , and possibly enhance the immunostimulatory activity of these genes , by effectively inducing TLR-9 mobilization in the endosomal compartment . According to the data presented in Table 2 , despite utilizing highly sensitive methods to determine parasite burden , there was no significant difference between cpa/cpb and cpa/cpb-CTE cocktail DNA vaccines ( G2 vs G4; Table 2 ) . On the other hand , the discrepancy between these cocktails was significant when they were formulated with cSLN and the parasite burden was determined by LN manipulation ( G1 vs G3 , Table 2 ) . This data further supports the importance of the lymph nodes as one of the most relevant tissues involved in the parasite-host interface during the stages of L . major infection as the cellular and humoral immune responses in the LN are able to better describe the major immunological changes due to parasite persistence during infection . Therefore , LN may reflect the profile of the host's immune response and the parasite burden intensity throughout L . major infection via both conventional ( microtitration ) and novel ( flow cytometry ) techniques . PI% were singnificantly different between the groups received formulated and non formulated cocktail vaccines ( G1 vs G2 and G3 vs G4 ) . These results emphasis induction of the immune responses by using delivery systems in such an extent that even the effect CTE deletion could be detectable in the disease progression . To further evaluate the precision of the obtained data , the cytokines produced by antigen-specific T-cells , were evaluated to determine the profile of an elicited antibody response . IL-5 is associated with high levels of IgG1 , whereas production of IgG2a is dependent on IFN-γ . Our results demonstrated that the immune response elicited by Spa/b and Spa/b-CTE formulations was dominantly Th1 response denoted by the higher ratio of IFN-γ/IL-5 secretion after stimulation with SLA and rCPs . However this ratio was significantly higher in Spa/b-CTE vaccinated animals . This ratio is approximately 3-fold higher when SLN has been used as a delivery system . T-cell immunogenicity of CPs had been shown in previous studies , where immunizations with L . mexicana recombinant CP resulted in the development of a potentially protective Th1 cell line , and that recombinant CPB from L . major efficiently induced CD8+ T-cells [9] , [26] . Therefore , SLN proceeded as a Th1 stimulator adjuvant and increased the cocktail vaccine efficiency in elicitation of protective responses . IgG1 and IgG2a antibody titers were also used as an indicator of Th2 and Th1 immune responses , respectively . The significantly ( p<0 . 05 ) highest IgG2a was seen in the sera of group 3 of mice immunized with Spa/b-CTE before challenge against recombinant CPA compared to other groups . This might be an indication that CTE-domain deletion in Spa/b-CTE formulation redirected the immune responses toward increasing IgG2a production . However , using cSLN formulation for this cocktail vaccine consequently induced a more potent antibody response compared with free pDNAs ( Fig . 7A , C and G3 , G4 ) . At week 9th after challenge , the IgG2a/IgG1 ratio in mice vaccinated with Spa/b-CTE formulation ( G3 ) was correlated with a Th1 response and further confirmed that could induce a potent Th1 type of immune response and protection against leishmaniasis , at least in murine model . Only G3 showed significantly ( p<0 . 05 ) highest ratio of IgG2a/IgG1 , revealing the induced protection in this group confirmed by a significant smaller FP swelling ( p<0 . 05 ) , lower LN parasite load ( p<0 . 05 ) , highest IFN-γ/IL-5 ratio ( p<0 . 05 ) and maximum average of parasitic inhibition percent by flowcytometry and imaging methods . Despite , using cSLN as a delivery system , the immune responses to the selected antigens ( cpb and cpb-CTE ) of the cocktail vaccine were differed . Generally , the titres of specific antibodies raised by DNA vaccination are lower than those obtained after vaccination with a recombinant protein . As it is shown in this study antibodies are induced to a very low extent against CPB-CTE , especially in comparison to antibody levels against CPB ( Fig . 7B , D ) . This is definitely attributed to the presence of immunogenic CTE-domain in the vaccine formulations administered to G1 and G2 that affect the character and potency of the responses against defined antigens in the mentioned cocktail vaccines . As a part of our experiments , we have performed an ELISA test in which pooled sera of G1 and G2 were tested against CPB-CTE and sera of G3 , G4 tested against recombinant CPB . As a result , G1 and G2 revealed reduced antibody responses while G3 , G4 did not show any difference in antibody responses ( data not shown ) . Thus , to avoid presenting exaggerated responses in the benefit for Spa/b-CTE formulation , the presented data show the results of the experiment in which each group plated against its own set of antigens . Therefore , CTE-domain deletion is shown to be an appropriate approach to design a protective vaccine candidate against L . major as well as L . infantum infectious challenge [13] . Above mentioned protective Th1 response demonstrated in G3 that was characterized by increased titres of IgG2a in sera and elevated IFN-γ production by LN cells both before and after challenge , was further supported by the report by Brewer J M et al . indicated that lipid vesicles with a mean diameter >225 nm preferentially induces Th1 responses in BALB/c mice [27] . On the other hand , pcDNA-cps also possesses several immunostimulatory CpG motifs within pcDNA3 . 1 vector backbone . These CpG motifs might also facilitate priming of CTL responses by activating DCs [28] . Nevertheless , this effect is often temporary because of the rapid degradation of DNA [5] , [15] and consequently repeated administrations or much higher doses are required to achieve the desired effects . Moreover , the pDNA delivery to the intracellular compartments for recognition by TLR-9 is hardened [5] , [29] without a delivery system , as shown for G2 and G4 . Therefore , we can conclude that cSLN as a delivery systems improved the storage stability [17] , transfection efficiency [17] and immunostimulatory effects of pDNA-cps ( Table 2 ) . This was possibly the result of pDNAs protection from nuclease activity in vivo , as we have previously reported this potential in vitro [17] and facilitation of pDNAs delivery to the cytoplasm because of cSLN positive zeta potential and the presence of cholesterol domains in cSLN formulation that enhances transfection efficiency by facilitating membrane fusion [17] , [30] . In addition , phagocytosis of cSLN-pDNAs by APCs as well as localization of them in the draining LNs occurs easily following SC administration due to the composition and physicochemical characterization of these nanoparticles . The presence of Tween 80 in the formulation enhances this phenomenon as recently Seeballuck and co-workers have also demonstrated that , this surfactant would increase lymphatic uptake by promoting chylomicron formation [31] . Since the draining LNs contain a greater number of cells that express TLR9 , localization of pDNAs in the draining LNs possibly will be an important mechanism by which cSLN formulation has enhanced the immunological activity of these antigens . Another important aspect in this formulation could be the presence of DOTAP in this cSLN formulation that can also activate the dendritic cells through a common binding partner with LPS [32] . Therefore , this formulation not only act as a delivery system but also as an adjuvant for Leishmania vaccine by improving the uptake of loaded antigens and also stimulating immune cells in specific way . In conclusion , this paper clearly demonstrates that cSLN is a promising and adaptable delivery system that can be modified rationally towards specific vaccine targets by varying composition . Simplicity , reproducibility and the scale up possibility of the manufacturing process together with the appropriate immunostimulatory effects of this formulation as a delivery system might be utilized to create a stronger protective vaccine in combination with Leishmania CPs . The current data , in murine model of L . major infection , showed promising role of cSLN as an adjuvant to enhance stronger immune response against Leishmania infection . The mean average of parasite load reduction for such a cocktail pDNA vaccination was determined to be 38% [1] . Here in this study , we report that the percent of parasite inhibition by a particulate cocktail DNA vaccination technology could be increased up to 73 . 36±15 . 95% , according to the precise methods for parasite burden determination in the different organs of the challenged animals via both conventional ( i . e . microtitration , Fig . 4A ) and more novel ( i . e . flow cytometry , Fig . 3 , 4B and imaging , Fig . 2 ) techniques . In the entire mentioned techniques parasite burden gave a discriminative view among control groups and Spb-CTE vaccinated animals . Amongst the disscussed methods , direct LN flowcytometry was found to be the most rapid , sensitive , and easily reproducible method for screening vaccination strategies . These promising data warrant further investigations in this regard . Our future studies are being designed to expand cSLN passive targeting to an active targeting to increase the vaccination efficiency . Coating cSLN harbouring pDNA-cps with ligands ( such as mannan ) are our main future visions to increase the cellular immune responses . | Cutaneous leishmaniasis ( CL ) is the most common form of leishmaniasis with an annual incidence of approximately 2 million cases and is endemic in 88 countries , including Iran . CL's continued spread , along with rather ineffectual treatments and drug-resistant variants emergence has increased the need for advanced preventive strategies . We studied Type II cysteine proteinase ( CPA ) and Type I ( CPB ) with its C-terminal extension ( CTE ) as cocktail DNA vaccine against murine and canine leishmaniasis . However , adjuvants' success in enhancing immune responses to selected antigens led us to refocus our vaccine development programs . Herein , we discuss cationic solid lipid nanoparticles' ( cSLN ) ability to improve vaccine-induced protective efficacy against CL and subsequent lesion size and parasite load reduction in BALB/c mice . For this work , we evaluated five different conventional as well as novel parasite detection techniques , i . e . , footpad imaging , footpad flowcytometry and lymph node flowcytometry for disease progression assessments . Vaccination with cSLN-cpa/cpb-CTE formulation showed highest parasite inhibition at 3-month post vaccination . Immunized mice showed reduced IL-5 level and significant IFN-ã increase , compared to control groups . We think our study represents a potential future and a major step forward in vaccine development against leishmaniasis . | [
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] | 2011 | C-Terminal Domain Deletion Enhances the Protective Activity of cpa/cpb Loaded Solid Lipid Nanoparticles against Leishmania major in BALB/c Mice |
Turnip yellow mosaic virus ( TYMV ) - a member of the alphavirus-like supergroup of viruses - serves as a model system for positive-stranded RNA virus membrane-bound replication . TYMV encodes a precursor replication polyprotein that is processed by the endoproteolytic activity of its internal cysteine proteinase domain ( PRO ) . We recently reported that PRO is actually a multifunctional enzyme with a specific ubiquitin hydrolase ( DUB ) activity that contributes to viral infectivity . Here , we report the crystal structure of the 150-residue PRO . Strikingly , PRO displays no homology to other processing proteinases from positive-stranded RNA viruses , including that of alphaviruses . Instead , the closest structural homologs of PRO are DUBs from the Ovarian tumor ( OTU ) family . In the crystal , one molecule's C-terminus inserts into the catalytic cleft of the next , providing a view of the N-terminal product complex in replication polyprotein processing . This allows us to locate the specificity determinants of PRO for its proteinase substrates . In addition to the catalytic cleft , at the exit of which the active site is unusually pared down and solvent-exposed , a key element in molecular recognition by PRO is a lobe N-terminal to the catalytic domain . Docking models and the activities of PRO and PRO mutants in a deubiquitylating assay suggest that this N-terminal lobe is also likely involved in PRO's DUB function . Our data thus establish that DUBs can evolve to specifically hydrolyze both iso- and endopeptide bonds with different sequences . This is achieved by the use of multiple specificity determinants , as recognition of substrate patches distant from the cleavage sites allows a relaxed specificity of PRO at the sites themselves . Our results thus shed light on how such a compact protein achieves a diversity of key functions in viral genome replication and host-pathogen interaction .
Plus-strand RNA ( RNA+ ) viruses are the largest class of eukaryotic viruses . They include significant pathogens of humans , animals and plants [1] . From the sequencing of their genomes , it has become clear that despite a huge diversity , these viruses possess high similarities at the molecular level [2][3] . Indeed , common strategies and regulatory mechanisms have been uncovered in the replication of RNA+ viruses [4] . Thus , all RNA+ viruses studied to date synthesize new viral genomes at an intracellular membrane . There , synthesis of the viral progeny requires the establishment of specific and regulated interactions between viral proteins and different cellular factors , assembled within a replication complex . In RNA+ viruses , the replication proteins are usually synthesized as a single polypeptide chain that may be subsequently processed by viral ( and sometimes also cellular ) proteinases . Another common feature of RNA+ viruses is that the highly compact viral genome codes for usually multifunctional proteins . Turnip yellow mosaic virus ( TYMV ) is a simple , model RNA+ virus whose replication is well characterized at the molecular and cellular levels [5][6][7] . It is included in the alphavirus-like supergroup of RNA+ viruses [3] that also comprises the animal alphaviruses ( including Sindbis virus , Semliki Forest virus and Chikungunya virus ) and rubiviruses ( including Rubella virus ) . Indeed , TYMV shares with these viruses striking similarities in the organization and processing of the replication polyprotein [8] . Its 6 . 3-kb genome codes for three proteins , the largest of which ( 206K ) is a polyprotein of 206 kDa that contains all the viral components of the replication machinery ( Fig . S1 in Text S1 ) . From N- to C-terminus , 206K harbors methyltransferase ( MT ) , cysteine proteinase ( PRO ) , helicase ( HEL or 42K ) and RNA-dependent RNA polymerase ( POL or 66K ) domains . In previous works , we established that the PRO domain is a key regulator of TYMV replication . First , its endopeptidase activity is required to proteolytically process 206K at the HEL/POL junctions to release the 66K polymerase , while a second cleavage at the PRO/HEL junction contributes to the regulation of viral RNA synthesis [8] . The PRO domain is also essential for the recruitment of 66K to the membrane replication sites [7] . Finally , we recently reported that TYMV PRO also displays an ubiquitin hydrolase ( DUB ) activity in vitro and in vivo , and identified 66K polymerase as a specific substrate of this activity [9] . PRO's DUB isopeptidase activity is thus also a key factor for the interaction of the virus with its host , in counteracting the ubiquitin-proteasome system and possibly subverting it into regulating availability of 66K for TYMV replication [10] . Here we describe the crystal structure of the recombinant PRO . Strikingly , PRO displays no homology to other processing proteinases from RNA+ viruses , including that of alphaviruses , and the closest structural homologs of PRO were identified as DUBs from the Ovarian tumor ( OTU ) family . Our crystal captures a view of TYMV PRO in its polyprotein processing mode that reveals dual substrate specificity determinants . Modelling of a PRO/ubiquitin complex , subsequent site-directed mutagenesis of PRO and enzymatic analysis of its DUB activity suggest that PRO structural elements used for specific recognition of ubiquitin overlap those used in its processing proteinase function . These findings provide a structural rationale for PRO's targeting of the diverse viral and cellular , endo- and isopeptide bonds whose hydrolysis allows TYMV to complete its replication cycle .
We report here the structure of the TYMV PRO domain to a resolution of 2 Å with a final Rfree of 20 . 1% ( Table 1 ) . As reported elsewhere [11] , all data including 3 derivative datasets obtained by heavy atom soaks were from crystals grown in a single crystallization drop . The asymmetric unit contains a single PRO molecule packing against the next PRO along the crystallographic 31 screw axis , making up continuous PRO helices in the crystal ( Fig . S2 in Text S1 ) , and an Escherichia coli contaminant ( ribosomal protein S15 ) . S15 bridges the separate PRO helices , explaining why diffraction-quality crystals only grew from a PRO preparation heavily contaminated by S15 [11] . PRO displays a three-lobed architecture . The N-terminal lobe ( in blue on Fig . 1A ) comprises two short helices flanking a two-stranded , distorted β-sheet . The catalytic domain is made up by the central and C-terminal lobes ( a bundle of five helices and a four-stranded β-sheet , respectively ) . The catalytic dyad Cys783-His869 ( TYMV polyprotein numbering ) lies at the interface between helix α3 ( the first helix in the central lobe ) and strand β6 ( the last strand of the C-terminal lobe ) . Indeed , Cys783 is the first residue of helix α3 and His869 the first residue of strand β6 ( Fig . 1B ) . We used the DALI server [12] ( http://ekhidna . biocenter . helsinki . fi/dali_server ) to seek homologs of PRO with available structures in the Protein Data Bank ( PDB ) . Strikingly , there is no detectable homology ( DALI Z-score below 2 ) to other processing proteinases from RNA+ viruses , including that of alphaviruses . It was previously remarked that PRO shares limited sequence similarities around the two catalytic residues with the OTU domain class of DUB enzymes and we recently reported that PRO is a functional DUB in vitro and in vivo [9] . Indeed , although no close homolog is available and the N-terminal lobe cannot be matched at all , the fold of the PRO catalytic domain is clearly the same as the core fold of the OTU1 cellular DUB ( yOTU1 , Saccharomyces cerevisiae , DALI Z-score 7 . 5 , 91 residues matched ) [13] and nairovirus DUB ( vOTU , Bunyaviridae , DALI Z-score 6 . 8 , 90 residues matched ) [14][15][16] . These two DUBs are assigned to clan CA of papain-like proteinases in the MEROPS peptidase database scheme [17] ( http://merops . sanger . ac . uk/ ) . Although clan CA contains several viral processing proteinases from Picornaviridae and Coronaviridae , only strict DUBs ( i . e . enzymes lacking endopeptidase activity ) have substantial DALI Z-scores in comparisons with PRO . Indeed , the nearest homolog of PRO with reported endopeptidase activity is the bacterial Staphopain ( Z-score 3 . 4 ) . A DALI superposition of yOTU1 and vOTU yields a Z-score of 12 . 2 . This higher score is due to yOTU1 and vOTU being structurally superimposable on a significantly larger number of residues ( 126 residues matched by DALI ) . Of note , in both yOTU1 and vOTU , the segment directly upstream of the homolog of helix α3 ( in green on Fig . 1C ) partially covers the exit from the active site . In contrast , the catalytic dyad of TYMV PRO Cys783-His869 is completely solvent exposed ( Figs . 1C , 2A and 2C ) . There is no pocket that could act as a stabilizer for the oxyanion intermediate in the reaction for cysteine and serine proteases . Indeed , due to the lack of a covering segment there is no counterpart for the main chain nitrogen of Asp37 of vOTU ( Fig . 2C ) , that has been proposed to participate in formation of this oxyanion hole [15] . Furthermore , the side chain of Trp99 of vOTU , that has been shown to take part in oxyanion hole formation [16] , is missing in TYMV PRO's Gly821 ( Fig . 2C ) . Similarly , there is no candidate in PRO for a catalytic residue acting as a general acid to stabilize and activate the side chain of His869 . Asp153 of vOTU , that has been shown to be implicated in the catalytic triad [16] , is replaced by a serine in Tymoviridae as Trp99 is replaced by a glycine ( Fig . 2C , Fig . 1B ) . Thus , TYMV PRO's catalytic site appears to be reduced to an exposed dyad , possibly explaining in part its poor DUB activity [9] ( see below ) compared to e . g . vOTU [15][14][16] . Furthermore , although the dyad itself is almost superimposable with the corresponding residues of yOTU1 and vOTU ( Fig . 2C and legend thereof ) , the Cys783 side chain is flipped and makes no interaction with the His869 side chain . Thus the PRO active site is most likely not in its catalytically competent state in the crystal , where we caught a product release state ( see below ) . The PRO active site's exposure is due to the long β2–α3 loop ( residues 771–782 ) connecting the N-terminal lobe to the central lobe coming to helix α3 from the other side of the α3–β6 interface . The β2–α3 loop threads through a cleft between helices α4 and α5 at the back of the central lobe . This results in the N-terminal lobe being apposed to the catalytic domain ( Fig . 1A ) but on the other side from the catalytic dyad ( Fig . 2A ) . Important residues in this positioning of loop β2–α3 are Arg769 , that participates in an extended network of interactions , including a salt bridge to Asp809 and a hydrogen bond to Pro808 at the base of α5; Pro777 , that positions the main chain to make two hydrogen bonds to the indole ring nitrogen of Trp800 in α4; and Pro779 , that inserts into a hydrophobic pocket lined by Trp800 , Leu785 and Leu822 . The pattern of conservation among Tymoviridae proteinases ( Fig . 1B ) indicates that this arrangement , and consequently the position of the N-terminal lobe , are very likely conserved in the family . Remarkably , the five-residue loop between strands β5 and β6 contains two successive cis-prolines 865-Gly-Pro-Pro-867 ( Fig . S3 in Text S1 , Fig . 2A ) . Such a conformation was recently found in only 7 out of 809 Pro-Pro segments in high resolution structures of proteins [18] . Downstream of strand β6 , the main chain makes a sharp turn so that the C-terminal residues of PRO 874-Lys-Arg-Leu-Leu-Gly-Ser-879 point away from the α3–β6 interface . Finally , the electrostatic potential at the surface of PRO displays three strong features ( Fig . 2B ) : First an apolar bulge made by the two cis-prolines 866–867; second , a basic patch on the N-terminal lobe on the other side from the entry to the catalytic cleft; and third , a small acidic pocket to the side of the entry to the catalytic cleft . The continuous helices of PRO in the crystal are formed by the insertion of the C-terminus of one molecule into the catalytic site of the next ( Fig . S2 and S4 in Text S1 ) . Thus , we have captured the N-terminal product complex resulting from the self-cleavage in trans of a viral polyprotein by its resident proteinase . The specificity of PRO is on the N-terminal ( P ) side of the scissile bond , while the C-terminal ( P' ) side is not important as defined by mutagenesis studies [19][8] . This structure thus reveals the molecular determinants of PRO specificity in its processing proteinase function ( Fig . 3 ) . The specificity of PRO is defined as P5- ( K/R ) LX ( G/A/S ) ( G/A/S ) -P1 [8] . The molecular determinants for this are now readily assigned by examining the interactions between one PRO molecule ( hereafter called “substrate” , with relevant residues with an “s” subscript ) and the next ( hereafter called “peptidase” , with relevant residues with a “p” subscript ) . Analysis of the peptidase-substrate interface using the PISA server [20] ( http://www . ebi . ac . uk/msd-srv/prot_int/pistart . html ) shows that 940 Å2 ( 11 . 9% ) and 825 Å2 ( 10 . 5% ) of solvent-accessible surface area are buried in the complex for the substrate and peptidase , respectively . This interface is not expected to be stable in solution . Accordingly , we find that PRO solutions up to 10 mg/ml are monodisperse as measured by dynamic light scattering ( not shown ) , with an hydrodynamic radius of 2 . 3 nm very close to the one calculated from the crystal structure for the PRO monomer ( 2 . 14 nm ) . The residues involved in the interface are mostly , but not exclusively ( Fig . 3A ) in and around the entry to the catalytic cleft for the peptidase and in the C-terminus for the substrate , respectively . The last five residues of the substrate 875-Args-Leus-Leus-Glys-Sers-879 are funneled in an extended beta conformation towards the catalytic dyad of the peptidase Cysp783-Hisp869 ( Fig . 3B , where the peptidase residues are labeled in white and the substrate residues in black ) . Indeed one of the carboxyterminal oxygens and the main chain nitrogen of Sers879 are hydrogen-bonded to the main chain nitrogen and carbonyl , respectively , of Phep870 . Likewise , the main chain polar atoms of Glys878 are hydrogen-bonded to the main chain polar atoms of Leup822 in the connection between helices α6 and α7 on the other side of the catalytic cleft . The net effect is a small three-stranded intermolecular beta-sheet firmly holding 878-Glys-Sers-879 in place . The other Sers879 carboxyterminal oxygen is hydrogen-bonded to the side chain of Hisp869 . This is the only interaction stabilizing this side chain in the crystal ( see above ) and it is less well ordered than the other residues in the catalytic cleft ( Fig . S4 in Text S1 ) . Further upstream the substrate , the extended conformation of the main chain is maintained by hydrogen bonds from side chains of the peptidase . Key side chains are those of Hisp862 ( that also participates in the P4 and P2 specificity , see below ) and Thrp824 ( Fig . 3B ) . In our crystal structure , the side chain hydroxyl of Sers879 hydrogen bonds to the main chain nitrogen of the catalytic Cysp783 ( Fig . 3B ) . The relaxed G/A/S specificity at P1 stems largely from the solvent exposure of the exit of the catalytic cleft ( see above ) . Still , the side chain of Leup781 caps the S1 site , precluding the presence of a large side chain at position P1 . Similarly , the constriction of the active site cleft at the S2 pocket is less pronounced than in yOTU1 and vOTU , where bulky residues strictly restrict specificity to GG . The S2 pocket , lined by Hisp862 , Phep870 and Serp868 and occupied by Glys878 in our crystal structure , could readily accommodate a small side chain . At first sight , the sharp difference in specificity between P3 ( seemingly no specificity ) and P4 ( strict specificity for a hydrophobic amino acid with a strong preference for Leu ) [8] is somewhat surprising . Both Leus877 ( P3 ) and Leus876 ( P4 ) make extensive contacts to conserved , shallow hydrophobic pockets at the surface of the peptidase's central and C-terminal lobes , respectively . These contacts bury respectively 89% of Leus877's and 96% of Leus876's solvent-accessible surface area , as reported by PISA . Two major differences though are that Leus877 ( P3 ) is buttressed only on one side and that the S3 pocket is lined by negatively charged residues ( the outer edge of the acidic pocket depicted on Fig . 2B ) . This explains why P3 may be mutated to Ala and may naturally be either a hydrophobic ( e . g . Leu ) , small polar ( e . g . Asn ) or arginine side chain , but an aspartate is never found at this position [8] . In contrast , Leus876 ( P4 ) is sandwiched between two strictly apolar surfaces on the two C-terminal lobes of the peptidase and substrate , respectively , with Hisp862 , Phep870 , Valp840 , Ilep847 and Serp842 on one side and Pros846 and Tyrs841 on the other side . This likely accounts for the specificity at position P4 . As for the strict P5 specificity for a positively charged amino acid , it is readily explained by the fact that P5 inserts its side chain into the acidic pocket depicted on Fig . 2B . Indeed , Args875 makes salt bridges to two conserved glutamates that protrude from the central lobe of the peptidase , Glup816 in the small helix α6 overhanging the S3 pocket and Glup825 in helix α7 . Molecular recognition of the substrate by the peptidase further involves patches recessed from the C-terminus of the substrate ( Fig . 3A ) . In the peptidase , these recognition patches are harbored by the N-terminal lobe . Thus , there is a prominent hydrophobic contact between the double cis-proline of the substrate Pros866-Pros867 and Prop733-Alap734-Prop735 at the base of helix α1 in the N-terminal lobe of the peptidase . A second patch in this lobe is centered on Asnp760 at the tip of the extended α2–β2 loop . Asnp760 makes both a hydrogen bond to Asps790 and a stacking interaction to the Pros872-Glys873 motif that makes up the sharp turn to the C-terminal residues mentioned above . This contact is completed on one side by an electrostatic interaction between Glup759 and Lyss793 . On the other side , the hydrophobic contact is taken up between the aliphatic part of the side chain of Lyss874 and Ilep847 , an interaction that is continuous with the S4 pocket . Using the HADDOCK program ( see Materials and Methods ) , we performed a docking simulation to explore the possible binding modes of ubiquitin in association with PRO . We first defined spatial ambiguous restraints for the interaction between ubiquitin and PRO by the involvement of: 1 ) the C-terminal residues of Ub 2 ) the corresponding catalytic cleft on PRO and 3 ) an apolar patch on the surface of ubiquitin ( hereafter referred to as the Ile44 patch ) that is recognized by most ubiquitin-binding proteins [21] . The ubiquitin residues most frequently targeted in this patch are Ile44 , His68 , Val70 , Gly47 , Leu8 and Arg42 ( in cyan on Fig . 4 ) [21] . The structures of both yOTU1 and vOTU have been reported as covalent complexes with ubiquitin [13][14][15][16] . These works have shown [14][15] that the Ile44 patch is recognized by nonhomologous regions of the two OTU DUBs due to a 75° rotation of Ub around an axis defined by the main chain of the 5 C-terminal Ub residues ( Fig . 4AB ) . To account for this known flexibility of the C-terminal tail of Ub [21] , multiple conformations were sampled prior to the rigid-body docking step so as not to bias the interface too heavily towards a given binding mode . Two clusters of solutions were found , among which only the largest cluster ( which also contains the complex with the best HADDOCK score ) had a binding mode consistent with the C-terminal residues of Ub in the PRO active site . This binding mode was cross-confirmed by other docking simulations using alternative methodologies , in particular without applying prior restraints between the putative binding regions ( see Protocol S2 in Text S1 ) . Further inspection of the lowest energy structure ( Fig . 4C ) shows that the orientation of ubiquitin is similar to that in the vOTU complex and that the predicted interface prominently involves PRO's N-terminal lobe . Indeed , the tip of the lobe's extended α2–β2 loop inserts into the patch ( Fig . 4D ) , suggesting that Glu759 and Asn760 , that participate in PROs binding ( Fig . 3A ) , may also be involved in ubiquitin recognition . Indeed , in the docking model Glu759 would be in a position to make salt bridges to Ub His68 in the Ile patch and/or Lys6 at its periphery . Since such interactions are usually good specificity but weak affinity determinants , we further looked for hydrophobic patches on PRO's surface that could come into contact with the Ile44 patch based on the model prediction . We found two such PRO patches on either side of the Ile44 patch ( Fig . 4D ) . One is made by Leu732/Leu765 in the N-terminal lobe . The other is centered on Ile847 . Although not part of the N-terminal lobe , Ile847 seemed an excellent candidate as it is an exposed residue with a conserved hydrophobic character ( Fig . 1B ) . This matches a known feature of interface evolution , where contacts between apolar patches are the most conserved although the residues themselves may not be [22] . Thus if the docking model was correct and Ile847 was an interface residue with the Ile44 patch , we expected a reduction in the bulk of its side chain to reduce the interaction and the substitution for a short charged residue to almost abolish it . In view of the docking results , we assessed the DUB activity of PRO and selected mutants . All mutants described below were produced in E . coli in a soluble form and purified to homogeneity ( Fig . S6 in Text S1 ) . Dynamic light scattering analysis of the mutants showed the same results as for the wild type , indicating that they were likely properly folded . As a deubiquitylating assay we used hydrolysis of the general substrate Ubiquitin-7-amino-4-methylcoumarin ( Ub-AMC ) . Determination of initial velocities up to the highest Ub-AMC concentration available to us showed that the wild type hexahistidine-tagged PRO whose structure is reported here is still far from saturating conditions at the highest substrate concentration we could reach ( 20 µM Ub-AMC , Fig . 5A ) . Accordingly , we compared the wild type and mutant enzymes by determining their pseudo first-order rate constants , Kapp , which approximate kcat/Km in conditions far from saturation ( Table 2 ) . For wild type PRO , the Kapp value of 2650 M−1s−1 we find is comparable to the Kapp of 1550 M−1s−1 previously reported from initial velocity measurements of a GST-tagged version of PRO [9] and thus ∼100-fold less than the Kapp reported for vOTU [14][15][16] . A L732A/L765A mutant was not significantly affected in its Kapp ( p = 0 . 34 , Mann-Whitney rank test ) . On the other hand , an E759G/N760G mutant showed a significant ( p<0 . 01 ) though slight ( 20% ) reduction in Kapp , suggesting that the α2–β2 loop is indeed involved in ubiquitin recognition . Further tampering with this loop , e . g . deleting its tip by replacing 758-PENT-761 with a diglycine motif , led to no soluble PRO production , so that we could not further probe this . We next assessed Ile847 mutants for their DUB activity . I847A is impaired in Kapp ( a 10-fold deterioration ) , while I847D is barely active ( a 150-fold reduction in Kapp ) . This behavior is exactly as predicted from the docking model , since I847A will reduce the size and complementarity of the PRO hydrophobic patch , while I847D will destroy its apolar character altogether . To further probe the docking model , we tested the I847A mutant initial velocity at higher substrate conditions ( Fig . 5B ) , where the slope of the wild type curve starts to decrease ( Fig . 5A ) . Within the same range , the I847A curve still appears linear in substrate concentration , suggesting that ubiquitin binding rather than turnover rate is impaired in the I847 mutants .
In the present work , we provide structural insights into viral polyprotein processing by a viral proteinase that cleaves at its own C-terminus . Such an event is common enough in the viral world , particularly among positive-stranded RNA viruses , and may in principle be achieved either in cis ( the proteinase domain cleaves the polypeptide of which it is a part ) or in trans ( it cleaves another polyprotein molecule ) . Our structure precludes the possibility of the C-terminus of PRO looping back towards the entry to the catalytic cleft in the same molecule . Therefore , cleavage of the TYMV replication polyprotein at the PRO/HEL junction occurs only in trans . This cleavage is a regulatory event in the replication of the TYMV RNA genome . It occurs in the replication complex comprising the two products of the first cleavage: the 66K RdRp and the 140K protein . 140K harbors PRO and localization determinants to the chloroplast envelope , where it recruits 66K . There , cleavage of 140K at the PRO/HEL junction into 98K and the 42K helicase contributes to the switch to synthesis of the +strand [7][8] ( Fig . S1 in Text S1 ) . A strictly trans cleavage likely takes part in the regulation by requiring a sufficient local concentration of 140K at the chloroplast membrane and/or remodeling of this membrane into a special compartment for viral replication before synthesis of new viral genomes . The interface in the crystal of the N-terminal complex of this trans cleavage reveals the molecular determinants for the peptidase sequence specificity . The fact that PRO proteinase specificity is confined to the P side is readily explained by the fact that the catalytic cleft ends abruptly at the catalytic dyad , leaving it completely solvent-exposed . Thus , residues on the P' side of the substrates will have little or no contact with PRO . In contrast , there are extensive interactions with the P-side residues up to P5 from both lobes making up the catalytic proteinase domain . The C-terminal β-sheet lobe thus provides a hydrophobic pocket for P4 and the central α-helical lobe an acidic pocket contributing salt bridges to the positively charged P5 and a shallow hydrophobic patch for P3 . The constriction of the active site cleft at the interface between the two lobes ensures that only small residues ( but not necessarily glycines ) can be at positions P2 and P1 . We recently reported that 98K has DUB activity in vivo and in vitro and that this DUB activity is localized in PRO [9] . Thus , PRO recognizes at least three different substrates that differ at positions P1 ( G for Ub , but A and S for the HEL/66K and PRO/HEL junctions , respectively ) and specifically cleaves either endopeptide bonds ( HEL/66K , PRO/HEL ) or isopeptide bonds ( Ub ) . Our crystal structure shows that the latter property is linked to an unusual solvent exposure of the active site of PRO on the P'/lysine sidechain side of the cleavage . The former property of relaxed sequence specificity is allowed by an also unusual lesser constriction of the PRO active site cleft at the P1 and P2 positions ( see below ) . Such features imply that PRO may be more heavily dependent on the recognition of additional molecular determinants away from the active site , in order to maintain sufficient substrate affinity and most importantly , high substrate specificity . Accordingly , the crystal structure we obtained allows us to identify two such determinants . First , an acidic pocket to the side of the entry to the active site strongly favors a positively charged residue in P5 of the substrate . Second and most important , we identify the Tymoviridae-specific N-terminal lobe of PRO as a recognition element for surface patches of the PRO/HEL substrate , as this lobe was found to recognize a signature bulge made of the two successive cis-prolines in the PRO substrate molecule . Whether the N-terminal lobe is also prominently used in recognition of the HEL/66K junction cannot be assessed at present . However , docking of the PRO/Ub complex and subsequent mutational analysis of the DUB activity of PRO suggest that PRO targets the Ile44 patch that is recognized by all characterized DUBs in part with elements also involved in recognition of PROs , such as Ile847 and possibly the α2–β2 loop . Of note , our docking model places the three residues that differ between plant ubiquitin ( the natural TYMV PRO target ) and human ubiquitin ( that we used in modeling and functional work ) on the side of ubiquitin opposite the interfaces with PRO ( Fig . S7 in Text S1 ) . This would rule out a different behavior of the natural substrate of PRO's DUB function ( plant ubiquitin ) compared to the readily available experimental substrates ( derivatives of human ubiquitin ) . Using a myc-tagged version of human ubiquitin , it was previously shown that , in contrast to other viral DUBs ( e . g . vOTU ) , 98K is a very specific DUB whose overexpression in cells does not lead to a global deubiquitylation of cellular proteins but rather to specific deubiquitylation of 66K [9] . Several lysine side chains of 66K are polyubiquitylated in vivo [10] and the types of these ubiquitin chain linkages are presently unknown . In vitro TYMV PRO may disassemble both Lys48-linked and Lys63-linked polyubiquitin chains , albeit with weak activity [9] . In the light of our findings , one may ask whether PRO may display specificity to particular ubiquitin linkages . Specificity may be achieved in several ways , e . g . on the P'/Lys side of the catalytic cleft ( Fig . 4E ) either by recognizing the sequence context of the modified lysine or by positioning the Lys-linked moiety . In either case , addressing the question of specificity would require modeling a diubiquitin chain across PRO's catalytic site . Such an exercise ( not shown ) must be highly speculative at the moment in the absence of structures for relevant complexes of OTU DUBs [23] . We may note that , as for other OTU DUBs , the isopeptide bonds in extended linkages ( such as Lys63-linked polyubiquitin ) can in principle be readily accessed by PRO , but compact chain conformations ( as in Lys48-linked polyubiquitin ) require an extensive conformational change to expose the isopeptide bond and allow binding and cleavage by PRO [23] . But the question of linkage specificity can also be addressed by modeling a diubiquitin chain on the P side of PRO's catalytic cleft ( Fig . 4E ) . Molecular recognition of Lys48-linked chains is poorly understood , as in their compact conformations their ubiquitin moieties interact through their Ile44 patches . It is proposed that structural flexibility allows transient access to the Ile44 patches to binding partners , and indeed a minor population of more open Lys48-linked diubiquitin has been modeled from nuclear magnetic resonance data [23] . Interestingly , placing this minor conformation onto our docking model results in PRO's N-terminal lobe being sandwiched between the catalytic domain and the Lys48-linked diubiquitin and making contact with both ot the latter's Ile44 patches ( Fig . 4E , top ) . On the other hand , similarly placing the structure of a Lys63-linked diubiquitin predicts no interactions to the second moiety ( Fig . 4E , bottom ) , due to the extended character of the Lys63 linkage . PRO counteracts the 66K polymerase degradation by the ubiquitin-proteasome system through polyubiquitin removal [10][9] . Since Lys48-linked polyubiquitylation is the canonical proteasome addressing signal , simultaneous recognition by the N-terminal lobe of several ubiquitin moieties on the P side ( Fig . 4E , top ) could be a mechanism allowing more efficient cleavage of Lys48-linked polyubiquitin chains . It might also explain in part why PRO displays rather poor activity for a DUB ( e . g . compared to vOTU [14][15][16] ) in a general deubiquitylation assay using a monoubiquitin derivative [9] ( this work ) , with a Km in the tens of micromolar range ( Fig . 5A ) . The other obvious feature of TYMV PRO explaining its lesser activity is the minimal character of its active site . It lacks altogether two important functional elements that are present in most cysteine proteinases , including the closest relatives of PRO ( clan CA , including yOTU1 and vOTU , see below ) : The oxyanion hole and a general acid as the third catalytic residue . Our structural work thus draws the picture of a barely complete proteinase that nonetheless effectively achieves cleavage of several endo- and isopeptide targets by combining co-localization with the targets and a versatile recognition lobe . Among peptidases that process polyproteins from RNA viruses with a Cys/His catalytic dyad , there are two known structural clans with unrelated folds . The first is clan CA , that comprises yOTU1 and vOTU . Another is clan CN , whose type is the nsP2 proteinase of alphaviruses [24] . Alphaviruses , including Sindbis virus , Semliki Forest Virus and Chikungunya virus , are animal relatives of tymoviruses . The two virus families share many features in their replication strategies , including successive cleavages of the replication polyprotein by the resident proteinase regulating RNA+ vs −strand synthesis [8] . Nevertheless , our data clearly show that PRO is unrelated to nsP2 and assign PRO to clan CA , a result that could not be firmly established by sequence comparisons alone ( http://merops . sanger . ac . uk/ ) [25][9] . The two other families of processing proteinases assigned to clan CA are also from positive-stranded RNA viruses: They are the coronavirus papain-like proteinases PLP1 and PLP2 [26][27] and the picornavirus leader proteinase [28] . These proteinases have also been reported to be ubiquitin hydrolases [27][29] . Yet PRO does not display detectable homology to these proteinases . Instead , the fold of PRO's two-lobed catalytic domain is clearly a more compact version of the OTU domain fold of ubiquitin hydrolases . The least dissimilar OTU domains to PRO are those of the cellular OTU1 DUB ( yOTU1 ) , whose structure is available in complex with Ub [13] , and the viral OTU domain encoded in the L protein of Crimean–Congo haemorrhagic fever virus ( vOTU ) , whose structure has been recently reported in complex with Ub and ISG15 [14][15][16] . Thus , PRO is closest to enzymes with no endopeptidase activity . Potential clues as to how TYMV acquired an ubiquitin hydrolase as a dual DUB/processing proteinase may be found in the family Flexiviridae of plant viruses . In this closest family to Tymoviridae , some of the replication proteins encode two peptidase domains , an OTU domain being N-terminal to the processing proteinase P [30] . One may therefore picture a scenario in which an ancestor to Tymoviridae harbored such a two-peptidase replication polyprotein . Subsequently , the OTU peptidase acquired specificity determinants allowing its use as processing proteinase and the P domain was lost . This report and previous works [13][14][15] establish that nonhomologous recognition modules have repeatedly evolved in the OTU family of DUBs , which is consistent with such a scenario . Whatever actually happened , the present diversity of specific functions performed by PRO is remarkable in a proteinase domain that is no larger ( 148 ordered residues ) than the more specialized vOTU ( 162 ordered residues ) or yOTU1 ( 170 ordered residues ) . Our results shed light on the molecular details that allow such a compact protein to perform a diversity of key functions in viral genome replication and host-pathogen interaction .
The production and purification of an N-terminally 6-histidine tagged PRO domain and of PRO mutants are described in details in protocol S1 in Text S1 . Briefly , the coding sequence of the PRO domain ( residues 728–879 of 206K ) was produced with an in-frame N-terminal 6His-tag . Purification was performed with two successive chromatography steps ( immobilized metal affinity chromatography followed by size exclusion chromatography ) . Crystallization is described elsewhere [11] . Briefly , a pool from all fractions of the size exclusion step in buffer 10 mM Tris-HCl pH 8 , 350 mM Ammonium Acetate , 1 mM DTT , was concentrated to 39 mg/ml as judged by OD280 nm . Hexagonal crystals of up to 50×50×40 µm3 grew in a single vapor diffusion drop where 1 µl protein solution plus 1 µl well solution ( 0 . 1 M Hepes pH 7 . 5 , 2 . 5 M Ammonium formate ) was equilibrated against a 0 . 5 ml reservoir volume . Prior to testing , crystals were transferred for ∼30 s in 0 . 1 M Hepes pH 7 . 5 , 4 M Ammonium formate , 16% glycerol and flash cooled by plunging into liquid nitrogen . Details of the structure determination are given elsewhere [11] . Briefly , the structure was solved by MIRAS from three poor derivatives thanks to the high ( 69% ) solvent content of the crystals . Heavy atom derivatives ( HgAc2 , NaI and CsCl ) were obtained by soaking . Data were processed with the XDS package [31] . Initial heavy atom sites were located with SHELXD [32] . This first heavy atom model was refined , completed and pruned and initial phases were computed and improved with autoSHARP [33] . The resulting map was interpretable and a first model was built with phenix . autobuild [34] . The model was manually rebuilt with COOT [35] and refined with phenix . refine [34] . Data processing and refinement statistics are collated in table 1 . Interfaces in the crystal were assessed using the PISA server [20] ( http://www . ebi . ac . uk/msd-srv/prot_int/pistart . html ) . Homologs of PRO were sought and superimposed with the DALI server [12] ( http://ekhidna . biocenter . helsinki . fi/dali_server ) . Structures were displayed and figures were prepared with Pymol ( www . pymol . org ) . Figure 1B was generated with ALINE [36] . 98 monomeric structures were generated from the Ub monomer extracted from the vOTU-Ub structure by sampling and clustering 2 , 000 C-terminal tail conformations using the Rosetta 3 . 4 FloppyTail application [37] . These conformations were used as a starting ensemble for Ub in the docking process . HADDOCK v2 . 1 [38] [39] was used to perform the docking with standard parameters , generating 5 , 000 rigid-body docking conformations followed by flexible explicit solvent refinement of the best 500 structures . The solutions were clustered and the most likely model was picked ( see details in Protocol S2 and Fig . S5 in Text S1 ) . This model was subsequently used for visualizing PRO/diubiquitin models . Lys48-linked diubiquitin was either the compact structure ( PDB 1AAR ) or the minor population structure ( PDB 2PE9 ) [23] . Lys63-linked diubiquitin was PDB 2JF5 . For each diubiquitin , one moiety was superimposed on the ubiquitin in the docking model . This was either the moiety with the lysine-linked C-terminus ( across-cleft modeling ) or the moiety with the free C-terminus ( P side modeling ) . In across-cleft modeling , this results in major clashes of the other moiety with PRO for both Lys48-linked diubiquitin conformations and still large clashes for Lys63-linked diubiquitin . In P side modeling , this results also in unrelievable clashes for the compact Lys48-linked diubiquitin , as with all proteins binding the ubiquitin Ile44 patch . There were few clashes with the minor population Lys48-linked diubiquitin in P side modeling and none with the Lys63-linked diubiquitin . Recombinant wild type and mutant his-PRO were generated , produced and purified as described in Protocol S1 in Text S1 . Samples were concentrated to 200–1096 µM , dialyzed in 50 mM HEPES pH 8 , 150 mM KCl , 1 mM DTT , 10% glycerol , aliquoted and kept at −80°C until use . DUB activity was assessed in Assay buffer ( HEPES-KOH 50 mM pH 7 . 8 , KCl 10 mM , EDTA 0 . 5 mM , DTT 5 mM , NP40 0 . 5% , DMSO 2% ) using the fluorogenic substrate Ub-AMC ( Boston Biochem ) . DMSO was adjusted to 2% in all assays to match the DMSO concentration in the highest Ub-AMC concentration tests . The rate of substrate hydrolysis was determined by monitoring AMC-released fluorescence as described previously [9] with some modifications . Assays were performed at 20°C in a temperature-controlled Perkin-Elmer LS50B spectrofluorimeter . The initial velocity V was derived from the linear increase in fluorescence at 460 nm ( excitation at 380 nm ) in minutes 1 to 11 after mixing in Ub-AMC . In order to determine the Kapp , the substrate concentration was kept at a concentration below 0 . 5 µM where the initial velocity is linear in substrate concentration . Enzyme concentrations were 100 nM for wild type PRO , L732A/L765A and E759G/N760G , 1 µM for I847A and I847D . The apparent kcat/Km ( Kapp ) values were determined according to the equation V/[E] = Kapp/[S] . Subsequently V was also determined at higher substrate concentrations ranging from 1 µM to 20 µM for PRO wild type ( 10 nM ) and Pro I847A ( 100 nM ) . Results were fitted to Michaelis-Menten kinetics by nonlinear curve fitting using Graphpad Prism ( Graphpad Software inc . , la Jolla , CA ) . Data were expressed as the means and standard deviations of these independent experiments . All experiments were performed at least in triplicates for Kapp values and at least in duplicates for the higher substrate concentrations experiments . | Positive-stranded RNA viruses are ultimate parasites . In order to replicate their genome , they first need to invade a host cell and , with usually very limited viral genetic material , subvert the host's molecular machinery . Turnip yellow mosaic virus ( TYMV ) is an excellent model system for studying positive-stranded RNA virus replication . As for many such viruses , TYMV genome replication is dependent on the activity of a viral proteinase ( PRO ) to properly process the virus' replication molecules . We have recently established that PRO is a multifunctional enzyme and is also used by TYMV to subvert a key host defense against pathogens . We report here the atomic structure of PRO as well as new functional data on PRO's interaction with the host . Our data shed light on how PRO can perform such multiple activities despite its small size , providing TYMV with a Swiss army knife in its ongoing fight with a vastly more complex host . | [
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] | 2013 | A Compact Viral Processing Proteinase/Ubiquitin Hydrolase from the OTU Family |
As proteins are synthesized , the nascent polypeptide must pass through a negatively charged exit tunnel . During this stage , positively charged stretches can interact with the ribosome walls and slow the translation . Therefore , charged polypeptides may be important factors that affect protein expression . To determine the frequency and distribution of positively and negatively charged stretches in different proteomes , the net charge was calculated for every 30 consecutive amino acid residues , which corresponds to the length of the ribosome exit tunnel . The following annotated and reviewed proteins in the UniProt database ( Swiss-Prot ) were analyzed: 551 , 705 proteins from different organisms and a total of 180 million protein segments . We observed that there were more negative than positive stretches and that super-charged positive sequences ( i . e . , net charges ≥ 14 ) were underrepresented in the proteomes . Overall , the proteins were more positively charged at their N-termini and C-termini , and this feature was present in most organisms and subcellular localizations . To investigate whether the N-terminal charges affect the elongation rates , previously published ribosomal profiling data obtained from S . cerevisiae , without translation-interfering drugs , were analyzed . We observed a nonlinear effect of the charge on the ribosome occupancy in which values ≥ +5 and ≤ -6 showed increased and reduced ribosome densities , respectively . These groups also showed different distributions across 80S monosomes and polysomes . Basic polypeptides are more common within short proteins that are translated by monosomes , whereas negative stretches are more abundant in polysome-translated proteins . These findings suggest that the nascent peptide charge impacts translation and can be one of the factors that regulate translation efficiency and protein expression .
The translation of messenger RNAs is the final step in translating the genetic code from DNA to proteins and is an essential , energetic expensive reaction[1] . The translation process is regulated by different mechanisms , primarily at the initiation phase . These mechanisms include initiation factor levels and posttranslational modifications , mRNA accessibility , microRNA activity and trans-acting protein activity[1] . The termination phase may also be subject to regulation by permitting frame-shifting or the insertion of amino acids where the translation should end[1] . The elongation rate can be influenced by the messenger RNA secondary structure[1] , rare codon usage[2 , 3] or nascent polypeptide charge[4] and appears to be tightly coupled to co-translational folding[5] . The translation efficiency is directly affected by the elongation rate as slow rates can , in certain cases , lead to ribosome stalling and interruption of translation[6] . The elongation and initiation rates can also determine whether the transcripts are translated in the monosomes or polysomes[7] . Most nascent polypeptides are synthesized in the ribosomal exit tunnel . This tunnel provides a passage that is approximately 100 Å long through the large subunit from the peptidyl transferase center to the cytosol[8] and houses approximately 30 amino acids residues of the nascent polypeptide[9] . The tunnel is composed primarily of nucleic acids and contains two proteins ( L4 and L17 ) that line the tunnel’s interior[9] . Because nucleic acids are the major constituents , the electrostatic potential is negative , ranging from -8 mV to -22 mV[10] . Experiments using recombinant proteins designed to function as molecular tape measures have shown that positively charged sequences ( rich in arginine/lysine residues ) are able to arrest translation , while neutral or negatively charged sequences promote faster elongation rates[4] . This finding indicates that positively charged residues are able to interact with the ribosome exit tunnel and possibly promote ribosome stalling . The effect of the nascent polypeptide composition on the translation rates has also been investigated using the ribosome profile technique , which is based on the deep sequencing of ribosome-protected mRNA fragments during translation[11] . Using this technique , there are certain contradictory conclusions that appear to originate from the use of translational inhibitors , such as cycloheximide , which are usually added to stabilize the ribosomes prior to the cell lysis[11–14] . When performed in the absence of such drugs , experiments tend to consistently show that positively charged sequences are able to promote slower elongation rates[14] . If the nascent polypeptide charge is an important factor affecting ribosomal translation efficiency , one might expect that the distribution of charges across a protein sequence should be shaped not only by structural and functional pressures but also by their effect on protein synthesis . It has already been demonstrated that there is a higher concentration of positively charged residues at the N- and C-termini of proteins[15–17] . Tentative explanations for this observation involve either the charge effect on translation or the importance of the positive stretches to the protein structure . An early work using the technique of ribosome profiling to assess the speed of translation showed that the translation rates were slower at the N-termini of proteins . The authors suggested that the so-called ribosomal ramp is an inherent component of the translation initiation and is important for organizing polysomal translation and avoiding jams[18] . This model could explain the enrichment of positive charges at the N-terminus because this could be one of the factors that causes the increase in the ribosomal density in this area . However , many aspects of this hypothesis have been debated in the literature[19 , 20] . Even the functional meaning of a ribosomal ramp has been questioned since the observed increase in the ribosome density at the beginning of the transcripts appears to be induced by the addition of cycloheximide during the sample preparation and , therefore , cannot be considered a natural characteristic of ribosomal translation[16 , 17] . An alternative hypothesis was offered by Charneski and Hurst[17] , who suggested that the enrichment of positively charged residues at the protein extremities could be explained by the presence of positive patches adjacent to the transmembrane protein segments . These basic domains lie on the cytoplasmic side and control the protein orientation by electrostatic interactions with the inner layer of phospholipids[17] . By analyzing the first 30 amino acids of E . coli and S . cerevisiae proteins , the authors found that basic N-termini are present in membrane proteins but not in cytoplasmic proteins . The authors also demonstrated that the specific distribution of Arg or Lys in the N- and C- termini could be caused by the natural tendency for positively charged patches to occur close to the protein extremities . Taken together , these data indicate that there is no consensus regarding whether the translation efficiency is a factor that influences the distribution of charges in the primary structure of proteins . In this study , 551 , 705 proteins from different organisms from E . coli to H . sapiens were analyzed to search for charged sequences and their biological relevance . We found that there are more negatively charged sequences than positively charged sequences , and extremely positively charged sequences are underrepresented in most proteomes . Proteins tend to concentrate their positively charged sequences at their N- and C-termini , and this feature appears to be strongly conserved across different organisms and subcellular locations , including cytoplasmic proteins . By analyzing previously published ribosome-profiling analyses of S . cerevisiae that were performed without cycloheximide , we concluded that the nascent polypeptide charge can modulate the ribosome occupancy , but this effect is only apparent in peptides with a net positive N-terminal charge ≥ +5 and ≤ -6 . Finally , using ribosome-profiling data from different monosomal/polysomal population , it is possible to observe that the N-terminal net charges are associated with monosomal translation , whereas neutral and negative stretches are more common in the N-termini in proteins that are translated by polysomes . Moreover , because most proteins contain basic stretches within the net-charge range that have little effect on the translation rate , our analysis suggests that a widespread ribosomal ramp is not important for organizing the polysomal translation . These data corroborate the notion that the translation rate can act as a selection force to shape the charge distribution along the primary structures of proteins .
To determine the charge frequency of protein segments in various proteomes , we developed a program that is able to screen the sequence of a given protein and calculate the net charge of every 30-amino acid segment , which is the estimated polypeptide length that occupies the ribosome exit tunnel[9] . The net charge was calculated by considering the protonation state of the amino acids according to their pKa values at a physiological pH of 7 . 4 . Positively charged residues ( lysine and arginine ) were considered +1; negatively charged residues ( glutamic and aspartic acid ) were considered -1; and all other residues were considered 0 . For the sake of simplicity , we eliminated the C- and N-termini charges and disregarded the cysteine contribution ( charge at 7 . 4 = − 0 . 085 ) . Histidine deserved special attention since its pKa value can vary from 2 . 4 to 9 . 2 depending on the chemical environment within the protein[21 , 22] . In an unfolded peptide of five amino acids ( Ala-Ala-His-Ala-Ala ) , the pKa of histidine was determined to be 6 . 5[21 , 22] . This finding means that at physiological pH values of 7 . 0–7 . 4 , histidines are more likely to be uncharged . However , because previous analyses considered histidine a positively charged amino acid[15 , 17] , calculations were performed by considering both His +1 and His 0 . The annotated and reviewed proteins in the UniProt database ( Swiss-Prot ) were analyzed , including 551 , 705 proteins from different organisms and 180 million 30-amino acid segments . The histidine charge affected the distribution of the net charge in the protein segments . As shown in Fig 1A , the presence of charged histidines caused a shift in the frequency distribution curve toward positive net-charge values , and as shown in Fig 1A ( right inset ) , the charged histidines increased the proportion of positively charged sequences relative to the negatively charged segments . Another observation was that super-charged segments with positive net-charges > +14 appeared less frequently than segments with negative net-charges < -14 ( Fig 1A , left inset ) . This feature can be better observed in Fig 1B , in which the ratio of the number of 30-amino acid sequences that possess equal but opposite net-charge values ( negative/positive ) was determined . If the number of negative and positive stretches of a given charge value is approximately the same , the result is one . Values greater than one reflect a ratio of negative over positive stretches , and values smaller than one reflect the opposite ( Fig 1B ) . The plot shows that the negatively charged sequences become more abundant than the positively charged stretches at net-charge values greater than 10 . Furthermore , the histidine charge primarily affects the sequences with moderate net-charge values ( i . e . , 5–10 ) . When His is considered +1 , the average ratio of these charge values shifts toward positively charged sequences; however , at net-charge values greater than 10 , the negatively charged segments become more frequent regardless of the histidine charge . In most organisms , the intracellular pH , except in certain specific organelles , varies between 7 . 0 and 7 . 5[23] . Therefore , a net charge of 0 was adopted to more closely resemble the physiological scenario in all subsequent analyses . Previous analyses by other groups indicated that the N- and C- termini of proteins tend to be enriched with positively charged residues . To determine whether these regions contain more positively charged stretches , the net charges of 30-residue segments along the primary structures of proteins were investigated . As shown in Fig 2A , a heat map was used to depict the charge distribution at the N-termini ( i . e . , first 100 net charges ) ; core ( i . e . , net charges 101–200 ) and C-termini ( i . e . , last 100 net charges ) of proteins . The color of each tile corresponds to the net charge of 30 consecutive amino acids ( i . e . , first tile 1–30 , second tile 2–31 , etc . ) . Consistently with other analyses [15–17] , it is possible to observe that the N- and C-terminal regions tend to be enriched with positive charges , while the core of most sequences contains more negative and neutral segments ( Fig 2A ) . Fig 2B shows that 49 and 46% of the sequences are positively charged at their first and last 30 amino acid residues , respectively , whereas 63% of the segments from the middle of the primary sequence are neutral or negatively charged . Fig 2C shows the correlation between the net-charge of the first and last 30 residues in each of the 6 , 663 proteins of the S . cerevisiae proteome . There was a positive correlation ( P < 0 . 0001 ) , but this was observed for only a small number of proteins ( R2 = 0 . 006479 ) ( Fig 2C ) . The subsequent analysis provided information regarding the charge at each position in the first and last 30 amino acid residues of proteins . ( Fig 3A–3F ) . In this analysis , each data point represents the average charge of the residues that occupy a specific position in the protein sequence . For example , the first point is always zero since it represents the starting methionine . A similar pattern was observed in all organisms analyzed . At the N-terminus , the second amino acid residue tends to be negatively charged , followed by positive charges up until amino acid number ten; from amino acid 11 to 30 , the average charge tends to be only slightly positive ( Fig 3 ) . The C-terminal analysis shows a different distribution in which there is a steep increase in positive charges around the last ten to fifteen amino acids ( Fig 3 ) . Since signal peptides , which are responsible for targeting proteins to the endoplasmic reticulum and , consequently , to the secretory pathway , are known to contribute positive charges to the N-terminus[24] , the analysis was repeated after excluding proteins that were identified as having signal peptides in the Uniprot database ( Fig 3 , red symbols ) . The same charge distribution profile was observed with both datasets ( Fig 3 ) . Certain aspects of the specific distribution of the amino acid charges can be readily explained . The first amino acid tends to be neutral , which is logical because the first amino acid is usually a methionine residue ( Fig 3 ) . The second amino acid tends to be negative ( Fig 3 ) . This observation can be explained by the Kozak consensus sequence , which predicts a high prevalence of G after the initiation codon AUG[25] . Every codon that starts with G codes for either a neutral or a negatively charged amino acid , which explains this observed phenomenon . When proteins with secretion signal peptides are removed from the analysis , the average charge of the second amino acid is even lower . This observation can be explained by the common presence of lysine residues at amino acid position two in the signal peptides , which tend to be rich in positively charged residues[24] . An increased occurrence of basic peptides in the C-termini of proteins was also observed . This result is consistent with previous analyses that show that the basic amino acids in the last two positions before the stop codon can greatly enhance the translation termination efficiency[26 , 27] . However , these observations alone cannot account for the general and conserved tendency of the positively charged protein segments to encompass dozens of residues at the N- or C-termini of proteins ( Fig 2A ) . To assess whether the positively charged segments occur more frequently in membrane proteins , we sorted the H . sapiens , D . melanogaster , C . elegans , A . thaliana and S . cerevisiae protein databases according to their subcellular localization based on Gene Ontology ( GO ) [28] and calculated the average charge distribution of the first ( Fig 4 ) and last ( S1 Fig ) 100 residues from proteins from each organelle . The positively charged N-termini could be observed in different organelles and cytoplasmic proteins from different species . Even when the average net charge of a given type of organelle was highly positive ( i . e . , nucleolus , mitochondria and ribosomes ) , the N-terminus showed an even higher concentration of positively charged residues ( Fig 4 , S2 Fig ) . Fig 5 shows the net charge of consecutive stretches of 30 amino acids ( i . e . , 1–30; 2–31 ) from proteins in H . sapiens , D . melanogaster , C . elegans , A . thaliana and S . cerevisiae localized in the cytoplasm and organelles that are enriched with membrane proteins ( mitochondria , endoplasmic reticulum , Golgi apparatus and vacuoles ) . Fig 5A shows that even though proteins from membrane organelles have a more positively charged N-terminus than cytosolic proteins , both groups are more positively charged up to their 50th amino acid position . When the mitochondrial proteins were removed , the distribution of positive charges changed considerably ( Fig 5B ) . This change can be explained by the presence of mitochondrial targeting sequences ( MTS ) that direct proteins to the mitochondria and undoubtedly contribute to the high net charges observed in the N-termini of this group[29] . Similarly , nuclear localization signals ( NLSs ) usually contain repeated lysine and arginine residues and could contribute to the N-terminal net charge in the nucleolus , chromosome , nucleoplasm , nuclear envelope and ribosome groups , even though they are not necessarily located at the N-terminal ( see next section ) . Unarguably , the post-translational function has a profound influence on the protein charge distribution as observed in the structure of membrane proteins[17] and the contribution of the MTS to the N-terminal charge ( Fig 5 ) . Furthermore , ribosome interacting proteins tend to present dynamic N and C termini that are enriched with positively charged residues , which facilitates the RNA interaction[30] . However , there is no clear explanation for the specific charge distribution in a vast number of proteins ( i . e . , cytoplasmic proteins ) . Taken together , these data suggested that while not all subcellular localizations showed an average positively charged N-termini , this feature was present in diverse organelles and proteins with different functional roles . While many proteins have positively charged stretches at their N and C-termini , not all proteins have net positive charges in this area . Therefore , we attempted to find a consistent correlation between the net charge of the first 30 amino acid residues of S . cerevisiae proteins and different aspects of protein synthesis and stability , such as the elongation rate , mRNA half-life or protein molecules per cell . The results of the correlation analysis are shown in S1 Table . Some analyses resulted in statistically significant P-values ( < 0 . 005 ) , but the squares of the correlation coefficient ( r2 ) were all very small . For example , the best correlation value , i . e . , a negative correlation between the net charge and the ORF length , had a P-value < 0 . 0001 with r2 = 0 . 01124 , indicating that only 44 of the 4 , 448 analyzed proteins would show net charge values that vary with the protein size . Another significant negative correlation was found between the net charge and the total elongation time ( P = 0 . 0002 ) and had even lower r2 values ( 0 . 0036 ) . In conclusion , it was not possible to draw any functional hypotheses from these analyses . One possibility is that the correlation analysis is too stringent to detect co-variations that are likely not linear ( see next sections ) ( S1 Table ) . Because positive charges are related to slower translation rates[4] and translation rates are related to co-translational protein folding[5 , 31] , we investigated whether the N-terminal positive net charges are associated with chaperone recruitment . The S . cerevisiae’s chaperone interaction database[32] was used to analyze the average net charge of each chaperone’s clients . Because proteins with 300 amino acids or less tend to fold without the aid of chaperones while proteins with more than 300 amino acids usually need chaperone assistance[33] , we also separated our database based on the protein length . Our analysis showed that both groups displayed the same distribution of N-terminal positive charges , suggesting that the protein charge is not an important factor in co-translational chaperone recruitment . To establish a link between the charge distribution along the protein primary structure and the translation efficiency , we analyzed the translation rate at the N-terminal of proteins as a function of the nascent polypeptide charge . The effect of the N-terminal net charge on the ribosome occupancy was assessed using cycloheximide-free S . cerevisiae ribosome profiling data that were generated by the Brown laboratory[34] . The S . cerevisiae proteins were divided based on the net charge of their first 30 amino acid residues , and the ribosome occupancy was determined by plotting the number of reads at each position normalized to the average number of reads in the entire sequence of the corresponding mRNA . Fig 6A shows that the transcripts that code for positively charged protein segments tend to accumulate more ribosomes than the transcripts that code for negatively charged peptides . However , the effect of the nascent protein charge on the ribosome occupancy is clearly nonlinear ( Fig 6B ) . Segments with net charges up to +5 occupy a number of ribosomes that is similar to those with neutral sequences , and only sequences with positive charges greater than +8 showed a two-fold increase in the ribosome density in their first codons . However , the mildly negatively charged segments showed a lower ribosome occupancy with values as small as -1 , but the observed effect stabilized at charges below -6 ( Fig 6B ) . The GO analysis of the 144 proteins with N-terminal net charges ≥ +8 revealed that this group is enriched with nucleosome , ribosome and nucleolus components ( S2 Table ) . Because most of these proteins have a nuclear localization , we investigated whether the NLS is present at the N-termini of these sequences and , therefore , is an important factor contributing to ribosome stalling . It is important to note that the NLS have diverse structures and compositions; therefore , prediction programs usually compute a probability score for putative motifs[35] . Fig 6C shows the cumulative distribution of the putative NLS determined by NucPred for the first 30 residues of proteins that belong to the charge groups analyzed in Fig 6A . Considering the recommended score threshold of 0 . 8 ( sensitivity of 0 . 30 and specificity of 0 . 61 ) [35] , only two proteins from the > +8 group presented an NLS at their N-terminal . Lowering the threshold to 0 . 5 did not substantially alter the number of putative NLS present in the positively charged group of proteins , excluding the possibility that the presence of an NLS accounts for the stalling of ribosomes , which is shown in Fig 6A . A mitochondrial targeting signal is also not common in the > +8 group , as shown in Fig 6A . Only six proteins had an MTS at their N-terminal , and the average ribosomal occupancy in the > +8 group was the same regardless of whether these proteins were excluded . Moreover , when we analyzed the ribosomal profiling of all yeast mitochondrial proteins ( n = 414 ) , the result was very similar to that in proteins with a 0 net charge at the N-termini ( Fig 6A , compare dashed line with black line ) . As shown in Fig 6D , it becomes clear that the group of proteins with N-terminal net charges ≥ +8 belong to an exceptional set of proteins with net-charge values that are much higher than those in any other group that is created by cellular localization ( Fig 6D ) . We also analyzed the presence of the MTS and NLSs in the 2 , 307 super-charged proteins with net charges ≥ +14 located at any position of the protein in all organisms ( Fig 1 ) . Only 1% ( 25 proteins ) were mitochondrial proteins . In contrast , we observed that NLSs are more common in the super-charged proteins than in a random group of yeast protein ( S3 Fig ) , and approximately half of the NLSs with probability scores ≥ 0 . 8 were within the 60 residues that contained the supercharged stretch . Therefore , the super-charged proteins are enriched with NLSs , but the signal alone is not responsible for the particular high charge in this group ( S3 Fig , compare blue symbols with red symbols ) . Because most of the basic stretches at the N-terminal were within the range in which no important effect on translation efficiency was observed , the association between a physiological ramp and positively charged sequences , as proposed by Tuller et al . [16] , may not be correct . Our conclusion is consistent with other work that has challenged this hypothesis . For example , studies suggest that cycloheximide influences ramp formation[19] , and more recently , it has been observed that the ribosome profile of the polysomal fraction of the translating ribosomes has no ramp[7] . Nevertheless , it is clear that highly charged negative or positive peptides can modulate the ribosome occupancy ( Fig 6 ) . Moreover , rather than organizing the translation process , the important delay that is observed in ribosome movements with extremely positively charged proteins may signal errors , thereby reducing the translation efficiency . This delay could be one of the selection forces that act against extremely positively charged protein segments in the proteomes of different organisms ( Fig 1 ) . Translating ribosomes exist in the following two different populations: monosomes , when a single ribosome is bound to the mRNA , and polysomes , when two or more ribosomes are bound to the mRNA . Polysomes are usually regarded as the active fraction of the ribosomal pool , but a recent study demonstrated that monosomes also actively translate and are responsible for processing transcripts that have initiation rates that are slower than the elongation and termination rates[7] . This condition was consistently found in ORFs shorter than 590 nt , in which the initiation time usually exceeds the elongation time; therefore , termination occurs before a second ribosome can initiate the translation . This condition was also present in longer ORFs , in which other factors may contribute to extending the initiation time , such as the presence of upstream open reading frames ( uORFs ) [7] . Polysomes , however , are formed in long transcripts with fast initiation rates . Because the ribosome occupancy analysis ( Fig 6 ) suggested that the nascent polypeptide charge can affect the translation rate , we investigated whether the charged stretches could be related to monosomal or polysomal translation . To answer this question , we analyzed S . cerevisiae proteins that were classified according to their distribution in the monosomal or polysomal fractions as previously determined experimentally as follows[7]: “ORFs <590” , with proteins shorter than 590 nt ( <590 ) ; “Monosome enriched” , with proteins longer than 590 nt but mainly found in the monosomal fraction; “No enrichment” ( NE ) , with proteins equally found in both groups; and “Polysome enriched” , with proteins mainly translated by polysomes . The net charge of the first 30-amino-acid stretches was calculated , and the frequency of the distribution of these sequences is shown in Fig 7A . A shift toward a more basic net charge was observed in the “ORFs<590” and “Monosome enriched” groups compared to the “No enrichment category” ( Fig 7A , 7A inset and 7B ) . Then , the average net charge of each group was analyzed with a focus on the N-terminal , core and C-terminal segments of each group of proteins ( Fig 7C ) . It is clear that the monosome-enriched proteins had a higher N-terminal net charge ( Monosome vs . Polysome , P < 0 , 05 , ** ) and that the polysome-enriched proteins had a lower , even negative , N-terminal net charge ( Fig 7C ) . This result indicates that proteins with positively charged N-termini are over-represented in the monosomal translation pool ( groups “ORFs<590” and “Monosome enriched” ) , while negatively charged stretches are more abundant in the polysomal translation pool . Fig 6B shows that the ribosome occupancy is more affected by the high values of the N-terminal net-charge . This observation prompted us to analyze the distribution of the different charge values in the monosomal and polysomal groups ( Fig 7D ) . Proteins with positively charged N-termini are over represented in the <590 nt monosomal fractions ( orange line ) , while the negatively charged N-termini tend to be translated by polysomes ( black and red lines ) . It should be noted , however , that the monosome group formed by mRNAs with more than 590 nts is not enriched with proteins with highly basic N-termini ( green line ) . Heyer and Moore noticed that this group is mainly populated by regulatory proteins with low expression levels . Some sequences contain uORFs that decrease the downstream reinitiating efficiency . However , for other sequences , it was not possible to find a simple explanation for the extremely long initiation time . Here , we show that a high concentration of basic charges cannot account for this effect , even though it could play a role together with other factors , such as the codon arrangement . The presence of more proteins with highly positive N-termini in the ORFS <590 nt monosomal group is not a direct consequence of the N-terminal charge because the elongation and termination of sequences of this size are typically faster than the initiation time[7] . Moreover , the GO analysis of this group showed an enrichment in ribosomal and mitochondrial proteins ( S3 Table ) , suggesting structural reasons for the presence of more positively charged sequences in the ORFS <590 nt group . However , we observed that the polysome and polysome top300 groups are even more enriched with ribosome components; however , in this high throughput mode of translation , sequences with neutral or negatively charged N-termini are more abundant than proteins with basic segments ( Fig 7D ) . This distribution may reflect a selection for a rapid translation initiation and efficient elongation on polysomes , while short proteins , which are usually translated by monosomes , allow longer initiation or elongation times due to the basic polypeptides .
Our study shows that , universally , there are more negatively charged sequences than positively charged sequences ( Fig 1 ) . This difference is even greater in extremely charged sequences ( charges greater than 14 ) , suggesting that there is a strong evolutionary pressure against super positively charged proteins . Consistently with previously published works , we observed that the N- and C-termini of proteins tend to be enriched with positive stretches ( Figs 2 , 3 , 4 , 5 , S1 and S2 ) . Only a part of this distribution can be explained by the presence of localization signals at their N-termini or functional and structural reasons . Even though there was no substantial correlation between the N-terminal charge and certain aspects of translation ( S1 Table ) , the super positively charged proteins were indeed able to significantly affect the translational speed ( Fig 6 ) . In fact , proteins with super positive N-termini are enriched with functions that are related to nucleic acid binding , such as ribosomal proteins and histones . This finding indicates that proteins that contain very positive stretches for a specific functional reason , such as nucleic acid binding and stabilization , may have to endure a lower translational efficiency . Since there is a translational stall in the synthesis of these positive proteins , it is possible that these proteins might require chaperone assistance to complete their folding or protect themselves from quality-control mechanisms , but no evidence for this hypothesis was observed . Finally , the N-terminal charge was compared across monosomal and polysomal populations . A positive charge and slow translation rates are suggested to aid polysomal translation because these factors can prevent ribosome jamming and collisions[16] , but we observed an opposite trend ( Fig 7 ) . Positive N-termini appear to be related to monosomal translation ( groups “ORFs > 590” and “Monosome enriched” ) , which is associated with slow initiation rates , while neutral or negative stretches are more common in proteins that are translated by polysomes , in which fast initiation and elongation rates are favored . The presence of basic peptides in short proteins that are translated by monosomes may be less disruptive than that in long proteins , in which a sudden drop in the translation speed may cause jams and abort translation . This analyses suggest that the translational efficiency may be one of the factors that shape the charge composition and distribution along the primary structures of the proteins .
To calculate the net charge in a given sequence , a program that is able to screen the primary sequence of a given protein and attribute different values to different amino acids was developed . To determine the charge values for the amino acid residues , we considered the contribution of individual pKa values to the Henderson-Hasselbach equation at pH 7 . 4 ( S1 Fig ) . The final peptide net charge was determined by summing each amino acid charge[36 , 37] . The following charge values were obtained: lysine = +0 . 999 ( rounded value = + 1 ) ; arginine = +1 . 000; histidine = +0 . 048 ( rounded value = 0 ) ; glutamic acid = -0 . 999 ( rounded value = -1 ) ; aspartic acid = -1 . 000; cysteine = -0 . 085 ( rounded value = 0 ) and all other amino acids = 0 . 000 . The N- and C-termini were attributed the charges +0 . 996 and -1 . 000 , respectively . After the charge attribution , the program calculated the net charge of every 30-amino-acid window in the primary sequence . The distribution of the protein fragments’ net charge from the S . cerevisiae proteome was determined considering charge values with 3 decimal places . The result was compared with a simpler calculation , which considered rounded charge values and disregarded the C- and N-terminal contributions ( S1 Fig ) . Because the distributions were very similar , the rounded values were used in all other calculations . To calculate the individual charge distribution , we developed a program that is able to screen the primary sequence of a given protein and attribute the same values as the last program , but this program provides the individual charges of the first and last 30 amino acids of the primary sequence . The database SwissProt from uniprot . org was used as our source of primary sequences . The advanced search option ( reviewed:yes ) was used to select all proteins from SwissProt . Advanced search options for H . sapiens ( reviewed:yes AND organism:"Homo sapiens ( Human ) [9606]" ) , D . melanogaster ( reviewed:yes AND organism:"Drosophila melanogaster ( Fruit fly ) [7227]" ) , C . elegans ( reviewed:yes AND organism:"Caenorhabditis elegans [6239]" ) , A . thaliana ( reviewed:yes AND organism:"Arabidopsis thaliana ( Mouse-ear cress ) [3702]" ) and S . cerevisiae ( reviewed:yes AND organism:"Saccharomyces cerevisiae ( strain ATCC 204508 / S288c ) ( Baker's yeast ) [559292]" ) were used as a source for the proteomes . For the analysis without the proteins that carry signal peptides , ( NOT annotation: ( type:signal 5840 ) AND reviewed:yes AND organism:"Homo sapiens ( Human ) [9606]" ) and ( NOT annotation: ( type:signal 5840 ) AND reviewed:yes AND organism:"Saccharomyces cerevisiae ( strain ATCC 204508 / S288c ) ( Baker's yeast ) [559292]" were used . For the subcellular localization analysis , proteins were separated according to their Gene Ontology ID ( GO:0005635 nuclear envelope , GO:0005654 nucleoplasm , GO:0005694 chromosome , GO:0005730 nucleolus , GO:0005739 mitochondrion , GO:0005764 lysosome , GO:0005768 endosome , GO:0005773 vacuole , GO:0005777 peroxisome , GO:0005783 endoplasmic reticulum , GO:0005794 Golgi apparatus , GO:0005811 lipid particle , GO:0005815 microtubule organizing center , GO:0005829 cytosol , GO:0005840 ribosome , GO:0005856 cytoskeleton , and GO:0005886 plasma membrane ) . To create the positive/negative heat maps displayed in this paper , Orange 3 . 2 ( orange . biolab . si ) software was used . After downloading the primary sequences and calculating the charge , our output files were uploaded onto Orange 3 . 2 , and the “Heat Map” option was selected from the “Visualize” menu . The first one hundred net-charge values were used for the N-terminal analyses , and the last one hundred net-charge values were used for the C-terminal analyses . For the core residue analyses , the 130th to 229th net-charge values were utilized . To analyze the correlations between the co-translational folding and the N-terminal net charges , the S . cerevisiae proteome interaction database was used[32] . The S . cerevisiae proteins were separated based on their chaperone interactions , and their first 100 average net-charge values were plotted . Chaperones with 30 clients or less were not included . To analyze the correlations between the ribosome density and the net charges , S . cerevisiae ribosome profiling data were used[33] . The data were analyzed as described previously[14] . Briefly , the data were downloaded from GEO , and the adaptors ( CTGTAGGCACCATCAAT ) were trimmed . The trimmed FASTA sequences were aligned to S . cerevisiae ribosomal and noncoding RNA sequences to remove the rRNA reads . The unaligned reads were aligned to the S . cerevisiae S288C genome , which is deposited in the Saccharomyces genome database . First , any reads that mapped to multiple locations were removed . Then , the reads were aligned to the S . cerevisiae coding sequence database , allowing two mismatches per read . Genes with <50% of positions covered were eliminated . To analyze the N-terminal correlation with the ribosome density , the S . cerevisiae proteins were separated into the following eight categories based on their first net charge: ≥+8 ( first net charges from +8 to +13 ) , “+7” , “+6” , “+5” , “0” , “-1” , “-6” and ≤-7 ( first net charges from -7 to -15 ) . Then , the first averaged ribosome density of each group was plotted . To evaluate whether N-termini with a net charge ≥ +8 were enriched with NLSs , we individually analyzed the N-terminal region ( first 30 amino acids ) of all 144 proteins with N-terminal charges ≥ +8 using NucPred[35] . To analyze whether peptides with a net charge ≥ +14 were enriched with NLSs , we used a program that was able to identify these highly charged peptides and provide their sequences plus the next thirty amino acids ( totalizing sixty amino acids ) as the output; we then used the NucPred batch predictor[35] on these data . To compare the net-charge values between monosomal or polysomal translation , the Monosome:Polysome scores were utilized[7] . Based on these data , the S . cerevisiae proteins were separated into five categories ( ORF<590 , monosome enriched , no enrichment , polysome enriched and polysome top 300 ) . After the protein categorization , the average first net charge in each category was calculated for the N-terminal net charge , the average 121 net charge for the core residues net charge , and the C-terminal net charge . | Which factors shape the sequence of amino acids that will form a protein ? The biochemical features of amino acids , such as their charge and hydrophobicity , are important drivers of protein tridimensional folding , which creates interaction sites for binding other molecules and directs proteins to specific cellular compartments . These features all impact the activity of the proteins after they are produced . Another less obvious factor that influences the protein’s primary structure may be how efficiently a given amino acid sequence is produced by the ribosome . It is known that a repetitive stretch of positively charged amino acids may interact with the negative charges in the ribosome exit tunnel , slowing , or even halting , translation . By analyzing the charge of protein stretches in different organisms , we observed that proteins tend to present positively charged stretches at their extremities , and high charge values can slow ( for positive charges ) or speed ( for negative charges ) translation . An interesting consequence of this trend is that proteins that are translated in high quantities by several ribosomes at the same RNA ( polysomes ) tend to have more negatively charged stretches than proteins that are translated by a single ribosome per RNA ( monosomes ) . | [
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] | 2017 | Protein charge distribution in proteomes and its impact on translation |
Dengue virus ( DENV ) is the most common mosquito-borne flavivirus; it can either cause mild dengue fever or the more severe dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . One of the characteristic features of DHF/DSS is vascular leakage; although DENV nonstructural protein 1 ( NS1 ) has been proved to induce vascular leakage after binding to Toll-like receptor 4 , the down-stream mechanism has not yet been fully understood . In the sera of DENV-infected patients , the concentrations of DENV NS1 and inflammatory cytokine macrophage migration inhibitory factor ( MIF ) are positively correlated with disease severity , but whether DENV NS1 induces vascular leakage through MIF secretion remains unknown . We demonstrated that recombinant NS1 induced vascular leakage and MIF secretion both in human endothelial cell line HMEC-1 and in mice . Furthermore , these phenomena were inhibited in the presence of anti-NS1 antibodies both in vitro and in vivo . DENV NS1 also induced LC3-I to LC3-II conversion and p62 degradation in endothelial cell line , which indicated the formation of autophagy . To clarify whether MIF or autophagy mediated DENV NS1-induced vascular leakage , various inhibitors were applied . The results showed that DENV NS1-induced vascular leakage and VE-cadherin disarray were blocked in the presence of MIF inhibitors , anti-MIF-antibodies or autophagy inhibitors . An Atg5 knockdown clone further confirmed that autophagy formation of endothelial cells was required in NS1-induced vascular leakage . Furthermore , DENV NS1-induced LC3 puncta were also decreased in the presence of MIF inhibitors , indicating that MIF mediated DENV NS1-induced autophagy . Taken together , the results suggest a potential mechanism of DENV-induced vascular leakage and provide possible therapeutic targets against DHF/DSS .
Dengue virus ( DENV ) is the most common mosquito-borne flavivirus that spreads in tropical and sub-tropical areas . The World Health Organization estimates that more than 2 . 5 billion people , over 40% of the world’s population , are now at risk of dengue infection [1 , 2] . DENV infection generally causes dengue fever ( DF ) , which is often asymptomatic or results in a mild flu-like illness with intense joint pain and fever . However , a small proportion of cases develop into severe illness termed dengue hemorrhagic fever ( DHF ) . DHF is characterized by vascular leakage , thrombocytopenia , and coagulopathy [3] . Among these characteristics , vascular ( plasma ) leakage results in hemoconcentration and serious effusions , which can lead to circulatory collapse and life-threatening dengue shock syndrome ( DSS ) [4 , 5] . It has been estimated that there are 50–100 million infections and approximately 500 , 000 people with severe dengue requiring hospitalization each year globally . The mortality of DF is less than 1% with adequate treatment; however , severe disease carries a mortality rate of 26% . Despite the high mortality of DHF/DSS , there are still no effective drugs or vaccines available because of a limited understanding of the pathogenic mechanism [6] . DENV nonstructural protein 1 ( NS1 ) is a 48 kDa glycoprotein that can be expressed on the cell surface as a dimer and secreted as a hexamer into the blood circulation of dengue patients . The NS1 hexamer is composed of three dimers , which forms a detergent-sensitive hydrophobic central cavity that carries a cargo of ~70 lipid molecules; the composition is similar to that of high-density lipoprotein [7–9] . The concentration of NS1 in the sera of DHF/DSS patients can reach 50 μg/ml , which is positively correlated with disease severity [10–12] . The secreted NS1 may bind to cell membranes via interactions with heparin sulfate and chondroitin sulfate [13] . NS1 can also interact with prothrombin to interrupt the coagulation cascade [14] . In addition , NS1 can activate complement to elicit complement-dependent cytotoxicity in endothelial cells or to escape from innate immunity attack [15–17] . Recently , NS1 has been shown to be able to induce vascular leakage via binding to Toll-like receptor 4 ( TLR4 ) [18 , 19] . Therefore , investigating the downstream effectors of NS1-induced vascular leakage may provide potential targets for treating DHF/DSS . Vascular permeability is normally maintained by the well-regulated endothelial barrier structure , which plays a crucial role in the control of exchange of small solutes and macromolecules between the intravascular and interstitial space [20 , 21] . The integrity of endothelial permeability is regulated by many factors . Under pathological conditions such as infection , vascular leakage may occur because of damage to endothelial cells or loss of endothelial barrier function [22] . The physical damage to endothelial cells can be a result of cell apoptosis , which will take time to repair . In contrast , dysfunction of the endothelial barrier is reversible and may occur because of exposure to various vasoactive mediators or cytokines leading to the disruption of cell-cell junctions [23] . Vascular leakage in DHF/DSS patients occurs on days 3–7 of the illness and will resolve within 1 to 2 days in patients who receive appropriate fluid resuscitation [24 , 25] . Therefore , it is generally believed that a mechanism that induces vasoactive cytokines rather than structural destruction of endothelial cells may be the major factor responsible for vascular leakage in DHF/DSS [6 , 26 , 27] . In a previous study , we found that DENV infection can induce macrophage migration inhibitory factor ( MIF ) secretion , which can cause an increase in vascular permeability both in vitro and in vivo [28] . Using recombinant MIF , we further demonstrated that MIF induces endothelial hyperpermeability through autophagy and that this process is related to the degradation of junction proteins [29] . MIF is a 12 . 5 kDa protein that is widely expressed in different cells , including immune cells , platelets , hepatocytes , and endothelial cells . Under physiological conditions , MIF exists in cells as a trimer consisting of three identical subunits , resulting in a catalytic site located in the intermonomeric pocket . Under stress conditions , such as inflammation and hypoxia , MIF is secreted into the blood circulation to modulate both innate and adaptive immune responses [30] . Secreted MIF can bind to cell surface receptors such as CXCR2 , CXCR4 and/or CD74 [31 , 32] , inducing downstream signals such as the phosphoinositide 3-kinase ( PI3K ) /Akt pathway or the mitogen-activated protein kinases ( MAPK ) /extracellular signal-regulated kinase ( ERK ) pathway [33 , 34] . It is known that MIF secretion can also be induced upon TLR4 stimulation [35] . Therefore , it is possible that MIF-induced by DENV NS1 may play an important role in DENV-induced vascular leakage . Autophagy is a degradation pathway that occurs when cells are under stress conditions such as starvation , hypoxia , and infection [36–38] . Autophagy begins with the sequestration of the area of the cytoplasm inside double-membrane vesicles called autophagosomes [39 , 40] , which subsequently fuse with lysosomes to form autolysosomes or late endosomes to produce amphisomes [41] . Two ubiquitin-like conjugation of autophagy proteins ( Atg5 and Atg12 ) are essential for autophagosome formation . Atg5 and Atg12 promote lipidation of a cytosolic form of light chain 3 ( LC3; LC3-I ) to form the LC3-phosphatidylethanolamine conjugate ( LC3-II ) . The lipidated LC3-II , which is tightly associated with the autophagosomal membranes , can be observed by immunofluorescence staining to monitor autophagy , in which LC3 puncta formation reflects the existence of autophagosomes . In addition , after fusion with lysosomes , adaptor protein p62 will be degraded in the autophagolysosomes . As a result , autophagy formation can be determined by the decrease of p62 or the increase of LC3-I to II conversion by immunoblotting analysis . It has been demonstrated that DENV infection promotes the formation of autophagy , which can enhance virus replication [42] . However , the role of autophagy in DENV-induced vascular leakage has not been studied . Therefore , we proposed and tested the hypothesis that dengue NS1 increases vascular permeability through MIF secretion and autophagy formation .
To assess the role of DENV NS1 protein in vascular permeability , recombinant serotype 2 DENV NS1 derived from human 293T cells ( 293T-NS1 ) and Drosophila S2 cells ( S2-NS1 ) were used in this study . Different concentrations of 293T-NS1 were incubated with human endothelial cell line ( HMEC-1 ) for 6 h and the endothelial permeability was determined by the transwell permeability assay . This result showed that 293T-NS1 increased endothelial permeability in a dose-dependent manner . At least 5 μg/ml of 293T-NS1 was required to increase the permeability ( Fig 1A ) . The kinetic changes of 293T-NS1-induced endothelial hyperpermeability were also measured . Endothelial hyperpermeability was induced 3 h after incubating with 293T-NS1 ( 20 μg/ml ) , which persisted to 24 h ( Fig 1B ) . Similar effects were found using S2-NS1 ( Fig 1C ) . To determine whether NS1 caused vascular leakage in vivo , protein extravasation in the abdominal cavity of mice was measured 6 h after i . p . injection of bovine serum albumin ( BSA ) or S2-NS1 ( Fig 1D ) . Protein concentrations in the abdominal lavages of S2-NS1-injected mice were significantly increased compared to those in BSA-injected mice , suggesting that S2-NS1 was able to induce vascular leakage in mice ( Fig 1D ) . To confirm that the vascular leakage was specifically induced by NS1 , we co-treated different anti-NS1 antibodies with 293T-NS1 and examined whether 293T-NS1-induced vascular leakage could be blocked . In addition , a real-time cell analysis ( RTCA ) system was used to monitor the kinetic change of endothelial permeability . These antibodies alone did not have any effect on the endothelial permeability of HMEC-1 cells either measured by RTCA or transwell assay ( S1A and S1B Fig ) . However , 293T-NS1-increased endothelial permeability was inhibited in the presence of monoclonal antibodies ( mAb ) or polyclonal antibodies ( pAb ) against NS1 as measured by RTCA ( Fig 2A ) . It was noted that different NS1 mAbs showed different blocking effect of which mAb 2E8 was better than mAb DN5C6 ( Fig 2A ) . On the other hand , isotype control mouse IgG ( CTRL mIgG ) did not block 293T-NS1-increased permeability ( Fig 2A ) . Similar results were also observed using the transwell permeability assay ( Fig 2B ) . Likewise , in vivo experimentation also showed that anti-NS1 mAb 2E8 and pAb could block S2-NS1-induced protein extravasation in mice nearly to the basal value of the abdominal cavity , whereas CTRL mIgG could not ( Fig 2C ) . To test whether MIF secretion was induced upon DENV NS1 stimulation of endothelial cells , the amount of MIF in the cell culture supernatant was determined by ELISA . As shown in Fig 3A , MIF secretion was induced by incubating 293T-NS1 with HMEC-1 cells . Anti-NS1 mAb 2E8 and pAb completely reversed 293T-NS1-induced MIF secretion , mAb DN5C6 showed partial inhibitory effect , while CTRL mIgG had no effect ( Fig 3A ) . In addition , these antibodies alone did not alter the basal level of MIF secretion of HMEC-1 cells ( S1C Fig ) . Similar to what we found in in vitro study , intraperitoneal or intravenous injection of S2-NS1 but not PBS into mice increased MIF concentrations in peritoneal lavage or plasma of mice , respectively ( Fig 3B and 3C ) . Furthermore , anti-NS1 mAb 2E8 and pAb , but not mAb DN5C6 or CTRL mIgG , significantly inhibited S2-NS1-induced MIF secretion in mice ( Fig 3B ) . In our previous study , we found that MIF was involved in DENV-induced vascular leakage [28]; therefore , we tested whether inhibition of MIF could block DENV NS1-induced endothelial hyperpermeability . Inhibition of MIF by its inhibitors , ISO-1 or p425 , decreased 293T-NS1-increased permeability as shown by RTCA ( Fig 4A ) and the transwell permeability assay ( Fig 4B ) . In addition , anti-MIF pAb could also block 293T-NS1-increased endothelial permeability ( Fig 4B ) . Rabbit IgG isotype control ( CTRL RaIgG ) was used as a negative control of anti-MIF pAb , which did not inhibit 293T-NS1-increased endothelial permeability . In addition , all these chemical inhibitors or antibodies alone did not have any effect on endothelial permeability , as shown in the supporting information ( S1A and S1B Fig ) . MIF was reported to induce vascular leakage through autophagy formation [29] , so we assessed whether DENV NS1 could induce autophagy of HMEC-1 cells . PBS- or 293T-NS1-treated HMEC-1 cell lysates were collected . Western blot analysis showed that 293T-NS1 induced p62 degradation and LC3-I-to-LC3-II conversion , which indicated autophagy formation in HMEC-1 cells ( Fig 5A ) . Furthermore , 293T-NS1 also decreased the protein level of VE-cadherin , which might result in endothelial hyperpermeability ( Fig 5A ) . Because the function of autophagy is to digest or degrade organelles or proteins , we wondered whether autophagy mediate DENV NS1-induced VE-cadherin degradation . Immunofluorescence staining was thus applied . Double staining of VE-cadherin and LC3 showed that the number of LC3 puncta was increased after 6 h of 293T-NS1 treatment ( Fig 5B and 5C ) . In addition , cytosolic VE-cadherin colocalized with the LC3 puncta was found in 293T-NS1-stimulated HMEC-1 cells , indicating that some of the VE-cadherin proteins were embedded by autophagosomes ( Fig 5B and 5D ) . Inhibiting MIF by its inhibitor ISO-1 decreased 293T-NS1-induced autophagy formation , LC3 puncta and the colocalization of LC3 puncta with VE-cadherin ( Fig 5B–5D ) . To clarify whether autophagy mediated 293T-NS1-induced vascular leakage , autophagy inhibitors were used . RTCA results showed that 293T-NS1-induced endothelial hyperpermeability was inhibited by co-treatment with PI3K inhibitor 3-methyladenine ( 3-MA ) or the reactive oxygen species ( ROS ) scavenger N-acetyl-L-cysteine ( NAC ) ( Fig 6A ) . The results from transwell permeability assay also showed that both 3-MA and NAC inhibited 293T-NS1-increased endothelial permeability ( Fig 6B ) , whereas neither 3-MA nor NAC alone had effect on endothelial permeability in vitro ( S1A and S1B Fig ) . The importance of autophagy in NS1-induced endothelial hyperpermeability was further supported by the stable Atg5 knockdown HMEC-1 cells ( shAtg5 ) , which , unlike the control shLuc cells , were resistant to S2-NS1-induced endothelial hyperpermeability ( Fig 6C ) . In vivo permeability assay was also applied to test whether inhibition of MIF or autophagy could rescue DENV NS1-induced vascular leakage in mice . The results showed that either inhibiting MIF or autophagy could rescue DENV NS1-induced vascular leakage in mice , indicating that both MIF and autophagy are involved in DENV NS1-induced vascular leakage ( Fig 6D ) . Because MIF was previously shown to increase vascular permeability through the disarray of endothelial junction proteins ZO-1 and VE-cadherin , we sought to determine whether NS1 alters the alignment of endothelial junction proteins [29] . The immunofluorescence staining results showed that 293T-NS1 increased the ratio of cytosolic/barrier VE-cadherin of HMEC-1 cells ( Fig 7A and 7B ) . To determine whether MIF and autophagy are involved in NS1-induced VE-cadherin disarray , we treated HMEC-1 cells with NS1 in the presence of MIF inhibitor ISO-1 or autophagy inhibitor 3-MA . The results showed that 293T-NS1-induced VE-cadherin translocation was inhibited in the presence of MIF or autophagy inhibitors and these inhibitors alone has no effects on VE-cadherin distribution ( Fig 7A and 7B ) .
Little was known about the pathogenic roles of NS1 during DENV infection until recently . Two independent groups published papers which demonstrated that DENV NS1 can induce vascular leakage via TLR4 [18] and anti-NS1 antibodies or that NS1 vaccination can block this effect [19] . In this study , our results confirmed their findings and further suggests that MIF-induced autophagy of endothelial cells may mediate NS1-induced vascular leakage . The hypothetical model of the pathway by which DENV NS1 increases vascular permeability is shown in Fig 8 . In this study , we found that 5 μg/ml 293T-NS1 was sufficient to induce endothelial hyperpermeability at 6 h ( Fig 1A ) . In in vivo experiments , we injected 50 μg S2-NS1 into mice . Because the total blood volume of a mouse is approximately 2–3 ml , the sera concentration of NS1 in mice is approximately 20 to 25 μg/ml . Furthermore , because the serum concentration of NS1 in dengue patients is estimated to range from 0 . 01 to 50 μg/ml [10] , our experiments mimic the pathological condition in dengue patients . Even though further study is required to understand the contribution of NS1 in vascular leakage of dengue patients , these results suggest that NS1 can directly bind to endothelial cells to cause vascular leakage in dengue patients . To further understand the interaction between NS1 and endothelial cells , we used different NS1 antibodies to block its effect . It is known that NS1 can also induce pathogenic antibodies that can cross-react with endothelial cells and induce endothelial cell apoptosis through molecular mimicry [43 , 44] . Some of these anti-NS1 antibodies can also recognize platelets , resulting in thrombocytopenia [45] . Other anti-NS1 antibodies can cross-react with thrombin and plasminogen , resulting in inhibition of thrombosis and enhanced fibrinolysis [46] . Therefore , we used two different anti-NS1 mAbs 2E8 and DN5C6 . Both of which did not bind to endothelial cells . We found that anti-NS1 mAb 2E8 showed better effect than mAb DN5C6 to block the activities of 293T-NS1 and S2-NS1 to stimulate endothelial cells . Similar results were also found by Beatty et al . which demonstrated that not all anti-NS1 antibodies can inhibit NS1-induecd vascular leakage [19] . Therefore , certain regions of DENV NS1 are more important for NS1 to interact with endothelial cells to induce vascular leakage . Identification of these regions may shed light to generate antibodies or vaccines to block NS1-induced vascular leakage . It is known that MIF knockout mice show lower hemoconcentration and lethality compared with normal mice during DENV infection [47] . In sepsis , knockout or inhibition of MIF also increased survival rate of mice [48–50] . Previously , we demonstrated that MIF could mediate DENV-induced junction disarray and increase permeability in endothelial cells [28] . In this study , we further demonstrated that MIF is involved in DENV NS1-induced vascular leakage . Inhibition of MIF by its inhibitors can prevent DENV NS1-induced vascular leakage both in vitro and in mice . It is known that in addition to endothelial cells , other cells such as peripheral blood mononuclear cells ( PBMC ) can secrete MIF during DENV infection . Therefore , MIF secretion can be induced by either DENV infection or NS1 stimulation of different cells in dengue patients . However , in addition to MIF , other cytokines may also contribute to vascular leakage during DENV infection . Modhiran et al . showed that the expression of several cytokines including IL-6 , TNF-α , IL-8 , and MCP-1 were up-regulated in PBMC after DENV NS1 stimulation [18] . Many of these cytokines can also increase endothelial permeability [51–54] . Furthermore , culture supernatants from DENV-infected macrophage can induce endothelial cell apoptosis which is blocked by anti-TNF-α antibodies [55] . Even though it is known that MIF can augment the secretion of TNFα and counteracts the anti-inflammatory action of glucocorticoids [56 , 57] , DENV-induced vascular leakage may involve different mechanisms and the importance of MIF as therapeutic target against DENV-induced vascular leakage should be further studied . It is known that autophagy is induced by DENV to prevent cell death and enhance viral replication during infection in human hepatoma cell lines [42 , 58 , 59] . Autophagy not only provides isolated environment but also provides energy and materials required for DENV replication by regulating lipid metabolism [60] . In addition , recent study showed that autophagy plays an important role in the antibody-dependent enhancement response in Fc receptor-bearing cells [61] . However , the role of DENV-induced autophagy in endothelial cells has not yet been discussed extensively . It has been reported that DENV NS4A is able to induce autophagy [62] , but whether NS1 can also induce autophagy has not yet been reported . In this study , we showed that DENV NS1 induced autophagy , which mediated NS1-induced vascular leakage . As autophagy is required during DENV infection , inhibition of autophagy may prevent vascular leakage as well as suppress DENV replication . However , further studies are required to validate the therapeutic effects of autophagy inhibitors as anti-DENV drugs . Taken together , our results suggest NS1-induced MIF secretion and autophagy may represent potential therapeutic targets for preventing vascular leakage in DHF/DSS . Our study highlights DENV NS1 as an important pathogenic factor in DHF/DSS . NS1-induced MIF secretion and autophagy may contribute to vascular leakage in DHF/DSS . Even though NS1 purified from DENV-infected cells or patients should be used to further confirm the pathogenic effects of NS1 on endothelial cells in the future , NS1-induced vascular leakage may represent a disease model in mice to develop potential therapeutic drugs and vaccines against dengue [63–66] .
All experiments were performed in conformity with the Guide for the Care and Use of Laboratory Animals ( The Chinese-Taipei Society of Laboratory Animal Sciences , 2010 ) and were approved by the Institutional Animal Care and Use Committee ( IACUC ) of National Cheng Kung University ( NCKU ) under the number IACUC 99057 . HMEC-1 cells were cultured in Medium 200 ( Thermo Fisher Scientific , Waltham , MA ) supplemented with 10% fetal bovine serum ( FBS; HyClone Laboratory , Logan , UT ) at 37°C in a 5% CO2 atmosphere . Stable clones of luciferase ( Luc ) -knockdown HMEC-1 cells were generated by a lentivirus-based short hairpin RNA ( shRNA ) system ( National RNAi Core Facility , Academia Sinica , Taipei , Taiwan ) targeting sequence 5’-GCCACAACATCGAGGACGGCA-3’ . A stable clone of Atg5-silenced HMEC-1 cells was a kind gift from Dr . Chiou-Feng Lin . Both the shLuc and shAtg5 HMEC-1 cells were selected with 2 μg/ml of puromycin ( MDBio , Inc . , Taiwan ) . In this study , we used two different commercialized recombinant NS1 proteins which were expressed in non-bacterial systems . Mammalian recombinant DENV serotype 2 NS1 protein , 293T-NS1 ( The Native Antigen Company , UK ) , was engineered and expressed in the human 293T cell line . Another recombinant DENV serotype 2 NS1 protein , S2-NS1 ( CTK biotech , San Diego , CA ) , was expressed in Drosophila S2 cells . Recombinant NS1 proteins were tested for endotoxin contamination by the Limulus amebocyte lysate ( LAL ) assay using the LAL Chromogenic Endotoxin Kit ( Thermo Fisher Scientific , Waltham , MA ) and shown to be endotoxin-free . Background endotoxin concentration of 0 . 036 EU/ml was found in 20 μg/ml 293T-NS1 and 0 . 018 EU/ml in 20 μg/ml S2-NS1 , respectively . In this study , BALB/c mice were purchased from and maintained at the Laboratory Animal Center of NCKU . Purified recombinant DENV2 NS1 was used to immunize 6- to 8-week-old female BALB/c mice at a dose of 50 μg as previously described [67] . The first dose was administered in complete Freund's adjuvant ( CFA ) , and the following three doses were administered in PBS . After sacrifice , mice splenocytes were fused with FO cells ( Taiwan Medical Cell and Microbial Resources ) . The resultant hybrid cells were selected in hypoxanthine-aminopterin-thymidine medium . An ELISA was performed to screen for the specific antibodies against NS1 . After the hybridoma clones 2E8 and DN5C6 were established , the hybridoma cells were i . p . injected into pristine-primed BALB/c mice to produce monoclonal antibodies in ascites . The antibodies were then purified using a Protein G column ( GE Healthcare ) . Rabbit polyclonal anti-NS1 antibodies were purified from purchased recombinant DENV2 NS1-immunized antiserum ( GeneTex , Inc . , Irvine , CA ) . Endotoxin concentrations in these antibodies were also measured by LAL assay . Endotoxin concentrations in 30 μg/ml mAb 2E8 , mAb DN5C6 and CTRL mIgG ( Leadgene Biomedical , Taiwan ) were 0 . 082 , 0 . 092 and 0 . 028 EU/ml , respectively . Nevertheless , none of these antibodies alone could alter endothelial permeability nor induce MIF secretion as shown in the supporting information ( S1 Fig ) . In the in vitro experiments , 20 μg/ml ( ~ 400 nM ) 293T-NS1 or S2-NS1 was applied . In the in vivo experiments , 50 μg S2-NS1 was applied by i . p or i . v . injection . Different anti-NS1 antibodies ( mAb 2E8 , DN5C6 and pAb ) were used to block recombinant NS1-induced effects . The concentration of antibodies utilized in the studies was at 30 μg/ml ( ~ 200 nM ) for in vitro experiments ( Fig 2A , 2B , 3A and S1 Fig ) , and at 40 μg per mouse for in vivo setting ( Figs 2C and 3B ) . Since IgG has two antigen binding sites , it can bind to more than one antigen by binding identical epitope carried on the surfaces of these antigens . Therefore , the amount of IgG antibodies used in current studies should be able to bind to most of the NS1 ( ~ 400 nM ) we added for in vitro experiments . To inhibit MIF activity , the MIF tautomerase inhibitor ( S , R ) -3- ( 4-hydroxyphenyl ) -4 , 5-dihydro-5-isoxazole acetic acid methyl ester ( ISO-1 ) ( 50 μM; Calbiochem , La Jolla , CA ) and 6 , 6'-[ ( 3 , 3-Dimethoxy[1 , 1'-biphenyl]-4 , 4'-diyl ) bis ( azo ) ]bis[4-amino-5-hydroxy-1 , 3-napthalenedisulphonic acid] ( p425 ) ( 100 μM; Calbiochem ) were mixed with 293T-NS1 or S2-NS1 before treatment . Polyclonal anti-MIF antibody was purified using a protein G column ( GE Healthcare ) , and 30 μg/ml was used to block MIF in 293T-NS1-treated cells . The endotoxin concentration in 30 μg/ml anti-MIF antibody and CTRL RaIgG ( GeneTex ) were 0 . 065 EU/ml and 0 . 02 EU/ml as determined by LAL assay , respectively . To inhibit autophagy , 5 mM of 3-MA ( Sigma-Aldrich , St . Louis , MO ) or 5 mM of NAC ( Sigma-Aldrich ) was used . To measure the permeability of endothelial cells in vitro , we used two different methods in this study: the transwell permeability assay and real-time cell analysis ( RTCA ) [68] . For the transwell permeability assay , cells ( 2 x 105 ) were grown on a Transwell insert ( 0 . 4 μm; Corning B . V . Life Sciences , The Netherlands ) until a monolayer was formed . The upper chambers were reconstituted with 10% FBS-containing medium with 293T-NS1 , S2-NS1 and the inhibitors . At the indicated time points , the media in the upper chambers were changed to 300 μl of serum-free media containing 4 . 5 μl streptavidin-horseradish peroxidase ( HRP ) ( R&D Systems , Minneapolis , MN ) . The medium ( 20 μl ) in the lower chamber was collected 5 min after adding streptavidin-HRP and was assayed for HRP activity by adding 100 μl 3 , 3' , 5 , 5'-tetramethylbenzidine ( TMB ) substrate ( R&D Systems ) . Color development was detected by a VersaMax microplate reader ( Molecular Devices , Sunnyvale , CA ) at 450 nm . RTCA was used to test cell-cell or cell-matrix adhesion by detecting the electric resistance of the monolayered endothelial cells . High resistance indicates strong endothelial barrier function . Using this device allowed us to detect the kinetic changes in endothelial permeability . For RTCA , 1 x 104 HMEC-1 cells were grown on a 96-well E-plate to form a confluent monolayer . After 293T-NS1 and the inhibitors were added , the resistance of the monolayer was recorded by an xCELLigence Real-Time Cell Analysis System ( Cambridge Bioscience , UK ) for 24 h . The method for testing vascular leakage in the peritoneal cavity was described previously [28] . Briefly , 8- to 12-week-old BALB/c mice were injected intraperitoneally with 50 μg of S2-NS1 , which was solubilized in 500 μl of PBS with or without the inhibitors . The mice were sacrificed 6 h after the treatments and the abdominal cavity was washed with 5 ml PBS after sacrificing the mice . The concentration of protein in the abdominal lavage was determined using the BCA method ( Pierce Biotechnology , Rockford , IL ) . Mean concentration was calculated with 3 to 5 mice in each condition . The MIF concentration in the cell culture medium was tested by using an ELISA kit ( R&D System , Minneapolis , MN ) following the manufacturer’s instructions . The MIF concentration in the peritoneal lavage fluid or plasma of mice was tested by using another ELISA kit ( BlueGene Biotech , China ) . For Western blot analysis , VE-cadherin ( BD Biosciences , Franklin Lakes , NJ ) , p62 ( Santa Cruz , Dallas , TX ) and LC3 ( GeneTex ) were detected using a 1:1 , 000 dilution of antibodies followed by a 1:6 , 000 dilution of HRP-conjugated anti-mouse or anti-rabbit immunoglobulin antibody ( Leadgene Biomedical ) . The β-actin ( Table 1 ) antibody ( Sigma-Aldrich ) was used at a 1:10 , 000 dilution as an internal control . Bound HRP-conjugated antibodies were detected using the Luminata Crescendo Western HRP substrate ( Merck Millipore , Germany ) . The Western blot results were quantified using the Image J software program . Cell monolayers were seeded onto microscope cover glass . After treatment , the cells were fixed in 4% paraformaldehyde for 5 min , followed by three washes with PBS . The cells were then blocked with SuperBlock blocking buffer ( Thermo Fisher Scientific ) for 1 h at room temperature . To detect VE-cadherin and LC3 localization , specific antibodies against VE-cadherin ( Beckman Coulter , Brea CA ) , and LC3 ( Genetex ) ( 1:200 dilutions in PBS ) were incubated with cells overnight at 4°C . After three washes with tris-buffered saline and Tween 20 , the cells were treated with an Alexa 488-conjugated goat anti-mouse IgG monoclonal antibody ( Invitrogen , Carlsbad , CA ) ( 1:500 dilution ) and Alexa 594-conjugated goat anti-rabbit pAb ( Invitrogen ) ( 1:1 , 000 dilution ) for 1 h , followed by three washes with tris-buffered saline-Tween 20 . Images were obtained using a confocal microscope ( Olympus FluoView FV1000 , Melville , NY ) . For quantifying barrier/marginal VE-cadherin , 3 μm across the cell border was defined as barrier/marginal area . And the remaining area within a cell was defined as cytosolic/perinuclear area . 50 cells in each condition were quantified by using Image J software . The data are expressed as the mean ± standard error of the mean ( SEM ) from more than three independent experiments . One-way ANOVA and Bonferroni's multiple comparison test as post-test , two-way ANOVA or Student’s t-test was used to analyze the significance of the difference between the test and the control groups by GraphPad Prism 5 software . P values < 0 . 05 were considered statistically significant . | Dengue is a viral disease transmitted by mosquitoes . The symptoms of dengue are often mild; however , severe dengue is one of the leading causes of hospitalization and death among children in Asian and Latin American countries . A symptom of severe dengue is vascular leakage , which can result in fluid accumulation , hypotension , circulatory collapse , and even death . For dengue and severe dengue , there is no specific treatment , and the only supportive treatment is to maintain a patient’s body fluids at normal levels . As a result , investigating the mechanism of how dengue virus ( DENV ) causes vascular leakage is an important and urgent issue . In this study , we demonstrated that DENV nonstructural protein 1 ( NS1 ) induced vascular leakage through the secretion of macrophage migration inhibitory factor ( MIF ) and the formation of autophagy . Inhibition of MIF or autophagy formation effectively reversed NS1-induced vascular leakage both in vitro and in mice . These results provide possible therapeutic targets for treating vascular leakage in severe dengue . | [
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] | 2016 | Dengue Virus Nonstructural Protein 1 Induces Vascular Leakage through Macrophage Migration Inhibitory Factor and Autophagy |
The majority of cilia are formed and maintained by the highly conserved process of intraflagellar transport ( IFT ) . Mutations in IFT genes lead to ciliary structural defects and systemic disorders termed ciliopathies . Here we show that the severely truncated sensory cilia of hypomorphic IFT mutants in C . elegans transiently elongate during a discrete period of adult aging leading to markedly improved sensory behaviors . Age-dependent restoration of cilia morphology occurs in structurally diverse cilia types and requires IFT . We demonstrate that while DAF-16/FOXO is dispensable , the age-dependent suppression of cilia phenotypes in IFT mutants requires cell-autonomous functions of the HSF1 heat shock factor and the Hsp90 chaperone . Our results describe an unexpected role of early aging and protein quality control mechanisms in suppressing ciliary phenotypes of IFT mutants , and suggest possible strategies for targeting subsets of ciliopathies .
The coordinated functions of multiple proteins in large macromolecular complexes is essential for many fundamental cellular processes including the building of multicomponent cellular structures . For instance , primary cilia , which are microtubule-based sensory organelles present on nearly all metazoan cells , are generated and maintained by large protein complexes that mediate the conserved process of intraflagellar transport ( IFT ) . These IFT complexes link cargo molecules to kinesin-2 and cytoplasmic dynein 1b molecular motors to build these critical signaling structures [1–3] ( Fig 1A ) . While null mutations in IFT genes result in severe disruption or loss of cilia , and embryonic lethality in vertebrates [4–6] , hypomorphic mutations in core IFT genes lead to weaker cilia structural defects and tissue-specific phenotypes , characteristic of syndromes collectively termed ciliopathies [7–12] . Thus , identifying conditions that suppress and/or bypass deleterious effects of IFT gene mutations and restore cilia growth is of great interest and medical relevance . Cilia are present at the dendritic endings of a subset of sensory neurons in C . elegans ( Fig 1A ) [13 , 14] . As in other animals , IFT is essential for ciliogenesis in C . elegans , and IFT-A and IFT-B core complex proteins are highly conserved [15] . Mutations in IFT-A complex genes such as daf-10/IFT122 result in accumulation of proteins at the cilia tips suggesting defects in retrograde transport , whereas mutations in core IFT-B genes such as osm-6/IFT52 and osm-5/IFT88 affect anterograde transport leading to severely truncated cilia with protein accumulation at the ciliary base [14 , 16] . In both cases , sensory neuronal function is severely impaired [17–19] . Thus , C . elegans provides an excellent experimental system in which to identify and analyze mechanisms of ciliogenesis and cilia function . C . elegans is also an established model organism for the study of aging [20–22] . Work in multiple systems has demonstrated that aging is a highly regulated process that is under tight genetic control [23–25] . A hallmark of aging is the decreased ability to maintain protein function or protein homeostasis ( proteostasis ) , which results in increased cellular damage and decline of cellular and organismal functions [26–29] . Compromised proteostasis in aged animals is in part due to reduced functionality of protein quality control mechanisms , thereby enhancing aggregation and accumulation of misfolded proteins [30–34] . Thus , protein complexes such as IFT particles that rely on defined stoichiometry of individual components [3 , 35 , 36] may be particularly vulnerable to aging . However , how aging affects primary cilia structure and function has not been examined in detail . Here we show that aging leads to transient structural and functional recovery of severely defective sensory cilia in hypomorphic IFT mutants in C . elegans . This age-dependent improvement of cilia morphology and properties occurs in multiple unique cilia types and is IFT-dependent . We find that the HSF1 heat shock factor , the Hsp90 molecular chaperone , and the ubiquitin-proteasome system are required for the observed suppression of cilia structural and functional defects in IFT mutants . Our results describe a protective role of early aging and protein quality control mechanisms in restoring sensory cilia function in hypomorphic IFT mutants , and raise the possibility that related mechanisms may similarly ameliorate cilia defects and improve cellular and organismal homeostasis in other contexts .
To investigate whether aging perturbs cilia structure and function in C . elegans , we began by examining the simple rod-like cilia of the ASI sensory neuron pair in the head amphid organs [13 , 14 , 37] . ASI cilia were visualized via cell-specific expression of a GFP-tagged SRG-36 pheromone receptor protein , which localizes specifically to ciliary membranes [38] ( Fig 1B ) . We found that ASI cilia reached their final length by the L4 larval stage , suggesting that cilium length is not grossly affected by animal size following transition into adulthood ( Fig 1C ) . However , we noted increased variability in ASI cilia length as animals aged ( Fig 1C ) , suggesting that cilium length may be affected upon aging . To further investigate this issue , we compared ASI cilia length in wild-type and the long-lived daf-2 ( e1370ts ) [39 , 40] insulin receptor mutants . Insulin/IGF-1 signaling is the major pathway that regulates aging in C . elegans as well as in other species [41 , 42] . At the restrictive growth temperature of 25°C , wild-type and daf-2 ( e1370ts ) mutants exhibit a mean lifespan of ~15d and ~23d , respectively [41] . ASI cilia length was more variable and on average , significantly shorter in 14d old compared to 1d old wild-type animals at this temperature ( Fig 1C ) . In contrast , although ASI cilia were shorter in daf-2 ( e1370ts ) mutants for unknown reasons , their length remained constant through 14d of adulthood at this temperature ( Fig 1C ) . We conclude that ASI cilia length becomes more variable in old age , and that this phenotype is under genetic regulation . Loss of function mutations in IFT-B complex genes such as osm-6/IFT52 result in severely truncated cilia [14 , 43] ( Fig 1B and 1C ) . Unexpectedly , we observed that the severely truncated ASI cilia in osm-6 ( p811 ) mutants lengthened in adults during early aging ( Fig 1B and 1C ) . While these cilia did not elongate to wild-type lengths , they were nevertheless nearly twice as long in 4-7d old , as compared to 1d old , osm-6 adults grown at 20°C ( Fig 1C ) . The cilia did not elongate with further aging and were shortened in older animals ( Fig 1C ) . We observed similar elongation of ASI cilia in 7d old osm-6 mutants grown at 25°C; these cilia were significantly shortened by 14d ( Fig 1C ) . In contrast , the elongated cilia were maintained in 14d old daf-2;osm-6 double mutants ( Fig 1C ) , consistent with delayed aging in these animals . The observed age-dependent elongation was not specific to the osm-6 ( p811 ) allele , since similar effects were observed in the osm-6 ( m533 ) mutant ( S1A Fig ) . We also observed ASI cilia elongation upon visualization of cilia using a soluble fluorescent reporter protein ( S1B Fig ) , indicating that the quantified ASI cilia length was not an artifact due to altered SRG-36 receptor protein trafficking or ciliary localization at different ages . These results indicate that the effects of osm-6 mutations on ASI cilia can be partially and transiently suppressed during early stages of aging . OSM-6/IFT52 comprises a core component of the larger IFT-B protein complex [3 , 16 , 35 , 43] . We asked whether cilia elongation with age is specific to mutations in osm-6 , or is a general phenomenon observed in animals mutant for other IFT-B complex genes . Cilia elongation was observed to a similar extent in 7d old osm-1/IFT172 and osm-5/IFT88 IFT-B complex component mutants grown at 20°C ( Fig 1D ) . Cilia in IFT-A complex genes are not shortened to the same extent as in IFT-B complex mutants [14] . Nevertheless , we also observed a lengthening of ASI cilia in 7d old daf-10/IFT122 IFT-A mutants ( Fig 1D ) . Moreover , ASI cilia in osm-6;osm-5 and daf-10;osm-6 double mutants also elongated significantly with age ( Fig 1D ) . Henceforth , we refer to this phenomenon as age-dependent cilia recovery ( AdCR ) . In the process of characterizing these IFT mutants , we noted that many of the isolated IFT mutant alleles in C . elegans are likely to be hypomorphs . These include the canonical osm-6 ( p811 ) allele which appears to encode a cilia-localized protein likely translated from a secondary or cryptic start codon ( S2A–S2C Fig ) , the osm-1 ( p816 ) allele which is an in-frame deletion predicted to encode a protein lacking 2 of the 14 TPR repeats , and the osm-5 ( p813 ) allele which is a nonsense mutation encoding a truncated protein that contains the coiled-coil domain and 5 of the 12 TPR repeats ( S2A Fig ) . We asked whether AdCR occurs only in strains carrying partial loss of function mutations in IFT genes , or whether the ciliary phenotypes of null mutations in these genes can also be bypassed upon aging . The osm-5 ( ok451 ) mutation is a large deletion/insertion in the gene , and is likely a null allele ( S2A Fig ) . Interestingly , we noted that the ASI cilia failed to elongate in aged osm-5 ( ok451 ) mutants , and instead were significantly shorter ( Fig 1D ) . These observations indicate that AdCR can occur in animals with compromised , but not entirely absent , IFT proteins . We next asked if AdCR is restricted to ASI cilia by examining the cilia of additional sensory neurons in IFT-B mutants . We visualized and quantified cilia morphologies in three other head amphid sensory neurons ( AWC , ASE and ASH ) via expression of neuron-specific cilia markers . While ASH and ASE cilia are simple and rod-like in shape similar to ASI cilia , AWC cilia exhibit complex membranous morphologies and are ensheathed by glial cells [14 , 37] . We observed marked restoration of the morphologies of all three cilia types in 7d old osm-6 adults ( Fig 2A–2D ) , indicating that AdCR is a general feature in multiple ciliated neuron types . To determine how closely these recovered cilia resemble their wild-type counterparts , we examined their ultrastructure via serial section electron microscopy . The cilia of eight pairs of amphid sensory neurons , including the ASI neurons , comprise ten axonemes that are bundled together and exposed to the environment via a channel created by the amphid socket and sheath glial cells ( channel cilia ) [14 , 37] . Each cilium harbors a proximal region ( transition zone and middle segments ) containing 9 outer doublet microtubules and a distal region ( distal segments ) containing 9 outer singlet microtubules ( Fig 2E , S3A and S3C Fig ) [14 , 37] . In IFT-B mutants such as osm-6 ( p811 ) that exhibit severely truncated cilia , the amphid channel is frequently deformed making it challenging to identify and visualize ciliary ultrastructure . We , therefore , examined osm-6 ( m533 ) mutants in which the amphid channel and cilia lengths are affected to a lesser degree ( S1A Fig ) . Few if any axonemes were observed in distal parts of the channel in 1d old osm-6 ( m533 ) mutants ( Fig 2E , S3A , S3C and S3D Fig ) , consistent with the shortened cilia in these animals ( S1A Fig ) . However , axonemes were consistently observed in distal sections of 7d old osm-6 animals ( Fig 2E , S3A–S3D Fig ) , indicating that the cilia of multiple sensory neurons elongate in aged osm-6 mutants . Since the longer cilia in aged osm-6 mutants were not full-length ( Figs 1B , 1C and 2A–2D , S1 Fig ) , we asked whether only the middle ciliary segments comprised of doublet microtubules elongate , or whether the elongated cilia also contain singlet microtubules characteristic of ciliary distal segments . As in wild-type animals , we observed distal singlets in a subset of cilia in 7d old osm-6 mutants ( Fig 2E , S3A–S3C Fig ) , indicating that both middle and distal segments recover upon aging . Together , these results indicate that the cilia of multiple sensory neurons in IFT mutants exhibit AdCR , and that the axonemal ultrastructures of these elongated cilia resemble those of wild-type cilia . We next examined whether in addition to partially restoring cilia morphology , AdCR restores ciliary function and sensory responsiveness of the affected neurons . A subset of ciliated sensory neurons responds to environmental chemical stimuli including volatile and aqueous chemicals produced by the bacterial food source of C . elegans , and drives attraction or avoidance behaviors [45–47] . Many IFT gene mutants exhibit strong defects in attraction to , or avoidance of , subsets of chemicals [17–19] , indicating that intact cilia are essential for chemosensation by many sensory neurons . We first compared the ability of 1d and 7d old wild-type and IFT mutants to be attracted to a point source of live bacteria . We found that while wild-type animals were robustly attracted regardless of age , 1d old osm-5 ( p813 ) and osm-6 ( p811 ) mutants exhibited weak or no attraction ( Fig 3A ) . However , attraction to bacteria was dramatically improved in 7d old osm-5 ( p813 ) and osm-6 ( p811 ) animals ( Fig 3A ) , implying that AdCR may contribute to improved sensory responsiveness . Decreased chemosensory responses in 1d old osm mutant animals were not simply due to movement defects since the velocities of 1d and 7d old osm-5 and osm-6 mutants were inversely correlated with their chemoattraction behaviors ( S1 Table ) . We could not examine chemotaxis behaviors of osm-5 ( ok451 ) mutants since these animals exhibited impaired locomotion due to unknown reasons . To further correlate AdCR with improved chemosensation , we examined the ability of wild-type and osm-5 and osm-6 mutants to avoid solutions of high osmolarity . Osmotic avoidance behavior is mediated by the ASH sensory neurons [48] , and IFT gene mutants exhibit strong defects in this avoidance response [17] . As expected , while nearly 100% of wild-type animals placed within a ring of 8M glycerol remained within the ring regardless of age , many 1d old osm-5 ( p813 ) and osm-6 ( p811 ) mutants escaped the ring within 2 mins ( Fig 3B ) , consistent with impaired ASH sensory functions in these mutants . As in the case of bacterial chemosensation , 7d old osm-5 and osm-6 mutants exhibited improved osmotic avoidance , such that a significantly larger number of animals remained within the ring ( Fig 3B ) . Improved chemosensory responses in aged IFT mutants could arise due to physiological changes unrelated to AdCR . To address this issue , we next correlated ASH cilia length with osmotic avoidance behavior in 7d old osm-5 ( p813 ) and osm-6 ( p811 ) mutants . We found that ASH cilia of both osm-5 and osm-6 mutant animals that escaped the ring were on average significantly shorter than those of animals that remained within the ring after 2 mins ( Fig 3C ) . Together , these results suggest that AdCR partially restores sensory cilia function . To begin to explore the mechanisms underlying AdCR , we first asked whether IFT motors are necessary for this process . The middle segments of channel cilia , including ASI cilia , are built via the cooperative and redundant actions of the heterotrimeric kinesin-II ( comprised of klp-11 , kap-1 and klp-20-encoded proteins ) and homodimeric OSM-3 kinesin-2 motors , whereas the distal segment requires OSM-3 function alone [49] ( Fig 1A ) . The osm-3 ( p802 ) allele is predicted to encode a protein that lacks the motor stalk and tail domains thereby likely abrogating interactions between OSM-3 and IFT particles [49] . The distal segments of ASI cilia are absent in osm-3 ( p802 ) mutants [14] , and we found that these shortened cilia did not elongate regardless of age ( Fig 4A ) . Although ASI cilia were also unexpectedly shorter in 1d old kap-1 ( ok676 ) putative null mutants , these cilia elongated in 7d old animals , likely via OSM-3 function ( Fig 4A ) . Consistent with this hypothesis , the severely truncated ASI cilia in aged kap-1;osm-3 double mutants did not elongate ( Fig 4A ) . We next tested whether loss of either motor function affects AdCR in osm-6 mutants . Although osm-3 ( p802 ) failed to fully suppress AdCR , loss of kap-1 suppressed AdCR in osm-6 mutants ( Fig 4A ) , suggesting that kinesin-II is the primary motor that mediates AdCR in IFT mutants . Thus , while OSM-3 can elongate ASI cilia in aged kap-1 mutants in the presence of wild-type IFT complexes , this motor is partly dispensable for AdCR in IFT mutants . Consistent with a possible altered function of OSM-3 in aged IFT mutants , kymograph analyses showed that OSM-3 moved anterogradely at a slower rate in the middle segments of ASH/ASI cilia in 7d old osm-6 mutants as compared to 7d old wild-type animals , whereas the velocity profile of kinesin-II was similar in both genetic backgrounds ( Fig 4 and 4C and S2 Table ) . We conclude that IFT motors , and in particular , kinesin-II , is essential for AdCR in IFT mutants . We next examined the requirement of signaling pathways implicated in regulating aging in mediating AdCR . In C . elegans and other organisms , loss or reduction of insulin signaling increases longevity primarily , but not exclusively , via activation of the DAF16/FOXO and HSF-1 transcription factors [50–54] . Both transcription factors in turn regulate the expression of genes including cellular chaperones , which maintain proteostasis and promote longevity [26 , 55] . We found that while loss of daf-16 had no effect on AdCR in osm-6 mutants ( Fig 5A ) , the hsf-1 ( sy441ts ) mutation significantly reduced AdCR in these animals at the restrictive temperature ( Fig 5B ) . Similarly ASI-specific knockdown of hsf-1 by RNAi abolished AdCR ( Fig 5C ) . ASI-specific overexpression of gfp-tagged wild-type hsf-1 sequences in osm-6; hsf-1 double mutants rescued AdCR ( Fig 5B ) , although no effects on cilia length were observed upon HSF-1 overexpression in 1d old osm-6 or hsf-1;osm-6 mutants ( Fig 5B ) . ASI cilia length was unaffected upon either overexpression or knockdown of hsf-1 in wild-type animals at any examined age ( Fig 5B and 5C ) . We conclude that HSF-1 acts cell autonomously to regulate AdCR . HSF1-regulated chaperones such as Hsp90 are upregulated during flagellar regeneration in Chlamydomonas [56 , 57] , although we did not observe transcriptional upregulation of the daf-21 Hsp90 C . elegans ortholog upon aging in wild-type or IFT mutant backgrounds ( S4 and S4B Fig ) . Since null mutations in daf-21 result in larval lethality [58] , we tested a requirement for Hsp90 in AdCR by knocking down daf-21 via cell-specific RNAi in ASI in osm-6 mutants and quantifying cilia length . As shown in Fig 5D , decreased DAF-21 function in ASI suppressed cilia elongation in osm-6 mutants . These results suggest that Hsp90 may also be required cell-autonomously for AdCR in hypomorphic IFT-B mutants . It has previously been shown that although expression of chaperones is not altered upon chronic expression of aggregation-prone proteins in C . elegans , the extent of aggregation remains HSF1-dependent [59] . We verified that similar to daf-21 , expression of the heat shock reporter hsp-16 . 2p::gfp [60 , 61] was also unaltered in aged wild-type or osm-6 animals ( S4C Fig ) . To mimic conditions of chronic stress potentially experienced by aged IFT mutants , we asked whether exposure of 1d old osm-6 mutants to repeated or prolonged heat stress is sufficient to induce AdCR . We subjected wild-type and osm-6 L3-L4 larvae to repeated acute heat shock ( 3 repeated exposures to 34°C for 15 min , interspersed with 15 min recovery at 20°C ) , or mild prolonged heat shock ( 28°C for 24°h ) , and quantified ASI cilia lengths in 1d old adults . However , cilia lengths were unaltered under either heat shock regime ( S4D Fig ) , indicating that exposure to prolonged heat stress is not sufficient to induce AdCR in young animals . Together , these results indicate that HSF-1 and Hsp90 are required for AdCR , but that expression of chaperone proteins in ciliated sensory neurons is unaffected upon aging in osm-6 mutants . Since AdCR only occurs in animals carrying hypomorphic alleles of IFT genes , we considered the possibility that AdCR is mediated by the accumulation of partially functional IFT proteins as a function of chronological age or due to age-dependent failure of proteostasis [26–29] . This hypothesis predicts that overexpression of the mutant IFT protein will promote cilia recovery in young adult animals , and may further enhance AdCR in aged IFT hypomorphic mutants . To test this notion , we overexpressed a GFP-tagged osm-5 ( p813 ) cDNA specifically in ASI in wild-type and osm-5 ( p813 ) mutants , and examined cilia length in 1d and 4d old animals . A wild-type OSM-5::GFP fusion protein was able to rescue osm-5 but not osm-6 mutant phenotypes and localized to cilia when expressed in ASI ( S5A Fig ) , indicating that addition of GFP coding sequences does not alter OSM-5 protein function . Contrary to our prediction , we found that ASI cilia length in 1d old osm-5 ( p813 ) mutants was unaffected upon overexpression of the mutant OSM-5 protein ( Fig 5E ) . Instead , overexpression of the mutant IFT protein abolished AdCR in 4d old osm-5 ( p813 ) animals ( Fig 5E ) . No effects on ASI cilia length were observed in a wild-type background ( Fig 5E ) . We could not perform similar experiments with an osm-6 ( p811 ) encoded protein since this mutation results in the production of multiple alternatively spliced mRNAs ( S2B Fig ) complicating experimental design . We conclude that overexpression of a mutant OSM-5 protein inhibits AdCR . Based on the above observation , we hypothesized that mutant IFT proteins may be toxic , and that removal of these proteins in aged animals permits productive IFT and AdCR . The ubiquitin-proteasome system ( UPS ) plays a major role in the degradation of misfolded and toxic proteins associated with aging and diseases [31 , 32 , 62 , 63] . The UPS has also been implicated in cilia biology [64 , 65] . To test whether UPS activity plays a role in AdCR , we transferred L4 larval stage animals to plates containing the 26S proteasome inhibitor Bortezomib and grew them to adulthood [66] . While Bortezomib treatment had no effect on ASI cilia length in 1d old osm-6 mutants , growth on this reagent significantly inhibited AdCR in 4d old osm-6 mutants ( Fig 5F ) . ASI cilia length in 4d old wild-type animals was weakly but significantly increased upon Bortezomib treatment ( Fig 5F ) for reasons that are currently unclear . Moreover , levels of Ub-G76V::GFP—an inverse reporter of UPS activity [67–71]—were significantly decreased in ASI neurons of 4d old wild-type and osm-6 mutants as compared to 1d old animals ( S5B Fig ) , suggesting that UPS activity is upregulated in these neurons during early aging . We infer that increased UPS activity in ciliated sensory neurons during early aging contributes to AdCR . Since AdCR requires both HSF-1 and UPS activity , we investigated whether AdCR is correlated with improved proteostasis in ciliated sensory neurons . Decreased aggregation of the human SOD1 ( G85R ) protein has been shown to correlate with improved protein quality control in C . elegans neurons [72–74] . As reported previously [73 , 75] , expression of SOD1 ( G85R ) ::YFP resulted in the formation of aggregates of heterogeneous sizes with large and small aggregates in body wall muscle and ASI neurons , respectively ( S5C Fig ) . While aggregates in muscle did not appear to be grossly affected by age or genetic background , the number of small aggregates in ASI decreased in both 4d old wild-type and osm-6 animals ( S5D Fig ) . This reduction in aggregate number was not correlated with reduced ASI promoter activity in aged animals ( S5E Fig ) . These results suggest that improved proteostasis may contribute to AdCR .
We report that aging partly suppresses the severe cilia structural defects of IFT hypomorphic mutants in C . elegans . Remarkably , AdCR correlates with significant recovery of cilia-dependent sensory behaviors; aged IFT mutants exhibit markedly improved chemosensory responses to both attractive and noxious cues . This result is surprising a priori since many IFT gene mutants were originally identified on the basis of their severe chemosensory defects [17–19] . However , the majority of behavioral screens were likely performed using 1-2d old young adult animals which exhibit highly defective cilia , thereby enabling the isolation of these chemotaxis-defective IFT mutants . While structural recovery is observed by 4d of aging in animals grown at 20°C , cilia of IFT mutants are again truncated during late stages of aging , indicating that AdCR is a transient process . AdCR is dependent on IFT . This conclusion is based on several observations . First , kinesin-II is essential for this process . In wild-type animals , kinesin-II and OSM-3 act redundantly to build the middle segments of the cilia of a subset of sensory neurons including ASI [49] . However , OSM-3 alone cannot extend the middle segments in aged osm-6 mutants , suggesting that OSM-3 functions are altered under these conditions . Consistent with this hypothesis , OSM-3 anterograde velocity is decreased in the middle segments of ASI cilia in 7d old osm-6 mutants as compared to its velocity in wild-type ASI cilia in animals of the same age . AdCR is also abolished in osm-3;kap-1 double mutants . Second , the structural recovery is observed in diverse cilia types during early aging , suggesting that AdCR is mediated by a process that is common to all cilia . Third , AdCR is only observed in animals carrying hypomorphic , but not null , alleles of IFT genes , indicating that partial IFT protein function is necessary for this process . Together , these results suggest that AdCR is mediated by partial restoration of IFT function in hypomorphic IFT mutants . HSF1/Hsp90 buffer the effects of partial loss-of-function mutations [76–78] . However , it is unlikely that simple genetic buffering via upregulation of HSF1/Hsp90 during aging is sufficient for AdCR since neither overexpression of HSF1 nor induction of the heat shock response in 1d old osm-6 mutants is sufficient to suppress their ciliary defects . Instead , we speculate that in younger animals , expression of a partly functional IFT protein in the absence of the wild-type protein disrupts IFT complex function [35 , 36 , 79 , 80] . Reduced levels of mutant protein in aged animals via increased UPS activity , coupled with chaperone-mediated stabilization of the complex or folding intermediates enables productive IFT and AdCR in IFT hypomorphic mutant animals ( S6 Fig ) . HSF-1/Hsp90 and UPS may also indirectly affect IFT to improve ciliogenesis . Hsp90 has been suggested to facilitate tubulin polymerization [81 , 82]; increased tubulin assembly mediated by Hsp90 may also promote productive IFT [83] in aged IFT hypomorphic mutant backgrounds . A positive effect of aging on cilia structure and function is unexpected given the association of aging with a decline in cellular functions . However , proteasome function may be regulated in a tissue-specific manner as a function of age [26 , 28 , 70 , 84 , 85] , indicating that the function of this proteolytic complex is under both local and global regulation . Interestingly , the time period during which AdCR is exhibited in C . elegans coincides approximately with their reproductive period . Since cilia are essential for the functions of sensory neurons , and sensory neuron functions in turn are required for behaviors such as egg-laying and mate-finding in C . elegans [86–90] , we speculate that AdCR may represent a homeostatic mechanism to maintain sensory cilia function and reproductive fitness under specific conditions . Similar mechanisms may operate in other organisms to maintain cilia function in IFT mutants . In Tetrahymena and Chlamydomonas , flagellar defects due to partial loss of IFT proteins can be bypassed in some suppressor strains under conditions of oxygen deprivation [80 , 91–93] , and it has been suggested that a stress-induced chaperone mechanism stabilizes the IFT-B complex to permit cilia function under these conditions [92] . Moreover , Hsp90 is localized to cilia , and regulates cilia stability in response to stress in mammalian cells [94–96] . Our observations indicate that while IFT protein function is essential for ciliogenesis , compromised IFT complex function can be partly bypassed during early stages of adult aging or under other conditions of stress to promote cilia lengthening . We propose that therapies relieving proteostatic stress may represent a promising avenue for targeting ciliopathies arising from specific mutations in IFT genes .
Worms were grown on E . coli OP50 bacteria using standard procedures . Double-mutants strains were generated using standard genetic methods , and the presence of the desired alleles was verified by PCR-based genotyping and/or sequencing . Co-injection markers for transgenic strains were unc-122p::gfp or unc-122p::dsRed injected at 30 ng/μl and 50 ng/μl , respectively . A complete list of strains is provided in S3 Table . To age animals , well-fed animals were maintained for at least two generations before analyses . To obtain worms of a specific age , animals were selected at the L4 stage and maintained until the required day of adulthood . Animals were transferred daily to new plates to remove progeny . All animals were grown at 20°C , unless indicated otherwise . For Bortezomib treatment , L4 larval stage worms were grown to adulthood on plates supplemented with 10 μM Bortezomib . 0 . 7 or 1 . 0 kb of srg-47 upstream regulatory sequences were used to drive expression of fluorescent reporters , or cDNAs with or without tagged reporter sequences , specifically in ASI . srg-36::gfp coding sequences were driven under str-3 upstream regulatory sequences in ASI [38] . The hsf-1::gfp containing plasmid was a gift from Ao-Lin Hsu ( University of Michigan ) . The srg-47p::osm-6 ( p811 ) ::gfp::SL2::mCherry construct was generated by introducing osm-6 genomic sequences amplified from the PR811 strain ( S3 Table ) into a construct containing SL2::mCherry ( gift of Cori Bargmann ) driven under srg-47 upstream regulatory sequences . The p813 mutation was introduced by deleting 3’ sequences in an osm-5 cDNA cloned under srg-47 promoter sequences . The G85R mutation was introduced by site-directed mutagenesis into a plasmid containing unc-54p::SOD1::YFP coding sequences ( kind gift of R . Morimoto ) . SOD1 ( G85R ) ::YFP and Ub-G76V::GFP encoding sequences ( plasmid #11941—Addgene ) were inserted under srg-47 regulatory sequences in a worm expression vector . All constructs were verified by sequencing . osm-6 ( p811 ) encoded transcripts were identified from mRNA pools isolated from 1d and 4d old animals by reverse transcription , followed by amplification , cloning and sequencing . RNAi constructs were generated as described previously [97] by fusing sense and antisense products obtained from amplifying exon 1 and exon 4 sequences from hsf-1 and daf-21 , respectively , to srg-47 upstream regulatory sequences . Sense and antisense fusion products were subcloned into the pGEM vector ( Promega ) , amplified , and injected at 100 ng/μl each . Primers used for RNAi constructs were the following ( 5’-3’ ) : To perform cilia length measurements , animals were anesthetized with 10 mM tetramisole hydrochloride ( Sigma ) or sodium azide , mounted on 10% agarose pads on microscope slides , and examined on an inverted spinning disk confocal microscope using a 100X objective ( Zeiss Axio Observer with a Yokogawa CSU-22 spinning disk confocal head ) , or on a Zeiss Axio Imager 2 epifluorescent microscope using a 63X objective . Optical sections were acquired at 0 . 1 or 0 . 2-μm intervals and images were z-projected at maximum intensity . Cilia length was measured using ImageJ ( National Institutes of Health ) . For optimal visualization of cilia , images were linear adjusted for brightness and contrast using ImageJ ( NIH ) . IFT analyses were performed as described previously [98] . In brief , movies of mobile GFP particles in the cilia were acquired on a spinning disk confocal microscope for 1–2 mins with a 300 ms exposure time . Kymograph analyses were performed using the Multiple Kymograph plugin in ImageJ ( NIH ) . Average velocities were calculated using data from at least 3 independent experiments . KAP-1::GFP and OSM-3::GFP movement in wild-type and osm-6 mutants of the same age were imaged together in individual experiments . For quantification of Ub-G76V::GFP fluorescence levels relative to TagRFP expressed in the same cells , animals were imaged on a spinning disk microscope using a 63X objective . Images were obtained at 0 . 5 μm intervals , z-projected at maximum intensities , and fluorescence quantification performed using ImageJ . Images were acquired with a 100 ms exposure for both fluorophores ensuring that fluorescence levels were not saturated . Mean GFP intensities in the region of interest were normalized to mean RFP intensities to obtain the normalized Ub-G76V::GFP fluorescence values . srg-47p::TagRFP levels in ASI were examined in animals co-expressing srg-47p::Ub-G76V::gfp and srg-47p::TagRFP . Animals were imaged using a 63X objective on a spinning disc confocal microscope . For quantification of fluorescence levels , the ASI cell bodies were marked manually , and quantification was performed on maximum intensity z-projected images using ImageJ ( NIH ) . Adult animals of the desired ages were fixed , sectioned and imaged essentially as previously described [99] , with the exception that worms were fixed overnight at 4°C in 2 . 5% gluteraldehyde , 1% paraformaldehyde in Sørensen phosphate buffer ( 0 . 133M , pH 7 . 2 ) . Serial ultrathin sections of 80 nm were examined on an electron microscope ( Tecnai Twin ) , and images were recorded using a MegaView 2 digital recording system ( Olympus ) . smFISH probes were designed against daf-21 sequences utilizing the Stellaris FISH Probe Designer ( Biosearch Technologies , Inc; ( www . biosearchtech/com/stellarisdesigner ) . Probe sets of 44 probes of 22 nucleotides each labeled with TAMRA dye ( Biosearch Technologies , Inc . ) were used . At least 10–20 1d and 4d old adult animals per strain were fixed using 4% paraformaldehyde and resuspended in 70% ethanol at 4°C for approximately 24 hours . Samples were then hybridized with the daf-21 Stellaris FISH Probe set following the manufacturer’s instructions ( www . biosearchtech . com/stellarisprotocols ) . For quantification of puncta , images were acquired on an Axio Observer A1 inverted microscope ( Zeiss ) using a 63X oil objective and a digital CCD camera ( Orca-R2 C10600-10B , Hamamatsu ) . All samples were imaged under identical settings . Mean pixel intensities in the regions containing ASH/ASI neuronal cell bodies were measured using ImageJ ( NIH ) . All statistical analyses were performed using the SPSS 21 statistical analyses software ( IBM ) . The Wilcoxon Mann-Whitney U or Kruskal-Wallis nonparametric tests were used for data with non-normal distributions . | Cilia are ‘antenna-like’ structures that are present on nearly all cell types in animals . These structures are important for sensing and signaling external cues to the cell . Most cilia are formed by a protein transport process called ‘intraflagellar transport’ or IFT . Mutations in IFT genes result in severe cilia defects , and are causal to a large number of diverse human disorders called ciliopathies . Since the genes and processes by which cilia are formed are similar across species , studies in experimental models such as the nematode C . elegans can greatly inform our overall understanding of cilia formation and function . Here we report the surprising observation that the structures and functions of severely defective cilia in nematodes with disrupted IFT genes markedly improve upon aging . We find that protein quality control mechanisms that normally decline in aging are required for this age-dependent recovery of cilia structure . Our results raise the possibility that the effects of some mutations in IFT genes can be bypassed under specific conditions , thereby restoring cilia functions . | [
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] | 2016 | Structural and Functional Recovery of Sensory Cilia in C. elegans IFT Mutants upon Aging |
No investigations have been undertaken of risk factors for intensity of soil-transmitted helminth ( STH ) infection in Timor-Leste . This study provides the first analysis of risk factors for intensity of STH infection , as determined by quantitative PCR ( qPCR ) , examining a broad range of water , sanitation and hygiene ( WASH ) and environmental factors , among communities in Manufahi District , Timor-Leste . A baseline cross-sectional survey of 18 communities was undertaken as part of a cluster randomised controlled trial , with additional identically-collected data from six other communities . qPCR was used to assess STH infection from stool samples , and questionnaires administered to collect WASH , demographic , and socioeconomic data . Environmental information was obtained from open-access sources and linked to infection outcomes . Mixed-effects multinomial logistic regression was undertaken to assess risk factors for intensity of Necator americanus and Ascaris infection . 2152 participants provided stool and questionnaire information for this analysis . In adjusted models incorporating WASH , demographic and environmental variables , environmental variables were generally associated with infection intensity for both N . americanus and Ascaris spp . Precipitation ( in centimetres ) was associated with increased risk of moderate-intensity ( adjusted relative risk [ARR] 6 . 1; 95% confidence interval [CI] 1 . 9–19 . 3 ) and heavy-intensity ( ARR 6 . 6; 95% CI 3 . 1–14 . 1 ) N . americanus infection , as was sandy-loam soil around households ( moderate-intensity ARR 2 . 1; 95% CI 1 . 0–4 . 3; heavy-intensity ARR 2 . 7; 95% CI 1 . 6–4 . 5; compared to no infection ) . For Ascaris , alkaline soil around the household was associated with reduced risk of moderate-intensity infection ( ARR 0 . 21; 95% CI 0 . 09–0 . 51 ) , and heavy-intensity infection ( ARR 0 . 04; 95% CI 0 . 01–0 . 25 ) . Few WASH risk factors were significant . In this high-prevalence setting , strong risk associations with environmental factors indicate that anthelmintic treatment alone will be insufficient to interrupt STH transmission , as conditions are favourable for ongoing environmental transmission . Integrated STH control strategies should be explored as a priority .
Surprisingly little evidence convincingly demonstrates the benefits of water , sanitation and hygiene ( WASH ) interventions on reducing soil-transmitted helminth ( STH ) infections [1 , 2] . Yet it is widely believed that WASH improvements together with anthelmintics could break STH transmission cycles in settings in which anthelmintics alone are insufficient [3 , 4] . There has been inadequate epidemiological investigation of the role of improved WASH in reducing the STH burden , but there is a growing need for evidence to enable more effective investment in WASH and integrated strategies for STH control . Intensity of STH infection is important to assess in epidemiological analyses . STH are highly aggregated in humans , with a small number of people harbouring large numbers of helminths , and the majority harbouring few or none [5] . As with prevalence , intensity of worm burden is marked within various groups of the community such as different age groups and gender [6] . This well-described phenomenon is a key feature of this macroparasite relationship with the human host . For quantitative investigations it is therefore problematic to use solely prevalence of infection as an indicator of STH burden or transmission , because large changes in intensity may only be accompanied by small changes in prevalence [6] . STH do not reproduce within the host; infection intensity depends on the time and extent of exposure [7] . Where STH are endemic , maximum worm intensity usually occurs at ages five to ten for Ascaris lumbricoides and Trichuris trichiura , and in adolescence or early adulthood for hookworm [6] . Whilst the reasons for this are unknown , it may be due to behavioural and social factors , nutritional status , genetic and immunological factors [5 , 8–11] . There is evidence that some individuals are predisposed to heavy or light STH infections [9 , 12] . Intensity of T . trichiura infection reacquired by an individual after treatment has been found to be significantly correlated with the intensity of infection prior to treatment [13] . Additionally , intensity of infection with STH has been identified as substantially greater when any of the species occurred in combination with one or more of the others [14] , probably also due to exposure , genetic and immunological factors , which could then act in determining risk of associated morbidities . Despite this knowledge , there is much focus on the use of prevalence to measure STH infection endemicity . The relationships between intensity of STH infection and risk factors have been inadequately explored , yet could provide useful information as to why intensities differ by host age , environment , and helminth species . Because a key feature of the STH life cycle is the soil-dwelling stage , STH survival , development and transmission potential all rely on a complex assortment of environmental , social , behavioural and host factors . Therefore , in addition to investigating associations between WASH and STH , community-based associations must be considered within their environmental context [15 , 16] . Although more evidence is required , STH associations with WASH have been systematically appraised [2] . Studies have additionally identified temperature , rainfall , soil porosity and pH , vegetation and elevation ranges as influencing N . americanus larval development and STH transmission [16 , 17] . We have previously separately reported on WASH [18] and environmental [19] risk factors for STH prevalence in Manufahi District , Timor-Leste . Given exposure-related risks , and associations between heavy-intensity infection and morbidity , this analysis was conducted to investigate whether WASH- and environmental-related risk factors in this district may also be associated with infection intensity , using categories derived from quantitative PCR ( qPCR ) , a highly sensitive and specific diagnostic technique [20] . By combining data on both WASH and environmental risk factors this analysis provides a more complete picture of risks and thereby augments the current knowledge of risk factors for STH in Timor-Leste . Knowledge of WASH risk factors will be used to inform control strategies in this country . Whilst many environmental risk factors may not be modifiable , the inclusion of these factors will enable targeting of control strategies to areas of greatest need . This is one of very few extensive investigations of combined WASH and environmental risk factors for STH undertaken . It is additionally the first epidemiological analysis of risk factors undertaken using categorised intensity of STH infection from qPCR .
Environmental factors were associated with N . americanus infection ( Table 2 ) . Of particular note , precipitation , measured in centimetres , was significantly associated with a six-fold increased risk of moderate-intensity ( adjusted risk ratio [ARR] 6 . 1 , 95% confidence interval ( CI ) 1 . 9 , 19 . 3 ) , and seven-fold increased risk of heavy-intensity infection ( ARR 6 . 6 , 95%CI 3 . 1 , 14 . 1 ) , compared to no infection . Sandy-loam soil around the house was associated with more than two-fold higher risk of moderate-intensity ( ARR 2 . 1 , 95%CI 1 . 0 , 4 . 3 ) , and heavy-intensity infection ( ARR 2 . 7 , 95%CI 1 . 6 , 4 . 5 ) , respectively , compared to other soil types . Increasing elevation above sea-level was associated with slightly reduced risk of heavy-intensity infection ( ARR 0 . 90 , 95%CI 0 . 83 , 0 . 97 ) , but was not associated with moderate-intensity infection ( ARR 0 . 94 , 95%CI 0 . 83 , 1 . 1 ) . Increasing normalised difference vegetation index ( NDVI ) was associated with increased risk for heavy-intensity infection ( ARR 1 . 1 , 95%CI 1 . 0 , 1 . 1 ) . Soil acidity was not included in N . americanus regression models ( P>0 . 2 on univariable analysis ) . Co-infection with Ancylostoma spp . was associated with four-fold higher risk of heavy-intensity N . americanus infection ( ARR 4 . 1 , 95%CI 2 . 1 , 8 . 0 ) . G . duodenalis was marginally non-significant for heavy-intensity infection ( ARR 0 . 71 , 95%CI 0 . 49 , 1 . 0 ) . Due to the sex by age interaction term results are reported separately for females and males within age groups . Relative to no N . americanus infection , a significant gradient of increased risk of heavy N . americanus infection intensity with increasing age group was evident for females ( ARRs increasing from 3 . 2 to 9 . 6; see Table 2 ) , however this was less evident for moderate-intensity infection ( with the exception of being aged 65 years or older having four-fold increased risk of infection; ARR 4 . 4 , 95%CI 1 . 6 , 11 . 9 ) . For males , relative to no infection , being aged 18 to 64 years was significantly associated with more than three-fold increased risk of any intensity infection ( moderate-intensity ARR 3 . 3 , 95%CI 1 . 3 , 8 . 7; heavy-intensity ARR 3 . 6 , 95%CI 1 . 8 , 7 . 3 ) . Sex in participants aged one to five years ( i . e . reference group ) was not associated with intensity of infection . A gradient of generally increasing risk of moderate- and heavy-intensity infection was also evident with worsening socioeconomic quintile ( being significant across most subgroups for heavy-intensity ) , with people in the poorest quintile having more than twice the risk of infection for both intensity levels ( moderate-intensity ARR 2 . 0 , 95%CI 1 . 1 , 3 . 7; heavy-intensity ARR 2 . 2 , 95%CI 1 . 3 , 3 . 6 ) . Few associations were found between WASH variables and STH outcomes in adjusted analyses . Of note is that a shared piped water supply was associated with strongly reduced risk of heavy-intensity infection compared to an unprotected stream ( ARR 0 . 32 , 95%CI 0 . 12 , 0 . 84 ) , and use of surface water was associated with twice the risk of moderate-intensity infection compared to an unprotected stream ( ARR 1 . 9 , 95%CI 1 . 1 , 3 . 2 ) . Boiling household water was associated with half the risk of moderate-intensity N . americanus infection compared to not boiling water ( ARR 0 . 52 , 95%CI 0 . 34 , 0 . 80 ) . Having one preschool-aged child in the household was protective against heavy-intensity N . americanus infection ( ARR 0 . 57 , 95%CI 0 . 40 , 0 . 82 ) . For moderate-intensity infection having one preschool-aged child in the house was not significant ( ARR 0 . 81 , 95%CI 0 . 52 , 1 . 3 ) , but having more than one was associated with reduced risk ( ARR 0 . 57 , 95%CI 0 . 34 , 0 . 94 ) . People reporting three or more bowel motions during the previous 24 hours ( indicating diarrhoea ) was associated with reduced risk of heavy-intensity infection compared to people who reported less than three bowel motions ( ARR 0 . 40 , 95%CI 0 . 17 , 0 . 96 ) . People who reported having access to anthelmintic drugs and people who reported actually taking deworming treatment within the previous 12 months , was not associated with risk of infection in adjusted models , despite these factors being highly significant in univariable analysis for heavy-intensity infection . Methods of post-defecation anal cleansing , and shoe wearing , all of which were highly significant in univariable analyses for heavy-intensity infection , did not emerge as risk factors in adjusted analyses . Factors significantly associated with Ascaris infection were age , and environmental variables , particularly alkaline soil and elevation above sea level ( Table 3 ) . Alkaline soil was significantly associated with highly reduced risks of moderate-intensity ( ARR 0 . 21 , 95%CI 0 . 09 , 0 . 51 ) , and heavy-intensity Ascaris infection ( ARR 0 . 04 , 95%CI 0 . 01 , 0 . 25 , note low numbers ) compared to acidic soils . Neutral pH soil showed no association with risk of infection . Increasing elevation was associated with Ascaris infection , with observations of a mild gradient of increasing risk with increasing infection intensity ( moderate-intensity ARR 1 . 3 , 95%CI 1 . 2 , 1 . 4; heavy-intensity ARR 1 . 4 , 95%CI 1 . 2 , 1 . 7 ) . Increasing NDVI was also associated with mildly increased risk of heavy-intensity infection ( ARR 1 . 2 , 95%CI 1 . 1 , 1 . 4 ) . No WASH variables emerged as risk factors for Ascaris infection . Increasing age was associated with reducing risk of both moderate and severe infection intensity on a gradient that was significant for many age groups ( particularly for heavy-intensity infections ) . Sex and socioeconomic status were not risk factors for Ascaris infection intensity .
This is an observational analysis and , as such , cause and effect cannot be determined . As has been noted previously [18] much of the WASH data collected involved self-report of infrastructure and behaviours . Presence , type and cleanliness of household and village latrines were verified by interviewer observation . Self-reporting is a frequently-encountered drawback of measuring WASH characteristics . Further , extensive heterogeneity in assessing WASH behaviours on STH outcomes makes assessment of WASH characteristics challenging [15 , 35] . An important research priority is to develop specific WASH measurement guidelines for STH control . Power calculations indicated power to detect low associations for N . americanus and moderate associations for Ascaris infection intensity in multinomial models . There are particular strengths to this study . This is one of very few epidemiological investigations of risk factors for STH infection intensity; this is particularly important to assess for environmental factors , given the links to STH transmission dynamics and correlations with morbidity . In this paper a community-based risk analysis is presented that combines high-resolution environmental , WASH and demographic variables in adjusted models . Advanced statistical techniques have been used to adjust for multinomial intensity outcomes , dependency of observations , effects of poverty , and confounding from other measured variables . As with all analyses , there is the possibility of residual confounding from unmeasured factors . However this provides the most comprehensive assessment of STH risk factors that we have identified in any setting . A further strength is the use of PCR; a highly sensitive and specific technique [20] that is increasingly used for STH diagnosis . PCR-derived intensity of infection categorisation is a recent development , and requires further validation in different epidemiological settings [23] . Notwithstanding the need for further refinement of cut-points , different risk factors for moderate and heavy-intensity STH infections were found in this study area , with some evidence of a scale of increasing risk for factors such as soil type . This contributes useful , and highly relevant , information on risk factors within these communities . Use of infection intensity to determine risk factor associations requires more investigation . In particular , use of prevalence alone could mask significant intensity-related associations . This may mean that key evidence for WASH benefits may be overlooked in epidemiological studies that use prevalence of infection as the outcome . The possibility that WASH significance may be underreported in this way has been inadequately explored . With intensity of STH infection as the outcome , a comprehensive risk analysis of environmental , WASH and demographic variables is presented for communities in Manufahi District , Timor-Leste . Strong risk associations with environmental variables were identified . However , generally few associations with WASH risk factors were evident . This raises the importance of accurate measurement of WASH , and the need for clear guidelines on measuring WASH epidemiological research . This result also has important implications for STH control activities . Even in the absence of WASH significance , WASH infrastructure and behavioural-related activities are the only identified mechanism that could reduce or prevent transmission in an environment of high STH transmission potential . In this setting , anthelmintic treatment alone will not interrupt STH transmission; this provides a strong justification for application of integrated STH control strategies in this district .
This analysis used baseline data from 18 communities in a cluster randomised controlled trial ( RCT ) , supplemented with data from an additional six communities , in Manufahi District , Timor-Leste ( Australian and New Zealand Clinical Trials Registry ACTRN12614000680662 ) [21] . STH have recently been reported as endemic in this community , with prevalence of N . americanus of 60% and Ascaris spp . of 24% , as detected by qPCR [18] . The University of Queensland Human Research Ethics Committee; the Australian National University Human Ethics Committee; the Timorese Ministry of Health Research and Ethics Committee; and the University of Melbourne Human Research Ethics Committee approved the study protocol . Participant informed consent processes included explaining the study purpose and methods , and obtaining signed consent from all adults and parents or guardians of children under 18 years [21] . Children aged less than 12 months were excluded [21] . The RCT commenced in May 2012 . Detail on the RCT design is provided in the trial protocol [21] . A baseline survey of 18 communities involved in the RCT , and six additional communities , was conducted between May 2012 and October 2013 . All communities surveyed were rural , and agrarian occupations predominated . Manufahi District has terrain varying from flat coastal plains to relatively mountainous inland areas ( with elevation exceeding 1100 metres in some communities ) . It is a tropical region , with very high average rainfall of 190cm [19] and a wet season extending for close to ten months of the year . The average annual temperature is 24 . 5°C [19] . A single stool sample per participant was collected and fixed in 5% potassium dichromate . Multiplex qPCR was used to analyse stool samples for the presence and intensity of STH infection . Details on the qPCR diagnostic method are provided elsewhere [20] . Village , household and individual level questionnaires encompassing a broad range of potential WASH and socioeconomic risk factors were administered by trained field workers [18 , 21] . Interviewer observation of household and village latrines , their type and cleanliness was undertaken; all other questions were self-reported . Data were collated and entered into a Microsoft Access database and extracted to STATA 13 . 0 ( Stata Corporation , College Station , Texas ) for error checking . Individual-level data were linked to questionnaire and parasitological outcomes and household GPS coordinates [18 , 21] . Principal component analysis was used to create a wealth index , based on ownership of household assets ( animals , transport and appliances ) , house floor type , reported income , and presence of electricity [18 , 22] . Using eigenvalues above 1 , four principal components were retained and used to produce a final wealth score which was categorised into quintiles of relative socioeconomic status [18] . Outcome variables were intensity of N . americanus and Ascaris infection , which were analysed separately . Intensity of infection was derived from qPCR DNA cycle threshold ( Ct ) values , and categorised into two groups: ( i ) heavy-intensity , and ( ii ) moderate- to light-intensity infection ( hereafter called “moderate intensity” ) using algorithms generated from seeding experiments to correlate Ct-values to eggs per gram of faeces ( epg ) equivalents . Full detail of this method is provided elsewhere [20 , 23] . Exposure variables were WASH variables from study questionnaires , grouped into domains of related variables ( e . g . household sanitation; household water supply; household hygiene; household socioeconomic status ) , and environmental variables that were sourced separately . Environmental variables were selected for analysis based on reported prior relationships with STH development [17] , and availability via open-access sources . Temperature , precipitation , elevation , soil texture , soil pH , landcover and vegetation data were selected for analysis ( Table 4 ) and processed using the geographical information system ArcMap 10 . 3 ( ESRI , Redlands , CA ) [19] . Very few environmental analyses incorporate information on soil texture and soil pH; it has been possible to incorporate these variables due to soil surveys conducted in the study region between 1960 and 1965 [24]; soil type was not considered to have changed dramatically since that time . A range of environmental variables related to the above factors was produced according to long-term average data , seasonal periods , and spatial resolution [19] , with household as the data point , and a 1 km buffer applied ( whereby the median raster value within a 1 km radius of the household was used [19] ) . Quality checks and exploratory analyses were undertaken to determine the most suitable version of each variable for analysis . Separate assessment of spatial autocorrelation was undertaken using semivariograms of residuals from multivariable models of selected environmental variables , with household and village random effects [19]; no additional autocorrelation was identified [19] . The analysis of environmental covariates in this study was limited to risk factor investigation . Predictive risk maps for STH infection in Manufahi District are published separately [19] . Variables were investigated for multicollinearity according to likely relationships determined from literature , using tetrachoric analysis and the STATA “collin” user written package , according to the type of variable . Temperature and elevation were collinear; each variable was analysed in separate univariable models and subsequent variable selection was based on lower Akaike’s Information Criterion ( AIC ) , indicating better predictive performance of the model . Chi-squared tests were conducted to compare intensity of infection by age , sex and socioeconomic quintile . Using categorised intensity of infection as the outcome , univariable and multivariable mixed effects multinomial regression was undertaken , with household and village random effects to account for dependence of observations . Regression analyses were undertaken for N . americanus and Ascaris spp . separately . Regression models were not age-stratified due to insufficient numbers for some combinations of outcome and explanatory variables . Univariable regression was undertaken for each risk factor , with inclusion of variables in multivariable regression if they had P<0 . 2 on the Wald test in univariable analyses . All multivariable models included age group , sex , and socioeconomic quintile as covariates . Forward stepwise variable addition was used with variables retained if P<0 . 1 within , then across , domains of variables , until the most parsimonious adjusted model for each outcome was achieved . A categorised age variable , and a sex*age interaction term , were investigated , as the association between sex and the outcome was anticipated to vary by age group . Interactions were investigated by developing models without , then with , the interaction term and comparing these using the likelihood ratio test , with P<0 . 1 being the inclusion criterion for the interaction . Applying this criterion , the interaction term was retained in the final N . americanus model , but not the Ascaris model . A 5% significance level was used , however this analysis reports results of up to 10% significance , which is important for epidemiological interpretation . Analyses were conducted using generalised structural equation models in STATA 14 . 1 ( Stata Corporation , College Station , Texas ) . Due to uncertainty regarding the linearity of the association of continuous environmental variables and the infection outcomes , quadratic terns were also investigated in all models; however as none of these quadratic terms were significant in the adjusted models , these results are not presented . Post-analysis power calculations indicated 80% power , with a 5% significance level , to detect relative risks of 1 . 2 to 1 . 8 for N . americanus infection intensity ( depending on level of intensity ) , and , reflecting lower prevalence overall , relative risks of 2 . 7 to 3 . 9 for Ascaris infection intensity . | We present a detailed analysis of WASH , environmental and demographic factors associated with intensity of STH infection in Manufahi District , Timor-Leste , using qPCR . Investigation of risk factors for intensity of STH infection is rarely undertaken , and prior analyses have used microscopic-based eggs per gram of faeces ( epg ) measures , which are of lower diagnostic accuracy than qPCR . Additionally , few analyses have investigated combined WASH and environmental risk factors in association with STH . This is important due to the extensive potential interrelatedness of environmental , social , behavioural and host factors in any given setting influencing STH survival and transmission . This analysis uses categorical intensity of infection variables for Necator americanus and Ascaris spp . , and advanced statistical modelling to adjust for multinomial intensity outcomes , dependency of observations , effects of poverty , and confounding from other measured variables . As such , this analysis provides a comprehensive assessment of risk factors for STH in Manufahi District , Timor-Leste . This is of importance for development of policy and programmatic decisions; risk factors need to be considered not only for their clinical and statistical significance , but more broadly in terms of what may represent modifiable pathways for STH transmission . | [
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] | 2017 | Water, Sanitation and Hygiene (WASH) and environmental risk factors for soil-transmitted helminth intensity of infection in Timor-Leste, using real time PCR |
West Nile virus ( WNV ) exists in nature as a genetically diverse population of competing genomes . This high genetic diversity and concomitant adaptive plasticity has facilitated the rapid adaptation of WNV to North American transmission cycles and contributed to its explosive spread throughout the New World . WNV is maintained in nature in a transmission cycle between mosquitoes and birds , with intrahost genetic diversity highest in mosquitoes . The mechanistic basis for this increase in genetic diversity in mosquitoes is poorly understood . To determine whether the high mutational diversity of WNV in mosquitoes is driven by RNA interference ( RNAi ) , we characterized the RNAi response to WNV in the midguts of orally exposed Culex pipiens quinquefasciatus using high-throughput , massively parallel sequencing and estimated viral genetic diversity . Our data demonstrate that WNV infection in orally exposed vector mosquitoes induces the RNAi pathway and that regions of the WNV genome that are more intensely targeted by RNAi are more likely to contain point mutations compared to weakly targeted regions . These results suggest that , under natural conditions , positive selection of WNV within mosquitoes is stronger in regions highly targeted by the host RNAi response . Further , they provide a mechanistic basis for the relative importance of mosquitoes in driving WNV diversification .
RNA viruses possess an extraordinary capacity to adapt to changing environments due to their high mutation rate [1] . Recent epidemics of West Nile virus ( WNV , Flaviviridae , Flavivirus ) and chikungunya virus ( CHIKV , Togaviridae , Alphavirus ) were driven by relatively small genetic changes that increased the efficiency with which the viruses were transmitted by mosquito vectors ( Culex spp . and Aedes albopictus , respectively ) [2]–[4] . In the case of WNV , a single conservative amino acid substitution is responsible for increased transmission efficiency at early timepoints after the mosquito acquires an infectious bloodmeal [2] , [3] . The change in transmission efficiency has led to the complete displacement of the parental WNV genotype by the derived strain , demonstrating the power of small genetic changes to profoundly influence arbovirus transmission patterns and the importance of mosquitoes in shaping their populations [5] . Studies examining the population genetics of WNV have shown that WNV consists of a genetically diverse population within hosts , and that infections within mosquitoes are more genetically diverse than are those within birds [6] . The underlying mechanism for the increased genetic diversity in mosquitoes , however , is poorly understood . Eukaryotic organisms generally possess pathways that respond to pathogen-associated molecular patterns ( PAMPs ) that , when activated , lead to suppression or elimination of the pathogen . Double-stranded RNA ( dsRNA ) is one such PAMP that is a powerful trigger of innate antiviral responses . Invertebrates , including mosquitoes and other dipterans , respond to virus infections through RNA interference ( RNAi ) [7]–[13] . In RNAi , dsRNA molecules are identified , processed and ultimately used to guide sequence-specific degradation of homologous RNA sequences . Because many RNA viruses , including WNV and other arboviruses , have complex secondary structures within their genomes and form dsRNA intermediates during replication , their genomes may be susceptible to RNAi-based degradation . Indeed , several studies have demonstrated that the RNAi pathway can function to limit viral infections in members of the diptera [7]–[12] . Virus-derived small interfering RNAs ( viRNAs ) have been detected in Aedes aegypti mosquitoes infected with the arboviruses dengue virus ( DENV ) and Sindbis virus ( SINV ) , and in Drosophila infected with flock house virus ( FHV ) [7] , [9] , [10] . In addition , suppression of RNAi machinery in Ae . aegypti resulted in transient increases in DENV and SINV titers [10] , [14] . Similar studies in RNAi deficient Anopheles gambiae showed increased viral dissemination rates and titers of intra-thoracically inoculated O'nyong-nyong virus [15] . An expanding body of literature thus suggests that RNAi functions as an innate antivirus response in mosquitoes . Interpreting this literature , however , has been problematic . First , the systems studied have generally not included ecologically relevant virus-vector pairs ( but with the notable exception of Sanchez-Vargas et al . [10] ) . For example , neither Aedes aegypti nor Drosophila are vectors of SINV or FHV , respectively . Second , although mosquitoes acquire infection orally during bloodfeeding , many published studies have examined intra-thoracically inoculated mosquitoes . Mosquito midgut epithelial cells are the first cells to become infected and midgut tissues present important infection and escape barriers that appear to control the vector competence of mosquitoes for arboviruses [16]–[20] . Consequently , surprisingly little is known about the innate antivirus response mounted by ecologically important vector mosquitoes in physiologically relevant tissues . Viruses possess counter-measures to escape host antiviral responses . One such counter-measure may be their characteristically high mutation rates . RNAi is highly sequence specific and single nucleotide mismatches between the guide and target sequences can drastically reduce or abolish silencing effects [21] , [22] . The sequence specificity of the RNAi response may therefore influence virus genetics . For example , artificial induction of the RNAi pathway using virus-specific siRNAs has been shown to result in ( a ) the accumulation of mutations in the targeted regions and ( b ) viral escape in poliovirus ( Picornaviridae; Enterovirus ) , hepatitis C virus ( Flaviviridae; Hepacivirus ) , LaCrosse virus ( Bunyaviridae; Bunyavirus ) and HIV-1 [23]–[26] . Whether this occurs spontaneously following oral infection of competent vector mosquitoes , however , is less clear . Since WNV genetic diversity is increased within mosquitoes , we hypothesized that RNAi might lead to increased mutational diversity by creating an intracellular environment in which mutant genomes are less likely to be degraded by viRNAs deriving from common WNV genome sequences . In particular , we used a deep sequencing approach ( technology reviewed extensively in [27] ) to determine whether siRNAs mapping to the WNV genome are produced in mosquito midguts in response to infection . Using these data , we then determined whether particular genome regions are consistently targeted and whether differential targeting of the genome leads to increases in genetic diversity in intensely targeted regions .
To ensure that only WNV-positive midguts were included in the infected group , and that all putatively negative mosquito midguts were indeed negative , midgut RNAs were screened for the presence of WNV RNA by RT-PCR . At both timepoints sampled , 12 of the 15 midguts analyzed were positive , and all control mosquito midguts were negative . To define the population of small RNAs in mosquito midguts , small RNA ( sRNA ) libraries from WNV-infected and uninfected midguts after a 7 or 14 day extrinsic incubation period ( EIP ) were subjected to high-throughput sequencing . The total number of sRNA reads obtained from each of the four libraries sequenced ranged from 3 . 7 to 4 . 9 million ( Table 1 ) . To determine the presence of viRNAs , sRNA reads were aligned to the WNV genome . There were 2 , 544 ( 1 , 701 unique ) and 4 , 419 ( 2 , 629 unique ) reads that aligned to the WNV genome following a 7 or 14 day EIP , respectively , where unique reads represent individual viRNAs that code for a specific nucleotide sequence . As expected , these reads had a mean and mode length of 21 nt ( Figure 1 , Table 1 ) . In addition , the average quality scores [Illumina scores , scaled from 1 ( minimum ) to 40 ( maximum ) ] were extremely high ( 90% of the reads >36 ) and greater than 80% of these reads perfectly aligned with the viral genome ( Table 1 ) . A small population of sRNAs from the uninfected mosquito midguts aligned to the WNV genome . A high proportion of these reads required 2 mismatches in order to align to the WNV genome; 50% and 86% at 7 and 14 days post bloodmeal , respectively , ( Table 1 ) . The orientation of the WNV strand targeted by viRNAs was determined . At both a 7 and 14 day EIP , 74% of all viRNAs were derived from the positive sense viral genome . Subsequently , genomic equivalents for both the positive and negative strands were determined by two-step RT-PCR . 81% and 80% of the total WNV RNA detected was from the positive strand at 7 and 14 days , respectively . The percent coverage of the WNV genome by viRNA was determined . 81 . 75% and 91 . 88% of the genome was targeted by at least one viRNA in the WNV infected samples following a 7 or 14 day EIP , respectively . To assess positional and regional differences in the intensity of viRNA targeting of the WNV genome in mosquito midguts , the frequency of viRNA reads mapping to each nucleotide in the WNV genome was computed ( Figure 2A ) . Inspection of these results revealed ( A ) an asymmetric distribution of viRNAs across the genome with some regions being highly targeted and others weakly or not targeted and ( B ) that peaks in the frequency distribution of hits along the genome at 7 days were also apparent at 14 days , although peaks at 14 days tended to be higher , with some exceptions . The most intensely targeted portion of the WNV genome , at both 7 and 14 days , was an approximately 200 nt region of the Capsid ( C ) coding sequence . Genome position 176 within this region was targeted by 64 and 120 viRNAs at 7 and 14 days , which was the highest peak in either dataset . Examination of the orientation of viRNAs targeting this region revealed roughly equal targeting of both positive and negative strand WNV genomes ( Figure 2C ) . A similar examination of a portion of the 3′UTR that possesses well-characterized secondary structures revealed less intense targeting , and a more pronounced bias toward viRNAs targeting the positive WNV genome ( Figure 2D ) . We then examined viRNAs that were apparently abundantly produced in mosquito midguts . At both 7 and 14 days the abundance of each unique read was calculated which ranged from 1 to 23 ( Table 1 ) . In order to assess whether the observed distribution of read abundance at both 7 and 14 days was significantly different than would be expected by chance , we conducted a permutation analysis . For each time point , n = 2544 ( day 7 ) or n = 4419 ( day 14 ) genomic positions were randomly sampled with replacement . The sampling procedure was repeated 100 , 000 times to obtain a theoretical distribution of read abundances , and this distribution was compared to the observed distribution by the Kolmorogov-Smirnov test ( Figure 3 ) . The observed abundance distribution was significantly different from the null expectation ( P<0 . 001 at 7 and 14 days ) . In addition , the intensity of viRNA targeting of each nucleotide of the genome was highly correlated at 7 and 14 days ( Figure 4A ) . Further , a subset of viRNAs was detected at both sampled timepoints . The abundance of these “common” reads at 7 and 14 days was also highly correlated ( Figure 4B ) . The RNAi pathway is highly sequence specific and mutations in the target dsRNA sequence can have profound effects on its efficiency . Therefore we sought to determine whether highly targeted regions would be more likely to contain mutations compared with relatively weakly targeted regions . Initially , we used imperfect reads ( i . e . reads that required one or two mismatches in order to be aligned to the WNV genome ) as a proxy for viral mutant sequences , hypothesizing that these reads derived from mutant genomes and had been processed by the host cell RNAi machinery . Putative mutant basecalls with quality scores of less than 30 were discarded . Thus , the probability that basecalls included as mutations in this analysis are read errors was less than 0 . 0010 . At both timepoints sampled , mismatched nucleotides within imperfect reads were covered by a significantly higher number of viRNAs compared to nucleotides that had only perfect reads and were covered by at least a single viRNA ( Table 2 , T-test P<0 . 0001 ) . Next , we tested the association between viRNA coverage and mutation frequency directly . To accomplish this , we identified regions in NS5 and the 3′-UTR of the WNV genome that had varying degrees of viRNA coverage and sampled WNV genomes from the same RNA specimens used to generate sRNA libraries . viRNA coverage per nucleotide along the entire genome was used to determine the frequency distribution of number of viRNA hits per nucleotide position . Positions were then ranked into quartiles and the interquartile range combined . At 7 days there were three mutations identified in NS5 and no mutations in the 3′-UTR . These mutations were evenly distributed across the three frequency distribution classifications . In WNV genomes sampled at 14 days , mutations were detected in both NS5 and 3′-UTR with increasing viRNA coverage associated with increasing mutation frequency . ( Chi-squared test for trend , p = 0 . 0393 ) ( Figure 5 ) .
We used massively parallel sequencing to comprehensively analyze viRNA from the midgut of Cx . p . quinquefasciatus . Mosquitoes were sampled after a 7 or 14 day EIP in order to assess differential production and positioning of viRNAs aligned to the WNV genome . At both sampling points , 80% of midguts were infected , indicating that the Cx . p . quinquefasciatus used in our studies were highly susceptible to infection by WNV . WNV-positive midgut RNA samples from each were then pooled in order to obtain sufficient RNA concentrations for sRNA library construction . In addition , pooling RNA allowed us to look at a generalized response to WNV within midguts . Additional studies will be required to characterize individual mosquito RNAi responses to WNV . Between two and five thousand high-quality reads from infected midguts mapped to the WNV genome at both sampling points ( Table 1 ) . The average read length was approximately 21 nt , and the vast majority were between 20 and 22 nt , suggesting that reads mapping to the WNV genome were mainly viRNAs ( Figure 1 ) . Furthermore , the majority of these reads matched perfectly to the sequence of the infectious clone-derived WNV used to infect the mosquitoes . In contrast , a very small population ( <70 reads ) of siRNAs mapping to the WNV genome were identified from uninfected control mosquitoes . These reads were similar in length and quality to those from infected midguts , but tended to require more mismatches to align to WNV ( Table 1 ) . The origin of these siRNAs is unknown , although the presence of insect-specific flaviviruses such as Culex flavivirus and T'Ho virus has been reported in wild Culex spp . populations and could have led to the observed data [28]–[31] . In addition , flavivirus-like genetic material , termed cell-fusing agent virus , has been identified from the genomes of Culex spp . populations in Puerto Rico [32] . To eliminate the possibility that these viruses were the source of the siRNAs in our uninfected control mosquitoes , three pools of ten male and female Cx . p . quinquefasciatus from our colony were screened for the presence of flavivirus RNA by one-step RT-PCR using flavivirus specific primers [28] . By this approach , we were unable to detect the presence of flavivirus RNA in our colony mosquitoes , suggesting that these siRNAs were not derived from a contaminating insect-specific flavivirus ( data not shown ) . Further , alignment of our sRNA libraries against multiple irrelevant Flavivirus genomes and one Alphavirus genome produced very few reads mapping to anything other than WNV ( Text S1 , Table S2 ) . Alternatively , there may have been cross contamination between the infected and uninfected samples during preparation of the sRNA libraries . However , this seems unlikely considering that the majority of the siRNAs identified in the uninfected control groups contained mismatches ( 61% and 88% at 7 and 14 days post bloodmeal , respectively ) while the viRNAs retrieved from the infected samples were predominantly perfect matches ( 83% and 84% at 7 and 14 days post infection , respectively ) with the WNV genome ( Table 1 ) . Finally , because the genome of Culex pipiens has not been completed we cannot rule out the possibility that these sequences were derived from the Culex genome . In any case , examination of basic quality metrics on read data from all four sRNA libraries allows us to conclude that the viRNA sequences obtained from infected midguts are of generally high quality and are products of the RNAi pathway . We observed viRNAs targeting both the positive and negative sense WNV genome . Flaviviruses have highly structured genomes with many secondary structures and replicate in an asymmetric manner through dsRNA replicative intermediates . While dsRNA clearly activates the RNAi pathway , it has not been clear whether replicative intermediate or stem-loop and other RNA secondary structures are mainly responsible for activation . Studies with ( + ) ssRNA plant viruses have demonstrated that either may predominate , depending on the virus-host system studied . For example , infection of two different plant species with Cymbidium ringspot tombusvirus resulted in the production of viRNAs almost exclusively from the positive-strand . These results suggest that imperfect duplexes in the secondary structure of the ( + ) ssRNA genome serve as targets for Dicer ( DCR ) cleavage [33] , [34] . Conversely , when analyzing the viRNA profile of Brassica juncea infected with Turnip mosaic potyvirus , viRNAs originated from both the positive- and negative-strands and were present in almost equal proportions suggesting that DCR targets dsRNA replicative intermediates in this system [33] . We found that RNAi targeting of the negative sense genome was proportional to the amount of negative sense genome in the infected midguts , which was also observed in the FHV-drosophila system [7] . In addition , a highly structured portion of the 3′UTR was not highly targeted in our data . These results differ from those recently reported by Myles and others whom reported a strong positive-strand bias in Aedes aegypti inoculated with SINV [9] . The reasons for this are likely to be complex , and possibly related to differences in the virus-vector system , route of infection and/or source of sRNAs ( midguts vs . whole body ) , among others . In any case , our data establish that the RNAi pathway in WNV-infected Culex midguts clearly targets dsRNA replicative intermediates and most likely secondary structures within the positive-sense genomic RNA as well . Computing the number of viRNA reads targeting each nucleotide of the WNV genome allowed us to determine whether particular regions are more intensely targeted by the RNAi response than others . Inspection of Figure 2 clearly shows that this is true . Further , regions targeted at 7 days were also targeted at 14 days: peaks in the figure tend to coincide with one another . Indeed , the per-nucleotide intensity of viRNA coverage along the genome at 7 and 14 days is highly correlated ( Figure 4A ) . This suggests that particular regions of the genome are more accessible to the RNAi pathway than others . Supporting this observation , some peaks are extremely high , while others are quite small . Notably , the most intense targeting of WNV by viRNAs at both timepoints is a region in the 5′ aspect of the C coding region ( Figure 2C ) . The reasons for this are unclear at present , but may be related to inefficient replication initiation , whereby a stalled replicase complex may leave dsRNA targets available for cleavage . In fact , this has been described in Drosophila using FHV [7] . Interestingly , the extreme 3′ portion of the 3′UTR , which has well characterized stem-loop structures , is not highly targeted ( Figure 2D ) . Upon further analysis of the distribution of viRNA aligning to the WNV genome a small number of sequences obtained from both infected libraries were detected very frequently . The possibility that this observation was due to a sampling artifact was ruled out by permutation analysis . This suggests that detecting any read greater than three times was unlikely to occur by chance ( Figure 3 ) . Therefore , we considered any viRNA with abundance greater than three to be highly abundant . We also observed that the abundance of read sequences detected at both 7 and 14 days ( i . e . “common reads” ) were highly correlated ( Figure 4B ) . Collectively , these data demonstrate that there are ‘hotspots’ within the WNV genome targeted by the RNAi pathway in the midgut of Cx . p . quinquefasciatus and suggest a stereotypical RNAi response to WNV infection that is characterized by the production of a small subset of common viRNA molecules . WNV populations within hosts are highly genetically diverse , with greater levels of population diversity ( i . e . more mutations ) found in mosquitoes compared to birds [6] . The mechanism ( s ) that give rise to this increase in genetic diversity within mosquitoes , however , remain poorly characterized . We hypothesized that the sequence specificity of the RNAi response in mosquitoes might increase viral genetic diversity . Mutant genomes would be favored because they would be less susceptible to degradation by the RISC loaded with wild-type , un-mutated viRNAs , i . e . , they could escape degradation by mutation . We observed two lines of evidence in support of this . First , viRNAs that were imperfect matches to the input WNV genome tended to occur on nucleotides that were significantly more highly targeted than un-mutated nucleotides , excluding uncovered positions ( Table 2 ) . Second , when viral genetic diversity was independently assessed using viral RNA from the midguts analyzed , it was found that after fourteen days extrinsic incubation , regions that were more intensely targeted by viRNAs had greater genetic diversity than regions that were weakly targeted ( Figure 5 ) . To ascertain whether the observed results were a result of differences in selective constraints across the genome , we analyzed the selective pressures influencing the WNV populations within the mosquito midgut ( Text S1 , Figure S1 , Table S1 ) . These analyses failed to detect significant correlation between intrahost mutational diversity ( estimated from sRNA reads mapping to the WNV genome ) and dN/dS or interhost genetic diversity . Thus , our data support the hypothesis that intense targeting of WNV by the RNAi pathway in mosquitoes might result in the observed increases in genetic diversity in mosquitoes relative to birds . Indeed , this phenomenon has been observed by other investigators . Multiple studies have demonstrated that artificial induction of the RNAi pathway with viral-specific siRNAs can drive viral evolution [22] , [25] , [26] . For example , exposure of HIV-1 to viral-specific siRNAs resulted in the accumulation of mutations and viral escape mutants . When the viral escape mutants were sequenced it was found that they contained a mutation distribution similar to sequence variants of naturally circulating HIV-1 [23] . It is notable that even with the power constraints implicit in our strategy for independently assessing genetic diversity in our samples ( a relatively small number of nucleotides were sequenced ) we observed a significant association between viRNA coverage and viral genetic diversity . High-throughput virus genome sequencing would undoubtedly enhance our ability to characterize the WNV genotypes in our samples . Nonetheless , our results on viral genetic diversity describe for the first time a direct correlation between the RNAi pathway and viral evolution under natural conditions . Further , they suggest a mechanism for the increased fitness observed in highly genetically diverse WNV populations in vitro [35] and may in part explain how WNV can persist in vivo in mosquitoes despite the presence of an apparently robust RNAi response . A genetically diverse virus population , whether acquired during bloodfeeding or arising through mutation in situ , may present a more complex target for the RNAi response .
WNV used in these experiments was generated from an infectious cDNA clone derived from the NY99 strain as previously described [36] . Virus was produced in baby hamster kidney ( BHK ) cells and used without subsequent passage . WNV obtained in this manner is highly genetically homogeneous [6] , and well characterized phenotypically [37] . The mosquitoes used for these experiments were obtained from our Culex pipiens quinquefasciatus colony . Mosquitoes were housed in environmental chamber at constant temperature of 27°C with a 16∶8 light∶dark photoperiod for the duration of these experiments . Artificial bloodmeals containing defibrinated goose blood alone ( controls ) or with 2×108 plaque-forming units ( pfu ) /ml of WNV were offered to adult female Cx . quinquefasciatus five to seven days post-eclosion using a Hemotek ( Accrington , UK ) membrane feeding apparatus . Mosquitoes were then cold anesthetized and engorged individuals reserved and held for either a 7 or 14 day EIP under the above standard conditions . After the 7 and 14 day EIPs , mosquitoes were cold-anesthetized and fifteen mosquitoes from each group were dissected . Midguts were isolated , washed four times in phosphate buffered saline ( PBS ) and placed in 300 µl lysis buffer ( mirVana , Ambion , Austin , TX ) . Forceps were flame sterilized between each dissection . RNA from individual midguts was extracted using the mirVana miRNA Isolation Kit according to the manufacturer's instructions . The midgut RNA was subsequently analyzed by one-step RT-PCR for the presence of WNV RNA using the SuperScript® One-Step RT-PCR with Platinum® Taq ( Invitrogen , Carlsbad , CA ) according to standard methods . WNV-positive midgut RNAs , were pooled . An equivalent number of uninfected midgut RNAs were pooled in our control groups . RNA was then precipitated with ethanol , resuspended in 100 ul H2O , and quantity and integrity determined on an Agilent 2100 Bioanalyzer ( Agilent Technologies , Santa Clara , CA ) . Approximately 6–7 µg of total RNA from pooled WNV-positive and negative specimens were size fractionated on a 15% TBE/ urea polyacrylamide gel and small RNA ( sRNA ) populations ( 18–30 nt ) recovered . 5′ and 3′ sequencing adapters were then ligated to sRNAs and reverse transcription and PCR amplification performed according the manufacturer's instructions ( Illumina , San Diego , CA ) . The resulting libraries were sequenced at the National Center for Genomic Resources ( Santa Fe , NM ) using an Illumina Cluster Station and Genome Analyzer . Reads from small RNA libraries were trimmed of adapter sequences and aligned to the WNV infectious clone reference genome using the Short Oligonucleotide Alignment Package v . 1 ( SOAP ) ( http://soap . genomics . org . cn/ ) with seed size of eight , and a maximum of two mismatches allowed . Further trimming and gaps were not permitted . Additional analyses were performed using R , Microsoft Excel and GraphPad . Intra-population genetic diversity was determined according to methods described elsewhere [6] . For these studies , 160 nt ( 10776–10936 ) and 454 nt ( 7800–8254 ) of the 3′-UTR and NS5 of the WNV genome , respectively , were chosen based on inspection of frequency distributions of the number of viRNA “hits” per nucleotide . Regions were chosen that had highly variable viRNA coverage . Viral RNAs extracted from WNV-infected pools were reverse transcribed using the AccuScript High Fidelity 1st Strand cDNA Synthesis Kit ( Stratagene , LaJolla , CA ) . The fragments were PCR amplified using high fidelity PfuUltra polymerase ( Stratagene ) using the following parameters: 94°C for 30 s , 60°C for 30 s and 72°C for 45 s repeated an additional 39 cycles followed by a 72°C final extension for 6 min . The primers used for both the RT and PCR reactions were WNV 10 , 524 F 5′- CGCCACCGGAAGTTGAGTAGAC -3′ and WNV 3′ End 5′- AGATCCTGTGTTCTCGCACCACCA - 3′ or WNV 7670 F 5′-GACTAAAAAGAGGTGGGGCAAAAG -3′ and WNV 8335 R 5′-GAAGCTCGACTCACCCAATACAT -3′ . Amplicons were gel purified and cloned into pCR Script Amp+ vector ( Stratagene ) . Clones were sequenced using the M13 Rev primer at the University of New Mexico's School of Medicine DNA Services laboratory . Sequences were aligned and analyzed for genetic diversity using DNAStar's SeqMan program . To determine the amount of positive and negative strand WNV genome in mosquito midguts at each sampling point , equal concentrations of RNA from WNV-infected midguts was reverse transcribed with strand specific primers using the AccuScript High Fidelity 1st Strand cDNA Synthesis Kit ( Stratagene ) . WNV RNA of each polarity was then quantified using real-time quantitative ( TaqMan ) RT-PCR as described elsewhere [38] . | West Nile virus ( WNV ) was introduced into New York state in 1999 and has since spread across the Americas . It is transmitted in nature between adult female mosquitoes and birds and occasionally infects humans and horses . Within the host , WNV exists as a diverse assortment of closely related mutants . WNV populations within mosquitoes are more complex genetically than are those within birds . The reasons for this discrepancy are unknown , but may be related to the host's innate antivirus response . We demonstrate that WNV is targeted by RNA interference , a highly sequence-specific pathway in the mosquito . Further , we present data that correlates the intensity of this targeting with virus mutation under natural conditions . These results provide a mechanistic explanation for the increasead complexity of WNV populations in mosquitoes: the RNAi response creates an intracellular environment where rare genotypes are favored . In addition , our results suggest that genetically diverse WNV populations may have an advantage over less diverse populations because they present a more complex target for the RNAi response . Finally , these data suggest that WNV , and possibly other viruses with high mutation rates , may escape an engineered antivirus intervention that is highly sequence-specific . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
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] | 2009 | RNAi Targeting of West Nile Virus in Mosquito Midguts Promotes Virus Diversification |
The computational and cognitive properties of neural systems are often thought to be implemented in terms of their ( stochastic ) network dynamics . Hence , recovering the system dynamics from experimentally observed neuronal time series , like multiple single-unit recordings or neuroimaging data , is an important step toward understanding its computations . Ideally , one would not only seek a ( lower-dimensional ) state space representation of the dynamics , but would wish to have access to its statistical properties and their generative equations for in-depth analysis . Recurrent neural networks ( RNNs ) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective . Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs ( PLRNNs ) within the statistical framework of state space models , which accounts for noise in both the underlying latent dynamics and the observation process . The Expectation-Maximization algorithm is used to infer the latent state distribution , through a global Laplace approximation , and the PLRNN parameters iteratively . After validating the procedure on toy examples , and using inference through particle filters for comparison , the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex ( ACC ) obtained during performance of a classical working memory task , delayed alternation . Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance , including stimulus-selective delay activity . The estimated models were rarely multi-stable , however , but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point . In summary , the present work advances a semi-analytical ( thus reasonably fast ) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series , and directly link these to computational properties .
Stochastic neural dynamics mediate between the underlying biophysical and physiological properties of a neural system and its computational and cognitive properties ( e . g . [1–4] ) . Hence , from a computational perspective , we are often interested in recovering the neural network dynamics of a given brain region or neural system from experimental measurements . Yet , experimentally , we commonly have access only to noisy recordings from a relatively small proportion of neurons ( compared to the size of the brain area of interest ) , or to lumped surface signals like local field potentials or the EEG . Inferring from these the computationally relevant dynamics is therefore not trivial , especially since both the recorded signals ( e . g . , spike sorting errors; [5] ) as well as the neural system dynamics itself ( e . g . , stochastic synaptic release; [6] ) come with a good deal of noise . The stochastic nature of neural dynamics has , in fact , been deemed crucial for perceptual inference and decision making [7–9] , and potentially helps to avoid local minima in task learning or problem solving [10] . Speaking in statistical terms , 'model-free' techniques which combine delay embedding methods with nonlinear basis expansions and kernel techniques have been one approach to the problem [11; 12] . These techniques provide informative lower-dimensional visualizations of population trajectories and ( local ) approximations to the neural flow field , but they may highlight only certain , salient aspects of the dynamics ( but see [13] ) and , in any case , do not directly return distribution generating equations or underlying computations . Alternatively , state space models , a statistical framework particularly popular in engineering and ecology ( e . g . [14] ) , have been adapted to extract lower-dimensional , probabilistic neural trajectory flows from higher-dimensional recordings [15–25] . State space models link a process model of the unobserved ( latent ) underlying dynamics to the experimentally observed time series via observation equations , and differentiate between stochasticity in the process and observation noise ( e . g . [26] ) . So far , with few exceptions ( e . g . [23; 27] ) , these models assumed linear latent dynamics , however . Although this may often be sufficient to yield lower-dimensional smoothed trajectories , it implies that the recovered dynamical model may be less apt for capturing highly nonlinear dynamical phenomena in the observations , and will by itself not be powerful enough to reproduce a range of important dynamical and computational processes in the nervous system , among them multi-stability which has been proposed to underlie neural activity during working memory [28–32] , limit cycles ( stable oscillations ) , or chaos ( e . g . [33] ) . Here we derive a new state space algorithm based on piecewise-linear ( PL ) recurrent neural networks ( RNN ) . It has been shown that RNNs with nonlinear activation functions can , in principle , approximate any dynamical system's trajectory or , in fact , dynamical system itself ( given some general conditions; [34–36] ) . Thus , in theory , they are powerful enough to recover whatever dynamical system is underlying the experimentally observed time series . Piecewise linear activation functions , in particular , are by now the most popular choice in deep learning algorithms [37–39] , and considerably simplify some of the derivations within the state space framework ( as shown later ) . They may also be more apt for producing working memory-type activity with longer delays if for some units the transfer function happens to coincide with the bisectrix ( cf . [40] ) , and ease the analysis of fixed points and stability . We then apply this newly derived algorithm to multiple single-unit recordings from the rat prefrontal cortex obtained during a classical delayed alternation working memory task [41] .
This article considers simple discrete-time piecewise-linear ( PL ) recurrent neural networks ( RNN ) of the form zt=Azt−1+Wmax{0 , zt−1−θ}+Cst+εt , εt∼N ( 0 , Σ ) , ( 1 ) where zt = ( z1t…zMt ) T is the ( M×1 ) -dimensional ( latent ) neural state vector at time t = 1…T , A = diag ( [a11…aMM] ) is an M×M diagonal matrix of auto-regression weights , W = ( 0 w12…w1M , w21 0 w23…w2M , w31 w32 0 w34…w3M , … ) is an M×M off-diagonal matrix of connection weights , θ = ( θ1…θM ) T is a set of ( constant ) activation thresholds , st is a sequence of ( known ) external K-dimensional inputs , weighted by ( M×K ) matrix C , and εt denotes a Gaussian white noise process with diagonal covariance matrix Σ=diag ( [σ112…σMM2] ) . The max-operator is assumed to work element-wise . In physiological terms , latent variables zmt are often interpreted as a membrane potential ( or current ) which gives rise to spiking activity as soon as firing threshold θm is exceeded ( e . g . [42 , 43] ) . According to this interpretation , the diagonal elements in A may be seen as the neurons’ individual membrane time constants , while the off-diagonal elements in W represent the between-neuron synaptic connections which multiply with the presynaptic firing rates . In statistical terms , ( 1 ) has the form of an auto-regressive model with a nonlinear basis expansion in variables zmt ( e . g . [44;45] ) , which retains linearity in parameters W for ease of estimation . Restricting model parameters , e . g . Σ , to be of diagonal form , is common in such models to avoid over-specification and help identifiabiliy ( e . g . [26; 46]; see also further below ) . For instance , including a diagonal in W would be partly redundant to parameters A ( strictly so in a pure linear model ) . For similar reasons , and for ease of presentation , in the following we will focus on a model for which K = M and C = I ( i . e . , no separate scaling of the inputs ) , although the full model as stated above , Eq 1 , was implemented as well ( and code for it is provided; of course , the case K>M could always be accommodated by pre-multiplying st by some predefined matrix C , obtained e . g . by PCA on the input space ) . While different model formulations are around in the computational neuroscience and machine learning literature , often they may be related by a simple transformation of variables ( see [47] ) and , as long as the model is powerful enough to express the whole spectrum of basic dynamical phenomena , details of model specification may also not be overly crucial for the present purposes . A particular advantage of the PLRNN model is that all its fixed points can be obtained easily analytically by solving ( in the absence of external input ) the 2M linear equations z*= ( A+WΩ−I ) −1WΩθ , ( 2 ) where Ω is to denote the set of indices of units for which we assume zm ≤ θm , and WΩ the respective connectivity matrix in which all columns from W corresponding to units in Ω are set to 0 . Obviously , to make z* a true fixed point of ( 1 ) , the solution to ( 2 ) has to be consistent with the defined set Ω , that is z*m ≤ θm has to hold for all m ∈ Ω and z*m > θm for all m ∉ Ω . For networks of moderate size ( say M<30 ) it is thus computationally feasible to explicitly check for all fixed points and their stability . For estimation from experimental data , latent state model ( 1 ) is then connected to some N-dimensional observed vector time series X = {xt} via a simple linear-Gaussian model , xt=Bϕ ( zt ) +ηt , ηt∼N ( 0 , Γ ) , ( 3 ) where ϕ ( zt ) ≔ max{0 , zt−θ} , {ηt} is the ( white Gaussian ) observation noise series with diagonal covariance matrix Γ=diag ( [γ112…γNN2] ) , and B an N×M matrix of regression weights . Thus , the idea is that only the PL-transformed activation ϕ ( zt ) reaches the ‘observation surface’ as , e . g . , with spiking activity when the underlying membrane dynamics itself is not visible . We further assume for the initial state , z1∼N ( μ0+s1 , Σ ) , ( 4 ) which has , for simplicity , the same covariance matrix as the process noise in general ( reducing the number of to be estimated parameters ) . In the case of multiple , temporally separated trials , we allow each one to have its own individual initial condition μk , k = 1…K . The general goal here is to determine both the model’s unknown parameters Ξ = {μ0 , A , W , Σ , B , Γ} ( assuming fixed thresholds θ for now ) as well as the unobserved , latent state path Z ≔ {zt} ( and its second-order moments ) from the experimentally observed time series {xt} . These could be , for instance , properly transformed multivariate spike time series or neuroimaging data . This is accomplished here by the Expectation-Maximization ( EM ) algorithm which iterates state ( E ) and parameter ( M ) estimation steps and is developed in detail for model ( 1 ) and ( 3 ) in the Methods . In the following I will first discuss state and parameter estimation separately for the PLRNN , before describing results from the full EM algorithm in subsequent sections . This will be done along two toy problems , a higher-order nonlinear oscillation ( stable limit cycle ) , and a simple 'working memory' paradigm in which one of two discrete stimuli had to be retained across a temporal interval . Finally , the application of the validated PLRNN EM algorithm will be demonstrated on multiple single-unit recordings obtained from rats on a standard working memory task ( delayed alternation; data from [41] , kindly provided by Dr . James Hyman , University of Nevada , Las Vegas ) . The latent state distribution , as explained in Methods , is a high-dimensional ( piecewise ) Gaussian mixture with the number of components growing as 2T×M with sequence length T and number of latent states M . Here a semi-analytical , approximate approach was developed that treats state estimation as a combinatorial problem by first searching for the mode of the full distribution ( cf . [16; 48]; in contrast , e . g . , to a recursive filtering-smoothing scheme that makes local ( linear-Gaussian ) approximations , e . g . [15; 26] ) . This approach amounts to solving a high ( 2M×T ) -dimensional piecewise linear problem ( due to the piecewise quadratic , in the states Z , log-likelihood Eqs 6 and 7 ) . Here this was accomplished by alternating between ( 1 ) solving the linear set of equations implied by a given set of linear constraints Ω ≔ { ( m , t ) |zmt ≤ θm} ( cf . Eq 7 in Methods ) and ( 2 ) flipping the sign of the constraints violated by the current solution z* ( Ω ) to the linear equations , thus following a path through the ( M×T ) -dimensional binary space of linear constraints using Newton-type iterations ( similar as in [49] , see Methods; note that here the ‘constraints’ are not fixed as in quadratic programming problems ) . Given the mode and state covariance matrix ( evaluated at the mode from the negative inverse Hessian ) , all other expectations needed for the EM algorithm were then derived analytically , with one exception that was approximated ( see Methods for full details ) . The toy problems introduced above were used to assess the quality of these approximations . For the first toy problem , an order-15 limit cycle was produced with a PLRNN consisting of three recurrently coupled units , inputs to units #1 and #2 , and parameter settings as indicated in Fig 1 and provided Matlab file ‘PLRNNoscParam’ . The limit cycle was repeated for 50 full cycles ( giving 750 data points ) and corrupted by process noise ( cf . Fig 1 ) . These noisy states ( arranged in a ( 3 x 750 ) matrix Z ) were then transformed into a ( 3 x 750 ) output matrix X , to which observation noise was added , through a randomly drawn ( 3 x 3 ) regression weight matrix B . State estimation was started from a random initial condition . True ( but noise-corrupted ) and estimated states for this particular problem are illustrated in Fig 1A , indicating a tight fit ( although some fraction of the linear constraints were still violated , ≈0 . 27% in the present example and <2 . 3% in the working memory example below; see Methods on this issue ) . To examine more systematically the quality of the approximate-analytical estimates of the first and second order moments of the joint distribution across states z and their piecewise linear transformations ϕ ( z ) , samples from p ( Z|X ) were simulated using bootstrap particle filtering ( see Methods ) . Although these simulated samples are based only on the filtering ( not the smoothing ) steps ( and ( re- ) sampling schemes may have issues of their own; e . g . [26] , analytical and sampling estimates were in tight agreement , correlating almost to 1 for this example , as shown in Fig 2 . Fig 3A illustrates the setup of the ‘two-cue working memory task’ , chosen for later comparability with the experimental setup . A 5-unit PLRNN was first trained by conventional gradient descent ( ‘real-time recurrent learning’ ( RTRL ) , see [50; 51] ) to produce a series of six 1’s on unit #3 and six 0’s on unit #4 five time steps after an input ( of 1 ) occurred on unit #1 , and the reverse pattern ( six 0’s on unit #3 and six 1’s on unit #4 ) five time steps after an input occurred on unit #2 . A stable PLRNN with a reasonable solution to this problem was then chosen for further testing the present algorithm ( cf . Fig 3C ) . ( While the RTRL approach was chosen to derive a working memory circuit with reasonably ‘realistic’ characteristics like a wider distribution of weights , it is noted that a multi-stable network is relatively straightforward to construct explicitly given the analytical accessibility of fixed points ( see Methods ) ; for instance , choosing θ = ( 0 . 5 0 . 5 0 . 5 0 . 5 2 ) , A = ( 0 . 9 0 . 9 0 . 9 0 . 9 0 . 5 ) , and W = ( 0 ω − ω − ω − ω , ω 0 − ω − ω – ω , − ω − ω 0 ω – ω , − ω − ω ω 0 − ω , 11110 ) with ω = 0 . 2 , yields a tri-stable system . ) Like for the limit cycle problem before , the number of observations was taken to be equal to the number of latent states , and process and observation noise were added ( see Fig 4 and Matlab file ‘PLRNNwmParam’ for specification of parameters ) . The system was simulated for 20 repetitions of each trial type ( i . e . , cue-1 or cue-2 presentations ) with different noise realizations and each ‘trial’ started from its own initial condition μk ( see Methods ) , resulting in a total series length of T = 20×2×20 = 800 ( although , importantly , in this case the time series consisted of distinct , temporally segregated trials , instead of one continuous series , and was treated as such an ensemble of series by the algorithm ) . As before , state estimation started from random initial conditions and was provided with the correct parameters , as well as with the observation matrix X . While Fig 3B illustrates the correlation between true ( i . e . , simulated ) and estimated states across all trials and units , Fig 3C shows true and estimated states for a representative cue-1 ( left ) and cue-2 ( right ) trial , respectively . Again , our procedure for obtaining ( or approximating ) the maximum a-posteriori ( MAP ) estimate of the state distribution appears to work quite well ( in general , only locally optimal or approximate solutions may be achieved , however , and the algorithm may have to be repeated with different state initializations; see Methods ) . Given the true states , how well would the algorithm retrieve the parameters of the PLRNN ? To assess this , the actual model states ( which generated the observations X ) from simulation runs of the oscillation and the working memory task described above were provided as initialization for the E-step . Based on these , the algorithm first estimated the state covariances for z and ϕ ( z ) ( see above ) , and then the parameters in a second step ( i . e . , the M-step ) . Note that the parameters can all be computed analytically given the state distribution ( see Methods ) , and , provided the state covariance matrices ( summed across time ) as required in Eq 17A , 17D and 17F are non-singular , have a unique solution . Hence , in this case , any misalignment with the true model parameters can only come from one of two sources: i ) estimation was based on one finite-length noisy realization of the PLRNN process , ii ) all second order moments of the state distribution were still estimated based on the true state vectors . However , as can be appreciated from Fig 1B ( oscillation ) and Fig 4 ( working memory ) , for the two ( relatively low-noise ) example scenarios studied here , all parameter estimates still agreed tightly with those describing the true underlying model . In the more general case where both the states and the parameters are unknown and only the observations are given , note that the model as stated in Eqs 1 & 3 is over-specified as , for instance , at the level of the observations , additional variance placed into Σ may be compensated for by adjusting Γ accordingly , and by rescaling W and , within limits , A ( cf . [52; 53] ) . In the following we therefore always arbitrarily fixed Σ ( to some scalar; see Methods ) , as common in many latent variable models ( like factor analysis ) , including state space models ( e . g . [27; 46] ) . It may be worth noting here that the relative size of Σ vs . Γ determines how much weight is put on temporal consistency among states ( “Σ<Γ” ) vs . fitting of the observations ( “Σ>Γ” ) within the likelihood , Eq 5 . The observations above confirm that our algorithm finds satisfactory approximations to the underlying state path and state covariances when started with the right parameters , and—vice versa—identifies the correct parameters when provided with the true states . Indeed , the M-step , since it is exact , can only increase the expected log-likelihood Eq 5 with the present state expectancies fixed . However , due to the system's piecewise-defined discrete nature , modifying the parameters may lead to a new set of constraint violations , that is may throw the system into a completely different linear subspace which may imply a decrease in the likelihood in the E-step . It is thus not guaranteed that a straightforward EM algorithm converges ( cf . [54; 55] ) , or that the likelihood would even monotonically increase with each EM iteration . To examine this issue , full EM estimation of the WM model ( as specified in Fig 4 , using N = 20 outputs in this case ) was run 240 times , starting from different random , uniformly distributed initializations for the parameters . Fig 5B ( Δt = 0 ) gives , for the five highest likelihood solutions across all 240 runs ( Fig 5A ) , the mean squared error ( MSE ) avg[ ( xit−x^it ) 2] between actual neural observations xit and model predictions x^it , which is close to 0 ( and , correspondingly , correlations between predicted and actual observations were close to 1 ) . With respect to the inferred states , note that estimated and true model states may not be in the same order , as any permutation of the latent state indices together with the respective columns of observation matrix B will be equally consistent with the data X ( see also [27] ) . For the WM model examined here , however , partial order information is implicitly provided to the EM algorithm through the definition of unit-specific inputs sit . For the present example , true and estimated states for the highest likelihood solution were nicely linearly correlated for all 5 latent variables ( Fig 6 ) , but some of the regression slopes significantly differed from 1 , indicating a degree of freedom in the scaling of the states . Note that if the system were strictly linear , the states would be identifiable only up to a linear transformation in general , since any multiplication of the latent states by some matrix V could essentially be reversed at the level of the outputs by back-multiplying B with V-1 ( cf . [27] ) . Likewise , in the present piecewise linear system , one may expect that there is a class of piecewise-linear transformations of the states which is still compatible with the observed outputs , and hence that the model is only identifiable up to this class of transformations ( a general issue with state space models , of course , not particular to the present one; cf . [53] ) . However , this might not be a too serious issue , if one is primarily interested in the latent dynamics ( rather than in the exact parameters ) . Fig 7 illustrates the distribution of initial and final parameter estimates around their true values across all 240 runs ( before and after reordering the estimated latent states based on the rotation that would be required for achieving the optimal mapping onto the true states , as determined through Procrustes analysis ) . Fig 7 reveals that a ) the EM algorithm does clearly improve the estimates and b ) these final estimates seemed to be relatively ‘unbiased’ ( i . e . , with deviations centered around 0 ) . How do the computational costs of the algorithm grow as the number of latent variables in the model is increased ? As pointed out in Paninski et al . [16] , exploiting the block-tridiagonal nature of the covariance matrices , the numerical operations within one iteration of the state inference algorithm ( i . e . , solving ∂QΩ* ( Z ) /∂Z=0 , Eq 7 ) can be done in linear , O ( M×T ) , time , just like with the Kalman filter ( due to the model’s Markov properties , full inversion of the Hessian is also not necessary to obtain the relevant moments of the posterior state distribution ) . This leaves open the question of how many more mode search iterations , i . e . linear equation solving ( Eq 7 ) and constraint-flipping ( vector dΩ ) steps , are required as the number of latent variables ( through either M or T ) increases . The answer is provided in Fig 8A which is based on the experimental data set discussed below . Although a full computational complexity analysis is beyond the scope of this paper , at least for these example data ( and similar to what has sometimes been reported for the somewhat related Simplex algorithm; [56] ) , the increase with M appears to be at most linear . Likewise , the total number of iterations within the full EM procedure , i . e . the number of mode-search steps summed across all EM iterations ( thus reflecting the overall scaling of the full algorithm ) , was about linear ( Fig 8B; in this case , single-constraint instead of complete flipping ( see Methods ) was used which , of course , increases the overall number of iterations but may perform more stably; note that in general the absolute number of iterations will also depend on detailed parameter settings of the algorithm , like the EM convergence criterion and error tolerance ) . Thus , overall , the present state inference algorithm seems to behave quite favorably , with an at most linear increase in the number of iterations required as the number of latent variables is ramped up . I next was interested in what kind of structure the present PLRNN approach would retrieve from experimental multiple ( N = 19 ) single-unit recordings obtained while rats were performing a simple and well-examined working memory task , namely spatial delayed alternation [41] ( see Methods ) . ( Note that in the present context this analysis is mainly meant as an exemplification of the current model approach , not as a detailed examination of the working memory issue itself . ) The delay was always initiated by a nose poke of the animal into a port located on the side opposite from the response levers , and had a minimum length of 10 s . Spike trains were first transformed into kernel density estimates by convolution with a Gaussian kernel ( see Methods ) , as done previously ( e . g . [12; 57; 58] ) , and binned with 500 ms resolution . This also renders the observed data more suitable to the Gaussian noise assumptions of the present observation model , Eq 3 . Models with different numbers of latent states were estimated , with M = 5 or M = 10 chosen for the examples below . Periods of cue presentation were indicated to the model by setting external inputs sit = 1 to units i = 1 ( left lever ) or i = 2 ( right lever ) for three 500 ms time bins surrounding the event ( and sit = 0 otherwise ) , and the response period was indicated by setting s3t = 1 for 3 consecutive time bins irrespective of the correct response side ( i . e . , non-discriminatively ) . The EM algorithm was started from a range of different initializations of the parameters ( including thresholds θ ) , and the 5 highest likelihood solutions were considered further for the examples below . Fig 10A gives the log-likelihoods across EM iterations for these 5 highest-likelihood solutions ( starting from 36 different initializations ) for the M = 5 model . Interestingly , there were single neurons whose responses were predicted quite well by the estimated model despite large trial-to-trial fluctuations ( Fig 9A , top row ) , while there were others with similar trial-to-trial fluctuations for which the model only captured the general trend ( Fig 9A , bottom row; to put this into context , Fig 9B gives the full distribution of correlations between actual and predicted observations across all 19 neurons ) . This may potentially indicate that trial-to-trial fluctuations in single neurons could be for very different reasons: For instance , in those cases where strongly varying single unit responses are nevertheless tightly reproduced by the estimated model , a larger proportion of their trial-to-trial fluctuations may have been captured by the latent state dynamics , ultimately rooted in different ( trial-unique ) initializations of the states ( recall that the states are not completely free to vary in accounting for the observations , but are constrained by the model’s temporal consistency requirements ) . In contrast , when only the average trend is captured , the neuron’s trial-to-trial fluctuations may be more likely to represent true intrinsic ( or measurement ) noise sources that the model’s deterministic part cannot account for . In practice , such conclusions would have to be examined more carefully to rule out that no other factors in the estimation procedure , like different local maxima , initializations , or over-fitting issues ( see below ) , could account for these differences . Although this was not further investigated here , this observation nevertheless highlights the potential of ( nonlinear ) state space models to possibly provide new insights also into other long-standing issues in neurophysiology . Cross-validation is an established means to address over-fitting [45] , although due to the presence of both unknown parameters and unknown states , its application to state space models and its interpretation in this context may be a bit less straightforward . Here the cross-validation error was first assessed by leaving out each of the 14 experimental trials in turn , estimating model parameters Ξ from the remaining 13 trials , inferring states zt given these parameters on the left-out trial , and computing the squared prediction errors ( xit−x^it ) 2 between actual neural observations xit and model predictions x^it on the left-out trial . As shown in Fig 10B , this measure steadily ( albeit sub-linearly ) decreases as the number M of latent states in the model is increased . At first sight , this seems to suggest that with M = 5 or even M = 10 the over-fitting regime is not yet reached . On the other hand , the latent states are , of course , not completely fixed by the transition equations , but have some freedom to vary as well ( the true effective degrees of freedom for such systems are in fact very hard to determine , cf . [59] ) . Hence , we also examined the Δt-step-ahead prediction errors , that is , when the transition model were iterated Δt steps forward in time , and x^i , t+Δt=bi⋅ϕ ( z^t+Δt ) estimated from the deterministically predicted states z^t+Δt=HΔt ( E[zt] ) ( with HΔt the Δt-times iterated map H ( zt ) = Azt + Wϕ ( zt ) + Cst ) , not from the directly inferred states ( that is , predictions were made on data points which were neither used to estimate parameters nor to infer the current state ) . These curves are shown for Δt = 1 and Δt = 3 in Fig 10C , and confirm that M = 5 might be a reasonable choice at which over-fitting has not yet ensued . ( Alternatively , the predictive log-likelihood , logp ( Xtest|Ξ^train ) =log∫p ( Xtest|Z^ ) p ( Z^|Ξ^train ) dZ^ , may be used for model selection ( i . e . , choice of M ) , with p ( Z^|Ξ^train ) either approximated through the E-step algorithm ( with all X-dependent terms removed ) , or bootstrapped by generating Z^-trajectories from the model with parameters Ξ^train ( note that this is different from particle filtering since p ( Z^|Ξ^train ) does not depend on test observations Xtest ) . This is of course , however , computationally more costly to evaluate than the Δt-step-ahead prediction error . ) Fig 11 shows trial-averaged latent states for both left- and right-lever trials , illustrated in this case for one of the five highest likelihood solutions ( starting from 100 different initializations ) for the M = 10 model . Recall that the first 3 PLRNN units received external inputs to indicate left cue ( i = 1 ) , right cue ( i = 2 ) , or response ( i = 3 ) periods , and so , not too surprisingly , reflect these features in their activation . On the other hand , the cue response is not very prominent in unit i = 1 , indicating that activity in the driven units is not completely dominated by the external regressors either , while unit i = 10 ( not externally driven ) shows a clear left-cue response . Most importantly , many of the remaining state variables clearly distinguish between the left and right lever options throughout the delay period of the task , in this sense carrying a memory of the cue ( previous response ) within the delay . Some of the activation profiles appear to systematically climb or decay across the delay period , as reported previously ( e . g . [1; 60] ) , but are a bit harder to read ( at least in the absence of more detailed behavioral information ) , such that one may want to stick with the simpler M = 5 model discussed above . Either way , for this particular data set , the extracted latent states appear to summarize quite well the most salient computational features of this simple working memory task . Further insight about the dynamical mechanisms of working memory might be gained by examining the system’s fixed points and their eigenvalue spectrum . For this purpose , the EM algorithm was started from 400 different initial conditions ( that is , initial parameter estimates and threshold settings θ ) with maximum absolute eigenvalues ( of the corresponding fixed points ) drawn from a relatively uniform distribution within the interval [0 3] . Although the estimation process rarely returned truly multi-stable solutions ( just 2 . 5% of all cases ) , one frequently discussed candidate mechanism for working memory ( e . g . [29; 32] ) , there was a clear trend for the final maximum absolute eigenvalues to aggregate around 1 ( Fig 12 ) . For the discrete-time dynamical system ( 1 ) this implies it is close to a bifurcation , with fixed points on the brink of becoming unstable , and will tend to produce ( very ) slow dynamics as the degree of convergence shrinks to zero along the maximum eigenvalue direction ( strictly , a single eigenvalue near 1 does not yet guarantee a slow approach , but makes it very likely , especially in a ( piecewise ) linear system ) . Indeed , effectively slow dynamics is all that is needed to bridge the delays ( see also [1] ) , while true multi-stability may perhaps even be the physiologically less likely scenario ( e . g . [61; 62] ) . ( Reducing the bin width from 500 ms to 100 ms appeared to produce solutions with eigenvalues even closer to 1 while retaining stimulus selectivity across the delay , but this observation was not followed up more systematically here ) . Linear dynamical systems ( LDS ) have frequently and successfully been used to infer smooth neural trajectories from spike train recordings [15; 16; 20; 22] or other measurement modalities [63] . However , as noted before , they cannot , on their own , as a matter of principle , produce a variety of dynamical phenomena essential for neural computation and observed experimentally , including multi-stability ( e . g . [29; 2] ) , limit cycles ( stable oscillations; e . g . [3] ) , chaos ( e . g . [33] ) , and many types of bifurcations and phase transitions . For instance , the question of whether working memory performance is better explained in terms of multi-stability or effectively slow dynamics ( see above , Fig 12 ) is largely beyond the realm of an LDS , due to its inherent inability to express multi-stability in the first place . An LDS is therefore less suitable for retrieving system dynamics or computations in general . Nevertheless , it may still be instructive to ask how much of the underlying dynamics could already be explained in linear terms . The most direct comparison of PLRNN to LDS performance is made by replacing the nonlinearity ϕ ( zt ) = max{0 , zt − θ} in Eq 1 simply by the linear function ϕ ( zt ) = zt−θ , yielding an LDS with exactly the same parameters Ξ as the PLRNN which can be subjected to the very same estimation and inference procedures ( only that state inference can now be done exactly in just one step ) . Fig 10B reveals that a LDS fits the observed neural recordings about as well as the PLRNN for M≤5 , and starts to excel PLRNN performance for M>5 . Since the major difference in this context is that the PLRNN places a tighter constraint on the temporal consistency of the states through the threshold-nonlinearity , it seems reasonable that this result is due to over-fitting , i . e . the LDS due to its smoothness allows for more freedom for the states to adjust to the actual observations ( cf . [64] ) . It is important to bear in mind that consistency with the actual observations is just one objective of the maximum-likelihood formulation , Eq 5; the other is consistency of states across time according to the model specification . Either way , the PLRNN starts to significantly outperform the LDS in terms of the Δt-step-ahead prediction errors ( see above ) , with the gap in performance widening as Δt increases ( Fig 10C ) . This strongly suggests that the PLRNN has internalized aspects of the system dynamics which the LDS fails to represent , i . e . supports the presence of nonlinear structure in the transition dynamics . Interestingly , looking back at Fig 5B , it turns out that even for simulated data generated by a PLRNN ( at least for this example ) , for Δt = 0 an estimated LDS is about as good in reproducing the actual observations as an estimated PLRNN itself ( with an MSE close to 0 ) , that is , although , unlike the PLRNN it does not have the correct model structure . However , similar to what has been observed for the experimental data ( Fig 10B and 10C ) , this performance rapidly drops and falls far behind that of the PLRNN ( which remains low ) as 1 or more time steps into the future are to be predicted ( note that for the simulated model , unlike the experimental example , the true number of states is known of course ) . This confirms that although the LDS may capture the actual observations quite well , it may not , unlike the PLRNN , be able to properly represent the underlying system within its internal dynamics . As a note on the side , an LDS could be utilized to find proper , efficient initializations for the corresponding PLRNN , or to first improve initial estimates ( although it remains to be examined whether this could potentially also bias the search space in an unfavorable way ) .
In the present work , a semi-analytical , maximum-likelihood ( ML ) approach for estimating piecewise-linear recurrent neural networks ( PLRNN ) from brain recordings was developed . The idea is that such models would provide 1 ) a representation of neural trajectories and computationally relevant dynamical features underlying high-dimensional experimental time series in a much lower-dimensional latent variable space ( cf . [20; 25] ) and 2 ) more direct access to the neural system’s statistical and computational properties . Specifically , once estimated to reproduce the data ( in the ML sense ) , such models may , in principle , allow for more detailed analysis and in depth insight into the system’s probabilistic computational dynamics , e . g . through an analysis of fixed points and their linear stability ( e . g . [28; 30; 32; 47; 65–70] ) , properties which are not directly accessible from the experimental time series . Model-free ( non-parametric ) techniques , usually based on Takens’ delay embedding theorem [71] and extensions thereof [72; 73] , have also frequently been applied to gain insight into neuronal dynamics and its essential features , like attracting states associated with different task phases from in-vivo multiple single-unit recordings [11; 12] or unstable periodic orbits extracted from relatively low-noise slice recordings [74] . In neuroscience , however , one commonly deals with high-dimensional observations , as provided by current multiple single-unit or neuroimaging techniques ( which still usually constitute just a minor subset of all the system’s dynamical variables ) . In addition , there is a large variety of both process and measurement noise sources . Measurement noise may come from direct physical sources like , for instance , instabilities and movement in the tissue surrounding the recording electrodes , noise properties of the recording devices themselves , the mere fact that only a fraction of all system variables is experimentally accessed ( ‘sampling noise’ ) , or may result from preprocessing steps like spike sorting ( e . g . [75; 76] ) . Process noise sources include thermal fluctuations and the probabilistic behavior of single ion channel gating [77] , probabilistic synaptic release [6] , fluctuations in neuromodulatory background and hormone levels , and a large variety of uncontrollable external noise sources via the sensory surfaces , including somatosensory and visceral feedback from within the body . In fact , the stochasticity of the neural dynamics itself has been deemed essential for a number of computational processes like those involved in decision making and inference [7–9] . This is therefore a quite different scenario from the comparatively low-dimensional and low-noise situations in , e . g . , laser physics [78] , and delay-embedding-based approaches to the reconstruction of neural dynamics may have to be augmented by machine learning techniques to retrieve at least some of its most salient features [11; 12] . Of course , model-based approaches like the one developed here are also plagued by the high dimensionality and high noise levels inherent in neural data , but perhaps to a lesser extent than approaches like delay embeddings that aim to directly construct the state space from the observations ( see also [79] ) . This is because models as pursued in the statistical state space framework explicitly incorporate process and measurement noise assumptions into the system’s description , performing smoothing in the latent space . Also , as long as the latent variable space itself is relatively small and related to the observations by simple linear equations , as here , the high dimensionality of the observations themselves does not constitute a too serious issue for estimation . More importantly , however , it is of clear advantage to have access to process equations generating state distributions consistent with the observations , as this allows for a more in depth analysis of the system’s stochastic dynamics and its relation to neural computation ( e . g . [2; 28; 30; 47; 68; 70; 33] ) . There have also been various attempts to account for the observed dynamics directly in terms of nonlinear time series models ( e . g . [13 , 78 , 80] ) , i . e . without reference to an underlying latent variable model , e . g . through differential equations expressed in terms of nonlinear basis expansions in the observations , estimated through strongly regularized ( penalized ) regression methods [11; 13; 80] . For neuroscientific data where usually only a small subset of all dimensions is observed , this implies that this approach has to be augmented by delay embedding techniques to replace the unobserved variables . This , in turn , may potentially lead to very high-dimensional systems ( cf . [11 , 13] ) that may necessitate further pre-processing steps to reduce the dimensionality again , in a way that preserves the dynamics . Also , there is no distinction between measurement and dynamical noise in these models , and , although functionally generic , the parameters of such models may be harder to interpret in a neuroscientific context . How these different assumptions and methodological steps affect the reconstruction of neural dynamics from high-dimensional , noisy neural time series , as compared to state space models , remains an open and interesting question at this point . State space models are a popular statistical tool in many fields of science ( e . g . [14; 63] ) , although their applications in neuroscience are of more recent origin [15 , 16; 18; 19; 21–24] . The Dynamic Causal Modeling ( DCM ) framework advanced in the human fMRI literature to infer the functional connectivity of brain networks and their dependence on task conditions [63; 81] may be seen as a state space approach , although these models usually do not contain process noise ( except for the more recently proposed ‘stochastic DCM’ [81] ) and are commonly estimated through Bayesian inference , which imposes more constraints ( via computational burden ) on the complexity of the models that could potentially be dealt with in this framework . In neurophysiology , Smith & Brown [15] were among the first to suggest a state space model for multivariate spike count data by coupling a linear-Gaussian transition model with Poisson observations , with state estimation achieved by making locally Gaussian approximations to Eq 18 . Similar models have variously been used subsequently to infer local circuit coding properties [18] or , e . g . , biophysical parameters of neurons or synaptic inputs from postsynaptic voltage recordings [82; 17] . Yu et al . [25] proposed Gaussian Process Factor Analysis ( GPFA ) for retrieving lower-dimensional , smooth latent neural trajectories from multiple spike train recordings . In GPFA , the correlation structure among the latent variables is specified ( parameterized ) explicitly rather than being given through a transition model . Buesing et al . [20] , finally , discuss regularized forms of neural state space models to enforce their stability . By far most of the models discussed above are linear in their latent dynamics , however ( although observations may be non-Gaussian ) . As demonstrated in the Results , linear state space models may potentially be similarly well fit for reproducing actual observations , at least for the particular model and experimental systems studied here . In fact , this is not at all guaranteed in general , if the underlying processes are highly nonlinear ( unlike those in Fig 5 where the nonlinearity was comparatively mild ( not depending on multi-stability ) ) . Thus , they may often be sufficient to obtain smoothed neural trajectories or lower-dimensional representations of the observed process [25] , to uncover properties of the underlying connectivity [63; 81] , or to estimate synaptic/neuronal parameters [16; 82] . However , as linear systems are strongly limited in the repertoire of dynamics and computations they can produce ( e . g . [65; 83] ) , they cannot serve as a model for the underlying computational processes and dynamics in general , and do not allow for the type of analyses which led into Fig 12 . A LDS can , on its own , express at most one isolated fixed point ( or a neutrally un-/stable continuum ) , or ( neutrally un-/stable ) sinusoidal-like cycles , but cannot represent any of the more complex phenomena which characterize physiological activity and are a hallmark of most computation . On the other hand , a direct comparison of LDS vs . PLRNN predictive performance may be highly revealing in itself: While some cognitive processes ( like decision making , sequence or syntax generation ) would clearly be expected to be highly nonlinear in their underlying dynamics [4; 84; 85] , others ( early stimulus responses , or value updating , for instance ) may follow more of a linear rule ( e . g . , if stimuli were projected into a high-dimensional space for linear separability; cf . [86] ) . Directly contrasting LDS with PLRNN predictions on the same data set ( as carried out in Fig 10 ) , may uncover such important differences in computational mechanisms , and hence constitute an interesting analysis strategy in its own right . There are a couple of other exceptions from the linear framework the current work builds on: Yu et al . [23] suggested a RNN with sigmoid-type activation function ( using the error function ) , coupled to Poisson spike count outputs , and used it to reconstruct the latent neural dynamics underlying motor preparation and planning in non-human primates . In their work , they combined the Gaussian approximation suggested by Smith & Brown [15] with the Extended Kalman Filter ( EKF ) for estimation within the EM framework . These various approximations in conjunction with the iterative EKF estimation scheme may be quite prone to numerical instabilities and accumulating errors , however ( cf . [26] ) . Earlier work by Roweis & Ghahramani [27] used radial basis function ( RBF ) networks as a partly analytically tracktable approach . Nonlinear extensions to DCM , incorporating quadratic terms , have been proposed as well recently [87] . State and parameter estimation has also been attempted in ( noisy ) nonlinear biophysical models [88; 89] , but these approaches are usually computationally expensive , especially when based on numerical sampling [89] , while at the same time pursuing objectives somewhat different from those targeted here ( i . e . , less focused on computational properties ) . A very recent article by Whiteway & Butts [90] discusses an approach closely related to the present one in that it also assumed piecewise linear latent states ( or , ‘rectified linear units ( ReLU ) ’ ) . Unlike here , however , the latent states were not connected through a dynamical systems model with separate process noise ( but just constrained through a smoothness prior ) . Indeed , the objectives of this work were different , as Whiteway & Butts [90] aimed more at capturing unobserved sources of input in accounting for observed neural activity ( more in the spirit of factor analysis ) , rather than attempting to retrieve an underlying stochastic dynamics as in the present work . They found , however , that the inclusion of nonlinearities may help in accounting for observed data and improve interpretability of the latent factors . In summary , nonlinear neural state space models remain a relatively under-researched topic in theoretical neuroscience . PLRNNs , as chosen here , have the advantage of being mathematically comparatively tracktable , which allowed for the present , reasonably fast , semi-analytical algorithm , yet they are computationally and dynamically still powerful [91–94] . A number of other inference schemes have been suggested for state space models , comprising both analytical approximations [22] and numerical ( sampling ) techniques ( e . g . [26] ) . Among the former are the Extended Kalman filter ( based on local Taylor series approximations ) , methods based on variational inference as reviewed in Macke et al . [22] , or the ( global ) Laplace approximation advertized in Paniniski et al . ( [16]; see also [22] ) . Durbin & Koopman [26] review different variants of particle filter schemes for sequential numerical sampling . These may often be simpler to use , but are usually computationally much more costly than the semi-analytical methods . The Unscented Kalman Filter may be seen somewhere in between , using a few deliberately chosen sample ( ‘sigma’ ) points for a local parametric assessment [26] . Here we chose a global approach rather than a recursive-sequential scheme , that is by solving the full M×T system of linear equations within each subspace defined by constraints Ω in one go . Apart from its generally nice computational properties as discussed in Paniniski et al . [16] , it seems particularly well-suited for the present piecewise-linear model Eqs ( 1 ) and ( 3 ) , in dealing with the combinatorial explosion which builds up along the chain from t = 1…T . However , the mathematical properties of the present algorithm , among them issues of convergence/monotonicity , local maxima/ saddles , and uniqueness and existence of solutions , certainly require further illumination which may lead to algorithmic improvements . In particular , identifiability of dynamics , that is to what degree and under which conditions the true underlying dynamical system could be recovered by the PLRNN-EM approach , remains an open issue ( one line of extension toward greater approximation power would be polynomial basis expansions , at the cost , however , of losing the straightforward interpretation in terms of ‘neural networks’ ) . Most commonly , different variants of gradient-based techniques are being used to train recurrent neural networks to fit observations [40 , 42 , 50 , 95 , 96] . For instance , recurrent network models have been trained to perform behavioral tasks [43] or reproduce behavioral data to infer the dynamical mechanisms potentially underlying working memory [97] or context-dependent decision making [68] . In these settings , however , the observations–that is behavioral data points or requested task outputs–are usually relatively sparse in time compared to the time scale of the underlying dynamics , unlike the neural time series settings studied here where the data can be as dense as the latent state vectors of the model . More importantly , in contrast to these previous gradient-based approaches , the present scheme embeds RNNs into a statistical framework that comes with explicit probability assumptions , thereby puts error bars on state and parameter estimates and returns the posterior probability distribution across latent states , which links in with the observations through a separate measurement function ( enabling , for instance , dimensionality reduction ) , and allows for likelihood-based statistical inference and model comparison . Some preliminary analyses using stochastic Adagrad [98] for training PLRNNs on the time series from the working memory example ( cf . Fig 3 ) seemed , on top , to indicate that the resulting parameter estimates may correlate less well ( <0 . 51 for A and W , after optimal reordering of states ) with the true model parameters than those obtained with the present EM approach ( >0 . 78 ) for the lowest error/ highest likelihood solutions ( this may potentially be improved through teacher forcing , which , however , is not applicable when the observed and latent space differ in dimensionality and are related through an , in general , not strictly invertible transform , as here ) . Other observation models , like the Poisson model for spike counts [15; 22] , are also relatively straightforward to accommodate within this framework ( see [16] ) . However , there are also other ways to deal with spike count observations , like simple Box-Cox-type transforms to make them more Gaussian , e . g . the sqrt-transform suggested for GPFA [25] , or kernel-density smoothing ( e . g . [58] ) as used here . The latter has the additional advantage of reducing the impact of ‘binning noise’ , due to the somewhat arbitrary mapping of real-valued spike times onto discrete ( user-defined ) time bins for the purpose of counting . In general , from a practical perspective , it may therefore still be an open question of whether the additional computational burden that comes with non-Gaussian observation models ( e . g . the requirement of Newton-Raphson steps for each mode-search iteration ) pays off in the end compared to these alternatives . In either case , for the time being , it seems useful to have a more general approach which can also deal with other measurement modalities , like neuroimaging data , which are not of a count-nature . The present approach could also be extended by incorporating various additional structural features . For instance , a distinction between units with excitatory vs . inhibitory connections [43] could be accommodated quite easily within the present framework ( requiring constrained optimization for weight parameters , however , e . g . through quadratic programming ) . Or special gated linear units which make LSTM networks so powerful [39 , 40] may potentially also yield improvements within the present EM/ state-space framework ( although , in general , one may want to be cautious about the assumptions that additional structural elements like these may imply about the underlying neural system to be examined ) . Although the primary focus of this work was to develop and evaluate a state space framework for PLRNNs , some discussion of the applicational example chosen here , working memory , is in order . Working memory is generally defined as the ability to actively hold an item in memory , in the absence of guiding external input , for short-term reference in subsequent choice situations [99] . Various neural mechanisms have been proposed to underlie this cognitive capacity , most prominently multi-stable neural networks which retain short-term memory items by switching into one of several stimulus-selective attractor states [28; 29; 32] . These attractors usually represent fixed points in the firing rates , with assemblies of recurrently coupled stimulus-selective cells exhibiting high rates while those cells not coding for the present stimulus in short-term memory remaining at a spontaneous low-rate base level . These models were inspired by the physiological observation of ‘delay-active’ cells [100–102] , that is cells that switch into a high-rate state during the delay periods of working memory tasks , and back to a low-rate state after completion of a trial , similar to the ‘delay-active’ latent states observed in Fig 11 . Nakahara & Doya [103] were among the first to point out , however , that , for working memory , it may be completely sufficient ( or even advantageous ) to tune the system close to a bifurcation point where the dynamics becomes very slow ( see also [1] ) , and true multi-stability may not be required . This is supported by the present observation that most of the estimated PLRNN models had fixed points with eigenvalues close to 1 but were not truly bi- or multi-stable ( cf . Fig 12 ) , yet this was sufficient to account for maintenance of stimulus-selectivity throughout the 10 s delay of the present task ( cf . Fig 11 ) and for experimental observations ( cf . Fig 9 ) . Recently , a number of other mechanisms for supporting working memory , however , including sequential activation of cell populations [104] or synaptic mechanisms [105] have been discussed . Thus , the neural mechanisms of working memory remain an active research area to which statistical model estimation approaches as developed here may contribute , but too broad a topic in its own right to be covered in more depth by this mainly methodological work .
As with most previous work on estimation in ( neural ) state space models [20; 22; 23; 26] , we use the Expectation-Maximization ( EM ) framework for obtaining estimates of both the model parameters and the underlying latent state path . Due to the piecewise-linear nature of model ( 1 ) , however , the conditional latent state path density p ( Z|X ) is a high-dimensional ‘mixture’ of partial Gaussians , with the number of integrations to perform to obtain moments of p ( Z|X ) scaling as 2T×M . Although analytically accessible , this will be computationally prohibitive for almost all cases of interest . Our approach therefore focuses on a computationally reasonably efficient way of searching for the mode ( maximum a-posteriori , MAP estimate ) of p ( Z|X ) which was found to be in good agreement with E ( Z|X ) in most cases . Covariances were then approximated locally around the MAP estimate . More specifically , the EM algorithm maximizes the expected log-likelihood of the joint distribution p ( X , Z ) as a lower bound on log p ( X|Ξ ) [27] , where Ξ = {μ0 , A , W , Σ , B , Γ} denotes the set of to-be-optimized-for parameters ( note that we dropped the thresholds θ from this for now ) : Q ( Ξ , Z ) ≔E[logp ( Z , X|Ξ ) ]=E[−12 ( z1−μ0−s1 ) TΣ−1 ( z1−μ0−s1 ) ]+E[−12∑t=2T ( zt−Azt−1−Wϕ ( zt−1 ) −st ) TΣ−1 ( zt−Azt−1−Wϕ ( zt−1 ) −st ) ]+E[−12∑t=1T ( xt−Bϕ ( zt ) ) TΓ−1 ( xt−Bϕ ( zt ) ) ]−T2 ( log|Σ|+log|Γ| ) . ( 5 ) For state estimation ( E-step ) , if ϕ were a linear function , obtaining E ( Z|X , Ξ ) would be equivalent to maximizing the argument of the expectancy in ( 5 ) w . r . t . Z , i . e . , E[Z|X , Ξ] ≡ arg maxZ[log p ( Z , X|Ξ ) ] ( see [16]; see also [106] ) . This is because for a Gaussian mean and mode coincide . In our case , p ( X , Z ) is piecewise Gaussian , and we still take the approach ( suggested in [16] ) of maximizing log p ( Z , X|Ξ ) directly w . r . t . Z ( essentially a Laplace approximation of p ( X|Ξ ) where we neglect the Hessian which is constant around the maximizer; cf . [16 , 48] ) . Let Ω ( t ) ⊆ {1…M} be the set of all indices of the units for which we have zmt ≤ θm at time t , and WΩ ( t ) and BΩ ( t ) the matrices W and B , respectively , with all columns with indices ∈ Ω ( t ) set to 0 . The state estimation problem can then be formulated as maximizeQΩ* ( Z ) ≔−12 ( z1−μ0−s1 ) TΣ−1 ( z1−μ0−s1 ) −12∑t=2T[zt− ( A+WΩ ( t−1 ) ) zt−1+WΩ ( t−1 ) θ−st]TΣ−1[zt− ( A+WΩ ( t−1 ) ) zt−1+WΩ ( t−1 ) θ−st]−12∑t=1T ( xt−BΩ ( t ) zt+BΩ ( t ) θ ) TΓ−1 ( xt−BΩ ( t ) zt+BΩ ( t ) θ ) w . r . t . ( Ω , Z ) , subjecttozmt≤θm∀t , m∈Ω ( t ) ANDzmt>θm∀t , m∉Ω ( t ) ( 6 ) Let us concatenate all state variables into one long column vector , z = ( z1 , … , zT ) = ( z11…zmt…zMT ) T , and unwrap the sums across time into large , block-banded MT×MT matrices ( see [16; 83] ) in which we combine all terms quadratic or linear in z , or ϕ ( z ) , respectively . Further , define dΩ as the binary ( MT×1 ) indicator vector which has 1s everywhere except for the entries with indices ∈ Ω ⊆ {1…MT} which are set to 0 , and let DΩ ≔ diag ( dΩ ) the MT×MT diagonal matrix formed from dΩ . Let Θ ≔ ( θ , θ , … , θ ) ( MT×1 ) , and Θ+M the same vector shifted downward by M positions , with the first M entries set to 0 . One may then rewrite QΩ* ( Z ) in the form QΩ* ( Z ) =−12[zT ( U0+DΩU1+U1TDΩ+DΩU2DΩ ) z−zT ( v0+DΩv1+V2diag[dΩ+M]Θ+M+V3DΩΘ+DΩV4DΩΘ ) − ( v0+DΩv1+V2diag[dΩ+M]Θ+M+V3DΩΘ+DΩV4DΩΘ ) Tz]+const . ( 7 ) The MT×MT matrices U{0…2} separate product terms that do not involve ϕ ( z ) ( U0 ) , involve multiplication by ϕ ( z ) only from the left-hand or right-hand side ( U1 ) , or from both sides ( U2 ) . Likewise , for the terms linear in z , vector and matrix terms were separated that involved zmt or θm conditional on zmt > θm ( please see the provided MatLab code for the exact composition of these matrices ) . For now , the important point is that we have 2M× T different quadratic equations , depending on the bits on and off in the binary vector dΩ . Consequently , to obtain the MAP estimator for z , in theory , one may consider all 2M×T different settings for dΩ , for each solve the linear equations implied by ∂QΩ* ( Z ) /∂Z=0 , and select among those for which the solution z* is consistent with the considered set Ω ( if one exists; see below ) the one which produces the largest value QΩ* ( z* ) . In practice , this is generally not feasible . Various solution methods for piecewise linear equations have been suggested in the mathematical programming literature in the past [107; 108] . For instance , some piecewise linear problems may be recast as a linear complementarity problem [109] , but the pivoting methods often used to solve it work ( numerically ) well only for smaller scale settings [49] . Here we therefore settled on a similar , simple Newton-type iteration scheme as proposed in [49] . Specifically , if we denote by z* ( Ω ) the solution to Eq 7 obtained with the set of constraints Ω active , the present scheme initializes with a random drawing of the {zmt} , sets the components of dΩ for which zmt > θm to 1 and all others to 0 , and then keeps on alternating between ( 1 ) solving ∂QΩ* ( Z ) /∂Z=0 for z* ( Ω ) and ( 2 ) flipping the bits in dΩ for which sgn[2dΩ ( k ) −1]≠sgn[z*k ( Ω ) −θk] , that is , for which the components of the vector c≔ ( 2dΩ−1 ) T∘ ( θ−z* ( Ω ) ) ( 8 ) are positive , until the solution to ∂QΩ* ( Z ) /∂Z=0 is consistent with set Ω ( i . e . , c ≤ 0 ) . For the problem as formulated in Brugnano & Casulli [49] , these authors proved that such a solution always exists , and that the algorithm will always terminate after a finite ( usually low ) number of steps , given certain assumptions and provided the matrix that multiplies with the states z in ∂QΩ* ( Z ) /∂Z=0 ( i . e . , the Hessian of QΩ* ( z* ) ) , fulfills certain conditions ( Stieltjes-type; see [49] for details ) . This will usually not be the case for the present system; although the Hessian of QΩ* ( z* ) will be symmetric and positive-definite ( with proper parameter settings ) , its off-diagonal elements may be either larger or smaller than 0 . Moreover , for the problem considered here , all elements of the Hessian in ( 7 ) depend on Ω , while in [49] this is only the case for the on-diagonal elements ( i . e . , in [49] DΩ enters the Hessian only in additive , not multiplicative form as here ) . For these reasons , the Newton-type algorithm outlined above may not always converge to an exact solution ( if one exists in this case ) but may eventually cycle among non-solution configurations , or may not even always increase Q ( Z ) ( i . e . , Eq 5 ! ) . To bypass this , the algorithm was always terminated if one of the following three conditions was met: ( i ) A solution to ∂QΩ* ( Z ) /∂Z=0 consistent with Ω was encountered; ( ii ) a previously probed set Ω was revisited; ( iii ) the constraint violation error defined by ‖c+‖1 , the l1 norm of the positive part of c defined in Eq 8 , went up beyond a pre-specified tolerance level ( this is essentially a fast proxy for assessing the likelihood , intended to speed up iterations by using quantities already computed ) . With these modifications , we found that the algorithm would usually terminate after only a few iterations ( <10 for the examined toy examples ) and yield approximate solutions with only a few constraints still violated ( <3% for the toy examples ) . As a caveat , unless condition ( i ) is met , this procedure implies that the returned solution may not even be locally optimal ( in the strict mathematical sense–it would still be expected to live within an ‘elevated’ region of the optimization landscape defined by Q ( Z ) ) . On the other hand , since Q ( Z ) cannot keep on increasing along a closed cycle ( it must ‘come back’ ) , cycling implies there must be local maxima ( or potentially saddles ) located on the rims that separate different linear subspaces defined by dΩ . Hence , for the elements k of z for which the constraints are still violated in the end , that is for which ck > 0 in Eq 8 , one may explicitly enforce the constraints by setting the violating states z{k} = θ{k} , then solve again for the remaining states z{l ≠ k} ( placing the solution on a ridge; or a quadratic program may be solved for the last step ) . Either way , it was found that even these approximate ( and potentially not even locally optimal ) solutions were generally ( for the problems studied ) in sufficiently good agreement with E ( Z|X ) . In the case of full EM iterations ( with the parameters unknown as well ) , it appeared that flipping violated constraints in dΩ one by one may often ( for the scenarios studied here ) improve overall performance , in the sense of yielding higher-likelihood solutions and less numerical problems ( although it may leave more constraints violated in the end ) . Hence , this scheme was adopted here for the full EM , that is only the single bit k* corresponding to the maximum element of vector c in Eq 8 was inverted on each iteration ( the one with the largest wrong-side deviation from θ ) . In general , however , the resultant slow-down in the algorithm may not always be worth the performance gains; or a mixture of methods , with dk*l+1=1−dk*lwithk*≔argmaxk{ck>0} early on , and d{k}l+1=1−d{k}l∀k:ck>0 during later iterations , may be considered . Once a ( local ) maximum zmax ( or approximation thereof ) has been obtained , the covariances may be read off from the inverse negative Hessian at zmax , i . e . the elements of V≔ ( U0+DΩU1+U1TDΩ+DΩU2DΩ ) −1 . ( 9 ) Note that this is a local estimate around the current maximizer zmax ( i . e . , oblivious to the discontinuities at the borders of the linear subspaces defined by dΩ ) . We then use these covariance estimates to obtain ( estimates of ) E[ϕ ( z ) ] , E[zϕ ( z ) T] , and E[ϕ ( z ) ϕ ( z ) T] , as required for the maximization step . Denoting by F ( λ;μ , σ2 ) ≔∫λ∞N ( x;μ , σ2 ) dx the complementary cumulative Gaussian , to ease subsequent derivations , let us introduce the following notation: Nk≔N ( θk;zkmax , σk2 ) , Fk≔F ( θk;zkmax , σk2 ) , σkl2≔cov ( zkmax , zlmax ) ≈vkl . ( 10 ) The elements of the expectancy vectors and matrices above are computed as E[ϕ ( zk ) ]=σk2Nk+ ( zkmax−θk ) Fk , E[ϕ ( zk ) 2]= ( [zkmax]2+σk2+θk2−2θkzkmax ) Fk+ ( zkmax−θk ) σk2Nk , E[zkϕ ( zl ) ]= ( σkl2−θlzkmax+zkmaxzlmax ) Fl+zkmaxσl2Nl . ( 11 ) The terms E[ϕ ( zk ) ϕ ( zl ) ] , for k ≠ l , are more tedious , and cannot be ( to my knowledge and insight ) computed exactly ( analytically ) , so we develop them in a bit more detail here: E[ϕ ( zk ) ϕ ( zl ) ]=∫θk∞∫θl∞p ( zk , zl ) ( zk−θk ) ( zl−θl ) dzkdzl=∫θk∞∫θl∞p ( zk , zl ) zkzldzkdzl−θk∫θk∞∫θl∞p ( zk , zl ) zldzkdzl−θl∫θk∞∫θl∞p ( zk , zl ) zkdzkdzl+θkθl∫θk∞∫θl∞p ( zk , zl ) dzkdzl ( 12 ) The last term is just a ( complementary ) cumulative bivariate Gaussian evaluated with parameters specified through the approximate MAP solution ( zmax , V ) ( and multiplied by the thresholds ) . The first term we may rewrite as follows: ∫θk∞∫θl∞p ( zk , zl ) zkzldzkdzl=∫θk∞p ( zk ) zk∫θl∞p ( zl|zk ) zldzkdzl=∫θk∞p ( zk ) zk[N ( θl;μl|k , λl−1 ) +μl|k ( 1−∫−∞θlN ( zl;μl|k , λlk−1 ) dzl]dzkwhereμl|k≔zlmax−λl−1λlk ( zk−zkmax ) λl≔σl2/ ( σk2σl2−σkl4 ) λlk≔−σkl2/ ( σk2σl2−σkl4 ) ( 13 ) These are just standard results one can derive by the reverse chain rule for integration , with the λ’s the elements of the inverse bivariate ( k , l ) -covariance matrix . Note that if the variable zk were removed from the first integrand in Eq 13 , i . e . as in the second term in Eq 12 , all terms in Eq 13 would just come down to uni- or bivariate Gaussians ( times some factor ) or a univariate Gaussian expectancy value , respectively . Noting this , one obtains for the second ( and correspondingly for the third ) term in Eq 12: θk∫θk∞∫θl∞p ( zk , zl ) zldzkdzl=θkλkNlF ( θk;μlk , λl−1 ) +θk ( zlmaxFk+σkl2Nk ) F ( θl;zlmax , λk−1 ) withμkl≔zlmax+σkl2/σk2 ( θk−zkmax ) ( 14 ) The problematic bit is the product term ∫θk∞p ( zk ) zkμl|k∫−∞θlN ( zl;μl|k , λlk−1 ) dzldzk in Eq 13 , which we resolve by making the approximation μl|k≈μl=zlmax . This way we have for the first term in Eq 12: ∫θk∞∫θl∞p ( zk , zl ) zkzldzkdzl≈λkNl[λl−1N ( θk;μlk , λl−1 ) +μlkF ( θk;μlk , λl−1 ) ]+[ ( σk2zlmax−σkl2zkmax ) Nk+ ( zkmaxzlmax+σkl2 ) Fk]F ( θl;μlk , λk−1 ) ( 15 ) Putting ( 13 ) – ( 15 ) together with the bivariate cumulative Gaussian yields an analytical approximation to Eq 12 that can be computed based on the quantities obtained from the approximate MAP estimate ( zmax , V ) . Once we have estimates for E[z] , E[zzT] , E[ϕ ( z ) ] , E[zϕ ( z ) T] , and E[ϕ ( z ) ϕ ( z ) T] , the maximization step is standard and straightforward , so for convenience we just state the results here , using the notation E1 , Δ≔∑t=1T−ΔE[ϕ ( zt ) ϕ ( zt ) T] , E2≔∑t=2TE[ztzt−1T] , E3 , Δ≔∑t=1+ΔT−1+ΔE[ztztT] , E4≔∑t=1T−1E[ϕ ( zt ) ztT] , E5≔∑t=2TE[ztϕ ( zt−1 ) T] , F1≔∑t=1TxtE[ϕ ( zt ) T] , F2≔∑t=1TxtxtT , F3≔∑t=2TstE[zt−1T] , F4≔∑t=2TstE[ϕ ( zt−1 ) T] , F5≔∑t=1TE[zt]stT , F6≔∑t=1TststT ( 16 ) With these expectancy sums defined , one has B=F1E1 , 0−1 ( 17A ) Γ=1T ( F2−F1BT−BF1T+BE1 , 0TBT ) ∘I ( 17B ) μ0=E[z1]−s1 ( 17C ) A=[ ( E2−WE4−F3 ) ∘I][E3 , 0∘I]−1 ( 17D ) Σ=1T[var ( z1 ) +μ0s1T+s1μ0T+E3 , 1T−F5−F5T+F6+ ( F3−E2 ) AT+A ( F3T−E2T ) +AE3 , 0TAT+ ( F4−E5 ) WT+W ( F4T−E5T ) +WE1 , 1TWT+AE4TWT+WE4AT]∘I ( 17E ) Note that to avoid redundancy in the parameters , here we usually fixed Σ = I ⋅ 10−2 ( for the toy models ) or Σ = I ( for the experimental data ) . For W , since we assumed this matrix to have an off-diagonal structure ( i . e . , with zeros on the diagonal ) , we solve for each row of W separately: P ( 0 ) ≔ ( E3 , 0∘I ) −1E4TP ( 1 ) ≔E5−[ ( E2−F3 ) ∘I]P ( 0 ) −F4∀m∈{1…M}:Wm , {1:M}\m=Pm , {1:M}\m ( 1 ) ( [E1 , 1−E4 , •mPm• ( 0 ) ]{1:M}\m , {1:M}\m ) −1 ( 17F ) where the subscripts indicate the matrix elements to be pulled out , with the subscript dot denoting all elements of the corresponding column or row ( e . g . , ‘•m’ takes the mth column of that matrix ) . Should matrices Γ , Σ , W of full form be desired , the derivations simplify a bit–in essence , the diagonal operator ‘∘I‘ in the equations above ( except Eq 17D ) would have to be omitted , and Eq 17F could be solved in full matrix form ( instead of row-wise ) . An expression for input scaling matrix C ( cf . Eq 1 ) is given by C= ( F5−z1s1T−WF4T−AF3T ) ( F6−s1s1T ) −1 , but note that C would also show up in ( 17C ) – ( 17F ) ( multiplying with st everywhere ) , as well as in the state inference equations; matrices A , W , and C would therefore need to be solved for simultaneously in this case ( complicating the above expressions a bit; see provided MatLab code for full details ) . Starting from a number of different random parameter initializations , the E- and M-steps are alternated until the log-likelihood ratio falls below a predefined tolerance level ( while still increasing ) or a preset maximum number of allowed iterations are exceeded . For reasons mentioned in the Results , sometimes it can actually happen that the log-likelihood ratio temporarily decreases , in which case the iterations are continued . If ( N−M ) 2 ≥ N + M , factor analysis may be used to derive initial estimates for the latent states and observation parameters in ( 3 ) [27] , although this was not attempted here . Another possibility is to improve initial estimates first through the much faster , corresponding LDS , before submitting them to full PLRNN estimation . For further implementational details see the MatLab code provided on GitHub ( repository ‘PLRNNstsp’ ) . To validate the approximations from our semi-analytical procedure developed above , a bootstrap particle filter as given in [26] was implemented . In bootstrap particle filtering , the state posterior distribution at time t , pΞ ( zt|x1 , … , xt ) =pΞ ( xt|zt ) pΞ ( zt|x1 , … , xt−1 ) pΞ ( xt|x1 , … , xt−1 ) =pΞ ( xt|zt ) ∫zt−1pΞ ( zt|zt−1 ) pΞ ( zt−1|x1 , … , xt−1 ) dzt−1pΞ ( xt|x1 , … , xt−1 ) ( 18 ) is numerically approximated through a set of ‘particles’ ( samples ) {zt ( 1 ) , … , zt ( K ) } , drawn from pΞ ( zt | x1 , … , xt−1 ) , together with a set of normalized weights {wt ( 1 ) , … , wt ( K ) } , wt ( r ) ≔pΞ ( xt|zt ( r ) ) ( ∑k=1KpΞ ( xt|zt ( k ) ) ) −1 . Based on this representation , moments of pΞ ( zt | x1:t ) and pΞ ( ϕ ( zt ) | x1:t ) can be easily obtained by evaluating ϕ ( or any other function of z ) on the set of samples {zt ( r ) } and summing the outcomes weighted with their respective normalized observation likelihoods {wt ( r ) } . A new set of samples {zt+1 ( r ) } for t+1 is then generated by first drawing K times from {zt ( k ) } with replacement according to the weights {wt ( k ) } , and then drawing K new samples according to the transition probabilities pΞ ( zt+1 ( k ) |zt ( k ) ) ( thus approximating the integral in Eq 18 ) . Here we used K = 104 samples . Note that this numerical sampling scheme , like a Kalman filter , but unlike the procedure outlined above , only implements the filtering step ( i . e . , yields pΞ ( zt | x1:t ) , not pΞ ( zt | x1:T ) ) . On the other hand , it gives ( weakly ) consistent ( asymptotically unbiased; [110; 111] ) estimates of all expectancies across this distribution , that is , it does not rely on the type of approximations and locally optimal solutions of our semi-analytical approach that almost inevitably will come with some bias ( since , among other factors , the local or approximate mode would usually deviate from the mean by some amount for the present model ) . Details of the experimental task and electrophysiological data sets used here could be found in [41 , 112] . Briefly , rats had to alternate between left and right lever presses in a Skinner box to obtain a food reward dispensed on correct choices , with a ≥ 10 s delay enforced between consecutive lever presses . While the levers were located on one side of the Skinner box , animals had to perform a nosepoke on the opposite side of the box in between lever presses for initiating the delay period , to discourage them from developing an external coding strategy ( e . g . , through maintenance of body posture during the delay ) . While animals were performing the task , multiple single units were recorded with a set of 16 tetrodes implanted bilaterally into the anterior cingulate cortex ( ACC , a subdivision of rat prefrontal cortex ) . For the present analyses , a data set from only one of the four rats recorded on this task was selected for the present exemplary purposes , namely the one where the clearest single unit traces of delay activity were observed in the first place . This data set consisted of 30 simultaneously recorded units , of which the 19 units with spiking rates >1 Hz were retained , on 14 correct trials ( only correct response trials were analyzed ) . The trials had variable length , but were all cut down to the same length of 14 s , including 2 s of pre-nosepoke , 5 s extending into the delay from the nosepoke , 5 s preceding the next lever press , and 2 s of post-response phase ( note that this may imply temporal gaps in the middle of the delay on some trials , which were ignored here for convenience ) . All spike trains were convolved with Gaussian kernels ( see , e . g . , [12; 57; 112] ) , with the kernel standard deviation set individually for each unit to one half of its mean interspike-interval . Note that this also brings the observed series into tighter agreement with the Gaussian assumptions of the observation model , Eq 3 . Finally , the spike time series were binned into 500 ms bins ( corresponding roughly to the inverse of the overall ( across all 30 recorded cells ) average neural firing rates of ≈2 . 2 Hz ) , which resulted in 14 trials of 28 time bins each submitted to the estimation process . As indicated in the section ‘State space model’ , a trial-unique initial state mean μk , k = 1…14 , was assumed for each of the 14 temporally segregated trials . | Neuronal dynamics mediate between the physiological and anatomical properties of a neural system and the computations it performs , in fact may be seen as the ‘computational language’ of the brain . It is therefore of great interest to recover from experimentally recorded time series , like multiple single-unit or neuroimaging data , the underlying stochastic network dynamics and , ideally , even equations governing their statistical evolution . This is not at all a trivial enterprise , however , since neural systems are very high-dimensional , come with considerable levels of intrinsic ( process ) noise , are usually only partially observable , and these observations may be further corrupted by noise from measurement and preprocessing steps . The present article embeds piecewise-linear recurrent neural networks ( PLRNNs ) within a state space approach , a statistical estimation framework that deals with both process and observation noise . PLRNNs are computationally and dynamically powerful nonlinear systems . Their statistically principled estimation from multivariate neuronal time series thus may provide access to some essential features of the neuronal dynamics , like attractor states , generative equations , and their computational implications . The approach is exemplified on multiple single-unit recordings from the rat prefrontal cortex during working memory . | [
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] | 2017 | A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements |
Plants integrate seasonal cues such as temperature and day length to optimally adjust their flowering time to the environment . Compared to the control of flowering before and after winter by the vernalization and day length pathways , mechanisms that delay or promote flowering during a transient cool or warm period , especially during spring , are less well understood . Due to global warming , understanding this ambient temperature pathway has gained increasing importance . In Arabidopsis thaliana , FLOWERING LOCUS M ( FLM ) is a critical flowering regulator of the ambient temperature pathway . FLM is alternatively spliced in a temperature-dependent manner and the two predominant splice variants , FLM-ß and FLM-δ , can repress and activate flowering in the genetic background of the A . thaliana reference accession Columbia-0 . The relevance of this regulatory mechanism for the environmental adaptation across the entire range of the species is , however , unknown . Here , we identify insertion polymorphisms in the first intron of FLM as causative for accelerated flowering in many natural A . thaliana accessions , especially in cool ( 15°C ) temperatures . We present evidence for a potential adaptive role of this structural variation and link it specifically to changes in the abundance of FLM-ß . Our results may allow predicting flowering in response to ambient temperatures in the Brassicaceae .
In plants , fertilization and reproduction are directly linked to the seasonal onset of flowering . Plants enter the reproductive phase when environmental conditions are favorable for seed set and thus reproduction . Since day length and temperature as well as temperature changes throughout the year provide the crucial information about the passage of the seasons and the environment , plants sense these cues for the adjustment of their flowering time [1] . Proper flowering time and reproductive success of a given species or ecotype , on the one side , and the differences in flowering time between species or ecotypes , on the other , are the result of the differential integration of temperature and day length information . The vernalization and the ambient temperature pathways control temperature-dependent flowering in plants . Whereas vernalization requires long periods ( weeks ) of cold , usually below 10°C , as experienced during the winter [2] , the ambient temperature pathway modulates flowering in response to short-term ( days ) temperature changes in the range between 12°C and 27°C [3–5] . In A . thaliana , the central mechanism of accelerating flowering in response to prolonged cold is achieved by repression of the negative regulator FLOWERING LOCUS C ( FLC ) , a MADS-box transcription factor [6–9] . Different mechanisms than in A . thaliana control vernalization in cereal crops such as wheat and barley , and the activity or inactivity of the vernalization pathway determines the flowering behavior of their winter and spring varieties [10 , 11] . To date , the understanding of the vernalization pathway in A . thaliana is already well advanced and it is possible to make predictions on the vernalization requirement based on the plants’ genotypes [12 , 13] . In contrast , the complexities of ambient temperature sensing are just beginning to be understood [5 , 14 , 15] . The finding that loss-of-function mutations of the gene FLM ( FLOWERING LOCUS M ) reduce the temperature-sensitivity of flowering in A . thaliana accessions suggested that this MADS-box transcription factor acts as a repressor in the ambient temperature pathway [16–18] . The molecular understanding of FLM is complicated by the fact that the FLM gene is alternatively spliced into at least four splice forms [18] . FLM-ß and FLM-δ , which result from the alternative use of the two exons 2 ( FLM-ß ) and 3 ( FLM-δ ) , represent the two predominant splice variants in the Columbia-0 ( Col-0 ) reference accession [19 , 20] . The observation that the abundance of FLM-ß declines from 16°C to 27°C while the abundance of FLM-δ increases over the same temperature range has motivated experiments to examine the effects of the FLM-ß and FLM-δ isoforms in isolation in a flm-3 loss-of-function background . These experiments indicated that the expression of the low temperature-abundant FLM-ß and the warm temperature-abundant FLM-δ can repress and promote flowering , respectively , and consequently a model was established according to which changes in the relative abundance of FLM-ß and FLM-δ control flowering time in response to changes in ambient temperature [19] . FLM directly interacts with several other MADS-box transcription factors to control flowering through the expression of flowering time genes such as FT ( FLOWERING LOCUS T ) and SOC1 ( SUPPRESSOR OF OVEREXPRESSION OF CO1 ) [19–21] . SVP ( SHORT VEGETATIVE PHASE ) is an important FLM interaction partner and , in this context , the flowering-repressive activity of FLM-ß and the flowering-promoting activity of FLM-δ have been explained by the differential effects of the FLM-SVP interactions [19]: It was proposed that a DNA-binding heterodimer of FLM-ß with SVP represses flowering by repressing FT and SOC1 expression . Conversely , FLM-δ could sequester SVP into an inactive complex that thereby indirectly promotes FT and SOC1 expression and consequently flowering . Although this experimentally validated model is very intriguing , it is at present not known whether the alternative splicing of FLM plays a role in flowering time adaptation in natural accessions of A . thaliana . There is increasing evidence for global warming due to climate change [8] . Temperature changes by only a few centigrade ( °C ) can already lead to ecological and physiological constraints that have negative impacts on agricultural production systems [22–24] . Thus , there is a need to better understand the ambient temperature pathway and to integrate this understanding in plant breeding programs [25] . Here , we identify a structural polymorphism in the first intron of FLM as being causative for the early flowering time of the A . thaliana accession Killean-0 ( Kil-0 ) . This structural polymorphism is present in several additional accessions and directly affects FLM transcript abundance , splicing , and flowering . We further correlate the abundance of the FLM-ß and FLM-δ splice variants with flowering behavior in several A . thaliana accessions and reveal an important role of intron 1 for FLM gene expression and a predominant role of FLM in flowering time control .
To understand the variation in flowering time in response to temperature , we compared the flowering behavior of a collection of A . thaliana accessions at 15°C and at 21°C . In this analysis , our attention was drawn to the Scottish accession Killean-0 ( Kil-0 ) , which flowered two weeks earlier than the Columbia-0 ( Col-0 ) reference when grown at 15°C but only one week earlier at 21°C ( Figs 1A , 1B , S1A and S1B ) . The vernalization pathway could potentially contribute to the early flowering behavior of Kil-0 at 15°C but we detected only minor flowering time effects after a six-week vernalization treatment ( S1C Fig ) . These flowering time effects were similar to those observed in the reference Col-0 and confirmed also the results from previous surveys that had classified Kil-0 as a summer annual [12 , 26] . Since the expression of the major vernalization-responsive gene FLC was also as strongly reduced in Kil-0 as in the summer annual accession Col-0 ( S1D Fig ) , we concluded that the temperature-dependent early flowering phenotype of Kil-0 at 15°C was vernalization-independent . The prominent early flowering of Kil-0 at 15°C ( hitherto FT15 ) reliably allowed distinguishing between Kil-0 and Col-0 . Analyses of F1 and F2 Kil-0 x Col-0 plants indicated that the Kil-0 flowering phenotype was determined by a major-effect recessive locus ( Fig 1C , 1D and 1E ) . We subsequently mapped the FT15 locus to a 968 kb genomic region ( S2A and S2B Fig ) . After selfing F2 plants with a recombination event in this interval , we identified 49 F3 plants with an early ( Kil-0 ) and 41 F3 plants with a late ( Col-0 ) flowering behavior ( S2C and S2D Fig ) . We further narrowed down the interval of interest to a 151 kb region between 28 . 9 and 29 . 1 Mb on chromosome 1 ( S2E Fig ) and sequenced pools of 15 early and 9 late flowering F3 recombinants ( S1 Table ) . Additionally , we sequenced the Kil-0 genome and identified 309 high confidence SNPs ( single nucleotide polymorphisms ) in the 151 kb mapping interval ( S2E Fig ) . We smoothed the allele frequencies of the 309 SNPs of the two pools of early and late flowering F3 plants using LOESS ( locally weighted scatterplot smoothing ) and calculated the difference ( Δf ) between them . A fraction of Δf > 25% defined a final mapping interval of 31 . 3 kb that comprised eleven annotated genes ( S2E Fig ) . Since the flowering phenotype of Kil-0 segregated in a recessive manner ( Fig 1C ) , we assumed that a potential candidate gene might show reduced transcript abundance in comparison to Col-0 . When we investigated RNA-seq data from 10 day-old Kil-0 and Col-0 plants grown at 21°C , we identified FLOWERING LOCUS M ( FLM ) as the only gene within the 31 . 3 kb region that was expressed at a significantly lower level in Kil-0 than in Col-0 ( Fig 2A and S2 Table ) . FLM was also the most strongly downregulated gene in Kil-0 when we specifically analyzed 267 genes with a role in flowering time regulation ( Fig 2A and S3 Table ) . These data suggested that FLM ( FLMKil-0 ) may be causative for early flowering in Kil-0 . Since the gene expression analyses had indicated that FLM may be the causative locus for early flowering im Kil-0 , we compared the FLM genomic loci from Kil-0 and Col-0 at the molecular level . We found , however , no SNPs in the FLM coding sequence and only a few SNPs in the FLM promoter or introns . Interestingly , read coverage was greatly reduced at the beginning of the first FLM intron in Kil-0 ( Fig 2B and 2C ) . Since a structural polymorphism could cause such a read coverage reduction , we de novo assembled the Kil-0 genomic sequencing reads and identified an insertion in FLMKil-0 that was flanked by two contigs corresponding to two halves of the FLMCol-0 locus at the predicted insertion site ( Fig 2C ) . We PCR-amplified this region and confirmed the presence of a 5 . 7 kb insertion in the first intron of FLMKil-0 ( Fig 2C ) . The inserted sequence showed similarity ( 68% identity , 86% coverage ) to A . thaliana ATLINE1_8 ( hereafter called LINE insert ) , a non-LTR ( NON-LONG TERMINAL REPEAT LONG INTERSPERSED NUCLEAR ELEMENTS ) retrotransposon with six typical retrotransposon domains as well as a perfect copy of the second exon of a RIBONUCLEASE H-LIKE ( AT1G04625 ) gene from chromosome 1 of the Col-0 genome ( S4 Table and Figs 2C and S3 ) . We considered this FLM insertion polymorphism as the most likely cause for early flowering in Kil-0 and subsequently conducted genetic experiments to confirm this hypothesis . Loss of FLM results in early flowering in the flm-3 allele in 23°C-grown Col-0 [17 , 18] . To be able to compare the effect of the FLMKil-0 locus with that of FLMCol-0 and the flm-3 loss-of-function allele , we backcrossed FLMKil-0 six times into Col-0 to establish ColFLM-Kil . ColFLM-Kil flowered much earlier than Col-0 but not as early as the flm-3 knock-out mutant ( Fig 3 ) . We also introgressed flm-3 with four backcrosses into Kil-0 to obtain Kilflm-3 . This introgression line flowered earlier than Kil-0 . Although we performed marker assisted backcrossing , we cannot exclude the possibility that a background effect has an additional influence on flowering time regulation in the two described backcross lines . However , since the results between the two backcross lines are consistent with their effects in the original backgrounds , we considered it highly likely that the genetic data reflect FLM activity and concluded that the activity of FLMKil-0 was intermediate between that of a functional FLMCol-0 allele and the flm-3 loss-of-function allele ( Fig 3 ) . The phenotypic differences between Col-0 and ColFLM-Kil as well as those between Col-0 and flm-3 were more pronounced at 15°C than at 21°C ( Fig 3 ) . This was in agreement with our analysis of flm-3 in a range of growth temperatures , which had revealed that FLM makes a particularly prominent contribution to flowering time control at 15°C ( S4 Fig ) . Since 15°C is closer to the average temperature in the native range of the species than the commonly used 21°C , the strong effect of FLM on flowering at 15°C should be considered a physiologically and ecologically relevant phenotype [11 , 27] . FLM produces two major splice isoforms , FLM-ß and FLM-δ , in the Col-0 accession [19 , 20] . FLM-ß is the predominant splice form in cooler and FLM-δ is the predominant splice form in warmer temperatures [19] . Since the FLMKil-0 allele had weaker effects on flowering time than the flm-3 loss-of-function allele and since FLMKil-0 did not have polymorphisms in the FLM coding region , we hypothesized that the temperature-sensitive flowering of Kil-0 may be caused by changes in FLM expression or FLM alternative splicing . To examine this , we transferred seven day-old 21°C-grown Col-0 and Kil-0 plants for three days to 9°C , 15°C , 21°C or 27°C and examined the effects on FLM-ß and FLM-δ transcript abundance . In agreement with published data , we observed a respective decrease of FLM-ß and an increase of FLM-δ in response to warmer temperatures ( Fig 4A ) [19 , 20] . Importantly , temperature-dependent changes in the abundance of the FLM isoforms were maintained in Kil-0 as well as in ColFLM-Kil but the overall FLM transcript abundance was strongly reduced compared to Col-0 . For example , when comparing the values at 21°C , Kil-0 and ColFLM-Kil had six-times less FLM-ß and 27-times less FLM-δ than Col-0 ( Fig 4A ) . It was previously shown that FLM-ß represses and that FLM-δ promotes flowering at 16°C when introduced as transgenes in the flm-3 background [19–21] . It was further proposed that FLM-ß forms heterodimers with SVP ( SHORT VEGETATIVE PHASE ) to prevent flowering by direct DNA-binding to repress the transcription of FT and SOC1 . Conversely , FLM-δ would form inactive heterodimers with SVP and would thereby indirectly induce flowering by relieving the repression from FT and SOC1 . To understand the effects of this differential regulation , we measured SVP , FT , and SOC1 expression levels . Whereas SVP expression was similar between the different genotypes , FT and SOC1 were expressed more strongly in the early flowering Kil-0 or ColFLM-Kil than in Col-0 ( Figs 4A and S5 ) . We thus concluded that the reduced expression of FLM was likely the cause for the early flowering of Kil-0 and ColFLM-Kil and that the temperature-dependent differential accumulation of FLM-ß and FLM-δ could be the basis of the temperature-sensitive flowering time in Kil-0 . Since Kil-0 flowered earlier than Col-0 , we assumed that the effect of the downregulation of the repressive FLM-ß isoform was dominant over the downregulation of the flowering activating isoform FLM-δ . To test whether the large insertion in FLMKil-0 was the causative polymorphism for low FLM expression and the specific reduction in the FLM-ß isoform , we transformed Kil-0 with a genomic fragment of FLMCol-0 ( including 2 kb promoter plus 5'-UTR [untranslated region] and 0 . 5 kb 3'-UTR sequence ) as well as an FLMKil-0 genomic variant with an engineered deletion of the LINE-insertion ( FLMKil-0ΔLINE ) . We found that FLMCol-0 as well as FLMKil-0ΔLINE delayed flowering in Kil-0 ( Fig 4B ) . We also tested whether the 5 . 7 kb insertion had a comparable effect on FLM transcript abundance when engineered into the FLMCol-0 reference and introduced a FLMCol-0 transgene with an engineered 5 . 7 kb LINE insertion into the flm-3 loss-of-function mutant . Indeed , the LINE insertion reduced FLM expression and changed FLM splicing , similar to the FLMKil-0 allele ( Fig 4C ) . We thus considered it very likely that the LINE insertion in the first intron of FLM was the causative polymorphism for reduced FLM transcript abundance , differential FLM splice isoform accumulation , and early flowering in Kil-0 . To gain an understanding of the molecular effect of the LINE insertion on FLM transcription and splicing , we compared the exon usage of Col-0 and Kil-0 using RNA-seq data . In Col-0 , we detected a strong differential use of alternative exons 2 ( FLM-ß ) and 3 ( FLM-δ ) that define the two dominant FLM isoforms ( Fig 4D ) , with the FLM-ß-specific exon 2 being more abundant than the FLM-δ-specific exon 3 . In Kil-0 , we detected generally fewer reads for all exons including exon 1 , indicating that the LINE insertion between exon 1 and exon 2 controls the overall FLM transcript abundance ( Fig 4D ) . Furthermore , we noted that the read coverage of the exons following intron 1 , including the alternatively used exons 2 ( ß ) and 3 ( δ ) was more strongly reduced in Kil-0 than in Col-0 , suggesting that the inserted LINE element may also negatively control FLM splicing efficiency ( Fig 4D , 4E and 4F ) . To approximate the rate of FLM transcription , we determined the levels of unspliced FLM pre-mRNA using primers located in intron 1 ( upstream of the LINE insertion ) , and intron 2 and 6 . No significant differences in pre-mRNA levels were detected when we amplified intron 1 indicating a comparable transcription rate between the two accessions . However , the relative abundance of intron 2 and intron 6 , which are located downstream from the insertion was strongly reduced in Kil-0 , indicative for a partial premature termination of FLM transcription ( Figs 4G , S6A and S6B ) . We next mapped the RNA-seq reads from Col-0 and Kil-0 to the FLMCol-0 genomic locus paying particular attention to the exon-intron junctions ( Fig 4H ) . Whereas the read coverage dropped sharply at the exon 1-intron 1 junction in Col-0 , reads covering the beginning of intron 1 could be readily retrieved in Kil-0 ( Fig 4H ) . This finding suggested that the LINE insertion resulted in aberrant splicing within Kil-0 intron 1 . This change in the splicing pattern had an impact on the abundance of the FLM-ß and FLM-δ full-length transcripts but , importantly , the respective full-length mRNAs were still generated as confirmed by semi-quantitative RT-PCR ( S6C Fig ) . We then used qRT-PCR with primers spanning only exon 1 or exon 1 and parts of intron 1 to validate the occurrence of transcripts containing intron 1 sequences ( Fig 4I ) . In Col-0 as well as in Kil-0 , we found transcripts including intron 1 sequences until 350 bp from the start codon ( Figs 4I and S6D ) . The abundance of intron 1 sequence-containing reads was much higher in Kil-0 than in Col-0 indicating that the insertion may indeed promote premature transcription termination possibly in combination with aberrant splicing . Since transposon insertions were reported to induce alternative polyadenylation [28–30] , we examined whether the corresponding intron 1 sequence-containing transcripts were polyadenylated and performed 3’-RACE ( rapid amplification of cDNA ends ) experiments . The RACE PCR yielded two abundant fragments , one corresponding in size to the full length transcript and one smaller fragment that was much more abundant in Kil-0 than in Col-0 ( S6E Fig ) . We cloned and sequenced products and determined six different polyadenylated transcripts containing intron 1 sequences . One of these fragments represented the most abundant species ( 75% ) among the 32 independent sequences ( Fig 4I ) . Premature translation termination codons ( PTC ) located distantly from splice sites frequently trigger the degradation of aberrant transcripts through the NMD- ( non-sense mediated decay- ) pathway [31 , 32] . We identified a PTC in intron 1 , just two bases downstream from the exon1-intron1 border and hypothesized that the aberrant FLMKil-0 transcripts may be NMD targets ( Fig 4I ) . When we used the translation inhibitor CHX ( cycloheximide ) to mimic the molecular phenotype of NMD-defective mutants [32 , 33] , we detected indeed a significant increase in the abundance of two aberrant transcripts containing intron 1 sequences ( S6F and S6G Fig ) . In summary , we concluded that the early flowering of Kil-0 correlated with the presence of a LINE insertion in FLM intron 1 . Further , the LINE insertion did not affect de novo FLM transcription initiation but partially impaired the formation of full length transcripts , possibly as a result of premature transcription termination and the formation of aberrantly spliced polyadenylated transcripts that are targeted for NMD . Alternative molecular mechanisms that were not examined here may of course also be suitable to explain the overall reduction in FLM transcript abundance in Kil-0 . To gain information about the distribution of the FLMKil-0 structural polymorphism across the native range of the species , we screened a genetically highly diverse set of accessions from a previously published HapMap population , which we supplemented with selected laboratory accessions to obtain a final population with 419 accessions ( S6 Table and Figs 2E and S7 ) [34] . Through PCR-based screening , we identified nine additional accessions with a LINE insertion in the same position as in Kil-0 from Scotland ( Kil-0 ) and Sweden ( Ull2-3 ) to Germany ( 8 accessions ) ( S5 Table and Fig 5A and 5B ) . We subsequently also analyzed genome sequences from 1128 A . thaliana accessions ( www . 1001genomes . org ) and confirmed by this approach seven FLMLINE accessions and identified El-0 as an additional FLMLINE accession from Germany ( S7 Fig ) . Sequence analyses of the LINE insertions revealed a high sequence similarity between the ten FLMLINE-accessions ( S9 Fig ) . Taking into account a spontaneous mutation rate of 6 to 7×10−9 per site per generation , approximately one to three seed generations per year , and the absence of any selective pressure on the insertion , we calculated that the common ancestor probably originated only 8 . 000 to 30 . 000 years ago [35 , 36] . We found that the FLMLINE accessions belonged to genetically differentiated clades and were thus truly independent [37] . Additionally we performed a phylogenetic analysis using the genomic sequence of the FLM locus of these ten FLMLINE accessions together with Col-0 and 88 randomly selected accessions , which revealed that the FLMLINE lines clustered into one clade when the FLM locus was analyzed in isolation ( Figs 5B and S8 ) . Similarly to Kil-0 , all FLMLINE-accessions had low expression of the FLM-ß and FLM-δ isoforms ( Fig 5C ) . Apart from two late-flowering vernalization-dependent accessions ( Sp-0 and Ste-0 ) , seven of the remaining eight FLMLINE accessions flowered earlier than Col-0 at 15°C ( Figs 5D , S9A and S9B ) [12 , 26] . Consistent with the fact that these accessions come from genetically highly diverse groups , these lines showed a substantial variation in flowering time between them . However , when flowering time data of the vernalization-independent FLMLINE accessions was averaged , we measured a significant reduction in flowering time at 21°C and 15°C in comparison to Col-0 ( Fig 5E ) . Thus , the LINE insertion correlated with early flowering in summer annual accessions . Our data thus suggests that the FLMKil-0 allele arose comparatively recently and subsequently spread geographically to contribute to flowering time regulation in a background- and temperature-dependent manner . When we analyzed the ten FLMLINE-accessions for FLM expression by qRT-PCR , we noted a prominent variation in the abundance of the FLM-δ isoform but relatively stable expression levels of FLM-ß ( Fig 5C ) . We therefore asked whether FLM polymorphisms other than the LINE insertion could explain these differences . However , all ten FLMLINE accessions were highly similar in this region and we identified only four different additional polymorphic sites ( S9C Fig ) . Since a Mann-Whitney test ( p > 0 . 05 ) indicated that none of these polymorphisms was significantly associated with FLM-ß and FLM-δ abundance or the ratio between these two isoforms , we concluded that the regulation of the FLM-ß and FLM-δ transcript abundance may be regulated in trans . Specifically within the family of MADS-box transcription factor genes , various cases are known where structural polymorphisms within the first intron enhance or repress gene expression [10 , 38–41] . To test if FLM intronic sequences contributed to FLM expression , we transformed flm-3 mutants with constructs for the expression of the FLM-ß or FLM-δ coding sequences under control of a 2 . 1 kb FLM promoter fragment ( S10 Fig ) . Importantly , none of the resulting FLM-ß or FLM-δ T1 transformants expressed significant levels of the respective transgene or showed a suppression of the flm-3 early flowering phenotype ( S10 Fig ) . Since FLM was expressed from corresponding genomic constructs containing all introns and since it was previously shown that FLM is expressed from the above-described construct when only intron 1 is included [19] , we concluded that intron 1 was essential for FLM expression . To address whether sequence identity , sequence length or the specific insertion site within intron 1 conferred the effect of the LINE element on FLM expression and splicing , we examined the effects of T-DNA or DS ( Dissociator ) transposon element insertions in FLM intron 1 [42–44] . These intron 1 insertions were of similar size ( 4 . 5 kb to 5 . 3 kb ) to the 5 . 7 kb LINE insertion and present in the Col-0 and No-0 ( Nossen-0 ) accessions: Salk_068360 ( Salk , Col-0 ) , RATM13-4593-1 ( RIKEN , No-0 ) and GK_487H01 ( GABI , Col-0 ) ( Fig 6A ) . Additionally , we included Co-1 that carries a ~1 . 5 kb insertion in FLM intron 1 ( Fig 6A ) . When we determined the effects of these structural variants on FLM transcript accumulation and alternative splicing in plants grown at 15°C and 21°C , we found that the insertion correlated in each case with reduced FLM transcript abundance when compared to the controls ( Fig 6B ) . Furthermore , FLM-δ was in each case more strongly reduced than FLM-ß , inviting the conclusion that increases in intron length , regardless of the molecular identities of the insertions , resulted in decreased FLM-ß and FLM-δ expression and changes in the ratio between the isoforms . This was further confirmed by the molecular analysis of pre-mRNA transcript abundance , the formation of aberrant polyadenylated transcripts and transcript targeting to the NMD pathway , which we performed in parallel for the Salk insertion line , Col-0 , and Kil-0 ( Figs 4G and S6 ) . In this analysis , we identified in each case the same molecular defects in the Salk insertion line as in Kil-0 . At the same time , we noted that the temperature-sensitive regulation of the FLM isoforms was maintained in all lines . Interestingly , insertions in the second half of the intron as present in the GABI and RIKEN lines caused a particularly strong reduction in the expression of FLM-δ expression ( Fig 6B ) . We thus concluded that insertions in the second half of the intron may have additional effects on FLM-δ splicing . To examine the phenotypic consequences of the observed transcriptional changes of the FLM insertion lines , we evaluated their flowering at 15°C and 21°C . We thereby focused on the Salk and ColFLM-Kil as well as the GABI and RIKEN lines , which had contrasting phenotypes with regard to the abundance of the FLM-δ isoform . Regardless of the differences in expression of the FLM-δ isoform , all lines flowered earlier than the respective wild type , and this effect was particularly prominent at 15°C ( Fig 6C and 6D ) . Importantly , we did not notice any pleiotropic effects on plant growth or plant height for the tested alleles suggesting that FLM acts specifically on flowering time regulation ( Figs 6D and S11 ) . We next evaluated to what extent changes in FLM-ß or FLM-δ abundance could explain the observed difference in flowering time . To this end , we correlated datasets on flowering time and FLM expression from three independent flowering time experiments ( Fig 6E ) . In each experiment , expression of FLM-ß correlated much better with flowering time than FLM-δ and this effect was particularly pronounced at 15°C ( Fig 6E ) . Thus , FLM-ß expression levels alone rather than the ratio between FLM-ß and FLM-δ have a prominent effect on flowering time regulation in A . thaliana .
We have identified a recently evolved FLM allele from the accession Kil-0 . The insertion of a LINE element in intron 1 of FLMKil-0 resulted in reduced FLM transcript abundance and correlated with an overall acceleration of flowering time that was particularly prominent at 15°C ( Fig 7 ) . We identified additional nine FLMLINE accessions that mainly represented lines collected from Germany . Although these FLMLINE accessions were highly homologous over the FLM locus , they represented accessions from genetically different clades indicating that FLMLINE was involved in recent adaptation to early flowering and that its rather narrow geographical distribution is likely due to the young demographic history of this allele . The LINE element insertion of FLMLINE shares 68% homology with LINE class I retrotransposons from A . thaliana . Transposable elements are typically suppressed by epigenetic mechanisms and this suppression can also negatively interfere with the expression of neighboring genes [45–48] . Epigenetic regulation is also known to control the expression of MADS-box transcription factor genes . For example , the chromatin of FLC is modified during vernalization by lysine 27 methylation of histone 3 ( H3K27me ) , a repressive mark , and several natural variants interfere with regulatory regions in the FLC intron 1 [41 , 49–53] . Thus , the LINE insertion could interfere with the direct transcriptional regulation but , alternatively , also with the epigenetic control of the FLMKil-0 locus . However , previous genome-wide studies failed to identify epigenetic marks such as H3K27me on intron 1 of FLM [54] and we detected no differences in transcription rate between the insertion lines and the Col-0 reference . Furthermore , transposon and T-DNA insertions were reported to mediate alternative polyadenylation through the utilization of alternative polyadenylation sites [28–30] . Although we detected a higher abundance of short aberrant polyadenylated transcripts in the insertion lines , we consider it unlikely that the insertion itself provides cis elements that result in their synthesis . Aberrrant transcripts did not extend into the insertion and no differences in quantity and composition of these transcripts were detected between Kil-0 and the Salk line , which have molecularly distinct insertions . We concluded that the reduction of FLM full-length transcript abundance in Kil-0 is caused by a combination of partial premature termination of transcription and aberrant splicing due to the enlargement of the first intron . Conversely , through experiments with FLM transgenes where intronic sequences were deleted , we could conclude that intron 1 was strictly required for FLM expression and activity . A contribution of intronic sequences in gene expression regulation , generally referred to as IME ( intron-mediated enhancement ) , was previously reported for many genes , and in plants specifically for members of the MADS-box transcription factor family [55 , 56] . Several studies have already identified corresponding intronic cis-regulatory elements , e . g . in intronic regions of the floral homeotic genes AG ( AGAMOUS ) and members of the AGL6 ( AGAMOUS-LIKE6 ) -subfamily [38–40 , 57] . Several independent structural intron polymorphisms were also reported for the MADS-box factor and flowering time regulator VRN1 ( VERNALIZATION1 ) from wheat and barley . There , these structural differences in intron 1 composition can promote high VRN1 expression and these differences are the main molecular cause for vernalization-independent flowering in many spring barley and wheat cultivars [10] . Thus structural intron polymorphisms , e . g . through transposon insertions , are a recurrent theme in the expression control of MADS-box transcription factors and the adaptation to the environment through these factors . Interestingly , our expression analysis showed that the expression of the two FLM isoforms , FLM-ß and FLM-δ were differentially affected by the intron 1 insertions . Since this behavior was found in several accessions and was recapitulated by inserting a LINE-bearing FLM transgene in the Col-0 FLM allele , we judge that this regulation again is not related to the nature of the insertion in FLM but rather to the position of the insertion or the corresponding increase in intron length . It remains to be investigated , however , what the underlying molecular basis of the differential effect of the insertions on FLM-ß and FLM-δ abundance is . Through investigations of insertion lines from different sources and ecotypes , we found that the position of the inserted sequence affected the relative abundance of FLM-ß and FLM-δ . In combination , the availability of these lines allowed the examination of flowering time and its correlation with the abundance of the two FLM splice variants . Importantly , our study did not support a role for FLM-δ in flowering time control but rather suggests that FLM-ß abundance alone is the predominant determinant of flowering time in natural variants under the ecologically relevant temperature of 15°C . Thus , previous experiments exploring the contribution of FLM-ß and FLM-δ to flowering time control using transgenic approaches may have overestimated the contribution of FLM-δ , at least in the diverse genetic material and at the temperatures used in our study . At the same time , it cannot be ruled out that FLM-δ levels reached a subcritical level in the insertion lines analyzed in our study so that the contribution of FLM-δ could not be accurately determined . Along these lines , a recent study showed no contribution of the FLM locus to flowering time regulation in a large set of wild A . thaliana ectoypes in warm ( 27°C ) temperature where FLM-δ is upregulated [58] indicating that even under temperature conditions that promote FLM-δ abundance no important regulatory role could be ascribed to it . Once a detailed understanding of factors controlling FLM gene expression and splicing will be obtained , it will be important to reexamine this aspect in more detail . The role of FLM in flowering time variation was previously established in the temperature-insensitive accessions Nd-1 and Ei-6 where FLM is deleted [16 , 17] . In our study , we report the first gene expression variation-allele for FLM and describe a molecular mechanism for the control of flowering time in ambient temperature through structural changes in FLM intron 1 . It is interesting to note that loss-of-function alleles of flowering time genes are generally very rare and are typically distributed within a small geographic region . This may indicate that adaptation through gene loss may be disadvantageous outside of the specific ecological niche simply because the loss of the gene will prevent its future reactivation [59 , 60] . In the case of FLM , this is exemplified by the Nd-1 and Ei-6 accessions , but also among the many known FLC alleles , only a few null alleles have been reported whereas gene expression-modulatory FLC alleles are more common [6 , 12 , 49 , 50 , 61] . In this regard , we perceive at least a trend towards a similar distribution among the A . thaliana FLM alleles . A deeper analysis about the FLM coding sequence polymorphisms will be required to make conclusive statements about the importance of strong and weak FLM alleles during adaptation of flowering time . We conclude that structural variations of FLM intron 1 as described here represent an adaptive mechanism for the control of flowering time in A . thaliana and possibly also in other closely related Brassicaceae . FLM might have a very specific role in flowering time regulation because modulations of its expression seemingly affect only flowering and not other plant growth traits . We , therefore , think that FLM is an excellent candidate gene to precisely and steadily modulate flowering time in a dynamic manner over a broad range of temperature conditions to overcome the impacts of climate change on flowering in plants .
The following A . thaliana accessions and genotypes were used in this study and , unless stated otherwise , provided by the Nottingham Arabidopsis Stock Centre ( NASC; Nottingham , UK ) : The Arabidopsis accessions Killean-0 ( Kil-0 ) , Columbia-0 ( Col-0 ) , and Nossen ( No-0 ) ; the insertion mutants flm-3 ( Salk_141971; Col-0 ) , GABI-KAT GK487H01 ( GABI; Col-0 ) , Salk_068360 ( Salk; Col-0 ) as well as RIKEN-13-4593-1 ( RIKEN; No-0 ) from the RIKEN Stock Center . In each case , the positions of the insertions were verified by DNA sequencing . The transgenic line gFLMCol-0 ( flm-3 ) was previously described [19] , flc-3 and the line FRISF-2 FLC [6] were a gift from Franziska Turck and George Coupland ( Max-Planck Institute of Plant Breeding Research , Cologne , Germany ) . A list of the A . thaliana accessions screened by PCR with the primers LP1 , LP2 , and RP1 for the FLMLINE structural polymorphism is provided as S6 Table . Primer sequences are provided as part of S7 Table . For flowering time analyses , plants were randomly arranged in trays and grown under constant white light ( 70–90 μmol m-2 s-1 ) or in long day-conditions with 16 hrs white light ( 110–130 μmol m-2 s-1 ) /8 hrs dark in MobyLux GroBanks ( CLF Plant Climatics , Wertingen , Germany ) or MLR-351 SANYO growth chambers ( Ewald , Bad Nenndorf , Germany ) . Trays were rearranged every two days and water was supplied by subirrigation . Analysis of large plant sets was performed in a walk-in chamber with constant white light as described above . Flowering time was quantified by determining the time until the macroscopic appearance of the first flower bud ( days to bolting , DTB ) or by counting rosette and cauline leaf numbers ( RLN , CLN ) . Student’s t-tests , ANOVA , and Tukey HSD tests were calculated with Excel ( Microsoft ) and R ( http://www . r-project . org/ ) , respectively . For the resequencing of the Kil-0 genome or the late and early flowering F3 recombinant pools , libraries were prepared from 600 ng genomic DNA following the standard protocol of the TruSeq DNA Sample Preparation Kit v2 ( Illumina , San Diego , CA ) . Paired-end sequencing with a read length of 100 bp was performed on a HiSeq 2500 ( Illumina , San Diego , CA ) . Post-sequencing quality trimming was performed with the CLC Genomics Workbench ( v . 7 . 0 ) and the following parameters: low quality limit = 0 . 05; ambiguous nucleotide = maximum 1; length minimum = 15 . Post-trimming , 15 x 106 and 20 x 106 reads were obtained for the early and late flowering samples , respectively . Read mapping was performed using the TAIR10 release of the A . thaliana reference ( The Arabidopsis Genome Initiative , 2000 ) genome reference sequence with the stringent settings: mismatch cost = 2; insertion cost = 2; deletion cost = 2; length fraction = 0 . 9; similarity = 0 . 9 . An average 57- or 79-fold coverage was obtained from the early and late flowering DNA pools . Variant calling was performed using the probabilistic variant calling tool of the CLC Genomics Workbench ( v . 7 . 0 ) and default settings . SNPs with a 30–120-fold coverage and frequency f > 20% as well as a presence call in both pooled samples were selected for allele frequency mapping . From those , SNPs that showed a frequency of < 80% in the resequencing analysis of the homozygous Kil-0 parental line were discarded . Smoothing using locally weighted scatterplot smoothing ( LOESS ) of SNP frequency values was achieved with R ( http://www . r-project . org/ ) . 95% confidence intervals , Δf > 25% , and Δfmax were calculated from the LOESS values . The de novo assembly of Kil-0 resequencing reads was performed using the CLC Genomics Workbench v . 7 . 0 with default settings . Contigs were identified by a simple search and were reassembled to the Col-0 genomic FLM sequence . The Kil-0 genomic sequence is available as LN866842 at www . ebi . ac . uk/ena . To identify the causative locus for early flowering in Kil-0 , the FT15 locus was mapped with polymorphic markers selected from a previously described marker collection [62] . Additional SSLP ( single sequence length polymorphisms ) markers were generated by searching the publicly available Kil-0 genomic sequence ( www . 1001genomes . org ) or the genome sequence that was determined as part of this project for InDel ( insertion/deletion ) polymorphisms . PCR primers spanning these sites were designed with Primer3 [63] and tested on Col-0 and Kil-0 genomic DNA . The PCR fragments were generally between 200 and 700 bp long and separated on 2–3 . 5% agarose gels or using the QIAxcel Advanced Capillary Electrophoresis high resolution kit ( Qiagen , Hilden , Germany ) . For rough mapping of FT15 , ten early and ten late flowering F2 plants were selected from the extreme phenotypic borders of an F2 population ( n = 124 ) . The genetic marker distances were calculated from the genotype data of all screened F2 plants with JoinMap v . 4 . 1 ( Kyazma B . V . ) . A list of markers and the respective primers is provided as S8 Table . ColFLM-Kil and Kilflm-3 were generated by marker-assisted backcrosses . In brief , heterozygous F1 plants were genotyped with the primers LP1 , LP2 , and RP1 to examine the lines for the presence of the Col-0 or Kil-0 FLM allele and with Salk_141971 forward and reverse primers to test for the flm-3 T-DNA insertion , respectively . Primer sequences are provided as part of S7 Table . For qRT-PCR analyses , total RNA was isolated from three biological replicates using the NucleoSpin RNA Plant kit ( Machery-Nagel , Düren , Germany ) . DNA was removed by an on-column treatment with rDNase ( Machery-Nagel , Düren , Germany ) . 2–3 μg total RNA were reverse transcribed with an oligo ( dT ) primer and M-MuLV Reverse Transcriptase ( Fermentas , St . Leon-Rot , Germany ) and the cDNA equivalent of 30–50 ng total RNA was used in a 10 μl PCR reaction with SsoAdvanced™ Universal SYBR Green Supermix ( BioRad , München , Germany ) in a CFX96 Real-Time System Cycler ( BioRad , München , Germany ) . The relative quantification was calculated with the ΔΔCt method with ACT8 as a control [64] . See S7 Table for a list of qRT-PCR primers . To investigate significant gene expression differences between Col-0 and Kil-0 , RNA was prepared from three biological replicate samples from ten day-old seedlings ( 21°C , long days ) as described above . Sequencing libraries were prepared following the standard protocol of the TruSeq RNA Sample prep v2 Kit ( Illumina , San Diego , CA ) . Paired-end sequencing with a read length of 100 bp was performed on a HiSeq 1000 ( Illumina , San Diego , CA ) . RNA-seq reads from each biological replicate were then aligned against the Col-0 TAIR10 release genome using Bowtie ( version 2 . 1 . 0 ) and Tophat2 ( version 2 . 0 . 8 ) with default parameters [65 , 66] . On the basis of the structural gene annotation for A . thaliana TAIR10 , exon- and gene-level transcription was quantified by using HTSeq , differential expression tests were performed by using DESeq2 [67 , 68] . Significantly differentially expressed genes were defined as genes with Benjamini-Hochberg-adjusted p-values < 0 . 01 . Tests for differential exon usage were conducted on basis of RNA-seq exon counts with DEXseq [69] . The RNA-seq data are publically available as PRJEB9470 at www . ebi . ac . uk/ena . A list of genes with a role in flowering time regulation was generated by searching the TAIR database ( www . arabidopsis . org ) for the term “flowering time” . The resulting list was reviewed manually and is presented in S3 Table . To subsequently analyze gene expression of the FLM locus at a nucleotide resolution , the corresponding Col-0 gene sequences were extracted from the Col-0 TAIR10 reference genome and the Kil-0 genome sequence as determined as part of this study . Subsequently , RNA-seq reads were aligned against the Kil-0 and Col-0 genomic sequences using Bowtie and Tophat2 as described above . The number of mapped RNA-seq reads from Col-0 and Kil-0 per nucleotide were counted with the toolset Bedtools and subsequently normalized to range between 0 ( no expression ) and 1 ( maximum expression ) . For visualization , the mean expression level and 5% and 95% confidence intervals were determined across the biological replicates of one sample . To obtain a genomic FLM fragment from Kil-0 with a deletion of the 5 . 7 kb LINE insertion , a 6 , 981 bp gFLM ( Kil-0 ) ΔLINE deletion fragment was amplified by overlap extension PCR from Kil-0 genomic DNA . Fragment 1 ( bp—2367 to bp + 631 ) was amplified with ULC-1 and ULC-2 and fragment 2 ( bp + 632 to bp + 4156 and 251 bp 3’-UTR plus 207 bp downstream sequence ) with ULC-3 and ULC-4 . After purification of the two subfragments , the full fragment was generated by overlap-extension PCR reaction with ULC-1 and ULC-4 . The insert of the full-length fragment in pCR2 . 1-TOPO ( Life Technologies , Carlsbad , CA ) was sequenced and subcloned as a BamHI and XhoI fragment into pGreen0229 [70] . To insert the LINE insertion into pDP34 , a previously described construct with the Col-0 genomic FLM fragment pFLM::gFLM [19] , pDP34 was mutagenized by PCR with ULC-12 to replace the sequence ATTGTTCA ( bp +632 to bp +640 ) with a unique AscI restriction site ( GGCGCGCC ) [71] . The LINE insertion was then amplified from Kil-0 genomic DNA with ULC-16 and ULC-17 and inserted as an AscI fragment into the modified pDP34 . All constructs were transformed into Agrobacterium tumefaciens strain GV3101 containing pSOUP and subsequently using floral dip transformation into Col-0 , Kil-0 , and flm-3 [70 , 72] . pDP79 and pDP80 were previously described [19] . T1 transformants were selected by spraying soil-grown plants with 0 . 1% BASTA . The list of primers is provided in S7 Table . To test for the presence of the FLMKil-0 allele in a large collection of accessions , we performed PCR with the primers LP1 , LP2 , and RP1 on genomic DNA using Phusion ( New England Biolabs , Frankfurt , Germany ) or TaKaRa LA Taq polymerases ( Takara Bio , Saint-Germain-en-Laye , France ) . Selected PCR products were analyzed by DNA sequencing following gel extraction . Sequencing primers are listed in S7 Table . A complete list of all accessions examined is provided as S6 Table . Kil-0 genome sequence information was used to identify accessions with a LINE insertion by analyzing genome sequences as determined in the frame of the 1001 Arabidopsis genome sequencing project ( www . 1001genomes . org ) . To render read mapping specific for the Kil-0 allele , the insertion at the experimentally retrieved insertion sites flanked by 140 bp sequence was extracted for each of the two insertion breakpoints and defined as target sequences for mapping . Subsequently , all reads of the individual A . thaliana accessions were mapped against the target sequences using SHORE and genomemapper allowing for up to 5% single base pair differences including gaps with regard to the read length [73 , 74] . To ensure that each supporting read spans at least 25% of its sequence across the insertion breakpoint , only reads that overlapped an insertion site with the inner 50% of its read sequence were counted as supporting an insertion breakpoint ( core-mapping read ) . Furthermore , only unique mapped reads were included . An insertion was defined as present if there were at least two unique core-mapping reads at the left and right insertion breakpoint . Several database searches with the Kil-0 LINE insert yielded no highly similar sequences , except for a 559 bp match to the second exon of Col-0 AT1G04625 , which is most likely a transposon-related gene based on its ribonuclease H-like description . The LINE sequence within the Kil-0 insert was identified with RepeatMasker ( http://www . repeatmasker . org ) against PGSP-REdat , v_9 . 3_Eudicot ( http://pgsb . helmholtz-muenchen . de/plant/recat/ ) . The Pfam domains where annotated with hmmsearch of hmmer3 in all 6 reading frames against PfamA v27 [75 , 76] . For phylogenetic analyses of the FLM locus , sequences of 88 randomly selected accessions were extracted from the A . thaliana 1001 genome project GEBowser ( http://signal . salk . edu/atg1001/3 . 0/gebrowser . php ) and aligned with sequences of the ten FLMLINE accessions and Col-0 . Alignments were calculated using ClustalW and Neighbor-Joining trees ( Maximum Composite Likelihood method , 1000 bootstrap replicates ) were constructed with MEGA5 [77] . Polyadenylated transcripts were amplified according to [78] . Transcript pools were analyzed on an agarose gel and the upper and lower bands as depicted in S6E Fig were purified from all samples , subcloned into pCR2 . 1-TOPO ( Life Technologies , Carlsbad , CA ) and sequenced . Primers sequences are listed in S7 Table . Nuclei were isolated as previously described [79] with two biological replicates per line . RNA from nuclei pellet was extracted as previously described [80] and DNA was digested using DNaseI ( Life Technologies , Carlsbad , CA ) . cDNA synthesis and qRT-PCR were performed as described above . Primer sequences are provided in S7 Table . | Plants control their flowering time in response to the temperatures of their environment , e . g . in response to the experience of winter or in response to cold and warm ambient temperatures experienced during spring . The knowledge about the evolutionary adaptation of plants to changing ambient temperatures is at present very limited . Understanding the latter is , however , becoming increasingly important due to the temperature changes associated with global warming and the anticipated changes in flowering time in ecosystems and agricultural systems . Here , we uncover an evolutionarily conserved molecular mechanism employed by Arabidopsis thaliana ecotypes for the adaptation of flowering time to cool temperatures . This structural change in the architecture of the gene FLOWERING LOCUS M can be found in multiple A . thaliana natural accessions and the knowledge gained in our study may be used to predict or modify flowering time in plants related to A . thaliana in the future . | [
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] | [] | 2015 | Modulation of Ambient Temperature-Dependent Flowering in Arabidopsis thaliana by Natural Variation of FLOWERING LOCUS M |
Homologous recombination provides a mechanism of DNA double-strand break repair ( DSBR ) that requires an intact , homologous template for DNA synthesis . When DNA synthesis associated with DSBR is convergent , the broken DNA strands are replaced and repair is accurate . However , if divergent DNA synthesis is established , over-replication of flanking DNA may occur with deleterious consequences . The RecG protein of Escherichia coli is a helicase and translocase that can re-model 3-way and 4-way DNA structures such as replication forks and Holliday junctions . However , the primary role of RecG in live cells has remained elusive . Here we show that , in the absence of RecG , attempted DSBR is accompanied by divergent DNA replication at the site of an induced chromosomal DNA double-strand break . Furthermore , DNA double-stand ends are generated in a recG mutant at sites known to block replication forks . These double-strand ends , also trigger DSBR and the divergent DNA replication characteristic of this mutant , which can explain over-replication of the terminus region of the chromosome . The loss of DNA associated with unwinding joint molecules previously observed in the absence of RuvAB and RecG , is suppressed by a helicase deficient PriA mutation ( priA300 ) , arguing that the action of RecG ensures that PriA is bound correctly on D-loops to direct DNA replication rather than to unwind joint molecules . This has led us to put forward a revised model of homologous recombination in which the re-modelling of branched intermediates by RecG plays a fundamental role in directing DNA synthesis and thus maintaining genomic stability .
In wild type Escherichia coli cells , DNA double-strand break repair ( DSBR ) is mediated by the RecBCD pathway of homologous recombination . In this pathway , DNA is unwound by RecBCD and cleaved five nucleotides 3’ of the sequence known as Chi ( 5’-GCTGGTGG-3’ ) [1] . Following recognition of Chi , RecBCD continues to unwind the substrate and facilitates the loading of RecA onto the 3’ strand close to Chi . In vitro , the fates of the DNA strands between the DNA double-strand break ( DSB ) site and Chi and of the strand terminating 5’ at Chi depend on the ATP/Mg2+ concentration ratio . Degradation of these strands increases in vitro as the ATP/Mg2+ concentration ratio increases but the extent of degradation in vivo is unknown . Two recent reviews of the RecBCD pathway of recombination describe this reaction in detail and depict the “Chi modulated DNA degradation” and “nick at Chi” models for the initiation of recombination shown in Fig 1A [2 , 3] . Following the formation of a D-loop through the strand exchange activity of RecA , Holliday junctions are formed , migrated and resolved by the RuvABC complex resulting in the formation of a structure resembling a replication fork . Subsequently , PriA is recruited to this fork-like structure , and is required to initiate a cascade of protein binding that ultimately results in the loading of the primary replicative helicase , DnaB , to the lagging-strand template [4] . DNA synthesis then proceeds to replace any genetic information lost at the site of the DSB ( see [5] for a recent review ) . RecG has been a mysterious player in these reactions . The observation that RecG not only plays a role in the RecBCD pathway of DSBR but also in the RecF and RecE pathways ( activated in mutant strains of E . coli ) suggests that , like RecA , it plays a fundamental role in DNA repair and acts on a DNA substrate that is common to different recombination pathways [6] . Indeed , its importance in DSBR has been confirmed using both cleavage of a chromosomal I-SceI target site with the I-SceI enzyme [7] and cleavage of a hairpin DNA structure by SbcCD nuclease [8 , 9] . Despite the early genetic evidence for a function common to three recombination pathways [6] , many different roles for RecG have been proposed . These range from the migration of Holliday junctions to facilitate their resolution [7 , 10 , 11 , 12 , 13 , 14] , the promotion and opposition of RecA strand exchange [15 , 16] , the reversal of replication forks [17 , 18 , 19 , 20 , 21 , 22 , 23] , the processing of flaps generated when DNA replication forks converge [24 , 25 , 26 , 27] and the stabilisation of D-loops [9] . Understanding the role of RecG has not been facilitated by the fact that the existence or identity of a eukaryotic homologue or functional orthologue has not been reported until recently [28] . If SMARCAL1 is indeed the human functional orthologue of RecG , there is hope that more light will be shed on the function of this important protein . Purified RecG protein is a helicase that can bind and unwind synthetic model Holliday junctions [12] . In vitro , RecG efficiently catalyses the re-pairing of template strands in substrates mimicking replication forks , in a reaction termed replication fork reversal or replication fork regression [18 , 19 , 21 , 22 , 23] . Interestingly , this RecG promoted reaction occurs preferentially on substrates mimicking replication forks with a new strand annealed to the lagging-strand template [20 , 21] . RecG also efficiently reverses a replication fork blocked at a DNA lesion in an in vitro replication system where the DNA polymerase and replicative helicase are associated with the DNA [29] . These studies have led to a current view that an important biochemical action of RecG in vitro is replication fork reversal [30] . However in live cells there is a lack of evidence for RecG mediated fork reversal in several in vivo fork reversal reactions ( e . g . [31] ) . Some indirect results imply that RecG might reverse replication forks following UV irradiation [19] . However following UV irradiation , the chromosome fragmentation by RuvABC-mediated cleavage of Holliday junctions present at reversed forks , which can be detected in a recBC mutant , is hardly affected by RecG [32] . This does not support even the view that RecG has a specific role in reversing forks following UV irradiation . The discordance between the substantial amount of evidence for RecG catalysed fork reversal in vitro and the small amount of evidence in vivo raises an interesting question: what is the substrate for RecG in live cells ? A clue as to the nature of the RecG substrate in vivo comes from the observation that a class of suppressors of the recG recombination deficient phenotype carry mutations in the helicase domain of PriA [33] . This is consistent with an interaction between RecG and PriA during the processing of recombination intermediates . PriA is required for the re-start of replication forks , during chromosomal DNA replication , recombination and replicative transposition , via the loading of the DnaB helicase [4 , 34 , 35 , 36] . Both RecG and PriA are known to remodel replication fork substrates in vitro . RecG binds the parental double-stranded part of a replication fork and unwinds the new strands ( see [30] for a recent review ) . It has a preference to unwind a model fork substrate with a 5’ new lagging-strand at the fork over a substrate with a 3’ new leading-strand at the fork [20 , 21] . RecG unwinds the 5’ new lagging-strand and pairs it to the 3’ new leading-strand to generate a reversed fork [18 , 19 , 21 , 22 , 23 , 29] . However , in a coupled reaction where RecG and PriA are both present , RecG unwinds the 5’ new lagging-strand until a recessed 3’ new leading-strand end is brought to the branch point of the fork whereupon PriA binds in a configuration that does not lead to unwinding of parental template strands by the PriA helicase or continued unwinding by RecG [37] . A replication fork with a 3’ end at the branch point is a favoured substrate for PriA binding through the combined action of its N-terminal 3’ end binding domain ( 3’DB ) , a parental-strand binding winged helix domain ( WH ) and the helicase domains ( HD1 and HD2 ) thought to contact the lagging-strand [38] . The biochemical literature supports the idea most clearly presented by Masai and colleagues [35] that RecG remodels replication forks to permit the 3’ end binding mode of PriA at a stalled fork or D-loop promoting the hand-off reaction to DnaB via PriB , DnaT and DnaC [39 , 40 , 41] . In the absence of a 3’ new leading-strand at the fork , PriA alone cannot be stabilised in the configuration in which its helicase is inactive for unwinding the parental duplex [35] . Instead , PriA moves from 3’ to 5’ on the leading-strand template to unwind the parental duplex and on the lagging-strand template to unwind the 5’ new lagging strand [35] . We show here that in the absence of RecG , abnormal DNA synthesis proceeds outwards and away from a specific site of attempted DSBR . Also , we show that in the absence of RecG attempted DSBR occurs at sites known to block DNA replication forks . Furthermore , we demonstrate that the DNA loss associated with the unwinding of joint molecules observed in the absence of both RecG and RuvAB requires PriA helicase activity . These results have led us to conclude that in vivo RecG plays a critical role in directing DNA synthesis at D-loops through its remodelling of the DNA to promote the correct binding of PriA . In turn , this has led us to reconsider the RecBCD recombination pathway in bacteria and to propose a mechanism in which the presence of a 5’ terminal DNA strand at a D-loop plays a more prominent role than generally envisaged .
We have used MFA by next generation genomic DNA sequencing to determine the DNA abundance profile in a recG deletion mutant following attempted DSBR at the site of an interrupted 246 bp palindrome ( Pal+ ) in the lacZ gene of the E . coli chromosome ( Fig 1B ) , following expression of the hairpin endonuclease SbcCD [8] . In the absence of a DSB at lacZ , the MFA pattern observed in a ΔrecG mutant was as previously published [27 , 42] . An excess of DNA reads was detected in the region of the chromosome between the unidirectional termination sites , terA and terB ( S1 Fig and S2 Fig ) . Normalisation of the number of mapped sequencing reads in a ΔrecG mutant to the number of mapped reads in a Rec+ strain clearly revealed the excess of reads in the terminus region of the ΔrecG mutant ( Fig 2A ) . In the strain undergoing DSBR at the palindrome in lacZ , a similar pattern as in the strain that was not attempting DSBR was observed in the terminus region . However , there was also a loss of reads in the immediate vicinity of the DSB in lacZ followed by an excess of reads on both sides of this DSB ( Fig 2B , S1 Fig and S2 Fig ) . The effect of attempted DSBR in the lacZ region is clearly visible ( Fig 2D ) when normalising the ΔrecG Pal+ dataset ( induced DSBR , in the presence of a 246 bp palindrome at lacZ ) to the ΔrecG dataset ( no induced DSBR ) . Extra DNA accumulates on both sides of the DSBR site in a ΔrecG mutant . This extra DNA , which is not observed in a Rec+ strain ( Fig 2C ) , extends back towards the origin for about 300 kb and towards the terminus for about 1 Mb . It has previously been demonstrated that UV irradiated recG mutant cells undergo excess DNA replication that is not associated with initiation of DNA replication from the origin ( oriC ) [25 , 26] . Our work now shows that this abnormal DNA synthesis occurs on both sides of a site-specific DSBR event directly linking the location of DNA synthesis to the location of DSBR . In order to confirm that there is increased divergent DNA replication from the site of attempted DSBR in lacZ in the abnormal direction towards the origin , we inserted an ectopic terB site 50 kb origin-proximal of the palindrome in the orientation predicted to block replication forks progressing back towards the origin . We detect a 9-fold increase in replication fork blockage at this ectopic terB site in a recG mutant over Rec+ under conditions of DSBR at lacZ ( S3 Fig ) consistent with an increased level of divergent DNA replication in the recG mutant . We have previously developed a method for visualising attempted DSBR that relies on chromatin immunoprecipitation of RecA cross-linked to DNA , followed by whole genome sequencing ( RecA ChIP-seq; [43] ) . RecA is bound to DNA at sites of attempted DSBR following its loading at Chi sites by RecBCD . The shape of the RecA binding profile is distinctive . Binding rises sharply to a maximum value close to the position of a correctly oriented Chi site and then decreases with a slow exponential decay . This binding profile coupled to the locations and orientations of the Chi sites can be used to identify the region of the chromosome in which a DSB has been generated . These characteristics can also be used to distinguish between one-ended and two-ended breaks and to determine the directionality of a one-ended break . As can be seen in Fig 3A–3D , attempted DSBR in the presence and absence of RecG occurs at the site of palindrome cleavage in the lacZ gene . As expected from the results obtained in a Rec+ strain [43] , RecA enrichment was observed on both sides of the break consistent with two-ended DSBR . In addition , three sites of attempted one-ended DSBR were specifically observed in the absence of RecG . The first of these was at a Chi site oriented appropriately if a replication fork proceeding from the site of the initial DSB in lacZ towards the origin of chromosomal replication generated a double-strand end at the closest ribosomal RNA operon ( rrnH ) , 120 kb on the origin-proximal side of the DSB ( Fig 3C ) . Because this replication fork would be proceeding in the reverse direction to normal chromosomal replication , it would have encountered the rrnH operon as it moved in the opposite direction to its transcription . Replication-transcription collisions of this kind are known to result in blocking of replication forks [44 , 45 , 46 , 47 , 48] and can generate one-ended DSBs [31] . It is worth noting that the rrnH operon itself is recognised by RecA but this recognition is independent of DSBR and independent of RecG . Furthermore , it bears no hallmarks of DSBR such as correlation with Chi sites or an asymmetric distribution ( see [43] for further details of recombination independent RecA binding to rRNA genes ) . In a ΔrecG mutant , RecA binding was also detected approximately 100 kb origin-distal to the DSB in lacZ in a ΔrecG mutant ( Fig 3C ) . This peak ( which can also be detected at a low level in the Rec+ data ) most likely corresponds to the origin-distal end of the DSB at lacZ being processed at a long distance . The elevated processing at a distance in a ΔrecG mutant may be caused by unwinding of joint molecules followed by re-invasion downstream of the first Chi array or simply from RecBCD enzymes that had failed to recognise the first Chi array . The second and third sites of ΔrecG specific attempted DSBR ( Fig 3G and 3H ) were located at positions of correctly oriented Chi sites for DSBs generated at the replication termination sites terA and terB [47 , 48] . Again these events were one-ended , consistent with replication fork processing , and were oriented appropriately for replication forks proceeding outward ( from terminus towards origin ) and being blocked at the termination sites . These sites of one-ended DSBR were also the boundaries of the extra terminal DNA replication that has been detected in ΔrecG mutants by MFA ( Fig 2 and S1 Fig ) and [27 , 42] . We have shown previously that intermediates of DSBR are lost in a ΔrecG ΔruvAB double mutant [9] and have hypothesised that the branch migration activities of RecG and RuvAB stabilise joint molecules . Since RuvAB is a complex known to branch migrate Holliday junctions and to facilitate their resolution by cleavage in the presence of RuvC ( see [5] ) , we considered it likely that the stabilising activity of RuvAB is mediated by branch migration of Holliday junctions . This was confirmed by the observation that 4-way junctions accumulated in a ΔruvAB mutant [9] . However , the branch migration activity of RecG implicated in stabilising the joint molecules was less clear . The fact that 4-way junctions accumulated in the presence of RecG in a ΔruvAB mutant indicated that they were not migrated away from the region of joint molecule formation by RecG . Instead , this suggested that RecG might stabilise joint molecules by remodelling the nascent fork end of the D-loop to promote DNA synthesis from the invading 3’ end . We have now tested whether the helicase activity of PriA is responsible for the DNA loss associated with destabilising joint molecules in the absence of RecG and RuvAB . The loss of DNA following induction of DSBR at lacZ was quantified by agarose gel electrophoresis and Southern hybridisation . The recovery of the 7 . 8 kb NdeI DNA fragment containing the DSB site in lacZ ( Fig 4A ) was compared to the recovery of the 10 kb NdeI cysN control fragment situated on the opposite side of the chromosome . As can be seen in Fig 4B and 4C , 40% of the DNA undergoing DSBR in a ΔrecG ΔruvAB mutant was lost from the lacZ region . This loss was prevented in a ΔrecG ΔruvAB priA300 mutant , in which the helicase activity of PriA is inactivated by the K230R mutation [49] . The nature of the intermediates accumulated in a ΔrecG ΔruvAB priA300 mutant was investigated by two-dimensional native-native agarose gel electrophoresis . In the absence of RecG and RuvAB , the priA300 mutation increased the recovery of X-spike intermediates in the 7 . 8 kb NdeI fragment containing the DSB site , consistent with the accumulation of 4-way junctions ( Fig 4E and 4F and S4 Fig ) . Our data suggest that the helicase activity of PriA is responsible for the unwinding of D-loops in the absence of the stabilising activities of RecG and RuvAB . Since the priA300 mutation also suppresses the recombination deficiency of a recG mutant [50] , we argue that it is RecG that prevents the unwinding activity of PriA helicase , suggesting that RecG is operating to facilitate the correct binding of PriA for DNA synthesis rather than D-loop unwinding .
Previous work has demonstrated that an excess of oriC-independent DNA replication occurs in a recG mutant following UV irradiation [25] . We have shown that at an induced DSB in lacZ , and adjacent to sites of one-ended breaks in the terminus region , there is DNA over-replication that proceeds away from the direction of appropriate replication ( the direction of reconstitution of a replication fork at a D-loop ) . This establishes that the over-replication observed following attempted DSBR in a ΔrecG mutant is associated with the site of DSBR itself . Previous work has shown that the over-replication observed following UV irradiation of a recG mutant is suppressed in priA helicase mutants implicating PriA in the over-replication phenotype [25] . Given the biochemical evidence that RecG remodels the DNA at a replication fork for the appropriate binding of PriA [37] , we have considered whether , in the absence of RecG , PriA might bind to direct DNA synthesis inappropriately . In order for PriA to load DnaB incorrectly at the site of a D-loop , we envisage that PriA would bind in its 3’ end recognition mode in an orientation appropriate for loading DnaB onto the strand ending 5’ at the D-loop . We only see this as possible if the strand ending 5’ at the D-loop extends further than the 3’ ended strand ( Fig 5 ) . How far this 5’ strand extends back towards the DSB site requires further investigation as does the fate of the 3’ strand from the DSB site to Chi . One can envisage two general scenarios based upon the known biochemistry of RecBCD enzyme ( see [2 , 3 , 5] for recent reviews ) and the models presented in Fig 1A . In one scenario , degradation of the 3’ end from the DSB site to Chi occurs frequently and the 5’ strand is cleaved infrequently leading to a recessed 3’ end at Chi . Following Chi recognition , unwinding by RecBCD continues but , in the presence of RecA , the Chi-activated 5’-3’ nuclease is inhibited , retaining the extended 5’ end . This would require an extension of the “Chi modulated DNA degradation” model [2] ( see Fig 1A ) . In this new scenario , RecA loading would inhibit 5’ end cleavage by RecBCD after Chi recognition . In an alternative scenario , DNA from the DSB to Chi is unwound and the 3’ end is cleaved at Chi while the 5’ end remains intact . Following Chi recognition and cutting , unwinding continues and RecA is loaded to the 3’ strand . In this scenario , the unwound 3’ strand from the DSB site to Chi is somehow prevented from annealing to the 5’ strand . This might be accomplished by cleavage of the 3’ or 5’ stands before Chi by unknown nucleases ( e . g . ExoI or RecJ ) or by the binding of SSB to both unwound strands . This would require an extension of the “nick at Chi” model [3] to explain the fate of unwound strands between the DSB site and Chi . Previous studies have demonstrated that SSB attenuates RecBCD nuclease action and inhibits reannealing of strands unwound by RecBCD [51 , 52 , 53 , 54] . These actions of SSB are likely to promote the persistence of a protruding 5’ single-stand provided the Chi-activated 5’-3’ nuclease of RecBCD is not operating ( e . g . because of the ionic conditions or because of RecA loading ) . Our model is summarised in Fig 5A . We envisage that RecBCD enables loading of RecA to a 3’ single-strand generated by unwinding beyond the cleaved Chi site and that a joint molecule is formed that retains a 5’ tail . RuvABC migrates and resolves the Holliday junction at one end of this joint molecule allowing the formation of a replication fork with an extended 5’ end . This is the preferred substrate for RecG [20 , 21] . RecG binds and unwinds the 5’ end while reannealing the parental template stands of the fork but hands off to PriA before unwinding of the 3’ end can occur [37] , thus preventing fork reversal . In Fig 5B we show how PriA is expected to bind to permit the loading of DnaB to the lagging-strand template . In Fig 5C we compare the two possible binding modes of PriA to a substrate with a 5’ new strand at the fork in the absence of a hand-off reaction from RecG . It can be seen that a simple rotation of strands coupled to displacement of the 5’ end can lead to alternative 3’ end-binding modes that predict either loading of DnaB onto the lagging-strand template ( correct loading ) or onto the new lagging-strand ( incorrect loading ) . Because the 3’ end is available and PriA can manipulate the junction both binding modes involve recognition of the 3’ end and lead to DnaB loading rather than helicase activity . Joint molecules are formed through the action of RecBCD and RecA . We have previously proposed that in the absence of RuvAB and RecG these joint molecules are unstable because D-loops cannot be converted to replication forks by RuvABC action and because RecG is not present to carry out an unknown stabilising role [9] . We considered that this stabilising role could either be the migration of the Holliday junction away from the site of DSBR or the establishment of correct DNA synthesis from the site of the D-loop . Given the known suppression of the recG recombination defective phenotype by helicase mutants of PriA and our observation of inappropriate backward-directed DNA synthesis at sites of attempted DSBR in a ΔrecG mutant we sought to test whether PriA helicase activity might unwind D-loops in the absence of RecG and RuvAB . Our data reveal that the helicase activity of PriA is indeed responsible for the DNA loss associated with destabilisation of joint molecules in a ΔruvAB ΔrecG mutant . Two possible modes of unwinding by PriA helicase that have been observed in vitro might be responsible for this . Unwinding of the 5’ end would directly unpair one of the D-loop double-strands , while unwinding the parental duplex strands would cause strand rotation that would unwind the D-loop ( D-loop migration ) . We consider that the unwinding of the parental duplex and the consequent unwinding of the D-loop by strand rotation , required to minimise accumulation of positive supercoils ( ahead of the D-loop ) and negative supercoils ( behind the D-loop ) during its migration , is likely to be the critical activity of PriA helicase in this situation . This is because this action would result in ejection of both the 3’ and the 5’ ends from the D-loop , which would be needed to unwind the joint molecules . This action of PriA helicase requires an extended 5’ end at the replication fork side of the D-loop , to provide the single-stranded DNA region for PriA binding on the leading-strand template . This is consistent with our view that such an end is indeed present . We envisage that remodelling of the replication fork end of the D-loop is prevented in the absence of RuvAB by a persistent Holliday junction that tethers the two strands of the fork . This prevents the binding of PriA in the 3’ end-binding mode required for DnaB loading and leaves only the helicase mode of PriA binding available as shown in Fig 5D . As seen previously in a recG mutant [27 , 42] , we observe DNA over-replication in the terminus region of the chromosome between the sequences terA and terB . This over-replication is eliminated in helicase mutants of PriA [27] . We show here that terminus over-replication in the absence of RecG is not influenced by attempted DSBR at lacZ but is associated with attempted DSBR at terA and terB as revealed by RecA binding at the positions of the first correctly-oriented Chi sites adjacent to these ter sites . We therefore propose that this over-replication is caused by a similar reaction to the backward replication from D-loops that we envisage happening at the DSBR event in lacZ . Because the DSBs at terA and at terB are one-ended and outward-facing , they do not arise from replication fork collision in the centre of the terminus region as envisaged in the model proposed by Lloyd and colleagues [24 , 25 , 26 , 27 , 42] . Furthermore , our demonstration of backward-directed replication at a site of attempted DSBR in lacZ and of one-ended DSBR at terA and terB do not fit with the model of Gowrishakar [55] that does not envisage replication initiation in the terminus region . A depiction of how we envisage terminus replication in the absence of RecG is shown in Fig 6 . We propose that in the absence of RecG , a replication fork that has been blocked by collision with a Tus/ter complex is no longer protected from incorrect binding of PriA helicase . This results in the deposition of DnaB on the newly synthesised strand ending 5’ close to ter and the establishment of a fork that moves back across the terminus region until it is stopped by encounter with another ter site . At this point , another backward-directed replication fork can be assembled and replication can copy the same region again in the opposite direction . In the meantime the ends generated by backward-directed replication will attempt recombination and so create more forks that can set up more backward-directed replication as well as forks that will collide with the original ter sites . This cascade of replication in the absence of RecG explains the DNA over-replication of the terminus region . The initial formation of replication forks blocked at the ter sites in a ΔrecG mutant is likely to be contributed to by stable DNA replication as suggested previously [55] . The precise molecular details of how PriA binds in the terminus region require further investigation . It is known that Tus protein blocks DNA synthesis initially leaving a recessed 5’ end of 50–100 nt [56] . It is possible that this is a poor substrate for the hand-off reaction from RecG to PriA but is converted to a good substrate via the action of 3’ to 5’ exonucleases , the absence of which can cause RecG independent replication in the terminus region [27 , 42] . Alternatively , Tus protein itself modifies the interaction of PriA with DNA in the absence of RecG . We have shown that in the absence of RecG attempted DSBR at either the site of an induced two-ended DSB in lacZ , or at a site in which a replication fork is predicted to collide with a transcription bubble ( at the rrnH operon ) , or at sites in which replication forks are expected to collide with the Tus/ter complex at ter sites , abnormal backward-directed DNA synthesis is observed . Furthermore , we have shown that D-loops that have not been acted upon by RuvABC or RecG are unwound by the helicase activity of PriA . These results strongly suggest that RecG acts at the replication fork end of a D-loop and possibly at a stalled replication fork to direct the correct loading of the DnaB replicative helicase through the correct binding of PriA . This conclusion is supported by the biochemical evidence that the action of RecG allows PriA to associate with a synthetic replication fork substrate with a recessed 3’ end in its 3’ end-binding mode in which it can promote the further hand-off reaction to DnaB rather than acting as a helicase [37] . This new understanding of the role of RecG reconciles many roles previously proposed . The synergistic action of RecG and RuvAB is explained by alternative modes of stabilising D-loops . The apparent contradiction that RecG strongly promotes replication fork reversal in vitro whereas little evidence for this reaction has been obtained in vivo is explained by the hand-off reaction from RecG to PriA , which captures a key DNA structure and prevents fork reversal in vivo . The single situation in which fork reversal has been proposed to occur in vivo is following UV irradiation [19] . It is possible that the extent of damage overwhelms the ability of PriA to capture all the precursors to fork reversal . There is no longer any need to propose a role for RecG in the processing of flaps hypothesised to occur at sites of convergent replication forks [24 , 25 , 26 , 27] as the fork collision model is not supported by the outward facing one-ended attempted DSBR that we infer at ter sites in the absence of RecG . Our new understanding also explains why RecG has a preference for action at a replication fork substrate with an extended 5’ end . This is indeed the substrate that we hypothesise normally to be present in a D-loop since we propose that the extended 5’ end is required for the inappropriate binding of PriA ( in its incorrect 3’ end-binding mode ) in the absence of RecG . It is also the structure that we hypothesise to be required for the incorrect binding of PriA ( in its helicase mode ) in the absence of RuvAB and RecG . According to this view , RecG may be considered an early participant in the hand-off reaction from PriA to DnaB , which is required for the re-start of replication during DSBR . This pathway may be considered to run from RecG to PriA to PriB to DnaT to DnaC to DnaB [39 , 57 , 58 , 59 , 60] . Given that a pathway of replication restart from a DSB has not yet been identified in eukaryotic cells it will be interesting to know whether the potential human functional orthologue of RecG ( SMARCAL1 ) opens a window on this important reaction in higher organisms .
All strains and oligonucleotide sequences used are listed in supporting information S1 and S2 Tables ( S1 Table: DNA oligonucleotide sequences used and S2 Table: Bacterial strains used ) . The plasmid pDL4922 ( CmR Ts Sucs ) was created in order to introduce a terB site ( 5’-AATAAGTATGTTGTAACTAAAGT-3’ ) site in between the pseudogenes ykgM and eaeH of the E . coli chromosome to pause counter clockwise replication forks specifically . Primer pairs used for the cross-over PCR on BW27784 genomic DNA were ykgMterB-F1 /R1 and ykgMterB-F2/R2 . These primers permit the insertion of a terB site between the two homology arms . This fragment was cloned in pTOF24 using PstI and SalI restriction enzymes [61] . The plasmid pDL4947 ( CmR Ts Sucs ) was created in order to introduce the priA300 mutation into the priA locus of the E . coli chromosome . The region was amplified from JJC1422 using priA300 . F and priA300 . R primers , digested using SalI and PstI and inserted into the temperature sensitive plasmid pTOF24 . Overnight cultures were grown in 5ml of LB medium . The following day , cultures were diluted to an OD600nm of 0 . 02 and grown shaking at 37°C to an OD600nm of 0 . 2 . Cultures were then re-diluted to an OD600nm of 0 . 02 and grown shaking at 37°C to an OD600nm of 0 . 2 . Expression from the PBAD-sbcDC construct was induced by the addition of 0 . 2% arabinose to the culture medium . Cultures were then incubated at 37°C for 1 hour before samples were isolated . DNA was isolated from cultures after 1 hour induction of sbcDC expression using the Promega Wizard® Genomic DNA purification kit by following the manufacturer’s instructions . RNase treatment was carried out for 50 minutes and the DNA was re-hydrated overnight in TE ( 10 mM Tris ( pH 7 . 4 ) , 1 mM EDTA ) at 4°C . To further eliminate potential RNA , 3 units of Riboshredder ( RNase Blend ) were added per sample according to the manufacturer’s instructions . Samples were purified by phenol/chloroform extraction and ethanol precipitation . The integrity of the DNA was verified by running the samples on a 0 . 8% agarose gel and the quantity of DNA was determined by Nanodrop analysis ( Thermo Scientific ) and by Qubit fluorometry ( Life Technologies ) . Finally , construction of libraries and DNA sequencing was carried out on an Illumina HiSeq 2000 platform by Edinburgh Genomics , using the Illumina TruSeq DNA Sample Prep kit according to manufacturer’s instructions . Paired-end raw datasets from an Illumina HiSeq 2000 sequencing platform ( obtained from Edinburgh Genomics ) were mapped against the genomic sequence of the reference strain ‘BW27784’ using BWA sequence aligner ( version 0 . 7 . 11 ) and subsequently analysed using SAMtools ( version 1 . 2 ) . ‘BW27784’ is a modified version of E . coli K12 MG1655 ( NC000913 . 3 ) including all published differences between the strains [62 , 63] . Replication profiles of exponentially growing cultures were calculated by normalizing to the number of uniquely mapped sequence reads ( to correct for differences in depth of sequencing ) and then to the normalised reads of a non-replicating stationary-phase wild-type culture ( a Rec+ strain without palindrome ) to correct for differences in sequence-based recovery across the genome . An in-lab R-script ( available on request ) has been used to calculate the enrichment ( normalised read depth ) in 1 kb non-overlapping windows across the genome and a non-parametric smoothing method ( LOESS , Local regression ) has been applied to the data points of the replication profiles of each strain . All ChIP experiments were performed with cells grown in exponential growth phase . RecA-DNA interactions were chemically cross-linked with formaldehyde ( Sigma-Aldrich , at a final concentration of 1% ) for 10 minutes at 22 . 5°C . Crosslinking was quenched by the addition of 0 . 5 M glycine ( Sigma-Aldrich ) . Cells were collected by centrifugation at 1 , 500 x g for 10 minutes and then washed three times in ice-cold 1X PBS . The pellet was then re-suspended in 250 μl ChIP buffer ( 200 mM Tris-HCl ( pH 8 . 0 ) , 600 mM NaCl 4% Triton X , Complete protease inhibitor cocktail EDTA-free ( Roche ) ) . Sonication of crosslinked samples was performed using the Diagenode Bioruptor at 30 seconds intervals for 10 minutes at high amplitude . After sonication , 350 μl of ChIP buffer was added to each sample , the samples were mixed by gentle pipetting and 100 μl of each lysate were removed and stored as ‘input’ . Immunoprecipitation was performed overnight at 4°C using 1/100 anti-RecA antibody ( Abcam , ab63797 ) . Immunoprecipitated ( IP ) samples were then incubated with Protein G Dynabeads® ( Life Technologies ) for 2 hours with rotation at room temperature . All samples were washed three times with 1 X PBS + 0 . 02% Tween-20 before re-suspending the Protein G dynabeads in 200 μl of TE buffer + 1% SDS . 100 μl of TE buffer were added to the input samples and all samples were then incubated at 65°C for 10 hours to reverse the formaldehyde cross-links . DNA was isolated using the MinElute PCR purification kit ( Qiagen ) according to manufacturer’s instructions . DNA was eluted in 100 μl of TE buffer using a 2-step elution . Samples were stored at -20°C . Input and ChIP samples were processed following NEB’s protocol from the NEBNext ChIP-Seq library preparation kit . Briefly , input and ChIP-enriched DNA were subjected to end repair to fill in ssDNA overhangs , remove 3’ phosphates and phosphorylate the 5’ ends of sheared DNA . Klenow exo- was used to adenylate the 3’ ends of the DNA and NEXTflex DNA barcodes ( Bioo Scientific ) were ligated using T4 DNA ligase . After each step , the DNA was purified using the Qiagen MinElute PCR purification kit according to the manufacturer’s instructions . After adaptor ligation , the adaptor-modified DNA fragments were enriched by PCR using primers corresponding to the beginning of each adaptor . Finally , agarose gel electrophoresis was used to size select adaptor-ligated DNA with an average size of approximately 275 bp . All samples were quantified on a Bioanalyzer ( Agilent ) before being sequenced on the Illumina® HiSeq 2000 by BGI International . 50 bp single-end reads were mapped to the E . coli K12 ‘BW27784’ genome using Novoalign version 2 . 07 ( www . novocraft . com ) . Novoalign uses the Needleman-Wunsch algorithm to determine the optimal alignment of reads . Before mapping , the 3’ adaptor sequences were removed using fastx_clipper and the data collapsed using fastx_collapser to remove identical sequence reads ( http://hannonlab . cshl . edu/fastx_toolkit/index . html ) . Sequences were mapped with default parameters , allowing for a maximum of one mismatch per read . In order to report reads that have multiple alignment loci we specified the–r parameter as “Random” . PyReadCounters was used to calculate the overlap between aligned reads and E . coli genomic features [64] . The distribution of reads along the E . coli genome was visualized using the Integrated Genome Browser [65] . Full details of all scripts are available upon request . The raw data are shown in grey and smoothed data are shown in red . The smoothed data were plotted using a moving average filter with a 4 kb window . The data have been normalised relative to the peak of RecA ChIP observed at the rrnH locus . This peak of RecA ChIP is independent of induced DSBR at lacZ , is independent of the recG genotype and does not have the characteristics of DSBR ( it is not correlated with the positions of Chi sites and the binding is uniform across the gene ) . Whether or not this binding is of biological interest or represents a ChIP artefact remains to be determined . However , it usefully provides a way of approximately normalising reads between experiments . This normalisation cannot be considered absolute as this peak may itself be influenced by unknown factors that differ between experiments . We are therefore careful not to infer absolute levels of RecA binding between experiments . Methods were adapted from [9 , 66] | DNA double-strand breaks are accurately repaired by homologous recombination . This accuracy is ensured by copying the correct genetic information present on a second unbroken copy of the DNA , normally a sister chromosome that is generated during DNA replication . This implies that DNA synthesis occurring during recombination must be directed to replace lost or damaged base pairs but not to over-replicate undamaged chromosomal regions . Here , we investigate the genomic consequences of the absence of RecG during DNA repair following a site-specific double-strand break introduced in only one of two homologous E . coli chromosomes . Our observations suggest that RecG can re-model branched intermediates of recombination to direct the correct binding of PriA . This establishes converging replication forks that replace lost DNA at the site of DSBR and prevents over-replication of flanking DNA regions . This has led us to re-evaluate our understanding of the pathway of homologous recombination in E . coli and to propose a model in which RecG plays a critical role in remodelling branched intermediates at the interface of recombination and DNA replication . | [
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] | 2016 | RecG Directs DNA Synthesis during Double-Strand Break Repair |
Transmission lies at the interface of human immunodeficiency virus type 1 ( HIV-1 ) evolution within and among hosts and separates distinct selective pressures that impose differences in both the mode of diversification and the tempo of evolution . In the absence of comprehensive direct comparative analyses of the evolutionary processes at different biological scales , our understanding of how fast within-host HIV-1 evolutionary rates translate to lower rates at the between host level remains incomplete . Here , we address this by analyzing pol and env data from a large HIV-1 subtype C transmission chain for which both the timing and the direction is known for most transmission events . To this purpose , we develop a new transmission model in a Bayesian genealogical inference framework and demonstrate how to constrain the viral evolutionary history to be compatible with the transmission history while simultaneously inferring the within-host evolutionary and population dynamics . We show that accommodating a transmission bottleneck affords the best fit our data , but the sparse within-host HIV-1 sampling prevents accurate quantification of the concomitant loss in genetic diversity . We draw inference under the transmission model to estimate HIV-1 evolutionary rates among epidemiologically-related patients and demonstrate that they lie in between fast intra-host rates and lower rates among epidemiologically unrelated individuals infected with HIV subtype C . Using a new molecular clock approach , we quantify and find support for a lower evolutionary rate along branches that accommodate a transmission event or branches that represent the entire backbone of transmitted lineages in our transmission history . Finally , we recover the rate differences at the different biological scales for both synonymous and non-synonymous substitution rates , which is only compatible with the ‘store and retrieve’ hypothesis positing that viruses stored early in latently infected cells preferentially transmit or establish new infections upon reactivation .
HIV evolutionary analyses generally focus on either within-host dynamics or on among-host epidemiological processes [1] . The rapid evolutionary rate of HIV allows the virus to accumulate significant sequence divergence over the time course of a single infection , ensuring that within-host HIV populations can escape both considerable immune and drug selective pressure . Across multiple infections , however , these selective dynamics and within-host evolutionary arms race do not appear to strongly impact the mode of HIV diversification , as multiple co-circulating lineages generally reflect more neutral epidemiological dynamics [2] . The mechanisms involved in HIV transmission are key to this distinction and they have received a great deal of attention due to their importance for the design of preventive strategies ( e . g . [3] ) . Although transmission generally imposes a strong bottleneck on HIV within-host populations [4] , [5] , no clear phenotypic constraints appear to act on transmission apart , perhaps , from co-receptor usage , and a multitude of viral phenotypical aspects are only loosely associated with enhanced transmission [6] . In addition to studies characterizing viral founder populations , phylogenetic studies also take great interest in sequence data sampled across multiple infections . Molecular phylogenetics represents a popular approach to elucidate transmission links in a wide variety of situations , including nosocomial transmission from health care workers [7] , [8] , mother-to-child transmission [9] , sexual transmission [10] , [11] , parenteral transmission [12] and even criminal transmission [13] , [14] . Its use as a forensic tool has led to a critical appraisal of viral phylogenetics e . g . [15] , [16] , and in this respect , known transmission histories may provide valuable data to evaluate the performance of the evolutionary reconstruction methods . By comparing inferred clustering patterns with the known phylogenetic relationships in a Swedish transmission chain , Leitner et al . [17] were the first to demonstrate that phylogenetic estimates were generally consistent with the transmission history , provided the evolutionary model accounts for rate variation . A more recent analysis of an HIV transmission cluster involving 9 patients also presented phylogenetic reconstructions that were largely compatible with the known transmission history , except for one particular transmission link that appeared to be confounded by multi-drug resistance patterns in the pol gene [18] . Whereas Leitner et al . examined topological differences between a single viral and transmission tree , the more recent study took a somewhat different perspective on compatibility and examined whether any conflict arises when attempting to superimpose the host transmission history onto the viral phylogeny . This was motivated by the fact that different viral evolutionary trajectories can be embedded within a particular host transmission tree , akin to gene trees and their containing species trees [19] . In addition to confirming transmission links , the question has also been raised to what extent transmission direction and even transmission times can be ascertained through phylogenetic approaches . The former may be inferred through paraphyletic clustering of the source viruses with respect to those of the recipient , which requires adequate sampling of the viral diversity within both source and recipient [20] or samples from the source both before and after transmission [18] . To explore the temporal dimension of viral transmission , phylogenetic trees need to be calibrated in time units . This is accommodated by the incorporation of molecular clock models in phylogenetic inference and has proven useful to test hypotheses on HIV-1 and HCV transmission [21] . Applications to next-generation sequencing data have further exploited time-measured trees to provide genetic estimates of dates of HIV infection [22] , although it needs to be acknowledged that - even when a bottleneck can generally be assumed - the transmission may have occurred anywhere between the divergence from the source and the most recent common ancestor of the recipient viruses [23] . The ability to estimate divergence times and evolutionary rates from time-stamped sequence data has provided a historical perspective on the emergence of different viruses ( e . g . [24] ) and resulted in detailed investigations into the tempo of evolution at different evolutionary scales [25] , [26] . Such studies also led to the suggestion that HIV evolutionary rates may be higher within hosts compared to among hosts . Although few attempts have been made to quantify such differences , different hypotheses have been put forward to explain a potential rate discrepancy [27] and modeling efforts have been undertaken to examine them [28] . From an evolutionary biology perspective , it is difficult to explain such differences in the tempo of evolution at the different scales , and similar to differences in the mode of phylogenetic diversification , they may be dependent on how transmission is linked to within-host evolutionary dynamics . A rate mismatch may arise from the preferential transmission of stored virus , which will be ancestral to the currently circulating diversity in the source patient , and this will result in the accumulation of fewer substitutions between hosts ( ‘store and retrieve’ ) [28] , [29] . This is in line with a recent phylogenetic study that provided a genome-wide quantification of rate differences within and among-host , and although based on limited within-host data , the consistently-elevated rates across the entire genome seem to support the hypothesis that HIV strains that are less adapted to the host have an advantage during transmission [30] . Alternatively , it has been proposed the within-host adaptive process will have little impact on between host evolutionary rate estimates because many transmissions will occur early in infection before the host mounts effective immune responses ( ‘stage-specific selection’ ) [27] , [31] . Finally , Herbeck et al . [32] explain the rate mismatch by invoking frequent reversion of adaptive mutations when virus enters a new host mounting different immune responses ( ‘adapt and revert’ ) . Here , we present a new Bayesian genealogical inference approach that reconstructs within-host viral evolution and population dynamics for different individuals linked in a transmission cluster . At the core of this approach lies a transmission model that requires viral genealogies to be compatible with a timed history of transmission events from a coalescent perspective . Specifically , the model constrains the coalescent time for the source and recipient viral population to be older than the transmission event and assumes a host transition in the viral genealogy upon transmission . This approach ( i ) further relaxes requirements for topological compatibility between host and viral evolutionary history , ( ii ) makes no assumption about transmission bottlenecks , and ( iii ) makes more explicit use of the temporal dimension in viral evolutionary reconstructions from serially-sampled data . Importantly , the transmission constraints and associated parameterization of transmission times allow partitioning of the viral genealogy into patient-specific evolutionary trajectories , each informing the parameters of an overall within-host demographic model . We apply this approach to new clonal HIV-1 subtype C data from a previously-described [18] , but extended heterosexual transmission chain . Before applying the model , we test molecular clock models and evaluate the compatibility of the viral evolutionary history with the transmission model constraints . We subsequently explore the model's ability to estimate transmission bottlenecks and transmission times , and use it to quantify evolutionary rates at the interface of within and among-host HIV evolution . Our analyses clearly indicate that transmission decreases HIV-1 evolutionary rates , and since this is the case for both synonymous and non-synonymous substitutions , the findings are consistent with the hypothesis of preferential transmission of ancestral virus .
We amplified and sequenced partial pol and env regions for multiple clones from 11 patients in a previously-studied HIV-1 subtype C transmission chain [18] . Our clonal sampling includes sequences from additional time points for six out of nine previously described patients as well as sequences from two newly identified patients in the transmission cluster ( K and L; Figure S1 ) . Written informed consent was obtained from each patient [18] . Patient A and B represent the earliest infected patients in this cluster , but the time and direction of transmission between these two patients has not been clearly established . For the other transmission events , patient interviews and clinical data were able to demarcate a relatively narrow time interval for transmission ( see Table S1 ) . Table S2 lists the sampling date , the number of pol and env clonal sequences obtained for each sample and the sample viral load ( if known ) . Our sampling is not very informative about the transmission-associated bottleneck size , but it does provide a unique opportunity to investigate the impact of transmission on evolutionary rates . Although formal evaluations are sparse , evolutionary rates among hosts are suggested to be lower than evolutionary rates between hosts [27] , [28] , [30] . To investigate this using our data , we separately estimated within-host and between host evolutionary rates for both pol and env . The within-host estimate was obtained using a Bayesian hierarchical phylogenetic model ( HPM ) fit across patients for which multiple samples are available . The HPM model posits patient-specific evolutionary rate parameters , but allows sharing of evolutionary rate information across patients through a hierarchical prior specification . We report estimates of the mean of the population-level ( hierarchical ) distribution as the within-host evolutionary rate . An among-host evolutionary rate for closely related patients was obtained using a transmission model analysis that only considered a single sample per patient ( cfr . Methods ) . In addition , we compared these inferences with evolutionary rate estimates from a data set representing epidemiologically-unrelated patients infected with subtype C for the same genome regions ( cfr . Methods ) . Despite the uncertainty associated with evolutionary rate estimates , this reveals a clear rate decrease from small ( within-host ) to large ( among-host ) evolutionary scales ( Figure 5 ) , with an intermediate rate for the epidemiologically-related patients in the transmission chain . For both pol and env , we observe about a twofold decrease in evolutionary rate among epidemiologically-unrelated patients compared to the within-host evolutionary rate . The patients in the subtype C transmission chain have received antiretroviral therapy for a substantial part of the period between the first and last sample ( see Figure S1 ) , and this can affect viral evolutionary rates in different ways [18] , [38] . Because the rate estimate for the transmission chain relies on the samples before or only shortly after treatment initiation for each patient , we do not expect a considerable effect on the among-host evolutionary rate . To examine whether treatment biases the within-host evolutionary rates however , we compared our rates to estimates for a control set of longitudinally sampled therapy-naive patients ( cfr Methods ) using a Bayesian HPM approach with fixed-effects [39] . This does not support any rate differences ( see Table S5 ) , suggesting that therapy does not confound our comparison within and between hosts . Because the control data sets include sampling over different times during infection , we took the opportunity to also stratify these patients into an ‘early’ and ‘chronic’ group , based on sample availability before or after the first year of infection , in order to test for stage-specific evolutionary rates . No substantial rate difference between both groups was detected ( see Table S5 ) , which argues against evolutionary rate differences due to stage-specific selection . To test more explicitly that transmission decreases evolutionary rates , we develop a new molecular clock approach that allows for rate variation according to a relaxed molecular clock model but also incorporates fixed effects to quantify a difference in rate along a specified subset of branches ( cfr . Methods and Figure S5 ) . We applied this model within the transmission chain framework in two different ways . First , we specified an estimable rate effect on the branches to which a transmission event can be unambiguously assigned and we estimate the support for a lower rate on these branches using ln Bayes factors ( BFs ) . Using this approach , we find about a twofold lower rate on the branches that accommodate a transmission event in the subtype C transmission chain for both pol and env with a strong ln BF support ( Table 2 ) . We also extended the fixed effects to the complete branch set representing the transmitted lineage in the chain as opposed to the within-host branches that can be generally considered as evolutionary dead-ends ( see Figure S5 ) . This results in similar rate differences and associated ln BF support and complements our comparison of intra-host and inter-host evolutionary rates in providing statistical evidence for a slower among-host ‘trunk or backbone’ rate in the transmission chain compared to lineages that do not get transmitted . We note that this rate difference is not enforced by the transmission constraints because we get consistent results when using a flexible coalescent prior ( the Bayesian skyride model , Table S6 ) , even though env shows a somewhat less pronounced rate difference . The different hypotheses that have been put forward to explain rate differences within and among hosts have different expectations concerning synonymous ( ) and non-synonymous ( ) substitution rates [28] . Whereas ‘store and retrieve’ is expected to affect and rates in a similar way , ‘adapt and revert’ and ‘stage-specific selection’ are predicted to have a greater influence on non-synonymous mutations and their substitution rates . Moreover , the latter two hypotheses may also imply a more pronounced rate decrease for non-synonymous substitutions in env because of the major immunological pressure it experiences . To assess these predictions for our data , we resort to recent techniques to map codon substitutions [40] and employ them to obtain posterior estimates of and ( see Methods ) . Comparing the and estimates for both pol and env to the overall substitution rates ( Figure 5 ) , we consistently find a similar rate decrease over the different evolutionary scales , and similar decreases for both pol and env . As expected for approximately silent substitutions , estimates are highly similar between pol and env for the same evolutionary scale ( they are the only estimates with the same Y-axis scale for the corresponding pol and env panels in Figure 5 ) . Estimates of on the other hand are much higher for env due to stronger immune pressure and relaxed constraints in this gene region .
In this study , we present a novel transmission model in a Bayesian genealogical inference framework that focuses on time-calibrated viral evolutionary histories and requires such genealogies to be compatible with a known transmission history . Before applying the model to estimate HIV-1 evolutionary rates , we investigate the compatibility assumptions on new clonal data from a subtype C transmission chain and assess the model's potential to estimate transmission bottlenecks . We consider viral genealogies to be compatible with a transmission history if the viral lineages from the source and recipient coalesce before the time of transmission and if the host transitions can be superimposed onto the genealogy according to the time-ordered chain of transmission events . This approach follows gene-species tree thinking [19] and relaxes the assumption that viral and transmission trees need to be a perfect match , but explicitly incorporates temporal constraints instead . The compatibility concept we introduce here , as well as the violations we identify in our data , are important considerations for phylogenetic studies that assess transmission linkage . Conditioning on the contact tracing information being correct , the major source for the 2 to 3 incompatible transmission events we observe for both pol and env appears to be a too recent divergence time estimate for the source-recipient lineages , as exemplified by the C–D coalescence patterns , and not anomalous clustering . In this respect , it is important to note that high compatibility statistics are only expected if a considerable ancestral divergence ( or pre-transmission interval [41] ) exists for each transmission event . If the source and recipient lineages coalesce almost immediately before the time of transmission , the stochasticity of the substitution process and the stochastic error in the divergence time estimates will inevitably result in credible intervals for the divergence time of source-recipient lineages that overlap with the upper boundary for the transmission time . However , since the ancestral divergence is generally pronounced in transmission chains [41] , the fact that we do not observe high compatibility for these transmission events may be due to the same reason we invoke for the lower inter-host evolutionary rates . A preferential transmission of ancestral viruses may in fact result in more similar source-recipient lineages than expected based on their transmission time and bias their divergence time estimates towards more recent times ( see Figure S6 ) . The only instance of incompatibility that appears to result from a clustering issue involves the clustering of patient B , H and I lineages in pol . The marked difference with the clustering for env might have resulted from a pattern of convergent evolution leading to higher similarity between patient I and patient H virus in pol . An analysis excluding the positions associated with drug resistance indicated that drug selective pressure may at least have been responsible for the unexpected paraphyletic clustering of patient I with respect to patient H , but their divergence time is still too recent to be compatible with patient B as a source for both these patients . We note that convergent evolution due to drug selective pressure also induced incompatible clustering in the original analysis of the population sequences from this transmission chain [18] . However , this concerned the viruses from patients F and G , and the convergent substitution patterns involved may have a lower impact on our analyses because we use longer and therefore more informative clonal sequences . Although we attempted to exclude recombinant sequences from our analysis , we note that undetected recombination within a gene region may also be responsible for incompatibly between the viral genealogy and transmission history . Also relevant to phylogenetic investigations of viral transmission is the ability to infer transmission direction . A recent study of HIV transmission in two criminal cases suggested that transmission direction can be deduced from paraphyletic relationships that show recipient virus clades nested within the larger diversity of the source virus population [20] . We demonstrate that such relationships can be easily reconstructed in a rooted phylogeny when source samples are available before and after transmission . In the absence of such samples from the source , however , both the source and recipient diversity may need to be sampled close to transmission to be able to recover paraphyletic relationships . We show that different gene regions are not necessarily consistent in revealing recipient sub-clusters within a source clade . For example , a paraphyletic relationship is reconstructed for the FG transmission in pol but not in env . In source F , a selective sweep in the env region , which is the dominant target for immune selective pressure , might already have erased the paraphyletic structure . Indeed , within-host HIV phylogenies generally have a strong temporal structure [42] and the continual strain turnover will reduce the probability of recovering source-recipient paraphyletic relationships . In addition to the differential impact of selective pressure ( e . g . drug selective pressure in pol and immune selective pressure in env ) and incomplete lineage sorting effects in the two genome regions that may be largely unlinked due to recombination , also experimental aspects leading to non-proportional representation of variants could explain the general differences we observe among the two gene regions . The transmission model incorporates a coalescent prior that models the within-host population dynamics for each patient starting from transmission from its respective source . Although not the focus of our study , we demonstrate that a model incorporating a transmission bottleneck with subsequent logistic growth in relative genetic diversity fits our data best . Accurately quantifying the bottleneck , however , remains challenging and requires more dense sampling , in particular close to transmission . In the absence of such data , prior information on the bottleneck size may be incorporated , which in our case indicates that the bottleneck size parameter may interact with the relative timing of the transmission and recipient MRCA , provided recipient diversity is sampled close to transmission . The latter is likely to be a general requirement to accurately estimate HIV-1 infection dates from recipient coalescent times [22] . Intensive sampling throughout transmission will not only assist in estimating transmission times or quantifying bottlenecks , but it may also help to resolve whether the bottleneck results from a single variant being transmitted as opposed to the outgrowth of a single lineage from multiple transmitted viruses in the recipient [43] . In addition to more samples , the genealogical inference may also benefit from a more detailed characterization of the diversity within each sample . Nowadays this can be efficiently pursued using next-generation sequencing ( NGS ) platforms , although this would result in shorter read lengths than the clonal sequences we obtained here . Finally , conventional ( RT- ) PCR followed by either molecular cloning or NGS may both suffer from a non-proportional representation of sequences due to the re-sampling of only certain templates . This as well as other confounders can be avoided by using single genome amplification followed by direct sequencing of the amplicons [44] . We note that the availability of more comprehensive sampling may not only better inform the current model but also stimulate the development of extensions such as patient-specific coalescent parameterizations as well as more complex coalescent models , perhaps in a hierarchical framework [39] . Using the transmission model , we scrutinize HIV-1 evolutionary rates in the subtype C transmission chain and find an intermediate rate compared to within-host evolution on the one hand and evolution among epidemiologically unrelated individuals on the other hand . This suggest that the more transmissions in the HIV-1 evolutionary history , the slower the evolutionary rate , which may be consistent with the different hypotheses put forward to explain a rate mismatch at different HIV-1 evolutionary scales . The subtype C transmission chain encompasses 15 years of HIV evolution and 10 transmission events , while the subtype C evolutionary history for 81 sequences from unrelated patients encompasses about 50 years of HIV evolution but at the very least more than 8 times the number of transmission events . The fact there are far more transmission events and therefore more opportunity for transmission-associated rate decrease in the latter explains why we find the lowest evolutionary at this scale . We test the transmission-associated rate decline more explicitly by applying a new molecular clock model that allows quantifying a different rate for the branches that accommodate a transmission event . In agreement with the twofold lower rate among epidemiologically-unrelated patients compared to within-host evolutionary rates , we demonstrate a similar rate difference between branches accommodating a transmission event or branches representing the entire transmitted lineage compared to background within-host evolution in the viral genealogy for both pol and env . This suggests that lineages that avoid accumulating particular substitutions within hosts , perhaps those resulting from the evolutionary arms race , do not compromise their transmissibility and will consequently be characterized by a lower divergence rate . Evidence for a rate difference within and among hosts across the entire genome was recently interpreted as support for the ‘store and retrieve’ hypothesis [30] . Indeed , it seems unlikely that the selection forces invoked by both the ‘stage-specific selection’ hypothesis and the ‘adapt and revert’ hypothesis operate strongly across the entire genome . Our study also finds similar differences in two different genome regions , but more importantly , we provide evidence for a similar decline in both synonymous and non-synonymous substitution rates . This argues more directly against hypotheses based on selective dynamics whereas it is compatibility with the ‘store and retrieve’ hypothesis . We acknowledge that synonymous substitutions are not necessarily selectively neutral , for example due to codon usage bias and secondary RNA structure , but the selection effect will still be considerably weaker on silent versus replacement changes [45] . It is therefore not surprising that we find similar synonymous substitution rates for both pol and env at the same evolutionary scale despite very different non-synonymous rates . By focusing on synonymous substitutions , we also avoid having to compare rates for a subset of branches , such as the internal branches [30] , which , unlike external or tip branches , are less likely to represent transient ( slightly ) deleterious mutations that will be eliminated by purifying selection [46] , [47] . Whereas [30] found a more pronounced rate difference in the env gene , suggesting that reversions may also contribute to the rate difference in this gene , we find similar rate differences for both pol and env . However , our study focuses on the gp41 region of the env gene which may experience less reversions compared to the C2V5 region of env gp120 for example . By focusing on subtype C , our study extends the rate differences within and among hosts that were previously established for subtype B . However , the rate mismatch between the intra-host and interhost level for epidemiologically unrelated patients appears to be less pronounced ( about 2-fold ) than that identified for subtype B complete genomes ( about 4 to 5 fold difference , [30] ) . This discrepancy may be due to the differences in the transmission dynamics underlying subtype C and subtype B spread . Based on a recent study that provided evidence against preferential transmission from the compartmentalized virus [48] , and on rates of evolution that are even slower among IDUs than among populations where the virus is transmitted sexually [31] , Lythgoe et al . [28] claim that an inherent transmission and/or establishment advantage is the most plausible hypothesis and speculate that larger inoculum sizes during high-dose rectal and intravenous transmission may result in slower among-host rates than for sexual transmission . In the latter case , stochastic effects may be more important . Following this argumentation , the less pronounced rate mismatch we find for subtype C may be due to the largely heterosexual nature of the this epidemic as opposed to a larger contribution of homosexual and intravenous drug user ( IDU ) transmission for subtype B . However , we note that a comparison of six subtype B within-host data sets for the pol region also pointed at lower differences ( 1 . 64 fold; [30] ) . While the role of latently-infected memory T cells in creating a long-term viral reservoir was already well established as a significant barrier to HIV eradication [49] , the ‘store and retrieve’ hypothesis also attributes a major role to HIV persistence and reservoir dynamics in the conflict between HIV selective pressures at the within and between host level . HIV evolution and adaptation within a particular host has been termed ‘shortsighted’ because it is unlikely to favor viral variants that are efficiently transmitted or that efficiently establish infection in new hosts [50] . The storage of HIV variants in latent cells at an early stage and preferential transmission upon reactivation later in infection provides a mechanism to respond to the different selective pressures within and between hosts [51] . Further studies need to determine how pervasive ‘store and retrieve’ can be because it has important implications for modeling the spread of drug resistant and immune escape variants . Our analysis of an HIV transmission cluster using dedicated Bayesian inference approaches corroborates recent findings about rate differences within and among hosts and hints at potential differences between different subtypes , perhaps linked to differences in main risk group-associated transmission routes .
We obtained PCR products for both the pol and env gp41 region ( HXB2 nucleotide positions 2097 to 2292 and 7173 to 8792 respectively ) using previously described procedures that were specifically adapted for the use of Expand High Fidelity PCR System ( Roche Diagnostics , Mannheim , Germany ) [52] , [53] . PCR products from 25 samples were cloned using TOPO XL PCR cloning kit ( Life Technologies , Gent , Belgium ) , and 1–19 clones were subsequently sequenced . TOPO ligated PCR fragments were transformed into TOPO 10 cells ( Life Technologies , Gent , Belgium ) . Single colonies were used to inoculate 5 mL LB aliquots and left overnight in a shaking incubator at 37°C . Plasmid DNA was extracted from cultured cells using a QIAprep Miniprep Kit ( Qiagen , Venlo , The Netherlands ) and clones were sequenced using an ABI PRISM Big Dye Terminator v3 . 1 Ready Reaction Cycle Sequencing Kit with previously described primer sets [52] , [53] . Sequencing reactions were run on an ABI3100 Genetic Analyzer ( Life Technologies ) . Sequence fragments were assembled and analyzed using Sequence Analysis v3 . 7 and SeqScape v2 . 0 ( Life Technologies , Gent ) . For env in particular , the clone sequences were considerably longer than the previously obtained population sequences [18] because numerous insertions and deletions seriously hamper unambiguous population sequencing [53] . Testing both datasets for recombination signal with the -test [54] using SplitsTree v . 4 . 12 . 6 [55] revealed significant recombination signal ( = 8 . 328E-5 for env and = 6 . 248E-4 for pol . We omit sequences with statistically significant recombination signal , as identified using RDP3 [56] , from further analyses . Sequences were aligned using Clustal W [57] and manually edited according to their codon reading frame in Se-Al ( http://tree . bio . ed . ac . uk ) . Because identical clones might have resulted from template re-sampling [58] , especially at lower viral loads , we analyzed only unique sequences obtained from the isolates . We conducted Bayesian evolutionary reconstructions using BEAST for both the pol and env gp41 alignments employing either the Skyride model [33] or the transmission model discussed below . The Skyride model was used as a flexible demographic tree prior in analyses aimed at testing molecular clocks and evaluating the coalescent compatibility of viral genealogy with the known transmission history , before enforcing this compatibility in analysis using the transmission model . The latter is based on the compatibility concept outlined below as the first part of the transmission model , and formalized into a compatibility statistic . Specifically , we record a statistic for each transmission in each sampled genealogy that evaluates whether the source-recipient coalescent events pre-date the specific transmission event and whether the correct host transition order can be superimposed onto the viral genealogy , allowing us to calculate the posterior compatibility probability for each transmission event . We perform our analyses with a codon position partitioning into first+second and third positions , each associated with a general-time reversible ( GTR ) model and among site rate heterogeneity modeled using a discrete -distribution and a proportion of invariable sites . We apply the same substitution model and among-site rate variability to the data discussed in the next sections . MCMC chains were run sufficiently long to ensure convergence , as inspected using Tracer v1 . 5 ( http://tree . bio . ed . ac . uk ) . Maximum clade credibility ( MCC ) trees were summarized using the TreeAnnotator tool in BEAST and visualized in FigTree v1 . 4 ( http://tree . bio . ed . ac . uk ) . Molecular clock models , including a strict clock assumption as well as the uncorrelated relaxed clock models with underlying exponential ( uced ) and lognormal distribution ( ucld ) , were tested using recent implementations of path sampling ( PS ) [59] and stepping-stone ( SS ) sampling [60] estimators of the marginal likelihood in BEAST [35] . Both PS and SS have been shown to outperform the widely-used harmonic mean estimators [35] and offer similar performance as Bayesian model averaging when proper priors are used [36] . The length of each power posterior MCMC length in the PS/SS approach was set to he number of states for the standard MCMC analysis divided by the number of steps taken to arrive at the prior . We implement a new genealogical model in the BEAST statistical inference software [61] that accounts for the known transmission links among patients and allows estimating evolutionary parameters as well as transmission times and within-host population dynamics from viral diversity sampled through time . BEAST infers rooted , time-calibrated genealogies with a coalescent or birth-death process as a prior distribution for the branching events [61] . Generally , the entire genealogy is assumed to be generated by a single coalescent or branching process . To accommodate the specific transmission structure and within-host population dynamics , we modify this standard prior specification in two ways . First , we enforce the viral genealogy to be compatible with the known transmission history by enforcing coalescent events between source and recipient lineages to exist before the transmission time and assuming a transmission-associated source-recipient host transition along the relevant lineages in the viral genealogy . In Figure 1 , we illustrate the coalescent compatibility concept for a hypothetical transmission chain of 4 patients and a particular genealogy of viruses sampled from each patient . For each transmission event , we show an upper and lower boundary for the transmission event which represents the fact that the actual transmission time is difficult to pinpoint , but a transmission interval can often be defined using external information ( e . g . based on the last negative and first HIV positive test for the recipient ) . In this case , we require the coalescent times for source and recipient lineages to predate the upper boundary for the transmission time and superimpose transmission-associated host transitions onto the viral genealogy as depicted by the transitions in branch colors in Figure 1 . The latter allows tracking the host transition history in the viral genealogy and ensures that also inadequate clustering of patient-specific lineages can lead to incompatibility despite the fact the relevant coalescent times may still be compatible with their transmission time ( as observed for the patient H and I lineages in the pol genealogy in the subtype C transmission cluster ) . In the example genealogy , both the coalescent events for lineages from patient 1 and patient 2 , and from patient 2 and patient 3 , are compatible with their transmission events , despite the fact that two lineages are transmitted during the latter event . In contrast , the most recent common ancestor for lineages from patient 3 and patient 4 appears to be more recent than the upper boundary for the transmission time between patient 3 and patient 4 , which is considered to be incompatible under our model . Therefore , no genealogies would be allowed to have such coalescent patterns under the transmission constraints . We explicitly parameterize the transmission times for each transmission event and integrate out their dates over the known transmission time intervals in our inference framework . The transmission time parameters naturally partition the genealogy into patient-specific lineages represented by the different branch colors in Figure 1 . This allows us to model a within-host coalescent process for each patient and estimate population parameters based on the distribution of waiting times in the patient-specific lineages . Because within-host sampling is generally sparse for transmission clusters , all patients share the same coalescent model in the current implementation of the model , except maybe for the ultimate source of the transmission cluster which cannot be related to its respective source patient . We consider simple demographic functions as coalescent models , including constant population size:where is the effective population size in the recipient ( ) at time and is the effective population size at time of transmission ( ) from the source ( ) to . Because this model assumes no transmission bottleneck ( ) , where is the effective population size in at ) , we also consider an exponential growth model:where represents an ancestral proportion ( ) of the effective population size in at time of transmission and represents the exponential growth rate , and extend this further to a logistic growth model:with So , the latter two demographic functions are explicitly parameterized in terms of a transmission bottleneck . Because this cannot be applied to the patient at the origin of the transmission chain ( e . g . patient 1 in Figure 1 ) , we allow specifying a separate demographic function for this patient using standard parametric formulations . Although constant , exponential and logistic models can therefore also be applied to this patient , we consistently opted for a simple constant model because the putative source patients are only sparsely sampled through time ( see Figure S1 ) . Our BEAST implementation enables the simultaneous inference of viral genealogical history , including the tempo and mode of viral evolution , and transmission times and bottlenecks . We sample from the posterior distribution using Markov chain Monte Carlo ( MCMC ) incorporating standard transition kernels . In order to compare evolutionary rate estimates at different scales , we distinguish between HIV-1 evolution within hosts , among epidemiologically-related hosts and among epidemiologically unrelated hosts . We study the first two processes based on data from the subtype C transmission cluster and discuss the data and associated analysis for the remaining evolutionary scale in the next section . To obtain a ‘pure’ within-host evolutionary rate estimate for pol and env gp41 across different patients , we apply a Bayesian Hierarchical Phylogenetic ( HPM ) model to the transmission cluster patients for which sequences from multiple time points are available ( see Table S2 ) [62] , [63] . This approach allows specifying independent genealogies for each patient while pooling information on evolutionary and population genetic parameters across patients through hierarchical prior specification . Due to the sparse sampling within the patients , we resort to a strict molecular clock model and apply a constant population size model to the patient-specific genealogy . We specify hierarchical prior distributions over the evolutionary rate and demographic parameters , allowing them to vary around an unknown common mean . We consider the mean estimate of the hierarchical prior distribution for the evolutionary rate as a quantification of the overall within-host evolutionary rate . To reduce the impact of within-host evolution among the epidemiologically-related patients , we only include the time point of each patient closest to the transmission event from its source . Because HIV-1 replication rate and evolutionary rate may be affected by drug treatment , which was common for the patients in our subtype C transmission chain , we sought to investigate how comparable our within-host rate was to estimates from untreated patients . For this purpose , we compiled control data sets based on a search for intra-patient sequences in the HIV sequence database ( http://www . hiv . lanl . gov/ ) according to the following criteria: ( i ) longitudinal samples , ( ii ) known time of sampling , ( iii ) untreated , ( iv ) pol or env genome region , including fragments with a minimum length of 200 . By screening the relevant publications , we identified 10 and 15 studies with pol and env data respectively that met our criteria . To ensure a close match in genomic region , all sequence data were aligned against the clonal data . Only sequences with >75% overlap with the clonal data of the respective region were kept for further analysis . Because only few pol sequences ( 9/30 patients ) spanned the entire length of the clonal sequence alignment , we trimmed the alignment from 421 AA to 330 AA ( = 78 . 5% of the original length ) . Finally , duplicates were removed and sequences were grouped per patient . This resulted in the inclusion of 30 patients for which the serially-sampled sequences start at AA 1 of protease and end at AA 231 of reverse transcriptase ( numbering according to HXB2 ) . For env , where the sequence overlap was less of an issue , the intermediate alignment consisted of 34 patients and comprised AA 468 to AA 856 of gp160 . We refer to Tables S7 and S8 for a detailed overview of all control datasets . In addition to serving as untreated controls , both the pol and env data sets were stratified in those sampled during ‘early’ and ‘chronic’ stage of infection by making use of the available disease stage information , in order to assess what impact this has on substitution rates estimates . In particular we classified sequences under ‘early’ when they were sampled in the first year of infection and ‘chronic’ when they were sampled later in infection . The data from the subtype C transmission chain corresponds better with data from the chronic stage of infection , which generally also allows for sampling over longer time periods providing potentially more calibration information . Following the within-host evolutionary analysis for transmission chain patients , we apply a Bayesian HPM procedure to this data , but extend it with fixed effects to test for differences among patient groups [63] . The fixed-effects HPM enables the estimation of Bayes Factor ( BF ) support for the early vs . chronic group effect on any evolutionary parameter , in our case the evolutionary rate . To complement our rate estimates within hosts and among epidemiologically-linked hosts , we compile a representative subtype C data set from epidemiologically-unlinked hosts . To this purpose , we retrieve and align all available HIV-1 subtype C full genomes with annotated sampling year from the Los Alamos HIV sequence database ( http://www . hiv . lanl . gov/ ) . From the resulting alignment containing 505 sequences , we select a diverse subset to minimize epidemiological relatedness and ensure that they are representative for the diversity of the subtype C epidemic . At the same time , we aim to spread the sampling density over the available sampling time interval . Therefore , we select the 5 most divergent sequences within each sampling year by constructing a BioNJ tree in Seaview [64] , followed by subsampling according to diversity . For the latter we make use of the Phylogenetic Diversity algorithm , which selects for the subtree of taxa connected by the longest branch length [65] . For the year 1989 , we kept only two sequences in our selection because 3 of the 4 available sequences were from the same patient . This selection procedure resulted in a dataset of 82 taxa spanning the period of 1986–2010 . Inspection of the temporal signal by plotting root-to-tip divergence as a function of sampling time in Path-O-Gen v1 . 3 ( http://tree . bio . ed . ac . uk/ ) lead us to remove 1 outlier sequence from 2009 , and showed clear signal for divergence accumulation over the sampling time interval ( R2 = 0 . 50 ) for the remaining 81 full genomes . We again used the -test [54] as implemented in SplitsTree v4 . 12 . 6 [55] to detect recombination; no significant signal was found . For the Bayesian genealogical inference , we partition the full genome by gene to allow for among-gene rate variation . We further subdivide the rev and tat genes according to their splicing parts , and split pol and env into the region that overlaps with our clonal data and the remainder of the gene . We specify a Bayesian HPM for the gene-specific GTR substitution model parameters , the shape parameter for -distribution modeling among-site rate variation , and for the proportion of invariant sites . Similar to the analyses of the other data sets , we specify a Bayesian Skyride model as a flexible demographic prior for the tree . To quantify and test for different evolutionary rates along an arbitrary branch set in the genealogy , we develop a novel mixed effects molecular clock approach in our Bayesian framework that combines both fixed and random effects . Following standard hierarchical modeling terminology , the random effects quantify possibly different rates for each branch and we posit that these effects arise from an uncorrelated relaxed clock process following [34] on the log-scale . Assuming effects are additive on the log-scale , we further incorporate fixed effects to allow for different overall rates on fixed subset of branches in the unknown genealogy . Specifically , we model the overall rate on branch as ( 1 ) where is the fixed design indicator or covariate associated with branch . We test two different fixed-effect designs for the transmission chain data: one that differentiates branches along which a transmission event occurred from the remaining branches and one that differentiates the branches representing the transmitted lineage from the remaining branches ( see Figure S5 ) . In the former case , we focus on single branches that unequivocally represent a transmission event . For these branches , we set ; for all other branches , we set . To achieve unequivocal events , we omit the sequence samples from patient G and the earliest sampling time point for patient C ( C94 ) , which complicate the unambiguous assignment of transmission events . The remaining nine transmission-associated branches are generally well-supported in the posterior according to the genealogical inference , but we enforce the descendent taxa to be monophyletic in the molecular clock inference to ensure that the effect is always associated with an identifiable branch . For the second approach , we specify ‘transmitted’ lineages for the full data set without monophyletic constraints as the set of branches from the root of the tree to the MRCAs of patients from which there is no more onwards transmission in the chain; these branches receive and represent the ‘trunk’ or ‘backbone’ lineages [47] for multiple patients in a transmission chain . To evaluate the significance of the fixed-effect specification , we conduct a Bayes factor ( BF ) test [66] that expresses the posterior odds over the prior odds that rates on the branches of interest ( transmission-associated or transmission lineage-associated ) are lower than the background within-host branches . We perform the BF test using the posterior sample obtained via MCMC directly since the restricted hypothesis is nested within the unconstrained model that we simulate . Under the unconstrained model , the posterior sample average of the indicator converges to the posterior probability of the constrained hypothesis . Since the prior odds in our case simplify to , we simply need to compute the odds ratio of the mean indicator value to estimate the BF . To estimate absolute synonymous and non-synonymous substitution rates , we integrate recently developed stochastic mapping procedures in the BEAST analyses described above [40] . We follow an approach that is conceptually similar to [47] , but is computationally more efficient in accommodating the uncertainty about the phylogenetic tree and about other nuisance parameters . Briefly , we fit codon position partitioned substitution models in a Bayesian framework and use standard MCMC integration to obtain a sample from the posterior distribution of model parameters . At each iteration of the MCMC , we use stochastic mapping to impute the full evolutionary history of each nucleotide position within each codon site in our alignment and subsequently summarize the resulting numbers synonymous ( ) and non-synonymous ( ) substitutions . To obtain posterior estimates of synonymous ( ) and non-synonymous ( ) evolutionary rates in substitutions per site per year , we divide the total and counts at each iteration by the total tree length in time units , and summarize these quantities across the posterior distribution of trees to arrive at mean estimates and credible intervals . To obtain an overall within-host and estimate for comparison with the estimates for the epidemiologically-linked and epidemiologically-unrelated data sets , we sum the and counts for the patient-specific genealogies at each iteration in the HPM analysis and divide them by the sum of the respective tree lengths , and then also summarize these quantities across all samples . | Since its discovery three decades ago , the HIV epidemic has unfolded into one of the most devastating pandemics in human history . When HIV replication cannot be completely inhibited , the fast-evolving retrovirus continuously evades intra-host immune and drug selective pressure , but diversifies according to more neutral epidemiological dynamics at the interhost level . Limited evidence suggests that the virus may evolve faster in a single host than in a population of hosts , and various hypotheses have been put forward to explain this phenomenon . Here , we develop a new computational approach aimed at integrating host transmission information with pathogen genealogical reconstructions . We apply this approach to comprehensive sequence data sets sampled from a large HIV-1 subtype C transmission chain , and in addition to providing several insights into the reconstruction of HIV-1 transmissions histories and its associated population dynamics , we find that transmission decreases the HIV-1 evolutionary rate . The fact that we also identify this decline for substitutions that do not alter amino acid substitutions provides evidence against hypotheses that invoke selection forces . Instead , our findings support earlier reports that new infections start preferentially with less evolved variants , which may be stored in latently infected cells , and this may vary among different HIV-1 subtypes . | [
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] | 2014 | The Genealogical Population Dynamics of HIV-1 in a Large Transmission Chain: Bridging within and among Host Evolutionary Rates |
General results from statistical learning theory suggest to understand not only brain computations , but also brain plasticity as probabilistic inference . But a model for that has been missing . We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations . This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values . It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience , how cortical networks can generalize learned information so well to novel experiences , and how they can compensate continuously for unforeseen disturbances of the network . The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling .
We reexamine in this article the conceptual and mathematical framework for understanding the organization of plasticity in networks of neurons in the brain . We will focus on synaptic plasticity and network rewiring ( spine motility ) in this article , but our framework is also applicable to other network plasticity processes . One commonly assumes , that plasticity moves network parameters θ ( such as synaptic connections between neurons and synaptic weights ) to values θ* that are optimal for the current computational function of the network . In learning theory , this view is made precise for example as maximum likelihood learning , where model parameters θ are moved to values θ* that maximize the fit of the resulting internal model to the inputs x that impinge on the network from its environment ( by maximizing the likelihood of these inputs x ) . The convergence to θ* is often assumed to be facilitated by some external regulation of learning rates , that reduces the learning rate when the network approaches an optimal solution . This view of network plasticity has been challenged on several grounds . From the theoretical perspective it is problematic because in the absence of an intelligent external controller it is likely to lead to overfitting of the internal model to the inputs x it has received , thereby reducing its capability to generalize learned knowledge to new inputs . Furthermore , networks of neurons in the brain are apparently exposed to a multitude of internal and external changes and perturbations , to which they have to respond quickly in order to maintain stable functionality . Other experimental data point to surprising ongoing fluctuations in dendritic spines and spine volumes , to some extent even in the adult brain [1] and in the absence of synaptic activity [2] . Also a significant portion of axonal side branches and axonal boutons were found to appear and disapper within a week in adult visual cortex , even in the absence of imposed learning and lesions [3] . Furthermore surprising random drifts of tuning curves of neurons in motor cortex were observed [4] . Apart from such continuously ongoing changes in synaptic connections and tuning curves of neurons , massive changes in synaptic connectivity were found to accompany functional reorganization of primary visual cortex after lesions , see e . g . [5] . We therefore propose to view network plasticity as a process that continuously moves high-dimensional network parameters θ within some low-dimensional manifold that represents a compromise between overriding structural rules and different ways of fitting the internal model to external inputs x . We propose that ongoing stochastic fluctuations ( not unlike Brownian motion ) continuously drive network parameters θ within such low-dimensional manifold . The primary conceptual innovation is the departure from the traditional view of learning as moving parameters to values θ* that represent optimal ( or locally optimal ) fits to network inputs x . We show that our alternative view can be turned into a precise learning model within the framework of probability theory . This new model satisfies theoretical requirements for handling priors such as structural constraints and rules in a principled manner , that have previously already been formulated and explored in the context of artificial neural networks [6 , 7] , as well as more recent challenges that arise from probabilistic brain models [8] . The low-dimensional manifold of parameters θ that becomes the new learning goal in our model can be characterized mathematically as the high probability regions of the posterior distribution p* ( θ∣x ) of network parameters θ . This posterior arises as product of a general prior p𝒮 ( θ ) for network parameters ( that enforces structural rules ) with a term that describes the quality of the current internal model ( e . g . in a predictive coding or generative modeling framework: the likelihood p𝒩 ( x∣θ ) of inputs x for the current parameter values θ of the network 𝒩 ) . More precisely , we propose that brain plasticity mechanisms are designed to enable brain networks to sample from this posterior distribution p* ( θ∣x ) through inherent stochastic features of their molecular implementation . In this way synaptic and other plasticity processes are able to carry out probabilistic ( or Bayesian ) inference through sampling from a posterior distribution that takes into account both structural rules and fitting to external inputs . Hence this model provides a solution to the challenge of [8] to understand how posterior distributions of weights can be represented and learned by networks of neurons in the brain . This new model proposes to reexamine rules for synaptic plasticity . Rather than viewing trial-to-trial variability and ongoing fluctuations of synaptic parameters as the result of a suboptimal implementation of an inherently deterministic plasticity process , it proposes to model experimental data on synaptic plasticity by rules that consist of three terms: the standard ( typically deterministic ) activity-dependent ( e . g . , Hebbian or STDP ) term that fits the model to external inputs , a second term that enforces structural rules ( priors ) , and a third term that provides the stochastic driving force . This stochastic force enables network parameters to sample from the posterior , i . e . , to fluctuate between different possible solutions of the learning task . The stochastic third term can be modeled by a standard formalism ( stochastic Wiener process ) that had been developed to model Brownian motion . The first two terms can be modeled as drift terms in a stochastic process . A key insight is that one can easily relate details of the resulting more complex rules for the dynamics of network parameters θ , which now become stochastic differential equations , to specific features of the resulting posterior distribution p* ( θ∣x ) of parameter vectors θ from which the network samples . Thereby , this theory provides a new framework for relating experimentally observed details of local plasticity mechanisms ( including their typically stochastic implementation on the molecular scale ) to functional consequences of network learning . For example , one gets a theoretically founded framework for relating experimental data on spine motility to experimentally observed network properties , such as sparse connectivity , specific distributions of synaptic weights , and the capability to compensate against perturbations [9] . We demonstrate the resulting new style of modeling network plasticity in three examples . These examples demonstrate how previously mentioned functional demands on network plasticity , such as incorporation of structural rules , automatic avoidance of overfitting , and inherent and immediate compensation for network perturbances , can be accomplished through stochastic local plasticity processes . We focus here on common models for unsupervised learning in networks of neurons: generative models . We first develop the general learning theory for this class of models , and then describe applications to common non-spiking and spiking generative network models . Both structural plasticity ( see [10 , 11] for reviews ) and synaptic plasticity ( STDP ) are integrated into the resulting theory of network plasticity .
In contrast , we assume here that not only a neural network 𝒩 , but also a prior p𝒮 ( θ ) for its parameters are given . This prior p𝒮 can encode both structural constraints ( such as sparse connectivity ) and structural rules ( e . g . , a heavy-tailed distribution of synaptic weights ) . Then the goal of network learning becomes: learn the posterior distribution p * ( θ | x ) defined ( up to normalization ) by p 𝒮 ( θ ) · p 𝒩 ( x | θ ) . ( 2 ) The patterns x = x1 , … , xN are assumed here to be regularly reoccurring network inputs . A key insight ( see Fig 1 for an illustration ) is that stochastic local plasticity rules for the parameters θi enable a network to achieve the learning goal Eq ( 2 ) : The distribution of network parameters θ will converge after a while to the posterior distribution p* ( θ ) = p* ( θ∣x ) —and produce samples from it—if each network parameter θi obeys the dynamics d θ i = b ( ∂ ∂ θ i log p 𝒮 ( θ ) + ∂ ∂ θ i log p 𝒩 ( x | θ ) ) d t + 2 b d W i , ( 3 ) where the learning rate b > 0 controls the speed of the parameter dynamics . Eq ( 3 ) is a stochastic differential equation ( see [16] ) , which differs from commonly considered differential equations through the stochastic term d𝒲i that describes infinitesimal stochastic increments and decrements of a Wiener process 𝒲i . A Wiener process is a standard model for Brownian motion in one dimension ( more precisely: the limit of a random walk with infinitesimal step size and normally distributed increments W i t − W i s ∼ N o r m a l ( 0 , t − s ) between times t and s ) . Thus in an approximation of Eq ( 3 ) for discrete time steps Δt the term d𝒲i can be replaced by Gaussian noise with variance Δt ( see Eq ( 7 ) ) . Note that Eq ( 3 ) does not have a single solution θi ( t ) , but a continuum of stochastic sample paths ( see Fig 1F for an example ) that each describe one possible time course of the parameter θi . Rigorous mathematical results based on Fokker-Planck equations ( see Methods and S1 Text for details ) allow us to infer from the stochastic local dynamics of the parameters θi given by a stochastic differential equation of the form Eq ( 3 ) the probability that the parameter vector θ can be found after a while in a particular region of the high-dimensional space in which it moves . The key result is that for the case of the stochastic dynamics according to Eq ( 3 ) this probability is equal to the posterior p* ( θ∣x ) given by Eq ( 2 ) . Hence the stochastic dynamics Eq ( 3 ) of network parameters θi enables a network to achieve the learning goal Eq ( 2 ) : to learn the posterior distribution p* ( θ∣x ) . This posterior distribution is not represented in the network through any explicit neural code , but through its stochastic dynamics , as the unique stationary distribution of a Markov process from which it samples continuously . In particular , if most of the mass of this posterior distribution is concentrated on some low-dimensional manifold , the network parameters θ will move most of the time within this low-dimensional manifold . Since this realization of the posterior distribution p* ( θ∣x ) is achieved by sampling from it , we refer to this model defined by Eq ( 3 ) ( in the case where the parameters θi represent synaptic parameters ) as synaptic sampling . The stochastic term d𝒲i in Eq ( 3 ) provides a simple integrative model for a multitude of biological and biochemical stochastic processes that effect the efficacy of a synaptic connection . The mammalian postsynaptic density comprises over 1000 different types of proteins [17] . Many of those proteins that effect the amplitude of postsynaptic potentials and synaptic plasticity , for example NMDA receptors , occur in small numbers , and are subject to Brownian motion within the membrane [18] . In addition , the turnover of important scaffolding proteins in the postsynaptic density such as PSD-95 , which clusters glutamate receptors and is thought to have a substantial impact on synaptic efficacy , is relatively fast , on the time-scale of hours to days , depending on developmental state and environmental condition [19] . Also the volume of spines at dendrites , which is assumed to be directly related to synaptic efficacy [20 , 21] is reported to fluctuate continuously , even in the absence of synaptic activity [2] . Furthermore the stochastically varying internal states of multiple interacting biochemical signaling pathways in the postsynaptic neuron are likely to effect synaptic transmission and plasticity [22] . The contribution of the stochastic term d𝒲i in Eq ( 3 ) can be scaled by a temperature parameter T , where T can be any positive number . The resulting stationary distribution of θ is proportional to p * ( θ ) 1 T , so that the dynamics of the stochastic process can be described by the energy landscape log p * ( θ ) T . For high values of T this energy landscape is flattened , i . e . , the main modes of p* ( θ ) become less pronounced . For T → 0 the dynamics of θ approaches a deterministic process and converges to the next local maximum of p* ( θ ) . Thus the learning process approximates for low values of T maximum a posteriori ( MAP ) inference [7] . We propose that this temperature parameter T is regulated in biological networks of neurons dependent on the developmental state , environment , and behavior of an organism . One can also accommodate a modulation of the dynamics of each individual parameter θi by a learning rate b ( θi ) that depends on its current value ( see Methods ) . For online learning one assumes that the likelihood p𝒩 ( x∣θ ) = p𝒩 ( x1 , … , xN∣θ ) of the network inputs can be factorized: p 𝒩 ( x 1 , … , x N | θ ) = ∏ n = 1 N p 𝒩 ( x n | θ ) , ( 4 ) i . e . , each network input xn can be explained as being drawn individually from p𝒩 ( xn∣θ ) , independently from other inputs . The weight update rule Eq ( 3 ) depends on all inputs x = x1 , … , xN , hence synapses have to keep track of the whole set of all network inputs for the exact dynamics ( batch learning ) . In an online scenario , we assume that only the current network input xn is available for synaptic sampling . One then arrives at the following online-approximation to Eq ( 3 ) d θ i = b ( ∂ ∂ θ i log p 𝒮 ( θ ) + N ∂ ∂ θ i log p 𝒩 ( x n | θ ) ) d t + 2 b d W i . ( 5 ) Note the additional factor N in the rule . It compensates for the N-fold summation of the first and last term in Eq ( 5 ) when one moves through all N inputs xn . Although convergence to the correct posterior distribution cannot be guaranteed theoretically for this online rule , we show in Methods that the rule is a reasonable approximation to the batch-rule Eq ( 3 ) . Furthermore , all subsequent simulations are based on this online rule , which demonstrates the viability of this approximation . Typically , synaptic plasticity in generative network models is modeled as maximum likelihood learning . Time is often discretized into small discrete time steps Δt . For gradient-based approaches the parameter change Δ θ i M L is then given by the gradient of the log likelihood multiplied with some learning rate η: Δ θ i M L = η ∂ ∂ θ i log p 𝒩 ( x n | θ ) . ( 6 ) To compare this maximum likelihood update with synaptic sampling , we consider a version of the parameter dynamics Eq ( 5 ) for discrete time ( see Methods for a derivation ) : Δ θ i = η ( ∂ ∂ θ i log p 𝒮 ( θ ) + N ∂ ∂ θ i log p 𝒩 ( x n | θ ) ) + 2 η ν i t , ( 7 ) where the learning rate η is given by η = b Δt and ν i t denotes Gaussian noise with zero mean and variance 1 , drawn independently for each parameter θi and each update time t . We see that the maximum likelihood update Eq ( 6 ) becomes one term in this online version of synaptic sampling . Eq ( 7 ) is a special case of the online Langevin sampler that was introduced in [23] . The first term ∂ ∂ θ i log p 𝒮 ( θ ) in Eq ( 7 ) arises from the prior p𝒮 ( θ ) , and has apparently not been considered in previous rules for synaptic plasticity . An additional novel component is the Gaussian noise term ν i t ( see also Fig 1G ) . It arises because the accumulated impact of the Wiener process 𝒲i over a time interval of length Δt is distributed according to a normal distribution with variance Δt . In contrast to traditional maximum likelihood optimization based on additive noise for escaping local optima , this noise term is not scaled down when learning approaches a local optimum . This ongoing noise is essential for enabling the network to sample from the posterior distribution p* ( θ ) via continuously ongoing synaptic plasticity ( see Fig 1F ) . The previously described theory for learning a posterior distribution over parameters θ can be applied to all neural network models 𝒩 where the derivative ∂ ∂ θ i log p 𝒩 ( x n | θ ) in Eq ( 5 ) can be efficiently estimated . Since this term also has to be estimated for maximum likelihood learning Eq ( 6 ) , synaptic sampling can basically be applied to all neuron and network models that are amenable to maximum likelihood learning . We illustrate salient new features that result from synaptic sampling ( i . e . , plasticity rules Eqs ( 5 ) or ( 7 ) ) for some of these models . We begin with the Boltzmann machine [24] , one of the oldest generative neural network models . It is currently still extensively investigated in the context of deep learning [25 , 26] . We demonstrate in Fig 2D and 2F the improved generalization capability of this model for the learning approach Eq ( 2 ) ( learning of the posterior ) , compared with maximum likelihood learning ( approach Eq ( 1 ) ) , which had been theoretically predicted by [6] and [7] . But this model for learning the posterior ( approach Eq ( 2 ) ) in Boltzmann machines is now based on local plasticity rules . Note that the Boltzmann machine with synaptic sampling samples simultaneously on two different time scales: In addition to sampling for given parameters θ from likely network states in the usual manner , it now samples simultaneously on a slower time scale according to Eq ( 7 ) from the posterior of network parameters θ . A Boltzmann machine employs extremely simple non-spiking neuron models with binary outputs . Neuron yi outputs 1 with probability σ ( ∑j wij yj + bi ) , else 0 , where σ is the logistic sigmoid σ ( u ) = 1 1 + e - u , with synaptic weights wij and bias parameters bi . Synaptic connections in a Boltzmann machine are bidirectional , with symmetric weights ( wij = wji ) . The parameters θ for the Boltzmann machine consist of all weights wij and biases bi in the network . For the special case of a restricted Boltzmann machine ( RBM ) , maximum likelihood learning of these parameters can be done efficiently [27] , and therefore RBM’s are typically used for deep learning . An RBM has a layered structure with one layer of visible neurons x and a second layer of hidden neurons z . Synaptic connections are formed only between neurons on different layers ( Fig 2A ) . The maximum likelihood gradients Δ w i j M L = ∂ ∂ w i j log p 𝒩 ( x | θ ) and Δ b i M L = ∂ ∂ b i log p 𝒩 ( x | θ ) can be efficiently approximated for this model , for example ∂ ∂ w i j log p 𝒩 ( x n | θ ) ≈ z i n x j n - z ^ i n x ^ j n , ( 8 ) where x j n is the output of input neuron j while input xn is presented , and x ^ j n its output during a subsequent phase of spontaneous activity ( “reconstruction phase” ) ; analogously for the hidden neuron zj ( see Methods and S3 Text ) . We integrated this maximum likelihood estimate Eq ( 8 ) into the synaptic sampling rule Eq ( 7 ) in order to test whether a suitable prior p𝒮 ( w ) for the weights improves the generalization capability of the network . The network received as input just five samples x1 , … , x5 of a handwritten Arabic number 1 from the MNIST dataset ( the training set , shown in Fig 2A ) that were repeatedly presented . Each pixel of the digit images was represented by one neuron in the visible layer ( which consisted of 784 neurons ) . We selected a second set of 100 samples of the handwritten digit 1 from the MNIST dataset as test set ( Fig 2B ) . These samples include completely different styles of writing that were not present in the training set . After allowing the network to learn the five input samples from Fig 2A for various numbers of update steps ( horizontal axis of Fig 2D and 2F ) , we evaluated the learned internal model of this network 𝒩 for the digit 1 by measuring the average log-likelihood log p𝒩 ( x∣θ ) for the test data . The result is indicated in Fig 2D and 2F for the training samples by the blue curves , and for the new test examples , that were never shown while synaptic plasticity was active , by the red curves . First , a uniform prior over the synaptic weights was used ( Fig 2C ) , which corresponds to the common maximum likelihood learning paradigm Eq ( 8 ) . The performance on the test set ( shown on vertical axis ) initially increases but degrades for prolonged exposure to the training set ( length of that prior exposure shown on horizontal axis ) . This effect is known as overfitting [6 , 7] . It can be reduced by choosing a suitable prior p𝒮 ( θ ) in the synaptic sampling rule Eq ( 7 ) . The choice for the prior distribution is best if it matches the statistics of the training samples [6] , which has in this case two modes ( resulting from black and white pixels ) . The presence of this prior in the learning rule maintains good generalization capability for test samples even after prolonged exposure to the training set ( red curve in Fig 2F ) . The improved generalization capability of the network is a result of the prior distribution . It is well known that the prior in Bayesian inference allows to effectively prevent overfitting by making solutions that use fewer or smaller parameters more likely . Similar results would therefore emerge in any other implementation of Bayesian learning in neural networks . A thorough discussion on this topic which is known as Bayesian regularization can be found in [6 , 7] . As a consequence , the choice of the prior distribution can have a significant impact on the learning result . In S3 Text we compared a set of different priors and demonstrate this effect more systematically . There it can also be seen that if the choice of the prior is bad , the learning performance can even get worse than in the case without a prior . In the following sections we apply our synaptic sampling framework to networks of spiking neurons and biological models for network plasticity . The number and volume of spines for a synaptic connection is thought to be directly related to its synaptic weight [28] . Experimental studies have provided a wealth of information about the stochastic dynamics of dendritic spines ( see e . g . [1 , 28–32] ) . They demonstrate that the volume of a substantial fraction of dendritic spines varies continuously over time , and that all the time new spines and synaptic connections are formed and existing ones are eliminated . We show that these experimental data on spine motility can be understood as special cases of synaptic sampling . The synaptic sampling framework is however very general , and many different models for spine motility can be derived from it as special cases . We demonstrate this here for one simple model , induced by the following assumptions: The last requirement can be met by encoding the state of the synapse in an abstract form , that represents synaptic connectivity and synaptic plasticity in a single parameter θi . We define that negative values of θi represent a current disconnection and positive values represent a functional synaptic connection . The distance of the current value of θi from zero indicates how likely it is that the synapse will soon reconnect ( for negative values ) or withdraw ( for positive values ) , see Fig 3A . In addition the synaptic parameter θi encodes for positive values the synaptic efficacy wi , i . e . , the resulting EPSP amplitudes , by a simple mapping wi = f ( θi ) . A large class of mapping functions f is supported by our theory ( see S4 Text for details ) . The second assumption which requires multiplicative synaptic dynamics supports an exponential function f in our model , in accordance with previous models of spine motility [28] . Thus , we assume in the following that the efficacy wi of synapse i is given by w i = exp ( θ i - θ 0 ) , ( 9 ) see Fig 3C . Note that for a large enough offset θ0 , negative parameter values θi ( which model a non-functional synaptic connection ) are automatically mapped onto a tiny region close to zero in the w-space , so that retracted spines have essentially zero synaptic efficacy . The general rule for online synaptic sampling Eq ( 5 ) for the exponential mapping Eq ( 9 ) can be written as ( see S4 Text ) d θ i = b ( ∂ ∂ θ i log p 𝒮 ( θ ) + N w i ∂ ∂ w i log p 𝒩 ( x n | w ) ) d t + 2 b d W i . ( 10 ) In Eq ( 10 ) the multiplicative synaptic dynamics becomes explicit . The gradient ∂ ∂ w i log p 𝒩 ( x n | w ) , i . e . , the activity-dependent contribution to synaptic plasticity , is weighted by wi . Hence , for negative values of θi ( non-functional synaptic connection ) , the activities of the pre- and post-synaptic neurons have negligible impact on the dynamics of the synapse . Assuming a large enough θ0 , retracted synapses therefore evolve solely according to the prior p𝒮 ( θ ) and the random fluctuations d𝒲i . For large values of θi the opposite is the case . The influence of the prior ∂ ∂ θ i log p 𝒮 ( θ ) and the Wiener process d𝒲i become negligible , and the dynamics is dominated by the activity-dependent likelihood term . Large synapses can therefore become quite stable if the presynaptic activity is strong and reliable ( see Fig 3B ) . Through the use of parameters θ which determine both synaptic connectivity and synaptic efficacies , the synaptic sampling framework provides a unified model for structural and synaptic plasticity . The prior distribution can have significant impact on the spine motility , encouraging for example sparser or denser synaptic connectivity . If the activity-dependent second term in Eq ( 10 ) , that tries to maximize the likelihood , is small ( e . g . , because θi is small or parameters are near a mode of the likelihood ) then Eq ( 10 ) implements an Ornstein Uhlenbeck process . This prediction of our model is consistent with a previous analysis which showed that an Ornstein Uhlenbeck process is a viable model for synaptic spine motility [28] . The weight dynamics that emerges through the stochastic process Eq ( 10 ) is illustrated in the right panel of Fig 3D . A Gaussian parameter prior p𝒮 ( θi ) results in a log-normal prior p𝒮 ( wi ) in a corresponding stochastic differential equation for synaptic efficacies wi ( see S4 Text for details ) . The last term ( noise term ) in our synaptic sampling rule Eq ( 10 ) predicts that eliminated connections spontaneously regrow at irregular intervals . In this way they can test whether they can contribute to explaining the input . If they cannot contribute , they disappear again . The resulting power-law behavior of the survival of newly formed synaptic connections ( Fig 3E and 3F ) matches corresponding new experimental data [32] and is qualitatively similar to earlier experimental results which revealed a quick decay of transient dendritic spines [30 , 31 , 33] . Functional consequences of this structural plasticity are explored in the following sections . We will explore in this and the next section implications of the synaptic sampling rule Eq ( 10 ) for network plasticity in simple generative spike-based neural network models . The main types of spike-based generative neural network models that have been proposed are [34–37] . We focus here on the type of models introduced by [36–38] , since these models allow an easy estimation of the likelihood gradient ( the second term in Eq ( 10 ) ) and can relate this likelihood term to STDP . Since these spike-based neural network models have non-symmetric synaptic connections ( that model chemical synapses between pyramidal cells in the cortex ) , they do not allow to regenerate inputs x from internal responses z by running the network backwards ( like in a Boltzmann machine ) . Rather they are implicit generative models , where synaptic weights from inputs to hidden neurons are interpreted as implicit models for presynaptic activity , given that the postsynaptic neuron fires . We focus in this section on a simple model for an ubiquitous cortical microcircuit motif: an ensemble of pyramidal cells with lateral inhibition , often referred to as Winner-Take-All ( WTA ) circuit . It has been proposed that this microcircuit motif provides for computational analysis an important bridge between single neurons and larger brain systems [39] . We employ a simple form of divisive normalization ( as proposed by [39]; see Methods ) to model lateral inhibition , thereby arriving at a theoretically tractable version of this microcircuit motif that allows us to compute the maximum likelihood term ( second term in Eq ( 10 ) ) in the synaptic sampling rule . We assumed Gaussian prior distributions p𝒮 ( θi ) , with mean μ and variance σ2 over the synaptic parameters θi ( as in Fig 3B ) . Then the synaptic sampling rule Eq ( 10 ) yields for this model d θ i = b ( 1 σ 2 ( μ - θ i ) + N w i S ( t ) ( x i ( t ) - α e w i ) ) d t + 2 b d W i , ( 11 ) where S ( t ) denotes the spike train of the postsynaptic neuron and xi ( t ) denotes the weight-normalized value of the sum of EPSPs from presynaptic neuron i at time t ( i . e . , the summed EPSPs that would arise for weight wi = 1; see Methods for details ) . α is a parameter that scales the impact of synaptic plasticity depending on the current synaptic efficacy . The resulting activity-dependent component S ( t ) ( xi ( t ) − α ewi ) of the likelihood term is a simplified version of the standard STDP learning rule ( Fig 4B and 4C ) , like in [36 , 40] . Synaptic plasticity ( STDP ) for connections from input neurons to pyramidal cells in the WTA circuit can be understood from the generative aspect as fitting a mixture of Poisson ( or other exponential family ) distributions to high-dimensional spike inputs [36 , 37] . The factor wi = exp ( θi − θ0 ) had been discussed in [36] , because it is compatible with the underlying generative model , but provides in addition a better fit to the experimental data of [41] . We examine in this section emergent properties of network plasticity in this simple spike-based neural network under the synaptic sampling rule Eq ( 11 ) . It is well documented that cortical dendritic spines are transient and that spine turnover is enhanced by novel experience and training [33 , 42 , 43] . For example , enhanced spine formation as a consequence of sensory enrichment was found in mouse somatosensory cortex [33] . In this study the animals were exposed to a new sensory environment by adding additional objects to their home cage . This sensory enrichment resulted in a rapid increase in the formation of new spines . If the exposure to the enriched environment was only brief , the newly formed spines quickly decayed . We wondered whether these experimentally observed effects also emerge in our synaptic sampling model . As in [33] we exposed the network to different sensory environments to study these effects . Sensory experiences typically involve several processing steps and interactions between multiple brain systems , and precise knowledge about their cortical representation is still missing . Therefore we used here a simple symbolic representation of the sensory environment . We represented each sensory experience by a point in some finite dimensional space which is covered by the tuning curves of a large number of input neurons . Their spike output was then communicated to the WTA circuit in the form of 200 ms-long spike patterns of the 1000 input neurons ( see Fig 4D and 4E and Methods for details ) . Independently drawn sensory experiences were presented sequentially and synaptic sampling according to Eq ( 11 ) was applied continuously to all synapses from the 1000 input neurons to the ten neurons in the WTA circuit . Each environment was represented as a mixture of Gaussians ( clusters ) of points in the finite-dimensional sensory space . Each cluster could represent for example different sensory experiences with some object in the environment . Consequently we modelled an enriched environment ( EE ) simply by adding a few new clusters to the standard environment ( SE ) . In phase 1 the network was exposed to an environment with 3 clusters ( standard environment ( SE ) , see Fig 4D ) . After 3 hours the network input was enriched by adding 4 additional clusters ( enriched environment ( EE ) , see Fig 4E ) . We found that exposure to EE significantly increased the rate of new synapse formation as in the experimental result of [33] ( Fig 4F ) . Most of the newly formed synapses decayed within a few hours after return to the standard environment ( EE-SE situation , see Fig 4G ) . In this case only about about 8% become stable . A fraction of about 30% becomes stable when the enriched environment was maintained ( EE-EE situation ) . These results qualitatively reproduce the findings from mouse barrel cortex ( compare Figures 1h and 2c in [33] ) . Note that we used here relatively large update rates b to keep simulation times in a feasible range , which results in spine dynamics on the time scale of hours instead of days as in biological synapses [33] . Numerous experimental data show that the same function of a neural circuit is achieved in different individuals with drastically different parameters , and also that a single organism can compensate for disturbances by moving to a new parameter vector [9 , 44–47] . These results suggest that there exists some low-dimensional submanifold of values for the high-dimensional parameter vector θ of a biological neural network that all provide stable network function ( degeneracy ) . We propose that the previously discussed posterior distribution of network parameters θ provides a mathematical model for such low-dimensional submanifold . Furthermore we propose that the underlying continuous stochastic fluctuation d𝒲 provides a driving force that automatically moves network parameters ( with high probability ) to a functionally more attractive regime when the current solution performs worse because of perturbations , such as lesions of neurons or network connections . This compensation capability is not an add-on to the synaptic sampling model , but an inherent feature of its organization . We demonstrate this inherent compensation capability in Fig 5 for a generative spiking neural network with synaptic parameters θ that regulate simultaneously structural plasticity and synaptic plasticity ( dynamics of weights ) as in Figs 3 and 4 . The prior p𝒮 ( θ ) for these parameters is here the same as in the preceding section ( see Fig 4G on the left ) . But in contrast to the previous section we consider here a network that allows us to study the self-organization of connections between hidden neurons . The network consists of eight WTA-circuits , but in contrast to Fig 4 we allow here arbitrary excitatory synaptic connections between neurons within the same or different ones of these WTA circuits . This network models multi-modal sensory integration and association in a simplified manner . Two populations of “auditory” and “visual” input neurons xA and xV project onto corresponding populations zA and zV of hidden neurons ( each consisting of one half of the WTA circuits , see lower panel of Fig 5A ) . Only a fraction of the potential synaptic connections became functional ( see Fig . S2A in S6 Text ) through the synaptic sampling rule Eq ( 11 ) that integrates structural and synaptic plasticity . Synaptic weights and connections were not forced to be symmetric or bidirectional . As in the previous demonstrations we do not use external rewards or teacher-inputs for guiding network plasticity . Rather , we allow the model to discover on its own regularities in its network inputs . The “auditory” hidden neurons zA on the left in Fig 5A received temporal spike patterns from the auditory input neurons xA that were generated from spoken utterings of the digit 1 and 2 ( which lasted between 320 ms and 520 ms ) . Simultaneously we presented to the “visual” hidden neurons zV on the right for the same time period a ( symbolic ) visual representation of the same digit ( randomly drawn from the MNIST database like in Fig . 2 ) . The emergent associations between the two populations zA and zV of hidden neurons were tested by presenting auditory input only and observing the activity of the “visual” hidden neurons zV . Fig 5B shows the emergent activity of the neurons zV when only the auditory stimulus was presented ( visual input neurons xV remained silent ) . The generative aspect of the network can be demonstrated by reconstructing for this case the visual stimulus from the activity of the “visual” hidden neurons zV . Fig 5B shows reconstructed visual stimuli from a single run where only the auditory stimuli xA for digits 1 ( left ) and 2 ( right ) were presented to the network . Digit images were reconstructed by multiplying the synaptic efficacies of synapses from neurons in xV to neurons in zV ( which did not receive any input from xV in this experiment ) with the instantaneous firing rates of the corresponding zV-neurons . Interestingly we found that synaptic sampling significantly outperforms the pure deterministic STDP updates introduced in [38] , which do not impose a prior distribution over synaptic parameters . The structural prior that favors solutions with only a small number of large synaptic weights seems to be beneficial for this task as it allows to learn few but pronounced associations between the neurons ( see S6 Text ) . In order to investigate the inherent compensation capability of synaptic sampling , we applied two lesions to the network within a learning session of 8 hours . In the first lesion all neurons ( 16 out of 40 ) that became tuned for digit 2 in the preceding learning ( see Fig 5D and S6 Text ) were removed . The lesion significantly impaired the performance of the network in stimulus reconstruction , but it was able to recover from the lesion after about one hour of continuing network plasticity according to Eq ( 11 ) ( Fig 5D ) . The reconstruction performance of the network was measured here continuously through the capability of a linear readout neuron from the visual ensemble to classify the current auditory stimulus ( 1 or 2 ) . In the second lesion all synaptic connections between hidden neurons that were present after recovery from the first lesion were removed and not allowed to regrow ( 2936 synapses in total ) . After about two hours of continuing synaptic sampling 294 new synaptic connections between hidden neurons emerged . These made it again possible to infer the auditory stimulus from the activity of the remaining 24 hidden neurons in the population zV ( in the absence of any input from the population xV ) , at about 75% of the performance level before the second lesion ( see bottom panel of Fig 5D ) . In order to illustrate the ongoing network reconfiguration we track in Fig 5C the temporal evolution of a subset θ′ of network parameters ( 35 parameters θi associated with the potential synaptic connections of the neuron marked in red in the middle of Fig 5D from or to other hidden neurons , excluding those that were removed at lesion 2 and not allowed to regrow ) . The first three PCA components of this 35-dimensional parameter vector are shown . The vector θ′ fluctuates first within one region of the parameter space while probing different solutions to the learning problem , e . g . , high probability regions of the posterior distribution ( blue trace ) . Each lesions induced a fast switch to a different region ( red , green ) , accompanied by a recovery of the visual stimulus reconstruction performance ( see Fig 5D ) . The random fluctuations were found to be an integral part of the fast recovery form lesions . In S6 Text we analyzed the impact of the diffusion term in Eq ( 11 ) on the learning speed . We found that it acts as a temperature parameter that allows to scale the speed of exploration in the parameter space ( see also the Methods for a detailed derivation ) . Altogether this experiment showed that continuously ongoing synaptic sampling maintains stable network function also in a more complex network architecture . Another consequence of synaptic sampling was that the neural codes ( assembly sequences ) that emerged for the two digit classes within the hidden neurons zA and zV ( see Fig . S2B in S6 Text ) drifted over larger periods of time ( also in the absence of lesions ) , similarly as observed for place cells in [48] and for tuning curves of motor cortex neurons in [4] .
We have shown that stochasticity may provide an important function for network plasticity . It enables networks to sample parameters from some low-dimensional manifold in a high-dimensional parameter space that represents attractive combinations of structural constraints and rules ( such as sparse connectivity and heavy-tailed distributions of synaptic weights ) and a good fit to empirical evidence ( e . g . , sensory inputs ) . We have developed a normative model for this new learning perspective , where the traditional gold standard of maximum likelihood optimization is replaced by theoretically optimal sampling from a posterior distribution of parameter settings , where regions of high probability provide a theoretically optimal model for the low-dimensional manifold from which parameter settings should be sampled . The postulate that networks should learn such posterior distributions of parameters , rather than maximum likelihood values , had been proposed already for quite some while for artificial neural networks [6 , 7] , since such organization of learning promises better generalization capability to new examples . The open problem how such posterior distributions could be learned by networks of neurons in the brain , in a way that is consistent with experimental data , has been highlighted in [8] as a key challenge for computational neuroscience . We have presented here such a model , whose primary innovation is to view experimentally found trial-to-trial variability and ongoing fluctuations of parameters such as spine volumes no longer as a nuisance , but as a functionally important component of the organization of network learning , since it enables sampling from a distribution of network configurations . The mathematical framework that we have presented provides a normative model for evaluating such empirically found stochastic dynamics of network parameters , and for relating specific properties of this “noise” to functional aspects of network learning . Reports of trial-to-trial variability and ongoing fluctuations of parameters related to synaptic weights are ubiquitous in experimental studies of synaptic plasticity and its molecular implementation , from fluctuations of proteins such as PSD-95 [19] in the postsynaptic density that are thought to be related to synaptic strength , over intrinsic fluctuations in spine volumes and synaptic connections [1–3 , 5 , 28 , 31 , 32] , to surprising shifts of neural codes on a larger time scale [4 , 48] . These fluctuations may have numerous causes , from noise in the external environment over noise and fluctuations of internal states in sensory neurons and brain networks , to noise in the pre- and postsynaptic molecular machinery that implements changes in synaptic efficacies on various time scales [18] . One might even hypothesize , that it would be very hard for this molecular machinery to implement synaptic weights that remain constant in the absence of learning , and deterministic rules for synaptic plasticity , because the half-life of many key proteins that are involved is relatively short , and receptors and other membrane-bound proteins are subject to Brownian motion . In this context the finding that neural codes shift over time [4 , 48] appears to be less surprising . In fact , our model predicts ( see S6 Text ) that also stereotypical assembly sequences that emerge in our model through learning , similarly as in the experimental data of [49] , are subject to such shifts on a larger time scale . However it should be pointed out that our model is agnostic with regard to the time scale on which these changes occur , since this time scale can be defined arbitrarily through the parameter b ( learning rate ) in Eq ( 3 ) . The model that we have presented makes no assumptions about the actual sources of noise . It only assumes that salient network parameters are subject to stochastic processes , that are qualitatively similar to those which have been studied and modeled in the context of Brownian motion of particles as random walk on the microscale . One can scale the influence of these stochastic forces in the model by a parameter T that regulates the “temperature” of the stochastic dynamics of network parameters θ . This parameter T regulates the tradeoff between trying out different regions ( or modes ) of the posterior distribution of θ ( exploration ) , and staying for longer time periods in a high probability region of the posterior ( exploitation ) . We conjecture that this parameter T varies in the brain between different brain regions , and possibly also between different types of synaptic connections within a cortical column . For example , spine turnover is increased for large values of T , and network parameters θ can move faster to a new peak in the posterior distribution , thereby supporting faster learning ( and faster forgetting ) . Since spine turnover is reported to be higher in the hippocampus than in the cortex [50] , such higher value of T could for example be more adequate for modeling network plasticity in the hippocampus . This model would then also support the hypothesis of [50] that memories are more transient in the hippocampus . In addition T is likely to be regulated on a larger time scale by developmental processes , and on a shorter time scale by neuromodulators and attentional control . The view that synaptic plasticity is stochastic had already been explored through simulation studies in [4 , 51] . Artificial neural networks were trained in [51] through supervised learning with high learning rates and high amounts of noise both on neuron outputs and synaptic weight changes . The authors explored the influence of various combinations of noise levels and learning rates on the success of learning , which can be understood as varying the temperature parameters T in the synaptic sampling framework . In order to measure this parameter T experimentally in a direct manner , one would have to apply repeatedly the same plasticity induction protocol to the same synapse , with a complete reset of the internal state of the synapse between trials , and measure the resulting trial-to-trial variability of changes of its synaptic efficacy . Since such complete reset of a synaptic state appears to be impossible at present , one can only try to approximate it by the variability that can be measured between different instances of the same type of synaptic connection . We have shown that the Fokker-Planck equation , a standard tool in physics for analyzing the temporal evolution of the spatial probability density function for particles under Brownian motion , can be used to create bridges between details of local stochastic plasticity processes on the microscale and the probability distribution of the vector θ of all parameters on the network level . This theoretical result provides the basis for the new theory of network plasticity that we are proposing . In particular , this link allows us to derive rules for synaptic plasticity which enable the network to learn , and represent in a stochastic manner , a desirable posterior distribution of network parameters; in other words: to approximate Bayesian inference . We find that resulting rules for synaptic plasticity contain the familiar term for maximum likelihood learning . But another new term , apart from the Brownian-motion-like stochastic term , is the term ∂ ∂ θ i log p 𝒮 ( θ i ) that results from a prior distributions p𝒮 ( θi ) , which could actually be different for each biological parameter θi and enforce structural requirements and preferences of networks of neurons in the brain . Some systematic dependencies of changes in synaptic weights ( for the same pairing of pre- and postsynaptic activity ) on their current values had already been reported in [41 , 52–54] . These can be modeled as impact of priors . Other potential functional benefits of priors ( on emergent selectivity of neurons ) have recently been demonstrated in [55] for a restricted Boltzmann machine . An interesting open question is whether the non-local learning rules of their model can be approximated through biologically more realistic local plasticity rules , e . g . through synaptic sampling . We have also demonstrated in Figs 3 and 4 that suitable priors can model experimental data from [32] and [33] on the survival statistics of dendritic spines . The transient behavior of synaptic turnover in our model fits a two-term exponential function , the long-term ( stationary ) behavior is well described by a power-law . Both findings are in accordance with experimental data . The results reported in [56] suggest that learned neural representations integrate experience with a priori beliefs about the sensory environment . The model presented here could be used to further investigate this hypothesis . Also the Fokker-Planck formalism was previously applied to describe the dynamics of dendritic spines in hippocampus [57] . The methods described there to integrate experimental data into computational models could be combined with the synaptic sampling framework to further improve the fit to biology . Finally , we have demonstrated in Figs 4 and 5 that suitable priors for network parameters θi that model spine volumes endow a neural network with the capability to respond to changes in the input distribution and network perturbations with a network rewiring that maintains or restores the network function , while simultaneously observing structural constraints such as sparse connectivity . Our model underlines the importance of further experimental investigation of priors for network parameters . How are they implemented on a molecular level ? What role does gene regulation have in their implementation ? How does the history of a synapse affect its prior ? In particular , can consolidation of a synaptic weight θi be modeled in an adequate manner as a modification of its prior ? This would be attractive from a functional perspective , because according to our model it both allows long-term storage of learned information and flexible network responses to subsequent perturbations . Besides the use of parameter priors , dropout [58] and dropconnect [59] can be used to avoid overfitting in artificial neural networks . In particular , dropconnect , which drops randomly chosen synaptic connections during training , is reminiscent of stochastic synaptic release in biological neuronal networks . In synaptic sampling , synaptic parameters are assumed to be stochastic , however , this stochastic dynamics evolves on a much slower time scale than stochastic release , which was not modeled in our simulations . An interesting open question is whether synaptic sampling combined with stochastic synaptic release would further improve generalization capabilities of spiking neural networks in a similar manner as dropconnect for artificial neural networks . We have focused in the examples for our model on the plasticity of synaptic weights and synaptic connections . But the synaptic sampling framework can also be used for studying the plasticity of other synaptic parameters , e . g . , parameters that control the short term dynamics of synapses , i . e . , their individual mixture of short term facilitation and depression . The corresponding parameters U , D , F of the models from [60 , 61] are known to depend in a systematic manner on the type of pre- and postsynaptic neuron [62] , indicative of a corresponding prior . However also a substantial variability within the same type of synaptic connections , had been found [62] . Hence it would be interesting to investigate functional properties and experimentally testable consequences of stochastic plasticity rules of type Eq ( 5 ) for U , D , F , and to compare the results with those of previously considered deterministic plasticity rules for U , D , F ( see e . g . , [63] ) . Early theoretical work on activity-dependent formation and elimination of synapses has been used to model ocular dominance in the visual cortex [64 , 65] . Theoretical models for structural plasticity have also shown that simple plasticity models combined with mechanisms for rewiring are able to model cortical reorganization after lesions [66 , 67] . In [68] a model was presented that combines structural plasticity and STDP . This model was able to reproduce the existence of transient and persistent spines in the cortex . A recently introduced probabilistic model of structural plasticity was also able to reproduced the statistics of the number of synaptic connections between pairs of neurons in the cortex [69] . Furthermore a simple model of structural synaptic plasticity has been introduced that was able to explain cognitive phenomena such as graded amnesia and catastrophic forgetting [70] . In contrast to these previous studies , the goal of the current work was to establish a model of structural plasticity that follows from a first functional principle , that is , sampling from the posterior distribution over parameters . We have demonstrated that this framework provides a new and principled way of modeling structural plasticity [10 , 11] . The challenge to find a biologically plausible way of modeling structural plasticity as Bayesian inference has been highlighted by [8] . In addition , the proposed framework does not treat rewiring and synaptic plasticity separately , but provides a unified theory for both phenomena , that can be directly related to functional aspects of the network via the resulting posterior distribution . We have shown in Figs 3 and 4 that this rule produces a population of persistent synapses that remain stable over long periods of time , and another population of transient synaptic connections which disappear and reappear randomly , thereby supporting automatic adaptation of the network structure to changes in the distribution of external inputs ( Fig 4 ) and network perturbation ( Fig 5 ) . On a more general level we propose that a framework for network plasticity where network parameters are sampled continuously from a posterior distribution will automatically be less brittle than previously considered maximum likelihood learning frameworks . The latter require some intelligent supervisor who recognizes that the solution given by the current parameter vector is no longer useful , and induces the network to resume plasticity . In contrast , plasticity processes remain active all the time in our sampling-based framework . Hence network compensation for external or internal perturbations is automatic and inherent in the organization of network plasticity . The need to rethink observed parameter values and plasticity processes in biological networks of neurons in a way which takes into account their astounding variability and compensation capabilities has been emphasized by Eve Marder ( see e . g . [9 , 47 , 71] ) and others . This article has introduced a new conceptual and mathematical framework for network plasticity that promises to provide a foundation for such rethinking of network plasticity .
Here we prove that p* ( θ ) = p ( θ∣ x ) is the unique stationary distribution of the parameter dynamics Eq ( 3 ) that operate on the network parameters θ = ( θ1 , … , θM ) . Convergence to this stationary distribution then follows for strictly positive p* ( θ ) . In fact , we prove here a more general result for parameter dynamics given by d θ i = ( b ( θ i ) ∂ ∂ θ i log p 𝒮 ( θ ) + b ( θ i ) ∂ ∂ θ i log p 𝒩 ( x | θ ) + T b ′ ( θ i ) ) d t + 2 T b ( θ i ) d W i ( 12 ) for i = 1 , … , M and b ′ ( θ i ) : = ∂ ∂ θ i b ( θ i ) . This dynamics includes a temperature parameter T and a sampling-speed factor b ( θi ) that can in general depend on the current value of the parameter θi . The temperature parameter T can be used to scale the diffusion term ( i . e . , the noise ) . The sampling-speed factor controls the speed of sampling , i . e . , how fast the parameter space is explored . It can be made dependent on the individual parameter value without changing the stationary distribution . For example , the sampling speed of a synaptic weight can be slowed down if it reaches very high or very low values . Note that the dynamics Eq ( 3 ) is a special case of the dynamics Eq ( 12 ) with unit temperature T = 1 and constant sampling speed b ( θi ) ≡ b . We show that the stochastic dynamics Eq ( 12 ) leaves the distribution p * ( θ ) ≡ 1 𝒵 q * ( θ ) ( 13 ) invariant , where 𝒵 is a normalizing constant 𝒵 = ∫q* ( θ ) dθ and q * ( θ ) = p ( θ | x ) 1 T . ( 14 ) Note that the stationary distribution p* ( θ ) is shaped by the temperature parameter T , in the sense that p* ( θ ) is a flattened version of the posterior for high temperature . The result is formalized in the following theorem , which is proven in detail in S1 Text: Theorem 1 . Let p ( x , θ ) be a strictly positive , continuous probability distribution over continuous or discrete states x = x1 , … , xN and continuous parameters θ = ( θ1 , … , θM ) , twice continuously differentiable with respect to θ . Let b ( θ ) be a strictly positive , twice continuously differentiable function . Then the set of stochastic differential Eq ( 12 ) leaves the distribution p* ( θ ) invariant . Furthermore , p* ( θ ) is the unique stationary distribution of the sampling dynamics . For learning the distribution over different writings of digit 1 with different priors in Fig 2 , a restricted Boltzmann machine ( RBM ) with 748 visible and 9 hidden neurons was used . A detailed definition of the RBM model and additional details to the simulations are given in S3 Text . Here we provide details to the network model and spiking inputs for the recurrent WTA circuits ( Fig 5 ) . Additional details to the data analysis and performance evaluation are provided in S6 Text . | Synaptic connectivity between neurons in the brain and the efficacies ( “weights” ) of these synaptic connections are thought to encode the long-term memory of an organism . But a closer look at their molecular implementation , as well as imaging experiments over longer periods of time , have shown that synaptic connections are subject to numerous stochastic processes . We propose that this seeming unreliability of synaptic connections is not a bug , but an important feature . It endows networks of neurons with an important experimentally observed but theoretically not understood capability: Automatic compensation for internal and external changes . This perspective of network plasticity requires a new conceptual and mathematical framework , which is provided by this article . Stochasticity of synapses is seen here not as noise of an inherently deterministic system , but as an inherent property , similarly as Brownian motion of particles in a physical system cannot be abstracted away if one wants to understand certain properties of a physical system . In fact , we find that this underlying stochasticity of synaptic connections enables a network of neurons to continuously try out new network configurations while maintaining its functionality . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Network Plasticity as Bayesian Inference |
Cystic fibrosis ( CF ) is associated with chronic bacterial airway infections leading to lung insufficiency and decreased life expectancy . Staphylococcus aureus is one of the most prevalent pathogens isolated from the airways of CF patients . Mucoid colony morphology has been described for Pseudomonas aeruginosa , the most common pathogen in CF , but not for S . aureus . From the airways of 8 of 313 CF patients ( 2 . 5% ) mucoid S . aureus isolates ( n = 115 ) were cultured with a mean persistence of 29 months ( range 1 month , 126 months ) . In contrast to non-mucoid S . aureus , mucoid isolates were strong biofilm formers . The upstream region of the ica operon , which encodes the proteins responsible for the synthesis of the polysaccharide intercellular adhesin ( PIA ) , of mucoid isolates was sequenced . Spa-types of mucoid and non-mucoid strains were identical , but differed between patients . Mucoid isolates carried a 5 bp deletion in the intergenic region between icaR and icaA . During long-term persistence , from two patients subsequent non-mucoid isolates ( n = 12 ) with 5 bp deletions were cultured , which did not produce biofilm . Sequencing of the entire ica operon identified compensatory mutations in various ica-genes including icaA ( n = 7 ) , icaD ( n = 3 ) and icaC ( n = 2 ) . Six sequential isolates of each of these two patients with non-mucoid and mucoid phenotypes were subjected to whole genome sequencing revealing a very close relationship of the individual patient’s isolates . Transformation of strains with vectors expressing the respective wild-type genes restored mucoidy . In contrast to the non-mucoid phenotype , mucoid strains were protected against neutrophilic killing and survived better under starvation conditions . In conclusion , the special conditions present in CF airways seem to facilitate ongoing mutations in the ica operon during S . aureus persistence .
Cystic fibrosis ( CF ) is one of the most common hereditary diseases in the Caucasian population caused by mutations of an important chloride channel ( cystic fibrosis transmembrane regulator ) and affects worldwide approximately 70 , 000 people [1] . The mutation leads to impaired mucociliary clearance by airway epithelial cells with ensuing recurrent suppurative bacterial infections [2] . Staphylococcus aureus is one of the first and today the most frequent isolated pathogen , which can be recovered from the airways of CF patients with increasing prevalence rates most likely due to early eradication strategies directed against Pseudomonas aeruginosa , which was the leading pathogen in CF for decades and which has been shown to be responsible for lung function decline [1 , 3] . Mucoid isolates of P . aeruginosa occur in late stages of CF after the patients experienced long-term persistence of non-mucoid P . aeruginosa phenotypes [4] . The recovery of mucoid isolates has been shown to play a greater role in lung disease progression than the recovery of non-mucoid P . aeruginosa isolates [4] . The underlying mechanism for mucoidy is caused by overproduction of alginate due to a mutation in the mucA gene [5 , 6] . It has been shown that biofilm formation of S . aureus occurs in CF patients in vivo , which in part explains persistence of S . aureus in this particular niche [7 , 8] . Significantly , biofilm formation protects S . aureus from the host’s immune response and renders the species intrinsically more resistant against antibiotics [9] . Different mechanisms contribute to S . aureus cell aggregation and subsequent biofilm formation [10 , 11] . Of significant importance is the polysaccharide intercellular adhesin ( PIA ) , also known as poly-N-acetyl-β- ( 1–6 ) -glucosamine ( PNAG ) , which is encoded by the icaADBC locus and regulated by icaR located upstream of the icaA start codon [12] . The functions for the single ica genes are only partly resolved . There is biochemical evidence that IcaA functions as a N-acetylglucosaminyl transferase , and IcaD might be a chaperone that directs the correct folding and membrane insertion of IcaA [13] . No published evidence for the function of IcaC is available , but it has been speculated that the protein is involved in the externalization of PIA/PNAG [14] , while IcaB acts as a deacetylase responsible for de-acetylation of mature PIA [15] . The ica operon is present in almost all isolates , but S . aureus usually only produces a scant biofilm under in vitro conditions [12] . Interestingly , agr-negative S . aureus strains produce higher amounts of biofilm [16] . Furthermore , Cramton et al . showed that anaerobic conditions as typically observed in mucus plugs within the airways of CF patients [17] , induced the expression of PIA/PNAG [18] . Later , Ulrich et al . identified SrrAB as a major activator of ica expression under anaerobic conditions , resulting in protection of S . aureus against neutrophil killing under anaerobic conditions [19] . Recently , Jefferson et al . described a hyper-biofilm forming S . aureus strain , which was isolated accidently in the laboratory [20] . The authors identified a 5 bp deletion upstream of the ica operon and downstream of the ica repressor , which was responsible for this unusual phenotype [20] . Mucoid S . aureus phenotypes have not been described to occur in vivo or during chronic CF airway infection . Since we occasionally isolated unusual mucoid S . aureus isolates from some patients of the two CF centers in Münster , Germany , we aimed to determine the prevalence , persistence and underlying mechanism responsible for mucoidy . Therefore , we retrospectively analyzed microbiological results of S . aureus strains collected from two independent prospective studies for the occurrence of mucoid isolates . Mucoid and non-mucoid isolates were characterized by spa- and MLST-typing , and biofilm formation was assessed using a static biofilm assay . The icaR-icaA intergenic region was sequenced to detect a possible 5 bp deletion [20] , which could be responsible for the hyper-biofilm formation . Furthermore , the mucoid isolates were compared to non-mucoid isolates by competition experiments to assess for fitness loss , survival under starvation conditions to determine survival advantages and in opsonophagocytic assays to analyze protection against neutrophilic killing .
In our laboratory , airway cultures from CF patients occasionally yielded unusual mucoid S . aureus isolates , which are not necessarily detectable by eye on Columbia blood agar plates ( Fig 1 ) , but which are especially recognizable by the unusual sticky phenotype , which can be assessed by sub-culturing ( Suppl . Material , movie ) . The mucoid phenotype is easily recognizable on Congo red agar ( CRA ) plates , because of the wrinkled dry colonies in contrast to smooth round colonies of the non-mucoid phenotype ( Fig 1 ) . To determine the prevalence of mucoid S . aureus isolates in S . aureus-positive CF patients from two CF centers in Münster , Germany ( n = 118 ) , and from a recent prospective longitudinal multicenter study ( n = 195 ) , we reanalyzed our microbiological data and identified 115 mucoid isolates , which were isolated from nasal or throat swabs or sputa from 8 patients ( 7 male , median age at first mucoid isolate 24 years ) with a mean persistence of 29 months ( range: 1 month to 126 months , Table 1 ) . To determine clonality , we performed spa- and MLST-typing of all isolates , which indicated that non-mucoid and mucoid isolates belonged to the same spa-type in each patient , but differed between patients ( Table 1 ) with two strains each belonging to ST5 and ST30 . These data show that mucoid phenotypes are not restricted to a special S . aureus clone but can occur in different genetic S . aureus lineages . S . aureus produces capsule polysaccharides ( CP ) also termed micro-capsules , which can be differentiated by rabbit serum antibodies [21] . These ( CPs ) represent an important virulence factor of S . aureus due to protection against phagocytosis . Most clinical S . aureus isolates belong to CP-type 5 or 8 [21] . Therefore , we hypothesized that hyper-expression of CPs could be responsible for the mucoid phenotype . We determined CP expression by a semi-quantitative colony immunoblot assay [22] for selected isolates of patient 8 . Mucoid strains varied in their CP expression between weak and strong and did not differ significantly compared to the non-mucoid strains ( S1 Table ) . Quantification of capsule production by ELISA inhibition confirmed the results of the colony immunoblot assays ( S1 Fig ) , indicating that CP expression did not explain the mucoid phenotype of the isolates . Another possible explanation for the mucoid phenotype could be overproduction of PIA/PNAG leading to enhanced biofilm formation . Assessment of biofilm formation using a conventional microtiter plate assay showed that all mucoid isolates displayed enhanced biofilm production , whereas almost no biofilm was formed by the non-mucoid strains as exemplified with strains 8 . 1 and 8 . 2 ( Fig 2 , Table 2 ) . To investigate the nature of the biofilm , we treated the biofilm with sodium metaperiodate , which breaks down PIA/PNAG-dependent biofilms , and by proteinase K or DNase , which disrupts protein-or DNA-dependent biofilms , respectively . The biofilm of the mucoid isolate was dissolved by sodium metaperiodate , but not by proteinase K or DNase , indicating that PIA/PNAG is functionally involved in the biofilm formation ( Fig 2 ) . To further characterize biofilm formation of mucoid isolates in more detail , confocal laser scanning microscopy was performed . Staining of adherent bacteria using the LIVE/DEAD BacLight kit detected no stable biofilm for non-mucoid isolates . Here , only occasionally cell clusters with different density and thickness were observed ( Fig 3A ) . Intriguingly , bacteria were only loosely attached to each other , resulting in wobbling of bacterial consortia on mechanical stress , and the average biofilm thickness was low ( <10 μM at 24 h ) . In contrast , the mucoid strain produced a stable biofilm , which revealed the typical mushroom-shaped multicellular structure ( Fig 3B ) . Cells in this biofilm were tightly attached and a biofilm progression over time was present . While the surface was not entirely covered after 12 h , full coverage was reached after 24 h combined with a mushroom-shaped multicellular structure . The average height of the biofilm was 23 μM after 24 h . Furthermore , we observed an accumulation of dead cells inside the mushroom structures ( Fig 3B ) . Staining of the cultures with antibodies against PIA revealed densely packed bacteria embedded in extracellular PIA/PNAG ( Fig 4B ) for the mucoid but not for the non-mucoid isolate ( Fig 4A ) . Since CF lung disease is characterized by enhanced inflammation with an accumulation of polymorphonuclear neutrophils ( PMNs ) [23] , we assessed uptake and killing by fresh human PMNs of representative non-mucoid and mucoid isolates from patient 8 ( strains 8 . 1 and 8 . 2 , Table 2 ) in an in vitro assay . In contrast to the non-mucoid strain , which was readily killed by PMNs , the mucoid isolate resisted opsonophagocytic killing ( Fig 5A ) . However , if antibodies against PIA/PNAG were added to the assay , robust phagocytic killing was achieved ( Fig 5B ) . In contrast , CP8 antibodies , which interfere with the microcapsule of this isolate , which has been shown to belong to CP8 , were poorly opsonic ( S2 Fig ) indicating that capsule formation had no impact on protection against neutrophilic killing . Therefore , these data revealed that opsonophagocytic killing of the mucoid S . aureus isolate was mediated by opsonic PIA/PNAG antibodies . Because mucoidy of the S . aureus isolates correlated with increased PIA/PNAG-mediated biofilm formation , we further characterized the ica operon of relevant S . aureus strains . As shown earlier by Jefferson et al . [20] , a spontaneous laboratory mutant of S . aureus MN8 harbored a 5 bp deletion in the icaR-icaA intergenic region of the ica operon , resulting in constitutive expression of PIA/PNAG . Therefore , at first we sequenced the upstream icaA region of our mucoid isolates . All mucoid isolates carried exactly the same 5 bp deletion at the same position in this intergenic region . Interestingly , in three patients mucoid isolates co-colonized with non-mucoid S . aureus for periods up to 126 months ( Table 1 ) , which is exemplified more extensively for patient 8 in S1 Table . Surprisingly , from patients 7 and 8 , who were persistently infected by mucoid S . aureus isolates for extended periods , non-mucoid isolates ( n = 12 ) , which carried the 5 bp deletion , were isolated . The non-mucoid isolates ( Table 2 ) did not produce rough colonies on CRA or biofilm and therefore did not seem to produce high amounts of PIA/PNAG related biofilm anymore . To determine whether compensatory mutations in these late non-mucoid isolates with 5 bp deletions had occurred , we sequenced the entire ica operon and identified various mutations in icaA ( n = 7 ) , icaD ( n = 3 ) and icaC ( n = 2 , Table 2 , Fig 6 ) . Patient 7 carried S . aureus isolates with two different mutations in icaA that were detected in two different isolates recovered from one visit , the same mutation in icaD in three independent isolates during a period of 3 months , and mutations in icaC at one visit ( Table 2 ) . Patient 8 carried S . aureus with mutations in icaC identified at one visit and in icaA at five visits . Two of these isolates recovered in April 2007 ( strain 8 . 4 ) and May 2008 ( strain 8 . 7 ) carried different mutations in icaA . Six years later , three isolates with the same mutation in icaA as the isolate recovered in 2008 were cultured from March until November 2014 ( strains 8 . 8 , 8 . 9 and 8 . 10 ) . Since there was a break of 6 years without the culture of isolates with such mutations , it is unlikely that these isolates persisted during this period without detection by culture . In line with this suggestion our whole genome sequencing data revealed that strain 8 . 7 is more distant from all other sequential isolates making it unlikely that this strain is the founding strain for strain 8 . 8 ( Fig 7 ) . Therefore , the same mutation in icaA emerged again in mucoid isolates . In summary , 10 of 12 isolates with 5 bp deletions but without PIA/PNAG hyper-expression showed mutations in icaA ( n = 7 ) and icaD ( n = 3 ) . To confirm that the mucoid isolates of patients 7 and 8 evolved from the non-mucoid S . aureus clone , we performed WGS of the first mucoid and nearest non-mucoid S . aureus isolates of these patients and also of the isolates with 5 bp deletion and compensatory mutations ( Table 2 ) . WGS and subsequent cluster analysis based on cgMLST ( core genome MLST ) allelic profiles of the each six strains of patient 7 and patient 8 exhibit a close relationship of all strains within each patient irrespective of the phenotype ( Fig 7 ) . Additional ANI ( average nucleotide identity ) calculation further corroborated this close relationship as all isolates of patient 7 had ANI values of 100% in pairwise comparisons . Similar ANI results were also observed for patient 8 , where the ANI values were ≥ 99 . 99% . To assess the role of the icaA , icaD and icaC mutations in non-mucoid S . aureus isolates with the 5 bp deletion , we used the xylose-inducible staphylococcal expression vectors pTXicaBC and pTXicaAD . Complementation of the isolates with the respective vectors restored the mucoid phenotype and allowed strong biofilm formation ( Fig 8 ) , corroborating that the compensatory mutations in icaA , icaD or icaC caused abrogation of biofilm and the non-mucoid phenotype . Excessive production of PIA/PNAG might confer a fitness loss to mucoid S . aureus compared to non-mucoid isolates . To investigate this , we performed competition experiments between two non-mucoid and mucoid S . aureus strain pairs , which were isolated from patients 7 and 8 . Whereas for patient 7 , the non-mucoid strain outcompeted the mucoid isolate during co-culture ( Fig 9A ) , for the strain pair of patient 8 no significant differences in fitness could be observed ( Fig 9C ) . These results indicate that the emergence of a mucoid phenotype in vivo can , but is not necessarily associated with a fitness loss . To assess whether there is a difference of mucoid and non-mucoid isolates in terms of survival during starvation conditions that may occur at different sites in CF airways , we exposed the same non-mucoid/mucoid strain pairs as we used for the competition experiments to nutrient limited conditions lacking any carbon source [24] . Both mucoid strains of patients 7 and 8 were significantly better able to survive under these harsh conditions ( Fig 9B and 9D ) . However , a non-mucoid strain without any mutations in the ica operon of patient 7 , which was isolated at a later time point from the airways , showed a significantly better survival compared to the mucoid isolate ( S3 Fig ) . Such results indicate that under starvation conditions mucoid isolates experience a survival advantage , which could be due to consumption of the extracellular polysaccharide . However , during in vivo persistence additional mutations might occur also in non-mucoid strains thereby facilitating survival under carbon-limited conditions . Recently , a role for icaC as a target for phase variation has been suggested [24] . The authors showed that a strain with a 5 bp deletion and a compensatory mutation in icaC survived significantly better during starvation than the non-mucoid isolate with an intact ica operon . Therefore , we tested the survival of three non-mucoid strains with 5 bp deletions in comparison to the isogenic mucoid isolate during starvation . All strains with compensatory mutations were less able to survive under nutrient-limited conditions than the parental mucoid isolate ( Fig 10 ) indicating that compensatory mutations in icaA , icaD or icaC are not advantageous for survival during starvation .
Although S . aureus chronically colonizes and infects the airways of CF patients , there are no previous reports of mucoid phenotypes of this pathogen recovered from the airways of these patients . Therefore , after occasional culture of mucoid S . aureus phenotypes from the specimens of some CF patients , we determined the prevalence of mucoid S . aureus isolates in S . aureus-positive patients from two independent prospective longitudinal studies , the persistence of mucoid isolates in the airways of these CF patients and the underlying molecular mechanism for mucoidy . Our study revealed that mucoid S . aureus isolates i ) can be isolated from app . 2% of CF patients with persistent S . aureus cultures without using a selective agar . Isolates with mucoid phenotypes might be missed during routine microbiological culture due to their distinct phenotype , which is sometimes only detectable by sub-culturing the mucoid isolate; ii ) carried a 5 bp deletion in the icaR-icaA intergenic region , iii ) evolve from non-mucoid S . aureus as indicated by molecular typing and WGS; iv ) occurred during CF lung disease in many different genetic backgrounds of S . aureus; v ) can persist in the airways for extended periods; vi ) possess a survival advantage compared to the non-mucoid phenotype due to protection against neutrophil killing under aerobic conditions and may be optimized for survival under nutrient limited conditions and vii ) that isolates with compensatory mutations in biofilm hyper-expressing clones in icaA , icaD or icaC emerge . Mucoid isolates were observed in 8 of 313 CF patients evaluated in two separate studies , and all investigated mucoid strains ( n = 115 ) carried the same 5 bp deletion in the icaR-icaA intergenic region . WGS of sequential S . aureus isolates of two CF patients confirmed that not only the mucoid isolates evolved from the non-mucoid strain , but also that the isolates with the non-mucoid phenotype and the 5 bp deletion , which additionally harbor compensatory mutations in ica genes , evolved from the mucoid isolates . The minor differences determined among the sequential isolates of the patients could be explained with microevolutionary events . It is known that approximately one mutation occurs in the core genome per six weeks [25] . Moreover , recent investigations demonstrated that even within S . aureus population of an individual with asymptomatic S . aureus nasal carriage a certain genomic diversity could be detected [26] . Therefore , considering the selective pressure present in the hostile environment of the CF lung it is conceivable that sequential isolated S . aureus strains are not necessarily 100% identical as determined by WGS . So far , only a single isolate with a 5bp deletion has been identified in S . aureus MN8m in vitro , but has never been reported to occur in vivo during S . aureus infections in patients [20] . Increased transcription of the ica operon was observed in strain MN8m . However , gel shift and DNase I footprint analyses revealed that this 5 bp motif within the ica promoter region did not affect binding of the repressor icaR . Therefore , the authors suggested that another protein must use this sequence to regulate icaADBC transcription . Just recently , Yu L . et al . identified a new repressor of the ica-locus , which binds to exactly the intergenic region , which is absent in our mucoid isolates due to the 5 bp deletion thereby causing the hyper-expression of PIA/PNAG [27] . The authors identified this repressor in a clinical isolate , which exhibited also an unusual mucoid phenotype but did not reveal any changes in the ica operon , by microarray analysis and DNA sequencing . They named this repressor "rob" , repressor of biofilm [27] . Compared to S . epidermidis , S . aureus produces only limited amounts of PIA/PNAG biofilm under aerobic conditions , whereas PIA/PNAG production is stimulated under anaerobic conditions , under the influence of SrrAB [18 , 19] . The S . aureus 5 bp deletion resulted in hyper-production of PIA/PNAG under aerobic conditions . Worlitzsch et al . reported that in the airways of CF patients anaerobic conditions are present in mucus plugs , resulting in biofilm formation in vivo [17] . However , within the environment of the airways there are different atmospheric levels of oxygen exposure . Therefore , hyper-production of PIA/PNAG under aerobic conditions could be advantageous for S . aureus in CF airways . The CF lung disease is characterized by inflammation and recruitment of large numbers of neutrophils to the airways [23] . In an in vitro opsonophagocytic killing assay the mucoid isolate was protected against phagocytosis and killing by human neutrophils , whereas S . aureus with the non-mucoid phenotype was killed , confirming that biofilm overproduction is a protective mechanism against neutrophil killing . Ulrich et al . proposed a model for neutrophil killing of S . aureus under aerobic and anaerobic conditions in the airways [19] . Under aerobic conditions PIA/PNAG negative isolates are killed within neutrophils by reactive oxygen species ( ROS ) and defensins , while PIA/PNAG positive isolates are protected against killing by ROS but are killed by defensins [19] . However , our results suggest that hyper-production of PIA/PNAG not only protects against ROS , but also against killing by defensins . In light of the high rate of survival of mucoid S . aureus isolates during in vivo persistence in the lungs of some CF patients , hyper-production of biofilm may be an efficient strategy for S . aureus to avoid killing by PMNs under in vivo aerobic conditions . Because biofilm formation requires additional energy output , it is possible that non-mucoid strains would be fitter than mucoid isolates . However , using two non-mucoid/mucoid strain pairs from two different patients our results did not necessarily support this hypothesis . In one strain pair the non-mucoid isolate out-competed the mucoid strain , while in the other strain pair the mucoid isolate was as much fit as the non-mucoid . Thus , mucoidy in clinical isolates might confer a fitness loss for some but not for all mucoid isolates . The comparable fitness of mucoid isolates compared to non-mucoid isolates might be due to additional mutations somewhere in the genome , which allow overcoming the energy costs due to excess biofilm formation . The results of the in vitro assays are also supported by our observation that mucoid isolates persisted for extended periods in vivo in the airways of CF patients . Adaptation to limited nutrients also drives mutations in CF isolates as shown for P . aeruginosa [28] . Therefore , we also investigated the behavior of mucoid and non-mucoid isolates under carbon-limited conditions . Interestingly , two early mucoid isolates survived significantly better under these starvation conditions ( Fig 9B and 9D ) than the non-mucoid strains isolated at the same time ( Table 2 ) . Lack of carbon in the growth medium might be compensated in the mucoid isolates by consumption of the surrounding polysaccharide substrate , which then might lead to a better survival during starvation , while non-mucoid isolates die . However , we also show that in a later strain pair of patient 7 ( S3 Fig ) the non-mucoid isolate survived significantly better than the mucoid isolate . This result is in contrast to the results shown for the early mucoid/non-mucoid strain pairs and is most likely to further ongoing mutations in other parts of the genome in the non-mucoid isolate , which facilitate survival under starvation conditions without hyper-biofilm formation . Interestingly , non-mucoid isolates that retained the 5 bp deletion were identified later during persistence in the airways . We hypothesized that a second mutation in the ica operon must have occurred in these strains , and therefore we sequenced the entire ica locus of these isolates . Sequencing results identified compensatory mutations , which were identified especially in icaA and icaD in 10 out of 12 isolates with the 5 bp deletion and a non-mucoid phenotype . The emergence of compensatory mutations in ica seems to be a reasonable strategy to avoid fitness costs required due to increased biofilm formation . However , most of the isolates with compensatory mutations were only isolated once or persisted only for a short period in the airways of CF patients , indicating that isolates with such compensatory mutations occur and might confer a short-term advantage but not an advantage for long-term persistence . In line with this are the data of decreased survival of non-mucoid isolates with compensatory mutations in minimal medium . In our experiments we compared survival under nutrient limitation for three isolates with 5 bp deletions and compensatory mutations in icaA ( 2 bp insertion ) , icaD ( 1 bp substitution ) and icaC ( ttta-mutation ) in comparison to the isogenic mucoid isolate . The observed decreased survival of these strains with compensatory mutations in icaA , icaD and icaC ( Fig 10 ) are in contrast to the data by Brooks and Jefferson [24] , who showed that their strain JB12 , which possessed the same 5bp deletion and a ttta-mutation in icaC as our strain 7 . 9 , survived better during nutrient starvation compared to the non-mucoid strain without 5bp deletion or the isogenic ica-deletion mutant . From their data Brooks and Jefferson suggested an important role for icaA , icaD and icaB genes in the absence of a functional icaC for bacterial survival under growth-limiting conditions [24] . Our contrasting results might be explained by different genetic backgrounds of the S . aureus strains tested or by the special conditions that S . aureus experiences during persistence in CF airways with ongoing mutations . In summary , S . aureus isolates with mucoid colony morphology were observed during long-term persistence of S . aureus in the airways of several CF patients . Such isolates carried a 5 bp deletion in the icaR-icaA intergenic region of the ica operon and persisted in some patients for extended periods . Mucoid isolates were protected against phagocytosis and were partially optimized for survival under nutrient limited conditions . Later , non-mucoid S . aureus isolates with 5 bp deletion with compensatory mutations in various ica genes emerged without excess biofilm production . Such isolates were readily purged from the S . aureus population , while mucoid isolates still persisted . Thus , biofilm hyper-production of S . aureus represents an efficient strategy against phagocytic killing and also facilitates survival under starvation conditions thereby supporting long-term survival of S . aureus in CF airways , where neutrophils are highly predominant and nutrient starvation occurs . In conclusion , the special conditions present in CF airways seem to facilitate ongoing mutations in the ica operon during persistence of S . aureus .
Two different study groups were evaluated retrospectively for the occurrence of mucoid isolates . The first group consisted of CF patients with positive S . aureus cultures treated in the two CF centers in Münster , Germany ( n = 118 ) . We searched the microbiological database for patients with mucoid S . aureus . The second study group consisted of patients of a recently conducted prospective multicenter study ( n = 195 ) , in which only CF patients with positive S . aureus cultures the year before recruitment but without chronic P . aeruginosa infection were included ( in revision ) . Several mucoid and non-mucoid S . aureus strains isolated from the respiratory tract of CF patients were characterized in this study ( Tables 1 , 2 and S1 ) . The biofilm-negative S . carnosus TM300 and the biofilm-positive S . epidermidis RP62A ( ATCC 35984 ) were used as controls . Staphylococci were grown in tryptic soy broth ( TSB ) ( Becton , Dickinson and Company , Sparks , MD , USA ) , brain heart infusion ( BHI ) ( Merck , Darmstadt , Germany ) , or on tryptic soy agar ( TSA ) ( Becton , Dickinson and Company , Sparks , MD , USA ) or Columbia blood agar plates and incubated for 24 h at 37°C . Congo red agar plates prepared with Columbia blood ( CRA ) were used to confirm the mucoid phenotype of S . aureus isolates [24] . When appropriate , erythromycin ( 10 μg/ml ) was added to the medium . Induction was achieved by the addition of 0 . 5% xylose . Cap-typing was performed by multiplex PCR [29]; spa-typing and MLST were done as described before [30 , 31] . To determine the clonal relationship of the strains of two patients with long-term persistence , we performed whole genome sequencing ( WGS ) of 12 S . aureus isolates of these patients as described [32] using the Illumina Nextera XT library preparation for a 250 bp paired-end sequencing run on a Miseq system ( Illumina Inc . , San Diego , CA , USA ) . Quality trimming , de novo assembly and subsequent core genome MLST ( cgMLST ) were performed as described recently [32] . For tree building using the Unweighted Pair Group Method with Arithmetic mean ( UPGMA ) method within the Ridom SeqSphere+ software ( Ridom GmbH , Münster , Germany ) , the allelic profiles of the up to 1 , 861 cgMLST targets [33] were used applying the parameter “pairwise ignoring missing values” . Moreover , we determined the average nucleotide identity ( ANI ) based on the de novo assembled contigs . Here , we used the ANI calculator ( http://enve-omics . ce . gatech . edu/ani/ [34] , which estimates the ANI using reciprocal best hits ( two-way ANI ) between two genomic datasets using default parameters . We assume that closely related isolates should exhibit > 99 . 9% ANI . For comparison , genome sequences of S . aureus reference strains N315 ( GenBank accession no . NC_002745; MLST ST5 ) and 55/2053 ( NC_022113; MLST ST30 ) were used for patient 7 and patient 8 , respectively , which reflect the most related S . aureus lineages to the patients’ isolates as determined by MLST . All raw reads generated were submitted to the European Nucleotide Archive ( http://www . ebi . ac . uk/ena/ ) under the study accession number PRJEB15647 . To assess if there is a survival fitness of non-mucoid or mucoid isolates during starvation , bacteria were cultured in minimal medium according to Brooks et al . [24] . Briefly , bacteria were grown in 5 ml tryptic soy broth containing 1% glucose ( TSBG ) in 50 ml conical cell reactor tubes ( Cellstar , No 227 245 ) at 160 rpm and 37°C . After 24h , cultures of mucoid isolates were supplemented with 5 μg/ml of Dispersin B ( Kane Biotech ) and cultivated for 30 min at 37°C to break up biofilm clusters . Bacteria were collected by centrifugation ( 4500 rpm , 7 min , RT ) and pellets were resuspended in 5 ml MOPS minimal media ( Teknova ) lacking glucose to achieve a concentration of 108 cells/ml . Bacteria were cultivated in 50 ml conical cell reactor tubes for up to 48h at 37°C and 160 rpm . After 0h , 4h , 8h , 12h , 24h and 48h cultures were vortexed , serially diluted and plated on Columbia blood agar incubated for 24h at 37°C to determine CFU/ml counts . Survival of isolates in minimal medium was calculated by comparing CFU/ml counts of respective time-points to the 0h CFU/ml counts ( presented in % ) . Transformants harboring the xylose inducible plasmids pTXicaBC or pTXicaAD were cultivated as described above with the exception that the growth medium was supplemented with 10 μg/ml tetracycline and 0 . 5% ( v/v ) xylose . Statistical analysis was performed using an unpaired two-tailed t-test . * p-value ≤ 0 , 05; ** p-value ≤ 0 , 01; *** p-value ≤ 0 , 001 . Competition experiments were performed according to Brooks et al . , 2014 . Bacteria were grown in 5 ml TSBG in 12 ml tubes at 160 rpm and 37°C . Growth medium of mucoid isolates was supplemented with 5 μg/ml of Dispersin B ( Kane Biotech ) to prevent strong clustering due to excessive biofilm formation . After 24 h , bacteria were collected by centrifugation ( 4500 rpm , 7 min , RT ) and pellets were resuspended in 5 ml TSBG . Cultures were diluted to a concentration of 108 cells/ml and mucoid and non-mucoid isolates were mixed 1:1 in 50 ml conical cell reactor tubes ( Cellstar ) . The medium was supplemented with 5 μg/ml of Dispersin B ( Kane Biotech ) to prevent biofilm formation and cultures were incubated for up to 24h at 37°C and 160 rpm . After 0h , 4h , 8h and 24h culture aliquots were vortexed , serially diluted and plated on CRA plates for CFU counting . CRA plates were incubated for 2–3 days at 37°C . Changes in population composition were calculated by comparing CFU/ml counts of mucoid or non-mucoid isolates to the CFU/ml counts of the whole population at the respective time-point ( presented in % ) . Fitness of non-mucoid and mucoid isolates was calculated using the following function developed by Sander et al . Mt = ln [ ( nt/mt ) / ( nt-1/mt-1 ) 1/gen] where nt and mt are the amount of non-mucoid and mucoid cells at a given time-point t , while nt-1 and mt-1 represent the amount of non-mucoid and mucoid cells at the preceding time-point [35] . For all isolates t0 was chosen to be the preceding time-point and fitness was calculated for the given time-points 4h , 8h and 24h according to http://textbookofbacteriology . net/growth_3 . html . The function fitt = 1+Mt was used to calculate the relative bacterial fitness . The fitness value is bigger than 1 , if the non-mucoid isolate is fitter than the mucoid isolate . If there is no difference in fitness between the isolates , the value equals 1 . If it is lower than 1 , the non-mucoid isolate has a reduced fitness compared to the mucoid isolate . Statistical analysis of the generated data was performed using an unpaired two-tailed t-test . * p-value ≤ 0 , 05; ** p-value ≤ 0 , 01; *** p-value ≤ 0 , 001 . A static biofilm assay followed by crystal violet staining was modified from a previous report [36] . Briefly , an overnight culture of S . aureus was diluted 200-fold with BHI containing 0 . 25% glucose , of which 200 μl were added to the wells of a 96-well polystyrene microtiter plate ( Greiner Bio-One , Frickenhausen , Germany ) and incubated for 24 h at 37°C . To determine the amount of biofilm produced by each clinical S . aureus isolate , all plates were washed three times with PBS and the on the bottom adhering biofilms were stained with 1% crystal violet for 15 min . Following three further washing steps with PBS , biofilms were solubilized in 100 μl ethanol-acetone ( 80:20 ) . The absorbance was determined at OD655nm with a microtiter plate reader ( Bio-Rad , Hercules , CA , USA ) . In parallel experiments , the nature of the formed biofilms was analyzed . Therefore , microtiter plates that were incubated for 24 h at 37°C were treated either with 100 μl sodium-metaperiodate in water ( 40 mM; AppliChem , Darmstadt , Germany ) , DNase I in 150 mM NaCl/1mM CaCl2 ( 100 μg/ml; Roche , Mannheim , Germany ) or proteinase K in 10 mM Tris-HCl ( pH 7 . 5 ) ( 100 μg/ml; MP Biomedicals , Santa Ana , California , USA ) for 3 h at 37°C . After three washing steps with PBS , biofilms were stained with crystal violet , washed again with PBS and solubilized in ethanol-acetone as described above . To confirm accuracy and reproducibility , each isolate was investigated in three biological replicates , always in eight wells per microtiter plate . S . epidermidis RP62A , which is known to form a strong biofilm with PIA as the major component [37] served as positive control , and the biofilm-negative and PIA-icaADBC-negative S . carnosus TM300 served as a negative control [38] . S . aureus isolates were grown overnight in 300 μl TSB under static conditions in six-well cell culture plates ( μ-Dish , Ibidi , Munich , Germany ) . In some experiments Alexa-568 labelled Wheat germ agglutinin or antibodies against PIA were added to the growth medium . Non-adherent cells were removed by washing with PBS and bacteria were stained using live staining ( Live/dead staining , Molecular Probes ) . Confocal image acquisition was performed on a Zeiss Axiovert 200M inverted microscope equipped with a Yokogawa CSU-22 confocal head and a Hamamatsu C9100-02 EM-CCD camera . Images were taken with a Zeiss Plan Apochromat 63x/1 . 4 Ph3 Oil objective . Improvision Velocity software was used for image acquisition and quantification . Capsule was semi-quantified by colony immunoblots as described before [22 , 39] , and bacterial polysaccharides ( CP and PIA/PNAG ) were quantified by enzyme-linked immunosorbent ( ELISA ) inhibition assays [40 , 41] . Briefly , 96-well plates were coated overnight at 4°C with purified PNAG ( 1 μg/ml ) or with CP5 or CP8 ( 4 μg/ml ) coupled to poly-L-lysine by the cyanuric acid chloride method . The microtiter plate was washed and blocked with 0 . 05% skim milk . S . aureus strains were harvested , washed in phosphate buffer , and then trypsinized ( 1 mg trypsin/ml of 0 . 1 M phosphate buffer , pH 8 ) for 60 min at 37°C to remove protein A . After washing , the bacterial suspensions were serially diluted , and the bacterial concentrations were verified by plating on tryptic soy agar plates . Polyclonal polysaccharide-specific antiserum was diluted and incubated overnight at 4°C with serial dilutions of the bacteria or purified polysaccharide ( standard curve ranging from 1 μg/ml to 1 ng/ml ) . Samples were centrifuged , and the absorbed serum samples ( supernatants ) were added to the coated microtiter plates . Following a 2-h incubation with absorbed or unabsorbed serum samples , the plates were washed with PBS/Tween , and alkaline phosphatase-conjugated protein A/G ( Thermo Scientific; 1:3000 ) was added to each well . After a 2-h incubation at ambient temperature , the plate was washed , and the substrate p-nitrophenyl phosphate was added . When the wells containing unabsorbed serum samples reached an OD405 nm of ~2 . 0 , the plate was read on a Bio-TEK Power Wave HT ELISA reader . The concentration of each sample ( CFU/ml ) that resulted in 50% inhibition of antibody binding ( IC50 ) was determined , and the polysaccharide content of the sample was calculated from the standard curve . The opsonophagocytic killing ( OPK ) activity of human polymorphonuclear neutrophils ( PMNs ) was performed and analyzed as described [42] . The staphylococcal expression vectors pTXicaBC and pTXicaAD [43] were used to complement the S . aureus strains with 5 bp deletions and non-mucoid phenotype . The plasmids were transformed into the cells by electroporation using standard procedures as described [44] . Plasmid purification was performed following the PrepEase Quick MiniSpin Plasmid kit protocol ( Affymetrix ) . Genomic DNA ( gDNA ) was isolated according to the manufacturer instructions of the QiAamp DNA Mini Kit ( Qiagen , Hilden , Germany ) . For sequencing of the icaADBC operon , amplified PCR products were purified with the QIAquick PCR Purification Kit ( Qiagen , Hilden , Germany ) according to the instructions of the manufacturer . Sequence analysis was conducted using the software Clone Manager Suite 7 ( Scientific & Educational Software , Durham , NC ) and the reference sequence of S . aureus ATCC35556 . Primers are shown in Table 3 . | Staphylococcus aureus is one of the most common pathogens isolated from the airways of cystic fibrosis ( CF ) patients . In this study , we identified unusual mucoid S . aureus isolates in 8 of 313 ( 2 . 5% ) CF patients . All mucoid isolates carried a 5 bp deletion upstream of the ica operon , which resulted in increased expression of PIA/PNAG biofilm . In three patients , mucoid isolates were recovered for extended periods up to 126 months . Surprisingly , later sequential non-mucoid isolates ( n = 12 ) of two patients also carried the 5 bp deletion . Sequencing of the entire ica operon identified compensatory mutations in different ica genes ( icaA , icaD , icaC ) in these isolates . A close relationship of these isolates and of the first mucoid and closest non-mucoid isolate without 5 bp deletion were confirmed by whole genome sequencing . Transformation with expression vectors with respective wild-type genes restored mucoidy . Mucoid isolates were protected against neutrophil killing and survived better under starvation conditions . In conclusion , the special conditions present in CF airways seem to facilitate ongoing mutations in the ica operon during persistence of S . aureus . | [
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] | 2016 | Dynamic in vivo mutations within the ica operon during persistence of Staphylococcus aureus in the airways of cystic fibrosis patients |
The global migration patterns of influenza viruses have profound implications for the evolutionary and epidemiological dynamics of the disease . We developed a novel approach to reconstruct the genetic history of human influenza A ( H3N2 ) collected worldwide over 1998 to 2009 and used it to infer the global network of influenza transmission . Consistent with previous models , we find that China and Southeast Asia lie at the center of this global network . However , we also find that strains of influenza circulate outside of Asia for multiple seasons , persisting through dynamic migration between northern and southern regions . The USA acts as the primary hub of temperate transmission and , together with China and Southeast Asia , forms the trunk of influenza's evolutionary tree . These findings suggest that antiviral use outside of China and Southeast Asia may lead to the evolution of long-term local and potentially global antiviral resistance . Our results might also aid the design of surveillance efforts and of vaccines better tailored to different geographic regions .
Yearly epidemics of influenza viruses are responsible for between 250 , 000 and 500 , 000 deaths globally , with influenza A causing the bulk of mortality and morbidity [1] . Influenza is concentrated in the autumn and winter in temperate regions , but shows less periodic transmission in the tropics . Influenza is not endemic to any particular region in the world but appears to be dynamically sustained; a local epidemic will sweep through a particular region , fade out and then be reseeded by contact with a different region's local epidemic the following year [2] , [3] . Understanding the geographic structure of influenza transmission is of critical importance to our efforts to combat the disease . Here , we identify previously unreported genetic structure in the global population of seasonal influenza A ( H3N2 ) viruses and show how this structure arises from the dynamics of the global transmission network . Whereas previous hypotheses propose a source-sink model of viral evolution , in which a network of populations in East and Southeast ( E-SE ) Asia seeds annual epidemics in temperate latitudes [3] , [4] , we find that strains of influenza often circulate outside Asia , sustained by complex migration dynamics . This persistence may have long-term effects on influenza's evolution . Through migration between regions , influenza may persist over time , even if no particular region serves as a reservoir of disease .
Genetic diversity in influenza A ( H3N2 ) is highly restricted and few unique hemagglutinin ( HA ) variants exist at any one time [5] . We find that 4355 sequences sampled from 1998 to 2009 show average nucleotide diversity of substitutions per site ( 95% confidence interval ) between pairs of contemporaneous sequences , defined as sequences sampled no more than 30 days apart from one another . This level of nucleotide diversity is approximately 15 times greater than that of human genes ( ) [6] . However , it is significantly lower than nucleotide diversity of HIV sequences isolated from a single patient ( 0 . 08 ) [7] . Despite showing limited genetic diversity , the virus evolves extremely quickly at a rate of substitutions per site per year , resulting in rapid genetic turnover from year-to-year [4] , [8] . At a continental scale , we find substantial geographic population structure in the influenza virus . We classified influenza samples into 7 regions: China ( encompassing mainland China , Hong Kong , Macau and Taiwan ) , Europe , Japan , Oceania , South America , Southeast Asia ( encompassing Cambodia , Indonesia , Malaysia , Myanmar , the Philippines , Singapore , Thailand , and Vietnam ) and the USA . These regions were chosen based upon geography as well as sampling density ( Table S1 , Figure S1 , Figure S2 ) . On average , genetic diversity among contemporaneous sequences is greater between regions , , than within regions , ( Table S2 ) . This distinction is commonly quantified as , equal to [9] . greater than 0 indicates genetic isolation among regions , referred to as population structure . In influenza , is 0 . 207 ( 0 . 134 , 0 . 270 ) . For comparison , continental genetic differences in humans show of [10] . To explain the genetic relationships among viral samples , we used a population genetic model based on the structured coalescent [11] , [12] , which describes the genealogical patterns connecting members of a reproducing population . This model explicitly incorporates sampling date [13] and sampling region [14] to reconstruct the genetic history of samples taken from an evolving population . In this analysis , we account for differences in the overall sampling resolution ( Figure S1 ) and for differences in temporal sampling patterns ( Figure S2 ) by taking 100 random subsamples from the available influenza A ( H3N2 ) sequences . In each resampled replicate , the number of sequences from each region was the same . Our migration rate estimates represent the mean across these replicates . Confidence intervals were produced by comparing estimates across the replicate pool . The statistic showed evidence of structure among influenza virus populations , arising from structure in the contact network of host populations . Our structured coalescent analysis goes further , revealing not only population structure , but also rates of migration of influenza viruses between regions ( Figure 1 ) . Migration rate estimates varied little over resampled replicates ( Table S3 ) suggesting that sampling particulars had little influence over our coalescent results . In support of a global metapopulation model , we find that all regions act to some extent as sources in the migration network . We observe frequent gene flow from China into the USA , but also from the USA into China . Both in terms of overall rate of emigration and in terms of network centrality , China , Southeast Asia and the USA make the strongest contributions to the migration network ( Table 1 , Figure 1 ) . The estimated migration network correlates well with the frequency of air travel between regions ( compare Figure 1 to Figure 1 of Hufnagel et al . [15] ) . For example , South America is relatively isolated in the global aviation network [15] , although it possesses more ample connections to North America . Consistent with this detail , we find that immigration of influenza into South America is rare and that when it occurs it most often comes from the USA . Additionally , influenza in China migrates most frequently to Japan and Southeast Asia , while influenza in Southeast Asia migrates most frequently to China and Oceania . Still , although the world has become tightly connected through travel , it appears that influenza sweeps through local populations fast enough to sustain substantial geographical population structure on a continental scale . A detailed genealogical history of the influenza A ( H3N2 ) virus population was reconstructed through further analysis of a large number of sampled sequences ( Figure 2 ) . Here , temporal patterns are of predominant interest . Samples that are spread out over time provide the most information toward this goal . To reduce the computational complexity of the data set while retaining the most temporal information , we pruned the 4355 sequences to 2165 sequences by taking at most 10 sequences per month from each region . Sequence counts in richly sampled regions were reduced by this procedure , while regions with poor sampling were left mostly intact ( Figure S2 ) . The influenza tree has the characteristic shape of a long trunk and short side branches [16] , [17] , resulting from the combined effects of temporal sampling and rapid coalescence . Looking backwards in time , two sorts of events occur: new lineages are sampled and existing lineages coalesce . If the rate of coalescence is large compared to the rate at which new lineages are sampled , then the genealogy will appear spindly with few contemporaneous lineages . The observed pattern of rapid coalescence arises from immune-driven adaptive evolution in the HA gene [5] , [18] , [19] . Contacts between regions produce migration events , which we depict as shifts in color in the virus genealogy ( Figure 2 ) . The genealogy shows that migration events between major geographic regions are uncommon , and thus the virus is not well-mixed among regions . Generally , we observe genetic diversification over the course of a regional epidemic , after which time few , if any , lineages persist . Local persistence appears in a genealogy as a side branch present in the same region over multiple seasons . It is clear from the influenza tree that this pattern is rare , suggesting lineages of influenza do not often persist from season to season within temperate regions . While a general lack of local persistence is consistent with previous results [2] , [3] , we find that contrary to previous hypotheses [3] , [4] seasonal epidemics in temperate regions can seed future epidemics around the world . For example , we find that the 1998–1999 USA epidemic seeds two major influenza lineages . The first of these lineages appears as a temperate lineage that circulates predominantly in Europe , Oceania , South America and the USA . This lineage persists for years . The second lineage is part of the trunk of the genealogy; it migrates from the USA into China , where it persists from 2000 to 2003 . After 2003 , this lineage spreads to the rest of the world . Thus , we find that global persistence is aided by metapopulation structure in which infection is dynamically sustained through contact between regions of different seasonality . At any given moment there is a strain of influenza that will eventually , through natural selection and genetic drift , become the progenitor of all future influenza strains . Looking backward in time , this is equivalent to the statement that all current strains of influenza share a common ancestor at some time in the past . This progenitor strain corresponds to the trunk of the influenza genealogy ( Figure 2 ) and is where historically relevant evolution occurs; only mutations that occur along the trunk are maintained indefinitely , while mutations that occur along other branches will eventually be lost . Still , mutations to side branches may have important , if transitory , effects . Antigenically novel variants will be more likely than other strains to become the progenitors of the influenza population . However , minor variants arising in poorly connected regions of the world , i . e . South America , will be less likely to spread than variants arising in highly connected regions , i . e . the USA . Thus , even in the presence of antigenic drift , we expect migrational structure to play a role in which strain eventually takes over the influenza population . Our structured coalescent approach explicitly models the location of the genealogy trunk over time , allowing direct calculation of the proportion of the trunk belonging to each geographic region ( Table 2 , Figure 3 ) . Consistent with previous predictions [3] , we find that from 1998 to 2007 the trunk of the genealogy predominantly resides in China ( 34% ) and Southeast Asia ( 32% ) . However , a significant proportion of the trunk resides in the USA ( 24% ) . As previously established , migration patterns near the tips of the tree support China , Southeast Asia and the USA as source populations ( Figure 1 ) . We would expect that as source populations , these regions would predominate the trunk of the genealogy . These results demonstrate that the last years of historically meaningful evolution in the virus population occurred primarily in China , Southeast Asia and the USA .
We have shown that the genetic population structure of influenza A ( H3N2 ) arises in part from global migration dynamics , with the most important contributions from China and Southeast Asia , but nonetheless significant contributions from temperate regions outside Asia ( Figure 1 ) . In contrast to the prevailing source-sink model , we find evidence of significant migration of viruses from temperate regions to tropic regions , and that lineages may exist outside of Asia for several seasons , persisting through dynamic migration between regions of different seasonality ( Figure 2 ) . Additionally , we find that China , Southeast Asia and the USA all contribute to the trunk of the influenza genealogy ( Figure 3 ) , and thus mutations occurring within these regions have shaped the global flu population . The evolution of H3N2 influenza over the past 10 years thus reflects the dynamics of a global metapopulation , rather than a metapopulation restricted to East and Southeast Asia . Our use of the structured coalescent model to analyze influenza evolution represents a significant step forward over previous techniques . The tree constructed by Russell et al . [3] using phylogenetic methods , is a single estimate of the HA genealogy . We use a Bayesian sampling technique to analyze a large number of trees concordant with the genetic data [20] . More importantly , our coalescent method explicitly incorporates sampling date , sampling location and also an underlying model of the demographic process . These details provide substantially more context , and thus allow for more accurate reconstruction . Rambaut et al . [4] use a similar Bayesian coalescent approach; however , their technique did not take into account population structure . By analyzing a large number of sampled trees and through resampled replicates , we establish the degree of uncertainty of our estimates . Each migration rate has a confidence interval attached to it ( Table S3 ) . Additionally , our estimates of the trunk location over time have a degree of confidence associated with them . Our statistical model strongly suggests that the 1998–1999 USA epidemic forms the trunk of the influenza genealogy ( Figure 3A ) . Consistent with this hypothesis , we observe that samples from the USA during this period coalesce to the trunk of the genealogy rapidly in absolute terms , not just relative to other samples ( Figure 3B ) . From 2000 to 2002 , Chinese samples are closer to the trunk than samples from the USA and Oceania , but are not close to the trunk in absolute terms ( Figure 3B ) . Because of this , there is considerable uncertainty as to whether the trunk of the genealogy resides in China or in Southeast Asia ( Figure 3A ) . This particular result is especially supportive of our method , as we lack samples in Southeast Asia prior to 2002 ( Figure S2 ) , yet still we infer that Southeast Asia may be contributing to the trunk of the genealogy . There are other time periods ( e . g . 2006 ) in which samples are far from the trunk , suggesting the possibility that the trunk may be located outside of sampled regions . Regardless of methodological differences and differences of interpretation , our results are compatible with the results of Russell et al . [3] and Rambaut et al . [4] . In their analysis of mean distance to the trunk , Russell et al . find that the USA is behind China , Taiwan , Hong Kong and South Korea , but ahead of every other country sampled , including all of the Southeast Asian countries . This itself should suggest that the USA plays an important role in the global migration dynamics . Additionally , the inference of the 1998–1999 USA epidemic as the trunk of the genealogy is congruent with the findings of Rambaut et al . In a genealogy produced from only USA sequences ( their Supplementary Figure 3e ) , it is clearly seen that while most USA epidemics occur as side branches , the 1998–1999 epidemic is distinctly part of the trunk of the genealogy . Russell et al . state: “the tree does show evidence for bidirectional seeding but no evidence for non-E&SE Asian strains contributing to long-term evolution of the virus during the study period . ” We suggest that if Russell et al . had analyzed samples from 1998–1999 , they would have obtained different results . It is possible , for example that some of the strength of the USA's contribution to the migration dynamics ( Figure 1 ) comes from its proximity to the Central American tropics . In this scenario , gene flow back and forth across the Pacific would be attributable to strains of influenza circulating in Central America that pass through the USA in their spread to the rest of the world . However , if this scenario were true , we might expect that the Central American influence would extend to South America in addition to the USA . We do not see evidence of this; South America contributes the least among studied regions to the global migration dynamics . Additional evidence for a temperate contribution to the migration network and to the trunk of the genealogy comes from epidemiological simulations ( see Materials and Methods ) . In simulations with equal rates of contact between hosts in a northern population , a tropical population and a southern population we observe that despite strong seasonality in the temperate regions , substantial emigration occurs out of the temperate populations ( Table 3 ) . In this scenario , we find that although the trunk of the genealogy resides predominantly in the tropics , it often passes through the temperate populations during the course of a seasonal epidemic ( Figure 4 ) . Examining the influenza genealogy ( Figure 2 ) , it is apparent that regional outbreaks often result from very few immigration events , consistent with previous results [2] . For example , the 2003 epidemic in Oceania appears almost completely monophyletic and can trace its history to a single migration event ( or perhaps multiple migration events of identical strains ) in early 2003 . Thus , even if there are millions of infected individuals at the peak an epidemic , the genetic diversity of the virus will be bottlenecked at the beginning of the outbreak [21] . The bottlenecking effect of low migration may have contributed to the observed pattern of restricted genetic diversity in influenza . We observe similar effective population sizes across all regions ( Table S4 ) , consistent with the hypothesis that epidemic influenza is passed from one region to another , persisting nowhere . If there were a reservoir of endemic influenza in E-SE Asia ( or elsewhere ) that repeatedly seeded epidemics in the rest of the world , then the coalescence of E-SE Asian lineages would not be bottlenecked to the same extent , in which case we would observe significantly deeper coalescence events in this region and a correspondingly larger effective population size . This is not , however , the pattern we observe , reinforcing the idea that a metapopulation structure underlies influenza's persistence , even in the tropics [3] , [4] . The global dynamics of influenza virus population influence a variety of public health decisions . Because influenza frequently migrates out of the USA , seeding epidemics in other parts of world , actions taken to combat the disease in the USA can have global impacts . For instance , the use of antivirals in the USA may promote the evolution of drug-resistant strains , which could then spread to the rest of the world . And conversely , vaccination programs outside of E-SE Asia have the potential to curb the global spread of the disease . Additionally , with increased knowledge of the patterns of flu migration , it may be possible to tailor vaccine design to particular areas of the globe . For instance , we observe that most influenza in South America arrives from the USA . This suggests that vaccines used in South America should be preferentially constructed from the USA strains of the previous season . Our research suggests that the majority of historically relevant evolution of the influenza virus occurs in China , Southeast Asia and the USA , with other regions of the globe playing significant , but relatively minor roles . This conclusion is , to some extent , contingent upon the restricted temporal and spatial patterns of viral sampling . There may be other regions of the world , such as Africa , Central America and India , that act as important sources in the worldwide influenza migration network . Increased worldwide sampling of the influenza virus would further clarify the complex migration dynamics of the virus .
Sequences belonging to the HA1 domain of the hemagglutinin ( HA ) gene were downloaded from the Influenza Virus Resource of GenBank [22] . Only non-lab strains of at least 900 bases with fully specified dates ( day , month , year ) and countries of origin were used . We restricted our analysis to sequences dated from 1998 to 2009 . We categorized the resulting 4355 samples into 7 geographic regions ( Figure S1 , Figure S2 ) . Regions were chosen with the intention of maximizing geographic distinctions while simultaneously maintaining enough samples to make accurate regional inferences . Sequences were aligned using MUSCLE v3 . 7 under default parameters [23] . Nucleotide diversity , measured in terms of substitutions per site , was calculated as the mean proportion of mismatches across all contemporaneous pairs of sequences . Only sequences whose sample dates were within 30 days of each other were considered contemporaneous . To avoid bias toward well sampled regions , the overall within-region nucleotide diversity was estimated as the average of region-specific diversity estimates:where regions and refers to diversity estimates where both samples in each pair are from region ( Table S2 ) . The overall between-region diversity was estimated in a similar fashion:where refers to diversity estimates where one sample is from region and the other sample is from region . Confidence intervals were estimated by taking 1000 bootstrap replicates from the total pool of sequences . We caution that our estimates of diversity may be over-estimates due to the fact that strains are often first characterized by HI cross-reaction; antigenically novel strains are then preferentially sequenced . If the bias towards antigenically novel strains is similar in each region , then we would expect estimates of to be robust to this effect as and should be biased equally . Evolutionary dynamics were estimated using a Bayesian Markov chain Monte Carlo ( MCMC ) approach . MCMC explores the parameter-space through a random walk , converging on the posterior distribution of the model parameters . Evolutionary parameters shared across locations were estimated using the MCMC techniques implemented in the coalescent inference program BEAST v1 . 4 . 8 [24] . Here , trees are constructed following a single-population coalescent process , which imposes a prior on the branch lengths of the tree . We used the HKY85 model [25] to parameterize the mutational process , with equilibrium nucleotide frequencies taken from observed nucleotide frequencies , and the evolutionary rate across sites held constant . The transition/transversion ratio was estimated to be 6 . 745 ( 95% credible interval 6 . 267–7 . 269 ) . The rate of nucleotide substitution was estimated to be substitutions per site per year ( substitutions per site per year ) . These mutational parameters were held constant in subsequent analyses to estimate coalescent parameters for each geographic region via a similar MCMC technique implemented by Migrate v3 . 0 . 8 [14] , [20] that allows joint analysis of multiple regions . Henceforth , we refer to these sampling regions as demes . Migrate estimates the parameter , where is the effective population size of deme . We measure in terms of years , rather than generations , corresponding to our measurement of in terms of substitutions per site per year . Thus , measures the expected number of years for two samples from within a deme to coalesce into a single lineage . We call this the ‘timescale of coalescence’ of deme . The prior distribution of was assumed to be exponential with a mean of 0 . 1 substitutions per site . Migrate estimates the rate of migration via the parameter . The rate of migration is measured in terms of migration events from deme into deme per lineage per year . The prior distribution of was assumed to be exponential with a mean of 0 . 1 migration events . To confirm that sampling patterns did not influence our results , we performed independent analyses of 100 resampled replicates . For each replicate , we limited each region to the same number of samples between the years 2002 and 2008 during which time each region was well represented ( Figure S2 ) . South America had only 61 samples during this span of time , and so sample counts in other regions were constrained to match . Migration rate estimates varied little across the 100 resampled replicates , suggesting sampling details had little impact on our results ( Table S3 ) . In our coalescent model , selective neutrality was assumed among lineages , however much of the effect of selection will be captured by the effective population size parameter [4] . Additionally , effective population sizes and rates of migration were assumed constant over time . However , given the strong seasonality exhibited by influenza [1] , we expect variation in the rates of migration and coalescence over the course of a year . By assuming constant rates of coalescence and migration , our estimates mask such rate variation . It is nonetheless noteworthy that a relatively high rate of migration was inferred from the USA to Oceania , despite their strongly asynchronous epidemic dynamics . We might expect that , during the Southern Hemisphere summer when influenza is common in the USA , migration events from the USA into Oceania should be rare , as seasonal forcing should prevent the newly emigrated lineage from getting a foothold in Oceania . Furthermore , we might expect most migration from the USA into Oceania to occur during the Northern Hemisphere spring/Southern Hemisphere fall , when levels of seasonality and immunity are most favorable to emigrant lineages . More complex statistical models will help explore the nuances of the seasonal migration dynamics of the disease . For each of the 100 bootstrap replicates , fifty MCMC chains were run for steps each , sampling genealogies and parameter values every 10 , 000 steps . The first steps of each chain were removed as ‘burn-in . ’ Convergence was assessed visually and through comparison among chains using the Gelman-Rubin convergence statistic . We combined the remaining samples from each chain to give a total of 5 , 000 samples for each of the resampled replicates . We performed a number of additional checks to confirm that our results were robust to the details of the analysis . Instead of equal sampling from region , we sampled from each region in proportion to its human population size ( Table S5 ) . We performed a number of analyses adjusting the migration rate prior . We found more variation in the migration network using larger priors , but the details of connectivity were highly similar ( Table S6 ) . Larger priors resulted in a larger USA contribution , suggesting that our choice of smaller prior is a conservative one . We also performed a number of analyses using alternative regional groupings , for example dropping South America and splitting China into two regions: China and Hong Kong ( Table S7 ) . In all cases , we still find support for a global meta-population model in which the USA plays a strong role . In addition , the relative rates of migration between regions were similar between analyses . Nevertheless , despite our best efforts to control for sampling effects , it remains possible that overlooked sampling details may have influenced our results . With progress in worldwide surveillance and sequencing technology , constructing a truly representative sample of influenza should eventually become tractable . For reconstruction of genealogical trees we cut down the full 4355 sequences to 2165 sequences by taking at most 10 sequences per month from each region . This served to make the analysis more computationally feasible , while retaining as much temporal information as possible . Sample counts from the USA , and to a lesser extent Japan and Oceania , were decremented by this method , while other regions were affected only slightly ( Table S1 ) . In our analysis of these sequences , we held migration rates and effective population sizes constant at the levels estimated from the preceding resampling analysis . The trunk of the influenza genealogy can only be identified in retrospect . All branches in trees sampled by Migrate are labeled with the demes they occupy . To assess deme-specific contributions to the trunk , we first extracted the trunk from the genealogy . This was done by taking random samples present between 2007 and 2009 and tracing their ancestry backwards in time . Thus , each random sample gives a slightly different trunk . Farther in the past , all samples share the same lineage as the trunk , while closer to the present samples may differ in which lineage they consider to be the trunk . Uncertainty is further encapsulated by analyzing the trunks of a sample of genealogies , rather than just using a single tree . Across all sampled trunks , we calculated the mean and credible interval for the proportion of each trunk belonging to a particular geographic region ( Table 2 ) . The temporal dynamics were assessed in a similar fashion , calculating the proportion of sampled genealogies belonging to a particular region at a particular point in time ( Figure 3 ) . Trunk extraction and processing was performing using the program PACT , which is freely available from the author's website ( http://www . trevorbedford . com/pact ) . Because of the larger dataset , MCMC chains had to run for significantly longer than in the previous analysis . Four MCMC chains were run for steps each . The first steps of each chain were removed as burn-in . Genealogical trees were sampled every steps . Combining the remaining data left a sample of 4000 genealogical trees in which to perform trunk reconstruction . To validate our methods , we implemented a stochastic , multi-strain , multi-deme susceptible-infected-recovered-susceptible ( SIRS ) model . Three host populations ( North , Tropics and South ) were simulated with epidemiological parameters derived from influenza A ( H3N2 ) . Here , we refer to these populations as demes . In these simulations , the North and the South were seasonally forced , so that every summer infection dies out . We tested two ecological scenarios . In the source-sink model , infected hosts within the Tropics can contact hosts in the North and in the South . Infected individuals in the North and the South never contact susceptibles outside their own demes . The second model was an equal contact model , where bidirectional migration occurred between all demes . We suggest that what is most important here is the concordance between simulation parameters and our estimates of these parameters , rather than that the simulation model perfectly reflect reality . The epidemiological model was run for 50 simulated years with the first 40 years removed as ‘burn-in’ to allow genetic diversity and population dynamics to equilibrate . All epidemiological and demographic parameters , except contact rates , were identical between demes . Host population sizes in each deme were kept constant at individuals with per-capita birth and death rates of 30 years . Strains had an intrinsic reproductive rate of 2 [26] , [27] , an average duration of infection of 5 days [28] , and an average duration of immunity of 2 years [29] . The North and South populations were seasonally forced using a sinusoidal function with amplitude 0 . 4 , and thus varied from 1 . 2 to 2 . 8 . A strain was defined as a sequence of 1000 bases . The rate of mutation was substitutions per site per year . In the source-sink model , the per capita probability of transmission from the Tropics to the North and from the Tropics to the South was 0 . 005 of the rate of within-deme transmission . In the equal contact model , between-deme transmission was 0 . 005 of the rate of within-deme transmission for each pair of demes . In each case , this translates to an expectation of migration events per lineage per year . Under these parameters , infection in the Tropics reaches an endemic equilibrium , while infection in the temperate regions shows seasonal epidemics ( Figure 4 ) . In the Tropics , there was an average of 248 . 8 and 251 . 0 infected individuals on any given day for the source-sink model and the equal contact model respectively ( Table 3 ) . This is the population size of each regional virus population . The generation time is given by duration of infection and is equal to 5 days , or 73 generations per year . Five hundred sequences were sampled from each deme over the final 10 years of the simulation ( Figure 4 ) . Sampling was proportional to the size of the regional population , so that the seasonality of the North and the South is reflected in the temporal distribution of samples . We ran Migrate v3 . 0 . 8 [14] , [20] with these samples to assess deme-specific rates of coalescence and migration . Estimates of effective population size of the Tropics agreed well with the true population size in the simulations of both models ( Table 3 ) . Estimates of the effective population size in the North and South are lower than the mean census population size owing to the large fluctuations in census size over time ( Figure 4 ) . Estimated rates of migration were highly compatible with the contact rates specified in the model ( Table 3 ) . In cases where there the true contact rate was 0 . 365 , the estimated contact rates ranged from 0 . 37 to 0 . 39 . In cases where the true contact rate was 0 . 0 , the estimated contact rates ranged from 0 . 01 to 0 . 06 . The overall appearance of the genealogical trees reconstructed from simulation data ( Figure 4 ) was very close to the reconstructed influenza genealogy ( Figure 2 ) . In the source-sink model , we see that seasonal epidemics in the North and in the South never contribute viral lineages to the trunk , while in the equal contact model , seasonal epidemics sometimes lead to chains of transmission in the Tropics and sometimes lead directly to the next seasonal epidemic in the other hemisphere ( Figure 4 ) . Here , the reconstruction of the genealogy trunk was in agreement with our expectations . In the source-sink model , the location of trunk of the genealogy is estimated to always reside in the Tropics ( Figure 4 ) . This makes intuitive sense in that with asymmetric migration only the Tropics may possibly contribute to the long-term evolution of the simulated virus population . In the equal contact model , the location of the trunk varies dynamically over time ( Figure 4 ) . Although the Tropics make the largest contribution , both the North and South sometimes comprise the trunk of the genealogy and thus shape long-term viral evolution . To further confirm that our coalescent inference methodology is robust to sampling details , we performed an additional analysis of simulation data from the source-sink model and from the equal contact model . In this analysis , sampling intensity was intentionally skewed with 500 samples were taken from the North and South , but only 100 samples taken from the Tropics . Results from this analysis were highly similar to the analysis with equal sampling intensity ( Table S8 ) . | Infections by the influenza A virus show highly seasonal patterns in temperate regions . Winter is flu season . Over the course of the autumn and winter , a small number of initial infections grow to encompass a significant proportion of the population . At the end of the winter , infection disappears . It has been suggested that the strains founding each temperate flu season originate from China and Southeast Asia , where influenza A is less seasonal . We test this hypothesis through analysis of genetic sequences from viruses sampled throughout the world between 1998 and 2009 . We find that although China and Southeast Asia play the largest role in the migration network , temperate regions , particularly the USA , also make important contributions . Not all temperate strains of influenza die out with the end of the winter season . Instead many strains emigrate to more favorable climes . Thus , we find patterns of influenza transmission to be highly dynamic . Because of emigration out of temperate regions , mutations harbored by temperate strains of influenza A can spread to the global virus population . This means that regional public health decisions regarding influenza may have global impacts . | [
"Abstract",
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] | [
"computational",
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] | 2010 | Global Migration Dynamics Underlie Evolution and Persistence of Human Influenza A (H3N2) |
Explaining the dramatic variation in species richness across the tree of life remains a key challenge in evolutionary biology . At the largest phylogenetic scales , the extreme heterogeneity in species richness observed among different groups of organisms is almost certainly a function of many complex and interdependent factors . However , the most fundamental expectation in macroevolutionary studies is simply that species richness in extant clades should be correlated with clade age: all things being equal , older clades will have had more time for diversity to accumulate than younger clades . Here , we test the relationship between stem clade age and species richness across 1 , 397 major clades of multicellular eukaryotes that collectively account for more than 1 . 2 million described species . We find no evidence that clade age predicts species richness at this scale . We demonstrate that this decoupling of age and richness is unlikely to result from variation in net diversification rates among clades . At the largest phylogenetic scales , contemporary patterns of species richness are inconsistent with unbounded diversity increase through time . These results imply that a fundamentally different interpretative paradigm may be needed in the study of phylogenetic diversity patterns in many groups of organisms .
One of the most striking large-scale patterns in biology is the uneven distribution of species richness across the tree of life . Some groups are characterized by nearly incomprehensible species diversity ( beetles , grasses ) , yet many other groups are species-poor ( tuataras , ginkgoes ) . Evolutionary biologists have long been preoccupied with identifying the causal mechanisms underlying these differences in species richness [1]–[3] . These mechanisms include a vast range of biological , historical , and geographic factors . For example , lineage-specific molecular evolutionary traits ( e . g . , rates of molecular evolution or genome duplication ) might be associated with net rates of species diversification [4] , [5] . Likewise , species diversification rates might be a function of ecological traits , including those associated with the use of novel resources or defense from natural enemies [6] , [7] . The list of factors that have been linked to differential diversification rates is substantial and continues to increase [8]–[11] . The most general explanatory variable of all is clade age [12]: clades vary in age , and this age variation should lead to differences in clade diversity , particularly if all clades have identical net rates of species diversification through time . If clade diversity is generally increasing through time , there is a strong theoretical expectation that species richness should be associated with their age ( Figure S1 ) . Even if individual clades are characterized by a “balanced” random walk in diversity , such that speciation and extinction rates are exactly equal , we may still observe a positive relationship between age and richness through time if clade diversity is conditioned on survival to the present day ( Figure S1 ) . Stochastic models of clade diversification through time consistently suggest that species richness and clade age should be correlated [13] , [14] . These expectations differ from patterns observed for extinct clades [15] , [16] , presumably because living clades have survived to the present to be observed . The expectation that age and diversity should be correlated does not minimize the importance of evolutionary “key innovations” [7] , [17] , [18] and other factors as determinants of clade richness . In fact , to the extent that such factors influence net diversification rates , their effects should further accentuate differences in richness attributable to age variation alone . Surprisingly , previous analyses have reached contrasting conclusions regarding the importance of clade age as a determinant of species richness [12] , [13] , [19] , [20] . For some groups , clade age does not appear to predict species richness , suggesting that clade richness is regulated by diversity-dependence of speciation and extinction rates [14] , [21] , [22] . Some have suggested that this pattern lacks generality and that that is merely to be expected when clades vary in net diversification rates [20] , [23] . The nature of the age-diversity relationship critically influences how we analyze and compare patterns of species richness among clades and between geographic regions . If age and richness truly are decoupled , then species richness in clades should not be modeled as the outcome of a simple time-constant diversification process , as is done in the overwhelming majority of evolutionary and biogeographic studies . In this study , we evaluate the relationship between clade age and species richness across 1 , 397 clades of multicellular eukaryotes , including fungi , plants , arthropods , and vertebrates . We explicitly incorporate phylogeny into our analyses to ask the following questions: ( i ) What is the overall relationship between clade age and species richness across major clades of eukaryotes ? ( ii ) Can simple models of among-clade variation in diversification rates account for the observed relationship between age and richness ? ( iii ) How does the nature of this relationship vary across major subclades of eukaryotes ?
We tested the relationship between clade age and species richness using a recent time-calibrated super-phylogeny [24] that spans virtually the entire tree of life and that contains a record of the phylogenetic relationships and stem clade ages of 1 , 592 higher taxonomic groups ( e . g . , families of beetles ) . We surveyed the literature for data on the extant species richness of all multicellular eukaryotic clades contained within this timetree , including fungi , plants , arthropods , and vertebrates . We obtained richness estimates for a total of 1 , 397 clades , totaling more than 1 . 2 million species ( Figure 1 ) . Using phylogenetic generalized least-squares ( PGLS ) regression [25] , we find no relationship between clade age and log-transformed species richness across the full set of 1 , 397 major clades of multicellular eukaryotes ( Figure 2; t = 0 . 438; p = 0 . 66; df = 1395; β = 0 . 0008 , where the regression coefficient β is the change in log-transformed diversity per million years ) . Use of non-phylogenetic regression models to analyze the age-richness relationship is inappropriate for these data , due to significant phylogenetic signal in clade size across the timetree ( variance in independent contrasts test: p<10−20 ) . We found that high phylogenetic signal in clade size can result in extremely high Type I error rates when the data are analyzed with OLS regression models , even when there is no true relationship between age and diversity ( see Materials and Methods; Figure S2 ) . Our results do not break down for younger clades: we found no relationship between age and log-transformed richness for the 307 clades younger than 50 Ma ( β = −0 . 0251; p = 0 . 122; df = 305 ) . Similar results were found for other subsets of the data ( e . g . , subsets of all clades less than 50 , 100 , 150 , 200 , and 250 Ma; Table S1; β≤0 for all analyses ) . Thus , there is no evidence that diversity increases asymptotically with respect to clade age . We then examined the relationships between age and richness for the most densely sampled higher taxonomic groups within the timetree ( Figure 3 ) . Within this set of 12 major groups ( 1 , 133 clades total ) , only beetles show a significant relationship between age and richness ( PGLS β = 0 . 017 , p = 0 . 004 ) . We repeated this analysis across all 352 subtrees within the timetree that contained at least 10 terminal clades and found no evidence that these patterns are simply an artifact of looking at “major” taxonomic groups ( Figure S3 ) . Moreover , the significant age-diversity correlation within beetles ( Figure 3 ) is almost entirely attributable to a single subtree containing just 22 terminal clades ( Figure S3 ) . Because beetles represent the sole group showing a positive age-diversity correlation , we repeated our analyses on a comprehensive time-calibrated tree of 327 beetle subfamilies from a previous study [26] , with the prediction that patterns observed at the family level should hold for more comprehensive subfamily-level sampling . We find no relationship between clade age and species richness at this scale ( Figure S4; PGLS β = −0 . 002 , t = −0 . 54; p = 0 . 59; df = 325 ) , raising the possibility that the results we observe for beetles are a consequence of the large number of statistical tests we performed . We note that our analyses should have been biased in favor of detecting a significant age-diversity relationship as we did not correct any tests for multiple comparisons . Substantial variation among clades in net rates of species diversification should weaken the expected relationship between clade age and species richness [14] , and previous studies have found that diversification rates show phylogenetic signal across the branches of phylogenetic trees [3] , [27] , [28] . To address among-clade rate variation , we used the MEDUSA model [3] to estimate the extent of diversification rate variation within each of the 12 major groups shown in Figure 3 . MEDUSA analyses strongly supported the presence of multiple rate shifts within each group ( Table 1 ) . The MEDUSA model assumes , but does not test , whether constant-rate diversification processes can account for observed patterns of species richness within higher taxa . To test whether the MEDUSA model of rate variation could result in the age-diversity relationships we report here , we performed a posteriori simulations under the fitted MEDUSA parameters and evaluated the model-predicted relationship between clade age and species richness . Performing simulations under the MEDUSA model is challenging , because it requires a stochastic model that can account for the origin of higher taxa as well as for the occurrence of diversification rate shifts on phylogenetic trees . Our implementation assumed a two-state birth-death process , where the units are ( i ) individual lineages and ( ii ) higher taxa ( see Materials and Methods ) . We modeled the origin of higher taxa as point occurrence events on the branches of phylogenetic trees; the occurrence of these events can be viewed as analogous to the acquisition of a phenotypic or ecological feature that defines a particular named higher taxon . We further assumed that diversification rate shifts occur within individual lineages under a Poisson process defined by the fitted MEDUSA model . We computed the Spearman correlation between clade age and species richness for each age-diversity dataset generated by the MEDUSA process and compared these distributions to the observed rank-correlations . Our results indicate that the MEDUSA model of rate variation cannot explain the observed lack of relationship between clade age and species richness ( Figure 4 ) . For 10 of the 12 groups , the observed correlation between clade age and species richness is significantly less than the model-predicted correlation ( p<0 . 05 ) . Even for beetles , the correlation between age and richness is much lower than expected under the MEDUSA model ( p<0 . 002 ) . The two groups for which the MEDUSA model could potentially explain the observed age-diversity correlation ( actinopterygiians and gymnosperms ) were characterized by the smallest number of subclades ( N = 12 in each case ) . The mean age-diversity correlation for each null distribution ( Figure 4 ) is highly correlated with the number of subclades in the dataset ( r = 0 . 88; p<0 . 001; Figure S5 ) , suggesting that the effects observed for actinopterygiians and gymnosperms may be manifestations of small sample sizes . The MEDUSA-based simulations described above are explicitly phylogenetic , in that closely related lineages tend to share common diversification parameters . We also considered a non-phylogenetic model of rate variation whereby each clade diversifies under a constant-rate birth-death process but with individual clade rates drawn from some overall distribution of rates [13] , [14] . We implemented this model in a Bayesian framework , assuming that clade rates were drawn from a lognormal distribution [14] but with no phylogenetic signal in the resulting distribution of rates . To test whether this “relaxed rate” model could explain the lack of relationship between age and richness , we conducted posterior predictive simulation by ( i ) sampling parameters from their joint posterior distributions under the model , ( ii ) using the sampled parameters to simulate clade species richness , and ( iii ) using PGLS to evaluate the relationship between clade age and ( simulated ) species richness . We then computed the standardized effect size ( SES ) for the observed PGLS slopes to determine whether the observed age-diversity correlation is less than expected if net diversification rates among clades follow a simple lognormal distribution . As with the MEDUSA simulations ( Figure 4 ) , our results reject the hypothesis that among-clade variation in net diversification rates can explain the lack of relationship between age and richness ( Table 2 ) . For every combination of subclade and relative extinction rate , the observed slope of the age diversity relationship is lower than the corresponding model-predicted value .
If diversity-dependent processes regulate species richness within clades [1] , then clade age should be a poor predictor of species richness [21] , [36] . Clade age will predict species richness only when clades are growing through time . This type of diversity-dependent control is fundamentally related to Simpson's notion of “adaptive zones” [18]: higher taxa , such as the clades we consider in this study , would thus represent monophyletic groups of species that have radiated into a set of related ecological niches . This line of reasoning also implies that diversity dynamics are governed by clade-specific carrying capacities . Macroevolutionary carrying capacities represent an important component of adaptive radiation [37] , [38] and are intrinsically linked to the notion that ecological opportunity influences the tempo and mode of species diversification through time [39]–[41] . We may not understand the ecological mechanisms underlying “carrying capacity” dynamics , but we must still wrestle with substantial neontological and paleontological evidence for their existence . These include patterns of lineage and phenotype diversification as inferred from molecular phylogenies [40] , [42]–[44] , diversity rebounds after mass extinction [45]–[47] , diversity-dependence of speciation and/or extinction rates [33] , [48] , long periods of diversity-constancy through time [32] , [49] , and double-wedge patterns of clade turnover through time [50] . Explosive radiations into novel adaptive zones have also been suggested to underlie long-term patterns of phenotypic evolution in a broad range of taxa [51] . In some groups , morphological innovations appear to have promoted shifts in carrying capacities even within geographically restricted radiations [35] . The central challenge in ascribing diversity-dependent causality to the age-diversity relationship in higher taxa is to explain why carrying capacity dynamics would pertain to sets of named higher taxa . The existence of a clade-specific carrying capacity implies that there is something special about named clades themselves , and there is no reason to accept this explanation if higher taxa are effectively random clades with no special meaning . However , higher taxa are clearly not random draws from the tree of life: major clades frequently comprise sets of taxa that are highly distinct in both phenotypic and ecological space ( e . g . , whales , bats , and carnivores within mammals ) . In a Simpsonian framework , recognized higher taxa are those clades that have acquired ecological innovations enabling them to radiate in new regions of ecological space , and there is nothing random about our recognizing them as such . We note that a positive relationship between age and richness need not imply an absence of diversity-dependent regulation of speciation-extinction dynamics . Indeed , positive relationships between stem clade age and richness are expected even under strong diversity-dependence , at least during the initial phase of diversity expansion [36] , [52] . However , once clades have reached carrying capacity , age and richness should become decoupled , as has been observed in analyses of several species-level molecular phylogenies [53] , [54] . An alternative explanation for the lack of relationship between age and species richness is that the dataset contains clades undergoing both diversity increase and diversity decline . Paleobiologists have long noted that clades in the fossil record tend to wax and wane through time [1] , [15] , [50] , [55] . At least intuitively , it seems reasonable that older clades are more likely to be on the “decline” phase of a diversity trajectory , as has been suggested for snakes [56] . This would provide an immediate explanation for the observed lack of relationship between age and diversity , and would link the patterns described here to the rise and fall of species richness in the fossil record [1] , [15] . We find little evidence for a “hump-shaped” relationship between species richness and time ( Figures 2–3 ) , one possible pattern that may be consistent with declining diversity scenarios [15] , [56] . However , we have only recently begun to explore the mechanisms by which diversity declines might shape age-diversity relationships in extant clades [56] . Recent studies suggest that it may be difficult to detect the signal of diversity declines even with complete species-level molecular phylogenies [57] . Fully addressing the role of diversity declines will presumably require the integration of neontological with paleontological data [58] . It is possible that the lack of relationship between clade age and richness is an artifact of the non-random manner by which higher taxa are recognized and which has nothing to do with the underlying process of diversity regulation [14] . Clearly , some property of clades causes us to recognize some as cohesive , named units ( Aves , Squamata , Actinopterygii ) ; we know very little about the consequences of such taxonomic ranking . Perhaps the clades we recognize as higher taxa represent a subset of clades that have accumulated exceptional phenotypic distinctiveness relative to other clades . Such clades might , in turn , be those clades that have had lengthy and independent evolutionary histories during which to accumulate sufficient evolutionary change to merit recognition as a distinct higher taxonomic group . One prediction of this model is that named higher taxa would represent crown clades with exceptionally lengthy stem branches . Thus , higher taxa themselves might represent units delimited ( albeit indirectly ) by a property related to their age , and this could potentially compromise general conclusions about the relationship between clade age and species richness . Likewise , named higher taxa might correspond to clades that have undergone substantial shifts in the tempo and mode of phenotypic evolution [59]; this property itself might be associated with shifts in the dynamics of species diversification . We can at best acknowledge the possibility that the age-diversity relationship might be a statistical artifact attributable to yet-unknown perceptual biases that cause us to name a select subset of the total set of available clades across the tree of life . Constant-rate estimators of “net diversification rate , ” which assume a sustained increase in species richness through time , remain exceedingly popular for studying the dynamics of diversification from molecular phylogenetic data [3] , [20] , [60] , [61] . This is undoubtedly due in part to the analytical tractability of these methods . Recent methods have been developed for accommodating temporal changes in rates of species diversification on complete species-level phylogenies [53] , [62]–[66] , but constant-rate estimates remain widely employed in the study of diversification patterns for higher taxonomic levels ( but see [13] , [14] , [56] ) . At the phylogenetic scales we consider here , constant-rate diversification rate estimates may not be meaningful . This may also be true for the widely used MEDUSA model of rate variation [3] , which appears to be incapable of recovering age-diversity relationships consistent with patterns observed in real datasets . If species richness is independent of stem clade age , time-constant models will misleadingly produce rate estimates that are negatively correlated with clade age . Our results suggest that , when age and diversity are not correlated , the significance of rate estimates in macroevolutionary studies should be interpreted with extreme caution since these estimates may offer little insight into the actual underlying processes that regulate species richness within clades [14] , [36] . This is true regardless of the underlying causes of the observed age-diversity relationship: even if the absence of an age-diversity relationship is a statistical artifact of the manner by which we recognize higher taxa , our results imply that estimates of diversification rates for higher taxa may have little to do with the factors that influence clade species richness . We are unaware of any theoretical or empirical evidence demonstrating that “constant rate” estimators of net diversification , as applied to stem ages for extant clades , provide any useful insight into evolutionary processes in the absence of a positive relationship between clade age and species richness . The relationship between clade age and species richness is fundamental to interpreting the effects of ecological , life-history , geographic , and other factors on clade diversity . A positive relationship between age and richness implies that species richness in clades is controlled by net rates of species proliferation . A decoupling between age and richness implies that other factors exert primary control on richness , or that clade diversity may be declining through time . The notion that species richness in clades can be decoupled from time seems counterintuitive , but is the expected outcome of diversity-dependent regulation of speciation-extinction dynamics . It is possible that species richness across the clades considered here is shaped by a mixture of processes , including diversity-dependence , declining rates , and rate heterogeneity . We are presently unable to determine the relative importance of these and other candidate processes , but integrating other data types ( paleontological data; species-level molecular phylogenies ) into studies such as this may provide a fruitful avenue for future research . In addition , further research is needed on the nature of higher taxa and the possibility that the results reported here might be a purely statistical consequence of the non-random process by which systematists have designated some clades as higher taxonomic groups . However , we are not presently aware of any non-biological mechanism that can account for this lack of relationship . Our results suggest that large-scale phylogenetic diversity patterns reflect constraints on species richness within clades rather than sustained diversity increases through time .
We used a recently published timetree for the tree of life in our analysis [24] . The timetree represents a synthesis of ∼70 time-calibrated , mostly interfamilial studies generated by experts on major taxonomic groups . Although diverse phylogenetic methods were used to generate and time-calibrate these topologies , high congruence in age estimates was observed between the most inclusive timetrees that linked major subsections of the tree of life together and the lower level timetrees contained within each subsection ( see Chapter 3 in reference [24] ) . The combined timetree thus broadly summarizes our current understanding of the timing of major splits across the tree of life and provides a framework for investigating the tempo of diversification of extant lineages . We tabulated data on species richness of each terminal clade represented in the timetree using counts taken from the literature . We preferentially used data from published compendia of species or online checklists that formed parts of ongoing species databasing efforts . These resources were supplemented with richness estimates from other primary literature sources where no checklists were available . Many higher level clades in the timetree were incompletely sampled . In these instances ( Table S2 ) , we assigned richness of missing lineages to their closest sister lineage that was present in the time tree , collapsing clades if necessary . This resulted in a total of 1 , 226 , 871 species assigned to 1 , 397 clades . We conducted simulations to test whether phylogenetic conservatism in clade size alone could generate significant age-richness correlations . Species richness is typically modeled as a geometric random variable , but incorporating covariance among clades due to shared evolutionary history is challenging . We assumed simply that the logarithm of species richness evolved across the phylogeny under a Brownian motion process . Strictly speaking , this is not a valid process-based model for the distribution of species richness across higher level phylogenetic trees . Specifically , this approach assumes that the “backbone structure” of the phylogeny is independent of the process that gives rise to richness at the tips of tree , as species richness is treated as a variable that can simply evolve across a pre-defined tree . This is unlikely to be valid in general , as both the phylogenetic backbone and the tip richness values presumably reflect common dynamic processes of speciation and extinction . However , our objective in these simulations was simply to test whether phylogenetic signal in clade size per se could lead to spurious relationships between clade age and species richness when no such relationship exists in the data , and we note that previous studies have analyzed this relationship in a non-phylogenetic framework [12] , [67] . To loosely parameterize our simulations , we first estimated Pagel's lambda [68] , which we denote by Λ , for the distribution of log-transformed species richness across the timetree . We found strong support for phylogenetic signal in log-transformed richness ( ΔAIC = 372 in favor of model with Λ>0 versus non-phylogenetic model with Λ = 0; maximum likelihood estimate of Λ = 0 . 724 ) . Using the maximum likelihood estimate of Λ and the corresponding Brownian motion parameters ( root state and variance ) , we simulated 500 datasets under an unconstrained Brownian motion process with the fitted root state and variance parameters . Each simulation thus generated a distribution of log-transformed richness values , with a level of phylogenetic signal ( Λ = 0 . 724 ) parameterized from the observed data , but with species richness values that are independent of clade age . Significant correlations between clade age and species richness were nonetheless observed in a majority of simulated datasets ( Figure S2 ) , despite no relationship between age and richness in the simulation model . This suggests that a simple tendency for closely related clades to be similar in size can lead to a highly misleading perspective on the relationship between age and richness and potentially explains positive age-diversity correlations reported in previous non-phylogenetic analyses [12] , [67] . The MEDUSA algorithm [3] attempts to identify a mixture of constant-rate birth-death processes that can explain patterns of species richness across higher level phylogenetic trees . We fit the MEDUSA model to the 12 core “higher taxa” with substantial within-group sampling ( see Figure 3 ) . It was not feasible to fit a single model to the full dataset of 1 , 397 clades . Briefly , the algorithm uses a forward stepwise model selection procedure to incrementally add rate-shifts to a phylogenetic tree . The process ends when the addition of a new rate shift fails to improve the log-likelihood of the data beyond a pre-determined AICc ( AICc , Akaike Information Criterion with finite sample size correction ) threshold . These AICc thresholds for each subtree of N taxa were determined using the threshold selection function as implemented in the GEIGER package [69] , where the threshold is computed as ΔAICc = A* ( N−B ) C+D . Default values for these parameters in GEIGER are A = −35 . 94105 , B = 6 . 73726 , C = −0 . 10062 , and D = 27 . 51668 . We modified the source code in the original MEDUSA implementation to allow extinction rates to exceed speciation rates , thus enhancing our ability to detect the signal of declining clade diversity through time . We tested whether the MEDUSA model of rate variation could explain the observed lack of relationship between clade age and species richness by performing a posteriori simulations under the fitted models . We developed a simulation model for the MEDUSA process that enabled us to generate a phylogenetic backbone tree as well as higher taxonomic groups and associated species richness values . We assumed a two-state birth-death process , with units of ( i ) individual lineages and ( ii ) higher taxa . Our model adds two parameters to the speciation ( λ ) and extinction ( μ ) rates of the simple birth-death process . First , we assumed that higher taxa originate from individual lineages at a per-lineage rate Φ . These transitions are irreversible: individual lineages can transition to higher taxa , but the reverse transition is not permitted . Second , we assumed that lineages undergo transitions to new diversification rate classes with rate α . Each simulation was initiated with n = 2 lineages , and simulations were run for a length of time equal to the crown age ( Tc ) of each major group shown in Figure 3 . For each lineage , we sampled the waiting time to the next event from an exponential distribution with parameter β = λ+μ+Φ+α; the identity of the event was then sampled with probability proportionate to the event rate . For example , the probability of a higher taxon formation event would be Φ/β . Upon formation of a higher taxon at time T1 , we assumed that the new taxon inherited the speciation and extinction parameters of the parent lineage; this is consistent with the MEDUSA model formulation , which allows rate shifts only along the internal branches of a phylogenetic tree . Given the remaining interval of time until the present day ( t = Tc−T1 ) , we then simulated clade richness ( given λ , μ , and t ) by sampling an integer-valued random variable from the expected distribution of progeny lineages under the birth-death process [70] , [71] . We allowed higher taxa to become extinct before the present . The precise time of origin of a particular higher taxon ( T1 ) cannot be inferred from the reconstructed phylogenetic trees generated by this simulation procedure; we can only know that the events that define higher taxa occurred at some time after the stem clade age of the group . Thus , phylogenetic trees generated by this algorithm are similar to the higher-level phylogenies analyzed in this and many other studies . We constrained the per-lineage rate of higher taxon formation to be equal to the rate of speciation at any point in time . This decision was motivated by the observation that these rates must be roughly balanced under the model: for each phylogeny containing N higher taxa , we note that the interior “backbone phylogeny” necessarily contains N−1 speciation events ( including the root node ) . Failing to allow approximate equality of these rates can lead to simulated trees consisting entirely of just a few higher taxa ( if Φ>λ ) , or to trees consisting primarily of individual lineages that reached the end of the simulation without forming a higher taxon ( if λ>Φ ) . Each simulation was initiated by sampling a matched pair of speciation and extinction rates from the set of fitted rate classes inferred under the MEDUSA model . For the diptera , for example , we inferred nine rate shifts under MEDUSA , corresponding to a total of 10 rate classes ( including the ancestral rates at the root ) . When a rate shift event occurred during the simulation , we sampled ( with replacement ) another matched pair of speciation-extinction rates from the set of fitted MEDUSA values . We set the shift rate equal to the maximum likelihood estimate under a Poisson process model of rate variation . This is obtained by noting simply that the observed number of rate shifts ( e . g . , nine for diptera ) occurred on the internal branches of the phylogeny; an estimate of the event rate is thus given by the number of inferred events divided by the summed internal branch lengths of the phylogeny . We automatically rejected any simulations that resulted in an exceptionally large or small number of terminals . We set the rejection threshold at 50% and 150% of the observed number of terminals for each dataset; for a dataset with 100 higher taxa , we would thus reject all simulated phylogenies with fewer than 50 or more than 150 terminals at the end of each simulation . We simulated 5 , 000 phylogenetic trees for each dataset . As an alternative to the MEDUSA-based simulations described above , we also used a hierarchical Bayes approach to fit a non-phylogenetic “relaxed rate” model of diversification rate variation [14] to each of the 12 core subsets of the data ( e . g . , angiosperms , beetles , squamate reptiles ) with substantial within-group sampling ( see Figure 3 ) . Here , we assumed that the net diversification rates for clades within each dataset were drawn from an uncorrelated lognormal distribution . We fit the model under both low ( ε = 0 ) and high ( ε = 0 . 99 ) relative extinction rates , where ε is the ratio of extinction to speciation rates . For each dataset ( e . g . , angiosperms ) , the model has two hyperparameters: the mean and standard deviation of the lognormal distribution of diversification rates . We used Markov Chain Monte Carlo ( MCMC ) to approximate the posterior distribution of all parameters and hyperparameters . To assess whether this model could explain the lack of relationship between clade age and species richness , we conducted posterior predictive simulations by simulating species richness values for each clade under the fitted relaxed rate models . Unlike the MEDUSA analyses described above , these simulations treated the phylogenetic backbone tree as fixed; we thus performed phylogenetic GLS analyses on each simulated dataset . For each set of simulations , we computed the standardized effect size for the observed age-diversity relationship as SES = ( βobs−βsim ) /σsim , where βobs is the observed PGLS slope , and βsim and σsim are the expected mean and standard deviations of the slope from posterior predictive simulation . A negative SES value thus indicates negative displacement of the observed value relative to simulations . | Species richness varies by many orders of magnitude across the evolutionary "tree of life . " Some groups , like beetles and flowering plants , contain nearly incomprehensible species diversity , but the overwhelming majority of groups contain far fewer species . Many processes presumably contribute to this variation in diversity , but the most general explanatory variable is the evolutionary age of each group: older groups will simply have had more time for diversity to accumulate than younger groups . We tested whether evolutionary age explains differences in species richness by compiling diversity and age estimates for nearly 1 , 400 groups of multicellular organisms . Surprisingly , we find no evidence that old groups have more species than young groups . This result appears to hold across the entire tree of life , for taxa as diverse as ferns , fungi , and flies . We demonstrate that this pattern is highly unlikely under simple but widely used evolutionary models that allow diversity to increase through time without bounds . Paleontologists have long contended that diversity-dependent processes have regulated species richness through time , and our results suggest that such processes have left a footprint on the living biota that can even be seen without data from the fossil record . | [
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] | 2012 | Clade Age and Species Richness Are Decoupled Across the Eukaryotic Tree of Life |
Naturally circulating lentiviruses are abundant in African primate species today , yet their origins and history of transmitting between hosts remain obscure . As a means to better understand the age of primate lentiviruses , we analyzed primate genomes for signatures of lentivirus-driven evolution . Specifically , we studied the adaptive evolution of host restriction factor APOBEC3G ( A3G ) in Old World Monkey ( OWM ) species . We find recurrent mutation of A3G in multiple primate lineages at sites that determine susceptibility to antagonism by the lentiviral accessory protein Vif . Using a broad panel of SIV Vif isolates , we demonstrate that natural variation in OWM A3G confers resistance to Vif-mediated degradation , suggesting that adaptive variants of the host factor were selected upon exposure to pathogenic lentiviruses at least 5–6 million years ago ( MYA ) . Furthermore , in members of the divergent Colobinae subfamily of OWM , a multi-residue insertion event in A3G that arose at least 12 MYA blocks the activity of Vif , suggesting an even more ancient origin of SIV . Moreover , analysis of the lentiviruses associated with Colobinae monkeys reveal that the interface of the A3G-Vif interaction has shifted and given rise to a second genetic conflict . Our analysis of virus-driven evolution describes an ancient yet ongoing genetic conflict between simian primates and lentiviruses on a million-year time scale .
HIV-1 was introduced into human populations in the early 20th century following multiple transmissions of a chimpanzee virus , known as SIVcpz [1] , [2] . The other , less virulent human lentivirus , HIV-2 , resulted from transmissions of SIVsm , a virus found in sooty mangabeys [3] . In fact , more than 40 non-human primate species in sub-Saharan Africa are infected with species-specific strains of SIV [4] . Known as “natural hosts , ” these species either co-evolved with their respective lentivirus or were infected more recently via cross-species transmission from other primates [5] . In either case , the association between natural hosts and SIV is thought to be considerably older than that of humans and HIV [6] , [7] . Natural SIV infections do not generally cause an AIDS-like immunodeficiency in their autologous host species , leading to the hypothesis that the virus-host relationship has evolved towards an apathogenic state [8]–[11] . However , the age and pathogenic potential of wild SIV infections in diverse primate taxa remain largely uncharacterized . Initial attempts at calculating the age of SIV using phylogenetics produced widely disparate , but all relatively recent , estimates [12] , [13] . However , two significant findings have pushed back considerably the age estimates of primate lentiviruses . First , the discovery of a full-length endogenous SIV in the genomes of lemurs indicates that lentiviruses were present in prosimians at least 4 million years ago ( MYA ) [14] , [15] . However , SIV is not currently found in prosimians , while it is common in simian primates like OWM . Thus , the age of lentiviral infections in current natural hosts of SIV cannot be addressed by endogenous lentiviruses . Second , a date to calibrate SIV phylogenetics in OWM was made possible with the identification of SIV strains endemic to the African island of Bioko . Here , each virus found on the island shares ancestry with a mainland virus , and their respective hosts belong to the same genus , demonstrating that lentiviruses have been infecting OWM for tens of thousands of years [16] . Nonetheless , the use of viral sequences to establish the age of virus families is problematic because rapid evolution obscures phylogenetic signals , and because many viral lineages have gone extinct in the past [17] , [18] . On the other hand , because of the process of virus-driven evolution of host innate immunity , it is possible to estimate the true evolutionary age of viruses by tracking and dating the evolution of antiviral genes [19] . Ideal candidate genes for this type of analysis are restriction factors , cellular proteins that coordinate the cell-intrinsic innate immune response to virus infections . Moreover , if the virus encodes an antagonist of the restriction factor , and the interactive interface between host and viral factors is known , then evolution at the site ( s ) of interaction can be used to infer past instances of infection [20] . The host restriction factor APOBEC3G ( A3G ) is a cytidine deaminase that restricts lentivirus replication by hypermutating viral DNA and by inhibiting reverse transcription [21]–[23] . To overcome this block , all known primate lentiviruses encode the accessory protein Vif [5] , which links A3G to a cellular E3 ubiquitin ligase complex and accelerates its turnover at the proteasome [24]–[27] . The early birth and ongoing retention of vif within all circulating primate lentiviruses [7] , [28] suggest that antagonism of A3G is crucial to lentivirus spread and survival . Therefore , A3G is a likely substrate for signatures of lentivirus-driven selection , from which a detailed account of past viral challenges can be reconstructed . Previously , we studied the co-evolution of A3G and vif in the setting of natural SIVagm infections in African green monkeys ( AGM ) . We found that the A3G is subject to recent diversifying selection in wild monkey populations , with single nucleotide polymorphisms ( SNPs ) encoding charge altering amino acid changes at surfaces targeted by Vif [29] . Our data support that these naturally occurring mutations in A3G were selected to allow evasion of SIVagm Vif proteins , implicating Vif as the selective pressure responsible . Adaptive evolution at the A3G-Vif interface in recently diverged primate populations implies that some modern SIV infections can incur a cost to host fitness , whether it be overt immunodeficiency or more subtle phenotypes that decrease host survivability or fertility [29] . In the present study , we trace the co-evolution of A3G and Vif through deep evolutionary time using an array of diverse primate species and SIV isolates . Our work allow us to provide a minimum age estimate for simian primate lentivirus infections , as well as an illustration of the dynamic flux of a host-pathogen interaction over time . We find that multiple species of the Old World Monkey ( OWM ) subfamily Cercopithecinae possess mutations in the Vif interaction site of A3G and that each allows escape from antagonism by Vif proteins . The recurrence and deep ancestry of such mutations suggest that a lentivirus encoding Vif existed at least 5–6 MYA . In response , contemporary Vif proteins have counter-evolved to these various Vif-resistant forms of A3G by tolerating amino acid variation at the canonical Vif interaction site . Moreover , we reveal an even older ancestral insertion event in the N-terminus of A3G of the Colobinae subfamily that conceals the Vif-binding site and precludes interaction with Vif proteins , suggesting that lentiviruses may have infected primates as much as 12 MYA . Coincident with this unique host adaptation , a Vif protein from a lentivirus currently infecting one of the Colobinae species has evolved to recognize a novel surface of A3G . Furthermore , we highlight the adaptability of lentiviral Vif proteins and the possible impact that this evolution may have on cross-species transmission and virus emergence . For example , Vif from a lentivirus infecting sooty mangabeys ( SIVsm ) and its descendants ( SIVmac and HIV-2 ) , exhibit exceptional breadth , possibly explaining in part how SIVsm was able to successfully colonize both humans and macaque species . Together , these data suggest that infections of OWM by primate lentiviruses are older than previous thought , driving selective changes in antiviral genes of their natural hosts and inciting an evolutionary arms race that continues to this day .
The bulk of known SIVs that circulate in the wild have been found within the Cercopithecinae subfamily of OWM , a group that includes AGM , mangabeys , macaques , and members of the Cercopithecus genus ( collectively known as guenons ) [5] . We previously identified naturally occurring amino acid changes within 126FWKPDYQ132 of A3G in AGM populations , a motif that is critical to the interaction between A3G and Vif [30]–[34] . Specifically , polymorphisms at codons 128 and 130 of AGM A3G were found to be adaptive because they confer resistance to Vif-mediated antagonism [29] . In order to further characterize the age and distribution of the genetic conflict between A3G and vif , we asked whether adaptive signatures in A3G were common to a wide range of OWM hosts . Full-length A3G was amplified from single representatives of OWM species , including members of the divergent Colobinae subfamily . In addition , the dataset was supplemented by previously published sequences from NCBI GenBank [35] , [36] ( Figure 1 ) . We found that mutations at codons 128 and 130 of A3G have emerged independently in several primate lineages ( Figure 1 ) . In particular , a D130A mutation was detected in four members of the Cercopithecus genus: the De Brazza's monkey ( C . neglectus ) , Wolf's guenon ( C . wolfi ) , the lesser white nosed monkey ( C . petaurista ) , and the mustached guenon ( C . cephus ) ( Figure 1 ) . We found D130A homozygosity in three of the four species , while the lesser white nosed monkey is heterozygous . The presence of the D130A mutation in four separate guenon species suggests that it has approached fixation since emerging in the ancestor of the Cercopithecus genus 5–6 MYA [37] . In addition to D130A , one allele of A3G from the mustached guenon contains a K128E mutation , a Vif-blocking SNP previously observed in a proportion of grivet monkeys ( AGM ) ( Figure 1 ) [29] . Previous work has demonstrated that A3G is undergoing adaptive evolution in primates , as measured by the relative rates of non-synonymous variation ( dN ) and synonymous variation ( dS ) [36] , [38] . However , given the additional interspecies and intraspecies A3G sequences reported in this study ( Figure S1 and S2 ) , we reexamined the gene's evolutionary history within the OWM clade to determine if the pattern of non-synonymous mutation is suggestive of selection , and moreover , of exposure to a common selective pressure [39] , [40] . In agreement with previous efforts , our results indicate that the A3G locus as a whole is evolving according to diversifying ( positive ) selection in OWM ( Table S1 ) . A model that allows sites to evolve under selection ( M8 ) provides a significantly better fit to the molecular data than does a model of neutral evolution ( M7 ) ( Table S1 ) . Using the mixed-effected model of evolution ( MEME ) [41] to identify specific residues that are subject to diversifying selection , we find strong signals of selection originating from 13 codons spread throughout the length of the gene , including codons 128 and 130 of the Vif binding site ( p = 0 . 0496 and p = 0 . 0493 , respectively ) ( Table S2 ) . The random effects likelihood ( REL ) analysis also identified codons 128 and 130 as being under positive selection , with both sites displaying dN/dS>1 with high posterior probabilities ( >0 . 99 ) ( Table S2 ) . Overall , our analyses support that the constellation of mutations identified at codons 128 and 130 of A3G result from natural selection , suggesting that a genetic conflict between A3G and vif is proceeding in multiple species of OWM . To test whether the D130A and K128E mutations in A3G are adaptive by affecting susceptibility to Vif-mediated antagonism , we measured the sensitivity of A3G variants to a panel of Vif proteins , including autologous isolates ( derived from viruses naturally circulating in a given host species ) and heterologous isolates ( those derived from other host species ) . Since viral adaptation can mask the adaptive phenotype of genetic mutations of the host , the use of a broad spectrum of Vif proteins , each with a different adaptive history , allows us to assess how mutations in A3G affect sensitivity to antagonism . The antiviral activity of A3G from De Brazza's monkey , Wolf's guenon , and mustached guenon was measured by co-expressing each variant with Vif-deficient HIV-1 . All A3G variants inhibit infectivity of the virus by more than 100-fold relative to virus produced in the absence of A3G , demonstrating that the antiviral activity of A3G orthologs has been conserved despite the observed variation ( Figure 2a ) . Using recombinant HIV-1 virus engineered to express SIV vif genes , we found that De Brazza's A3G is resistant to antagonism by Vif from SIVagm . Ver , a heterologous isolate that infects vervet monkeys ( AGM ) ( Figure 2a ) . However , reversion of D130A ( A130D , restoration of ancestral 128KPD130 ) renders De Brazza's A3G fully sensitive , demonstrating that a single alanine residue at position 130 allows escape from antagonism . Conversely , Vif from the virus that naturally circulates among De Brazza's monkeys in the wild , SIVdeb [42] , readily antagonizes De Brazza's A3G despite the D130A adaptation . A3G from two other members of the Cercopithecus genus ( Mustached guenon ( allele I ) and Wolf's guenon ) exhibits the same sensitivity as De Brazza's A3G ( Figure 2a ) . These data suggest that the emergence of D130A in the ancestor of the Cercopithecus genus drove Vif ( e . g . SIVdeb Vif ) to adapt to this highly prevalent A3G variant . Furthermore , De Brazza's A3G is also sensitive to SIVagm . Sab Vif , which antagonizes A3G carrying D130A or A130D . This activity is likely the result of prior adaptation to the D130H polymorphism in A3G from sabaeus monkeys ( AGM ) , as previously described [29] . Western blot analysis confirms that antagonism of A3G variants by Vif results in depletion of intracellular A3G protein . De Brazza's A3G expression is reduced substantially in the presence of SIVdeb Vif , relative to expression in the absence of Vif , whereas SIVagm . Ver Vif has no impact ( Figure 2b ) . Upon reversion of the D130A mutation , however , SIVagm . Ver Vif is capable of depleting A3G levels ( Figure 2b ) . These results suggest that the derived D130A mutation was selected 5–6 MYA to evade ancestral SIV Vif proteins . One variant of A3G specific to the mustached guenon , the 128EPA130 variant encoding K128E in addition to D130A ( Mustached II ) , is resistant to four heterologous Vif proteins: SIVagm . Ver Vif , SIVagm . Sab Vif , SIVagm . Gri Vif , and SIVdeb Vif isolates ( Figure 2c ) . This demonstrates that K128E , like D130A , prevents Vif-mediated antagonism . However , this variant of A3G is sensitive to Vif from SIVmus-1 , indicating that at least one of the three lentivirus strains currently circulating in mustached guenons [43] , [44] has counter-evolved while adapting to this species ( Figure 2c ) . Concordantly , only SIVmus-1 Vif depletes expression levels of the 128EPA130 variant ( Mustached II ) , while the 128KPA130 variant ( Mustached I ) common to other Cercopithecus monkeys is degraded by several Vif proteins ( Figure 2d ) . These data demonstrate that K128E and D130A were selected at different times during primate evolution to prevent Vif-mediated antagonism of A3G , with the latter occurring 5–6 MYA in the common ancestor of the Cercopithecus genus and the former appearing recently in a single species ( the mustached guenon ) . Recurrent virus-driven evolution of A3G over time suggests that natural host species are engaged in a prolonged , antagonistic relationship with lentiviruses . While most examples of variation at the Vif binding site of A3G were identified in natural hosts of modern SIV strains , we also found unique variation among rhesus macaques ( Macaca mulatta ) . Captive macaques have experienced simian AIDS stemming from accidental and experimental cross-species transmissions of SIVsm ( giving rise to SIVmac ) in the 1970s [45] , [46] , but they are not thought to harbor a lentivirus in wild Asian habitats . Using a previously published dataset from 36 Indian-origin rhesus macaques , a D130N polymorphism in A3G was identified in 59/74 ( 80% ) of chromosomes examined [35] ( Figure 1 ) . Like the D130H and D130A mutations observed in sabaeus monkeys and members of the Cercopithecus genus , respectively , rhesus A3G encoding D130N ( variant 128KPN130 ) resists antagonism by SIVagm . Ver Vif ( Figure 3a right side ) . Since SIVsm was able to cross-transmit into both humans and macaques , with both species exhibiting specific variation at the Vif-binding site of A3G ( Figure 1 ) , we tested the activity of SIVsm Vif . Similarly to the macaque-adapted strain SIVmac , Vif from SIVsm is capable of antagonizing rhesus A3G despite the D130N mutation ( Figure 3a ) . Western blot analysis demonstrates that both variants of rhesus A3G are depleted by SIVsm Vif , but not by HIV-1 Vif , while SIVagm . Ver Vif is only capable of degrading the variant encoding the ancestral 128KPD130 motif ( Rhesus I ) ( Figure 3c ) . Furthermore , human A3G is susceptible to antagonism by HIV-2 Vif as well as Vif from SIVsm , in agreement with a prior report [47] , but not SIVagm . Ver Vif ( Figure 3a left side and 3b ) . These data demonstrate that SIVsm Vif , in exhibiting broad cross reactivity for the A3G substrate , was ‘pre-optimized’ to target both rhesus A3G and human A3G prior to cross-species transmission . Moreover , this capacity for widespread antagonism has been maintained by SIVmac and HIV-2 following emergence in rhesus macaques and humans , respectively . Our characterization of SIV Vif proteins suggests that some have evolved to tolerate variation at the Vif-binding site of A3G . To determine vif counter-evolution produces antagonists that continue to rely on residues 128 and 130 or whether it shifts the stage of the genetic conflict to distinct surfaces on A3G , we tested the activity of Vif proteins against seven A3G variants representing each variation of the Vif-binding site . While the range of A3G variants targeted by each Vif varies , no Vif was capable of antagonizing the full spectrum ( Table 1 ) . SIVmus-1 Vif fails to inhibit human A3G and only minimally inhibits the two AGM A3G variants . Furthermore , Vif from SIVagm . Sab recognizes A3G from AGM , rhesus , and De Brazza's , but cannot tolerate 128EPA130 present in mustached guenon A3G . The broadest acting Vif species , encoded by SIVsm and its descendants ( SIVmac and HIV-2 ) , exhibit specificity for nearly all variants of A3G reported here . However , all three are defective at targeting the 128KPH130 variant found in sabaeus monkeys ( Table 1 ) . These data indicate that , despite differences in substrate specificity , Vif isolates from viruses infecting Cercopithecinae monkeys share a dependency on residues 128 and 130 for antagonism of A3G . Therefore , Vif is most likely the selective agent responsible for the recurrent selection of ‘escape’ mutations at these positions ( Figure 1 ) . In studying the species-specificity of SIV Vif proteins , we found that A3G from the mantled colobus monkey ( Colobus guereza ) is widely resistant to most SIV Vif proteins ( Table 1 ) , despite carrying the ancestral 128KPD130 at the Vif binding site ( Figure 1 ) . This observation suggests that residues in A3G lying outside of the canonical Vif binding motif can govern susceptibility to antagonism . The mantled colobus species ( hereafter referred to as colobus ) belongs to the Colobinae subfamily of OWM , a group of primates that diverged from Cercopithecinae about 18 MYA [37] . It is naturally associated with a specific SIV strain termed SIVcol , and this is the case for other closely related species ( SIVwrc in western red colobus and SIVolc in olive colobus ) [48]–[52] . In testing the sensitivity of colobus A3G to Vif from viruses naturally associated with Colobinae hosts , we found that SIVolc Vif was unique in its ability to target it for destruction ( Figure S3 ) . In fact , SIVolc Vif antagonizes solely colobus A3G and not A3G from any other primate species tested ( Table 1 ) . Conversely , SIVagm . Sab Vif exhibits the opposite specificity , readily counteracting AGM A3G and several OWM A3G orthologs but not colobus A3G ( Table 1 ) . To learn how colobus A3G remains resistant to nearly all Vif proteins except SIVolc Vif , we constructed chimeric A3G proteins containing portions of the N-terminus of AGM A3G and the C-terminus of colobus A3G . These chimeras were co-expressed with virus encoding SIVagm . Sab Vif or SIVolc Vif to test for sensitivity to antagonism . The critical constructs are shown in Figure 4a . Chimera C and chimera D differ by only seven amino acids , yet the former is sensitive to SIVagm . Sab Vif while the latter is resistant ( Figure 4a ) . Interestingly , a multi-residue insertion unique to members of the Colobinae subfamily is contained within this sequence ( Figure 4b ) . Upon removal of the insertion ( 66SCK68 ) from wild-type colobus A3G , a full gain in sensitivity to SIVagm . Sab Vif is achieved ( Figure 4a , compare Colobus del 64–66 to Colobus A3G ) . Therefore , a three amino acid insertion that emerged in the N-terminus of A3G in the Colobinae ancestor prevents antagonism by Vif . The Vif-blocking activity of 66SCK68 is context dependent in that it only blocks other Vif proteins when in combination with residues 66–199 of colobus A3G ( data not shown ) . In comparison to SIVagm . Sab Vif , SIVolc Vif displayed different specificities for the same chimeric A3G proteins , demonstrating that it has diverged to target distinct surfaces of the A3G substrate ( Figure 4a , compare Chi B to Chi A ) . Mutagenesis of residues within 110–165 that are divergent between AGM A3G and colobus A3G reveal that E133 , N137 , K141 , and A145 ( depicted as ‘ENKA’ ) are major recognition determinants of SIVolc Vif ( Figure 4c and 4d ) . Single mutations of E133 or N137 alone completely prevent antagonism by SIVolc Vif , while mutation of K141 and A145 in combination also blocks antagonism ( Figure 4d ) . Interestingly , residues 133 , 137 , and 145 are divergent between members of the Colobinae subfamily , suggesting that this motif may be diversifying in response to Vif from SIV infecting these primates ( Figure 4c and Figure S2 ) . In order to determine if SIVolc Vif antagonizes A3G independently of the “canonical” Vif interaction motif involving residues 128 and 130 , we tested its ability to antagonize A3G encoding against naturally occurring mutations at these sites . Indeed , we found that A3G constructs that are sensitive to SIVolc Vif remain so after the introduction of the K128E and D130A mutations ( the 128EPA130 motif found in mustached A3G ) ( Figure 4e ) . Conversely , these mutations completely abrogated antagonism by SIVagm . Sab Vif ( Figure 4e ) . Therefore , SIVolc Vif has diverged to utilize unique surfaces of A3G while adapting to its natural host , targeting residues that are divorced from those targeted by all other Vif proteins studied to date .
Using maximum likelihood methods , we reveal that the region of A3G targeted by Vif has been independently diversifying in several primate lineages . While a previous report of positive selection in primate A3G concluded that Vif ( and by extension , lentiviruses ) did not play a major role in the gene's evolution [36] , the data set used was limited in the number of natural hosts of SIV . Using a data set enriched for OWM species , we discover recurrent charge-altering mutations at residues 128 and 130 of the Vif interaction site that are evolving under positive selection . Importantly , in vitro infections reveal that single amino acid changes affect sensitivity to Vif-mediated degradation . Thus , in a remarkable case of convergent evolution , diverse Vif isolates have independently selected for mutations at the same amino acid residues of A3G in multiple lineages of simian primates . While this scenario is often loosely inferred from genetic data alone , our functional demonstrations that convergent amino acid changes affect the host-virus interface between Vif and A3G provide strong support that lentiviruses can shape the evolution of the hosts that they infect . Importantly , virus-driven evolution of A3G is apparent in ancestral primate species that existed many millions of years ago as well as extant primate species , suggesting that lentiviruses are an enduring selective pressure . It is important to note that we cannot exclude the possibility that this region of A3G is subject to selective pressures other than lentiviral Vif . However , recurrent selection at precisely the same sites targeted by most SIV Vif proteins supports that the selective agent responsible is a Vif-encoding element . These data suggest that Vif drives the emergence of ‘escape’ mutations in A3G that allow evasion of Vif-mediated degradation , which in turn promotes vif counter-evolution and the perpetuation of a genetic conflict between host and virus . What remains unresolved is whether or not the ancient pathogens inferred by this study are the direct ancestors of contemporary SIV strains . We raise two possibilities: 1 ) extant lentiviruses are themselves ancient , having coexisted continuously with their specific hosts for millions of years , or 2 ) extant lentivirus infections are young , such that adaptive evolution at the A3G-Vif interface was driven by lentiviruses that no longer exist ( paleoviruses ) . The second scenario posits that modern SIV strains may not necessarily bear semblance to the lentiviruses that drove selection in A3G , a distinct possibility given the prevalence of cross species transmission [53] , dual lentivirus infections and circulating recombinant forms [43] , [54] , [55] , and virus lineage extinction [56] . That is , the evolutionary histories of natural host species may be punctuated by periodic lentivirus infections rather than by a single , enduring lentiviral threat . Along with our previous discovery of adaptive evolution in A3G of AGM [29] , the emergence of ‘escape’ mutations in members of the Cercopithecus genus yield insight into the age and pathogenic potential of SIV infections in natural host species . The D130A mutation common to the four members of the genus indicates that Cercopithecus ancestors were exposed to a form of SIV prior to speciation , one that impacted host fitness and selected for adaptive mutations in innate immunity . Furthermore , the subsequent emergence of E128K within one of those species , the mustached guenon , suggests the selective pressure applied by SIV is not only ancient but also ongoing . This particular mutation is unlikely to be found in other members of Cercopithecus , since SIVmus-1 is unique in its ability to degrade it . Vif from SIVdeb , a strain infecting a closely related host , the De Brazza's monkey , does not tolerate variation at this site ( Figure 2c ) . Therefore , E128K likely represents a more recent adaptive change than the D130A mutation common to the genus . The sequential emergence of two mutations that each allowed escape from Vif proteins suggests that the mustached guenon lineage has been subjected to continuous ( scenario 1 , above ) or periodic ( scenario 2 ) selective pressure by SIV since diverging from other members of the genus 5–6 MYA . This is considerably older than previous phylogenetic analyses have indicated , and presents an alternative route to dating viral infections that does not suffer from the limitations of virus sequence-based methods [17] , [18] . Moreover , our analysis of virus-driven evolution provides an age estimate of SIV in the ancestors of present day natural hosts that complements the finding of an endogenous lentivirus in lemurs [14] , [15] , which also suggested an ancient association between primates and lentiviruses extending back at least 4 million years . Another example of variation in the Vif-binding motif of A3G was found in the rhesus macaque , a species of Asian descent that is not known to carry a circulating lentivirus . The genetic heterogeneity of rhesus macaques is also evident in the TRIM5 gene , which is highly polymorphic and gives rise to seven distinct variants with anti-lentivirus activity [57] . Moreover , one allele encodes a TRIM5-CypA fusion protein that restricts HIV-2 and SIVagm , suggesting that it was selected for 5–6 MYA by a virus with similar characteristics [58]–[61] . Together with the data presented here on A3G polymorphism , the rhesus genome abounds with genetic clues alluding to a former lentiviral presence that is now extinct . However , further study of diverse Asian primates is needed to support this speculation . Our results indicate that , upon adaptation to one or more variants of A3G in a polymorphic host species , SIV evolves Vif proteins with broader specificity . That is , by counter-evolving to antagonize resistant variants of A3G from their respective host species , SIV Vif gains the ability to target A3G variants present in other species . This is evidenced by SIVagm . Sab Vif which , having adapted to target 128KPD130 and 128KPH130 variants of A3G in sabaeus monkeys , exhibits the capacity to antagonize the 128KPA130 variant in Cercopithecus monkeys and the 128KPN130 variant in Rhesus macaques ( Figure 2a , Table 1 ) . This is in stark contrast to the related SIVagm . Ver Vif , which solely antagonizes A3G bearing the 128KPD130 motif ancestral to OWM . Furthermore , SIVmus-1 Vif has adapted to persist among mustached guenons , which have presented adaptive variation at both residues 128 and 130 of A3G . In doing so , SIVmus-1 Vif can cross-react with a broad array of A3G orthologs that present different combinations of characters at these two sites ( Table 1 ) . Likewise , in the cases of SIVmac and HIV-2 , the broad activity of their Vif proteins may have been pre-determined by events that played out in sooty mangabey populations ( Table 1 ) . Our data suggest that SIVsm was ‘pre-optimized’ to target both rhesus A3G and human A3G prior to cross-species transmission , and that this activity has been maintained by SIVmac and HIV-2 following emergence in rhesus macaques and humans , respectively . Although we did not see “escape” mutations in the sooty mangabey A3G , we did not have a large enough population sample to detect polymorphisms that were not fixed or at high frequency . Nonetheless , we believe that the cross-reactivity or ‘promiscuity’ of Vif proteins , as exemplified by SIVsm Vif , can shine light on the unique adaptive history of lentivirus strains and provide clues about the A3G diversity of its host species . While the canonical Vif binding site of A3G was conserved in members of the divergent Colobinae subfamily of OWM , we found that a multi-residue insertion ( 66SCK/E68 ) in the N-terminus renders A3G resistant to nearly all Vif proteins . As of yet there is no crystal structure of the A3G-Vif interaction , but our experimental results demonstrate how the insertion disrupts the ability for Vif to counteract A3G . Upon removal of these three residues from colobus A3G , this variant becomes sensitive to antagonism by SIVagm . Sab Vif , revealing that all the determinants necessary for binding and degradation are intact elsewhere in the protein . Thus , we hypothesize that the 66SCK68 insertion serves to conceal the typical Vif binding site by altering protein conformation and masking distal epitopes necessary for Vif-mediated antagonism . Furthermore , our studies using chimeric A3G proteins demonstrate that the insertion functions as such only in the context of colobus A3G . Thus , the multi-residue insertion may represent an alternative strategy to evade antagonism by Vif proteins that evolved within the Colobinae lineage . Our functional analysis suggests that this adaptive feat drove the evolution of SIV Vif proteins with different targeting preferences . That is , in adapting to the presence of the 66SCK68 insertion in colobus A3G , SIVolc Vif has evolved to recognize a patch of residues offset from the binding site preferred by other Vif proteins . Therefore , the stage of the genetic conflict between A3G and Vif has shifted at least once during primate evolution . The consequences of this switch in substrate recognition may already be evolutionarily apparent , as residues of A3G important for SIVolc Vif-mediated antagonism are divergent in Colobinae species used in this study ( Figure 4c ) . In summary , the data reported herein allow us to infer the presence of lentiviruses that applied pathogenic selective pressure at different points in primate evolutionary history . In a marked display of convergent evolution , two residues of A3G that coordinate an interaction with SIV Vif are diversifying in multiple primate species . Moreover , a divergent strategy of Vif-evasion has emerged in a separate branch of the primate phylogeny , giving rise to a second genetic conflict and altering the interface of the A3G-Vif interaction . The pattern of adaptive mutation suggests that SIV has been infecting OWM on timescale of millions of years .
The following fibroblast or lymphoid cell lines derived from primate species were obtained from Coriell Cell Repositories ( Camden , NJ ) : patas monkey ( Erythrocebus patas; AG06116A ) , mustached guenon ( Cercopithecus cephus; PR00527 ) , lesser white nosed monkey ( Cercopithecus petaurista; PR00949 ) , Wolf's guenon ( Cercopithecus wolfi; PR01241 ) , De Brazza's monkey ( Cercopithecus neglectus; PR01144 ) , Allen's swamp monkey ( Allenopithecus nigroviridis; PR01231 ) , red capped mangabey ( Cercocebus torquatus; PR00485 ) , sooty mangabey ( Cercocebus atys; G077 ) , Francois' leaf monkey ( Trachypithecus francoisi; PR01099 ) , proboscis monkey ( Nasalis larvatus; PR00674 ) , and the mantled colobus monkey ( Colobus guereza; PR00980 ) . The following transformed lymphoid cell lines were obtained from the NIH Nonhuman Primate Reagent Resource: Olive baboon ( Pabio anubis , GAG-LCL ) . Whole RNA was extracted using the RNeasy Mini Kit ( QIAGEN ) . Full-length A3G was amplified via one-step RT-PCR with the SuperScript III Reverse-Transcriptase Kit ( Invitrogen ) using primers specific for OWM A3G ( FOR 5′-ATG AAG CCT CAA ATC AGA AAC ATG G-3′ , REV 5′-CAG TTT CCC TGA TTC TGG-3′ ) . Bulk PCR product was subcloned , and six to ten clones were sequenced . If two distinct A3G sequences were detected , the individual was considered to be heterozygous . OWM A3G sequences were appended with a 5′ hemagglutinin ( HA ) tag by PCR and cloned into the mammalian expression vector pcDNA3 . 1 . Alignment of newly derived A3G nucleotide sequences from OWM plus those previously published in NCBI GenBank was executed in ClustalW ( Figure S3 ) . Phylogenetic reconstruction by maximum likelihood was performed with the web-based version of PhyML [62] ( Figure S2 ) . The resulting A3G phylogeny and the currently accepted phylogeny of OWM species [37] are similar but not identical , most likely due to rampant selection . The A3G phylogeny does not recapitulate that macaques , baboons , and mangabeys share a single common ancestor , and some intraspecies variants do not share immediate common ancestry ( De Brazza's and red capped mangabey ) . Moreover , Allen's swamp monkey is placed ancestral to the Cercopithicini tribe ( AGM , Patas , guenons ) , reflecting a previous classification [63] . A phylogeny consistent with the currently accepted phylogeny of OWM species ( the placement of Allen's swamp monkey is the exception ) ( Figure S2a ) was uploaded to the Codeml program ( of the PAML suite ) and to the web-based version of HyPhy ( DataMonkey , www . datamonkey . org ) for molecular evolution analysis . Given an alignment and phylogenetic tree of primate A3G , these packages assess whether or not models of neutral evolution can recapitulate the observed molecular data . A3G sequences were screened for recombination with GARD and SBR programs in DataMonkey and the data set was partitioned according to breakpoints [64] . Analyses were performed for the full-length alignment as well as for each partition to consider possible effects of recombination . The mixed effects model of evolution ( MEME ) analysis was performed in DataMonkey to identify individual codons subject to diversifying selection with a p-value threshold of 0 . 05 , as determined by a significant proportion of branches in the tree exhibiting a bias towards non-synonymous variation at these sites [41] . The MEME analysis is recommended for analyses of diversifying selection in host genes because it is sensitive to cases of transient or episodic selection , whereas traditional methods are not [41] . The Codeml program was used to determine whether A3G is evolving under positive selection ( comparison of models M7 and M8 ) and to identify the individual residues undergoing selection ( Nsites ) [40] . Maximum likelihood scores were calculated under each model and significant differences were calculated using the Chi-square test . Bayes Empirical Bayes ( BEB ) analysis was used to pinpoint residues with a posterior probability >0 . 95 that dN/dS>1 . The following SIV vif sequences were synthesized by GenScript ( without codon optimization ) : SIVdeb CM5 , SIVmus-1 CM1085 , SIVsm E041 , SIVcol CGU1 , SIVwrc 98CI04 , SIVolc 97CI12 . HIV-2ROD9 vif and SIVmac239 vif were PCR amplified from the full-length molecular clone [65] , [66] and other vif genes were previously described in [29] . SIV vif sequences were appended with a 5′ Kozac sequence and 5′Mlu1 and 3′ Xba1 restriction sites by PCR and cloned into the HIV-1Δvif molecular clone pLaiΔenvLuc2Δvif , generated after Nde1-Stu1 deletion in pLaiΔenvLuc2 . Epitope-tagged versions of select vif isolates ( SIVcol vif , SIVwrc vif , and SIVolc vif , ) were produced by appending a 3′ 3X-FLAG , and they too were cloned into the HIV-1Δvif molecular clone pLaiΔenvLuc2Δvif . The resulting proviral plasmids lack env , contain a firefly luciferase gene into nef , and encode SIV vif in the context of the HIV-1 backbone . 293T cells were plated in 12-well plates at 2 . 5×105 cells/mL . The following day , cells were cotransfected with 0 . 4 µg of A3G expression plasmid of an empty expression plasmid , 0 . 1 µg of L-VSV-G ( vesicular stomatitis virus glycoprotein , for pseudotyping ) , and 0 . 6 µg of proviral plasmid in a 100 µL transfection volume with TransIT-LT1 lipid transfection reagent ( Mirus Bio ) . Virus supernatants were harvested at 48 hrs and clarified by centrifugation for 5 min at 1 , 800 rpm , while transfected cells were lysed with NP-40-doc buffer ( 1% NP-40 , 0 . 2% sodium deoxycholate , 0 . 12 M NaCl , 20 mM Tris [pH 8 . 0] , 2 . 4 mM dithiothreitol ( DTT ) and protease inhibitor cocktail ( Roche ) ) and pelleted for 5 min at 10 , 000 rpm . Total protein concentration was quantified by Bradford assay and 20 µg was resolved by 10% SDS-PAGE , transferred to polyvinylidene difluoride ( PVDF ) membranes , and probed with anti-HA ( Santa Cruz Biotechnology ) or anti-actin ( Sigma ) antibodies . Virus in the supernatant was quantified by p24 Gag enzyme-linked immunosorbent assay ( Advanced Bioscience Laboratories ) . Two ng of virus was used to infect supT1 cells plated at 3 . 8×105 cells/mL in the presence of 20 µg/mL DEAE-Dextran , in a total volume of 100 µL . Virus infections were performed in triplicate for 48 hrs . Luciferase activity was measured with 100 µL of Bright-Glo Luciferase Assay Reagent ( Promega ) . Chimeric A3G plasmids Chi A , B , and D were produced between mantled colobus A3G and AGM haplotype I A3G [29] by restriction digest with BamH1 , BstX1 , and Apa1 , respectively . The remaining chimeras were produced by overlap PCR with reaction-specific primer sets . Mutagenesis of colobus A3G and chimeras Chi A and C was performed using the Quikchange II XL Site-Directed Mutagenesis Kit ( Agilent Technologies ) . The GenBank accession numbers for OWM A3G sequences produced from this study are KC176173-KC176194 . | The emergence of AIDS in the late 20th century has provoked studies to better understand the evolutionary history of viruses and the factors that govern their spread . Pandemic human immunodeficiency virus-type 1 ( HIV-1 ) , which currently infects 34 million people worldwide , emerged following the transmission of a lentivirus between chimpanzees and humans . A growing list of apparently nonpathogenic , species-specific strains has now been characterized in dozens of African primates , suggesting that primate lentiviruses are older and more widespread than originally thought . To estimate the extent to which primates and lentiviruses have coexisted , we examined the interaction between host and virus on a molecular level and tracked its dynamics over evolutionary time . We report that the immunity factor APOBEC3G is evolving in tandem with the lentiviral accessory gene vif , allowing us to associate instances of host evolution with instances of lentivirus infection in deep and shallow timescales . Specifically , we show that the region of APOBEC3G targeted by Vif is adaptively diversifying in independent primate lineages in a manner that suggests that lentiviruses are millions of years old . Our study reveals that , while primate lentiviruses may have modern consequences for human health , they have ancient origins in our non-human primate relatives . | [
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] | 2013 | Convergence and Divergence in the Evolution of the APOBEC3G-Vif Interaction Reveal Ancient Origins of Simian Immunodeficiency Viruses |
Chromosome segregation is fundamental to all cells , but the force-generating mechanisms underlying chromosome translocation in bacteria remain mysterious . Caulobacter crescentus utilizes a depolymerization-driven process in which a ParA protein structure elongates from the new cell pole , binds to a ParB-decorated chromosome , and then retracts via disassembly , pulling the chromosome across the cell . This poses the question of how a depolymerizing structure can robustly pull the chromosome that disassembles it . We perform Brownian dynamics simulations with a simple , physically consistent model of the ParABS system . The simulations suggest that the mechanism of translocation is “self-diffusiophoretic”: by disassembling ParA , ParB generates a ParA concentration gradient so that the ParA concentration is higher in front of the chromosome than behind it . Since the chromosome is attracted to ParA via ParB , it moves up the ParA gradient and across the cell . We find that translocation is most robust when ParB binds side-on to ParA filaments . In this case , robust translocation occurs over a wide parameter range and is controlled by a single dimensionless quantity: the product of the rate of ParA disassembly and a characteristic relaxation time of the chromosome . This time scale measures the time it takes for the chromosome to recover its average shape after it is has been pulled . Our results suggest explanations for observed phenomena such as segregation failure , filament-length-dependent translocation velocity , and chromosomal compaction .
Several processes involved in DNA partitioning rely on depolymerization of filaments for translocation . In eukaryotes , depolymerizing microtubules [1] position chromosomes before cell division via macromolecular couplers and/or molecular motors bound to the microtubules [2] , [3] . In prokaryotes , however , no such coupler or motor has been identified . Instead , proteins bound to the chromosome or plasmid bind directly to filaments and trigger their depolymerization [4] , [5] . This poses the question of whether in the absence of a coupler , DNA can be pulled in a robust fashion , without becoming detached from the filaments as they disassemble . Type I low-copy-number-plasmids [6] , [7] , chromosome I of Vibrio cholerae [8] , and the chromosome of Caulobacter crescentus [9]–[12] all share a common segregation mechanism that relies on pulling mediated by filament depolymerization . This conserved system relies on three central components: the ATPase ParA , the DNA-binding protein ParB , and a centromere-like DNA locus . ParA is a deviant Walker-type ATPase that upon binding ATP forms dimers that can polymerize and associate with DNA [10] , [13] . ParB interacts with ParA directly and stimulates ATP hydrolysis , causing ParA to dissociate into free monomers [13] . The spatial and temporal organization of ParA and the ParB-binding parS chromosomal locus can lead to robust chromosome segregation in vivo . For example , in C . crescentus , the chromosomal origin ( ori ) is initially localized at a single cell pole ( the “stalked” pole ) [14] , and must translocate to the opposite “swarmer” pole before cell division . In predivisional cells , approximately one thousand ParB are bound via parS near the origin of the chromosome ( ori ) [9] , [15] . There appear to be several distinct stages of ParB-parS-ori complex translocation [11]; our focus is on the final , most rapid stage in which the complex binds to filaments of ParA and translates from partway across the cell to the swarmer pole at a velocity of [9] , [11] , [16] , [17] . As the ParA bundle depolymerizes , presumably due to ParB-induced ATP hydrolysis or nucleotide exchange [7]–[11] , [13] , [15] , [18] , [19] , the ParB-parS-ori complex remains localized near the edge of the ParA structure [8] , [10]–[12] . For eukaryotic chromosome segregation driven by depolymerization of microtubules [2] , [3] , models generally assume the existence of a “coupler” that attaches the chromosome to the depolymerizing microtubules . This coupler moves along the microtubule ahead of the depolymerizing end , either because it slides along it diffusively [20]–[24] , because it is pushed by conformational changes near the tip of the microtubule [23]–[26] , or because it has a complex internal structure that causes it to process [3] , [27] . Of the existing models of bacterial chromosome segregation [28]–[33] , only a few address the question of how depolymerizing proteins can cause translocation . Typically , these models attempt to explain ParAB partitioning systems with reaction-diffusion models or general thermodynamic arguments , but do not address the conditions required for robust translocation [31] , [32] . Here we ask whether depolymerization of ParA by ParB without a coupler is sufficient to explain the observed translocation in prokaryotic DNA partitioning . We performed Brownian dynamics simulations that explicitly incorporate the biochemistry of the primary constituents of the ParABS segregation system . In our simulations , a polymer representing the ParB-parS-ori complex ( henceforth referred to as the “ParB polymer” ) , binds to a filamentous ParA bundle and initiates disassembly of ParA . We find that the ParB polymer can indeed exhibit robust , depolymerization-driven translocation via a novel mechanism ( Fig . 1 ) , provided certain conditions are met .
To understand the mechanism by which ParA translocates ParB , we performed Brownian dynamics simulations of a ParB polymer interacting with an anchored ParA filament bundle ( Fig . 1c ) . The ParB polymer , shown in Figs . 1b–c , corresponds to the ParB-parS-ori complex . It is represented by a semi-flexible chain of monomeric subunits , typically of length 100 subunits . The center section ( dark green in Fig . 1b ) , typically of length 50 subunits , represents the part of the chromosome that binds to ParA via ParB , while the two peripheral segments ( light green in Fig . 1b only ) cannot bind to ParA . During robust translocation , the ParB polymer remains localized near the tip of the ParA bundle and moves across the cell ( see snapshots in Fig . 1c and Video S1 ) . By inducing disassembly , ParB creates a concentration gradient of ParA filaments that remains fixed with respect to the center of mass of the ParB polymer . Thus , the ParA concentration profile translocates with the ParB , and exhibits only small , short-lived fluctuations around a well-defined steady-state mean ( Fig . 1c ) . Since the precise nature of the ParB–ParA interaction is unknown , we used our simulations to identify the modes of binding and disassembly that provide robust translocation . In our model ( see Methods ) , ParB binds to ParA subunits in the filament bundle ( Fig . 1c ) . The ParB polymer hydrolyzes ParA subunits that it binds to; once a subunit at the tip of a ParA filament is hydrolyzed , it can depolymerize from the filament . Monomers rapidly diffuse away once they have depolymerized . Some interaction/disassembly mechanisms or parameter ranges lead to robust translocation of the ParB polymer , while others lead to failure by rapid detachment: To understand how ParA translocates ParB , we identified variables controlling the translocation velocity , . In all cases , we find that is given by the mean rate , , of disassembly of a ParA filament , so that , where is the length of a ParA subunit . In order for a subunit to disassemble from the tip of a ParA filament , the subunit must bind to ParB , its ATP must hydrolyze , and the subunit must fall off . therefore depends on the distance , , that the ParB polymer typically penetrates into the ParA bundle and causes ParA-ATP hydrolysis , the rate , , of ParA-ATP hydrolysis , and the rate , , at which a ParA subunit depolymerizes once hydrolyzed . In turn , the penetration length , , depends on the shape of the ParB polymer . In our simulations , the freely diffusing ParB polymer adopts an isotropic , globular equilibrium shape . The maximum value , , of the penetration length , , is achieved if the ParB polymer is able to maintain this equilibrium shape as it is pulled by ParA . If the disassembly rate , , is too high , the ParB polymer is pulled along so rapidly that it does not have time to relax to its equilibrium shape . In this case , the ParA bundle pulls the leading region of the ParB polymer faster than the rear of the polymer can respond to the perturbation and the ParB polymer stretches out . Because the part of ParB polymer does not keep pace with the retraction of the depolymerizing ParA bundle , the ParB polymer does penetrate as deeply into the ParA bundle , so . We now estimate the time for the ParB polymer to relax to its equilibrium size . In our simulations , since ParB decorates the center section of the polymer and binds to ParA , the undecorated peripheral segments of the chain are the first ones to stretch out when the ParB polymer is pulled too rapidly ( Video S3 ) . The stretching of the peripheral segments is governed by the equation: ( 1 ) where is the ensemble-averaged -distance between the ends of a peripheral segment pulled by one end in the -direction , is the diffusion coefficient of the segment , is the -component of its equilibrium radius of gyration , and the relaxation time , , is the ratio of its internal drag , , to the effective spring constant , ( see Text S1 ) . Stretching is appreciable if , so for translocation in steady state ( ) , stretching becomes appreciable for , or , equivalently , ( inset to Fig . 3a ) . The shape of the pulled ParB polymer is therefore governed by the product , where we have defined ( 2 ) The penetration length , , depends directly on the shape of the ParB polymer . For large the ParB polymer is pulled rapidly and is small . This is because the ParB polymer is pulled away from the ParA bundle , leading to less overlap of the volume of the ParB polymer with the volume of the ParA bundle . As a result , there is less binding between individual ParB subunits with ParA subunits . As decreases , increases and saturates at for ( inset to Fig . 3b ) . In the latter regime , the disassembly rate is , where ( 3 ) Thus , the translocation velocity is controlled by the effective relaxation time , , and the maximum disassembly rate . We find that the translocation velocity , , falls into three regimes , depending on : ( 4 ) For ( regime I ) , the ParB polymer retains its equilibrium shape as it is pulled across the cell at the velocity . For ( regime II ) , the ParB polymer stretches as it is pulled and does not penetrate deeply into the ParA bundle . Since fewer ParA subunits bind to ParB , fewer are hydrolyzed and drops below . For ( regime III ) , the ParB polymer is so elongated that ParB binds to very few ParA subunits and the ParB polymer quickly detaches from the ParA bundle , leading to . This physical picture explains the results shown in Fig . 3 , where we vary both the disassembly rate , ( Figs . 3a–b ) and the effective relaxation time , ( Figs . 3c–d ) . Specifically , Fig . 3a shows how depends on the depolymerization rate , . For the black circles in Fig . 3a , the hydrolysis rate , , is effectively infinite so that ( Eq . 3 ) . In this case , for sufficiently small , the system is in regime I and . As increases , also increases; as a result , the ParB polymer stretches ( inset to Fig . 3a ) and the system crosses into regime II , where drops below . At very large , the system reaches regime III , and . In contrast , if is small ( red triangles in Fig . 3a ) , then cannot exceed as increases ( Eq . 3 ) . Therefore , for small , the ParB polymer remains in regime I , , for all , so that and translocation is robust for any . Thus , by decreasing the overall rate of disassembly by lowering , the system can achieve robust translocation , albeit at a cost to velocity . Fig . 3b shows how varies as increases . In this case , saturates to at large ( Eq . 3 ) . Since is chosen to be small , we find over the entire range of , meaning the system is in regime I and . The different velocity regimes can also be explored by varying instead of . Fig . 3c shows that is insensitive to the total drag , , on the polymer when and thus are small . In this case , is small , and the system is in regime I . As increases , increases , causing to drop below as the system crosses into regime II . Fig . 3d shows the effect of the total contour length , , of the ParB polymer . For small , is constant since the system is in regime I . As increases , increases , a9nd when , crosses into regime II and drops below . Fig . 4 shows that has a threshold dependence on the ParB–ParA binding energy , . As shown in Figs . 2c , 4 , ParB rapidly detaches from the ParA bundle if is too small . However , as long as is sufficiently large , the ParB polymer remains attached to the bundle throughout the simulation and translocates with a velocity that is insensitive to and is set by ( Eq . 4 ) . We observe similar behavior as the number of binding sites on the ParB polymer is varied . If there are too few binding sites , the ParB polymer quickly detaches from ParA . Above a threshold value , however , does not sensitively depend on the length of the binding strip ( Fig . S1 ) . The translocation velocity is also insensitive to the filament density within the ParA bundle , the arrangement of filaments in the bundle , and stiffness of the ParB polymer ( Figs . S2 , S3 , S4 ) . Finally , we have also verified that our main results hold when the form of the ParB–ParA binding potential is altered to allow binding by multiple points on ParB and/or ParA subunits . We next investigate the extent to which motility is robust to an external force on the ParB polymer that opposes translocation . The external force , , opposes translocation by pulling on each end of the ParB polymer . In our simulations , we find that is unperturbed for ( Fig . S5 ) . For , however , the ParB polymer rapidly detaches from the ParA bundle and translocation stalls . In order to understand this behavior , we analytically estimate the “detachment force , ” , required to pull the ParB polymer off of the ParA bundle in a time , , that is approximately equal to the time required for the ParB polymer to translocate across the cell ( see Text S1 for details ) . In our simulations , we model the ParB-parS-ori complex as a polymer chain comprised of monomeric subunits . Each subunit in the central strip of the ParB polymer binds with a binding energy , , to a subunit in the ParA bundle . Thus , the total strength of the attraction between the ParB polymer and the ParA bundle is approximately proportional to , where is the number of ParB subunits actually bound to ParA . Since ParB subunits lie in approximately a Gaussian distribution about the center of mass of the ParB polymer [34] , , depends on the location , , of the center of mass of the ParB polymer . Now consider the effect of a force on the ParB polymer that opposes translocation in the direction . At the simplest level , based on the above analysis , the ParB polymer may be replaced by a point particle at the center of mass of the ParB polymer , , in an effective potential given by ( 5 ) The first term is due to ParB binding to ParA and the second term is the work done by the external pulling force , . As increases , the minimum of shifts to lower values of and the number of bound ParB sites decreases , eventually leading to unbinding of the ParB polymer from the ParA bundle . The mean time for the particle to escape from the potential well ( to detach from the ParA bundle ) is well approximated by the Kramers escape time , for this potential [35] , [36]: ( 6 ) Given these expressions , we calculate the detachment force to be the force for which the escape time , , is equal to , the time required for the ParB polymer to translocate across the cell . In simulations with our standard model , the central binding strip has and . There are ParB subunits that bind to ParA with energy , , so the maximum total binding energy is . The ParB polymer translocates at , so that the time to translocate is . With these parameters , we estimate that the detachment force is . An estimate for the detachment force under more realistic conditions ( in vivo ) is given in the Discussion section . This order of magnitude estimate agrees with our simulations at high depolymerization rates , ( Fig . 3a ) , large drag coefficients , ( Fig . 3c ) , and large external pulling forces , ( Fig . S5 ) . In the first case , the mean time to first detachment is shorter than the translocation time for ; this suggests that the force , , required for rapid detachment is . Similarly , we find that the ParB polymer fails to translocate for , giving a detachment force of . In addition , we have conducted simulations in which we apply an external force , , to each of the ends of the polymer . For these simulations , we find robust translocation up to a detachment force of . So far , we have assumed that the ParA bundle is anchored to the pole . Recent reports suggest that in C . crescentus , ParA is localized to the swarmer pole by TipN [10] , [12] , but it is unclear if TipN actually anchors ParA . We therefore examined whether ParB translocation could occur if the ParA bundle is localized but not anchored . Fig . 5 shows that the ParB polymer translocates even when the ParA bundle is unanchored . We understand this through Newton's third law , which dictates that the force , , that pulls ParB to ParA is equal in magnitude but opposite in direction to the force on ParA . Thus ParB is pulled towards the swarmer pole while ParA is simultaneously pulled away from it: ( 7 ) 10 . 1371/journal . pcbi . 1002145 . g005Figure 5The ParB polymer translocates even when the ParA bundle is unanchored . ( A ) Snapshots of a simulation in which the ParA bundle is not anchored at its right end ( swarmer pole ) . The ParA bundle ( red ) is pulled towards mid-cell as the ParB ( green ) moves towards the swarmer pole . ( B ) Dependence of speeds of ParA ( red ) and ParB ( green ) on the ratio of drags , . In these simulations , and . where and are the drag coefficients of the ParB polymer and ParA filament bundle , respectively . In the case of a long , unanchored ParA bundle , and the ParB polymer translocates across the cell while the ParA bundle remains relatively stationary ( Fig . 5b ) . However , if the ParA bundle is sufficiently small ( e . g . , when the ParB has nearly reached the swarmer pole ) , is small , so the large ParB polymer remains relatively stationary while pulling the smaller , disassembling ParA bundle towards mid-cell ( Fig . 5b ) .
Our simulations point to a specific physical mechanism underlying translocation in the ParABS system . We find that disassembly of ParA generates a steady-state ParA filament concentration gradient that remains fixed in the center-of-mass frame of the translocating ParB polymer ( Fig . 1c ) . In other words , disassembly of ParA allows the ParA filament concentration gradient to translocate with the particle across the cell so that at all times the ParB polymer is moving up the concentration gradient of ParA to satisfy its attraction to ParA . Our simulations do not include fluid flow , but it is known that external concentration gradients can also drive motion of a particle in a fluid environment; the latter phenomenon is known as “diffusiophoresis . ” If the particle ( in this case , the ParB-parS-ori complex ) is attracted to the solute ( the ParA filament bundle ) , it will translocate up the concentration gradient towards high solute concentrations [37] . In “self-diffusiophoresis , ” the particle itself ( the ParB-parS-ori complex ) generates and sustains the solute concentration gradient [38] , [39] via disassembly of ParA . We emphasize that ParB-induced depolymerization ( particle-induced destruction of solute ) is central to this process . Without depolymerization , the ParA bundle would remain intact and the concentration of ParA filaments would not change with time . As a result , the ParA concentration profile would not be able to move with the particle and translocation would not occur . This intrinsically many-body mechanism is distinct from biased diffusion . In contrast to biased diffusion mechanisms which apply to a coupler that attaches a load to a single filament or fiber [20]–[22] , [26] , self-diffusiophoretic translocation can occur even if the ParB polymer does not diffuse , as long as the ParB-ParA interaction range is finite . In self-diffusiophoresis , “diffusio” refers not to diffusion of a coupler , but to the key role of the solute gradient , just as the prefix in “electrophoresis” refers to an electric potential gradient [37] . The self-diffusiophoretic mechanism also differs from ones involving motion of a coupler [3] , [20]–[27]; in our case , the load is not attached to a coupler that cannot detach from the depolymerizing filaments . Instead , the load is attached directly to the depolymerizing filaments via many non-permanent bonds . It has been suggested that polymerization-driven motility , as in the case of F-actin in the lamellipodium of eukaryotic cells , also constitutes an example of self-diffusiophoretic motility [40] , [41] . In that case , the object to be moved ( e . g . , the cell membrane ) is repelled by the structure ( the branched actin network ) that it builds in order to move . In depolymerization-driven translocation , on the other hand , the object to be moved ( the ParB-DNA complex ) is attracted to the structure ( ParA ) that it destroys in order to move . The self-diffusiophoretic mechanism suggests modes of failure for translocation . For example , overexpression of ParA leads to segregation defects , and it has been suggested that these defects arise due to the increase in the quantity of delocalized ParA [12] , [15] . This effect may be analogous to what we observe in our simulations with severing ( Video S2 ) , where instead of binding to the ParA bundle , ParB can bind to severed ParA filaments . This disrupts the steady-state generation of a translating ParA concentration gradient so that it does not support steady-state ParB polymer translocation . Similarly , when ParA is overexpressed , extra ParA monomers or protofilaments may diminish or erase the ParA concentration gradient created by depolymerization . Alternatively , the extra ParA could saturate ParB , preventing translation of the ParA gradient . We observe robust translocation over a wide range of physical parameters only if ParB binds to the sides of ParA filaments , triggering disassembly only from the tips of filaments ( Fig . 1b–c ) . If ParB binds only to the tips of filaments , translocation is far less robust for two reasons . First , there are many fewer ParA subunits to which ParB can bind so the overall attraction between ParB and ParA is weaker . Second , the ParB polymer is localized near the tip of the bundle , at the very edge of the concentration gradient of ParA that drives translocation . In contrast , in the side-binding model , the ParB polymer penetrates further into the bundle so that it is localized near the steepest , central section of the concentration gradient ( Fig . S6 ) . Thus , in the tip-binding-only model , the ParB polymer is much more likely to detach from the ParA bundle due to thermal noise ( Fig . 2a ) . This failure mode can only be averted by greatly increasing the binding energy or the number of filaments , and thus tips , in the ParA bundle . We also find that ParA disassembly via severing does not provide robust translocation ( Fig . 2b ) because severed protofilaments can bind to ParB , reducing the attraction between the ParB polymer and the main ParA bundle , leading to detachment . We therefore predict that ParB binds to the sides of ParA filaments and ParA filaments disassemble primarily from the tip . This prediction can be tested with in vitro experiments . Our model is sufficiently versatile to account for a range of experimental observations . For example , by varying the initial density and cross-linking of the ParA filament bundle in our simulations , we find cases in which some ParA filaments remain partially assembled even though the ParB polymer has translocated across the cell ( Fig . S7 ) . This is in agreement with the observations of Ptacin et al . [10] , who found that in some cases , a fiber of ParA extended across the predivisional cell after ori had translocated . We find that the robustness of translocation is primarily controlled by the quantity , the product of an effective relaxation time ( Eq . 2 ) and the maximum rate of disassembly of ParA ( Eq . 3 ) . The underlying details of the ParB polymer are only important insofar as they affect quantitative results such as the precise value of the relaxation time; they do not affect the qualitative physical principles described above . Specifically , if is too high , the ParB polymer stretches out and can detach from the ParA bundle . This finding suggests a possible role for chromosome organizing factors such as the SMC protein [14] , [42] . In order to translocate reliably and efficiently , the chromosome of four million base pairs [14] , [16] must be organized such that it does not overload the pulling mechanism . We propose that one important physical function of chromosomal organization and condensation is to minimize the effective relaxation time , , so that the chromosome can keep up with the retracting ParA bundle , to ensure robust translocation . In addition , we find that the velocity is simply the product of the ParA subunit length and the maximum disassembly rate , , provided disassembly is slow enough to guarantee that ( Eq . 4 ) . From the observed ori translocation velocity , [9] , [11] , [16] , [17] , we estimate the in vivo ParA disassembly rate to be , which is slower than the measured disassembly rate of dynamically unstable ParM filaments [43] , but comparable to the disassembly rate of actin filaments [44] . The translocation velocity in our simulations is considerably higher , typically several , because we used high disassembly rates . Translocation is robust in our simulations at these high values of because the effective relaxation time , , of our ParB polymer is fairly short . In the real system , where the effective relaxation time of the chromosome is likely to be considerably longer , it could be a biological necessity that both ParA disassembly and ori translocation proceed at slower than the simulated rates . Likewise , in our simulations the ParB polymer detaches when it is pulled with a force of order tens of pN , but this detachment force is likely to be much higher in the real system . The most important difference between our simulations and the actual bacterium lies in the number of ParB binding sites . To estimate the detachment force , , under realistic conditions , we first estimate , the maximum possible binding energy , the extent of the chromosome , and the diffusion coefficient of the chromosome . We first estimate by assuming that ParB decorates the approximately kilobase segment of the chromosome that was found to be the site of force exertion during translocation in [9] . For we therefore obtain a maximum binding energy of . For ideal polymer chains [34] , and . Thus , we estimate and . This crude estimate of actually agrees well with experimental snapshots of C . crescentus during chromosome segregation [10] , [11] . The estimate of falls within the range , which is measured in E . coli for DNA segments of varying sizes [45] , [46] . We note that is insensitive to , and varies by less than over that range . According to experiments [9] , [11] , [16] , [17] , the ParB-parS-ori complex translocates across the cell in about minutes . Using Eq . 6 , we find that the detachment force is . This value is of the same order of magnitude as the stall force for chromosome segregation along kinetochore fibers in eukaryotes [47] , [48] . Thus , this estimate suggests that the mechanism we have proposed is both physically reasonable and biologically relevant . Insights from our results may extend to plasmid segregation by ParAB . In Escherichia coli , the ParA concentration profile is known to oscillate as plasmid pB171 is partitioned [6] , [19] , [49] . This dynamic behavior appears to be required for proper plasmid partitioning [7] , [19] . We suggest that ParB creates a moving ParA filament concentration gradient that pulls the plasmid along as ParA disassembles . In addition , our findings suggest an alternative explanation for observations that the distance that plasmid pB171 translocates in a given time interval increases approximately linearly with the initial ParA filament length [7] . Ringgaard et al . [7] suggest that this effect arises from a ParA filament-length-dependent plasmid detachment rate . However , we have shown that the relative velocities of the ParB polymer and the ParA bundle depend on the ratio of the viscous drags on ParA and ParB , ( Fig . 5 ) . Thus , the observed dependence of plasmid translocation distances and velocities on ParA filament length may simply be a result of Newton's third law , due to the variation of with ParA filament length . Our simulations with unanchored ParA filaments suggest a new possibility for the mechanism of terminus segregation in C . crescentus . As translocation begins , the ParA filaments are long , so and the ParB polymer is pulled rapidly towards the swarmer pole . However , as the ParB polymer nears the swarmer pole the ParA filaments are much shorter and may be satisfied , so that the ParA bundle is pulled toward mid-cell . Experiments have indicated that ParA binds non-specifically to DNA [7] , [10] , [18] . Thus , we propose that DNA near the terminus is non-specifically bound to ParA and translocates away from the swarmer pole as ParA filaments are pulled toward mid-cell by the ParB-parS-ori complex . In contrast to previously suggested passive mechanisms [16] , [30] , [33] , this is an active process , directly linked to ori translocation . Our results provide a new paradigm for understanding depolymerization-driven translocation in prokaryotic DNA segregation systems . Since self-assembly and disassembly are ubiquitous in cellular systems , the creation of concentration gradients by these processes provides a general and robust mechanism for translocation .
The process of ParA disassembly begins when a ParB subunit binds to a ParA-ATP subunit . If the interaction energy , , exceeds a certain threshold , , the ParA-ATP hydrolyzes at rate . Once the ParA subunit hydrolyzes , it may detach from the ParA filament by depolymerization at rate ( after which it continues to interact with other subunits by the interaction ) . In our standard model , ParB binds to the sides of ParA filaments , and a hydrolyzed ParA subunit can only depolymerize if it is located at the tip of a ParA filament . Simulation units are converted into physical units by taking the subunit length to be . The typical subunit diffusion coefficient is taken to be , as measured in [50] , and the diffusion coefficient for a particular subunit is ( typically or , see below ) , giving a cell viscosity and a characteristic time scale . Typical runs are approximately and simulation steps are . Several interactions are included in the model; their specific forms are given below . All subunits are spheres with diameter that repel each other if they overlap: ( 8 ) where is the center-to-center distance between subunits and and . Within a ParA or ParB polymer chain , neighboring subunits are held together through an attractive harmonic potential: ( 9 ) with . In order to hold the ParA bundle together , we typically take 40% of ParA subunits to be cross-linked to a subunit in a nearby filament through an attractive potential: ( 10 ) where is the initial spacing of filaments in the ParA bundle and . ParA filaments are stiffened by a bending potential [51]: ( 11 ) where is the angle between the bond vector , , between ParA subunits and , and the bond vector , , between subunits and . Thus , , where . We take and . Similarly , the stiffness of the ParB polymer can be controlled by an interaction potential of form of Eq . 11 ( however , in our standard model , in the ParB polymer ) . In addition , we introduce interactions so that binding between ParA and ParB occurs in specific spatial locations on the spheres representing the subunits . Each subunit has a unit polarization vector , , that determines the location of the binding site for the ParB–ParA interaction , and the following interaction potential aligns it to be at an angle to the bond vectors connecting adjacent subunits: ( 12 ) We choose so that tends to be perpendicular to the bond vectors , and fix for ParA filaments and in the ParB polymer , which is relatively more flexible . Binding sites are arranged helically on the ParA filaments and the ParB polymer due to two additional interaction potentials . The first constrains polarization vectors on nearest-neighbor subunits on a given chain: ( 13 ) where and sets the pitch of the helix . Here , for ParA and for ParB . The second potential has the same form , ( 14 ) but constrains polarization vectors on the next-nearest-neighbor subunits with and . Here , in ParA and for ParB . Note that in addition to regulating the locations of the binding sites , Eqs . 13 and 14 implicitly regulate torsion within the ParB polymer . Finally , ParB binds to ParA with a site-specific , short-ranged interaction potential: ( 15 ) where is the vector distance between the ParA and ParB subunits and is the binding energy . In our standard model , . The normalization factor ensures that is the relevant energy scale for binding . The distance sets as the minimum of the binding potential . Binding site specificity is implemented through regulation of the angles between the polarization vectors on the ParA and ParB subunits as well as . In Eq . 15 , , , and . Binding is strongest when the two polarization vectors point towards each other and along . We have also studied several variations of these models . For example , in a separate set of simulations , we set and for both ParA and ParB , so that the binding sites were not arranged helically on the ParA filaments and ParB polymer . The orientation of the polarization vectors was set by , where for tip binding and for side-on binding . We also studied cases in which monomeric ParB subunits did not possess specific orientations ( polarization vectors ) . In these cases , ParA polarization vectors were set by , where for both tip-binding and side-binding . Binding only weakly depended on the orientation of the ParA-ParB bond through a modified version of , which we denote and for tip-binding and side-binding , respectively . For tip-binding without ParB polarization vectors: ( 16 ) For side-binding without ParB polarization vectors:: ( 17 ) where , , , and are as defined above . All subunits in the system translate and rotate according to Brownian dynamics [52] . Thus , we solve a system of coupled Langevin equation where the velocity of each subunit is governed by the forces exerted by other subunits in the system as well as thermal forces , from the surrounding liquid medium: ( 18 ) ( 19 ) and ( 20 ) ( 21 ) The subunit friction constant is , where is the viscosity , and is a constant that determines the relative magnitude of the drag on subunit . Typically , for ParA and normal ParB subunits , and for ParB subunits that cannot bind to ParA . is the rotational friction coefficient . | Reliable chromosome segregation is crucial to all dividing cells . In some bacteria , segregation has been found to occur in a rather counterintuitive way: the chromosome attaches to a filament bundle and erodes it by causing depolymerization of the filaments . Moreover , unlike eukaryotic cells , bacteria do not use molecular motors and/or macromolecular tethers to position their chromosomes . This raises the general question of how depolymerizing filaments alone can continuously and robustly pull cargo as the filaments themselves are falling apart . In this work , we introduce the first quantitative physical model for depolymerization-driven translocation in a many-filament system . Our simulations of this model suggest a novel underlying mechanism for robust translocation , namely self-diffusiophoresis , motion of an object in a self-generated concentration gradient in a viscous environment . In this case , the cargo generates and sustains a concentration gradient of filaments by inducing them to depolymerize . We demonstrate that our model agrees well with existing experimental observations such as segregation failure , filament-length-dependent translocation velocity , and chromosomal compaction . In addition , we make several predictions–including predictions for the specific modes by which the chromosome binds to the filament structure and triggers its disassembly–that can be tested experimentally . | [
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] | 2011 | Filament Depolymerization Can Explain Chromosome Pulling during Bacterial Mitosis |
Delphinids produce large numbers of short duration , broadband echolocation clicks which may be useful for species classification in passive acoustic monitoring efforts . A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying patterns across many detections . An automated unsupervised network-based classification method was developed to simulate the approach a human analyst uses when categorizing click types: Clusters of similar clicks were identified by incorporating multiple click characteristics ( spectral shape and inter-click interval distributions ) to distinguish within-type from between-type variation , and identify distinct , persistent click types . Once click types were established , an algorithm for classifying novel detections using existing clusters was tested . The automated classification method was applied to a dataset of 52 million clicks detected across five monitoring sites over two years in the Gulf of Mexico ( GOM ) . Seven distinct click types were identified , one of which is known to be associated with an acoustically identifiable delphinid ( Risso’s dolphin ) and six of which are not yet identified . All types occurred at multiple monitoring locations , but the relative occurrence of types varied , particularly between continental shelf and slope locations . Automatically-identified click types from autonomous seafloor recorders without verifiable species identification were compared with clicks detected on sea-surface towed hydrophone arrays in the presence of visually identified delphinid species . These comparisons suggest potential species identities for the animals producing some echolocation click types . The network-based classification method presented here is effective for rapid , unsupervised delphinid click classification across large datasets in which the click types may not be known a priori .
Dolphins produce echolocation clicks while socializing , foraging and traveling [1] . The prevalence of echolocation clicks makes these signals useful for monitoring delphinids using passive acoustic methods; however , only a few delphinid click types can currently be classified to species . Echolocation clicks have a suite of characteristics that make them challenging to classify in acoustic recordings . For example , echolocation clicks are highly directional signals which can be received “on-axis” ( animal oriented in the direction of the recording sensor while clicking ) or “off-axis” ( animal oriented away from the sensor ) , leading to differences in amplitude and interference patterns [2] . Dolphin echolocation click signals also significantly attenuate over relatively short distances due to their high frequency acoustic content; therefore , the orientation and proximity of a clicking animal relative to an acoustic sensor has a large effect on the frequency structure of the recorded click [3 , 4] . Behaviorally , individual dolphins may vary click source levels and beam widths [5–8] . Furthermore , dolphins are typically found in large , sometimes multi-species groups in which animals vocalize simultaneously . All of these factors contribute to click variability and therefore complexity in classification . Despite these sources of variability , echolocation clicks of a few delphinid species as well as many beaked whale species have known species-specific spectral features [9–12] . Consistent features have typically been recognized by expert analysts manually reviewing large amounts of data . Previously identified characteristic spectral features include mean frequency , bandwidth , and peaks or troughs in frequency spectra indicating dominant or diminished frequencies . Typical inter-click interval ( ICI ) ranges also differ between beaked whale species [13] , and ICI is used to identify porpoise click trains to species [14 , 15] , although ICI may vary as a function of depth or behavior in some cases [1 , 16 , 17] . A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying consistent patterns . One approach is to train analysts to recognize patterns . Humans are particularly adept at pattern recognition tasks: With enough training time , contextual information and training data , an analyst can distinguish within-type and between-type click variations , and develop a sense of the major click categories in a dataset . However this is an iterative , time-consuming and potentially subjective process . An alternative is to develop automated methods to perform echolocation click classification . Within a computational framework , one approach to the click variability problem is to consider a set of clicks as a group of objects that are similar but not identical to one another . In a simple example with five clicks labeled A through E , consider a case where clicks A , B and C are very similar , click D is slightly different , and click E is very different than A-C , with some similarity to D . In this case , clicks A , B and C are regarded as the most informative for classification , as they contain consistent features among them , while clicks D and E are likely outliers . We might consider A , B and C to be members of a group characterized by their common feature set . In practice , an actively echolocating dolphin produces multiple clicks per second . Therefore , a similar but more complex case exists in which a subset of those clicks will be highly interrelated , while others are only weakly associated . This approach to the variability problem can be represented as a weighted network [18] , in which clicks are represented by nodes and the lines or edges between nodes represent the strength of the similarity between them . In the example above of echolocation clicks A through E , the click characteristic inter-relationships are represented by a network with larger edge weights among similar clicks A-C and lower value edge weights among clicks D and E and their neighbors which show their greater dissimilarity from clicks A-C and each other ( Fig 1 ) . A network of N nodes can also be represented as an adjacency matrix G in which G ( i , j ) represents the weight of the edge between nodes i and j , for i and j ∈ the set of nodes N [19] . Once the relationships between a set of clicks are represented as a network , an unsupervised learning algorithm can be used to identify clusters of highly similar clicks . Here we use an agglomerative clustering routine [20] that seeks to identify structure within the network without a priori information about what that structure might be . Using this method , nodes within the network are iteratively grouped together based on the strengths of the edges between them . This method can converge to a single large cluster if all nodes are highly interrelated , but multiple clusters can be identified if interrelationships are not evenly spread across the network . In this work , unsupervised network-based classification methods are applied to the problem of delphinid echolocation click classification in the Gulf of Mexico ( GOM ) . Long-term passive acoustic monitoring efforts using autonomous near-seafloor hydrophones at five sites in the GOM have resulted in a dataset of over 52 million unlabeled dolphin echolocation clicks . Thirteen delphinid species are known to inhabit the GOM , including five members of the genus Stenella , and five species belonging to the subfamily Globicephalinae ( Table 1 ) . Three of these five species , Risso’s dolphin ( Grampus griseus ) , false killer whale ( Pseudorca crassidens ) and short-finned pilot whale ( Globicephala macrorynchus ) can be distinguished based on echolocation click characteristics [11 , 21]; however , few other species have been conclusively identified . Our objectives are to develop a technique for recognizing candidate click types in this dataset which may be associated with species that are not yet acoustically identifiable , and to demonstrate a method for recognizing these click types automatically in novel data . Further , we support the utility of this method by comparing automatically identified types with clicks recorded using towed hydrophone arrays in the presence of vocalizing animals from the western Atlantic whose species identity has been verified by trained visual observers . The described click types are informative for passive acoustic delphinid population monitoring efforts , while the methods offer an approach for automated classification of variable signals in large unlabeled acoustic datasets .
Long term passive acoustic recordings were collected at three continental slope sites ( sites MC , GC , and DT ) , and two shelf sites ( sites DC and MP ) . Delphinid clicks were automatically detected in large numbers during all deployments at each site , with click counts ranging from 5 . 2x105 to over 8 . 1x106 analyst-confirmed detections per deployment ( between 6 , 000 and 67 , 000 clicks per day; Table 2 ) . Detections were grouped into 5-minute bins marked as click-positive or negative . The number of click-positive 5-minute bins per deployment varied from almost 5 , 000 to close to 12 , 000 bins ( unnormalized for recording effort ) . The average number of delphinid echolocation encounters ( periods of continuous click detections bounded before and after by at least 15 minutes without click detections ) per recording day ranged from 1 . 4 to 7 . 9 across deployments . Average encounter durations were generally shorter at the shelf sites MP and DC; however , encounter durations were highly variable at all sites and ranged from 1 to 640 minutes . Across all deployments , between 0 . 1% and 10 . 1% of click-positive bins contained more than 5000 clicks and were sub-sampled for classification purposes . The most sub-sampled site was site DT . Phases 1 and 2 were run on the full training set following the exploratory analysis . In Phase 1 , the average number of automatically identified clusters per time bin ranged from 1 . 02 to 1 . 14 ( CV = 0 . 14 and 0 . 35 respectively ) across sites and deployments ( Table 2 ) . In Phase 2 , seven dominant and recurrent click types ( A-G ) characterized by consistent spectral shapes and modal ICIs were identified ( Table 3 , Fig 3 ) . We define the modal ICI as the most frequently observed ICI during a period of clicking . Click type A was identified in the training data from the three deep sites , and one shallow site . Most instances came from site GC . This type was characterized by a minor narrow low frequency peak near 12 kHz , dominant energy between 20 and 35 kHz , and 0 . 15 sec modal ICI . Click type B was identified in the training data from all sites except site GC . This click type , presumed to be Risso’s dolphin based on Soldevilla et al . [11] and has distinct narrow energy peaks at ~ 22 , 26 , and 33 kHz . The ICI distribution for this type was bimodal with shorter ICIs near 0 . 12 sec at the northern sites , and longer ICIs over 0 . 23 sec at the southern site DT . Click type C was identified in the training data from the deep sites only . This click type had the lowest frequency content of dominant energy between ~15 and 30 kHz , and a modal ICI of 0 . 16 sec . Click type D was identified in the training data from site DC , and in one bin from site MP . This click type was characterized by two low frequency peaks at 12 and 18 kHz , dominant energy between 30 and 50 kHz , and a bimodal modal ICI with peaks at 0 . 03 and 0 . 09 sec . Click type E was identified in the training data from all five sites and represented 22% of the training set . It was particularly common at the southern site DT . Click type E was characterized by minimal energy below 20 kHz , a dominant spectral peak near 30 kHz , and a modal ICI of 0 . 06 sec . Spectral variability below 20 kHz may indicate the presence of multiple subtypes , or overlap with click type F . Click type F was identified in the training data from all five sites and represented 47% of the training set . This type was similar to type E , had a minor energy peak at approximately 20 kHz . Some examples had a third peak at 16 kHz . High variability of this type in the 10–25 kHz band suggests that it may include multiple subtypes . This type had a strong modal ICI at 0 . 06 sec . Click type G was only identified in the training data from the two shallow sites only: Sites DC and MP . It was characterized by a broad high energy band between 15 and 52 kHz , and a peak frequency of 36 kHz and a modal ICI of 0 . 03 sec .
Delphinid clicks are very short duration , highly variable signals which contain limited information when considered individually . The automated clustering strategy was designed to mimic a human analyst by comparing large numbers of clicks to identify persistent features . Leveraging multiple sources of information such as spectral shape and ICI distributions across bins of similar clicks further facilitated pattern recognition and click type distinction . The two-step training process tackled the large dataset by reducing the number of comparisons necessary through use of filtered means and modes . A variety of different pruning and clustering techniques were implemented during the algorithm development process . In the final implementation , edge pruning was executed using a dynamic metric in which the weakest N% of edges were pruned from each network . Using this approach , networks of highly similar nodes and networks of weakly similar nodes were pruned by the same amount . An alternate approach would be to prune all edges weaker than a static threshold value . Using the static approach , a network of weakly interrelated nodes would be pruned more heavily than a network of strongly interrelated nodes . Both approaches were tested during development of the clustering protocol , but the dynamic metric was ultimately chosen as the more conservative pruning method for preserving click types with smaller sample sizes . More aggressive pruning at site MP might reduce inclusion of false positives associated with snapping shrimp and improve classification accuracy if snap spectra are more variable than click spectra . A more complex , greedy clustering algorithm [modularity; 24 , 25] , preliminarily used during the development process , was not able to reliably identify clusters of different sizes . The simpler CW algorithm used in the final implementation identified both small and large clusters within a network , which is essential in identifying less common click types . Further click type separation may be possible however . In this dataset , some click types had very different spectral shapes and ICIs from one another such as type A and B clicks , while others were similar , such as type E and F clicks . This is a challenging situation for clustering purposes , because some types separate well , while others remain intermingled , as in the case of types E and F where spectral variability may represent multiple sub-types . In Phase 2 , a multi-pass clustering approach in which thresholds were incrementally increased might enable better distinction between similar types such as those within type E without over-pruning highly distinct types . Reduced within-cluster variability would probably also reduce classifier confusion and improve accuracy . ICI and spectral similarities ( both values between 0 and 1 ) , were combined in Phase 2 of the automated classification process by simple multiplication . The multiplicative rule was used because analysts typically needed both robust ICI and spectral information to make a confident classification . The two metrics did not necessarily contribute equally to the overall similarity scores because although they are both values between [0 , 1] , they did not have identical distributions . Transforming the distribution of either parameter prior to multiplication would modify the influence of the parameter on the Phase 2 network . For example , if spectra were deemed more reliable than ICI , SSPEC could be transformed prior to Eq ( 2 ) to give it more influence on the network . For classification of the test set , the multiplication method requires that both score high to achieve a high overall similarity score . An earlier implementation of this algorithm used correlation distance between ICI distributions instead of distance between modal ICIs . This strategy produced similar results but performance suffered when classifying bins with high click counts . As the number of detections per bin increased , click trains tended to become interleaved , resulting in higher numbers of low ICIs . While true ICIs from a single animal’s click train may be a species-specific feature [26] , the interval between clicks received from multiple individuals’ trains is not informative . Similarly , high false positive rates associated with snapping shrimp at site MP affected ICI distributions . Modal ICI , which likely represents individuals’ ICIs , was found to be less sensitive to differences in click counts per bin and more robust to false positives . Modal ICI may be more difficult to detect for species that are often found in very large groups . The unsupervised click classification routine identified seven distinct delphinid click types in the training data across five sites in the Gulf of Mexico based on frequency content and modal ICI . All types were identified at a minimum of two sites , and over half were identified at four or more sites . One hypothesis of what is driving the persistent features leading to the click type clusters is site-specific propagation and noise conditions; however , a number of features demonstrated here are inconsistent with this explanation . First , site-specific noise and propagation do not explain why multiple click types were found at each site , often within the same day or in overlapping encounters , nor do they explain why the same click types were found at multiple sites , despite differences in noise , site depth , and site location . Second , site-specific propagation and noise would be expected to affect all clicks in the same way; therefore , they do not explain why some click types have complex spectra with peaks and troughs , or why frequency distributions differ between types under similar noise conditions . Third , site-specific conditions do not offer an explanation for the consistent relationships between click type spectral shape and ICI distributions across deployments spanning multiple years , or why ICI distributions have consistent modal values . Alternative hypotheses are that the distinct click types identified in this dataset represent different dolphin species or echolocation clicks used in different contexts [e . g . 27] . Species differences may explain these observations . Echolocation click frequency content and click rates have been shown to differ between odontocetes such as sperm whales , beaked whales , dolphins , and porpoises [e . g . 11 , 12 , 13 , 28]; therefore , it is reasonable to expect that these features may also differ between delphinid genera and/or species . Consistent ICIs have been reported for beaked whale species [e . g . 13] and similar consistency may be typical of some delphinids [29] . Spectral content may vary depending on target prey [9] , and ICI may be related to click source level , frequency content , and/or prey search distance [e . g . 30 , 31] . Low frequency , high amplitude clicks have the potential to propagate farther than high frequency or low amplitude clicks . This may result in a longer two-way travel time for each click . Delphinids may compensate with a longer ICI to allow for the longer travel times . The majority of clicks detected at the three deepest sites were associated with types E and F which had similar spectral shapes and modal ICIs . According to the most recent NOAA stock assessments [22 , 23] based on summer visual surveys , approximately 80% of offshore delphinids in the GOM are members of the Stenella genus , of which spinner and pantropical spotted dolphins are the most common species . Two additional Stenellid species , striped and Clymene dolphins , are also found offshore , although population estimates vary widely between surveys . A fifth species , Atlantic spotted dolphin , is found primarily on the continental shelf . Based on the high abundance of Stenellids as a proportion of GOM delphinids , Stenellid dolphins are the most likely match for type E and F clicks . Considerable variability below 20 kHz within sites in the type E and F clusters suggests that they may include multiple subtypes , possibly representing different species . Towed hydrophone array recordings made in the presence of pantropical and Atlantic spotted dolphins revealed ICIs that were consistent with type E and F clicks . Distributions of the various Stenellid species differ in the GOM [32] , and this may account for the different ratios of these types across sites . Based on visual survey data , species composition and abundance is expected to differ between the three deeper slope sites ( GC , MC , and DT ) and two shallower shelf sites ( MP and DC ) . Primary species at the shallow sites include Atlantic spotted dolphin ( also a member of the genus Stenella ) and bottlenose dolphin [32] . Rough-toothed dolphins have also been observed near site DC , although in much lower numbers . Click type G which was common at the two shallow sites but was not identified at deeper locations , and click type D which was predominantly identified at site DC , are likely associated with some of these species . Snapping shrimp snaps were a common source of false positives at site MP , where click type G was primarily detected . Distributions associated with this click type may have been contaminated by snap signals . In future work , click train tracking could be used to improve ICI estimates in noisy , shallow water environments , and encounters with very high click counts . Click Type B likely represents Risso’s dolphin clicks as it contains the consistent peaks and notches described for Risso’s dolphins in the Southern California Bight , and further matches the peak structure documented here from a towed array recording of visually-verified Risso’s dolphins from the western Atlantic . Modal ICI differed between the three northern sites ( MC , DC , and MP ) and the southern site ( DT ) , suggesting possible behavioral or population differences . Click type A may represent short-finned pilot whale clicks as it is similar to Atlantic pilot whale ( presumed short-finned ) recordings collected using towed hydrophone arrays . However , it differs from a recent description of Pacific short-finned pilot whale clicks which found spectral peaks at 12 and 18 kHz collected in the Hawaiian Islands [21] . Click type A was most common at site GC in this dataset , which is consistent with short-finned pilot whales’ predominantly eastern GOM distribution based on visual surveys [32] . The narrower bandwidth of click type C centered at lower frequencies is consistent with published descriptions of false killer whale ( Pseudorca crassidens ) echolocation clicks [9 , 21] from the Eastern Pacific . However , there are no published estimates of modal ICI for false killer whales . Across all sites , 1 . 3% of bins were classified as Type C . The most recent stock assessment estimates place false killer whales as approximately 1% of offshore GOM delphinids . Melon-headed whales are expected in low densities the GOM , but information regarding distinguishing features of these clicks is limited [12] , and no clear match was identified . Killer whale , pygmy killer whale and Fraser’s dolphin , although present in the GOM , may be too rare at these sites to be identified using these methods [23] . Use of a larger training set with a multi-pass strategy in which dominant types , such as E and F , were identified and removed could facilitate recognition of rare types . A subset of the identified click types had characteristics in common with clicks recorded in the presence of visually-identified species recorded using the towed hydrophone array . Unfortunately , with the exception of the pantropical spotted dolphin data , these recordings were collected in the Atlantic and can only be tentatively compared with GOM click types . Towed array hydrophones are typically much shallower than seafloor instruments , therefore the effect of acoustic propagation on recorded signals differs . Further work will seek to solidify and extend comparisons between seafloor sensor types and towed array recordings of known species , with an emphasis on collecting recordings of visually identified species in the GOM . The towed array environment is different from that of the seafloor sensor . Towed array recordings are much more affected by vessel , ship-based electronic and wind-generated sea-surface noise , and shallow sound-speed profiles than autonomous seafloor recordings . The orientations of animals relative to the sensors differ between the two types of recordings , for example , during a ship survey , dolphins are often oriented toward the bow , while the sensor is towed behind the vessel; whereas seafloor instruments are located below dolphins traveling near the sea surface , and do not typically influence dolphin orientations . Animal behaviors likely differ as well because marine mammal surveys require daylight for visual marine mammal identification , but seafloor sensor recordings typically show that most delphinid clicks are detected at night [29] . In addition , comparisons of simultaneous towed array and HARP recordings in the GOM have shown that towed array detection ranges may be limited by signal refraction associated with warm surface mixed layer [33] . Towed array delphinid click recordings were often characterized by short encounters and highly variable click amplitudes . When animals were close enough to the towed array to be detectable , both on-axis ( transmission beam oriented directly toward the sensor ) and off-axis clicks were likely received , and on-axis clicks could be clipped due to high amplitudes at close range . In contrast , delphinid encounters recorded by near-seafloor HARPs were often longer in duration due to larger detection ranges . Click amplitudes tended to be lower , because delphinids were farther from the sensor , and off-axis clicks were less detectable according to click propagation simulations [34] . Several improvements could be made to the automated classification approach in future work . First , different distance metrics could be evaluated . In this study , a correlation distance metric was used to assess similarity between spectra as it was found to capture shape similarities more effectively than a simpler Euclidean distance . However , the correlation distance used assigns equal weight to all frequencies in the spectra , while high frequency amplitudes are expected to vary more than low frequencies because of acoustic attenuation . To account for this expectation , a weighted distance metric could be used that emphasizes low frequency shape . Alternatively , click shapes could be summarized as cepstra ( inverse FFT of spectra , e . g . [28] ) to emphasize particular aspects of overall shape . Preliminary experiments using cepstra and perceptual weighting were conducted as part of this study , however clustering results were poor . Nonetheless , more complex weighting strategies might be useful in future work . Another improvement that could be considered is to evaluate the impact of pre-filtering spectra prior to classification . In this implementation , frequencies below 10 kHz were removed by a bandpass filter . Future classification efforts might benefit from including lower frequency spectral content . Recent work by Finneran et al . [4] suggests that delphinid clicks may have consistent spectral features below 10 kHz which may be useful for click classification [e . g . 21] . Improvements could also focus on using different metrics to capture persistent features of ICIs . In this study , clear modal ICI peaks were associated with the click types , and ICI previously has been found to be useful for classifying beaked whale clicks [13] . While delphinids have been shown to vary their ICI in wild and captive studies [1 , 16] , this typically occurs during terminal buzzes [35] which are much lower amplitude and occur less frequently than regular clicks [35 , 36] and therefore , are much less likely to be detected in wild recordings [34] . Deep seafloor instruments ( at depths of roughly 80 m or more ) often receive only a single animal’s click train at a given time due to the animals’ narrow transmission beam patterns and distance from seafloor sensors; therefore ICI often is accurately calculated and modal ICI is representative of persistent features . On occasions when a group of animals is large and/or close to the sensor , multiple click trains will overlap and modal ICI values may become subject to saturation . Click train tracking [37] could be used to improve modal ICI estimates in saturated cases and in noisy or shallow environments . Additional improvements could be made to fully automate the classification process . For example , false positives were manually removed from this dataset prior to classification . However , many sources of false positives , including beaked whales , sperm whales , and ships , have their own spectral and ICI characteristics . A similar approach to that described here could be used to build template clusters for false positive sources , allowing them to be identified and excluded automatically during classification . In addition to accelerating the analysis process , this approach could improve the removal of false positives within bouts of true detections ( such as at shallow sites ) , facilitate false positive rate calculations , and provide certainty scores for removed detections . Future work will likely seek to combine clustering with deep learning methods as a possible refinement for improved classification . Finally , future improvements should focus on evaluating sources of variability within click types and on linking distinct click types with delphinid species identity or behavior states . This work focused on identifying distinct click types , however , more work needs to be done to describe within-type variability . Delphinids have been shown to vary their clicks depending on context [e . g . 6 , 16 , 27] . The types described here are broad groupings , and are not meant to indicate a lack of variability within each type . Concurrent visual identifications with HARP recordings are needed to conclusively validate potential species associations . Future steps should include applying this method to a labeled dataset associated with visually-identified species to ground truth the approach . Continued development of unsupervised learning strategies for identifying consistent dolphin click types will advance marine mammal monitoring programs by facilitating delphinid and toothed whale species identification in data collected using autonomous passive acoustic sensors .
Long-term autonomous datasets were collected using High-frequency Acoustic Recording Packages ( HARPs ) at three continental slope and two shelf locations in the GOM between 2010 and 2012 ( Fig 6 ) . Details of each HARP deployment are presented in Table 2 . HARPs are autonomous bottom-mounted acoustic recorders containing a hydrophone , data logger , battery power supply , ballast weights , acoustic release system , and flotation [39] . All of the seafloor recording instruments used in this study were of the same type with equivalent hardware and software . Each instrument used a calibrated hydrophone ( Channel Group Technologies , Santa Barbara , CA , ITC-1042 ) buoyed approximately 10 m above the seafloor . Hydrophones had an approximately flat ( ±2 dB ) sensitivity from 10 to 100 kHz of -200 dB re V/μPa . Preamplifier calibrations were performed at Scripps Institution of Oceanography and at the U . S . Navy’s Transducer Evaluation Center facility in San Diego , California [38] . All HARPs sampled continuously at 200 kHz throughout each deployment . Towed hydrophone array recordings were collected in 2011 and 2012 ( Table 6 ) during National Oceanographic and Atmospheric Administration’s ( NOAA ) National Marine Fisheries Service ( NMFS ) Southeast Fisheries Science Center ( SEFSC ) marine mammal abundance surveys aboard the R/V Gordon Gunter , conducted in the eastern GOM and within the southeastern U . S . Atlantic coastal exclusive economic zone ( EEZ ) . A team of visual observers identified dolphins to species whenever possible , thereby providing ground-truthed species identifications which acousticians could associate with concurrent array recordings . A five-element hydrophone array was towed 274 m behind the ship , at an estimated depth of 15 to 18 m at typical survey speed ( 10 kn ) . Hydrophone elements consisted of custom-built preamplifiers , with band-pass filters set for 3 dB roll-off at 1 kHz and 200 kHz , connected to an omni-directional spherical hydrophone ( HS-150 Sonar Research and Development , Ltd . , Beverley , UK ) . Two hydrophones separated by 2 . 12 m were sampled continuously at 500 kHz using a data acquisition board ( NI USB 6251 , National Instruments Corporation , Austin , TX ) and recorded directly to hard disk drives using Logger 2000 ( International Fund for Animal Welfare , IFAW , Yarmouth Port , MA ) . The towed array recording setup differs considerably from the seafloor sensors , therefore any comparisons are considered tentative . The set of summary nodes identified using in the training set were used to automatically classify clicks in the test dataset ( Table 2 ) . As in the classifier training , Phase 1 of the automated clustering routine was executed on click-positive bins from test data to produce a set C of n test summary nodes spanning each test deployment . To classify each test summary node Ci in C ( for i = 1 to n ) from the test data to one of the click type clusters T from the training data , the spectrum and modal ICI of the test node was compared to all of the training nodes in each click type Tj of P , ( for j = 1 , … , m ) , to obtain a similarity metric following similar methods as for Phase II described above . The set of similarity scores was pruned , and Ci was automatically assigned to the cluster Tj with the highest mean similarity score between the test and training summary nodes . The mean similarity between Ci and its matching cluster Tj was retained as a metric of classification certainty . In this classification exercise , the goal was to find the best click type match for Ci , even if Ci was a poor quality example ( e . g . noisy or sparse ) so a lower pe threshold ( pe = 0 . 90 ) was used to allow matching across a range of qualities by retaining more edges . Note also that this method of fusing spectral and ICI similarity scores using a product requires both scores to be strong in order to produce a strong match . Implications of this approach are further explored in the discussion . To evaluate classifier performance , a systematic random sample of 200 test summary nodes from each site were manually assigned to a template cluster by a trained analyst reviewing mean spectra and ICI distributions of the test nodes . Test nodes that were not clearly similar to any of the click type clusters were labeled “unknown” by the analyst and counted as disagreements . The manual classifications were then compared with the automated classifications to evaluate classification confusion and to examine the relationship between automated classifier certainty and agreement between automated and manual classifications . Based on the evaluation , a minimum certainty threshold of 0 . 3 was established for automated classification . When evaluating classification confusion from the test subset , test summary nodes identified as unknown by either the manual or automated method were considered mismatches . Total detection rates of each click type at each site were evaluated for the full test set . | Health of marine mammal populations is often considered an indicator of overall marine ecosystem health and resilience , particularly in highly-impacted regions such as the Gulf of Mexico . Marine mammal populations are difficult to monitor given the many challenges of observing animals at sea ( e . g . weather , limited daylight , ocean conditions , and expense ) . An increasingly common approach is the use of underwater acoustic sensors capable of recording marine mammal calls at remote locations for months at a time . Acoustic sensors generate large datasets in which dolphin echolocation clicks are commonly present . Dolphins are the most diverse family of marine mammals , and distinguishing click characteristics have only been described for a small subset of species . We developed a workflow to automatically identify distinct dolphin click types within large datasets without prior knowledge of their distinguishing features . Our algorithm then recognizes these click types in novel recording data across a range of monitoring locations . Known species-specific click types emerge from the data using this approach , as well as new click types potentially associated with additional species . This technique is a key step toward determining species identification for passive acoustic monitoring of offshore populations of dolphins and other toothed whales under a big data paradigm . | [
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] | 2017 | Automated classification of dolphin echolocation click types from the Gulf of Mexico |
Mycobacterium tuberculosis ( Mtb ) uses efficient strategies to evade the eradication by professional phagocytes , involving—as recently confirmed—escape from phagosomal confinement . While Mtb determinants , such as the ESX-1 type VII secretion system , that contribute to this phenomenon are known , the host cell factors governing this important biological process are yet unexplored . Using a newly developed flow-cytometric approach for Mtb , we show that macrophages expressing the phagosomal bivalent cation transporter Nramp-1 , are much less susceptible to phagosomal rupture . Together with results from the use of the phagosome acidification inhibitor bafilomycin , we demonstrate that restriction of phagosomal acidification is a prerequisite for mycobacterial phagosomal rupture and cytosolic contact . Using different in vivo approaches including an enrichment and screen for tracking rare infected phagocytes carrying the CD45 . 1 hematopoietic allelic marker , we here provide first and unique evidence of M . tuberculosis-mediated phagosomal rupture in mouse spleen and lungs and in numerous phagocyte types . Our results , linking the ability of restriction of phagosome acidification to cytosolic access , provide an important conceptual advance for our knowledge on host processes targeted by Mtb evasion strategies .
The pathogenic potential of Mycobacterium tuberculosis ( Mtb ) , the etiologic agent of human tuberculosis ( TB ) , depends largely on the type VII secretion system ESX-1 [1 , 2] , which is responsible for the secretion of the 6-kDa Early Secreted Antigenic Target ( ESAT-6 ) , its protein partner , the 10-kDa Culture Filtrate Protein ( CFP-10 ) , and several ESX-1 associated proteins ( Esps ) [3 , 4] . ESX-1 secretion is evolutionary conserved in most members of the M . tuberculosis complex [5] , and the more distantly related tubercle bacilli of the Mycobacterium canettii clade [6 , 7] , as well as in some non-tuberculous mycobacteria such as Mycobacterium marinum [8] . This secretion system governs numerous aspects of interaction between pathogenic mycobacteria and the host cell [1 , 2] , including membrane-damaging activity [9–11] , thought to be implicated in phagosomal escape at later stages of infection [12–16] . Although this phenomenon is a matter of debate [2 , 17–20] , by use of a single-cell Fluorescence Resonance Energy Transfer ( FRET ) -based technology [21] , we recently demonstrated that ESX-1-proficient Mtb and recombinant Mycobacterium bovis BCG::ESX-1 were able to induce phagosome rupture in human THP-1 macrophage ( MΦ ) -like cells [15] . This assay uses the ability of the surface-exposed BlaC β-lactamase of Mtb [22 , 23] to cleave the FRET substrate CCF-4 , which consists of a cephalosporin core linking 7-hydroxycoumarin to fluorescein that has also been used for exploring effector injection and intracellular localization of Gram-negative bacteria [21 , 24 , 25] . The ESX-1-induced rupture of the phagosomal membrane , which results in the exit of mycobacterial products from the endosomal pathway and in extra-phagosomal localization of bacilli [13–16] is of relevance for the outcome of the immune control and bacterial dissemination [26–29] . Phagosomes are reported to be specialized platforms for pathogen recognition [30] and there is also growing evidence of a link between the functionality of the ESX-1 secretion system and the presence of mycobacteria-associated molecular patterns in the host cytosol . Peptidoglycans [31 , 32] and extracellular mycobacterial DNA [33] were reported to be sensed by the cytosolic receptors of the innate system with multiple biological consequences . Indeed , the Mtb-mediated induction of Nucleotide binding Oligomerization Domain ( NOD ) -Like Receptor pathways , i . e . , NOD2 / Receptor-interacting protein 2 kinase ( Rip2 ) / TANK-Binding Kinase 1 ( TBK1 ) / Interferon regulatory factor ( Irf ) 5 , is responsible for a significant part of type I interferon ( IFN ) production [31 , 32] . On the other hand , the signaling through the Stimulator of IFN Genes ( STING ) / TBK1 / Irf3 pathway [33] leads to a type I IFN signature on which depends the expression of CCL5 , CXCL10 and Nitric Oxide Synthase 2 [34 , 35] . The formation of Nucleotide-binding domain and Leucin-rich Repeat pyrin–containing Protein-3 ( NLRP-3 ) / ASC ( Apoptosis-associated Speck-like protein containing a carboxy-terminal CARD ) / caspase-1 inflammasome complex , is required in humans for the processing of the pro-IL-1β into biologically active pleïotropic immune mediatorIL-1β following Mtb infection [36 , 37] . Moreover , the ubiquitination of Mtb prior to its delivery to the autophagic machinery also necessitates the ESX-1-dependent translocation of extracellular Mtb DNA to the cytosol [16 , 33 , 38 , 39] . Thus , the events arising from mycobacterial cytoplasmic access may substantially influence both the immune control of Mtb and the inflammation-induced tissue damage . The impact of selected components of the ESX-1 system on phagosomal rupture has recently been assessed [13 , 15 , 16] , however , other potential intervening factors , including those from the host cell remain largely unexplored . Here , we have investigated the host parameters modulating the Mtb-mediated vacuolar breakage , by developing a CCF-4 FRET-based approach that can be used for the study of Mtb-infected cells by flow cytometry . This approach , which permits to combine the detection of phagosomal rupture with the analysis of numerous host cell phenotypic and functional parameters , allowed us to explore multiple phagocyte types , including those isolated from mouse airways . Our results provide first and unique evidence that Mtb-induced phagosomal rupture does occur in vivo inside the lungs and spleens of infected experimental animals and lasts over several days . Moreover , we here explore the impact of vacuolar acidification that constitutes a fundamental cellular defense mechanism [40] and demonstrate that the characteristic partial prevention of phagosomal acidification by Mtb is a prerequisite for phagosomal escape of the pathogen . Our study thus reveals novel details and presents a refined model of cellular events during infection with Mtb .
To evaluate mycobacteria-mediated phagosomal rupture in different phagocyte types and different physiological contexts , we adapted the previously used microscopy-based CCF-4 FRET technique [15] for flow cytometry . The latter approach not only allows monitoring of bacteria-induced phagosomal rupture or tracking of endosome-to-cytosol antigen translocation [25 , 41] , but also permits the simultaneous inspection of surface markers and analysis of hundreds of thousands of host cells . At first , we infected differentiated THP-1 cells at a multiplicity of infection ( MOI ) of 1 either with Mtb H37Rv WT or the isogenic ΔESX-1 derivative , Mtb H37Rv-ΔRD1 [10] , which both display similar β-lactamase activity [15] . These THP-1 cells were then incubated with CCF-4-AM , an esterified , lipophilic form of the CCF-4 substrate that can readily enter into cells , where it is converted by endogenous cytoplasmic esterases into negatively charged CCF-4 , which is retained in the cytosol and emits green fluorescence ( 500–550 nm ) upon stimulation at 320–380 nm , due to FRET from the coumarin moiety to the fluoroscein part ( S1 Fig . ) . In the case of Mtb-induced phagosomal rupture , cleavage of CCF-4 by the intrinsic Mtb BlaC β-lactamase leads to loss of FRET and a change of the CCF-4 emission spectrum from green to blue coumarin fluorescence ( 410–470 nm ) . As depicted in Fig . 1A , the CCF-4 emission signals of CD11b+ gated THP-1 cells , infected with wild-type ( WT ) Mtb H37Rv , showed a marked shift of the CCF-4 emission towards blue at 4 days post infection ( dpi ) . In contrast , a much weaker shift of the CCF-4 spectrum was observed for Mtb ΔESX-1-infected cells , validating our experimental setup and confirming the fundamental virulence differences between the used ESX-1-proficient and ESX-1–deficient Mtb strains [10 , 15] . The residual blue shift in Mtb ΔESX-1-infected cells relative to non-infected cells is likely a consequence of paraformaldehyde ( PFA ) fixation prior to signal acquisition ( S2A–B Fig . ) . These results were further corroborated by ratios of Mean Fluorescence Intensities ( MFI ) of blue vs . green signals ( Fig . 1B ) , and blue MFI447 nm ( Fig . 1C ) . Moreover , we also used fluorescent Mtb ( DsRed-Mtb H37Rv ) to infect THP-1 cells , at a weaker initial dose ( MOI = 0 . 3 ) , and thereby observed that the CCF-4 blue emission shift selectively occurred in cells that had engulfed the bacteria ( Fig . 1D ) . This approach thus allowed a quantitative study of phagosomal rupture in host cells that have engulfed Mtb , and whose subtype can be identified/determined by staining of the specific surface markers . Hence , our experimental setup was adapted to be used for various cell types and physiological situations , including the detection of vacuolar rupture in rare ( infected ) cells that were dispersed in a large and heterogeneous cell background population . Dendritic cells ( DC ) and MΦ do not play the same roles during the infection . DCs that have engulfed Mtb , are more prone to process and present pathogen-derived antigens and to prime T cells than Mtb-laden MΦ which are thought to initiate the inflammatory program and are considered as long-term Mtb reservoirs . We thus comparatively evaluated the potential of Mtb to induce phagosome rupture in bone-marrow-derived ( BM ) -DC and -MΦ At first , by using fluorescent DsRed WT and ΔESX-1 Mtb variants , we showed similar uptake and infectivity of both strains at the beginning of the infection ( Fig . 2A ) . Infection of BM-DC and BM-MΦ with WT Mtb then resulted in a strong blue shift at 3 dpi and thereafter , whereas for cells infected with the ΔESX-1 Mtb strain only a minor blue shift was detected ( Fig . 2B ) . The relatively stable CCF-4 green signal and its progressively increasing blue shift for WT Mtb resulted in a blue/green ratio of 15 in BM-DC and 10 in BM-MΦ respectively , at 6 dpi ( Fig . 2C ) . Similar as observed for THP-1 cells ( Fig . 1D ) , infection of BM-DC with DsRed expressing Mtb showed that cells , which had engulfed DsRed Mtb , progressively increased their CCF4 blue shift over the observation period of 3 to 5 dpi ( S3 Fig . ) . Together , these results suggest that ESX-1-dependent , Mtb-induced phagosomal rupture does occur in DC and MΦ . To ascertain that the absence of FRET inhibition in cells infected with the ΔESX-1 Mtb mutant was not due to other molecular reasons than the absence of the ESX-1 secretion system , we complemented the Mtb ΔESX-1 strain with the integrative cosmid p2F9 , containing 32 kb of the ESX-1 encoding genomic region from Mtb H37Rv [42] . This complementation reconstituted the ability of the resulting strain to induce phagosomal rupture , and thereby validated the ΔESX-1 mutants used throughout this study ( S2C Fig . ) . When uncontrolled inside the host cell , Mtb infection may lead to necrosis [27 , 43] , which could theoretically allow exchanges between phagosome and cytosol and thereby establish a contact between mycobacterial β-lactamase located within the phagosome and CCF-4 located inside the cytosol . To investigate this key question , we determined whether the cytosolic access of Mtb was a consequence of host cell necrosis . In a dose-response experiment , changes in the FRET signal for the Mtb WT strain were seen as a function of the MOI ( Fig . 2D ) . Except for an MOI below 1 , the proportions of BM-DC displaying FRET inhibition were higher than the percentages of necrotic cells ( Fig . 2E ) . In contrast , BM-DC infected with Mtb ΔESX-1 at the same MOIs displayed much weaker CCF-4 blue shifts . These data suggest that ESX-1-mediated phagosomal rupture progressively occurs in phagocytes in an MOI-dependent manner and that the resultant presence of mycobacterial β-lactamase activity in the host cell cytosol does not arise from host cell necrosis but rather precedes cell death . So far , Mtb-induced phagosomal rupture has only been observed at later stages of infection , i . e , 3–5 dpi , a kinetic situation , which cannot explain the very early , ESX-1-dependent release of type I IFNs or IL-1β , that requires recognition of mycobacterial components by the host cytosolic sensors [44] . However , our highly sensitive approach allowed now detection of minor levels of FRET inhibition indicated by enhanced MFI447 nm ( blue ) , as early as 3 hours post infection ( hpi ) with WT Mtb ( Fig . 3A-B ) . The blue shift then progressively increased at 24 and 48 hpi , although it remained still low compared to values obtained for later time points ( Fig . 2B-C ) . Comparison of these results with those from infection experiments using the Mtb ΔESX-1 deletion mutant , which overall showed much lower MFI447nm ( blue ) values ( Fig . 3B ) , suggests that Mtb-mediated phagosomal rupture begins already at such early time-points , likely caused by initial ESX-1-induced pore forming activity , and progresses into stronger phagosomal disassembly over time . These findings suggest that the time during which the Mtb-infected host cell displays phagosomal rupture and Mtb cytosolic access , prior to host cell death , is longer than previously estimated [15] . Considering the long Mtb replication time of ≈ 20h , such early initiation of Mtb-mediated phagosomal rupture suggests that this phenomenon does not depend on bacterial replication , but on the functions of the implicated bacterial virulence factors . The levels of phagosome disruption were entirely proportional to the amounts of secreted IFN-β ( Fig . 3C ) . A partially ESX-1-dependent increase in the IFN-α secretion was also detected , which might be linked to the induction of Irf7 subsequent to IFN-β induction [45] . Therefore , minute levels of early phagosomal rupture are in direct correlation with the kinetics of the induction of type I IFN production . In contrast , no differences were found between ESX-1-proficient and ΔESX-1 Mtb strains when IL-1β secretion was studied ( S4 Fig . ) , which is consistent with the inflammasome/caspase-1-independent IL-1β secretion in mice during Mtb infection [46] and which is different to the situation in humans [36] . We next evaluated whether the characteristic Mtb-mediated partial inhibition of phagosome acidification was connected to the phenomenon of phagosome rupture . Given the previously established role of Natural resistance-associated macrophage protein ( Nramp ) -1 , a phagosomal bivalent cation transporter , in phagosomal acidification and pH regulation [47–49] , we evaluated its possible impact on mycobacteria-mediated phagosomal rupture . We thus used Mtb WT or ΔESX-1 strains to infect cells from the murine MΦ cell line Raw264 . 7 , deficient in functional Nramp-1 , which had been transfected with a non-functional nramp-1S ( Sensitive ) or a functional nramp-1R ( Resistance ) allele [50] . At 3 dpi , intense CCF-4 blue shifts were observed in WT Mtb-infected parental Raw264 . 7 cells and Raw264 . 7::Nramp-1S cells , whereas much less FRET inhibition was detected in Raw264 . 7::Nramp-1R cells ( Fig . 4A-C ) . As assessed for various MOI , the intracellular mycobacterial load inside parental , Nramp-1S- or Nramp-1R-transfected Raw264 . 7 cells was comparable at 3 dpi , when the phagosomal rupture was monitored ( Fig . 4D ) . Thus , the functional Nramp-1R seems to provide protection against Mtb-induced phagosomal rupture for the benefit of the host cell . The Nramp-1-mediated rescue of the host cells occurred at any MOI and independently of the host cell proliferation rate , which as we noticed , both influence the control of the infection ( S5 Fig . ) . We obtained further confirmation of our results by using an nramp-1 gene silencing strategy in Raw264 . 7::Nramp-1R cells ( Fig . 4E ) , which reversed the phenotype and promoted Mtb-mediated phagosomal rupture ( Fig . 4F-G ) . We further treated Raw264 . 7::Nramp-1R cells or , as primary phagocytes , BM-DC from Sv129 ( nramp-1R ) mice with bafilomycin , a specific inhibitor of vacuolar proton ATPases , prior to infection with WT Mtb H37Rv . As shown in Fig . 5A-B , the bafilomycin-mediated reduction of phagosomal acidification resulted in enhanced phagosomal rupture in both cell types . This observation provides additional evidence for a link between restriction of phagosome acidification and the strength of observed phagosomal rupture . In this FRET-based method , the β-lactamase operates on CCF-4 located in the host cytosol , where the pH remains neutral [25 , 41] . However , to further ascertain that the micro-environmental acidity did not affect the functionality of mycobacterial BlaC , we tested the β-lactamase enzymatic activity of Mtb at different pH levels by the use of nitrocefin , a chromogenic β-lactamase substrate . These experiments confirmed that Mtb , grown at different pH , ranging from 5 to 7 , preserves entirely its β-lactamase enzymatic activity ( Fig . 5C ) . Thus , acidification of the phagosomal lumen seems to be a critical host cell parameters , which exerts an antagonistic effect on Mtb-mediated phagosomal rupture in phagocytes . The finding that both phenomena are linked provides a new basis for elucidating the molecular key players that govern the host-pathogen interaction during Mtb infection . Previous studies on vacuolar rupture and phagosomal escape of M . marinum [12 , 51] and Mtb [13 , 15 , 16] used infected MΦ or DC under in vitro conditions . To extend our investigations towards cells from the lung , we examined the Mtb-mediated phagosomal rupture in different phagocyte types of mouse airways . To this end , low-density cells isolated from mouse lung parenchyma were infected ex vivo at an MOI of 1 with ΔESX-1 or WT Mtb strains . CCF-4 signals obtained from monocytes/MΦ ( CD11bhi CD11c- ) and DC ( CD11bint CD11c+ ) were analyzed at 4 dpi , when changes in the FRET signal were detected in lung monocytes/MΦ and DC ( Fig . 6A ) , showing the occurrence of Mtb-mediated phagosomal rupture in the primary lung phagocytes . To assess the relevance of mycobacteria-mediated phagosomal rupture in phagocytes in vivo , in a first attempt we used T-/B-cell deficient recombination activation gene ( rag ) 2 knock-out mice in which infection with Mtb is more persistent and the innate cell compartments more developed than in their immunocompetent counterparts . However , flow cytometric analysis of lung- or spleen-derived MΦ/monocytes , DC and neutrophils obtained from infected ( 1 x 106 CFU i . v . /mouse of WT or ΔESX-1 Mtb ) or uninfected rag2°/° mice displayed indistinguishable CCF-4 blue profiles ( S6 Fig . ) . The apparent failure in the detection of phagosomal rupture in this experimental setting seems to be related to the very low frequencies of mycobacteria-infected cells within each innate cell subset and/or a possible furtive feature of the phenomenon in vivo due to possible efferocytosis [52] of the primary phagocytes , in which phagosomal rupture and certain damage signals would have been initiated . To distinguish infected and non-infected cells , we then used fluorescent DsRed-WT Mtb ( 1 x 106 CFU/mouse ) for intravenous ( i . v . ) infection of C57BL/6 mice , which allowed us to focus on the relatively few Mtb-infected phagocytes present during the initial phase of chronic infection . At 3 weeks p . i . mice were sacrificed , the spleens homogenized and resulting cells enriched and subjected to flow cytometric analysis . We have focused on the phagocytes of the spleen because this organ is particularly targeted by the i . v . route of infection . When the CCF-4 blue signal of the innate immune cells that contained DsRed Mtb was compared to the other cells inside each cell subset in the spleen ( Fig . 6B ) , a slight increase in CCF-4 blue signal was notably detected in Mtb-containing cells in the subsets of neutrophils ( CD11bhiCD11c-Ly6G+ ) and MΦ/monocytes ( CD11bintCD11c-Ly6G- ) ( Fig . 6C ) , which suggests the occurrence of weak , albeit reproducible , levels of phagosomal rupture in these infected cells . Interestingly , no DsRed+ cells were detected inside the CD11bloCD11c+Ly6G- DC subset , which might be due to possible rapid turnover of infected DC or to their CD11b up-regulation . In this chronic infection model , it was however not possible to compare WT and ΔESX-1 Mtb strains , because of the non-persistence of the latter . To overcome this limitation we developed an alternative in vivo model whereby mice were instilled intra-nasally with cells that were infected with Mtb in vitro prior to transfer , and whose infection status in vivo could be specifically monitored . To this end , BM-DC from mice with CD45 . 1 hematopoietic allelic marker were infected in vitro with WT or ΔESX-1 Mtb , in conditions that allowed up to 70% of the cells to be infected ( Fig . 2A ) , whereas control cells were left uninfected . At 16 hpi , the cells were instilled into the airways of congenic CD45 . 2 recipients . At different time points post-transfer , the lung low-density cells were isolated and the CCF-4 blue shift in the CD11b+ CD45 . 1 cell subset of the different experimental groups assessed ( Figs . 7A and S7 ) . Strikingly , at day 4 and day 6 post-transfer , in the CD11b+ CD45 . 1 population infected with WT Mtb , a blue shift was detected in comparison to the non-infected or ΔESX-1-infected transferred cells ( Fig . 7B-C ) . Moreover , independent flow cytometric examination of cells extracted directly from surface lung granuloma tissue of Mtb-infected C57BL/6 mice revealed a small , distinct cell population that displayed a clear-cut blue signal and a CD11b+ CD11c+ phenotype ( Fig . 7D ) , which points to the presence of innate cells in these lungs wherein Mtb-mediated phagosomal rupture had occurred . Altogether , our data suggest that the Mtb-induced phagosomal rupture does indeed happen in vivo , in Mtb-infected cells in the organs of small laboratory animals . The detected phagocytes containing intracellular bacteria seem to have a life-time of several days , which however does not exclude the possibility that a portion of the total number of infected phagocytes might get eliminated by efferocytosis [52] , as suggested by the relatively modest differences in blue shift observed in the in vivo settings .
The pathogenic potential of Mtb is intimately linked to the interplay between the host defense and the persistence of the mycobacteria . The intracellular localization and cytosolic access of the bacterium has substantial consequences on the recognition of mycobacteria-associated patterns by the cytosolic receptors of the innate immunity that determine innate and adaptive immune responses and ultimately the fate of the host cell and the bacterium [27] . Subsequent to phagocytosis , in order to avoid the acidified environment generated by the phagosome-lysosome fusion , some specialized intracellular bacteria , such as S . flexneri , Listeria monocytogenes or Francisella tularensis , evolved to rapidly escape from phagosomes into the cytosol [21 , 53 , 54] . In contrast , Mtb has been described as a bacterium that resists degradation in the phagosome by inhibiting the fusion with lysosomes , a characteristic feature that seems to protect the bacilli from bactericidal mechanisms of the phagocytes and allows intracellular survival and multiplication [10 , 18 , 55–57] . However , recent reports based on in vitro infection of phagocytes also suggest that at later stages of infection ESX-1-dependent vacuolar breakage might be an important requirement for the pathogenic potential of Mtb , given that ESX-1-deficient bacilli that are unable to perforate and lyse the phagosomal membrane are—in general—attenuated [13 , 15 , 16 , 18 , 56–59] . In previous studies , Mtb-mediated phagosomal escape has only been reported at late time points like 2–5 dpi , a kinetic feature that was not reconcilable with the intracellular host immune events , like type I IFN induction , which require the early recognition of mycobacterial components by cytosolic sensors . Here , the use of highly sensitive FRET-based cytometry enabled us to highlight minor levels of cytosolic contact of Mtb and its products initiated as soon as 3 hpi , which is kinetically concordant and proportional with the amounts of IFN-β released by DC . While we cannot exclude the possibility that some of this effect may have been caused by bacterial products translocating through permeable phagosomal membranes [30] , the reproducible differences observed between the WT and the ΔESX-1 Mtb strains argue for a specific , ESX-1-mediated impact . We also noted that distinct cell types might display different susceptibility to phagosomal rupture , with THP-1 cells as the most susceptible ones , followed by BM-DC/BM-MΦ , and the Raw264 . 7 MΦ as the least affected cell types , tested . Our results show that the phagosomal bivalent cation transporter Nramp-1 interferes with Mtb-induced phagosomal rupture as observed at 3 dpi , i . e . , a time point at which mycobacterial loads were still comparable in Mtb-infected MΦ harboring Nramp-1S ( non functional ) - or Nramp-1R ( functional ) allelic forms . In line with that , the effect of bafilomycin , reported to inhibit phagosomal acidification [60] , reconstituted in Nramp-1R-proficient phagocytes the capacity of Mtb to enhance phagosomal rupture to the level of Nramp-1S phagocytes . Thus , the partial inhibition of phagosome acidification emerges as a prerequisite to mycobacterial phagosomal rupture . Plausibly , only when phagosome acidification is partially inhibited , mycobacteria may survive , use their virulence factors and induce phagosomal membrane disruption . Although cellular models may provide important new insights into cell biological mechanisms , evaluation of the accuracy of the findings in an in vivo model , i . e . in tissues or organs is of crucial importance to emphasize their relevance . Previous electron microscopy analyses of lung innate cells isolated from TB patients or mycobacteria-infected mice have led to discrepancies with regards to intracellular location [18] . In alveolar MΦ of TB patients and in granuloma or lung homogenates of infected mice , Mtb has been detected as single bacterium or pairs of bacilli inside phagosomes [61 , 62] , whereas Mtb has also been observed in membrane-disrupted compartments or free in the cytosol in the mouse granulomas [63 , 64] . Moreover , heavily infected human alveolar MΦ [62] and damaged mouse MΦ of inflammatory sites [65] contain multiple mycobacteria per phagosome . In this context , our results from carefully designed in vivo infection experiments add new elements to the discussion . Although the strength of the FRET-inhibition was found weaker under in vivo conditions ( Figs . 6 and 7 ) than observed for the cell culture-based infection assays ( Figs . 1 and 2 ) , the reproducibility and complementarity of the results from the three distinct in vivo settings analyzed , point to biological relevance of mycobacteria-induced phagosomal rupture in the organs of Mtb-infected laboratory animals . It should be noted that in our experiment with BM-DC from mice with the CD45 . 1 hematopoietic allelic markers ( Fig . 7 ) , we cannot exclude that in the infected DC some minor cytosolic contact might develop already in vitro , prior to their instillation to the CD45 . 2 recipient mice . However , the finding that FRET inhibition remains detectable for several days after the transfer into the lungs of the CD45 . 2 recipients suggests that the phagocytes in which cytosolic access of Mtb progressively builds up , can survive in the host environment for some days . Together with ex vivo results from MΦ/monocytes and DC isolated from the lung parenchyma , the in vivo demonstration of cytosolic access of Mtb provides important new insights into the cellular events during infection inside the organs . Our data suggest that after infection , the concerned phagocytes may persist in the organs long enough to have a potential impact on host defense mechanisms that likely also include key cellular processes , such as autophagy , which requires Mtb ubiquitination in an ESX-1-dependent manner [16 , 33 , 38 , 39] . The intracellular localization of mycobacteria and mycobacteria-mediated phagosomal rupture have been subject of numerous controversies , which may be explained by the differences between the level of virulence of mycobacterial strains used , the MOI and the conditions of the mycobacterial cultures in vitro [18] . For the virulent strains , here we used WT and DsRed Mtb previously passaged in immunocompetent mice to maintain a normal degree of virulence and to remain as close to natural infection as possible . We only used mycobacterial cultures in mid-log10 growth phase to minimize bacterial mortality , and we cultured the bacteria in the presence of Tween 80 to avoid clumping , as phagocytosis of non-viable or clumped mycobacteria may lead to rapid phagosome-lysosome fusion and prevent visualization of phagosomal rupture [18] . In addition , we systematically compared the ESX-1-proficient and ESX-1-deficient mycobacterial strains and detected a relevant phagosomal rupture only with ESX-1-proficient strains . Previous observations with numerous virulent and attenuated Mtb strains suggest that the capacity of a strain to induce phagosomal rupture in vitro is often correlated with its virulence [15 , 16] . Hence , the ESX-1-dependent , mycobacteria-induced phagosomal rupture emerges as a major characteristic feature of Mtb infection , which likely initiates the first damages caused by this intracellular pathogen to the host cell . Consequently , modulation of the parameters , which orchestrate this phenomenon , may constitute a promising base for vaccinal or therapeutic interventions against TB . For example , we have previously noticed that recombinant BCG and M . microti strains with a reconstituted ESX-1 secretion system showed enhanced protective efficacy [66 , 67] . More recently , a dedicated study identified small molecule inhibitors belonging to the benzyloxybenzylidene-hydrazine and the benzothiophene chemical classes , which interfered with ESAT-6 secretion and thereby protected host cells from Mtb-induced lysis [68] . Molecules belonging to closely related chemical scaffolds were also identified in a high content phenotypic screen as agents that interfered with the intracellular growth and the virulence of Mtb [69] . Hence , it is conceivable that future phenotypic library screening might identify novel pharmacological compounds that inhibit Mtb-mediated phagosomal rupture in the host cell . Such molecules would represent interesting anti-virulence compounds to be tested as addition to conventional treatment regimens against TB . In conclusion , our study suggests that Mtb is not the passive pathogen that induces pathology only by the over-boarding reaction of the host immune system . We show that ESX-1-mediated phagosomal rupture contributes in a significant way to establish mycobacterial cytosolic contact , which is however only possible if the maturation / acidification of the phagosome is limited in a first process . In this direction , our study also opens new perspectives for future studies on the mycobacterial components involved in the modulation of phagosomal acidification such as the phthiocerol dimycocerosates and other mycobacterial factors , reported to intervene in this process [70 , 71] . The ESX-1 system might thus represent one of the final members in a chain of virulence factors that determine the pathogenicity of Mtb through the induction of phagosomal rupture , and its function might therefore have been evolutionary preserved [5 , 7] . As such , our work has the potential to reconcile the outcome of previous studies on mycobacterial virulence factors that interfere with vacuolar acidification [71–74] and studies on cellular localization of Mtb [13–16] and establishes Mtb-mediated phagosomal rupture as a basic biological mechanism involved in TB pathogenesis .
C57BL/6 mice , rag2°/° or CD45 . 1 were obtained from Animal Facilities of Institut Pasteur . C57BL/6 mice were purchased from Janvier Le Genest-Saint-Isle France ) . CD45 . 2 mice were anesthetized by i . p . injection of 100 mg/kg Ketamine ( Lyon , France ) and 10 mg/kg Xylazine ( KCP Kiel , Germany ) before cell transfer by i . n . route . Mouse infection with Mtb via aerosol route was performed as previously described [75] . Granuloma were recovered from the surface lung parenchyma of infected C57BL/6 mice at 6 weeks p . i . Mouse studies were approved by the Institut Pasteur Safety Committee , in accordance with French and European guidelines and regulations ( Directive 86/609/CEE and Decree 87–848 of 19 October 1987 ) and the Animal Experimentation Ethics Committee Ile-de-France-1 ( reference number 2012–0005 ) . THP-1 cells ( our laboratory stock collection , initially originating from ATCC provided cells ) were maintained in RPMI , complemented with 10% heat-inactivated FBS and were treated with 20 ng/ml of Phorbol 12-Myristate 13-Acetate for 72h to induce their differentiation into MΦ . Raw264 . 7 cells transfected with nramp-1S or -1R allele ( kind gift of Pr J . Blackwell ) [50] were treated with 8 μg/ml of the selective antibiotic puromycin . BM-MΦ or -DC were generated from femur hematopoietic precursors , respectively by use of M-CSF or GM-CSF . Rat anti-mouse IFN-α mAb ( RMMA-1 ) , biotinylated polyclonal rabbit anti-mouse IFN-α ( R&D ) , rat anti-mouse IFN-β ( 8 . S . 415 ) ( LifeSpan BioSciences ) and biotinylated polyclonal rabbit anti-mouse IFN-β ( R&D ) were used to quantify the cytokines produced in the culture supernatants by ELISA . Mtb H37Rv , WT , ΔESX-1 ( kind gift of Pr . W . Jacobs ) [10] or ΔESX::ESX-1 [42] were maintained in 7H9 medium supplemented with ADC ( Difco ) . Seven-to-10 days before cell infection , bacteria were transferred into Dubos medium , which contains Tween 80 , to avoid mycobacterial clumping . DsRed-WT or -ΔESX-1 strains were obtained by complementation with the pMRF plasmid containing a DsRed cassette , under the hsp60 promoter ( kind gift of Dr . S . Cho ) and were cultured in the continuous presence of 20 μg/ml of the selective antibiotic kanamycin . In in vivo experiments , we used an Mtb H37Rv strain with a plasmid containing the DsRed and hygromycin resistance genes ( kind gift of Dr . O . Neyrolles ) . Only mycobacteria grown to mid-log10 phase were used to minimize the frequency of death bacteria . Raw264 . 7 cells were infected at various MOI with Mtb in complete antibiotic-free RPMI . At 3 dpi , equal numbers of cells were lysed by addition of 0 . 1% Triton X-100 in PBS and the intracellular CFU was determined by plating serial dilutions of cell lysates on 7H9 Agar medium and incubation at 37°C for 3 weeks . The principle of the β-lactamase CCF-4 FRET assay is summarized in S1 Fig . . To measure the Mtb phagosomal rupture , cells were stained during 1h at RT , with 8 μM CCF-4 ( Invitrogen ) in EM buffer ( 120 mM NaCl , 7 mM KCl , 1 . 8 mM CaCl2 , 0 . 8 mM MgCl2 , 5 mM glucose and 25 mM Hepes , pH 7 . 3 ) complemented with 2 . 5 μM probenecid . Cells were then stained with anti-CD11c-PE-Cy7 , anti-CD11b-PerCp-Cy5 . 5 ( eBiosciences ) or anti-CD11b-APC ( BD ) mAbs andfixed with 4% PFA overnight at 4°C . Cell mortality in the same cultures of infected cells was determined by use of Pacific Blue Dead/Live reagent ( Invitrogen ) , which reacts with free amines both inside and outside of the plasma membrane , yielding log10 1 more intense fluorescent staining of dead cells . Anti-CD45 . 1-PE-Cy7 and anti-CD45 . 2-PerCpCy5 . 5 were from eBiosciences . To avoid fluorochromes with emission signals overlapping with those of CCF-4 ( λem 500–550 nm and λem 410–470 nm ) , APC ( λem 660 nm ) - , PerCp-Cy5 . 5 ( λem 696 nm ) - or PE-Cy7 ( λem 778 nm ) -conjugated mAbs were chosen for concomitant cell surface staining . Cells were analyzed in a CyAn cytometer using Summit software ( Beckman Coulter , France ) . At least 100 , 000 events per sample were acquired for in vitro assays . For in vivo detection of CCF-4 signal in CD45 congenic mouse model , 1 , 000 , 000 events per sample have been acquired . Data were analyzed with FlowJo software ( Treestar , OR ) . siRNA transfection to cells was performed by using reverse transfection method . A pool of four Nramp-1-specific siRNAs , GGUCAAGUCUAGAGAAGUA , GAUCCUAGGCUGUCUCUUU , GGGCGACUGUGCUAGGUUU and GAAGUCAUCGGGACGGCUA , at final concentration of 50 nM , was mixed with 6 μl of lipofectamine ( Invitrogen ) in 500 μl of PBS in 6-well plates . After 30 min incubation at RT , 3 x 105 cells contained in 2 ml of complete RPMI were added to the mixture and incubated for 3 days at 37°C . The efficiency of gene silencing was determined by qRT-PCR before the infection . One mg of total RNA was transcribed into cDNA . Then , 4 μL of cDNA was tested by qRT-PCR with LightCycler 480 SYBR Green using GCCACTGTGCTAGGTTTGCT and AATGGTGATCAGTACACCGC primers . All experiments were run in triplicate and the Livak method [76] was applied for relative quantification with β-actin . The β-lactamase activity of Mtb , grown in Dubos broth with various pH , was measured by use of the chromogenic β-lactamase substrate , nitrocefin . Briefly 1 x 106 bacteria , re-suspended in 100 μl of Dubos broth at indicated pH , were incubated in 96-well plates with 50 μl of nitrocefin , reconstituted at 0 . 5 mg/ml in PBS which contained 5% DMSO . Absorbance by nitrocefin at 486 nm was measured after 3 hours of incubation at 37°C . Lungs or spleen were removed aseptically and were digested by treatment with 400 U/ml type IV collagenase and DNase I ( Roche ) . Following a 45 min incubation at 37°C , single-cell suspensions were prepared by use of a Gentle Macs ( Miltenyi ) and by passage through 100-μm nylon filters ( Cell Strainer , BD Falcon ) . When indicated , cell suspensions were enriched in low-density cells on iodixanol gradient medium ( OptiPrep , Axis-Shield ) , according to the manufacturer’s protocol . Notably this gradient only selects alive cells , as confirmed by blue Trypan exclusion assay . These cells were either used directly in flow cytometry analyses or were plated in 12 well culture plates in complete RPMI to be infected ex vivo with mycobacteria . | The intracellular fate of the agent of the human tuberculosis agent in phagocytes is a question of great biological relevance . Among the mycobacterial survival strategies , the escape of Mycobacterium tuberculosis from phagosomes has been subject of scientific debate for a long time . However , technically improved methods recently reinforced the occurrence of this phenomenon . Here , we focused on the host factors involved in phagosomal rupture and provide first and singular evidence of M . tuberculosis-mediated phagosomal rupture in vivo in mouse lungs and inside the granuloma . We show that partial blockage of phagosomal acidification , induced by mycobacteria , is a prerequisite for efficient vacuolar breakage by M . tuberculosis and link maturation arrest , cytosolic contact and the corresponding immune responses . From our results we conclude that vacuolar breakage induced by M . tuberculosis is not an ex vivo artifact of cell cultures , but an important process that occurs inside infected phagocytes within organs during several days that strongly determines the outcome of infection with this key pathogen . | [
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] | [] | 2015 | Cytosolic Access of Mycobacterium tuberculosis: Critical Impact of Phagosomal Acidification Control and Demonstration of Occurrence In Vivo |
Recent experiments revealed that the fruit fly Drosophila melanogaster has a dedicated mechanism for forgetting: blocking the G-protein Rac leads to slower and activating Rac to faster forgetting . This active form of forgetting lacks a satisfactory functional explanation . We investigated optimal decision making for an agent adapting to a stochastic environment where a stimulus may switch between being indicative of reward or punishment . Like Drosophila , an optimal agent shows forgetting with a rate that is linked to the time scale of changes in the environment . Moreover , to reduce the odds of missing future reward , an optimal agent may trade the risk of immediate pain for information gain and thus forget faster after aversive conditioning . A simple neuronal network reproduces these features . Our theory shows that forgetting in Drosophila appears as an optimal adaptive behavior in a changing environment . This is in line with the view that forgetting is adaptive rather than a consequence of limitations of the memory system .
Drosophila melanogaster forgets [1] , [2] . In itself this is unremarkable because forgetting as a behavioral phenomenon appears in any adaptive system of limited capacity; storing new associations will lead to interference with existing memories . Forgetting , in this sense , is just the flip side of learning . When capacity is not an issue , forgetting may nevertheless be caused by a useful mechanism: one that keeps a low memory load and thus prevents a slowdown of retrieval [3] , [4] . Consequently , capacity or retrieval limitations lie at the heart of standard theories of non-pathological forgetting [5] , [6] , which focus on interference and decay explanations . Alternatively , forgetting has been proposed to be an adaptive strategy that has evolved in response to the demands of a changing environment [7] . It is the latter explanation that seems to apply to Drosophila where the experimental evidence suggests that the cause underlying forgetting is an active process which is modulated by the learning task and not by internal constraints of the memory system; in particular in olfactory conditioning tasks , reversal learning leads to faster forgetting [8] whereas spaced training leads to slower forgetting compared to single or massed training [9] . Further , forgetting in Drosophila seems rather idiosyncratic in that aversive conditioning is forgotten approximately twice as quickly as appetitive conditioning [10] , [11] . In psychology , the term forgetting commonly refers in “to the absence of expression of previously properly acquired memory in situations that normally cause such expression . ” ( [6]; see also [12] ) . Similarly , in conditioning experiments , one speaks of forgetting , when the conditioned stimulus fails to evoke the conditioned response at some point after successful conditioning [8] , [13] . In the basic protocol for behavioral studies of memory in Drosophila [1] a group of flies is placed into a tube for conditioning . There the flies are exposed to a specific odor and the exposure is paired with a reinforcer ( sugar or electrical shock ) . Having experienced the pairing once or multiple times , the flies are removed from the conditioning tube . After a predefined delay time , the group is placed into the middle of a second , elongated tube for assessment . One side of the elongated tube is baited with the conditioned odor and , after a while , the fraction of flies is determined which exhibit the conditioned response by comparing the number of flies which are closer to the baited side of the tube with the number of flies closer to the un-baited side . The setup allows to measure memory performance ( c . f . Fig . 1 D ) , i . e . expression of the conditioned response , as function of the delay time and of the conditioning protocol ( e . g . magnitude of reinforcement , number of pairings ) . To check for bias in the setup , one typically in addition uses a second odor as a control which was not paired with a reinforcer . That Drosophila has a dedicated mechanism to control forgetting was convincingly demonstrated by Shuai et al . [8] and Berry et al . [2] . Inhibition of the small G-protein Rac leads to slower decay of memory , extending it from a few hours to more than one day [8] . Conversely , elevated Rac activity leads to faster forgetting [8] . Similar results were achieved by modulation of a small subset of Dopamine neurons [2] . Stimulating these neurons leads to faster forgetting after aversive and appetitive conditioning , while silencing these neurons leads to slower forgetting [2] . Given the importance of decision making , it appears unlikely that forgetting in Drosophila is a behavioral trait which is maladaptive in an ecological sense . Hence we investigated what generic model of the environment would justify the observed forgetting and in particular the asymmetry between aversive and appetitive conditioning . For this we mathematically determined optimal decision making strategies in environments with different associations between stimulus and reinforcement .
For our model we assumed a simplified scenario where the conditioning pertains directly to the appetitive reaction . In particular , depending on the state of the environment , approaching the odor can lead to reward ( ) or punishment ( ) but it can also result in no reinforcement ( ) ( Fig . 1 ) . Fleeing the odor , i . e the aversive reaction , never leads to reinforcement ( ) . An agent ( fruit fly ) , whose goal is to maximize reinforcement , chooses between the appetitive and aversive reaction depending on past experience . To model the non-deterministic behavior observed in the experiments we assume that the two available behavioral options involve different costs of responding . These costs of responding , however , fluctuate from trial to trial causing no bias on average . For instance , a fly which happens to find itself to the right of the group initially could well have a smaller cost of responding for staying on this side of the assessment tube on this trial . More generally , the stochastic costs of responding can be seen as incorporating all other factors that also influence the behavior but do not depend on the past experiences that involve the conditioned stimulus . The total reward received by the agent is the external reinforcement ( ) minus the cost of responding . Our agent takes this into account in decision making , and so the costs of responding result in trial to trial fluctuation in the behavior . Whether the appetitive reaction results in depends on the state of the environment . This state changes slowly over time ( according to a Markov chain , see Methods and Fig . 1A ) . So when the appetitive reaction results in on one trial , the same outcome is likely on an immediately subsequent trial , but as time goes by the odds increase that the appetitive reaction results in or even punishment . If the agent knew the environmental state , the best policy would be simple: choose the appetitive ( aversive ) reaction if the environmental state is rewarding ( punishing ) . Typically however , the agent does not know the actual environmental state but , at best , maintains a belief about it ( see Fig . 2A and Methods ) . In our model , the belief consists of the probabilities , and to receive rewarding , neutral or punishing reinforcement , respectively , after selecting the appetitive reaction . Geometrically , the belief can be represented as a position in a 2-dimensional belief space that stepwise changes after the appetitive reaction and thus gaining new information about the current environmental state and otherwise drifts towards an equilibrium ( forgetting ) , see Fig . 2B ( note that , since the three probabilities sum to one , the probability of the neutral state can be computed given the probabilities of the rewarding and punishing state , i . e . ) . If e . g . a fly gets punished , the probability to be punished again on the next trial is high ( initial point of red trajectory in Fig . 2B ) . If subsequently the fly chooses the aversive reaction , the belief will drift towards a stationary value ( end point of red trajectory in Fig . 2B ) . We assume that the agent has implicit knowledge , e . g . gathered by experience or through genetic encoding , about the transition rates of the environmental state . Based on belief values and costs of responding one may define different policies . A greedy policy selects the appetitive reaction if the agent believes that reward is more probable than punishment and costs of responding are equal for both actions , i . e . ( Fig . 2C top , middle ) . If costs for one reaction are larger than for the other , the region in the belief space favoring this higher-cost reaction becomes smaller ( Fig . 2C top , left and right ) . Immediately after conditioning , an agent has a strong belief that the environment is still in the same state as during conditioning . Thus , if the greedy policy determines action selection , an agent most likely chooses the conditioned response . As the belief drifts towards the stationary point , the stochastic costs of responding gain more influence on the decision making and thus an agent is more likely to have already forgotten the conditioning , i . e . the agent is more likely to choose the opposite of the conditioned response . We call this policy “greedy” , because it maximizes reward if only one choice is made but it is not necessarily optimal with respect to gaining future rewards . Technically , the greedy policy is equivalent to the optimal future discounted policy with discount factor , i . e . the policy that neglects future rewards . In order to conveniently analyze the forgetting behavior under the greedy policy for different choices of the environmental parameters and ( Fig . 1A ) , we use a re-parametrization with the “probability of the neutral state” and the “average reward” , where denotes the stationary state probability of state ( see Methods for the relationship between and ) . Changing the probability of the neutral state has almost no effect on the forgetting curve ( Fig . 3A , solid vs . dashed line ) . Increasing the average reward has the consequence that in the stationary state more agents select the appetitive reaction than the aversive reaction ( Fig . 3A , solid vs . dotted line ) . The speed of forgetting a conditioned state ( p , n or r ) is determined by the rate of transitioning away from this state . Fig . 3A ( solid vs . dash-dotted line ) shows the effect of changing the rate , whose inverse is equal to the average number of timesteps the environment spends in the rewarding state: forgetting is faster for a larger rate . The variance of the costs of responding determines the impact of the costs of responding on decision making . For large variance the forgetting curve is closer to 0 . 5 than for small variance , since for large variance it is more likely that the costs of responding have a strong impact on decision making ( Fig . 3B ) . While the difference in forgetting speed after appetitive and aversive forgetting could be a consequence of different transition rates and , such a difference also arises if these rates are equal but the agent uses a provident policy , i . e . a policy that also takes into account future rewards . In the long run the provident policy is superior to the greedy policy ( Fig . 4B ) . We therefore determined numerically a policy which approximately maximizes the reward rate , i . e . the total reward accumulated over a long period divided by the length of this period ( see Methods ) . The resulting policy is such that there are beliefs for which the appetitive reaction is chosen , even when the probability of punishment is larger than the probability of reward , i . e . , and the costs of responding are equal for both actions ( Fig . 2C bottom , middle ) . The reason for this becomes clearer when we look at what economists call the opportunity cost , i . e . the additional gain that has not been harvested because of missing to choose the ( often by hindsight ) better option [14] . For the appetitive reaction , the agent's opportunity cost is given by the potentially lower cost for the aversive reaction . But for the aversive reaction , the agent's opportunity cost is not only the potentially lower cost for the appetitive reaction but also the lack of further information about the actual environmental state . This information is required for best exploitation in future trials . Assume , for instance , that at some point in time the agent believes that punishment is slightly more probable than reward and therefore sticks to the aversive reaction . Now , if the actual environmental state would be rewarding , the agent would not only miss the current reward but also misses subsequent rewards that could potentially be harvested while the state is still rewarding . When taking this opportunity cost into account , the agent will choose the appetitive reaction despite the belief state slightly favoring the aversive reaction . For an external observer this optimal choice behavior appears as a faster forgetting of the aversive memory . In short , the asymmetry in forgetting after aversive and appetitive conditioning ( Fig . 4 ) arises because choosing the appetitive reaction is always informative about the current environmental state whereas choosing the aversive reaction is not . The probabilistic calculations needed to derive the optimal provident behavior can be quite involved . We do not suggest that there is a neuronal circuitry in Drosophila which actually does these calculations . Yet it is interesting to note that a much simpler mechanistic decision making model already results in close to optimal behavior ( Fig . 4B ) . This simple model allows an interpretation of the variables as synaptic strengths from odor sensitive neurons to decision neurons ( Fig . 4C ) . In the absence of odor and behavioral feedback the synaptic strengths decay with different time scales towards a stationary level: decay is faster for synapses targeting the “avoid” neurons than for the “approach” neurons . One could speculate that the speed of this decay is governed by e . g . the concentration of Rac [8] or dopamine [2] . So far we have assumed that the transition rates between the environmental states are fixed . This is not an assumption Drosophila seems to make and in fact , would be an unrealistic model of the environment . The experiments by Tully et al . [9] show that forgetting depends not only on the number of conditioning trials but also on their frequency . In particular , forgetting is slower when the same number of learning trials is spaced out over a longer period of time . Spaced training is more informative about the environment being in a slowly changing mode than the temporally compressed massed training . Furthermore , reversal training during which in fast succession an odor is aversively , neutral and again aversively conditioned [8] results in faster forgetting and is informative about a fast changing environment . So the observed behavior provides rather direct evidence that adaptation in Drosophila does indeed take non-stationarity into account . To include adaptation as a response to changing transition rates , we extended our model by a slowly varying meta variable which can either be in state “fast change” or “slow change” ( Fig . 5A ) . The dynamics of the meta variable is governed by a Markov process with small transition rates . In state “fast change” , the environmental reward state changes more rapidly than in state “slow change” . In this setting , an optimal agent maintains a belief about both the environmental reward state and the “hidden” state that sets the time scale of the changes in . Spaced training increases the belief that the environment is in a slowly changing mode , whereas reversal learning leads to a strong belief about the environment being in the fast changing mode . The resulting greedy-optimal behavior is in qualitative agreement with the known behavior after spaced , massed and reversal learning ( Fig . 5B ) as observed for flies [8] , [9] , honey bees [15] , pigeons [13] , and humans [16] .
We demonstrated that forgetting appears when an agent , subject to costs of responding , acts optimally in an environment with non-stationary stimulus-reinforcement associations . Based on reward maximization in a non-stationary environment , which is a reasonable objective not only for the fruit fly but for other species as well , our normative theory of forgetting includes an asymmetry in forgetting speed after aversive and appetitive conditioning and an adaptation of forgetting speed after spaced , massed and reversal learning . The asymmetry is the result of an economically optimal provident policy , which forages not only for immediate reward but also for information required for future exploitation . The adaptation of forgetting rate after spaced , massed and reversal learning is a consequence of the agents estimation of the current rate of environmental changes . That costs of responding influence the action selection is an assumption which is in agreement with test-retest experiments [9] , [11] , [17] . In these classical conditioning experiment the flies are grouped according to whether they choose the conditioned response or not . Both groups are immediately retested to examine whether the flies stick to their decision . The outcome is: they do not . An equal fraction of flies chooses the conditioned response in both retest groups and this fraction is the same as in the first test containing all flies . This suggests that all flies maintain traces of the conditioning but that also other factors influence the choice in a stochastic way . Similarly , in our model the belief is a sufficient statistic of the past experiences that involve the conditioned stimulus and the stochastic costs of responding account for other factors that influence the choice . A key assumption in our normative explanation of the differential forgetting in Drosophila is that the relationship between conditioned stimulus and reinforcement is non-stationary . Now , if this relationship were completely stationary , it would not need to be learned by the phenotype because it would already have been learned by the genotype , i . e . in this case the stimulus would be an unconditioned stimulus . Hence , from an evolutionary perspective , our assumption is close to being a truism . Nevertheless , many biological models of reinforcement learning have , for the sake of simplicity , assumed a stationary stimulus-reinforcement relationship [18] , [19] . Experiments and models with non-stationary stimulus-reinforcement associations have suggested , similar to our findings , that in a more volatile environment the learning should be faster [20]–[24] . However , faster learning does not unconditionally imply faster forgetting . The asymmetry in forgetting speed after appetitive and aversive conditioning additionally requires an evaluation of the behavioral relevance of a specific memory content . Since the aversive reaction is not informative about the current state of association , aversive conditioning should be forgotten faster than appetitive conditioning . Finding the optimal policy in an environment with a non-stationary stimulus-reinforcement relationship , as considered here , is computationally involving . As we have shown , however , approximately optimal decision making is still possible with a simplified neuronal model using experience induced synaptic updates . This model incorporates forgetting in the decay time constant of the synaptic strengths . As the parameters describing the changing environment are assumed to be constant across generations , the neuronal architecture and the forgetting rates can be considered to be genetically encoded . Since the work of Ebbinghaus [25] on the forgetting rate of non-sense syllables and the observation of Jenkins and Dallenbach [26] that sleep between learning and recalling reduces forgetting , cognitive psychologists debate about the role of natural decay and interference in explaining forgetting [5] . While interference based explanations are favored by many [5] , [12] , Hardt et al . [6] recently advocated active processes behind decay-driven forgetting . They suggested a memory system that engages in promiscuous encoding and uses a flexible mechanism to remove irrelevant information later , mostly during sleep phases . In their view , different forgetting rates are a sign of such a flexible removal mechanism . But why do biological organisms need to actively remove irrelevant memories at all ? Popular answers so far implicitly assumed that forgetting is ultimately the result of some limitation of the memory system , for instance , limited storage capacity , a limit on the acceptable read-out time for the memory or a decay of the biological substrate similar to unused muscles atrophy [6] , [27] . In our model , however , forgetting does not result from a memory limitation , but emerges as an adaptive feature of the memory system to optimally cope with a changing environment while accounting for the relevance of different memory contents .
In time bin an odor can be associated with one of three environmental states: ( reward ) , ( neutral ) , ( punishment ) . The time-discrete dynamics of the environmental state is given by a Markov Chain with state space and transition probabilities and , where for . For simplicity we did not include direct transitions between the rewarding and punishing state , i . e . . Including them would also allow for a behavior where the preference switches from the conditioned response to the opposite of the conditioned response before reaching the stationary state . The stationary distribution of this Markov chain , satisfying the self-consistency equation , is given by and , where . In each time bin the agent has two behavioral options: approach the odor ( ) or avoid the odor ( ) . If the agent avoids , a neutral reinforcement signal is always returned . If the agent approaches , the external reinforcement signal depends on the environmental state: there will always be a positive signal if , always a negative signal if and if the odor is associated with the neutral state ( ) , the agent will stochastically get a neutral signal with probability 0 . 99 , while with probability 0 . 005 the agent will get a positive or a negative reinforcement signal . Positive and negative reinforcement signals during the neutral state are included to model situations , where reward or punishment depends on odor unrelated factors . For further use we summarize the information in this paragraph in the probabilities , with non-zero entries , , and , , . The agent maintains a belief over the current environmental state given past reinforcement and actions . The belief state is updated by Bayesian filtering ( 1 ) with normalization . We use the abbreviation to denote the update of the belief given action and reinforcement signal . We modeled costs of responding with exponentially distributed and uncorrelated random variables and with parameter , i . e . the probability density function of is given by if and otherwise . This distribution has mean and standard deviation . We assumed , that the agent receives an effective reward , which is a sum of the external reinforcement signal and the momentary cost of responding for the action chosen . During decision making , the agent knows the costs of responding for both actions but only has an expectation of the external reinforcement signal . If the goal is to maximize immediate reward , the agent's policy depends on the expected return in the next step , which for action ap can be simplified to and for action av is always zero , i . e . . Including costs of responding , the policy that maximizes immediate reward selects the action for which is maximal . A canonical choice of the objective to be maximized by a provident policy is the reward rate , i . e . with expected reward in time bin when acting according to policy . We approximately determined the policy which maximizes the reward rate by two methods: dynamic programming and linear programming on a quantized space . Dynamic Programming allows to find a policy that maximizes the future discounted valueswith discount factor and expected reward in time bin after starting in belief state and acting according to policy . For finite state spaces and sufficiently close to 1 a policy that maximizes future discounted reward also maximizes the reward rate [28] . Without costs of responding one could directly apply the Incremental Pruning algorithm [29] to find a policy that maximizes the future discounted values . Here we derive dynamical programming in the presence of costs of responding . Dynamic programming proceeds by iteratively constructing optimal finite-horizon values for some operator . Assume that we have the horizon- policy that maximizes the future discounted values of an episode of length . The horizon- policy consists of instructions for each step in the episode , where tells which action to take at the -th step before the end of the episode , given belief and costs of responding and . To construct the horizon- policy we need to extend the horizon- policy by the instruction for the first step , i . e . the -th step before the end of the episode . Without considering the costs of responding in the first step , the expected future discounted values for choosing action are given by ( 2 ) where and the value function is given by ( we will use the indicator function , given by if is true and otherwise ) : With the change of variables , the resulting probability density function ( Laplace probability density ) , and the abbreviations , and , we get ( 3 ) In the same manner we find the value function , which depends through on ( see Eq . 2 ) ( 4 ) where now . Due to the discount factor this recursion will eventually converge . In practice we will stop after iterations and define the policy , which approximates the future discounted policy . Note that in contrast to the finite horizon policies the policy is stationary: in a sequential setting there is no end of an episode on which the policy may depend . The number of terms in a naive implementation of grows exponentially with . Without costs of responding the exponential growth can sometimes be prohibited by Incremental Pruning [29] . With costs of responding we are not aware of a way to prevent exponential growth . In Fig . 4 we approximated the stationary policy by taking the policy after 5 iteration with discount factor , i . e . . Since it is not clear whether for this choice of discount factor and number of iterations the resulting policy is a good approximation of the reward rate maximizing policy , we compared the result of dynamic programming with the policy obtained by linear programming on a quantized belief space . For finite state and action space Markov Decision Processes linear programming can be used to find a policy that maximizes the reward rate [30] , [31] . In our case , the policies act on the continuous space of belief states and cost of responding differences . Analogous to the finite state space problem , the optimization problem could be formulated as: find functions that implicitly define the policy [30] and satisfywhere denotes the expected reward for action , belief state and costs of responding differences and denotes the probability density to transition from and to and given action . A straightforward approach is to quantize the belief space and space of cost of responding differences , replace the integrals by sums and find through linear programming an approximation to the reward rate maximizing policy . We quantized the two dimensional belief simplex on a square lattice with different lattice spacings . Values that did not fall on lattice points where stochastically assigned to neighboring lattice points . The space of real valued cost of responding differences was quantized by segmenting the real line into adjacent intervals with equal mass of the probability density function . For each interval the average costs of responding for each action where computed . Using increasingly finer quantization we estimated the total reward to be between 600 and 655 for trials of time bins , which is in agreement with the estimate obtained with dynamic programming ( Fig . 4B provident ) . In Fig . 4 we demonstrate that also an agent with two uncoupled low-pass filters can show near to optimal behavior . The agent's decision to approach ( ) or avoid ( ) the odor depends on whether , where ( ) are variables interpretable as synaptic strengths and where represents stochastic input due to costs of responding . The values of decay with different time-constants , in the case of no feedback , because the agent either stays away or no odor is present . If the agent approaches the odor and experiences reward , is set to a maximal value , while is set to zero; for odor plus punishment is set to a maximal value , while is set to zero . Formally , with the subscript standing for either ap or av , we get ( 5 ) The parameter controls the speed of forgetting , sets a baseline value and sets a maximum value . In Fig . 4 the parameter values where fit to the curves in sub-figure A ( approx provident: , , , , ) and to the curves in sub-figure B ( approx greedy: , , ) . To study the behavior of an agent that additionally has to estimate the rate of change we extended the basic model of the environment with a meta variable that controls the rate of change of the environmental state . In time bin the meta variable can be in one of two states: ( fast ) or ( slow ) . The dynamics of the meta variable is described by a Markov Chain with transition probabilities and . If the meta variable is in the slow ( fast ) state the transition parameters of the environmental state are , and , . In the extended model the state space is given by the product space and the transition parameters are given by . The agent maintains a belief about both the environmental state and the state of transition speed . In spaced training , the agent was aversively conditioned six times with intermittent waiting periods of 9 time bins . In massed training , the agent was aversively conditioned in 6 subsequent time bins . In reversal learning , the agent was exposed to the punishing , neutral and punishing environmental state in subsequent time bins . Forgetting curves are shown for the computationally less involving greedy policy . In order to compare massed with spaced training we choose a finer time discretization in the extended model , i . e . 10 time bins in the extended model correspond to 1 time bin in the basic model . In figure 5B the result is plotted in units of the basic model . | The dominant perception of forgetting in science and society is that it is a nuisance in achieving better memory performance . However , recent experiments in the fruit fly show that the forgetting rate is biochemically adapted to the environment , raising doubts that slower forgetting per se is a desirable feature . Here we show that , in fact , optimal behavior in a stochastically changing environment requires a forgetting rate that is adapted to the time constant of the changes . The fruit fly behavior is compatible with the classical optimality criterion of choosing actions that maximize future rewards . A consequence of future reward maximization is that negative experiences that lead to timid behavior should be quickly forgotten in order to not miss rewarding opportunities . In economics this is called “minimization of opportunity costs” , and the fruit fly seems to care about it: punishment is forgotten faster than reward . Forgetting as a trait of optimality can further explain the different memory performances for multiple training sessions with varying inter-session intervals , as observed in a wide range of species from flies to humans . These aspects suggest to view forgetting as a dimension of adaptive behavior that is tuned to the environment to maximize subjective benefits . | [
"Abstract",
"Introduction",
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"learning",
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] | 2014 | A Normative Theory of Forgetting: Lessons from the Fruit Fly |
A fundamental question in biology is how sharp boundaries of gene expression form precisely in spite of biological variation/noise . Numerous mechanisms position gene expression domains across fields of cells ( e . g . morphogens ) , but how these domains are refined remains unclear . In some cases , domain boundaries sharpen through differential adhesion-mediated cell sorting . However , boundaries can also sharpen through cellular plasticity , with cell fate changes driven by up- or down-regulation of gene expression . In this context , we have argued that noise in gene expression can help cells transition to the correct fate . Here we investigate the efficacy of cell sorting , gene expression plasticity , and their combination in boundary sharpening using multi-scale , stochastic models . We focus on the formation of hindbrain segments ( rhombomeres ) in the developing zebrafish as an example , but the mechanisms investigated apply broadly to many tissues . Our results indicate that neither sorting nor plasticity is sufficient on its own to sharpen transition regions between different rhombomeres . Rather the two have complementary strengths and weaknesses , which synergize when combined to sharpen gene expression boundaries .
The specification of segmental domains of gene expression is a fundamental aspect of animal development and a critical first step in bilaterian body plan organization [1 , 2] . Within these domains , differential gene expression determines the functional properties of cells . For example , alternating domains of pair rule gene ( e . g . fushi tarazu , even skipped [eve] ) expression in the early Drosophila embryo organize the segmented body plan [3 , 4] . In vertebrates , segmentally-organized somites form muscle segments and the vertebrae of the backbone [5–7] . In both cases , cells acquire their segmental identities along the anterior-posterior ( A-P ) axis through the functions of Hox genes . Further anteriorly , Hox paralogue groups 1–5 specify segments of the hindbrain ( rhombomeres ) [8–10] . How these segmented domains form has been the subject of intense investigation . Morphogen gradients control the formation of segmental domains of gene expression , specifying distinct domains in a concentration-dependent manner . In the Drosophila embryo , maternal gradients of bicoid and caudal promote expression of different gap genes [11–15] . In vertebrates , secreted signaling molecules such as Fibroblast growth factor 8 ( FGF ) , Wnt3a , and retinoic acid ( RA ) form gradients that influence somite formation [16–20] . Similarly , in the developing hindbrain , a network of FGF , Wnt and RA induce differential expression of Hox genes and Krox20 in adjacent rhombomeres [20–28] . However , morphogens are unlikely to be the only mechanism controlling segmentation in each of these cases . In particular , cell rearrangements are known to play a role . Steinberg’s differential adhesion ( DA ) hypothesis ( DAH ) predicts that cell sorting can generate distinct cell aggregates [29] . This mechanism works particularly well if cells of adjacent segments differ in the number or type of adhesion proteins they express , such as E-cadherin [30] . Similarly , contact-mediated repulsion can promote sorting . Repulsion between cells expressing Eph and Ephrin receptors is required for proper boundary formation between segments in both somites [31 , 32] and rhombomeres [33–36] . When these two surface receptors come into contact , they elicit bi-directional signaling that causes cells to repel each other [37] . Wolpert’s classic French flag model posits that morphogens form well-defined gradients and that cells can precisely read the level of the signal at their location [38] . However , like any biochemical signal , morphogens are inherently noisy and the process of transducing the signal is stochastic . Noise can lead to mis-specification of cells , which will in turn produce a rough transition region between segments where multiple cell types co-exist in a salt-and-pepper arrangement . Actin cables or other physical barriers form at the interface between tissues in systems such as developing germ layers in early embryos [39] , the Drosophila embryonic epidermis [40] , or the zebrafish hindbrain [41] ( reviewed in [42] ) . Thus , it is paramount that transition regions sharpen prior to the formation of these structures . The question thus becomes , how can a morphogen-organized system cope with stochasticity and generate refined , segmented zones of different cell types . Cells may physically sort but the effectiveness of sorting is unclear , particularly in cases involving relatively small numbers of cells . For example , in the embryonic zebrafish hindbrain , rhombomeres are comprised of tens to a few hundred cells , depending on the stage [33 , 34] . Very few cells occupy the local region near the interface between segments . The DAH assumes that tissues are liquid-like cell aggregates and that a sorted state is achieved as the system minimizes a tension/adhesion free energy . This however is primarily valid at macroscopic scales with large cell numbers [29 , 30] . Furthermore , tissues do not necessarily behave as immiscible fluids [43] . Thus it is important to determine the effectiveness of cell sorting at smaller scales where the macroscopic assumptions of the DAH are not necessarily valid . Previous work in the zebrafish hindbrain suggests that while cell sorting is important [33 , 34 , 36 , 40 , 44] , cellular plasticity ( e . g . transcription of target genes–hoxb1a and krox20 ) in response to morphogens ( e . g . RA , Fgf and Wnt ) also promotes sharpening of segment boundaries [45 , 46] . Here , we use computational modeling to investigate the influences of these two different mechanisms . Since it involves both mechanical ( e . g . adhesion , repulsion ) and biochemical ( e . g . gene transcription ) processes , we develop a multi-scale model that accounts for both . Using this framework , we investigate each mechanism individually as well as in combination with others to determine effectiveness and potential interactions . Agent-based models treat each cell as a discrete entity with dynamically evolving properties [47 , 48] , while the Potts/Glazier-Graner-Hogeweg ( GGH ) model [49 , 50] uses a lattice-based approach to account for dynamically evolving cell shapes [51] and cell-cell interactions . We use a sub-cellular element method ( SCEM ) , which is similar to GGH , but allows more explicit descriptions of forces arising from cell-cell interactions [52 , 53] . Each cell is treated as a collection of elements that interact according to user-defined forces . This has been used successfully to study the dynamics of epithelia [52 , 53] , the influence of Notch signaling on cell division [54] , and homeostatic regulation in intestinal crypts [55] . We use SCEM to build a multi-scale , stochastic model of rhombomere boundary sharpening and investigate the effectiveness of cell sorting and plasticity ( based on a stochastic description of hoxb1a and krox20 in cells ) . We show that adhesion , repulsion , and plasticity all have a role in this process , none of which sharpens boundaries efficiently on its own . Instead , each has benefits and weaknesses , which are complementary and appear to work synergistically to accomplish this goal .
How do distinct gene expression domains form in response to noisy positional information ( Fig 1 ) ? To address this question , we developed a set of hybrid computational models to investigate the effectiveness of different mechanisms at refining gene expression boundaries . Three possible mechanisms were considered: 1 ) differential adhesion , 2 ) cellular repulsion , and 3 ) cellular plasticity in gene expression . We constructed three models–Sorting ( S ) , Plasticity ( P ) , and Sorting + Plasticity ( SP ) . Model S includes only mechanical interactions such as cell-cell adhesion and repulsion . Model P assumes cells are stationary but allows for plasticity-mediated changes in cell fate . Model SP combines both . We used a discrete stochastic model formulation to account for low cell numbers . An SCEM framework endows each cell with a size , stiffness , and deformability ( Fig A in S1 Text ) . The foundations of this method have been explained previously [52 , 56] . To describe the influence of morphogens and gene regulation on cell identity , we constructed a spatial stochastic model of cell fate regulation . For each model , we utilized similar computational domains and initial conditions to aid direct comparison of results produced by each set of analyses . Motivated by rhombomere formation in the zebrafish , we consider a simplified computational domain consisting of a rectangular array of 6 cells in height with varying widths ( Fig 1 ) . We note that while this is a simplification , this dimension is on a similar scale in the horizontal direction; in the vertical direction , increasing cell numbers is computationally intensive yet does not give further valuable results , and thus we considered this simplified scenario . In most of our simulation , we compute our simulations in a time window corresponding to 10 . 7 to 12 . 7 hours post fertilization ( hpf ) , during which the zebrafish rombomere 3/4 ( r3/4 ) and 4/5 ( r4/5 ) boundaries are sharpened [45] . The models discussed herein are necessarily complex . We thus focus on their aspects that are most relevant to this discussion . Specifically , we will consider how effective each is at sharpening gene expression boundaries , and where there are deficiencies , we will assess the source of that deficiency . Where possible , we will consider the sensitivity of results to model details . However , we note that given the complexity of these models , an exhaustive sensitivity analysis is not possible . We thus leave a detailed discussion of the sub-cellular element model that is the basis of Model S and the gene expression model that is the bases of Model P for references provided herein . Instead , we focus on the qualitative properties of these mechanisms and how they operate individually and in combination . Model S was simulated under a range of conditions including varied levels of adhesion and repulsion between cells . With a rectangular array of cells , we considered multiple initial conditions in which we varied initial transition width ( ITW = 2 , 3 , or 4-cell wide transition regions ) . We manually populated the transition region with a random array of the two cell types with precisely a half-half mixture , and performed an ensemble of simulations for each condition . By comparing either the number of boundary formed and boundary nearly formed simulations under different conditions , we found ( Table 1 ) that mechanical cell sorting was effective when the initial transition region was narrow , especially in cases with stronger cell adhesion strength ( see Morse potentials in Fig B in S1 Text ) . A substantial fraction of these simulations ended with boundary nearly formed rather than formed . However , most outlying cells were at the top/bottom edges of the boundary where they have fewer neighbors ( due to the structure of the domain ) and are subjected to weaker sorting influences . For wider initial transition regions ( ITW = 3 ) , sharpening was reduced and strongly dependent on the strength of sorting . For ITW = 4 and wider , mechanical cell sorting was ineffective at boundary sharpening , no matter how strong the sorting forces . Since both differential adhesion and repulsive interactions between cells can lead to sorting independently [29] , we next assessed the relative influence of each ( Fig 2 ) . Simulations were performed starting with identical initial conditions and adhesion or repulsion was either attenuated or strengthened . Inclusion of both adhesion and repulsion led to effective boundary sharpening ( Fig 2A , bottom; S1 Movie ) . Removal of repulsion disrupted sorting , leading to a transition region that not only did not sharpen , but in many cases actually expanded ( Fig 2A , top; S2 Movie ) . In contrast , removal of adhesion led to contiguous boundaries between regions of cells , though the resulting boundaries were far from straight ( Fig 2A , middle; S3 Movie ) . In cases where boundary sharpening failed , individual cells ( Fig 2B ) or cell groups ( Fig 2C , top ) were isolated from their preferred zone , mainly at the top or bottom edges of boundaries , as discussed previously . Since the only sorting interactions in this model were physical cell-cell interactions , once cells became isolated they encountered an isotropic environment with nothing biasing their direction of motion . Increasing the strength of cell-cell interactions reduced the frequency of these events ( Table 1; Fig 2C , bottom ) . However , since there is a significant random component to the isolation of these cells , optimizing the properties of cell-cell interactions only marginally improved the outcome . Additionally , the likelihood of cells becoming isolated strongly depends on initial transition region width ( ITW ) and its noisiness . This is the primary reason that Model S became increasingly ineffective as the ITW increased ( Fig 2D , S4 Movie ) . These results show that both adhesion and repulsion are required for proper sorting , and that these mechanical processes are only effective in boundary sharpening if the ITW is relatively narrow . With model P we asked how effective plasticity alone is at sharpening boundaries between cellular zones ( Fig 3 ) . If morphogen signals are noise free and gene regulation is deterministic , morphogens will always form precisely placed , sharp boundaries . In reality , however , this regulatory system is stochastic at every level . We have hypothesized that “noise-induced switching” helps sharpen rhombomeres in the zebrafish hindbrain [41] . This is based on the idea that while morphogen stochasticity introduces disorder near the boundary between cellular zones ( i . e . a transition region ) , stochasticity in gene regulatory processes can also help cells to transition to the correct gene expression state [41] . To test this in our model , we omitted cellular motion so that sharpening relied solely on this mechanism . In this model , initial cell fates were determined by a single morphogen , which was assumed to direct fate specification by influencing transcription levels of A and B ( which for r3-5 of the hindbrain was modeled as hoxb1a and krox20 ) . Upon application of the morphogen M , two expression domains formed with an intervening transition region ( Fig 3B ) . Ensemble simulation results confirmed that the transition region partially sharpened after initial cell specification . When relatively little stochasticity ( noise ) in gene regulation was included in the simulations ( gene expression noise strength ηA = ηB = 0 . 03 , see equations S2 . 4 , S2 . 5 in S1 Text ) , the transition region narrowed but did not sharpen ( Fig 3C ) . Too much noise ( ηA = ηB = 0 . 09 ) overwhelmed the system ( Fig 3E ) . When moderate noise ( ηA = ηB = 0 . 06 ) was included , however , sharpening was more effective ( Fig 3D; S5 Movie ) . Fig 3A shows the average ( across simulations ) locations of the anterior ( red ) and posterior ( blue ) ends of transition regions as a function of time under different conditions . To ensure cell distributions have reached a steady state , we double the simulation time . Inspection of each of 16 replicate simulations shows that after T = 12 . 03 hpf cell fate transition rates drop and the system achieves a steady distribution of A and B cells ( Fig M in S1 Text ) . These results confirmed that , for the medium noise case , the width of the transition region was reduced ( but not completely ) to about 2 cell diameters in width . This indicates that noise-driven sharpening can narrow an initially wide transition region , but is less effective at sharpening it completely . It is also instructive to consider how this refinement occurs . Fig 3A shows that with moderate or weak stochasticity , sharpening in Model P occurred with the posterior edge of the transition region steadily moving toward a fixed anterior edge over time , reducing the region’s width . This results from an asymmetry in the underlying gene regulatory network that generates noise driven red → blue transitions ( with the reverse much more rare ) . When noise levels are even higher , this red → blue transition was so prominent throughout the domain , that the posterior edge converged to the anterior edge and blue zone overtook the red zone over time ( Fig 3E ) . This contrasted with mechanical sharpening ( Model S ) , where red cells tended to move anteriorly and blue posteriorly , leaving a border mostly in the middle of the original transition region ( at least when sharpening occurs ) –though it also depended on the numbers of red and blue cells . This suggests that the cell switching from type A to B that occurs in Model P , but not S , leads to a fundamental difference in the directionality of boundary sharpening . We make a final note about the role of stochasticity in promoting sharpening . The idea underlying the theory of noise-induced plasticity is that cell states ( A and B ) are represented by stable wells in an energy landscape ( see Fig 3B for a schematic ) . Depending on the cells local environment ( determined by position in our case ) , the relative depth ( e . g . stability ) of those wells may be different . In this context , for plasticity to aid sharpening , the “correct” state should be a deeper well and the incorrect a shallower well . In this way , an incorrect → correct ( e . g . shallow to deep , see Fig 3B ) transition would be more likely than the reverse . If the two wells are of roughly equal stability , both transitions would occur with equal likelihood , which would provide no benefit . Thus a sufficient level of asymmetry is required for noise to aid sharpening . Of course , if both wells were either too deep or too shallow relative to noise strength , stochasticity would either have no effect or overwhelm the system ( illustrated in Fig 3E ) . Thus while stochasticity can provide a benefit , the system must be in an appropriate operating regime to take advantage of it . Our simulations with Models S and P show that mechanical sorting is effective at sharpening narrow transition regions while plasticity effectively narrows wider transition regions . How effective are these two mechanisms when combined ? We hypothesized that plasticity narrows a transition region sufficiently to allow subsequent cell movements to complete sharpening . To test this , we considered the model SP , which essentially adds local cell-cell interactions that drive sorting to model P ( Table 2; Figs 4 and 5D ) . Fig 4C–4F shows temporal snapshots of sorting , where each simulation begins from the same initial state , which is generated by the morphogen regulatory system ( Fig 4C ) . These results provide a direct comparison of sorting resulting from plasticity alone ( Model P , Fig 4F ) , mechanical sorting alone ( Model S , Fig 4E ) , and the two combined ( Model SP , Fig 4D , S6 Movie ) . In Model S , after the initial state is specified by the morphogen , the gene regulation system is turned off , and all cells are unable to alter their gene expression levels . Thus the morphogen system serves only to generate the initial condition for Model S . Results indicate that boundaries sharpen more effectively with SP than either S or P individually , based on tracking the average ( over 16 simulations ) position of the transition region borders and the transition region width ( Fig 4A and 4B ) . With SP all simulations led to formed or nearly formed boundaries and a larger fraction formed completely ( Table 2 ) . Tracking the SI changes over time ( Fig 5D ) showing that model SP is the best among the three ( end SI = 0 . 26 ) , while model S is the worst that only reduces SI a little ( end SI = 0 . 95 ) , and model P sits in between ( end SI = 0 . 66 ) . The standard deviation of SI of the model S , P and SP is shown in Fig K in S1 Text . Tracking the boundaries of the transition region over time also revealed that rather than sharpening to either the center or one side of the transition region , the final boundaries were within the initial transition region but biased toward the anterior ( Fig 4A ) . This is consistent with a combination of the two mechanisms ultimately driving sharpening . Additionally , simulation results indicated that the final location of the boundary was precise when the sharpening was driven by cell sorting and/or plasticity ( see S1 Text section S7 for further details ) . We next sought to determine if the order of action or duration of these different sharpening mechanisms influence the outcome . To do so , we performed numerical simulations ( results in Table 2 ) where 1 ) plasticity was only active early , up to a pre-determined time point ( T = 11 . 37 or 11 . 7 hpf ) after which sorting became active , 2 ) the reverse , sorting was followed by plasticity , and 3 ) the two mechanisms occurred simultaneously and for the full duration of the simulations ( i . e . the SP model discussed previously ) . When sorting was active early and plasticity occurred later , outcomes ( Table 2 , “S followed by P” ) were better than with sorting alone and comparable to plasticity alone , but still ineffective . Sorting followed by plasticity ( “P followed by S’ ) on the other hand yields a substantial effect ( Fig L in S1 Text ) . This indicates that the early action of plasticity followed by later action of sorting improves outcomes over either mechanism alone . The combination of the two ( model SP ) acting in concert for the full sharpening window however yields yet further improvement ( Fig L in S1 Text ) . Combined , these results suggest that the two mechanisms , mechanical sorting and plasticity , can work synergistically with plasticity serving to narrow transition regions to a manageable width and sorting serving to finalize the sharpening process . The zebrafish hindbrain consists of 7 rhombomeres . While these segments utilize different signals and potentially different mechanisms to form and sharpen , the RA morphogen along with the Hoxb1/Krox20 regulatory system are vital to the formation of rhombomeres 3–5 ( r3-r5 ) ( Fig 1 ) . Up to this point , we have modeled sharpening between two domains , but we now consider how effectively cell movements and plasticity ( Models S , P , and SP ) are at forming and sharpening three cellular zones . To initialize the domain , the simulated RA morphogen generates a 20x6 domain of cells ( Fig 7 ) , which is similar in scale to the horizontal dimension of the r3-r5 zones at the onset of hindbrain specification in zebrafish [45] . We scaled the morphogen system such that the readout of the Hoxb1/Krox20 gene regulatory system in response to the RA gradient generates three zones of roughly the same size with transition regions in between . All three models ( S , P , and SP ) were simulated and the dynamics of the two transition regions were tracked over time . The combined model ( SP ) was highly effective at sharpening the r4/5 boundary ( Table 3 , Fig 7A and 7B , S8 Movie ) . When comparing the three models , models S or P individually were not as effective as SP , as was the case earlier in the 2-domain models . However , compared to 2-domain models , all three models ( S , P , and SP ) appeared to be more effective in the 3-domain scenario ( Tables 2 and 3 , Fig 7E ) . This results from the fact that the length scale of the RA morphogen was reduced to generate a sufficiently steep gradient to produce three cellular zones . Since the width of transition regions depends on the relationship between noise in the morphogen and steepness of the gradient , the transition widths in all 3-domain models were narrower than in the previous 2-domain simulations . The dynamics of the r3/4 boundary were however significantly different than r4/5 . At this boundary , plasticity was completely ineffective at sharpening in any simulations ( Table 3; Fig 7 ) . This reflects the fact that the interplay between stochasticity and the underlying gene regulatory network depends on morphogen levels . At the r3/4 boundary , morphogen levels are too low for plasticity to induce any state transitions . Models S and SP at this boundary performed nearly identically–especially when we compare the SI changes ( Fig 7F ) . Additionally , simulations of model S at each of these two boundaries performed nearly identically . These results suggest that either mechanical sorting is the only manner of sharpening at the r3/4 boundary or some alternative type of cellular plasticity ( e . g . other morphogens , other gene regulatory networks ) is required to sharpen this boundary , unlike r4/5 . As we discussed above , contact-based cell sorting only appears to be effective at forming a sharp boundary when transition regions are narrow . This could of course be the result of sub-optimal cell-cell interaction parameters . However modulating the strength of inter-cellular forces does not improve the situation much ( Fig J in S1 Text ) . Furthermore , simulations of r3-r5 formation , where two boundaries must form and sharpen , suggest that while sorting may be effective at one boundary , it is unlikely to be as effective at adjacent boundaries . Recent experimental studies suggest that long ranged cell sorting or chemotaxis may be very effective at forming a clear and sharp boundary [59 , 60] . To investigate the effect of inter-cellular force range on boundary formation , we perform a similar simulation study with the same parameter values as in the mild adhesion and repulsion combination , but increase the effective range of cell-cell interactions to about 3 cell diameters ( see S1 Text section S1 ) to mimic longer range chemotactic effects . With this addition , boundary formation becomes much more effective ( Fig J in S1 Text ) : starting with ITW = 3 , all 16 simulations ended up with a clear boundary formed , while with ITW = 4 , 13 out of 16 simulations formed a clear boundary , the other 3 failing as a result of isolated cells that are pushed to corners of the domain . The key observation from these and results above is that local , cell-cell contact based sorting forces appear to only be effective at sharpening domain boundaries if the initial imperfections are confined to a narrow band . Our prior results indicate that plasticity based effects could improve this process by narrowing initially broad transition regions . This is not however the only possibility . Results here indicate longer range chemotactic forces between cells could play a similar role . Alternatively , chemotaxis towards or away from an organizing center could provide the same benefits . While we cannot rule out any of these on the basis of the data available , we do note that the plasticity based mechanism discussed above would yield qualitatively different predictions than these chemotaxis based mechanisms . The essential failure of the simple , contact based sorting process is its locality . Each of these additions provides a separate means of providing more global information to the cells . In the case of chemotactic mechanisms , that global information would drive longer length scale motions of cells . With the plasticity based mechanism on the other hand , narrowing of transition zones would arise from fate transitions rather than longer length scale motions . Thus the qualitative movement patterns of cells could provide a means to delineate these mechanisms in future experimentation .
Embryonic segments are fundamental building blocks of the body plan of many animals . While intense research has been directed at elucidating how segmentation occurs [1–10] , it remains unclear how the borders between different segments sharpen . For example , in any morphogen signaling system that controls segmentation ( such as bicoid or RA ) , noise in both the morphogen itself and in the transduction of the morphogen signal may reduce precision in the ability of responding cells to form sharp boundaries between segmental domains , which could have long lasting effects on development . To date , multiple theories for boundary sharpening and maintenance have been proposed . Mechanical structures such as actin cables have been posited to generate tension that robustly separates segments [40 , 42] . These structures however could be a double-edged sword . While they might help maintain a boundary once it is established , the presence of noise could impair their development , potentially leading to a permanent inability to sharpen further . Thus , a relatively well-defined boundary should be present before the formation of these structures . The “Differential Adhesion” ( DA ) hypothesis [29] argues that mechanical cell-cell interactions promote segment formation and sharpening . This hypothesis is however most often discussed in the context of systems containing thousands of cells that can be thought of as an active fluid . It is thus unclear how effective DA may be when smaller numbers of cells are present , such as in the zebrafish hindbrain . Our modeling results suggest that local , contact based adhesion/repulsion mediated sorting is only effective at sharpening narrow ( approximately ≤3 cell diameters ) transition regions between segments . This is because once individual cells or cell groups become isolated from their respective aggregate , the local nature of contact-mediated cell-cell interactions cannot provide sufficient information to direct cells to the correct location . In this sense , an initially wide transition region is meta-stable state and can only resolve through random movements over time . Thus , when small numbers of cells are present , purely local , contact based sorting ( e . g . differential adhesion sorting ) appears to be insufficient to guarantee robust organization . There are a number of possible processes that could be layered on top of contact based sorting to improve sharpening . Motility based processes such as chemo-attraction or chemo-repulsion of like or different cells could provide additional , longer range information to coax cells toward the appropriate region of the domain [60] . Our results show that this is an effective mechanism . Chemotaxis toward or away from a pre-existing organizing center could serve a similar purpose ( as suggested in [59] ) . Here we investigate an alternative possibility , the “cellular plasticity” theory , which argues cells can change their fate by up- or down-regulating the expression of critical genes [45] . This is fundamentally distinct from the motility based processes discussed above in that long range taxis information is not required . Here we show that this mechanism has its strengths and weaknesses , but that when combined with local , cell contact based sorting , the two work together synergistically to promote sharpening . Plasticity-mediated sharpening can be explained from the standpoint of an energy landscape view ( Fig 3B ) of cell fate regulation [61 , 62] . This theory posits that cell fates can be thought of as , loosely speaking , minima ( or wells ) in an underlying energy landscape . Each valley in the landscape is associated with a particular fate and thus multiple wells indicate multi-potency . However , cells do not prefer all of these local energy minima equally ( i . e . they differ in stability ) . Different depths are associated with relative stability and the greatest minimum is typically associated with the “correct” fate . In this view , a cell in the wrong fate ( based on its position ) simply resides in a different , shallower energy well and stochasticity can drive the cell to the correct fate . The shallower the minimum , the more likely noise can lead to fate switching . Thus , cells at a significant distance from the presumptive boundary are quickly driven to the correct fate in our model by differences in morphogen concentration , narrowing the transition region . Once it is narrow , however , morphogen differences from cell to cell near the boundary become small and cells have a nearly equal preference for the two possible states ( i . e . the two fates are represented by wells that have roughly the same depth ) . This process of course requires the landscape to have the appropriate structure , which can vary with the state of the system ( represented by parameters ) . Our sensitivity results however suggest that the qualitative structure of the underlying landscape does not change dramatically as parameters are changed . For this reason , plasticity-mediated sharpening is effective at rapidly narrowing a wide transition region , but is very slow at completing the sharpening process and in many cases fails to do so . Our results for cell-contact based sorting show the opposite . This mechanism is effective at sharpening narrow boundaries , but performs poorly for wider transition regions where cells are intermingled . Thus the two mechanisms have complementary strengths and weaknesses . When combined however , their strengths synergistically promote sharpening , plasticity narrows the transition region while cell contact based sorting completes the process . Given that many segmented tissues ( e . g . the zebrafish hindbrain ) are comprised of more than two cellular zones , we further investigated the effectiveness of this joint mechanism at sharpening the boundaries between multiple zones of different cell types . We found that the two mechanisms have different roles at different boundaries . Specifically , while plasticity-induced sorting is effective at reducing the width of transition regions at the r4/5 boundary , it is ineffective at the r3/4 boundary , a result of different morphogen levels placing the system in a different dynamical regime of the underlying gene regulatory network . Given the distinct strengths of plasticity versus sorting , plasticity is of most value early in the sharpening process while the primary value of sorting becomes apparent later in the process ( though a model where they are jointly active for the entire sharpening time frame is most effective ) . We are not aware of quantitative , time-dependent gene expression data here for the hindbrain , but in the developing mammalian embryo , cell fate decisions have been found to be gradual with gene expression diverging over the course of hours and multiple cell divisions [63] . This gradual fate specification ( if present here ) could have two effects . First , the relative ease and frequency of stochastically driven cell fate transitions is related to the level of gene expression distinctiveness ( or in other terms , the level of specialization ) of the two fates . That is , the more distinct two cell fates are , the more difficult it becomes to stochastically drive transitions between them . Second , it is possible that the difference in adhesion / repulsion properties between multiple cell types is directly related to distinctiveness of those cell types ( e . g . more distinctive cells exhibit stronger sorting ) . Combined , these would lead to a scenario where plasticity would be more active early while sorting would become active at later times . We note that Calzolari et al . [40] did not observe cell fate transitions during rhombomere formation . However , they focused primarily on stages when actin cables form between segments , which occurs near the end of the boundary sharpening process when plasticity effects would be less likely to be observed . Zhang et al [45] on the other hand report co-expression of hoxb1a and krox20 posterior to the future r4/5 boundary , consistent with cellular plasticity contributing to sharpening of this rhombomere boundary . The most direct test of the hypothesis that plasticity and sorting both contribute to this process would be to simultaneously track cell positions and gene expressions from the earliest stage of rhombomere formation . An alternative , more indirect test of how the sharpening process occurs would be to track the spatial location of the resulting boundary as a function of time . If sorting alone is at work , the final location of a sharpened boundary will be near the center of the transition region . Alternatively , if plasticity plays a major role , that final boundary will consistently form near one end of the preceding transition region . Thus , these two mechanisms could potentially be distinguished with a smaller number of experimental observations and without tracking of individual cells . We make one final note about the potential differences between these mechanisms . It is reasonable to expect that in addition to forming well-delineated boundaries , developmental systems should strive to ensure different segmented domains are the appropriate size . For example , the size of one rhombomere relative to its neighbors should be consistent ( e . g . reproducible ) across different embryos at the same developmental stage . A sharpening process that relies solely on sorting ( whether it be contact based and / or chemo-repulsion / chemo-attraction / chemo-taxis based ) of cells will have an inherent level of imprecision since the relative number of cells initially allocated to each of the cell lineages , which is stochastic , determines the final location of a boundary . A plasticity-based mechanism however provides a built-in control that could improve precision ( see Fig G in S1 Text ) since the final location of the boundary will be primarily determined by the morphogen itself . While our results provide a general framework for explaining how initially noisy boundaries generated by a morphogen sharpen to form distinct segments , they also raise new questions . How do multiple segments ( e . g . the zebrafish hindbrain or the neural tube ) refine all zones jointly ? While plasticity is vital to the refinement of noisy boundaries , the dynamics of morphogen signal transduction and the influence of stochasticity are highly dependent on the local levels of morphogen , which depend on position . Furthermore , morphogens have a spatial range of action limited by rates of diffusion , receptor binding , and the fidelity of signal transduction . In light of this , it is important to ask , can the same mechanisms and machinery be used to sharpen [1] unwanted transition regions . In large tissues beyond the length scales over which morphogens can act , other signals must be involved . But what about on smaller scales ( e . g . multiple rhombomeres composed of only tens of cells ) ? Addressing these questions and gaining a more comprehensive understanding of how more complex systems organize will require moving beyond this setting and considering the presence of multiple morphogens ( e . g . RA and Fgf in the hindbrain for example [26 , 64–67] ) , how they function in parallel , and how they potentially interact in signaling pathways .
The modeling framework was based on a hybrid approach incorporating both noise-driven plasticity and sorting-driven mechanics . Plasticity was modeled by stochastic PDEs for the morphogens and several ODEs for the interaction among gene expression and the morphogen evaluated in each cell [45] . The equations were solved using an explicit finite difference scheme . The mechanics were modeled using SCEM , with forces included to describe both cell-cell adhesion and repulsion . For model and simulation details , please see supplementary material S1 Text . | In many developing systems , chemical gradients control the formation of segmental domains of gene expression , specifying distinct domains that go on to form different tissues and structures , in a concentration-dependent manner . These gradients are noisy however , raising the question of how sharply delineated boundaries between distinct segments form . It is crucial that developing systems be able to cope with stochasticity and generate well-defined boundaries between different segmented domains . Previous work suggests that cell sorting and cellular plasticity help sharpen boundaries between segments . However , it remains unclear how effective each of these mechanisms is and what their role in sharpening may be . Motivated by recent experimental observations , we construct a hybrid stochastic model to investigate these questions . We find that neither mechanism is sufficient on its own to sharpen boundaries between different segments . Rather , results indicate each has its own strengths and weaknesses , and that they work together synergistically to promote the development of precise , well defined segment boundaries . Formation of segmented rhombomeres in the zebrafish hindbrain , which later form different components of the central nervous system , is a motivating case for this study . | [
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] | 2017 | Cell Sorting and Noise-Induced Cell Plasticity Coordinate to Sharpen Boundaries between Gene Expression Domains |
Rabies is a vaccine-preventable viral zoonosis belonging to the group of neglected tropical diseases . Exposure to a rabid animal may result in a fatal acute encephalitis if effective post-exposure prophylaxis is not provided . Rabies occurs worldwide , but its burden is disproportionately high in developing countries , including Nepal . We aimed to summarize current knowledge on the epidemiology , impact and control of rabies in Nepal . We performed a systematic review of international and national scientific literature and searched grey literature through the World Health Organization Digital Library and the library of the National Zoonoses and Food Hygiene Research Centre , Nepal , and through searching Google and Google Scholar . Further data on animal and human rabies were obtained from the relevant Nepalese government agencies . Finally , we surveyed the archives of a Nepalese daily to obtain qualitative information on rabies in Nepal . So far , only little original research has been conducted on the epidemiology and impact of rabies in Nepal . Per year , rabies is reported to kill about 100 livestock and 10–100 humans , while about 1 , 000 livestock and 35 , 000 humans are reported to receive rabies post-exposure prophylaxis . However , these estimates are very likely to be serious underestimations of the true rabies burden . Significant progress has been made in the production of cell culture-based anti-rabies vaccine and rabies immunoglobulin , but availability and supply remain a matter of concern , especially in remote areas . Different state and non-state actors have initiated rabies control activities over the years , but efforts typically remained focalized , of short duration and not harmonized . Communication and coordination between veterinary and human health authorities is limited at present , further complicating rabies control in Nepal . Important research gaps include the reporting biases for both human and animal rabies , the ecology of stray dog populations and the true contribution of the sylvatic cycle . Better data are needed to unravel the true burden of animal and human rabies . More collaboration , both within the country and within the region , is needed to control rabies . To achieve these goals , high level political commitment is essential . We therefore propose to make rabies the model zoonosis for successful control in Nepal .
Rabies is a neglected zoonotic disease caused by an RNA virus of the family Rhabdoviridae , genus Lyssavirus . All mammals can be infected with the rabies virus , but dogs are the most important source of human rabies . Although the necessary evidence and tools are in place to control and eliminate rabies , the virus still has a worldwide distribution and is causing a significant health and economic burden to mainly developing countries in Africa and Asia [1] . Rabies is a vaccine-preventable disease . Modern cell culture-based and embryonated egg-based anti-rabies vaccines ( ARV ) have proven to be safe and effective in preventing human rabies [2] . Earlier nerve tissue ARV induce severe adverse reactions and are less immunogenic . As a result , the production and use of nerve tissue ARV has been discouraged by the World Health Organization ( WHO ) since 1984 , although they are still in use in a few countries [3] . Pre-exposure prophylaxis ( PrEP ) is recommended for individuals who will be at continual , frequent or increased risk of exposure to the rabies virus [2] , such as animal handlers , laboratory technicians , and veterinarians in endemic countries . Once exposed to a rabid animal , timely post-exposure prophylaxis ( PEP ) can be lifesaving . The WHO-recommended PEP protocol consists of immediate and proper primary wound management , accompanied by a recommended course of ARV and , for high risk exposures , administration of rabies immunoglobulin ( RIG ) . Intradermal administration of ARV is recommended over intramuscular administration , as it reduces the volume used and thus the direct cost of the vaccine by 60–80% , without compromising on safety or immunogenicity [3] . Although proper implementation of PrEP and PEP can significantly reduce the human rabies burden , it is neither a sustainable nor a cost-effective approach for controlling rabies . Indeed , human rabies prophylaxis alone does not reduce the rabies transmission and can induce an unbearable economic burden on households , communities and governments . The Partners for Rabies Prevention , an international group of agencies and experts involved in rabies , developed a blueprint for rabies prevention and control [4] . The main intervention strategy in the dog rabies control blueprint is mass dog vaccination , possibly complemented with dog population management measures . However , proper planning and evaluation are equally crucial components of the blueprint . In the planning phase , information should be gathered on the local rabies epidemiology and the extent of the dog population . Also , awareness should be created and support elicited from both the local population and the relevant governmental agencies . Once a programme is in place , the change in epidemiological , economic and social impact of the disease needs to be monitored to evaluate the effectiveness of the programme . Reliable baseline data and effective rabies surveillance are inevitable to accomplish this goal . This review focuses on the rabies situation in Nepal . Landlocked between India and China , Nepal has a population of approximately 28 million and a surface of 147 , 000 km² , administratively divided in 75 districts . Geographically , the country can be divided in three ecological belts , i . e . , the northern Himalayas , the central hills , and the southern Terai plains . Due to a concurrent history and an open border , Nepal has similar socioeconomic conditions as India , the country with the largest rabies burden worldwide [1 , 5] . Nevertheless , little is known about the actual status of rabies in Nepal . This review summarizes current knowledge on epidemiology , impact and control of rabies in Nepal , and ends with recommendations for a way forward .
We used a variety of sources to search for information on the epidemiology , impact and control of rabies in Nepal ( S1 and S2 Files ) . Rabies epidemiology was defined as transmission , geographical distribution , seasonality and molecular diversity , while rabies impact was defined as the number of outbreaks , cases , deaths and Disability-Adjusted Life Years ( DALYs ) . We performed a systematic review of scientific literature indexed in PubMed , Web of Knowledge and Nepal Journals Online ( http://www . nepjol . info/ ) , complemented by manual searches of the main Nepalese journals and the conference proceedings of the Rabies in Asia ( RIA ) foundation ( http://www . rabiesinasia . org/ ) . We searched for the following key words: ( "rabies" OR "rabid" OR "dog" ) AND "Nepal" . After removing duplicates , we first excluded items for which we could not retrieve an abstract or full text , and subsequently excluded items that did not pertain to rabies in Nepal . No time restrictions were applied . Further grey literature was collected through searching the WHO Digital Library ( http://apps . who . int/iris/ ) and the library of the National Zoonoses and Food Hygiene Research Centre ( NZFHRC ) , a non-governmental organization actively working in the prevention of zoonosis in Nepal . We also searched Google and Google Scholar for additional documents , but acknowledge that these searches are not replicable , due to the continuous updating of the Google databases and the user-specific ranking of database items . For each eligible document , a narrative synthesis was made , which were then further digested into a qualitative review . Additionally , we obtained data on animal and human rabies from the relevant Nepalese government agencies , and generated numerical and graphical summaries by district , year and month . Rabies surveillance in Nepal is passive and based on decentralized data collection systems . The Veterinary Epidemiology Centre ( VEC ) , resorting under the Directorate of Animal Health ( DAH ) , Department of Livestock Services ( DLS ) , Ministry of Agricultural Development ( MoAD ) , is the national focal point for animal disease surveillance , including rabies . Passive surveillance for bite incidents ( due to dogs and other animals ) is comprised in the Health Management Information System ( HMIS ) , managed by the Department of Health Services ( DoHS ) , Ministry of Health and Population ( MoHP ) . The Epidemiology and Disease Control Division ( EDCD ) under the DoHS is responsible for prevention and control of rabies in Nepal , and for recording human rabies cases and PEP administration . Finally , we searched news items on rabies in Nepal from the 2010–2014 archives of The Himalayan Times , a large English-language Nepalese daily . We acknowledge that this data source does not provide reliable quantitative information , but believe that it is a useful source of qualitative information on the rabies situation in Nepal .
Little original research has been conducted on the epidemiology and impact of rabies in Nepal . Fig 1 shows a flow diagram summarizing the results of the systematic review . From the database searches , we retained a total of 36 documents ( S1 File ) , while the further manual searches revealed another 28 documents ( S2 File ) . In the period 2010–2014 , 41 articles were published about rabies in The Himalayan Times ( S3 File ) . We retrieved data from the VEC for the period 2005–2014 . Similar data from previous time periods have been discussed by Gongal [6] and Karki and Thakuri [7] . The VEC receives a monthly Animal Disease Epidemiological Report in a specified format from all 75 District Livestock Service Offices ( DLSO ) . Each DLSO , in its turn , receives animal health and disease data from 999 livestock service centres strategically located in the various Village Development Committees and Municipalities , the lowest administrative levels in the Nepalese system [6] . Reported cases are mostly based on clinical diagnosis without lab confirmation . In the absence of a standardized case definition , diagnosis of animal rabies depends on the clinical experience of the practitioner . Further information on rabies in Nepal was available from the five most recent DAH Annual Technical Reports [8–12] . Since 1994 , the DoHS publishes Annual Reports which analyse the performance of different programmes and present information collected by the HMIS . In fiscal year 2013/14 , 81% of public hospitals , all 75 District ( Public ) Health Offices , and all Primary Health Care Centres , Health Posts , and Sub Health Posts reported to HMIS [13] . A total of 441 NGO and 669 private health institutions also reported to HMIS that year . The Annual Report contains information on the number of dog bites and other animal bites , by district , reported to the concerned health centres . It also contains hospital inpatient data on rabies morbidity and mortality , based on the ICD codes A82 ( Rabies ) and A82 . 8 ( Rabies , unspecified ) . We obtained the ten most recent DoHS Annual Reports , i . e . , for fiscal years 2004/05 to 2013/14 [13–22] . Rabies in Nepal occurs in two interrelated epidemiological cycles: an urban cycle involving domesticated dogs and a sylvatic cycle involving wildlife [23] . The urban cycle is the predominant source of human rabies , with more than 96% of rabies patients reported during 1991–2000 showing a history of rabid dog exposure [24] . Nevertheless , overlap between both cycles does occur . Indeed , spill-over between cycles has recently been demonstrated by the isolation of a virus from a human rabies case that showed 100% identity over the studied region to viruses previously isolated from two dogs and a mongoose ( family Herpestidae ) in Nepal [25] . The urban cycle is maintained by the stray and community dog population , with spill-overs to pet dogs adding to the human rabies burden . There is little current information on the extent of the stray dog population in Nepal . Based on a dog census carried out by the NZFHRC in 1998 , it was estimated that there were nearly 2 million dogs in Nepal at that time ( or 1 per 10 humans; [26] . However , most other surveys have been conducted in the Kathmandu Valley of Nepal , comprising the Kathmandu , Bhaktapur and Lalitpur districts . In 1989 , Bögel & Joshi [27] estimated a dog population of 12 , 500 in Lalitpur city , or 700 per km² . In October 1997 , a much higher stray dog density of 2 , 930 dogs per km² was established in Kathmandu [28] , corresponding to over 170 , 000 stray dogs ( assuming a total area of 58 km² ) . More recently , animal welfare organizations have undertaken several dog population surveys in the Kathmandu Valley . Within the Ring Road area of the Valley , the estimated dog population was 31 , 000 in 2006 , dropping to 22 , 500 in 2010 and 22 , 300 in 2012 [29 , 30] . In Pokhara , a sub-metropolitan city in western Nepal , a total of 1767 street dogs ( 32 per km² ) were counted during a three-month survey in 2011–2012 [31] . One cause of this problematic size is believed to be the religious adoration of the dog in Nepalese culture [27] . The culmination of dog worship takes place on Kukur Tihar , the second day of Tihar , the festival of lights , when dogs receive religious ornaments and food . However , more important factors for the sustenance of the stray dog population are probably the bad garbage policy and open slaughter facilities , especially in the Kathmandu Valley [32 , 33] . The rapid urbanization and the growth of slum areas further create favourable conditions for the sustenance of stray dog populations [34 , 35] . Finally , the decline in the vulture population since the 1990s in the Indian subcontinent , including Nepal , also implied the loss of a competitor for food [36] . The sylvatic cycle is maintained by wild carnivores living in forest zones , national parks , or wildlife reserves , such as mongooses ( family Herpestidae ) and jackals ( Canis aureus ) [24] . In Nepal , the direct importance of this cycle is thought to be less important , although it probably has a significant indirect importance as continuous source of infection for the urban cycle [23 , 25] . Nevertheless , a proper understanding of the sylvatic cycle is lacking . Rhesus macaques ( Macaca mulatta ) are abundant in certain temple areas inside and outside of the Kathmandu Valley . Although these temple monkeys can become infected through the urban and sylvatic cycle , their role in rabies transmission is unclear . Nevertheless , monkey bites or scratches are reported to occur frequently in tourists and expats staying in Kathmandu [37–40] , and in India , a rhesus macaque is believed to have transmitted rabies to a 10-year-old Australian boy [38 , 41] . Furthermore , monkeys are occasionally reported to menace the local population , although it is not always clear if this is due to rabies infection ( S3 File ) . The risk of rabies infection is believed to be highest in the southern Terai plains , which are densely populated agricultural areas and contain various wildlife areas [6 , 24] . The open border with India may also allow for spill-over between both countries . Nevertheless , animal and human rabies is reported in a much wider range of districts . Fig 2 shows the distribution of animal rabies outbreaks reported to the VEC during 2005–2014 . The districts with the highest number of outbreaks were Nawalparasi and Tanahu ( 45 each ) . No outbreaks were reported from several mountain and hill districts during this period . Fig 3 shows the number of reported outbreaks by month . Over the ten-year period , the average monthly number of reported outbreaks ranged from 3 . 8 ( November ) to 7 . 6 ( June and July ) . Some authors associated the apparent seasonality with the breeding seasonality of dogs and wild carnivores [6 , 7] . Molecular identification and phylogenetic analysis of animal rabies virus has identified both the Indian subcontinent lineage as the Arctic lineage to occur in animals in Nepal [42–44] . So far , only one wildlife sample has been analysed , i . e . , from a mongoose , which was found to cluster with dog and livestock isolates [25 , 44] . No systematic monitoring for sylvatic rabies is in place .
A proper understanding of the epidemiology and impact of rabies is crucial for planning , implementing and evaluating rabies control programmes . In this review , we have tried to generate the best possible summary of data on animal and human rabies in Nepal . However , as our review was mainly narrative in nature , and as a substantial amount of information was obtained through grey literature searches , we acknowledge that replicability may be limited . To accommodate this limitation , we ensured full transparency by including full details on our search strategy and results as Supporting Information files . Per year , rabies is reported to kill about 100 livestock and 10–100 humans , while about 1 , 000 livestock and 35 , 000 humans are reported to receive rabies PEP . However , these estimates very likely represent serious underestimations of the true rabies burden . Indeed , underreporting is very likely to occur in both the animal and human passive surveillance systems [84] , and a proper understanding and quantification of the various reporting biases is a major research gap . Illustrative for these problems are the discrepancies in human rabies deaths between the DoHS Annual Reports and the EDCD reports and the lack of inpatient data from three districts ( Lalitpur , Parsa , and Rupandehi ) since the last five years . Further perturbation is introduced by putative cases typically being diagnosed based on history and symptoms , without lab confirmation . Although rapid testing is done in regional laboratories , confirmation of rabies is currently limited to the Central Veterinary Laboratory , Kathmandu [42 , 61] , which sees around 20 positive human and animal samples a year [8–12] . This corresponds to 10–20% of all reported cases , but to an unknown proportion of all cases . Underreporting of human rabies cases may further result from the fact that rabies patients sometimes prefer to visit traditional healers or prefer to stay home when rabies symptoms have appeared , resulting in a discrepancy between inpatient morbidity and inpatient mortality cases ( S3 File ) . Human rabies cases may further be refused hospital admission due to fear of exposure to health workers and the absence of effective treatment . The reporting of animal rabies cases may depend on the economic value of the affected species , the remoteness of the area , and the motivation of the practitioner . Rabies is estimated to cause around 10–20 , 000 DALYs per year in Nepal [54 , 55] . This is in line with the total burden of the three major parasitic zoonoses in Nepal ( i . e . , cysticercosis , toxoplasmosis , cystic echinococcosis; [85] ) , showing that rabies still is a major zoonosis in Nepal . However , in the absence of reliable data , the burden estimates generated by WHO and IHME are based on extrapolations from neighbouring countries , warranting cautious interpretation ( http://ihmeuw . org/3o6q ) . Only with more reliable local data can these estimates be further refined . Significant progress has been made in the production of ARV and RIG . The abandonment of nerve tissue vaccines has been mitigated by the production of cell culture vaccines , and efforts are ongoing to produce ARV and RIG for human use . Nevertheless , availability and supply of vaccine remains a matter of concern , especially in remote areas where transportation and cold chain maintenance are big challenges . Also , increasing production costs and quality requirements may impede future production of ARV and RIG for human use in the public sector . As it is to be expected that the number of people taking PEP will continue to rise , introduction of cost-effective intradermal rabies vaccination is essential for sustaining the supply of human ARV . Furthermore , it should be clear that prophylaxis alone is not sufficient to control rabies . Unfortunately , much less success has been made in the formulation and implementation of effective rabies control programmes . Such programmes appear to have been initiated since the late 1970s , yet although some individual projects reported successes , the overall impact has probably been limited due to limited duration and geographical coverage . Most projects seem to have been limited to the Kathmandu Valley , likely because of accessibility , yet available data showed that 94% of all reported dog bites occurred outside of this area . Different state and non-state actors have been involved in rabies control over the years , but collaboration between these different groups has been limited . Illustrative of this is that most projects included awareness and mass dog vaccination , but lacked dog population management activities such as animal birth control or waste management . Prevailing cultural and religious practices should be taken into account when designing dog population management strategies , as for instance the important role of dogs in Hinduism may be an impediment for successful programme adoption [67] . Canine rabies control programmes could further be complemented with deworming against endemic dog-borne parasitic zoonoses such as Echinococcus granulosus and Toxocara canis [85] . New policy-relevant research , e . g . , on the ecology of stray dog populations across the country and the true contribution of the sylvatic cycle , is crucial to develop realistic , long-term control programmes . Finally , with 40% of all rabies occurring in the South Asian region , several regional control efforts are emerging , providing new opportunities for rabies control in Nepal [86] .
Limited data indicate that rabies probably still is a major zoonosis in Nepal . However , more and better data are needed , especially from rural areas , to estimate the true burden of animal and human rabies and to plan , implement and evaluate rabies control programmes . The current control of rabies is hampered by insufficient vaccine availability across the country . The way forward for effective rabies control programmes lies in more collaboration , both within the country and within the region . We believe this disease can be controlled only through a coordinated one health approach [87 , 88] . To accomplish these recommendations , high-level political commitment is essential . Making rabies the model zoonosis for successful control could be a powerful step towards achieving this . | Rabies has been known as a deadly disease in Nepal for decades , but information on epidemiology , impact and control remains scattered . We collected and summarized information from a variety of sources , including scientific literature and government agencies . Only little original research has been conducted on the epidemiology and impact of rabies in Nepal , leaving many questions unanswered . Official reports show that each year 100 livestock and 10–100 humans die of rabies , but these numbers very likely underestimate the true rabies burden . Availability and supply of anti-rabies vaccines have remained suboptimal and rabies control activities have been hampered by a lack of collaboration and comprehensiveness . High level political commitment is essential to overcome these problems and to reduce the burden of rabies . We therefore propose to make rabies the model zoonosis for successful control in Nepal . | [
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] | 2016 | Epidemiology, Impact and Control of Rabies in Nepal: A Systematic Review |
Animal African trypanosomosis is a major threat to the economic development and human health in sub-Saharan Africa . Trypanosoma congolense infections represent the major constraint in livestock production , with anemia as the major pathogenic lethal feature . The mechanisms underlying anemia development are ill defined , which hampers the development of an effective therapy . Here , the contribution of the erythropoietic and erythrophagocytic potential as well as of hemodilution to the development of T . congolense-induced anemia were addressed in a mouse model of low virulence relevant for bovine trypanosomosis . We show that in infected mice , splenic extramedullary erythropoiesis could compensate for the chronic low-grade type I inflammation-induced phagocytosis of senescent red blood cells ( RBCs ) in spleen and liver myeloid cells , as well as for the impaired maturation of RBCs occurring in the bone marrow and spleen . Rather , anemia resulted from hemodilution . Our data also suggest that the heme catabolism subsequent to sustained erythrophagocytosis resulted in iron accumulation in tissue and hyperbilirubinemia . Moreover , hypoalbuminemia , potentially resulting from hemodilution and liver injury in infected mice , impaired the elimination of toxic circulating molecules like bilirubin . Hemodilutional thrombocytopenia also coincided with impaired coagulation . Combined , these effects could elicit multiple organ failure and uncontrolled bleeding thus reduce the survival of infected mice . MIF ( macrophage migrating inhibitory factor ) , a potential pathogenic molecule in African trypanosomosis , was found herein to promote erythrophagocytosis , to block extramedullary erythropoiesis and RBC maturation , and to trigger hemodilution . Hence , these data prompt considering MIF as a potential target for treatment of natural bovine trypanosomosis .
African trypanosomosis ( AT ) is a neglected tropical disease of medical and veterinary importance that adversely affects human health and welfare , as well as the economic development in sub-Saharan Africa [1 , 2] . AT is caused by blood-borne hemoflagellated protozoan parasites from the Trypanosoma genus that are transmitted by the hematophagous tsetse fly ( Glossina spp . ) vector [3] . Trypanosoma-induced diseases in mammals include sleeping sickness in humans or nagana in domestic livestock , with fatal consequences unless treated [4 , 5] . Given that so far no effective vaccine is available , that certain trypanosome strains have become resistant to curative and preventive treatments , and that eradication of tsetse flies remains impossible in most regions [1 , 6] , strategies focusing at reducing AT-associated pathogenicity might be an alternative approach to reduce the economic losses in cattle production . In case of bovine trypanosomosis , the main difference between so-called trypano-susceptible and -tolerant animals relies in their capacity to control anemia development , the major cause of death associated with the disease [7 , 8] , and hereby to remain productive [9] . Differences in erythropoietic potential have been suggested as a contributing factor to anemia development [8] . Yet , the mechanisms underlying this phenomenon remain poorly understood , which hampers the design of effective therapeutic strategies . Given the similarities between the anemic responses of cattle and C57Bl/6 mice upon trypanosome infection [8] , the underlying mechanisms were mainly scrutinized in murine models [10 , 11] . The data collectively suggest that the pro-inflammatory type I immune response , involving TNF , IFN-γ and M1-type ( classically activated ) myeloid cells , contributes to pathogenicity in general and anemia in particular , and in combination with impaired B-cell functionality , results in reduced survival of the mice [10 , 12 , 13] . Thus , identification of gene-products regulating pro-inflammatory signals during the course of the disease might pave the way to develop novel intervention strategies . In this context , we previously identified macrophage migration inhibitory factor ( MIF ) as a potential susceptibility marker for African trypanosomosis . This ubiquitously produced cytokine is a prominent inducer of systemic inflammation in many inflammatory diseases [14 , 15] that acts by recruiting and activating myeloid cells towards M1-type cells to the site of inflammation [16 , 17] , and by suppressing apoptosis of inflammatory cells [18] . We have shown using a trypanosusceptible model based on C57Bl/6 mice infected with T . brucei brucei , that MIF contributes to tissue pathogenicity by sustaining throughout infection a persistent type I pro-inflammatory chemokine ( CXCL1 , CCL2 ) and cytokine ( IFN-γ , TNF , IL-6 ) response , and by enhancing the recruitment of Ly6C+ monocytes and neutrophils ( PMNs ) in the liver with concomitant hepatomegaly [19] . Moreover , PMN- but not monocyte-derived MIF was mainly responsible for liver damage . In addition , MIF promotes the development of so-called anemia of inflammation in trypanosusceptible mice by enhancing red blood cell ( RBC ) clearance from the blood , and by triggering the storage of iron in liver myeloid cells that deprives iron from erythropoiesis and impairs the generation of mature RBCs . However , despite reduced liver injury and anemia levels in T . b . brucei-infected Mif-/- mice or mice treated with a neutralizing anti-MIF antibody , the host survival time was not affected [19] . Compared to T . b . brucei , T . congolense infection in C57Bl/6 mice is considered a trypanotolerant model more relevant for bovine trypanosomosis [20 , 21] . In contrast to T . b . brucei , T . congolense causes a chronic infection ( 3–4 months versus 1 month ) , due to the capacity of T . congolense-infected mice to restrain the type I immune response and to switch to an IL-10-mediated tissue protective anti-inflammatory response [21 , 22] . This model could thus allow a thorough analysis of the mechanisms underlying anemia development in an immune environment different from that of anemia of inflammation . Here , we evaluated the role of MIF in T . congolense infection-associated anemia development , by focusing on the modulation of the erythropoietic and erythrophagocytic potential in tissues including the bone marrow , the spleen and the liver . Additionally , the contribution of hemodilution to anemia was addressed .
The systemic production of MIF increased progressively during the course of T . congolense infection ( S1 Fig ) . Hence , the potential role of MIF in the outcome of i . p . infection was evaluated by comparing wild type ( WT ) and MIF-deficient ( Mif-/- ) C57Bl/6 mice . Although parasitemia development was similar in WT and Mif-/- mice , a prolongation of median survival time occurred in Mif-/- mice ( Fig 1A and 1B ) . Similar observations were obtained using a natural route , tsetse fly-mediated infection model ( S2A and S2B Fig ) . Considering the similar capacity of WT and Mif-/- mice to control parasite growth , the increased survival of Mif-/- mice could result from lower tissue pathogenicity [23 , 24] . In agreement , as compared to WT mice , Mif-/- mice exhibited reduced serum AST ( alanine aminotransferase , reflecting systemic tissue injury ) and ALT ( aspartate aminotransferase , reflecting liver injury ) levels ( Fig 1C and 1D ) , as well as reduced hepato- and splenomegaly that coincided with a lower increase in the number of white blood cells ( WBC ) in the liver and the spleen of Mif-/- mice ( Figs 1E and 1F and S3 ) . No WBC accumulation was observed in the bone marrow of infected WT and Mif-/- mice ( Fig 1F ) . The differences in pathogenicity between WT and Mif-/- infected mice were clearly established from 3 months post infection ( p . i . ) ( Figs 1C–1F and S3 ) , hence we focused at this time point . At 3 months p . i . , MIF secretion was enhanced in the liver , spleen and bone marrow of infected WT mice ( Fig 2 ) , mirroring the increased MIF level measured in the blood . In agreement with the reduced tissue injury in infected Mif-/- mice , the levels of neutrophil ( CXCL1 ) and monocyte ( CCL2 ) chemoattractants , as well as of pro-inflammatory cytokines documented to contribute to T . congolense-induced tissue destruction ( TNF , IL-6 , IFN-γ ) [25 , 26] , were increased to a lesser degree in the liver , spleen , bone marrow and blood of Mif-/- than WT mice ( Fig 2A–2D ) . This was also true for IL-12p70 in the blood ( Fig 2D ) [27] . However , the systemic and tissue levels of IL-10 , which increases upon T . congolense infection and is crucial to limit tissue destruction [28] , did not differ between WT and Mif-/- mice ( Fig 2 ) . In addition to differences in pro-inflammatory cytokine production , the decreased tissue pathogenicity and increased survival of Mif-/- mice also could be due to a superior ability of the Mif-/- mice to mount a parasite-specific antibody response [13] . As shown in Figs 3A and S4 , similar serum levels of parasite-specific IgG antibodies were recorded in WT and Mif-/- mice until 1 . 5 months p . i . ; thereafter and from 3 months p . i . , the IgG levels declined in WT mice while they remained elevated in Mif-/- mice . The drop in IgG levels in infected WT mice did not correlate with a decrease in the number of total ( B220+ ) or germinal center ( GL-7+Fas+B220+ ) splenic B-cells ( Figs 3B and 3C , S5A and S5B ) , but could be due to an increase in B-cell apoptosis ( Fig 3D ) . The increased IgG levels observed in infected Mif-/- mice as compared to WT mice was associated with an increased number of total and germinal center B-cells as well as with lower B-cell apoptosis ( Fig 4A–4D ) . Collectively , in T . congolense-infected mice , the absence of MIF results in a reduced pro-inflammatory immune response , which in turn could contribute to a lower hepato-splenomegaly and an enhanced B-cell response that collectively could enhance the survival time . Anemia is the prominent pathogenic feature of a natural T . congolense infection , which is mediated by hematopoietic cells , but not by T lymphocytes or antibodies [10 , 29] . Since MIF ( i ) contributes to the accumulation of WBC ( including phagocytes; Fig 1F ) in tissues that are potentially involved in erythrophagocytosis and extramedullary erythropoiesis , and ( ii ) stimulates the production of the erythroid lineage development-blocking cytokine IL-6 [30] in the two main erythropoietic tissues ( bone marrow , spleen; Fig 2B and 2C ) , we investigated MIF’s role in anemia development during T . congolense infection . Anemia is characterized by two distinct phases: ( i ) a rapid decline in RBC levels followed by partial recovery during the early phase of infection and ( ii ) a more progressive decline in RBC levels during the chronic phase of infection ( Fig 4A ) . During the early phase of infection ( i . e . day 5–10 p . i . ) , the RBC percentages initially dropped to about 50% of non-infected mice in both WT and Mif-/- mice ( Fig 4A ) . Between day 10–14 p . i . , a partial recovery that reaches about 75% of the RBC level in non-infected mice occurred in WT mice , while in Mif-/- mice this recovery reached about 95% ( Fig 4A ) . Subsequently , during the chronic phase of infection , the RBC levels declined progressively and remained significantly lower in WT than Mif-/- mice . Mif-/- mice also exhibited reduced anemia in a natural tsetse fly-mediated infection ( S2C Fig ) . The serum hemoglobin and iron levels were reduced in WT mice as compared to non-infected animals during the course of infection ( Fig 4B and 4C ) . In infected Mif-/- mice , these reductions were less pronounced when compared to infected WT mice . In both groups of mice , the serum hemoglobin and iron levels reached nadir levels at 3 months p . i . , the time point when the differences in tissue pathogenicity were established ( Figs 1C and 1D and 4A ) . Collectively , during the chronic stage of T . congolense infection , MIF partially impaired recovery from early stage anemia and contributed to the decline in serum hemoglobin and iron levels . Typically , in response to chronic anemia , splenomegaly and increased blood reticulocyte content are indicative of inefficient erythropoiesis in the bone marrow and extramedullary erythropoiesis in the spleen [31] . Compared to non-infected mice , the numbers of splenic Ter119+ RBCs increased more prominently in T . congolense-infected WT than Mif-/- mice from 3 months p . i . ( Fig 4D ) . Remarkably , the largest increase in cell number in the spleens of infected mice was found in the RBC and not the WBC compartment ( Fig 4D ) , showing the expansion of the erythroid compartment as a main cause for T . congolense-associated splenomegaly . The relative abundance of Ter119+CD71+ reticulocytes and mature Ter119+CD71- RBCs ( identified as described in S6A Fig ) was quantified in the blood of WT and Mif-/- mice at 3 months p . i . , which corresponds with a time point when maximal cell numbers , level of hepato-splenomegaly and tissue pathogenicity were reached in both groups of mice ( Figs 1C–1F and S3 ) . The concentration of reticulocytes increased in the blood of infected WT mice when compared to non-infected animals , and this increase was less pronounced in Mif-/- mice ( Fig 5A ) . Concomitantly , the reduction in the concentration of mature RBCs was more pronounced in the blood of WT than Mif-/- mice . Within the erythropoietic tissues of infected WT mice , reticulocyte and mature RBC accumulation was not affected in the bone marrow , but was dramatically increased in the spleen ( Fig 5B and 5C ) , suggesting extramedullary erythropoiesis . A comparison of infected WT and Mif-/- mice revealed a detrimental contribution of MIF to mature RBC accumulation in the bone marrow , and to reticulocyte accumulation in the spleen ( Fig 5B and 5C ) . Next , we assessed the stage at which erythropoiesis could be affected ( from nucleated erythroblasts ( I ) until enucleated erythrocytes ( VI ) ) , by gating for Ter119+ RBCs in a CD44 versus FSC-A plot [32] ( S6B Fig ) . In the bone marrow of infected WT mice , a blockade in the two last steps of RBC differentiation resulted in decreased percentage ( S6C Fig ) and numbers ( Fig 5D ) of nucleated reticulocytes ( stage V ) and enucleated reticulocyte/erythrocyte ( stage VI ) cells . In the spleen , infected WT mice exhibited increased accumulation of the RBC differentiation stage III polychromatic erythroblasts to stage VI mature RBCs ( Fig 5E ) , confirming extramedullary erythropoiesis in this organ . The erythropoiesis efficacy in the bone marrow and the spleen was improved in infected Mif-/- as compared to WT mice . Indeed , Mif-/- mice exhibited a more efficient RBC maturation mainly during the transition from orthochromatic erythroblasts and nucleated reticulocytes ( stage IV and V ) to the last step of differentiation i . e . enucleation of erythrocytes ( stage VI , Fig 5D and 5E ) . Of note , the gene expression levels of Vcam1 ( Vascular cell adhesion molecule-1 ) and Maea ( EMP: erythroblast-macrophage protein ) , two molecules crucial for erythroblast—macrophage interaction in the terminal stage of erythropoiesis , i . e . during the erythroblast enucleation [33 , 34] , were higher in Mif-/- than in WT mice ( Fig 5F ) , suggesting a negative effect of MIF on the reticulocyte terminal maturation . Collectively , these data indicated that mice exhibited reticulocytosis during the chronic stage of T . congolense infection , which was likely due to an increased production of RBCs , mainly in the spleen , to overcome the chronic loss of mature RBCs observed in the blood . Moreover , RBCs were impaired in their terminal stages of maturation both in the bone marrow and spleen of infected mice . Finally , MIF contributed to the reticulocytosis and to the impairment of the terminal differentiation of RBCs from the orthochromatic erythroblast to the enucleated reticulocyte/erythrocyte stage . The percentage of blood Annexin-V+ RBCs increased in T . congolense-infected WT mice , and this increase was lower in Mif-/- than in WT mice ( Fig 6A ) . Phosphatidylserine exposure , which forms the basis of the Annexin-V staining assay , is an “eat-me” signal observed during apoptosis of senescent cells . Thus , we investigated whether an increased RBC elimination through phagocytosis in the liver and the spleen contributed to anemia in T . congolense- infected mice . An assay consisting of i . v . injection of pHrodo-labeled RBCs in infected WT or Mif-/- mice followed by analysis of the appearance of a fluorescent signal in phagocytes from the liver and the spleen that have engulfed labeled RBCs [35] , was used . In infected WT mice , PMNs , monocytes and macrophages ( gated as in S7A Fig ) exhibited RBC phagocytic activity . This activity was more pronounced and MIF-dependent for PMNs in the liver and for monocytes and macrophages in the spleen ( Fig 6B and 6C ) . In parallel , a higher number of phagocytic PMNs , monocytes and macrophages was observed in the liver of infected WT mice when compared to Mif-/- mice ( Fig 6B and 6C ) . The same held true for macrophages in the spleen . Together , these data suggest that the reduced anemia in T . congolense-infected Mif-/- mice is a combined effect of a reduced apoptosis/senescence of RBCs , a reduced number of phagocytic cells and a reduced phagocytic capacity of these cells . Erythrophagocytosis results in the release of hemoglobin within liver and spleen phagocytes , where the heme is catabolized to iron , carbon monoxide and bilirubin . The latter is then released in the circulation and coupled to albumin to be transported to hepatocytes [36] . Accordingly , histological analyses revealed iron accumulation mainly in the splenic tissue of infected WT mice ( Fig 7A and 7B ) . Furthermore , the observed erythrophagocytosis in T . congolense-infected WT mice coincided with a progressive increase in total bilirubin and decrease in albumin serum levels ( Fig 7C and 7D ) . The decreased anemia and erythrophagocytosis observed in infected Mif-/- mice correlated with lower intracellular iron retention , lower bilirubinemia and higher albuminemia as compared to WT mice ( Fig 7A–7D ) . Hemodilution can also contribute to anemia [37] . Moreover , the hypoalbuminemia observed in T . congolense-infected mice may also reflect hemodilution . APC-labelled hydroxyethyl starch ( HES ) is used to monitor hemodilution and is not affected by differences in RBC numbers . Hence , HES was injected i . v . in WT and Mif-/- mice at different time points post T . congolense infection . After 5–10 minutes , the blood was collected and the blood volume and concentration of HES were evaluated . As compared to non-infected mice , the blood HES concentration dropped in infected WT mice while the volume of the blood collected increased , whereby there was about a 3-fold change from 3 months p . i . ( Fig 8A and 8B ) . These data suggest that hemodilution was occurring in T . congolense-infected mice during the later stage of infection . In agreement with observations in T . congolense-infected cattle [38] , the packed cell volume ( PCV ) of the collected blood was reduced in WT mice as compared to non-infected animals ( Fig 8C ) . However , when taking into account the blood volume in the whole animal , it appeared that the total PCV , i . e . the total amount of RBCs , was not affected by the infection while the plasma volume was increased ( Fig 8D ) . When compared to infected WT mice , the blood and plasma volume were lower , and the HES concentration and total PCV were higher in infected Mif-/- mice ( Fig 8A–8D ) . Together , these data indicated that anemia resulted from MIF-dependent hemodilution and not from lower production of RBCs in T . congolense-infected mice . Hemodilution can also give rise to a reduction in platelet concentration , which in turn contributes to inefficient blood coagulation . As shown in Fig 8E , the concentration of FSClo/SSClo CD41+ platelets ( gated as in S7B Fig ) declined in T . congolense-infected WT mice and to a lesser extent also in Mif-/- mice . However , when taking into consideration the total blood volume , the number of platelets per animals was not affected by the infection ( Fig 8F ) . Furthermore , the platelet dilution was associated with a drastic increase in clotting time in infected WT mice . Indeed , while the clotting time of non-infected mice was about 2 minutes , >70% of the infected WT mice continued to bleed 15 minutes after the tail cut ( time at which the wound was sealed following ethical guideline to avoid otherwise lethal hemorrhage ) ( Fig 8G ) . The coagulation time in infected Mif-/- mice was higher than in non-infected mice but remained lower than in infected WT mice , with all mice coagulating in about 10 minutes . Collectively , these data suggest that MIF contributed to hemodilution that coincided with decreased blood platelet concentration . Combined , these effects can result in inefficient coagulation during T . congolense infection . To further assess whether MIF triggered hemodilutional anemia , Mif-/- mice at 3 months p . i . were treated with recombinant mouse MIF ( rMIF ) every second day for 1 week and tested for anemia-associated parameters . rMIF-treated Mif-/- mice exhibited a more severe anemia than untreated Mif-/- mice , reaching a percentage of RBC levels close to that of infected WT mice ( Fig 9A ) . Moreover , the number of erythrocytes and reticulocytes—albeit to a non-significant extent , decreased in the blood of rMIF-treated Mif-/- mice ( Fig 9B ) . This effect coincided with a drop in the terminal stage VI of erythroid development in the spleen , which paralleled the results of WT mice ( Fig 9C ) , thereby strengthening the notion that MIF affected the reticulocyte enucleation process . In addition , rMIF treatment increased the level of annexin-V+ apoptotic RBCs ( Fig 9D ) . Finally , infected rMIF-treated Mif-/- mice exhibited an increased plasma volume recovered from the whole animal , increased splenomegaly , and a decreased concentration but not number of platelets ( Fig 9E–9H ) . Collectively , these data showed that rMIF treatment in T . congolense-infected Mif-/- mice could partially recapitulate the pathogenic features associated with anemia and hemodilution development in infected WT mice . These rMIF treatment data further support the conclusion that MIF exerted negative effects on anemia and hemodilution development during the later stage of T . congolense infection .
We have recently reported that MIF , an upstream regulator of the inflammatory response , contributed to anemia in trypanosusceptible T . b . brucei-infected C57Bl/6 mice [19] . As compared to T . b . brucei-infected mice that are locked in a type I inflammatory immune response , T . congolense-infected C57Bl/6 mice are trypanotolerant due to their ability to restrict the type I driven immune response and to mount a tissue-protective IL-10-mediated immune response in the chronic phase of infection [10 , 26] . Based on the observation that MIF was produced in the chronic phase of T . congolense infection , we hypothesized that this less virulent model of African trypanosomosis could allow a refined analysis of the MIF-dependent pathogenic mechanisms at play during infection-induced tissue damage and anemia . As in trypanosusceptible mice , in the absence of MIF , the systemic and tissue-restricted production of pathogenic chemokines ( CXCL1 , CCL2 ) and cytokines ( IFN-γ , TNF , IL-6 , IL-12p70 ) was impaired during the chronic stage of T . congolense infection . MIF deficiency also limited hepato-splenomegaly and tissue destruction , including anemia . In both T . b . brucei- and T . congolense-infected mice , the reduced anemia in Mif-/- mice coincided with a partial recovery of serum hemoglobin and iron levels . The higher iron bioavailability partially restored erythropoiesis , which was reflected by a decreased concentration of reticulocytes and increased concentration of RBCs in the blood and the spleen of infected Mif-/- mice . The absence of MIF also improved the terminal stage of erythroid development , i . e . the differentiation from nucleated reticulocytes to enucleated RBCs . The latter effect can result from the reduced circulating IL-6 levels in infected Mif-/- mice [30] . The reduced anemia observed in T . b . brucei- and T . congolense-infected Mif-/- mice also could result in part from the better RBC recovery during the early stage of infection ( up to 15 days p . i . ) , which in turn could require a lower erythropoietic demand during the latter stages of infection . In cattle , anemia development can result from the extravascular destruction of RBCs due to massive erythrophagocytosis by activated macrophages in the spleen and the liver [39] . We found that enhanced erythrophagocytosis indeed occurred in both the spleen and the liver of T . congolense-infected WT mice . This phenomenon was less pronounced in Mif-/- mice despite their better IgG response , and likely due to the reduced number of phagocytic hepatic macrophages , Ly6C+ monocytes and PMNs , and of splenic macrophages . Because of the increased erythrophagocytic activity throughout T . congolense infection , a compensatory demand for increased production of RBCs was evidenced in the spleen but not in the bone marrow . This extramedullary erythropoiesis led to a massive generation and accumulation of reticulocytes and mature RBCs that could account for the splenomegaly observed in infected mice . In the absence of MIF , an increased maturation of reticulocytes to mature RBCs occurred and coincided with reduced splenomegaly and anemia . One week of rMIF treatment in infected Mif-/- mice in turn recapitulated anemia development , including splenomegaly , increased apoptosis/senescence of RBCs and impaired maturation of reticulocytes . Our accumulated evidence argues for hemodilution , and not erythrophagocytosis , as the main contributor to the chronic anemia developing in T . congolense-infected mice in a MIF-dependent manner . Despite a study showing no increase in the blood volume in T . congolense-infected calves [40] , others found a marked hypervolemia in T . congolense-infected sheep and calves [41–45] . In line with the latter observations , the blood and plasma volume was augmented in infected WT mice . However , the total number of RBCs in the blood of infected WT mice , calculated on the basis of the blood volume and the PCV , did not differ from that of non-infected mice . These data suggested that hypervolemic hemodilution developed in infected mice . They also suggested that , in infected WT mice , the increased erythropoiesis and maturation of RBCs in the spleen could compensate for both the impaired maturation of RBCs in the bone marrow and the increased clearance of RBCs through erythrophagocytosis in the liver and the spleen . Remarkably , in anemic T . b . brucei-infected WT mice , the blood and plasma volumes were not affected and the PCV decreased ( S8 Fig ) , in line with observation in T . b . brucei-infected calves [46] . Collectively , these data support the view that a predominantly inflammatory anemia , i . e . anemia of inflammation , develops in a type I immune environment in T . b . brucei-infected mice [10 , 47] , while a predominantly hemodilutional anemia occurs in a type II environment in T . congolense-infected mice . rMIF treatment in infected Mif-/- mice phenocopied the hemodilution development , confirming a MIF-dependent mechanism in infected WT mice . Hemodilution in rMIF-treated Mif-/- mice however did not reach the level observed in WT mice . Although this result could argue for the occurrence of MIF-independent mechanisms of hemodilution , we cannot exclude the possibility that rMIF treatment for a period longer than one week may be necessary . Hemodilution could also account for the reduced concentration of platelets in the circulation of T . congolense-infected WT mice . This hemodilutional thrombocytopenia concurred with a delayed blot clotting time in infected mice that could lead to lethal haemorrhage when the tail cut for blood sampling was not sealed . Of note , thrombocytopenia was reported to occur in T . congolense-infected cattle and sheep [41 , 48] . Whether hemodilution of the clotting factors also accounts for the inefficient coagulation in infected animals deserves further investigation . Despite similarly decreasing tissue pathogenicity , a difference in the pathogenic role of MIF between trypanosusceptible and trypanotolerant mice nevertheless was observed . In T . b . brucei-infected mice , MIF deficiency resulted in increased production of IL-10 , the main anti-pathogenic cytokine in experimental African trypanosomosis , but did not affect the survival time of the infected hosts [19] . In T . congolense-infected mice , the absence of MIF had no effect on IL-10 production but resulted in prolonged survival time . Although MIF-independent mechanisms could determine the survival of T . b . brucei-infected mice , we could not exclude that these mice are not sufficiently responsive to IL-10 [49 , 50] . Alternatively , the virulence of T . b . brucei due to its tissue-invading capacity could be higher than that of T . congolense , which remains strictly in the blood vessels [51] . It is postulated that T . b . brucei- and T . congolense-infected mice die from inflammation-mediated multiple organ failure [12] , but the cause of death remains unclear and may differ between the two parasite species . In this respect , a refined mechanism for the death of T . congolense-infected WT mice could be envisaged based on the data reported herein . Our results support the interpretation that in these animals , the continual and month-lasting low-grade inflammatory response drives erythrophagocytosis and that the ensuing catabolism of hemoglobin resulted in iron accumulation mainly in the spleen , followed by the enhanced release of bilirubin in the blood circulation . Importantly , hyperbilirubinemia could favour the externalisation of phosphatidylserine on RBCs observed herein and thus further contribute to erythrophagocytosis or eryptosis during T . congolense infection [36 , 52] . The hyperbilirubinemia and the hypoalbuminemia—with the latter resulting most likely from the hemodilution and liver damage in infected mice , could contribute to a greater degree of systemic tissue destruction , including not only the hepatic tissue but also the heart and the brain [53 , 54] . The severe hemodilutional anemia could also reduce cerebral oxygen delivery and further promote cerebral damage [55 , 56] , although T . congolense is not a blood brain barrier penetrating parasite . Combined , the hyperbilirubinemia , hypoalbuminemia , and hemodilution could thus contribute importantly to the increased mortality of T . congolense-infected WT mice . Thrombocytopenia and delayed coagulation , as correlates of hemodilution , could also negatively impact the survival of infected WT mice . MIF deficiency partially alleviated iron accumulation in tissues , hyperbilirubinemia , hypoalbuminemia and defective coagulation , most likely because T . congolense-infected Mif-/- mice exhibited reduced erythrophagocytosis combined with reduced hemodilution and liver injury . Each of these effects may be contributory to the enhanced survival of Mif-/- mice . Of course , our data do not exclude the reduced inflammatory cytokine response and the better parasite-specific antibody response , both vital for the control of African trypanosomosis [13 , 28 , 57] , as reasons for the enhanced survival of T . congolense-infected Mif-/- mice . Collectively , our results suggest that during the chronic phase of T . congolense infection , anemia did not result from the impaired production of mature RBCs . MIF-induced splenic extramedullary erythropoiesis could compensate for the impaired differentiation of erythroblasts in the bone marrow and for the enhanced erythrophagocytosis in the liver and the spleen ( Fig 10 ) . In contrast , anemia induced by T . congolense mainly occurred through MIF-dependent hemodilution . The heme catabolism ensuing erythrophagocytosis could lead to iron accumulation in tissue and to hyperbilirubinemia . Hypoalbuminemia resulting from hemodilution in infected mice impaired the elimination of toxic circulating molecules , including bilirubin . Hemodilution with thrombocytopenia as a consequence could also account for impaired coagulation in infected mice . Combined , these effects could trigger multiple organ failure and uncontrolled bleeding hereby reducing the survival time of infected mice . Together , this study suggests that interfering with MIF signaling could represent an approach to limit inflammation-associated anemia complications during natural T . congolense trypanosomosis . Given that polymorphisms in the human MIF gene contribute to differences in susceptibility in several inflammatory diseases [58 , 59] , it could be interesting to assess whether differences between trypanosusceptible and trypanotolerant cattle associates with genetically-predetermined differences in MIF expression .
All experiments , maintenance and care of the mice complied with the European Convention for the Protection of Vertebrate Animals used for Experimental and Other Scientific Purposes guidelines ( CETS n° 123 ) and were approved by the Ethical Committee for Animal Experiments ( ECAE ) at the Vrije Universiteit Brussel ( Permit Numbers: 14-220-4 and 14-220-6 ) . Infection of tsetse flies with T . congolense parasites was performed in compliance with the regulations for bio-safety and under approval from the Environmental administration of the Flemish government ( Permit number: BM2012-6 ) . Clonal T . congolense parasites ( Tc13 ) were kindly provided by Dr . Henry Tabel ( University of Saskatchewan , Saskatoon ) and stored at -80°C . Wild type ( WT ) C57Bl/6 mice were obtained from Janvier . MIF deficient ( Mif-/- ) C57Bl/6 mice generated as described in [60] were bred in our animal facility . Female mice ( 7–8 weeks old ) were infected intraperitoneally ( i . p . ) with 2 x 103 Tc13 trypanosomes . Alternatively , tsetse fly infection was achieved by feeding teneral flies on T . congolense-infected mice as described in [19] . When required , infected mice were treated i . p . with 200 ng of mouse recombinant MIF ( rMIF , Abcam , ab191658 ) every second day for 1 week . Parasite and red blood cell ( RBC ) numbers in blood were determined via hemocytometer by cold tail-cut ( 2 . 5 μl blood in 500 μl RPMI ) . Anemia was expressed as the percentage of reduction in RBC counts compared to non-infected animals . Packed cell volume ( PCV ) was measured following collection of anti-coagulated blood in heparinized capillaries and centrifugation at 9500g for 7 min . using a micro-centrifuge ( Fisher BioBlock Scientific ) . Liver cell isolation was performed as described by Stijlemans et al . [61] . Briefly , livers from CO2 euthanized mice were perfused with 30 ml heparinized saline ( 10 units/ml; Leo Pharma ) containing 0 . 05% collagenase type II ( Clostridium histolyticum; Sigma-Aldrich ) , excised and rinsed in saline . Following mincing in 10 ml digestive media ( 0 . 05% collagenase type II in Hanks' Balanced Salt Solution ( HBSS ) without calcium or magnesium; Invitrogen ) and incubation at 37°C for 30 min . , the digested liver was homogenized and filtered ( 40 μm pore filter ) . The cell suspension was centrifuged ( 7 min . , 300×g , 4°C ) and the pellet treated with erythrocyte-lysis buffer . Following centrifugation ( 7 min . , 300×g , 4°C ) the pellet was resuspended in 2–5 ml RPMI/5% FCS medium , cells counted and adjusted at 107 cells/ml for flow-cytometric analysis and cell culturing . Spleen and bone marrow ( tibia and femur ) cells were obtained by homogenizing the organs in 10 ml RPMI/5%FCS medium , passing the suspension through a 40 μm pore filter and centrifugation ( 7 min . , 300×g , 4°C ) . Cells were counted and brought at 107 cells/ml in RPMI/5% FCS medium for RBC analysis via flow cytometry . Remaining cells were pelleted ( 7 min . , 300×g , 4°C ) and subsequently treated with erythrocyte-lysis buffer and processed as described for the liver ( see above ) for analysis of white blood cells ( WBCs ) . Cells were diluted at 2x106 cells/ml in complete medium ( RPMI-1640 medium , 10% FBS , 1% sodium pyruvate ( Gibco ) , 1% non-essential amino acids ( Gibco ) , 1% glutamate , 1% penicillin-streptomycin ) . Next , 500 μl of cell suspension/well were cultured ( 36–48 hours , 37°C , 5% CO2 ) in 48 well plates ( Nunc ) and the supernatant was tested in ELISA . To analyze the RBC composition and platelet counts , the blood , spleen and bone marrow cells were analysed omitting RBC lysis . Briefly , total blood ( 2 . 5 μl diluted in 500 μl RPMI/5% FCS ) and 106 spleen or bone marrow cells ( in 100 μl ) were incubated ( 15 min . , 4°C ) with Fc-gamma blocking antibody ( 2 . 4G2 , BD Biosciences ) , and subsequently stained with labelled antibodies ( S1 Table ) and matching control antibodies . Samples were washed with PBS , measured on FACSCanto II ( BD Bioscience ) and results were analysed using FlowJo software by excluding CD45+ cells and gating on Ter-119+ or CD41+ cells . The WBC composition within the bone marrow , spleen and liver cells and the B cell compartment in the spleen ( 106 cells/100 μl ) were analyzed after RBC lysis as described above using labelled antibodies ( S1 Table ) . The results were analysed after selection of CD45+ cells , followed by exclusion of aggregated and death cells ( 7AAD+ , BD Pharmingen ) . Annexin-V staining was performed as described by the suppliers ( TACS Annexin-V FITC Apoptosis Detection kit , R&D Systems ) . Blood was collected from CO2 euthanized mice via cardiac puncture , centrifuged ( 15 minutes , 10 . 000xg , 25°C ) , and serum was kept at -20°C . Serum levels of IFN-γ , IL-6 , IL-10 , IL-12p70 , TNF and KC ( CXCL1 ) were determined using the V-PLEX Custom Mouse Cytokine assay ( Meso Scale Discovery , Maryland , USA ) . Serum MIF levels were measured by ELISA as recommended by the suppliers ( R&D Systems ) . Alternatively , culture medium concentrations of MIF , TNF , CCL2 and KC ( R&D Systems ) as well as IFN-γ , IL-6 and IL-10 ( Pharmingen ) were determined by ELISA as recommended by the suppliers . Parasite-specific IgG responses were determined using soluble lysate freshly prepared from DEAE-purified T . congolense parasites recovered from WT mice at the peak of infection ( around day 7 ) . Lysate was coated overnight at 10 μg/ml PBS in 96-well Maxisorp plates ( NUNC ) . Plates were washed ( 0 . 1% Tween 20 in PBS ) and blocked ( 1% BSA in PBS ) for 1 hour . Next , plates were washed and the sera ( 100 μl ) serially diluted starting from 1/100 in blocking buffer were added . The ELISA was subsequently performed as described by the suppliers ( SBA Clonotyping system-HRP kit ( SouthernBiotech , USA ) ) . As negative controls , blood samples incubated on lysate-free plates were used . The OD450nm recorded on lysate-free plates was subtracted from the OD450nm recorded on lysate-coated plates . One μg of total RNA prepared from 107 cells ( RNeasy plus mini kit , Qiagen ) was reverse-transcribed using oligo ( dT ) and Superscript II Reverse Transcription following the manufacturer's recommendations ( Roche Molecular Systems ) . RT-QPCR was performed in an iCycler iQ , with iQ SYBR Green Supermix ( Bio-Rad ) . Primer sequences were Vcam-1-F: 5’-CTCTCCCAGGAATACAACGA-3’ , Vcam-1-R: 5’-CACGTCAGAACAACCGAATC-3’ and Maea-F: 5’-GAGTGGTCTCCTCTCAACAG-3’ , Maea-R: 5’-AGCTACCATCTGTC TGGATG-3’ . PCR cycles consisted of 1-minute denaturation at 94°C , 45-second annealing at 55°C , and 1-minute extension at 72°C . Fold change in gene expression was expressed as compared to non-infected animals after normalization against the Ct value of the ribosomal S12 ( Mrps12 ) protein as household gene . The pHrodo-labeling of red blood cells ( RBCs ) was described in [35] . 109 pHrodo-labelled RBCs isolated from non-infected WT mice were injected i . v . in WT or Mif-/- mice . 18 h later , mice were sacrificed , liver and spleen CD11b+Ly6CintLy6G+ PMNs , CD11b+Ly6ChighLy6G- monocytes and CD11b+Ly6C-Ly6G-F4/80+ macrophages were tested for delta median fluorescent intensity ( MFI ) of the intracellular pHrodo signal determined by subtracting the PE signal of cells from mice receiving unlabeled RBCs from the PE signal of cells from mice receiving pHrodo-labeled RBCs . Serum AST and ALT levels were determined as described by the suppliers ( Boehringer Mannheim Diagnostics ) . 100 μl of APC-labelled hydroxyethyl starch ( APC-HES ( 130/0 . 4 ) at 100 μg/ml in 0 . 9% NaCl ) was injected i . v . ( as described in [62] ) . After 5–10 min . , blood was collected and the APC signal measured via cytofluorimeter ( OD660nm , Ultra Microplate reader , ELx808 , Bio-Tek instruments . inc ) . A standard curve consisting of a serial dilution of APC-HES ( starting from 200 μg/ml ) diluted in blood from non-infected mice was used to calculate the concentration of APC-HES in collected blood . The OD660nm from blood of non-infected mice was subtracted from all samples . For hemoglobin quantification , 2 μl of blood collected via cold tail cut was diluted in 200 μl distilled water in a 96 well round bottom plate ( Falcon ) . After incubation for 30 min . at 37°C and centrifugation ( 600xg , 10 min . ) , the supernatant was collected and the OD550nm measured . The hemoglobin concentration was calculated using a standard ( Sigma ) curve . Total iron ( IRON FZ kit , Chema Diagnostics ) , bilirubin and albumin ( Chema Diagnostica , Italy and Sigma Aldrich , respectively ) were measured as recommended by the suppliers . Bleeding times of mice were obtained by using the tail-cut model [63] . Briefly , anesthetized animals were transected at the 5-mm mark from the tip of the tail and incubated in warm saline ( 37°C ) . The time for cessation of bleeding was recorded . The experiment was terminated after 15 min . to avoid lethality , whereby the tail was cauterized and the bleeding time was taken as 15 min . Total spleens and the largest lobe of the liver were embedded in Tissue-Tek O . C . T . compound ( Sakura Belgium B . V . B . A . ) and kept at -80°C . Next , 5 μm cryosections were cut using a Leica microtome , fixed in cold acetone for 15 min . and washed shortly in distilled water ( 2–3 changes ) . The sections were stained for 20 min . in equal volumes of warm HCl 4% and K4[Fe ( CN ) 6]·3H2O 4% at 45°C . After washing shortly 3x with distilled water , the slides were counterstained with NFR ( Nuclear Fast Red , Sigma-Aldrich ) for 5 min . After rinsing 3x with distilled water , the slides were dehydrated ( short in EtOH 96% , short in EtOH 100% ( 2x ) , 3 min . in xylol ( 2x ) ) and mounted with DPX mounting medium ( Sigma-Aldrich ) . Images were obtained using an OLYMPUS BX41 fluorescent microscope . The CellSens Dimension 1 . 9 software was used for quantification . For each sample , an average quantification of 5 representative images was determined . The results are expressed as % stained area within the region of interest . The GraphPad Prism software was used for statistical analyses ( Two-way ANOVA or student t-test ) . Values are expressed as mean ± SEM . Values of p≤ 0 . 05 are considered significant . | Bovine African trypanosomosis is a parasitic disease of veterinary importance that adversely affects the public health and economic development of sub-Saharan Africa . Anemia is a major cause of death associated with this disease . Yet , the mechanisms underlying anemia development are not elucidated , which hampers the design of effective therapeutic strategies . We show here that in a Trypanosoma congolense infection mouse model relevant for bovine trypanosomosis , red blood cells ( RBCs ) are generated in the spleen . This compensates for the impaired maturation of RBCs occurring in the bone marrow , the normal site of RBC generation , and for the destruction of RBCs taking place in the liver and the spleen . Instead , anemia results from an increase in blood volume ( hemodilution ) . The immune molecule Macrophage Migration Inhibitory Factor ( MIF ) was found to drive RBC destruction , to block RBC maturation , as well as to trigger hemodilution . Iron accumulation in tissue due to sustained RBC destruction and hemodilution causes tissue damage , which culminates in the release of toxic molecules like bilirubin , in impaired production of blood detoxifying molecules like albumin , and in defective coagulation . Combined , these effects initiate multiple organ failure that can reduce the survival of infected mice . Given the unmet medical need for this parasite infection , our findings offer promise for improved treatment protocols in the field . | [
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] | 2016 | MIF-Mediated Hemodilution Promotes Pathogenic Anemia in Experimental African Trypanosomosis |
Fungal plant pathogens are persistent and global food security threats . To invade their hosts they often form highly specialized infection structures , known as appressoria . The cAMP/ PKA- and MAP kinase-signaling cascades have been functionally delineated as positive-acting pathways required for appressorium development . Negative-acting regulatory pathways that block appressorial development are not known . Here , we present the first detailed evidence that the conserved Target of Rapamycin ( TOR ) signaling pathway is a powerful inhibitor of appressorium formation by the rice blast fungus Magnaporthe oryzae . We determined TOR signaling was activated in an M . oryzae mutant strain lacking a functional copy of the GATA transcription factor-encoding gene ASD4 . Δasd4 mutant strains could not form appressoria and expressed GLN1 , a glutamine synthetase-encoding orthologue silenced in wild type . Inappropriate expression of GLN1 increased the intracellular steady-state levels of glutamine in Δasd4 mutant strains during axenic growth when compared to wild type . Deleting GLN1 lowered glutamine levels and promoted appressorium formation by Δasd4 strains . Furthermore , glutamine is an agonist of TOR . Treating Δasd4 mutant strains with the specific TOR kinase inhibitor rapamycin restored appressorium development . Rapamycin was also shown to induce appressorium formation by wild type and Δcpka mutant strains on non-inductive hydrophilic surfaces but had no effect on the MAP kinase mutant Δpmk1 . When taken together , we implicate Asd4 in regulating intracellular glutamine levels in order to modulate TOR inhibition of appressorium formation downstream of cPKA . This study thus provides novel insight into the metabolic mechanisms that underpin the highly regulated process of appressorium development .
Fungal pathogens cause some of the most devastating crop diseases and constitute globe-wide challenges to socioeconomic growth and food security . To facilitate entry into their hosts , many filamentous pathogens form highly specialized infection structures , known as appressoria , on the leaf surface [1 – 3] . Appressoria breach the host cuticle and allow access to the underlying epidermal cells . Appressoria have varying morphologies that range from undifferentiated germ tube swellings to discrete dome-shaped cells separated from the germ tube tip by septa [1 , 4 , 5] . In addition to facilitating plant invasion , appressoria can act as sites of effector delivery and thus mediate the molecular host-pathogen interaction [6 , 7] . Despite their widespread occurrence and long-acknowledged importance to plant health , detailed mechanistic descriptions of the regulatory pathways necessary for appressorium formation are limited to two molecular pathways , the cAMP/ PKA- and MAP kinase—signaling cascades [2 , 5 , 8 – 10] . One filamentous pathogen that has been widely studied as a model to understand the molecular biology of appressorium development is the rice blast fungus Magnaporthe oryzae [5 , 10] . This pathogen is notable for the serious threat it poses to rice production worldwide , destroying 10–30% of the global rice harvest each year . Infection begins when a three-celled spore of M . oryzae adheres to the surface of a rice leaf and germinates . At 4 hours post inoculation ( hpi ) , the germ tube hooks and begins to swell . By 8 hpi the swelling has developed into a dome-shaped appressorium that becomes melanized , pressurized , and infection competent by 16–24 hpi [3 , 5 , 11] . The tightly regulated morphological transitions that occur during appressorium development are dependent on a range of external cues , including surface hardness and hydrophobicity [12 , 13] , that act to trigger internal regulatory processes such as adenylate cyclase activation and cAMP production [5 , 8 , 9] . cAMP acts by binding the regulatory subunit of protein kinase A ( PKA ) to release the protein kinase A catalytic subunit ( cPKA ) . Genetic lesions in the cAMP/ PKA signaling pathway significantly reduce appressorium formation and those that do form are small and non-functional [5 , 14 , 15] . Appressorium formation can be remediated by the addition of cAMP when pathway mutations occur upstream of PKA . Moreover , activating cPKA by exogenous cAMP can induce appressorium formation in wild type strains ( WT ) on non-inductive hydrophilic surfaces [14] . Another internal regulatory process that has been well documented to control appressorium morphogenesis is the Pmk1 MAP kinase signaling cascade . The MAP kinase orthologue of Fus3/Kss1 , Pmk1 , is essential for appressorial formation and works in a MAP kinase cascade instigated by hydrophobicity and cutin monomer sensing [9 , 10 , 16] . Disruptions to MAP kinase signaling abolish the initiation of appressorium formation , and the germ tubes of Δpmk1 mutants remain undifferentiated [17] . Thus , the positive-acting cAMP/ PKA and MAP kinase morphogenetic regulatory cascades are integral to appressorium initiation and development . Here , we present genetic and biochemical evidence for a previously unknown , negative-acting regulator that inhibits appressorium formation downstream of cPKA . In a previous study [18] , we showed that the GATA family [19] transcription factor Asd4 was essential for sporulation , optimal growth on undefined complete media ( CM ) and appressorium formation [18] . Spores of Δasd4 mutant strains lacking a functional ASD4 allele due to homologous gene replacement produced germ tubes that could not elaborate appressoria at the apical tips . However the mechanisms involved , and any relationship of the GATA factor Asd4 to cAMP/ PKA and MAP kinase signaling , were unknown [18] . Here , we show that Asd4 regulates the expression of genes involved in nitrogen assimilation and glutaminolysis in order to modulate intracellular glutamine pools . Elevated intracellular pools of glutamine in Δasd4 mutant strains activated the target of rapamycin ( TOR ) nutrient-sufficiency signaling pathway [20] and prevented appressorium formation . Remediating glutamine levels in Δasd4 mutant strains by genetic manipulation , or bypassing the elevated glutamine signal using the specific TOR inhibitor rapamycin , promoted appressorium formation by Δasd4 mutant strains . Rapamycin treatment also induced appressorium formation in ΔcpkA mutant strains . However , cAMP treatment did not restore appressorium formation to Δasd4 mutant strains , and rapamycin treatment did not stimulate appressorium formation in Δpmk1 mutant strains . When considered together , the results presented here implicate Asd4 , glutamine metabolism and TOR as fundamental but previously unknown regulators of plant disease that act on the cAMP/ cPKA signaling pathway to control appressorium formation .
To investigate the role of Asd4 in appressorium formation , we first turned our attention to the connection between Asd4 function and optimal axenic growth on plate media . In common with other fungi , M . oryzae preferentially utilizes glucose and ammonium ( NH4+ ) over other carbon and nitrogen sources [21 , 22] . When grown on defined minimal media containing 1% ( w/v ) glucose ( GMM ) and 10 mM NH4+ as the sole carbon and nitrogen source , respectively , Δasd4 mutant strains , compared to the Guy11 wild type ( WT ) isolate used in our studies , were reduced for growth ( Fig 1A and S1 Table ) . Reduced growth on GMM with 10 mM NH4+ was similarly observed for Δasd4 mutant strains generated from the M . oryzae reference isolate 70–15 ( S1A Fig ) and , consistent with previous observations , loss of ASD4 function in 70–15 also abolished appressorium formation ( S1B Fig ) . Reduced growth on NH4+- media was not observed , however , for a Δasd4 ASD4GFP complementation strain expressing Asd4 fused to GFP from its native promoter in the Guy11-derived Δasd4 mutant background ( Fig 1A ) . The Δasd4 Asd4GFP complementation strain was also restored for appressoria formation on hydrophobic surfaces ( S1C Fig ) and Asd4GFP localized , as expected for a transcription factor , to the appressorial nucleus ( S1D Fig ) . Thus , the role of Asd4 in growth and appressorium formation is not idiosyncratic to the Guy11 isolate ( nor does the Δasd4 phenotype result from off-target gene deletion effects ) but is rather a fundamental function of this GATA factor in M . oryzae . Additional plate testing revealed that Δasd4 mutant strains ( but not WT or Δasd4 ASD4GFP complementation strains ) were also defective for growth on glucose MM ( GMM ) regardless of the nitrogen source , including compounds less preferred than NH4+ such as amino acids and nitrate ( NO3- ) ( Fig 1A and S1 Table ) . Δasd4 radial growth was not further restricted by low ( < 10 mM ) concentrations of NH4+ , amino acids or GABA as sole nitrogen sources ( Fig 1B and S1 Table ) , suggesting Asd4 is not involved in nitrogen uptake . Asd4 is also not involved in sugar utilization because we observed poor growth of Δasd4 mutant strains on MM with L-glutamine as a sole carbon and nitrogen source ( Fig 1C ) ; on MM lacking glucose but containing the less preferred , derepressing sugar xylose with L-glutamine or L-glutamate as a nitrogen source ( S2 Table ) ; and on MM with L-glutamine or L-GABA as sole carbon sources and with NH4+ as a nitrogen source ( S2 Table ) . Taken together , these observations indicate Asd4 is not required for nitrogen uptake ( Fig 1B ) or sugar metabolism ( S2 Table ) , but is required for nitrogen metabolism ( Fig 1A ) including glutaminolysis ( Fig 1C ) . Impaired nitrogen assimilation in Δasd4 mutant strains could account for its poor growth on all the nitrogen sources tested regardless of the carbon source ( Fig 1A and 1C ) . Based on sequence homology , we identified genes in the M . oryzae genome [23] encoding likely components of the nitrogen assimilatory and glutaminolytic pathways [24 – 26] , including two glutamine synthetase-encoding orthologues , GLN1 and GLN2 ( Fig 2 and S3 Table ) . To determine the expression profiles of these genes we used quantitative real-time PCR ( qPCR ) to analyze RNA extracted from WT and Δasd4 mutant strains grown in liquid shake GMM with 10 mM NH4+ for 3 and 16 h [21] . Loss of Asd4 function induced the expression of GLN1 and up-regulated the expression of GLN2 , GDH1 and MGD1 compared to WT ( Figs 2 and S2 ) . Thus , genes for assimilating and metabolizing nitrogen are misregulated in Δasd4 mutant strains when compared to WT . To understand how the gene expression perturbations in Fig 2 might affect nitrogen metabolism , we measured the steady-state concentrations of amino acids in WT and Δasd4 mycelia following growth in GMM with 10 mM NH4+ for 16 h using LC-MS/MS ( Table 1 ) . Steady-state intracellular pools of glutamine were significantly ( Student’s t-test p ≤ 0 . 05 ) increased in Δasd4 mycelial extracts compared to WT ( Table 1 ) . This suggests that defects in glutaminolysis ( Fig 1C ) and/ or the misregulation of nitrogen assimilation genes ( Fig 2 ) significantly affected glutamine biosynthesis and turnover in Δasd4 mutant strains . The concentrations of other intracellular amino acids pools were also altered in Δasd4 mutant strains under these growth conditions . For instance , Table 1 shows that steady-state intracellular pools of aspartate , alanine and arginine were reduced , while asparagine and valine levels were increased , in Δasd4 mycelia compared to WT . Collectively , these results demonstrate that glutamine turnover and the distribution of assimilated nitrogen into other nitrogenous compounds is perturbed in Δasd4 mutant strains compared to WT . These observations are consistent with our plate tests ( Fig 1A , 1B and 1C ) showing Δasd4 strains were impaired in nitrogen source utilization and glutaminolysis . We hypothesized that the misregulation of GLN1/2 , MGD1 and GDH1 gene expression might account for the accumulation of glutamine in Δasd4 mutant strains compared to WT . Of particular note , GLN1 expression was detected in Δasd4 mutant strains on NH4+ media but not in WT ( Figs 2 and S2 ) . To determine how GLN1 expression might contribute to the observed Δasd4 phenotypes , we first characterized how GLN1 was expressed under different developmental and growth conditions . We found that , in contrast to GLN2 , GLN1 gene expression was not detected in WT during appressoria development [27] ( S3A Fig ) . Furthermore , our qPCR transcript analysis showed that , unlike GLN2 , GLN1 was not expressed during early in planta colonization by WT ( S3B Fig ) , or during the growth of WT on a range of nitrogen sources in addition to NH4+ ( S3C Fig ) . However , GLN1 was highly expressed in Δasd4 mutant strains compared to WT on all the nitrogen sources tested ( S3C Fig ) . These expression data prompted us to perform chromatin immunoprecipitation ( ChIP ) in order to determine whether Asd4 interacted with GLN1 DNA in vivo . Using Anti-GFP , we immunoprecipitated chromatin samples from Δasd4 ASD4GFP strains , and from the WT lacking the ASD4GFP allele , following growth on 1% GMM with 10 mM NH4+ as the sole nitrogen source . ChIP-qPCR detected a significant enrichment ( Student’s t-test p ≤ 0 . 05 ) of GLN1 DNA in ChIP samples from strains expressing Asd4GFP compared to WT ( Fig 3A ) . The GLN1 signal/ input ratio for Asd4GFP ChIP was 9 . 6-fold higher than for WT ChIP ( ie . the background ) , thus demonstrating a physical interaction between Asd4GFP and GLN1 DNA that is consistent with the transcriptional data ( Figs 2 , S2 and S3 ) . Also , the positioning of the ChIP-qPCR primers used to detect GLN1 suggests Asd4 binding occurs in the 5 ‘ region of the gene , which might be consistent with the presence of predicted GATA-binding sequences in the promoter region of GLN1 [23] . Taken together , we conclude that GLN1 is a cryptic glutamine synthetase-encoding gene normally silenced in WT by Asd4 . To determine if GLN1 expression in Δasd4 mutant strains affected appressorium development , we deleted GLN1 from the WT and Δasd4 genomes using targeted homologous gene replacement . As expected for a gene that is not normally expressed ( S3 Fig ) , loss of GLN1 in WT did not affect colony morphology , sporulation , appressorium formation or pathogenicity ( S4A–S4D Fig ) . However , in the Δasd4 Δgln1 double mutant strain , steady-state intracellular glutamine pools were restored to WT levels when grown on NH4+-media ( Fig 3B and Table 1 ) , indicating that inappropriate GLN1 expression in Δasd4 mutant strains affects nitrogen assimilation and/ or distribution into amino acids . Furthermore , Δasd4 Δgln1 germ tubes were found to develop melanized appressorium on artificial hydrophobic surfaces ( Fig 3C ) at a significantly higher rate ( Student’s t-test p ≤ 0 . 05 ) than Δasd4 mutant strains ( Fig 3D ) . Thus , Asd4-dependent silencing of GLN1 might be required for maintaining intracellular glutamine pools at levels optimal for promoting appressorium formation in WT . GLN2 expression is also upregulated in Δasd4 mutant strains compared to WT ( although not to the same extend as GLN1; S2 Fig ) and deleting GLN2 in Δasd4 mutant strains might also affect glutamine levels and appressorium formation . However , despite numerous attempts , we were unable to generate Δasd4 Δgln2 double mutant strains in this study , perhaps indicating that whereas GLN2 functions without GLN1 under a range of developmental conditions ( S3 Fig ) , GLN1 cannot substitute for GLN2 in Δasd4 mutant strains . The relationship between GLN2 and ASD4 requires more articulation but does not affect our central conclusion that altering intracellular glutamine levels in Δasd4 mutant strains due to GLN1 expression affects appressorium formation . In addition to restoring glutamine levels , deleting GLN1 in the Δasd4 background also restored intracellular pool levels of asparagine and valine ( Table 1 ) . However , three lines of evidence suggested that the reduction in intracellular glutamine levels ( rather than global changes in nitrogen assimilation and distribution ) was linked to appressorium formation in the Δasd4 Δgln1 double mutant compared to the Δasd4 parental strain . Firstly , Δasd4 Δgln1 strains continued to grow poorly on NH4+- media ( S4E Fig ) , and aspartate , alanine and arginine levels were not remediated in the Δasd4 Δgln1 double mutant strain ( Table 1 ) . This indicates that nitrogen assimilation and/ or distribution remained defective in Δasd4 Δgln1 strains but did not prevent appressorium formation . Secondly , treating Δasd4 spores with the glutamine synthetase inhibitor L-methionine sulphoximine ( MSX ) , shown in yeast to reduce intracellular glutamine levels [28] , significantly improved ( Student’s t-test p ≤ 0 . 05 ) Δasd4 appressorium formation rates on artificial surfaces ( Fig 3E ) . Thirdly , in yeast [20 , 28 , 29] and mammals [30] , glutamine ( amongst other metabolites ) acts as a signal to activate the conserved target of rapamycin ( TOR ) pathway and facilitate growth under nutrient-sufficient conditions . The specific TOR kinase inhibitor rapamycin inactivates TOR and induces starvation-like responses in yeast and mammals [20 , 31 , 32] . Fig 3F shows that treatment of Δasd4 spores with rapamycin significantly ( Student’s t-test p ≤ 0 . 05 ) induced appressorium formation on inductive , artificial hydrophobic surfaces compared to untreated controls ( Fig 3F ) . Rapamycin treatment also induced appressorium formation in Δasd4 mutant strains derived from 70–15 ( S5 Fig ) . Intracellular glutamine levels were not affected by treatment with rapamycin and remained elevated in Δasd4 mutant strains compared to the Guy11 WT ( Fig 3G ) , suggesting rapamycin bypasses the elevated glutamine signal in Δasd4 mutant strains to promote appressoria formation . Moreover , whereas WT appressorium formation rates were not affected by rapamycin treatment on inductive hydrophobic surfaces ( Fig 3F ) , rapamycin treatment significantly ( Student’s t-test p ≤ 0 . 05 ) enhanced the rate of appressorial formation for WT and Δasd4 mutant strains on non-inductive hydrophilic surfaces ( glass coverslips ) ( Fig 3H ) . Taken together , these results suggest that ( i ) Asd4-dependent glutamine metabolism and the resulting glutamine pool sizes are important determinants of appressorium formation , and ( ii ) glutamine signaling might regulate appressorium formation via the TOR signaling pathway . The previous section suggested that Asd4 might modulate intracellular glutamine levels to control appressorium formation via TOR . We next sought more evidence for a functional connection between Asd4 and TOR signaling . Firstly , we intended to confirm that rapamycin could affect appressorium formation by acting directly on TOR , as opposed to having off-target effects on unrelated processes . To achieve this goal , we identified MoFRP1 , the M . oryzae homologue of the yeast FRP1 gene encoding the FK506/rapamycin-binding protein FKBP12 . The FKBP-rapamycin complex physically interacts with TOR to inhibit its activity , and TOR is the conserved target of FKBP-rapamycin [20] . However , FKBP12 does not interact with TOR in the absence of rapamycin and consequently in yeast , FRP1 deletion strains are viable but are not responsive to rapamycin [33] . We generated Δfpr1 mutant strains that were indistinguishable from WT on plates ( Fig 4A ) and formed appressoria on hydrophobic surfaces ( Fig 4B ) . These results are consistent with previous studies that showed how the Botrytis cinerea FKBP12 ortholog is not required for plant pathogenicity [34] . However , rapamycin failed to induce appressorium formation by Δfpr1 mutant strains on hydrophilic surfaces ( Fig 4C ) . These results demonstrate that rapamycin treatment requires FKBP12 to affect appressorium formation and thus , a priori , FKBP-rapamycin must be acting on its conserved target TOR . Secondly , we sought genetic evidence that TOR inactivation restored appressoria formation in Δasd4 mutant strains in order to corroborate our pharmacological data . We hypothesized that disrupting the sole copy of the TOR-encoding geneTOR1 in Δasd4 mutant strains would restore appressorium formation . However , we were unable to generate Δtor1 mutant deletion strains in Guy11 or Δasd4 strains , likely due to an essential role for the TOR protein in cell viability . This is consistent with studies in Fusarium graminearum that were also unable to yield targeted deletions of the single FgTOR gene [35] . Future work might involve gene silencing of TOR1 rather than deletion , but we did not attempt that here , in part because gene silencing in M . oryzae has not been developed to the stage where targeted genes can be switched off at specific stages of development , and in part because we had an alternative strategy involving the Δrbp35 mutant strain . RBP35 encodes an M . oryzae RNA-binding protein involved in processing RNA transcripts essential for rice root colonization [36] . Loss of RBP35 leads to downregulation of the TOR signaling pathway [36 , 37] . We hypothesized that deleting ASD4 in the Δrbp35 mutant strain would permit appressorium formation because the downregulation of TOR signaling resulting from the Δrbp35 allele would counteract the upregulation of the TOR signaling pathway resulting from intracellular glutamine accumulation in the Δasd4 mutant strain . Fig 4D shows that , as predicted , the Δasd4 Δrbp35 double mutant produced significantly more appressoria on inductive , hydrophobic surfaces than the Δasd4 single mutant strain . This provides genetic evidence that TOR signaling lies downstream of Asd4 and is activated in the Δasd4 mutant strains to prevent appressorium formation . Finally , we sought to demonstrate that TOR signaling was perturbed in Δasd4 strains by analyzing the expression of TOR readout genes in WT and Δasd4 mutant strains . RS2 and RS3 encode ribosomal proteins that have been shown previously to be elevated in expression when TOR is active but reduced in expression when TOR is inactivated following rapamycin treatment [38] . Fig 4E shows that the RS2 and RS3 genes were elevated in expression in Δasd4 mutant strains following axenic growth compared to WT , and this expression pattern was reversed when rapamycin was added to the growth media . Furthermore , ATG8 is an autophagy gene whose expression is repressed by active TOR . Autophagy is required for appressorium maturation in M . oryzae [39] , and is a processes inhibited by active TOR in yeast and mammals [40] . Fig 4F shows that ATG8 expression was repressed in Δasd4 mutant strains following axenic growth compared to the Δasd4 Δgln1 double mutant . When considered together , these results indicate that Asd4 acts upstream of TOR ( via glutamine ) in order to regulate appressoria formation . Consequently , TOR signaling is perturbed in Δasd4 mutant strains . A previously unknown outcome of this work is the discovery that rapamycin treatment can generate appressoria on non-inductive hydrophilic surfaces ( Fig 3H ) . cAMP treatment , or mutations that constitutively activate cAMP/ PKA signaling , also enable appressoria to form on non-inductive hydrophilic surfaces [8] . We next asked if a relationship existed between TOR and the cAMP/ PKA- and MAP kinase-signaling pathways by first treating Δasd4 spores with cAMP . cAMP treatment did not restore appressorium formation to Δasd4 mutant strains on either inductive hydrophobic ( Fig 5A ) or , in contrast to WT , on non-inductive hydrophilic surfaces ( Fig 5B ) . This indicates that activated TOR in Δasd4 mutant strains blocks appressorium formation downstream of cPKA . However , although downstream of cAMP/ PKA signaling , Asd4 is not under direct cPKA control because if so , cPKA would be required for Asd4 function and Δcpka strains would be expected to phenocopy Δasd4 strains . However , S6A Fig shows that Δcpka mutant strains grew better than Δasd4 strains on NH4+ media . Thus , CPKA is not likely epistatic to ASD4 . Further evidence that TOR acts downstream of cPKA is shown in Fig 5C and 5D . Spores of the cAMP/ PKA signaling mutant Δcpka and the MAP kinase mutant Δpmk1 were treated with rapamycin . In common with previous reports [15] , by 24 hpi , Δcpka mutant strains had formed appressoria on inductive hydrophobic surfaces at the same rate as WT ( Fig 5C ) . On non-inductive hydrophilic surfaces , Δcpka spores treated with rapamycin formed significantly more ( Student’s t-test p ≤ 0 . 05 ) appressoria than untreated spores ( Fig 5D ) . In contrast , appressorium formation by Δpmk1 strains was not induced by rapamycin treatment on hydrophobic ( Fig 5C ) or hydrophilic ( Fig 5D ) surfaces . Thus , inactivating TOR promotes appressorium formation in a cPKA-independent , Pmk1-dependent manner . Treatment with cAMP results in germ tube tip differentiation in Δpmk1 strains [16] resulting in hooking and swelling but not appressorium formation S6B Fig ) . This places Pmk1 function downstream of cAMP/ PKA [16] . In contrast , Δasd4 mutant strains treated with 10 mM cAMP on hydrophilic surfaces did not exhibit hooking or swelling and the germ tube tips remained undifferentiated ( S6B Fig ) . This places Asd4 function upstream of Pmk1 . Together , these results are consistent with the model shown in Fig 5E which shows that appressorium formation requires both activation of the cAMP/ PKA and MAP kinase signaling pathways and inactivation of the TOR signaling pathway , the latter via Asd4-dependent glutamine metabolism . Conversely , activated TOR in Δasd4 strains inhibits appressorium formation downstream of cAMP/ PKA but upstream of Pmk1 ( Fig 5E ) . We next sought to determine the physiological relevance of the connection between Asd4 and TOR under infection conditions . Δasd4 spores that had been treated with rapamycin and applied to detached rice leaf sheath surfaces formed melanized appressoria ( Fig 6A ) at rates that were not significantly different to rapamycin treated WT spores ( p = 0 . 08; Fig 6B ) . However , the resulting Δasd4 appressoria were non-functional and unable to penetrate rice leaf surfaces ( Fig 6C and 6D ) . Similarly , untreated spores of the Δasd4 Δgln1 double mutant , compared to WT and the Δasd4 parental strain , formed appressoria on leaf sheaths ( Fig 6E ) , but none were observed penetrating the host leaf surface ( Fig 6D and 6F ) . These results provide evidence that , on the one hand , Asd4-dependent TOR inactivation is required for appressorium formation during rice infection . On the other hand , Asd4 is shown here to have roles in the pre-penetration stage of infection that might be independent of TOR and which are currently unknown .
New insights into the molecular pathways that regulate plant invasion by pests could reveal attractive targets for effectively managing a range of diseases . Many fungal pathogens rely on appressoria to infect host cells [2] , and appressorial developmental , at least in M . oryzae , is dependent on positive-acting cAMP/ PKA- and MAP kinase-signaling pathways [5 , 10] . Here , we undertook the first steps in providing a mechanistic account of a negative-acting regulator of appressorium formation in M . oryzae . Appressoria form under nutrient-free , hydrophobic conditions , and we showed here that an activated TOR signaling pathway blocks this process . TOR signaling was found to be constitutively active in the GATA factor mutant strain Δasd4 , and characterizing Asd4 function provided several unique insights into the biology of infection-related development . Our results are consistent with a model whereby Asd4 represses the expression of a glutamine synthetase orthologue , GLN1 , and down-regulates the expression of other structural genes involved in nitrogen assimilation and glutamine turnover . We propose this maintains intracellular glutamine pools at levels that are not sufficient to activate TOR . Perturbing glutaminolysis and activating GLN1 expression in Δasd4 mutant strains affected the intracellular steady-state pools of glutamine and activated TOR , resulting in inhibition of the cAMP/ PKA signaling pathway and loss of appressorium formation . Inactivating TOR restored appressorium formation by Δasd4 mutant strains . This was most evident on host leaf surfaces where Δasd4 appressoria formed at rates indistinguishable from WT after rapamycin treatment . When taken together , the key novel features of the work described here include elucidating a role for TOR in inhibiting appressorial formation; discovering TOR inactivation requires Asd4; and identifying TOR as a regulator of cAMP/ PKA signaling downstream of cPKA but upstream of its connection with the MAP kinase pathway . In yeast , the GATA transcription factors Gln3 and Gat1 are required for utilizing non-preferred nitrogen sources and are downstream targets of TOR [20 , 41] . TOR is activated in response to carbon and nitrogen sufficiency cues , including glutamine [20 , 28 , 29 , 42] , leading to the induction of anabolic processes and growth . Under these conditions , Gln3 and Gat1 are maintained in the cytoplasm . TOR inactivation due to nutrient limitation results in increased autophagy , reduced protein synthesis and increased nitrogen catabolic gene expression following Gln3 and Gat1 nuclear localization [20 , 28 , 29 , 43] . Thus , TOR controls Gln3 and Gat1 activity in yeast . In addition to the Gln3 and Gat1 transcriptional activators , Dal80 and Gzf3 are yeast GATA factors that , like Asd4 , act as transcriptional repressors [42] . However , in contrast to the situation described here ( whereby Asd4 is upstream of TOR and mediates its activity by controlling glutamine metabolism ) , no comparable roles in controlling TOR signaling have been described for the yeast Dal80 and Gzf3 GATA factors [20 , 44] . Moreover , GATA factors were not found in a recent screen of yeast genes necessary for TOR inactivation [44] . Thus , Asd4 is revealed here as a novel TOR regulator , but whether this role is conserved in other fungi , or necessitated in M . oryzae due to specific demands for TOR-related processes during the infection cycle , is not known . This work has provided new mechanistic insights into the control of TOR during the M . oryzae-rice interaction . In general , though , the role of TOR in phytopathology is not well understood , although nonselective macroautophagy- an output of inactive TOR signaling in yeast—is necessary for the maturation of incipient appressoria in M . oryzae [45] . Evidence of a role for TOR in root colonization by M . oryzae has also been presented [36] whereby TOR signaling is downregulated in the non-pathogenic RNA processing mutant Δrbp35 . In the wheat pathogen F . graminearum , loss of FgFKBP12 and mutations in FgTOR1 abolished the toxicity of rapamycin , while downstream components of the TOR pathway—FgSit4 , FgPpg and FgTip41—were shown to have roles in virulence , development and mycotoxin production [35] . A separate study has characterized the serine/threonine-protein kinase SCH9 , an important downstream target of yeast TORC1 , in F . graminearum and M . oryzae [46] . ΔFgsch9 deletions strains were impaired for conidiation , mycotoxin production and virulence on wheat heads , and produced smaller spores than the F . graminearum parental strain . ΔMosch9 mutant strains exhibited reduced conidia and appressorial sizes than WT and were defective , though not abolished , in plant infection [46] . Recently , we have shown how the biotrophic growth of M . oryzae in rice cells requires a transketolase—dependent metabolic checkpoint involving the activation of TOR [38] . Loss of transketolase function resulted in Δtkl1 mutant strains that formed functional appressoria , penetrated the rice cuticle and elaborated invasive hyphae . However , Δtkl1 strains were depleted for ATP , an agonist of TOR , and these strains underwent mitotic delay and reduced hyphal growth in rice cells due to the inactivation of TOR [38] . How TOR controls the cell cycle during M . oryzae biotrophy is not known . Thus , extending these observations on the roles of TOR signaling during plant pathogenesis , and integrating them into testable models of phytopathogen growth and development , will be a future challenge . Our results presented here indicate how nitrogen turnover is an important feature of appressorial morphogenesis . Because appressoria develop on the nutrient-free surface of the leaf , internal nitrogen sources that contribute to the glutamine pools affecting TOR must be generated from the recycling of endogenous proteins during autophagic cell death of the spore . This would be consistent with previous work that determined ubiquitin-mediated proteolysis was required for many aspects of M . oryzae development , including appressorium function [47] . How protein turnover during appressorium formation integrates with Asd4-dependent nitrogen assimilation , glutaminolysis and TOR activity is therefore an important question for rice blast research that could shed light on the relationships between GATA and TOR in different systems . Downstream of Asd4 , how the TOR pathway intersects and inhibits the cAMP/ PKA signaling pathway is not known . In yeast , controversy has developed regarding how TOR and PKA signaling pathways regulate common protein targets , with some models suggesting the pathways act in parallel , and some models suggesting TOR is upstream of PKA [48] . Both models are likely valid because recent work has shown that the regulatory subunit of PKA can be a direct target of TOR in yeast , although PKA phosphorylation by TOR occurs selectively and is not global [48] . Our study of M . oryzae places TOR downstream and inhibitory to cAMP/ PKA signaling during appressorium formation and thus provides an opportunity to uncover new relationships between these two fundamental pathways . This would include identifying common targets that control appressorium formation . One point of shared control for the two pathways could be autophagy because Δcpka and Δpmk1 mutants are unable to undergo autophagy in M . oryzae [39] , and PKA is necessary for autophagy in yeast [49] . Thus , focusing on TOR , PKA and autophagy will likely yield important insights into appressorial biology . Another important area of future study will be uncovering the role of Asd4 in cuticle penetration . Although Δasd4 Δgln1 strains and Δasd4 spores treated with rapamycin could form melanized , mature appressoria , they were unable to form penetration pegs . This suggests Asd4 might play a TOR-independent role in the late stages of appressorium maturation and/ or the regulation of penetration peg formation . Successful peg penetration is dependent on the Pmk1 MAP kinase target Mst12 [9 , 50] , and on NADPH oxidase-dependent control of septin and F-actin reorganization [51] . Also , in addition to the Pmk1 MAP kinase pathway that is essential for appressorium formation , another MAP kinase pathway in M . oryzae , involving Mps1 , is not involved in appressorium formation but is required for penetration [52] . Determining if and how Asd4 intersects with these processes will likely yield important new discoveries about appressorium function . In summary , this work demonstrates the utility of performing axenic physiological analyses to make testable inferences regarding the metabolic strategies underlying M . oryzae infection of host plants . This has revealed that TOR inactivation requires Asd4 , and that the TOR pathway is a previously unknown negative-acting regulator of cAMP/ PKA signaling . The results presented here thus provide mechanistic insights that extend our basic knowledge of regulatory networks in fungi by revealing novel connections between GATA- , TOR- and PKA-mediated signaling within the context of appressorium morphogenesis . Given the wealth of knowledge about the role of TOR in yeast physiology and human pathologies , further explorations of the function of TOR in M . oryzae and other appressorium-forming phytopathogens could provide new tools and avenues for alleviating the global burden of plant diseases attributable to fungi . Conversely , revealing Asd4 as a new TOR regulator might shed light on aspects of developmental control that could be applicable to a wide range of cellular processes across taxa .
The strains used in this study are listed in Table 2 . Strains were grown on complete medium ( CM ) containing 1% ( W/V ) glucose , 0 . 2% ( W/V ) peptone , 0 . 1% ( W/V ) yeast extract and 0 . 1% ( W/V ) casamino acids , or on minimal medium ( MM ) containing 1% glucose and 0 . 6% sodium nitrate , unless otherwise stated , as described in [18] . For the growth tests , nitrogen sources were used in MM at 10 mM concentrations , unless otherwise specified . Plate images were taken with a Sony Cyber-shot digital camera , 14 . 1 mega pixels . For spore counts , 10 mm2 blocks of mycelium were transferred to the center of each plate , and the strains grown for 12 days at 26 °C with 12 hr light/dark cycles . Spores were harvested in sterile distilled water , vortexed vigorously and counted on a haemocytometer ( Corning ) . Spores were counted independently at least three times . The spore suspensions were adjusted to a concentration of 1 x 105 spores/ml to perform the appressoria formation tests . Rapamycin ( Rap; LC Laboratories , USA ) and monobutyryl cyclic AMP ( cAMP; Sigma-Aldritch , USA ) were added to the spore suspensions at a concentration of 55 nM and 10 mM , respectively . Appressorial development was evaluated on inductive , hydrophobic plastic coverslips and non-inductive , hydrophilic glass slides . Both substrates were inoculated with 200 μl of each spore suspension and appressoria were observed 24 hrs post-inoculation ( hpi ) . Rates were determined by counting the number of appressoria formed from 50 conidia per coverslip , repeated in triplicate for each strain [53] . Concentrations of 55 nM—1 μM rapamycin could induce appressorial formation of WT on hydrophilic surfaces . Rice plant infections were made using a susceptible rice ( Oryza sativa ) cultivar , CO-39 , as described previously [18] . Fungal spores were isolated from 12–14 day-old plate cultures and spray-inoculated onto rice plants of cultivar CO-39 in 0 . 2% gelatin at a concentration of 1 x 105 spores/ml , and disease symptoms were allowed to develop under conditions of high relative humidity for 120 hrs . Live-cell imaging was performed as described previously [53] using 3 cm-long leaf sheath segments from 3–4 week-old rice plants and injecting one end of the sheath with a spore suspension of 1 x 105 spores/ml in 0 . 2% gelatin . At the time points indicated , leaf sheaths were trimmed and observed using a Nikon Eclipse 50i microscope and a Nikon D100 digital net camera . The average rates of appressorium formation and penetration were determined for each strain , in triplicate , by analyzing 50 spores or appressoria per rice cuticle [53] . Asd4GFP was imaged as described previously for H1:RFP [38] using 488 nm and 500–550 nm for excitation and emission wavelengths , respectively . Gene functional analysis was achieved by the split marker method described in [18] , using the oligonucleotide primers shown in S4 Table . GLN1 was replaced in the Δasd4 parental strain using the hygromycin B resistance selectable marker , hph . MoFPR1 and GLN1 were replaced in the Guy11 genome using the ILV1 gene conferring resistance to sulphonyl urea [18] . ASD4 was deleted from the 70–15 background using ILV1 . ASD4 was deleted in the Δrbp35 parental strain using the Bar gene conferring bialaphos resistance [18] . Gene deletions were verified by PCR as described previously [18] . The original Δasd4 mutant strain [18] was complemented with ASD4GFP under its native promoter , constructed using the vector pDL2 and the primers ASD4-G F/R ( S4 Table ) , following the protocol of Zhou et al . [54] . Strains were grown for 48 h in CM before switching to minimal media for 3 h and 16 h , as indicated . For in planta expression studies , detached rice leaf sheaths were inoculated with WT and harvested at the indicated timepoints . Mycelia and leaves were frozen in liquid nitrogen , and lyophilized for 36 hrs . RNA was extracted from fungal mycelium using the RNeasy mini kit from Qiagen . RNA was converted to cDNA using the qScript reagents from Quantas . Real time quantitative PCR ( qPCR ) was performed on an Eppendorf Mastercycler Realplex using the recommended reagents with primers designed using the netprimer software program ( S4 Table ) . qPCR data was analyzed using the Realplex software . Thermocycler conditions were: 10 min at 95°C , followed by 40 cycles of 95°C for 30 sec , 63°C for 30 sec and 72°C for 30 sec . ChIP was performed as described in [55] . WT and Δasd4 ASD4GFP complementation strains were grown in liquid CM for 48 h before switching to 1% GMM with 10 mM NH4+ for 16 h . Three biological replicates were performed per strain . Thirty per cent of each DNA aliquot was saved prior to ChIP and served as the input controls . Anti-GFP mAB-Agarose ( D125-8 , MBL ) was used to precipitate Asd4GFP-bound chromatin . A control ChIP was run in parallel using Mouse IgG-Agarose ( A0919 , Sigma ) . The quantification of input and precipitated GLN1 DNA was performed at least in triplicate using qPCR and the specific GLN1 primers shown in S4 Table . GLN1 DNA enrichment by Asd4GFP ChIP was confirmed by calculating the values of GLN1 DNA obtained following Anti-GFP immunoprecipitation ( the signal ) relative to the levels of GLN1 in the input controls , then comparing the GLN1 signal-to-input ratio derived from the Δasd4 ASD4GFP samples against those of the WT negative control lacking Asd4GFP . Amino acid analysis was performed by LC-MS/MS using the aTRAQ kit provided by ABSciex ( Framingham , MA ) . Samples of lyophilized ground mycelia were first washed with water by suspension and centrifugation at 4 oC . The supernatants were aspirated and the pellets were used for extraction . The extractions were performed using 90% MeOH with 128 μM Norleucine added as internal standard . After incubation at -55 oC , a 10 μL aliquot was removed and concentrated by SpeedVac Centrifugation followed by derivatization according to the aTRAQ protocol . The parameters for the MRM acquisition , chromatography and ion source operation were also according to the aTRAQ protocol ( Curtain gas = 20 , CAD = Medium , IS = 1500; TEM = 600 , GS1 = GS2 = 60 , heater on ) employing a Nova Pak C-18 4 μm 3 . 9x150mm from Waters Corp . ( Milford , MA ) for the separation of the tagged amino acids with a sample injection volume of 2 μL . Amino acid concentrations were calculated from the ratio of areas ( Heavy aTRAQ/light aTRAQ labeled standards ) and corrected for losses for the entire procedure by means of the Nle Internal Standard area recovery . | Many fungal pathogens destroy important crops by first gaining entrance to the host using specialized appressorial cells . Understanding the molecular mechanisms that control appressorium formation could provide new routes for managing severe plant diseases . Here , we describe a previously unknown regulatory pathway that suppresses appressorium formation by the rice pathogen Magnaporthe oryzae . We provide evidence that a mutant M . oryzae strain , unable to form appressoria , accumulates intracellular glutamine that , in turn , inappropriately activates a conserved signaling pathway called TOR . Reducing intracellular glutamine levels , or inactivating TOR , restored appressorium formation to the mutant strain . TOR activation is thus a powerful inhibitor of appressorium formation and could be leveraged to develop sustainable mitigation practices against recalcitrant fungal pathogens . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | GATA-Dependent Glutaminolysis Drives Appressorium Formation in Magnaporthe oryzae by Suppressing TOR Inhibition of cAMP/PKA Signaling |
Plasmodium ovale is comprised of two genetically distinct subspecies , P . ovale curtisi and P . ovale wallikeri . Although P . ovale subspecies are similar based on morphology and geographical distribution , allelic differences indicate that P . ovale curtisi and P . ovale wallikeri are genetically divergent . Additionally , potential clinical and latency duration differences between P . ovale curtisi and P . ovale wallikeri demonstrate the need for investigation into the contribution of this neglected malaria parasite to the global malaria burden . In order to detect all P . ovale subspecies simultaneously , we developed an inclusive P . ovale-specific real-time PCR assay based on conserved regions between P . ovale curtisi and P . ovale wallikeri in the reticulocyte binding protein 2 ( rbp2 ) gene . Additionally , we characterized the P . ovale subspecies prevalence from 22 asymptomatic malaria infections using multilocus genotyping to discriminate P . ovale curtisi and P . ovale wallikeri . Our P . ovale rbp2 qPCR assay validation experiments demonstrated a linear dynamic range from 6 . 25 rbp2 plasmid copies/microliter to 100 , 000 rbp2 plasmid copies/microliter and a limit of detection of 1 . 5 rbp2 plasmid copies/microliter . Specificity experiments showed the ability of the rbp2 qPCR assay to detect low-levels of P . ovale in the presence of additional malaria parasite species , including P . falciparum , P . vivax , and P . malariae . We identified P . ovale curtisi and P . ovale wallikeri in Western Kenya by DNA sequencing of the tryptophan-rich antigen gene , the small subunit ribosomal RNA gene , and the rbp2 gene . Our novel P . ovale rbp2 qPCR assay detects P . ovale curtisi and P . ovale wallikeri simultaneously and can be utilized to characterize the prevalence , distribution , and burden of P . ovale in malaria endemic regions . Using multilocus genotyping , we also provided the first description of the prevalence of P . ovale curtisi and P . ovale wallikeri in Western Kenya , a region holoendemic for malaria transmission .
Plasmodium ovale , the causative agent of benign tertian malaria , was identified as a distinct malaria parasite species in 1922 based on its characteristic oval morphology in infected erythrocytes [1] . P . ovale rarely causes severe disease in humans living in malaria endemic regions , but can cause serious clinical disease in naive travelers [2–9] . The actual prevalence and clinical relevance of P . ovale is likely underestimated for the following reasons . First , P . ovale is often found as a mixed infection with other malaria parasite species [10–12] . This can confound microscopic identification of P . ovale due to difficulties in differentiating P . ovale from other morphologically similar malaria parasites , such as P . vivax . Second , the characteristic low-level parasitemia of P . ovale infection further complicates microscopic detection due to the difficulty in finding and identifying low numbers of P . ovale parasites [13] . Finally , malaria Rapid Diagnostic Tests ( RDTs ) show a reduced ability to detect P . ovale compared to other human malaria parasites , resulting in false negative cases [14–16] . However , the use of extremely sensitive molecular detection methods , such as polymerase chain reaction ( PCR ) , have revealed a higher prevalence of P . ovale and expanded the geographical distribution of this malaria parasite compared to what was previously identified based on microscopy [10 , 17–20] . Recent findings demonstrated that P . ovale exists as two genetically distinct sympatric subspecies , P . ovale curtisi and P . ovale wallikeri[21–24] . Morphological differences between the two P . ovale subspecies have not been identified , thereby limiting the use of microscopy to differentiate P . ovale curtisi and P . ovale wallikeri . As recent studies suggest potential clinical and latency duration differences between the two P . ovale subspecies , [25 , 26] , a discriminatory assay to differentiate P . ovale curtisi and P . ovale wallikeri is clinically relevant . Additionally , initial P . ovale-specific assays developed by our group and others were unknowingly designed based on gene sequences specific to only one subspecies , thereby failing to detect the other P . ovale subspecies . PCR assays that target conserved genetic regions between the two subspecies are , therefore , necessary to determine the true P . ovale prevalence and distribution [27–30] . Small-subunit ribosomal RNA ( ssrRNA ) genes are common targets for malaria parasite species-specific assays based on nucleotide polymorphisms that facilitate specific detection of the species of interest [28 , 29 , 31] . Although rRNA based PCR assays have proven useful for the detection of low-level parasitemias of a single malaria parasite species , Demas et al . demonstrated that alternative gene targets may be more sensitive for species-specific detection in the context of mixed species infections [32] . A quality control program to determine the ability of 10 different laboratories to detect malaria parasite species based on rRNA PCR revealed detection of P . ovale to be the most difficult , with a detection rate of 70% [33] . Additionally , allelic diversity within the P . ovale ssrRNA alleles may further limit the ability of rRNA specific PCR assays to detect P . ovale infections [34] . Due to these difficulties in the detection of P . ovale , we designed a novel P . ovale-specific assay based on a gene found only in P . ovale curtisi and P . ovale wallikeri and not present in other human malaria parasite species . This approach reduces aberrant amplification of non-target malaria species and allows for the detection of low-level P . ovale infections in the presence of high parasitemias of other malaria parasite species , such as P . falciparum . Several epidemiology surveys of exant malaria species have established the endemicity of P . ovale in Western Kenya based on microscopic identification , entomological studies , and nucleic acid detection methods [13 , 35–38] . Clinical cases due to P . ovale relapse in non-immune individuals after traveling to Western Kenya have also been reported , including a single case of a returned traveller with P . ovale curtisi infection [25 , 39] . However , the lack of data on the prevalence and distribution of P . ovale curtisi and P . ovale wallikeri in Western Kenya represents a critical gap in our understanding of the true malaria epidemiology in this region that could impact both patient treatment and malaria control strategies . In this study , we developed a novel , highly specific , real-time PCR ( qPCR ) assay to detect all P . ovale subspecies simultaneously based on a conserved region of the P . ovale-specific reticulocyte binding protein 2 ( rbp2 ) gene . This inclusive P . ovale rbp2 qPCR assay was characterized and validated to determine the sensitivity , limit of detection , limit of quantification , specificity , repeatability , and reproducibility . In addition , the occurrence of both P . ovale subspecies ( P . ovale curtisi and P . ovale wallikeri ) was documented in Western Kenya using multilocus genotyping . Our P . ovale species-specific assay can be utilized to better characterize the presence , parasitemia , geographical distribution , and the contribution of this malaria parasite species to mixed species infections and to clinical disease in malaria endemic regions .
Anonymized human whole blood samples were collected with signed informed consent under approved protocols ( Walter Reed Army Institute of Research Human Use and Review Committee Protocols #1720 and 1306 , Kenya Medical Research Institute ( KEMRI ) SSC#2008 and 1111 ) . Clinically healthy ( asymptomatic ) adult individuals in Nyanza Province , Kenya were screened ( active detection ) with the Parascreen Pan/Pf ® malaria Rapid Diagnostic Test ( Zephyr Biomedicals , Verna , Goa , India ) for the presence/absence of malaria parasites from March through September of 2008 . Thin and thick smears were examined subsequently by up to 5 expert microscopists in the Malaria Diagnostic Centre ( MDC ) , Kisumu , Kenya for malaria species designation and estimation of quantitative parasitemia [40] . Samples identified as positive for P . ovale ( n = 22 ) via microscopy , in which all were mixed infections with other malaria species , were targeted for DNA extraction and PCR based analysis . DNA was extracted from 200 microliters of whole blood using the QIAamp DNA Minikit ( Qiagen , Venlo , Netherlands ) following the manufacturer’s protocol . DNA was eluted in 200 microliters of Buffer EB and samples were stored at −20°C until time of use . A human-specific RNaseP based qPCR assay was performed for each sample in duplicate to confirm successful nucleic acid extraction [41] . Tryptophan-rich antigen ( tra ) gene . The P . ovale-specific tryptophan-rich antigen ( tra ) gene was recently identified as a target to discriminate between P . ovale subspecies based on DNA sequence length and single nucleotide polymorphisms ( SNPs ) [22 , 23 , 30] . We utilized the PoTRA fwd3 and PoTRA rev3 primers reported in Oguike et al . 2011 for PCR analysis [23] . Primers ( Table 1 ) were synthesized by Integrated DNA Technologies ( IDT , Coralville , IA , USA ) and purified by standard desalting methods . Each PCR assay consisted of 1X Sigma JumpStart REDTaq ReadyMix ( 20 mM Tris-HCl , 100 mM KCl , 4 mM MgCl2 , 0 . 4 mM of each dNTP , 0 . 03 unit/μl of Taq DNA polymerase , Sigma , Balcatta , WA , USA ) , 8 . 75 picomoles of each primer , and one microliter of template with a final volume of 25 microliters . PCR cycling conditions were: initial denaturation for 2 minutes at 95°C followed by 45 cycles of 95°C for 30 seconds , 58°C for 45 seconds , 72°C for 1 min and a final extension at 75°C for 5 minutes . All conventional PCRs were performed on a DNA Engine PTC-200 Thermal Cycler ( MJ Research , Waltham , MA , USA ) . Reticulocyte binding protein 2 ( rbp2 ) gene . The reticulocyte binding protein 2 ( rbp2 ) gene was utilized by Oguike et al . 2011 to differentiate between P . ovale subspecies using qPCR melt curve profiles based on six SNPs present within a 120 base pair fragment . We designed a novel set of primers ( Table 1 , IDT ) using Primer Express software ( Life Technologies , version 3 . 0; Frederick , MD , USA ) to amplify a smaller , 74 base pair region of the rbp2 gene for assay development . Our primers ( PoRBP2f and PoRBP2r ) are located within conserved DNA sequences of the P . ovale subspecies to ensure detection and amplification of both P . ovale subspecies . The amplicon also contains a single SNP to distinguish P . ovale subspecies by DNA sequencing . Fig . 1 shows the single SNP in the rbp2 amplicon at position 431 , in which P . ovale curtisi contains an adenine and P . ovale wallikeri contains a thymine . Primer BLAST was utilized to ensure our primers were specific for P . ovale and would not amplify non- P . ovale malaria parasite DNA or human DNA . PCRs consisted of 1X Sigma JumpStart REDTaq ReadyMix Reaction Mix , 25 picomoles of each primer , and one microliter of template , with a final volume of 25 microliters . PCR cycling conditions were as follows: initial denaturation at 95°C for 2 minutes followed by 40 cycles of 95°C for 30 seconds , 55°C for 30 seconds , 72°C for 30 seconds , and a final extension at 72°C for 10 minutes . Small subunit ribosomal RNA ( ssrRNA ) gene . We utilized P . ovale-specific primers ( Table 1 , IDT ) reported by Fuehrer et al . 2012 ( rOVA1WC and rOVA2WC ) to further characterize P . ovale positive samples based on differences within the small subunit ribosomal RNA ( ssrRNA ) gene [29] . PCRs consisted of 1X Sigma JumpStart REDTaq ReadyMix Reaction Mix , 25 picomoles of each primer , one microliter of template , and a final volume of 25 microliters . PCR cycling conditions were as follows: initial denaturation at 95°C for 4 minutes followed by 35 cycles of 94°C for 1 minute , 58°C for 2 minutes , 72°C for 2 minutes , and a final extension at 72°C for 5 minutes . DNA sequencing . PCR products were visualized on 0 . 7% agarose gels stained with ethidium bromide . PCR products were cloned into the pCR 2 . 1-TOPO TA vector ( Life Technologies ) based on manufacturer’s guidelines . Plasmid purification was performed using the QIAprep Spin Miniprep kit ( Qiagen ) and used as template for sequencing reactions . PCR products were sequenced using the M13 Forward ( −20 ) Primer ( Life Technologies ) at the Biomedical Instrumentation Center at the Uniformed Services University or GENEWIZ Inc ( Germantown , MD , USA ) using the ABI 3500XL Genetic Analyzer and the ABI 3730XL DNA Analyzer , respectively . Sequencing facility was chosen based on temporal availability . DNA sequences were aligned and analyzed with previously published sequences using SeqMan software ( DNAStar Lasergene Version 8 . 1 . 5 , Madison , WI , USA ) . Reference sequences utilized for DNA alignments are shown in Table 2 . Primer Express software ( Life Technologies , version 3 . 0 ) was utilized to design a hydrolysis probe ( Table 1 ) for use with our rbp2 primers on the ABI 7500 fast real-time PCR ( qPCR ) platform ( Life Technologies ) . An alignment of the P . ovale rbp2 DNA sequences was constructed using the Clustal Omega Program provided by the European Molecular Biology Laboratory—European Bioinformatics Institute ( EMBL-EBI ) [42 , 43] . We utilized the Jalview output tool to visualize the DNA sequence alignment ( Fig . 1 ) [44] . Primers and probe were designed in order to amplify a conserved region within the rbp2 gene to ensure detection of both P . ovale subspecies by our qPCR assay at the same time . In silico analyses were performed to ensure primers and probe were specific to P . ovale and would not amplify genes of other malaria parasites or human DNA . Each qPCR reaction consisted of the following: 1X TaqMan Fast Universal PCR Master Mix , No AmpErase UNG ( Life Technologies , Cat No . 4364103 ) , 5 picomoles of each primer and probe , and one microliter of template in a final volume of 20 microliters . Real-time PCR was performed utilizing fast thermal cycling conditions ( 95°C for 20 seconds , followed by 40–60 cycles of 95°C for 3 seconds and 60°C for 30 seconds ) . Analysis of qPCR results was performed using ABI 7500 Fast Real-Time PCR Systems Software ( Life Technologies , Version 2 . 0 . 5 ) . Basic statistical analyses ( means , standard deviations , coefficient of variation ) , generation of standard curve graphs , calculation of slopes , and coefficient of correlation were performed in Microsoft Excel or GraphPad Prism ( GraphPad Prism Software Version 6 , La Jolla , CA , USA ) . Plasmid standard curve . We cloned the 74 base pair rbp2 amplicon into the pCR 2 . 1-TOPO TA vector ( Life Technologies ) following manufacturer’s guidelines and eluted the rbp2 plasmid in PCR grade water . The approximate rbp2 amplicon copy number per microliter was determined based on spectrophotometer ( Nanodrop 2000c ) concentration in nanograms per microliter . Plasmids with the rbp2 amplicon ( rbp2 plasmid ) were diluted in water to generate a ten-fold serial dilution from 100 , 000 rbp2 copies per microliter to 0 . 1 rbp2 copies per microliter . The resulting non-linearized ten-fold serial dilution series was utilized as a standard curve in subsequent validation experiments including determination of the linear dynamic range , specificity , reproducibility , repeatability , and limit of detection . The effect of the conformation of the rbp2 plasmid on standard curve linearity was analyzed by linearizing the rbp2 plasmid using the NotI restriction enzyme ( New England BioLabs Inc , Ipswich , MA , USA ) according to the manufacturer’s protocol . Rbp2 plasmid linearization was confirmed by gel electrophoresis on a 0 . 7% agarose gel stained with ethidium bromide . Linearized rbp2 plasmid was purified using the Qiagen PCR Purification Kit following the manufacturer’s protocol . The approximate rbp2 copy number per microliter of the linearized rbp2 plasmid was determined and diluted in water to generate a ten-fold serial dilution ( 100 , 000 to 0 . 1 copies per microliter ) . The rbp2 standard curve PCR efficiency and coefficient of correlation ( R2 ) were determined and the Pearson product-moment correlation was used to compare the linearized and non-linearized rbp2 plasmid standard curves ( GraphPad Prism ) . Validation experiments . Real-time PCR efficiency was determined using a standard curve of 10-fold serial dilutions of the non-linearized rbp2 plasmid . Efficiency ( E ) was calculated using the following formula: E = 10 ( − 1/ slope ) −1 . Rbp2 plasmid standard curve samples were run at least in duplicate and the mean quantification cycle ( Cq ) value was utilized to generate the standard curve . The limit of detection was defined as the concentration of rbp2 plasmid in copies per microliter that gave a positive signal in at least one replicate well in two separate qPCR experiments . Limit of quantification was defined as the range of rbp2 plasmid concentrations that maintained linearity and therefore could be used to quantify P . ovale concentration from test samples . Specificity was analyzed using DNA template from non- P . ovale malaria parasite species and uninfected human DNA . Genomic DNAs from P . falciparum strains 3D7 ( WRAIR ) , FCR3CSA ( ATCC/BEI Resources , MR4 , Manassas , Virginia ) , Dd2 ( ATCC/BEI Resources , MR4 ) , and NF54 ( ATCC/BEI Resources , MR4 ) were utilized as template to assess specificity . P . vivax genomic DNA was extracted from frozen whole parasites ( kind gift of Dr . J . Prachumsri , Mahidol University , Bangkok , Thailand ) . Since pure P . malariae positive samples were unavailable , we utilized three samples collected as part of the blood collection protocol in Kenya that were positive for P . malariae as well as P . falciparum by microscopy and PCR , but were negative for P . ovale . The P . malariae parasitemias ranged from approximately 30 to 2400 parasites per microliter . Additionally , genomic DNAs from P . knowlesi , P . simiovale , P . fragile , and P . cynomolgi ( ATCC/BEI Resources , MR4 ) , were also utilized as templates . Specificity was further analyzed by performing spiking experiments in which a known concentration of rbp2 plasmid was added to template containing P . falciparum 3D7 DNA ( 10 , 000 parasites per microliter ) or P . vivax DNA ( 517 parasites per microliter ) . One-way analysis of variance ( ANOVA ) was used to determine differences in Cq values for spiking experiments ( GraphPad Prism ) . Within-run repeatability was defined as the variation of Cq values within a single run and was analyzed by calculating the percent coefficient of variation ( %CV ) of Cq values in replicate wells . Between-run repeatability was defined as the variation of Cq values in separate qPCR runs and was determined by calculating the percent coefficient of variation ( %CV ) of mean Cq values based on six separate qPCR experiments . Reproducibility was evaluated by comparing the assay performance by a technician at the USAMRU-K laboratory in Kisumu , Kenya and the Uniformed Services University in Bethesda , Maryland , USA . Quantification comparison: Microscopy versus rbp2 qPCR . Parasitemias were determined for P . ovale positive blood films based using standard microscopic methods at the Malaria Diagnostic Centre , affiliated with both USAMRU-K and KEMRI , in Kisumu , Kenya . DNA was extracted from microscopy-positive P . ovale samples and tested using the P . ovale-specific rbp2 qPCR assay . Approximate rbp2 copy number per microliter was determined based on the rbp2 plasmid standard curve . Parasitemias as determined by expert microscopy ( parasites per microliter ) were compared to rbp2 copy number per microliter as determined by the P . ovale-specific qPCR in order to examine potential correlation between rbp2 plasmid copy number and microscopic parasitemias .
Human-specific RNaseP qPCR . A previously described qPCR assay based on the human-specific RNaseP gene was performed to confirm the presence of nucleic after DNA extraction [41] . The human RNaseP gene was detected from all 22 samples ( Average Cq = 29 . 12 , Cq Range = 28 . 2–32 . 87 , standard deviation = 1 . 02 ) , indicating extraction methods yielded DNA suitable for subsequent PCR experiments . Tryptophan-rich antigen ( tra ) gene . Alignments of tra gene sequences revealed nine samples ( 40 . 9% ) positive for P . ovale curtisi type 1 , two samples ( 9 . 1% ) positive for P . ovale curtisi type 2 , six samples ( 27 . 3% ) positive for P . ovale wallikeri type 1 , and three samples ( 13 . 6% ) positive for P . ovale wallikeri type 2 ( Table 3 ) . Previously published GenBank accession numbers were utilized as reference sequences for alignment and are shown in Table 2 . Representative P . ovale curtisi type 1 , P . ovale curtisi type 2 , P . ovale wallikeri type 1 , and P . ovale wallikeri type 2 tra DNA sequences were deposited under GenBank accession numbers KM494978-KM494981 , respectively , and are identical to the reference sequences . As shown in Table 4 , unique polymorphisms within the tra gene were also detected and confirmed by at least two separate sequencing reactions for 5 samples: Po05 , Po12 , Po20 , Po06 , and Po07 ( Accession numbers KM494982-KM494986 , respectively ) . Samples Po12 and Po20 contained an 18 base pair insertion between nucleotide positions 171 and 172 ( based on P . ovale wallikeri type 1 HM594180 reference sequence ) , which represents a short sequence repeated throughout the tra gene . Two samples , Po9 and Po18 , failed to amplify with the tra primers despite multiple PCR attempts . Reticulocyte binding protein 2 ( rbp2 ) gene . DNA sequences of the rbp2 gene were obtained for all 22 P . ovale samples ( Table 3 ) . S1 Table contains the 74 pair rbp2 amplicon for both P . ovale curtisi and P . ovale wallikeri . These sequences were not eligible for submission as the minimum length requirement for GenBank is 200 nucleotides . P . ovale subspecies results based on rbp2 gene sequences agreed with subspecies results based on the tra gene sequences . Thirteen ( 59% ) of the P . ovale samples were positive for P . ovale curtisi and 9 ( 41% ) were positive for P . ovale wallikeri . None of our samples failed to amplify with the rbp2 primers . Small subunit rRNA ( ssrRNA ) gene . Nineteen of the 22 P . ovale positive samples were detected by the ssrRNA gene assay ( Table 3 ) . P . ovale curtisi and P . ovale wallikeri ssrRNA sequences were approximately 99% identical to previously published sequences at this locus . Representative P . ovale curtisi and P . ovale wallikeri ssrRNA sequences were deposited in GenBank as KM494987 and KM494988 , respectively . P . ovale subspecies results based ssrRNA gene sequences agreed with subspecies results based on tra and rbp2 gene sequences . Three samples , Po9 , Po11 , and Po18 , failed to amplify using the ssrRNA primers despite a second attempt using an additional microliter of template DNA . Plasmid standard curve analysis of rbp2 qPCR assay . Since all 22 P . ovale microscopy positive samples were successfully amplified and sequenced using the rbp2 primers , we developed an rbp2 based qPCR assay to detect all P . ovale subspecies simultaneously in a single assay . Efficiency of the rbp2 qPCR assay was analyzed using the non-linearized rbp2 plasmid 10-fold serial dilution standard curve . Efficiency ranged from 90%–99% for six consecutive qPCR experiments with a coefficient of correlation ( R2 ) greater than 0 . 99 . A representative qPCR amplification plot and standard curve are shown in Fig . 2 and 3 , respectively . All 22 P . ovale samples identified as P . ovale positive by expert microscopy were detected using our rbp2 qPCR assay . There was no difference in PCR efficiency or R2 value based on the conformation ( linearized vs . non-linearized ) of the rbp2 plasmid standard curve ( Pearson product-moment correlation = 0 . 998 , P<0 . 001 ) . Limit of quantification and limit of detection . The linear dynamic range of the rbp2 qPCR assay was determined to be between 6 . 25 copies per microliter and 100 , 000 copies per microliter based on serial dilutions of the rbp2 plasmid . Two-fold serial dilutions of known concentrations of the rbp2 plasmid were performed in at least duplicate to determine the limit of detection . Dilutions containing 1 . 5 copies per microliter of the rbp2 plasmid were detected by at least one replicate well in two separate qPCR experiments . Specificity . In order to test the specificity of our rbp2 assay for P . ovale , we performed qPCR using DNA isolated from cultured P . falciparum 3D7 ( 10 , 000 parasites per microliter ) and P . vivax DNA ( 517 parasites per microliter ) . Based on a series of ten separate qPCR experiments , DNA from P . falciparum and P . vivax were uniformly negative . To ensure no background from other P . falciparum strains , we tested genomic DNAs from strains Dd2 , NF54 , and FCR3CSA , which were also not detected by our assay . We tested DNA from P . knowlesi , P . fragile , and P . cynomolgi and found DNA from these malaria parasite species were undetectable by our rbp2 qPCR assay . As we were unable to obtain pure P . malariae samples , we examined DNA samples isolated from the blood of individuals co-infected with both P . malariae and P . falciparum . These P . falciparum and P . malariae co-infected samples were also negative , indicating that our rbp2 qPCR assay does not detect P . malariae DNA . Two different control DNA samples from malaria uninfected human blood were also uniformly negative . All specificity experiments were carried out to 60 cycles in an attempt to capture non-specific amplification , which was never seen , although the standard curve and the P . ovale-containing field samples amplified appropriately . Spiking experiments , in which P . falciparum DNA or P . vivax DNA was added to the rbp2 plasmid standard curve samples and subsequently utilized as template for the rbp2 qPCR did not significantly alter the Cq values compared to when the standard curve plasmid samples were run alone ( ANOVA , P = 0 . 9993 , Fig . 4 ) . Interestingly , our rbp2 qPCR assay detected P . simiovale genomic DNA isolated from filter paper . DNA sequencing utilizing the rbp2 primers revealed that the 74 base pair rbp2 region in P . simiovale is identical to that in P . ovale curtisi . Subsequent attempts using additional primers to sequence the full-length rbp2 gene of P . simiovale were not successful . As these additional primers successfully amplified P . ovale positive samples , the inability to amplify the full-length P . simiovale rbp2 gene is likely due to sequence polymorphisms between P . ovale and P . simiovale in the primer binding regions . Repeatability . Within-run repeatability of the rbp2 plasmid standard curve Cq values was high , with the percent coefficient of variation ( %CV ) of dilution series replicates between 0 . 00–2 . 23% ( Table 5 ) . Results were also repeatable between runs , with the percent coefficient of variation ( %CV ) between 1 . 17–3 . 43% ( Table 5 ) . Repeatability was determined using results from six separate consecutive qPCR experiments . Reproducibility . Analysis of the efficiency of the rbp2 assay was performed independently at the USAMRU-K laboratory . A known concentration of non-linearized rbp2 plasmid was diluted in PCR grade water to generate a 10-fold dilution standard curve for PCR efficiency analysis . The assay was performed with the same P . ovale-specific primers and probe utilized in validation experiments in a final volume of 50 microliters of Life Technologies TaqMan Fast Master Mix for the USAMRU-K ABI 7500 . Despite slight variations in qPCR set up and cycling conditions , the Kenya laboratory obtained a PCR efficiency of 93 . 6% with an R2 >0 . 99 for the standard curve analysis . These results are identical to the PCR efficiencies and R2 values obtained at USU . The USAMRU-K laboratory also performed specificity experiments and demonstrated no amplification from P . falciparum DNA , DNA from uninfected human blood , or from negative template controls . Quantification comparison: Microscopy versus rbp2 qPCR . Quantitative parasitemia determined by expert microscopy ( parasites per microliter ) was compared to the rbp2 copy number per microliter based on the rbp2 plasmid standard curve ( Fig . 5 ) . A modest correlation was determined ( R2 = 0 . 6595 ) . This lack of a strong correlation is not surprising , as all P . ovale parasitemias were low , ranging from 16–3800 parasites/μl , and such low-level parasitemias are notoriously difficult to quantify accurately by microscopy [40 , 45–47] . Additionally , the samples utilized for comparison were mixed malaria species infections , mainly with P . falciparum . Mixed species infections create further difficulties for the accurate quantification of P . ovale-specific parasitemia based on light microscopy , but single-species P . ovale infected samples were not available .
Based on multilocus genotyping using the rbp2 , ssrRNA , and tra genes , we detected both P . ovale curtisi and P . ovale wallikeri in approximately equal frequencies in a small sample set from Western Kenya , a region in which P . ovale subspecies characterization had not been previously performed . The presence of both P . ovale subspecies in Western Kenya is in agreement with other studies in sub-Saharan Africa and P . ovale endemic regions that describe the sympatric distribution of P . ovale curtisi and P . ovale wallikeri [23 , 27 , 48] . We also identified additional allelic diversity within the tra gene in P . ovale samples from Kenya ( Table 4 ) compared to what was previously identified in P . ovale samples from other malaria endemic regions [23] . This allelic diversity at the P . ovale tra gene is consistent with reports of other tra variants identified by DNA sequencing , however our tra sequences are unique from previously published tra gene sequences [30] . Our new inclusive P . ovale-specific qPCR assay is based on rbp2 , a gene that contains conserved regions between P . ovale curtisi and P . ovale wallikeri but that is absent from other human malaria parasite species . The rbp2 qPCR assay described herein allows simultaneous detection of both P . ovale subspecies using a single set of primers and probe . All 22 samples were detected and sequenced using our rbp2 primers , highlighting the utility of these primers for P . ovale identification . P . ovale subspecies differentiation by DNA sequencing of the 74 base pair rbp2 sequence region was in absolute agreement with tra and ssrRNA DNA sequencing results . This again emphasizes the utility of the PoRBP2fwd1 and PoRBP2rev1 primers for P . ovale subspecies discrimination based on a single SNP at position 431 ( Fig . 1 ) located between these primers . In agreement with other previous studies , these data demonstrate perfect dimorphism between P . ovale curtisi and P . ovale wallikeri , providing further support for the separation of the two P . ovale subspecies [21–24 , 48–50] . As we begin to understand potential clinical , pathological , and biological differences between the two P . ovale subspecies , molecular methods to distinguish P . ovale curtisi and P . ovale wallikeri will aid in these research efforts . Additionally , as genomic data and full genome sequences become available for P . ovale curtisi and P . ovale wallikeri , phylogenetic analyses to determine the evolutionary relatedness between these and other malaria species will likely further our understanding of these newly characterized but poorly understood human parasites . Using the rbp2 plasmid as a standard curve , the linear dynamic range of our assay was determined to be between 6 . 25 copies of rbp2 per microliter to 100 , 000 copies of rbp2 per microliter . The lower , non-linear but still clearly positive limit of detection of our assay was determined to be 1 . 5 copies of rbp2 per microliter , confirming this assay’s capacity to detect low-level parasitemias . P . ovale parasitemias are characteristically lower than other malaria species , so we limited the testing of our upper dynamic range to 100 , 000 rbp2 copies per microliter , as higher copy numbers would likely be epidemiologically and clinically irrelevant . We used the rbp2 plasmid to determine the linear dynamic range and limit of detection because of difficulties obtaining pure P . ovale infected samples from malaria endemic regions and the inability to culture P . ovale parasites . The paucity of published genomic information for P . ovale also hinders the determination of copy number of P . ovale-specific genes , such as the rbp2 , tra , and ssrRNA genes , utilized in this study . Thus , we are further limited in our attempts to appropriately correlate rbp2 copy number and P . ovale parasitemias . Despite these limitations , we demonstrate the utility of our P . ovale-specific assay to detect low-levels of the rbp2 plasmid and to detect low P . ovale parasitemias ( as low as 16 parasites per microliter ) from human blood samples collected in Western Kenya . Our study was also limited by only testing samples collected in Western Kenya and additional validation is therefore needed to confirm the ability of the rbp2 qPCR assay to detect total P . ovale from other malaria endemic regions . As the 22 samples included in this study were identified as P . ovale by microscopy , further studies are needed to test the P . ovale rbp2 qPCR assay with submicroscopic and asymptomatic P . ovale infections with a range of parasitemias . Repeatability and reproducibility of qPCR assays are important components of assay validation as they indicate the assay’s capacity to provide consistent and reliable results in different environments . Different users under modified laboratory conditions performed this assay successfully , with high PCR efficiency and equivalent quantification . Specificity experiments showed no cross reactivity of our assay with P . falciparum , P . vivax , P . malariae , P . cynomolgi , P . knowlesi , P . fragile , and DNA from uninfected human blood even when qPCR was performed for 60 cycles . The complete lack of background amplification from human and other malaria parasite DNA , verifies the exquisite specificity of the assay . Further , assay performance was unchanged in the presence of DNA from other malaria parasite species . This is of particular importance for P . ovale , as this malaria species is often found as a co-infection with other malaria species . Interestingly , our rbp2 qPCR assay also detected DNA obtained from P . simiovale . As P . simiovale rbp2 sequence information is not available , we attempted to amplify the full-length P . simiovale rbp2 gene using additional primers based on the P . ovale rbp2 gene . However , we were unable to amplify the full P . simiovale rbp2 gene , suggesting the P . ovale and P . simiovale rbp2 genes may be similar but not identical . These results warrant further investigation of the P . simiovale rbp2 and additional specificity experiments of other P . ovale assays that may also unknowingly detect P . simiovale . Of the 22 samples identified as P . ovale positive by expert microscopy , three samples ( Po9 , Po11 , Po18 ) failed to amplify at two of the three loci tested despite multiple attempts ( Table 3 ) . However , the rbp2 gene was successfully amplified for all 22 samples as was a human-specific RNaseP endogenous control . These data , along with the parasitemia data from multiple expert microscopists , indicate that the 22 samples were P . ovale positive and that DNA template quality was unlikely to be the cause of the failed amplifications at the tra and ssrRNA loci . The inability to successfully amplify at all three loci could be explained by several reasons including: sequence polymorphisms , template degradation , low P . ovale density , and inter-laboratory variability due to reagents , equipment , and personnel . Additional investigation into potential reasons for the failure to amplify at all loci was prevented due to limited sample volume . The limited correlation between microscopy and rbp2 qPCR results ( Fig . 5 ) is not surprising as parasitemia calculations for P . ovale human samples at low parasitemias are notoriously difficult , particularly in co-infected samples [45] . Our P . ovale positive samples from Western Kenya are all co-infected with either P . falciparum or P . malariae , thus likely complicating the microscopy quantitation further . Variation between parasitemia and rbp2 copy number could also be explained by the P . ovale parasite stage . For example , a P . ovale ring stage counts as a single parasite by microscopy and DNA extracted from a P . ovale ring stage parasite represents one genome . However , a P . ovale schizont is counted as a single parasite by microscopy but DNA extracted from a P . ovale schizont may contain up to 14 genomes . This is a limitation of our study , as any relationship between P . ovale parasitemia and rbp2 copy number based on qPCR would depend on the parasite stages observed under the microscope and present in the blood sample obtained for DNA extraction . Utilizing a plasmid standard curve for qPCR assays provides an efficient method for standardizing assays that does not require culturing organisms or using human samples . However , recent studies have highlighted important concerns regarding the plasmid template conformation that could lead to quantification bias of plasmid template by qPCR [51 , 52] . After linearizing our template plasmid to compare with a non-linearized plasmid standard curve , we found no difference in Cq value , R2 , slope , or PCR efficiency with the rbp2 qPCR assay . This is in agreement with another recent study , which also found no difference in plasmid standard curve based on the plasmid confirmation ( linearized versus non-linearized ) [53] . These results indicate that the effect of plasmid conformation on standard curve quantification may be assay specific . In addition to plasmid conformation , several additional quality control factors were optimized , including plasmid isolation methods , purification , storage , and developing appropriate laboratory protocols to minimize freeze-thawing , handling , and contamination . Conventional PCR assays targeting the multi-copy small subunit ssrRNA genes are sensitive methods to detect and differentiate malaria species [54] . Initial P . ovale-specific ssrRNA PCR protocols showed limited capability to detect both P . ovale subspecies and have since been adapted to target conserved regions between the two subspecies . [29 , 55 , 56] . Although ssrRNA conventional PCR protocols have shown high sensitivity and specificity for malaria detection , we aimed to develop a novel P . ovale-specific assay based on a gene target that is found only in P . ovale and is absent from other malaria species infecting humans . We believe this approach enhances the specificity of our P . ovale-specific assay and eliminates the potential for nonspecific amplification of non- P . ovale species . Additionally , allelic variation within the ssrRNA genes of P . ovale curtisi and P . ovale wallikeri may limit the ability of ssrRNA based assays to capture all P . ovale infections due to sequence polymorphisms [34] . We found no allelic variation in the primer and probe-binding regions of the rbp2 gene from 22 P . ovale positive samples , indicating the potential utility of rbp2 for P . ovale subspecies detection . While nested PCR is often utilized to enhance sensitivity for malaria PCR detection , we chose a single step qPCR protocol , as a nested PCR approach requires additional labor and cost to perform the second PCR . Nested PCR also increases the risk of laboratory contamination of PCR product and requires separate laboratory space to minimize the risk of contamination . Our P . ovale-specific qPCR assay maintains high sensitivity while also minimizing the additional cost , labor , designated laboratory space , and potential PCR product contamination that can be associated with nested PCR protocols . Our P . ovale-specific qPCR assay provides several advantages for our future epidemiological studies of this neglected , and clinically relevant , malaria parasite species . First , fast qPCR conditions allow for a reaction to be completed in less than 1 hour . Second , the qPCR platform bypasses the need for gel electrophoresis , reducing the risk of amplicon contamination of the laboratory . Third , the use of a hydrolysis probe increases specificity compared to double stand DNA ( dsDNA ) based qPCR product detection . Our P . ovale-specific rbp2 qPCR assay can be utilized to better characterize the presence , parasitemia , geographical distribution , and the contribution of P . ovale to mixed-species infections and to clinical disease in malaria endemic regions . | Humans can be infected with five malaria parasite species: Plasmodium falciparum , P . vivax , P . malariae , P . knowlesi , and P . ovale . Although the vast majority of malaria morbidity and mortality worldwide can be attributed to P . falciparum , non-falciparum malaria parasites can also cause clinical disease . Researchers use nucleic acid based detection methods , such a polymerase chain reaction ( PCR ) , to detect low-density malaria parasitemias that can evade microscopic detection . P . ovale was recently identified to exist as two subspecies , P . ovale curtisi and P . ovale wallikeri , that look identical but differ genetically . In this study , we developed a novel real-time PCR ( qPCR ) assay to detect all P . ovale parasites , based on a conserved gene between P . ovale curtisi and P . ovale wallikeri . We also used DNA sequencing to differentiate between P . ovale curtisi and P . ovale wallikeri from a small sample of P . ovale asymptomatic infections in Western Kenya . Through the use of our novel rbp2 qPCR assay , we aim to characterize the prevalence of P . ovale in future epidemiological studies in order to better understand this neglected malaria parasite species . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Characterization of Plasmodium ovale curtisi and P. ovale wallikeri in Western Kenya Utilizing a Novel Species-specific Real-time PCR Assay |
Gametes are the source and carrier of genetic information , essential for the propagation of all sexually reproducing organisms . Male gametes are derived from a progenitor stem cell population called spermatogonial stem cells ( SSCs ) . SSCs give rise to male gametes through the coordination of two essential processes: self-renewal to produce more SSCs , and differentiation to produce mature sperm . Disruption of this equilibrium can lead to excessive proliferation of SSCs , causing tumorigenesis , or can result in aberrant differentiation , leading to infertility . Little is known about how SSCs achieve the fine balance between self-renewal and differentiation , which is necessary for their remarkable output and developmental potential . To understand the mechanisms of SSC maintenance , we examine the planarian homolog of Nuclear Factor Y-B ( NF-YB ) , which is required for the maintenance of early planarian male germ cells . Here , we demonstrate that NF-YB plays a role in the self-renewal and proliferation of planarian SSCs , but not in their specification or differentiation . Furthermore , we characterize members of the NF-Y complex in Schistosoma mansoni , a parasitic flatworm related to the free-living planarian . We find that the function of NF-YB in regulating male germ cell proliferation is conserved in schistosomes . This finding is especially significant because fecundity is the cause of pathogenesis of S . mansoni . Our findings can help elucidate the complex relationship between self-renewal and differentiation of SSCs , and may also have implications for understanding and controlling schistosomiasis .
Spermatogenesis is highly prolific , relying on SSCs for continual production of progeny . This prodigious output must employ multiple mechanisms to maintain the fine balance between SSC self-renewal and differentiation . Understanding the mechanisms of SSC maintenance is crucial for the treatment of several physiological and disease conditions . Self-renewal of SSCs without differentiation can result in tumor formation . For instance , seminoma-like growth of undifferentiated spermatogonia is seen upon expression of activated RAS , or overexpression of GDNF , or Cyclins D2 and E1 , or BCL6B [1–3] . In contrast , aberrant development and differentiation of spermatozoa , due to insufficient sperm production , inadequate sperm motility , or abnormal sperm morphology , are the principal causes underlying male infertility [4] . The maintenance of germline stem cells is also a key feature behind the fecundity of trematodes such as Schistosoma mansoni , a causative agent of schistosomiasis , a disease affecting over 200 million people worldwide . The pathogenicity of schistosomiasis is due to the body’s immune response to eggs laid by adult worms in their human hosts . The spermatogenic output of these parasites is clear from the observation that individuals with schistosomiasis can pass eggs over 30 years after initial infection [5 , 6] . Thus , in addition to illuminating the causes behind infertility and tumorigenesis , a better understanding of molecules that play a role in the maintenance of SSCs may provide new approaches for preventing and treating schistosomiasis . One such molecule is a planarian homolog of Nuclear Factor-Y B ( NF-YB ) , which belongs to the NF-Y family of transcription factors [7–9] . The NF-Y complex has been studied in several developmental contexts in D . melanogaster [10–12] , C . elegans [13] , and D . rerio [14] , and a function in germ cells for this gene family has been described in the freshwater planarian Schmidtea mediterranea [15] . More recent work has shown that members of this complex also play roles in somatic stem cell maintenance in the asexual strain of S . mediterranea [16] . In the sexual strain , upon NF-YB knockdown , animals initially lost their SSC pool followed by more differentiated male germ cells . After over a month of NF-YB ( RNAi ) , mature sperm were seen in sperm ducts of sexual planarians , and some animals had small testes filled with mostly spermatids and some sperm . Thus , NF-YB ( RNAi ) animals appeared to complete the initial rounds of spermatogenesis , but failed to maintain sperm production over time , possibly due to the loss of SSCs . This phenotype is strikingly similar to that seen in Plzf and TAF4b mutant mice [17 , 18] . How NF-YB coordinates the balance between self-renewal and differentiation decisions of SSCs at both cellular and molecular levels needs further exploration . In this study , we provide a phenotypic characterization of planarian NF-YB ( RNAi ) using new markers to track individual stages of spermatogenesis [19] . Our experiments indicate that in S . mediterranea , NF-YB does not control germ cell specification or differentiation , but instead promotes self-renewal and proliferation of early germ cells . Interestingly , the NF-YB ( RNAi ) phenotype in the male germline is strikingly similar in both S . mediterranea and the trematode S . mansoni . Our findings provide mechanistic insight into the role of NF-YB , show the conserved function of this molecule in the testes of both free-living and parasitic flatworms , and may have implications for combating schistosomiasis .
To observe the different stages of NF-YB ( RNAi ) phenotype progression in the male germ cells of S . mediterranea ( schematic Fig 1A ) , we tracked the following male germ cell populations and their respective signature transcripts: SSCs ( = nanos ) , spermatogonia ( = germinal histone H4/gH4 ) , spermatocytes ( = tektin1/tkn-1 ) , and spermatids ( = protein kinase A/pka ) [20–22] . Loss of SSCs and spermatogonia was observed at the earliest stages of NF-YB ( RNAi ) , indicated by loss of nanos and gH4 labeling ( Figs 1B , 1C and S1 ) . Although the spermatocyte layer was initially unaffected , upon continued knockdown , a reduction in tkn-1-labeled spermatocytes was seen ( Figs 1D and S1 ) . NF-YB ( RNAi ) animals also show varying degrees of mature spermatozoa loss during the RNAi timecourse . At later time points the testes only contained clusters of spermatids , labeled with pka ( Figs 1E and S1 ) and some sperm . Eventually , there was a complete loss of all male germ cells after NF-YB ( RNAi ) . Although all animals showed a progressive loss of male germ cells starting with the least differentiated cells ( SSCs and spermatogonia ) , there was some variability both between samples and within samples in NF-YB ( RNAi ) animals . We hypothesize that this variability could be a reflection of the NF-YB mRNA/protein half-life in the system , or possibly reflect the variability of germ cell turnover among animals and between different testis lobes ( S1 Fig , S1 Table ) . Since NF-YB is part of a hetero-trimeric complex , requiring its partners NF-YA and NF-YC for transcriptional activation or repression [23–25] , we also examined whether other components of the planarian NF-Y complex function in the gonad . We identified and cloned two planarian paralogs of NF-YA ( A1 and A2 ) , two of NF-YB ( B and B2 ) and one of NF-YC . ClustalW analyses showed a high degree of conservation between the histone-fold motifs of these proteins with their human counterparts ( S2A Fig ) . By in situ hybridization , the NF-YB2 transcript was detected only in somatic cells and excluded from the testes ( S2B Fig ) . NF-YA1 , NF-YA2 , and NF-YC were detected in the male gonads as well as somatic tissues ( S2B Fig ) . Knockdown of NF-YB2 , NF-YA1 , and NF-YC resulted in lesions , head regression , and lethality ( S2C Fig ) , suggesting a role for these genes in neoblast ( adult somatic stem cells ) or somatic maintenance . Our observation is consistent with experiments performed in the asexual strain of S . mediterranea [16] . Due to the early lethality of these RNAi treatments , we could not ascertain whether these genes also play roles in testes maintenance , and if they phenocopy NF-YB ( RNAi ) . NF-YA2 ( RNAi ) had no somatic or germ cell phenotype ( S2C and S2D Fig ) , and its function may be redundant with NF-YA1 . NF-YB appears to be the only subunit of the planarian NF-Y complex with a germline-specific function and this gene belongs to the relatively small group of planarian genes required for early germ cell maintenance . Thus , we directed our focus on its functional characterization . The early germ cell loss seen in NF-YB ( RNAi ) animals is reminiscent of the knockdown of planarian nanos [21] , a gene with conserved germ cell functions across metazoans ( S3 Fig ) . The similar phenotypes of NF-YB ( RNAi ) and nanos ( RNAi ) led us to speculate that the two genes might act in concert to control germ cell development . The presence of a CCAAT box , the NF-Y DNA binding motif , -118bp upstream of the nanos transcription start site made NF-YB an attractive candidate regulator of nanos expression . In S . mediterranea sexual hatchlings , expression of nanos is detected within 3 days post hatching [21] . If NF-YB plays a role in the regulation of nanos expression , we reasoned that NF-YB expression would precede that of nanos . In situ analyses showed that the NF-YB transcript was seen in the soma in early hatchlings . However , germline NF-YB transcript expression is observed only at later time points relative to nanos expression ( Fig 2A ) . This observation suggests that NF-YB does not activate the expression of nanos . Planarians specify germ cells from tissue fragments completely devoid of reproductive structures [21 , 26 , 27] . We modified a previously established experimental paradigm [27] to further test whether NF-YB is required for the specification of nanos-expressing cells . Briefly , sexually mature planarians were fed NF-YB double-stranded ( dsRNA ) 2–3 times and amputated anterior to the ovaries . The resulting head fragments ( lacking reproductive structures at the time of amputation ) were monitored for the re-appearance and maintenance of nanos-expressing cells at various regeneration time points ( Fig 2B ) . To ensure that NF-YB protein levels were depleted below the threshold required for the maintenance of nanos+ cells , we performed NF-YB ( RNAi ) in sexually mature planarians ( 6 feedings over a month ) until nanos+ cells were lost ( S4A Fig ) . At fifteen days of regeneration , both control ( RNAi ) ( n = 11/11 ) and NF-YB ( RNAi ) ( n = 11/11 ) ( Fig 2C ) head fragments showed de novo nanos expression , indicating that NF-YB is not required for the respecification of nanos+ SSCs . There was no significant difference in the number of respecified nanos+ cells between control and NF-YB ( RNAi ) animals at this early time point ( n = 11/11 for both , S5A Fig ) . The respecified nanos+ cells in NF-YB ( RNAi ) animals persisted through regeneration for over a month ( Fig 2D–2E’ ) . In later stages of regeneration ( 45 days post amputation ) , control animals had numerous nanos+ clusters and many nanos+ cells per cluster , indicating proliferation of SSCs ( n = 10/10 , Figs 2C–2E’ , S5B and S5C ) . By contrast , NF-YB ( RNAi ) animals had fewer SSC clusters and the nanos+ cells remained mostly as single cells in these clusters ( n = 10/10 , Figs 2C–2E’ , S5B and S5C ) . nanos transcripts in male germ cells were not detected in dmd1 ( RNAi ) animals ( S4B Fig ) , consistent with the previously reported role for this gene in SSC specification [24] . We validated the effectiveness and specificity of NF-YB knockdown with quantitative real time PCR and in situ hybridization experiments to ensure that nanos expression in NF-YB knockdown animals was not due to residual NF-YB , defective regeneration , or off-target effects ( S4C and S6 Figs ) . Together , these data suggest that NF-YB is not required for SSC specification , but may function later in SSC self-renewal or proliferation . The NF-Y complex is associated with cell cycle regulation in other systems [28–32] , and is enriched in many stem cell populations [33–37] . After ruling out a role for NF-YB in SSC specification , we tested if NF-YB is required for cell cycle progression of SSCs and spermatogonia and whether early germ cell loss in NF-YB knockdown was through differentiation or apoptosis . We knocked down NF-YB in juvenile sexual planarians; in these animals , the testes contain clusters of SSCs and spermatogonia , but lack the more differentiated germ cells . Thus , aberrations in spermatogonial differentiation are more easily assayed in these animals compared to mature sexual animals that already possess differentiated male germ cells . The dsRNA-fed animals were processed at early and late knockdown timepoints , cryosectioned , and sections of the same animal were used for phospho-histone H3 ( PH3S10 ) and TUNEL labeling ( Fig 3A ) . In planarian testes , the SSCs give rise to spermatogonia , undergoing three rounds of mitosis with incomplete cytokinesis , which can be easily visualized by PH3S10 staining [38 , 39] . NF-YB ( RNAi ) animals showed a dramatic reduction in mitotic cell number following two feedings of dsRNA ( Fig 3B and S1 Table ) . We confirmed that NF-YB ( RNAi ) animals do not show a reduction in nanos+ SSCs or the nanos transcript at this RNAi timepoint ( S7 Fig ) . Not surprisingly , this difference was more pronounced after four feedings , following loss of the mitotic spermatogonial layer ( Fig 3C and S1 Table ) . Our analysis clearly indicates a reduction in proliferation of SSCs and spermatogonia upon NF-YB knockdown . We next tested whether germ cells were undergoing apoptosis in NF-YB ( RNAi ) animals . We found low levels of TUNEL labeling in NF-YB ( RNAi ) animals after two feedings of dsRNA ( Fig 3D , S1 Table ) . However , following four feedings of NF-YB dsRNA there was an increase in apoptosis of the differentiated male germ cells ( Fig 3E , S1 Table ) . NF-YB ( RNAi ) animals in later stages of RNAi ( after four feedings ) still have differentiated male germ cells ( spermatocytes and spermatids ) , indicating that the early germ cells are unlikely to be undergoing apoptosis themselves and are capable of differentiation . Conditional deletion of both NF-YB alleles in primary mouse embryonic fibroblasts causes a block in progression of the cell cycle and induction of apoptosis [40] . We speculate that a similar mechanism of NF-YB-mediated testis-maintenance could be acting here . We have previously shown that molecular similarities exist between planarian and schistosome somatic stem cells [41–43]; however , similarities between the germ cells of these two flatworms remain unexplored . To test if the NF-Y complex plays a similar role in the gonads of free-living and parasitic flatworms , we examined the role of NF-Y components in the parasite S . mansoni . We were especially interested in this comparison because the morbidity associated with schistosomiasis is a result of the tremendous reproductive output of the parasite . Inhibiting fertilization or propagation by blocking germ cell production may open novel avenues for treating this disease . Although schistosomes are dioecious , we restricted our analysis to male schistosomes ( Schematic in Fig 4A ) due to the testis-specific function of NF-YB in planarians [15] . In situ hybridization revealed that Sm-NF-YB , Sm-NF-YA , and Sm-NF-YC ( Figs 4A and S8A ) were enriched in the parasite testes , with possible low levels of somatic expression . Further , we found that Sm-nanos-1 is expressed in the testes of S . mansoni ( Fig 4A ) . Next , we asked if NF-YB plays a similar role in schistosome testis maintenance . We knocked down the NF-Y complex components in schistosomes by culturing the worms in vitro in the presence of dsRNA [41] . Similar to the planarian NF-YB knockdown phenotype , we found that following seven days of dsRNA treatment , Sm-NF-YB ( RNAi ) animals , and surprisingly Sm-NF-YA ( RNAi ) and Sm-NF-YC ( RNAi ) animals , showed a loss of Sm-nanos-1 labeling ( Figs 4B and S8B ) . To determine whether loss of nanos expression is due to reduced proliferation of the male germ cells , we performed a 24-hour EdU pulse in vitro at early ( 7 days ) and late ( 14 days ) RNAi time points . After 7 days of knockdown , control ( RNAi ) animals showed a large number of EdU+ cells in the testes ( Fig 4C ) . In contrast , Sm-NF-YB ( RNAi ) animals had fewer EdU+ male germ cells ( Fig 4C ) , while maintaining an intact testis structure ( assessed using DAPI labeling ) . Similarly , Sm-nanos-1 ( RNAi ) animals had fewer proliferating male germ cells but greater male germ cell loss compared to Sm-NF-YB ( RNAi ) animals ( Fig 4C ) . Following 14 days of RNAi , most male germ cells were lost in both Sm-NF-YB ( RNAi ) and Sm-nanos-1 ( RNAi ) animals ( Fig 4D and 4E ) . Sm-NF-YA and Sm-NF-YC knockdown animals showed a similar RNAi phenotype ( S8C Fig ) . Together , we conclude that the role of NF-YB in male germ cell proliferation is conserved in both free-living and parasitic flatworms .
Significant progress has been made in understanding post-transcriptional regulation and the role of RNA-binding proteins in the germline [44–46] . However , mechanisms of transcriptional regulation in germ cells have received relatively less attention . A variant of TFIIA , ALF or TFIIAτ , is expressed in male and female gonads in mice and Xenopus , and can substitute for TFIIA in core promoters [47 , 48] . A TATA-Binding Protein ( TBP ) variant , TLF/TRF2 is not required for the female reproductive system , but loss of TLF/TRF2 results in an inability to complete spermatogenesis in males [49–51] . Several TBP-associated factors ( TAFs ) have germ cell-specific roles . Deletion of TAF4b in both female [52] and male [19] gonads results in sterility . A TAF-II-80 homolog , cannonball , is expressed in Drosophila spermatocytes , and mutations in this gene block spermatid differentiation [53] . Here , we studied a planarian male germ cell-specific transcription factor , NF-YB , exploring the cellular mechanisms of NF-YB-mediated maintenance of early planarian male germ cells . We also found gonadal enrichment of NF-Y components in male schistosomes , and a requirement for NF-Y genes in the proliferation and maintenance of male germ cells in these parasites . Members of the NF-Y complex are enriched in stem cell populations in many other systems . In proliferating skeletal muscle cells the NF-Y complex , and its target cyclin B1 , are expressed at high levels; however , in terminally differentiated cells there is a loss or reduction in NF-Y components [33] . In hematopoietic stem cells the NF-Y complex activates HOXB4 , a homeobox gene that is expressed abundantly in primitive HSCs . During HSC differentiation there is a decline in NF-Y binding to the HOXB4 promoter and a concomitant reduction in HOXB4 transcript levels [54] . Subsequent work showed that NF-Ya overexpression in HSCs shifts the balance towards HSC-self-renewal rather than differentiation [34] . By contrast , deletion of NF-Ya caused an accumulation of HSCs in G2/M phase of the cell cycle , followed by apoptosis , possibly as a result of dysregulation of key genes involved in cell cycle control , apoptosis , and self-renewal [36] . In human embryonic stem cells ( ESCs ) , the NF-Y complex is required for proliferation and isoforms of NF-Ya are differentially expressed during differentiation [35] . A recent study showed that , in addition to its housekeeping functions , the NF-Y complex regulates ESC identity by coordinating the binding of ESC master transcription factors to core self-renewal and pluripotency genes [37] . Our finding that planarian NF-YB is necessary for self-renewal and proliferation of SSCs and spermatogonia is consistent with the known functions of the NF-Y complex in these other stem cell systems , and provides insight into the role of this transcription factor family in germ cells . We also performed functional characterization of the NF-Y complex in the parasite Schistosoma mansoni . Previous work reported that the S . mansoni NF-YA protein is expressed in both male and female gonads and levels of Sm-NF-YA decreased as maturation of the male germ cells progressed [55] . We found that NF-Y components are necessary for the proliferation of male germ cells in S . mansoni . The NF-YB ( RNAi ) phenotype in planarians is strikingly similar to those observed in Plzf [17 , 18] and TAF4b [19] mutant mice , both of which undergo progressive loss of spermatogonia with age . This progressive loss , from the least differentiated to the most differentiated germ cells , is not expected in the case of meiotic or maturation defects , strongly indicating that all three genes function in early male germ cell maintenance . Both Plzf and TAF4b mutant mice are born with normal number of gonocytes/primordial germ cells , indicating proper specification of the germ cells; planarian tissue fragments lacking germ cells and NF-YB activity regenerate normal number of SSCs , consistent with proper specification of these cells . Plzf and TAF4b mutant mice complete the initial rounds of spermatogenesis but show decreasing fertility with age; in planarians , NF-YB ( RNAi ) SSCs and spermatogonia are able to differentiate initially but fail to do so over time . Plzf and TAF4b mutant mice show a decrease in proliferative spermatogonia over time; NF-YB ( RNAi ) animals also show reduced proliferation of male germ cells in both S . mediterranea and S . mansoni . Given the striking similarities between Plzf , TAF4b , and NF-YB functions in the male gonad , it is not unreasonable to speculate that these three genes might be controlling similar targets , or genes that perform similar functions . We also observed an increase in apoptotic germ cells in later stages of NF-YB ( RNAi ) . NF-YB ( RNAi ) results in loss of nanos+ SSCs , but these cells do not show obvious TUNEL labeling . Several possibilities exist to explain this observation . SSCs may be undergoing apoptosis but the signal may be too weak or transient to be detected , or they may use a non-apoptotic mechanism of cell death . It is also possible that the early germ cells could be entering the differentiation pathway aberrantly , resulting in apoptosis of the differentiating cells . NF-YB ( RNAi ) results in loss of elongated spermatids and sperm in addition to early germ cells ( Figs 1 and S1 ) . Although the primary phenotype of NF-YB ( RNAi ) is the loss of early germ cells , the expression of NF-YB transcript in the more differentiated male germ cells leaves open the possibility that NF-YB regulates additional target ( s ) vital for the survival of these differentiated cells . The identification of a testis-specific component of the planarian NF-Y complex , and our finding that NF-YB is required for the maintenance of planarian SSCs , provides a valuable tool for understanding the dynamics of early , undifferentiated germ cells . Putative SSC-specific targets of NF-YB will help reveal the function of the NF-Y complex in the planarian , in which tissue-specific knockdown is not possible . Several genes required for the maintenance of various stages of planarian germ cell development have homologs in other species [15] . Many of these genes , such as rap55 , ELAV , and MSY4 [56–60] , are known to play roles in germ cell development in vertebrates . Given the observation that the NF-YB transcript is upregulated in mouse spermatogonia relative to other male germ cells [59] , we predict that the role of NF-YB in SSC self-renewal and proliferation might be conserved in vertebrates . Thus , a better understanding of SSC-specific NF-YB targets in planarians is expected to yield insight into the workings of early germ cells across different systems .
In adherence to the Animal Welfare Act and the Public Health Service Policy on Humane Care and Use of Laboratory Animals , all experiments with and care of vertebrate animals were performed in accordance with protocols approved by the Institutional Animal Care and Use Committee ( IACUC ) of the University of Illinois at Urbana-Champaign ( protocol approval number 13017 ) . Clonal lines of hermaphroditic S . mediterranea [20] were maintained in 0 . 75X Montjuïc salts at 18°C [61] . Clonal asexual lines [62] were maintained in 0 . 5 g/L Instant Ocean Sea Salts at 20°C . Coding DNA sequences of the NF-Y complex were obtained from SmedGD [63] . Mixtures of sexual and asexual planarian cDNA were used as templates to clone NF-Y components , using primers in S2 Table . Total RNA was extracted using TRIzol ( Invitrogen ) according to manufacturer’s instructions , DNase treated ( Fisher Scientific ) and purified using an RNA clean-up kit ( Zymo ) before reverse transcription ( iScript , Bio-Rad ) . Prior to RNA extraction , animals were starved for 7 days after the last RNAi feeding to ensure that any remnant dsRNA was cleared from the system . qPCR was performed using GoTaq qPCR master mix ( Promega ) using Applied Biosystems StepOne Plus RT-PCR system . All experiments were done in biological and technical triplicates . Transcript levels were normalized to β-tubulin ( primers in S2 Table ) . Relative mRNA levels were calculated using ΔΔCT [64] . Planarian double-stranded RNA ( dsRNA ) synthesis and feeding were performed as previously described [65] . Briefly , dsRNA diluted to 15 μg/ml in 2:1 minced liver:planarian salts was fed to planarians once every 4–5 days . The ccdb bacterial gene encoded in pJC53 . 2 [65] served as the negative control . Whole-mount in situ hybridization of planarians was performed as previously described [66] with modifications for the sexual strain [21 , 22 , 67] . Samples developed through the NBT/BCIP colorimetric method were mounted in 80% glycerol and imaged using a Leica M205A stereomicroscope ( Leica , Wetzlar , Germany ) , equipped with Leica DFC420 camera . Whole-mount FISH animals were mounted in Vectashield ( Vector Laboratories , Burlingame , CA ) and imaged on a Zeiss Stereo Lumar V12 ( Carl Zeiss , Germany ) . For confocal FISH images , samples were mounted in Vectashield and imaged using a Zeiss LSM710 confocal microscope running ZEN 2011 . Images were processed using Adobe Photoshop CS5 . Planarians were cut longitudinally and one half was killed with 2% HCl for 3 minutes on ice and fixed in Methacarn . Cryosectioning was performed as previously described [68] . Anti-Phospho-histone H3 ( Cell Signaling , number: 3377S ) was used at 1:500 dilution overnight at 4°C . Secondary antibody ( anti-rabbit HRP-Jackson labs 111-035-003 ) was used at 1:500 . DAPI ( 1 μg/ml ) was added to the secondary antibody solution . Tyramide signal amplification was performed using FITC-tyramide ( Perkin Elmer ) . Slides were rinsed in PBSTx and mounted in Vectashield . A whole-mount TUNEL protocol [69] was modified for cryosections . Planarians were cut longitudinally and one half was treated with 10% N-acetyl-L-cysteine for 7 . 5 minutes and fixed in 4% formaldehyde in PBSTx ( 0 . 3% Triton X-100 ) for 20–30 minutes at room temperature . Cryosectioning was done as described previously [68] . Rehydration included treatment with pre-chilled ethanol:acetic acid ( 2:1 ) at –20°C for 5 minutes . After equilibration , slides were rinsed twice in DI water , and equilibrated in equilibration buffer ( 100 mM Tris-HCl pH 7 . 5 + 1 mg/ml IgG-free BSA ) . Slides were covered with TdT solution ( 0 . 5 μl NEB TdT ( Cat . No . M0252L ) , 2 μl NEB buffer 4 , 2 μl 2 . 5 mM CoCl2 , 0 . 8 μl 1:50 DIG-dUTP in dATP , 14 . 7 μl water ) . After rinsing 3X with PBSTx , the sections were blocked with 5% Horse Serum ( Sigma H1138 ) in PBSTx for 30 minutes . Block was replaced with 1:1000 anti-DIG-POD ( Roche 11207733910 ) diluted in block solution . DAPI ( 1 μg/ml ) was added at this step . Sections were covered with coverslips and incubated for 1 hour at RT . Signal was revealed using Cy3-tyramide ( Perkin-Elmer ) . Slides were rinsed in PBSTx and mounted in Vectashield . Schistosoma mansoni , Strain NMRI—exposed Swiss Webster mice ( NR-21963 ) were provided by the NIAID Schistosomiasis Resource Center at the Biomedical Research Institute ( Rockville , MD ) through NIH-NIAID Contract HHSN272201000005I for distribution through BEI Resources . Mice were perfused with DMEM containing 10% heat-inactivated serum and schistosomes were cultured in vitro [41] . In situ hybridization was performed as previously described [41] . For RNAi , animals ( in quadriplicates ) were soaked in dsRNA generated by in vitro transcription ( 30 μg RNA per 10–12 pairs in 3 ml of Basch 169 medium [70 , 71] ) . Animals were incubated for 7 and 14 days at 37°C . EdU pulse chase and detection were performed as described previously [41] . Nucleotide sequences have been deposited in GenBank with the following accession numbers: NF-YB—KU366699; NF-YB2—KU366700; NF-YA1—KU366701; NF-YA2—KU366702; NF-YC—KU366703 . | Sexually reproducing organisms require gametes–sperm and eggs–for the perpetuation of life and transmission of genetic information between generations . Male gametes ( sperm ) arise from a dedicated population of stem cells known as spermatogonial stem cells ( SSCs ) . Identification of factors involved in SSC maintenance has important biomedical implications , including deciphering the etiology of testicular tumors and optimizing fertility treatments . Here we show that a male germ cell-specific homolog of the ubiquitous Nuclear Factor-Y family of transcription factors , NF-YB , is required for the self-renewal and proliferation of SSCs in the freshwater planarian , Schmidtea mediterranea . Additionally , we extend our study to the parasitic flatworm Schistosoma mansoni , the causative agent of the major neglected tropical disease schistosomiasis and evolutionary cousin of the free-living planarian . We find that there is a loss of proliferating cells in the testes of the parasite when schistosome NF-Y components are inhibited . This observation is significant since the reproductive output of S . mansoni is the primary cause of the morbidity associated with schistosomiasis . Together , our results establish NF-YB as an important regulator of SSC maintenance , and may open avenues for combating schistosomiasis . | [
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] | 2016 | NF-YB Regulates Spermatogonial Stem Cell Self-Renewal and Proliferation in the Planarian Schmidtea mediterranea |
Patients with valvulopathy have the highest risk to develop infective endocarditis ( IE ) , although the relationship between valvulopathy and IE is not clearly understood . Q fever endocarditis , an IE due to Coxiella burnetii , is accompanied by immune impairment . Patients with valvulopathy exhibited increased levels of circulating apoptotic leukocytes , as determined by the measurement of active caspases and nucleosome determination . The binding of apoptotic cells to monocytes and macrophages , the hosts of C . burnetii , may be responsible for the immune impairment observed in Q fever endocarditis . Apoptotic lymphocytes ( AL ) increased C . burnetii replication in monocytes and monocyte-derived macrophages in a cell-contact dependent manner , as determined by quantitative PCR and immunofluorescence . AL binding induced a M2 program in monocytes and macrophages stimulated with C . burnetii as determined by a cDNA chip containing 440 arrayed sequences and functional tests , but this program was in part different in monocytes and macrophages . While monocytes that had bound AL released high levels of IL-10 and IL-6 , low levels of TNF and increased CD14 expression , macrophages that had bound AL released high levels of TGF-β1 and expressed mannose receptor . The neutralization of IL-10 and TGF-β1 prevented the replication of C . burnetii due to the binding of AL , suggesting that they were critically involved in bacterial replication . In contrast , the binding of necrotic cells to monocytes and macrophages led to C . burnetii killing and typical M1 polarization . Finally , interferon-γ corrected the immune deactivation induced by apoptotic cells: it prevented the replication of C . burnetii and re-directed monocytes and macrophages toward a M1 program , which was deleterious for C . burnetii . We suggest that leukocyte apoptosis associated with valvulopathy may be critical for the pathogenesis of Q fever endocarditis by deactivating immune cells and creating a favorable environment for bacterial persistence .
Infective endocarditis ( IE ) has long been recognized as a fatal disease . Despite the availability of antimicrobial agents and cardiac surgery , IE still causes high morbidity and mortality . About 75% of patients with IE have pre-existing cardiac diseases [1] , including congenital cardiac malformations , acquired valvular dysfunction and prosthetic cardiac valves [2] . Normal endocardium is resistant to colonization by bacteria [3] unless it exhibits pre-existing lesions . Lesions expose underlying extracellular matrix proteins and enable deposition of fibrin-platelet clots [4] , bacterial adhesion [5] and recruitment of monocytes , which produce tissue factor and inflammatory cytokines [6]: this usually leads to the growth of vegetation . Cardiac valve lesions are associated with pathological fluid shear stress [7] . In vitro , fluid shear stress modifies the structure and the function of the endothelium [8] and increases apoptosis of neutrophils [9] , platelets [10] and monocytes ( Mo ) [11] , suggesting that leukocyte apoptosis may be related to cardiac valvulopathy . IE associated with negative blood culture constitutes 5% of all IE cases . It is often caused by obligate intracellular pathogens , such as Coxiella burnetii [12] . This bacterium that resides in Mo and macrophages [13] is the agent of the so-called Q fever . In patients with acute Q fever and valve disease , chronic endocarditis will develop in 30% to 50% of cases [14] . Q fever endocarditis is characterized by the lack of vegetations [15] and granuloma formation and impaired systemic cell-mediated immune response [13] , whereas acute Q fever is usually controlled by the cell-mediated immune system [13] , including the interferon ( IFN ) -γ pathway [16] , [17] . This suggests that mechanisms distinct from endothelial lesions are involved in the immune impairment associated with the development of Q fever endocarditis . It is tempting to speculate that leukocyte apoptosis induced by cardiac valvulopathy may impair the immune response to C . burnetii through the modulation of macrophage polarization induced by the binding of apoptotic cells . Indeed , the phagocytosis of apoptotic cells by phagocytes and neighboring cells results in a powerful anti-inflammatory and immunosuppressive response [18] via the secretion of anti-inflammatory molecules , such as interleukin ( IL ) -10 and transforming growth factor ( TGF ) -β1 [19] . Different activation states of macrophages induced by microbial products , cytokines , glucocorticoids or immune complexes have been described [20] . By referring to the Th1/Th2 nomenclature , many now refer to M1 and M2 macrophages . M1 macrophages , stimulated by lipopolysaccharide ( LPS ) and/or IFN-γ , have a high capacity for antigen presentation , express CCR7 , exhibit high levels of inducible nitric oxide synthase ( iNOS ) and secrete inflammatory cytokines , such as tumor necrosis factor ( TNF ) , IL-1 , IL-6 and IL-12 , and chemokines , such as CXCL8 , CCL2 and CCL5 . M2 macrophages , induced by IL-4 , IL-13 or IL-10 , express the Fcγ-receptor type 2 ( Fcγ-R2 , CD23 ) , the mannose receptor ( MR ) and CD14 and secrete anti-inflammatory cytokines , such as IL-10 , TGF-β1 and IL-1 receptor antagonist ( IL-1ra ) , and specific chemokines , such as CCL16 , CCL18 and CCL24 [21] . The expression of arginase-1 by M2 macrophages shifts L-arginine metabolism toward the production of ornithine and polyamines by arginase-1 , which , in turn , contributes to blocking the iNOS pathway [22] . The aim of this study was to determine the mechanisms by which valvulopathy creates favorable conditions for the establishment of Q fever endocarditis . We showed that apoptosis of circulating leukocytes was increased in patients with valvulopathy , independently of Q fever . We also showed that the binding of apoptotic lymphocytes ( AL ) by Mo and Mo-derived macrophages ( MDM ) increased C . burnetii replication and polarized Mo and MDM toward a M2 profile . Interestingly , IFN-γ prevented C . burnetii replication and re-directed Mo and MDM toward a M1 program . Leukocyte apoptosis associated with valvulopathy may be critical in the pathogenesis of IE by creating a favorable environment for pathogen persistence through the deactivation of immune competent cells .
Apoptosis was investigated by measuring circulating nucleosomes and caspase activity in leukocytes from control subjects , patients with valvulopathy and patients with Q fever ( Figure 1 ) . In patients with valvulopathy , acute Q fever and valvulopathy or Q fever endocarditis , circulating nucleosomes ( Figure 1A ) and the percentage of active caspases in leukocytes ( Figure 1B–F ) significantly ( p<0 . 001 ) increased as compared to control subjects . Interestingly , circulating nucleosomes ( p<0 . 001 ) and the percentage of active caspases in leukocytes ( p<0 . 01 ) was significantly higher in patients with acute Q fever and valvulopathy than in patients with acute Q fever without valvulopathy . These results showed that patients with valvulopathy exhibited increased levels of apoptotic leukocytes . To determine which type of leukocytes was apoptotic , cells with active caspases were quantified in CD3- and CD14-gated populations ( Figure 1G ) . In patients with valvulopathy , with or without acute Q fever , the percentages of CD3+ and CD14+ cells that expressed active caspases were significantly higher ( p<0 . 01 ) as compared to control subjects and patients with acute Q fever without valvulopathy . They were similar to those found in patients with Q fever endocarditis ( Figure 1G ) . Taken together , these results show that increased leukocyte apoptosis was associated with valvulopathy . Impaired immune responses associated with Q fever endocarditis may result from the binding of apoptotic cells to professional phagocytes . Consequently , we created an experimental model to test in vitro this hypothesis . Apoptosis of lymphocytes was induced by 10−6 M dexamethasone , which stimulated apoptosis via a caspase-dependent pathway ( Figure S1 ) . As a control , necrosis of lymphocytes was induced by a 95°C shock ( Figure S1 ) . As apoptotic cells stimulates cytoskeleton reorganization in phagocytic cells [23] , we analyzed the morphological changes induced by AL and necrotic lymphocytes ( NL ) in Mo ( Figure 2A ) and MDM ( Figure 2B ) . Resting Mo and MDM were rounded and did not present ruffles or filopodia . The F-actin distribution was roughly homogeneous in quiescent Mo and MDM . AL induced intense morphological changes consisting of membrane ruffles in Mo and MDM . In contrast to AL , NL induced the formation of numerous filopodial extensions in Mo and MDM . F-actin was redistributed around membrane ruffles and filopodial extensions induced by AL and NL , respectively . The percentage of Mo and MDM with membrane ruffles or filopodia was quantified: AL induced membrane ruffles in about 70% of Mo ( Figure 2C ) and MDM ( Figure 2D ) whereas NL induced filopodia in about 75% of Mo ( Figure 2C ) and MDM ( Figure 2D ) . These results showed that AL and NL induced distinct cytoskeleton reorganization in Mo and MDM . Finally , we determined the time course of AL and NL binding . The binding of AL and NL to Mo and MDM was maximal after 2 h . Indeed , about 80% of Mo ( Figure 2E ) and MDM ( Figure 2F ) had bound at least one AL or NL after 2 h . Thus , a 2-h incubation time of AL and NL with Mo and MDM was used in further experiments . Mo and MDM were incubated with AL or NL for 2 h and infected with C . burnetii . The binding of AL and NL to Mo ( Figure 3A , inset ) and MDM ( Figure 3B , inset ) had no effect on C . burnetii phagocytosis . C . burnetii survived without replication in control Mo ( Figure 3A ) and moderately replicated in control MDM ( Figure 3B ) , as determined by real-time quantitative PCR ( qPCR ) . Mo that had bound AL allowed C . burnetii replication after 9 days of culture ( 29 , 000±3 , 000 vs . 12 , 600±2 , 000 bacterial DNA copies in control Mo , p<0 . 001; Figure 3A ) . In MDM that had bound AL , C . burnetii replication was detectable after 3 days and was significantly ( p<0 . 05 ) higher at day 9 ( 45 , 000±2 , 000 vs . 24 , 280±2 , 200 bacterial DNA copies in control MDM , Figure 3B ) . The effect of AL was independent of the apoptosis-inducer agent since the replication of C . burnetii was increased in Mo and MDM that have bound apoptotic cells induced by either corticoids or staurosporine ( data not shown ) . In contrast to AL binding , the binding of NL to Mo ( Figure 3A ) and MDM ( Figure 3B ) induced C . burnetii killing . After 9 days of culture , the bacterial load was significantly ( p<0 . 05 ) decreased by 66% in Mo ( Figure 3A ) and by 50% in MDM ( Figure 3B ) , as compared with control cells . Bacterial infection was also determined by immunofluorescence . The percentage of Mo ( Figure 3C ) and MDM ( Figure 3D ) that contained more than 5 bacteria increased after the binding of AL . It reached about 25% after 9 days of culture , while this percentage did not exceed 15% in control Mo and MDM ( p<0 . 001 ) . In contrast , the number of Mo and MDM containing more than 5 bacteria was significantly ( p<0 . 001 ) lower after NL binding ( Figure 3C and D ) . Taken together , these results showed that AL binding stimulated C . burnetii replication in Mo and MDM , while the NL binding led to C . burnetii killing . Finally , the effect of AL and NL on C . burnetii replication was cell-contact dependent since the culture of Mo ( Figure 3E ) and MDM ( Figure 3F ) with AL and NL in separate chambers had no effect on C . burnetii replication . In macrophages , C . burnetii survives in a late phagosome that fails to fuse with lysosomes [17] . The effect of AL binding on the intracellular traffic of C . burnetii in MDM was studied by determining the colocalization of C . burnetii with Lamp-1 ( Lysosomal associated membrane protein-1 ) , a marker of the late endosomes-early lysosomes , and the lysosomal protease cathepsin D . In control MDM , C . burnetii resided in a late phagosome unable to fuse with lysosomes ( Figure 4A ) . Indeed , 78±6% of C . burnetii phagosomes were Lamp-1+ and cathepsin D− while only 22±8% of phagosomes expressed both Lamp-1 and cathepsin D ( Figure 4D ) . The binding of AL ( Figure 4B ) and NL ( Figure 4C ) to MDM increased the maturation of the C . burnetii phagosome toward a mature phagolysosome: about 60% of C . burnetii phagosomes were Lamp-1+ and cathepsin D+ after binding of AL or NL ( Figure 4D ) . Thus , AL and NL binding to MDM stimulated the maturation of C . burnetii phagosomes in the early phase of infection . We hypothesized that AL binding may orient Mo and MDM toward a M2 program that favors C . burnetii replication . Transcriptional patterns of AL-Mo and AL-MDM were compared by clustering algorithm analysis ( Figure 5A ) . Mo and MDM stimulated with C . burnetii exhibited distinct transcriptional programs that combine M1 and M2 features . The expression of CCL18 , CCL24 and Fcε-R2 , associated with the M2 profile , and CD1B , CD1C and CD1D were up-regulated in both cell types . In Mo , C . burnetii up-regulated the expression of CCL20 , which is associated with M2 polarization; CCR7 and TNF , which are associated with M1 polarization; and CD1A and Fcα-R . It only down-regulated the expression of IL-20 . In C . burnetii-infected MDM , the expression of CCL16 and IL-1ra , which are associated with M2 polarization , and that of CXCL8 , CXCL11 and IL-6 , which are associated with M1 polarization , were up-regulated . The expression of the two M1 cytokines IFN-γ and TNF were down-regulated . AL binding directed C . burnetii-infected Mo and MDM toward a M2 program . In Mo , AL binding up-regulated the expression of CCL16 , CD14 , IL-10 , IL-1ra , which are associated with M2 polarization , IL-6 , IL-13 and the three members of the IL-10 family , IL-19 , IL-20 and IL-24 . AL binding also down-regulated numerous genes involved in M1 polarization , such as CCR7 , IL-12p40 , IL-1β , TNF and iNOS . In MDM , AL binding up-regulated the expression of IL-10 , IL-1ra and TGF-β1 , which are associated with M2 polarization; and CXCL10 and IL-6 , which are associated with M1 polarization . The expression of the M1 markers CXCL8 , CCR7 , IL-12p40 , IL-1β and TNF were down-regulated . In contrast to AL binding , NL binding stimulated a typical M1 profile in C . burnetii-infected Mo and MDM . Indeed , NL binding up-regulated the expression of the M1 molecules CCL5 , CXCL8 , CXCL11 , CCR7 , CD80 , IFN-γ , TNF , IL-12p40 , IL-1β and iNOS in both Mo and MDM ( Figure 5A ) . Functional tests confirmed the transcriptional studies . After AL binding , Mo exhibited a M2 program characterized by high levels of IL-10 and IL-6 , decreased TNF release ( Figure 5B ) and increased expression of CD14 ( p<0 . 05 , Figure 5D ) . As found for transcriptional responses , the response of MDM was characterized by M2 features that were partly different from those of Mo . Indeed , AL-MDM released high levels of TGF-β1 ( Figure 5C ) and the percentage of MDM that expressed MR was significantly ( p<0 . 001 ) increased ( Figure 5E ) . The effect of AL binding was specific since NL binding induced a M1 program in both Mo and MDM stimulated with C . burnetii . Indeed , the release of TNF by Mo and MDM was increased whereas those of IL-6 , TGF-β1 and IL-10 were inhibited ( Figure 5B and C ) . In addition , the expression of CD14 in Mo ( p<0 . 001 , Figure 5D ) and that of MR in MDM ( p<0 . 01 , Figure 5E ) were significantly decreased . Finally , we showed that IL-10 was critically involved in the replication of C . burnetii since adding blocking anti-IL-10 antibodies ( Abs ) to AL-Mo ( Figure 5F ) and AL-MDM ( Figure 5G ) prevented bacterial replication . Anti-TGF-β1 blocking Abs prevented the replication of C . burnetii only in AL-MDM and had no effect in AL-Mo ( Figure 5F and G ) . Taken together , these results showed that AL binding stimulated a clear-cut M2 profile in Mo and MDM , in which IL-10 appears critical in both Mo and MDM and TGF-β1 in MDM . As IFN-γ stimulates C . burnetii killing in Mo and MDM [24] , we wondered if it may correct the permissive effect of AL binding on C . burnetii replication . IFN-γ inhibited C . burnetii replication in AL-Mo ( Figure 6A ) and AL-MDM ( Figure 6B ) . After 9 days of culture , the bacterial DNA copy number decreased from 31 , 100±1 , 330 in AL-Mo to 8 , 300±450 in IFN-γ-treated AL-Mo ( p<0 . 001 ) . In AL-MDM , the bacterial DNA copy number decreased from 47 , 340±2000 to 18 , 400±3 , 800 in IFN-γ-treated cells ( p<0 . 05 ) . The number of bacterial DNA copies was similar to that found in Mo and MDM treated with IFN-γ . The percentage of cells that contained more than 5 bacteria was also significantly ( p<0 . 05 ) lower in IFN-γ-treated AL-Mo and AL-MDM ( Figure S2 ) , demonstrating that IFN-γ completely reverted the effect of AL binding on C . burnetii replication . In addition , IFN-γ increased the maturation of C . burnetii phagosomes toward phagolysosomes independently of AL binding: more than 90% of C . burnetii phagosomes were Lamp-1 and cathepsin D positive after IFN-γ treatment ( Figure 6C–E ) . Finally , IFN-γ reverted the response of AL-Mo and AL-MDM toward a M1 profile . First , it caused up-regulation of CXCL8 , TNF and iNOS genes and down-regulation of IL-6 and IL-10 genes in AL-Mo . It also down-regulated the expression of TGF-β1 in AL-MDM ( Figure 6F ) . Second , the IFN-γ treatment of AL-Mo and AL-MDM induced the release of high levels of TNF and inhibited the release of IL-6 , IL-10 and TGF-β1 ( Figure 6G ) . These results showed that IFN-γ prevented the effect of AL binding on C . burnetii replication and polarized AL-Mo and AL-MDM toward a M1 profile .
Patients with cardiac valve lesions have the highest risk to develop IE [2] , although the role of valvulopathy in the establishment of IE is not clearly elucidated . We showed here , for the first time , that patients with valvulopathy exhibited increased levels of circulating apoptotic leukocytes , suggesting a link between valvulopathy and leukocyte apoptosis . We have previously demonstrated that Q fever endocarditis is associated with immune impairment [25] , [26] . The immune impairment may be due to lymphopenia induced by lymphocyte cell death but lymphopenia was not reported in Q fever endocarditis [14] . We hypothesized that the binding of apoptotic leukocytes to Mo and macrophages may be responsible for the immune impairment observed in Q fever endocarditis . To test this hypothesis , we have established an in vitro model to study the effect of apoptotic cells on C . burnetii replication . Binding of AL to Mo and macrophages induced the formation of large membrane ruffles with local F-actin redistribution . This result is in accordance with previous reports that showed that adhesion of apoptotic cells to specific membrane receptors leads to the formation of large membrane ruffles and cytoskeleton reorganization [27] , [28] . We showed for the first time that NL binding induced cytoskeletal reorganization characterized by the formation of filopodia with F-actin concentration at their basis . The differences in cytoskeletal changes induced by AL and NL in Mo and MDM were not due to differences in binding levels: about 80% of Mo and MDM ingested AL and NL after 2 hours . They may result from the engagement by AL and NL of specific receptors that further modulate cytoskeleton reorganization in different ways [18] , [29] . The binding of AL increased C . burnetii replication in Mo and MDM as do IL-10 , the only cytokine able to stimulate C . burnetii replication [30] . Our results emphasized recent studies on the impact of the binding of apoptotic cells to phagocytes on survival of intracellular pathogens . The binding of apoptotic cells to murine macrophages increases the replication of the avirulent form of C . burnetii [31] and the growth of Trypanosoma cruzi [32] and Leishmania major [33] . In addition , the replication of C . burnetii induced by AL is dependent on AL contact with Mo and MDM . It has been previously demonstrated that the contact between monocytes and apoptotic cells is required for inducing the immunosuppressive response of monocytes [34] . Finally , the receptors engaged by AL binding are likely critical for C . burnetii replication . Indeed , when AL were opsonized ( Protocol S1 ) with specific Abs to engage the Fc-receptor ( FcR ) pathway , the replication of C . burnetii was prevented ( Figure S3 ) . These results are supported by the fact that apoptotic cells opsonized with antibodies , particularly IgG , are recognized by macrophage FcR and stimulate a pro-inflammatory response [35] . This also suggests that the entry pathway orients the intracellular fate of C . burnetii . Interestingly , the binding of AL or NL to macrophages increased the maturation of C . burnetii phagosomes . It has been recently shown that the uptake of apoptotic cells by macrophages or fibroblasts results in a rapid maturation of phagosomes by stimulating Rho GTPases [36] . It is tempting to speculate that the maturation of C . burnetii phagosomes observed after NL binding results also in the activation of Rho GTPases in Mo and MDM . However , the increased maturation of C . burnetii phagosomes , which occurs early during the C . burnetii infection , did not seem to govern the intracellular fate of bacteria in the later stages of infection . AL binding by C . burnetii-infected Mo and MDM reprogramed them toward a M2 profile , but the properties of the M2 programs were different in Mo and MDM . In AL-Mo , the expression of genes encoding four members of the IL-10 family , namely IL-10 , IL-19 , IL-20 and IL-24 , were up-regulated . The four genes are expressed within a highly conserved cluster [37] . IL-10 is highly secreted by macrophages following ingestion of apoptotic cells [38] . IL-10 is also critically implicated in the persistence of microorganisms and the chronic evolution of Q fever [39] . IL-19 and IL-20 down-regulate IFN-γ expression and up-regulate that of IL-4 and IL-13 in T cells , supporting a Th2 polarization [40] . In Mo , AL binding up-regulated also the expression of IL-1ra , which is increased in patients with Q fever [41] . In MDM , AL binding up-regulated the expression of TGF-β1 , which is known to interfere with the activities of IFN-γ , iNOS and superoxide anion [42]–[44] , alter the expression of co-stimulatory molecules [45] , inhibit Th1/Th2 differentiation [46] and favor the expansion of regulatory T cells [47] . Interestingly , patients with acute Q fever and valvulopathy or Q fever endocarditis exhibit higher numbers of regulatory T cells as compared to patients with acute Q fever and healthy persons ( our unpublished data ) . We also found that IL-6 was up-regulated in both AL-Mo and AL-MDM . IL-6 is largely considered as an enhancer of the inflammatory response . However , IL-6 can also act as a modulator of inflammatory responses since it shifts the T cell response toward a Th2 response by inducing B cell maturation [48] . Recently , it has been reported that IL-6 and TGF-β1 act together in inducing IL-10 production in T cells [49] . We can suppose that , in Q fever , IL-6 may contribute to the defective control of C . burnetii infection by macrophages . Finally , the replication of C . burnetii stimulated by AL binding was strongly associated with the production of M2 cytokines since IL-10 and TGF-β1 neutralization abolished bacterial replication . These results suggest a direct role of IL-10 and TGF-β1 in the signaling pathway leading to C . burnetii replication . In contrast to AL binding , NL binding induced C . burnetii killing and a M1 transcriptional program in Mo and MDM . These results confirmed previous studies on the bactericidal response of Mo and MDM against C . burnetii induced by inflammatory cytokines , such as IFN-γ [24] . In addition , the expression of genes associated with a M2 program was down-regulated: peculiarly , the gene encoding IL-10 was inhibited in NL-Mo while the gene encoding TGF-β1 was inhibited in NL-MDM . Our results are consistent with other reports . The binding of necrotic cells induces the release of inflammatory cytokines by MDM [50] . It also induces the expression of major histocompatibility complex class II molecules by antigen-presenting cells and increases their ability to activate T cells [51] . Finally , IFN-γ inhibited the effect of AL binding on the C . burnetii replication in Mo and MDM . It induced C . burnetii killing and complete maturation of C . burnetii phagosomes . In contrast to LPS that is unable to inhibit the anti-inflammatory response of peripheral blood mononuclear cells ( PBMCs ) and Mo after binding of apoptotic cells [19] , [34] , IFN-γ reverted the M2 program induced by AL . It is likely that C . burnetii infection amplifies the inflammatory signal induced by IFN-γ to engage Mo and MDM toward a M1 profile . We can also suppose that the engagement of a broad number of receptors by C . burnetii , such as TLR4 [52] , TLR2 [53] and the αvβ3 integrin [54] , in the presence of IFN-γ counter-balances the anti-inflammatory signals delivered by AL to phagocytic cells . In conclusion , we showed that valvulopathy increased the rate of circulating apoptotic leukocytes and we provided a model in which apoptotic cells play a key role in the establishment of Q fever endocarditis ( Figure 7 ) . The binding of apoptotic cells increased C . burnetii replication by subverting phagocyte responses; Mo and MDM , polarized toward M2 profiles , stimulate type 2 responses that are non-protective against most pathogens . If the systemic apoptosis of leukocytes occurs in an inflammatory context , such as that found in the presence of IFN-γ , the effect of AL binding is inhibited; Mo and MDM , polarized toward a M1 program , are able to kill C . burnetii as do patients with acute Q fever without valvulopathy . Our results give new comprehensive insights into the pathological processes resulting from valvulopathy that are associated with the high risk to develop rare and atypical IE .
Informed and written consent was obtained from each subject and the study was approved by the Ethics Committee of the Université de la Méditerranée . Nine patients with valvulopathy ( 5 men and 4 women , mean age of 69 . 5 years ) , 10 with acute Q fever ( 3 men and 7 women , mean age of 46 . 3 years ) , 11 with acute Q fever and valvulopathy ( 6 men and 5 women , mean age of 51 . 3 years ) and 11 with Q fever endocarditis ( 7 men and 4 women , mean age of 47 . 6 ) were included . Ten healthy persons ( 6 men and 4 women , mean age of 35 . 0 years ) were included as controls . The diagnosis of acute and chronic Q fever was based on epidemiological and clinical features , as previously described [55] . Plasma levels of nucleosomes were measured using the ELISA cell death detection plus kit from Roche Diagnostics . This assay is based on a quantitative sandwich enzyme immunoassay that recognizes DNA and histones [56] . The specific enrichment factor in nucleosomes , expressed in arbitrary units , was calculated as previously described [57] . Caspase activity of leukocytes was measured with the Apoptosis Detection Polycaspase Assay Kit ( Immunochemistry Technologies ) , as previously described [57] . Briefly , 100 µl of EDTA-collected blood were incubated with 5 µl of 30× FLICA solution for 1 h and then with 20 µl of CD3-PE or CD14-APC for 15 min . After washing , leukocytes were fixed and analyzed by flow cytometry ( EPICS XL , Coulter Beckman ) . The percentage of CD3+ or CD14+ leukocytes with active caspases was determined using the Expo32 ADC and the WinMDI 2 . 8 software . C . burnetii organisms ( Nine Mile strain ) were cultured as previously described [54] . Dilacerated spleens of BALB/c mice infected with 108 C . burnetii organisms for 7 days were added to L929 cells . Infected cells were sonicated and centrifuged at 300×g for 10 min . Supernatants were collected and centrifuged at 10 , 000×g for 10 min . Bacteria were then washed and stored at −80°C . The concentration of organisms was determined by Gimenez staining and the bacterial viability was assessed using the LIVE/DEAD BacLight bacterial viability kit ( Molecular Probes ) . PBMCs were isolated from leukopacks ( Etablissement Français du Sang , Marseille , France ) by Ficoll gradient ( MSL , Eurobio ) and suspended in RPMI 1640 containing 20 mM HEPES , 10% fetal calf serum ( FCS ) , 2 mM L-glutamine , 100 U/ml penicillin and 100 µg/ml streptomycin ( Invitrogen ) . PBMCs were incubated in flat-bottom 24-well plates ( Nunc ) for 60 min at 37°C . After washing , adherent cells were designated as Mo ( 90% of cells expressed CD14 ) and MDM were obtained by a 7-day culture , as recently described [58] . Non-adherent cells were designated as lymphocytes ( 90% of them expressed CD3 ) . Lymphocyte apoptosis , induced by incubation with dexamethasone ( Merck ) for 24 h [59] , and necrosis , induced by heat shock , were determined by flow cytometry using the annexin V/Propidium Iodide ( PI ) kit ( Roche ) ( Figure S1 ) . Mo and MDM were incubated with AL or NL ( ratio of 1∶5 ) for different periods , washed to remove unbound AL or NL , and infected with C . burnetii . In some experiments , Mo and MDM were separated from AL or NL using culture inserts ( 0 . 4-µm pore size; Transwell , Costar ) in 24-well plates before C . burnetii infection . Mo and MDM were incubated with C . burnetii ( bacterium-to-cell ratio of 200∶1 ) for 4 h . After washing to remove free bacteria ( time designated as day 0 ) , infected cells were cultured for 12 days . In some experiments , recombinant human IFN-γ ( 1000 U/ml , Peprotech Inc . ) , monoclonal anti-IL-10 ( 10 µg/ml , R&D Systems ) or anti-TGF-β1 ( 10 µg/ml , R&D Systems ) Abs were added every 3 days . Infection was quantified by immunofluorescence [52] and qPCR . DNA was extracted with the QIAamp Blood Mini Kit ( Qiagen ) and stored in a volume of 100 µl at −20°C . qPCR was performed using 5 µl of DNA extract and the LightCycler FastStart DNA SYBR green system ( Roche ) , as previously described [60] . Primers ( Table S1 ) amplified a 75-bp fragment of the C . burnetii com1 gene ( GenBank accession no . AF318146 ) . MDM were infected with C . burnetii for 4 h ( bacterium-to-cell ratio of 200∶1 ) , washed and cultured for 24 h . Cells were fixed in 3% paraformaldehyde , permeabilized with 0 . 1% Triton X-100 and immunofluorescence labeling was performed according to standard procedures [61] . Briefly , MDM were incubated with human anti-C . burnetii ( 1∶4 , 000 dilution ) , mouse anti-Lamp-1 ( 1∶1 , 000 dilution , DHSB , Developmental Studies Hybridoma Bank ) and rabbit anti-cathepsin D ( 1∶1 , 000 dilution , a gift from S . Kornfeld , Washington University School of Medicine , St . Louis , Missouri ) Abs for 30 min . Bacteria were revealed by Alexa 546-conjugated F ( ab' ) 2 anti-human IgG Abs , Lamp-1 by Alexa 488-conjugated anti-mouse IgG Abs and cathepsin D by Alexa 647-conjugated anti-rabbit IgG Abs . All secondary Abs were used at a 1∶500 dilution . The colocalization of bacteria and intracellular markers was examined by laser scanning microscopy using a confocal microscope ( Leica TCS SP2 ) with a 63×/1 . 32-0 . 6 oil CS lens and an electronic Zoom 3× . Optical sections of fluorescent images were collected at 0 . 25-µm intervals using the Leica Confocal software and processed using the Adobe Photoshop V5 . 5 software . At least 65 MDM were examined for each experimental condition and the results are expressed as the percentage of bacteria that colocalized with fluorescent markers . Mo and MDM were stimulated with C . burnetii for 4 h . Total RNA was purified using the RNeasy Mini Kit ( Qiagen ) , according to the manufacturer's protocol . DNase treatment was performed with the RNase-free DNase set ( Qiagen ) . The transcriptional pattern of cells was studied using a cDNA chip containing 440 arrayed sequences ( Oligo GEArray Human Hematology/Immunology , SuperArray ) . Ten µg of RNA were transcribed into biotin-labeled cDNA by the MMLV reverse transcriptase ( RT ) . Membranes were then hybridized with biotin-labeled cDNA and incubated with streptavidin-conjugated alkaline phosphatase . Chemiluminescence was visualised by autoradiography . Datasets were analyzed with the GEArray Expression Analysis Suite software ( SuperArray ) and the TIGR's Multiexperiment Viewer . Data were submitted to the ArrayExpress database ( MIAME Accession number E-MEXP-1289 ) . For qRT-PCR studies , reverse transcription of RNA was performed with the MMLV-RT kit ( Invitrogen ) , according to the manufacturer's protocol . The primers ( Supplemental data , Table I ) were designed using the primer3 tool ( http://frodo . wi . mit . edu/cgi-bin/primer3/primer3_www . cgi ) . RT was omitted in negative controls . The fold change in target gene cDNA relative to the β-actin endogenous control was determined as follows: fold change = 2−ΔΔCt , where ΔΔCt = ( CtTarget−CtActin ) test condition− ( CtTarget−CtActin ) reference condition . Ct values were defined as the number of cycles for which the fluorescence signals were detected [62] . Mo and MDM were incubated with heat-killed ( 100°C for 30 min ) C . burnetii organisms ( bacterium-to-cell ratio of 10∶1 ) for 24 h . Supernatants were stored at -80°C before immunoassays . IL-10 , TGF-β1 and TNF assays were purchased from R&D Systems . IL-6 assay was purchased from Beckman Coulter . The intra- and interspecific coefficients of variation ranged from 5% to 10% . Mo and MDM ( 5×105 cells per well ) were incubated with AL and NL for 2 h , washed and then stimulated with C . burnetii ( bacterium to cell ratio of 10∶1 ) for 24 h . After washing , Mo and MDM were scrapped and washed once with ice-cold PBS . Mo and MDM were then incubated with 10 µl of CD14-PE ( Beckman Coulter ) and MR-FITC ( BioLegend , San Diego , California , USA ) Abs for 30 min at 4°C . Cells were washed three times in ice-cold PBS and resuspended in PBS containing 10% FCS and 1% sodium azide ( Sigma-Aldrich ) . Cells were then stored at 4°C in the dark and analyzed by flow cytometry ( EPICS XL , Beckman Coulter ) . Ten thousand events were acquired for each sample . The percentage of positive cells was determined using the Expo32 ADC and the WinMDI 2 . 8 software . Results are expressed as medians or means±SEM and compared with the non-parametric Mann-Whitney U test . Differences were considered significant when p<0 . 05 . | Infective endocarditis ( IE ) is a problem of public health that still causes high mortality despite antibiotic treatments . Most of the patients who develop an IE have pre-existing cardiac lesions , although the relationship between IE and valvulopathy is not clearly understood . We showed here that patients with valvulopathy exhibited increased levels of circulating apoptotic leukocytes . As the binding of apoptotic cells to monocytes and macrophages is known to inhibit their inflammatory activity , we hypothesized that the high levels of circulating apoptotic leukocytes may be responsible for the immune impairment observed in Q fever endocarditis , an IE due to Coxiella burnetii , a bacterium that survives in monocytes and macrophages . The binding of apoptotic lymphocytes to monocytes and macrophages increased the replication of C . burnetii by stimulating their anti-inflammatory response . In contrast , the binding of necrotic lymphocytes to monocytes and macrophages induced C . burnetii killing and stimulated an inflammatory response . Interferon-γ , which is associated with the control of C . burnetii infection , prevented the replication of C . burnetii in monocytes and macrophages that have bound apoptotic lymphocytes by stimulating their inflammatory response . In conclusion , we suggest that leukocyte apoptosis associated with valvulopathy may be critical for the pathogenesis of Q fever endocarditis by deactivating immune cells and creating a favorable environment for pathogen persistence . | [
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] | 2008 | The Uptake of Apoptotic Cells Drives Coxiella burnetii Replication and Macrophage Polarization: A Model for Q Fever Endocarditis |
Biophysically detailed models of single cells are difficult to fit to real data . Recent advances in imaging techniques allow simultaneous access to various intracellular variables , and these data can be used to significantly facilitate the modelling task . These data , however , are noisy , and current approaches to building biophysically detailed models are not designed to deal with this . We extend previous techniques to take the noisy nature of the measurements into account . Sequential Monte Carlo ( “particle filtering” ) methods , in combination with a detailed biophysical description of a cell , are used for principled , model-based smoothing of noisy recording data . We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner . Biophysically important parameters of detailed models ( such as channel densities , intercompartmental conductances , input resistances , and observation noise ) are inferred automatically from noisy data via expectation-maximisation . Overall , we find that model-based smoothing is a powerful , robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters in the face of recording noise .
The aim of this paper is to fit biophysically detailed models to noisy electrophysiological or imaging data . We first give an overview of the kinds of models we consider; which parameters in those models we seek to infer; how this inference is affected by the noise inherent in the measurements; and how standard machine learning techniques can be applied to this inference problem . The overview will be couched in terms of voltage measurements , but we later also consider measurements of calcium concentrations . Expectation-Maximisation ( EM ) is one standard machine-learning technique that allows estimation of parameters in precisely the circumstances just outlined , i . e . where inference depends on unobserved variables and certain expectations can be evaluated . The EM algorithm achieves a local maximisation of the data likelihood by iterating over two steps . For the case where voltage is recorded , it consists of: The EM algorithm can be shown to increase the likelihood of the parameters at each iteration [24] , [25] , [30] , [31] , and will typically converge to a local maximum . Although in combination with the Monte-Carlo estimation these guarantees no longer hold , in practice , we have never encountered nonglobal optima .
We first assume that the true parameters are known , and in the E-step infer the conditional marginal distributions for all times . The conditional mean is a model-based , smoothed estimate of the true underlying signal at each point in time which is optimal under mean squared error . The E-step is implemented using standard sequential Monte Carlo techniques [7] . Here we present the detailed equations as applied to noisy recordings of cellular dynamic variables such as the transmembrane voltage or intracellular calcium concentration . The smoothed distribution is computed via a backward recursion which relies on the filtering distribution , which in turn is inferred by writing the following recursion ( suppressing the dependence on for clarity ) : ( 12 ) This recursion relies on the fact that the hidden variables are Markovian ( 13 ) Based on this , the smoothed distribution , which gives estimates of the hidden variables that incorporate all , not just the past , observations , can then be inferred by starting with and iterating backwards: ( 14 ) where all quantities inside the integral are now known . The filtering and smoothing equations demand integrals over the hidden variables . In the present case , these integrals are not analytically tractable , because of the complex nonlinearities in the kinetics . They can , however , be approximated using Sequential Monte Carlo methods . Such methods ( also known as “particle filters” ) are a special version of importance sampling , in which distributions and expectations are represented by weighted samples with . If samples are drawn from the distribution directly , the weights . In the present case , this would mean drawing samples from the distributions and , which is not possible because they themselves depend on integrals at adjacent timesteps which are hard to evaluate exactly . Instead , importance sampling allows sampling from a different “proposal” distribution and compensating by setting . Here , we first seek samples and forward filtering weights such that ( 15 ) and based on these will then derive backwards , smoothing weights such that ( 16 ) Substituting the desideratum in equation 15 for time into equation 12 ( 17 ) As a proposal distribution for our setting we use the one-step predictive probability distribution ( derived from the Markov property in equation 13 ) : ( 18 ) where is termed the “particle” . The samples are made to reflect the conditional distribution by adjusting the weights , for which the probabilities need to be computed . These are given bywhich involves a sum over that is quadratic in . We approximate this by ( 19 ) which neglects the probability that the particle at time could in fact have arisen from particle at time . The weights for each of the particles are then given by a simple update equation: ( 20 ) ( 21 ) One well-known consequence of the approximation in equations 19–21 is that over time , the variance of the weights becomes large; this means that most particles have negligible weight , and only one particle is used to represent a whole distribution . Classically , this problem is prevented by resampling , and we here use stratified resampling [8] . This procedure , illustrated in Figure 2 , results in eliminating particles that assign little , and duplicating particles that assign large likelihood to the data whenever the effective number of particles drops below some threshold , here . It should be pointed out that it is also possible to interpolate between observations , or to do learning ( see below ) from subsampled traces . For example , assume we have a recording frequency of but wish to infer the underlying signal at a higher frequency , with . At time points without observation the likelihood term in equation 21 is uninformative ( flat ) and we therefore set ( 22 ) keeping equation 21 for the remainder of times . In this paper , we will run compartmental models ( equation 1 ) at sampling intervals , and recover signals to that same temporal precision from data subsampled at intervals . See e . g . [32] for further details on incorporating intermittently-sampled observations into the alternative predictive distribution . We have so far derived the filtering weights such that particles are representative of the distribution conditioned on the past data . It often is more appropriate to condition on the entire set of measurements , i . e . represent the distribution . We will see that this is also necessary for the parameter inference in the M-step . Substituting equations 15 and 16 into equation 14 , we arrive at the updates for the smoothing weightswhere the weights now represent the joint distribution of the hidden variables at adjacent timesteps: The maximum likelihood estimate of the parameters can be inferred via a maximisation of an expectation over the hidden variables:where . This is achieved by iterating over the two steps of the EM algorithm . In the M-step of the iteration , the likelihood of the entire set of measurements with respect to the parameters is maximised by maximising the expected total log likelihood [25]which is achieved by setting the gradients with respect to to zero ( see [31] , [33] for alternative approaches ) . For the main linear parameters we seek to infer in the compartmental model ( ) , these equations are solved by performing a constrained linear regression , akin to that in equation 7 . We write the total likelihood in terms of the dynamic and the observation models ( equations 10 and 11 ) :Let us assume that we have noisy measurements of the voltage . Because the parametrisation of the evolution of the voltage is linear , but that of the other hidden variables is not , we separate the two as where are the gates of the conductances affecting the voltage ( a similar formulation can be written for [Ca2+] observations ) . Approximating the expectations by the weighted sums of the particles defined in the previous section , we arrive at ( 23 ) where , and parametrise the distribution over the initial hidden variables at time , and is the row of the matrix derived from particle . Note that because we are not inferring the kinetics of the channels , the evolution term for the gates ( a sum over terms of the form ) is a constant and can be neglected . Now setting the gradients of equation 23 with respect to the parameters to zero , we find that the linear parameters can be written , as in equation 7 , as a straightforward quadratic minimisation with linear constraints ( 24 ) where we see that the Hessian and the linear term of the problem are given by an expectation involving the particles . Importantly , this is still a quadratic optimisation problem with linear constraints , and which is efficiently solved by standard packages . Similarly , the initialisation parameters for the unobserved hidden variables are given bywhich are just the conditional mean and variance of the particles at time ; and the evolution and observation noise terms finally byThus , the procedure iterates over running the particle smoother in section Sequential Monte Carlo and then inferring the optimal parameters from the smoothed estimates of the unobserved variables .
We first present results on model-based smoothing . Here , we assume that we have a correct description of the parameters of the cell under scrutiny , and use this description to infer the true underlying signal from noisy measurements . These results may be considered as one possible application of a detailed model . Figure 2A shows the data , which was generated from a known , single-compartment cell with Hodgkin-Huxley-like conductances by adding Gaussian noise . The variance of the noise was chosen to replicate typical signal-to-noise ratios from voltage-dye experiments [2] . Figure 2B shows the particles used here , and Figure 2C the number of particles with non-negligible weights ( the “effective” number of particles ) . We see that when hits a threshold of , resampling results in large jumps in some particles . At around 3 ms , we see that some particles , which produced a spike at a time when there is little evidence for it in the data , are re-set to a value that is in better accord with the data . Figure 2D shows the close match between the true underlying signal and the inferred mean , while Figure 2E shows that even the unobserved channel open fractions are inferred very accurately . The match for both the voltage and the open channel fractions improves with the number of particles . Code for the implementation of this smoothing step is available online at http://www . gatsby . ucl . ac . uk/̃qhuys/code . html . For imaging data , the laser often has to be moved between recording locations , leading to intermittent sampling at any one location ( see [34]–[36] ) . Figure 3 illustrates the performance of the model-based smoother both for varying noise levels and for temporal subsampling . We see that even for very noisy and highly subsampled data , the spikes can be recovered very well . Figure 4 shows a different aspect of the same issue , whereby the laser moves linearly across an extended linear dendrite . Here , samples are taken every timesteps , but samples from each individual compartment are only obtained each . The true voltage across the entire passive dendrite is shown in Figure 4A , and the sparse data points distributed over the dendrite are shown in panel B . The inferred mean in panel C matches the true voltage very well . For this passive , linear example , the equations for the hidden dynamical system are exactly those of a Kalman smoother model [37]; thus the standard Kalman smoother performs the correct spatial and temporal smoothing once the parameters are known , with no need for the more general ( but more computationally costly ) particle smoother introduced above . More precisely , in this case the integrals in equations 12 and 14 can be evaluated analytically , and no sampling is necessary . The supplemental video S1 shows the results of a similar linear ( passive-membrane ) simulation , performed on a branched simulated dendrite ( instead of the linear dendritic segment illustrated in Figure 4 ) . We emphasize that the strong performance of the particle smoother and the Kalman smoother here should not be surprising , since the data were generated from a known model and in these cases these methods perform smoothing in a statistically optimal manner . Rather , these results should illustrate the power of using an exact , correct description of the cell and its dynamics . We have so far shown model-based filtering assuming that a full model of the cell under scrutiny is available . Here , we instead infer some of the main parameters from the data; specifically the linear parameters , the observation noise and the evolution noise . We continue to assume , however , that the kinetics of all channels that may be present in the cell are known exactly ( see [23] for a discussion of this assumption ) . Figure 5 illustrates the inference for a passive multicompartmental model , similar to that in Figure 4 , but driven by a square current injection into the second compartment . Figure 5B shows statistics of the inference of the leak conductance maximal density , the intercompartmental conductance , the input resistance and the observation noise across 50 different randomly generated noisy voltage traces . All the parameters are reliably recovered from 2 seconds of data at a 1 ms sampling frequency . We now proceed to infer channel densities and observation noise from active compartments with either four or eight channels . Figure 6 shows an example trace and inference for the four channel case ( using Hodgkin-Huxley like channel kinetics ) . Again , we stimulated with square current pulses . Only 10 ms of data were recorded , but at a very high temporal resolution Δ s = Δ = 0 . 02 ms . We see that both the underlying voltage trace and the channel and input resistance are recovered with high accuracy . Figure 7 presents batch data over 50 runs for varying levels of observation noise . The observation noise here has two effects: first , it slows down the inference ( as every data point is less informative ) , but secondly the variance across runs increases with increasing noise ( although the mean is still accurate ) . For illustration purposes , we started the maximal K+ conductance at its correct value . As can be seen , however , the inference initially moves away from the optimum , to compensate for the other conductance misestimations . ( This nonmonotonic behavior in is a result of the fact that the EM algorithm is searching for an optimal setting of all of the cell's conductance parameters , not just a single parameter; we will return to this issue below . ) Parametric inference here has so far employed densely sampled traces ( see Figure 6A ) . The algorithm however applies equally to subsampled traces ( see equation 22 ) . Figure 8 shows the effect of subsampling . We see that subsampling , just as noise , slows down the inference , until the active conductances are no longer inferred accurately ( the yellow trace for Δ s = 0 . 5 ms ) . In this case , the total recording length of 10 ms meant that inference had to be done based on one single spike . For longer recordings , information about multiple spikes can of course be combined , partially alleviating this problem; however , we have found that in highly active membranes , sampling frequencies below about 1 KHz led to inaccurate estimates of sodium channel densities ( since at slower sampling rates we will typically miss significant portions of the upswing of the action potential , leading the EM algorithm to underestimate the sodium channel density ) . Note that we kept the length of the recording in Figure 8 constant , and thus subsampling reduced the total number of measurements . As with any importance sampling method , particle filtering is known to suffer in higher dimensions [38] . To investigate the dependence of the particle smoother's accuracy on the dimensionality of the state space , we applied the method to a compartment with a larger number of channels: fast ( ) and persistent Na+ ( ) channels in addition to leak ( L ) and delayed rectivier ( ) , A-type ( ) , K2-type ( K2 ) and M-type ( ) K+ channels ( channel kinetics from ModelDB [39] , from [9] , [40] ) . Figure 9 shows the evolution of the channel intensities during inference . Estimates of most channel densities are correct up to a factor of approximately 2 . Unlike in the previous , smaller example , as either observation noise or subsampling increase , significant biases in the estimation of channel densities appear . For instance , the density of the fast sodium channel observed with noise of standard deviation 20 mV is only about half the true value . It is worth noting that this bias problem is not observed in the passive linear case , where the analytic Kalman smoother suffices to perform the inference: we can infer the linear dynamical parameters of neurons with many compartments , as long as we sample information from each compartment [23] . Instead , the difficulty here is due to multicollinearity of the regression performed in the M-step of the EM algorithm and to the fact that the particle smoother leads to biased estimation of covariance parameters in high-dimensional cases [38] . We will discuss some possible remedies for these biases below . Somewhat surprisingly , however , these observed estimation biases do not prove catastrophic if we care about predicting or smoothing the subthreshold voltage . Figure 10A compares the response to a new , random , input current of a compartment with the true parameters to that of a compartment with parameters as estimated during EM inference , while Figure 10B shows an example prediction with . Note the large plateau potentials after the spikes due to the persistent sodium channel . We see that even the parameters as estimated under high noise accurately come to predict the response to a new , previously unseen , input current . The asymptote in Figure 10A is determined by the true evolution noise level ( here σ = 1 mV ) : the more inherent noise , the less a response to a specific input is actually predictable . Some further insight into the problem can be gained by looking at the structure of the Hessian of the total likelihood around the true parameters . We estimate by running the particle smoother with a large number of particles once at the true parameter value; more generally , one could perform a similar analysis about the inferred parameter setting to obtain a parametric bootstrap estimate of the posterior uncertainty . Figure 11 shows that , around the true value , changes in either the fast Na+ or the K2-type K+ channel have the least effect; i . e . , the curvature in the loglikelihood is smallest in these directions , indicating that the observed data does not adequately constrain our parameter estimates in these directions , and prior information must be used to constrain these estimates instead . This explains why these channels showed disproportionately large amounts of inference variability , and why the prediction error did not suffer catastrophically from their relatively inaccurate inference ( Figure 10A ) . See [23] for further discussion of this multicollinearity issue in large multichannel models . We saw in the last section that as the dimensionality of the state vector grows , we may lose the ability to simultaneously estimate all of the system parameters . How can we deal with this issue ? One approach is to take a step back: in many statistical settings we do not care primarily about estimating the underlying model parameters accurately , but rather we just need a model that predicts the data well . It is worth emphasizing that the methods we have intrduced here can be quite useful in this setting as well . As an important example , consider the problem of estimating the subthreshold voltage given noisy observations . In many applications , we are more interested in a method which will reliably extract the subthreshold voltage than in the parameters underlying the method . For example , if a linear smoother ( e . g . , the Kalman smoother discussed above ) works well , it might be more efficient and stable to stick with this simpler method , rather than attempting to estimate the parameters defining the cell's full complement of active membrane channels ( indeed , depending on the signal-to-noise ratio and the collinearity structure of the problem , the latter goal may not be tractable , even in cases where the voltage may be reliably measured [23] ) . Of course , in many cases linear smoothers are not appropriate . For example , the linear ( Kalman ) model typically leads to oversmoothing if the voltage dynamics are sufficiently nonlinear ( data not shown ) , because action potentials take place on a much faster timescale than the passive membrane time constant . Thus it is worth looking for a method which can incorporate a flexible nonlinearity and whose parameters may not be directly interpretable biophysically but which nonetheless leads to good estimation of the signal of interest . We could just throw a lot of channels into the mix , but this increases the dimensionality of the state space , hurting the performance of the particle smoother and leading to multicollinearity problems in the M-step , as illustrated in the last subsection . A more promising approach is to fit nonlinear dynamics directly , while keeping the dimensionality of the state space as small as possible . This has been a major theme in computational neuroscience , where the reduction of complicated multichannel models into low-dimensional models , useful for phase plane analysis , has led to great insights into qualitative neural dynamics [26] , [41] . As a concrete example , we generated data from a strongly nonlinear ( Fitzhugh-Nagumo ) two-dimensional model , and then attempted to perform optimal smoothing , without prior knowledge of the underlying voltage nonlinearity . We initialized our analysis with a linear model , and then fit the nonlinearity nonparametrically via a straightforward nonparametric modification of the EM approach developed above . In more detail , we generated data from the following model [41]: ( 25 ) ( 26 ) where the nonlinear function is cubic in this case , and and denote independent white Gaussian noise processes . Then , given noisy observations of the voltage ( Figure 12 , left middle panel ) , we used a nonparametric version of our EM algorithm to estimate . The E-step of the EM algorithm is unchanged in this context: we compute and , along with the other pairwise sufficient statistics , using our standard particle forward-backward smoother , given our current estimate of . The M-step here is performed using a penalized spline method [42]: we represent as a linearly weighted combination of fixed basis functions :and then determine the optimal weights by maximum penalized likelihood:The first term here corresponds to the expected complete loglikelihood ( as in equation ( 23 ) ) , while the second term is a penalty which serves to smooth the inferred function ( by penalizing non-smooth solutions , i . e . , functions whose derivative has a large squared norm ) ; the scalar serves to set the balance between the smoothness of and the fit to the data . Despite its apparent complexity , in fact this expression is just a quadratic function of ( just like equation ( 24 ) ) , and the update may be obtained by solving a simple linear equation . If the basis functions have limited overlap , then the Hessian of this objective function with respect to is banded ( with bandwidth equal to the degree of overlap in the basis functions ) , and therefore this linear equation can be solved quickly using sparse banded matrix solvers [42] , [43] . We used 50 nonoverlapping simple step functions to represent in Figures . 12–13 , and each M-step took negligible time ( ≪1 sec ) . The penalty term was fit crudely by eye here ( we chose a that led to a reasonable fit to the data , without drastically oversmoothing ) ; this could be done more systematically by model selection criteria such as maximum marginal likelihood or cross-validation , but the results were relatively insensitive to the precise choice of . Finally , it is worth noting that the EM algorithm for maximum penalized likelihood estimation is guaranteed to ( locally ) optimize the penalized likelihood , just as the standard EM algorithm ( locally ) optimizes the unpenalized likelihood . Results are shown in Figures 12 and 13 . In Figure 12 , we observe a noisy version of the voltage , iterate the nonparametric penalized EM algorithm ten times to estimate , then compute the inferred voltage . In Figure 13 , instead of observing the noise-contaminated voltage directly , we observe the internal calcium concentration . This calcium concentration variable followed its own noisy dynamics , where denotes white Gaussian noise , and the term represents a fast voltage-activated inward calcium current which activates at −20 mV ( i . e . , this current is negligible at rest; it is effectively only activated during spiking ) . We then observed a noisy fluorescence signal which was linearly related to the calcium concentration [32] . Since the informative signal in is not its absolute magnitude but rather how quickly it is currently changing ( is dominated by during an action potential ) , we plot the time derivative in Figure 13; note that the effective signal-to-noise in both Figures 12 and 13 is quite low . The nonparametric EM-smoothing method effectively estimates the subthreshold voltage in each case , despite the low observation SNR . In Figure 12 , our estimate of is biased towards a constant by our smoothing prior; this low-SNR data is not informative enough to overcome the effect of the smoothing penalty term here; indeed , since this oversmoothed estimate of is sufficient to explain the data well , as seen in the left panels of Figure 12 , the smoother estimate is preferred by the optimizer . With more data , or a higher SNR , the estimated becomes more accurate ( data not shown ) . It is also worth noting that if we attempt to estimate from using a linear smoother in Figure 13 , we completely miss the hyperpolarization following each action potential; this further illustrates the advantages of the model-based approach in the context of these highly nonlinear dynamical observations .
This paper applied standard machine learning techniques to the problem of inferring biophysically detailed models of single neurones automatically and directly from single-trial imaging data . In the first part , the paper presented techniques for the use of detailed models to filter noisy and temporally and spatially subsampled data in a principled way . The second part of the paper used this approach to infer unknown parameters by EM . Our approach is somewhat different from standard approaches in the cellular computational neuroscience literature ( [12] , [14] , [15] , [19] , although see [18] ) , in that we argue that the inference problem posed is equivalent to problems in many other statistical situations . We thus postulate a full probabilistic model of the observations and then use standard machine learning tools to do inference about biophysically relevant parameters . This is an approach that is more standard in other , closely related fields in neuroscience [44] , [45] . Importantly , we attempt to use the description of the problem in detail to arrive at as efficient as possible a method of using the data . This implies that we directly compare recording traces ( the voltage or calcium trace ) , rather than attempting to fit measures of the traces such as the ISI distribution , and the sufficient statistics that are used for the parameter inference involves aspects of the data these parameters influence directly . One alternative is to include a combination of such physiologically relevant objective functions and to apply more general fitting routines [46] , [47] . A key assumption in our approach is that accurately fitting the voltage trace will lead to accurate fits of such other measures derived from the voltage trace , such as the inter-spike interval distribution . In the present approach this means that variability is explicitly captured by parameters internal to the model . In our experience , this is important to avoid both overfitting individual traces and neglecting the inherently stochastic nature of neural responses . A number of possible alternatives to sequential Monte Carlo methods exist , such as variations of Kalman filtering like extended or unscented Kalman filters [48] , [49] , variational approaches ( see [50] ) and approximate innovation methods [45] , [51] , [52] . We here opted for a sequential Monte Carlo method because it has the advantage of allowing the approximation of arbitrary distributions and expectations . This is of particular importance in the problem at hand because a ) we specifically wish to capture the nonlinearities in the problem as well as possible and b ) the distributions over the unobserved states are highly non-Gaussian , due to both the nonlinearities but also due to unit bounds on the gates . Model-based smoothing thus provides a well-founded alternative to standard smoothing techniques , and , importantly , allows smoothing of data without any averaging over either multiple cells or multiple trials [53] . This allows the inference of unobserved variables that have an effect on the observed variable . For example , just as one can infer the channels' open fractions , one can estimate the voltage from pure [Ca2+] recordings ( data not shown ) . The formulation presented makes it also straightforward to combine measurements from various variables , say [Ca2+] and transmembrane voltage , simply by appropriately defining the observation density . We should emphasize , though , that the techniques themselves are not novel . Rather , this paper aims to point out to what extent these techniques are promising for cellular imaging . The demand , when smoothing , for an accurate knowledge of the cell's parameters is addressed in the learning part of the paper where some of the important parameters are inferred accurately from small amounts of data . One instructive finding is that adding noise to the observations did not hurt our inference on average , though it did make it slower and more variable ( note the wider error bars in Figure 7 ) . In the higher-dimensional cases , we found that the dimensions in parameter space which have least effect on the models' behavior were also least well inferred . This may replicate the reports of significant flat ( although not disconnected ) regions in parameter space revealed in extensive parametric fits using other methods [19] . A number of parameters also remain beyond the reach of the methods discussed here , notably the kinetic channel parameters; this is the objective of the non-parametric inference in the last section of the Results , and also of further ongoing work . A number of additional questions remain open . Perhaps the fundamental direction for future research involves the analysis of models in which the nonlinear hidden variable is high-dimensional . As we saw in section EM – inferrring cellular parameters , our basic particle smoothing-EM methodology can break down in this high-dimensional setting . The statistical literature suggests two standard options here . First , we could replace the particle smoothing method with more general ( but more computationally expensive ) Markov chain Monte Carlo ( MCMC ) methods [54] for computing the necessary sufficient statistics for inference in our model . Designing efficient MCMC techniques suitable for high-dimensional multicompartmental neural models remains a completely open research topic . Second , to combat the multicollinearity diagnosed in Figure 11 ( see also Figure 6 of [23] ) , we could replace the maximum-likelihood estimates considered here with maximum a posteriori ( maximum penalized likelihood ) estimates , by incorporating terms in our objective function ( 7 ) to penalize parameter settings which are believed to be unlikely a priori . As discussed in section Estimation of subthreshold nonlinearity by nonparametric EM , the EM algorithm for maximum penalized likelihood estimation follows exactly the same structure as the standard EM algorithm for maximum likelihood estimation , and therefore our methodology may easily be adapted for this case . Finally , a third option is to proceed along the direction indicated in section Estimation of subthreshold nonlinearity by nonparametric EM: instead of attempting to fit the parameters of our model perfectly , in many cases we can develop good voltage smoothers using a cruder , approximate model whose parameters may be estimated much more tractably . We expect that a combination of these three strategies will prove to be crucial as optimal filtering of nonlinear voltage- and calcium-sensitive dendritic imaging data becomes more prevalent as a basic tool in systems neuroscience . | Cellular imaging techniques are maturing at a great pace , but are still plagued by high levels of noise . Here , we present two methods for smoothing individual , noisy traces . The first method fits a full , biophysically accurate description of the cell under study to the noisy data . This allows both smoothing of the data and inference of biophysically relevant parameters such as the density of ( active ) channels , input resistance , intercompartmental conductances , and noise levels; it does , however , depend on knowledge of active channel kinetics . The second method achieves smoothing of noisy traces by fitting arbitrary kinetics in a non-parametric manner . Both techniques can additionally be used to infer unobserved variables , for instance voltage from calcium concentration . This paper gives a detailed account of the methods and should allow for straightforward modification and inclusion of additional measurements . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"physiology/neuronal",
"signaling",
"mechanisms",
"biophysics/experimental",
"biophysical",
"methods",
"biophysics/theory",
"and",
"simulation",
"computational",
"biology/computational",
"neuroscience",
"neuroscience/theoretical",
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] | 2009 | Smoothing of, and Parameter Estimation from, Noisy Biophysical
Recordings |
Soluble circulating proteins play an important role in the regulation of mating behavior in Drosophila melanogaster . However , how these factors signal through the blood–brain barrier ( bbb ) to interact with the sex-specific brain circuits that control courtship is unknown . Here we show that male identity of the blood–brain barrier is necessary and that male-specific factors in the bbb are physiologically required for normal male courtship behavior . Feminization of the bbb of adult males significantly reduces male courtship . We show that the bbb–specific G-protein coupled receptor moody and bbb–specific Go signaling in adult males are necessary for normal courtship . These data identify sex-specific factors and signaling processes in the bbb as important regulators of male mating behavior .
Male courtship in Drosophila melanogaster consists of a series of consecutive behavioral steps that are well characterized ( for reviews see [1]–[3] ) and include orientation towards the female , tapping of the female's abdomen with the male's forelegs , performing of a courtship “song” by wing extension and vibration , licking of the female's genitalia , attempted copulation and copulation . The behavior is controlled by the master regulatory genes of the general somatic sex determination pathway Sxl , tra , dsx and fru [4]–[6] . A cascade of sex-specific alternative splicing results in males that express the transcription factors DSXM and FRUM , and females expressing DSXF ( the FRU protein is not made in females ) . Both FRU and DSX are expressed in defined subsets of neurons in the brain and abdominal ganglia , with expression overlapping in some of these neurons . A number of experiments have demonstrated that both dsx and fru are required for normal male courtship behavior ( [7]–[12]; reviewed in [13]–[16] ) and several recent studies have identified dsx and fru neuronal projections that form putative circuits that confer the competence for male courtship behavior [10] , [17]–[19] . While fru and dsx neurons are indispensible for male courtship , another , non-neuronal tissue has emerged as an important regulator: the fat body . The fat body ( which is often compared to the mammalian liver ) is a secretory tissue that surrounds the brain , the abdominal ganglia and the abdominal organs . Among other important aspects it functions in metabolism , energy storage , immune function and yolk protein synthesis in females . It also produces sex-specific factors that are required for full male courtship behavior [20] , [21] . When , by genetic means , the fat body is made female in an otherwise normal adult male , courtship behavior is strongly reduced , indicating that it produces sex- specific factors [22] . These experiments are feasible because sex determination in flies is mostly cell-autonomous . In agreement with this finding , several other screens for sex-specifically expressed genes in fly heads , and for genes that are controlled by dsx and fru , resulted in the identification of genes that are expressed in the fat body [23]–[26] . The fat body secretes proteins into the hemolymph , the open circulatory system of the fly . Takeout , one of the male-enriched fat body proteins with a role in male courtship , has been shown to be secreted into the hemolymph and act as a secreted protein [22] . This is likely also the case for other sex specific fat body proteins . How such hemolymph proteins interact with the brain circuits that control courtship is unknown . Since flies possess a blood–brain barrier ( bbb ) , hemolymph proteins do not have unrestricted access to brain neurons . Nothing is known about the mechanisms by which these proteins negotiate the blood–brain barrier . The Drosophila bbb consists of two glial cell layers that ensheathe the entire brain , ventral ganglion and peripheral nerves . The inner or subperineurial glia ( SPG ) cell layer is situated next to the neuronal cell bodies and forms the tight barrier that is characteristic of vertebrate and invertebrate blood–brain barriers [27]–[30] . The vertebrate bbb is formed by tight junctions between blood endothelial cells [31] . In contrast , in Drosophila , septate junctions form the basis of the barrier , similar to mammalian myelinating glia at the paranodal junctions . Several studies have identified important proteins that are required for the development of a tight and functional barrier , several of which are evolutionarily conserved [29] , [30] , [32]–[35] . While insight into the development of the Drosophila bbb has been gained , very little is still known about the adult bbb and the mechanisms that underlie its function ( for a review see [36] ) . Interaction of hemolymph components with the brain will likely involve specific receptors , transporters and signaling events . A Drosophila ABC transporter Mdr65 that functions similarly to the mammalian human MDR1/Pgp has been described [37] . Furthermore , the putative GPCR moody that is specifically expressed in the SPG barrier cells has been shown to mediate the behavioral response to ethanol and cocaine independent of its developmental function in barrier set up [28] . In order to approach the question how sex-specific circulating factors communicate/pass through the bbb in order to exert their function , we examined whether the sexual identity of the bbb itself might be required , and whether moody signaling in the bbb is a component in the regulation of courtship behavior . We report here that the bbb contains sex specific factors that are important for male courtship , and that moody and Go protein signaling in the bbb are important regulators of this behavior .
To examine whether the bbb has a sex-specific role in male courtship behavior , we used the sub-perineurial glia specific Gal4 driver SPG-Gal4 [30] to express a UAS- TraF transgene . TraF is a female-specific regulator of the sex determination cascade and expression of TraF has been shown to specifically feminize the cells in which it is expressed [38]–[40] . We used two different SPG-Gal4 lines with insertions on the second or third chromosome , respectively , to express TraF ( gifts from R . Bainton ) . Males whose bbb layer was feminized court females significantly less than the wild-type control males ( Figure 1a ) . Their overall courtship index ( CI ) was significantly reduced . The CI is the fraction of time a male spends performing any of the steps of the courtship ritual within the observation period . When we quantified individual courtship steps , we found that latency ( the time to first orientation towards the female ) was the same in control and experimental animals . This indicates that the mutants are not deficient in their perception of the female and her pheromones . In contrast , the fraction of time spent extending a wing to perform the courtship song and the number of copulations were significantly reduced , in agreement with the overall reduction in courtship ( Table 1 ) . These data show that males with feminized blood–brain barrier are capable of all steps of courtship , but perform them with reduced probability . This is not due to locomotion defects , since males with feminized bbb perform indistinguishably from control flies in a short term activity assay [41] ( Figure 1g ) . In this assay , individual males are placed in a courtship chamber and the number of time they cross a drawn line is counted . To corroborate that the observed courtship reduction is caused by the feminization of glial cells , we used repo-Gal4 , a driver that is expressed not just in the bbb but generally in glial cells [42] . We observed a similar reduction , confirming that the male identity of glial cells is important for male courtship ( Figure 1b , 1h ) . This would predict that a similar effect should be observed if these cells were made “less male” . Since TraF acts through its downstream targets fru and dsx we next examined the effect of expressing RNAis that target these transcripts in bbb cells . Indeed , SPG-Gal4/UAS-fruRNAi and SPG-Gal4/UAS-dsxRNAi males showed similar reductions as SPG-Gal4/UAS-TraF animals ( Figure 1c , 1i ) . This suggests that FRU and DSXM both have a role in regulating sex-specific molecules in the bbb . It also argues that the effect of TraF is not due to merely overexpressing female-specific DSXF . An important question that arises from these results is whether the feminization of the bbb affects the blood–brain barrier permeability . To address this question we performed a dye penetration assay [28] , [37] . An intact bbb will exclude a 10 kDa Texas-red-coupled dextran ( TR-dextran ) molecule from the brain , whereas in mutants with a defective bbb , the dye will penetrate through the bbb ( Figure 1e ) . We injected 10 kDa TR-dextran into adult males that prior to the injections were raised and treated identical to the males in the courtship assay . After a 24 h recovery period , fly brains were dissected and dye penetration into the brain was examined by confocal microscopy ( Figure 1d–1f ) . TR-dextran was efficiently excluded from the brain of males with feminized bbb ( Figure 1d ) , indistinguishable from the CS wild-type control ( Figure 1f ) . These results suggest that feminization of the sub-perineurial layer of the bbb does not affect blood–brain barrier permeability in an obvious way , but rather affects sex-specific physiological processes that are required for male courtship behavior . Because the sub-perineurial layer is formed during blood–brain barrier development [29] , but also has a physiological role in adults [28] , the observed reduction in male courtship behavior could be a result of developmental effects , an effect on the adult physiological function of the bbb , or both . To distinguish between these possibilities , we added a temperature sensitive tubP-Gal80ts transgene [43] to induce feminization of the sub-perineurial layer exclusively in the adult . Males were raised at 18°C and after eclosion kept at 18°C . At that temperature , the Gal80ts inhibitor is active and inhibits Gal4 from activating UAS-TraF . Mature adult males were then transferred to 32°C or 30°C before testing in the courtship assay . At 30°C and 32°C , Gal80ts becomes inactive , Gal4 is active and TraF is induced [43] . Control animals were continuously kept at 18°C ( uninduced controls ) . The results are shown in Figure 2 . Control genotypes at the uninduced ( 18°C ) and induced temperatures courted normally . Uninduced Gal80ts20/Gal80ts10; SPG-Gal4/UAS-TraF males courted females normally . In contrast , induced Gal80ts20/Gal80ts10; SPG-Gal4/UAS-TraF males showed a reduction in courtship behavior . This effect was dependent on the length of induction . Induction at 32°C for 16 hours ( Figure 2a ) reduced courtship to a lesser extent than induction for 48 hours ( Figure 2c ) . Extended induction at 30°C for four days did not reduce courtship further ( Figure 2c ) ( 30°C was chosen to reduce the impact of the extended heat-shock period ) . As observed in the feminization experiments not using Gal80 , all experimental males were capable of performing all courtship steps , but they performed them less frequently . The reduction in the extended time-induced males was close to the reduction observed in the absence of Gal80 , indicating that most of the observed effect was due to the effect of feminization during the mature adult stage . To explore this further , we examined Gal80ts20/Gal80ts10; SPG-Gal4/UAS-TraF males that spent most of their development at 30°C . Due to lethality of the higher temperature during earlier stages , we moved the flies from 25°C to 30°C on day 5 , when third instar wandering larvae began climbing up the wall of the vial . The critical time period for behavioral sex-determination has been shown to lie in pupal stages [4] . As shown in Figure 2d , extended heat conditions lowered overall courtship somewhat in the controls . In the Gal80ts20/Gal80ts10; SPG-Gal4/UAS-TraF males the courtship index was reduced similarly to the reduction in the SPG-Gal4/UAS-traF males shown in Figure 1a , relative to control flies . However , these males rarely showed courtship behaviors beyond their initial orientation to the female . These results may indicate a developmental component when feminization occurs throughout extended stages . We next tested whether males that had spent most of their development at 30°C could be rescued by shifting them back to 18°C after eclosion . As shown in Figure 2e , a fraction of these flies was rescued and showed wild-type courtship scores ( 8 out of 20 ) . The rest , rather than being partially rescued , did not improve . This “all or none” rescue may indicate a threshold effect . However , the males that did show rescue demonstrate the importance of correct male identity of the bbb cells in the adult . Taken together , our experiments demonstrate a significant sex-specific role of the bbb in the control of male courtship and suggest that sex-specific molecules in the bbb play a crucial physiological role in the regulation of courtship . Signaling molecules are likely candidates for mediating the courtship role of the bbb , but very little is known about proteins that are expressed in this layer . Among them , moody is a good candidate for such a role . moody encodes a putative GPCR that has been shown to be specifically expressed in the sub-perineurial glia ( SPG ) layer of the blood–brain barrier . While moody is required developmentally for the formation of a tight bbb [29] , it has an independent role in the response of adult animals towards ethanol and cocaine [28] . The gene encodes two different protein isoforms , Moody-α and Moody-β that are formed by differential alternative splicing [28] . moody null mutant animals ( moodyΔ17 ) are lethal with a leaky bbb , although a few escapers can survive into adulthood . Addition of either a moody-α or a moody-β encoding transgene is sufficient to rescue the lethality and the leakiness of the bbb [28] . However , despite their intact bbb , moodyΔ17; moody-α or moodyΔ17; moody-β flies are mutant in their sensitivity to ethanol and cocaine , demonstrating that both protein isoforms are required for the behavior [28] . We tested the courtship of moodyΔ17; moody-α and of moodyΔ17; moody-β flies and found that their courtship was significantly reduced ( Figure 3a ) . However , the reduced courtship behavior was rescued when both transgenes were present . The mutant genotypes did not affect short term activity , as shown in Figure 3b . We conclude from these experiments that moody-α and moody-β are both required in the bbb for normal courtship behavior . When we analyzed RNAseq data generated from adult male and female heads [44] we found that the moody isoforms are expressed differently in the two sexes ( Figure 4 ) . There are four moody RNA isoforms that differ in their use of two transcription start sites and by differential splicing ( by 2 nucleotides ) of one of the introns that generates either the α or β protein isoform [28] . Of the four previously identified isoforms ( http://flybase . org/reports/FBgn0025631 . html ) , one ( FBtr0303041-moody-RA ) was not detected in adult heads . FBtr0303043 ( moody-RB ) which encodes an alpha isoform is significantly enriched in males , whereas FBtr0303044 ( moody-RC ) and FBtr0303045 ( moody-RD ) which encode beta isoforms are enriched in females . However , our courtship data presented in Figure 3a show that both α and β isoforms are required for courtship in males . Since Moody is a putative GPCR that potentially signals through G proteins , we next examined the importance of G-protein signaling in the bbb for male courtship behavior . We tested mutant flies for each of the known G- proteins in Drosophila: Go , Gs , Gi , and Gq . In addition , we tested a RNAi mutant of a fifth Gα subunit ( concertina ) that has been shown to be present in the fly genome [45] . It has been reported that Go signaling is required for blood–brain barrier insulation during development [29] and G-protein mediated signaling is required throughout development . Therefore , to circumvent developmental lethality , we conditionally expressed mutant forms of the proteins only in adult mature males using the Gal80ts system . To specifically inhibit Go we used the bbb specific SPG-Gal4 to drive expression of pertussis toxin ( PTX ) ( Figure 5a , 5b ) . The S1 subunit of PTX from B . pertussis specifically ADP-ribosylates vertebrate G ( i/o/t ) proteins , resulting in their inability to bind to activated GPCRs [46] . Flies do not have Gt , and their Gi lacks the site for ADP ribosylation . Thus , PTX in flies is specifically inhibiting Go [47]–[49] . After the induction of PTX expression in the SPG layer for 12 hours at 32°C , we observed a significant reduction in male courtship behavior ( Figure 5a ) . A breakdown of courtship into the different steps indicates that in the mutants latency is normal , but the probability that a male will re-initiate and sustain courtship beyond the first steps is lowered , although all steps , including copulation , can be carried out ( Table 2 ) . In addition , flies with induced PTX expression are normal in the short-term activity assay ( Figure 5b ) . As a complementary approach to the inhibition of Go signaling by PTX , we conditionally reduced Go signaling in bbb cells by Gαo-RNAi . In agreement with the PTX results , Gαo reduction in adult mature males reduced male courtship ( Figure 5c ) . Short-term activity of the mutants was normal ( Figure 5d ) . To examine whether interference with Go signaling in adult males affects the integrity of the bbb , we performed TR-dextran dye penetration assays . Conditional adult expression of PTX or Go-RNAi in the blood–brain barrier does not affect the insulating properties of the blood–brain barrier ( Figure 5e–5h ) . Conditional bbb expression of dominant mutant forms of Gs and Gi [50] , [51] , and of Gq-RNAi and cta-RNAi had no effect on courtship ( Figure 5i–5l ) , although the mutant Gs and Gi proteins have previously been shown to be active and affect development [29] , [50] , [52] . We also did not observe a courtship phenotype when we disrupted PKA signaling , a potential downstream effector , by expressing PKA* , a dominant persistently active mutant catalytic subunit [53] . Taken together , our results strongly suggest that Go signaling in the bbb is physiologically required for the regulation of normal male courtship behavior .
The blood–brain barrier is an important selective interface between circulating factors and the brain . We show here that the bbb also plays a crucial role in the regulation of male courtship behavior in Drosophila . When the bbb is feminized in an otherwise normal male animal , the courtship index drops significantly , indicating the presence of male-specific factors and processes in these cells . Importantly , while some of these sex-specific factors may affect sex-specific development of the bbb , our results demonstrate that feminization of the bbb exclusively in the adult is sufficient to reduce male courtship . Thus , male-specific factors are physiologically required in courting males . It is worth noting that the integrity of the bbb was not affected by feminization or by any other of our manipulations using a standard approach to examine bbb barrier function , although we can not rule out small defects . Therefore , the observed effects support the interpretation that feminization affects physiological sex- specific processes within the bbb . We show here that moody GPCR signaling is one of these processes . Normal courtship requires both moody isoforms , α and β , similar to the previously reported response to alcohol and cocaine . As has been described , moody appears to have two distinct roles: While either one of the moody isoforms is sufficient for a functional and intact barrier , both isoforms are required for adult signaling processes . Our RNA sequencing data suggest that the two isoforms are not present in equal abundance and that the ratio of the two isoforms is sex-specifically regulated . It is not clear at present why two forms of the moody protein are required in behavior . It is unlikely that a strict stoichiometric ratio of the two isoforms is required , since we have observed normal courtship in wild-type flies that express additional Moody-β ( +/moody−β in Figure 3a ) . The two isoforms differ in their intracellular domain , which could indicate that they interact with different effector molecules that are both contributing to the behavioral response . We have observed courtship defects when either isoform is missing , indicating that both forms have a role in regulating courtship . Interestingly , we have observed that the ratio of the two isoforms is under the control of the sex-specific splicing factor TraF [44] , raising the possibility that the moody pre-mRNA is a target for splicing regulation by TraF or one of its downstream effectors . It will be of interest to identify other sex-specific factors in the bbb and examine their contribution to the regulation of male courtship . We did not observe a courtship phenotype when we expressed dominant mutants for Gs and Gq that have been shown to act as dominant negative mutations in developmental processes [50] . Likewise , expression of Gi-RNAi or concertina-RNAi did not result in reduced courtship either . This suggests that these G proteins do not play a significant role in courtship in this layer . In contrast , we have observed courtship defects when Go signaling was compromised . We have employed two approaches to show that the heterotrimeric protein Go is required for male courtship behavior: inhibition by PTX and mRNA reduction by RNAi . The S1 subunit of PTX from Bordetella pertussis catalyzes the transfer of an ADP-ribose onto the Gα subunit of the heterotrimeric G protein . In contrast to mammals , where PTX inhibits both Go and Gi , in Drosophila PTX is a specific inhibitor for Go , since the only Gi present ( G ( i ) 65A ) does not contain the PTX recognition site . PTX will only ribosylate heterotrimers ( not individual alpha subunits ) , and the consequence of this ribosylation is inhibition of the heterotrimer activation [46] , [54] . The inhibition of Go signaling by PTX is therefore very specific; since the ADP-ribosylated Go heterotrimers cannot be activated , they do not generate ectopic Gβγ subunits , nor do they sequester free Gβγ subunits away from other Gα subunits . Conditional induction of PTX only in adult mature flies , as well as conditional adult reduction of Gαo by RNAi reduced male courtship . This demonstrates that physiological signaling through Go is an important signaling pathway that regulates courtship in the bbb . Given these findings it is likely that moody signals through Go to exert its function in courtship . In embryos , Go , Gi , moody and loco mutations each disrupt the formation of the bbb [29] and lead to bbb leakiness , as shown by dye penetration . In contrast , we did not observe dye penetration in the PTX and Go-RNAi mutants that we generated , further evidence that the developmental and physiological roles of moody and Go signaling differ in their mechanisms . We do not know what the downstream pathways are that are mediating the Go action . Few Go effectors have been demonstrated and its α or βγ subunits could be mediating the signal . In many cases in vertebrates it is the βγ subunits that are responsible for actuating signaling . In neurons , presynaptic voltage-gated Ca2+ channels have been shown to represent an effector for Go [55] . Studies of the role of Go in learning and memory in Drosophila have suggested that Go signaling does not occur through the rut adenylyl cyclase [47] . Signaling through Go is not generally thought to occur through PKA , consistent with our finding that disruption of PKA signaling in the bbb did not affect courtship . It is unknown whether potential downstream signaling molecules like loco , Gγ13F or PKC are sex-specifically expressed in the blood–brain barrier and might have a courtship role in this layer . In whole heads , PKC98E is male-preferentially expressed [44] . It is unknown what the ligand is for Moody and it remains to be seen what the exact role is for moody bbb signaling in courtship . Hemolymph factors that influence courtship could conceivably do so by initiating signaling pathways at the bbb , or by passage and transport through the bbb . Moody could be playing a role in signaling , as well as through a possible effect on transport , perhaps in processes similar to its previously demonstrated effects on the actin cytoskeleton during development [29] . Here we have demonstrated that sex-specific molecules in the bbb are important regulators of male courtship behavior in Drosophila . The Moody GPCR and Go signaling in this layer are an important part of this regulation . It will be of importance to identify the ligand ( s ) and downstream signaling events that ultimately interact with the brain circuits that control male courtship behavior .
All flies strains were reared on standard corn meal/sugar-based medium at room temperature under non-controlled light conditions , except for Gal80ts flies that were grown at 18°C and induced as adults at 32°C or 30°C as indicated . TraF is w1118; P{UAS-tra . F}20J7 [39]; SPG-Gal4/TM3 , SPG-Gal4/CyO , Δ17/FM6K; Δ17/FM6K;α- moody/+ and Δ17/FM6K; β- moody/+ and Δ17/FM6K;α- moody/+; β- moody/+ were a gift from Dr . Roland Bainton ( University of California at San Francisco , San Francisco , California ) . y , repo-Gal4 on X was a gift from Takeshi Awasaki ( University of Massachusetts , Worcester , Massachusetts ) [27]; Prior to using the flies in courtship crosses , we removed the y mutation by recombination . w; UAS-G-salpha60A . Q215L}16/TM3 ( BL6490 ) [51] and UAS-concertina RNAi TRiP . JF01607}attP2 ( BL 31132 ) were obtained from the Bloomington Drosophila stock center; The strains w; UAS-G- ialpha65A . Q205L}16 [50]; P{UAS-PTX}16 , ry506/TM3; w; Sco/CyO; tubP-Gal80ts2 , w; tubP-Gal80ts10 , w; tubP-Gal80ts20 were a gift from Dr . Gregg Roman ( University of Houston , Houston , Texas ) . The Go RNAi strain UAS- CG2204 RNAi19124 ( Transformant ID :19124 ) was obtained from the VDRC stock collection , Vienna . For Gal80ts experiments , control and experimental flies were raised at 18°C . Virgin males were collected at eclosion and kept for 5–8 days at 18°C . Matured flies were then placed at 32°C or at 30°C for the times indicated in the text . For some of the temperature shift experiments , crosses were placed at 25°C for 5 days , when third instar wandering larvae began climbing up the walls . At that time the vials were moved to 30°C . Freshly eclosed males were individually put in vials and matured at the temperatures and for the time spans indicated in the figures . Only flies that eclosed within the first 24 hours were used . All flies were let rest for 1–2 hours at RT prior to the behavioral assay . Non-induced controls from 18°C were subjected to the same resting period of 1–2 hours at room temperature before testing . The courtship assay and activity assay were performed as previously described [20] . In short , males were placed in a plexiglas “mating wheel” ( diameter 0 . 8 cm ) , together with a 2–4 hrs old Canton-S virgin female . The courtship index was calculated as the fraction of time the male spent displaying any element of courtship behavior ( orienting , following , wing extension , licking , attempted copulation , copulation ) within a 10 minute observation period [6] . Short-term activity assays were performed as previously described [41] . Individual males were placed into the “mating wheel” containing a filter paper with a single line dividing the chamber in half . After 2–3 minutes of acclimation time , the number of times the male crossed the center line within the three minute observation time was counted . Each graph represents a full set of control and experimental genotypes that were grown , collected and aged in parallel . In each behavioral session , equal numbers of all genotypes were tested . The number of tested flies was equal for all genotypes in an experiment . We injected 10 kDa TR-dextran into adult males that prior to the injections were raised and treated identical to the males in the courtship assay . The animals were anesthetized on ice and injected with 2 . 5 mM 10 kDa Texas-Red conjugated dextran ( Invitrogen D-1863 ) in H2O as previously described [28] with slight modifications . Immobilized flies were kept cold by ice packs during dye injection . 0 . 1–0 . 3 µl of dye was microinjected into the scutellum of the fly . After injection flies were allowed to recover overnight . Flies were anesthetized on ice and fly heads were separated and fixed in 4% Paraformaldehyde ( EM grade , Polysciences Inc . ) in 1× PBHS for 30 minutes at RT . Then the proboscis was removed and the heads were fixed for an additional 5 minutes at RT . The brain was finally dissected and washed in 1× PBS 3 times for 30 minutes each . The brains were mounted on a coverslip with Vectashield mounting media with DAPI ( VectorLab ) and observed with an Olympus FV100 confocal microscope . DAPI- stained cell nuclei were visualized at 405 nm , Texas red Dextran at 633 nm . Statistical analysis was performed by one-way or two-way ANOVA and Bonferroni multiple comparison post hoc test . Statistical analyses were performed with Statview ( Adept Scientifics , Bethesda , MD ) or GraphPad Prism5 . | Complex behaviors such as mating behavior are controlled by the brain . Ensembles of brain cells work in networks to ensure proper behavior at the right time . While the state of these cells plays an important role in whether and how the behavior is displayed , information from outside the brain is also required . Often , this information is provided by hormones that are present in the circulating fluid ( such as the blood ) . However , the brain is protected by a layer of very tight cells , the so-called blood–brain barrier , that keeps unwanted molecules out . So how then do hormones and other regulatory factors “talk” to the brain ? We are studying this question by examining the mating behavior of males of a model organism , the fruit fly Drosophila melanogaster . We have found that the blood–brain barrier cells themselves contain male-specific molecules that play an important role . When they are absent , courtship behavior is compromised . We have also identified how outside factors talk to the brain: by using a cellular signaling protein and a particular signaling pathway . Together they are well suited to pass on outside information to the brain network that regulates mating behavior . | [
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] | 2013 | Sex-Specific Signaling in the Blood–Brain Barrier Is Required for Male Courtship in Drosophila |
The viral Tax oncoprotein plays a key role in both Human T-cell lymphotropic virus type 1 ( HTLV-1 ) -replication and HTLV-1-associated pathologies , notably adult T-cell leukemia . Tax governs the transcription from the viral 5’LTR , enhancing thereby its own expression , via the recruitment of dimers of phosphorylated CREB to cAMP-response elements located within the U3 region ( vCRE ) . In addition to phosphorylation , CREB is also the target of O-GlcNAcylation , another reversible post-translational modification involved in a wide range of diseases , including cancers . O-GlcNAcylation consists in the addition of O-linked-N-acetylglucosamine ( O-GlcNAc ) on Serine or Threonine residues , a process controlled by two enzymes: O-GlcNAc transferase ( OGT ) , which transfers O-GlcNAc on proteins , and O-GlcNAcase ( OGA ) , which removes it . In this study , we investigated the status of O-GlcNAcylation enzymes in HTLV-1-transformed T cells . We found that OGA mRNA and protein expression levels are increased in HTLV-1-transformed T cells as compared to control T cell lines while OGT expression is unchanged . However , higher OGA production coincides with a reduction in OGA specific activity , showing that HTLV-1-transformed T cells produce high level of a less active form of OGA . Introducing Tax into HEK-293T cells or Tax-negative HTLV-1-transformed TL-om1 T cells is sufficient to inhibit OGA activity and increase total O-GlcNAcylation , without any change in OGT activity . Furthermore , Tax interacts with the OGT/OGA complex and inhibits the activity of OGT-bound OGA . Pharmacological inhibition of OGA increases CREB O-GlcNAcylation as well as HTLV-1-LTR transactivation by Tax and CREB recruitment to the LTR . Moreover , overexpression of wild-type CREB but not a CREB protein mutated on a previously described O-GlcNAcylation site enhances Tax-mediated LTR transactivation . Finally , both OGT and OGA are recruited to the LTR . These findings reveal the interplay between Tax and the O-GlcNAcylation pathway and identify new key molecular actors involved in the assembly of the Tax-dependent transactivation complex .
Human T-lymphotropic virus type 1 ( HTLV-1 ) is the only retrovirus associated to a cancer in humans . HTLV-1 is indeed the etiologic agent of adult T-cell leukemia/lymphoma ( ATLL ) , a very aggressive malignant proliferation of CD4+ T lymphocytes , which appears in 2–5% of infected individuals ( reviewed in [1] ) . In addition , HTLV-1 is also associated with various inflammatory disorders , including HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) [2] . The oncogenic power of HTLV-1 is due in large part to the properties of the viral oncoprotein Tax . Tax is a powerful inducer of T-cell proliferation through its ability to activate a broad range of cellular promoters , promote cell cycle and inhibit apoptosis and repair machineries ( reviewed in [3] ) . As a consequence , Tax has been shown to induce immortalization of primary T cells in vitro [4] as well as tumor formation in transgenic animals [5] . Tax is also critical for HTLV-1 gene expression by virtue of its capacity to transactivate the 5’ LTR that controls the transcription of all HTLV-1 structural , enzymatic and regulatory genes , including Tax itself , and auxiliary genes with the exception of the antisense product HBZ [6] . The transactivation of the 5’LTR depends on Tax interaction with the cellular transcription factor cAMP response element binding protein ( CREB ) that , together with Tax , binds to three conserved copies of a cyclic AMP-response element ( CRE ) located in the LTR U3 region ( viral CRE/vCRE ) . CREB-mediated activation of cellular promoters has been shown to critically depend on CREB phosphorylation at Ser133 [7 , 8] . It was initially proposed that CREB phosphorylation was dispensable in the context of Tax transactivation of the HTLV-1 promoter [9 , 10] . However , further studies demonstrated on the one hand that the transactivation complex contains Ser133-phosphorylated CREB and on the other hand , that Tax is able to increase CREB phosphorylation [11–13] . The binding of Tax/CREB complexes to the vCRE then allows the recruitment of the CREB-Regulated Transcription Coactivator/Transducer Of Regulated CREB-Binding Protein ( CRTC/TORC ) [14] , the CREB binding protein ( CBP ) [15] and CBP-associated factor ( p/CAF ) [16] general co-activators and ultimately , of components of the basal transcription machinery ( reviewed in [17] ) . O-GlcNAcylation is a reversible post-translational modification [18] that has been shown to regulate stability , sub-cellular localisation and/or activity of a large set of proteins , notably transcription factors or co-factors [19] , including CREB [20–22] . O-GlcNAcylation consists in the addition of N-acetyl glucosamine ( GlcNAc ) on Serine and Threonine residues . Only a unique couple of enzymes controls O-GlcNAc level on proteins: OGT ( O-GlcNAc transferase ) , which adds the GlcNAc motif on proteins , and OGA ( O-GlcNAcase ) , which removes it [19] . OGT and OGA are known to be physically associated in a molecular complex ( the O-GlcNAczyme complex ) , and this association was shown previously to be important for their regulatory activity on cell signaling and transcriptional processes [23] . Numerous studies have reported alterations in OGT , OGA and O-GlcNAc levels in solid tumors as well as hematopoietic cancers [24] . O-GlcNAcylation may promote tumor development through perturbation of signalling pathways and cell cycle regulators [24 , 25] . In addition , major oncogenic factors were shown to be directly O-GlcNAcylated [24 , 25] . Finally , O-GlcNAcylation has been recently recognized as a novel epigenetic mark ( reviewed in [26] ) . O-GlcNAcylation of CREB was initially described in rat brain [20] . Serine 40 of CREB was identified as a major O-GlcNAcylation site and found to function as a negative signal by preventing CREB association with CRTC/TORC [21] . CREB can be simultaneously O-GlcNAcylated at Ser40 and phosphorylated at Ser133 and indeed , CREB O-GlcNAcylation was shown to preferentially occur on the population of Ser133-phosphorylated CREB [21 , 22] . In this study , we explore for the first time the status of O-GlcNAcylation in HTLV-1-transformed T cells . By using a combination of BRET , enzymatic and biochemical assays , we report that the HTLV-1 Tax protein binds to the O-GlcNAczyme complex , blocks the activity of OGA and increases total O-GlcNAcylation in both adherent cells and HTLV-1-transformed T cells . Moreover , we show on the one hand that Tax increases CREB O-GlcNAcylation and on the other hand that increasing O-GlcNAcylation through OGA inhibition enhances both Tax-induced LTR transactivation and CREB recruitment to the promoter . We also report that in contrast to wild-type CREB , the CREB S40A mutant fails to enhance Tax-mediated LTR transactivation . Finally , we show that both OGT and OGA are recruited to the HTLV-1 LTR . These findings identify new functional interacting partners of Tax and shed new light on the composition of the transactivation complex assembled by Tax on the HTLV-1 5’ LTR promoter .
To determine the status of O-GlcNAcylation in T cells upon HTLV-1-induced transformation , the levels of OGT and OGA were compared between T cells transformed or not by HTLV-1 . Greater level of OGA mRNA was found in four HTLV-1-transformed T cell lines , compared to four non-HTLV-1 transformed T cells ( Fig 1A ) , whereas OGT mRNA expression was not affected ( Fig 1B ) . To determine whether increased OGA mRNA expression could be related to the activated phenotype of HTLV-1 transformed T cells , we evaluated the levels of OGA and OGT mRNA upon T-cell activation . In contrast to HTLV-1-induced transformation , activation of peripheral blood mononuclear cells with PHA and IL-2 strongly reduced the level of OGA mRNA , while increasing OGT mRNA expression ( S1 Fig ) . Hence , HTLV-1-induced T-cell transformation and T-cell activation differentially modulate OGA and OGT mRNA expression . As shown in Fig 1C , western blot analysis confirmed increased OGA protein expression with no change in OGT protein expression in HTLV-1-transformed compared to non-HTLV-1 transformed T cells . The enzymatic activity of OGA in each T cell line was then quantified . Cells were lysed and equal amounts of total proteins were used to measure either total OGA enzymatic activity or OGA protein level ( Fig 1D ) . A statistically significant increase in total OGA activity ( p = 0 . 0317 ) was found in HTLV-1-transformed T cells as compared to control transformed T cells ( Fig 1D , left panel ) . Because OGA protein expression level was higher in HTLV-1-transformed T cells , OGA activities were normalized to the amount of OGA protein present in each assay , determined by quantification of the signal obtained by western-blotting using the same cell extracts ( Fig 1D , middle panel ) . To validate this procedure , we verified that a linear relationship exists between OGA activity and the OGA signal obtained by western-blot ( S2 Fig ) . When corrected for OGA expression levels , OGA specific activity was much lower ( p = 0 . 0159 ) in HTLV-1-transformed T-cells than in control transformed T cells ( Fig 1D , right panel ) . These findings show that OGT and OGA expression levels are differentially affected by HTLV-1 transformation . They also show that OGA production is increased at both mRNA and protein levels in HTLV-1-transformed T cells but that the activity of the enzyme is impaired in these cells . The HTLV-1 Tax protein is capable of interacting with and deregulating numerous cellular proteins and machineries [3] . We therefore evaluated the impact of Tax on OGA activity using a Tax-negative HTLV-1 transformed T cell line ( TL-om1 ) , which allowed us to study Tax activity in an HTLV-1-transformed T cell context . A Tax expressor plasmid was transfected into TL-om1 T cells and OGA activity was measured 24 hours post-transfection . We observed that Tax-expressing TL-om1 T cells exhibited lower OGA activity than TL-om1 T cells transfected with the control plasmid ( Fig 2A ) . This reduction in OGA activity was not due to a change in OGA expression level ( Fig 2A , insert ) . In order to determine whether Tax-induced OGA inhibition was associated with a change in O-GlcNAcylation , we developed a BRET biosensor based on a previously described FRET O-GlcNAc biosensor ( Fig 2B ) [27] . This BRET O-GlcNAc biosensor is composed of Rluc8 fused to a lectin domain ( GafD ) , a known OGT substrate peptide derived from casein kinase II , followed by the Venus variant of the yellow fluorescent protein . Upon O-GlcNAcylation , the casein kinase peptide binds to the lectin , resulting into a conformational change detected as an increased BRET signal ( Fig 2B ) . We observed higher BRET signal in TL-om1 cells expressing Tax compared to control cells ( Fig 2C , left panel and statistical analysis of Tax-induced delta BRET in middle panel ) . This result was confirmed by western-blotting with an anti-O-GlcNAc antibody , which showed increased O-GlcNAcylation of proteins in Tax-transfected cells ( Fig 2C , right panel ) . Since TL-om1 T cells still express the viral antisense product HBZ , we investigated the effect of Tax in an HTLV-1-independent context . In transfected HEK-293T cells , Tax expression also resulted in a marked reduction in OGA enzymatic activity , as compared to control cells ( Fig 2D ) . Again , this effect was not due to a change in OGA expression level ( Fig 2D , insert ) . In contrast to OGA , OGT activity was not affected by Tax expression ( S3 Fig ) . Inhibition of OGA activity coincided with a significant increase in the BRET signal of the biosensor ( Fig 2E left panel and statistical analysis of Tax-induced delta BRET in middle panel ) . An increase in O-GlcNAcylation level of HEK-293T cell proteins was also detected by western-blotting using the anti-O-GlcNAc antibody ( Fig 2E , right panel ) . These results suggest that Tax inhibits OGA activity independently of the HTLV-1 context , and that this inhibition results in increased cellular O-GlcNAcylation . OGT and OGA have been previously shown to form a molecular complex , referred to as the O-GlcNAczyme , which plays an important role in their biological functions [23] . To determine whether Tax may alter O-GlcNAcylation by interacting with this complex , we first evaluated by BRET the interaction of Tax with either OGA or OGT . HEK-293T cells were transfected with a cDNA coding for a luciferase-tagged Tax ( Rluc8-Tax ) together with YPET-OGT , YFP-OGA , or YFP alone . Western blot analysis showed correct expression of each of these fusion proteins at their expected molecular weights ( S4 Fig ) . A much higher BRET signal was observed with YPET-OGT or YFP-OGA than with YFP , indicating a specific interaction of Tax with the O-GlcNAc cycling enzymes ( Fig 3A ) . We then studied the effect of Tax expression on the formation of the OGT/OGA complex by BRET in HEK-293T cells co-transfected with OGT-Rluc and OGA-YFP constructs . As shown in Fig 3B , such complex could be readily detected as a BRET signal between OGT-Rluc and OGA-YFP . A higher BRET signal was found upon Tax expression , suggesting that Tax modulates OGT/OGA interaction ( Fig 3B and statistical analysis of Tax-induced delta BRET in the insert ) . To further analyze the effect of Tax on OGT/OGA interaction , BRET saturation assays were performed ( Fig 3C ) . This analysis permits to determine whether a change in BRET signal between two partners corresponds to an increased affinity between the two partners ( reflected by decreased BRET50 ) [28] or , rather , a conformational change within the complex that modifies the relative orientation between the luciferase and the YFP , resulting in a higher efficiency of energy transfer , without change in BRET50 [29] . Analysis of the saturation curves using Prisme software indicated that Tax expression reduces the BRET50 ( Fig 3C left panel and statistical analysis of BRET50 in the right panel ) , suggesting that Tax may regulate O-GlcNAcylation by increasing the affinity between OGA and OGT . We next measured the enzymatic OGA activity in the OGT/OGA complex after immunoprecipitation of OGT . HEK-293T cells were co-transfected with OGT-Luc , OGA-YFP and either the Tax or control plasmid . OGA activity was measured on the immune complex and normalized to YFP fluorescence of the precipitated proteins . We found that Tax significantly reduced the activity of OGA co-immunoprecipitated with OGT ( Fig 3D ) . Taken together , these data support the notion that Tax regulates O-GlcNAcylation by modulating OGT/OGA interaction , resulting in inhibition of OGA activity in the O-GlcNAczyme complex . We next studied the impact of increasing O-GlcNAcylation by using the specific OGA inhibitor Thiamet G on the activity of the HTLV-1 LTR in Tax expressing cells . C8166 T cells transfected with the HTLV-1-U3R-Firefly Luciferase construct ( U3R-Luc ) and the pRL-TK normalisation plasmid were cultured for 2 days with or without Thiamet G . As shown in Fig 4A , increased protein O-GlcNAcylation induced by Thiamet G ( right panel ) was associated with a significant increased activity of the U3R-Luc reporter construct compared to untreated cells ( left panel ) , while comparable amount of Tax was produced in each condition ( right panel ) . Importantly , similar results were obtained in HEK-293T cells transfected with the Tax plasmid ( Fig 4B ) . Hence , enhancing O-GlcNAcylation by pharmacological inhibition of OGA , to mimic the effect of Tax on OGA activity , significantly increases Tax-mediated LTR transactivation . Tax activates the viral LTR via the recruitment of CREB , which has been shown previously to be modified by O-GlcNAcylation [20–22] . This raises the hypothesis that the higher level of LTR transactivation upon OGA inhibition was linked to higher O-GlcNAcylation of CREB . To investigate this point , the impact of Tax on CREB O-GlcNAcylation was studied using capture on wheat germ agarose ( WGA ) , as previously described [30] . HEK-293T cells were transfected or not with the Tax plasmid and were also treated or not with Thiamet G . Two-days after transfection , cells were lysed and same amounts of total proteins were incubated with WGA . O-GlcNAcylated proteins captured on WGA were analyzed by western blot using an anti-O-GlcNAc antibody . As expected , Thiamet G treatment dramatically increased the amount of WGA-bound O-GlcNAcylated proteins ( Fig 4C left panel , compare lanes 1 and 3 and quantification on middle panel ) . Expression of Tax in HEK-293T cells also increased the binding of O-GlcNAcylated protein on WGA ( Fig 4C , left panel , compare lanes 1 and 2 ) , albeit at a much lower level than in cells treated with 10 μM Thiamet G . In agreement with this observation , we found that the inhibitory effect of Tax on OGA enzymatic activity in HEK-293T cells corresponds to the inhibitory effect of a much lower concentration of Thiamet G ( 0 . 01 μM , S5 Fig ) . Adding N-acetylglucosamine during incubation of cell lysates with WGA almost completely abolished the anti-O-GlcNAc signal , showing the specificity of the enrichment method ( S6 Fig ) . Reprobing the membrane with the anti-CREB antibody indicated a massive increase in binding of CREB to WGA upon Thiamet G treatment , confirming CREB as a target of O-GlcNAcylation ( Fig 4C , left panel , compare lanes 1 and 3 and quantification on right panel ) . Expression of Tax also significantly increased CREB retention on WGA , as demonstrated by the higher WGA/lysate ratio for CREB ( Fig 4C left panel , compare lanes 1 and 2 and quantification on right panel ) suggesting that Tax may induce CREB O-GlcNAcylation . In contrast , Tax was detected in the lysates but not among WGA-bound proteins , neither in absence or presence of Thiamet G . This suggests that Tax does not induce its own O-GlcNAcylation and is unlikely to be an O-GlcNAcylation target , as it is not retained on WGA even in conditions where a major general increase in protein O-GlcNAcylation is induced by pharmacological inhibition of OGA ( S7 Fig ) . We also evaluated in TL-om1 cells the effect of Tax on CREB retention on WGA ( Fig 4D ) . Same amounts of total proteins were incubated with WGA . Immunodetection were performed on WGA-bound proteins ( WGA ) or total proteins ( Lysates ) with either the anti-O-GlcNAc or anti-CREB antibody . First , we observed that the level of total O-GlcNAcylated proteins retained on WGA was higher in lysates from Tax-transfected than in control cells ( Fig 4D , left panel and quantification on middle panel ) . Importantly , the amount of CREB retained on WGA was also higher in Tax-transfected TL-om1 cells than in control cells ( Fig 4D , left panel and quantification on right panel ) . These data strongly suggest that Tax expression is sufficient to enhance CREB O-GlcNAcylation both in T cells and adherent HEK-293T cells . Serine 40 was previously described as a CREB O-GlcNAcylation site [21] . To determine whether Tax induces CREB O-GlcNAcylation on this particular residue , we used YFP-tagged wild-type ( wt ) and S40A mutant versions of CREB . HEK-293T cells were transfected with Tax and either wt or S40A YFP-tagged CREB and lysed 48 hours post-transfection . After normalization for equivalent amount of YFP-CREB fluorescence , cell lysates were incubated with WGA beads . Western-blotting with the anti-CREB antibody indicated that Tax expression significantly increased the amount of YFP-CREB retained on WGA ( Fig 5A ) . However , residual binding of mutated CREB on WGA suggests that either other O-GlcNAcylation sites still exists on S40A mutant , or that part of this binding occurs through O-GlcNAcylation of some CREB partner . As a complementary approach , Tax-mediated O-GlcNAcylation of Serine 40 of CREB was analyzed by immunoprecipitation ( Fig 5B ) . Cell lysates from HEK-293T cells transfected or not with Tax and either wt or S40A YFP-tagged CREB were normalized for YFP fluorescence and then immunoprecipitated with an anti-GFP antibody . Western-blotting using the anti-O-GlcNAc antibody revealed that mutation of S40 totally abolished Tax-induced O-GlcNAcylation of CREB , indicating that Serine 40 is indeed the main glycosylation site regulated by Tax . These findings also confirm that WGA binding of CREB mainly depends on O-GlcNAcylation of CREB itself . We then investigated the effect of expressing either wt or S40A YFP-CREB on Tax-induced LTR transactivation . As expected , transfection of wt YFP-CREB into HEK-293T cells significantly enhanced Tax-mediated transactivation ( Fig 5C , left panel ) . CREB S40A was produced at higher level than wt CREB ( Fig 5C , right panel ) , as previously reported [21] . However , despite this higher expression level , significantly less transactivation was found in cells expressing the S40A mutant than in those producing wt CREB ( Fig 5C , left panel ) . These findings provide direct evidence that CREB O-GlcNAcylation , especially at Serine 40 , is involved in Tax-mediated LTR activation . Since CREB activity on the HTLV-1 LTR is linked to its recruitment to the vCRE regions , we directly analyzed the impact of increasing O-GlcNAcylation on protein recruitment to the vCRE LTR sequences by chromatin immunoprecipitation ( ChIP ) experiments . As CREB phosphorylated at Serine 133 was shown to be preferentially recruited to the vCRE , ChIP experiments were performed using an anti-phospho CREB ( Ser 133 ) and primers specific for the distal U3 vCRE sequence . Thiamet G treatment of C8166 T cells dramatically increased the amount of amplified vCRE products as compared to untreated cells ( Fig 6A ) . Whether the O-GlcNAczyme complex was also recruited to the vCRE region was finally investigated by ChIP in C8166 T cells . Both anti-OGT and anti-OGA ChIP allowed the amplification of vCRE-specific products to levels significantly higher than the control IgG ( Fig 6B ) . Moreover , very low amplification signals were detected with primers targeting alpha-satellite ( alpha-sat ) regions , showing the specificity of the anti-OGT and anti-OGA ChIPs . Importantly , similar results were obtained with another HTLV-1-transformed T cell line ( MT2 , Fig 6C ) as well as with HTLV-1-immortalized T cells ( CIB , Fig 6D ) . Hence , CREB recruitment to the LTR is facilitated by O-GlcNAcylation and the OGT/OGA O-GlcNAczyme complex is recruited to the vCRE sequences of the HTLV-1 LTR .
The HTLV-1 Tax oncoprotein is critical for both HTLV-1 expression and HTLV-1-mediated T-cell immortalization . Therefore , the characterization of activators or co-factors responsible for the transactivation of the HTLV-1 5’ LTR is an important issue . In this study , we provide the first demonstration that a novel molecular actor , the O-GlcNAczyme complex , interacts with Tax and is recruited to the LTR as a positive co-factor in both HTLV-1 immortalized and transformed T cells . We first documented that HTLV-1-transformed T cells express higher level of OGA than control transformed T cells but that this coincides with a dramatic reduction in the specific activity of OGA . Furthermore , expressing only Tax was sufficient to inhibit OGA activity ( Fig 2A and 2D ) and to increase O-GlcNAcylation of a BRET-based biosensor ( Fig 2C and 2E ) in both Tax-negative HTLV-1-transformed TL-om1 T cells and HTLV-1-negative HEK-293T cells . This suggests that the ability of Tax to inhibit OGA and increase O-GlcNAcylation is independent of HBZ , as this inhibition is observed in HEK-293T cells which do not express any HTLV-1 protein . However , a potential blocking effect of HBZ on Tax-induced inhibition of OGA activity cannot be ruled-out and should be investigated in future studies . The inhibitory effect of Tax appeared to be specific for OGA activity , as OGT enzymatic activity was not affected by Tax transfection ( S3 Fig ) . Increased OGA expression associated with impaired OGA activity is not unprecedented . Indeed , previous studies reported that pharmacological inhibition of OGA also leads to OGA accumulation , presumably as the result of a regulatory feedback mechanism compensating for loss of enzymatic activity [31 , 32] . We propose therefore that the increased OGA expression found in HTLV-1 transformed T cells is an adaptive response , operating through a yet unknown mechanism , to counteract the inhibition of OGA activity by Tax . Previous studies have indicated that OGA and OGT associate into a molecular assembly denominated O-GlcNAczyme [23] . Using BRET experiments , we found on the one hand that Tax interacts with both OGT and OGA ( Fig 3A ) , and on the other hand , that Tax expression significantly increases the affinity between OGT and OGA ( Fig 3C ) . This was associated with a significant reduction of OGA enzymatic activity in the complex ( Fig 3D ) . Further experiments are needed to unravel the mechanism by which Tax regulates OGA activity within the O-GlcNAczyme complex , resulting in increased protein O-GlcNAcylation . Our data also provided evidence that an important consequence of Tax-induced OGA inhibition is the higher O-GlcNAcylation of CREB . Indeed , we showed that expressing Tax in either HEK-293T cells or TL-om1 T cells significantly increased the amount of WGA-bound CREB ( Fig 4C and 4D ) . Strikingly , higher WGA-bound CREB was also found upon pharmacological inhibition of OGA by Thiamet G , and Tax expression did not further increase CREB binding to WGA in Thiamet G-treated cells ( Fig 4C ) . This confirms that Tax effect on O-GlcNAcylation is mediated by inhibition of OGA , as it is not anymore detectable in cells in which OGA activity is maximally inhibited by the drug . O-GlcNAcylation of CREB was confirmed in experiments showing that CREB binding to WGA was markedly reduced by mutation of Serine 40 , a previously identified O-GlcNAcylation site on CREB ( Fig 5A ) . Importantly , the effect of Tax on O-GlcNAcylation of CREB was directly shown by immunoprecipitation of CREB followed by western-blotting using anti-O-GlcNAc antibody ( Fig 5B ) . Moreover , these experiments demonstrated that Tax induced O-GlcNAcylation of wt but not of S40A CREB ( Fig 5B ) . Regarding the functional impact of CREB O-GlcNAcylation , we found that treating cells with the selective OGA inhibitor Thiamet G increased Tax-mediated LTR transactivation both in C8166 T cells and adherent HEK-293T cells ( Fig 4A and 4B ) . Moreover , Thiamet G treatment strongly enhanced the recruitment of Ser133-phosphorylated CREB to the vCRE region of the LTR ( Fig 6A ) . This finding suggests that CREB O-GlcNAcylation and phosphorylation are not mutually exclusive , in agreement with a previous report [21] . Hence , our results indicate that OGA inhibition upon Tax expression or Thiamet G treatment increases CREB O-GlcNAcylation and thereby its activity on the LTR . In agreement with this hypothesis , we showed that Tax-mediated transactivation was enhanced upon expression of wt CREB but not of O-GlcNAcylation-defective S40A CREB mutant ( Fig 5C ) , directly linking CREB O-GlcNAcylation , notably at Serine 40 , and Tax-induced LTR activation . Interestingly , O-GlcNAcylation of CREB at Serine 40 was previously shown to block CREB transcriptional activity in neuronal cells by preventing CREB association with CRTC [21] . In contrast , we report here that CREB S40A is impaired for Tax-mediated transactivation , indicative of an activating role of this O-GlcNAcylation site in our model . This suggests that interaction with CRTC required for CREB function in neuronal cells is not a key determinant in the case of Tax-mediated LTR activation . CRTC/TORC was described as a coactivator of Tax-mediated LTR transactivation [14 , 33] . However , Siu and collaborators showed that silencing all three CRTC/TORC family members only partially reduced Tax-mediated LTR activation [14] , indicating that Tax can still activate the LTR without CRTC/TORC . Moreover , ATF4/CREB2 , which does not need CRTC as coactivator [14] , is able to activate the LTR in presence of Tax [34 , 35] . Hence , depending of the promoter context , CREB O-GlcNAcylation at Serine 40 may mediate either activating or repressive functions . Interestingly , such opposite effect of O-GlcNAcylation has previously been reported for other transcription factors , notably RelA and Sp1 [19 , 36–40] . OGT is now considered as an epigenetic regulator by virtue of its capacity to add O-GlcNAcylation on epifactors and histones ( reviewed in [26 , 41] ) . Consequently , OGT binding to promoters has been described [42] . We show here that not only OGT but also OGA are recruited to the vCRE region of the HTLV-1 LTR , suggesting the presence of the O-GlcNAczyme complex at the promoter and its involvement in HTLV-1 gene regulation . Our data therefore support a model in which , via the deposition of the O-GlcNAczyme complex onto the vCRE region , Tax facilitates the O-GlcNAcylation of CREB and possibly other transcription factors and co-factors while concomitantly modulating local chromatin architecture . This ultimately increases promoter activation , as documented here by the positive effect of OGA inhibition on the transactivation by Tax of the HTLV-1-U3R-Luc reporter construct . Importantly , recruitment of OGT and OGA to the 5’LTR was found in both HTLV-1-immortalized primary T cell and HTLV-1-transformed T cell lines . This provides the notion that the O-GlcNAczyme complex may play a key role in both HTLV-1-replication in vivo and HTLV-1-induced pathologies . Only few studies have investigated the impact of O-GlcNAcylation on virus transcription . It has been shown that enhancing O-GlcNAcylation inhibits the expression of human immunodeficiency virus type 1 or herpes virus simplex [43 , 44] . In these cases , the effect was linked to the modification of transcriptional regulators , Sp1 and HCF-1 respectively , involved in virus expression . Our data showing that O-GlcNAcylation increases the transactivation of the HTLV-1 LTR provide therefore the first example of a positive impact of the O-GlcNAcylation machinery on viral transcription via the recruitment of the O-GlcNAczyme complex to the viral promoter .
HEK-293T cells ( American Type Culture Collection CRL-3216 ) were grown in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal calf serum ( Dutcher , S1402851810 ) and with 2 mM glutamine , 1 mM pyruvate and antibiotics ( Invitrogen ) and were transfected using the Fugene 6 reagent ( Invitrogen ) . The non-infected CD4+ T-cell lines Jurkat ( kindly provided by Dr . Schwartz , Institut Pasteur , Paris , France ) , Molt4 ( American Type Culture Collection CRL-1582 ) , CEM ( American Type Culture Collection CRL-1992 ) and HUT-78 ( American Type Culture Collection TIB-161 ) and the HTLV-1-transformed CD4+ T-cell lines C8166 and MT2 ( NIH AIDS Research and Reference Reagent Program , USA ) and TL-om1 ( kindly provided by Dr . Harhaj , Johns Hopkins School of Medicine , Baltimore , USA ) were grown in RPMI 1640 medium containing 25mM glucose and supplemented as above but with the addition of 20 mM HEPES and 5mL of 100X non-essential aminoacid solution ( Invitrogen ) . TL-om1 T cells were transfected by nucleofection using the cell line nucleofector kit V ( Lonza , France ) and the program O-017 . The HTLV-1-immortalized CIB T cells described in [45] were generated from peripheral blood mononuclear cells of a TSP/HAM patient . These cells were grown in supplemented RPMI medium in the presence of 50U/ml of IL-2 ( Roche , France ) . The pSG5M empty vector , pSG5M-Tax and pRL-TK plasmids have been described elsewhere [46] . The U3R-Luc construct described in [47] was kindly provided by Dr . A . Kress ( Erlangen , Germany ) . The YFP-CREB wild-type plasmid was kindly provided by Prof . Montminy ( La Jolla , USA ) . YFP-CREB S40A was generated by PCR-mediated mutagenesis using the following primers: Fw: TGCCACATTAGCCCAGGTAgCCATGCCAGCAGCTCATG and Rev: CATGAGCTGCTGGCATGGcTACCTGGGCTAATGTGGCA and the presence of the mutation was verified by sequencing . The pcDNA3Rluc8 plasmid was a kind gift of Prof . Gambhir [48] . The pcDNA3 . 1 Rluc8-Tax plasmid was generated following PCR amplification of the Tax sequence from the pSG5M-Tax vector using primers creating NheI restriction sites at both extremities of Tax cDNA ( forward: GGCGCTAGCCACCATGGCCCACTTCCCAGGG; reverse: GCCGCTAGCTCCGA-CTTCTGTTTCTCGGAAATG ) . The PCR product was then inserted into the pcDNA3 . 1 RLuc8 after NheI digestion . YFP-OGA has been described previously [30] . Rluc8-OGT was generated by inserting OGT coding sequence [49] into the pcDNA3 . 1 Rluc8 vector after HinDIII/ BamHI digestion . YPet-OGT was obtained by insertion of cDNA OGT sequence into YPet-pcDNA3 vector after digestion with EcoRV-Apa1 . To monitor O-GlcNAcylation in living cells , we developed a BRET-biosensor based on the previously described FRET OS2-O-GlcNAc biosensor [27] by replacing the CFP by an Rluc8 sequence . The BRET biosensor is composed of Rluc8 fused to the fimbrial adhesin lectin domain GafD , a known OGT substrate peptide derived from casein kinase II placed between two flexible linkers ( GGSGG ) followed by a variant of the yellow fluorescent protein Venus ( Fig 2B ) . Tax was detected using sera from HTLV-1 infected individuals ( kindly provided by Dr Gessain , Institut Pasteur , Paris , France ) or the anti-Tax monoclonal antibody ( mab ) 168-A51 ( NIH AIDS Research and Reference Reagent Program , USA ) . The following primary antibodies were used: anti-GFP recognizing GFP as well as the YFP and YPET variants ( Roche Applied Science ) , anti-OGT ( Sigma , DM-17 06264 ) , anti-OGA ( Santa Cruz , sc135093 or Sigma , SAB4200311 ) , anti-O-GlcNAc ( Abcam , RL2 ) , anti-CREB ( Millipore , CS 203204 ) , anti-phospho CREB ser133 ( Millipore , CS 204400 ) , anti-actin ( Santa Cruz , sc1616 ) , anti-tubulin ( GeneTex , GT114 ) and GAPDH ( Santa Cruz , sc32233 ) . HRP-conjugated anti-human , anti-mouse and anti-rabbit IgG ( Promega ) were used as secondary antibodies . Thiamet G ( Sigma , SML 0244 ) was used at 10μM concentration . C8166 T cells ( 2x106/12 well in duplicates ) were cotransfected by nucleofection with 700 ng of the U3R-Luc reporter plasmid and 200 ng of the Renilla reporter plasmid pRL-TK . 293T cells seeded in duplicates in 24-well ( 3x104/well ) were co-transfected with 500 ng of the U3R-Luc plasmid and 50 ng of pRL-TK , and with 500 ng of the control or the Tax plasmids with or without 200 ng of the YFP-CREB constructs . Luciferase activity was determined using the Dual Luciferase Assay System ( Promega ) and values were normalized with Renilla activity . Cells were lysed in lysis buffer ( 50 mM Tris-HCl pH8 , 1% NP40 , 0 . 5% deoxycholate , 0 . 1% SDS and 150 mM NaCl ) supplemented with protease and phosphatase inhibitors ( Roche ) . Immunoprecipitations were carried out as follow: cell lysates were incubated overnight with primary antibodies at 4°C , and antibody complexes were captured on protein G-sepharose beads ( GE Healthcare ) 1h at 4°C . Sepharose beads were then washed 5 times in washing buffer ( 120 mM NaCl , 20mM Tris-HCl pH8 , 0 . 2 mM NaF , 0 . 2 mM EGTA , 0 . 2% deoxycholate , 0 . 5% NP40 ) before elution in Laemmli buffer . O-GlcNAcylated proteins were precipitated on 40 μL of WGL-agarose ( WGA ) beads ( Vector Laboratories , Paris , France ) for 2h at 4°C . WGA beads were then washed 5 times in washing buffer and captured proteins then eluted in Laemmli buffer as described in [50] . In some experiments , N-acetylglucosamine ( 500 mM ) was added during incubation with WGA beads as a control for non-specific binding of protein to WGA . Immunoprecipitated , WGA-precipitated proteins , and total cell lysates were separated by SDS-PAGE , transferred to membranes and blotted with specific antibodies . Total RNAs were prepared with the Nucleospin RNAII kit ( Macherey Nagel , France ) and 1μg of RNA was reverse transcribed using the Maxima first strand cDNA synthesis kit ( Thermo Scientific , France ) , according to the manufacturer’s procedure . Real-time-PCR was performed in the Lightcycler 2 . 0 ( Roche , France ) on 10 ng of reverse transcribed RNA using the following primers: OGT ( forward: GCCCTGGGTCGCTTGGAAGA , reverse: TGC CAC AGC TCT GTCAAAAA ) , OGA ( forward: TCTGCGGTGTGGTGGAAGGA , reverse: TGGGGTTAGAAAAAGTGATA ) and the housekeeping gene HPRT ( forward: 5’TGACACTGGCAAAACAATGCA3’ , reverse: 5’GGTCCTTTTCACCAGCAAGCT3’ ) for normalization . PCR was conducted using the Sybr Green method with the following conditions: a first step of denaturation at 95°C for 8 min , followed by 40 cycles of denaturation ( 95°C for 10 sec ) , annealing ( 60°C for 10 sec ) , and extension ( 72°C for 8 sec ) and a final step of melting curve ( 95°C for 5 sec , then 65°C for 15 sec . and finally 95°C for 10 sec ) . Before the experiment , 107 C8166 , MT2 or CIB cells were crosslinked using first 0 , 08% Disuccinimidylglutarate ( SantaCruz Biotechnologies ) during 30 min at room temperature and 1% Formaldehyde ( Electron Microscopy Sciences ) for 10 minutes at room temperature . Chromatin was then sheared using a Bioruptor Pico sonicator to obtain fragments of around 300 bp . Ten μg of chromatin were used for each condition . ChIP experiments were performed using the ChIP-IT high sensitivity kit from active motif . Primer pairs that specifically amplify the distal vCRE ( position 201–275: Forward 5’ATCATAAGCTCAGACCTCCGGGAA3’ , reverse 5’CCTGAGGACGGCTTGACAAACAT3’ ) were used for PCR . HEK-293T cells were transfected in 12 well plates as described previously [51] using 300 ng of each cDNA construct , unless otherwise stated in the figure legend . One day after transfection , cells were transferred into 96-well microplate , and BRET measurements were carried out on the following day . TL-om1 T cells were transfected by nucleofection in 12 well plates . On the following day , cells were distributed into 96 well microplate and BRET measurements were performed . For BRET measurements , cells were pre-incubated for 5 min in PBS in the presence of 5 μM coelenterazine . Light-emission acquisition at 485 nm and 530 nm was then started , and signal acquisition was performed every minute during 20–30 min using TECAN Infinite F200 Pro apparatus . BRET signal was expressed in milliBRET units ( mBU ) . The BRET unit has been defined previously as the ratio 530 nm/485 nm obtained when the two partners are present , corrected by the ratio 530 nm/485 nm obtained under the same experimental conditions , when only the partner fused to Renilla luciferase is present in the assay [52] . Each measurement corresponded to the signal emitted by the whole population of cells present in a well ( i . e . , approximatively 4x104 HEK 293 cells or 106 TL-om1 T cells ) . OGA activity was measured using 4-methylumbellifery-N-acetylβ-D-glucosamine ( MU-GlcNAc , Sigma ) , which is converted into fluorescent 4-methylumbelliferon upon hydrolysis by OGA and other hexosaminidase [53] . 4-methylumbelliferon fluorescence was measured at 448 nm after excitation at 362 nm after 30 min and 60 min incubation at 37°C , to ensure that the determination was performed during the linear phase of the reaction . To determine the concentration of 4-methylumbelliferon , a standard curve was performed in each experiment using commercial 4-methylumbelliferon ( Sigma ) . To specifically determine OGA activity versus other glycosydases , all reactions were performed in absence or presence of the highly specific OGA inhibitor Thiamet G . The difference of the fluorescent signal obtained in absence and presence of Thiamet G reflected the amount of 4-methylumbelliferon produced by OGA . To measure OGT activity , OGT was immunoprecipitated using an anti-OGT antibody ( Sigma-Aldrich ) for 2h at 4°C . Precipitation was performed by incubating 50μL equilibrated protein G-sepharose beads ( GE Healthcare ) for 30 min at 4°C . After 3 washes , the precipitated proteins were submitted to an additional wash in OGT assay buffer containing 50 mM Tris-HCl and 12 . 5 mM MgCl2 , pH7 . 5 and 1μM Thiamet G . OGT assay was then performed on protein-G sepharose bound OGT using the bioluminescent UDP-GloTM glycosyltransferase assay ( Promega ) exactly as described in the manufacturer instructions [54] . The use of peripheral blood mononuclear cells from patient CIB was approved by the French Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale ( CCPPRB ) and the patient provided a written informed consent . | Human T-cell lymphotropic virus type 1 ( HTLV-1 ) is the only human retrovirus associated to a cancer . Indeed , HTLV-1 is responsible for adult T-cell leukemia , an aggressive malignant proliferation of CD4+ T lymphocytes . The regulatory protein Tax governs HTLV-1 transcription from the 5’LTR , driving expression of all viral proteins , including itself , at the exception of the antisense product HBZ . Besides this critical role in HTLV-1 expression , Tax acts as an oncoprotein able to induce T-cell immortalization in vitro and tumor formation in mice . In this study , we report that Tax interacts with the O-GlcNAczyme OGT/OGA complex that catalyzes O-GlcNAcylation , a post-translational modification often deregulated in cancers . We found that Tax interacts with the OGT/OGA complex and inhibits the activity of OGA , increasing thereby cellular O-GlcNAcylation . Strikingly , we found that O-GlcNAcylation of CREB , the cellular transcription factor recruited by Tax on the viral promoter , is increased in a Tax-dependent manner . Moreover , increased CREB O-GlcNAcylation strongly enhances Tax-induced LTR transactivation as well as CREB binding to the viral promoter . Finally , both OGT and OGA are part of the transactivation complex . These findings shed new light on the mechanism of Tax-dependent LTR transactivation and may open the way to new molecular interventions targeting HTLV-1 expression . | [
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] | 2017 | Hijacking of the O-GlcNAcZYME complex by the HTLV-1 Tax oncoprotein facilitates viral transcription |
Taenia solium , a zoonotic parasite that is endemic in most developing countries where pork is consumed , is recognised as the main cause of acquired epilepsy in these regions . T . solium has been reported in almost all of the neighboring countries of Democratic Republic of Congo ( DRC ) but data on the current prevalence of the disease in the country itself are lacking . This study , focusing on porcine cysticercosis ( CC ) , makes part of a first initiative to assess whether cysticercosis is indeed actually present in DRC . An epidemiological study on porcine CC was conducted ( 1 ) on urban markets of Kinshasa where pork is sold and ( 2 ) in villages in Bas-Congo province where pigs are traditionally reared . Tongue inspection and ELISA for the detection of circulating antigen of the larval stage of T . solium were used to assess the prevalence of active CC in both study sites . The overall prevalence of pigs with active cysticercosis did not significantly differ between the market and the village study sites ( 38 . 8 [CI95%: 34–43] versus 41 . 2% [CI95%: 33–49] , respectively ) . However , tongue cysticercosis was only found in the village study site together with a significantly higher intensity of infection ( detected by ELISA ) . Pigs reared at village level are sold for consumption on Kinshasa markets , but it seems that highly infected animals are excluded at a certain level in the pig trade chain . Indeed , preliminary informal surveys on common practices conducted in parallel revealed that pig farmers and/or buyers select the low infected animals and exclude those who are positive by tongue inspection at village level . This study provides the only recent evidence of CC presence in DRC and gives the first estimates to fill an important gap on the African taeniasis/cysticercosis distribution map .
Taenia solium taeniasis/cysticercosis is a zoonotic disease with serious public health and agricultural consequences , which is endemic in most developing countries where pork is consumed [1] . The adult tapeworm occurs only in humans ( taeniasis ) but infection with the larval stage ( cysticercosis ( CC ) ) can affect both pigs and humans . Taeniasis has only mild clinical manifestations and may go unnoticed . Human cysticercosis occurs when cystic larvae lodge in muscles , subcutaneous tissues , eyes or brain . Localization in the brain or in the spinal cord causes neurocysticercosis ( NCC ) [2] . Seizures are the most common symptom of NCC and NCC has been reported to be the main cause of acquired epilepsy in developing countries [3] , [4] . Porcine cysticercosis , equally caused by the establishment of larvae in tissues , is an economical important disease , because of condemnation of carcasses and/or reduction of meat price of infected pigs [5] , [6] . The life cycle of the disease is sustained in regions with low hygienic standards , lack of sanitary conditions and traditional pig-production systems with free roaming pigs , thereby facilitating pig's access to contaminated feces from tapeworm carriers . Taeniasis/cysticercosis is a poverty-related disease [7] . It has been seriously neglected due to the lack of information and awareness of the extent of the problem in many countries , combined with the absence of suitable and sensitive diagnostic tools which can be applied at low cost and large scale in disease-endemic areas [8] , [9] . This so far neglected situation issues also in part from the fact that CC has no overt disease-specific manifestations , neither in pigs nor in humans , which makes it difficult to sensitize responsible authorities , both in the veterinary and the medical sectors . However , recently , the World Health Organization included cysticercosis in its 2008–2015 strategic plans for the control of neglected tropical diseases NTDs . The Democratic Republic of Congo ( DRC ) is one of the largest , and also poorest countries of Sub Saharan Africa ( SSA ) with a prevalence of undernourishment of 76% as compared to 30% for SSA [10] . Moreover , the economy of the DRC is only slowly recovering from two decades of decline caused by conflicts and war . This war situation dramatically reduced national output and government revenue , increased external debt , and resulted in the deaths of more than 3 . 5 million people from violence , famine , and disease and consequently also reduced the government's priorities on public health [11] . It is evident that health priorities in resource poor countries focus on major diseases such as HIV/AIDS , malaria or tuberculosis . However , this does not preclude the need of addressing neglected diseases , for which relatively simple control and prevention measures are often available , but only effective if applied in an integrated and sustainable way [12] . Common NTD's such as , soil-transmitted helminth infections ( STH ) , schistosomiasis , filariasis and onchocerciasis are known to be widespread among the poor in SSA , including the DRC [13] . NCC and CC have been reported in almost all of the neighboring countries of DRC [14] , [15] , but data on the current prevalence of the disease in the country itself are lacking . The last reports on human and porcine CC in DRC originate from 1958 [16] and 1990 [17] , respectively ( reviewed in [18] ) . In 2004 , a report of preliminary abattoir surveys revealed the presence of cysticercosis in approximately 3% of the pigs presented at slaughter ( Sumbu J . , proceedings of the 3ème congrès de pathologies infectieuses et parasitiare , 10–12 Decembre 2004 , Kinshasa , abstract N° F12 ) . Based hereon , recent incentives have emerged both from the political and scientific sectors , to catch up for these decades of negligence . The data reported here focus on porcine cysticercosis and are part of a first initiative to assess whether cysticercosis is indeed actually present in DRC and to estimate its potential economical and public health consequences .
The study protocol was approved by Laboratoire Vétérinaire de Kinshasa ( LABOVET ) , national veterinary reference laboratory of The Democratic Republic of Congo . The study permissions were obtained from LABOVET , from the village leaders and from the pig owners . Lingual examination and blood sampling on pigs were conducted by professional veterinarians , according to Congolese guidelines for animal husbandry . The protocol for the village survey was approved by the Ethical Committee of the University of Kinshasa , Democratic Republic of Congo , the Institutional Review Board of the Institute of Tropical Medicine of Antwerp , Belgium , and by the Ethical Committee of the University Teaching Hospital of Antwerp , Belgium . Written informed consent was obtained from all pig owners . An epidemiological study on porcine cysticercosis was conducted ( 1 ) on urban markets of Kinshasa where pork is sold and ( 2 ) in villages in Bas-Congo province where pigs are traditionally reared and consumed and where risk factors for the disease are ubiquitous . The first study was conducted between November and December 2008 in 5 important markets across the city of Kinshasa . All markets were visited approximately 3 times a week in the study period . The selection of the markets was driven by the objective to target all major import routes of pigs from outside into the city , and included the following markets: Liberté , Zikida , Gambela , Matete and Grand Marché de Kinshasa ( Figure 1 ) . All pigs presented for slaughtering during the visits of the markets were included in the study . The second study was conducted in April 2009 in 5 villages ( Viaza , Kiandu , Malanga , Kimaku and Kiasungua ) , in the health zone of Kimpese and located within a distance radius of 30 km around Kimpese city . Agriculture represents the most important income in the area . The villages were selected based on ( 1 ) the presence of the major risk factors for transmission of T . solium ( lack of latrines , free-roaming pigs and poor hygienic conditions ) and ( 2 ) the willingness of the community to participate in the study . All pig owners were asked to participate and to present all their pigs for CC diagnosis . All villages shared cultural , commercial , social and economical characteristics . Water supply system depends on the proximity of a river and water is non potable . Roads are not paved and there is no electricity . Inhabitants have access to primary health care through health centres , either located in their own village , or in a neighboring village . The pig phenotypes in the villages were very diverse and likely descend from mixed races with the major phenotypes of Pietrain or Landrace . Pigs were kept roaming during the day , and enclosed at night . Pig demand and consumption may differ throughout the year . As we currently do not have evidence to support the latter , a comparable trade off was assumed for the period of November-December 2008 ( market study ) and April 2009 ( village study ) . Tongue inspection ( TI ) and blood sampling were performed in both surveys . TI was performed while the mouth was opened using a wooden rod , the examiner , using a cloth , gently pulled the tongue , examined and palpated it throughout the base . The pig was considered positive for cysticercosis if cyst-like nodules were either seen or felt [19] . Blood samples of approximately 10 ml were collected in dry tubes , allowed to clot at ambient temperature , centrifuged , and serum was collected . Serum was stored at −20°C until analysis and later tested with the ELISA for the detection of circulating antigen of the larval stage of T . solium ( antigen ELISA , [20] ) , which enables the detection of active infections ( presence of viable cysts ) . The optical density of each sample was divided by the cut off value in order to obtain standardized ratios . These ratio values allow determining the positivity of each pig ( ratio>1 ) and also reflecting the intensity of infection [21] . To illustrate the comparison of infection intensity between both surveys , 4 classes of antigen ratio ( above 1 , infected animals only ) were arbitrarily defined: 1≤ratio<2 ( low intensity ) , 2≤ratio<5 ( medium intensity ) , 5≤ratio<10 ( high intensity ) , ratio ≥10 ( very high intensity ) . The data were analyzed in STATA 11 . Prevalence data and their 95% confidence intervals were calculated by dividing the number of positive pigs ( TI or ELISA ) by the total number of pigs . Multivariate logistic regression was applied to assess whether the risk of being infected depended on the market or province of origin ( market study ) or on the village of origin ( village study ) . A negative binomial-logit hurdle regression model [22] was used to compare the proportions and infection intensity of antigen ELISA positive pigs in both market and village study sites . The p value threshold for significance was set at 0 . 05 for all statistical analyses .
In the first study , a total of 498 pigs from the Kinshasa markets were blood sampled . Pigs originated from different types of pig-breeding systems , i . e . traditional ( free-roaming and scavenging pigs ) and industrial ( confined pigs ) . In 133 of these pigs the tongue palpation was either not performed or not conclusive . Tongue cysticercosis was not detected in any of the remaining 364 pigs . The overall prevalence of active cysticercosis ( as measured by antigen ELISA ) was 38 . 4% ( CI95%: 34–43 ) with no significant differences in percentages of positives between the respective markets . The proportion of pigs with circulating T . solium antigen was significantly higher in pigs originating from the province of Kinshasa as compared to the other provinces ( p value <0 . 01; Figure 2 ) . In the second study , a total of 153 pigs from the Bas-Congo villages were blood sampled . A conclusive tongue inspection was performed in 145 pigs . Tongue cysticercosis was detected among 5 . 5% ( CI95%: 2 . 4–10 . 6 ) . The prevalence of active cysticercosis by antigen ELISA measured among the free roaming pigs of the villages was 41 . 2% ( CI95%: 33–49 ) . No significant difference in percentage of positives was observed between the 5 villages ( Figure 2 ) . The overall prevalence of pigs with active cysticercosis as measured by the detection of circulating T . solium antigen did not significantly differ between the market and the village study sites ( 38 . 8 versus 41 . 2% respectively; p = 0 . 561 ) . However , tongue cysticercosis was only found in the village study site together with a significantly higher intensity of infection as reflected by the higher proportion of infected pigs having a high or a very high ratio value in the villages ( p<0 . 001; Figure 3 ) .
Twenty years ago the last official report on the presence of porcine CC in DRC was issued [17] . Despite the high prevalence figures reported at that time ( between 10 and 30% ) , no further research has so far been conducted on the disease within the country , neither in pigs nor in humans . Based on the knowledge that T . solium CC causes major economical losses and a considerable public health burden in countries surrounding DRC , joined with the presence of similar socio-economical and environmental conditions , the disease was anticipated to be equally important in DRC . The current study is a first attempt to estimate the importance of this zoonosis in DRC , thereby focusing on the veterinary part , i . e . porcine CC , which may represent a major constraint to increase pig production in developing countries , especially affecting the rural poor . Our data demonstrate that the disease indeed still prevails in the country with apparent prevalence figures of active CC in pigs above 25% in provinces surrounding the Kinshasa Region . The genus-specific ELISA for the detection of T . solium circulating antigen may show cross-reactions in pigs infected with T . hydatigena . The presence of this parasite in pigs in DRC needs to be further evaluated . However , veterinary carcass inspection was performed in 356 out of the 547 pigs included in the market study and T . hydatigena was never observed . In addition , based on abattoir surveys performed in Ituri [17] and on observations that T . hydatigena infections in pigs in African countries appear to be scarce [20] , [23] , we assume a low interference with the obtained serological results . In order to have a more improved picture of the occurrence of porcine CC , we estimated the prevalence at two different levels of the pig trade chain: ( 1 ) in rural villages , at farming level , representing the source of infection sites and ( 2 ) in Kinshasa , at urban markets level , representing the site where veterinary inspection should take place , and prevalence of porcine CC are thus expected to be lower . Interestingly , the mean proportion of active infections detected by antigen sero-detection did not significantly differ between both study sites , but the intensity of infection was significantly higher in pigs sampled in the villages as compared to those in the markets which was evidenced by a higher proportion of animals with antigen ratio's above 5 . It seems that highly infected animals are excluded at a certain level in the pig trade chain . Indeed , preliminary informal surveys on common practices in pig husbandry and pig trading were conducted in parallel in the two study sites . These revealed that the majority of both farmers in the villages and pig traders on the markets knew how to detect the parasite using tongue and/or carcass inspections , as it has been previously reported in other endemic regions [23] , [24] , [25] . Moreover , they pointed out that pig farmers and/or buyers select the low infected animals and exclude those who are positive by tongue inspection at village level . Based on their observations in slaughterhouses Ngowi et al . suggested a similar practice among pig traders in Tanzania [23] . More heavily infected pigs are subsequently used for villagers' own consumption or sold at local ( clandestine ) markets ( data not shown ) . This is in agreement with the observations of Zoli et al . [14] , who notified that due to the lack of well organized meat inspection , partial or total condemnation of infected carcasses barely occurred and that a pig carcass infected with CC was sold at a decreased price . Because of technical reasons , carcass dissection of a sub-sample of the pig populations could not be performed in these studies . However , this should be done in the future in order to confirm active infection in antigen ELISA positive pigs . Pork sold on Kinshasa markets originated from different regions in the country and from different types of pig-breeding systems , i . e . traditional ( free-roaming and scavenging pigs ) and industrial ( confined pigs ) . Since the latter would keep animals from contact with human faeces , the proportion of CC positive pigs in such conditions was expected to be low or even absent , thereby influencing the overall prevalence of the disease on the markets . However , this was not reflected by our results , which showed similar prevalence figures in the market and village study sites . The question arises whether CC is indeed absent in industrial pig farms , which deserves further investigations . This study is the first one to provide well-documented evidence on the presence of CC in different regions of DRC . Moreover , it confirms the higher probability to find infected pigs in rural areas where pigs are traditionally reared , despite the presence of some knowledge on the disease in the rural communities . The relatively high prevalence figures for porcine CC as presented here warrant similar studies on the status of human ( neuro ) cysticercosis , which are currently conducted by our research group . Moreover , our work points out the importance of conducting socio-economical surveys to understand the behavior of the different stakeholders acting in the pig meat production and trade chain , from the farmer to the consumer including the middle-men . The respective studies will enable to determine the economic and public health impact of the parasite in DRC and fill an important gap on the African taeniasis/cysticercosis distribution map . | Taenia solium is a parasite that can affect both humans and pigs , causing important economic losses in pig production and being the main cause of acquired epilepsy in endemic areas . However , the parasite has been neglected in many African countries and particularly in the Democratic Republic of Congo ( DRC ) , where recent data are non-existent . The present study is part of a first initiative to assess whether cysticercosis is actually present in DRC and to estimate its potential economic and public health importance . Focusing our work on porcine cysticercosis , we demonstrated high prevalence figures of active infections in villages in a rural area of DRC and in markets in the city of Kinshasa . Moreover , the intensity of infection was higher in pigs sampled in villages as compared to pigs sampled on urban markets . Preliminary surveys conducted in parallel in both study sites suggest an effect of pork trade on the transmission of the parasite selecting highly infected pigs at village level . | [
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] | 2010 | Taenia solium Cysticercosis in the Democratic Republic of Congo: How Does Pork Trade Affect the Transmission of the Parasite? |
The role of regulatory T cells ( Tregs ) in vaccination has been poorly investigated . We have reported that vaccination with ex vivo-generated dendritic-cells ( DC ) loaded with HIV-lipopeptides ( LIPO-5-DC vaccine ) in HIV-infected patients was well tolerated and highly immunogenic . These responses and their relation to viral replication following analytical treatment interruption ( ATI ) were variable . Here , we investigated whether the presence of HIV-specific Tregs might explain these differences . Co-expression of CD25 , CD134 , CD39 and FoxP3 was used to delineate both antigen-specific Tregs and effectors T cells ( Teffs ) . Median LIPO-5 specific-CD25+CD134+ polyfunctional T cells increased from 0 . 1% ( IQR 0-0 . 3 ) before vaccination ( week -4 ) to 2 . 1% ( IQR 1 . 1-3 . 9 ) at week 16 following 4 immunizations ( p=0 . 001 ) and were inversely correlated with maximum viral load following ATI ( r=-0 . 77 , p=0 . 001 ) . Vaccinees who displayed lower levels of HIV-specific CD4+CD134+CD25+CD39+FoxP3+ Tregs responded better to the LIPO-5-DC vaccine . After vaccination , the frequency of HIV-specific Tregs decreased ( from 69 . 3 at week -4 to 31 . 7% at week 16 ) and inversely correlated with HIV-specific IFN-γ-producing cells ( r=-0 . 64 , p=0 . 002 ) . We show that therapeutic immunization skewed the HIV-specific response from regulatory to effector phenotype which impacts on the magnitude of viral replication following ATI .
AIDS-related mortality and morbidity have decreased considerably since the introduction of highly active antiretroviral therapy ( HAART ) . Yet , HIV infection cannot be eradicated and lifelong HAART treatment is associated with several co-morbidities [1–3] . It is currently thought that the control of the HIV-1 epidemic will require both prophylactic and therapeutic vaccines . Despite considerable investments , potent HIV vaccines are not yet available [4 , 5] . Prophylactic vaccine development had mainly been focused on the induction of neutralizing humoral responses [6] . Several studies conducted in HIV-infected individuals or in Non-Human Primates have shown that vaccines which could induce HIV-specific T-cell responses may be effective against HIV replication [6–10] . Monocyte-derived dendritic cells ( moDCs ) pulsed ex vivo with tumor- or pathogen-derived antigens can induce T-cell responses in animal models [11 , 12] . This strategy has been used in the context of HIV infection in several studies [13 , 14] . We and others [15–18] have shown that DC-based vaccines were safe and efficient in inducing HIV-specific immune responses . Ex vivo generated autologous DCs loaded with HIV-derived long lipopeptides covering gag , nef and pol epitopes ( LIPO-5-DC vaccine ) were immunogenic in vivo . The induced polyfunctional HIV-specific responses were negatively correlated with the maximum viral load after HAART cessation [18] . In the present study , we have extended the characterization of vaccine-elicited T-cell responses to regulatory T-cell ( Tregs ) responses . Induction of Tregs by an HIV-vaccine is not a desired outcome as these cells can suppress HIV-specific effector T-cells ( Teffs ) responses [19] . Current assays used to evaluate antigen-specific responses , including effector cytokine or proliferative capacity measurements , are limited as they do not take into account antigen-specific Tregs because these cells are known to be anergic in vitro [20] . Moreover , detection of antigen-specific CD4+ T-cell responses by cytokine production ( intracellular staining ) after exposure to antigen can be misleading since the kinetics of cytokines secretion such as IFN-γ , IL-17 , IL-2 or IL-10 , is very variable . Therefore , we used here the “OX40 assay” [21] to simultaneously detect a full range of Th responses including antigen-specific Tregs responses [22] . CD134 ( OX40 ) is an inducible co-stimulatory molecule from the TNFR superfamily . It is expressed on recently activated T cells and its interactions with its ligand promote survival , proliferation as well as cytokine production [23] . The coexpression of CD134 and CD25 along with Tregs-specific markers , FoxP3 and CD39 , allowed the detection of both HIV-specific Tregs and cytokine-producing Teffs . We report that HIV-infected individuals harbor high levels of HIV-specific Tregs at baseline . The LIPO-5-DC vaccine preferentially induces Teffs responses and shifts the HIV-specific Tregs:Teffs ratio towards polyfunctional effector responses that inversely correlate with maximum viral load rebound after treatment interruption . Interestingly , vaccinees who display lower levels of HIV-specific CD4+CD134+CD25+CD39+FoxP3+ Tregs , show better Teffs responses to the LIPO-5-DC vaccine .
Nineteen HIV-1 infected individuals under successful antiretroviral therapy have been included in this pilot study ( Table 1 ) out of which we had access to frozen samples of 14 participants . Patients received LIPO-5-DC vaccine every 4 weeks during 16 week period . Blood was drawn 4 weeks prior to first vaccination ( week -4 ) and 4 weeks after the last ( week 16 ) . Virological endpoints following analytical treatment interruption ( ATI ) starting at week 24 , were defined at the study entry due to safety issues . Primary endpoint was the maximum viral load while predefined secondary virological endpoints were the time to viral rebound , the area under the curve of viral load , and the slope of the initial viral rebound [18] . We first determined both frequency and phenotype of CD4+ and CD8+ T-cell subsets ex-vivo to verify whether the vaccine influenced these parameters . A slight , although statistically significant increase in the CD4+/CD8+ T-cell ratio after vaccination ( week 16 ) was observed ( Table 2 ) . No changes in CD8+ Tregs percentages or in activation ( CD38/HLADR ) and/or exhaustion ( PD-1/2B4/Blimp-1 ) markers within the CD4+ and CD8+ T-cell compartments were found . Bulk CD4+CD25+CD127low Tregs fraction increased slightly after vaccination probably reflecting the increase in CD4+ T-cell compartment ( Table 2 ) . We stratified ( using symbols- square , triangle and circle ) the patients according to the magnitude of maximum viral rebound following ATI . Thus , patients with good ( squares ) , intermediate ( triangles ) and poor ( circles ) virological responses were defined according to the maximum viral load post-ATI ( VL ATI <40x103 , 40x103 <VL ATI <120x103 and VL ATI >120x103 copies/ml respectively ) . The three subgroups correspond to the tertiles of the VL distribution . We then compared the levels of antigen-specific CD4+ T cells measured using the “OX40 assay” , between these patient groups . PBMCs from before and after vaccination were stimulated with either HIV-derived peptide pools ( gag p24 ) , LIPO-5 vaccine ( which is a pool of 5 lipopeptides , 2 gag , 2 nef and 1 pol ) or CMV lysate for 44-hrs in vitro . A significant increase in both LIPO-5- and gag p24- specific responses ( CD4+CD25+CD134+ cells ) after vaccination was observed , while the responses to CMV remained unchanged ( Fig . 1A-B upper panel ) . Good virological responders showed the greatest increase in immune responses ( Fig . 1A-D ) . To check whether vaccine-induced immune responses and post-ATI viral load , was not driven by pre-HAART viral load levels , we performed additional analysis using the historical viral loads prior to any HAART . These analyses showed that the maximum viral load post-ATI in the trial was not associated with patient’s pre-HAART viral load ( r = -0 . 03 , p = 0 . 93 ) . The increase in activated LIPO-5-specific CD4+ T cells was accompanied by an increase in the frequency of cells expressing intracellular IFN-γ , TNF-α and IL-2 ( Fig . 1C ) . Similar increases of cytokine-secreting cells were observed when gag p24 , but not CMV ( S1 Fig ) , was used as eliciting antigen . In 9 out of 14 patients from whom sufficient cell numbers were available , we confirmed the results by additional testing of HIV-peptide pools representing each of the individual immunogens in the LIPO-5 vaccine . Interestingly , there was a significant increase in pol- , nef- or gag p17-specific responses ( CD4+CD25+CD134+ ) but not to gag p2-6 ( Fig . 1B , lower panel ) that was not contained in the LIPO-5 vaccine . The specificity of the CD4+CD25+CD134+ T cells was further demonstrated by the co-expression of CD154 , a marker of recently-activated antigen-specific cells [24] ( S2 Fig ) . Antigen-specific CD4+CD25+CD134+ cells are heterogeneous and express a wide range of transcription factors such as Tbx21 , Gata3 , Rorc , Foxp3 and Bcl-6 [25] . They comprise Th1-like cells that are commonly measured in standard ICS protocols but also other Th subtypes . To evaluate the functional profile of HIV-specific responses , we measured by Luminex the cytokines in the supernatants collected from the “OX40 assays” described above ( 44-hrs post-culture ) . Increases in IFN-γ , IL-2 , IL-4 , IL-21 , IL-17F , TNF-α , MIP-1β , IL-3 , IL-5 , IL-9 , IL-10 , IL-13 , IL-27 and sCD40L ( S3A Fig ) were observed after the vaccination . Notably , the increased levels of cytokines correlated with the increase of antigen-specific CD4+CD25+CD134+ T cells , thus indicating their polyfunctionality ( S3B Fig ) . Moreover , we calculated multivariate immune scores ( see Statistical analysis in Methods ) to summarize the data across several immune markers . Based on cytokine-producing CD4+CD25+CD134+ T cells as well as IFN-γ , IL-2 , IL-13 and IL-21 secretion assessed by Luminex , the median immune score increased significantly from median -6 ( IQR -10 to -4 ) to 9 ( IQR 9 to 10 ) between baseline and the post-vaccination time point ( p = 0 . 008 ) . Consistent with our previous report [18] , the post-vaccination immune score showed a significant negative correlation with the maximum viral load after ATI ( r = -0 . 79; p = 0 . 010 ) . In addition , the relative increases in LIPO-5-specific cells inversely correlated with the maximum observed viral load rebound after ATI ( Fig . 1D ) . As mentioned above , in this phase I trial , the follow up post-ATI was limited to a duration of 24 weeks ( from wk24 to wk48 ) to ensure participants’ safety , therefore several patients did not reach stable levels of viral load within this short period . In order to verify our observations reported in Fig . 1D , we used average viral load levels after ATI ( S4A-B Fig ) as well as viral loads observed at the end of the follow up ( week 48 , 6 months post ATI except for two patients who resumed HAART prior to that time point , S4C Fig ) and we have reached the same conclusions . The good virological responders ( low maximum viral load after ATI ) displayed the highest specific CD4+CD25+CD134+ T-cell responses . Similar inverse correlation was observed with gag p24 ( S5 Fig ) , though the correlation was stronger for LIPO-5 than for gag p24 ( r = -0 . 77 , p = 0 . 001 for LIPO-5 vs r = -0 . 60 , p = 0 . 026 for gag p24 pool ) . This suggests that the responses covering more than 1 peptide pool ( breadth ) might be more predictive of vaccine efficacy outcome . The graph showing the frequency of CD25+CD134+ T-cell specific responses for each peptide pool and for each patient , reveals that the good virological responders responded to more than one peptide pool , suggesting that vaccine efficacy is linked to the breadth of the response ( Fig . 2A ) . Our functional assay allows us to further determine the strength of the HIV-specific responses . We gave empirical scores to the antigen-specific responses for each peptide pool from 1 to 4 based on the percentages of CD4+CD25+CD134+ antigen-specific cells measured at week 16 post-vaccination ( S1 Table ) . Importantly , the overall strength of the response inversely correlated with the maximum of viral load after ATI ( r = -0 . 78 , p = 0 . 017 ) ( Fig . 2B ) . In addition , patient N19 ( black square ) , who did not experience viral rebound after ATI , showed the highest combination of breadth and strength of HIV-specific responses ( S1 Table ) . These data underline that LIPO-5-DC vaccination elicited a robust polyfunctional T-cell response which relies on both strength and breadth of the responses , a feature commonly desired for a functional HIV vaccine . Antigen-specific CD4+ T cells include both CD25+CD134+CD39+FoxP3+ Tregs and CD25+CD134+CD39-FoxP3- Teffs that can produce IFN-γ , TNF-α and IL-2 ( S6A Fig ) . CD25+ cells that have not upregulated CD134 post 44hrs stimulation , include ~90% of FoxP3+ positive cells . These cells produce no or very little IFN γ , TNF-α or IL-2 ( S6B Fig ) . We sought to determine the origin of the two antigen-specific CD4+CD25+CD134+CD39+FoxP3+ Tregs and CD4+CD25+CD134+CD39-FoxP3- Teffs subsets . CD4+ T cells were sorted based on their high , intermediate or low expression of CD25 ( gating strategy on Fig . 3A ) and then mixed with CD4neg cells ( fraction 1 that includes all cells that are outside the CD4 T-cells gate ) at 1:4 ratio . We used CMV lysate to stimulate the cells . Forty-four hours later , cells were stained for IFN-γ , FoxP3 and CD39 . The results in Fig . 3B show that antigen-specific CD4+CD25+CD134+CD39+FoxP3+ Tregs originated from CD25hi cells that upregulated CD134 upon stimulation . These cells did not produce IFN-γ ( Fig . 3B , right panel ) . In contrast , CD4+CD25+CD134+CD39-FoxP3- Teffs , secreting high levels of IFN-γ , originated from CD25lo cells . Finally , cells expressing intermediate levels of CD25 prior stimulation contained a mixture of antigen-specific Tregs and Teffs ( Fig . 3B ) . To check whether CD4+CD25+CD134+CD39+FoxP3+ Tregs are thymically derived or induced in the periphery , we included an anti-Helios monoclonal antibody in our experiments . This molecule was recently proposed as a marker of thymically derived Tregs [26] , although these studies are still quite controversial [27] . We observed that CD39+ Tregs , regardless of their antigen specificity , are Helios+ suggesting they might be of thymic origin ( S7 Fig ) . This observation surely needs confirmation since more reliable markers will hopefully be available in the future . To fully define CD4+CD25+CD134+CD39+FoxP3+ cells as Tregs , we performed functional assays [28–30] . Depleting CD25hi Tregs [31] prior to stimulation led to an increase in antigen-specific IFN-γ-producing cells ( Fig . 4A right panel ) and a decrease in CD4+CD25+CD134+CD39+FoxP3+ T cells ( Fig . 4A left panel ) . These results confirm that antigen-specific Tregs originate from CD25hi Tregs . As shown in Fig . 4B and C , CD25hi but not CD25lo cells suppressed CD4+ and CD8+ IFN-γ and TNF-α responses ( ratio 1:2 , Tregs:Teffs ) after in vitro stimulation with a pool of gag p24 peptides . Due to the scarcity of the isolated Tregs , we could not test higher ratios ( 1:1 , Tregs:Teffs ) , which can explain lower levels of suppression ( 30–35% ) we detected in our experiments ( Fig . 4C ) . As previously shown [32] , likely a Treg:Teffs ratio of 1:1 would show a higher suppressive activity . To investigate the influence of Tregs on the LIPO-5-DC-induced responses , we measured antigen-specific CD4+CD25+CD134+CD39+FoxP3+ Tregs in patients’ peripheral blood prior to and after vaccination . The frequency of HIV-specific Tregs prior to vaccination was elevated , accounting for a median of 43 . 8% ( IQR 24 . 3–61 . 2 ) of gag p24- and 69 . 3% ( IQR 55 . 8–75 . 2 ) of LIPO-5-specific response ( Fig . 5A ) . CMV-specific Tregs in the same patients accounted for 24 . 2% ( IQR 14 . 3–41 . 4 ) of the total CMV-specific CD4+ T-cell response . Following vaccination , proportions of HIV-specific Tregs significantly decreased ( 26 . 3% ( IQR 20 . 2–48 . 5 ) , p = 0 . 002 , of gag p24- and 31 . 7% ( IQR 22 . 1–38 . 2 ) , p = 0 . 008 , of LIPO-5-specific CD4+ T cells ) and this was accompanied by an increase in IFN- γ-producing HIV-specific CD134+CD25+ CD4+ T cells: from median 0 . 0% to 5 . 6% ( p = 0 . 009 ) among gag p24-specific and from median 0 . 0% to 4 . 6% ( p = 0 . 001 ) among LIPO-5-specific CD4+ T cells . Thus , while Tregs responses were dominant ( 69 . 3% ) over Teffs ( 30 . 7% ) before vaccination ( Fig . 5B ) , the balance shifted after vaccination and the proportion of Tregs decreased ( 31 . 7% ) simultaneously with an increase in both IFN- γ-producing cells ( 4 . 6% ) and in “other responses” ( 63 . 7% ) . These “other responses” that we have not determined yet are probably associated ( directly or indirectly ) with the significant production of IL-2 , IL-4 , IL-13 , IL-17F , TNF-α , MIP-1β , IL-3 , IL-5 , IL-9 , IL-10 , IL-21 , IL-27 and sCD40L , as measured in bulk PBMCs using Luminex technology ( S3A-B Fig ) . When patients were stratified according to the magnitude of maximum viral rebound following ATI , good ATI-responders showed decreased HIV-specific Tregs responses after vaccination as compared to poor ATI-responders ( Fig . 5C-D ) . Fig . 5C ( upper left and middle panels ) illustrates the change in the flow cytometry plots from a representative good ATI responder ( patient 11 ) showing the decrease in frequency of CD39+FoxP3+ specific Tregs within the CD134+CD25+ cells . In contrast , the lower panels ( left and middle ) illustrates the lack of change in the high frequency of CD39+FoxP3+ specific Tregs within the CD134+CD25+ cells from a representative poor ATI responder ( patient 10 ) . Right upper and lower panels in Fig . 5C show LIPO-5 specific IFN-γ responses for both patients . When these parameters were combined for all patients , we could see that majority of patients with high specific Tregs frequency and low IFN- γ levels are mainly poor ATI-responders ( circles ) and can be clustered together in Fig . 5D ( right circle ) . Patients with low Tregs-specific responses ( <40% ) included mainly good and medium virological responders ( squares and triangles in left circle , Fig . 5D ) and showed medium to high IFN-γ responses ( > 1% ) . Finally , CMV-specific responses , including Tregs and IFN-γ-producing cells , were unchanged before and after vaccination ( Fig . 5A and S1 Fig ) . We explored further the data and used the multivariate immune score ( See Statistical Analysis in Methods ) to assess correlations between CD39+FoxP3+ LIPO-5-specific Tregs and effector functions after vaccination . Although this did not reach statistical significance likely due to the small sample size and limited statistical power , we found a consistent signal for a negative correlation between baseline Tregs and post-vaccination immune score ( Fig . 6A ) , as well as between Tregs after vaccination and the immune score ( Fig . 6B ) . Together , these data suggest that the low IFN-γ responses usually found in HIV+ patients might be due to the presence of high percentages of HIV-specific Tregs among HIV-specific cells that might not be detected with current assays .
Efficient vaccines are characterized by the establishment of long-lived immunity . CD4+ T cells play an important role and are necessary for the control of viremia either directly or by providing help to B and CD8+ T cells [33 , 34] . CD4+ T cells comprise diverse populations , namely Th1 , Th2 , Th17 , Tregs , Tfh and probably others [35] . We and others have previously shown that DC-based vaccines for HIV are feasible , safe , and well tolerated [17 , 18] . Our vaccine induced polyfunctional CD4+ and CD8+ T-cell responses , with a more prominent CD4+ response , that resulted in partial control of the viral load [18] . We also observed an inverse correlation between HIV RNA values after HAART interruption and frequencies of polyfunctional HIV-specific CD4+ T-cell responses detected 16 weeks after the start of vaccination protocol . One of the caveats of our study design is the fact that safety requirements for this phase I trial did not allow longer follow up periods after ATI . This resulted in the fact that more reliable measurement of post-ATI viral load rebound , such as viral load setpoint could not be clearly established . Therefore , we decided ( consensus meeting with experts ) to use maximum viral load rebound as a primary virological endpoint . This parameter is considered to be relevant as it reflects the capacity of the immune responses to control viral replication . Also , to strengthen our findings , we show that the average viral load post-ATI , as well as the viral load at the end of the follow up , inversely correlates with vaccine elicited CD4+ T-cell responses . However , these findings will be further corroborated in phase II trial ( DALIA II ) , in which we will further address the effectiveness of the vaccine . In this study , we explored in depth the frequency and function of antigen-specific CD4+ T-cell responses that were induced by the vaccine using the “OX40 assay” that allows the measurement of a whole range of antigen-specific cells regardless of their functional profile . Notably , this assay is very useful as it is able to detect HIV-specific CD4+ Tregs along with Teffs [21 , 22] . The role of Tregs in HIV infection has been extensively studied [36] . These cells may play a dual role firstly by decreasing immune activation , which is beneficial for HIV-infected individuals , but also secondly by suppressing anti-HIV responses . Even though the induction of Tregs was assessed in cancer [37 , 38] as well as in HIV vaccine trials [39] , the induction of HIV-specific Tregs following vaccination has not been studied before . Indeed , the lack of tools that one can easily use in clinical trials setting has been preventing the measurement of Tregs-specific responses . Angin et al . , recently reported the presence of gag-specific Tregs in infected individuals [40] by using MHC Class II tetramer loaded with gag peptide . Although interesting , this approach is challenging in clinical trials due to the genetic variability of MHC Class II as well as the limited availability of Class II tetramers . Tregs could also have different affinity with MHC comparing to Teffs , which could lead to differential staining and probable under- or over- estimation of their frequencies . We were able to circumvent all these issues by the use of the inductive expression of CD134 on antigen-specific Tregs following an in vitro stimulation . Using this approach , the first surprising observation was that , prior to vaccination , a large proportion of HIV-specific Tregs with an activated phenotype ( CD4+CD25+CD134+CD39+FoxP3+ ) were found . Forty-four percent of gag p24- and 69 . 3% of LIPO-5-specific CD4+ T cells were Tregs , as compared to 24 . 2% of CMV-specific CD4+ T-cell response . Whether these high proportions of Tregs among antigen-specific cells are a peculiarity of HIV-specific responses is a question that is currently being studied in our laboratory . Chronic HIV infection is thought to induce higher proportions of Tregs as a mechanism preventing long-term damage caused by chronic immune activation [36] . On the other hand , these high levels of circulating Tregs could dampen Teffs responses and inadvertently help maintain viral persistence which , in turn , would lead to immune exhaustion . Therefore , the study of HIV-specific Tregs is a crucial aspect to consider in the quest for an efficient HIV-1 vaccine . The low levels suppression ( 30–35% ) we obtained in our in vitro assays might not translate to what would have happened in vivo and more investigation using animal models would be more informative . Nevertheless , our point in this study was not to make a statement that the magnitude of Tregs’ suppression could be translated to a clinical impact but to show that these cells exert a suppressive effect . When investigating whether vaccination shifted the balance of HIV-specific Tregs and Teffs , we found that the relative proportions of HIV-specific Tregs decreased significantly following vaccination . In contrast , Teffs increased in proportions , as measured by higher percentages of IFN-γ- , IL-2- and TNF-α- producing cells as well as increases in secretion of several other cytokines . Interestingly , the increase in these cytokines strongly correlated with the increase in LIPO-5-induced CD4+ specific responses . These results are in line with the fact that CD4+CD134+CD25+ antigen-specific cells contain several Th-subtype-defining transcription factors [25] , and show that our vaccine indeed induced highly polyfunctional Th responses . In addition , we found that besides polyfunctionality , the breadth of the response is also an important predictive mark of vaccine effectiveness . Notably , patient N19 , the only patient who did not experience viral rebound , responded strongly to all peptide pools after vaccination . These HIV-specific responses were not detected at entry prior to therapeutic immunization , thus suggesting that a shift to a less immunodominant response ( such as the response to gag p17 ) , could lead to a better distribution of the overall response and possibly a more effective viral control . This concept will be examined more in depth in our future trials . Of note , our vaccine contains palmitoyl-lysylamide lipid tail , known to signal through Toll-Like Receptor 2 and affect Tregs expansion and function in mouse studies [41 , 42] . Palmitoyl-lysylamide however may not have a similar role in human , as reflected by the decreased Tregs proportions observed after vaccination in our study . An impact of HIV-specific Tregs on the elicited vaccine response was further supported by a consistent signal for an inverse correlation between both baseline and post-vaccination LIPO-5- specific Tregs , respectively , and post-vaccination immune scores . Although this did not reach statistical significance , as the analyses were likely underpowered due to the small sample size , these results suggest a negative role for Tregs in the induction of vaccine induced effector responses . It would be of importance to know whether there is a clinical benefit in adding a Tregs blocker along with the vaccine in future studies . Outcomes from the cancer field clearly showed that Tregs suppress vaccine-induced immune responses and correlate with poor clinical benefit . In melanoma patients , reduction of suppressor cells by cyclophosphamide enhanced responses to vaccination [43] . Another study including patients with human papillomavirus type 16 ( HPV16 ) -induced vulvar intraepithelial neoplasia , clearly showed that those with larger lesions mounted higher frequencies of HPV16-specific CD4+CD25+Foxp3+ T cells and displayed a lower HPV16-specific IFNγ/IL-10 ratio after vaccination [37] , suggesting that high frequency of antigen-specific Tregs is predictive of poor clinical benefit . To circumvent the potential side-effects Tregs blocker could have on non-targeted immune responses , dendritic-cell based vaccination offers an interesting alternative . Pen et al . recently reported that multifunctional T cells could be induced without the induction of Tregs by vaccination with dendritic cells in which soluble PD1 or PD-L1 were induced by mRNA electroporation [44] . Also , with the future discovery of novel markers , we will be able to address the question of central versus peripheral origin of HIV-specific Tregs which could facilitate the in vivo targeting of these cells . Another question that remains to be answered is whether effector specific-responses measured in patients after vaccination , were induced by naïve T cells priming or whether they originated from the preexisting pool of memory T cells . Although probably both priming of naïve cells and expansion of memory pool took place , we would need to use animal models to be able to track precursors and clearly address this question . In addition , agonistic OX40 signaling itself could represent a good candidate for modulating vaccine responses towards a Th1 or Tregs in viral infections or autoimmune settings respectively . It was shown that when DCs were pulsed with KLH and injected to mice together with an anti-OX40 antibody , there was an increase in Th1 responses . In re-challenge experiments , OX40 stimulation led to the amplification of preexisting memory responses . These data suggest that skewing of the response based on OX40 ligation might be achieved only in unexposed individuals [23] . Of note , these findings need to be taken with caution as OX40 , unlike in humans , is constitutively expressed on murine Tregs . Therefore , the modulation of the response by OX40 ligation in human and mouse is probably very different and needs further study . Nevertheless , this molecule may be an interesting target for future immunomodulation protocols , not only in HIV infection , but also in cancer and autoimmune settings . In conclusion , we show here that the vaccination with DC-based vaccine pulsed with LIPO-5 construct , induced strong polyfunctional and polyspecific CD4+ T-cell responses . The strength of the induced responses inversely correlated with maximum viral load after antiretroviral treatment interruption . Importantly , the fact that we were able to measure Tregs and Teffs-specific cells in a single readout , gives our approach a significant advantage over other described approaches addressing the induction of CD4+ T-cell responses of different functional properties , especially in clinical trial settings .
Peripheral blood mononuclear cells ( PBMCs ) were obtained from healthy volunteers or vaccinees . Blood was collected in either heparin tubes or after apheresis . PBMCs were isolated from blood preparations by Ficoll density gradient centrifugation . All experiments were performed on freshly thawed cells that were left to rest for 5–6 hours in human serum-supplemented medium at 37°C . ANRS/VRI DALIA 1 , a phase I single-center study was performed at the North Texas Infectious Diseases Consultants in Dallas , TX . The study was sponsored by Baylor Institute for Immunology Research ( BIIR ) and the Agence Nationale de Recherches sur le SIDA et les hépatites virales ( ANRS ) . DC-based vaccines were generated from blood monocytes by culturing with GM-CSF and IFN-α and additionally activated with LPS , as previously described [45] . Briefly , monocytes were obtained from the apheresis product of HAART-treated HIV-infected patients by elutriation and cultured in a closed system with GM-CSF/IFN-α for 3 days . Differentiating DCs were pulsed for the last 24 hours with the ANRS HIV LIPO-5 peptides: gag ( 17–35; 253–284 ) ; pol ( 325–355 ) ; and nef ( 66–97; 116–145 ) . DCs were then activated with LPS ( purified lipopolysaccharide prepared from Escherichia coli O:113; U . S . Standard Reference Endotoxin vialed under Good Manufacturing Practice guidelines ) for 6 hours , harvested and frozen in autologous serum with a final concentration of 10% DMSO . After thawing , the DC vaccine cells suspended in 1 ml of freezing solution were diluted with 9 ml of saline to give a total volume for injection of 10 ml . Approximately 15x106 viable frozen-thawed HIV lipopeptide-loaded DCs were injected subcutaneously in 3 separate injection sites ( 3 . 3 ml per site ) in the upper and lower extremities . Subsequent DC injections were rotated to different locations on the upper and lower extremities . The vaccine was administered 4 times , at 4-weekly intervals . The blood samples ( apheresis ) analyzed were from wk -4 , corresponding to the blood draw 4 weeks prior to first vaccination and wk 16 , corresponding to the blood draw 4 weeks after the last vaccine . Antiviral treatment was stopped at wk 24 and viral load was measured thereafter . Ethical committee approval and written informed consent from all subjects , in accordance with the Declaration of Helsinki , were obtained prior to study initiation . Committee and institutional review board ( s ) of EFS and INSERM ( REF: C CPSL UNT—N° 12/EFS/079 and Convention reference number: I/DAJ/C2675 ) approved our study . The study was approved by the IRB of Baylor Research Institute ( BRI ) ( Clinical Trials Registration Number NCT 00796770 ) . All patients gave written informed consent . All staining experiments were performed at 4°C for 30 minutes . Antibodies used were CD3-PerCPCy5 . 5 , CD8-APCCy7 , CD25-APC , CD134-PE , TNF-α-PECy7 , CD154-APC ( ( Becton Dickinson ( BD ) Biosciences ) ) , CD4-Alexa Fluor 700 , IFN-γ-eFluor450 , IL2-PerCPeFluor710 , Streptavidin-Alexa Fluor 700 ( eBioscience ) , FoxP3-Alexa Fluor 488 , CD25-Brilliant Violet 421 ( BioLegend ) , CD39-biotin , CD127-PE ( Miltenyi biotec ) , Streptavidin-ECD , CD45RO-ECD ( Beckman Coulter ) . LIVE/DEAD fixable aqua staining kit ( Life technologies ) was used to discriminate live and dead cells . For intracellular staining , FoxP3 buffer set ( eBioscience ) was used . The “OX40 assay” is described in details elsewhere [21 , 22] . Briefly , two million PBMCs or Tregs-depleted cells were plated in 24-well plate and stimulated with 1μg/mL CMV lysate ( Behring ) or 2μg/mL of LIPO-5 or HIV peptide pools ( 192 peptides contained in 18 pools of 15-mers peptides ( NeoMPS , Strasbourg , France ) covering HIV-1 gag ( G1 to G11 including 3 pools covering gag p17 , 5 pools covering p24 and 3 pools gag p2/p6/p7 ) , 4 pools of pol ( RT12 to RT15 ) and 3 pools of nef ( N16 to N18 ) ) for 44 hours . In the last 6 hours , 1μg/mL of Brefeldin A ( Sigma ) was added to block the secretion of IFN-γ , IL-2 and TNF-α . Cells were then collected and stained for subsequent analysis by flow cytometry ( BD LSR II ) . Tregs-depleted PBMCs were obtained after efficient depletion of CD25+ Tregs as described previously [31] . The method comprised labeling total PBMCs with anti-CD25 beads ( Miltenyi biotech ) and one passage over LS columns . Briefly , 10 to 20 million PBMCs were used in all experiments . Ten microliters of anti-CD25 beads were added per 10x106 PBMCs resuspended in 90μL of cold MACS buffer . Cells were then incubated for 20 minutes at 4°C then washed with 2–3 mL of MACS buffer before their passage through an LS column which has been placed on a manual magnetic separator . Both flow-through ( Tregs-depleted ) and remaining ( Tregs ) fractions were collected for further analysis and functional studies . Tregs obtained by the above method were used in suppression assays in Tregs:Tresp ratio of 1:2 . Either Tregs or non-Tregs were mixed with responding cells and incubated overnight in the presence of 2μg/mL of gag p24 peptide pool , 1 μg/mL of αCD28 and αCD49d ( both from BD biosciences ) and 10μg/mL of Brefeldin A . Responding cells were discriminated from Tregs ( or non-Tregs ) by labeling with carboxyfluorescein succinimidyl ester ( CFSE , Life technologies ) at 0 . 025mM final concentration for 15 min at 37°C . FACS sorting of CD25hi , CD25int and CD25lo fractions were performed using MoFlo ( Beckman Coulter , Hialeah , FL , USA ) . These CD4+ T-cell populations were subsequently cultured with non-CD4+ T cells in 1:4 ratio to “reconstitute” the conditions as for a standard PBMC “OX40 assay” . After 44 hours of stimulation with LIPO-5 , 500μL of each supernatant was collected and frozen at -80°C . Cytokine secretion measurement for TGF-β1 , TGF-β2 , IL-17F , IL-17A , IL-21 , IL-22 , IL-27 , IL-31 , IFN-γ , IL-10 , IL-12p40 , IL-12p70 , IL-13 , sCD40L , IL-9 , IL-1β , IL-2 , IL-3 , IL-4 , IL-5 , IL-6 , IL-8 , IP-10 , MCP-1 , MIP-1β and TNF-α was performed using Luminex multiplex bead-based technology and a Bio-Plex 200 instrument ( BioRad ) , according to the manufacturer’s directions . Data were analyzed both in terms of fluorescence intensity ( FI ) and after transformation to concentration ( pg/ml ) by a 5-parameter logistic curve , according to the manufacturer’s directions . Analyses of differences between pre- and post-vaccination time points were done by Wilcoxon matched-pairs signed rank test . Correlations were assessed by Spearman correlation coefficients . To summarize the immune response to vaccination across several immune markers we used a method for multivariate ordinal data based on U-scores . Allowing for ties between variables , a partial ordering of the individuals is established based on their multivariate immunogenicity data , and an immune U-score for each individual is calculated by the difference in the numbers of individuals with superior versus inferior orders [46] . With this method we calculated a multivariate immune score across the following immune markers , best reflecting those correlated with maximum viral load post-ATI in the core trial analyses [18]: Luminex IL2 , IL13 , IL21 and IFN-γ after LIPO-5 stimulations of PBMC and % of IL-2 , IFN-γ and TNF-α among CD134+CD25+ after LIPO-5 stimulation . Prism 5 . 0 , version 5 . 0d , ( GraphPad Software , Inc . ) and SAS V9 . 2 ( SAS Institute , Cary , NC , USA ) were used for statistical analyses . P values were considered significant when < 0 . 05 , without adjustment for multiple testing in this exploratory study . | Highly active antiretroviral therapy ( HAART ) has considerably decreased AIDS-related mortality and morbidity in recent years . Nevertheless , the search for effective vaccine to combat HIV is in the limelight of modern medical research . In clinical trial settings , T-cell responses are routinely measured following vaccinations . However , the measurement of antigen-specific regulatory T-cell ( Tregs ) responses is omitted most of the time , since their detection is not possible with the use of standard assays . Following a phase I clinical trial in which autologous dendritic-cells pulsed with HIV-lipopeptides were used to induce T-cell responses , we used a novel assay to detect a whole range of T-helper responses , including Tregs . We report very high levels of HIV-specific Tregs responses in infected patients and interestingly , we observed that the dendritic cell-based vaccine shifted the responses from regulatory to effector phenotype , which impact on the magnitude of viral rebound after treatment interruption . | [
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"Methods"
] | [] | 2015 | Decreased HIV-Specific T-Regulatory Responses Are Associated with Effective DC-Vaccine Induced Immunity |
Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry ( MALDI-TOF MS ) has recently emerged in the field of entomology as a promising method for the identification of arthropods and the detection of associated pathogens . An experimental model of Ctenocephalides felis ( cat fleas ) infected with Bartonella quintana and Bartonella henselae was developed to evaluate the efficacy of MALDI-TOF MS in distinguishing infected from uninfected fleas , and its ability to distinguish fleas infected with Bartonella quintana from fleas infected with Bartonella henselae . For B . quintana , two groups of fleas received three successive blood meals , infected or not . A total of 140 fleas ( 100 exposed fleas and 40 control fleas ) were engorged on human blood , infected or uninfected with B . quintana . Regarding the second pathogen , two groups of fleas ( 200 exposed fleas and 40 control fleas ) were fed in the same manner with human blood , infected or not with Bartonella henselae . Fleas were dissected longitudinally; one-half was used for assessment of B . quintana and B . henselae infectious status by real-time PCR , and the second half was subjected to MALDI-TOF MS analysis . Comparison of MS spectra from infected fleas and uninfected fleas revealed distinct MS profiles . Blind queries against our MALDI-TOF MS arthropod database , upgraded with reference spectra from B . quintana and B . henselae infected fleas but also non-infected fleas , provided the correct classification for 100% of the different categories of specimens tested on the first model of flea infection with Bartonella quintana . As for Bartonella henselae , 81% of exposed qPCR-positive fleas , 96% of exposed qPCR-negative fleas and 100% of control fleas were correctly identified on the second model of flea infection . MALDI-TOF MS successfully differentiated Bartonella spp . -infected and uninfected fleas and was also able to correctly differentiate fleas infected with Bartonella quintana and fleas infected with Bartonella henselae . MALDI-TOF MS correctly identified flea species as well as their infectious status , consistent with the results of real-time PCR . MALDI-TOF is a promising tool for identification of the infection status of fleas infected with Bartonella spp . , which allows new possibilities for fast and accurate diagnosis in medical entomology and vector surveillance .
Fleas are wingless insects characterized by a laterally flattened body between 1 . 5 and 4 mm long . Males and females are strict hematophagous parasites of mammals and birds [1] . Fleas are vectors of human infectious diseases such as bubonic plague , caused by Yersinia pestis , and murine typhus , caused by Rickettsia typhi [2 , 3] . Fleas can also transmit Bartonella henselae , the agent of cat-scratch disease . Recently , Ctenocephalides felis , the cat flea , was described as a potential vector of Bartonella quintana , the agent of trench fever , known to be transmitted by the human body louse ( Pediculus humanus humanus ) [4 , 5] . Currently , the most widely used method for flea identification is based on morphological criteria [6] . This approach relies on the use of identification keys that require entomological expertise and specific documentation . However , the number of entomologists specialized in flea’s taxonomy is very small [7] . Also , the accurate identification of most flea species requires specimens to be slide mounted for stereoscopic examination , which makes them unusable for further molecular studies [8] . During entomological surveys , the determination of the infection status of fleas is also a critical step in assessing the risk of disease transmission . Molecular biology has been used in the last 20 years for arthropod identification and detection of their associated pathogens [8] . However , this approach is limited by the availability of reference sequences in the GenBank database . The length of time and the cost associated with molecular biology approaches can be limiting as well . Recently , Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry ( MALDI-TOF MS ) , a technique based on the analysis of protein fingerprints , has revolutionized clinical microbiology , enabling the rapid identification of bacteria and fungi by comparing their protein profiles to a reference database [9] . More recently , MALDI-TOF MS has been successfully applied to the identification of arthropods , including fleas [8 , 10] . Moreover , this tool has been tested for its ability to detect pathogens in arthropods , and it has been reported that MALDI-TOF is able to successfully differentiate ticks infected or uninfected by Borrelia or Rickettsia spp . [11 , 12] . In this context , we set up an experimental model of flea infection with B . quintana and Bartonella henselae . We ranked the flea groups according to their infection status as confirmed with molecular biology , and we evaluated MALDI-TOF's ability to distinguish the different groups and different pathogens .
Cat fleas ( Ctenocephalides felis ) were maintained under standard laboratory rearing conditions as previously described [4] . Adult stages were fed with human blood previously heated in a water bath at 37°C for 30 minutes . It was made available to the fleas via glass tubes closed with parafilm ( Sigma-Aldrich , Saint Louis , Missouri , USA ) , which were fixed to the plates containing the fleas [4] . The breeding cells containing the fleas are in contact with the lower part of the membrane . The eggs are collected with adult feces 1–2 times a week in sterile tubes containing 10 grams of larval nutrient medium , composed of 20 g rodent food , 40 g sand , 3 g lyophilized blood , and 2 g yeast . The tubes were placed in the laboratory incubator in the same temperature and humidity conditions used for the adults ( 80% humidity , 27°C ) . The device is kept in the dark until the emergence of the adult stages [4] . All experiments conducted with Bartonella quintana and Bartonella henselae were performed inside a Biosafety cabinet in a Biosafety Level 2 ( BSL2 ) room . The two strains of bacteria ( B . quintana strain Oklahoma ATCC 49793 , Bartonella henselae strain Marseille ) were grown on Columbia sheep blood agar plates ( 5% , BioMerieux , Marcy l’Etoile , France ) at 37°C in atmosphere enriched with 5% CO2 [13] . After 10 days of culturing , the bacteria were collected and transferred to a tube containing 400 μL of phosphate buffered saline ( PBS ) , pH 7 . 2 ( PBS , BioMerieux , Marcy l’Etoile , France ) . Two hundred microliters of this bacterial suspension mixed with 2 mL of human blood , like a simple mixture of Bartonella sp . and erythrocytes not an erythrocyte infection , were used for artificial infection of fleas . An aliquot of 2 ml of human whole blood containing 200 μl of PBS was used to feed the control group . The bacterial suspension ( 100 μl ) was used for bacterial quantification by flow cytometry . The inocula were marked by DAPI ( 4' , 6-diamidino-2-phénylindole ) and counting beads to quantify the number of bacteria . The absolute number of cells of interest ( cells/μL ) was calculated according to the manufacturer’s recommendations . The last 100 μl of inoculum was used for bacterial culture [14] . For secure experimentation , a Georgie and Wade confined equipment ( BSL3 ) was used for flea infection with B . quintana and B . henselae [4] . It is composed of a glove box with a safety airlock to load and unload material . For artificial infection with Bartonella quintana , two groups were created , an exposed group containing 100 Ctenocephalides felis fleas ( 40 males and 60 females ) and a control group containing 40 Ctenocephalides felis fleas ( 20 males and 20 females ) . For artificial infection with Bartonella henselae , two groups were formed; an exposed group containing 200 Ctenocephalides felis fleas ( 120 females and 80 males ) and a control group containing 40 Ctenocephalides felis fleas ( 20 males and 20 females ) . Each flea group received three successive human blood meals . Control fleas were fed with Bartonella-free blood mixed with 200 μL of PBS and exposed fleas were fed with blood infected with 200 μL of bacterial suspension [15] . These meals were delivered every 48 hours [4] . Three days after the last infective meal , ten fleas from each group were subsequently collected every two days . They were washed individually with 70% ethanol , and then rinsed twice with distilled water . Each flea was dissected using a sterile scalpel . Half of the body was selected for real-time PCR detection of the presence of Bartonella quintana and Bartonella henselae and the rest of the body was saved for MALDI-TOF MS analysis . Flea samples destined for molecular biology were incubated overnight at 56°C in 180 μl of G2 buffer ( Qiagen , Hilden , Germany ) ( 30 mM Tris-Cl , 30 mMEDTA; 5% Tween 20 , 0 . 5% Triton X-100; 800 mMGuHCl ) and 20 μl of proteinase K ( Qiagen , Hilden , Germany ) ( activity of 600 mAU/ml solution or 40 mAU/mg of protein ) . After lysis of the samples , DNA extraction was performed in the automatic extractor EZ1 ( Qiagen , Hilden , Germany ) . The presence of B . quintana and B . henselae DNA in these fleas was assessed by Bartonella-specific real-time PCR targeting the yopP3 gene [16] and pap31 gene , respectively [17] . The other half of the body of each flea was used to detect the presence of Bartonella quintana and Bartonella henselae in fleas by MALDI-TOF MS . The samples were ground using a TissueLyser II device ( Qiagen , Hilden , Germany ) in 30 μl of 50% ( v/v ) acetonitrile , 30 μl of 70% ( v/v ) formic acid ( Fluka , Buchs , Switzerland ) and a small amount of glass powder ( Sigma , Lyon , France ) . They were crushed at a frequency of 30 Hz for 1 minute and this cycle was repeated 3 times , in accordance with the previously published sample preparation optimization tests [18] . After centrifugation , 1 μl of supernatant was deposited in quadruplicate on the MALDI-TOF plate and covered by 1 μl of CHCA matrix solution composed of saturated α-cyano-4-hydroxycynnamic acid ( CHCA ) ( Sigma , Lyon , France ) , 50% ( v/v ) acetonitrile ( Fluka , Buchs , Switzerland ) , 2 . 5% trifluoroacetic acid ( v/v ) ( Aldrich , Dorset , UK ) and HPLC-grade water as previously described [8] . Protein mass profiles were obtained using a Microflex LT MALDI-TOF ( Bruker Daltonics , Germany ) mass spectrometer , with detection in positive ion linear mode at a laser frequency of 50 Hz in a mass range 2–20 kDa . The acceleration voltage was set to 20 kV , and the time of extraction delay was 200 ns . Each spectrum corresponds to the ions obtained from 240 laser shots fired in six regions of the same deposit on the ground plate and acquired automatically using the function of the Flex Control AutoXecute V . 2 . 4 software ( Bruker Daltonics ) . The spectra are displayed with Flex analysis v . 3 . 3 software and exported to ClinProTools2 . 2 and MALDI Biotyper v . 3 . 0 ( Bruker Daltonics , Germany ) for data processing . The reproducibility of the spectra of each flea group was evaluated using the ClinProTools 2 . 2 software . Reference spectra ( MSP , Main Spectrum Profile ) were generated by the automated function of the MALDI-Biotyper software v3 . 0 ( Bruker Daltonics , Germany ) by combining the results of the spectra of at least three specimens per condition in order to create a database . MSP were produced based on an unbiased algorithm based on the peak position , intensity and frequency . Flea spectra were selected based on their reproducibility and intensity for all MS analyses , including database creation . The database was created by selecting the spectra of at least 3 to 14 specimens from infected , exposed and control groups for both trials . The ability of MALDI-TOF MS to distinguish infected , exposed but uninfected , and control fleas was evaluated by blind test analysis . After sorting the spectra , high quality and reproducible MALDI-TOF MS spectra from the half body of 8 control fleas from the first trial ( B . quintana ) and 2 from the second trial ( B . henselae ) , 8 fleas infected with B . quintana and 7 fleas infected with B . henselae , and 14 fleas that fed on infected blood with B . quintana and 6 fleas that fed on infected blood with Bartonella henselae but tested negative in qPCR , were introduced into our in-house arthropod database ( Table 1 ) . This database already contains spectra obtained from protein extracts of the cephalothorax and legs of specimens of 4 species of fleas , including Ctenocephalides felis felis , Archaeopsylla erinacei , Xenopsylla cheopis and Ctenocephalides canis , a total of 6 tick species ( Am . variegatum infected by Rickettsia africae , Rh . sanguineus , Rh . bursa , Rh . annulatus , Rh . turanicus , Argas persicus , Hy . rufipes , Hy . detritum , I . ricinus , D . marginatus and D . reticulatus ) , 30 mosquito species ( Anopheles gambiae Giles , An . coluzzii , 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 , Aedes albopictus , Ae . 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 lice ( Pediculus humanus corporis ) , triatomines ( Triatoma infestans , Rhodnius prolixus , R . pictipes , R . robustus , Panstrongylus geniculatus , Eratyrus mucronatus ) and bedbugs ( Cimex lectularius ) [8 , 10 , 12 , 19–22] . Then , after the elimination of poor quality spectra , 33 spectra of infected fleas with Bartonella quintana , 30 spectra of infected fleas with Bartonella henselae , 44 spectra of fleas that fed on infected blood but that tested negative to Bartonella quintana in qPCR , 22 spectra of fleas that fed on infected blood but that tested negative to Bartonella henselae in qPCR , and a total of 34 spectra of control fleas from the first and second trial were queried against the upgraded database ( Table 1 ) . The results of the database queries are presented as Log Score Values ( LSVs ) for each spectrum , corresponding to a matched degree of signal intensities of mass spectra of the query and the reference spectra . LSVs range from 0 to 3 . LSVs allow good evaluation of the reproducibility between a queried spectrum and a reference spectrum , as they are the result of a thorough comparison of peak positions and intensity between those two spectra . An LSV was obtained for each spectrum of the samples tested blindly . For each specimen , the spectrum with the highest LSVs was selected for identification [22] . To appraise reproducibility of the MS profiles , spectra from the three categories ( infected , exposed but PCR-negative , and control ) were imported in the ClinProTools 2 . 2 software for both pathogens ( B . quintana , B . henselae ) . For the determination of discriminating peak masses associated with infection status , we compared the average spectrum of the three categories of fleas . The software was used to generate a peak list for each group in the 2 to 20 kDa mass range and to identify discriminating peaks . Regarding the discrimination between control profiles and B . quintana-infected flea derived profiles , the parameter settings in ClinProTools 2 . 2 software for spectra preparation were as follows: a resolution of 800; a noise threshold of 2 . 50; a maximal peak shift of 800 ppm and a match to calibrant peaks of 20% . For the peak calculation , peak peaking was performed on single spectra with a signal-to-noise threshold of 2 . 50 and an aggregation of 800 ppm . As for B . henselae , the parameters were as follows: a resolution of 800; a noise threshold of 2 . 20; a maximal peak shift of 800 ppm and a match to calibrant peaks of 20% . For the peak calculation , peak peaking was performed on single spectra with a signal-to-noise threshold of 2 . 20 and an aggregation of 800 ppm . The spectra were then analyzed with the genetic algorithm ( GA ) model , which provided a list of discriminating peaks . Manual inspection and validation of the peaks by the operator gave a “recognition capability” ( RC ) value together with the highest “cross-validation” ( CV ) value .
For Bartonella quintana inocula , bacterial cell numbers in the first inoculum were 8 . 106 , 7 . 106 in the second inoculum and 7 . 106 in the last inoculum ( cells/μL ) , as determined by flow cytometry . For Bartonella henselae inocula , bacterial cell numbers in the first inoculum were 6 . 106 , 4 . 107 in the second inoculum , and 8 . 106 in the last inoculum ( cells/μL ) . Three days after the infective blood meal , 28 control fleas and 80 Bartonella quintana-exposed fleas were collected for the first trial , and 20 control and 160 Bartonella henselae-exposed fleas were collected for the second trial . The qPCR results indicated the presence of Bartonella quintana DNA in 33 ( 41 . 25% ) of the 80 fleas , with cycle threshold ( Ct ) values ranging from 27 . 19 to 35 . 85 , and the presence of Bartonella henselae in 40 Ctenocephalides felis ( 25% ) of the 160 fleas , with cycle threshold ( Ct ) values ranging from 28 . 95 to 35 . 80 . All control fleas tested negative . All collected fleas were analyzed by MALDI-TOF MS . Spectra analysis with Flex analysis v . 3 . 3 and ClinProTools 2 . 2 software revealed reproducibility of the profiles within the same category ( Figs 1 and 2 ) . All profiles were compared using the ClinProTools 2 . 2 software to appraise global reproducibility and create an average profile . Visual inspection revealed that some peaks were present in fleas infected with Bartonella quintana and Bartonella henselae profiles , but absent in control and exposed fleas ( Figs 3 and 4 ) . The Genetic Algorithm tool of ClinProTools software was used to better identify the discriminating peaks between infected and control fleas . Spectra from 33 Ctenocephalides felis specimens infected with Bartonella quintana were compared to 22 control Ctenocephalides felis , same for the second pathogen; spectra from 24 Ctenocephalides felis specimens infected with Bartonella henselae were compared to 12 control Ctenocephalides felis . The genetic algorithm model displayed 13 peak masses that discriminated control and infected fleas with Bartonella quintana ( Table 2 ) , and 20 peak masses that discriminated control and infected fleas with Bartonella henselae ( Table 3 ) , with recognition capability ( RC ) and cross validation ( CV ) values of 100% for both comparisons . Bartonella quintana qPCR positive fleas ( n = 33 ) , Bartonella henselae qPCR positive fleas ( n = 30 ) , fleas exposed to blood infected with B . quintana but PCR negative ( n = 44 ) , fleas exposed to blood infected with B . henselae but PCR negative ( n = 22 ) and control fleas ( n = 34 ) were queried blindly against the MALDI-TOF arthropod database upgraded with reference spectra from these 4 category . All fleas from the artificial infection with Bartonella quintana were correctly identified to the species level and MALDI-TOF MS differentiated correctly the three categories of fleas ( Table 1 ) . Regarding artificial infection with Bartonella henselae , the identification was correct for 24/30 infected fleas ( 80% ) , 21/22 exposed negative fleas ( 95% ) and 100% correct identification for all control fleas . For all tested samples , LSVs ranged from 2 . 149 to 2 . 859 for exposed positive fleas , from 2 . 091 to 2 . 838 for exposed negative fleas and from 2 . 147 to 2 . 713 for control fleas .
Recently , MALDI-TOF MS technology has been successfully used for the identification of arthropods such as fleas [10] , mosquitoes [20 , 23] , Culicoides [21] , ticks [19 , 24] , sand flies [25] tsetse flies [26] and triatomines [22] . The success of this method requires the standardization of sample preparation protocols to allow sharing and comparison of MS reference spectra and results between research laboratories . As is true for each innovative method , application of this tool is best with some limitations , such as the cost of the device and the comprehensiveness of the databases [23] . Furthermore , some parameters may play a role in the efficiency of MALDI-TOF MS identification , such as the conservation of arthropods after collection ( 18 ) . Several recent studies have shown the efficacy of MALDI-TOF in identifying arthropods conserved in alcohol . Ticks collected in the field in Ethiopia , which were preserved in 70% ethanol for about two years , were correctly identified by MALDI-TOF . More recently , different species of ticks collected on mammals in Mali , also preserved in alcohol , were successfully identified as well by MALDI TOF , after the development of a de-alcoholization protocol providing even better results for MS identification for samples preserved in alcohol [27] . Arthropod body part selection for MALDI-TOF arthropod identification is based on the comparison of the spectra quality from different parts of the body . Flea reference spectra in our database were obtained from cephalothorax and leg protein extracts [10] . In this study , half of the body was chosen for MALDI-TOF MS and the other half for molecular biology , based on previous data indicating that the Bartonella species is localized in the infected flea gut tract [4] . Intraspecies reproducibility and interspecies specificity are then evaluated and the most reproducible spectra are selected to upgrade the database [8] . After the emergence of this method to identify arthropods , our research has been oriented toward the detection of associated pathogens . Preliminary encouraging work has shown that MALDI-TOF can differentiate Rhipicephalus sanguineus ticks infected or not by Rickettsia conorii , the agent of Mediterranean spotted fever [12] . It can detect Plasmodium parasites in anopheles [23] and Borrelia spp . in Ornithodoros sonrai ticks [11] based on spectra obtained from the cephalothorax or legs , respectively . Here , we obtained reproducible and intense spectra from control fleas , and also from fleas infected with B . quintana and B . henselae whose infection status was previously confirmed by quantitative PCR . In these models ( B . quintana , B . henselae ) , the bacterial loads of the infectious blood meals is higher than the bacteremia of humans with trench fever , which is around 104 to 105 [28] . However , our work focuses on the capacity of MALDI-TOF MS to distinguish fleas infected or not by Bartonella species . So , for this purpose we needed to obtain infected fleas . In previous published work , we have shown that in experimental models , it is difficult to infect fleas using bacteremia lower than 105 [4] . The aim of this work was not to study or explain how fleas are infected in natural cycles . This is why we chose to use higher concentrations , knowing that fleas will be successfully infected , as others have done in their models of infection [29 , 30] . Although the performance of the tool was assessed for the detection of the presence of Bartonella species in fleas , sensitivity and specificity are important parameters to consider when a new method is proposed . However , it is difficult to definitively determine the sensitivity of our method in the absence of a gold standard to determine the infectious status of fleas . While the bacterial concentration of the inoculum was measured , the exact bacterial load in each flea at the moment of the MALDI-TOF assay was unknown here . This work however was a preliminary work to determine the usefulness of MALDI TOF in differentiating infected and non-infected fleas in comparison with qPCR . For that purpose , groups were chosen regarding infection status . These groups corresponded respectively to fleas with low Ct values ( below 35 ) for the infected group , fleas that were exposed to infected blood but had a negative qPCR test , and control fleas . Interestingly , exposed but PCR-negative fleas were classified by MALDI-TOF MS as a single category , different than infected and control categories . While all fleas were perfectly identified for each category for the B . quintana model , there were nevertheless limitations in the distinction between fleas infected with B . henselae and fleas exposed to the same bacteria but negative in qPCR , since six infected specimens were identified as exposed PCR-negative specimens . The qPCR Ct values of these specimens varied between 35 . 53 and 35 . 85; they were all collected on the first day of collection , which corresponded to the third day after the last infective blood meal . We can hypothesize that the identification of infected fleas is based on discriminating peaks associated with the flea immune response . Because this approach is strictly based on the comparison of a profile to a reference spectrum , which is a representation of the extracted global proteome of the selected body part of the flea , it is not here possible to correlate a peak position to a protein identification . To obtain such information , complementary proteomic analyses would be necessary and would help to decipher interactions between fleas and flea-borne pathogens . These peaks could be partially present in exposed fleas , perhaps temporarily , causing misidentification of these specimens as infected fleas . A specific arthropod immune response to bacterial infection has already been described . Indeed , the production of three proteins in Anopheles gambiae hemolymph were increased following bacterial injection of Escherichia coli ( XL1 Blue ) and Micrococcus luteus ( UW-Madison strain ) . These proteins are associated with the immune response of these mosquitoes [31] . We can therefore hypothesize that immune response proteins played a role in allowing MALDI-TOF MS to clearly distinguish between negative exposed and control fleas . During this response , some genes coding for the proteins involved in innate immunity are regulated or reduced , which could explain the disappearance of some peaks on the average profile of the control fleas [23] . The microbiome of arthropods is usually different in the field than in lab colonies [32] . It may be questioned if the microbiome of wild fleas might be more extensive and varied and might interfere with the interpretation of the MALDI TOF-MS assay . We can’t exclude the part played by the microbiome in the resulting MALDI TOF-MS spectra . Therefore , it would be interesting to study its impact on the differentiation between Bartonella-infected and uninfected fleas in future studies . The one-step detection of the species identity of fleas and their infection status would be revolutionary for medical entomology studies , vector surveillance and movement of pathogens . | Fleas are known vectors of human infectious diseases . Identification of fleas and their associated pathogens is essential for the prevention of flea-borne diseases . Currently , the morphological identification of arthropods based on dichotomous keys , as well as molecular techniques , are the most common approaches for arthropod identification and entomological surveillance . In recent years , MALDI-TOF MS has revolutionized clinical microbiology in enabling the rapid identification of bacteria and fungi by comparing the protein profiles obtained to a database . This proteomic approach has recently been used for arthropod identification and pathogen detection . Here , we developed an experimental model to test MALDI-TOF's ability to differentiate fleas infected with human pathogens , Bartonella quintana and Bartonella henselae , from uninfected fleas . | [
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] | 2018 | Detection of Bartonella spp. in fleas by MALDI-TOF MS |
Gammaherpesviruses , including the human pathogens Epstein-Barr virus ( EBV ) and Kaposi’s sarcoma-associated herpesvirus ( KSHV ) , establish lifelong latent infection in B cells and are associated with a variety of tumors . In addition to protein coding genes , these viruses encode numerous microRNAs ( miRNAs ) within their genomes . While putative host targets of EBV and KSHV miRNAs have been previously identified , the specific functions of these miRNAs during in vivo infection are largely unknown . Murine gammaherpesvirus 68 ( MHV68 ) is a natural pathogen of rodents that is genetically related to both EBV and KSHV , and thus serves as an excellent model for the study of EBV and KSHV genetic elements such as miRNAs in the context of infection and disease . However , the specific targets of MHV68 miRNAs remain completely unknown . Using a technique known as qCLASH ( quick crosslinking , ligation , and sequencing of hybrids ) , we have now identified thousands of Ago-associated , direct miRNA-mRNA interactions during lytic infection , latent infection and reactivation from latency . Validating this approach , detailed molecular analyses of specific interactions demonstrated repression of numerous host mRNA targets of MHV68 miRNAs , including Arid1a , Ctsl , Ifitm3 and Phc3 . Notably , of the 1 , 505 MHV68 miRNA-host mRNA targets identified in B cells , 86% were shared with either EBV or KSHV , and 64% were shared among all three viruses , demonstrating significant conservation of gammaherpesvirus miRNA targeting . Pathway analysis of MHV68 miRNA targets further revealed enrichment of cellular pathways involved in protein synthesis and protein modification , including eIF2 Signaling , mTOR signaling and protein ubiquitination , pathways also enriched for targets of EBV and KSHV miRNAs . These findings provide substantial new information about specific targets of MHV68 miRNAs and shed important light on likely conserved functions of gammaherpesvirus miRNAs .
Gammaherpesviruses are a family of large double-stranded DNA viruses that establish lifelong latent infections in their hosts . This group includes the human pathogens Epstein-Barr virus ( EBV ) and Kaposi’s sarcoma-associated herpesvirus ( KSHV ) , as well as murine gammaherpesvirus 68 ( MHV68 , MuHV-4 , γHV68 ) . These viruses gain an initial foothold in the host by replicating in epithelial cells at the site of inoculation , and then subsequently establish lifelong latent infection in the periphery in circulating B cells . Although infection with gammaherpesviruses is typically asymptomatic , virus-driven malignancies such as B cell lymphomas may manifest during chronic infection , particularly within the setting of immunocompromise . In addition to encoding an array of proteins with conserved functions , EBV , KSHV , and MHV68 all encode multiple microRNAs ( miRNAs ) [1–5] . miRNAs are 21–23 nt noncoding RNAs that are critical regulators of gene expression [6] . miRNAs integrate into the RNA-induced silencing complex ( RISC ) , and are directed to target mRNAs through partial sequence complementarity [6] . In the context of RISC , the miRNA-mRNA binding interaction can result in mRNA silencing through translational repression or cleavage of the mRNA target [7 , 8] . Viral miRNAs are thought to play roles in diverse biological processes to promote viral persistence within the host . EBV and KSHV miRNAs are expressed in latently infected primary human cells and have been proposed to be important for the establishment and maintenance of chronic infection . EBV and KSHV encode 44 and 25 mature miRNAs , respectively [1–3 , 5] . Many host targets for EBV and KSHV miRNAs have been identified and validated , including host transcripts that code for proteins involved in cell cycle progression , apoptosis , and immune regulation [9–11] . For example , multiple EBV and KSHV miRNAs have been shown to target and decrease the expression of caspase-3 , a key effector of apoptosis [12–14] . Likewise , both EBV and KSHV miRNAs have been shown to target MICB , a stress-induced ligand for the activating natural killer ( NK ) cell receptor NKG2D [15] . Numerous other putative targets of EBV and KSHV miRNAs have been identified in tumor cells [16–21]; however , the true in vivo function of most EBV and KSHV miRNAs are largely unknown due to the complex nature of studying virus-host interactions in vivo and the strict species specificity of these viruses . Murine gammaherpesvirus 68 is a natural pathogen of rodents [22] that is genetically and pathogenically related to both EBV and KSHV [23–25] . Like the human gammaherpesviruses , MHV68 establishes lifelong latent infection in B cells [26–28] , and results in the development of lymphoproliferative disease and B cell lymphoma [29 , 30] . MHV68 encodes up to 28 mature miRNAs , many of which are abundantly expressed during long-term latency and in lymphoproliferative lesions and tumors [2 , 31–34] . While the specific targets of the MHV68 miRNAs have not yet been determined , we have previously demonstrated the biological importance of these miRNAs during in vivo infection: a MHV68 mutant lacking expression of all 28 miRNAs is significantly attenuated for latency establishment and displays complete absence of pathology in a lethal pneumonia model [34] . Interestingly , similar combinatorial miRNA mutant viruses display increased number of infected cells in immunodeficient mice during early infection [35] and in wild-type mice during long-term infection [36] , suggesting that the MHV68 miRNAs may fine-tune the delicate balance between latency and reactivation throughout chronic infection and viral pathogenesis . In work described here , we utilized a powerful new technique known as qCLASH ( quick crosslinking , ligation , and sequencing of hybrids ) to reveal the precise mRNA targets of MHV68 miRNAs [37–39] . Unlike crosslinking and immunoprecipitation ( CLIP ) approaches , which require bioinformatic identification of putative mRNA targets within a coupled data set following sequencing of separate miRNA and mRNA libraries , qCLASH directly identifies specific miRNA-target interactions through the sequencing of ligated miRNA-target mRNA hybrids . Like CLIP , the basis of CLASH is the purification of crosslinked RISC-RNA complexes by immunoprecipitation of Argonaute-2 ( Ago-2 ) , a major protein component of RISC . However , in contrast to CLIP protocols , CLASH utilizes RNA ligase to ligate miRNAs to their Ago-protected binding partners . Libraries generated from the resultant miRNA-mRNA hybrids are then subjected to high throughput sequencing . Sequencing results are stringently processed using the bioinformatic pipeline Hyb [40] , which identifies miRNA and mRNA sequences and complementarity sequences within each hybrid . Recently , qCLASH , a modified version of this procedure which allows for a reduced amount of input material , was used to define precise targets of KSHV miRNAs [37 , 41] . Here , we utilized the qCLASH approach to identify host mRNA targets of MHV68 miRNAs during lytic infection , latent infection , and reactivation from latency . Cumulatively , we defined 2 , 493 unique , high-confidence MHV68 miRNA-host mRNA interactions . Follow-up molecular studies validated specific repression of individual targets . Analysis of host pathways targeted by MHV68 miRNAs revealed a high number of targets shared with EBV and/or KSHV miRNAs , including numerous shared targets within host translation and protein modification pathways .
This study was conducted in accordance with all institutional and federal guidelines . All animal protocols were approved by the Institutional Animal Care and Use Committee at the University of Florida ( protocols 201609615 and 201708626 ) . NIH 3T12 murine fibroblasts ( ATCC , CCL-164 ) were maintained in Dulbecco’s modified Eagle’s medium , DMEM ( Corning , 11013CM ) supplemented with 10% heat inactivated fetal bovine serum ( FBS ) and 1X penicillin/streptomycin ( pen/strep; Corning , 30002CI ) . HE2 . 1 B cells ( generated by Dr . Craig Forrest , provided by Dr . Laurie Krug ) were maintained in RPMI 1640 medium ( Corning , MT10040CM ) supplemented with 10% FBS , 1X pen/strep ( Corning , 30002CI ) , and 50μM 2-mercaptoethanol . HE2 . 1 cells were cultured in the presence of 300 μg/mL hygromycin as described previously [42] . For preparation of lytic samples , NIH 3T12 cells were infected with WT MHV68 at MOI of 5 and incubated at 37°C . Lytically infected cells were harvested 16 hours post infection ( hpi ) . For preparation of latent samples , HE2 . 1 cells , a B lymphocyte cell line which is latently infected with WT MHV68 , were collected in log phase growth . For preparation of reactivation samples , HE2 . 1 cells were treated with tetradecanoylphorbol acetate ( TPA ) at a concentration of 20 ng/mL and incubated at 37°C . Reactivated cells were harvested 16 hours after TPA treatment . qCLASH was performed on lytically infected NIH 3T12 cells , latently infected HE2 . 1 B cells , and TPA treated HE2 . 1 B cells , each in triplicate . The qCLASH analysis was performed as described previously by Gay et al . [37] , but with modifications . Briefly , for each condition , 5 . 0x107 cells were collected , washed twice in 1X PBS , resuspended in 10 mL 1X PBS , and transferred to a cell culture dish on ice . Cells were UV-irradiated , then cell pellets were frozen at -80°C . Protein G beads Dynabeads ( Invitrogen , 10004D ) were washed and then resuspended in AffiniPure Rabbit Anti-Mouse IgG ( Jackson ImmunoResearch , 315-005-008 ) . After washing , beads were resuspended in 2A8 anti-Ago antibody ( generously provided by Dr . Zissimos Mourelatos ) . Cell lysates were resuspended in Lysis Buffer and incubated with RQ1 DNase ( Promega , M610A ) . Lysate was centrifuged , and supernatant was incubated with RNAse T1 . Prepared lysate was incubated with antibody-coated beads , then beads resuspended in Lysis Buffer containing RNase T1 . Following high stringency washes , phosphorylation and intermolecular ligation was performed using T4 PNK and T4 RNA Ligase . Subsequently , dephosphorylation and 3’ linker addition were performed using Alkaline Phosphatase ( Roche , 10713023001 ) , then T4 RNA Ligase 2 truncated K227Q ( NEB , M0351S ) with miRCat-33 3’ linker ( 5’-TGGAATTCTCGGGTGCCAAGG-3’ ) . Ago/RNA complexes were then eluted , proteins degraded by proteinase K ( Roche , 03115887001 ) treatment , and RNA was extracted using Phenol/Chloroform/Isoamyl Alcohol ( 25:24:1 ) . For library preparation , RNA was incubated with T4 PNK mixture , then T4 RNA Ligase with 5’ RNA linker . RNA was extracted with Phenol/Chloroform/Isoamyl Alcohol , then RNA was resuspended in RT buffer containing reverse transcription primer ( Illumina TruSeq Small RNA Sample Prep Kits RTP ) . Reverse transcription was carried out using SuperScript III ( Invitrogen , 18080093 ) . PCR was performed using 2x Phusion High-Fidelity Master Mix plus Primer 1 ( Illumina TruSeq Small RNA Sample Prep Kits RP1 ) , and Index Primers 1 , 2 , or 3 ( Illumina TruSeq Small RNA Sample Prep Kits RPI1 , RPI2 , or RPI3 ) . The resulting DNA libraries were separated on a 2% agarose gel , and regions corresponding to 175–300 bp were excised . DNA was gel purified from gel slices using the NucleoSpin Gel and PCR Clean-Up Kit ( Clontech , 740609 . 250 ) according to the manufacturer’s instructions . qCLASH libraries were sequenced on a HiSeq 2500 with a read length of 100 bases . The raw sequences were pre-processed with Trimmomatic [43] to remove adapter sequences and then analyzed with Hyb , a bioinformatics pipeline developed specifically for the analysis of CLASH data [40] . Determination of base-pairing along the length of the miRNA , categorization of miRNA seed-pairing and 3’ end pairing , and determination of mRNA transcript region origin were all carried out with custom scripts adapted from qCLASH scripts for KSHV miRNAs ( available at the GitHub page: http://github . com/RenneLab/qCLASH-Analysis ) . Regions of 500–1000 bp flanking the miRNA binding site of select genes were PCR amplified from NIH 3T12 cDNA using Q5 High-Fidelity DNA Polymerase ( NEB , M0491S ) according to the manufacturer’s instructions . The resulting PCR fragments were cloned into pmirGLO Dual-Luciferase miRNA Target Expression Vector ( Promega , E1330 ) using Gibson Assembly Cloning Kit ( NEB , E5510S ) according to the manufacturer’s instructions . Minimal miRNA binding sites ( approximately 30 bp ) were generated by annealing complementary oligos in annealing buffer ( 10mM Tris , pH 7 . 5 , 50mM NaCl , and 1mM EDTA ) at 100°C for 10 min , then incubating overnight at room temperature . The annealed oligos were cloned into the SacI and XbaI sites in the pmirGLO Vector using T4 DNA Ligase ( NEB , M0202S ) according to the manufacturer’s instructions . A list of all primers used are listed in S1 Table . For luciferase assays , 1 . 0x104 NIH 3T12 cells were plated per well of a 96-well plate and incubated overnight . Culture medium was removed and 50 μL transfection mixture was added to each well containing: 0 . 3 μL Lipofectamine-2000 , 1 μL 50ng pmirGLO Plasmid , 0 . 5 μL 5 μM mirVana custom miRNA mimic ( Life Technologies ) , 48 . 2 μL Opti-MEM Reduced Serum Medium ( ThermoFisher , 11058021 ) . Cells were then incubated with transfection mixture for 4 hours . 150 μL complete DMEM was added to each well and incubated overnight . Luciferase assays were performed with the Dual-Glo Luciferase Assay System ( Promega , E2940 ) according to the manufacturer’s instructions . All luciferase assays were performed in biological triplicates and technical quadruplicates . Total RNA was obtained using Qiagen RNeasy Mini Kit ( Qiagen , 74104 ) according to the manufacturer’s instructions . First-strand cDNA was synthesized from 1μg of total RNA using the NEB ProtoScript II Reverse Transcriptase ( NEB , M0368S ) per the manufacturer’s instructions . Quantification of selected genes were performed on an iCycler with an iQ5 multicolor real-time PCR detection system ( Bio-Rad Laboratories , Hercules , CA ) . The reaction mixture contained 5 pmol forward and reverse primer , 2x iQ SYBR green super mix ( Bio-Rad Laboratories ) , and 2 μl of template cDNA . Standard curves were prepared for each gene using 10-fold dilutions of a known quantity ( 300 ng/μL ) of cDNA from 3T12 Cells . The quantities were calculated using iQ5 optical detection system software . Each sample was normalized to GAPDH mRNA . The primer sequences utilized in this analysis are listed in S2 Table . All qRT-PCR assays were performed in biological and technical triplicates . Approximately 1x106 NIH 3T12 cells per sample were resuspended in 150 μL Lysis Buffer ( 150 mM NaCl , 1% NP-40 , 50 mM Tris , pH 8 . 0 , and 1X complete mini EDTA-free protease inhibitor ) and stored at -80°C . A 1:1 mixture lysate and 2X Loading Buffer ( 100 mM Tris-HCl , pH 6 . 8 , 4% SDS , 20% Glycerol , 0 . 05% Bromophenol Blue , and 10% 2-mercaptoethanol ) . Approximately 10 μg of total protein was then separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS PAGE 10% gel ) , transferred to a nitrocellulose membrane and probed with antibodies directed to β-actin ( Cell Signaling , 8H10D10 ) , ARID1A ( Novus Biologicals , NB100-55334 ) , CTSL ( R&D Systems , AF1515 ) , EWSR1 ( Abcam , EPR4647 ) , IFITM3 antibody ( R&D Systems , AF3377 ) , or FOXJ3 antibody ( R&D Systems , AF5786 ) . Bound antibodies were detected by HRP-conjugated secondary goat anti-rabbit ( Southern Biotech , 4050–05 ) , goat anti-mouse ( Southern Biotech , 1010–05 ) , or rabbit anti-goat ( Southern Biotech , 6160–05 ) , then visualized by enhanced chemiluminescence . Western blots were performed in triplicates . NIH 3T12 cells were infected at MOI 5 or MOI 10 and 35S labeling was carried out at either 5 or 10 hours post infection . For 35S labeling , infected cells were washed 3X with methionine- and cysteine-free Dulbecco’s modified Eagle’s medium ( Thermo Fisher , 21013024 ) and were then incubated in met-cys-free DMEM for 1 hour at 37°C . Media was removed from cells and incubated with Met-Cys-Free DMEM containing 0 . 1 mCi/mL 35S-methionine ( Perkin Elmer , NEG772007MC ) for 30 minutes at 37°C . The labeling was stopped by addition of DMEM containing 10% FBS . Media was then removed , cells washed 3X with DMEM containing 10% FBS , and then washed 3X with 1X PBS . Cells were resuspended in lysis buffer and stored at -80°C . Equal amounts of total protein was then separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE 10% Gel ) and the gel was dried using a gel drying kit ( Promega , V7120 ) according to the manufacturer’s instructions . Dried gel was then exposed to film for 24 hours and developed using a Kodak X-OMAT 2000 film developer . Pathway data sets were analyzed in the context of canonical pathways generated by Ingenuity Pathway Analysis ( IPA ) ( Qiagen; https://www . qiagenbio-informatics . com/products/ingenuity-pathway-analysis ) . To determine if any major cellular pathways are specifically targeted by MHV68 miRNAs , we analyzed the 2 , 493 genes present in two of three qCLASH biological replicates using IPA core analysis . The most significantly enriched canonical pathways were defined by IPA , and individual pathways were selected for visual representation here , with minor editing to clarify labels . For flow cytometry-based sorting of infected cells , mice were infected with 104 PFU MHV68-H2bYFP , a phenotypically wild-type virus that expresses eYFP under control of the H2b promoter [44] . At 16 dpi , splenocytes were prepared and blocked as described above . Cells were then stained with APC rat anti-mouse CD4 at 1:200 ( BD Biosciences , 553051 ) , APC rat anti-mouse CD8α at 1:200 ( BD Biosciences , 553035 ) , APC rat anti-mouse CD14 at 1:100 ( BD Biosciences , 560634 ) , and APC-Cy7 rat anti-mouse CD19 at 1:200 ( BD Biosciences , 557655 ) . Infected B cells ( CD4-CD8-CD14-CD19+YFP+ ) and non-infected B cells ( CD4-CD8-CD14-CD19+YFP- ) were sorted using a BD FACSAria II flow cytometer ( BD Biosciences ) . Sorted cells were immediately subjected to RNA extraction using an RNAqueous-Micro kit ( Ambion , AM1931 ) prior to qRT-PCR analyses .
To define the Ago-associated binding interactions between MHV68 miRNAs and cellular mRNAs , we performed a modified version of CLASH called quick CLASH ( qCLASH ) [37] . To assess miRNA targeting during latent infection , reactivation from latency , and lytic infection , three biological replicates were prepared from the MHV68+ B cell line HE2 . 1 [42] , TPA-treated HE2 . 1 cells , and MHV68-infected NIH 3T12 fibroblast cells . At time of harvest , cells were crosslinked and then Ago complexes were precipitated . Following RNA-RNA ligation , RNA hybrids were eluted and cDNA libraries were prepared and sequenced . Each library yielded between 10 and 20 million reads , which were bioinformatically analyzed using the Hyb program [40] to identify any RNA-RNA chimeras that included any combination of host miRNA , viral miRNA , host mRNA , viral mRNA , or host lncRNA ( sequences containing ribosomal RNAs were filtered ) . Consistent with previous CLASH studies [37 , 45] , 0 . 1 to 1 . 5% of reads were classified as RNA-RNA hybrids ( S3 Table ) . Of those miRNA-mRNA hybrids that aligned to a host mRNA , between 11 and 15% carried a MHV68 miRNA across all three sample groups ( Fig 1A ) . In all , we detected 844 to 1 , 316 MHV68 miRNA hybrids per latency replicate , 4 , 135 to 6 , 949 per reactivation replicate , and 4 , 331 to 7 , 993 per lytic replication replicate ( S3 Table ) . Although the abundance of individual MHV68 miRNAs varied between sample groups , five to six MHV68 miRNAs were consistently represented among the top 25 most abundant qCLASH miRNA-containing hybrids ( Fig 1B ) . MHV68 miRNAs miR-M1-1-3p , -2-5p , -2-3p , -7-3 , -8-5p and -9-3p were most commonly detected during all phases of infection ( Fig 1B and Table 1 ) . The current view of miRNAs is that they repress translation through binding interactions at the 3’ UTR of target transcripts . However , recent CLASH studies have found that numerous miRNAs target mRNA transcripts through binding to regions outside of the 3’ UTR , including the protein coding sequence [37 , 45 , 46] . To assess which domains of mRNA target transcripts were bound by Ago-associated MHV68 miRNAs , we defined whether the mRNA target sequence in individual hybrids aligned to the mRNA 5’ untranslated region ( 5’UTR ) , coding region ( CDS ) , or 3’ untranslated region ( 3’UTR ) ( Fig 2A ) . In some case , target sequences spanned two regions and thus were classified as 5’UTR-CDS or CDS-3’UTR . Consistent with other CLASH-based findings [37] , the majority of host ( 44 to 54% ) and viral ( 47 to 60% ) miRNAs aligned to the CDS of cognate target transcripts . The 3’ UTR was the next most frequently targeted region , accounting for 40 to 51% of host miRNA and 32 to 45% of viral miRNA binding . For both viral and host miRNAs , targeting of the 5’ UTR , 5’UTR-CDS boundary , and the CDS-3’UTR boundary cumulatively accounted for less than 10% of binding interactions . In general , viral miRNAs targeted CDS regions more frequently than host miRNAs; however , these differences were not statistically significant . Likely reflecting the distinct cellular transcriptomes during lytic infection , latency and reactivation , both host and viral miRNA targeting of specific transcript regions varied moderately among latency , reactivation , and lytic datasets , with targeting at the 3’ UTR highest during latency . While many factors contribute to miRNA target binding and target repression , the miRNA seed sequence ( defined as nucleotides 2 to 8 ) is conventionally thought to be one of the most important [7] . To examine the binding characteristics of host vs . viral miRNAs to their target transcripts along the length of miRNAs , we calculated the percentage of cumulative targets that were bound at each nucleotide ( Fig 2B ) . As expected , the seed sequence nucleotides of host miRNAs participated in target binding at a high frequency , with lower complementarity over nucleotides 13 to 21 . Interestingly , although the viral miRNAs utilized canonical seed sequence binding less frequently than host miRNAs , this was complemented by a high frequency utilization of downstream nucleotides through position 15 . These characteristics were reflected in an overall rightward shift of the cumulative viral miRNA target binding curve . However , this cumulative shift reflected a strong bias toward those individual hybrid binding interactions most highly represented in the viral miRNA qCLASH binding set ( S1 to S3 Figs ) . For example , within the latency group , two of the three most highly represented miRNAs , mghv-miR-M1-7-3p and mghv-miR-M1-1-3p , demonstrated unusual , non-canonical binding profiles with target mRNAs . However , other top miRNAs , including mghv-miR-M1-2-5p and mghv-miR-M1-6-5p demonstrated increased seed sequence binding , indicating that the cumulative binding profile does not reflect an overall skewing of binding by the entire viral miRNA population . In total , analysis of qCLASH hybrids from latency , reactivation , and lytic datasets revealed 5 , 924 unique host transcripts that were cumulatively targeted by MHV68 miRNAs across three biological replicates . Of those , 2 , 493 and 957 were observed in two of three and three of three biological replicates , respectively ( Fig 3A ) . Notably , numerous miRNA targets were shared across different phases of infection and different cell types ( Fig 3B ) . For example , 76% ( 96 of 127 ) of targets detected in latently infected B cells were also detected in both reactivating B cells and lytically infected fibroblasts . Likewise , 778 targets from B cells undergoing reactivation from latency were shared with lytically infected fibroblasts . Because gammaherpesviruses including MHV68 establish lifelong infection in B cells , we elected to focus our in-depth studies on those viral miRNA targets identified in B cell latency and/or reactivation qCLASH libraries . Of the unique host targets present in two of three biological replicates , 1 , 505 were observed in B cells , 57% ( 862 of 1 , 505 ) of which were also detected in fibroblasts ( S4 Fig ) . To select for transcripts whose targeting may be conserved across gammaherpesviruses , we compared these CLASH-identified B cell targets to putative EBV and KSHV targets previously identified in CLIP datasets from B cells [16–21] ( Fig 3C ) . Impressively , of the 1 , 505 MHV68 miRNA targets identified in B cells , 82% ( 1 , 204 ) were common with EBV , 68% ( 1 , 016 ) were common with KSHV , and 64% ( 969 ) were common with both EBV and KSHV . To validate repression of individual host targets by MHV68 miRNAs , we selected a subset of nine transcripts , eight of which were shared with EBV and/or KSHV miRNAs ( S4 Table ) . These targets include mRNAs that encode proteins relevant to chromatin remodeling , transcription , translation , and apoptosis: ARID1A , a chromatin remodeling protein [47] , CTSL , a lysosomal cysteine proteinase [48] , EWSR1 , an RNA binding protein [49] , FUS , an RNA binding protein [50] , IFITM3 , an interferon stimulated transmembrane protein [51] , PHC3 , a member of the Polycomb Group chromatin remodeling complex [52] , FOXJ3 , a transcription factor [53] , KDM5B , a histone demethylase [54] , and TRP53INP1 , a proapoptotic protein [55] . To stringently assess MHV68 miRNA repression of these select targets we utilized complementary molecular approaches including luciferase-based 3’ UTR targeting , in vivo transcript stability , and protein expression . To first determine whether MHV68 miRNAs could directly repress transcripts carrying target sequences from qCLASH hybrids , we performed luciferase knockdown assays using luciferase 3’ UTR constructs carrying: ( a ) specific miRNA target sequences with 500 to 1000 bp native flanking sequence ( “region” ) , ( b ) ~30 bp specific miRNA target site sequences ( “site” ) , or ( c ) ~30 bp specific miRNA target site sequences with a 3 bp mutation in the binding sequence complementary to the miRNA seed ( “mutant” ) . Specific target knockdown was determined for each transcript using the cognate CLASH-identified targeting MHV68 miRNA versus a control non-targeting MHV68 miRNA . For this study , we considered statistically significant reduction of luciferase activity by 30% or greater compared to control to constitute biologically relevant target repression . By this standard , transcripts carrying Arid1a , Ctsl , Ewsr1 , Fus , Ifitm3 , or Phc3 target sequences demonstrated specific repression by the qCLASH-identified MHV68 miRNAs mghv-miR-M1-6-3p , -8-5p , -7-5p , and -6-3p respectively ( Fig 4A ) . For individual targets , the level of transcript repression was similar between those transcripts carrying large target regions and those transcripts carrying minimal target sites . Moreover , in each case mutation of 3–4 bases within the target site completely ablated repression , demonstrating the specificity of miRNA targeting . As expected , not all qCLASH-identified transcripts were repressed by their targeting miRNA , as transcripts carrying FoxJ3 , Kdm5b , or Trp53inp1 target sequences were not affected by cognate miRNA binding . To determine whether MHV68 miRNAs could also directly repress endogenously expressed mRNAs , we quantified the relative level of specific target transcripts in cells transfected with individual miRNAs ( Fig 4B ) . Consistent with results from luciferase assays , the endogenous levels of Arid1a , Ctsl , Ewsr1 , Fus , Ifitm3 , or Phc3 mRNAs were repressed by their respective qCLASH-identified miRNAs . Moreover , the endogenous levels of mRNAs FoxJ3 , Kdm5b , or Trp53inp1 , which carry target sites not repressed in luciferase assays , were not reduced in the presence of targeting miRNAs . In fact , endogenous Trp53inp1 levels were significantly increased in the presence of targeting miRNA mghv-miR-M1-5-5p , implicating miRNA binding in stabilization of this transcript . To determine whether reduced levels of target mRNAs correlated with reduced endogenous protein expression , we assessed protein levels of select targets in the presence of individual miRNAs ( Fig 5 ) . Consistent with the mRNA knockdown results , we observed reduced expression of CTSL , EWSR1 , and IFITM3 proteins in the presence of their qCLASH-identified mRNA binding partners miR-M1-8-5p , -7-5p and -7-5p , respectively . Likewise , expression of FOXJ3 protein , whose mRNA was not repressed by miR-M1-12-3p binding , was not altered in the presence of miR-M1-12-3p . Notably , although EWSR1 protein levels were reduced by its qCLASH-identified interacting miRNA but not by other MHV68 miRNAs , the levels of CTSL and IFITM3 protein were repressed by their respective qCLASH-identified miRNAs as well as by miR-M1-6-3p , which was not identified by qCLASH . These observations may reflect miRNA-mRNA interactions that were not efficiently recovered by qCLASH , or miRNA repression of targets that indirectly affect expression of CTSL and IFITM3 . Together , these findings validate the identification of specific miRNA-mRNA interactions by qCLASH , and demonstrate that the MHV68 TMERs yield mature miRNAs that function to suppress expression of host proteins . Like EBV and KSHV , MHV68 predominantly establishes latent infection in circulating mature B cells in vivo [26–28 , 44 , 56–59] . To determine whether host transcripts which were validated for repression in vitro were also repressed in latently infected B cells in vivo , we performed qRT-PCR for host transcripts of interest on pure populations of non-infected or infected B cells sorted from mice during chronic infection . Wild-type C57BL/6J mice were infected with MHV68 . H2bYFP , a eYFP-marked recombinant MHV68 that is phenotypically wild-type[44] . At 16 days , splenocytes were harvested and sorted into infected ( CD4-CD8-CD14-CD19+YFP+ ) versus non-infected ( CD4-CD8-CD14-CD19+YFP- ) B cell populations . Following RNA extraction , transcript levels of Arid1a and Ctsl were determined by qRT-PCR ( Fig 6 ) . As compared to YFP- non-infected B cells , the levels of Arid1a and Ctsl transcripts were significantly decreased in YFP+ latently infected B cells . Due to the difficulty recovering large numbers of pure populations of infected cells from in vivo samples , we were only able to quantify transcript levels for these two transcripts . Nevertheless , these data clearly demonstrate that these two qCLASH-identified targets of MHV68 miRNAs were repressed in infected cells in vivo , strongly supporting the concept that a large subset of the host targets identified in this study are likely bona fide targets repressed by MHV68 miRNAs in vivo . To determine if there were major cellular pathways which were highly targeted by MHV68 miRNAs , pathway enrichment analysis was performed using the 1 , 851 and 1 , 505 genes which were identified in at least two of three qCLASH biological replicates for fibroblasts and B cells , respectively ( S4 Fig ) . Ingenuity Pathway Analysis ( IPA ) defined significant gene enrichment in numerous canonical cellular pathways with known relevance to cancer biology , including more than 35% of the 394 host genes categorized within the molecular mechanisms of cancer pathway ( Fig 7A , S5 Table ) . Notably , eIF2 signaling , protein ubiquitination , regulation of eIF4 and p70S60K signaling , and mTOR signaling , were the four most highly enriched pathways for both fibroblasts and B cells . Moreover , nine of the top twelve pathways in fibroblasts were represented within the top eleven pathways in B cells , demonstrating conserved pathway targeting between cell types . Despite the significant overlap between target sets , analysis of individual cell types nevertheless revealed enhanced enrichment of some pathways in one cell type versus the other . For example , using the targets identified in B cells revealed a high level of enrichment in pathways highly active or exclusive to lymphocytes , including B cell receptor signaling , B cell PI3K signaling , IL-7 signaling pathway , and JAK/Stat signaling . Likewise , the DNA methylation and transcriptional repression pathway was very highly enriched in fibroblasts but not B cells , perhaps reflecting the need for the virus to counteract host transcriptional repression mechanisms during lytic replication . One of the most notable observations for both cell types was the high representation of pathways directly involved in protein translation or protein modification within the target sets demonstrating the most significant enrichment ( Fig 7A , S5 Table ) . This group includes eIF2 signaling , eIF4 and p70S6K signaling , mTOR signaling , protein ubiquitination pathway , and sumoylation pathway . Interestingly , comparison of MHV68 miRNA targets derived from B cells with EBV and KSHV miRNA targets previously identified in B cells [16–21] , revealed significant conservation of targeting within individual pathways . In particular , a high percentage of the MHV68 B cell-derived miRNA targets integral to translation and protein modification pathways were shared with EBV and/or KSHV miRNA B cell targets . These include the protein ubiquitination pathway ( Fig 7B ) , eIF2/eIF4 signaling ( Fig 7C ) and mTOR signaling ( Fig 7D ) . For example , numerous critical components of the eIF4 complex , including eIF4A , eIF4B , eIF4E , eIF4G , eIF3 and eIF5 , were targeted by MHV68 and at least one of the human viruses . These findings clearly demonstrate conserved targeting of protein translation and modification pathways by gammaherpesvirus miRNAs . To determine if there were functional consequences for MHV68 miRNA targeting of pathways involved in host translation , we assessed global translation in cells infected with wild-type MHV68 or with MHV68 . Zt6 , a previously published MHV68 mutant deficient in the expression of all 14 pre-miRNAs [34] . After 10 hours of infection , newly synthesized proteins were labeled through 35S-methionine incorporation , and cell lysates where then subjected to SDS-PAGE gel electrophoresis . As expected , imaging of radiolabeled proteins revealed a reduction in both individual resolved bands and unresolved background bands in wild-type MHV68-infected cells as compared to mock-infected cells ( Fig 7E ) , indicating that infection resulted in a significant reduction in the translation of new proteins . Notably though , translation was nearly restored to a level equivalent to mock in cells infected with the miRNA-deficient virus MHV68 . Zt6 . These findings demonstrate that viral miRNAs contribute to the global reduction in translation observed following MHV68 infection .
In qCLASH experiments presented here , we identified 2 , 493 unique host transcripts that were targeted by MHV68 miRNAs in at least two of three biological replicates . Likely owing to the very low efficiency of the ligation of Ago-associated RNAs , the recovery of RNA-RNA hybrids was very low ( S3 Table ) . This was expected and highly consistent with previously published CLASH-based studies [37 , 45] . Among Ago-associated RNA-RNA hybrids recovered , approximately 41% contained either host or viral miRNAs . Of these , 13% contained MHV68 miRNAs . Although the total number of viral miRNA hybrids recovered during latency was 3- to 4-fold lower than the numbers recovered during lytic infection and reactivation , this was a reflection of the total number of reads , as viral miRNAs still represented 13% of the total miRNA hybrids recovered during latency . Moreover , 76% ( 96 of 127 ) of hybrids detected during latency were also found in lytic and/or reactivation samples ( Fig 3B ) . Despite the low efficiency of ligation , it is anticipated that the vast majority of RNA hybrids obtained through CLASH-based approaches represent legitimate miRNA targeting of mRNAs . This is due to the stringency of the procedure , in which ( a ) Ago-associated RNA hybrids are recovered only after Ago precipitation , RNase treatment , and ligation of protected RNAs , and ( b ) valid miRNA-mRNA hybrids are further identified through computational assessment of sequence complementarity within the short read . Nevertheless , bona fide miRNA-mRNA binding associations do not necessarily equate with reduction of target transcript and/or protein . Thus to validate repression of individual host targets of MHV68 miRNAs , we performed extensive molecular assessment ( Fig 4 ) of nine select targets in B cells that were targets shared with EBV and/or KSHV miRNAs . Of these , six mRNA targets were significantly repressed by their cognate miRNA binding partner , and three were not altered , equating to a 67% rate of repression for miRNA-mRNA interactions in this limited sample set . Importantly , the inclusion of entire mRNA target regions versus specific mRNA target site resulted in equivalent repression , demonstrating the specificity of target site recovery through qCLASH hybrid sequencing . It is not surprising that some miRNAs did not reduce mRNA or protein levels , as it has been previously noted across species and across target identification approaches that a sizeable percentage of miRNA binding interactions do not lead to target repression [7] . The findings presented here demonstrate the utility of the qCLASH approach , and strongly suggest that the majority of qCLASH-identified hybrids represent legitimate Ago-associated binding interactions between MHV68 miRNAs and their specific mRNA targets . Despite the legitimacy of the qCLASH approach , based on the data presented here it also reasonable to conclude that the procedure does not capture all miRNA-mRNA interactions . For example , in agreement with data presented here , miR-M1-1-3p and miR-M1-8-5p are among the most highly expressed MHV68 miRNAs during lytic and latent infection [34] . In contrast , while miR-M1-15-5p is clearly expressed in infected cells [34] , we detected no miR-M1-15-5p hybrids . Further , our finding that CTSL and IFITM3 were repressed by miR-M1-6-3p , but that this miRNA was not recovered as a qCLASH hybrid with CTSL or IFITM3 mRNAs , may suggest that these specific miRNA-mRNA interactions were not recovered due to low abundance or poor RNA ligation efficiency . Alternatively , it is possible that CTSL and IFITM3 are not direct miR-M1-6-3p targets , and that instead this miRNA represses a particular gene or set of genes responsible for the expression or stability of CTSL or IFITM3 . Likewise , some miRNAs frequently recovered in qCLASH have been observed to be expressed at low levels in infected , strongly suggesting that miRNA expression profiles are not predictive of Ago-mediated target repression . It is important to note that we have limited this study to the specific examination of MHV68 miRNA targeting of host mRNAs . However , it is well-established that gammaherpesviruses not only utilize their miRNAs to regulate the expression of host genes but also use their miRNAs in order to target and regulate the expression of their own genes to regulate their lifecycle and virulence . For example , it has been demonstrated that viral miRNAs of some herpesviruses directly target lytic genes to suppress lytic reactivation and maintain latency . For example , EBV miR-BART20-5p targets the 3’ UTRs of the transcripts that encode the key latent to lytic switch proteins Rta and Zta [60] . Similarly , KSHV miR-K12-7 and -9 target the Rta transcript to maintain latency and prevent reactivation [61–64] . While the viral targets of MHV68 miRNAs have not been discussed here , future studies will investigate the viral targets of MHV68 miRNAs and how this targeting plays a role in the maintenance of latency . The current view for miRNA function incorporates a set of well-accepted rules [7 , 65] for miRNA-mRNA binding interactions: ( i ) binding does not have to be perfectly complementary across the entire length of the miRNA , ( ii ) binding is largely dominated by complementarity within nucleotides 2–7 of the miRNA , a region that is defined as the miRNA seed sequence , ( iii ) base pairing outside of the seed sequence does not influence miRNA function , but stabilizes miRNA-mRNA binding , ( iv ) miRNA binding generally occurs in the 3’ UTR of target transcripts within relatively unstructured regions . Thus , while base pairing within the seed sequence is thought to be the most important determinant of miRNA targeting and function , base pairing outside of the seed sequence at the 3’ end of the miRNA , is thought to play a secondary role by stabilizing the miRNA-target transcript binding and increasing target specificity [7 , 65] . However , the recent application of RNA-RNA ligation-based approaches is challenging the rigidity of these rules . For example , a recent study demonstrated preferential miRNA binding of both host and viral miRNAs to the coding region ( CDS ) of target transcripts as comparted to the 3’ UTR [37] . Consistent with these findings , here we present data demonstrating that although a large proportion of virus and host miRNAs do bind the 3’ UTR of target transcripts , a majority of miRNAs instead bind to the coding region ( CDS ) of the mRNA . Interestingly , while we observed canonical seed paring with less 3’ binding in host miRNA-host mRNA hybrids , viral miRNA-host mRNA hybrids displayed decreased 5’ complementarity and a reciprocally increased 3’ complementarity . The genesis of this altered miRNA binding is unclear , but is not simply a reflection of skewing for an individual miRNA or miRNA-mRNA hybrid: Only one of the top six most highly represented miRNAs in each of the three infection data sets demonstrates a canonical target complementation with high seed sequence complementarity and lower 3’ complementarity ( S1–S3 Figs ) . Nevertheless , these miRNAs are fully functional , as miR-M1-8-5p and miR-M1-5-5p both repressed target mRNAs in our validation studies . Interestingly , it is has been very recently reported that some miRNAs may repress translation by targeting specific recognition elements within CDS sequences , and that these interactions typically require extensive base pairing in the 3’ portion of the miRNA [66] . It is also conceivable that at least some of these binding profiles could be explained in part by competing endogenous RNAs , which have been postulated to regulate the stability of some miRNA targets through miRNA sponging; however the high frequency of repressive miRNA-mRNA interactions in our validation studies argues against this possibility . Though individual miRNA molecules typically only affect a handful of individual mRNA targets , the synergistic actions of multiple miRNAs can substantially influence entire signaling pathways . Here we analyzed the potential influence of MHV68 miRNAs on host processes by examining the enrichment for qCLASH-identified mRNA targets in IPA-defined pathways . Transcripts targeted by MHV68 miRNAs were involved in a wide of array of key cellular pathways , including those associated with translation , protein modification , B cell signaling , and DNA damage . In particular , pathways involved in protein translation were among the most frequently targeted , with three of the top four most significant pathways in both B cells and fibroblasts directly influencing or participating in translation: eIF2 signaling , eIF4 and p70S6K signaling , and mTOR signaling . Importantly , numerous components of these same translation and protein modification pathways were also targeted by EBV and/or KSHV miRNAs ( Fig 7C and 7D ) , revealing a conserved strategy among gammaherpesvirus miRNAs for targeting these pathways . The specific functional consequences of miRNA targeting of translation pathway components are not yet understood . However , in support of a possible contribution of MHV68 miRNA-mediated repression of translation , the reduced level of global translation observed in cells infected with wild-type MHV68 was largely ablated in cells infected with an MHV68 mutant deficient in miRNA expression . This is perhaps surprising considering that numerous viruses , including EBV , KSHV and MHV68 , are known to induce shutoff of host protein synthesis through the use of a virus-encoded alkaline exonuclease [67–69] . However , it is plausible that , in the context of virus infection , gammaherpesvirus miRNA repression of translation factors is a critical step in initiation of exonuclease-mediated expression or activity , or that regulation of translation factors is crucial for tipping the counterpoise between host protein loss and new protein synthesis in favor of the viral exonuclease . Alternatively , it is possible that viral miRNA regulation of other host or viral factors are required for exonuclease activity , or to counteract repressors of exonuclease expression . Nevertheless , the finding that EBV , KSHV , and MHV68 miRNAs all target numerous factors within pathways important for regulation of translation strongly implies that this activity may be conserved among gammaherpesviruses . The comparison of the top fifteen pathways most significantly targeted in fibroblasts versus B cells revealed both substantial commonality at the top of the lists , and substantial differences at the bottom of the lists . In addition to the common targeting of pathways involved in host translation between cell types , protein ubiquitination and sumoylation pathways featured highly in both lists . This is perhaps not surprising , considering the wide number of viruses that are known to suppress host post-translational modification processes as a means to counteract obstacles imposed by the host cell [70 , 71] . In contrast , the observation of estrogen receptor signaling as a significantly enriched pathway in both cell types was perhaps surprising . However , estrogen receptor signaling has recently gained increasing attention for its functions in regulation of gene expression , particularly with regard to its involvement in regulation of epigenetic modifiers [72] . Interestingly , estrogen receptor signaling has previously been connected to regulation of B cell activation and B cell development [73] , and accumulating evidence has implicated a functional role for estrogen receptor signaling in multiple B cell malignancies [74] . The common miRNA targeting of estrogen receptor signaling in both fibroblasts and B cells strongly suggests that this pathway may play a more integral role in gammaherpesvirus infection than previously appreciated . As may be expected , pathways specific to , or particularly important for , lymphocytes were uniquely enriched in miRNA target sets identified in B cells . For example , B cell receptor signaling , IL-7 signaling , and JAK/Stat signaling were among the six pathways enriched in B cells but not fibroblasts . Repression of selective host B cell activation signaling components would be consistent with the need for these viruses , as an integral part of their lifestyles , to directly manipulate normal B cell biology in favor of virus-encoded signaling cues [75 , 76] . Considering the need of these viruses to counter host defenses aimed at shutting down viral gene expression , the finding that DNA methylation and transcriptional repression was among the top 6 most significantly enriched pathways in fibroblasts was not surprising . The fact that targeting of this pathway was prominent in fibroblasts but not B cells was unexpected; however , this finding may reflect the specific need for the virus to overcome repressive host mechanisms during robust lytic replication . The findings presented here reveal a wide range of host pathways targeted by MHV68 miRNAs , and suggest that cumulative targeting of factors within these pathways may have important functional outcomes during particular stages of infection . However , it should be noted that more than 30% of qCLASH-identified miRNA-mRNA interactions did not result in repression of the target mRNA or protein in the validation of a limited number of targets presented here . This observation serves to remind that the binding of a specific miRNA to a specific mRNA target does not always result in inhibition of that target; indeed in some scenarios binding can result in target stabilization . That said , the majority of qCLASH-identified targets were significantly repressed in validation assays , implying that most pathways enriched in MHV68 miRNA target sets will likely demonstrate some level of functional repression . Nevertheless , significant efforts to validate both the targeting of individual pathway components and the effects on entire pathways will be needed in order to make more definitive conclusions about the consequences of these myriad interactions . The use of RNA-RNA ligation approaches is quickly transitioning the miRNA target identification field from a bioinformatics-driven narrowing of potential mRNA targets which fit within defined parameters , to the outright discovery of bona fide mRNA targets that in many cases defy expected parameters . Here , we have defined a set of miRNA-mRNA binding interactions , a large proportion of which result in target repression , but that do not necessarily target the mRNA 3’ UTR and do not always bind through robust seed sequence-based complementarity . The reasons for these deviations from “normal” are unknown; however , the consequences of miRNA binding appear to be largely the same as would be expected , with repression of target mRNAs . Moreover , as combined miRNA targeting of pathways associated with translation signaling resulted in overall lower protein synthesis , it is plausible that combined target repression may result in modulation of numerous other crucial cell signaling pathways . The analysis presented here will allow for continued investigation of the functional consequences of targeting of these pathways and the role that these miRNA-mRNA interactions play in the biology of gammaherpesviruses . In particular , detailed in vivo analyses of the shared targets of MHV68 , EBV and KSHV targets should shed important new light on the consequences of the conserved repression of host pathways during chronic gammaherpesvirus infection and pathogenesis . | Gammaherpesviruses , including the human pathogens Epstein-Barr virus ( EBV ) and Kaposi’s sarcoma-associated herpesvirus ( KSHV ) , establish lifelong infections and are associated with a variety of tumors . These viruses encode numerous molecules called microRNAs ( miRNAs ) within their genomes , which target and suppress the products of specific genes within infected host cells . However , the function of these miRNAs during in vivo infection is largely unknown . Murine gammaherpesvirus 68 ( MHV68 ) is a natural pathogen of rodents that is genetically related to both EBV and KSHV , and thus serves as an excellent model for the study of EBV and KSHV . Here , we describe the identification and validation of thousands of new MHV68 miRNA targets . Notably , 86% of the MHV68 miRNA targets identified were shared with either EBV or KSHV , and 64% were shared among all three viruses . Further analyses revealed enrichment of cellular pathways involved in protein synthesis and protein modification , including pathways also enriched for targets of EBV and KSHV miRNAs . These findings provide substantial new information about specific targets of MHV68 miRNAs and shed important light on likely conserved functions of gammaherpesvirus miRNAs . | [
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"organisms"
] | 2019 | Identification of murine gammaherpesvirus 68 miRNA-mRNA hybrids reveals miRNA target conservation among gammaherpesviruses including host translation and protein modification machinery |
Noroviruses are the most common cause of viral gastroenteritis . An increase in the number of globally reported norovirus outbreaks was seen the past decade , especially for outbreaks caused by successive genogroup II genotype 4 ( GII . 4 ) variants . Whether this observed increase was due to an upswing in the number of infections , or to a surveillance artifact caused by heightened awareness and concomitant improved reporting , remained unclear . Therefore , we set out to study the population structure and changes thereof of GII . 4 strains detected through systematic outbreak surveillance since the early 1990s . We collected 1383 partial polymerase and 194 full capsid GII . 4 sequences . A Bayesian MCMC coalescent analysis revealed an increase in the number of GII . 4 infections during the last decade . The GII . 4 strains included in our analyses evolved at a rate of 4 . 3–9 . 0×10−3 mutations per site per year , and share a most recent common ancestor in the early 1980s . Determinants of adaptation in the capsid protein were studied using different maximum likelihood approaches to identify sites subject to diversifying or directional selection and sites that co-evolved . While a number of the computationally determined adaptively evolving sites were on the surface of the capsid and possible subject to immune selection , we also detected sites that were subject to constrained or compensatory evolution due to secondary RNA structures , relevant in virus-replication . We highlight codons that may prove useful in identifying emerging novel variants , and , using these , indicate that the novel 2008 variant is more likely to cause a future epidemic than the 2007 variant . While norovirus infections are generally mild and self-limiting , more severe outcomes of infection frequently occur in elderly and immunocompromized people , and no treatment is available . The observed pattern of continually emerging novel variants of GII . 4 , causing elevated numbers of infections , is therefore a cause for concern .
Noroviruses ( NoV ) are the most common cause of acute viral gastroenteritis [1] , [2] , with the numbers of reported outbreaks peaking characteristically between November and March in the northern hemisphere [3] . Illness is usually self-limiting and symptoms , comprising acute onset vomiting and watery diarrhea , subside within one to three days [4] . The relevance of studying NoV lies in their high prevalence in the population [5] , and in the more severe and prolonged illness that is seen among elderly and immunosuppressed patients [6]–[8] . NoVs are highly infectious , due to the combination of an extremely low infectious dose ( an estimated ID50 of less than 20 viral particles [9] ) , very high levels of shedding ( around 108 but up to >1010 RNA copies per gram of stool ) and prolonged shedding after clinical recovery [10] , [11] . NoV outbreaks , which may affect hundreds of people and are notoriously difficult to control , are primarily associated with places where people are in close contact , for example hospitals and long-term care facilities . NoVs are a genetically diverse group of positive sense single-stranded RNA viruses from the Caliciviridae family . Their 7 . 5 kb genome includes three open reading frames ( ORFs ) . The first ORF encodes a polyprotein that is post-translationally processed to form the non-structural proteins , the second and third ORFs encode the major and minor structural proteins; VP1 or the capsid protein and VP2 . The viral capsid is formed by 180 copies of the major capsid protein , and governs antigenicity , host-specificity and environmental stability . NoVs are classified into five distinct genogroups , which are further subdivided into genotypes , based on their amino acid capsid sequence . Molecular epidemiological studies have shown that in recent years approximately 70% of NoV outbreaks among humans have been caused by one dominant genotype , GII . 4 [12]–[17] . With continuous surveillance systems in place in some countries since the mid-1990s , it has become apparent that the number of reported NoV outbreaks , and especially those caused by GII . 4 strains has risen since the appearance of the 2002 variant of GII . 4 [12] , [15] , [18]–[21] . Since then , genetically distinct GII . 4 variants have emerged , and spread rapidly across the world causing epidemic waves of NoV illness [20] , [22] . To date , three variants named after the year when they were first detected have been identified in populations across the world ( the 2002 , 2004 and 2006b variants [22] ) . The emergence of each of these three variants was followed by ‘hot’ NoV winters with sharply increased numbers of reported outbreaks . Older strains belonging to the lineage designated 1996 were also detected around the world , although surveillance was limited at that time . The pattern of continuous lineage turnover , referred to as epochal evolution [23] , with emerging new variants replacing previously predominant circulating ones , is strongly reminiscent of what is observed in the molecular epidemiology of Influenza A virus ( IAV ) . The evolutionary interaction between IAV and the human immune response results in antigenic drift , illustrated by the characteristic ladder-like tree shapes for hemagglutinin and neuraminidase surface proteins [24] . Long-term partial immunity to the virus induces sharp fitness differences among strains and drives rapid amino acid replacement at key antigenic sites , pinpointed by in vitro and in silico analyses [25] , [26] . Whereas antigenic data can be readily generated for IAV , allowing the comparative mapping of antigenic and genetic evolution [27] , research of NoV antigenic properties has been hampered by the lack of a simple cell culture model [28] . However , recent publications indicate that the genetic differences between NoV genotypes , and also between variants of the GII . 4 genotype , translate into distinct antigenic types , although molecular determinants remain largely unclear [23] , [29] . Thus , individuals may be repeatedly infected by strains belonging to different genotypes , and also , because immunity against NoV infection is short-lived at best [30]–[32] , possibly repeatedly by strains of the same genotype . As a result , the impact of immune responses on NoV epidemiology remains poorly understood and phylodynamic and molecular adaptation studies may provide some key insights . In this study , we aimed to provide a rigorous measurement of NoV GII . 4 diversity through time , and we investigated viral population expansions in relationship to the increased numbers of infections reported in recent years . Evolutionary and population dynamics of GII . 4 NoVs were estimated by a Bayesian coalescent approach , using two different datasets of sequences from strains with known detection dates , between 1987 and 2008 . One set of sequences contained full capsid sequences , the other short partial polymerase sequences , which had been obtained for standard-procedure genotyping in NoV surveillance in Europe [13] ( http://www . noronet . nl/fbve/ ) and from the global NoV surveillance network Noronet [22] ( http://www . noronet . nl/ ) . We also tested whether these dynamics differed from neutral expectations , so whether and how they were shaped by selective pressure , and we attempted to further elucidate the molecular determinants of NoV evolutionary and epidemiological dynamics using in silico techniques . To identify the molecular characteristics of NoV GII . 4 strain replacement , we investigated both directional and diversifying selection and elucidated capsid protein positions showing evidence for co-evolutionary dynamics acting between sites .
To examine the extent to which recombination has shaped NoV evolution , we analyzed an alignment of 20 GII . 4 sequences , two for each variant , spanning the genome from region A ( a gene region in ORF1 that is commonly used for genotyping purposes , see Materials and Methods , and [22] and [33] ) up to the 3′end of the capsid . Using GARD ( Genetic Algorithm for Recombination Detection ) , VisRD ( Visual Recombination Detection ) and RDP3 ( Recombination detection program ) , significant phylogenetic variability was identified in this genome region , which could be attributed to a recombination event for the 2003Asia variant , a GII . 4 variant previously identified as a recombinant lineage , mainly detected in Asia , and rarely in Europe , Oceania or the Americas [22] . The crossover point lay in the ORF1/2 overlap , a position previously identified as a recombination hotspot in NoV [34] , [35] . Further analyses below were based on polymerase and capsid gene sequences that do not include this breakpoint , and no recombination could be detected in those individual data sets using the Phi test [36] . To test whether GII . 4 evolution deviated from selective neutrality , we applied a genealogical neutrality test that involved Bayesian coalescent inference , a tree-based summary statistic ( DF ) , and posterior predictive simulation [37] . Using a constant population size demographic prior , the capsid data set ( 194 sequences , 1623 nt ) showed significantly more negative DF than expected ( P<0 . 01 ) , suggesting a selective process that generated a significant excess of mutations on terminal branches . The same was true using an exponential growth prior , but a Bayes factor test did not support an exponential growth scenario ( ln BF constant versus exponential growth = −2 . 47 ) . A Bayes factor comparison also favored a constant population size model over an exponential growth model for the matched polymerase data set ( 172 sequences , 247 nt ) ( ln BF = −2 . 23 ) . In this case , we did not observe a significant difference in DF ( P = 0 . 15 ) . Because the polymerase sequences were considerably shorter , they provided less information to evaluate branch length properties . To counteract the loss of power we increased the number of sequences , and indeed , an analysis of the complete polymerase sequences dataset ( 1383 sequences , 247 nt ) rejected the model of neutral evolution ( P = 0 . 001 ) . As previously introduced [37] , this neutrality test relies on relative restricted demographic models governed by a limited number of parameters to capture large-scale demographic trends . To investigate the sensitivity to demographic detail , we extended the posterior predictive simulation procedure to accommodate highly parametric demographic models , which result in a more complex picture of norovirus dynamics ( see below ) . Using a Bayesian skyline plot ( BSP ) model as demographic function [38] , similar conclusions could be drawn from the neutrality test: significantly more negative DF values than expected under neutrality were observed for the capsid data set ( P = 0 . 019 ) and the complete polymerase data set ( P = 0 . 018 ) , whereas the matched polymerase lacked power in rejecting neutrality ( P = 0 . 029 ) . The demographic inference using the BSP model is summarized in Figures 1A and 1B , which essentially plot Neτ as a function of time . Ne τ can be considered a measure of relative genetic diversity that , in turn , reflects the number of effective infections established by the virus ( see also the Materials and Methods section ) . Uncertainty in the estimated parameters was evaluated using 95% Highest Probability Density ( HPD ) intervals . The Maximum Clade Credibility ( MCC ) trees from the same Bayesian analyses ( Figures 1C , D ) summarize the NoV evolutionary histories , and the stepwise emergence of the subsequent variants on a time scale . For comparison , surveillance data of reported NoV outbreaks with confirmed GII . 4 variant type were imposed on the BSPs . The changing patterns of NoV genetic diversity generally revealed seasonal dynamics , albeit with markedly varying resolution among the two datasets . The BSP for the polymerase dataset ( Figure 1A ) showed peaks for Neτ that coincided with the epidemic peaks observed in norovirus surveillance systems in the northern hemisphere winters 2002–03 , 2004–05 and 2006–07 . The BSP obtained from the capsid dataset ( Figure 1B ) showed a pattern that was more difficult to reconcile with epidemiological observations . Values for Neτ were highest in the years 1997–1999 , and the emergence of the 2002 variant , which had a strong impact in the population according to surveillance data , did not coincide with a pronounced upsurge in the BSP . Comparison of the BSPs obtained for both genes , illustrated that unraveling seasonal population dynamics with associated population bottlenecks for viruses like NoV , may require a sufficiently high sampling density . In fact , reducing the partial polymerase dataset to a similar number of sequences drastically diminished the resolution of the BSP analysis ( Figure S1 ) . In particular the 2004–05 and 2006–07 epidemics were not well reflected in the BSP derived from this sub-set of polymerase sequences that matched the capsid dataset in size , both genetically and temporally . The 2002–03 epidemic , following the replacement of the 1996 variant by the 2002 variant , wás however clearly noticeable in the matched polymerase set , whereas it was not in the capsid data set . Considering the associated MCC trees ( Figures 1C , 1D ) , it is conceivable that following the relatively long build-up of genetic variation during the circulation of the 1996-variant , its replacement by the 2002 variant signified a massive and sudden loss of diversity; a population bottleneck . The 2002 variant split into two distinct subclusters for the polymerase dataset . These lineages arose almost immediately after the emergence of the 2002 variant , and individually coalesced to a Most Recent Common Ancestor ( MRCA ) shortly before their diversification . The capsid 2002 variant cluster also grouped in two sublineages , but they coalesced more gradually to their MRCA . Comparison of the variant dynamics in the MCC trees to their respective BSPs suggested that variant replacement was not always absolute across subsequent epidemic seasons . To investigate this in more detail , we performed the coalescent analyses on partial polymerase datasets for the individual major GII . 4 variants separately ( Figure 2 ) . Whereas the pattern of rapid emergence , followed by an ( epidemic ) peak and later peaks of diminishing size observed for the 2002 , 2004 and 2006a variants were very similar , the patterns obtained for 1996 and 2006b were quite different . The 1996 variant , that was detected in the population during a relatively long period , but at low reporting frequencies after the initial epidemic ( winter of 1995–1996 , in the northern hemisphere ) ( Figure 1A/B ) , showed an increasing trend in the Neτ values persisting long after this first peak . The 2006b strain showed a less defined pattern , with multiple , smaller peaks . The demographic component is part of a full Bayesian model that enables the inference of time-scaled evolutionary histories and rates of molecular evolution from temporally-spaced sequence data . Rates of nucleotide substitution and the MRCA's of the included GII . 4 sequences were listed in Table 1 . The substitution rates found for the less densely sampled datasets , namely the complete capsid sequences ( 5 . 33×10−3 substitutions per site per year ) and the matched polymerases set ( 4 . 32×10−3 substitutions per site per year ) are lower than the rate found for the large partial polymerase dataset ( 8 . 98×10−3 substitutions per site per year ) . The estimated MRCA for these GII . 4 strains lies in the first half of the 1980s ( Table 1 ) . Because the genealogical test rejected selective neutrality for the capsid gene , we attempted to identify the molecular determinants of this selective process through two different approaches that were not previously applied on NoV data , namely DEPS [38] and co-evolutionary analysis of amino acids [39] , and complemented this with novel extensions of previously performed codon substitution model analyses [23] , [40] . The partial polymerase sequences under analysis in this study are very short and have therefore not been included in these analyses . In order to apply a codon model based on a general bivariate discrete distribution ( GBDD ) of dN and dS [41] , we employed a small sample AIC , which suggested that six rate classes ( D = 6 ) provided the best fit to the capsid data . The proportion of sites within these classes and corresponding dN and dS estimates are represented by Figure 3A and 3B . The model included one rate class describing positive selection ( dN ( = 0 . 77 ) >dS ( = 0 . 00 ) ) , with an estimated 0 . 93% of sites occupying this class . An empirical Bayes approach identified sites 6 , 9 , 15 , 47 and 534 ( 0 . 93% ) to be under diversifying positive selection ( Table 2 ) . Three of the sites ( 6 , 9 and 534 ) were confirmed by a site-by-site Fixed Effects Likelihood ( FEL ) analysis at p<0 . 05 , while the remaining two ( 15 and 47 ) were borderline significant ( p = 0 . 06 and p = 0 . 10 ) . The rates of false positives for FEL analyses at p = 0 . 05 was approximately 0 . 04 and 0 . 08 at p = 0 . 1 , based on dataset-matched neutral simulations , suggesting that the putatively selected sites were not due to elevated rates of false positives at given nominal significance values . To uncover population level selection processes , FEL analysis may be more appropriately applied to internal branches ( iFEL ) [42] . That this approach suited our data was also suggested by our genealogical tests , which identified an excess of slightly deleterious mutations on terminal branches , indicative of within host evolution . The use of iFEL enabled us to avoid this effect and revealed 8 codons under positive selection at the population level including 6 , 9 , 47 , 352 , 372 , 395 , 407 , 534 , with p≤0 . 05 . Codon models are powerful tools to detect an unusually high rate of nonsynonymous replacement , which generally occurs under a scenario of diversifying selection . However selection of episodic nature , e . g . directional selection or frequency-dependent selection is more difficult to detect and involves the question of which residues are being selected for or against [38] . A directional evolution in protein sequences analysis ( DEPS ) of NoV capsid sequences revealed elevated substitution rates towards 4 residues: V , S , A , T . Four sites were identified to be involved in this directional evolution; amino acids 9 ( with inferred amino acid substitution pattern: N→T/S→N ) , 294 ( ( V→ ) A→S/P→A→T ) , 333 ( L→M/V/L→M→V ) , and 395 ( -→T→A ) ( Figure S2 ) . In folded proteins amino acids are not arranged linearly; many functionally interact , making their evolution dependant on that of others . Various types of interactions exist , and interacting sites are not necessarily direct neighbors in either the protein sequence or in the 3D protein structure . We used Bayesian graphical models ( BGM ) to detect co-evolving sites . The sites identified , are shown as a network in Figure 4 , and sites for which co-evolution was detected but seemed less supported are shown in Figures S3A and S3B . Two values for the posterior probabilities are given , obtained from the analyses allowing for either one or two co-dependencies . Sites 231 and 209 , and 238 and 504 , which co-evolved as two coupled sets ( Figures S3A and S3B ) , were not involved in recent variant transitions . Therefore we conclude that they were not under selective pressure that governed variant replacement dynamics . All sites for which molecular adaptation was detected are listed presented in Table 3 . We marked relevant sites located on top of the capsid dimer in Figure 5 . Our study identified codon 6 to be under positive , diversifying selection and codon 9 to be under diversifying as well as directional selection; others performing dN/dS analyses detected positive selection at these sites as well [23] , [40] . The variation in codon 6 is due to AAT ( Asn ) to AGT ( Ser ) changes . The signal for codon 9 is solely attributable to substitutions at the second codon position of this site; AAC ( Asn ) , ACC ( Thr ) and AGC ( Ser ) . The N-terminal region of ORF2 was otherwise highly conserved . Because contrary to the other amino acids that were identified to be under selective pressure , the part of the protein encoded by these two amino acids is located on the inside of the virus capsid structure , and not surface exposed , we investigated the potential RNA secondary structure encoded by this region . In silico replacement of nucleotides at position 17 ( codon 6 ) did not lead to secondary structure changes ( not shown ) . Secondary structure predictions of the RNA encoding the 5′end of ORF2 were performed with all four possible nucleotides modeled at position 26 ( Figure 6 ) . The 4 nucleotides upstream of the ATG , generally thought to form the boundary of the subgenomic RNA [43] were included in the predictions . The presence of A , C , or G generated similar structures , when however a T ( U ) was modeled , extra pairing possibilities arose , lengthening the stem of the first stem-loop structure , and thus shortening the stretch of free nucleotides , available for ribosome binding , from 11 to 9 .
The first rise in the BSP estimates of Neτ ( a measure of effective population size ) in the polymerase dataset was seen just before January 1996 , around the time the first global GII . 4 epidemic was noted [45] ( Figure 1 ) . Interestingly , rather than appearing as a defined epidemic peak lasting one winter season , Neτ increased continuously through January 2000 . Surveillance data from around the world identified relatively few GII . 4 outbreaks among NoV positive outbreaks in the period between 1997 and spring 2002 [12] , [22]; instead , a relatively high diversity of other , non-GII . 4 genotypes was detected in this period . The high Neτ observed for this period is congruent with the long branch-lengths in the MCC tree during this period . Given the prolonged circulation time of the 1996 variant compared to the later variants , it seems likely that the BSP in this instance is a better representative of the relatively high genetic diversity built up in an extended period of co-circulation of the 1996 variant with other genotypes , rather than of the number of infections with this particular variant . The three most recent GII . 4 epidemics caused by emerging GII . 4 variants were clearly visible in the polymerase BSP . The 2002 epidemic was preceded by a sharp decline in Neτ , indicative of a purifying selection event against the previously dominating variant , in which the diversity that had been gradually built up by the long circulation of the 1996 variant collapsed . Next , coinciding with the off-seasonal peak observed in public health surveillance systems in the northern hemisphere , Neτ in the BSP rose sharply . After a brief and insignificant decline another peak occurred in the winter season of 2002–03 . During the epidemic caused by the 2004 variant Neτ peaked slightly less high and less long than during the 2002 epidemic peak . During the 2006–07 epidemic , with two distinct GII . 4 variants circulating , Neτ was the highest ( Figure 2 ) , and after this winter Neτ values stayed high , corresponding with continued circulation of the 2006b variant , as also observed in surveillance . Altogether , from the first half of the 1990s to present time , two major changes were observed . First , epidemic waves have arisen that could not be detected in earlier times , and second , the number of infections has gone up . The baseline before 1996 corresponds to the trough levels in between contemporary epidemics . Hence , the increased amount of GII . 4 outbreaks observed in outbreak surveillance seems to reflect an actual increase of GII . 4 infections in the population . These conclusions are undoubtedly impacted by the comparatively few available sequences from earlier years , which are , unfortunately , not widely available , and as shown by the recent publication by Bok et al . , not easily attainable [40] . A search in archival stool samples revealed that during the years 1974–1981 and 1987–1991 GII . 4 was not the most prevalent NoV genotype in hospitalized children with gastroenteritis , but GII . 3 was . Our sampling , albeit at lower frequency during this period , would probably have detected possible unidentified epidemic surges had they occurred . Altogether , although surveillance data and demographic estimates are very different types of information , their dynamics match remarkably well . The surveillance data is presented on a linear scale and reflects the reported outbreaks of NoV-gastroenteritis , which is probably an incomplete description of NoV circulation , as many cases of NoV illness remain unreported . The BSPs are presented on a log-scale , which makes for less sharp peaks than the peaks observed in the surveillance data graphs . The analysis of each major GII . 4 variant separately ( Figure 2 ) indicated that Neτ values , a measure for relative genetic diversity , and a proxy for the number of effective infections , of each variant reached approximately similar proportions . The 2002 , 2004 and 2006a variants each had one epidemic season , but the turnover was relatively slow compared to e . g . influenza [46] , resulting in repeated seasons ( of decreasing magnitude ) of illness caused by the same variant . For example , one main peak for Neτ was observed for the 2004 variant during the 2004–05 winter , but a smaller peak followed during the 2005–06 winter . This slow turnover may very well have been caused by the fact that only incomplete immunity is mounted against NoV after infection or alternatively that the pool of susceptibles is not depleted within one season . Different BSPs were obtained for GII . 4 variants 1996 and 2006b , that persisted longer than one season . Interestingly Neτ for the 1996 variant increased just before the emergence of the 2002 variant . The 2006b variant showed no defined epidemic peak , but a series of smaller peaks that did not coincide with annual winter-peaks . Two sublineages of the 2006b variant , distinguishable by up to 5 amino acid differences in the full capsid sequence [47] circulated in the population . Of these only S368G has been recognized as a polymorphism relevant for antigenic properties . Interestingly , while only two 2006b strains with this mutation are present in our capsid dataset , later full capsid sequencing revealed more strains of this sublineage , and the polymerase dataset included more sequences from this cluster . Thus , the 2006b variant may have persisted in the population by changing its antigenic properties . While Bayesian coalescent analyses of the large partial polymerase dataset reflected seasonal epidemic dynamics , the analysis of the longer capsid sequences offered little detail about the phylodynamic patterns of NoV . To investigate the nature of this difference , we performed an analysis of polymerase sequences matching the capsid sequences genotypically as well as temporally , which showed that a lack of phylodynamic detail can generally be attributed to a lower sampling density ( supplemental materials , Figure S1 ) . This may not be so surprising as it was previously thought that coalescent analyses would not be so effective at capturing cyclical population dynamics [48] . Only recently , a comprehensive analysis of a large H3N2 influenza virus dataset ( 1302 taxa ) was able to uncover seasonal dynamics from genetic data [46] . Interestingly , this data set was similar in size compared to the large dataset of partial NoV polymerases ( 1383 taxa ) presented here , although in the influenza study full gene segments were analyzed . The population bottlenecks in the NoV GII . 4 population history are to a large extent comparable to those seen for influenza and constitute repetitive large scale losses of genetic diversity . We note that viruses with seasonal dynamics do not necessarily have to exhibit such dynamics in genetic diversity . Short infections with strong cross-immunity , as seen for measles virus , allow many strains to co-circulate with frequencies contingent on neutral epidemiological processes [24] . For such viruses , seasonal epidemics may arise from repeated exhaustion of susceptible host populations [49] . Therefore , our phylodynamic analysis predicts that there may only be partial subsequent cross-immunity against GII . 4 variants . It is important to note that sampling size impacts the resolution of phylodynamic inference , but the actual sampling scheme does not dictate a pattern of fluctuating population size . Rambaut et al . [46] performed simulations using various demographic scenarios but with a sampling scheme used to obtain influenza genetic data from seasonal epidemics . In all cases , the simulated demographic history was accurately recovered . A sparse sampling prior to 2000 also makes it difficult to unequivocally conclude an increase in GII . 4 infections . However , we note that the value for Neτ before the first documented epidemic resulting of the emergence of a new genetic variant ( the 1996-variant ) in the BSP is lower than the estimates between subsequent epidemic peaks . This suggests an increase in the number of NoV GII . 4 infections , which is further reinforced by a recent study of archival stool samples from the Children's Hospital , Washington , DC ( 1974 to 1991 ) ( Bok et al . , 2009 ) . Although this study identified GII . 4 strains in the early seventies , this was not the predominant genotype before 1991 ( Bok et al . , 2009 ) . An increase of the GII . 4 variant therefore seems to provide a plausible explanation for the coincident increase in the number of norovirus infections . The estimated substitution rates ( 9×10−3substitutions per site per year for the partial polymerase sequences and 5 . 3×10−3 substitutions per site per year for the capsid sequences ) corresponded to the values recently reported for NoV GII . 4 [40] and were well within the range of what is commonly found for RNA viruses , e . g . between 3 . 5×10−3 and 8 . 5×10−4 for HMPV complete genomes [50] and for influenza A virus the highest rate , reported for the HA gene , was 5 . 72×10−3 substitutions per site per year [46] . A lower rate was observed for the polymerase subset matched to the capsid data set . Since the TMRCA estimates were consistent between these two polymerase data sets , the lower rate may be explained by differences in rate variation among sites , in particular for the proportion of invariant sites , which is sensitive to the number of taxa in the data set [51] . Dating the MRCA for these strains back to the early 1980's does not mean that the GII . 4 lineage arose only then , but rather suggests that the strains that circulated during the past two decades share a common ancestor at that time . It seems not unlikely that the GII . 4 lineage was less diverse before the 1980's , not comprising different variants as during the past decades . Alternatively , if multiple GII . 4 variants did exist before the MRCA of the current GII . 4 variants , the occurrence of a population bottleneck may have left progeny virus of only one variant . The strains detected in the 1970s reported recently seem to confirm that multiple GII . 4 variants existed before the Camberwell cluster arose [40] . Analyzing data of multiple NoV genotypes will provide a more detailed insight into the branching times of these different genotype clusters , and also in this case , including older sequences will be more elucidating . Ideally we would have used full genome sequences . However , for ( GII . 4 ) NoV these are only sparsely available . Instead , we showed that Bayesian coalescent demographic analyses of a large dataset containing very short sequences offered important and reliable insights into GII . 4 variant dynamics . For the GII . 4 NoV datasets presented here , the densely sampled but short polymerase sequences provided data that better defined the epidemic history than fewer longer capsid sequences . Perhaps additional to the limited size of the capsid dataset , strong selective pressure on the capsid protein confounded analysis of the capsid gene . We were also aware of the possibility that our capsid sequences dataset may have been biased in sampling . Sequencing of full capsid genes is not standard practice; the viruses of which sequences were available have all previously been selected by various researchers as ‘interesting enough to sequence’ . Thus , relatively many sequences of strains belonging to the 1996 variant are present in the dataset , especially compared to strains belonging to the younger variants , 2004 , 2006a and 2006b . Our genealogical test clearly rejected neutral evolution for the NoV capsid dataset . Although P-values were somewhat higher using a Bayesian skyline plot model in the posterior predictive simulation compared to more restricted demographic functions , we arrived at the same conclusions for all analyses . This demonstrates that the neutrality test is not overly sensitive to the demographic detail in the analysis . Nevertheless , through the advances made here , we demonstrate that this test can now be performed under any complex demographic scenario , a generalization that may further promote its use . To investigate the selective forces in more detail , we fitted different evolutionary models to identify sites in the capsid under selective pressure . We examined an extension of previously performed dN/dS analyses [23] , [40] to detect positively selected sites involved in population level selection , avoiding the effect of within host evolution . Eight positively selected codons were identified at p<0 . 05 with the iFEL approach . Of these sites , four ( 352 , 372 , 395 and 407 ) are located in the protruding regions of the protein . These sites have also been identified previously as ‘informative’ sites ( at least two shared an identical amino acid mutation in the alignment ) [20] . Amino acid 395 , that was also detected as directionally evolving , is located in a surface exposed loop of the capsid protein , that is part of a variable site of carbohydrate interaction ( amino acids 393-394-395 ) that has been identified by a number of studies as a locus for ligand binding and specificity [23] , [52] , [53] . Codon 394 , located in this same domain , is part of an intricate network of co-evolving positions , also containing codons 297 , 368 and 372 . Amino acid 297 is part of a site identified by Allen et al , ( 296-297-298 ) [52] , predicted as another of two host ligand binding sites . This particular site was not identified by Cao et al . [53] who performed co-crystallization assays with P-particle dimers and A and B-trisaccharides , the host ligands of NoV . It is structurally close to amino acids 368 and 372 , on top of the protein , flanking the ligand binding pockets , and to 294 , under directional evolution , which is located on the outside of the 296–298 loop , relative to the binding pocket . Lindesmith et al . also identified sites under positive selection , using fewer sequences . They used three different methods , single likelihood ancestor counting ( SLAC ) , fixed effects likelihood ( FEL ) and random effects likelihood ( REL ) , under the Tamura-Nei model of evolution . More codons under positive selection were identified in this study , but less strict nominal significance values were used . Bok et al . [40] applied SLAC analyses for detection of positive selection and found six amino acids under positive selection . We chose to identify sites under selective pressure for internal branches in a phylogeny using iFEL because external branches are prone to deleterious mutational load . Such mutations are expected to be young and more likely fall on the external branches of a population-level phylogeny [54] , where they can confound the identification of positively selected sites . To avoid this adverse effect we focused on internal branches only , on which advantageous mutations are more likely to fall . Codon 333 was previously identified as an informative site [20] and our analyses found it to be under directional selection . It is located in the hydrophobic part of the P-dimer interface , just below the carbohydrate binding site described by Tan et al . [55] , facing its counterpart in the other protomer of the same dimer ( distance 4 Å ) [53] . Changes in this amino acid are not likely to be involved in antigenic change but more likely structural compensation for other mutations . Codons 6 and especially 9 have consistently been identified as sites under strong selective pressure , and involved in defining the distinction between variants , not only in this study but in others as well [23] , [56] . Given their positions at the N-terminus of the protein , inside the shell , they seemed unlikely to have been under selective pressure through antibody recognition . This notion led us to investigate the RNA encoding the 5′end of ORF2 . The nucleotide sequence upstream of codon 9 ( nucleotide 26 ) is strongly conserved among all NoV genotypes , and a highly similar sequence is found at the 5′end of ORF1 in NoVs . Mutations have rarely been detected here , apart from at nucleotide 26 , and at nucleotide 17 ( codon 6 ) . We propose that secondary structure predictions of these RNA regions provide an explanation for this pattern ( Figure 5 ) . Highly conserved stem-loop structures create the circumstances necessary for translation initiation of ORF1 and ORF2 . Nucleotides A , C and G at position 26 all result in almost identical structures , in which 7 nucleotides from the start of ORF2 , or 11 when including the 4 nucleotides upstream from the AUG , are left free . When theoretically a U/T is inserted here the predicted structure changes , resulting in a diminished length of the free nucleotide strand of 5 nucleotides . We are unaware of sequences with nucleotide T at position 26 present either in our or the public databases ( data not shown ) , leading us to believe that a length of at least 7 unpaired nucleotides counting from the first AUG is necessary for efficient RNA translation from the subgenomic RNA . Thus , while normal capsids could be formed from strains with ( silent ) mutations in this area , the replication process of the virus may be disrupted by altering the secondary RNA structures . This theory is further supported by the observation that no other synonymous or non-synonymous mutations are found in the 9th codon , e . g . at the third nucleotide position , nor at other nucleotides in this genomic area , apart from the previously mentioned nucleotide 17 , in an RNA loop . Tentative analyses of other NoV genotypes demonstrated that the 5′ region is equally conserved within the different genotypes and yielded similar secondary RNA structures , which allow for point mutations in the loops of the structures ( data not shown ) . To further substantiate this hypothesis site-directed mutagenesis studies are required , which go beyond the scope of this study . All of the substitutions described above were associated with at least one variant transition; they appear at branches that give rise to new variants ( Figure 5 and Figures S2 , S3A and S3B ) . This indicates that these mutations include the molecular determinants of cluster replacement . Previously identified antigenic sites [23] , [52] did not enable distinction between all the different GII . 4 variants ( e . g . considering amino acids 296–298 and 393–395 , the 2006b and 2007 variants that are clearly phylogenetically distinct , share identical amino acids ) . When amino acids 6 , 9 , 294 , 333 , 352 , 368 , 372 , 407 and 534 , identified here as under positive , directional or co-evolutionary pressure , are added to these six amino acids , all currently identified GII . 4 variants ( excluding the Bristol and Camberwell strains , that circulated before the 1996 variant ) are separated by at least two amino acid differences ( Figure S4 ) . Thus , specific analysis of these sites will aid early recognition of novel variants in the future . The two most recent distinct GII . 4 variants , that have both been detected throughout the world in both 2008 and 2009 , albeit at low prevalence , the 2007- and 2008-variants , are identical to the still dominant 2006b variant in amino acids 296–298 , that were identified by Allen et al , and 2007 is also identical in site 393–395 , whereas the 2008 variant has two substitutions on those sites ( 2006b: STT , 2008: D/NTA ) . Thus , considering the sites listed above , the 2008 variant would have the best chance of becoming the next dominant strain , whereas , when considering the full capsid sequence , the 2008 variant is more similar to the 2006b variant than is the 2007 variant . Using Bayesian phylodynamic techniques we showed that since 2002 the number of GII . 4 infections has experienced expansion dynamics . Additionally we further substantiated the evidence for signature sites for variant transition , which may aid in the early recognition of potential new epidemic variants , although we stress that examining pre-defined amino acids does not enable certain identification of GII . 4 variants , for which full capsid sequences should be determined . We showed that it is important to select the genomic region to analyze by phylodynamic , coalescent methods with care , and our different datasets illustrated that for the phylodynamic analysis of pathogens undergoing repeated selective bottlenecks a considerable sampling density through time is required .
We compiled two different NoV GII . 4 datasets . First , partial polymerase gene sequences with known detection month and year were collected . These sequences , encoding a short genomic region commonly referred to as Region A , have been collected for genotyping purposes , as an essential part of the ongoing surveillance practice in institutions around the world [22] , [33] . This dataset includes sequences from participants of the Foodborne Viruses in Europe ( www . fbve . nl ) and of the Noronet ( www . noronet . nl ) networks , the contributing institutions of which are listed in the acknowledgements . Sequences of sufficient length ( i . e . covering at least the final region ) were included , generating a dataset of 1383 taxa , 247 nt in length . Strains originated from systematic surveillance collections , and form the best representative reflection of the circulating strains currently available . Second , complete capsid sequences of GII . 4 NoV strains with known sampling date were collected . This resulted in a dataset of 194 taxa , 1623 nt long . To allow comparison of results from capsid based versus polymerase based analysis , a set of 172 partial polymerase gene sequences matching the sequences in the capsids dataset ( identical variant typing and similar detection dates ) were selected from the total polymerase dataset . The 2003Asia variant was excluded from this mirror-dataset , since it was identified as recombinant and ORF1 does not belong to the GII . 4 genotype . Details on the nature of the strains comprised by the two generated datasets are provided in the Supporting Materials Table S1 . The distribution of the sampling dates of the included sequences is depicted in Figure S5 . For recombination analyses , a set comprising 20 sequences , two of each GII . 4 variant , spanning the genome region between Region A , in ORF1 , and the complete capsid sequence was collected . Sequences were aligned using the Clustal W algorithm implemented in Bioedit ( version 7 . 0 . 9 . 0 ) and edited where necessary . Sequence alignments can be obtained from the authors on request . Recombination within the genomic area under study invalidates the use of phylogenetic approaches . Therefore , we checked for possible recombination signal by analyzing 20 sequences ( two for each variant ) spanning Region A through the complete capsid protein ( 2404 nt ) . Different evolutionary histories across this genome region were inferred using the genetic algorithm for recombination detection ( GARD ) [57] and specific recombinants were identified using a modified VisRD algorithm [58] and using RDP3 [59] . In addition , we used the Phi test , shown to perform well under strong population growth and to be able to distinguish recurrent mutations from recombination events , to identify recombination signal in the NoV alignments [36] . Evolutionary dynamics were estimated using a Bayesian Markov chain Monte Carlo ( MCMC ) approach implemented in BEAST ( BEAST version 1 . 4 . 7 [60] ) . BEAST MCMC analysis estimates marginal posterior distributions for every parameter in a full probabilistic model comprising the timed evolutionary history , based on the incorporation of sampling times in a molecular clock model , the substitution process and demographic history . We used the GTR+I+Γ4 model of substitution and the uncorrelated lognormal relaxed clock model to accommodate variation in substitution rates among different branches [61] . To test selective neutrality of GII . 4 molecular evolution , we adopted the genealogical framework presented by Drummond and Suchard [37] . This involves the full model-based Bayesian analysis to obtain a posterior distribution of trees , genealogical summary statistics , and posterior predictive simulation to detect departures from the neutral expectations for these statistics . We employed the genealogical Fu and Li statistic ( DF ) , which compares the length of terminal branches to the total length of the coalescent genealogy . Strongly negative values for this statistic indicate terminal branch lengths being larger than expected , which reflects an excess of slightly deleterious mutations on these branches . This statistic has proven to be most sensitive in uncovering non-neutral evolution , and has for example rejected neutrality for human IAV hemagglutinin genes [37] . Posterior predictive simulation is performed according to the same demographic model as used in to obtain the posterior tree distribution . To evaluate the impact of large demographic trends , we compared analyses using both constant and exponential growth population size priors . To further investigate the impact of demographic detail on the neutrality test we extended the simulation procedure to highly parametric demographic models , including piecewise constant demographic functions that define a Bayesian skyline plot model . We validated the simulation procedure by comparing the reconstructed Bayesian skyline plot from the trees generated by posterior predictive simulation with the Bayesian skyline plot inferred from the sequence data , which yielded consistent results . To reconstruct the NoV GII . 4 demographic history in more detail , we employed the Bayesian skyline plot ( BSP ) model , which generates piecewise constant population size trajectories [62] . In this coalescent setting , demography is measured as the product of effective population size ( Ne ) and generation time ( τ ) , Ne τ , through time . To obtain a detailed measurement of NoV GII . 4 diversity through time , given the large dataset , we specified 40 groups in the piecewise constant population size function . All chains were run sufficiently long to achieve stationarity after burn-in , as checked using TRACER ( http://tree . bio . ed . ac . uk/software/tracer/ ) . Additionally , the polymerase dataset was split up into separate subsets , each comprising all available sequences from a major GII . 4 variant , and these were analyzed individually using the same model and settings as was used for the whole polymerase dataset . To examine how different sampling densities through time can impact our demographic estimates , we performed an additional analysis of a subset of the polymerase data , containing sequences selected to best mirror the sequences in the capsid dataset , using the same specifications as described above . Secondary structure predictions of the 5′end of the ORF2 encoding RNA were generated using the web based RNA Fold Webserver ( http://rna . tbi . univie . ac . at/cgi-bin/RNAfold . cgi ) [69] . | Noroviruses , known as the viruses that cause the ‘stomach flu’ or as the ‘cruise ship virus’ , cause sporadic cases and large outbreaks of gastrointestinal illness in humans . An increase in norovirus outbreaks was reported globally around 2002 . Doubts remained as to whether this increase was real , or caused by improved detection-techniques and increased awareness . This study was performed to address this ambiguity , and to determine the possible virological causes for such changes . Using a population genetic approach , we studied sequences of epidemic norovirus strains collected through time and we indeed demonstrated expanding epidemic dynamics . Global epidemics were caused by subsequent variants of norovirus , observed in 2002 , 2004 and 2006 and at a smaller scale in 1996 , whereas no evidence for such epidemic evolutionary patterns occurring previous to these peaks . Based on the sequences analyzed the strains of the genotype under study here were shown to have circulated at least since the early 1980s , and likely earlier . We showed that not only surface exposed sites on the outside of the virus shell were under selective pressure , involved in avoiding host immune responses , but also codons that are apparently conserved for the purpose of virus replication . | [
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] | 2010 | Phylodynamic Reconstruction Reveals Norovirus GII.4 Epidemic Expansions and their Molecular Determinants |
Recent experimental evidence suggests that coordinated expression of ion channels plays a role in constraining neuronal electrical activity . In particular , each neuronal cell type of the crustacean stomatogastric ganglion exhibits a unique set of positive linear correlations between ionic membrane conductances . These data suggest a causal relationship between expressed conductance correlations and features of cellular identity , namely electrical activity type . To test this idea , we used an existing database of conductance-based model neurons . We partitioned this database based on various measures of intrinsic activity , to approximate distinctions between biological cell types . We then tested individual conductance pairs for linear dependence to identify correlations . Contrary to experimental evidence , in which all conductance correlations are positive , 32% of correlations seen in this database were negative relationships . In addition , 80% of correlations seen here involved at least one calcium conductance , which have been difficult to measure experimentally . Similar to experimental results , each activity type investigated had a unique combination of correlated conductances . Finally , we found that populations of models that conform to a specific conductance correlation have a higher likelihood of exhibiting a particular feature of electrical activity . We conclude that regulating conductance ratios can support proper electrical activity of a wide range of cell types , particularly when the identity of the cell is well-defined by one or two features of its activity . Furthermore , we predict that previously unseen negative correlations and correlations involving calcium conductances are biologically plausible .
In well studied neuronal networks , it is often observed that each neuron has a specific role in the function of the circuit . In some cases , this role is unique and vital , and the health of the animal depends on a robust cellular identity . One example of this occurs in the pyloric circuit of the crustacean stomatogastric ganglion ( STG ) . This well-studied system must produce robust rhythmic activity for successful digestion [1] , and does so through the dependable cellular properties of its component neurons . These neurons are identified by their reliable network activity , morphology , and connectivity [1] . This reliability is surprising , because identified cells can have different sets of ion channel maximal conductances in different animals of a population , despite generating the same predictable electrical activity [2] , [3] . Furthermore , these ion channels are constantly replaced or modified due to synaptic input , sensory feedback , and neuromodulatory action [4] , [5] . In light of these sources of variability , how stomatogastric cells produce consistent , stable output is an open question . Recent experimental evidence suggests that coordinated expression and regulation of ion channels may play a role in constraining cellular electrical properties . The first such relationship found in the lobster STG was a positive co-regulation between the transient K+ current ( IA ) and the hyperpolarization-activated mixed ion current ( Ih ) [6] , [7] . The investigators discovered that cellular injection of mRNA coding for the ion channel underlying IA caused an endogenous increase in the opposing Ih current , without changing the electrical activity of the cell . The same conductance pair was shown to be correlated in a population of identified neurons in the crab STG , and this correlation was found to be independent of the effects of neuromodulators [8] . The latter study compared correlations of current density in the pyloric dilator ( PD ) neuron before and after removal of top-down neuromodulatory input , and found two additional K+ conductance correlations that were neuromodulator-dependent . A variety of positive linear correlations in ion channel mRNA copy numbers have been found in other identified STG cell types , as well [9] . Each cell type appears to express a unique set of ion-channel correlation types and linear relationship slopes . In some cell types , correlations have been seen to involve three or four channel types . Similar relationships , involving conductances [10] , [11] , [12] , or ion-channel kinetics [13] , have been found in other model systems at the single cell ( co-regulation ) and population ( correlation ) levels . Combined , the data suggest a means for constraining cellular activity despite the often several-fold conductance variability seen in these cell types . To date , all experimentally demonstrated conductance correlations within a wild type population of identified neurons have had a positive slope; no negative correlations have been found . This finding raises several questions about the mechanisms underlying the conductance correlations and their relation to maintenance or definition of activity type . At this time , the mechanisms are unknown , though there are several possibilities . Two membrane channel genes may be concurrently regulated by a single transcription factor in a cell-type specific manner [14] . Alternatively , a cleaved section of one ion channel can act as the transcription factor ( or repressor ) for another [15] . A unidirectional mechanism of this type may explain why the IA–IH co-regulation is not reciprocal in STG neurons [16] . In addition , correlations may be controlled by coordinated post-transcriptional or -translational modification of one or more channel gene products [17] , [18] . Though there is evidence for activity-independent co-regulation [6] , [8] , changes in activity are needed for co-regulation to be expressed during Xenopus laevis development [11] . This raises the possibility that activity-dependent regulatory cascades may also be involved in conductance co-regulation in some cases [19] , [20] . A number of conductance correlations are also neuromodulator dependent [8] . There is a possibility that any of the above mechanisms are influenced by the presence of neuromodulator , and that correlation differences between cell types are due to differences in neuromodulator sensitivity [21] , [22] . In addition to questions about the underlying mechanisms , questions remain regarding the cellular effects of linear conductance correlations . The effects on activity are likely different for each conductance pair; however , recent modeling studies have shed light on general trends in the structure of the conductance space with regard to activity type . Goldman et al . undertook a study of the pair-wise conductance space of stomatogastric neurons and neuron models , and demonstrated quasi-linear grouping of broadly categorized activity types [23] . Similar results have been obtained with a high-dimensional visualization technique of a model neuron database , wherein all conductance parameters are considered [24] . Hand-tuned models have also been a useful tool for demonstration of conductance correlation effects on activity . In one stomatogastric neuron model , a linear relationship between IA and IH maintains a specific number of spikes per burst [6] . Further theoretical studies demonstrated that correlated changes in these conductances can adjust neuronal input-output gain , while maintaining other features of activity [25] . Parameter changes in a leech heartbeat neuron model with narrowly constrained burst period and spike frequency demonstrated a correlation in the spike-mediated synaptic conductance and the inactivation time constant of the transient calcium current ICaS [26] . A tempting conclusion to draw from these results would be that as more features of activity are constrained , more correlations may be required to maintain proper activity . This may not be true , as has been suggested previously [26] . Taylor et al . recently published a detailed model of the lateral pyloric ( LP ) neuron constrained to reproduce 9 experimentally measured activity characteristics , but found no strong linear correlations between conductance parameters [27] . Here , we expand on previous work by investigating the general relationship between the presence of conductance correlations and the manifest features of activity type . We hypothesize that conductance correlations do contribute to the robustness of critical features of electrical activity . To test this , we use a database of generic STG conductance-based model neurons [28] . These model neurons were first partitioned into groups based on ranges of a single characteristic of the electrical activity , or combinations of biologically-inspired activity characteristics . The conductance space for each group was examined and individual conductance pairs were tested for linear dependence . The conductance correlations found were further evaluated for their effect on activity type by building correlation-based populations of models . Our results show that while regulating conductance ratios can support the maintenance of a single cellular activity characteristic , the effects of correlations are less clear when multiple characteristics are required to specify activity type .
A model neuron database was used to investigate the relationship between conductance correlations and intrinsic activity type . This database has been previously described [28] . Briefly , a single-compartment conductance-based model was used . Seven of the eight conductance types used in this model were derived from experiments on unidentified stomatogastric cells in culture [29] . They include: a fast Na+ ( gNa ) , fast transient Ca2+ ( gCaT ) , slow transient Ca2+ ( gCaS ) , fast transient K+ ( gA ) , Ca2+-dependent K+ ( gKCa ) , delayed-rectifier K+ ( gKd ) , and a voltage-independent leak conductance ( gleak ) . The hyperpolarization-activated mixed-ion inward conductance ( gH ) was modeled after that found in guinea pig lateral geniculate relay neurons [30] . Each maximal conductance parameter was independently varied over 6 equidistant values , ranging from zero to a physiologically relevant maximum . By simulating the model neurons corresponding to all 68 = 1 , 679 , 616 possible combinations of these parameters , a large variety of intrinsic electrical activity patterns were created . This systematic variation of parameters created an eight-dimensional grid of simulated parameter sets within the conductance space of the model . A single model neuron , with its particular intrinsic activity type , inhabits one grid point in this eight-dimensional space . The currents used to generate this model were not based on any one STG cell type . To approximate distinctions between cell types , which are identified in part by activity , the database was partitioned based on intrinsic activity type of each model neuron . The first level of categorization divided the model neurons into five subsections: silent , periodic spiking , periodic bursting , irregular ( non-periodic ) spiking or irregular bursting ( Figure 1 ) . Silent models exhibited no membrane voltage maxima . Periodic spiking models had inter-maximum intervals that were consistent within 1% of their mean . Periodic bursting activity was detected and defined as follows . First , the peaks and troughs of the membrane potential were subjected to a spike detection threshold . Any peak greater than −30 mV was defined as a spike , and all others were ignored as sub-threshold activity . Next , the inter-burst interval was defined as any inter-spike interval that deviated from the length of the greatest inter-spike interval by less than 30% . These definitions are different from previous burst classification metrics [28] to avoid minor classification errors associated with alternating burst features . The second level of categorization further partitioned the periodic spiking and bursting models . The large group of periodic bursting neurons was either partitioned based on the duty cycle , defined as the fraction of the burst period taken up by the burst duration , or by the average slope between the inter-burst minimum and the start of the next burst , hereafter referred to as the rising phase of the slow wave ( see Figure 2C ) . Briefly , the slope was calculated by first locating the inter-burst minimum ( point 1 on the inset of Figure 2C ) . Then , the next crossing of the spike detection threshold ( −30 mV ) was recorded ( point 5 ) . The distance between the two points was divided into four equal sections ( demarcated by points 2–4 ) . Average slope was calculated between points 1 and 4 , omitting the portion between points 4 and 5 to avoid any artifact generated by the sharp incline of the first spike in the burst . Initial slope ( average slope between points 1 and 2 ) and central slope ( average slope between points 2 and 4 ) were also investigated . These characteristics were chosen because both duty cycle and the average slope of the rise phase are thought to be regulated within a narrow range in biological cells [6] , [31] . Periodic spiking neurons were similarly partitioned by spike frequency . Group size and boundaries were chosen based on population distributions shown in Figure 2 . Clustering of models was seen in the spiking models ( Figure 2A ) and the bursting models segmented by the rising phase of the slow wave ( Figure 2C , D ) . In the absence of clear subpopulations arising from model clustering , an effort was made to achieve optimal resolution within the conductance space by avoiding large differences in group size . For activity metrics without clear subpopulations , shifting the arbitrarily chosen population boundaries shown in Figure 2 did not drastically change the correlations found . Additional metrics were used to partition the periodic spiking and bursting models , such as spike height and the number of spikes per burst , respectively . However , these metrics resulted in a lower overall success rate for finding correlations and were therefore not reported here . The segmentation scheme described above investigates the relationship between correlations and single activity characteristics . We also looked for correlations in model populations based on multiple activity characteristics . To do this , we constructed model populations based on subsets of the pacemaker activity criteria previously described , including characteristics of the “slow wave” voltage oscillation underlying bursts in STG pacemaker neurons [28] . Briefly , the slow wave is the membrane potential of a bursting model after spikes have been subtracted . The peak of the slow wave was approximated by the last maximum in a burst , and the slow wave amplitude is then the difference between this maximum and the between-burst minimum . The ranges of activity characteristics used are based on experimental data from the spontaneously bursting pacemaker kernel of the STG , which consists of one anterior burster ( AB ) neuron electrically coupled to two PD neurons . The complete dataset , including the descriptive statistics used to classify models , is available online as a supplemental file ( datasets S1 and S2 ) . After model neurons were sorted based on intrinsic activity , conductance plots were generated for each activity subsection ( Figure 1 ) . As shown in Figure 3 , these plots graph the value of one conductance parameter versus another , for a particular activity subsection . Each model in the activity subsection falls on one grid point on this plot , positioned by the maximal conductance parameters it was assigned for the pair in question . The other 6 conductance parameters were not constrained when plotting . The grid structure used to generate simulation points for this database creates a conductance space wherein all conductance values are initially equally represented . After segmentation of the database , this is no longer the case and each activity type has a unique distribution of each conductance parameter . Bursting models , for example , have a higher average value of gKCa than spiking models . These non-standard one-dimensional ( 1D ) conductance distributions violate the assumptions of parametric tests and significance testing . Instead , we used two non-parametric tests with simple cutoff values to define correlations . For each activity subsection: if the value of one conductance was determined to be dependent on the value of another ( by a chi-squared test of independence χ2>500 ) , and this dependence was confirmed to have a linear trend ( by a Spearman's rank correlation |ρ|>0 . 2 ) , the relationship was considered a correlation . Note that , in the absence of significance testing , these definitions are not dependent on the number of observations ( models in each population ) . This was chosen purposefully because , due to the sparse sampling of the conductance space , populations with very few models may not contain enough information to reliably identify correlations . Cutoff values independent of the number of observations ensures that those correlations reported are strong in all cases . In addition to the described statistics , a simple visual check for a linear dependence was performed as follows . Assuming independence between conductances , it is possible to generate the expected two-dimensional ( 2D ) conductance distribution for a particular activity type by multiplying normalized single-conductance histograms for that activity type ( hereafter referred to as an independence matrix ) . By looking at the deviations in the actual data from this independent assumption , a linear dependence or lack thereof is more clearly visible than when looking at the correlation plots alone ( Figure 3E , F ) . We chose to use this simple , graphical representation of dependence as a visual check to distinguish correlations from conductance relationships that can be explained by independently varying parameters . Specifically , the independence matrix was created by multiplying two conductance histograms for a particular activity type ( units in % of models of that activity type ) . Then , this independence plot was subtracted from the actual data ( units in % of models of that activity type ) . Note that the resulting plot of percent difference ( hereafter referred to as a difference matrix , shown in Figure 3E , F ) is distinct from , but complementary to , the chi-squared test of independence . Defined correlations were used to generate populations of model neurons that fit within those correlation boundaries . The shape of each correlation was defined by setting a threshold of 3% ( models/all models in the activity type , the latter hereafter referred to as typei ) per grid point of the correlation plot raw data ( see the white outline in Figure 3B for an example ) . The entire database , regardless of activity type , was scanned for models that fell within the boundaries of one defined correlation or a combination of correlations . We call the resulting “correlation-based” population cbi . If a correlation is successful in restricting activity to a particular type , it is expected that a large number of models in the correlation-based population would be of that type . Specifically , we would expect that there would be a greater percentage of typei models in cbi when compared to the original database . We therefore calculated a statistic we termed success percentage ( %Success ) , by dividing the number of models in cbi that are of typei by the total number of models in cbi ( %SuccessCorrelation-based ) . The percent of typei models in the original database ( %SuccessOriginal ) was also calculated ( see percentages shown in Figure 1 ) . The two were compared as follows:where O represents the original database , N denotes a function returning the number of models in the parameter population , and O∩typei should be read as “the intersection of the original database and the set of models of typei” . The success factor ( fSuccess ) is therefore a multiplicative factor by which implementing a correlation increases or decreases the likelihood of a particular activity type . An increase in % success for the correlation-based population versus the original database ( fSuccess>1 ) indicates a correlation that is useful in supporting a desired activity type . As a control , fSuccess was also calculated for a randomly distributed conductance plot applied to a random conductance pair . For the case of multiple correlations , the control case employed the same number of randomly generated conductance plots as there are defined correlations for that activity type . As further verification of our methods , we also applied an ideal linear correlation shape to randomly selected conductance pairs and activity types , and found no unexpected increases in fSuccess ( data not shown ) .
A model neuron database partitioned by activity type was screened for pair-wise conductance relationships , to elucidate the possible functional role of linear conductance relationships seen in experiments [6] , [8] , [9] . However , linear dependence was not the only relationship type seen in this database . A wide variety of nonlinear relationship types were also seen ( for examples , the correlation plots of activity type “spiking models 50–75 Hz” are used , Figure 4 ) . Many conductance pairs were clearly independent of one another; examples include completely flat distributions ( gH and gleak , Figure 4 ) or correlation plots with a striped appearance ( gNa and gH , Figure 4 ) . In these cases , one or both of the conductances involved has a flat 1D distribution , and therefore the activity type in question does not require a particular value or range of values of that conductance . Another type of relationship commonly seen was the “ramp” type . These conductance plots had a high concentration of models in one corner , few models in the opposite corner , and a gradient in between . Intuitively , this would suggest dependence . On the contrary , many of these apparent relationships could be explained by independent variation of conductances . For example , the gCaS and gA conductance plot shown in Figure 4 is one example of a ramp type relationship where the corresponding chi-squared statistic was low , and the percent-difference plot between actual data and the independence assumption ( difference matrix ) shows no interesting trend . Indeed , this relationship appears to be fully explained by independent variation of parameters , as many of the ramp-type relationships we saw were ( Figure 3A , C ) . Finally , both positive ( gNa and gCaT , Figure 4 ) and negative ( gCaT and gCaS , Figure 4 ) linear correlations were seen and will be described in detail . The statistical criteria used to define correlations were chosen with the goal of distinguishing those conductance pairs linearly dependent on one another . To verify the statistics , each plot and its corresponding difference matrix were checked for visual confirmation of a linear trend ( Figure 4 ) . In a small number of cases ( 16/174 ) we found that a plot would fit the statistical criteria for linear dependence but linearity was not evident . This was most often an edge effect; for instance , a relationship wherein one or both conductances were zero for a majority of the models ( Figure 5 ) . Notably , most false positives were due to low average values of gKCa or gCaT or both , and were found in activity types such as silent and fast spiking models . False-positives , though they met the statistical criteria , were not considered correlations ( see Supplemental files , Table S2 ) . Statistical criteria were chosen based on minimization of false-positive results . Excluding those cases discussed above , our statistical criteria identified 174 pair-wise linear correlations out of a possible 1316 conductance pair combinations in 47 model sub-populations ( see Figure 6B and Supplemental files Table S1 ) . We did not tally the relationships that appeared linear but did not meet the cutoff criteria ( apparent false-negatives ) because they tended to occur only in activity subsections with relatively low numbers of models . A property of the chi-squared test of independence , that relationships with more data points will more easily reach a cutoff value , was intentionally used to compensate for the sparse sampling of the conductance space . One example of an apparent false negative is shown in Figure 7 . The activity type “bursting models with a duty cycle greater than 0 . 6” appears to have a linear dependence between gA and gKCa; however , the chi-squared statistic is less than 500 for this plot . A possible solution to avoid excluding this apparent correlation would be to use a chi-squared statistic that is scaled by the total number of models in the population , because this group contains only 1776 models compared to an average of 100 , 000 ( Table S1 ) . However , that approach would exclude other , possibly more reliable , relationships . For example , a scaled cutoff would exclude the gA and gKCa correlation from the activity type “bursting models with a duty cycle between 0 . 2 and 0 . 4” which appears very similar to the ‘false negative’ discussed above ( Figure 7 ) . We chose to use the raw statistic ( unscaled ) because we found this result counter-intuitive . A relationship found in a population with a large number of models should be more reliable than an equivalent relationship found in a population with very few models , especially on a sparse grid . This is not to say that relationships fitting our statistical criteria were not apparent in populations with low numbers of models . On the contrary , we found several correlations in this and other populations with relatively few models ( Table S1 ) . When model populations were defined by a single activity characteristic , a correlation was more likely to appear in a group with a narrow range of that characteristic ( Figure 1 ) . For example , the group “all periodic spikers” had no correlations; however , when this group was segmented by spike frequency every subsection had at least one correlation . Similarly , the group “all periodic bursters” demonstrated no correlations , whereas all of its subsections partitioned by duty cycle did . Although the categories without correlations each included over 200 , 000 model neurons ( see Figure 1 and Supplemental files , Table S1 ) , this phenomenon was not linked to the size of the activity subsection . For example , the group “bursters with duty cycle <0 . 05” contains a large number of models ( 392 , 398 ) and has three correlations . It is reasonable then to assume that correlations arise by virtue of the narrowly defined activity type of a group rather than simply the number of models in a group . Very few conductance pairs were seen to demonstrate a positive correlation in one activity type and a negative correlation in others ( Figure 6A ) . Thirteen conductance pairs exhibited only positive correlations , eight conductance pairs exhibited only negative correlations , and two conductance pairs had both relationship types . We found no correlations for the remaining five possible conductance pairs . As shown in Figure 7 , the relationship between gA and gKCa is one example of a negative correlation . This conductance pair was negatively correlated for spiking models with a frequency of 25–75 Hz ( subsections 25–50 Hz and 50–75 Hz ) and bursting models with a duty cycle between 0 . 2 and 0 . 6 ( subsections 0 . 2–0 . 4 , and 0 . 4–0 . 6 ) ( Figure 7 ) . Interestingly , though all of the correlations for the gA/gKCa pair are negative , they appear to have a slightly different slope in each case . This is only one example of slope differences seen between activity types for a single conductance pair , though there are also cases of correlations with the same slope in several activity groups . In the latter case , correlations in different activity groups generally inhabit different areas of the conductance space ( Figure 8 ) . There was a strong presence of calcium conductance correlations in this database ( Figure 6A ) . In the model sub-populations we investigated , there were 140 correlations involving one or both calcium conductances , compared to only 34 that did not involve either calcium conductance . All conductances showed correlations with at least one of the calcium conductances . The gKCa and gCaS conductance pair had the highest number of correlations and was strongly correlated in several activity types . In contrast with populations based on a single criterion , there was no straightforward trend in the appearance or disappearance of correlations when multiple activity criteria were used to generate a population . Individually , each population based on a single pacemaker criterion has a unique set of correlations ( Figure 6B , bottom row ) . When criteria are combined , however , correlations appear or disappear with a seeming disregard to those seen in the “parent” populations . For example , starting with the population based on slow wave amplitude ( SWA ) between 10 and 30 mV , adding the slow wave peak ( SWP ) range as a criterion leads to a loss of the gCaT vs . gleak correlation . Adding duty cycle as a criterion brings back the gCaT vs . gleak correlation , even though the duty cycle parent population does not use it . Adding burst period again leads to loss of that correlation . There is no path from bottom to top in Figure 6 along which the number of correlations increases steadily with an increase in criteria . When all 5 criteria were used , no correlations were found , though this may be influenced by the small number of models that fit all 5 criteria ( 56 ) . Finally , defined correlations were used to create correlation-based sub-populations of models as described in the Methods section . A correlation-based population was , in all cases , found to have a larger percentage of models of the desired activity type than the original database . We found a 2 . 3 fold increase for individual correlations , on average ( σ = 0 . 9 ) . Individual correlations ranged from having a small positive effect on success rate to increasing it 5 fold ( Figure 9 ) . This is in contrast to the control condition of random conductance relationships , in which decreases in success were as likely as increases . On average , there was no difference between the control condition and the original database ( average fSuccess = 0 . 98 , σ = 0 . 2 ) . The difference between control and correlation-based populations was even larger when multiple correlations were used to generate each population . When the set of all correlations for an activity type was used to create a population of models , the percent success increased 10 fold on average ( Table S3 ) . In contrast , the average fSuccess for the control case with multiple random conductance distributions was below 1 ( μ = 0 . 89 , σ = 0 . 17 ) .
Our results illuminate the functional benefit of linear conductance correlations for maintaining activity type . When a population of models was gathered based on adherence to a correlation rule , there was always an increase in a particular feature of activity . In some cases , this increase was admittedly modest , particularly when the population was based on a single correlation . However , there were several cases in which the constraint on activity type was impressive ( Figure 9 ) . In one case , imposing a single correlation caused an increase in the desired activity type from 18% ( in the original database ) to 53% ( in the new , correlation-based population ) . Another population increased its percentage of a single activity type by 72% when multiple correlations were used . Put another way , the combined correlation-based populations demonstrated up to 81% of a single activity type . We find this to be a notable contribution to robustness of activity . This model implicates two types of correlations not previously seen in experiments . As of this writing there have been no published calcium conductance correlations . We have shown that calcium correlations are plausible , and if implemented by a cell , would assist maintenance of a wide range of activity types . This is also true of correlations with a negative slope , which have not yet been seen experimentally . Thirty percent of all correlations seen ( 56/174 ) were negative , spanning 10 out of the 23 conductance pairs with correlations in this study ( see Supplemental files , Table S1 ) . Though the mechanisms underlying biological correlations are uncertain , and not represented in the details of our model , our results suggest that negative correlations can be just as useful as positive ones for maintaining cellular activity . Negative correlations have been hypothesized for conductances that may compensate for one another . For example , a study of the solution space of a multi-compartment cerebellar Purkinje cell model reports negative correlations between several pairs of conductances , including two calcium conductances that appear to be anti-correlated so as to preserve the total calcium influx into the cell [32] . When fitting model neuron parameters to reproduce specific experimentally recorded neuronal voltage traces , such functionally compensatory conductances can lead to irreducible model parameter uncertainty [33] . An analysis of the correlation between the two potassium conductances gKCa and gA shows this type of relationship in our model . The Ca2+-dependent K+ current is often associated with bursting cells , and contributes in our model to determining burst length . Keeping all other conductances constant in a bursting model neuron , a higher value of gKCa will correspond to a shorter burst length [34] . The A current is also a K+ current , though it is not calcium dependent and it acts on a much faster time scale . Keeping all other conductances constant in a bursting cell , a higher value of gA will result in a slower spike rate , or fewer spikes per burst [35] . The effects of these conductances appear to sum for bursting neurons with a duty cycle between 0 . 2–0 . 6 ( Figure 7 ) . To maintain duty cycle in this range , a model needs a minimal amount of one or both conductances to counter-balance depolarizing ( inward ) currents . The exception to this rule is a small population ( ∼300 models ) of elliptic bursters in the 0 . 4–0 . 6 duty cycle group . These models all have the same low value of gNa ( 100 mS/cm2 ) , which may allow the absence of both gKCa and gA . Interestingly , bursting models with a low duty cycle show no dependence between gA and gKCa . A second type of negative correlation was seen between gNa and gKd . Initially , this negative correlation was surprising . Both conductances are involved in generation of the action potential and would therefore be expected to require balance via a positive correlation . Instead , only negative correlations were seen , and they appeared in two activity subsections: spiking models with frequency greater than 75 Hz , and bursting neurons with a duty cycle less than 0 . 05 . We found , based on where these correlations appeared , that a negative relationship between these two conductances was useful for maintaining very fast spiking . If a model in either of these activity groups was adjusted to make both conductances very low , then the model became silent . If both conductances were very high , the spike would broaden and the spiking frequency would decrease . It is interesting to note that both the conductance pairs discussed so far have been found to be positively correlated experimentally in stomatogastric neurons [9] . However , the activity types found to have these correlations in the database are not typical of stomatogastric intrinsic activity types [1] , [36] , [37] and thus should not be compared directly to experiment . There are several reasons why directly relating our results to the published experimental data is a challenge . First , conductance is not directly measured in experiments . Mapping conductance ( used in the database ) to mRNA copy number or current density involves several tenuous assumptions . For example , conversion between mRNA copy number and conductance value has been studied for three conductance types in the STG , of which only two were found to relate linearly [3] , [38] . Therefore , it cannot be assumed that the other 5 conductances types used in this model have a linear relationship between mRNA copy number and conductance value . A larger problem is the choice of comparison: which activity characteristics should be assigned to a biological cell in a particular experiment ? Our analysis of activity type of the model did not consider the effects of current injection or neuromodulation , and therefore should be compared to intrinsic activity of the biological cells . Unfortunately , there are conflicting accounts of the intrinsic activity of cells in the stomatogastric ganglion . While they have reliable and identifiable activity types in the intact circuit , isolating a cell to measure intrinsic activity often involves direct harm to its own processes or neighboring cells . The method of isolation may influence activity in unknown ways . For example , when the PD neuron is cut off from all synaptic input and neuromodulation it can be spontaneously silent [39] , spike tonically [39] , [40] , burst with a periodic rhythm [39] or burst irregularly [40] . The one exception in the STG is the AB neuron , which is an intrinsic burster and has the same intrinsic and network behavior . Unfortunately , no experiments have been done examining conductance correlations in the AB neuron . For these reasons , we chose to avoid comparing our results for a particular activity type with the experimental data for a single cell type . Even so , there are general comparisons that can be made between our results and experiment . For instance , we found differences in slope for the same correlation in different activity types ( Figure 7 ) . This is reminiscent of the cell-type specific correlations found by Schulz et al . ; the correlation slope for a single conductance pair was different in each cell type [9] . In addition , we saw correlations which maintained slope but differed between activity types with regard to intercept ( Figure 8 ) . We can predict from this result that the general location within the conductance space is also important for determining activity , which has been suggested before [23] . Furthermore , each identified cell type studied by Schulz et al . had a unique combination of correlated conductance pairs , similar to our results ( see Supplemental files , Table S1 ) . This suggests that not only the slope and location of correlations , but the combinations in which they are used , are important for definition of cellular identity . This view is bolstered by our results with correlation-based populations . We showed that combinations of correlations , drawn from a particular activity type , generated much larger increases in percent success compared to individual correlations or controls ( Figure 9 ) . Another important comparison to experiment involves the hyperpolarization-activated inward conductance , gH . Correlations involving this conductance were only found when the slope of the slow wave was used to identify activity type ( Figure 6C ) . We found this result to be particularly interesting because correlations involving gH have been found repeatedly in experiments in the stomatogastric ganglion [6] , [7] , [8] , [9] . In experiments on STG PD and LP cells , both the slope of the slow wave and the ratio of IA to IH were shown to be conserved after injection of shal mRNA [6] . This implies that the correlation between the values of IA and IH may have something to do with maintaining the shape of the slow wave . In these experiments , the average slope of the rising phase of the slow wave was found to range , roughly , from 0 . 02–0 . 08 mV/ms . We found this correlation in a slightly higher range when looking at the central 50% of the rise phase ( >0 . 1 , see supplemental Table S1 ) . Though these results are slightly different , both correlations were positive and both suggest that IH is involved in maintaining a specific activity characteristic: inter-burst dynamics . Interestingly , IH was not correlated in any other activity types . We hypothesize that this is because the other activity types are defined more by conductances which are active during the depolarized phase of the spike or burst . If this is the case , it would follow that correlations involving IH would be more prevalent if response to hyperpolarizing current injection were used as a metric for defining activity type . For cells dependent on inhibitory network connections for a bursting phenotype , such as LP and pyloric ( PY ) neurons in the STG , this may be the case and should be an interesting area of further research . Recently , Taylor et al published a study of a population of LP neuron models , constrained by 9 activity characteristics , in which no strong linear correlations were found . We report a similar result in that the usefulness of correlations for supporting activity characteristics did not scale up as we expected . When multiple biologically-realistic activity criteria were used to generate a population of models , the appearance and disappearance of correlations was not easily explained by the correlations found in the “parent” single-criteria populations . This suggests that the correlations which would be helpful for each feature individually might interact in complex ways , inhibiting or enhancing the influence of the other , when multiple activity characteristics are imposed on a group . In our case , a likely explanation for these results lies in the complexity of the conductance space . The conductance plots we analyzed were 2D approximations of an 8-dimensional space . This means that a perfect linear correlation between two parameters will appear as such . However , dependencies between multiple parameters will form subspaces within the overall conductance space that may , or may not , be detectable in a flattened 2D analysis . Furthermore , the perfect population definition for one conductance pair may not be ideal for another conductance pair , resulting in the occasional conductance plot that contains two local dependency rules ( see Figure 5A for a possible example ) . Finally , the scale of analysis is important . We had the opportunity to examine raw experimental data from correlations published by Schulz et al . [9] . It has been previously shown that every STG cell type has a unique range of conductance values [3] , [38] . By binning the experimental data based on the conductance ranges of each cell type , it was apparent that bin size could contribute to the appearance of correlations ( analysis not shown ) . For example , when binning conductance measurements from the gastric mill ( GM ) cell type based on the range of conductance values present in the PD cell type , otherwise apparent correlations are lost . Our model database is limited in this way , due to the grid structure of the conductance space , which can be interpreted as a type of binning . Combined , this complex behavior can easily give rise to situations in which correlations appear , or disappear , as the population of models is further reduced . Though this result highlights the inability of our analysis to capture all possible linear dependencies between conductances , it is important to note that it does not shed doubt on the correlations we did find . Though our set of apparent correlations should not be considered an inclusive list for these activity types , the correlations found in our database are useful for considering the possible effects of conductance relationships on activity . We argue that the presence of a large number of defined conductance relationships lends to the validity of the database as a tool for investigating correlation utility . With this tool , we have shown that linear conductance correlations can shape neuronal activity . Furthermore , we made specific predictions about the presence of negative or gCa correlations and situations in which they might be useful . | Most motor neurons receive input from the brain before transmitting to the muscle , resulting in a muscle contraction . In some cases , a small group of motor neurons can act independently to control rhythmic muscle contractions . Locomotion in mammals is thought to arise , in a large part , due to neuronal networks of this type residing in the spinal cord . However , the cellular machinery that guarantees the needed rhythmic pattern of electrical activity in these neurons is not fully understood . Here , we use a small circuit that controls stomach contractions in crustaceans like crabs and lobsters , called the pyloric circuit , to investigate potential mechanisms for regulation of neuronal activity . Ion channel proteins are integral to determination of electrical activity type . Recently , experimental studies using cells of the pyloric circuit have shown correlations in the expression of these proteins . Our study uses a mathematical model of neuronal electrical activity to detail how these correlations may be influencing activity type . We found that correlations imposed on model parameters increase the likelihood of a desired behavior , and we therefore conclude that a biological cell utilizing ion-channel correlations will have the advantage of increased robustness of activity type . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
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] | 2010 | Conductance Ratios and Cellular Identity |
The intestine is a common site for a variety of pathogenic infections . Helminth infections continue to be major causes of disease worldwide , and are a significant burden on health care systems . Lysine methyltransferases are part of a family of novel attractive targets for drug discovery . SETD7 is a member of the Suppressor of variegation 3-9-Enhancer of zeste-Trithorax ( SET ) domain-containing family of lysine methyltransferases , and has been shown to methylate and alter the function of a wide variety of proteins in vitro . A few of these putative methylation targets have been shown to be important in resistance against pathogens . We therefore sought to study the role of SETD7 during parasitic infections . We find that Setd7-/- mice display increased resistance to infection with the helminth Trichuris muris but not Heligmosomoides polygyrus bakeri . Resistance to T . muris relies on an appropriate type 2 immune response that in turn prompts intestinal epithelial cells ( IECs ) to alter differentiation and proliferation kinetics . Here we show that SETD7 does not affect immune cell responses during infection . Instead , we found that IEC-specific deletion of Setd7 renders mice resistant to T . muris by controlling IEC turnover , an important aspect of anti-helminth immune responses . We further show that SETD7 controls IEC turnover by modulating developmental signaling pathways such as Hippo/YAP and Wnt/β-Catenin . We show that the Hippo pathway specifically is relevant during T . muris infection as verteporfin ( a YAP inhibitor ) treated mice became susceptible to T . muris . We conclude that SETD7 plays an important role in IEC biology during infection .
The gastrointestinal tract is responsible for absorption of nutrients and water , but at the same time it has an important role in acting as a barrier to the external environment [1] . This barrier function is further complicated by the requirement to respond appropriately to pathogens , but remain tolerant to innocuous antigens like commensal organisms and food . Understanding the molecular pathways that control intestinal homeostasis is critical for promoting immunity and limiting inflammation . Intestinal homeostasis is the result of a complex interplay between the environment , intestinal epithelial cells ( IECs ) , mesenchymal cells , vascular endothelial cells , and cells of the innate and adaptive immune systems . This interconnected system relies on a multitude of signaling pathways in the various cell types , and aberrant signaling is a key feature in chronic intestinal inflammatory diseases [2] . However , for immunity against certain pathogens , a temporally controlled high level of immune activation is required , including strong inflammatory cues that may lead to significant tissue damage [3–6] . A repair process is then initiated that is essential to regain barrier function and prevent sustained inflammation . IECs play an important role in many of these processes as they can act as the first sensor of pathogens [7] , they execute immune responses by responding to specific cues [8] , and initiate repair processes that require intestinal stem cells ( ISCs ) [9–11] . Despite their importance , the molecular pathways that regulate IEC function in immunity , inflammation and repair remain poorly described . IECs have a remarkable turnover of around 3–5 days [12] . During homeostasis this turnover is driven by ISCs that reside at the bottom of crypts and divide every day [13] . Upon division ISCs leave the stem cell zone to become progenitors for either enterocytes or one of the secretory lineages such as goblet cells and Paneth cells [12] . A variety of signal transduction pathways , including Wnt , Notch , and Hippo are important regulators of ISC and IEC biology . Although several studies have emerged identifying the importance of IECs in immunity to pathogens [6 , 8 , 14 , 15] , the molecular pathways that control IEC dynamics during infection remain unknown . Lysine methyltransferases represent part of a family of novel druggable targets that are currently being investigated for a variety of diseases [16] . SETD7 is a member of the Suppressor of variegation 3-9-Enhancer of zeste-Trithorax ( SET ) domain-containing family of lysine methyltransferases , and has been shown to methylate and alter the function of a wide variety of proteins in vitro [17] . SETD7 has been shown to have in vitro effects on a wide variety of signaling intermediates including NF-κB and STAT3 [18 , 19] that are crucial for immunity to pathogens [6 , 8] . We have recently found that there is an interplay between SETD7 and the Hippo and Wnt pathways , which are evolutionarily conserved signaling pathways that are important for IEC homeostasis , regeneration and tumorigenesis [20–22] . In this study we identify a critical role for IEC-intrinsic expression of Setd7 in immunity to helminth infection .
Immunity to infection with the intestinal helminth parasite T . muris is mediated by a complex interplay between IECs and the innate and adaptive immune systems [5 , 8 , 23] . Commonly , resistance relies on the development of an adaptive TH2 cell response as opposed to a non-protective TH1 cell response that leads to susceptibility . This TH2 cell response is mediated by a wide variety of innate and adaptive immune cells that require appropriate signaling [5] . To test whether Setd7 plays a role in the development of intestinal immunity , we infected Setd7+/+ and Setd7-/- mice with 200 embryonated T . muris eggs [24] . Setd7-/- mice display no overt developmental phenotypes and we did not observe compensational expression of related lysine methyltransferases in the intestine upon deletion of Setd7 ( S1 Fig ) . At day 21 post infection both wild type and knock out mice had fully cleared the infection , indicating that complete loss of Setd7 does not render mice susceptible to T . muris infection ( Fig 1A ) . In contrast , at day 14 we found that although Setd7+/+ mice were in the midst of expelling the worms , Setd7-/- mice had already cleared most of their worm burden ( Fig 1A ) , suggesting that Setd7-/- mice were more resistant to T . muris infection . Enhanced resistance to infection with T . muris was not due to an intrinsic difference in microbiota-mediated hatching [25] , as fecal contents from either strain of mice were equally able to induce egg hatching in vitro ( S2A Fig ) and we detected equal worm burdens at day 12 post infection ( S2B Fig ) . Despite the increased immunity to infection in Setd7-/- mice , we did not detect any significant differences in expression of TH1 cell- or TH2 cell-mediated cytokine genes such as Ifng and Il13 in the gut by qPCR ( Figs 1B and S2C ) . These results suggest that the enhanced resistance to infection in SETD7-deficient mice was neither due to an increased protective type 2 nor a decreased non-protective type 1 immune response . Consistent with this hypothesis , infection of mice with a hematopoietic cell-intrinsic deletion of Setd7 ( Setd7ΔVav mice , generated by crossing Setd7f/f mice with Vav-Cre mice ) revealed that SETD7-deficiency in immune cells had no effect on resistance to infection . At day 14 post-infection , both Setd7f/f and Setd7ΔVav mice had equal worm burdens ( Fig 1C ) and similar gene expression levels of Ifng and Il13 in the intestine ( Fig 1D ) , suggesting that SETD7 expression in hematopoietic cells was not responsible for the increased immunity to T . muris . Importantly , Setd7-/- mice that lack an adaptive immune system ( Rag1-/- / Setd7-/- mice , generated by crossing Setd7-/- mice with Rag1-/- mice ) also displayed increased resistance to infection with T . muris compared to Rag1-/- / Setd7+/- littermate controls , with a significant reduction in worm burden at day 28 post-infection ( Fig 1E ) . Thus , in the absence of SETD7 , an adaptive immune system is dispensable for the development of immunity to T . muris , suggesting that SETD7 expression in non-hematopoietic cells is a critical component of the response to T . muris infection . Several studies have shown that immunity to T . muris is associated with a variety of IEC responses , including goblet cell hyperplasia and mucin production , expression of secreted molecules such as thymic stromal lymphopoietin ( TSLP ) and resistin-like molecule-β ( RELMβ ) , and increased proliferation and turnover [8 , 15 , 26–28] . We next directly tested whether IEC-intrinsic deletion of Setd7 would lead to increased resistance to T . muris infection . We infected mice with an IEC-specific deletion of Setd7 ( Setd7ΔIEC mice ) [20] with T . muris and found that similar to Setd7-/- mice , Setd7ΔIEC mice displayed enhanced resistance to infection , with reduced worm burden at day 14 post-infection ( Fig 2A ) . We failed to observe any differences in levels of IFN-γ or IL-13 produced by restimulated mesenteric lymph node ( mLN ) cells ( Fig 2B ) or in gene expression by qPCR of intestinal tissues ( S3A Fig ) between Setd7f/f and Setd7ΔIEC mice . Further , we detected equal worm burdens and Ifng and Il13 expression between infected Setd7f/f and Setd7ΔIEC mice at day 12 post infection ( S3B and S3C Fig ) . Thus , IEC-intrinsic expression of SETD7 negatively regulates resistance to infection with T . muris . IECs have been shown to differentiate into goblet cells following infection with T . muris in response to TH2 cell-derived cytokines and produce effector molecules such as the mucins Muc5AC [28] and Muc2 [27] , cytokines such as TSLP [29] as well as the small protein RELMβ [26] . Consistent with our results demonstrating equivalent TH2 cell responses , we did not observe any differences in expression of Muc5ac ( S3D Fig ) , Muc2 or Tslp ( Fig 2C ) or secretion of RELMβ into the intestinal lumen ( Fig 2D ) between Setd7f/f and Setd7ΔIEC mice following T . muris infection . Thus , the increased resistance to T . muris in Setd7ΔIEC mice is not due to the enhanced production of effector molecules by IECs . In addition to IEC differentiation , it has been shown that IEC turnover is important for resistance to infection with T . muris [15] . IEC turnover can be defined by combining crypt length ( or cells per crypt ) with the number of proliferating cells [15 , 28] . For example , short crypts with many proliferating cells have high turnover whereas long crypts with few proliferating cells have slow turnover . In resistant mice , IEC turnover is induced to clear T . muris , whereas in susceptible mice this increased turnover does not occur [15] . First , we carefully analyzed crypt length and found that Setd7ΔIEC mice have shorter crypts compared to Setd7f/f mice before and throughout T . muris infection ( Fig 3A and 3B ) [20] . Crypt length was highly correlated with the number of cells per crypt as counted by DAPI ( S4A–S4C Fig ) . Next , we analyzed the number of IECs that are proliferating by enumerating Ki67+ cells per crypt ( Fig 3C and S4A Fig ) . We found that prior to infection ( day 0 ) an increased number of IECs from Setd7ΔIEC mice stained positive for Ki67 , suggesting increased proliferation , consistent with our previous study [20] . To measure IEC turnover we calculated the frequency of cells proliferating by dividing the number of Ki67+ cells by the DAPI+ ( total number ) cell numbers ( Fig 3D ) . Combining proliferation with crypt length ( or number of cells per crypt ) is essential because increased proliferation does not always correlate with turnover , i . e . it may just lead to longer crypts . We found that under homeostatic conditions , Setd7ΔIEC mice display faster IEC turnover compared to Setd7f/f mice ( Fig 3D ) . Following infection with T . muris , Setd7f/f mice increase IEC turnover ( Fig 3D ) [15] , suggesting that this is the peak turnover that is required for parasite expulsion . Our results suggest that the turnover induced by T . muris in Setd7f/f mice that is associated with worm expulsion is already present during homeostasis in naive Setd7ΔIEC mice , resulting in enhanced worm expulsion . Thus , loss of SETD7 is associated with increased IEC turnover that promotes resistance to T . muris . Although a high-dose T . muris infection ( >100 eggs ) is very suitable for studying various immunological processes in mice , it is arguably not the perfect model for studying chronic helminth infections that dramatically affect human lives , mainly in developing countries [30] . In contrast , a low-dose ( <50 eggs ) T . muris infection leads to a persistent infection with a chronic worm burden [31] . To test if SETD7 played a role in the development of chronic helminth infection , we infected mice with a low-dose ( ~30 eggs ) of T . muris infection . We found that Setd7ΔIEC mice were also more resistant to chronic infection with T . muris compared to Setd7f/f mice at day 32 post infection ( Fig 4A ) . At day 21 post infection we observed equivalent infection rates between Setd7f/f and Setd7ΔIEC mice , which was associated with equivalent expression of Ifng and Il13 in the intestine ( S5A and S5B Fig ) . Further , we did not observe differences in adaptive immune responses as measured by cytokine secretion following polyclonal restimulation of mLN cells , serum levels of T . muris-specific IgG2a and IgG1 , as well as cytokine gene expression in the intestinal tissue at day 32 post infection ( Fig 4B and 4C and S5C and S5D Fig ) . However , consistent with our results with high-dose infection , we did observe differences in crypt length between Setd7f/f and Setd7ΔIEC mice , albeit with equal robust reduction of goblet cells ( Fig 4D and 4E ) . Thus , loss of SETD7 in IECs leads to increased resistance to chronic helminth infection independent of the immune response , identifying a potential new therapeutic target to treat persistent helminth infections . T . muris resides in the caecum embedded in the intestinal epithelium in close association with IECs [32] . IEC proliferation and turnover has been shown to play a role in expulsion via an ‘epithelial escalator’ [15] . In contrast , larvae of Heligmosomoides polygyrus bakeri penetrate the epithelium into the submucosa , undergo two molts , and the worms resurface in the lumen wrapped around small intestinal villi [33] . Resistance to H . p . bakeri is accomplished by a TH2-cell-mediated ‘weep and sweep’ response , which consists of goblet-cell-mediated secretion of mucins ( weep ) and muscle contraction ( sweep ) [34] . To test whether Setd7ΔIEC mice also have altered resistance against H . p . bakeri , we infected Setd7f/f and Setd7ΔIEC mice with ~200 larvae . At 28 days post infection we found no difference in worm burden or induction of mesenteric lymph node cell numbers ( Fig 5A and 5B ) . There was an upregulation of expression of indicators of TH1 ( Ifng ) and Treg ( Foxp3 ) cell responses at this time point ( Fig 5C ) . However , we failed to observe any differences in expression of Il5 , Il13 , Relmb , Ifng , Foxp3 between infected Setd7f/f and Setd7ΔIEC mice ( Fig 5C ) . We observed a reduction of periodic acid-Schiff ( PAS ) positive cells in infected animals compared to naïve controls , but this too was equivalent between Setd7f/f and Setd7ΔIEC mice ( Fig 5D and 5E ) . Thus , in contrast to T . muris , IEC-intrinsic deletion of Setd7 does not affect immunity against H . p . bakeri . We have previously shown that increased IEC proliferation and turnover in the absence of SETD7 during homeostasis correlated with dysregulated signaling by the Hippo pathway [20] , a pathway well known to control IEC proliferation [35–38] . SETD7 is required for the proper subcellular localization of the Hippo pathway transducer YAP [20] . In the absence of SETD7 , YAP is enriched in the nucleus where it interacts with the TEAD family of transcription factors , resulting in heightened YAP/TEAD-dependent gene expression . Recent studies have also linked Hippo/YAP signaling with Wnt/β-Catenin signaling in the control of IEC proliferation and turnover [38–41] . Indeed , we have recently shown that SETD7-dependent methylation of YAP is required for optimal Wnt/β-Catenin signaling during intestinal regeneration and tumorigenesis [22] . We therefore analyzed Wnt and Hippo activity in IECs during T . muris infection by measuring specific target genes of each pathway . We found that both Wnt ( Lgr5 and Axin2 ) and Hippo ( Ctgf and Gli2 ) target genes are upregulated in IECs during T . muris infection of control Setd7f/f mice compared to naïve mice ( Fig 6A and S6 Fig ) . In the absence of SETD7 , we found that Wnt target gene expression is reduced while Hippo target gene expression is increased in IECs following infection ( Fig 6A and S6 Fig ) . Thus , IEC-intrinsic expression of SETD7 regulates IEC turnover during T . muris infection , possibly by influencing Wnt and Hippo signaling . We next tested whether blocking YAP transcriptional activity would affect resistance to infection with T . muris . To do this , we treated wild type mice with liposome-encapsulated verteporfin ( VP ) , a recently discovered inhibitor of YAP-TEAD interactions [42] . Following infection with T . muris , we found that VP-treated mice failed to fully clear their infection by day 21 in comparison to vehicle-treated mice ( Fig 6B ) . Consistent with the increased susceptibility to infection , we observed a significant increase in gene expression of the TH1 cell-associated cytokine Ifng in the intestine of VP-treated mice , even though Il13 levels were equal ( Fig 6C ) . Further , induction of IEC-specific genes associated with resistance to infection such as Muc5ac and Tslp was abrogated by VP treatment ( Fig 6D ) . As expected , VP treatment abolished T . muris-mediated upregulation of YAP target gene Ctgf ( Fig 6E ) , but we did not observe a striking effect on Wnt/β-Catenin target genes such as Lgr5 ( Fig 6E ) . Together , these experiments show that YAP-TEAD interactions are important for the development of immunity to T . muris .
In this study we describe the role of SETD7 during helminth infection . We find that deletion of Setd7 in IECs renders mice resistant to the helminth T . muris but does not affect H . p . bakeri infection . Interestingly , Setd7 does not affect T helper cell responses or goblet cell differentiation , both of which are deemed very important for resistance against T . muris [27 , 28 , 43 , 44] . Instead , we find that epithelial turnover is affected by the lack of Setd7 , and therefore , IEC turnover can be dominant over adaptive immune responses in terms of importance for resistance to T . muris . In Setd7ΔIEC mice we observed higher turnover during homeostasis compared to Setd7f/f mice , demonstrating that SETD7 regulates IEC turnover independently of infection-induced immune cell cues . In contrast , control Setd7f/f mice relied on type 2 immune responses to increase its IEC turnover to expel T . muris worms . We quantified turnover by examining crypt length , number of cells per crypt , and number of cells proliferating . However , we did not directly measure the migration of cells up the crypt , for example by pulse-chase experiments , nor did we quantify cell shedding into the lumen , both of which are also important elements of turnover [15 , 45] . IECs originate from stem cells at the bottom of crypts , divide several times in the transit-amplifying zone ( bottom half of crypts ) , after which they differentiate and finally are shed into the lumen due to crowding [13 , 45] . Nevertheless , Setd7 deletion provides resistance to infection even in mice that completely lack an adaptive immune system . Thus , these results uncover an additional pathway to target in the design of therapies to treat helminth infection . We also provide evidence that the developmental Hippo/YAP and Wnt/β-Catenin signaling pathways are important components of resistance to T . muris infection . Both Hippo and Wnt gene expression programs are induced upon infection and are mediated by SETD7 . It would be particularly interesting to identify what cues drive these pathways during helminth infection , and whether the previously identified regulator of epithelial turnover , CXCL10 , plays any role [15] . A potential alternative pathway that merges inflammation with regeneration is the recently described gp130-Src-YAP axis that is induced during intestinal inflammation [46] . Although this study did not identify a link with Wnt/β-Catenin , it remains to be seen if intestinal infection relies on this pathway . Nevertheless , our results show that SETD7-dependent regulation of Hippo and Wnt signaling plays a critical role in the development of resistance to intestinal helminth infection . We used VP to test our hypothesis that YAP-TEAD mediated gene expression programs are important for T . muris resistance . Indeed , we find that VP treated animals become susceptible to T . muris compared to vehicle treated mice . However , we observed that VP treated animals also had increased TH1 cell-associated cytokines ( IFN-γ ) and reduced epithelial markers for resistance ( TSLP and Muc5AC ) , indicating that the Hippo pathway may play a wider role . This is in line with a recent study identifying a role for YAP in goblet cell function [40] . In addition , it suggests that a YAP-TEAD mediated regenerative gene expression program is required to avoid a shift towards a TH1 response upon infection with T . muris . In summary , we have identified a SETD7-dependent regulatory pathway in IECs that regulates immunity in the intestine . Modulation of SETD7 activity may provide a therapeutic strategy to improve anti-helminthic treatments independently of the innate and adaptive immune systems .
Villin-Cre and Rag1-/- mice were obtained from Jackson Laboratories . Vav-Cre mice were obtained from T . Graf ( Centre for Genomic Regulation , Barcelona , Spain ) . Setd7-/- and Setd7f/f mice were described previously [20 , 47] . We did not observe any physiological effects from Cre expression during homeostasis or infection . Animals were maintained in a specific-pathogen-free environment and tested negative for pathogens in routine screening . All experiments were carried out at the University of British Columbia following institutional guidelines . We used both males and females that were littermates and age matched ( ranging from 7–15 weeks old ) for all experiments . Isolation of T . muris eggs was carried out as described previously [24] . Mice were infected on day 0 with high dose ( ~200 ) or low dose ( ~35 ) of embryonated eggs by oral gavage , and parasite burdens were assessed microscopically on days 14 , 21 , 28 , or 32 post-infection . Mice were infected with 200 H . polygyrus bakeri L3 larvae by oral gavage and parasite burdens were assessed microscopically on day 28 . Liposome-encapsulated verteporfin ( VP , Visudyne ) was a kind gift by Novartis and was used at 50 mg/kg [42] by intraperitoneal injection at days -3 , 0 , 4 , 7 , 10 , 13 , 17 , 20 . Control mice were injected with the same volume of vehicle with the same schedule . All experiments were performed according to protocols ( A11-0290 , A11-0329 , A13-0010 ) approved by the University of British Columbia's Animal Care Committee and in direct accordance with The Canadian Council on Animal Care ( CCAC ) guidelines . Mesenteric lymph node cells from T . muris-treated mice were isolated and single-cell suspensions were plated at 4 × 106 per ml in the medium or in the presence of antibodies against CD3 ( 145-2C11 ) and CD28 ( 37 . 51 ) ; 1 μg ml−1 each; ( eBioscience , San Diego , CA ) or T . muris antigen ( 50 μg ml-1 ) for 72 h . Cytokine production from cell-free supernatants was determined by standard sandwich enzyme-linked immunosorbent assay ( ELISA ) using commercially available antibodies ( eBioscience ) . T . muris-specific serum IgG1 and IgG2a levels were determined by ELISA on plates coated with T . muris Ag ( 5 μg ml-1 ) . Total protein was isolated from fecal samples , resolved by sodium dodecyl sulfate- polyacrylamide gel electrophoresis , and immunoblotted using a rabbit anti-mouse RELM-β antibody ( PeproTech , Rocky Hill , NJ ) . RNA was purified from whole intestine using mechanical disruption followed by TRIzol according to the manufacturer’s instructions , or from isolated IECs using RNeasy isolation kit ( Qiagen ) . Reverse transcription using High Capacity cDNA Reverse Transcription kit ( Applied Biosystems ) was used to generate cDNA and qPCR was performed using SYBR green with primers from the Primer Bank ( http://pga . mgh . harvard . edu/primerbank ) using SYBR green chemistry on an ABI 7900 real-time PCR system ( Applied Biosystems ) . Samples were normalized against Actb or Gapdh and are presented as fold over wild type , naïve , or relative to housekeeping gene as is indicated in figure legends . Tissues were fixed in formalin and paraffin-embedded . Sections ( 5 μm ) were stained with hematoxylin and eosin ( H&E ) or periodic acid-Schiff ( PAS ) . Slides were analyzed on a Zeiss Axioplan2 microscope and images captured using a Qimaging Retiga EX CCD camera and Openlab 4 . 0 . 4 software ( PerkinElmer ) . For immunofluorescence , 5 μm sections of paraformaldehyde-fixed , paraffin-embedded tissues were incubated with anti-Ki67 ( SP6 clone , Sigma ) ) followed by Alexa568-conjugated goat-anti-rabbit and DAPI . IEC turnover was calculated by Ki67+ cells / total cells ( DAPI+ ) per crypt X 100 . Of note , crypt length was under all conditions equally associated with total cells per crypt . Results represent the mean ± s . e . m . Statistical significance was determined by Student’s t-test or 1-way ANOVA with subsequent post hoc test . | The gastrointestinal tract is a common site for infection by a variety of pathogens . For example , gut-dwelling parasitic worms currently infect over a billion people , mostly in developing nations . Deworming strategies have been shown to improve physical and intellectual development of infected children , but current therapies do not offer a sustainable solution . We still have too little insight into how these pathogens are causing disease and how immunity to them is regulated . In this study we show that SETD7 , an enzyme that modifies the function of other proteins by methylation , plays an important role in the development of intestinal immunity to the helminth parasite Trichuris muris but not Heligmosomoides polygyrus bakeri . Specifically , we show that SETD7 affects intestinal epithelial turnover , a key mechanism through which T . muris worms are extruded from the body . Our studies identify pathways that are important for immunity to infection , that were previously believed to be involved primarily during embryonic development . | [
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] | 2016 | Intestinal Epithelial Cell-Intrinsic Deletion of Setd7 Identifies Role for Developmental Pathways in Immunity to Helminth Infection |
Heterogeneous mosquito biting results in different individuals in a population receiving an uneven number of bites . This is a feature of many vector-borne disease systems that , if understood , could guide preventative control efforts toward individuals who are expected to contribute most to pathogen transmission . We aimed to characterize factors determining biting patterns of Aedes aegypti , the principal mosquito vector of dengue virus . Engorged female Ae . aegypti and human cheek swabs were collected from 19 houses in Iquitos , Peru . We recorded the body size , age , and sex of 275 consenting residents . Movement in and out of the house over a week ( time in house ) and mosquito abundance were recorded on eight separate occasions in each household over twelve months . We identified the individuals bitten by 96 engorged mosquitoes over this period by amplifying specific human microsatellite markers in mosquito blood meals and human cheek swabs . Using a multinomial model assuming a saturating relationship ( power ) , we found that , relative to other residents of a home , an individual's likelihood of being bitten in the home was directly proportional to time spent in their home and body surface area ( p<0 . 05 ) . A linear function fit the relationship equally well ( ΔAIC<1 ) . Our results indicate that larger people and those who spend more time at home are more likely to receive Ae . aegypti bites in their homes than other household residents . These findings are consistent with the idea that measurable characteristics of individuals can inform predictions of the extent to which different people will be bitten . This has implications for an improved understanding of heterogeneity in different people's contributions to pathogen transmission , and enhanced interventions that include the people and places that contribute most to pathogen amplification and spread .
Mosquito blood feeding behavior is epidemiologically important because of its central role in determining which vertebrate hosts and mosquitoes are exposed to a pathogen . Aedes aegypti , the principal mosquito vector of dengue ( DENV ) and urban yellow fever viruses [1] is highly anthropophilic , feeding predominantly on people during daylight hours and tending to travel short distances to obtain its blood meals [2] , [3] , [4] , [5] . Females often take more than one blood meal per gonotrophic cycle [6] , increasing their probability of ( 1 ) imbibing an infected blood meal and ( 2 ) after surviving an extrinsic incubation period , becoming infectious , and transmitting virus to an uninfected person [7] . These behaviors lead to the assumption that the risk of DENV infection is highest at the scale of individual locations; the places where female Ae . aegypti feed and people live or visit [8] , [9] , [10] , [11] , [12] . Even at this fine scale , however , predicting infection risk remains difficult because some individuals are bitten more often than others for reasons that are poorly understood [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] . A better understanding of who gets bitten more often and why would be useful for designing targeted methods of dengue prevention as well as for developing mathematical models of virus transmission . Although models have traditionally assumed that mosquitoes bite people randomly [21] , growing empirical evidence indicates that mosquito biting patterns are heterogeneous and theoretical work indicates that this can have important impacts on transmission dynamics [22] , [23] , [24] . In particular , people who receive many more mosquito bites than others could act as superspreaders of a pathogen , infecting a disproportionate number of vectors and thus playing a central role in pathogen transmission dynamics [25] . Identifying these people is , therefore , key for effective , targeted disease control strategies [20] . A number of factors have been identified that may make some people more likely to be bitten than others: host body size ( larger people being bitten more often ) , infection with parasites , body temperature , age ( perhaps as a proxy for other biological factors ) , sex , semiochemicals , microflora on the skin , and host movement and defensive behavior [10] , [12] , [14] , [16] , [18] , [19] , [26] , [27] , [28] , [29] . In the case of Ae . aegypti , results from a study conducted in Puerto Rico indicated that people under 20 years of age received fewer bites than those 20 years and older , regardless of gender [15] . There are several plausible explanations for the detected differences , including variation in individual body size and host movement patterns [10] . Our understanding of why some hosts are bitten more often by Ae . aegypti is incomplete , in part , because most studies do not account for the many potentially important differences among human hosts that could influence the chance of receiving a mosquito bite . Variation in biting patterns could be due to differences in inherent attractiveness to mosquitoes , determined by body size or smell , or some other characteristic that has yet to be identified . Observed variation in biting could also be due to the amount of time an individual spends in the same house as biting mosquitoes . We suspect that the most likely explanation combines individual characteristics and exposure time as principal determinants governing which individuals mosquitoes tend to bite most often . For instance , children may receive fewer bites than adults because they are smaller , exposed to fewer mosquitoes during the day or more active than adults . In Iquitos , Peru , for instance , mosquito abundances were found to be very low in schools compared to households [30] . During major portions of the day , when they are at school , children in Iquitos may be physically removed from biting mosquitoes . It is also important to consider the other individuals available at a particular location for mosquitoes to bite . Although mosquitoes may find a given individual suitable for biting , he or she may not be bitten if there are other people in the home that spend more time there or are more attractive to biting mosquitoes . Likewise , if mosquitoes only ever encounter a single individual , they will likely bite that person regardless of how attractive or unattractive they are . Making inferences about the factors that contribute to one's risk of being bitten requires simultaneously accounting for the characteristics of other potential blood meal hosts in the locations where mosquito encounters take place . In this study , we sought to isolate individual-level factors driving Ae . aegypti biting patterns by identifying which people living in 19 houses in Iquitos , Peru were bitten most often over a 12-month period . The person bitten was determined by DNA profiling of blood in engorged mosquitoes collected inside each house . We then assessed how a number of factors affected each participant's probability of receiving a bite . Our analysis revealed that some individuals are indeed bitten more often than others and that human exposure time and body surface area are associated factors with this heterogeneity .
There was a high correlation between body surface area and age ( until adulthood ) , and between the number of entrances and total weekly time in house . Additionally , the fitted relationship between biting score and either age , entrances , or both was weaker than those with surface area and time in house ( age and gender were not significant predictors ) . Our primary analysis thus only includes time in house and surface area . The aggregated data on time in house and surface area ( Figure 2 ) indicate that the majority of smaller individuals ( children ) spent more than half of their time in the home . There was no indication that smaller individuals ( less than 1 m2 body surface area ) that spent less than 50 hours in a week in their home were bitten by an engorged mosquito . Ultimately , however , whether an individual received a bite depended not only on their attributes , but also on the attributes of other residents in their house . In many of the houses in which blood meals were positively identified , larger people and those who spent more time at home tended to be the ones who were bitten ( Figure 3 ) . When weekly time-in-house alone was included in the model as a linear predictor of biting score , the fit was poor ( Table 3; LRT: p = 0 . 247; Fig . 4A ) . The shallow slope of the fitted curve indicates that individuals who spent little time in a house did not have significantly lower biting scores than those who spent more time in the same house . Surface area , by contrast , was highly significant by itself ( LRT: p<0 . 001; Figure 4B ) . Combining time in house and surface area improved the model's fit ( Figure 4C; lower AIC ) , but this was not significantly better than the model with surface area by itself ( LRT: p = 0 . 066; Table 3 ) . Models using power functions gave similar qualitative results to the linear models ( Table 4 ) , but somewhat different quantitative results ( Figure 5 ) . Weekly time-in-house was still a poor predictor by itself , and with the power functional form there was a sub-linear response ( fitted power term 0 . 322 ) . In other words , as time in house doubles , the biting score less than doubles . As with the linear models , a power function of surface area by itself also did a good job of explaining heterogeneous biting patterns in the data ( Table 4; Figure 5B; p<0 . 001 ) . In contrast to time in house , surface area had a super-linear relationship ( fitted power term 1 . 4 ) , indicating that incremental increases in surface area result in more than equivalent increases in biting score . Combining surface area with time in house again had the best AIC of all power models , and significantly improved model fit ( p = 0 . 038; Table 4; Figure 5C ) . Biting probabilities predicted by the power model with time in house and surface area are shown in Figure 3 for each house in which a human source of a blood meal was positively identified . Neither linear nor power functions provided better fits to the data , both having similar optimal AIC scores ( 321 . 84 vs 321 . 286 ) .
Understanding how female Ae . aegypti distribute their bites among human hosts is necessary to develop accurate models that ultimately assist in the design and implementation of more efficacious surveillance and disease control strategies . Our results indicate that , within a given household in Iquitos , Ae . aegypti more often bit larger people and those spending more time in the house , highlighting the importance of human movement behavior in determining individual risk of exposure to the viruses Ae . aegypti transmit . These factors predispose some individuals to receive more bites than others , with potentially important epidemiological effects . For instance , we expect the role of children in transmission to be less during the invasion of a new serotype , when immunologically naïve adults can become infected with , amplify , and transmit the virus . Under endemic transmission , however , infective bites are likely to fall on previously infected and thus immune adults , dampening transmission potential . Although future studies may elaborate on the determinants of heterogeneous biting , our results present a methodological advance in the analysis of DNA profiling data and empirical insight into the causal factors of Ae . aegypti biting and , by extension , DENV transmission . Previous studies identified human body size as a potentially important predictor of who receives the most bites from anopheline mosquitoes [18] . Explanations include more surface area for biting , easier detectability due to increased CO2 production , a larger heat signature , reduced defensive behavior , and differences in host activity level [7] , [26] , [27] , [28] , [29] . Although our study design does not allow us to determine which of these or other mechanisms might explain the pattern we observed in Iquitos , the significance of our result across multiple models for body surface area in a house is consistent with the idea that mosquitoes are following cues ( olfactory and/or visual ) when selecting a host to feed upon . This effect of body surface area does appear , however , to be modulated somewhat by the amount of time that individuals spend at home . The significant increase of fit in the power model when incorporating both body surface area and time-in-house , and the marginally significant increase in the linear model ( p-value = 0 . 066 ) , are consistent with the hypothesis that people accumulate more bites at a location if they spend more time there . Our results indicate that this effect of total time-in-house is saturating and relatively weak , and other work is suggestive of an even weaker effect whereby frequency of visitation , but not duration , drives exposure to Ae . aegypti bites and infection risk [12] . To clarify what appears to be a nuanced effect of time-in-house on biting risk , we also considered models with more complex representations of time-in-house , but found them to be inconclusive given the available data . In combination , our results suggest that one's risk of being bitten is driven primarily by sensory cues that Ae . aegypti use to detect people . Future work with larger sample sizes and more detailed accounting of time-in-house and movement in and out of the house would help to further resolve the determinants of relative biting risk within a person's home . As we and others have shown [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , not all hosts have an equal probability of being bitten by mosquito vectors . The assumption of homogeneous biting has historically been used in calculations to determine how difficult an infectious disease is to control [21] . The most common measure of this , the basic reproductive number [37] , is predicted to be higher in calculations based on models that allow for heterogeneous biting than in calculations based on models that assume homogeneous biting [22] , [23] , [24] . This indicates that controlling transmission could be more difficult than predicted by models that assume that all hosts have the same probability of being bitten . If , however , individuals who receive the most bites are identifiable , it may be possible to target interventions and more efficaciously control disease [20] . There were several limitations in our study regarding collection of adequate data for fitting our models . Due to technical issues associated with not being able to fingerprint all of the engorged mosquitoes we collected , we were limited in our ability to test alternative models defining biting risk . This included more complicated relationships between biting and the specific times at which participants were home . Although we had detailed time in house information for human household residents , we did not keep track of non-residents visiting the house or the risk of a resident being bitten at other places they visited during their daily activities [38] . Visitors might have influenced mosquito-biting decisions . The design of our study also precluded us from defining some individual attributes that might independently influence host attractiveness to mosquitoes , such as skin microflora [26] . We were , however , able to isolate important effects that influence how Ae . aegypti bites are distributed among its natural human hosts . Doing so required introducing a new statistical framework for assessing the contributions of different personal factors to one's relative risk of being bitten . Follow-up studies on Ae . aegypti or other household-biting mosquitoes should similarly account for the time people spend in a house and weight each individual's risk relative to other household residents . In particular , our results validate previous studies pointing to adults and/or larger people as the primary recipients of mosquito bites and underscore the importance of the time people spend at a location where mosquitoes bite . Moreover , our analyses reveal that the relationships between such factors can have nonlinear effects on an individual's risk , with time in house having a sub-linear effect and body surface area having a super-linear effect . More detailed understanding of these and other factors that contribute to an improved understanding of biting risk will be an important component of efforts to target interventions , such as vaccines for dengue virus that are currently under development . | We studied the biting habits of Aedes aegypti , the principal vector of dengue virus , to determine why certain people are bitten more often by this day-active mosquito . Over one year in dengue-endemic Iquitos , Peru , we collected blood fed mosquitoes from 19 households . Mosquito blood meals were then matched to household residents using genetic fingerprinting . We found that within a household , larger individuals and those spending more time in the home were bitten more often than other household residents . Importantly , our results show that one's probability of being bitten is dependent on the characteristics of other household residents and visitors . These results indicate that measurable characteristics of individuals do predict who is most exposed to mosquito-borne pathogens , which contributes to our understanding of pathogen transmission processes , informs development of mathematical disease models , and can enhance the design of targeted control programs . | [
"Abstract",
"Introduction",
"Results",
"Discussion"
] | [
"zoology",
"entomology",
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] | 2014 | Determinants of Heterogeneous Blood Feeding Patterns by Aedes aegypti in Iquitos, Peru |
The ortholog conjecture posits that orthologous genes are functionally more similar than paralogous genes . This conjecture is a cornerstone of phylogenomics and is used daily by both computational and experimental biologists in predicting , interpreting , and understanding gene functions . A recent study , however , challenged the ortholog conjecture on the basis of experimentally derived Gene Ontology ( GO ) annotations and microarray gene expression data in human and mouse . It instead proposed that the functional similarity of homologous genes is primarily determined by the cellular context in which the genes act , explaining why a greater functional similarity of ( within-species ) paralogs than ( between-species ) orthologs was observed . Here we show that GO-based functional similarity between human and mouse orthologs , relative to that between paralogs , has been increasing in the last five years . Further , compared with paralogs , orthologs are less likely to be included in the same study , causing an underestimation in their functional similarity . A close examination of functional studies of homologs with identical protein sequences reveals experimental biases , annotation errors , and homology-based functional inferences that are labeled in GO as experimental . These problems and the temporary nature of the GO-based finding make the current GO inappropriate for testing the ortholog conjecture . RNA sequencing ( RNA-Seq ) is known to be superior to microarray for comparing the expressions of different genes or in different species . Our analysis of a large RNA-Seq dataset of multiple tissues from eight mammals and the chicken shows that the expression similarity between orthologs is significantly higher than that between within-species paralogs , supporting the ortholog conjecture and refuting the cellular context hypothesis for gene expression . We conclude that the ortholog conjecture remains largely valid to the extent that it has been tested , but further scrutiny using more and better functional data is needed .
Orthologs , or orthologous genes , are genes in different species that originated by vertical descent from a single gene of the last common ancestor [1] . By contrast , paralogs , or paralogous genes , are homologous genes separated by a gene duplication event [1] . They are referred to as inparalogs when the gene duplication postdated a particular speciation event of reference [2] . Otherwise , they are known as outparalogs [2] . Paralogs residing in the same species are called within-species paralogs , whereas those residing in different species are between-species paralogs . It is widely believed that orthologs are functionally more similar than paralogs , especially after the control of protein sequence dissimilarity or divergence time between genes [3] . This belief , formally termed the “ortholog conjecture” [3]–[4] , is commonly used by molecular biologists in designing experiments and interpreting data and by computational biologists in predicting gene functions and annotating genome sequences [3] , [5]–[9] . The theoretical basis of the “ortholog conjecture” is the consideration that , without duplication , a gene is unlikely to change its basic function because such a change would require the loss of the original function , which is usually harmful . Indeed , a recent evolutionary study of protein interaction suggests that the molecular function of a gene , in the absence of duplication , is highly conserved [10] , although the biological processes in which the gene participates [11] and the importance of the gene [12]–[14] may be less conserved . With duplication , however , one gene copy may retain the original function , while the second copy can acquire new functions ( i . e . , neofunctionalization ) , resulting in functional divergence between the paralogs [15] . Alternatively , the two paralogs may each inherit some but not all of the progenitor gene's functions such that they together are functionally equivalent to the progenitor gene [16] . This process of subfunctionalization also leads to functional divergence between paralogs . Nonetheless , not all paralogs are expected to diverge in function . If an increased amount of gene product conferred by gene duplication is beneficial , the paralogs are expected to maintain their functions unaltered [17] . Additionally , subfunctionalization may occur with respect to the amount of gene expression , resulting in the total expression level of the two paralogs equivalent to that of the progenitor gene , which would also prevent the paralogs from functional divergence [18] . Overall , it seems likely that paralogs will diverge more rapidly than orthologs in gene function . Although many genes from genetic model organisms have been extensively characterized functionally , the ortholog conjecture had never been systematically tested [19] until recently [4] . In a provocative paper , Nehrt and colleagues used experiment-based annotations in the Gene Ontology ( GO ) database [20] and microarray gene expression data [21] to compare the functional and expression similarities of orthologs and paralogs in human and mouse [4] . They showed that , given the same level of protein sequence divergence , ( i ) orthologs are less similar than paralogs and ( ii ) between-species paralogs are less similar than within-species paralogs , in function and expression [4] . They further showed that ( iii ) functional and expression similarities between orthologs are independent of the protein sequence identity between the orthologs . These results are inconsistent or contradictory to the ortholog conjecture , prompting the authors to propose that the primary determinant of the evolutionary rate of gene function and expression is the cellular context in which the genes act . This cellular context hypothesis could explain why within-species paralogs were observed to be more similar in function and expression than between-species paralogs and orthologs , when the degree of protein sequence divergence is controlled . If the ortholog conjecture is indeed incorrect as claimed by Nehrt et al . [4] , some fundamental models of molecular evolution and numerous computational predictions of gene functions would require major revisions . We , however , have doubts about the suitability of GO annotations for testing the ortholog conjecture , for several reasons . First , because of the wide belief of the ortholog conjecture , functional differences between orthologs may be perceived as more surprising than those between paralogs and may thus be preferentially published . Second , functional data of genes from different species tend to be annotated by different teams which may adopt different rules of annotation , which would artificially increase the functional dissimilarity of orthologs and between-species paralogs , compared with within-species paralogs . These and other potential biases in reporting and annotation may affect the test of the ortholog conjecture [22]–[23] . Nehrt et al . were aware of some potential biases in GO annotations . They thus also used microarray gene expression data from human and mouse to compare expression similarities between orthologs and between paralogs . But , microarray was primarily designed to compare the expressions of the same gene from the same species across conditions or tissues . As shown previously , comparison of microarray expression data of different genes or different species can be misleading , because of different microarray probes , designs , and normalizations [24]–[25] . Given the fundamental importance of the ortholog conjecture in biology and the above concerns , Nehrt et al's results require further scrutiny . Here we report biases and errors in GO annotations that prevent a fair evaluation of the ortholog conjecture . By contrast , the ortholog conjecture is strongly supported by RNA-Seq gene expression data , which are known to be superior to microarray data , especially for comparisons among different genes or species [24] , [26]–[28] . The RNA-Seq data also reject the cellular context hypothesis for gene expression .
The rapid accumulation of gene function data means that the number of annotations in GO has increased quickly in recent years [29] . It is interesting to examine whether functional similarities of orthologs and paralogs calculated based on GO annotations have remained relatively stable over time . Such stability is a necessary , albeit not sufficient , condition for drawing any meaningful conclusion from GO . Based on human and mouse GO releases from 2006 to 2011 , we estimated the experiment-based functional similarities of orthologs and paralogs , using Nehrt et al . 's method [4] . Briefly , the functional similarity of a pair of homologous genes is the fraction of common experimentally derived functional annotations of the two genes ( see Materials and Methods ) . To ensure comparability over years , we used the same set of genes for all years . That is , for each orthologous or paralogous gene pair , we estimated their functional similarity in 2006 . We then calculated their functional similarity in each subsequent year , relative to that in 2006 . By averaging across orthologs or paralogs , we measured the average functional similarity of orthologs or paralogs in each year , relative to that in 2006 . Following Nehrt et al . [4] , we examined three sets of gene pairs: ( i ) human-mouse orthologs , ( ii ) human and mouse within-species outparalogs , which were generated prior to the human-mouse separation , and ( iii ) human and mouse ( within-species ) inparalogs , which were generated after the human-mouse separation . The ortholog conjecture predicts a higher functional similarity between ortholog than the two types of paralogs . By contrast , Nehrt et al . 's cellular context hypothesis predicts a lower functional similarity between orthologs than the two types of within-species paralogs . We did not examine between-species outparalogs , because both hypotheses predict them to have relatively low functional similarities . For convenience , we refer to within-species outparalogs simply as outparalogs . GO annotations are organized into three aspects: biological process , molecular function , and cellular component . Based on biological process GOs , the average functional similarity increased from year 2006 to 2011 for all three gene sets , but the increase was significantly faster for orthologs than the two types of paralogs ( Fig . 1A; Table S1 ) . The average annual increase in functional similarity is 14 . 8% , 5 . 6% , and 1 . 4% for orthologs , outparalogs , and inparalogs , respectively ( P<10−4 , P<10−3 , and P = 0 . 073 , respectively , n = 5 , two-tail t-test ) [30] , and these annual increases are all significantly different from one another ( P<10−6 , two-tail Z-test ) [31] . Thus , relative to paralogs , orthologs have become more similar in GO-annotated biological process functions over the last five years . We confirmed that this difference is not due to the difference in sample size between orthologs , outparalogs , and inparalogs ( Fig . S1 ) . Based on molecular function GOs ( Fig . 1B ) , functional similarity increased for orthologs ( 0 . 6% per year , P = 0 . 068 , n = 5 , two-tail t-test ) but decreased for outparalogs ( −0 . 9% per year , P<10−3 , n = 5 , two-tail t-test ) and remained unchanged for inparalogs ( −0 . 03% per year , P = 0 . 566 , n = 5 , two-tail t-test ) . Although the magnitudes of these changes are all small , the differences in annual change between orthologs and the two types of paralogs are both statistically significant ( P<10−7 and 0 . 02 , respectively , two-tail Z-test ) . Based on cellular component GOs ( Fig . 1C ) , the annual increase in functional similarity is 5 . 9% , 0 . 9% , and 0 . 5% for orthologs , outparalogs , and inparalogs , respectively ( P<10−5 , P<10−4 , and P = 0 . 053 , respectively , n = 5 , two-tail t-test ) . The annual increases are significantly different between orthologs and the two types of paralogs ( P<10−6 , two-tail Z-test ) , but are not significantly different between inparalogs and outparalogs ( P = 0 . 08 , two-tail Z-test ) . Thus , relative to the functional similarity between paralogs , functional similarity between orthologs has been increasing over the last five years in each of the three GO aspects , and there is no apparent deceleration of this increase ( Fig . 1A–C ) . Although the absolute functional similarities of orthologs ( 0 . 40 , 0 . 65 , and 0 . 60 for biological process , molecular function , and cellular component , respectively ) are still lower than those of outparalogs ( 0 . 55 , 0 . 75 , and 0 . 69 ) and inparalogs ( 0 . 67 , 0 . 85 , 0 . 79 ) in the latest GO version analyzed ( Nov . 2011 ) , these relations may be reversed in the future if the trend in Fig . 1A–C continues . Specifically , we predict based on the trend in Fig . 1A–C that functional similarity in biological process , molecular function , and cellular component would become greater for orthologs than outparalogs in 2013 , 2018 , and 2013 , respectively . Similarly , functional similarity in the three GO aspects would become greater for orthologs than inparalogs in 2013 , 2043 , and 2015 , respectively . These findings suggest that the current GO annotations do not allow a definitive conclusion about the ortholog conjecture . What might have caused the differential rates of change in functional similarity between orthologs and paralogs over the last five years ? Because of the wide acceptance of the ortholog conjecture , similar functions between orthologs may have been deemed uninteresting and hence underreported , especially for those orthologs with high sequence similarities . Nonetheless , as more and more gene function data from each species accumulate , the impact of such biases should decline , resulting in a relative increase in the functional similarity of orthologs over time . Alternatively , the patterns in Fig . 1A–C could be due to a slower increase in the number of GO annotations for orthologs than paralogs , because this number is the denominator in the definition of functional similarity [4] ( see Materials and Methods ) . But , this potential explanation is incorrect . The numbers of GO annotations for the three types of homologs increased for each of the three aspects of GO ( P<0 . 002 , n = 5 , two-tail t-test , Fig . 1D–F ) , and the increase in the number of GO annotations for orthologs is faster than those for outparalogs and inparalogs for each of the three aspects of GO ( P<10−6 , two-tail Z-test ) , with the exception of the comparison in biological process GOs between orthologs and outparalogs ( P = 0 . 09 , two-tail Z-test ) . Another potential explanation is that there may be fewer organism-specific GO terms in later versions , which would boost the functional similarity between a randomly picked human gene and a randomly picked mouse gene as well as that between human-mouse orthologs . This possibility , however , can be ruled out for biological process GOs ( Fig . 1G ) and cellular component GOs ( Fig . 1H ) , because the percentage of shared GO terms in human and mouse decreased by 1 . 3% ( P = 0 . 012 , n = 5 , two-tail t-test ) and 2 . 5% ( P<0 . 001 , n = 5 , two-tail t-test ) per year between 2006 and 2011 for these two aspects of GO , respectively . For molecular function GOs , the percentage of shared GO terms increased by 3 . 8% annually ( P<0 . 001 , n = 5 , two-tail t-test; Fig . 1I ) , which may account for the relative increase of the molecular function similarity of orthologs over the years ( Fig . 1B ) . Overall , these analyses suggest that the rising functional similarity of orthologs , especially in biological processes and cellular components , is likely due to previous underreporting of shared functions of orthologs . In the remainder of the paper , we analyze the latest GO version ( Nov . 2011 ) unless otherwise noted . Another potential bias in GO annotations is the source of functional data . We examined all papers used by GO that simultaneously studied a pair of homologous genes ( co-study papers ) , and found that 21% of orthologs , 35% of outparalogs , and 62% of inparalogs in our dataset have been investigated in those co-study papers ( Fig . 2A ) . Not unexpectedly , functional similarity between homologous genes appears higher in co-study papers than in other sources of information for both orthologs and paralogs in all three GO aspects ( Fig . 2B–D ) . Thus , compared with paralogs , the under-representation of orthologs in co-study papers causes their functional similarity to appear lower . To examine whether the temporal changes of functional similarity shown in Fig . 1 are primarily caused by the biases created by the co-study papers , we repeated the analysis after removing all co-study papers . We found the results to be qualitatively unaltered ( Fig . S2 ) , suggesting that the co-study papers cannot account for the temporal patterns of functional similarity in Fig . 1 . An unexpected observation in Fig . 2B–D is that , while the functional similarity is approximately equal among orthologs , outparalogs , and inparalogs when functional data outside co-study papers are used , the functional similarity is much greater for paralogs than orthologs in co-study papers . We wonder whether GO annotations based on co-study papers have underestimated the functional similarity of orthologs , compared with paralogs . To address this question , we investigated among the co-study papers those that studied homologs with identical protein sequences , because these gene pairs should have highly similar if not identical functions that are dependent on protein sequences ( Table S2 ) . These homologs include 31 orthologous pairs , five inparalogous pairs , and four outparalogous pairs . All nine paralogous pairs have 100% functional similarity in the GO annotations based on the co-study papers , while this occurs to only nine of the 31 orthologous pairs ( P = 0 . 0002 , Fisher's exact test ) . More extremely , eight of the 31 orthologous pairs show 0% functional similarity ( Table 1 ) . Surprisingly , none of the co-study papers [32]–[39] of these eight orthologous pairs explicitly mentioned functional dissimilarity between these orthologs . Several biases and errors are apparent . First , many so-called experiment-based GO annotations are inferred based on the experiments of a homolog of the gene being annotated . For example , the molecular function of “protein binding” for human gene encoding GABA ( A ) receptor-associated protein ( ENSG00000170296 ) was inferred from an experiment with a monkey homolog rather than the human gene itself ( first case in Table 1 ) . Similarly , the three cellular component annotations for the mouse ortholog of the human gene were inferred from rat ( first case in Table 1 ) . Such between-species functional inferences in so-called experiment-based GO annotations make the test of the ortholog conjecture circular . Second , different experiments were often conducted for two orthologs in the same co-study paper , probably because many experiments are not equally feasible in two species . This practice necessarily renders the estimated functional similarity of orthologs low . The second case in Table 1 illustrates this point , where the human ortholog was examined for molecular function while the mouse ortholog was examined for cellular component . Third , annotation errors are also prevalent . For example , GO annotated human interleukin enhancer binding factor 2 ( ILF2 , ENSG00000143621 ) as having a molecular function of “DNA binding” based on the paper with a PMID of 11804788 ( third case in Table 1 ) , but nowhere in this paper was this molecular function experimentally demonstrated . Sometimes , an experiment was conducted in one species but annotated for another species . For instance , the mouse but not the human gene encoding ras-related C3 botulinum toxin substrate 3 ( Rac3 , ENSG00000169750 ) was annotated for the biological process of “neuron projection development” , despite that the experiment was done in human cells ( fourth case in Table 1 ) . These homology-based functional inferences , experimental biases , and annotation errors , together with the biases identified earlier and the temporariness of the GO-based finding , suggest that the ortholog conjecture cannot be tested with the current GO annotations . Because the biases and errors in GO are hard to control , we , like Nehrt et al . [4] , turned to genome-wide gene expression data , which are not subject to the type of biases in GO , because they were systematically generated at the genomic scale . While gene expression does not equal gene function , the expression level and pattern of a gene must be more or less concordant with its function such that expression similarity between genes should reflect their functional similarity to some degree [40] . The problems of using microarray data to measure expression similarities between genes and species have been well documented [24]–[25] . For instance , without appropriate normalization , an earlier study of microarray data reported the unexpected finding that the gene expression patterns of two different tissues from the same species ( e . g . , human heart and human testis ) are more similar than those from the same tissue of different species ( e . g . , human heart and mouse heart ) [41] . After the control of differential hybridizations between orthologs caused by probe differences , the above relation is reversed [25] . By contrast , RNA-Seq is immune to the probe bias [26] and has correctly revealed the lower expression similarity between different tissues of the same species than the same tissue of different species [42]–[43] . The dynamic range of RNA-Seq is also much greater than microarray and the linear relationship between cDNA concentrations and expression estimates is better in RNA-Seq than microarray , making RNA-Seq a preferred method of expression quantification [24] , [26] . Here we use a recently published RNA-Seq dataset that includes six male and four female tissues of human and mouse [42] to test the ortholog conjecture . In the RNA-Seq data , gene expression is measured in RPKM , standing for reads per kilobase of exon model per million mapped reads [44] . Because the distributions of gene expression levels differ substantially between human and mouse , it is inappropriate to compare the expression levels of human and mouse orthologs directly [18] . We thus transformed the expression levels of human and mouse genes to Z-scores after a log2 transformation of RPKM values ( see Materials and Methods ) [18] . That is , we draw a distribution of log2 ( RPKM ) for all genes in the genome and calculate the mean and standard deviation of the distribution; the Z-score of a gene is the distance between its log2 ( RPKM ) value and the mean of the distribution , divided by the standard deviations of the distribution . The expression similarity between homologs is a measure of similarity in Z-score between the two genes ( see Materials and Methods ) . We analyzed each tissue separately , because recent studies have shown that comparing across-tissue expression-profile similarities of different gene pairs using either Pearson's correlation or Euclidian distances is problematic , because the variation of tissue-specificity of expression among genes interferes with such a comparison [45]–[46] . We first describe the observations in the male liver . We found the expression similarity between orthologs significantly higher than that between inparalogs and that between outparalogs , with or without the control of protein sequence identity ( Fig . 3A; Table S3 ) . Further , expression similarity of orthologs declines with the decrease of protein sequence identity ( n = 11 , Pearson's correlation coefficient r = 0 . 96 , P<10−5 ) , suggesting that gene expression evolution and protein sequence evolution are correlated [25] , [47] . Using expression ranks ( see Materials and Methods ) instead of Z-scores yielded the similar result of higher expression similarities between orthologs than between inparalogs and between outparalogs ( Fig . 3B ) . Similarly , rank-based expression similarity of orthologs declines with the decrease of sequence identity ( n = 11; r = 0 . 97 , P<10−6 ) . All nine other tissues examined show generally similar patterns , except that in a number of tissues ( e . g . , testis ) inparalogs become more similar than orthologs when the protein sequence identity is low ( Figs . S3 and S4 ) . This is probably an artifact caused by the low expression levels of the inparalogs with low protein sequence identities ( Fig . S5 ) . By definition , these inparalogs evolve rapidly in protein sequence . They tend to be lowly expressed , because of the strong negative correlation between protein expression levels and evolutionary rates [48]–[51] . If two genes are both lowly expressed , their expression divergence would tend to be small . This bias is less severe for orthologs and outparalogs with similar levels sequence identity because they have longer divergence times and thus lower evolutionary rates than the inparalogs . Consequently , their expression levels are not so low ( Fig . S5 ) and their expression similarities are not so high . We also estimated the average expression similarity across all 10 tissues by Z-scores ( Fig . 3C; Table S3 ) or expression ranks ( Fig . 3D ) . The results are similar to those from individual tissues . In addition to measuring the functional similarity of orthologs between human and mouse , we also measured it between human and all other species in the RNA-Seq data , including chimpanzee , gorilla , orangutan , macaque , opossum , platypus , and chicken [42] . We found that the Z-score-based expression similarity of human inparalogs generated after the human-mouse separation is lower than the expression similarity of orthologs between human and all above species , with ( Fig . 4A; Table S4 ) or without ( Fig . 4B ) the control of protein sequence identity , when the male liver is examined . We also found the mean expression similarity of orthologs correlated with the divergence time of orthologs ( n = 8 , Spearman's correlation coefficient ρ = −0 . 95 , P = 0 . 001; Fig . 4B ) . Similar results were observed when rank-based expression similarities were used ( Fig . 4C , D ) . These patterns are generally common among all tissues examined ( Figs . S6 and S7 ) . Nehrt et al . 's cellular context hypothesis [4] asserts that the divergence of cellular context in which genes act is the key determinant of the functional divergence of homologous genes . It predicts that functional similarity between orthologs is lower than that between within-species paralogs , regardless of the divergence time between the orthologs and that between the paralogs . Hence , the above results ( Fig . 4; Figs . S6 and S7 ) are inconsistent with the cellular context hypothesis . Using male liver expression similarities of homologous genes estimated from the RNA-Seq data , we further tested Nehrt et al . 's cellular context hypothesis . Contrary to its prediction , we found the Z-score-based expression similarity between human-mouse orthologs always higher than that between within-human paralogs ( P≤0 . 031 , one-tail Z-test ) or within-mouse paralogs ( P≤0 . 003 , one-tail Z-test ) , irrespective of the divergence time of the paralogs ( Fig . 5 ) . This pattern is also generally observed in nine other tissues examined ( Fig . S8 ) . Use of rank-based expression similarities yielded similar results ( Fig . S9 ) . Together , the RNA-Seq data support the ortholog conjecture and refute the cellular context hypothesis .
Stimulated by Nehrt et al . 's pioneering test of the ortholog conjecture with large-scale functional and expression data [4] , we here examined the suitability of such data for testing this conjecture . We found several biases and errors in the current GO that limit its utility . First , we observed a steady increase over the last five years of the functional similarity between orthologs , compared with that between paralogs . Hence , even if today's GO shows a lower functional similarity between orthologs than between paralogs , this relation may be reversed in the future when there are more GO annotations . Second , compared with paralogs , orthologs are underrepresented in co-study papers that simultaneously examined homologous genes . Because co-study papers tend to report higher functional similarities than other papers , functional similarity of orthologs is underestimated , relative to that of paralogs . This result is consistent with a recent analysis of GO [52] . Third , a close examination of 31 co-study papers that studied homologs with identical protein sequences revealed that orthologs are more likely than paralogs to be subjected to different experiments , causing underestimation of functional similarity of orthologs . The high prevalence of this experimental bias observed from our relatively small sample is consistent with the recent report of the GO Consortium [23] . Fourth , the above examinations also revealed GO annotation errors that reduce the functional similarity of orthologs . Fifth and most disturbingly , the so-called experiment-based functional evidence in GO often originates from species other than the one being annotated . In other words , such functional data were inferred from those of homologous genes , rendering the entire test of the ortholog conjecture by GO circular . Because spotting such problems requires careful reading of original papers , we do not know the prevalence of this problem in GO at large . But four of the eight orthologous gene pairs in Table 1 involve such homology-based functional inferences . Very recently , Altenhoff et al . also reported a number of biases in GO annotations [52] . In addition to the co-study bias discussed above , they also found variation of GO term frequency among species , variation of background similarity among species pairs , and propagated annotation bias . These authors suggested that functional similarity between orthologs is slightly but significantly higher than that between paralogs , when these four biases are controlled . We are unsure about the reliability of their conclusion , because of the additional biases we detected that were not controlled in their study . Of special concern is the “contamination” of so-called experiment-based functional data by those inferred from homologs . The steady increase of the GO functional similarity between orthologs , relative to that between paralogs , over the last five years means that Altenhoff et al . 's result is at best temporary . With these considerations , we believe that the current GO cannot serve as a solid base for any conclusion regarding the ortholog conjecture . But we are aware of the ongoing improvement of GO annotations [29] and hope that GO will become useful for resolving the ortholog conjecture and other important biological problems in the future . Our analysis of RNA-Seq gene expression data , which are more suitable than microarray data for comparing expression levels of different genes and different species , provides strong evidence for the ortholog conjecture . We observed that expression similarity between orthologs is generally higher than that between paralogs , with or without controlling the protein sequence identity or divergence time between genes . We believe that Nehrt et al . 's finding of a lower expression similarity of orthologs than that of within-species paralogs was likely caused by the incomparability between different microarrays that artificially reduces between-species expression similarities [25] . This bias may be alleviated when orthologs and between-species paralogs are compared due to the occurrence of the same bias to both types of gene pairs . Indeed , when orthologs and between-species outparalogs are compared , both Nehrt et al . [4] and another recent study [53] found orthologs to be more similar in expression than outparalogs , consistent with the ortholog conjecture . Our RNA-Seq results also refute the cellular context hypothesis proposed by Nehrt et al . [4] , because we found orthologs to be more similar in expression than within-species paralogs ( Figs . 4 and 5 ) , opposite to the prediction of this hypothesis . We note that the definition of the ortholog conjecture is vague in that it does not clearly state whether ( i ) any orthologs and paralogs , ( ii ) only those with the same divergence time , or ( iii ) only those with the same sequence identity can be compared . Nehrt et al . [4] compared orthologs and paralogs of the same protein sequence identity . In our analysis of the gene expression data , all three comparisons were made . For example , Fig . 3 used ( i ) and ( iii ) . Fig . 4 used ( ii ) to show that inparalogs are functionally less similar than orthologs of various divergence times , including those that are older than the inparalogs . Fig . 5 also used ( ii ) to show that orthologs are functionally more similar than paralogs of similar divergence times or even smaller divergence times . Although our genome-wide gene expression analysis supports the ortholog conjecture , we caution that this is not the final proof of the ortholog conjecture , because it should be further tested with large-scale gene function data . Even in terms of gene expression , the ortholog conjecture could be further tested with splicing variants and protein expression levels when such data in multiple species/tissues become available . It is notable that , in addition to Nehrt et al . [4] , there are other lines of evidence from large-scale functional data that appear to be at odds with the ortholog conjecture [19] , [54] . For example , based on experimental as well as predicted protein subcellular localization data , we previously showed that the rate of protein subcellular relocalization during fungal evolution is not lower for orthologs than paralogs [55] , although a recent GO-based analysis suggests otherwise [52] . Another example is the report that protein interactions appear to be more similar between within-species paralogs than between orthologs [56] , but this finding is likely an artifact arising from vastly different coverages of high-throughput protein interaction data of different species . In fact , a low-throughput targeted study found that protein interactions are highly conserved between orthologs [10] , but a comparable study of paralogs is currently lacking . What is clear , however , is the complexity and difficulty of testing the ortholog conjecture with large , systematic , and reliable functional data . The biases and errors in GO identified here , coupled with those reported recently [23] , [52] , caution the interpretation of findings made from GO annotations . Despite its wide applications , GO information is registered and annotated by individual bioinformaticians based on studies designed and conducted by individual investigators . The shear size of the information in GO does not necessarily translate to quality or reliability . We conclude that to date there is no unambiguous genome-wide evidence against the ortholog conjecture , but further tests are needed .
Human and mouse within-species paralogs , and one-to-one orthologs between human and chimpanzee , gorilla , orangutan , macaque , mouse , opossum , platypus , and chicken were downloaded from EnsEMBL ( release 64 , Sept . 2011 ) . Protein sequence identity , information of the most recent common ancestor of a pair of paralogs , mapping of EnsEMBL Gene ID to UniProt/SwissProt Accession in human and MGI ID in mouse were also downloaded from EnsEMBL [57] . The information of the most recent common ancestor of within-species paralogs allows us to determine if the duplication occurred before ( outparalogs ) or after ( inparalogs ) the separation between human and mouse . In total , our dataset is composed of 20 , 799 human genes and 23 , 255 mouse genes , including 15 , 588 pairs of orthologs , 55 , 578 pairs of inparalogs , and 233 , 295 pairs of within-species outparalogs . The number of orthologs between human and chimpanzee , gorilla , orangutan , macaque , opossum , platypus , and chicken is 17 , 689 , 15 , 692 , 16758 , 16 , 211 , 13 , 475 , 10 , 832 , and 11 , 205 , respectively . GO annotations [58] in biological process , molecular function , and cellular component were retrieved from the GO database . We downloaded the lite version of GO-gene associations and used annotations with the evidence code of EXP ( inferred from experiment ) and its children ( IDA , inferred from direct assay; IEP , inferred from expression pattern; IGI , inferred from genetic interaction; IMP , inferred from mutant phenotype; and IPI , inferred from physical interaction ) . Because GO is represented by directed acyclic graphs ( DAGs ) , the original functional terms were propagated towards the root of each DAG ( with the root node excluded ) , producing a complete set of terms for each gene . OBO v1 . 2 ( version 12:10:2011 ) was used for GO term propagation . The “Is-A” type of relations from OBO was used to propagate GO terms . We used the same version of OBO ( 12:10:2011 ) in the time series analysis ( Fig . 1 ) . Using OBO versions from individual years did not alter our results . In total , 1 , 818 human and 3 , 515 mouse proteins had at least one propagated GO term in all GO annotation releases in the November of each year from 2006 to 2011 . Homologous gene pairs with protein sequence identity greater than 50% were used for estimating functional similarity . The final number of homologous gene pairs in our GO time series assay was 1 , 077 orthologs , 105 inparalogs , and 1 , 027 within-species outparalogs . The significance of the annual increase in functional similarity was assessed by the significance of the regression coefficient in a t-test [30] . The difference in annual increase between two independent samples was evaluated by testing the equality of regression coefficients in a Z-test [31] . In the GO annotation release of November 2011 , there were 4 , 494 orthologous pairs , 404 inparalogous pairs , and 13 , 449 within-species outparalogous pairs with associated PubMed IDs for both genes . Homologous pairs sharing at least one PubMed ID were identified as being studied in a co-study paper . If there is more than one co-study paper for a homologous pair , we chose the paper that annotated more shared GO terms for further analysis . In total , 924 orthologous pairs , 251 inparalogous pairs , and 4 , 751 within-species outparalogous pairs were co-studied . The RNA-Seq-based gene expression levels were obtained from Brawand et al . [42] , which included six male and four female tissues from humans and six male and five female tissues from mice . Six male tissues and four female tissues shared between human and mouse were used for further analysis . We calculated RPKM values of each gene from the Rz values in [42] . Rz is the mean per-base read coverage for each gene , computed for Ensembl-annotated exons with reads unambiguously mapped by TopHat ( version 1 . 0 . 13 ) [42] . To make the expression levels comparable between species and tissues , we first calculated log2 ( RPKM ) and then transformed it to a Z-score within each tissue of each species . As a result , gene expressions within a tissue of a species have a mean of 0 and a standard deviation of 1 [18] . To be included in subsequent analyses , both members of a homologous gene pair must have non-zero RPKM values . In addition to the Z-score-based analysis , we ranked genes in each tissue of each species according to their expression levels and converted the ranks to percentile ranks . In total , we analyzed the expression levels of 12 , 219 human genes and 12 , 048 mouse genes in all 10 tissues . There are several different measures of functional similarity in the literature [59] , but a recent study found them to yield similar results [52] . We used the measure proposed by Nehrt et al . [4] to calculate GO-based functional similarity between a pair of genes so that our results are directly comparable to theirs . Let Ti be the set of propagated GO terms for gene i and Tj be the corresponding set for gene j . Functional similarity between i and j is defined by We used the expression Z-scores in each tissue to estimate expression similarity between genes . Let Zi be a Z-score for gene i and Zj be the Z-score for gene j in the tissue concerned . The expression similarity between i and j was calculated by , which has a maximal value of 1 . We similarly estimated the expression similarly using percentile ranks , with the replacement of Z-scores by expression percentile ranks . | Today's exceedingly high speed of genome sequencing , compared with the generally slow pace of functional assay , means that the functions of most genes identified from genome sequences will be annotated only through computational prediction . The primary source of information for this prediction is the functions of orthologous genes in model organisms , because orthologs are widely believed to be functionally similar , especially when compared with paralogs . This belief , known as the ortholog conjecture , was recently challenged on the basis of experimentally derived Gene Ontology ( GO ) annotations and microarray gene expression data , because these data revealed greater functional and expressional similarities of paralogs than orthologs . Here we show that GO-based estimates of functional similarities are temporary and unreliable , due to experimental biases , annotation errors , and homology-based functional inferences that are incorrectly labeled as experimental in GO . RNA sequencing ( RNA-Seq ) is superior to microarray for comparing the expressions of different genes or in different species , and our analysis of a large RNA-Seq dataset provides strong support to the ortholog conjecture for gene expression . We conclude that the ortholog conjecture remains largely valid to the extent that it has been tested , but further scrutiny using more and better functional data is needed . | [
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] | 2012 | The Ortholog Conjecture Is Untestable by the Current Gene Ontology but Is Supported by RNA Sequencing Data |
This article reviews essential topics of canine visceral leishmaniasis ( CVL ) due to Leishmania infantum infection . It focuses on the current serological and molecular diagnostic methods used in epidemiological research and veterinary clinics to diagnose CVL and includes new point-of-care ( POC ) tests under development . The efficacy of different treatment regimens on the clinical improvement and infectiousness of dogs is also addressed . In the last section , the review provides a critical appraisal of the effectiveness of different control measures that have been implemented to curb disease transmission .
CVL negatively impacts society from medical , veterinary , and societal standpoints . Studies on risk factors for human infection with L . infantum have yielded opposing results , but a meta-analysis suggested that owners of infected dogs and household members could be at high risk of infection , at least in the Americas [1] . Based on this generalized concept , strategies to comply with public health guidelines typically lead to difficult or expensive decisions . In developed countries , infected dogs are subjected to different treatment protocols that improve the clinical condition but do not clear L . infantum , while in developing countries , the recommended euthanasia of infected dogs generates societal conflicts [2–4] . Herein , we review current serological and molecular tools for the diagnosis of CVL , the impact of treatment on infectiousness , and control strategies to prevent infection and disease development .
The selection of studies referenced in this review was based on searches in PubMed , Library of Congress , Web of Science , Scielo , and Google Scholar , with no specific year range . The search strategy included combinations of the following key words: “canine visceral leishmaniasis , ” “leishmaniasis , ” “diagnosis , ” “molecular diagnostic methods , ” “serology , ” “treatment , ” “xenodiagnosis , ” “infectivity , ” “prevention , ” “control , ” “dog culling , ” and “vaccination . ” Paper selection was grounded in the specific information each article provided to the different sections of this review .
The analytical sensitivity of molecular tests suggests they can detect between 0 . 001 and 0 . 1 parasite/reaction [5–8] . However , determining the actual diagnostic efficacy at different infection stages could be problematic due to the relatively heterogeneous clinical criteria used in different studies . Before guidelines were established [9] , categorization as oligosymptomatic or polysymptomatic animals according to the number of CVL signs and symptoms could vary between studies , making them difficult to compare . The seroconversion to parasite antigens can be as early as one month after an infective phlebotomine bite [32] . Active CVL is usually associated with significant antibody titers of all classes , whereas low antibody levels are characteristic of subclinical infections or exposed but uninfected dogs [33] . Although the use of different antibody isotypes was proposed for improving serological evaluations [34–36] , the most recent data from a cohort of 134 dogs suggest that isotype responses have no major predictive value [37] . Increased levels of immunoglobulin G2 ( IgG2 ) were associated with protective responses , while rising IgG1 production was considered as bad prognosis [35] . IgE and IgA seem to be detected mostly in active CVL but with low predictive value in asymptomatic dogs [35 , 38 , 39] . Clinical and epidemiological studies have used serological tests involving whole parasites , soluble parasite extracts , or recombinant proteins derived from genes of interest . The recent development of chimeric antigens with relevant protein epitopes demonstrated the capacity of this method to detect specific antibodies during active disease or asymptomatic infection [40 , 41] . The direct agglutination test ( DAT ) is based on the agglutination of trypsinized Coomassie-stained Leishmania promastigotes by anti-Leishmania antibodies . It was the first serological test developed for field use . It is simple , cheap , and reliable , with proven clinical accuracy ( S1 Table ) [42 , 43] . Moreover , it can be performed in laboratories not requiring electrical equipment and has up to 2 years of shelf life . DAT has long incubation times and requires some level of expertise to run and read the test [43 , 44] . It has a sensitivity and specificity of 91% to 100% and 72% to 100% , respectively , yet subjective reading of end-point titers leads to interobserver discrepancy [43 , 45] . Despite those drawbacks , DAT is well accepted as a routine serologic test usually applied to a large number of samples [46] . The fast agglutination screening test ( FAST ) is a modification of DAT based on a single serum dilution above the cutoff point of normal sera . It requires shorter incubation times and has been optimized for screening large dog populations [46] . The immunofluorescence antibody test ( IFAT ) against Leishmania promastigotes is the reference qualitative serological method for CVL diagnosis [47] . The use of IFAT is restricted to laboratory settings because it needs specialized equipment and trained personnel [48] . The specificity and sensitivity are close to 100% in symptomatic animals . Some notable limitations are the cross-reactivity with other pathogens such as trypanosomes [47 , 48] and the significantly lower sensitivity for identifying asymptomatic dogs compared with ELISA [41] . ELISA allows screening large numbers of samples utilizing antigen-coated microplates and a spectrophotometer that determines antibody titers by optical density . The potential for absolute antibody quantification renders ELISA as a powerful tool that is less susceptible to operator bias . One of its strengths is the possibility of using combinations of multiple antigens , thereby increasing the sensitivity and/or specificity of the method ( S2 Table ) [49 , 50] . Flow cytometry ( FC ) is an emerging technology [51] that quantifies antibodies against Leishmania surface antigens , avoiding cross-reactivity against more conserved intracellular structures . Using amastigotes or promastigotes , this method achieved high levels of sensitivity and specificity and has been shown to distinguish serological profiles of infected but clinically healthy versus sick dogs [52] .
In Europe , the treatment of CVL has been almost exclusively limited to the use of pentavalent antimony meglumine antimoniate . The recommended regimen of 35 to 50 mg/kg subcutaneously twice daily for 4 to 6 weeks [9] demonstrated good clinical efficacy but without clearing the infection . The combination with allopurinol showed better response to treatment of sick dogs , with good clinical recovery and improvement of hematological and biochemical abnormalities [73] . Allopurinol administration ( 10 mg/kg by mouth twice daily ) for 6 to 12 months after an antimonial course was highly leishmaniostatic , maintaining treated dogs in long-term clinical remission [74] . Other leishmanicidal drugs such as miltefosine at a dosage of 2 mg/kg by mouth once daily for 4 weeks , in combination with allopurinol , demonstrated leishmanicidal efficacy in naturally infected dogs [75] . Other drugs against CVL have been studied in vivo or in vitro such as aminosidine , pentamidine , enrofloxacine , and marbofloxacine , but further controlled clinical trials are needed [76 , 77] . Some of them might be used as an alternative when first line therapy fails or renal function is altered [78] . A new therapeutic trend is the combination of parasiticidal–parasitostatic drugs and immunomodulators aimed at reducing parasite burden and establishing an appropriate immune response ( domperidone or Protein Aggregate Magnesium-Ammonium Phospholinoleate-Palmitoleate Anhydride ( P-MAPA ) [79 , 80] . Nevertheless , most dogs remain infected and might relapse , becoming infectious to healthy dogs and other hosts , including humans . Until new therapies that consistently clear L . infantum are found , dog infectivity can be managed by applying topical repellents that can reduce transmission risks to near zero ( see section “Are prevention and control strategies working ? ” ) . Xenodiagnosis ( sand fly feeding on host ) is the best alternative to determine dog infectiousness , but this method can only be applied in specialized research centers [81] . The first research using posttreatment xenodiagnosis that demonstrated significant reduction of dog infectiousness was published by Gradoni et al . [82] . Afterwards , Alvar et al . [83] evaluated six dogs treated with meglumine antimoniate in combination with allopurinol , reporting that all dogs were noninfective for a “few months” after chemotherapy . Guarga et al . [84] evaluated dogs ( n = 10 ) treated with meglumine antimoniate and found a significant reduction in dog infectivity , which persisted until the end of the study ( 120–180 days ) . Another study used Lutzomyia longipalpis to evaluate the effectiveness of a liposome formulation of meglumine antimoniate [85] . Dogs were treated parenterally with antimonials , empty liposomes , or isotonic saline solution . A significant reduction of dog infectivity was found in the group treated with antimonials as determined 150 days post treatment . More recently , Miró et al . [86] evaluated 32 dogs with CVL that were subjected to three different treatments: antimonials , antimonials plus allopurinol , or allopurinol alone . They showed a considerable reduction of infectivity of all three groups and significant decrease of parasite burden in bone narrow . A study conducted in Brazil by da Silva et al . [87] included 52 dogs distributed in six treatment groups: liposomal formulation of antimonials , allopurinol , liposomal formulation of antimonials plus allopurinol , empty liposomes plus allopurinol , empty liposomes , and saline solution . The negative xenodiagnosis and qPCR quantification of L . infantum in the skin below the infectious threshold indicated that antimonials plus allopurinol was the most efficient regime to decrease infectivity to sand flies . A recent study in Brazil ( n = 36 dogs ) assessed the infectivity of sick dogs after a conventional miltefosine treatment . After three months of treatment , there was a significant reduction of parasite load in the bone marrow , lymph nodes , and skin . These results correlated with the xenodiagnoses in which 74 . 2% of dogs were noninfectious for sand flies [88] . In conclusion , treatment of sick dogs in endemic areas decreases canine infectiousness , thus diminishing the epidemiological risks for humans and other uninfected dogs . Assessment of parasite loads in the ear skin by qPCR has been proposed as surrogate marker of infectiousness [89] and may be used whenever xenodiagnosis is not available . Nevertheless , further studies on posttreatment infectiousness of canines using different drugs are still needed . Despite not being a simple method , xenodiagnosis is a useful tool to assess the infectious capacity of dogs treated with new drugs and/or new treatment regimens .
Several strategies have been proposed for preventing and controlling CVL at both individual and population levels . Prevention of infection can be achieved by applying insecticide-impregnated collars or spot-on products ( e . g . , deltamethrin , permethrin , flumethrin , fipronil ) on dogs , whereas the risk of disease development can be reduced by vaccination or immunomodulation . The effectiveness of some of these strategies has been assessed by recent systematic reviews and meta-analyses [90 , 91] . The conclusions based on either parasitological or serological evidence were that repellents and prophylactic medication ( i . e . , domperidone ) tended to reduce the proportion of dogs infected with L . infantum [90] . Nonetheless , Wylie et al . [91] also concluded that well-designed , adequately powered , and properly reported randomized clinical trials are needed to clearly establish the efficacy of vaccines against CVL . Culling of infected dogs has been recommended as a control strategy in many endemic countries . A cluster-randomized trial in Northeast Brazil reported a low to moderate effectiveness of dog culling and concluded that there is an urgent need for revision of the Brazilian VL control program [92] . González et al . [93] reviewed two trials from Brazil that evaluated the effects of culling infected dogs compared to no intervention or indoor residual spraying . Although these trials reported a reduction in seroconversion over 18-month follow-up , they did not measure or report effects on clinical disease in humans [93] . Mathematical models suggest that dog culling alone is not effective in areas of high transmission [94] . According to Costa et al . [94] , the indiscriminate culling of healthy , seropositive dogs may jeopardize the effectiveness of the control program if low specificity tests are used , thus increasing the chance of generating outrage in the population and reducing the adherence to the program . In Iran , the utilization of insecticide-impregnated dog collars in nine villages showed significantly decreased seroconversion in children ( odds ratio [OR] 0 . 57; 95% CI 0 . 36–0 . 90; p = 0 . 017 ) and dogs ( OR 0 . 46; CI 0 . 30–0 . 70; p = 0 . 0003 ) compared with nine nonintervened villages [95] . While the above-mentioned strategies ( e . g . , repellents , vaccination , and immunomodulation ) may work at the individual level , their effectiveness at the population level still needs to be demonstrated when the intervention is transferred to the communities . Indeed , the effectiveness of strategies like community-wide application of insecticide-impregnated collars is directly dependant on coverage and loss rate [96] . This is particularly unrealistic in developing countries , considering the limited economic and human resources , especially in periods of crisis and political upheavals . Moreover , in these countries , stray dogs will not be targeted by such strategies and may function as infection reservoirs . As for any zoonosis , stray dog population management should be part of any VL control programs .
Molecular tools are mainly restricted to research laboratories , but progress is being made towards field applicability . The costs of real-time PCR machine and the need for sophisticated laboratory infrastructure highlight the importance of validating and implementing POC molecular tests that could be used in-clinic and in the field . The veterinarians should rely also on complementary serological information to make the best decision regarding both the animal’s health and the epidemiological risk it entails . The remaining challenge for serological tests is to improve the capacity to differentiate clinically healthy but infected and vaccinated dogs . The evaluation of new antileishmanial drugs should be complemented by standardized follow-up that includes the infectiousness status of dogs at different times post treatment . Nowadays , veterinarians and dog owners have different options to decrease infection risks , of which repellents are still the most important prevention tools as demonstrated by laboratory and field studies . New vaccines that could reduce the risk of disease development and infectiousness of vaccinated dogs are urgently needed . | Dogs are the principal reservoir hosts of L . infantum and consequently play a critical role in the transmission cycle of urban VL , which also affects humans . This review provides updated information on important topics such as diagnostic tests and dog treatments that improve dog health and decrease their transmission efficacy to insect vectors . A critical review of control measures is also provided . | [
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] | 2018 | Canine visceral leishmaniasis: Diagnosis and management of the reservoir living among us |
Humans have been shown to combine noisy sensory information with previous experience ( priors ) , in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration . However , when the prior distribution becomes more complex than a simple Gaussian , such as skewed or bimodal , training takes much longer and performance appears suboptimal . It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior , or from additional constraints in performing probabilistic computations on complex distributions , even when accurately represented . Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution , thereby removing the need to remember the prior . Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations , which changed on each trial . Different classes of priors were examined ( Gaussian , unimodal , bimodal ) . Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal . The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue , suggesting that suboptimality in dealing with complex statistical features , such as bimodality , may be due to a problem of acquiring the priors rather than computing with them . We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality . Our analysis rejects several models of stochastic behavior , including probability matching and sample-averaging strategies . Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors , and by variability in the decision process , which we represent as a noisy or stochastic posterior .
Humans have been shown to integrate prior knowledge and sensory information in a probabilistic manner to obtain optimal ( or nearly so ) estimates of behaviorally relevant stimulus quantities , such as speed [1] , [2] , orientation [3] , direction of motion [4] , interval duration [5]–[8] and position [9]–[11] . Prior expectations about the values taken by the task-relevant variable are usually assumed to be learned either from statistics of the natural environment [1]–[3] or during the course of the experiment [4]–[6] , [8]–[11]; the latter include studies in which a pre-existing prior is modified in the experimental context [12] , [13] . Behavior in these perceptual and sensorimotor tasks is qualitatively and often quantitatively well described by Bayesian Decision Theory ( BDT ) [14] , [15] . The extent to which we are capable of performing probabilistic inference on complex distributions that go beyond simple Gaussians , and the algorithms and approximations that we might use , is still unclear [14] . For example , it has been suggested that humans might approximate Bayesian computations by drawing random samples from the posterior distribution [16]–[19] . A major problem in testing hypotheses about human probabilistic inference is the difficulty in identifying the source of suboptimality , that is , separating any constraints and idiosyncrasies in performing Bayesian computations per se from any deficiencies in learning and recalling the correct prior . For example , previous work has examined Bayesian integration in the presence of experimentally-imposed bimodal priors [4] , [8] , [9] , [20] . Here the normative prescription of BDT under a wide variety of assumptions would be that responses should be biased towards one peak of the distribution or the other , depending on the current sensory information . However , for such bimodal priors , the emergence of Bayesian biases can require thousands of trials [9] or be apparent only on pooled data [4] , and often data show at best a complex pattern of biases which is only in partial agreement with the underlying distribution [8] , [20] . It is unknown whether this mismatch is due to the difficulty of learning statistical features of the bimodal distribution or if the bimodal prior is actually fully learned but our ability to perform Bayesian computation with it is limited . In the current study we look systematically at how people integrate uncertain cues with trial-dependent ‘prior’ distributions that are explicitly made available to the subjects . The priors were displayed as an array of potential targets distributed according to various density classes – Gaussian , unimodal or bimodal . Our paradigm allows full control over the generative model of the task and separates the aspect of computing with a probability distribution from the problem of learning and recalling a prior . We examine subjects' performance in manipulating probabilistic information as a function of the shape of the prior . Participants' behavior in the task is in qualitative agreement with Bayesian integration , although quite variable and generally suboptimal , but the degree of suboptimality does not differ significantly across different classes of distributions or levels of reliability of the cue . In particular , performance was not greatly affected by complexity of the distribution per se – for instance , people's performance with bimodal priors is analogous to that with Gaussian priors , in contrast to previous learning experiments [8] , [9] . This finding suggests that major deviations encountered in previous studies are likely to be primarily caused by the difficulty in learning complex statistical features rather than computing with them . We systematically explore the sources of suboptimality and variability in subjects' responses by employing a methodology that has been recently called factorial model comparison [21] . Using this approach we generate a set of models by combining different sources of suboptimality , such as different approximations in decision making with different forms of sensory noise , in a factorial manner . Our model comparison is able to reject some common models of variability in decision making , such as probability matching with the posterior distribution ( posterior-matching ) or a sampling-average strategy consisting of averaging a number of samples from the posterior distribution . The observer model that best describes the data is a Bayesian observer with a slightly mismatched representation of the likelihoods , with sensory noise in the estimation of the parameters of the prior , that occasionally lapses , and most importantly has a stochastic representation of the posterior that may represent additional variability in the inference process or in action selection .
We first performed a model-free analysis of subjects' performance . Figure 3 shows three representative prior distributions and the pooled subjects' responses as a function of the cue position for low ( red ) and high ( blue ) noise cues . Note that pooled data are used here only for display and all subjects' datasets were analyzed individually . The cue positions and responses in Figure 3 are reported in a coordinate system relative to the mean of the prior ( set as ) . For all analyses we consider relative coordinates without loss of generality , having verified the assumption of translational invariance of our task ( see Section 1 in Text S1 ) . Figure 3 shows that subjects' performance was affected by both details of the prior distribution and the cue . Also , subjects' mean performance ( continuous lines in Figure 3 ) show deviations from the prediction of an optimal Bayesian observer ( dashed lines ) , suggesting that subjects behavior may have been suboptimal . Our model-free analysis showed that subjects' performance in the task was suboptimal . Here we examine the source of this apparent suboptimality . Subjects' performance is modelled with a family of Bayesian ideal observers which incorporate various hypotheses about the decision-making process and internal representation of the task , with the aim of teasing out the major sources of subjects' suboptimality; see Figure 1e for a depiction of the elements of decision making in a trial . All these observers are ‘Bayesian’ because they build a posterior distribution through Bayes' rule , but the operations they perform with the posterior can differ from the normative prescriptions of Bayesian Decision Theory ( BDT ) . We construct a large model set with a factorial approach that consists in combining different independent model ‘factors’ that can take different ‘levels’ [8] , [21] . The basic factors we consider are: Observer models are identified by a model string , for example ‘BDT-P-L’ indicates an observer model that follows BDT with a noisy estimate of the prior and suffers from occasional lapses . Our basic model set comprises 24 observer models; we also considered several variants of these models that are described in the text . All main factors are explained in the following sections and summarized in Table 2 . The term ‘model component' is used through the text to indicate both factors and levels . For each observer model and each subject’s dataset we evaluated the posterior distribution of parameters , where is in general a vector of model-dependent parameters ( see Table 2 ) . Each subject's dataset comprised of two sessions ( training and test ) , for a total of about 1200 trials divided in 32 distinct conditions ( 8 priors 2 noise levels 2 sessions ) . In general , we assumed subjects shared the motor parameter across sessions . We also assumed that from training to test sessions people would use the same high-noise to low-noise ratio between cue variability ( ) ; so only one cue-noise parameter ( ) needed to be specified for the test session . Conversely , we assumed that the other noise-related parameters , if present ( , , , ) , could change freely between sessions , reasoning that additional response variability can be affected by the presence or absence of feedback , or as a result of the difference between training and test distributions . These assumptions were validated via a preliminary model comparison ( see Section 5 in Text S1 ) . Table 2 lists a summary of observer models and their free parameters . The posterior distributions of the parameters were obtained through a slice sampling Monte Carlo method [29] . In general , we assumed noninformative priors over the parameters except for motor noise parameter and cue-estimation sensory noise parameter ( when present ) , for which we determined a reasonable range of values through an independent experiment ( see Methods and Text S3 ) . Via sampling we also computed for each dataset a measure of complexity and goodness of fit of each observer model , the Deviance Information Criterion ( DIC ) [30] , which we used as an approximation of the marginal likelihood to perform model comparison ( see Methods ) . We compared observer models according to a hierarchical Bayesian model selection ( BMS ) method that treats subjects and models as random effects [31] . That is , we assumed that multiple observer models could be present in the population , and we computed how likely it is that a specific model ( or model level within a factor ) generated the data of a randomly chosen subject , given the model evidence represented by the subjects' DIC scores ( see Methods for details ) . As a Bayesian metric of significance we used the exceedance probability of one model ( or model level ) being more likely than any other model ( or model levels within a factor ) . In Text S1 we report instead a classical ( frequentist ) analysis of the group difference in DIC between models ( GDIC ) , which assumes that all datasets have been generated by the same unknown observer model . In spite of different assumptions , BMS and GDIC agree on the most likely observer model , validating the robustness of our main findings . The two approaches exhibit differences with respect to model ranking , due to the fact that , as a ‘fixed effect’ method , GDIC does not account for group heterogeneity and outliers [31] ( see Section 4 in Text S1 for details ) . Finally , we assessed the impact of each factor on model performance by computing the average change in DIC associated with a given component . After establishing model SPK-P-L as the ‘best’ description of the data among the considered observer models , we examined its properties . First of all , we inspected the posterior distribution of the model parameters given the data for each subject . In almost all cases the marginalized posterior distributions were unimodal with a well-defined peak . We therefore summarized each posterior distribution with a point estimate ( a robust mean ) with minor loss of generality; group averages are listed in Table 3 . For the analyses in this section we ignored outlier parameter values that fell more than 3 SDs away from the group mean ( this rule excluded at most one value per parameter ) . In general , we found a reasonable statistical agreement between parameters of different sessions , with some discrepancies in the unimodal test session only . In this section , inferred values are reported as mean SD across subjects . The motor noise parameter took typical values of screen units ( mm ) , somewhat larger on average than the values found in the sensorimotor estimation experiment , although still in a reasonable range ( see Text S3 ) . The inferred amount of motor noise is lower than estimates from previous studies in reaching and pointing ( e . g . [10] ) , but in our task subjects had the possibility to adjust their end-point position . The internal estimates of cue variability for low-noise and high-noise cues ( and ) were broadly scattered around the true values ( and screen units ) . In general , individual values were in qualitative agreement with the true parameters but showed quantitative discrepancies . Differences were manifest also at the group level , as we found statistically significant disagreement for both low and high-noise cues in the unimodal test session ( -test , ) and high-noise cues in the bimodal test session ( ) . The ratio between the two likelihood parameters , , differed significantly from the true ratio , ( ) . A few subjects ( ) were very precise in their decision-making process , with a power function exponent . For the majority of subjects , however , took values between and ( median ) , corresponding approximately to an amount of decision noise of of the variance of the posterior distribution ( median ) . The range of exponents is compatible with values of ( number of samples ) previously reported in other experiments , such as a distance-estimation task [33] or ‘intuitive physics’ judgments [35] . In agreement with the results of our previous model comparison , the inferred exponents suggest that subjects' stochastic decision making followed the shape of a considerably narrower version of the posterior distribution ( ) which is not simply a form of posterior-matching ( ) . The Weber's fraction of estimation of the parameters of the priors' density took typical values of , with similar means across conditions . These values denote quite a large amount of noise in estimating ( or manipulating ) properties of the priors . Nonetheless , such values are in qualitative agreeement with a density/numerosity estimation experiment in which a change of in density or numerosity of a field of random dots was necessary for subjects to note a difference in either property [36] . Although the two tasks are too different to allow a direct quantitative comparison , the thresholds measured in [36] suggest that density/numerosity estimation can indeed be as noisy as we found . Finally , even though we did not set an informative prior over the parameter , the lapse rate took reasonably low values as expected from a probability of occasional mistakes [28] , [37] . We found , and the inferred lapse rate averaged over training and test session was less than for all but one subject . We examined the best observer model's capability to reproduce our subjects' performance . For each subject and group , we generated datasets simulating the responses of the SPK-P-L observer model to the experimental trials experienced by the subject . For each simulated dataset , model parameters were sampled from the posterior distribution of the parameters given the data . For each condition ( shape of prior and cue type ) we then computed the optimality index and averaged it across simulated datasets . The model's ‘postdictions’ are plotted in Figure 10 as continuous lines ( SE are omitted for clarity ) and appear to be in good agreement with the data . Note that the postdiction is not exactly a fit since ( a ) the parameters are not optimized specifically to minimize performance error , and ( b ) the whole posterior distribution of the parameters is used and not just a ‘best’ point estimate . As a comparison , we also plotted in Figure 10 the postdiction for the best BDT observer model , BDT-P-L ( dashed line ) . As the model comparison suggested , standard Bayesian Decision Theory fails to capture subjects' performance . For each subject and group ( training and test ) we also plot the mean optimality index of the simulated sessions against the optimality index computed from the data , finding a good correlation ( ; see Figure 11 ) . Lastly , to gain an insight on subjects' systematic response biases , we used our framework in order to nonparametrically reconstruct what the subjects' priors in the various conditions would look like [2] , [3] , [8] , [9] ( see Methods ) . Due to limited data per condition and computational constraints , we recovered the subjects' priors at the group level and for model SPK-L , without additional noise on the priors ( P ) . The reconstructed average priors for distinct test sessions are shown in Figure 12 . Reconstructed priors display a very good match with the true priors for the Gaussian session and show minor deviations in the other sessions . The ability of the model to reconstruct the priors – modulo residual idiosyncrasies – is indicative of the goodness of the observer model in capturing subjects' sources of suboptimality .
Subjects integrated probabilistic information from both prior and cue in our task , but rarely exhibited the signature of full ‘synergistic integration’ , i . e . a performance above that which could be obtained by using either the prior or the cue alone ( see Figure 5 ) . However , unlike most studies of Bayesian learning , on each trial in our study subjects were presented with a new prior . A previous study on movement planning with probabilistic information ( and fewer conditions ) similarly found that subjects violated conditions of optimality [23] . More interestingly , in our data the relative degree of suboptimality did not show substantial differences across distinct classes of priors and noise levels of the cue ( low-noise and high-noise ) . This finding suggests that human efficacy at probabilistic inference is only mildly affected by complexity of the prior per se , at least for the distributions we have used . Conversely , the process of learning priors is considerably affected by the class of the distribution: for instance , learning a bimodal prior ( when it is learnt at all ) can require thousands of trials [9] , whereas mean and variance of a single Gaussian can be acquired reliably within a few hundred trials [11] . Within the same session , subjects' relative performance was influenced by the specific shape of the prior . In particular , for Gaussian priors we found a systematic effect of the variance – subjects performed worse with wider priors , more than what would be expected by taking into account the objective decrease in available information . Interestingly , neither noise in estimation of the prior width ( factor P ) nor occasional lapses that follow the shape of the prior itself ( factor L ) are sufficient to explain this effect . Model postdictions of model BDT-P-L show large systematic deviations from subjects' performance in the Gaussian sessions , whereas the best model with decision noise , SPK-P-L , is able to capture subjects' behavior; see top left and top right panels in Figure 10 . Moreover , the Gaussian priors recovered under model SPK-L match extremely well the true priors , furthering the role of the stochastic posterior in fully explaining subjects' performance with Gaussians . The crucial aspect of model SPK may be that decision noise is proportional to the width of the posterior , and not merely of the prior . In the unimodal test session , subjects' performance was positively correlated with the width of the main peak of the distribution . That is , non-Gaussian , narrow-peaked priors ( such as priors 1 and 6 in Figure 12b ) induced worse performance than broad and smooth distributions ( e . g . priors 4 and 8 ) . Subjects tended to ‘mistrust’ the prior , especially in the high-noise condition , giving excess weight to the cue ( is significantly lower than it should be; see Table 3 ) , which can be also interpreted as an overestimation of the width of the prior . In agreement with this description , the reconstructed priors in Figure 12b show a general tendency to overestimate the width of the narrower peaks , as we found in a previous study of interval timing [8] . This behavior is compatible with a well-known human tendency of underestimating ( or , alternatively , underweighting ) the probability of occurrence of highly probable results and overestimating ( overweighting ) the frequency of rare events ( see [27] , [38] , [39] ) . Similar biases in estimating and manipulating prior distributions may be explained with an hyperprior that favors more entropic and , therefore , smoother priors in order to avoid ‘overfitting’ to the environment [40] . In building our observer models we made several assumptions . For all models we assumed that the prior adopted by observers in Eq . 2 corresponded to a continuous approximation of the probability density function displayed on screen , or a noisy estimate thereof . We verified that using the original discrete representation does not improve model performance . Clearly , subjects may have been affected by the discretization of the prior in other ways , but we assumed that such errors could be absorbed by other model components . We also assumed subjects quickly acquired a correct internal model of the probabilistic structure of the task , through practice and feedback , although quantitative details ( i . e . model parameters ) could be mismatched with respect to the true parameters . Formally , our observer models were not ‘actor’ models in the sense that they did not take into account the motor error in the computation of the expected loss . However , this was with negligible loss of generality since the motor term has no influence on the inference of the optimal target for single Gaussians priors , and yields empirically negligible impact for other priors for small values of the motor error ( as those measured in our task; see Text S3 ) . Suboptimality was introduced into our observer models in three main ways: ( a ) miscalibration of the parameters of the likelihood; ( b ) models of approximate inference; and ( c ) additional stochasticity , either on the sensory inputs or in the decision-making process itself . Motor noise was another source of suboptimality , but its contribution was comparably low . Miscalibration of the parameters of the likelihood means that the subjective estimates of the reliability of the cues ( and ) could differ from the true values ( and ) . In fact , we found slight to moderate discrepancies , which became substantial in some conditions . Previous studies have investigated whether subjects have ( or develop ) a correct internal estimate of relevant noise parameters ( i . e . the likelihood ) which may correspond to their own sensory or motor variability plus some externally injected noise . In several cases subjects were found to have a miscalibrated model of their own variability which led to suboptimal behavior [33] , [41]–[43] , although there are cases in which subjects were able to develop correct estimates of such parameters [10] , [44] , [45] . More generally , it could be that subjects were not only using incorrect parameters for the task , but built a wrong internal model or were employing approximations in the inference process . For our task , which has a relatively simple one-dimensional structure , we did not find evidence that subjects were using low-order approximations of the posterior distribution . Also , the capability of our models to recover the subjects' priors in good agreement with the true priors suggest that subjects' internal model of the task was not too discrepant from the true one . Crucial element in all our models was the inclusion of extra sources of variability , in particular in decision making . Whereas most forms of added noise have a clear interpretation , such as sensory noise in the estimation of the cue location , or in estimating the parameters of the prior , the so-called ‘stochastic posterior’ deserves an extended explanation . We introduced the stochastic posterior model of decision making , SPK , with two intuitive interpretations , that is a noisy posterior or a sample-based approximation ( see Figure 7 and Text S2 ) , but clearly any process that produces a variability in the target choice distribution that approximates a power function of the posterior is a candidate explanation . The stochastic posterior captures the main trait of decision noise , that is a variability that depends on the shape of the posterior [33] , as opposed to other forms of noise that do not depend on the decision process . Outstanding open questions are therefore which kind of process could be behind the observed noise in decision making , and during which stage it arises , e . g . whether it is due to inference or to action selection [46] . A seemingly promising candidate for the source of noise in the inference is neuronal variability in the nervous system [47] . Although the noisy representation of the posterior distribution in Figure 7b through a population of units may be a simplistic cartoon , the posterior could be encoded in subtler ways ( see for instance [48] ) . However , neuronal noise itself may not be enough to explain the amount of observed variability ( see Text S2 ) . An extension of this hypothesis is that the noise may emerge since suboptimal computations magnify the underlying variability [49] . Conversely , another scenario is represented by the sampling hypothesis , an approximate algorithm for probabilistic inference which could be implemented at the neural level [19] . Our analysis ruled out an observer whose decision-making process consists in taking the average of samples from the posterior – operation that implicitly assumes a quadratic loss function – showing that averaging samples from the posterior is not a generally valid approach , although differences can be small for unimodal distributions . More generally , the sampling method should always take into account the loss function of the task , which in our case is closer to a delta function ( a MAP solution ) rather than to a quadratic loss . Our results are compatible with a proper sampling approach , in which an empirical distribution is built out of a small number of samples from the posterior , and then the expected loss is computed from the sampled distribution [19] . As a more cognitive explanation , decision variability may have arisen because subjects adopted a probabilistic instead of deterministic strategy in action selection as a form of exploratory behavior . In reinforcement learning this is analogous to the implementation of a probabilistic policy as opposed to a deterministic policy , with a ‘temperature’ parameter that governs the amount of variability [50] . Search strategies have been hypothesized to lie behind suboptimal behaviors that appear random , such as probability matching [51] . While generic exploratory behavior is compatible with our findings , our analysis rejected a simple posterior-matching strategy [25] , [26] . All of these interpretations assume that there is some noise in the decision process itself . However , the noise could emerge from other sources , without the necessity of introducing deviations from standard BDT . For instance , variability in the experiment could arise from lack of stationarity: dependencies between trials , fluctuations of subjects' parameters or time-varying strategies would appear as additional noise in a stationary model [52] . We explored the possibility of nonstationary behavior without finding evidence for strong effects of nonstationarity ( see Section 6 in Text S1 ) . In particular , an iterative ( trial-dependent ) non-Bayesian model failed to model the data in the training dataset better than the stochastic posterior model . Clearly , this does not exclude that different , possibly Bayesian , iterative models could explain the data better , but our task design with multiple alternating conditions and partial feedback should mitigate the effect of dependencies between trials , since each trial typically displays a different condition from the immediately preceding ones . In summary , we show that a decision strategy that implements a ‘stochastic posterior’ that introduces variability in the computation of the expected loss has several theoretical and empirical advantages when modelling subjects' performance , demonstrating improvement over previous models that implemented variability only through a ‘posterior-matching’ approach or that implicitly assume a quadratic loss function ( sampling-average methods ) .
The Cambridge Psychology Research Ethics Committee approved the experimental procedures and all subjects gave informed consent . Twenty-four subjects ( 10 male and 14 female; age range 18–33 years ) participated in the study . All participants were naïve to the purpose of the study . All participants were right-handed according to the Edinburgh handedness inventory [53] , with normal or corrected-to-normal vision and reported no neurological disorder . Participants were compensated for their time . Subjects were required to reach to an unknown target given probabilistic information about its position . Information consisted of a visual representation of the a priori probability distribution of targets for that trial and a noisy cue about the actual target position . Subjects held the handle of a robotic manipulandum ( vBOT , [54] ) . The visual scene from a CRT monitor ( Dell UltraScan P1110 , 21-inch , 100 Hz refresh rate ) was projected into the plane of the hand via a mirror ( Figure 1a ) that prevented the subjects from seeing their hand . The workspace origin , coordinates , was cm from the torso of the subjects , with positive axes towards the right ( axis ) and away from the subject ( axis ) . The workspace showed a home position ( 1 . 5 cm radius circle ) at cm and a cursor ( 1 . 25 cm radius circle ) that tracked the hand position . On each trial 100 potential targets ( 0 . 1 cm radius dots ) were shown around the target line at positions , for , where the formed a fixed discrete representation of the trial-dependent ‘prior’ distribution , obtained through a regular sample of the cdf ( see Figure 1d ) , and the were small random offsets used to facilitate visualization ( Uniform ( −0 . 3 , 0 . 3 ) cm ) . The true target was chosen by picking one of the potential targets at random with uniform probability . A cue ( 0 . 25 cm radius circle ) was shown at position . The horizontal position provided a noisy estimate of the target position , , with the true ( horizontal ) position of the target , the cue variability and a normal random variable with zero mean and unit variance . The distance of the cue from the target line , , was linearly related to the cue variability: cues distant from the target line were noisier than cues close to it . In our setup , the noise level could only either be low for ‘short-distance’ cues , cm ( cm ) , or high for ‘long-distance’ cues , cm ( cm ) . Both the prior distribution and cue remained on the screen for the duration of a trial . After a ‘go’ beep , subjects were required to move the handle towards the target line , choosing an endpoint position such that the true target would be within the cursor radius . The manipulandum generated a spring force along the depth axis ( N/cm ) for cursor positions past the target line , preventing subjects from overshooting . The horizontal endpoint position of the movement ( velocity of the cursor less than 0 . 5 cm/s ) , after contact with the target line , was recorded as the subject’s response for that trial . At the end of each trial , subjects received visual feedback on whether their cursor encircled ( a ‘success’ ) or missed the true target ( partial feedback ) . On full feedback trials , the position of the true target was also shown ( 0 . 25 cm radius yellow circle ) . Feedback remained on screen for 1 s . Potential targets , cues and feedback then disappeared . A new trial started 500 ms after the subject had returned to the home position . For simplicity , all distances in the experiment are reported in terms of standardized screen units ( window width of 1 . 0 ) , with and 0 . 01 screen units corresponding to 3 mm . In screen units , the cursor radius is and the SD of noise for short and long distance cues is respectively and . Subjects performed one practice block in which they were familiarized with the task ( 64 trials ) . The main experiment consisted of a training session with Gaussian priors ( 576 trials ) followed by a test session with group-dependent priors ( 576–640 trials ) . Sessions were divided in four runs . Subjects could take short breaks between runs and there was a mandatory 15 minutes break between the training and test sessions . Each session presented eight different types of priors and two cue noise levels ( corresponding to either ‘short’ or ‘long’ cues ) , for a total of 16 different conditions ( 36–40 trials per condition ) . Trials from different conditions were presented in random order . Depending on the session and group , priors belonged to one of the following classes ( see Figure 2 ) : | The process of decision making involves combining sensory information with statistics collected from prior experience . This combination is more likely to yield ‘statistically optimal’ behavior when our prior experiences conform to a simple and regular pattern . In contrast , if prior experience has complex patterns , we might require more trial-and-error before finding the optimal solution . This partly explains why , for example , a person deciding the appropriate clothes to wear for the weather on a June day in Italy has a higher chance of success than her counterpart in Scotland . Our study uses a novel experimental setup that examines the role of complexity of prior experience on suboptimal decision making . Participants are asked to find a specific target from an array of potential targets given a cue about its location . Importantly , the ‘prior’ information is presented explicitly so that subjects do not need to recall prior events . Participants' performance , albeit suboptimal , was mostly unaffected by the complexity of the prior distributions , suggesting that remembering the patterns of past events constitutes more of a challenge to decision making than manipulating the complex probabilistic information . We introduce a mathematical description that captures the pattern of human responses in our task better than previous accounts . | [
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] | 2014 | On the Origins of Suboptimality in Human Probabilistic Inference |
The pre-fibrillar stages of amyloid formation have been implicated in cellular toxicity , but have proved to be challenging to study directly in experiments and simulations . Rational strategies to suppress the formation of toxic amyloid oligomers require a better understanding of the mechanisms by which they are generated . We report Dynamical Monte Carlo simulations that allow us to study the early stages of amyloid formation . We use a generic , coarse-grained model of an amyloidogenic peptide that has two internal states: the first one representing the soluble random coil structure and the second one the -sheet conformation . We find that this system exhibits a propensity towards fibrillar self-assembly following the formation of a critical nucleus . Our calculations establish connections between the early nucleation events and the kinetic information available in the later stages of the aggregation process that are commonly probed in experiments . We analyze the kinetic behaviour in our simulations within the framework of the theory of classical nucleated polymerisation , and are able to connect the structural events at the early stages in amyloid growth with the resulting macroscopic observables such as the effective nucleus size . Furthermore , the free-energy landscapes that emerge from these simulations allow us to identify pertinent properties of the monomeric state that could be targeted to suppress oligomer formation .
A wide range of normally soluble proteins and peptides are known to have a propensity to aggregate into -sheet rich amyloid fibrils . Such structures do sometimes posses functional roles , for instance as functional coatings and catalytic scaffolds [1] . However , more often than not , the formation of amyloid structures is a pathogenic event – it is the hallmark of a range of neurodegenerative disorders , including Alzheimer's and Parkinson's diseases [2] , [3] . A diversity of experimental [4]–[9] , and theoretical [10]–[15] approaches have been developed to probe the mechanisms of amyloid formation . Such studies have shed light on the structural and kinetic aspects of amyloid growth; it has , however , proved to be very challenging to characterise the very early stages of this reaction , in particular the primary nucleation events and the subsequent formation of low relative molecular weight oligomers . Yet there is substantial evidence that these small oligomers , rather than mature fibrils act as neurotoxins , and are implicated in the pathological cascades that underlie neurodegeneration [16]–[18] . Hence , despite the technical challenges associated with the study of this phenomenon [19] , [20] , a quantitative understanding of the kinetics of oligomer formation is of great practical and fundamental importance in the context of neurodegeneration and more generally in relation to aberrant protein self-assembly . The growth of linear aggregates following a nucleation event is described in its simplest form by the classical theory of nucleated polymerisation , developed by Oosawa originally to study the formation of cytoskeletal filaments [21] . Within this framework , the time dependence of the mass fraction of the fibrils can be expressed as follows [21] , [22]: ( 1 ) where is the nucleus size , is an effective rate constant which contains contributions from both the nucleation rate and the elongation rate of the filaments , is the initial mass concentration of the monomers , the mass concentration of the fibrils and the mass fraction of the fibrils . The key parameters in Oosawa's theory are and , since is known a priori . This formalism has been extended by Ferrone , Eaton and coworkers in their pioneering studies of the aggregation of sickle haemoglobin to include secondary nucleation events such as filament fragmentation and surface catalysed nucleation [23] , [24]; in the present paper we focus on homogeneous nucleation which is crucial in the formation of the primary nuclei . Several groups have reported numerical simulations of the nucleation and growth of amyloid fibrils [25]–[35] . Such simulations provide invaluable microscopic insight into the mechanism of amyloid formation . The focus of the present paper is different: we wish to compute quantities that are directly accessible to experiment , such as the time dependence of fibril formation , and apply to these quantities the same analysis that is applied to experimental data . This approach allows us to test whether a reliable estimate of the critical nucleus size can be obtained by fitting the experimental fibril-growth curve to an analytical approximation , and sheds light on the microscopic and mechanistic interpretation of the nucleus size . In addition , our simulations allow us to follow in detail the pathway by which amyloid fibril nucleation takes place and shed light on the condensation-reorganisation mechanism that underlies the primary nucleation process [36] . Since primary nucleation processes are very rare events , and the critical nucleus is by definition the species after the highest free energy barrier and therefore lowest relative concentration within the aggregation pathway , simulations are currently the most fruitful avenue to access the structural determinants of the critical nucleus and to follow its formation and the subsequent conversion to amyloid fibrils . In the present paper we study fibril nucleation by considering a highly simplified model of amyloidogenic peptides that captures the salient features of this self-assembly process . A key property of amyloidogenic peptides is that they change conformation when converting from their normal soluble form into the amyloid state [3] . Our model has two internal states: one ( denoted the -state ) has weak intra-peptide interactions and represents the soluble random coil structure , the other ( the -state ) has a significantly higher intrinsic internal energy , but has stronger interpeptide attractions . The higher internal energy of the -state results from the loss of conformational degrees of freedom relative to the -state , and its higher propensity to form inter-peptide contacts is a consequence of the availability of the residues for hydrogen bonding with neighboring peptides in the sheet conformation . In this picture , fibril nucleation takes place once the free-energy gain due to aggregation compensates the free energy cost of converting the monomers to the -state . The model that we use is based on a peptide model reported in ref [37] . that has been extended to account for the two-state nature of amyloidogenic peptides ( see section methods ) . Because this model is simple and therefore computationally highly tractable , it allows us to study the behavior of large numbers of peptides . Specifically , we can use it to compute the fibrillar growth profile and the free-energy landscape for oligomer formation . We used dynamic Monte Carlo simulations to generate trajectories for an ensemble of peptides in a system with periodic boundary conditions . In what follows , we relate the properties of the present model system to solutions of A peptides with a length of 40–42 amino acid residues , which are the major components of the aberrant deposits found in connection with Alzheimer's disease [2] , [3] . With this mapping , the conditions of our simulations correspond to a concentration range of 0 . 2–8 mM . For more details on the model and the simulation method , see section methods .
We used Dynamic Monte Carlo ( DMC ) [38]–[41] for our simulations . In this method , the displacements , that can occur are so small , that no unphysical moves can occur . Moreover , the parameters of individual moves ( translation and rotation ) are fitted to experimental timescales such as diffusion and rotational constants . The values were taken from one of the most studied amyloid forming peptides A and were averaged between its 40 and 42 amino acid long forms ( and at 300 K ) [42] . Hence , maximum displacement was d nm and maximum rotation and consequently the time of our simulation step ( when on average all particles move , i . e . , sweep ) can be roughly related to 0 . 02 ns . The amyloidogenic peptides were modeled as Patchy Spherocylinders ( PSC ) [37] , i . e . cylinders with hemispherical caps at both ends and with an attractive stripe on its side . As was shown in ref . [37] such particles can either occur in an oligomeric form or assemble into amyloid-like structures with two filaments , depending on the model parameters . Unlike the model described in ref . [37] , the present model peptides can occur in two possible states: the first one ( denoted as the -state ) corresponds to the random coil conformation of peptide in solution [43]; the second state ( called -state ) corresponds to the -sheet structure found in the fibrils . The free energy difference corresponding to change from the to the -state , is denoted by . In what follows , we chose . These values were chosen to reflect the fact that , in experiments , amyloidogenic proteins are typically not found at detectable concentrations in the -sheet conformation in solution [44] , [45] . The attractive stripe is responsible for self-assembly and the cross-section of small oligomers in ideal conformation are depicted in Figure 1 . Chirality was introduced into the model in order to reproduce the relatively long persistence length of the amyloid fibrils . This was achieved by rotating the attractive patch off the cylinder axis around the vector connecting the middle of the cylinder axis with the middle of the patch . The attractive stripe was thus misaligned with the body of the spherocylinder represented by repulsive potential . The aspect ratio of the PSC was chosen to mimic the elementary -sheet unit of A peptides with dimensions . We use an implicit-solvent model where the interaction between the attractive stripes ( patches ) on different peptides effectively includes all possible interactions such as hydrophobic interaction , hydrogen bonds , salt-bridges , etc . The potential minimum of two interacting -states was −21 with an attractive stripe size of running length-wise in order to mimic the interaction potential of A peptides [11] and being able to form cross beta fibrils . We considered both a chiral and a non-chiral version of the model . The -states were interacting with a minimum of −8 . 4 and had a patch size of , which was inspired by the hydrophobic patch of the random coil structure covering 25% of its surface [46] and its presence mainly in monomeric form in solution . The interaction between and state was calculated using Berthelot's rule [47] . The interaction between the particles is effective , i . e . taking into account all the interactions . The probability of a PSC to attempt to switch its conformation from the -state to the -state or vice versa was per particle move . The value was estimated based on the rearrangement time of a polypeptide with a size of 18 Kuhn lengths [5] . The fibrillar growth with switching probabilities one order of magnitude larger or smaller was without any significant difference . In all our simulations we employed an NVT ensemble and periodic boundary conditions . The systems contained 600 PSC with the box sizes ( in nm ) of 50 , 75 , 100 , 125 , 150 and 175 corresponding to concentrations ( in mM ) of 7 . 97 , 2 . 36 , 1 . 00 , 0 . 51 , 0 . 30 , and 0 . 19 . For each concentration at least three separate runs were conducted with different random initial configurations and the obtained growth profiles were averaged over all runs . The size of a fibril for the relative mass profiles was defined as all oligomers with a size of at least four monomers .
A representative snapshot of the late stage of a simulation of fibril growth is shown in Figure 2: it reveals that aggregation results in fibrillar species with a morphology similar to that observed in experiments [48] . Our simulations allow us to follow the time dependence of the aggregation number and hence of the mass of individual aggregates as they grow . Figure 3 shows a representative time trace . Initially the system is in a purely monomeric state . As a result of the collision of two peptides , a dimer can be formed . The dimer can either dissociate into monomers , or can grow to a trimer through monomer addition . The oligomers with an aggregation number below four are highly dynamic and interconvert readily between different aggregation states , including dissociation to monomer . However , tetramers , once formed , always develop into a fiber , which suggests that the size of the critical nucleus is 4 or just below . In what follows , every oligomer containing four or more monomers is counted as a fiber . At later times , as larger aggregates emerge , the numbers of monomers and oligomers ( dimers and trimers ) decreases through the incorporation of peptides into larger structures . Figure 4 shows the average increase in the aggregate mass concentration obtained at a fixed concentration ( 0 . 51 mM ) . We first tested whether these data could be fitted to the Oosawa theory . We find that , as is the case for experimental studies [15] , analysis of the system under a single set of conditions does not yield strong enough constraints to allow a reliable estimate of the parameters in Oosawa's theory to be obtained . Indeed , fits of similar quality were obtained with critical nucleus sizes varying between 2 . 0 and 5 . 0 . This finding therefore highlights the difficulty in resolving microscopic parameters , in this case the critical nucleus size , from macroscopic bulk data at a single concentration since a similar shape of the curve can be obtained for different combinations of the nucleus size and the nucleation rate . These two parameters can be disentangled , however , when data at different concentrations are considered . The average growth profiles of the simulation with different initial monomer concentrations were simultaneously fitted to Eq . 1 ( see Figure 5 ) . The best fit was obtained for a critical nucleus size = 3 . 8 and a growth rate of , where is the unit time of our simulation roughly corresponding to 0 . 02 ns ( see Methods ) . We note that , in this formulation , corresponds to the first species in the aggregation pathway that has a higher than 50% probability to grow into a fibril . The results of the simulation also allow us to test the connections between the characteristic scaling behaviour [15] , [22] , [23] , [49] of the half-time and the critical nucleus size that follows from Eq . 1 given as the powerlaw: ( 2 ) where is the standard concentration and is the half-time of an aggregation reaction at this concentration . Following this procedure we obtain a slightly smaller estimate for the critical nucleus size: 3 . 4 . To test the sensitivity of Oosawa's theory in identifying processes other than nucleation and growth , we also employed a model characterized by the absence of chirality compared to the model used previously ( see Methods ) . In such a system , the persistence length of the polymers is low for bending mode parallel to the narrow dimension of the rectangular cross-section . In Figure 6A we show that there is a systematic deviation from the fit according to Oosawa's theory . In particular , the growth is always faster at the beginning and slower at the end of the growth than the fit . The reason is depicted in Figure 6B , where we can see that a flexible fiber can bent into a ring like structure , thereby effectively removing a potential growing site from the system ( i . e . , the fibrillar end is not accessible for further monomer addition ) . The fusion of fibrils , which we observed for both the non-chiral and the chiral models , also leads to a decrease in the late-stage rate of growth of fibrils . Neither ring formation nor fusion are accounted for in Oosawa's theory , and this fact could explain the slight deviation of the simulation data from the fit shown in Figure 6 . Note that at lower concentrations not all the monomers are depleted from the bulk at the end of simulation . This is also the case in the chiral model and reflects the finite probability for monomers to dissociate from the aggregates leading to an equilibrium between the monomeric and aggregated forms of the peptides as is observed in experiments [50] . The simulations allow us to follow the time evolution of single aggregates as they form . This information makes it possible to relate the macroscopic average quantities such as the scaling exponents characterising the lag-time , to microscopic events taking place on the level of individual peptides . Based on classical nucleation theory , the free energy profile along the reaction coordinate ( aggregation number ) increases from the monomer up to a maximum , after which it monotonically decreases under supersaturated conditions . The point of the maximum free energy is related to the size of the critical nucleus . We define the nucleus as the first oligomer after this free energy barrier , in other words the nucleus is the smallest oligomer , which has a higher probability to grow than to shrink . In order to look at the nucleation from yet another perspective , we constructed a free energy landscape for the different kinds of oligomers up to tetramer using the following procedure: The free energy of a mixed oligomer can be decomposed into several parts: ( 3 ) The enthalpic contribution , , to the free energy of an oligomers ( i-mer , where ) was determined as the minimum of the interaction energy of a given oligomer , which is schematically depicted with the enthalpic values in Figure 1 . Naturally , the bigger the oligomer and the larger the patch , the stronger the interaction is . Importantly , due to the patch size , a tetramer of -particles ( ) has no enthalpy gain compared to its trimer counterpart ( ) . The next contribution is the free energy associated with changes in the internal degrees of freedom of the protein molecule , ; this value is large compared to since the experimental measurements only report evidence for the state in solution: no free molecules in the state are observed . The free energy difference is fixed at 15 for each monomer which changes its state from the soluble state to the state that it assumes in the aggregates ( for more details see methods ) . The last contribution is entropic , where is the temperature and is the entropy; this contribution stems mainly from the loss of translational entropy upon binding to the cluster , but it also includes the rotational and internal entropy . Two additional simulations were carried out , each with fixed type of achiral monomers ( pure and pure ) . The free energy of the i-mers was determined based on their relative populations ( see supplementary information Text S1 ) . The enthalpic contributions can be determined directly by computing the relevant interaction terms in an ideal configuration . By subtracting it from the free energy and by dividing by we obtain the entropy per monomer for each ( pure ) i-mer . Assuming that the entropy of a single particle in a given state is similar in all ( mixed ) i-mers independent of their composition , we can construct the free energy landscape of the oligomers ; again using the enthalpic contribution for an ideal configuration ( see Figure 7 and the supplementary information Text S1 for a detailed description of how to construct it ) . Note , that the achiral monomers were employed for this calculation as it easier to determine the ideal maximum interaction enthalpy and therefore enthalpic contribution to the free energy . The nucleation for achiral and chiral model is very similar as they differ mainly in the later stage as self-assembled fibrils in their rigidity . The analysis of the energy landscape reveals the microscopic details through which primary nucleation occurs in the coarse grained system . The path of the lowest energy connecting the monomeric states with the aggregates starts with the assembly of a dimer of molecules in an unstructured state . At this stage , the free energy cost for the conversion of one of the molecules in the dimer to the state is 9 . 2 , less than the cost of the conversion in solution , 15 , but still sufficiently high for this mechanism not to be the major contribution to the overall production of aggregates . However , the unstructured trimer , obtained through monomer addition from the dimer , possesses a lower free energy than the mixed dimer , and at this stage the conversion of one of the molecules to the state is associated with a significantly lower cost of 3 . 2 resulting in the species . Subsequent conversions to the state are associated with an even smaller energy cost , and the species is only 0 . 3 higher in free energy relative to the species . This mixed aggregate represents the species with the highest free energy on the aggregation pathway; subsequent additions of monomers to this nucleus lower its free energy and result in the formation of -sheet fibrils . Therefore the nucleus size in the formulation of Eq . 1 is 4 , in excellent agreement with the analysis performed on the average kinetic data . The first fully -sheet aggregate is the tetramer . The overall nucleation pathway that emerges from our simulations is that of a nucleation process followed by a conformational conversion . The conversion step is , however , dependent on the addition of monomers and does not take the form of one step cooperative conversion [36] . In particular the critical nucleus is a mixed , partially converted , aggregate rather than the fully unconverted species . This finding is likely to be of general value since the mechanism identified in the present study allows the conversion of a number of monomers in discrete steps combined with monomer addition steps to avoid the high free energy barrier associated with a conversion of the entire aggregate in a single step . These results therefore generalise the nucleated conformational conversion model [36] to include multi-step conversion .
We have devised a simulation scheme which allows us to study a system of aggregating peptides and compute quantities that are directly accessible in experiments , such as the scaling behaviour of the lag-time with aggregate concentration , and to relate these characteristics to the microscopic events that underlie the generation of single nuclei and their subsequent growth . In order to reach the long time scales required for such a study , we have employed a coarse-grained model , which includes a representation of the internal degrees of freedom of the polypeptide chain as well as the possibility to assemble into both oligomers and elongated fibrils . The obtained sigmoidal growth of the oligomers is in agreement with previous studies employing different two state models [25] , [29] . The nucleus size was found to be in mutual agreement from all the employed methods . The most trivial one is the empirical observation of the oligomers' time distribution , where our results show that all tetramers develop into a fibril . The second method is the fit of the growth profile to Oosawa's theory , which has to be performed with data at varying initial monomer concentration for unambiguous results . The fitted nucleus size is 3 . 8 . The same result was obtained from our analysis of the free energy landscape for the oligomer formation . The coarse grained system that we study possess parameters that are representative of experimental systems , where nucleus sizes of the order of 2–4 are commonly reported [6] , [15] , [51] . We found very good agreement of our simulated fibril growth with the theory of Oosawa , especially for the chiral model . The small deviations can be due to the fact that our simulation time is rather small ( somewhere around 2 s , compared to hours in many experiments ) , a factor which required high concentrations in order to observe fibril growth . Some processes such as fibril fusion could , therefore , be enhanced under the conditions used in the simulations . Note that the current implementation of our model does not lead to any secondary nucleation [22] , [23] , [51] and the scale of our simulations prevents the fibril breakage [15] , [52] . As a result we have not observed secondary nucleation pathways which would lead to different kinetics of fibril formation [15] , [22] . The ability to compute the free energy landscape of the oligomers allowed us to study the sequence of events that leads to the generation of a fully -sheet aggregate from the monomeric precursor state . We found that the that this conversion occurs concomitantly with the growth of the oligomers through monomer addition . Furthermore , the critical nucleus is found to be a mixed species including both converted and unconverted peptides , and its size is in good agreement with that determined from the analysis of the scaling behaviour of the average lag-time . This concerted mechanism allows the energy penalty from the conversion of individual peptides to be compensated by the energy gain from an increase in the number of favorable inter-peptide contacts . | A number of normally soluble proteins can form amyloid structures in a process associated with neurodegenerative diseases such as Alzheimer's and Parkinson's diseases . Mature amyloid structures consist of large fibrils containing thousands of individual proteins aggregated into linear nanostructures; there is increasing evidence , however , that the toxic species responsible for neurodegeneration are not the mature fibrils themselves but rather lower molecular weight precursors commonly known as amyloid oligomers . Unfortunately , these early oligomers are commonly thermodynamically unstable and of nanometer scale dimensions , factors which make them highly challenging to probe in detail in experiments . We have used computer simulations of a model inspired by Alzheimer's Abeta peptide to investigate the early stages of protein aggregation . The results that we obtain were shown to fit Oosawa's polymerization theory , a finding which allows us to provide a connection between the microscopic molecular parameters and macroscopic growth . One crucial parameter is size of the nucleus , i . e . the basic oligomer existing at origin of the formation of each fiber . We have revealed a path for the formation of this nucleus and validate its size by several methods . Our results provide fundamental information for influencing the early stages of amyloid formation in a rational manner . | [
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"simulations",
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] | 2012 | Connecting Macroscopic Observables and Microscopic Assembly Events in Amyloid Formation Using Coarse Grained Simulations |
The surface proteins hemagglutinin ( HA ) and neuraminidase ( NA ) of human influenza A virus evolve under selection pressures to escape adaptive immune responses and antiviral drug treatments . In addition to these external selection pressures , some mutations in HA are known to affect the adaptive landscape of NA , and vice versa , because these two proteins are physiologically interlinked . However , the extent to which evolution of one protein affects the evolution of the other one is unknown . Here we develop a novel phylogenetic method for detecting the signatures of such genetic interactions between mutations in different genes – that is , inter-gene epistasis . Using this method , we show that influenza surface proteins evolve in a coordinated way , with mutations in HA affecting subsequent spread of mutations in NA and vice versa , at many sites . Of particular interest is our finding that the oseltamivir-resistance mutations in NA in subtype H1N1 were likely facilitated by prior mutations in HA . Our results illustrate that the adaptive landscape of a viral protein is remarkably sensitive to its genomic context and , more generally , that the evolution of any single protein must be understood within the context of the entire evolving genome .
One of the central obstacles in controlling many pathogen-borne diseases is their exceptional ability to adapt through evolutionary changes [1] . Large population sizes and high mutation rates in many pathogens make them extremely effective at evolving to evade the immune system or resist drug treatments [2–6] . Our ability to prevent or even predict such escape mutations is hampered by limited knowledge of the effects of new mutations on pathogen fitness . This problem is made especially difficult because the effect of any particular mutation is often dependent on the genetic background in which it occurs , a phenomenon called epistasis [7–16] . Epistasis is particularly common among mutations that arise in response to strong selection pressures . For example , resistance mutations that arise under drug treatments often carry substantial fitness costs which are alleviated by secondary , compensatory , mutations [7 , 10 , 14–16] . Likewise , mutations that facilitate immune escape are in several cases known to be epistatic with other , compensatory or permissive , mutations [17 , 18] . The surface proteins hemagglutinin ( HA ) and neuraminidase ( NA ) of the human influenza A virus evolve under strong selection pressures imposed by the human immune system and , possibly , antiviral drugs [4 , 19] . It is therefore expected that epistasis may play an important role in the evolution of these proteins . Several previous studies have found that epistasis within each of these proteins is widespread , so that mutations in a given protein are often beneficial only in the presence of mutations at other sites in the same protein [19–21] . Aside from intra-gene epistasis , we also might expect inter-gene epistasis , especially in the case of the HA and NA proteins of influenza viruses , which serve complementary physiological functions . HA facilitates the attachment of the virus to the cell surface , whereas NA catalyzes the separation of the ready-made virus particles from the cell . Thus , mutations that increase receptor-binding avidity of HA should promote mutations in NA that increase its cleavage activity [22 , 23] and vice versa [24 , 25] . HA and NA jointly determine sensitivity to neuraminidase inhibitors , with mutations in HA compensating for the reduction in binding affinity of NA caused by the inhibitors [26 , 27] . Other , as yet unknown , molecular interaction mechanisms may also lead to inter-gene epistasis . Indirect evidence also suggests that interactions between HA and NA may be strong; for example , reassortments giving rise to new combinations of HA and NA lead to a temporary increase in the rate of accumulation of mutations in these genes , likely due to changes adjusting the genes to each other [28 , 29] . Here we present a method toWe set out to detect signatures of inter-gene epistasis , and apply it to understand the evolutionary history of influenza surface proteins . The method we develop is an extension of techniques previously developed for detecting intra-gene epistasis [21 , 30] . The idea behind it is simple: epistasis will tend to induce temporal clustering of mutations along the phylogeny of an adapting protein , with mutations at one site followed rapidly by mutations at another , interacting site . In the case of mutations within a single protein it is straightforward to develop this idea into a rigorous statistical test , by quantifying the time that separates subsequent mutations along the protein’s phylogeny . All the sites within a single influenza protein share a common phylogenetic history: recombination events within an influenza virus RNA segment are exceedingly rare [31] , and so sites that reside on the same segment of the viral genome are completely linked . However , influenza viruses undergo frequent reassortment events , so that sites residing on different segments typically have different genealogies—a complication that obscures the temporal order of mutations occurring on different RNA segments . To resolve this complication , here we develop a method for inferring the relative temporal order of mutations at sites that have different evolutionary histories , and then use this information to detect temporal clustering of such mutations in influenza viruses . We find that origination of mutant alleles at many sites in NA facilitated the spread of subsequent mutations in HA and vice versa , implying that inter-gene epistasis has shaped the molecular evolution of influenza viruses .
We reconstructed individual phylogenetic trees for each of the two surface proteins HA and NA for the two major influenza subtypes circulating in humans , H3N2 and H1N1 . As expected , HA and NA phylogenies of the same subtype were incongruent . Using software GIRAF [32] , we identified taxa that descended from within-subtype reassortant ancestors and thus inferred the positions of reassortment events on the phylogenies of an individual segment ( see Materials and Methods for details ) . We inferred a total of 15 reassortment events between these two segments in subtype H3N2 , and 5 events in subtype H1N1 . We found that 847 out of 1 , 376 H3N2 isolates and 201 out of 745 H1N1 isolates are descendants of at least one reassortment event , which is consistent with previous findings [29 , 33 , 34] . To completely resolve incongruences between individual segment phylogenies , we assumed that reassortments are the only source of true differences between the phylogenies of individual segments . This assumption imposes a constraint that phylogenies of different segments may differ by at most as many rooted subtree prune-regraft ( rSPR ) operations as there are reassortment events , and otherwise be identical . We reconstructed such “constrained” phylogenies of individual segments using previously inferred “unconstrained” individual segment phylogenies as templates ( see Materials and Methods for details ) . Accelerated origination of mutations at one site ( referred to as “trailing” site ) following a genetic change at another site ( referred to as “leading” site ) indicates that mutations at the trailing site are more beneficial after a mutation occurs at the leading site , and thus indicate positive epistasis [21 , 30] . Here we are specifically interested in situations when leading and trailing sites are located in different genes and therefore have potentially different evolutionary histories . This fact complicates the inference of the temporal order of mutations . Consider mutations i and ii in the toy example presented in Fig 1A . While both of them obviously occurred on the line of descent of isolate b , it is not immediately clear whether mutation i in segment 1 occurred before or after mutation ii in segment 2 . We therefore cannot say a priori whether mutation ii facilitated mutation i , or mutation i facilitated mutation ii , or there was no interaction between them at all . To resolve such ambiguities , we estimate the temporal order of mutations in different genes using the constrained phylogenies constructed above . Specifically , in order to study accelerated origination of mutations in one gene ( referred to as the “foreground” gene ) that follow mutations in the other gene ( referred to as the “background” gene ) , we map all mutations in the background gene onto the phylogeny of the foreground gene . Since the constrained phylogenies are topologically identical with the exception of a relatively small number of reassortment events , most branches of the background tree correspond to unique branches of the foreground tree . Most mutations in the background gene are therefore unambiguously mapped onto branches of the foreground-gene phylogeny . In the toy example shown in Fig 1 branches gb , ec , and ed in the segment 2 phylogeny correspond to branches fb , ec , ed of the segment 1 phylogeny , respectively . Therefore , when considering segment 2 as the background gene , mutation iv unambiguously occurs on branch fb of the segment 1 phylogeny ( Fig 1B ) . Ambiguities in mapping background-gene mutations onto the foreground-gene phylogeny arise at branches that precede and follow reassortment events , such as branches rg , ga , and re in the segment 2 phylogeny in Fig 1A . For instance , mutation iii in segment 2 could occur either on branch rf or on branch fe of the segment 1 phylogeny . We resolve such ambiguities by placing background mutations onto the distal branch of the foreground phylogeny ( e . g . , in Fig 1 , mutation iii is placed on branch fe ) . This choice minimizes the potential number of mutation pairs that contribute to our epistasis statistic ( see below and Materials and Methods ) . Finally , reassortment events themselves represent genetic changes in the background gene which may potentially elicit epistatic responses in the foreground gene . Indeed , when viewed as an event on the line of descent of a foreground-gene isolate , each reassortment event is a replacement of the genetic background gene , equivalent to gain of multiple simultaneous mutations which we call “virtual” . To account for the possibility that some of such virtual mutations in the background gene lead to acceleration in rates of origination of mutations at foreground-gene sites , we mark each reassortment events by a “virtual” node on the foreground-gene phylogeny . All foreground-gene mutations that occur on the respective branch are then placed after the virtual node . Here we make a simplifying assumption that reassortment events precede all mutations on the respective branch ( see Materials and Methods for details ) . Even though this assumption introduces an error in our inference of relative order of mutations , this error is small because the fraction of branches involved in reassortment events is small . To illustrate this procedure , consider again the toy example in Fig 1 . When considering segment 1 as the foreground gene , we posit that the reassortment ( virtual node h ) precedes mutation i on branch fb ( segment 1 ) and mutation iv on branch gb ( segment 2 ) which is also mapped onto branch fb of the segment 1 phylogeny ( Fig 1B ) . This reassortment event replaces background segment 2 variant that carries no mutations ( present at node f ) with a segment 2 variant descendent from node g that has mutation ii . Thus , mutation ii is a virtual mutation in the background gene , and is placed on the virtual branch fh . Note that mutation ii is also mapped onto branch ra of segment 1 phylogeny . An alternative approach where we do not introduce the virtual node but assume a random order of all mutations ( including virtual ones ) within an edge yields qualitatively similar results ( see Materials and Methods ) . Once all genetic changes in the background gene are mapped onto the foreground-gene phylogeny , we can use our previously developed method [21] for detecting acceleration in the rate at which mutations arise on our phylogeny at sites in the foreground gene following mutations in the background gene . To do so , we compute the epistasis statistic for each pair of sites ( i , j ) where the leading site i is in the background gene and the trailing site j is in the foreground gene ( see Materials and Methods ) . The epistasis statistic tends to be large for those pairs of sites in which a mutation at the trailing site quickly follows a mutation at the leading site and for which such mutations at the trailing site occur in multiple descendant lineages . We measure time between the leading and the trailing mutation as the number of synonymous mutations in the foreground gene that occur between them . As in our previous study [21] , we exclude all mutations at terminal branches because many such mutations are likely to be deleterious or spurious . Finally , to identify the pairs of sites with the epistasis statistic greater than expected by chance ( which we call “putatively epistatic pairs” ) , we randomly reshuffle foreground-gene mutations among branches of the foreground-gene phylogeny while keeping the mapped background-gene mutations fixed . This permutation procedure preserves the number of mutations on each branch and the number of mutations observed at each sites , but breaks all potential associations between background- and foreground-gene mutations . It produces the null distributions of the epistasis statistic for all pairs of sites simultaneously and allows us to estimate the false discovery rate ( FDR ) for the number of putatively epistatic pairs at a desired nominal P-value threshold [21] . Importantly , our procedure does not take into account linkage between sites or temporal variation in the external selection pressure , which can inflate the epistasis statistic for some pairs and will lead to an underestimate of FDR . Thus , our list of putatively epistatis pairs of sites will likely contain some pairs that do not actually interact but have a significantly elevated value of the epistasis statistic for other reasons , e . g . , hitchhiking . We discuss this important caveat in section “Confounding effect of hitchhiking on inference of epistasis” below and provide an estimate of the fraction of truly interacting pairs in our list . We considered both HA and NA as foreground and background genes , for both subtypes . In all cases , we found a higher than expected number of nonsynonymous site pairs with high values of the epistasis statistic in our data , implying abundant positive inter-gene epistasis between amino acid-changing mutations ( Tables1 and S3 , Figs 2–4 and S1 ) . The observed number of epistatic pairs was significantly greater than expected for all considered nominal P-value thresholds below 0 . 05 in three of the four comparisons: ( N2 , H3 ) , ( H3 , N2 ) , and ( H1 , N1 ) . Here and hereafter , the first segment listed in a pair is the background , and the second segment is the foreground . In the fourth comparison ( N1 , H1 ) , it was significant for nominal P-values of 0 . 005 and below ( S1 Fig ) . Hereafter , we refer to these thresholds as “liberal P-value thresholds” . To form conservative lists of putatively epistatic pairs of nonsynonymous sites , we chose the threshold nominal P-values that minimize the FDR , while still retaining enough sites for the downstream analyses ( Table 1 , see Materials and Methods; hereafter , “conservative P-value thresholds” ) . At conservative thresholds , the number of epistatically interacting pairs of nonsynonymous sites is about 5 times greater than expected by chance in three of the four comparisons: ( N2 , H3 ) , ( H3 , N2 ) , and ( H1 , N1 ) , and about 2 . 5 times greater than expected in the remaining comparison ( N1 , H1 ) ( Table 1; S1 Fig ) . At conservative P-value thresholds , between 11% and 19% of nonsynonymous sites were involved in epistasis as leading , and between 4% and 8% , as trailing , depending on the considered pair of genes . For example , among the variable nonsynonymous sites in H3 , 8% ( 13/173 ) were involved as trailing sites in epistasis with N2 , and 12% ( 21/173 ) were involved as leading sites ( Table 1 ) . Overall , between 20% ( 53/261 for N1 ) and 31% ( 123/392 for N2 ) of all observed nonsynonymous mutations occurred at sites that we classify as epistatically interacting ( either leading , trailing , or both ) . The mean time between putatively epistatic leading and trailing mutations in different genes was about 5 years ( S2 Fig ) , similarly to our finding for intra-gene epistasis [21] . Evolution in large populations with limited recombination ( such as influenza A ) proceeds via selective sweeps whereby neutral and deleterious “hitchhiker” mutations linked to one or multiple advantageous “driver” mutations proceed to fixation all together [35–40] . Linkage may confound inferences of epistasis from phylogenetic patterns of mutations . For example , imagine that mutations at multiple sites sweep to fixation together but only one of these mutations facilitates a rapid spread of subsequent mutations at a trailing site . On the resulting genealogy , all mutations that participate in the sweep will form consecutive pairs with the trailing mutations , which will elevate the epistasis statistic for all such site pairs . Since our permutation procedure does not account for linkage , it may call all of these pairs as putatively epistatic . Thus , we expect a certain number of putatively epistatic pairs to be hitchhiking-induced false positives . In this section we show that this effect indeed takes place . We also show that it cannot account for all of the signal of epistasis that we see , and we provide a conservative estimate for the fraction of truly epistatic site pairs among all putatively epistatic pairs . To show that hitchhiking confounds our ability to detect epistasis , we repeated our analyses using synonymous mutations in the background gene as leading and synonymous or nonsynonymous mutations in the foreground gene as trailing ( syn-syn or syn-nsyn pairs , respectively ) . We found many putatively epistatic site pairs among syn-nsyn mutations ( S3 Fig ) . Since synonymous mutations have little or no effect on protein structure , we expect that there would be few ( if any ) real epistatic interactions between synonymous mutations in one gene and non-synonymous mutations in another gene . ( However , true syn-nsyn epistasis may potentially arise from viral RNA-protein interactions during packaging . ) Synonymous substitutions nonetheless participate in selective sweeps that also involve non-synonymous mutations [38] , some of which may experience epistatic interactions . Thus , significant values of the epistasis statistic among syn-nsyn pairs most likely arise as a result of hitchhiking . To confirm this , we found that for most of putatively epistatic syn-nsyn pairs , there exists a putatively epistatic pair of non-synonymous sites ( nsyn-nsyn pair ) with the same trailing mutations and a leading site whose phylogenetic distribution of non-synonymous mutations is identical to that of the leading synonymous mutations in the syn-nsyn pair . Such cases comprise 53% of all syn-nsyn pairs for ( N1 , H1 ) , 64% for ( H1 , N1 ) , 74% for ( N2 , H3 ) , and 67% for ( H3 , N2 ) for the liberal P-value thresholds . As expected , the fraction of syn-nsyn site pairs that have a corresponding nonsyn-nonsyn pair with an identical phylogenetic distribution of mutations is lower for those syn-nsyn pairs that are formed by multiple leading mutations ( e . g . , 36% for ( H3 , N2 ) ) , compared with pairs formed by just one leading mutation ( e . g . , 75% for ( H3 , N2 ) ) . Finally , as expected , the signal of epistasis among syn-syn pairs was very weak or non-existent ( S4 Fig ) . The small residual signal may still be attributed to hitchhiking or to a small number of as yet unexplained real genetic interactions . Overall , our method does not reliably identify which of the leading mutations that co-occur on the same edges of the phylogeny actually precipitate subsequent epistatic trailing mutations . However , we can provide a conservative estimate for the number of truly epistatic pairs among all putatively epistatic pairs . To do this , we grouped together all nsyn-nsyn pairs with identical phylogenetic distributions of leading and trailing mutations and ordered them according to the lowest P-value in such “phylogenetic group” ( S3 Table ) . Each group with a low group P-value signifies that the trailing mutations raise in frequency together unexpectedly rapidly after a previous selective sweep that involves the leading mutations . This implies that at least one of the leading sites exhibits positive epistasis with at least one of the trailing sites . An average phylogenetic group involves 1 . 54 site pairs for ( N1 , H1 ) , 1 . 85 for ( H1 , N1 ) , 1 . 59 for ( H3 , N2 ) , and 1 . 64 for ( N2 , H3 ) . Thus , assuming that each group includes only one truly epistatic pair , we estimate that the fractions of truly epistatic site pairs constitute 65% for ( N1 , H1 ) , 54% for ( H1 , N1 ) , 63% for ( H3 , N2 ) , and 61% for ( N2 , H3 ) ; the actual fractions may be even higher , as there may be multiple drivers with identical phylogenetic distributions [38 , 41] . As an additional evidence supporting our claim that not all putatively epistatic pairs are results of hitchhiking , we repeated our analysis for a restricted subset of site pairs where a mutation at the trailing site follows a mutation at the leading site in at least two independent locations on the genealogy ( Fig 5 ) . In this smaller subset of data , we still observe more site pairs with high values of the epistasis statistic than expected , and this excess is statistically significant ( S5 Fig , S3 Table ) . Putatively epistatic site pairs revealed in this analysis must have a smaller fraction of hitchhiking-induced pairs because the same non-interacting mutations will only rarely follow each other closely in two or more distinct sweeps . The direct effect of reassortments on our results was moderate: between 67% and 91% of pairs of nonsynonymous mutations at putatively epistatic site pairs were not separated by any reassortment events ( S1 Table ) . In over 75% of all putatively epistatic site pairs , the majority of consecutive mutations did not span any reassortment events . To analyze the association between reassortments and inter-gene epistasis , we asked whether putatively epistatic pairs are formed more frequently than expected with leading mutations that arose during reassortments , i . e . , mutations at virtual nodes . We found that for 6% ( N2 , H3 ) , 20% ( H3 , N2 ) , 0% ( N1 , H1 ) and 10% ( H1 , N1 ) of trailing mutations at putatively epistatic pairs of sites the corresponding leading mutation was at a virtual node ( S1 Table ) . This is more than expected ( 10% ) for N2 ( binomial P-value = 4 × 10−4 ) , less than expected ( 19% ) for H3 ( binomial P-value = 2 × 10−4 ) , and not significantly different from what is expected for N1 and H1 . Therefore , in N2 , a substantial number of trailing mutations compensates for the changes in the genetic background brought about by reassortment events . How does the extent of inter-gene epistasis compare to the extent of intra-gene epistasis ? To address this question , we repeated the analyses of intra-gene epistasis from Ref . [21] with the data for each of the four genes analyzed here ( H1 , N1 , H3 , and N2 ) and compared the number of putatively epistatic inter- and intra-gene site pairs for each FDR value ( S6 Fig ) . We found that the number of intra- and inter-gene putatively epistatic pairs is comparable when trailing mutations occur in HA and leading mutations occur in HA or NA , respectively ( S5 Fig ) . At the same time , the number of putatively epistatic inter-gene pairs exceeds that of intra-gene pairs by as much as a factor of 3 when trailing mutations occur in NA ( S6 Fig ) . We also compared the sets of sites involved in inter-gene epistasis to sets of sites involved in intra-gene epistasis [21] . The overlap between these two groups of sites was slightly higher than expected by chance for the H3 leading and the N2 trailing sites in the ( H3 , N2 ) gene pair ( Table 2 ) , but this difference was not significant after Bonferroni correction . Next , we investigated whether sites that were implicated in inter-gene epistasis occurred preferentially in parts of the HA and NA proteins with known functional significance . In particular , we compared the sets of putatively epistatic sites with the sets of epitopic sites [20 , 42–46] , glycosylation sites [47 , 48] , sites that are responsible for antigenic cluster transitions [36 , 49–51] , as well as sites that evolve under uniform or lineage-specific positive selection . The sites in HA identified as interacting with NA occurred in all parts of HA protein , with the majority of them located in known antigenic epitopes . This is expected because our method has more power to identify epistasis at sites that are more variable , and most of variable sites are also epitopic . However , we can control for this bias by comparing the set of putatively epistatic sites that we discover in real datasets with the corresponding sets of sites discovered in permuted datasets ( see Materials and Methods ) . Using this approach , we find that leading sites in HA are actually not enriched for epitopic sites or sites under uniform or lineage-specific positive selection ( Table 3 ) . But they are enriched for sites responsible for differences between antigenic clusters [36 , 49] ( Table 3 ) . On the other hand , cluster-transition sites are slightly underrepresented among trailing sites in H3 . Finally , glycosylation sites [52] are underrepresented among trailing sites in both H3 and H1 ( Table 3 ) . We found no enrichment of epitopic , positively selected , or glycosylation sites among sites in NA that are involved in inter-gene epistasis ( Table 3 ) . To better understand what types of sites in NA comprise the putatively epistatic set , we searched the literature for evidence of functional consequences of mutations at sites that we identified . ( Such a systematic analysis was impractical for HA because much less site-specific functional data is available for this protein . ) We carried out this search for each of the 31 distinct sites in N2 ( 23 leading , 11 trailing sites , and 3 sites falling into both types ) , and for each of the 19 epistatic sites in N1 ( 13 leading and 6 trailing ) . We found that inter-gene epistasis in both N1 and N2 may be related to NA catalytic activity and resistance to inhibitors ( S2 Table ) . Specifically , we found that N2 sites 370 , 372 , 401 and 432 , which are homologous to the second sialic-binding site ( hemadsorption site ) of avian influenza viruses , are the trailing sites in 58% ( 14/24 ) of the discovered epistatic pairs , including 13 pairs with the lowest P-values , and that they are leading sites in 10% ( 3/29 ) of the discovered epistatic pairs ( S2 Table ) . Although the function of these sites in human influenza has not been directly demonstrated , it is thought that they affect catalytic efficiency of NA [53] . Among the remaining epistatic sites , leading sites 126 and 248 and trailing site 127 affect binding of NA inhibitors . In addition , two more leading sites , 215 and 332 , although not shown to affect NA activity , were reported to often mutate in response to NA inhibitor treatment . Finally , two leading sites , 172 and 399 , and one trailing site 263 were previously inferred to distinguish the reassortant H3N2 clades [33] . In N1 , three leading sites 59 , 386 and 388 ( 70 , 390 , and 392 in N2 numbering ) , and trailing site 434 , undergo host-specific position-specific glycosylation , likely affecting enzymatic activity of NA [54] . Leading sites 6 and 14 and trailing site 15 are located in the transmembrane domain , which affects viral sialidase activity through its effect on NA tetramer assembly and transport to the membrane [55 , 56] . Leading site 149 and trailing sites 83 , 275 , 267 and 287 ( 274 , 266 , and 286 in N2 numbering ) affect sialic acid binding; mutations at sites 267 and 275 were also shown to affect resistance to oseltamivir , including the mutation at site 275 which gave rise to the common oseltamivir-resistant H1N1 subtype . Finally , mutations at leading site 78 , and trailing site 83 , arise with H275Y in naturally oseltamivir-resistant strains . Finally , we present several examples of implicated epistatic site pairs with biologically plausible explanations for the mechanism of their epistatic interactions .
We developed a phylogeny-based method for detecting positive epistasis between mutations at sites that are incompletely linked . This approach provides the first systematic procedure for identifying such genetic interactions from sequence data sampled over time . We demonstrated the power of this method by applying it to data from human influenza A virus where we found dozens of putative epistatic interactions between sites in the surface proteins HA and NA . Our analysis cannot take the place of a direct experimental assay to unambiguously demonstrate epistasis between a specific pair of mutations in influenza viruses . Still , several of the most significant pairs of sites implicated by this statistical procedure have known biological functions that provide a plausible mechanistic basis for the observed patterns of coordinated molecular evolution . While powerful , our method of detecting epistasis between incompletely linked sites has three limitations . First , it relies on our knowledge of recombination breakpoints and on our ability to accurately infer phylogenies , detect recombination events and map mutations from one phylogeny onto another . Since within-segment recombination in influenza is rare [31] and the main source of horizontal exchange of genetic material is reassortment , RNA segments represent well-defined linkage blocks , which simplifies our analysis . Although in principle our method of detecting epistasis through temporal clumping of mutations on the phylogeny should be applicable to systems with recombination ( e . g . , HIV ) , a practical implementation becomes cumbersome because recombination breakpoints need to be determined and mutations need to be mapped onto several different phylogenies . Even in influenza , an accurate detection of reassortments is difficult , especially between closely related taxa [29 , 32] , and the mapping of mutations is inherently ambiguous . The second limitation is inherent to the problem of detecting epistasis from temporal mutation data , and is discussed in detail in a previous study using such techniques [21] . The problem is that our method ( as well as any method utilizing the same data ) will identify sites as trailing in epistatic pairs if mutations at these sites are temporally clustered for any reason—including reasons that are not caused by epistatic interactions per se . In theory , temporally correlated substitutions may arise due to episodes of positive selection or relaxed negative selection that are correlated between sites of the two genes . If this scenario is common , we would typically expect the inverse of a site pair with a high epistatic score to also be high-scoring . However , among the pairs defined under the conservative P-value threshold , we do not observe a single such pair . While there were several site pairs with a significant inverse pair among the pairs defined under the liberal P-value threshold , these pairs do not have lower P-values than the remaining pairs , arguing against the temporally correlated selective constraint as the predominant explanation for our results . Furthermore , the sites detected by our method do not tend to experience lineage-specific positive selection ( Table 3 ) , again arguing against this explanation . Finally , hitchhiking of mutations at linked sites is a major confounding factor for our method . This problem is relevant for human influenza A because hitchhiking is widespread in its evolution [35–39] . However , hitchhiking alone cannot account for all of the detected epistasis signal because our randomization procedure preserves the number of mutations at each branch , and thus accounts for temporal non-uniformity of evolutionary rates . Nevertheless , the list of epistatic pairs is likely contaminated with hitchhikers that co-occur on branches together with the actual leading mutations [21] . Hitchhiking is apparently the leading cause of most of the epistatic signal that we observe between leading synonymous sites and trailing non-synonymous sites . By contrast , by grouping site pairs into phylogenetic groups with identical phylogenetic distributions of mutations , we estimate that less than 50% of putatively epistastic non-synonymous pairs arose due to hitchhiking . In each phylogenetic group , those site pairs with lower P-values and those that have multiple leading mutations are more likely to be the true epistatic pairs . Keeping these caveats in mind , we turn to the interpretation of our observation of epistasis between mutations in the HA and NA . Between 20% and 31% of all mutations occurred at sites involved in epistatic interactions as either leading or trailing . The numbers of putatively epistatic site pairs where the leading mutation occurs in NA and the trailing mutation occurs in HA and vice versa are similar . The mean times between consecutive mutations at such sites are also similar between each other ( S2 Fig ) and to those in intra-gene epistasis [21] . Thus , both mutations in HA facilitated by prior mutations in NA and mutations in NA facilitated by prior mutations in HA appear to be common . The evolution of these two segments of the human influenza virus is therefore tightly coordinated . Moreover , trailing sites in NA more frequently follow a leading mutation in HA than a leading mutation in NA itself . What is the molecular basis for such coordinated evolution ? We searched for enrichment of various properties among epistatically interacting sites . In HA , we found no enrichment of epistatic sites among the positively selected sites . In fact , it is somewhat surprising that the detected putatively epistatic sites are not particularly rapidly evolving , despite the fact that our method has more power to detect epistasis at sites with more mutations [21] . We do observe , however , an enrichment of leading sites among the HA sites responsible for antigenic shifts . This suggests that the changes in HA driven by immune system pressure are frequently compensated by mutations in NA . Conversely , antigenic sites and glycosylation sites were underrepresented among the trailing sites of HA , suggesting that the HA trailing sites compensating for the mutations in NA comprise a novel potentially interesting set of functional sites in this protein . We also found no enrichment of positional or functional categories in epistatic sites in NA . This lack of clear pattern is consistent with experimental data and implies that genetic interactions occur through a wide range of mechanisms , and that the sites involved in them are hard to predict a priori [60 , 61] . However , we observed that many epistatic sites in NA are involved in NAI resistance , modulation of NA activity , or both . Why do the sites affecting these traits interact with HA ? Some of the observed interactions ( e . g . , site N1-78 ( Fig 2 ) , and sites N1-274 and N1-430 ( Fig 3 ) ) could be directly attributed to the requirement to balance the activities of HA and NA to maintain viral fitness , especially in the presence of NAI [55] . Other interactions may affect this balance indirectly . For example , sites in the signal peptide of HA appear to occasionally interact with sites in the transmembrane domain of NA , e . g . , site H1-16 forms a putatively epistatic pair with site N1-15 ( S3 Table ) . These types of mutations likely affect the efficiency of membrane localization of the respective surface proteins [62] , and mutations in the transmembrane domain may also influence NA activity through their effect on tetramer assembly [63] . Some of the putatively epistatic site pairs that we detected have been experimentally confirmed . For example , a number of mutations in HA of H1N1 closely predated the 2007 spread of the H275Y ( 274 in N2 numbering ) oseltamivir resistance mutations in NA . Recently , 7 of these HA sites were experimentally tested for interactions [61] . These experiments showed that HA that carries the derived residues at all seven sites is well adapted to both the ancestral H275 ( sensitive ) and the derived Y275 ( resistant ) variant of NA . At the same time , three out of seven reconstructed reversions in HA ( at sites 82 , 141 and 189 ) had large fitness defects in the context of the derived NA variant , implying that mutations at these sites compensated for the H275Y mutation in NA [61] . Remarkably , all three of these HA sites form high-ranking pairs in our analysis with the site 275 in NA ( S3 Table , sites 99 , 157 , 205 in our numbering ) . Finally , a recent experimental study [27] confirmed the involvement of site 275 in intragenic epistatic interactions predicted in our previous work [21] . Trailing mutations in N2 frequently compensate for the changes in H3 , and possibly in other genes [29] , brought about by reassortments . Furthermore , the N2 sites that are involved in intra-gene epistasis as trailing are enriched in sites that experience post-reassortment mutations [29] , and in sites involved in inter-gene epistasis as trailing ( Table 2 ) . These findings support our interpretation that NA is the gene most actively involved as trailing in epistatic interactions , with mutations at it compensating a range of other events both in the same [21] and in other genes ( Table 2; [29] ) ; and show that the same set of sites in N2 might tune the protein function in response to various changes of genetic background of H3N2 IAV . More generally , our results suggest that the evolution of a protein depends strongly on its genomic context , with a substantial number of adaptive mutations representing responses to mutations that previously occurred in other proteins . Such evolutionary coupling between different proteins has also been observed in several experimental systems [13 , 15 , 23 , 64–66] . However , estimating the fraction of mutations that are driven by direct adaptation to the external environment versus by selection to balance or compensate the effects of prior mutations elsewhere in the genome remains an important open problem .
We downloaded all complete human H3N2 influenza A isolates ( N = 2 , 205 ) available on 27 October 2011 and all complete human seasonal H1N1 influenza A isolates ( N = 1 , 180 ) available on 12 November 2012 from the flu database [67] . The amino acid sequences were aligned using MUSCLE [68 , 69] , and the alignments were reverse translated using PAL2NAL [70] . Genotypes containing truncated sequences or long stretches of unidentified nucleotides were discarded . The 3 genotypes of H3N2 subtype carrying indels were discarded . We also discarded all genotypes of H1N1 that were sampled prior to 1936 because they had large ( 15–16 amino acids ) gaps between amino acid positions 42 and 77 in the NA protein . In all sequences , the alignment columns with gaps in more than 10% of all sequences were excluded from further consideration; in the remaining alignment columns , gaps were substituted with the consensus nucleotide . Four isolates of H1N1 subtype ( A/New Jersey/1976 , A/Wisconsin/301/1976 , A/Iowa/CEID23/2005 , A/Switzerland/5165/2010 ) were discarded as swine-origin influenza virus ( SOIV ) [71–73] . Three isolates of H3N2 subtype ( A/Ontario/RV123/2005 , A/Ontario/1252/2007 and A/Indiana/08/2011 ) were discarded as SOIV triple reassortants [74] . Many of the genotypes had NA genes with identical nucleotide sequences; among each such set of genotypes , we only retained one random genotype . This reduced our sample to 1 , 376 isolates for H3N2 subtype , and 745 isolates for H1N1 subtypes . For HA and NA proteins of H1N1 , the numbering scheme used through the text is relative to the proteins of the A/AA/Huston/1945 isolate , unless stated otherwise . We asked whether a mutation at a particular site in HA segment facilitates a subsequent mutation at a particular site in NA segment , or vice versa . To address this , we need to reconstruct the phylogenetic trees for each of the two segments , infer the position of reassortments on these trees , and establish the temporal order of mutations in different segments relative to each other . We achieve this goal in three steps , which are described in detail below . Briefly , in the first step , based on topological incongruencies between the phylogenetic trees of individual segments , we identify the so-called reassortment sets , i . e . , sets of taxa that are likely descendants of reassortant viruses . In the second step , we reconstruct the so-called constrained phylogenies of the segments , i . e . , phylogenies that are topologically identical everywhere except for branches that correspond to reassortment events . This allows us to map , in the third step , the mutations that occur on branches of one phylogeny to the branches of another phylogeny . Lists of epitopic sites of HA were taken from [42 , 43] for H3N2 and from [20] for H1N1 . Lists of epitopic sites of NA were taken from [43–45] for H3N2 , and from [46] for H1N1 . Sites involved in intra-gene epistasis in HA and NA of H3N2 and H1N1 were taken from [21] . Sites that may carry mutations changing the antigenic properties of isolates were taken from [36 , 49 , 50] for H3 , and from [51] for H1 . Glycosylation sites were taken from [47] for H3-HA1 , and from [48] for H1-HA1 and H1-NA . Sites in H1 , N1 , H3 and N2 under uniform positive selection were inferred by HyPhy IFEL method [82] , and under lineage-specific selection , by HyPhy MEME method [83] ( P-value < 0 . 05 ) using Datamonkey web service [84 , 85] ( http://www . datamonkey . org ) . To test whether a particular set of sites S is enriched or depleted among the top-ranking epistatic leading sites compared to the random expectation , we used the following procedure . First , we defined for each site its leading P-value as the lowest nominal P-value among all site pairs with this site as leading . We then defined the leading test statistic as the difference between the medians of site’s leading P-value for sites in S and for sites not in S . The null distribution of this test statistic was determined from the 400 fake datasets generated from the no-epistasis null hypothesis as explained above . An analogous procedure was used to find enrichment among the top-ranking epistatic trailing sites . For each pair of genes , we considered “leading-trailing” pairs of mutations to be provisionally “reassortment-induced” if the leading mutation was virtual , and the trailing mutation occurred in the same reassortment set . To test whether the reassortment-induced pairs are overrepresented , we analyzed the fraction of such mutations among all pairs of mutations at epistatic site pairs in the same reassortment set . The expected fraction was obtained from our ‘fake’ datasets ( see above ) . We tested the null hypothesis that the observed number of reassortment-induced pairs of mutations was sampled from a binomial distribution with the probability of success equal to the expected fraction of such mutation pairs . | The fitness of an organism depends on the coordinated function of many genes . Thus , how a mutation in one gene affects fitness often depends on what mutations are present in other genes . This dependence is called “genetic interaction” or “epistasis” . The prevalence and type of such interactions are not well understood . Epistasis can be inferred from time-series sequencing data when a mutation in one gene is observed to facilitate the spread of a mutation in another gene . However , the situation is much more complicated when new combinations of genes are formed by processes such as recombination or reassortment . In such cases , deducing the time and order of genetic changes is difficult . Here , we devise a method to infer pairs of mutations in different genes which closely follow one another in the presence of reassortment . We apply it to evolution of two surface proteins of influenza A virus , hemagglutinin and neuraminidase , which are important targets for the human immune system and drugs . We show that mutations in one of these proteins are often facilitated by prior mutations , or compensated by subsequent mutations , in the other protein . In particular , drug-resistance mutations in neuraminidase were likely made possible by prior mutation in hemagglutinin . Knowledge of such interactions is necessary to fully understand and predict evolution . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Coordinated Evolution of Influenza A Surface Proteins |
Infections caused by dengue virus are a major cause of morbidity and mortality in tropical and subtropical regions of the world . Factors that control transition from mild forms of disease such as dengue fever ( DF ) to more life-threatening forms such as dengue hemorrhagic fever ( DHF ) are poorly understood . Consequently , there are no reliable methods currently available for early triage of DHF patients resulting in significant over-hospitalization . We have systematically examined the proteome , cytokines and inflammatory markers in sera from 62 adult dengue patients ( 44 DF; 18 DHF ) with primary DENV infection , at three different times of infection representing the early febrile , defervescence and convalescent stages . Using fluorescent bioplex assays , we measured 27 cytokines in these serum samples . Additionally , we used multiple mass spectrometry methods for iTRAQ-based comparative analysis of serum proteome as well as measurements of protein adducts- 3-nitrotyrosine and 3-chlorotyrosine as surrogate measures of free radical activity . Using multiple methods such as OPLS , MRMR and MSVM-RFE for multivariate feature selection and classification , we report molecular markers that allow prediction of primary DHF with sensitivity and specificity of >80% . This report constitutes a comprehensive analysis of molecular signatures of dengue disease progression and will help unravel mechanisms of dengue disease progression . Our analysis resulted in the identification of markers that may be useful for early prediction of DHF during the febrile phase . The combination of highly sensitive analytical methods and novel statistical approaches described here forms a robust platform for biomarker discovery .
Infection with dengue virus ( DENV ) causes a spectrum of clinical manifestations ranging from mild dengue fever ( DF ) to the potentially lethal dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) [1] . In humans , the major cellular targets of dengue appear to be dendritic cells of the skin , macrophages and monocytes [2] . Dengue is endemic to the tropical and sub-tropical regions of the world , which are home to over half the population of the world as well as being popular tourist destinations . It has also emerged in new areas such as south Florida and Mediterranean France . With a significant proportion of the world population at risk of infection annually , coupled with the absence of a licensed vaccine , dengue is emerging as a global health concern . The majority of dengue patients recover uneventfully after 5–7 days of acute illness . In a small proportion of patients , however , the initial febrile period is followed by a rapid onset of vascular leakage , thrombocytopenia and hemorrhage indicating DHF . The continual loss of intravascular volume from plasma leakage can very rapidly lead to hypotension and cardiovascular collapse which , if not carefully managed , can result in death . In the absence of an effective antiviral drug , the management of dengue patients is primarily supportive . Early recognition of patients with plasma leakage is thus critical for the initiation of appropriate fluid management to prevent onset of hypovolemic shock . However , because these symptoms become evident only in the critical phase of infection , it is currently not possible to distinguish DF and DHF accurately during the early stages of illness , when the disease is less well differentiated [3] . The mechanisms that trigger transition from mild DF to more life threatening DHF are poorly understood , hampering early classification of dengue patients who will progress to DHF . This not only delays treatment but frequently results in the over-hospitalization of patients contributing significantly to the financial burden imposed by dengue [4] , [5] . The availability of reliable markers that predict DHF during the early stages of infection could be useful in triaging patients for management . In the last decade , numerous efforts have been made to identify serum markers that may predict severe dengue disease , with an emphasis on cytokines [6] . A number of studies propose that innate immune cytokines ( e . g . IFN-α , IL-8 ) are elevated during the early febrile phase while adaptive immune cytokines ( e . g . TNF-α , IL-10 , IFN-γ ) appear to increase during the defervescence phase of dengue infection [6] . Several factors have traditionally limited the usefulness of these studies in biomarker development . Firstly , the highly variable nature of patient cohorts ( e . g . pediatric versus adults; ethnicity ) used makes it difficult to compare the results of these various studies . Secondly , most studies have examined ‘case versus control’ type of sample population instead of longitudinal studies to distinguish ‘predictors’ from ‘indicators’ . Finally , a lack of follow-up in larger population base to test the prognostic potential of proposed markers limits their clinical application . The early dengue infection and outcome ( EDEN ) study in Singapore prospectively recruits and follows-up adult dengue patients in Singapore through early febrile , defervescence as well as convalescence stages [7] of the disease . This makes this longitudinal study highly suited for the identification of prognostic markers of severe dengue disease . In this study , we report a systematic characterization of serum cytokines , proteome , and markers of macrophage and neutrophil activity in a subset of adult dengue patients with primary dengue infections obtained from the EDEN cohort . In addition to identifying molecular signatures of disease progression , we describe a comprehensive multivariate statistical analysis to identify serum markers for early prediction of DHF .
The EDEN study is a multi-center longitudinal study of adult febrile infections that was carried out at a number of clinics island-wide in Singapore . Enrollment of eligible individuals was based on written informed consents and the protocols were approved by the National Healthcare Group ( DSRB B/05/013 ) . The study protocols have been described earlier [7] . In brief , adult patients ( >21 years ) presenting with acute onset fever ( ≥38 . 0°C for less than 72 hours ) without rhinitis or other clinical alternatives were included in the study . Initial dengue diagnosis and viremia levels were determined by real time RT-PCR using a previously described method [8] . This was followed by serology and subsequent serotyping by virus isolation and immunofluorescence using serotype specific monoclonal antibodies ( ATCC: HB46-49 ) . Venous blood samples were also collected at fever day 4 to 7 ( visit-2 ) and weeks 3 to 4 ( visit-3 ) , aliquoted and frozen at −80°C . ‘Fever day’ here refers to number of days post onset of fever . Classification of DF or DHF was made based on the guidelines provided by the WHO [9] . In brief , acute febrile patients positive for dengue with one or more of the following: headache , retro orbital pain , myalgia , rash , leucopenia , hemorrhage were classified as DF while patients with fever lasting 2–7 days combined with bleeding , thrombocytopenia ( <100 , 000/mm3 ) as well as evidence of plasma leakage shown by a 20% or greater rise in hematocrit relative to the blood sample obtained at convalescence or pleural effusion on chest X-ray were classified as DHF . Of the 133 dengue patients that were finally enrolled in this study ( September 2005–October 2006 ) , 62 patients ( 44 DF , 18 DHF ) tested negative for dengue IgG antibodies in the acute sera , using a commercial ELISA kit ( PanBio , Brisbane , Australia ) . These patients were deemed to have primary DENV infection , all of which were included in this study . A detailed hematological and virological analysis was performed and a subset of 15 clinical indicators was selected for our statistical analysis . These included white blood cell count ( WBC ) , red blood cell count ( RBC ) , blood hemoglobin ( HGB ) , hematocrit ( HCT ) , macrophage cell volume ( MCV ) , MCH , MCHC , platelet count ( PLT ) , lymphocyte percentage ( LYMPH% ) , lymphocyte count ( LYMPH ) , mixed cell distribution ( MXD ) , neutrophil percentage ( NEUT% ) , neutrophil count ( NEUT ) , red blood cell distribution width-coefficient of variation ( RDW-CV ) , and viral titers . Additionally , we used plasma samples from 50 asymptomatic healthy army recruits collected during their annual physical examination in Singapore as controls in our analyses . A comparison of cytokines in dengue patient sera with healthy plasma is shown in supplementary data ( Figure S1 and Table S1 ) . This study was approved by the National University of Singapore Institutional Review Board and samples were collected with individual informed written consents ( see checklist S1 ) . Cytokine measurements were performed with 12 . 5 µl sera in duplicates using the Bioplex 27-plex human cytokine kit from BioRad as per manufacturer's instructions . The standard curves were optimized automatically by the software ( Bioplex manager ) and verified manually . The Bioplex manager software was used to calculate cytokine concentrations and only measurements that showed a coefficient of variability ( CV ) of <10% were included for further analysis . Levels of interferon-induced cytokine IP-10 in 30% of the dengue patient samples during visit-1 were above upper limit of detection . We repeated the analysis after diluting the serum 100 fold for this subset of samples . Six of the visit-1 samples in DF group and 3 in DHF group still had very high levels of IP-10 and for the statistical analyses , we included these as missing values since levels of other cytokines for these samples were within detectable range . Measurement of 9 acute phase proteins was performed using the Bioplex Pro Acute phase multiplex kit ( BioRad laboratories ) as per manufacturer's instructions . Two different dilutions of sera were used-1∶1000 for ferritin ( FT ) , serum amyloid A2 ( SAA ) , procalcitonin ( PCT ) , tissue plasminogen activator ( tPA ) , fibrinogen ( FB ) and 1∶100 , 000 for alpha-2-macroglobulin ( A2M ) , haptoglobin ( HPT ) , C-reactive protein ( CRP ) and serum amyloid P ( SAP ) . Nitrotyrosine ( NT ) and chlorotyrosine ( CT ) in human serum were measured by a liquid chromatography-triplequadrupole MS method . Briefly , 2 mg of serum protein was spiked with 4 pmol internal standards ( IS ) L-3-chloro-[13C9 , 15N]-tyrosine and L-3-nitro-[13C9 , 15N]-tyrosine , and digested in the sodium acetate solution 0 . 1 M ( pH 7 . 4 ) with 0 . 4 mg pronase E ( freshly treated by the size-exclusive micro bio-spin column ) . The mixture was incubated at 50°C for overnight ( 16 hrs . ) and filtered by Vivospin500 3KMW centrifuge filter at 15 , 000 rpm to remove undigested protein . The amino acids were further purified by Agilent 1200 series HPLC system ( Waldbronn , Germany ) on an Xbridge TM Phenyl column ( 3 . 5 µm , 4 . 6×50 mm , Waters , Milford , MA ) . The fractions containing nitrotyrosine and chlorotyrosine , together with internal standards , were collected and dried by SpeedVac for subsequent LC/MS/MS analysis . Subsequent mass spectrometry analysis of target compounds involved separation on an Xbridge TM Phenyl column ( 3 . 5 µm , 1 . 0×100 mm , Waters , Milford , MA ) online injection into an Agilent 6460 triple quadrupole mass spectrometer . Two microliters of each sample was injected and eluted by isocratic 25% methanol ( 0 . 1% formic acid ) for 13 min at 15 µL/min . CT along with IS were analyzed by regular multiple reaction monitoring ( MRM ) as follows: 216/170 ( CT ) and 226/179 ( CT , IS ) . NT along with IS were measured by modified MS3 based in-source fragmentation as follows: 181/117 ( NT ) and 190/125 ( NT-IS ) by elevating the potential to 135 V at the ion source . The limits of quantitation achieved were 8 . 1 and 7 . 3 nM for CT and NT , respectively .
We selected 62 adult dengue patients from the EDEN cohort , of which 44 were diagnosed as DF , and 18 as DHF . The patients selected in DF and DHF groups had similar age and ethnic distribution ( Table 1 ) . Serotyping analysis indicated that DENV serotypes-1 and -3 were the most common , followed by serotype-2 , while no serotype-4 was present . Average duration between fever onset and first sample collection was <48 hours , and the average duration between samples were <80 hours ( visit 1 & 2 ) and <21days ( visit 2 & 3 ) respectively ( Table 1 ) . We examined the key clinical indicators commonly used for the diagnosis of DHF . Blood platelet count dropped significantly from febrile phase to defervescence in both DF and DHF patient groups with DHF patients exhibiting significantly ( p<0 . 05 ) lower platelet levels during defervescence ( visit-2 ) than DF patients ( Figure 1A ) . DHF patient groups also exhibited significantly ( p<0 . 05 ) lower WBC and lymphocyte counts especially during defervescence ( Figure 1B &C ) . Viral titer measured at visit-1 was higher in DHF patients ( Figure 1D ) consistent with several previous studies that have reported higher plasma viral loads in DHF patients [15] , [16] . Our study population thus recapitulated most of the hallmark clinical features of dengue progression in DF and DHF during the early febrile , defervescence and convalescence stages of infection . We measured the levels of 27 serum cytokines in our dengue patient cohort , using a multiplex assay . A majority of cytokines was maximally elevated in dengue patients during the early febrile phase ( visit-1 ) of infection ( Table S1 , Figure S1 ) . These included IL-1ra , IL-4 , IL-7 , IL-8 , IL-10 , IL-12 , Eotaxin , G-CSF , IFN-γ , IP-10 , MCP-1 , MIP-1b and VEGF . Cytokines IL-1b , IL-5 , IL-6 , IL-9 , IL-12 , IL-17 and FGF-basic , remained elevated during defervescence ( visit-2 ) and convalescence ( visit-3 ) stages . When compared with plasma samples from an independent cohort of healthy individuals , cytokines IP-10 , VEGF and PDGF-BB were found to be elevated >20 times over controls during the febrile phase of infection , while IL-4 , IL-9 , IL-10 and IL-1ra were elevated by 10–20 times over controls ( Table S2 ) . Cytokines IL-6 , IL-7 , IL-8 , Eotaxin , G-CSF , IL-17 and MIP-1b were elevated 4–8 fold ( Table S2 ) . These values may however be an overestimation of the actual changes since an independent cohort may not be an ideal control for the study population . To identify temporal patterns in cytokine flux in patient sera , we performed K-means clustering to group cytokines in DF patients exhibiting similar patterns across the three stages of disease as detailed in the methods section . The cytokine IP-10 was the sole member of cluster-1 ( Figure 1E ) with very high levels during the febrile phase followed by a rapid decline to near control levels at convalescence . A majority of cytokines fell into a second cluster ( Figure 1E and F , Cluster-2 ) that exhibited a peak at the febrile phase but declined modestly , with levels remaining significantly higher than controls even at the late convalescent stage ( visit-3 ) . A third cluster of 7 cytokines ( Figure 1E and F , Cluster-3 ) increased at febrile phase and decreased during defervescence but increased again to peak levels during late stages ( visit-3 ) . While overall clustering profile of cytokines was similar between DF and DHF , cytokines IL-1b , IL-4 , IL-6 , IL-8 , IFN-γ , IL-17 , G-CSF , VEGF , IP-10 , and PDGF-BB ( marked by asterisk in Figure 1F ) either clustered differently or showed different slopes ( not shown ) between the disease stages in DF and DHF groups suggesting that there may be changes in the temporal profile of these cytokines . Overall , our results indicated that cytokines and chemokines associated with innate immune activity ( e . g . IFN-γ , IP-10 ) , Th2 cell response ( IL-4 , IL-10 , and IL-13 ) , inflammation ( IL-1b , IL-6 , and IL-8 ) , chemotaxis of macrophages and neutrophils ( Eotaxin , MIP-1b ) are all maximally elevated in dengue patients during the early febrile phase . Cytokines IL-12 , growth factors FGF and PDGF increased even at convalescence . TNF-α remained below detection levels in our analysis likely because production is transient and missed in our timeline of sample collection . Similarly , levels of IL-2 , IL-15 , GM-CSF and MIP-1a were below the detection limit in >85% of the samples and were excluded from further analysis . Differences in temporal profile of a subset of cytokines between DF and DHF patients , identified in the clustering analysis outlined above , prompted us to examine these cytokines more closely across different time points of infection in DF and DHF groups . We observed that DHF patients had lower levels of IFN-γ during febrile phase , a time of peak interferon activity ( Figure 2A–B ) . Although levels of IP-10 ( an interferon-induced cytokine ) were also lower in the DHF group , this was statistically significant ( p<0 . 05 ) only at defervescence ( Figure 2B ) . Low levels of IFN-γ as well as IP-10 during the febrile phase point to an attenuated interferon response in DHF patients , which may be associated with diminished viral clearance . There was a marginal but significant correlation between viral titers and IFN-γ levels during the early febrile stage ( visit-1 , ( r = 0 . 370; p<0 . 05 ) . The correlation was especially strong between IFN-γ at visit-1 and IP-10 at visit-2 in DHF patients ( r = 0 . 66; p<0 . 05 ) . We observed decreased levels of Th2 cytokine IL-4 , in DHF patients during the febrile stage , ( Figure 2C ) compared with DF . Unlike DF patients , IL-1b levels in DHF patients were indistinguishable from healthy controls until the convalescence stage , indicating a depressed IL-1b response ( Figure 2D ) . Levels of IL-17 as well as Granulocyte-Colony Stimulating Factor ( G-CSF ) were lower in DHF patients especially during the febrile stage ( Figure 2E and F ) . The serum profiles for platelet-derived growth factor ( PDGF-BB ) as well as vascular endothelial growth factor ( VEGF ) were similar to G-CSF and markedly lower during the febrile phase in DHF patients compared to DF patients ( Figure 2G , H ) . A similar comparison of other cytokines IL-6 and IL-8 that were found altered in the clustering analysis ( Figure 1E &F ) indicated that the differences between DF and DHF groups were not statistically significant ( data not shown ) . The number of patients in this study was too low to allow stratification by days from fever onset ( Figure S2 ) . Quantitative proteomics by isobaric tagging of peptides allows multiplexing of biological samples thereby reducing variability while increasing accuracy of protein quantitation [17] . We adopted an iTRAQ-based approach to quantify the serum proteome of pooled dengue patient sera during the different stages of the disease . Overall , we identified 90 proteins with high confidence , and determined their fold-change over control samples , in both DF and DHF patient groups ( Table 2 ) . Of a total of 35 proteins that showed a >1 . 5 fold enrichment or depletion , 25 proteins were unique to DHF patient group while 6 proteins - serum amyloid A2 , leucine-rich-alpha-2 glycoprotein , hemoglobin alpha , actin , haptoglobin and alpha-1-antitrypsin , changed in both DF and DHF samples ( Table 2 ) . The acute phase reactants were the most abundant class , followed by serpin class of protease inhibitors and complement pathway proteins ( Figure 3A ) . A majority of these proteins were maximally elevated during the febrile phase although some remained high or increased further during defervescence ( Table 2 ) . Five proteins were depleted from sera during the febrile and defervescence stage but returned to near control levels during the convalescent stage ( Table 2 ) . Overall , the proteomic analysis indicated that the most readily observable predominant serum protein response in dengue infections was the acute phase response . A major caveat of the sample pooling approach described above is the averaging effect which may result in a gross underestimation of fold changes despite the high accuracy and sensitivity of the proteomic quantification . As an alternative , we used a commercially available multiplex fluorescent-bead based ELISA assay , which simultaneously measures levels of 9 well-known acute phase proteins including two serum proteins ( serum amyloid A2 ( SAA ) and haptoglobin ( HPT ) ) that were identified in our proteomics analysis ( Table 2 ) . Using this method , we analyzed individual serum samples from 10 DHF , 24 DF patients and 10 healthy asymptomatic controls . SAA and HPT were elevated in dengue patients during the early febrile ( visit-1 ) and defervescence ( visit-2 ) stages ( Figure 3B–C ) . Other acute phase proteins that were elevated in dengue patients included C-reactive protein ( CRP ) , alpha-2 macroglobulin ( A2M ) and ferritin ( FT ) ( Fig . 3D–F ) , while serum amyloid P ( SAP ) , pro-calcitonin ( PCT ) , tissue plasminogen activator ( t-PA ) and fibrinogen ( FB ) remained unchanged ( not shown ) . With the exception of SAA , which was higher in DHF patients during the febrile phase , the differences in levels of other proteins between DF and DHF patient groups were not statistically significant . We used a previously established mass spectrometry based method [18] to measure levels of total serum 3-nitro-tyrosine ( NT ) and 3-chloro-tyrosine ( CT ) , in 44 DF patients and 10 DHF patients at three different stages of the disease . Compared to healthy individuals where CT and NT levels in sera are below detection , there was a significant elevation of both CT and NT in dengue patient sera ( Figure 4 ) . Levels of CT were elevated in all dengue patients during the febrile phase compared to controls , and continued to increase during defervescence and remained high at convalescence ( Figure 4A ) . This suggests that neutrophil activity remains high even after viral clearance . Interestingly , DHF patients displayed higher levels of CT compared to DF patients during the early febrile phase and although higher levels were also seen during defervescence and convalescence , the differences at the latter stages were not statistically significant ( at p<0 . 05 ) . NT peaked during the early febrile phase of the infection but declined to near basal levels during the convalescence stage ( Figure 4B ) . We did not observe statistically significant ( p<0 . 05 ) differences in NT levels between DF and DHF groups in our experiment . We adopted a multiple-feature selection strategy to identify subsets of features from among the 47 blood parameters described above that may have predictive value in the identification of DHF during the early febrile phase . By analyzing the various feature classes ( i . e . cytokines , serum proteins , protein adducts , and clinical features ) measured at the early febrile phase ( visit-1 ) , both independently , as well as together we evaluated the relative predictive power of these various molecules . First , we analyzed 23 cytokines and identified a subset of 7 cytokines which displayed sensitivities and specificities >75% ( Table 3 ) . A receiver operator characteristics ( ROC ) curve analysis indicated that this subset performed well with area under curve ( AUC ) of 0 . 87±0 . 05 ( Table 3 , Figure 5 ) . We next combined 15 laboratory clinical features ( listed in the methods section ) along with the cytokines and reanalyzed the data . This resulted in a new subset ( Table 3 ) and achieved sensitivities and specificities >80% with an AUC of 0 . 92±0 . 03 ( Table 3 , Figure 5 ) . While cytokines IFN-ϒ , IL-1b , IL-8 and IL-17 were common with subset A , combining them with lymphocyte , platelet counts and viral titers improved the predictive performance of subset-B compared to subset-A ( Table 3 ) . The addition of two more features- CT and NT- to the dataset resulted in a new subset that retained cytokines- IFN-ϒ and IL-1b , IL-8 , and blood lymphocyte count , but also had additional set of cytokines ( Table 3 ) along with CT . However , overall predictive performance of subset group-C was poorer likely due to reduction of population size ( n = 54 as compared with n = 62 ) . Finally , we expanded the dataset to include all measured features ( i . e . 23 cytokines , 5 serum proteins , 2 protein adducts and 15 clinical features ) . The number of patients in this analysis was much lower ( n = 34 ) than the previous analysis ( n = 62 and n = 54 ) due to further exclusion of samples where the data was incomplete due to missing values . The subset from this analysis included a variety of features including serum proteins ( SAA and HPT ) , cytokines ( IFN-ϒ , IL-17 ) and protein adducts ( CT ) that achieved a sensitivity and specificity of >75% and AUC of 0 . 90±0 . 06 ( Table 3 ) .
We have performed a comprehensive molecular analysis of serum molecules in a cohort of adults with primary dengue infections with the objective of identifying predictive markers of DHF . Traditionally , biomarkers studies have relied mostly on case versus control studies ( reviewed in [6] ) with one sample per patient , collected in a 1–10 day period . Some of these studies have reconstructed temporal profiles via data grouping based on fever day [19] , [20] , [21] . However , variability in sample size within groups ( e . g . fever day ) and lack of patient follow-up often result in poor statistical performance and inadequate modeling of individual immune responses . Prospective follow-up of patients across disease stages , although most desirable for biomarker development , are scarce . A good example is a 1997 pediatric dengue study in Thailand , where a positive dengue diagnosis was followed by daily blood sampling till one day post defervescence [22] , [23] . The EDEN study combines the convenience of asynchronous patient recruitment during the early febrile phase , with patient follow-up , and is designed to specifically model adult dengue infections [7] . A detailed cytokine analysis indicated that DHF patients are characterized by an attenuated serum cytokine response especially during the early febrile phase . In DHF , low levels of IFN-γ during febrile phase correlated with reduced levels of IP-10 , indicating that an inability to mount a timely anti-viral response may result in high viremia . In cell culture models , pretreatment with interferons inhibits dengue viral replication [24] although treatment after infection has no effect due possibly to active inhibition of IFN-signaling pathways by dengue viral protein NS4B [25] . Whether higher viral titers reported in DHF patients is a consequence or cause of an impaired interferon response remains to be confirmed . In recent human challenge studies , development of infection correlated with extremely low or undetectable IFN-γ production by PBMCs suggesting a role for sustained IFN-γ production in protection [26] . An attenuated innate response may in turn affect the kinetics of adaptive immune and pro-inflammatory responses , as suggested by the lower levels of Th2 cytokines IL-4 and IL-13 , growth factors G-CSF , VEGF and PDGF , observed during the febrile stages in DHF patients in this study . In contrast our findings , a number of previous studies have reported elevated levels of IFN-γ [19] , IL-8 [27] , IL-6 , TNF-a [28] , MIP-1b [19] , IL-10 [29] , and free VEGF [23] , in DHF patients . However these studies differ significantly from the present study in the types of clinical cohorts evaluated . For example , comparable longitudinal cohort studies reporting higher levels of IL-10 and IL-6 have focused exclusively on pediatric cases [29] , [30] . Importantly , primary infections made up less than ten percent of the cohorts in previous studies reflecting the higher incidence of DHF in secondary infections . Hence , different cytokine profiles observed in the present and previous studies are likely related to differences in immune responses to primary and secondary infections . The few studies that have included a subset of primary infections have reported conflicting results , with some reporting higher levels of cytokines in secondary compared to primary infections [31] , [32] , while others reporting no differences [33] . It is noteworthy that no DHF patients were included in these studies , and therefore it is not possible to compare cytokine profiles specific to DHF . We hypothesize that timely interferon-regulated antiviral responses are critical determinants of outcome in primary infections , whereas inflammatory mediators and regulators of antibody-dependent enhancement , including IL-6 , IL-8 , and IL-10 may dominate in secondary infections . Ethnic background of patients can also affect the type of cytokine responses to dengue infections [34] , and may contribute to cytokine profiles described here . In an attempt to identify serum protein markers of DHF , several groups have reported proteomic analysis of dengue patient sera [35] , [36] , [37]; using a variety of methods and clinical cohorts . We used a highly sensitive isobaric-tag method of quantitation that allowed us the compare the proteomic changes across different stages of infection . We focused on the most prominent functional protein group identified ( i . e . acute phase reactants ) , and observed elevated levels of CRP , SAA , HPT , A2M and FT in individual patient samples . Maximum elevation of CRP and SAA in the early febrile phase was consistent with elevated production of IL-6 , a hepatic inflammatory cytokine . Interestingly , acute phase reactants PCT , FB and SAP were not altered , and this may be related to liver dysfunction observed in dengue patients [38] . With the exception of SAA , there were no significant differences between DF and DHF groups suggesting that acute phase response is not a dominant mechanism of pathology in primary infections . Understanding the specific functional role of other proteins shortlisted in our proteomics study will require detailed validation . Nitric oxide ( NO ) production by phagocytes is an important inflammatory response to pathogens and although increased levels of both inducible NO synthase ( iNOS ) and NO levels have been reported in dengue patients [28] , [39] , their role in dengue viral clearance is unknown . Protein adducts CT and NT formed from NO-mediated reactions are sensitive surrogate measures of neutrophil and macrophage activities during inflammation [18] , [40] . We observed elevated levels of CT in dengue patient sera compared to healthy controls , which continued to rise from early febrile to defervescence stage indicating robust and sustained neutrophil activity . Interaction of activated neutrophils with the endothelium has been known to modulate vascular permeability [41] , [42] . Whether elevated levels of CT can serve as an early indicator of plasma leakage remains to be tested . In contrast to CT , the transient nature of NT accumulation suggests that macrophage activity is limited to the acute phase of the infection , possibly linked to viral titers . The comprehensive database of 47 blood parameters from dengue patients described in this study provides a unique opportunity to statistically query this dataset to identify -1 ) most significant molecules and 2 ) their relative importance in distinguishing DHF from DF during the early febrile stage . In the final analysis , a subset of 9 features was identified that included 5 cytokines , chlorotyrosine , blood lymphocyte count , and two serum proteins . Overall , cytokines involved in attenuated antiviral response; up regulation of acute phase proteins , and elevated neutrophil activity; together appear to be early signatures of DHF resulting from primary infections . The precise role of other cytokines IL-17 , FGF-basic , and RANTES that were included in the predictive subset , in DHF pathogenesis is currently unclear and does not rule out the involvement of other cytokines in regulation of immune mechanisms in DHF patients . Previously , a variety of statistical methods including classification and regression tree ( CART ) analyses [43] , [44] , as well as decision tree algorithms [45] have been used to identify clinical markers that achieved high sensitivity but poor specificity in classification of DHF . These clinical parameters , however , require daily monitoring . Identifying and measuring the molecules that are directly involved in pathogenesis could improve our predictive capabilities . Recently , Brasier et al used a logistic regression approach to report a 3 component biomarker panel consisting of platelet count , lymphocyte count and IL-10 that , classified DF from DHF patients with an accuracy of >85% during the first week following onset of fever [21] . In a second study Brasier et al used a multivariate adaptive regression splines ( MARS ) method to evaluate cytokines and plasma proteome from a cohort of secondary dengue infections and reported a panel consisting of IL-10 and seven serum proteins that achieved 100% sensitivity and specificity in prediction of DHF in the first week of fever onset [37] . However , these two studies applied a broad window of measurement , which may not capture the dynamic processes of DHF pathogenesis . It also raised the possibility that biomarkers of DHF in secondary infections may be qualitatively and quantitatively different from primary infections . Determining which of these biomarkers reflect differences in primary versus secondary infections and which inform on DHF development , whether in primary or secondary infections , will be critical for the development of robust biomarkers to stratify dengue patients for medical care . In conclusion , this study describes a comprehensive and systematic molecular analysis of serum samples from a cohort of patients with primary dengue infection . The analytical approach and statistical workflow we have outlined forms a robust platform for both future discovery and validation of biomarkers for prediction of severe dengue disease . | While the majority of patients who exhibit febrile dengue infection recover within a week , a small proportion of the patients progress to develop severe symptoms that can be life-threatening if not managed in a hospital setting . Because there is no method to accurately identify this subgroup of patients , many dengue patients are hospitalized unnecessarily , which causes significant burden to the healthcare system . In our study , we have systematically measured a large number of molecules including cytokines and serum proteins in blood samples from a dengue patient cohort using highly sensitive mass spectrometry-based methods . We have further developed novel statistical methods that allow us to identify small panels of measureable blood markers , which can distinguish dengue patients that develop milder , self-limiting form of the disease from those that progress to develop severe symptoms . Because these markers can be applied within 48–72 hours of onset of febrile symptoms , we expect them to be useful for early classification of severe dengue disease . | [
"Abstract",
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] | 2012 | Serum Proteome and Cytokine Analysis in a Longitudinal Cohort of Adults with Primary Dengue Infection Reveals Predictive Markers of DHF |
The larva of cestodes belonging to the Echinococcus granulosus sensu lato ( s . l . ) complex causes cystic echinococcosis ( CE ) . It is a globally distributed zoonosis with significant economic and public health impact . The most immunogenic and specific Echinococcus-genus antigen for human CE diagnosis is antigen B ( AgB ) , an abundant lipoprotein of the hydatid cyst fluid ( HF ) . The AgB protein moiety ( apolipoprotein ) is encoded by five genes ( AgB1-AgB5 ) , which generate mature 8 kDa proteins ( AgB8/1-AgB8/5 ) . These genes seem to be differentially expressed among Echinococcus species . Since AgB immunogenicity lies on its protein moiety , differences in AgB expression within E . granulosus s . l . complex might have diagnostic and epidemiological relevance for discriminating the contribution of distinct species to human CE . Interestingly , AgB2 was proposed as a pseudogene in E . canadensis , which is the second most common cause of human CE , but proteomic studies for verifying it have not been performed yet . Herein , we analysed the protein and lipid composition of AgB obtained from fertile HF of swine origin ( E . canadensis G7 genotype ) . AgB apolipoproteins were identified and quantified using mass spectrometry tools . Results showed that AgB8/1 was the major protein component , representing 71% of total AgB apolipoproteins , followed by AgB8/4 ( 15 . 5% ) , AgB8/3 ( 13 . 2% ) and AgB8/5 ( 0 . 3% ) . AgB8/2 was not detected . As a methodological control , a parallel analysis detected all AgB apolipoproteins in bovine fertile HF ( G1/3/5 genotypes ) . Overall , E . canadensis AgB comprised mostly AgB8/1 together with a heterogeneous mixture of lipids , and AgB8/2 was not detected despite using high sensitivity proteomic techniques . This endorses genomic data supporting that AgB2 behaves as a pseudogene in G7 genotype . Since recombinant AgB8/2 has been found to be diagnostically valuable for human CE , our findings indicate that its use as antigen in immunoassays could contribute to false negative results in areas where E . canadensis circulates . Furthermore , the presence of anti-AgB8/2 antibodies in serum may represent a useful parameter to rule out E . canadensis infection when human CE is diagnosed .
The larval stage ( metacestode ) of Echinococcus granulosus sensu lato ( s . l . ) causes cystic echinococcosis ( CE , traditionally referred to as hydatid disease ) , one of the most important and widespread parasitic zoonoses . It is a fluid-filled cyst that establishes and grows in the host viscera ( mainly liver and lung ) of several ungulate livestock ( among others sheep , cattle , horse , goat , and pig ) and wild animals [1] . Recently , phylogenetic studies have led to split E . granulosus s . l . into five species , showing preference for infecting different hosts: E . granulosus sensu stricto ( including G1-G3 genotypes ) , E . equinus ( G4 ) , E . ortleppi ( G5 ) , E . canadensis ( G6–G10 ) and E . felidis [2 , 3] . These species seem to diverge in their transmission dynamics , morphology , rate of development , antigenicity , sensitivity to drugs and , particularly , in their infectivity and pathogenicity in humans , which might therefore influence the design of therapeutic and prophylactic programmes for CE control . This emphasises the need of studies focused on the molecular characterisation and the geographical distribution of E . granulosus s . l . species/genotypes . E . granulosus sensu stricto ( s . s . ) uses mostly sheep as intermediate hosts , but is also capable of infecting other livestock such as cattle as well as humans . Epidemiological studies for examining E . granulosus s . l . species associated with human CE have determined that E . granulosus s . s . has an extensive geographical distribution and causes between 73% and 88% of human CE worldwide ( reviewed by [4 , 5] ) . On the other hand , E . canadensis G6 and G7 genotypes , which use mainly camels , goats and pigs as intermediate hosts , are also geographically widely distributed and ranked as the second cause of human CE in the world , being responsible for between 11% and 21% of human CE cases according to more recent studies [4–6] . However , these values may be underestimated since E . canadensis seems to exhibit a lower and/or slower growth than E . granulosus s . s . in humans , leading to more benign or asymptomatic infections [3 , 4] . Moreover , in countries such as Austria , Poland , Egypt and Sudan , E . canadensis is the predominant cause of human CE [3] . Regarding E . canadensis genotypes , G6 has been preferably associated with human CE but , a recent systematic revision of the species and genotypes of E . granulosus s . l . responsible for human infections suggests a scenario with a slightly lower prevalence rate for G7 comparing to G6 ( 9 . 6% vs 12 . 2% , respectively ) [5] . Interestingly , the geographical distribution of these genotypes differ; G6 genotype is mainly present in human CE cases from America , Asia and Africa whereas the G7 genotype seems to affect mostly some countries in Central Europe . It is worth to mention that there is little or no genotype information on human CE cases reported in many geographical regions/countries , which might influence the epidemiological data cited above . Despite some progress achieved by prevention campaigns , CE continues being a major public health problem in several countries while represents an emerging or re-emerging disease in others ( reviewed by [7 , 8] and [9–13] ) . Regarding CE diagnosis , antigen B ( AgB ) , an abundant parasite component present in the HF of the E . granulosus s . l . metacestode , is the most immunogenic and specific Echinococcus-genus antigen . It is a 230 kDa lipoprotein that carries a huge amount of both neutral and polar lipids ( around 50% in mass ) including fatty acids ( FA ) and sterols , which Echinococcus is not capable of synthesising ( reviewed by [14] ) . This has led to emphasise its hypothetical role in parasite lipid metabolism , taking up host lipids as building blocks for parasite metabolic demands . Moreover , this hypothesis is supported by the fact that AgB belongs to a cestode-specific family of proteins exhibiting ability to bind hydrophobic ligands ( HLBP for hydrophobic ligand binding protein ) [15 , 16] . This family has emerged by independent gene expansion events , giving rise to species and gene-specific monophyletic clades . Interestingly , HLBP members are all immunodominant antigens . AgB antigenicity has been associated with its protein moiety ( apolipoprotein components ) [17–19] that is encoded by a multigene and polymorphic family with five AgB gene products named AgB1 to AgB5 ( revised by [20] ) . The recent assembly of Echinococcus granulosus G1 genotype and E . multilocularis genomes confirmed that this scenario is highly conserved among Echinococcus species [21] . The mature protein products of these genes are small ( around 8 kDa in mass ) , α helix-rich secreted polypeptides , with ability to self-assembly generating high-molecular-mass oligomers [22 , 23]; they are thus named AgB8/1 to AgB8/5 subunits . The native antigen , the recombinant AgB8/1 and AgB8/2 subunits , as well as various synthetic peptides derived from them , have shown to be valuable for CE diagnosis [24–26]; all of them have shown similar diagnostic performance in comparison with crude HF preparations , but in some clinical studies recombinant AgB8 subunits ( rAgB8 ) seem to yield better specificity with little or no loss in sensitivity [27–30] . AgB gene expression in Echinococcus s . l . species has been examined suggesting differences between them; this might be relevant for epidemiological investigations intended to discriminate the contribution of distinct E . granulosus s . l . species to human CE . In the larva of E . granulosus s . s . all AgB genes were found to be expressed at mRNA level [31] , even though only AgB8/1 to AgB8/4 protein products have been certainly detected in HF [23] . On the other hand , no evidence of AgB5 expression or of the generation of AgB8/2 and AgB8/5 was achieved in E . canadensis ( G6 and G7 genotypes ) and E . ortleppi ( G5 genotype ) metacestode [32 , 33] . In particular , AgB2 was proposed to be a pseudogene in E . canadensis . In fact , a low-scale sequencing analysis of E . canadensis genomic DNA , revealed that AgB2-related sequences ( named EgB2G6v15 to EgB2G6v17 and EgB2G7v15 , EgB2G7v18 and EgB2G7v19 ) contained a substitution at the splicing site ( GT-TG instead of GT-AG ) that probably interferes with the splicing , leading to the formation of a premature stop codon [32 , 33] . Taking advantage of the recently available genome of E . canadensis G7 genotype ( published at http://parasite . wormbase . org as echinococcus_canadensis . PRJEB8992 . WBPS5 . protein ) , we confirmed the existence of this substitution in ECANG7_10984 , which corresponds to the first hit by Blastn analysis using the E . granulosus s . s . AgB2 sequence Q27275 as a query at the http://parasite . wormbase . org webpage . However , the generation of a functional AgB2 product may occur by a non-canonical transcriptional mechanism using the TG dinucleotide as splice acceptor site [34] . Studies at transcriptional level failed to identify mRNA coding for a functional AgB2 product in E . canadensis G7; detected AgB2 mRNA transcripts were compatible with the use of an upstream AG dinucleotide in the second exon as splice acceptor site that would yield a protein considerably shorter than AgB8/2 due to a premature stop codon [33] . Nevertheless , these studies were carried out using protoscoleces derived from a single cyst of G7 origin ( Muzulin et al , 2008 ) , and the germinal layer constitutes a metacestode structure relevant in terms of AgB expression . On the other hand , a deep-sequencing analysis of the transcriptome of E . canadensis G7 metacestode has not been performed yet . Taken together , the conversion of AgB2 into a mature and functional product in the larva of E . canadensis remains uncertain and has not been explored using proteomic tools yet . It is important to remark that predictions based on draft genomes and transcriptional studies are not the ultimate proof of the absence or presence of a protein . Post-transcriptional control of gene expression could play an important role; a gene with low or undetectable expression at the transcriptional level could be efficiently translated allowing the detection of the encoded protein . Various proteomic studies have analysed the parasite and host components present in the HF of E . granulosus s . l . [23 , 35 , 36] , nevertheless , none of them provide data about E . canadensis AgB . In this work , we have employed high sensitivity proteomic tools to determine the apolipoprotein composition of AgB present in the HF of E . canadensis G7 genotype . For this proteomic study , we used swine HF as a source of AgB because pigs constitute the main intermediate hosts for E . canadensis G7 genotype , and HF collects products secreted/excreted by the germinal layer as well as protoscoleces , representing the parasite material where AgB accumulates . Complementary and high-sensitivity approaches including two-dimensional gel electrophoresis ( 2-DGE ) and liquid chromatography ( LC ) coupled to mass spectrometry ( MS ) were used as proteomic tools . For a complete biochemical characterisation of E . canadensis AgB , the lipids carried by the lipoprotein were also examined by high performance thin layer chromatography ( HPTLC ) . Results highlight the concept that AgB is a complex lipoprotein in E . granulosus s . l . species , including E . canadensis , being AgB8/1 the predominant apolipoprotein . Furthermore , in contrast with E . granulosus s . s . [23] , AgB8/2 was not detected in E . canadensis G7 genotype , supporting the concept that AgB2 is a pseudogene in this species . Since AgB is the most relevant antigen for human CE immunodiagnosis , and the use of rAgB8 subunits offers several advantages for standardising immunoassays ( reviewed by [26] ) , the possible implications of our findings on diagnostic and epidemiological studies on human CE are discussed .
Fertile hydatid cysts ( containing protoscolex , n = 24 ) were collected from livers of naturally infected pigs during the routine work of local abattoirs in Buenos Aires ( Argentina ) . HF was obtained by aspiration of the content of cysts , and preserved by addition of 5 mM EDTA and 20 μM 3 , 5-di-tert-butyl-4-hydroxytoluene ( BHT ) at -20°C until use . Protoscolex were used to analyse parasite genotype on individual cysts . Cyst genotyping was performed by amplification and sequencing of a fragment of the mitochondrial cytochrome c oxidase subunit 1 ( COX1 ) [37] . The sequencing reactions were performed at Macrogen ( Korea ) . All HF samples of swine origin were confirmed to belong to E . canadensis G7 genotype . For controlling the sensitivity of our proteomics tools , we prepared a pool of bovine HF samples ( similarly obtained from local abattoirs in Montevideo , Uruguay ) . This bovine pool was mainly representative of E . granulosus s . s . as it contained material from 20 and 3 cysts belonging to E . granulosus s . s . ( 18 of G1 and 2 of G3 genotypes ) and of E . ortleppi ( G5 genotype ) , respectively . An AgB-enriched fraction was prepared from pooled HF by removing the bulk of host albumin and immunoglobulins by anion exchange chromatography . HF was centrifuged at 10000 x g for 20 min at 4°C and the resulting supernatant filtered through 0 . 45 μm filter membranes ( Millipore ) . The clarified HF ( 700 mL ) was then fractioned by anion exchange chromatography on a Q-Sepharose column ( 2 . 5 cm x 10 cm , Pharmacia Biotech , Uppsala , Sweden ) previously equilibrated in 20 mM phosphate buffer , pH 7 . 4 containing 200 mM NaCl , 5 mM EDTA and 20 μM BHT . After washing in equilibration buffer , the retained material was eluted by changing ionic strength to 500 mM NaCl in a single step . The eluted fraction , Q-Sepharose retained fraction ( QSf ) , was concentrated 10-times , equilibrated in 20 mM phosphate buffer , pH 7 . 4 containing 150 mM NaCl , 5 mM EDTA and 20 μM BHT ( PBSEDTA-BHT ) , and used to characterise AgB apolipoprotein composition by mass spectrometry as described below . A second purification step was performed based on ultracentrifugation of QSf in a KBr density gradient . Briefly , 2 . 45 g of KBr were dissolved in 5 ml of QSf in an ultracentrifuge tube and slowly covered with a solution containing 0 . 15 M NaCl and 0 . 42 M KBr . After ultracentrifugation ( 4 h at 332 . 000 x g ) two bands were carefully recovered named low ( Ldf , yellowish-brown band ) and high ( Hdf ) density fractions . All fractions were equilibrated in PBSEDTA-BHT , and maintained at 4°C under a N2 atmosphere until use . 2-DGE and MS analysis was performed as described previously [38] but using 150 μg ( protein ) of the AgB-enriched fraction ( QSf ) for the electrofocusing step in order to detect poorly represented subunits . Briefly , the first dimension was performed with commercially available IPG-strips ( 7 cm , linear 3–10 , GE Healthcare ) . QSf was prepared and concentrated by using the 2-D Clean-Up kit ( GE Healthcare ) and dissolved in rehydration solution ( 7 M urea , 2 M thiourea , 2% CHAPS , 0 . 5% IPG buffer 3–10 ( GE Healthcare ) , 0 . 002% bromophenol blue , 17 mM DTT ) . Samples in rehydration solution were loaded onto IPG-strips by passive rehydration during 16 h at room temperature . The second-dimensional separation ( SDS-PAGE ) was performed in 15% polyacrylamide gels using a SE 260 mini-vertical gel electrophoresis unit ( GE Healthcare ) . The molecular size marker used was Low Molecular Weight Calibration Kit for SDS Electrophoresis ( Amersham GE Healthcare ) . The gels were colloidal coomassie stained and images were digitalised using a UMAX Power-Look 1120 scanner and LabScan 5 . 0 software ( GE Healthcare ) . Selected spots were submitted to in gel trypsin digestion ( sequencing-grade , Promega ) at 37°C overnight . Peptides were extracted from gels using 60% acetonitrile in 0 . 1% TFA , concentrated by vacuum drying , and then desalted using C18 reverse phase micro-columns ( OMIX Pipette tips , Varian ) . Peptide elution from micro-column was performed directly into the mass spectrometer sample plate with 2 μl of matrix solution ( α-cyano-4-hydroxycinnamic acid in 60% aqueous acetonitrile containing 0 . 1% TFA ) . Mass spectra of digestion mixtures were acquired using a matrix-assisted laser desorption/ionization time-of-flight mass spectrometer ( MALDI-TOF/TOF , 4800 Analyzer , ABi Sciex ) in positive reflector mode and were externally calibrated using a mixture of peptide standards ( Mix 1 , ABi Sciex ) . Collision induced dissociation ( CID ) MS/MS spectra of selected peptides ions were also acquired . Proteins were identified with measured m/z values in MS and MS/MS acquisition modes and using the MASCOT search engine ( Matrix Science , http://www . matrixscience . com ) in the Sequence Query search mode . AgB8 subunits were identified by searching in both , the NCBInr and an in-house Echinococcus databases using the following search parameters: unrestricted taxonomy , monoisotopic mass tolerance , 0 . 05 Da; fragment mass tolerance , 0 . 2 Da; carbamidomethyl cysteine and methionine oxidation as variable modifications and up to one missed tryptic cleavage allowed . Significant protein scores ( p < 0 . 05 ) were used as criteria for positive protein identification . In addition , at least two unique peptides with ion significant score ( p < 0 . 05 ) were required for AgB8 subunit identification . The in-house Echinococcus database was built comprising all sequences of E . canadensis ( G7 genotype , published in http://parasite . wormbase . org as echinococcus_canadensis . PRJEB8992 . WBPS5 . protein ) and of E . granulosus s . s . ( G1 genotype , published in www . genedb . org as EGU_proteins_29042013_products . fa ) plus a total of 102 full length sequences , including polymorphic variations at the level of the AgB mature products as well as the orthologous products in other Echinococcus species ( available on NCBInr , March 2015 ) . Furthermore , to study E . canadensis AgB8/2 presence , we took into account the previous characterisation of this gene ( at DNA and mRNA level , [32] ) and added to the database those protein sequences that would be generated by non-canonical splicing of E . canadensis AgB2-related sequences EgB2G6v15 to EgB2G6v17 , EgB2G7v15 , EgB2G7v18 and EgB2G7v19 , as well as of AgB ECANG7_10984 gene ( in all putative open reading frames , S1 Appendix ) . Samples ( QSf ) were analysed by LC tandem-mass spectrometry ( LC-MS/MS ) using five analytical replicates . Proteins were reduced , carbamidomethylated , and digested in solution with sequencing-grade trypsin ( Sequencing-grade Promega; 1:50 enzyme to total protein ratio ) in 70 mM ammonium bicarbonate pH 8 . 0 buffer containing 2 M guanidine hydrochloride for 12 h at 37°C . Peptides were further concentrated , desalted using C18 reverse phase micro-columns ( OMIX Pipette tips , Varian ) and eluted with 60% aqueous acetonitrile containing 0 . 1% TFA . Peptide mixtures were dried and resuspended in 5% aqueous acetonitrile containing 0 . 1% formic acid . Five micrograms of each sample were analysed in an EASY-nLC II nanoflow liquid chromatography ( Thermo Fisher Scientific , USA ) coupled to a LTQ-Orbitrap Velos mass spectrometer ( Thermo Fisher Scientific ) . Peptide mixture was injected into a trap column ( I . D . 100 μm x O . D . 360 μm x 50 mm ) packed with Jupiter C18 10 μm beads ( Phenomenex Inc . , USA ) for desalting with 100% solvent A ( 0 . 1% formic acid ) . Peptides were then fractionated on an analytical column ( I . D . 75 μm x O . D . 360 μm x 100 mm ) packed in-house with Aqua C-18 5 μm beads ( Phenomenex Inc . ) at a flow rate of 200 nL/min using a 60 min linear gradient from 5 to 35% of solvent B ( 0 . 1% formic acid in acetonitrile ) . Afterwards , a gradient from 35 to 85% of B in 5 min was applied for ensuring a complete elution . Nano-electrospray voltage was set to 2 . 3 kV , the source temperature to 250°C and mass spectrometer was operated in a data-dependent acquisition mode , where the top ten precursor ions in each cycle were selected for fragmentation event by CID . Ion trap injection time was set to 100 ms and FT-MS injection time was set to 1000 ms with a resolution of 60 , 000 across m/z 300–1800 . For IT scans , fragmentation was carried out on ions above a threshold of 200 counts , and dynamic exclusion was enable with an exclusion list size of 500 for 90 seconds , repeat duration of 30 seconds and a repeat count of 1 . Raw mass data files ( . raw ) were analysed in Maxquant ( v . 1 . 5 . 5 . 1 ) and its built-in Andromeda search engine . Parasite and host proteins were identified using MaxQuant software by searching MS and MS/MS data against a merged database comprising the Echinococcus database ( built as described above ) and the Bos taurus/Sus scrofa database ( downloaded from UniProt , April/2016 ) . Trypsin was set for enzyme specificity with a maximum of two missed cleavages , mass tolerance for precursor ions was set to 10ppm , and fragment ion mass tolerance was set to 0 . 5 Da . MS/MS spectra searches incorporated fixed modifications of carbamidomethylation of cysteine , oxidation of methionine and protein N-terminal acetylation were set for variable modifications . Maximum false peptide and protein discovery rate was set to 0 . 01 . Proteins matching to the reverse database were eliminated . Statistical analysis for protein identification was performed using Perseus ( v . 1 . 4 . 0 . 11 ) based on unique peptides MS intensities , the presence of a minimum of two unique peptides and PEP ( posterior error probability ) < 0 . 01 . To evaluate the abundance of each AgB protein species ( AgB8 subunit ) the intensity-based absolute quantification ( iBAQ ) was used as it has been reported as a useful label-free quantification method provided by MaxQuant . In the iBAQ algorithm the sum of all identified peptide intensities ( maximum detector peak intensities of the peptide elution profile , including all peaks in the isotope cluster ) is divided by the number of theoretically observable tryptic peptides , and expressed as log2 values [39]; this operation transforms a measure that is expected to be proportional to mass ( intensity ) into one that is proportional to molar amount ( iBAQ ) . To determine the relative abundance of each AgB8 subunit in AgB ( riBAQAgB ) , we divided the iBAQ value corresponding to each AgB8 subunit by the sum of the iBAQ values obtained for all AgB8 subunits , and expressed this ratio as a percentage . Replicate results were merged with Perseus and values for iBAQ , riBAQAgB , score and the percentage of the protein sequences covered by identified peptides ( % CO ) are expressed as the mean of all runs ( n = 5 ) . The total number of the identified peptide spectra matched for a protein ( PSM ) was also estimated as the sum of all runs . Only proteins present in at least 3 of the 5 analytical replicates were considered as positively identified . AgB total lipids were analysed using Ldf ( between 0 . 25 and 0 . 5 mg of protein ) following the methodology that we have already described [38] . Qualitative analysis of lipid classes was performed by HPTLC using double development for neutral and polar lipids as described previously [38] , but lipid bands were visualised under iodine vapour . Identification of lipid classes was performed by comparison with primary and secondary standards run on the same HPTLC plate .
The apolipoprotein composition of AgB present in fertile HF of E . canadensis G7 genotype was analysed using MS based methodologies . For this purpose , we prepared a biological representative pool of E . canadensis HF from 24 individual swine cysts , each one of G7 origin according to COX1 genotyping . However , to achieve an adequate sample for AgB apolipoprotein characterisation , we firstly carried out an enrichment step since AgB is poorly represented in HF compared to host albumin and immunoglobulins . Taking advantage that AgB can be selectively separated from these host proteins employing a Mono-Q [40] or Q-Sepharose beads [41] , we prepared an AgB enriched-fraction by a single step anion exchange chromatography of HF on Q-Sepharose; this step concentrates AgB favouring the detection of lower represented apolipoproteins . S1 Fig shows the SDS-PAGE analysis of fractions obtained by this chromatography . As expected , AgB was retained by Q-Sepharose beads and eluted with 500 mM NaCl pH = 7 . 4 ( fraction QSf ) since QSf , but not the flow through fraction ( FTf ) , showed 8 , 16 and 24 kDa bands in agreement with the typical AgB ladder-like pattern ( S1 Fig , small head arrows ) [42] . In contrast , the majority of the most abundant host proteins present in HF ( albumin and immunoglobulins ) did not bind to Q-Sepharose , being recovered in FTf ( S1 Fig ) . Additional steps based on ultracentrifugation on a KBr density gradient achieved to purify AgB ( see below ) . Nevertheless , since these purification steps led to protein losses , we rather to use QSf for characterising AgB protein species; we cannot ruled out that AgB includes particles of different densities and/or less abundant AgB apolipoproteins would be not represented in the purified AgB preparation ( Ldf , see below ) . The presence of AgB8 subunits in swine QSf ( sQSf ) was examined by 2-DGE followed by MALDI-TOF/TOF MS . As shown in Fig 1A , AgB was detected in several spots corresponding to the monomer , dimer and trimer ( indicated with bold circles and numbers in the Fig ) . Interestingly , the monomeric as well as oligomeric AgB forms comprised several components spread over a wide range of pH ( between 9 . 4 and 4 . 5 ) . AgB protein species identified in those spots included AgB8/1 , AgB8/3 and AgB8/4 subunits ( Fig 1A and S1 Table ) . AgB8/1 was the predominant subunit detected in all spots belonging to AgB ( n = 25 ) ; the presence of both Q86BY8 and Q3YFQ5 isoforms is plausible accordingly to the set of unique peptides identified by MALDI-TOF/TOF ( Table 1 ) . Q86BY8 and/or Q3YFQ5 are basic proteins ( pI = 9 . 11 ) that would agree with their identification in spots focused at around pH 9 . 4 ( named #1 and #2 , Fig 1A ) , but not in more acidic ones . Similarly , two AgB8/4 protein species with a theoretical pI of 6 . 15 ( named Q6J0W7 and Q6Q0G2 , Table 1 ) were detected in 9 spots focused in a wide range of pH ( Fig 1A and S1 Table ) . Thus , these results suggest the presence of post-translational modifications in both AgB8/1 and AgB8/4 . Neither signals corresponding to phosphorylated peptides nor the formation of carbonyl groups in AgB8 subunits ( because of oxidative reactions with oxides of nitrogen or metal catalysed oxidation ) were detected by MS and Western Blot , respectively ( S2 Appendix ) . Thus , further studies are needed to elucidate which molecular modifications explain the AgB pattern obtained by 2-DGE . On the other hand , AgB8/3 was identified in spots #1 and #2 based on various unique peptides ( Table 1 and S1 Table ) ; while these peptides cannot distinguish between the AgB8/3 isoforms named Q6VXZ8 and Q6VXZ9 , the presence of Q6VXZ8 seems to be more likely according to its pI . Finally , AgB8/2 and AgB8/5 were not detected in sQSf . The presence of AgB8/2 , but not AgB8/5 , has been previously reported in bovine HF collected from E . granulosus s . s . cysts [23] . We performed thus a similar study by 2-DGE plus MALDI-TOF/TOF using a pool of bovine HF ( mostly belonging to E . granulosus s . s . according to COX1 genotyping ) to evaluate whether our proteomic tool reached enough sensitivity for AgB8/2 analysis . Results showed the presence of AgB8/2 , but not of AgB8/5 in bovine QSf ( bQSf , Table 1 , S3 Appendix ) in accordance with the earlier report . Overall , detection of AgB8/2 in bQSf but not in sQSf suggests that AgB8/2 is not present in G7-HF . In addition to AgB protein species , analysis of sQSf and bQSf by 2-DGE plus MALDI-TOF/TOF showed the presence of Echinococcus Ag5 ( 22 and 38 KDa subunits ) as well as of some host components ( remaining albumin and immunoglobulin light chains , as well as apolipoprotein A-I ( Apo A-I ) ; see S1 Table and S3 Appendix ) . Confirmation of the observations described above was achieved using LC-MS/MS since this methodology enables a high sensitivity quantitation of proteins in complex biological samples . Results are summarised in Table 2 , in which the iBAQ parameter ( expressed as log2 values ) is proportional to the protein molar amounts while the riBAQAgB refers to the relative abundance of each protein species in AgB . Analysis of sQSf showed that AgB8/1 was the major protein component , representing 71% of total AgB apolipoproteins followed by AgB8/4 ( 15 . 5% ) , AgB8/3 ( 13 . 2% ) and AgB8/5 ( 0 . 3% ) . AgB8/2 was not detected in sQSf although our database included all AgB8/2 sequences available for E . granulosus s . l . species ( comprising those protein products that could be generated because of non-canonical splicing of all available AgB2 related sequences ) . As expected , E . granulosus s . s . AgB8/2 was identified in bQSf . Because the bovine HF pool contained samples from E . ortleppi G5 genotype , we looked for E . ortleppi AgB8/2 specific peptides in bQSf with no success . This may be consequence of the low proportion of E . ortleppi components in the bovine HF pool ( around 15% of the total volume ) , or of the lack of AgB2 functionality in this species , as proposed previously [32] . On the other hand , we detected in sQSf two unique peptides that make reliable the identification of AgB8/5 in E . canadensis metacestode ( Table 2 ) , contrasting with previous findings at the RNA level [33] . This contrast can be explained by the fact that AgB5 would be poorly expressed in the metacestode and/or that previous mRNA expression studies were performed using primers , which were not specifically designed for E . canadensis AgB5 . On the other hand , our results endorse previous data at mRNA [31] and protein levels [35] for AgB5 expression in E . granulosus s . l . metacestode . Finally , the detection in sQSf of AgB8/5 , an AgB subunit barely expressed in the metacestode of Echinococcus species , denotes the high sensitivity reached in our proteomic study . AgB is likely involved in taking up host lipids , which are essential for Echinococcus spp . , as building blocks for parasite needs [14] . For a complete biochemical characterisation , we purified E . canadensis AgB from QSf and characterised the lipid classes present in its lipid moiety . AgB purification was performed by a novel procedure based on density-gradient ultracentrifugation; this method preserves AgB native structure and yields AgB particles independently of its apolipoprotein composition . AgB was mainly recovered in the low density fraction , Ldf , but consecutive ultracentrifugation rounds were needed to achieve a good-quality AgB preparation ( about 95% pure , according to SDS-PAGE , S1B Fig ) , although these steps goes against the final AgB yield . Several lipid classes including highly polar ( phosphatidylcholine and , to a lesser extent , phosphatidylethanolamine ) and neutral lipids ( sterols , free FA , triacylglycerols and sterol esters ) were detected in E . canadensis AgB ( Fig 1B ) , just as we have already described for AgB immunopurified from bovine pooled HF ( [38] and S3 Appendix ) . In sum , the observed differences in the protein composition of AgB preparations from distinct E . granulosus s . l . species ( Table 2 ) , did not affect the lipid class composition of the lipoprotein . However , qualitative and or quantitative differences in lipid components within each class cannot be excluded and require further studies . The proteomic analysis of sQSf and bQSf by LC-MS/MS allowed identifying several parasite and host proteins in HF ( S4 Appendix ) . Regarding parasite proteins , we identified several proteins with putative diverse functions , but taking into account the iBAQ values in both samples , AgB subunits were found to be the most abundant components , followed by Ag5 . In particular , we significantly identified in sQSf and/or bQSf parasite proteins with a potential role in lipid metabolism . One of the most interesting was an E . granulosus s . s . HLBP detected in bQSf ( EgHLBP , Uniprot protein accession A0A068WMS7_EGHR ) . The gene that encodes EgHLBP , referred to as EgrG_000549200 ( http://www . genedb . org/ ) , mapped outside AgB cluster [43] . This novel EgHLBP has a higher similarity to an uncharacterised protein of E . granulosus s . s . ( Uniprot protein accession W6UNU2_ECHGR , 87% identity ) and to Taenia solium HLBP1 and HLBP2 ( Uniprot protein accession G3FJ94_TAESO and G3FJ95_TAESO , with 68% and 71% of identity , respectively ) than to AgB ( 45% identity ) ; the alignments of EgHLBP with these proteins are shown in S5 Appendix . Interestingly , we detected EgHLBP in HF , while both TsHLBP1 and TsHLBP2 were not detected by Western Blot in the metacestode and showed a high expression in the T . solium adult [44] . In addition , we identified the Lipid transport protein N-terminal in both sQSf and bQSf . This protein exhibits only significant similarity with its orthologous in E . multilocularis ( 96% identity ) and with an E . granulosus s . s . apolipophorin ( Uniprot accession W6UHB7_ECHGR , 98% identity ) , neither of which have been characterised yet . All of them are high MW macromolecules , containing various conserved regions found in several lipid transport proteins , including vitellogenin , microsomal triglyceride transfer protein and apolipoprotein B-100 ( Smart accession number SM00638; PROSITE PS51211 ) . Finally , we identified various host apolipoproteins , including Apo A-I , in sQSf and bQSf . Apo A-I has previously been detected in E . granulosus s . l . HF [35] . Interestingly , Apo A-I and an Apo A-I binding protein ( EmABP ) were found to be present in HF of E . multilocularis [45] . In our study , EmABP orthologues in E . granulosus s . l . species were not found in QSf or bQSf , but their presence in HF requires further investigation .
This work contributes to widen the information available on E . granulosus s . l . AgB subfamilies , particularly at the level of the presence and abundance of their protein products in the metacestode of E . canadensis G7 genotype . Using high sensitivity and quantitative proteomic analysis of a representative number of hydatid cysts , we showed that AgB8/1 is the major AgB apolipoprotein in the HF of E . canadensis G7 genotype . This strengthens the concept of AgB8/1 predominance in the HF of various E . granulosus s . l . species [23 , 35] . Since AgB likely contributes to the mechanisms used by the metacestode to transport lipids , particularly those that the parasite is unable to synthesise , this result would indicate that AgB8/1 is the main AgB apolipoprotein involved in this transport , and , in consequence , the presence of AgB8/1 receptors in parasite and host cells is worth to be further studied . To this respect , AgB8/1 was found to bind selectively to monocyte and macrophages , but the molecular partners involved have not been identified yet [41] . Furthermore , we identified an additional Echinococcus HLBP ( EgrG_000549200 ) and host apolipoproteins ( particularly Apo A-I ) in QSf , which suggests that several lipid carriers are involved in parasite mechanisms aimed at providing essential lipids to metacestode tissues . However , taken into account their abundance in HF ( iBAQ values ) , their contribution to lipid transport within metacestode tissues seems to be lower than that of AgB . On the other hand , our results support a differential expression of AgB2 among E . granulosus s . l . species; AgB8/2 was not detected in the HF of E . canadensis G7 genotype contrasting with their detection in E . granulosus G1 genotype ( [23] and this work ) . Despite this difference , we did not found significant differences in the lipid moiety of AgB purified from sQSf and bQSf , at least in terms of lipid classes . This may be a result of the fact that AgB8/2 showed a low relative abundance in comparison with AgB8/1 in E . granulosus s . s . , and that no differences have been observed between the lipid binding properties of AgB8/1 and AgB8/2 using in vitro assays [46] . Regarding to the lack of AgB8/2 in E . canadensis , it would be explained at the molecular level by the occurrence of an A/T transversion at the splicing site that likely interferes with canonical splicing mechanisms and the synthesis of a functional protein product . Bearing in mind that AgB is diagnostically valuable for human CE , differences in AgB apolipoprotein composition between E . granulosus s . s . and E . canadensis are informative for diagnosis and epidemiological studies on this zoonosis . In particular , rAgB8/1 and rAgB8/2 subunits , as well as peptides derived from them , have yielded reasonable diagnostic performance in immunoassays using panels of sera from patients with CE and other helminth infections ( reviewed by [26] ) . In most of these studies rAgB subunits were assessed as single antigens , although combination between them ( AgB subunit cocktail ) or with other HF antigens would help to achieve more sensitive and specific tests [27 , 47–51] . In any case , our results indicate that the use of AgB8/2 as antigen in immunoassays might contribute to false-negative results in patients infected by E . canadensis G7 genotype . Probably the same holds for infections with E . canadensis G6 genotype , although the lack of AgB8/2 at the protein level requires confirmation . As we have mentioned above E . canadensis accounts for between 11% and 21% of CE human cases worldwide reaching a higher prevalence in some countries , therefore , our results denote the importance of adapting the diagnostic tools to the epidemiological situation of each geographical region . Taking into account the global distribution of E . canadensis , our observations may thus be of major importance for regions where cases of human infection by E . canadensis have been reported ( including countries in all continents , such as Argentina , Egypt , Iran , Kenya , Mauritania , Mongolia , Poland , South Africa and ex-Yugoeslavia , [4 , 5] ) . However , because of the scarce information about the E . granulosus species/genotypes associated with human CE in many countries/regions , our data might be of interest for any region where E . canadensis is known to circulate ( for instance , Southern Brazil and the Mediterranean region [52 , 53] ) . The determination of E . granulosus s . l . genotype/species responsible for human CE cases is a subject of relevance since , as we have already mentioned , species belonging to E . granulosus complex differ in biological features ( i . e . infectivity and pathogenicity in humans ) , influencing control program design as well as disease follow-up and treatment [54] . Genotyping is thus a critical task . However , nowadays , it can only be performed after surgery in order to obtain parasite samples , and using molecular biology tools . Since E . granulosus s . s . and E . canadensis are the most common cause of human CE , it would be worth to develop simple tools to differentiate the infections caused by them . The presence of antibodies against AgB8/2 in serum would be easy to determine through conventional immunoassays based on the use of rAgB8/2 or AgB8/2-derived peptides as antigen . Thus , including this kind of immunoassays during routine diagnosis of human CE would allow ruling out E . canadensis infection based on the presence of anti-AgB8/2 antibodies . | Cystic echinococcosis ( CE ) , a worldwide-spread zoonosis , affects livestock mammals and humans with significant economic and public health impact . It is caused by the infection with the larva of cestodes belonging to Echinococcus granulosus complex , a series of parasite species with preference for different hosts . Among them , Echinococcus canadensis larva uses mainly camels , goats and pigs as hosts . Species/genotypes belonging to E . canadensis are considered the second most common cause of human CE , but its contribution may be underestimated since causes asymptomatic or more benign infections than other E . granulosus complex species . The most relevant antigen for CE diagnosis is a lipoprotein called antigen B ( AgB ) . AgB antigenicity is linked to its protein moiety that is encoded by several genes . One of these genes , AgB2 , seems to be differentially expressed within E . granulosus complex . Using high sensitivity proteomic tools we analysed the composition of AgB obtained from E . canadensis larva , detecting the protein products of all AgB genes , except AgB2 . Since AgB subunits have been widely used as antigens in immunoassays for human CE diagnosis , our results indicate that using AgB2 protein product in these assays may lead to false-negative results , particularly in geographical areas where E . canadensis species/genotypes circulate . | [
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] | 2017 | Characterisation of Antigen B Protein Species Present in the Hydatid Cyst Fluid of Echinococcus canadensis G7 Genotype |
Heavy glycosylation of the envelope ( Env ) surface subunit , gp120 , is a key adaptation of HIV-1; however , the precise effects of glycosylation on the folding , conformation and dynamics of this protein are poorly understood . Here we explore the patterns of HIV-1 Env gp120 glycosylation , and particularly the enrichment in glycosylation sites proximal to the disulfide linkages at the base of the surface-exposed variable domains . To dissect the influence of glycans on the conformation these regions , we focused on an antigenic peptide fragment from a disulfide bridge-bounded region spanning the V1 and V2 hyper-variable domains of HIV-1 gp120 . We used replica exchange molecular dynamics ( MD ) simulations to investigate how glycosylation influences its conformation and stability . Simulations were performed with and without N-linked glycosylation at two sites that are highly conserved across HIV-1 isolates ( N156 and N160 ) ; both are contacts for recognition by V1V2-targeted broadly neutralizing antibodies against HIV-1 . Glycosylation stabilized the pre-existing conformations of this peptide construct , reduced its propensity to adopt other secondary structures , and provided resistance against thermal unfolding . Simulations performed in the context of the Env trimer also indicated that glycosylation reduces flexibility of the V1V2 region , and provided insight into glycan-glycan interactions in this region . These stabilizing effects were influenced by a combination of factors , including the presence of a disulfide bond between the Cysteines at 131 and 157 , which increased the formation of beta-strands . Together , these results provide a mechanism for conservation of disulfide linkage proximal glycosylation adjacent to the variable domains of gp120 and begin to explain how this could be exploited to enhance the immunogenicity of those regions . These studies suggest that glycopeptide immunogens can be designed to stabilize the most relevant Env conformations to focus the immune response on key neutralizing epitopes .
Glycosylation , one of the most common intracellular modifications of proteins[1] , is the covalent attachment of one or more carbohydrates ( glycans ) at specific amino acid sequence motifs . In N-linked glycosylation , the glycan is attached to an asparagine ( Asn ) residue in an Asn-Xaa-Ser/Thr motif , where Xaa can be any amino acid residue except proline . Based on secondary structure predictions of protein sequences , there appears to be a strong preference for N-linked glycosylation at beta-bends[2] , where approximately 70% of N-linked glycan motifs occur , while 10% and 20% occur in alpha-helices and beta-sheets , respectively[1] . Lentiviral envelope proteins are among the most heavily glycosylated proteins in nature[3] . Carbohydrates constitute half of the HIV-1 Env gp120 mass , and cover much of its surface[4] . It has long been known that gp120 can accommodate a remarkable heterogeneity in terms of the number and location of glycosylation sites [5] . This variably glycosylated protein mediates the interactions with CD4 and coreceptor molecules that are critical for viral entry . However , the effects of glycosylation on the conformation and biology of gp120 are not well understood . In general , glycosylation can stabilize protein conformation[6] , accelerate protein folding[7] , promote secondary structure formation[8] , reduce protein aggregation [6 , 9] , shield hydrophobic surfaces[10] , promote disulfide pairing[11] , and increase folding cooperativity[12] . Others have shown that glycosylation can stabilize a protein structure against thermal unfolding due to entropic effects[13 , 14] . In some cases , glycosylation can slow down the folding process by stabilizing the on-pathway folding intermediates[15] . These varied effects of glycosylation on protein stability are sensitive to the number and location of glycans in the tertiary protein structure[16–20] . Furthermore , modeling approaches typically neglect the influence of non-specific and specific protein-protein and protein-glycan interactions , which play an important role in glycosylation effects[19 , 21–25] . Despite recent computational studies of glycosylation[14 , 23 , 26–28] , the effects of carbohydrate moieties on protein conformation and folding are incompletely understood , particularly when glycosylation occurs in or near a region with an unstructured conformation . HIV-1 gp120 contains multiple highly immunogenic regions and serves as the major target for neutralizing antibodies . The network of glycans on gp120 is of particular interest with regards to HIV-1 vaccine design , because the glycans both serve as targets for many classes of broadly neutralizing antibodies[29–32] , and contribute to patterns of immune evasion and escape during HIV-1 infection[33–39] . Elucidating the relevant forms of glycans for neutralizing antibody epitope formation could aid in the design of glycopeptide-based vaccine immunogens for HIV ( for examples , see: [33 , 40–42] ) . In this study , we investigated glycosylation patterns in the gp120 variable domains using an updated set of 4633 HIV-1 sequences from the 2014 Los Alamos HIV Database reference alignment . Our analysis highlights the enrichment of glycosylation at the base of the 4 disulfide bonded variable loops in gp120 ( Fig 1A ) . This led us to focus on the effects of glycosylation at two conserved sites in the V1V2 domain that are proximal to the Cys at 157: the site at 156 which is immediately adjacent , and one at 160 that is nearby ( Fig 1B ) . This region constitutes an important glycan-dependent target of broadly neutralizing antibodies ( Fig 1B ) [29–31 , 43 , 44] . This region of V1V2 contains both conserved and highly variable positions as captured in Fig 1C . It tends to maintain a region of high positive charge , and appears to have a flexible conformation , exhibiting a tendency to adopt different conformations . In the native trimer form of HIV-1 , this region of V1V2 adopts a beta-strand conformation , which persists when it is bound to broadly neutralizing antibodies PG9 and PG16 [30 , 44–46] . However , the V1V2 region is found in several conformations such as beta-strand , helical and random coil when bound to monoclonal antibodies ( mAbs ) elicited by RV144 vaccination that have limited neutralization breadth [45 , 47] . These findings suggest that certain conformations of V1V2 could preferentially elicit antibodies with neutralization breadth , and several groups are actively working towards this goal by developing V1V2-derived glycopeptide immunogens [40 , 48] . However , the conformational variability of this region , further complicated by glycosylation , could present substantial obstacles to this strategy . To better understand how glycosylation influences this immunogenic but disordered region of V1V2 , we utilized an unbiased all atom MD simulations approach . A peptide construct ( Fig 1B ) was generated to mimic this immunologically important region of V1V2; 6 amino acids from V1 ( 129–134 HXB2 numbering ) and 33 amino acids ( 152–184 HXB2 numbering ) from V1 and V2 ( including the region that was targeted by vaccinees in the RV144 trial and by other V2-directed antibodies from chronic HIV-1 infection ) , connected by a disulfide bond between the two cysteine residues Cys 131 and Cys 157 that close the V1 loop ( Figs 1B and 2A ) . The folding of this peptide , with and without the carbohydrates at positions N156 and N160 , was studied . We show that glycosylation has numerous effects on the stability and accessibility of this disulfide linked V1V2 peptide that could influence the antigenicity and immunogenicity of this region .
The all-atom replica exchange molecular dynamics ( REMD ) simulations were used to analyze conformational aspects of a peptide construct derived from the V1V2 variable loop region of HIV-1 gp120 . The peptide construct contains 6 amino acids from V1 ( HXB2 gp120 residues 129–134 ) and 33 amino acids from V2 ( HXB2 gp120 residues 157–184 ) connected by a disulfide bond between Cys131 in V1 loop and Cys157 in V2 loop as shown in Fig 2A . The sequence of the peptide corresponds to that of the CAP45 strain , ( Genbank accession number GQ999974 ) it was selected because a PG9 bound structure using CAP45 is available [30] . Our model included two glycans attached to the peptide at positions N156 and N160 , which , as noted , have been shown to be important for recognition by several broadly neutralizing antibodies . Also , N156 is immediately adjacent to the disulfide bridge formed by cysteines 131 and 157 . This specific sequence did not contain a glycan at position 130 that is found in many HIV-1 strains ( Fig 1C ) . Several studies using virion-associated envelope or SOSIP trimer proteins have shown that the carbohydrate moieties of HIV-1 virions are almost entirely oligomannose , consisting mainly of varying proportions of Man5-9GlcNAc2 . However , Man5GlcNAc2 is consistently present at key glycan positions in gp120 , including N156 and N160 [50–53] . Given the consistent presence of Man5GlcNAc2 , in relevant forms of HIV-1 envelope , and that this biosynthetic intermediate reflects the inherently restricted glycan processing of HIV-1 envelope , the carbohydrate moieties used in our model are high-mannose Man5GlcNAc2 ( Fig 2B ) . Furthermore , molecular dynamics simulations of the BG505 SOSIP trimer with Man5GlcNAc2 , Man7GlcNAc2 , or Man9GlcNAc2 at every sequon produced concordant results in terms of overlap with volume occupied by broadly neutralizing antibodies [53] . Separate REMD simulations were carried out for the unglycosylated and glycosylated peptide construct to quantitate the effects of glycosylation . Additionally , we carried out REMD simulations on a single peptide fragment ( NCSFNVTTIVRDKTTK ) derived from the HIV-1 subtype C consensus sequence ( ConC; Los Alamos Database Consensus Alignments , 2013 ) with or without glycosylation at N160 , and in the absence of a disulfide bond . These simulations were used to independently verify the glycosylation effects seen in the primary simulation studies on the peptide construct . All systems were composed of peptide/glycopeptide , water , and counter-ions . A total of 84 replicas were performed for the primary simulations , with a temperature range covering 275 K to 550 K . Each replica was run for 500 ns . A total of 76 replicas were included in the second set of simulations , with the temperature range covering 285 K to 558 K . Each replica was run for 100 ns . The details of the four REMD simulations are shown in Table 1 . The second halves of the REMD trajectories were used for analysis . The AMBERff99SB[54] force field parameters were used for the peptide , and GLYCAM06[55] force fields were used for the carbohydrate moiety , as they have been shown to be compatible with each other for glycoprotein studies[55] . Constant temperature and constant volume ( NVT ) REMD simulations were conducted to sample the conformational space [56] , REMD is an enhanced sampling technique based on the parallel tempering Monte Carlo method [56–59] , where copies of identical systems are simulated at different temperatures . Periodically state exchange between replicas is attempted , and the acceptance rule for each move between states i and j , dictated by a Boltzmann distribution , is Pacc=min{1 , exp[ ( βi−βj ) ( U ( r→iN ) −U ( r→jN ) ) ]} , where β = 1/kBT and U ( r→iN ) represents the configurational energy of the system in state i . Together with the exchange , the particle momenta are scaled by ( Ti/Tj ) 1/2 , such that the kinetic energy terms in the Boltzmann factor cancel out[56] . REMD sampling can also be described in terms of umbrella sampling[60] . The temperature spacing between replicas was chosen to ensure sufficient energy distribution overlap between neighboring replicas such that exchange attempts were , on average , accepted with a 20% probability[61] . The potential energy distributions of the system were simulated at constant volume and constant temperature for 30 different temperatures to establish the distribution for the replicas[62] . In addition , thermal unfolding studies of the peptide construct were carried out to characterize the effects of glycosylation on unfolding . Conventional MD simulations at high temperature were conducted on the fragment of the V1/V2 variable loop starting from the beta-strand structure ( PDB ID: 3U4E ) . Two sets of simulations were done , with each set having 20 replicas: one set was for peptide with two glycans at N156 and N160 , and the other set was for peptide without glycans . All 20 replicas in each set started from the same conformation but ran with different random seeds at 450 K . Simulations of 60 ns were run for each one without glycan to see the peptide completely unfold , while simulations of 100 ns were run for each one with glycan , and significant amount of beta-strand remained at the end of simulations . The details of the systems are listed in Table 1 . The Nose Hoover thermostat was used for the temperature coupling with a coupling time constant τT = 1 . 0 ps . The protein/glycoprotein and solvent are coupled separately to thermostats with the same coupling parameters . Van der Waals interactions are treated using a 1 . 0 nm cutoff . The electrostatic interactions are treated by smooth particle mesh Ewald summation . All bond interactions involving hydrogen atoms are constrained using SETTLE and SHAKE to allow a 2 fs integration time step . A total of 102 . 4 μs simulations have been carried out in the current study . Configurational entropy calculations were performed following the formulation of Schlitter[63] . This approach provides an approximate value ( upper bound ) S to the true configurational entropy Strue of the simulated system , Strue<S=kB2lndet[1+kbTe2ℏ2D_] where kB is the Botlzmann’s constant , T is the absolute temperature , e is Euler’s number , and ℏ is Planck’s constant divided by 2 π . Here D_ is the covariance matrix of mass-weighted atomic Cartesian coordinates , defined as D_=〈[M_12 ( r−〈r〉 ) ]⊗[M_12 ( r−〈r〉 ) ]〉 where r is the 3N-dimensional Cartesian coordinate vector of N particles ( atoms or beads ) considered for the entropy calculation after least-squares fitting onto a referenced structure , M_ is the 3N-dimensional diagonal matrix containing the masses of these particles , <…> denotes ensemble averaging , and the notation a ⊗ b stands for the matrix with elements μ , ν equal to aμ * bν . In our case , the structures after minimization were used as reference for the least square fitting of 1200 snapshots of the trajectory . Moreover , during the fitting procedure , the rotational and translational contribution was removed , considering only the internal degrees of freedom of the peptide backbone . Finally , we carried out large-scale all-atom MD simulations of the glycosylated and the unglycosylated Env spike containing gp120 trimers [64] . Initial coordinates of the glycoprotein complex were downloaded from the protein data bank repository ( PDB code 4NCO ) [65] . Missing loops and residues in the structure were built using the Modeller package [66] using the full sequence of the BG505 SOSIP gp140 Env trimer in complex with broadly neutralizing antibody PGT122 [65] . The X-ray structure is partially glycosylated with mannose sugar derivatives and all glycosylation positions were completed to have the same Man5 carbohydrate moieties . The system is represented using the same force field as used in the simulations of the V1V2 peptide fragment ( described above ) . Env trimeric spike was placed in a cubic box of ~ 4000 nm3 and was solvated with 100000 water molecules . Two independent MD simulations were carried out for the Env spike with and without glycosylation . Each simulation was run for one microsecond and carried on using the GROMACS 4 . 6 . 5 [67] molecular simulation package . All atom simulations were performed using a 2 fs time step to integrate Newton’s equations of motion . The LINCS algorithm [68] was applied to constrain all bond lengths with a relative geometric tolerance of 10−4 . Non-bonded interactions were handled using a twin-range cutoff scheme . Within a short-range cutoff of 0 . 9 nm , the interactions were evaluated every time step based on a pair list recalculated every five-time steps . The intermediate-range interactions up to a long-range cutoff radius of 1 . 4 nm were evaluated simultaneously with each pair list update and were assumed constant in between . A PME approach [69] was used to account for electrostatic interactions with a grid spacing set to 0 . 15 nm . Constant temperature ( 300 K ) was maintained by weak coupling of the solvent and solute separately to a Berendsen heat bath [70] with a relaxation time of 1 . 0 ps . Similarly , an isotropic approach was used to couple the pressure of the system to 1 . 0 bar . Trajectories were stored every 20 ps for further analysis .
HIV-1 gp120 contains between 18 to 33 N-linked glycosylation motifs , and maintains a median of about 25 despite very high levels of genetic variability[71] . Some of the glycan sites are highly conserved across all of the major HIV-1 clades and circulating recombinant forms , while a subset show clade-specific patterns[71 , 72] . However , considerable diversity in gp120 amino acid sequence and glycosylation is evident even in single individuals over time[71] , and this variation can mediate antibody immune escape[34–39 , 73 , 74] , including cases where the carbohydrate is a direct component of the epitope [30] . The range in number of glycan sites is largely a consequence of mutations , insertions and deletions that occur within the four hyper-variable domains of gp120 ( V1 , V2 , V4 and V5 ) ( see Fig 1A ) , with insertions often reflecting local imperfect direct repeats of varying lengths[75] . The V3 domain is also variable , however , it is more conserved than the four hyper-variable regions in terms of length and variation of glycosylation sites [71] . To investigate patterns of glycosylation in gp120 , we first performed an updated analysis of the relationship between hyper-variable loop length and glycosylation in gp120 . We included the most current set of global sequence data ( n = 4 , 633 ) and used a bioinformatics tool that is newly available at the Los Alamos HIV database ( http://www . hiv . lanl . gov/ ) . Fig 3 illustrates that for the four loops that are hyper-variable in terms of insertions and deletions ( V1 , V2 , V4 , and V5 ) , the loop length and number of glycan sites are highly variable and correlated with each other; in contrast these parameters for the V3 region are almost invariant . The V2-epitope region we have used as a basis for the peptide we model here is similar to V3 in that it is almost invariant in terms of length and number of potential glycosylation sites; the hyper-variable regions that evolve by insertion and deletion in the V1 and V2 loop flank the key epitope region represented in the peptide , in the context of the natural protein ( In Fig 1A , the hyper-variable regions within the loops are indicated in blue ) . In contrast , there is broad net charge distribution in all of these variable regions ( Fig 4 ) , including the epitope region , which varies from -4 to +7 for V1 , 2 , 4 and 5 , and ranges from -1 to +10 for V3 . Whereas V3 has a positive charge , with a median of +4 , the median charge is close to neutral for the other variable domains [76 , 77] . The V1 , V2 , V3 , and V4 loops are each delineated by an invariant cysteine-cysteine disulfide bond , and for 6 of these 8 loop-bounding cysteines , N-linked glycosylation sites tend to be immediately adjacent to one or both of the cysteines . There are two possible sequence motifs for these patterns: NCS/T or CNXS/T , where X can be any amino acid except for proline ( Fig 1A and S1 Fig ) . Among the 4633 sequences analyzed , there are 8 conserved Cys residues at the bases of the variable loops , and on average , another 16 Cys residues per Env . Outside of the variable loops , these other Cys residues rarely have a proximal N-linked glycosylation site ( Fig 5; 1357 of 73354 , or 1 . 8% ) . In contrast , the majority of the conserved Cys at the bases of the variable loops are proximal to a glycan ( Fig 5; 21 , 837 out of 37 , 064 , 59% ) . The enrichment for Cys-proximal N-linked glycosylation at the bases of the hyper-variable loops is highly significant ( 6/8 vs 0/16 , p = 0 . 0002 ) . In particular the two N-linked glycosylation sites ( N156 , N197 ) that are proximal to the Cys residues at the base of the V2 loop ( corresponding to C157 , C196: Fig 5 ) are both very highly conserved across HIV-1 subtypes ( S2 Fig ) . This suggests that these Cys-proximal glycans are important for Env function , and may impact the structural conformation of the variable loops in the intact protein , and V2 in particular . A beta-strand of 33 amino acids that contains key neutralizing antibody epitopes is embedded in our peptide construct ( Fig 1A ) . The portion that resides within V2 is conserved in terms of length and number of glycosylation sites; 94% of the sequences are 33 amino acids long in this region , and 88% carry 2 glycosylation sites ( generally the highly conserved sites at N156 and N160 ) , while 11% have only one ( S2 Fig ) . Despite overall conservation in length and number of glycans ( Fig 3 and S3 Fig ) , this V2 ‘epitope’ region has many highly variable positions ( Fig 1C ) , and it can vary dramatically in net charge ( Fig 4 and S3 Fig ) . Nevertheless , this region is of great interest from a vaccine perspective because it serves as the contact region for glycan-dependent broadly neutralizing antibodies such as PG9 and PG16 that recognize the V2 region with a preference for the quaternary structure [29 , 30] . This region is also recognized by other broadly neutralizing antibodies , PGT141-PGT145 [31] , CH01-CH04 [32] and the VRC26 group of mAbs [43] as well as by a number of antibodies with narrower neutralization breadth , such as C108g [78] , 10/76b [79] and 2909 [80] isolated from infected or immunized animals . The two conserved N-linked glycans that are in this region form parts of these epitopes and directly contact antibodies PG9 and PG16 [29 , 30] . In addition , in the RV144 vaccine trial immune responses to this linear epitope region were correlated with reduced risk of infection , and the protective effect was associated with antibody-dependent cellular cytotoxicity ( ADCC ) , not neutralization [81 , 82] . An initial set of REMD simulations was carried out to examine how glycosylation at the conserved positions N156 and N160 affects the secondary structure propensities of the unstructured , flexible V1V2 peptide construct . This isolated peptide exists predominantly as a random coil , and the fraction of residues that form a helix , beta-strand , or turn structures as a function of temperature is plotted in Fig 6 . While the fraction of residues in the glycosylated and unglycosylated peptides involved in beta-strand or turn structures decreases with increasing temperature , the fraction of residues that form a helix structure increases until around 440 K , followed by a slight decline . Importantly , a larger fraction of the residues are prone to form secondary structures other than random coil ( helix , beta-strand , or turn structures ) in the unglycosylated form compared to the glycosylated form ( Fig 6 ) . Thus , the overall probability of the V1V2 peptide to form folded secondary structures is reduced upon glycosylation . The free energy landscape of the V1V2 peptide in terms of end-end distance and radius of gyration is shown in Fig 7 . In the absence of glycosylation , 43% of the configurations were enclosed within a single state ( Rg = 1 . 1 nm , DN-N = 1 . 8 nm ) ( Fig 7A ) . Upon glycosylation however , 20% of the total configurations populated two states: ( Rg = 1 . 2 nm , DN-N = 1 . 8 nm ) and ( Rg = 1 . 2 nm , DN-N = 2 . 5 nm ) ( Fig 7B ) . These results suggest that the overall ensemble of the V1V2 peptide is more extended upon glycosylation . This is consistent with the reduction of secondary structural preference as seen in Fig 6 . Such an effect can arise with increased entropy of a peptide backbone , disruption of intra- and inter-peptide interaction , and between peptide and glycan , each of which were subsequently investigated . While N-linked glycosylation is often linked with global conformational effects , it is possible that the addition of carbohydrate could affect the N residue itself . A Ramachandran plot was therefore determined for the N156 backbone dihedral angles ( Fig 8 ) . Without glycosylation at N156 , the most sampled region shows characteristics shared with polyproline II motifs ( -65° , 135° ) . It is followed by the right-handed and left-handed alpha-helix regions ( Fig 8A ) . Glycosylation at N156 reduces the backbone sampling of polyproline II and left-handed alpha-helix regions and enhances the sampling of the extended beta-basin ( -135° , 135° ) and right-handed alpha-helix ( -60° , -30° ) regions ( Fig 8B ) . To further quantify the differences in sampling , Shannon entropy was calculated with and without glycosylation at N156 . The equation S = −R * Pi * log ( Pi ) was used to calculate Shannon entropy , with R equal to the molar gas constant and Pi equal to the probability to sample each bin . This calculation gives entropic values of 63 . 01 kJ K-1 mol-1 n and 60 . 93 kJ K-1 mol-1 with and without glycosylation , respectively . The slightly higher Shannon entropy for N156 backbone sampling with glycosylation is consistent with varying bin size . Thus , by introducing a carbohydrate moiety , increased dimensionality is added to the peptide construct , resulting in higher backbone sampling and entropy . Glycosylation of the V1V2 peptide at N156 and N160 could also alter the configurational entropy of the entire peptide . To investigate this , calculations were performed following the formulation of Schlitter[63] . S4 Fig shows the configurational entropy of the peptide backbone at 300K and 450K , averaged over a 250 ns simulation . Clearly , glycosylation increases the configurational entropy of the peptide , and is greatest at higher temperatures , consistent with the flexibility in secondary structure of the peptide . Interestingly , the inclusion of the glycans increases the entropy of the peptide backbone at higher and lower temperatures by ~100 and ~60 J mol-1 K-1 , respectively . However , such an increase in entropy of the peptide backbone is somehow compensated by the enthalpic contribution of the glycan , as discussed below . The effect of glycosylation on intermolecular interactions can be quantified in terms of hydrogen bonding within the peptide , between peptide and solvent , and between glycan and peptide . The total number of hydrogen bonds in these different types of interactions in terms of configuration is shown in Fig 9 . Regardless of glycosylation , as expected , peptide-solvent hydrogen bonding dominates ( Fig 9A ) . Overall , there are much more hydrogen bond interactions between the peptide and water molecules compared to intra-peptide hydrogen bond interactions . However , glycosylation does disrupt hydrogen bonding between peptide and solvent , and between peptides . As shown above , glycosylation of the V1V2 peptide disrupts the intra- and inter-molecular hydrogen bonding of the peptide . However , this effect could potentially be compensated by the hydrogen bond interactions that arise due to introduction of glycan . Additionally , the electrostatic nature of the glycan can lead to specific interactions with charged residues of the peptide . Therefore , the different coulombic contributions to the overall energetics were considered to capture any compensating electrostatic interactions introduced by glycosylation . Accordingly , even though several hydrogen bonding interactions involving charged residues are reduced by addition of the glycans , the reduction is compensated by de novo hydrogen bonding between the glycan and other polar residues ( Fig 9B ) . Fig 10A shows the averaged coulombic contribution as a function of time for the interactions between the peptide , glycans , and solvent . The addition of the glycan clearly affects the intra-molecular interactions of the peptide , as well as the peptide interactions with water molecules . However , the presence of the glycans adds ~ 3000 kJ mol-1 to the stability of the peptide ( Fig 10 , sum of the energies from panels C and D ) . On average , most of the contribution comes from the interaction between the two glycans and the interaction of glycans with the solvent . This observation was also recorded at a higher temperature . Overall , these results suggest that the glycan itself contributes enthalpically to the stability of the peptide in solution . The interaction between the two glycans at N156 and N160 was characterized in terms of their contact distance . The inter-glycan distance is calculated as the distance between the two C1 atoms of the first mannose in each carbohydrate moiety . The fraction of configurations as a function of inter-glycan distance is plotted in Fig 11 . Two snapshots of the glycopeptide are also shown , one with an inter-glycan distance of 0 . 4 nm ( left panel ) and the other with a distance of 3 . 1 nm ( right panel ) . A short inter-glycan distance corresponds to the glycans interacting with each other . The inter-glycan distance distribution is highest between 0 . 75 nm and 1 . 0 nm , clearly demonstrating that the smaller inter-glycan distances are preferred . It is likely that antibodies that target that region can interrupt such glycan-glycan interactions . In the context of the flexible unstructured peptide construct considered in this study , spatially proximal glycans prefer to interact with each other . The V1V2 peptide construct utilized here is predominantly disordered when in solution . However , as mentioned above , it can adopt beta-strand or alpha-helical structures when bound to antibodies . To understand whether glycosylation can stabilize pre-formed beta-strand conformation , unfolding simulations were carried out at 450 K for the peptide construct starting from an initial beta-strand configuration similar to that in complex with the broadly neutralizing antibody PG9 . Both the unglycosylated and glycosylated peptide systems were considered ( see Methods section ) . The average number of residues in beta-strand as a function of time for the two systems is plotted in Fig 12 . The beta-strand structures unfolded rapidly , disappearing within 60 ns of simulation for the unglycosylated peptide ( Fig 12A ) . A fitted exponential curve provided a decay time constant of 14 . 56 ns . Only two residues remained in the beta-strand structure at the end of the simulation . In contrast , beta-strand structures unfolded at a much slower rate in the glycosylated system , and a significant amount of secondary structures remained at the end of the 100 ns simulation for the glycosylated peptide ( Fig 12B ) . The decay time constant was 27 . 78 ns for the glycosylated peptide , and there were six residues remaining in the beta-strand at the end of the simulation . Thus , glycosylation of the V1V2 peptide retards the decay of preformed secondary structure by almost two-fold and preserves more residues with secondary structure . This potentially demonstrates the ability of glycosylation to stabilize the desired conformation of this region of V1V2 in terms of antibody recognition . To explore whether some aspects of stabilization due to glycosylation could be attributed to shielding of solvent , the average surface accessible surface area ( SASA ) for the unglycosylated and glycosylated V1V2 peptide systems was determined , and is shown as a function of time in Fig 12C and 12D , respectively . The SASA for the glycosylated peptide is less than that of the unglycosylated peptide , likely due to glycan shielding that may also contribute to the stabilization seen above . Previous studies have shown that desolvation of helix or beta-strand peptides stabilize the conformation by strengthening the intra-peptide hydrogen bond interactions[83 , 84] . In addition , interactions between the glycan and the peptide residues can also provide stability as discussed above . Next , we computationally investigated the possibility that disulfide bonds such as those found in V1V2 promote the formation of beta-strand structures in unstructured peptides by bringing two peptide regions close together . To investigate whether the disulfide bridge impacted the stabilizing effects of glycosylation on the V1V2 peptide , we considered a V2 loop fragment derived from a clade C consensus ( ConC ) sequence that did not contain the V1 fragment and the disulfide bond . The propensity to form secondary structures for the CAP45 V1V2 ( with disulfide bond ) and the ConC V2 peptide ( without disulfide bond ) is shown in Fig 13 . In ConC V2 peptide , like CAP45 V1V2 peptide , glycosylation reduces the propensity for secondary structure formation . However , the fraction of residues that form a beta-strand structure is higher when there is a disulfide linkage , as seen in the glycosylated and unglycosylated forms of the V1V2 peptide ( Fig 13A ) . Though , the helical content is lower for both peptides , in contrast , the fraction of residues that form a helix is reduced to the same level by glycosylation in the presence or absence of the disulfide bond ( Fig 13B ) . Thus , our calculations tend to suggest that the propensity to form beta-strand structure is noticeably increased in the presence of the disulfide bridge . To this point , we have presented computational results from studies performed on an isolated peptide fragment from the V1V2 region of gp120 . An inevitable question is whether the effects of glycosylation described for this fragment are the same in the context of the entire Env trimer spike . To address this , we performed all-atom MD simulations of the Env spike using the BG505 SOSIP gp140 Env trimer in complex with broadly neutralizing antibody PGT122 [65] . A more extensive characterization of the global effect of glycosylation and its contributions to stability of the Env trimer are in progress elsewhere ( manuscript in preparation ) . We find that many of the physical trends of glycosylation are preserved in the context of Env spike for the same regions of gp120 considered in the V1/V2 peptide construct . From the all-atom simulations of the Env spike , Fig 14A shows the root mean square fluctuations ( RMSF ) of the backbone atoms encompassing the V1/V2 construct sequence . The RMSF observed for the non-glycosylated sequence are at least two times greater than the glycosylated counterpart , indicating a significant reduction in flexibility imposed by glycosylation . Furthermore , the accumulated configuration entropy over time for the same V1/V2 region ( Fig 14B ) shows reduced entropy upon glycosylation . We also calculated the different coulombic contributions to the overall energetics within the V1/V2 sequence stabilization . Again , as shown in Fig 14C for the different interactions between the local protein region representing the peptide construct , glycans , and solvent , the results in the context of Env spike are similar to those from the isolated construct ( see Fig 10 top panel for comparison ) . Therefore , whether in the context of the Env trimeric spike or as an isolated fragment , the backbone mobility of this V1V2 peptide is restricted by glycosylation . Furthermore , the presence of glycan affects the local intra-molecular interactions among protein residues well as their interactions with water molecules . The studies described above indicate an important contribution of the highly conserved glycan at position at 156 adjacent to Cys157 in folding of the adjacent V2 peptide . The extreme conservation of this glycan in HIV-1 and among all primate lentiviruses ( Cys196 ) also supports its role as an important element in the structural framework of the region ( S5 Fig ) . These observations raise the question of whether this and other Cys-proximal glycans regulate the efficiency of processing and conformational folding in the context of the native Env protein . This was investigated by removing the 156 and 197 glycans and evaluating their effect on Env processing . To simplify this analysis , these studies were performed with SF162 Env , which conserves glycans 156 and 197 but lacks a glycan at position 160 ( Fig 15A ) . For comparison , glycans in the same region that were not adjacent to the Cys residues , N136 and N188 , were also mutated . The glycans at 156 , 197 , 136 , and 188 were each eliminated by mutating the Ser or Thr residues in the corresponding motifs to Ala residues ( Fig 15A ) . Mutation of 156 and 197 glycosylation motifs resulted in a reduction in size of the corresponding gp120 proteins ( Fig 15B ) . This was consistent with the loss of an N-linked glycan , and showed that both of these positions were glycosylated in the wild type ( wt ) Env protein . The effects of these mutations on Env folding were examined by comparing the intracellular forms of Env present after a 5 hr labeling period for the wt Env ( lane 1 ) , mutants containing the 156 or 197 mutation ( lanes 2 and 3 ) , and a 156/197 double mutant ( lane 4 ) ( Fig 15C ) . Immunoprecipitation performed with polyclonal HIVIG showed that the majority of the mutant Env protein remained in the unprocessed gp160 form . Processing to gp120 appeared to be impaired to a greater extent for the 156 glycan ( lane 2 ) compared to the 197 glycan ( lane 3 ) , while processing was completely abrogated for the double mutation ( lane 4 ) . Similarly impaired processing to gp120 was observed for samples immunoprecipitated with mAbs b12 and 5145A , which are specific for conformational epitopes in the CD4-binding domain ( Fig 15C , upper panels ) . These antibodies recognized similar levels of the wt and mutant gPr160 precursors , indicating that these mutations did not affect the folding events required for the formation of the CD4-binding domain . However , recognition of gPr160 by mAbs to three conformational epitopes in the V2 domain was significantly reduced by mutation of the 197 glycan , and almost completely inhibited by the loss of the 156 glycan and the double mutation ( Fig 15C , lower panels ) . Previous epitope mapping studies with mAb 830A indicated that this antibody recognizes a discontinuous conformational epitope that overlaps the α β -integrin binding site at positions 179–181 . This epitope also involves other residues in both the V1 and V2 domains , and a crystal structure of a V1V2 scaffolded molecule complexed with 830A demonstrated that this epitope did not include any glycans [45] , suggesting that the reduced recognition of the glycosilation mutants by the V2-specific antibodies was not due to direct mutation of these epitopes . A similar analysis of the 136 and 188 glycan mutants , which are not adjacent to the Cys residues , revealed that processing of the single and double mutant proteins was similar to the wt protein ( Fig 15D ) . These results indicate that processing of gp160 to gp120 was significantly impaired by loss of the 156 and 197 glycans , but not by loss of the 136 and 188 glycans . Furthermore , the conformation of the V1/V2 domain was altered by removal of the two Cys-adjacent glycosylation sites , such that recognition by three conformationally-dependent V2-directed mAbs was significantly reduced .
Structural information about the effects of glycosylation on HIV-1 gp120 , and in particular the hyper-variable domains , is still limited . This is due in part to the difficulties in obtaining crystal structures with glycans and hyper-variable domains intact . MD simulations can thus provide unique insight that enhances our understanding of the glycosylated gp120 structure . In this study , we used the enhanced sampling approach of replica exchange MD simulations to investigate the effects of glycosylation on a flexible unstructured region of the V1V2 domain that is of immunologic interest . We generated a peptide fragment containing portions of the V1 and V2 loops , including an antigenic region of gp120 that was recognized by antibodies generated by vaccination and during HIV-1 infection . The two regions were linked by a disulfide bond , and contained high mannose glycans at positions N156 and N160 , which are targeted by a class of broadly neutralizing antibodies . These studies were undertaken to provide information about how to mimic salient structural features of V1V2 that could enhance its immunogenicity , as well as to understand the selective pressures that underlie strongly conserved features within a highly variable domain . The addition of glycans to the V1V2 peptide caused a strong enthalpic compensation that resulted in two complementary effects on this disordered flexible fragment . First , glycosylation stabilized the pre-formed conformation of this peptide . Second , it reduced the propensity of the unstructured peptide to form secondary structures . Paradoxically , glycosylation destabilized this disordered V1V2 fragment by reducing its secondary structure propensities , while at the same time stabilizing it by preventing the peptide fragment from unfolding . It is possible that with glycosylation , the free energy of the unfolded state is lower due to the increase in entropic and enthalpic components . In addition , glycosylation also disrupts intra- and inter-peptide interactions that might be important to the folding process of the peptide . The large volume of a carbohydrate moiety could also impede the rearrangement of the V1V2 structure during its folding process . On the other hand , it is possible that the free energy of the folded structure is also lower with glycosylation due to introduction of strong interactions between glycan-glycan and glycan-solvent . Also , glycosylation reduces the solvent accessible surface area of the peptide , thereby shielding the unfolding process from the solvent . Based on our results , we postulate that this destabilization of secondary structure is a generalized effect of the glycan attached to unstructured regions of proteins . At the same time , our results show that glycosylation can prevent a pre-formed beta-strand structure in the peptide from thermal unfolding; the unfolding process was much slower in the presence of glycosylation . One could envision that such a situation arises during oligomerization of gp120 monomers or when V1V2 is bound to an antibody . Even though addition of glycans to the V1V2 peptide disrupted significant peptide-peptide and peptide-solvent interactions , a much larger favorable enthalpic contribution was obtained from the glycan-glycan and the glycan-solvent interactions . In fact , the large enthalpic contribution from solvation leads to better hydration of the peptide . This is reflected in the free energy landscape that favors an extended conformation of the peptide in the water solution . Additionally , a significant enthalpic contribution originates from glycan-glycan interactions in the case of glycosylation sites that are spatially proximal in the peptide , such as N156 and N160 . Our studies show that if two glycans occur at spatially close sites in a flexible region of the protein , they will cluster together . There is an additional site , N130 , often found next to a disulfide linkage in HIV Env ( Fig 1C ) , although not in the CAP45 sequence that we used in this study; when it is present , it might also impact the conformation of both the peptide and the intact trimer as discussed below . In this study , we characterized the changes in the energy landscape and thermodynamics of an isolated , disulfide bound V1V2 peptide fragment upon glycosylation . Such a characterization is critical for designing an immunogen construct involving glycosylation to ensure that the important conformational characteristics of the peptide are not significantly altered . It is also possible that the effect of glycosylation is more dramatic in a peptide construct when the scaffolding influence of the rest of protein is absent . In order to address this potential limitation , we performed extensive all-atom MD simulations of the entire Env spike and found that glycans exert similar effects on the V1V2 immunodominant region even when considered in the context of the whole Env gp120 trimer . Thus , backbone mobility is restricted when glycosylation is present in the context of the isolated peptide and the Env trimer , Furthermore , our simulations revealed that the presence of the glycan affects the local intra-molecular interactions among protein residues well as their interactions with water molecules . All-atom MD simulations of the Env spike also demonstrated that additional glycans from the trimeric complex interact with the glycan moieties at positions 156 and 160 . Preliminary results from the Env spike simulations show that the glycan at position 156 makes contact with other glycans within the same protomer , whereas the glycan at position 160 interacts with glycans from neighboring protomers at top of the spike ( S6 Fig ) . Thus , it is likely that glycan interactions with 156 and 160 contribute to the stabilization of the V1/V2 immunodominant region and , more importantly , to the integrity of the trimer . Indeed , studies are currently underway to understand how glycan-glycan interactions contribute to the stability of the trimer . Another study used simulations to investigate the effects of glycosylation on the mobility and conformation of the V3 domain in gp120 [85] . In that study , unglycosylated gp120 was compared with gp120 containing either a single or multiple proximal high mannose N-linked glycans . That study reported that glycans surrounding the disulfide bounded V3 domain modulate its dynamics and conformational properties . The glycans tended to constrain the movement of V3 , and cause it to adopt a more narrow conformation than the non-glycosylated gp120 form . Interestingly , the glycans flanking the V3 domain are less well conserved across HIV-1 clades and circulating recombinant forms ( CRFs ) than those in V1V2 . The N-linked glycan motif at N295 , which is N-terminal to V3 , is found less frequently in clades A and C compared to other group M clades and CRFs . At the C-terminal flank of V3 , the N334 glycan addition site is highly conserved in CRF01_AE , but is more variable in other clades and CRFs , which tend to have higher frequencies of the N332 glycan addition site instead . Thus , the cysteine-bounded loops of gp120 are often flanked by glycans that are conserved to varying degrees , and therefore may be particularly susceptible to their influence . Consistent with this concept is strong evidence that the placement of N-linked glycosylation sites adjacent to disulfide bonds is a highly conserved feature at the base of the V2 loop ( and also modestly conserved at the base of V1 , V3 , and V4 ) . Disulfide bonds are commonly found in proteins , but their effects on protein structure are still under investigation[10 , 86–89] . Moreover , the effect of having a glycan juxtaposed to a disulfide bond has not been addressed from a structural perspective . In the peptide construct considered in this study , the glycan at 156 is adjacent to a disulfide bond . Past studies have shown that elimination of 156 and 160 glycans in the HIV-1 DH12 infectious molecular clone resulted in a loss of infectivity that was attributed to a defect in CD4 binding [90] . Furthermore , our studies demonstrated that removal of 156 and 160 from the HIV-1 SF162 Env impaired gp160 processing to gp120 and disrupted the conformation of V1V2 . Mutation of V1V2 glycans that were not adjacent to Cys residues did not reproduce these effects . Thus , it is likely that Cys-adjacent glycans play an important role in HIV-1 Env processing , folding , and function . We also evaluated a peptide region of V2 , based on the consensus C sequence , that lacked the disulfide bond and the V1 fragment . By comparing the effects of the disulfide bond with or without glycosylation in the V1V2 and a second V2 peptide , we found that the residues adjacent to the disulfide bonded cysteine residues had a higher propensity to form beta-strand structure compared to other residues in the peptide ( S7 Fig ) . However , addition of a glycan next to the disulfide diminished the propensity to form beta structures . In the context Env trimer , examination of other V1V2 and other disulfide regions with proximal glycans revealed that secondary structures are slightly diminished by the presence of glycosylation ( Fig 14D ) . Taken together , these findings suggest that the preservation of the glycan next to the disulfide bond is most beneficial during folding or upon pre-forming a stable secondary conformation induced by binding to other proteins or antibodies . Presumably , a major function of the Cys-adjacent glycans is to regulate the efficiency and specificity of disulfide bond formation . Intuitively , the large size of the glycans would limit degrees of rotation , which could regulate the orientation of the two peptide strands on either end of the disulfide . This may be particularly important in cases where there are proximal glycans to both partners of a disulfide bond , such as is observed for the Cys 131- Cys 157 disulfide bond that closes V1 and the disulfide bond at the base of the V3 loop . A somewhat under-appreciated consideration is that recombinant gp120 proteins possess considerable heterogeneity specifically in the V1V2 region [91] , which could be influenced by the adjacent glycosylation . Consistent with this concept , mass spectroscopy studies have provided evidence for alternative disulfide pairing in the V1V2 region of the recombinant CON-S gp140 ΔCFI protein [91] and this may be influenced by glycosylation . There is also evidence that disulfide reorganization occurs after receptor binding and is mediated by membrane-associated protein-disulfide isomerases [92] . This could also be affected by proximal glycans . Thus , further studies are warranted to explore whether these effects are present only in lentiviral glycoproteins . Here , we addressed the effect of glycosylation of a V1V2 peptide that generally exists in a disordered conformation in solution . From the standpoint of immunogen design , thermodynamics elucidated from the current study provide insightful strategies to stabilize the V1V2 peptide and drive it towards the formation of beta-strand structures that could be desirable for eliciting broadly neutralizing antibodies over those that recognize linear , non-neutralizing or strain-specific V1V2 epitopes . The RV144 human vaccine trial elicited cross-reactive but weakly neutralizing antibodies directed against epitopes in V2 that were inversely correlated with the rate of HIV-1 infection [93] . Structural studies indicated that the key region of V2 ( residues 168–176 ) recognized by vaccine elicited non-neutralizing mAbs CH58 and CH59 , and the V1V2-targeted broadly neutralizing mAb PG9 , can exist in multiple conformations [46 , 47] . PG9 appears to preferentially bind to a beta strand conformation , whereas CH58 and CH59 may recognize alternate forms [46 , 47] . An additional key feature of V1V2 directed broadly neutralizing antibodies such as PG9 is their ability to bind to glycans at N156 and N160 , in addition to the underlying peptide ( 17 ) . Alam et al . [40] described V1V2 glycopeptide immunogens that bind with high affinity to mature V1V2 broadly neutralizing antibodies and their putative germlines , but with much lower affinity to the vaccine-elicited , strain-specific V2 antibodies [40] . Also , they elegantly showed the importance of disulfide bonds in their peptide constructs , which is consistent with our findings . Therefore , glycopeptide immunogens represent a viable strategy to elicit V1V2-directed broadly neutralizing antibodies , but the effects of glycan-proximal disulfide bonds will also need to be considered . The region that intervenes between the V2 ‘epitope’ region and the Cys involved in V2 loop closure is a hyper-variable segment . Thus , peptides and scaffolds that encompass the end of the V2 loop and include the conserved glycosylation sites at the base of the V1V2 region , such as those described in[93] , may better mimic the structure found in a native Env trimer . However , this region also spans sequences that are unique and highly distinctive between every isolate , a balance to consider in immunogen and reagent design . Finally , one aspect that was not considered in the current study is the heterogeneity of carbohydrate forms at glycan sites , in particular at N156 and N160 . A recent study using BG505 trimer protein demonstrated that N156 and N160 participate in a glycan ring at the trimer apex [52] . Both glycans were found to be predominantly of the oligomannose type , consisting of varying proportions Man₅-9GlcNAc₂ . The authors proposed that glycan processing at N156 and N160 is likely to be constrained by inter-protomer contacts , resulting in minimal processing , although N160 tends to be more heterogenous than N156 [52] . Likewise , a study using a clade G SOSIP trimer demonstrated that the trimer apex is a region of glycan crowding , with less processing than glycans occurring in more dispersed regions [53] . The results of these trimer-based studies stand in contrast with that of Amin et al . [48] , which evaluated cyclic V1V2 peptides that contain glycans attached at N156 ( or N173 which substitutes for N156 in some isolates ) and N160 . Interestingly , the N-glycans at N156 and N173 have been shown to be spatially equivalent in terms of antibody recognition . The authors found that Man₅GlcNAc₂ glycan was required at N160 for recognition by broadly neutralizing antibodies PG9 and PG16 . Furthermore , a sialylated N-glycan at the secondary site ( N156 or N173 ) was also necessary for antibody binding to the glycopeptide . However , in the context of the BG505 trimer , PG9 binds regardless of whether the protein was produced in the presence or absence of glycan processing , supporting that these glycans are likely to be composed mainly of oligomannose forms in the native envelope trimer [94] . Taken together , these studies suggest that a better understanding of the structural features and immunogenicity of V1V2 , and the conformational forms recognized by broadly neutralizing antibodies , could lead to the development of novel glycopeptide immunogens .
A major difficulty in the pursuit of incorporating V1V2 epitopes into HIV vaccine design is the structural heterogeneity and variable glycosylation of this immunogenic region . Limited knowledge of how glycosylation and disulfide bonds affect the conformation and dynamics of short intrinsically disordered peptides complicates the design of immunogenic peptides . Thus , the development of strategies to define and exploit optimal configurations of V1V2 epitopes is important . Here , we used extensive replica exchange and conventional MD simulations to characterize the effects of glycosylation on the free energy landscape of a disulfide bound V1V2 peptide and dissect the enthalpic and entropic components upon addition of a glycan . Our analyses demonstrated that glycosylation stabilizes the pre-existing conformation of this peptide , and reduces its propensity to form other secondary structures . However , glycosylation also stabilizes the V1V2 peptide against thermal unfolding , and exhibits specific effects in relation to the adjacent disulfide linkage . These complementary effects originate from a combination of multiple factors , including the observation that having a disulfide bond adjacent to the glycan sites further promotes the formation of beta-strand structure in this peptide . Glycosylation and disulfide linkage are therefore likely important components that contribute to the immunogenecity of this region of V1V2 , and will influence whether the appropriate conformation is adopted . The observation that HIV-1 is under strong selective pressure to conserve glycans adjacent to disulfide bonds could perhaps be exploited in the design of immunogens . | Heavy glycosylation of the envelope surface subunit , gp120 , is a key adaptation of HIV-1 , however , the precise effects of glycosylation on the folding , conformation and dynamics of this protein are poorly understood . The network of glycans on gp120 is of particular interest with regards to vaccine design , because the glycans both serve as targets for many classes of broadly neutralizing antibodies , and contribute to patterns of immune evasion and escape during HIV-1 infection . In this manuscript , we report on how glycosylation influences an immunogenic but disordered region of gp120 . Glycosylation stabilizes the pre-existing conformation , and reduces its propensity to form other secondary structures . It also stabilizes preformed conformation against thermal unfolding . These complementary effects originate from a combination of multiple factors , including the observation that having a glycosylation site adjacent to the disulfide bond further promotes the formation of beta-strand structure in this peptide . | [
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] | 2016 | Effect of Glycosylation on an Immunodominant Region in the V1V2 Variable Domain of the HIV-1 Envelope gp120 Protein |
Finding a multidimensional potential landscape is the key for addressing important global issues , such as the robustness of cellular networks . We have uncovered the underlying potential energy landscape of a simple gene regulatory network: a toggle switch . This was realized by explicitly constructing the steady state probability of the gene switch in the protein concentration space in the presence of the intrinsic statistical fluctuations due to the small number of proteins in the cell . We explored the global phase space for the system . We found that the protein synthesis rate and the unbinding rate of proteins to the gene were small relative to the protein degradation rate; the gene switch is monostable with only one stable basin of attraction . When both the protein synthesis rate and the unbinding rate of proteins to the gene are large compared with the protein degradation rate , two global basins of attraction emerge for a toggle switch . These basins correspond to the biologically stable functional states . The potential energy barrier between the two basins determines the time scale of conversion from one to the other . We found as the protein synthesis rate and protein unbinding rate to the gene relative to the protein degradation rate became larger , the potential energy barrier became larger . This also corresponded to systems with less noise or the fluctuations on the protein numbers . It leads to the robustness of the biological basins of the gene switches . The technique used here is general and can be applied to explore the potential energy landscape of the gene networks .
In the post-genome era , with a wealth of data on genomic sequences , the crucial question becomes how to understand the organization of these sequences in nature and how genes function [1–4] . This is a challenging task . According to the central dogma , turning gene switches on and off controls certain types of protein synthesis and production . Furthermore , the on and off of gene switches determines the developmental plans of the cell . On the other hand , the protein products generated by the gene switches act back on the genes to control their expression patterns . The gene regulations thereby form a network with inherent many-body interactions and feedback loops . That is why the system often becomes quite complicated and hard to study . The underlying nature of cellular networks has been explored by many experimental techniques [4] . It has often been found that cellular networks are in general quite robust and perform their biological functions in the midst of environmental perturbations . There have recently been an increasing number of studies on the global topological structures of cellular networks [5–8] . However , so far there are very few studies from the physical point of view of why the networks are so robust and why they perform their biological functions [9–20] . Theoretical models of cellular networks have often been formulated with a set of chemical reaction equations in bulk . These averaged dynamical descriptions are inherently local . To probe the global properties , one often has to explore different parameters . Since the parameter space is huge , the issue of global robustness is hard to address directly from these approaches . Here we will explore the nature of the network from another angle: we formulate the problem in terms of the potential energy function or potential energy landscape . If the potential landscape of the cellular network is known , the global properties can be explored [21 , 22] . This is analogous to the fact that the global thermodynamic properties can be explored when knowing the inherent interaction potentials in a system . There is another intriguing factor controlling the gene expression patterns . In the cell , there are a finite number of molecules ( typically on the order of several hundreds or thousands ) . The intrinsic statistical fluctuations , usually not encountered in bulk due to the large-number averaging , can be significant and play an important role in the dynamics of gene expression . This gives the source of intrinsic statistical fluctuations or noise . On the other hand , the fluctuations from highly dynamical and nonhomogeneous environments of the interior of the cell give the source of the external noise for the networks [23–30] . It is important to investigate the roles of the statistical fluctuations or noises on the robustness and stability of the network . In general , instead of studying the averaged chemical reaction network equations in bulk , we should use statistical descriptions to model the cellular process . This can be realized by constructing a master equation for the evolution of probability instead of average concentration for the corresponding chemical reaction network equations [26 , 31–35] . One can also study the steady state properties of these probabilistic chemical reaction network equations . The generalized potential energy for the steady state of the network can be shown to be closely associated with the steady state probability of the network in general [10–12 , 15–20 , 31 , 32] . Once the network problem has been formulated in terms of the generalized potential function or potential landscape , the issue of the global stability or robustness is much easier to address . In fact , some explicit illustrations of the potential energy landscape and robustness for the MAP Kinase signal transduction network and cell cycle have been given recently [19 , 20] . It is the purpose of this paper to study the global robustness problem directly from the properties of the potential landscape for a simple yet important gene regulatory network: a toggle switch . Figure 1 shows a toggle switch . Gene networks often involve many degrees of freedom . To resolve the issue of multidimensionality , instead of using the direct Monte Carlo simulation [33] for solving the master equations , a Hartree mean field approximation can be applied to reduce the dimensionality and address the global issues [11 , 12 , 36–38] . There are three aims of this paper . Our first aim is to develop a time-dependent Hartree approximation scheme [36] to solve the associated master equations to follow the evolution of multidimensional probability of the network . Our second aim is to construct the underlying potential energy landscape for a toggle switch [39] and explore both the steady state and time evolution of the landscape . Our third aim is to study the phase diagram of the system and the kinetic time scale from one stable basin of attraction to another in different conditions . We will address the global robustness condition for a toggle switch .
Gene expression is regulated in various and complex ways , and can be represented by many coupled biochemical reactions . In this report , our goal was not just to explain some specific gene network system as accurately as possible , but to illustrate mathematical tools for exploring the general mechanisms of transcriptional regulatory gene networks . We therefore took abstractions of some essential biochemical reactions from complicated reactions of diverse systems . Let us start with the explanation of some terminologies used in this manuscript: “activator” is a regulatory protein that increases the level of transcription , “repressor” is a regulatory protein that decreases the level of transcription . By “operator” we mean the DNA site or the gene where regulatory proteins ( either an activator or a repressor ) bind . First we are interested in the effect of “operator fluctuation” by which we mean the biochemical reactions that change the state of the operator . The operator is said to be in an occupied state if a regulatory protein is bound to it , and in an unoccupied state if the protein is not bound to it . For the repressor we include the following reaction . where stands for the active ( inactive ) operator state of gene α , Mβ represents the regulatory protein synthesized or produced by gene β , and qαβ is for the multimer-type of proteins . For example , if qAB = 2 ( 3 ) , dimer ( tetramer ) proteins produced from gene B repress the expression of gene A , and are reaction probabilities per unit time . In a similar way , we may also consider the activator: Notice that the superscript 1 ( 0 ) in indicates the activity state of the operator and does not represent the bound state of regulatory protein . We will say the gene is on ( off ) when the operator of the gene is active ( inactive ) . The gene will be on when it is occupied by activators or when repressors are unbound from it . Next we include the transcription and translation steps . Here we ignore mRNA and consider only one step combining transcription and translation: where ∅︀ denotes a protein sink or source , bα stands for the burst size of produced proteins ( Mα ) , g1 ( 0 ) is a protein synthesis probability per unit time , and kα is the degradation probability per unit time . We can say that Equations 1–7 are “effective reactions” of the transcriptional regulatory gene network system . Roughly speaking , we can say the other biochemical reactions could be taken into account by adjusting the parameters of the reaction probabilities per unit time . In this sense , the reaction parameters are not really constants but functions of time . Furthermore , the proteins may not be well-mixed in the cell , and the number of proteins could be a function of position . So we can generalize this formalism in a space-dependent manner . We also can add more species and reactions to the master equations . In this report we will assume homogeneity of the number of proteins and ignore the time delay ( for example , due to the translation process ) so that all the parameters are constants . Now we can construct the master equation based on the above assumptions and chosen effective reactions . The master equation is the equation for the time evolution of the probability of some specific state P: where A , B , C , … is the label of each gene; nA , nB , nC , … is the number of proteins expressed by gene A , B , C , … , respectively . SA , SB , SC , … is 1 or 0 , and represents the activity state of the operator . The number of states , N , is nA × 2 × nB × 2 × nC × 2 × … . We expected to have N-coupled differential equations , which are not feasible to solve . Following a mean field approach [11] , we used the Hartree approximation to split the probability into the products of individual ones: First , let us assume and sum over all indexes except one specific index that we are interested in , say α . This effectively reduces the dimensionality from exponential nA × nB × ··· nN × 2N to multiples ( nA + nB + … ) × 2 × N , and therefore the problem is computationally tractable . Finally we are left with two equations , one for P ( nα , 1 ) and one for P ( nα , 0 ) . ( In fact , these are not just two equations because nα varies from 0 to hundreds . ) With the two component vector notation , we have the compact form for the network: where Notice that Equation 11 is simply a “birth–death” process without the last term . We will call the first two terms in Equation 11 the birth–death part or “drift and diffusion” part from the viewpoint of the diffusional Fokker–Plank equation [10 , 35] . Furthermore , we will call the last term the operator fluctuation part . In Equation 11 , all other indices except α appear only in Hαβ in the ensemble-averaged form ( fαβ is just some number ) . If we deal with the one gene case , there is no ensemble average in Equation 12 . The first effect of the operator fluctuation is the sum over nβ and Sβ . The second effect is to cancel out many of the birth–death terms of other genes . Since α = A , B , C , … , we have the vector equation set of the same numbers as those of the genes . They are coupled to each other through the term Hαβ . All network interactions can be determined by assigning every hαβ . bα is the number of proteins produced in bursts from gene α , and θ is a step function . In Equation 12 , we take into account several kinds of binding proteins , and use proper combinatorics and ensemble average . The techniques of quantum field theory can be used to solve the master equation [11 , 37] . The first step is to construct a many-body quantum state . Notice that the probabilities defined by Equation 10 are imbedded in the quantum state as coefficients ( Equation 14 ) where In Equation 13 we make an ansatz of Hartree-type product for the many-body state . Then non-Hermitian “Hamiltonian” of only repressive proteins , Ω , yields: where For each protein concentration , a creation and an annihilation operator are introduced , such that a+|n〉 = |n + 1〉 and a|n〉 = n|n − 1〉 . These operators satisfy [a , a+] = 1 . The generalization to include activating proteins is straightforward . While the state vector is a simple product of individual genes , the operator product form of Ω is chosen deliberately to reproduce the original master Equation 11 . The Ω of a many-gene system seems to be Ω = Σ Ωi [11] , but it would not be the simple sum of individual operators because of the interaction terms . Like the master equation , Dα is the birth–death part and plays a role in the diffusion and drift terms in the context of Fokker–Plank equation . The second term and third term in Equation 15 are repressor-related terms , and Hαβ is the counterpart of Mαβ in Equation 12 . Finally , we have the following quantum system: To complete the mean-field approximation , we need to average all interaction effects by doing an inner product with some reference state , which is a two-component generalization of the Glauber state [37] . If we are interested in an α-gene ( operator ) state and the associated protein , we may define the reference state: Then , is equivalent to the master Equation 11 . The final output we get from these equations is basically moments . From these moments we need to construct the total probability . There are several important features to be pointed out . We start with the single gene case . First , notice that the total probability does not have the structure of C1P1 + C0P0 . We started with a two-component column vector and to extract the physical observables we needed to do the inner product with a two-component row state vector . ( We never added the spin up and down component directly in quantum mechanics . ) The total probability should therefore not follow the steps of constructing P1 and P0 first and then weighing by C1 and C0 . The correct procedure is the following . With the moments , the solutions of equations , we construct new moments: In principle , we can get arbitrary order of moments and construct the corresponding probability if the equations are closed . In practice , however , we may choose one of two probability distributions: Poisson or Gaussian distributions . Second , the probability obtained above corresponds to one limit point or basin of attraction . One solution of the equations determines one of the limit points and also gives the variation around the basin of attraction , so it is intrinsic . If the system allows multistability , then there are several probability distributions localized at each basin of attraction , but with different variations . Thus , the total probability is the weighted sum of all these probability distributions . The weighting factors ( wa , wb ) are the size of the basin , which is nothing but the relative size of the set of initial values ending up with a specific basin of attraction . Notice that the steady state solution is not enough to describe the total probability . It does not say anything about the volume of the basin , it only tells us the limit point . So the effort to derive an effective potential energy from the steady state solution on general grounds needs to take into account the volume of the basin of attraction . One simple exception is the symmetric toggle switch , where the weighting factors are simply ( 0 . 5 , 0 . 5 ) by symmetry . Third , the total probability of many genes is simply the product of each gene based on our basic assumption , the mean field approximation . For example , the probability of a toggle switch can be written as where a and b denote each limit point , and wa and wb are the weighting factors . Even though it is simply multiplication , the interactions between them are already taken into account from the coupled equations . Finally , once we have the total probability , we can construct the potential energy ( or potential energy landscape ) by the relationship with the steady state probability: This is the reverse order of the usual statistical mechanics of first obtaining the potential energy function , exponentially Boltzman weighting it , and then studying the partition function or probability of the associated system . Here we look for the inherent potential energy function from the steady state probability . In the gene-network system , every chemical parameter , such as the protein production/decay rates and binding/unbinding rates , will contribute to the fluctuation of the system . All these effects are encoded in the total probability distribution , and , consequently , in the underlying potential energy landscape .
For the symmetric switch , we first solved the equations of motion determining the amplitude , the mean , and the higher order moments of the probability distribution of the protein concentrations of the corresponding genes . These are given below . We solved the master equation with two methods . One is the Poisson ansatz , mentioned above , by assuming the inherent Poisson distribution , and the other is the exact method , using the moment equation . For the inherent Poisson distribution , we can write down the equations of motion for the amplitude and mean . By giving some initial conditions , and taking the long time limit , we obtained the steady state solution . We fixed all parameters except the protein synthesis rate gA1 ( = gB1 ) . We looked at the probability of genes that were in the active state versus the relative importance of synthesis rate versus degradation rate . By increasing the synthesis rate , gA1 , we could observe the bifurcation from the monostable state to the bistable state after passing a certain critical point . Figure 2 shows the result of taking the long time limit of the equations of motion . The two curves ( with subscript Moment ) are from moment equations , and the others are from the Poisson ansatz . This is consistent with the results directly from time-independent equations ( Figure 3A in [12] ) . We used the parameter Xad = ḡ/k as the horizontal axis variable . It is simply g1/2 in our choice . In the parameter range in which the bistability occurs , we found two limit points ( named a and b ) in the numerical analysis . Now from the solution of the equations we constructed the probability of protein numbers or concentrations ( all illustrations in this paper were based on the Poisson ansatz for simplicity , but it can be easily done with the moment equations and qualitative features will not be changed ) , For the symmetric toggle switch case , the weight factor was simply ( 0 . 5 , 0 . 5 ) due to symmetry . The change of the probability distribution shape in terms of the adiabatic parameter of the relative importance of the protein synthesis rate compared with the degradation rate is shown in Figure 3 . These figures show the monostability to bistability of the symmetric toggle switch . For a large enough protein synthesis rate relative to degradation rate , bistability emerges . As we discussed , the steady-state distribution function P↛ for the state variable ↛ can be expressed to be exponential in a function U↛: where P↛ is already normalized . From the steady state distribution function , we can therefore identify U as the generalized potential energy function of the network system . In this way , we map out the potential energy landscape . Figure 4 shows the potential energy landscape corresponding to Figure 3 . We can see that when the protein synthesis rate is small relative to degradation rate , only a single basin of attraction exists for the underlying potential energy landscape . For large enough protein synthesis rate relative to degradation rate , two basins of attraction emerge . Once we have the potential energy landscape , we can discuss the global stability of the gene regulatory networks . The time scale of the transition between the two stable minimum basins of attraction can be estimated by τ ∼ τ0exp[U≠ − Umin] [41] . Here , τ0 is the pre-factor and τ is the time scale of transition from one basin of attraction to the other . U≠ is the potential energy at the saddle point between the two stable basins of attraction . Umin is the potential energy at one of the basins of attraction . Thus U≠ − Umin represents the potential energy barrier height between two stable basins of attraction . In Figure 5 we can see that as the synthesis rate and unbinding rate of protein to DNA increase relative to the degradation rate , the potential barrier height between the two basins of attraction increases . The time scale of the transition from one basin of attraction to the other exponentially increases with the barrier height . Figure 5 shows the phase diagram of the parameter ranges for the monostable basin and two bistable basins of attraction . We can see that when the synthesis rate and unbinding rate of protein to DNA are low relative to the degradation rate , the potential energy landscape prefers one stable basin of attraction . As the synthesis rate and unbinding rate of protein to DNA increases relative to the degradation rate , the potential energy landscape gradually develops the two stable basins of attraction from the monostable one . There is a transition from monostable to bistable basins of attraction of the underlying potential energy landscape at certain parameters . This illustrates how biological robustness is realized for the toggle switch . As the protein synthesis rate and unbinding rate of protein to DNA increase relative to the degradation rate , more proteins are synthesized . These proteins are strong repressors . This leads to smaller fluctuations . Furthermore , the associated barrier height between the two basins of attraction becomes large , and the two basins of attraction become more stable since it is harder to go from one well to another . So , small fluctuations and large barrier heights both serve as the source for the robustness and stability of the gene toggle switch . In other words , it is more unlikely for the system to change from one basin of attraction to the other . Therefore , the system becomes robust . The robustness issue is not yet well-understood for cellular networks in general . Here we explored the robustness of the switches against the intrinsic statistical fluctuations coming from the finite number of protein and DNA molecules . This is clearly very important and has potential applications to the robustness problem of lambda phage in bacteria . We also studied the time evolution of the probability and the potential energy landscape with dynamic equations . We chose the specific parameters and initial conditions to illustrate the idea . The results are shown in Figures 6 and 7 . In Figures 6 and 7 , we see the evolution in time of the probability and the underlying potential energy landscape from the flat land at the beginning to the full development of two basins of attraction at the steady state . This is the first illustration of the dynamical evolution or formation of development of the potential energy landscape of a toggle switch .
Finding the multidimensional potential energy landscape is the key to addressing important global issues such as the robustness of cellular networks . We have uncovered the underlying potential energy landscape of a simple gene network: toggle switch . We found that as the protein synthesis rate and the unbinding of protein to DNA rate relative to degradation change from small to large , the underlying potential energy landscape changes from having monostable to bistable basins of attraction . These basins correspond to stable , biologically functional states . The potential barrier between the two basins determines the time scale of conversion from one to the other . We found that as the protein synthesis rate and unbinding of protein with DNA rate relative to degradation became greater , the potential energy barrier became greater and the statistical fluctuations were effectively more severely suppressed . This leads to the robustness of the biological basins of the gene switches . In principle , our approach can be generalized to more realistic networks involving multiple genes as well as additional levels of regulations . This could be realized by averaging the interactions among genes in the corresponding master equations . It effectively reduces the dimensionality of the problem from exponential to polynomial number of degrees of freedom . It is worthwhile to note the limitation of this approach . When the interactions among genes are very strong , our approach is less effective . Recently , synthetic biology became an important part of systems biology [42–45] . There has been significant progress in this field . However , there still seems to be a lack of general principles and algorithms guiding the design and construction of synthetic gene networks . The robustness condition ( see Figure 5 ) found in this study would help us to identify the parameter and connectivity region to reach global robustness and function of the network . The optimal network design will be based on that . Furthermore , we can vary the parameters and connections to design different distinct features while maintaining the stability of the network . The adaptive landscape idea was first introduced into biology by S . Wright in the 1930s [46–49] . Landscape construction for one dimension is rather straightforward . However , even the two-dimensional case becomes nontrivial . The recent efforts to understand global systems biology need the concept of landscape . Progress was made towards this from the dynamic system point of view , where the nontrivial nature of low-dimensional systems was illustrated [20 , 36 , 50] . There are still conceptual and methodological issues remaining for high dimensional systems . The stochastic method introduced here may pave the road towards solving this problem . This model can be modified to include more biochemical reactions . To investigate the role of mRNA , we can consider the transcription and translation process separately . To focus on the statistical fluctuations of genes turning on and off , it is possible to generalize the formalism to compute the statistical fluctuations quantitatively . We also can take into account the spatial variation of the state variables , such as the number of proteins . | Cellular networks are at the heart of systems biology at present . To understand how cellular networks function in these highly fluctuating environments , a global approach is needed . Here we provide a global framework , in terms of potential landscapes , for studying the gene regulatory networks in the presence of the intrinsic statistical fluctuations . We uncovered the underlying landscape for the network . We identified the basins of attraction of the landscape as the biological functional states . The potential barrier between the two basins determines the time scale of conversion from one to the other . The robustness of the biological functional states of the network , the gene switches in this case , can be guaranteed if the conversions among the basins of attraction are not frequent , or , in other words , the barriers among the basins are relatively large . More detailed features of the network , such as the key genes or regulating links relevant to diseases ( i . e . , cancers ) , can be uncovered from the underlying landscape . Our technique is general and can be applied to explore the potential landscape of more realistic gene networks . Furthermore , our approach can also be helpful in guiding the network optimal design for synthetic biology . | [
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] | 2007 | Potential Energy Landscape and Robustness of a Gene Regulatory Network: Toggle Switch |
The 8-aminoquinoline antimalarials , the only drugs which prevent relapse of vivax and ovale malaria ( radical cure ) , cause dose-dependent oxidant haemolysis in individuals with glucose-6-phosphate dehydrogenase ( G6PD ) deficiency . Patients with <30% and <70% of normal G6PD activity are not given standard regimens of primaquine and tafenoquine , respectively . Both drugs are currently considered contraindicated in pregnant and lactating women . Quantitative G6PD enzyme activity data from 5198 individuals were used to estimate the proportions of heterozygous females who would be ineligible for treatment at the 30% and 70% activity thresholds , and the relationship with the severity of the deficiency . This was used to construct a simple model relating allele frequency in males to the potential population coverage of tafenoquine and primaquine under current prescribing restrictions . Independent of G6PD deficiency , the current pregnancy and lactation restrictions will exclude ~13% of females from radical cure treatment . This could be reduced to ~4% if 8-aminoquinolines can be prescribed to women breast-feeding infants older than 1 month . At a 30% activity threshold , approximately 8–19% of G6PD heterozygous women are ineligible for primaquine treatment; at a 70% threshold , 50–70% of heterozygous women and approximately 5% of G6PD wild type individuals are ineligible for tafenoquine treatment . Thus , overall in areas where the G6PDd allele frequency is >10% more than 15% of men and more than 25% of women would be unable to receive tafenoquine . In vivax malaria infected patients these proportions will be lowered by any protective effect against P . vivax conferred by G6PD deficiency . If tafenoquine is deployed for radical cure , primaquine will still be needed to obtain high population coverage . Better radical cure antimalarial regimens are needed .
Plasmodium vivax is an important cause of malaria outside Sub-Saharan Africa . The WHO estimates that P . vivax comprises 41% of the malaria burden outside of Africa . This translates into 6–11 million cases/year with an estimated 1800–4900 deaths . India , Indonesia and Pakistan account for just over 80% of the global vivax malaria case burden [1] . Relapse frequencies vary by geographical region . South East Asia and Oceania have the highest incidence with relapse rates exceeding 50% [2] . In this context relapse from liver hypnozoites is the main cause of P . vivax malaria illness and asymptomatic carriage [3] . The only currently available treatment to eliminate liver hypnozoites and thus prevent future relapses ( ‘radical cure’ ) of vivax or ovale malaria is primaquine , a rapidly eliminated 8-aminoquinoline . Radical curative efficacy depends on the total dose administered [4] . Treatment courses of 14 days are recommended by the WHO but the effectiveness of unsupervised primaquine is often poor [5 , 6] . Primaquine has one major adverse effect–it causes dose related acute haemolytic anaemia ( AHA ) in individuals with glucose-6-phosphate dehydrogenase deficiency ( G6PDd ) [7] . G6PDd is a common inherited X-linked red blood cell disorder prevalent in tropical and subtropical regions , where in some ethnic groups 35% of males are G6PDd [8] . Males are either deficient ( hemizygotes ) or normal , whereas females can be fully deficient ( homozygotes ) , partially deficient ( heterozygotes ) or normal . Due to random X-inactivation ( Lyonisation ) , heterozygous females have two red blood cell populations , one with normal G6PD enzyme activity and the other with reduced activity . On average heterozygote females have half the red cell enzyme activity of normal individuals [9] . However , because X-inactivation occurs early in embryogenesis , there is significant variation between individual heterozygous females in the ratio of deficient to normal red cells . Standard radical cure regimens of daily primaquine ( 0 . 25 or 0 . 5 mg/kg/day x 14d ) are not given to patients who test as G6PDd with currently available qualitative rapid diagnostic tests ( RDTs ) . These tests identify subjects with < 30% of normal activity [10–12] and so detect all male hemizygotes and female homozygotes but only some heterozygous females [13] . The majority of heterozygous females have G6PD enzyme activities above 30% but they may still experience AHA when given daily primaquine or single dose tafenoquine [14–16] . Data in this vulnerable group are limited . The WHO currently recommends eight weekly primaquine [0 . 75 mg/kg ( 45 mg in adults ) ] doses for those with mild G6PD deficiency variants but its safety is uncertain in the more severe G6PDd variants such as the Mediterranean variant in the Middle East and west Asia , and the variants ( e . g . Mahidol , Viangchan , Vanua Lava , Canton ) prevalent in South East Asia and Oceania [17] . Weekly primaquine in Cambodia , where G6PD Viangchan predominates [11] , resulted in one of 18 vivax infected G6PDd patients requiring a blood transfusion [18] . Accordingly , the WHO recommends a careful risk benefit assessment and medical supervision if weekly primaquine is given . Tafenoquine is a slowly eliminated primaquine analogue ( half life ~14d ) that will soon be introduced as a single dose regimen to provide radical cure . Tafenoquine also causes dose dependent AHA in G6PDd individuals [16] , but because its slow elimination provides a protracted oxidant effect , its use will be restricted to individuals whose G6PD enzyme activity is > 70% of normal . For tafenoquine , this will require the use of a G6PD test that can quantify the G6PD activity , posing a significant challenge for malaria control programmes . Both primaquine and tafenoquine are contraindicated in pregnancy and during lactation for fear of causing AHA in the foetus or in a G6PDd breast-fed infant . However , recent work has shown that concentrations of primaquine in breast milk are very low and likely to be safe for G6PDd infants outside the neonatal period [19] . Current dosing restrictions curtail the use of primaquine and tafenoquine [12] . We examined the effect of these restrictions on the potential coverage of radical cure with primaquine today and tafenoquine in the future .
All data used in this analysis were from trials with ethical approval where all subjects gave fully informed consent . G6PDd is X-linked . The polymorphic variants prevalent in areas where malaria is or was prevalent confer varying levels of enzyme deficiency . We assume that the population distribution of the polymorphic G6PDd genotypes conforms to the Hardy-Weinberg equilibrium [20] . Hereafter , `allele frequency’ refers to the allele frequency in hemizygous males . In the studies ( all studies except for [10 , 21 , 22] ) where genotype data were not available , the expected number of homo- and heterozygous females was calculated from the Hardy-Weinberg proportions with deficient allele frequency estimated from the sampled male population . There are few large data sets of G6PD quantitative activities . Raw and meta-data were collected from nine recent studies ( 2013–2017 ) that reported quantitative measurements of G6PD activity . Three of these datasets were from studies in malaria patients ( P . vivax and P . falciparum ) ; four were in healthy volunteers; one was in pregnant women; and one was from a mass primaquine treatment study [10 , 13 , 21–27] . These nine studies represent a convenience sample of G6PD activity data . Raw data were available or made available for seven of these studies and for the other two studies , the corresponding authors kindly provided the meta-data . The full extracted meta-data are provided in the supplementary materials . The methods are calibrated to the adjusted population median activity [28] . Total G6PD enzyme activity in heterozygous females is assumed to be normally distributed ( data based assumption from [22] ) . The mean and variance of this distribution is expected to depend on the severity of the deficiency conferred by the hemizygous/homozygous genotypes . As the majority of studies did not have genotype corrected data , a Bayesian hierarchical model was used to estimate the quantiles of the distributions of enzyme activity corresponding 30 and 70% of the population median as a function of two separate categories of severity using a Bayesian beta-binomial model . This categorisation allows for partial correction of the over-dispersion in the data . We defined two categories , which roughly separate out the severity of the deficiencies as follows: Category 1: A- and Mahidol; Category 2: Viangchan , Orissa , and Vanua Lava . These categories were defined by calculating the ratio of the median activity in hemizygous deficient males over the median adjusted normal activity . This visually clustered the genotypes into these two categories . Note that these categories do not correspond to the WHO categories [29] . The Bayesian beta-binomial model was fitted in R using stan [30] . See supplementary materials for full model specification and code . To estimate the distributions of G6PD activities in hemizygous males and homozygous females ( theoretically identical ) we pooled all activity data from G6PD normal males ( classified by phenotype ) and G6PD normal females ( classified by genotype to avoid bias from heterozygotes ) . To adjust for inter-study variability , each G6PD activity was then scaled by 10studymedianactivity so that the pooled data had a median of 10 ( arbitrary value ) . Currently , neither primaquine nor tafenoquine can be given during pregnancy or breast-feeding and are not recommended in children < 6 months . The incidence of vivax malaria is usually low in young infants so they were excluded from the calculations . In our main scenario , we assume that the average woman of reproductive age ( 15–40 y ) has three children who survive at least two years and that each child is breast-fed for two years ( the minimum recommended by WHO [31] ) . Fertility rates in Indonesia , Pakistan and India ( which comprise more than 80% of annual cases ) range from 2 . 4–3 . 4 ( data . worldbank . org/indicator/SP . DYN . TFRT . IN/ ) . This totals a period of 8 . 25 years during which women on average cannot take radical curative regimens . We assume that in countries where P . vivax is endemic , women of reproductive age comprise 40% of the total female population ( this corresponds to current Asian populations , see www . populationpyramid . net/asia/2016/ ) . For both tested thresholds , we have assumed that in a given population the male to female ratio is 1:1 and that the G6PDd allele frequency in hemizygous males can vary between 0 and 25% . For primaquine dosing , qualitative G6PD tests with thresholds of 30% are in use currently . Quantitative point-of-care tests are still being developed . In these calculations , we have assumed a suitable quantitative test is available ( i . e . detects accurately at least 70% of normal activity ) . For males , it is assumed that the qualitative test has 100% specificity and sensitivity . For the quantitative test with a 70% of population median threshold all deficient males are correctly identified and 5% of normal males are misclassified as deficient ( see Results ) . Thus , the proportion of males who cannot receive either radical cure is equal to the background allele frequency ( q ) for primaquine , and slightly higher for tafenoquine ( 0 . 05 + 0 . 95q ) . The proportions of females who cannot receive radical cure ( XPQ for primaquine & XTQ for tafenoquine ) under current prescribing restrictions were calculated as follows: XPQ=ΔdefPQ+0 . 4×PBF25− ( 0 . 4×PBF25 ) ΔdefPQ , XTQ=ΔdefTQ+0 . 4×PBF25− ( 0 . 4×PBF25 ) ΔdefTQ . Where ΔdefPQ and ΔdefTQ are the proportions of females who are classified as G6PD deficient at enzyme activity thresholds of 30 and 70% of the population median: ΔdefPQ=q2+2pqQ30% , ΔdefTQ=q2+2pqQ70%+0 . 05 ( 1−q ) . q: G6PDd allele frequency; p = 1-q; Q30% & Q70%: proportions of heterozygous females classified as G6PD deficient under a 30% & 70% cut-off , respectively; 0 . 4: the proportion of women between 15–40 years of age; PBF: mean number of years during which 8-aminoquinolines are restricted due to pregnancy or breast-feeding ( this is a function of the fertility rate ) and 25 is the number of years of reproductive age; 0 . 05: proportion of wild type ( WT ) homozygous females misclassified as deficient . Extracted meta-data from studies included in analysis are available in the supplementary materials . Publicly available datasets used were: https://doi . org/10 . 1371/journal . pone . 0116143 . s001 https://doi . org/10 . 1371/journal . pone . 0169930 . s002 https://doi . org/10 . 1371/journal . pone . 0151898 . s003 .
Seven of the nine studies were in South East Asian populations ( dominant G6PDd genotypes: Viangchan , Mahidol and Vanua Lava ) ; one study was from Bangladesh ( likely dominant genotypes Orissa , Kalyan-Kerala [32] and Mahidol ) ; and one study was in African Americans ( dominant genotype: A- ) ( Table 1 ) . These studies recorded quantitative enzyme data for a total of 2803 females and 2395 males . Estimated G6PDd allele frequencies varied from 7–15% . The estimated proportions of heterozygous females with enzyme activity < 30% of the population median varied considerably from 0% ( A- variant: estimated 0 out of ~27 expected heterozygotes ) to 29% ( Vanua Lava , exact numbers not reported ) . The estimated proportions of heterozygous females with activity < 70% of the population median also varied considerably from 43% ( A- variant: estimated ~12 out of ~27 expected heterozygotes ) to 85% ( Orissa/Mahidol are likely variants: estimated ~113 out of ~133 expected heterozygotes ) . Pooled individual enzyme activity data on G6PD WT males ( classified by phenotype ) and G6PD WT females ( only those classified by genotype ) were used to estimate the proportion of all G6PD WT individuals who would have G6PD enzyme activity below a 70% threshold . There was substantial variation in this proportion across studies , with estimates varying from 1–20% in males and 2 . 6% in females , with a mean estimate of 5 . 6% ( Fig 1 ) . We estimate that between 8% ( African A- and Mahidol variants , 90% credible interval ( C . I . ) : 2–19% ) , and 19% ( Orissa , Viangchan and Vanua Lava variants , 90% C . I . : 9–36% ) of heterozygous females test as G6PD deficient at a 30% threshold . The same model estimates that between 50% ( African A- and Mahidol variants , 90% C . I . : 30–70 ) and 71% ( Orissa , Viangchan and Vanua Lava variants , 90% C . I . : 51–86 ) of heterozygous females test as G6PD deficient at a 70% threshold . Both models conclude that an increasing level of G6PDd severity is associated with lower mean enzyme activities in heterozygotes [33 , 34] . To simplify results , all following calculations assume that 10 and 70% of heterozygous females classify as deficient at 30 and 70% thresholds , respectively . Current prescribing restrictions imply that the proportion of males who cannot receive radical cure is the same as the background G6PDd allele frequency . For females , the relationship is more complex due to restrictions in pregnancy and lactation , and G6PD heterozygosity ( Fig 2 ) . Under our assumptions , independent of G6PD considerations , pregnancy and lactation result in ~13% of women ineligible for radical cure ( 6 . 5% of total population ) . If the background allele frequency is 10% , then 10 and 14 . 5% of males cannot receive radical cure ( primaquine and tafenoquine , respectively ) , and 16 and 25% of females cannot receive radical cure ( primaquine and tafenoquine , respectively ) . This results in 13 and 20% of the total population being excluded , respectively . The breakdown of excluded proportions for tafenoquine radical cure with a background allele frequency of 10% is shown in Fig 3 . Sensitivity to these estimates can be computed simply by adjusting the model parameters to fit a variety of epidemiological contexts via an interactive RShiny app found at: https://moru . shinyapps . io/8_Aminoquinoline_Coverage/ . Lifting the breast-feeding restrictions for primaquine or tafenoquine would significantly increase potential radical cure coverage . Recent pharmacokinetic studies indicate that very little primaquine is excreted in breast milk , and therefore that primaquine is likely to be safe in breast-feeding mothers with infants older than 28 days [19] . Based on our assumptions [31] , 9–10% of women will be breast-feeding at any given time point in the population . If there were an alternative primaquine regimen which could be prescribed safely to G6PDd individuals , this would reduce substantially the proportion of excluded individuals [35] ( Fig 2 ) . All males could be treated and only 13% of females ( those breast-feeding and pregnant ) would be excluded from treatment . This would achieve 93 . 5% population coverage irrespective of background allele frequency . This compares with 80% coverage for tafenoquine when the background G6PDd allele frequency is 10% ( Fig 4 ) .
The majority of patients with vivax malaria who should receive radical curative regimens currently do not receive them . There are many reasons for this including the widespread unavailability of G6PD deficiency testing , concerns over haemolysis in both identified and unidentified G6PD deficiency , restrictions in pregnancy and lactation , the unavailability of primaquine , poor adherence to prescribed regimens and inertia . There is increasing recognition , however , that if vivax malaria is to be controlled and eliminated then the coverage of radical cure does need to increase . Tafenoquine provides an excellent solution to the problem of poor adherence to standard 14-day primaquine regimens as it is given in a single dose treatment . However , this comes at the price of increased haemolytic risk . Tafenoquine is eliminated slowly and once taken persists at active concentrations in the blood for several weeks ( mean half-life 2 weeks ) whereas the rapidly eliminated primaquine can be stopped at the first signs of severe AHA . To mitigate the risk of severe AHA in G6PD heterozygous females , most of whom appear G6PD normal on current qualitative RDTs , biosensors have been developed that give a quantitative G6PD enzyme activity result . The percentage of G6PD activity in an individual with reference to the normal population median can be calculated easily . This will allow tafenoquine to be given safely to all males with G6PD activity > 30% and all females with activity > 70% of normal G6PD activity . The field performance of these newly developed sensors has not been assessed yet at scale so it is unknown how many genotypically normal females will be identified as deficient by these tests . However , even if biosensors are made widely available , and they do prove consistently accurate in operational use , substantial proportions of patients will not receive radical cure . Because of the higher G6PD safety threshold ( 70% ) a greater proportion of individuals , mainly females , will be excluded from tafenoquine compared to daily-administered primaquine ( Fig 4 ) . A policy of tafenoquine only in a vivax control programme could mean more than 20% of all individuals would not be eligible to receive radical cure if the G6PDd prevalence is over 10% . This underscores the need to continue provision of alternative safer regimens of primaquine for G6PD deficient patients if tafenoquine is deployed widely . Although once weekly primaquine provides a potential treatment of G6PDd patients , its safety in areas where more severe variants are prevalent has not been well established . The provision of primaquine could be greatly simplified if a regimen were developed that replaced both daily and weekly primaquine and could be used without the need to test for G6PDd [35] . These calculations are illustrative and dependent on assumptions that may not be applicable widely . Population demographics vary widely , mean duration of breast-feeding can often be longer than two years [36] , the epidemiology of vivax malaria varies ( vivax malaria is mostly a paediatric disease in high transmission areas like New Guinea island ) , and there is good evidence that severe G6PDd ( Mediterranean variant ) protects against symptomatic disease [37] . Thus , the proportions of vivax malaria patients excluded from radical cure treatment would be lower than predicted from the allele frequencies . This would be proportional to the severity of the prevalent G6PDd genotypes . In a very recent evaluation using G6PD prevalence data across 95 P . vivax endemic countries it was estimated that 14 . 3% of the population would be precluded from primaquine radical cure treatment on safety grounds [12]; in 70% because of G6PD deficiency ( in this estimate all heterozygotes were considered excluded ) , in 12% because of infancy ( <6 months ) , and in 12% because of either pregnancy or lactation ( where breast-feeding was for 6 months ) . Another important consideration is genetic polymorphism in primaquine bioactivation , notably by CYP 2D6 . A large number of CYP 2D6 variants have been described which vary from conferring substantial loss of function to gain in function . The *10 variant which confers moderate loss of function is the most common in East Asian populations , reaching an allele prevalence of 43% . Individuals homozygous ( or mixed heterozygotes ) for loss of function alleles have been reported to have reduced primaquine radical curative efficacy [38 , 39] , and also presumably reduced risk of haemolytic toxicity . Tafenoquine may be less affected by this genetic polymorphism [40] . The current focus of vivax elimination is the administration of radical cure to patients who present with acute disease . However , there is growing evidence that asymptomatic reservoirs of vivax parasitaemia are substantial , most of which are derived from hypnozoites [2 , 41 , 42] . If vivax malaria is to be eliminated rapidly , then one approach is to provide focussed mass treatment with 8-aminoquinolines as was done extensively in the past [43] . In that context the protective effect of G6PD deficiency against vivax malaria will not affect these predictions on the proportions of patients who cannot be provided with radical cure . Tafenoquine may provide substantial operational advantages but it will not obviate the need for primaquine . More work needs to be done to establish the safety or otherwise of alternative primaquine regimens in areas of severe and moderately severe G6PDd variants . High coverage is key to the successful elimination of Plasmodium vivax . Tafenoquine will be a significant advance in the management of vivax malaria providing single dose radical cure but a significant proportion of the population ( predominantly females ) will be unable to receive it . Safer primaquine regimens are needed for these patients . | More than half of the malaria outside of Sub-Saharan Africa is caused by the parasite Plasmodium vivax which is characterised by multiple relapses of malaria from parasites which persist in the liver . The only drugs which prevent these relapses ( radical cure ) are the 8-aminoquinolines primaquine and tafenoquine , and they both cause haemolytic anaemia in G6PD deficiency , the most common enzymopathy of man . Neither can currently be prescribed in pregnancy or lactation . Tafenoquine is given as a single dose regimen and is a significant advance over primaquine ( recommended as a 14 day regimen ) . However , a greater number of individuals , mostly females , will be ineligible for tafenoquine treatment due to a tighter restriction on the minimum G6PD enzyme activity considered safe for use of the drug . Using enzyme activity data from over 5000 individuals , we estimate the proportions ineligible due to G6PD deficiency as a function of the deficient allele prevalence . Adding this to simple estimates of pregnancy and lactation , we estimate the proportions of populations who cannot receive either tafenoquine or primaquine radical cure . For the elimination of vivax malaria in areas with a high prevalence of G6PD deficiency , then if tafenoquine is deployed primaquine will still be needed , so better regimens should be developed . | [
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] | 2018 | Implications of current therapeutic restrictions for primaquine and tafenoquine in the radical cure of vivax malaria |
Infection of mammalian cells by the strictly intracellular pathogens Chlamydiae requires adhesion and internalization of the infectious Elementary Bodies ( EBs ) . The components of the latter step were unknown . Here , we identify Chlamydia pneumoniae Pmp21 as an invasin and EGFR as its receptor . Modulation of EGFR surface expression evokes correlated changes in EB adhesion , internalization and infectivity . Ectopic expression of EGFR in EGFR-negative hamster cells leads to binding of Pmp21 beads and EBs , thus boosting the infection . EB/Pmp21 binding and invasion of epithelial cells results in activation of EGFR , recruitment of adaptors Grb2 and c-Cbl and activation of ERK1/2 , while inhibition of EGFR or MEK kinase activity abrogates EB entry , but not attachment . Binding of Grb2 and c-Cbl by EGFR is essential for infection . This is the first report of an invasin-receptor interaction involved in host-cell invasion by any chlamydial species .
The genus Chlamydia comprises obligate intracellular , Gram-negative pathogens that infect a variety of organisms . The Chlamydia pneumoniae infection is ubiquitous in humans , with an antibody prevalence of 50% by age 20 years . C . pneumoniae is a common cause of community-acquired pneumonia and other respiratory infections . Moreover , its persistent infection may play a role in chronic inflammation and atherosclerosis [1] . All Chlamydia species share a common biphasic developmental cycle , characterized by adhesion and internalization of infectious , metabolically inactive elementary bodies ( EBs ) into a membrane-bounded compartment , termed inclusion . How the bacteria are internalized by host cells is largely unknown . After attachment of Chlamydiae to host cells subsequent internalization may occur either by clathrin-mediated endocytosis or via caveolin-rich domains or lipid rafts [2] , [3] , [4] . Activation of specific signaling pathways upon attachment , and subsequent rearrangement of actin networks , are essential for entry [5] , [6] . Infection by C . pneumoniae is associated with activation of tyrosine kinases , PI3-dependent and MAP kinases , and leads within minutes to activation of ERK via the Ras-Raf-MEK cascade [6] , [7] . The focal adhesion kinase ( FAK ) is tyrosine phosphorylated within minutes of exposure to C . pneumoniae , and may recruit additional signaling molecules to sites of bacterial attachment [6] . Isoform-specific tyrosine phosphorylation of the docking protein SHC also occurs at the time of C . pneumoniae attachment and entry suggesting activation of yet unknown receptors [6] . Since Chlamydia species can infect different cell types in vitro , they may use widespread host-cell receptors and/or a broad repertoire of specific chlamydial adhesins . The C . pneumoniae Pmp6 , Pmp20 and Pmp21 proteins are recently identified adhesins essential for EB adhesion to human cells [8] . However , the receptor ( s ) for these adhesins remain ( s ) unknown . In this study , we demonstrate that Pmp21 acts as an invasin protein for C . pneumoniae and identify the epidermal growth factor receptor ( EGFR ) as its direct interaction partner . The interaction leads to activation of EGFR . Furthermore , we show that the activated receptor is tightly associated with internalized Pmp21-coated beads and is also clustered in ring-like structures around the internalized C . pneumoniae EBs . Expression of functional EGFR on human cells is essential for binding and internalization of the bacteria . Finally , recruitment of the adaptor proteins Grb2 and c-Cbl by EGFR is essential for infection by C . pneumoniae . The Pmp21-EGFR interaction thus represents the missing link between chlamydial attachment and the subsequent host cell entry .
The recent identification of Pmp21 as a C . pneumoniae adhesin led us to ask whether it can be internalized by host cells . Infectious EBs bear proteolytically processed forms of Pmp21 on their surfaces ( summarized in [8] , [9] , [10] ) . N-Pmp21 , M-Pmp21 and N/M-Pmp21 all mediate adhesion of EBs to human epithelial ( HEp-2 ) cells and ( in soluble form ) block infection by C . pneumoniae [8] . We incubated microbeads coated with one of four recombinant ( His-tagged ) proteins with HEp-2 cells at 4°C . Beads loaded with tagged GST showed little binding , while beads bearing recombinant invasin from Yersinia pseudotuberculosis , GroEL1 from C . pneumoniae or M-Pmp21 clearly bound to the cells ( Figs . 1A , S1 ) . After further incubation at 37°C , 7% of GST and of GroEL1 beads respectively , were found in cells ( Figs . 1B , S1 ) , while 90% of adherent invasin-coated beads and 31% of Pmp21-coated beads were internalized ( Figs . 1B , 1C and S1 ) . Hence M-Pmp21 induces bead uptake into these epithelial cells via a specific receptor . We chose a biochemical approach to identify the host receptor for Pmp21 . rM-Pmp21 labeled with NHS-SS-biotin was incubated with a monolayer of HEp-2 cells to allow it to interact with its cellular target ( s ) ( Fig . S2A ) . Bound M-Pmp21 was then cross-linked to its partner ( s ) on the cell surface using the membrane-impermeable reagent DTSSP . The cells were then lysed , and the lysates were applied to a NeutrAvidin column to capture biotinylated complexes ( see Experimental Procedures ) . Biotin-bound protein complexes were eluted , and crosslinks simultaneously cleaved , with DTT , and eluted fractions were subjected to SDS/PAGE . As a control , an identical set-up was used with invasin as the probe ( data not shown ) . Several bands not found in the control lanes ( no M-Pmp21 added ) were analyzed by MALDI-MS and peptide mass fingerprinting ( Figs . 2A , S2B ) . Three bands ( >170 kDa ) detected only in lysates of HEp-2 cells exposed to biotinylated M-Pmp21 were identified as the human epidermal growth factor receptor ( EGFR/HER1 or ErbB-1 ) . An anti-EGFR antibody confirmed the presence of EGFR in the rM-Pmp21 lysate and its absence in the control invasin lysate . Conversely , the invasin receptor integrin-β1 was detectable only in the latter lysate ( Fig . 2B ) . To confirm the interaction of Pmp21 with EGFR in vivo , the crosslinking/affinity purification procedure was applied to cell lysates after surface-biotinylated EBs had been incubated with non-biotinylated HEp-2 cells or vice versa , and immunoblots were probed with anti-EGFR or anti-Pmp21 antibodies ( Fig . 2C ) . When biotinylated EBs or biotinylated HEp-2 cells were used in this test , the EGFR and the Pmp21 signals were strongly increased in the elution fractions compared to input . Probing of eluates with antibodies against PDGFR , integrin-β1 , human transferrin receptor , the tyrosine receptor kinase Met ( data not shown ) or the bacterial cell surface protein Momp gave no signals , indicating that the EGFR-Pmp21 interaction is specific ( Fig . 2C ) . Immunoprecipitation experiments on infected cells confirmed these data . HEp-2 cells exposed to C . pneumoniae for 1 h were crosslinked , and membrane protein complexes were solubilized and immunoprecipitated with EGFR- , PDGFR- or M-Pmp21-specific antibodies . A specific interaction was again detected between M-Pmp21 and EGFR , but not between M-Pmp21 and PDGFR ( Fig . 2D ) . Thus , affinity labeling and immunoprecipitation experiments strongly suggest that Pmp21 interacts specifically with EGFR . To show that this interaction is direct , yeast two-hybrid ( Y2H ) analyses were performed ( Figs . 2E , S4A ) . Human EGFR ( aa 1 – aa 1209; see [11] ) was expressed as a fusion to the Gal4 activation domain and tested for interaction with its natural ligand EGF , or with M-Pmp21 , each fused to the Gal4 binding domain . Patch tests revealed that only yeast cells that co-expressed EGFR with either EGF or M-Pmp21 could grow on selective medium , indicating that M-Pmp21 physically interacts with EGFR , but not with other receptors ( e . g . the LDL receptor ) in this surrogate system ( Figs . 2E , S4A+B ) . Since EGFR on the host-cell surface interacts with the EB-associated Pmp21 early in infection , we followed its later relationship with C . pneumoniae EBs by indirect immunofluorescence microscopy . At 5 min and 15 min pi , between 0 . 3 and 1 . 8 clustered EGFR signals ( termed cups ) were found to be associated with C . pneumoniae EBs attached to single cells , and this increased to about 6 EGFR cups per cell by 30 min pi ( Figs . 3A , 3C ) . At 60 min pi EBs were frequently colocalized with the EGFR , which formed cups or ring-like structures around internalized bacteria , often near the nucleus ( Figs . 3B , 3C ) . Similarly , in CHO-K1 cells expressing EGFR-YFP , recruitment of the receptor to rings surrounding chlamydial particles was detectable ( Fig . 3D ) . A 3D model of the structures seen at 60 min pi revealed the clustering of the receptor ( Fig . 3D; see also movie S1 ) . The morphology of these ring-like forms implies that the EGFR is recruited to the membrane around the EBs as they are endocytosed and remains associated with the early inclusion thus formed . The EGFR/Pmp21 interaction data and colocalization of EGFR with EBs in endocytic vesicles imply an important function for EGFR in the initiation of infection . To further study this role , the level of the protein was down-regulated using specific siRNA in epithelial HeLa229 cells , which could be transfected efficiently . By 24 h after transfection , EGFR levels were estimated to be less than half those in control cells ( Fig . 4A ) and the infectivity ( based on the number of inclusions at 48 h pi ) of C . pneumoniae was reduced by 64% compared to the mock-transfected control ( Fig . 4B ) . Binding of labeled C . pneumoniae EBs to transfected cells was found to be reduced by some 50% ( Fig . 4C ) . Hence the expression level of EGFR correlates with the level of EB attachment and subsequent infection , indicating that the receptor is involved in mediating infection . Preincubation of human cells with EGF transiently removes EGFR from the cell surface by inducing EGFR signaling and internalization of the receptor-ligand complex [12] . When cells were incubated with EGF for 2 h , and then exposed to C . pneumoniae , infectivity was reduced by 47% compared to the PBS control ( Fig . 4D ) . Interestingly , the number of EBs associated with the cells fell by only about 7% compared to the control , while the number of internalized chlamydial cells was reduced by 42% ( Fig . 4E ) . Thus , after exposure of cells to EGF , sufficient EGFR remains on the surface to bind most of the EBs on offer . To confirm these results , HEp-2 cells were treated for 2 h with the antibody cetuximab , which blocks the ligand-binding site of EGFR [13] and simultaneously triggers receptor endocytosis , thus depleting EGFR from the cell surface [14] . Treatment of cells with the antibody prior to exposure to C . pneumoniae reduced infectivity by 54% compared to control ( Fig . 4D ) . The total number of EBs associated with the HEp-2 cells fell by only 32% , but a 60% reduction in internalization of the infectious EBs was observed ( Fig . 4E ) . These data are all consistent with the idea that Pmp21 mediates binding of EBs to EGFR . To prove this directly we tested whether rM-Pmp21-coated beads could bind to CHO-K1 cells , which lack EGFR [15] . Indeed CHO-K1 cells bind Pmp21-bearing beads no more efficiently than BSA control beads , while HEp-2 cells , which express high levels of EGFR showed very significant M-Pmp21 bead binding ( Fig . 4F ) . In contrast , more than three times as many M-Pmp21 beads as BSA control beads bound to CHO-K1 cells transfected with EGFR ( Fig . 4G ) . These data directly demonstrate that the Pmp21 adhesin binds to EGFR . EGFR belongs to the family of receptor tyrosine kinases [16] , [17] . It is activated by several natural ligands , but direct binding of a microbial pathogen by this receptor had not been described hitherto [18] . We therefore asked whether EGFR is activated upon binding of Pmp21 and chlamydial EBs . Binding of EGF to EGFR leads to dimerization of the receptor , activation of its intrinsic kinase function and autophosphorylation of critical tyrosine residues located in the C-terminal tail facing the cytosol [19] , [20] . The phosphorylated tyrosines provide an interaction platform for cytosolic proteins involved in endocytosis of the activated receptor and for members of downstream signaling cascades [21] , [22] . Serum-starved HEp-2 cells were infected at increasing MOIs with C . pneumoniae EBs for 60 min . Progressive , dose-dependent autophosphorylation of Y1068 was detected , while total amounts of EGFR remained unchanged ( Fig . 5A ) . Next we determined the kinetics of EGFR activation by incubating HEp-2 cells with recombinant EGF , viable or non-viable C . pneumoniae EBs , rPmp21 or rOmcB protein ( Fig . 5B ) . The natural ligand EGF induced rapid phosphorylation of Y1068 . Viable EBs also evoked strong autophosphorylation of Y1068 , beginning after 5 min of incubation and lasting for 3 h . HEp-2 cells incubated with heat-inactivated EBs showed no phosphorylation . Most strikingly , incubation of HEp-2 cells with rM-Pmp21 also triggered fast phosphorylation of Y1068 . As expected , rM-Pmp21-induced EGFR activation was abrogated by co-incubation with the receptor-blocking antibody cetuximab . Importantly , exposure of human cells to a different chlamydial adhesin , rOmcB , did not activate the receptor ( Fig . 5B ) . These data indicate that binding of soluble Pmp21 also activates EGFR . Next we tested whether catalytically activated EGFR was recruited to endocytic vesicles containing C . pneumoniae EBs 60 min pi using immunofluorescence microscopy . Endogenous EGFR phosphorylated at Y1068 was found to colocalize in ring-like structures at bacterial entry sites ( Fig . 5C ) . Quantification revealed that 85% of EB signals colocalized with activated EGFR signals , while this was only the case for 20% of the human transferrin receptor hTfR ( Fig . S3A+B ) . This confirms that binding of C . pneumoniae activates EGFR and that the activated receptor specifically clusters with the bacteria during internalization . To test for Pmp21-induced EGFR activation directly , we followed the fate of rM-Pmp21-coated beads upon incubation with CHO-K1 cells transfected with EGFR-YFP . Internalized Pmp21 beads were surrounded by ring-like structures bearing EGFR-YFP phosphorylated at Y1068 EGFR , proving that Pmp21 both binds and activates EGFR ( Fig . 5D ) . That activation of EGFR is needed for EB uptake was demonstrated with the EGFR-specific kinase inhibitor AG1478 . Pretreatment of host cells for 2 h with AG1478 ( Fig . 5E ) reduced infectivity by 63% , which correlates well with the 41% reduction in EB internalization , while EB attachment was unaffected ( Fig . 5F ) . Hence EGFR kinase activity is indeed important for endocytosis of chlamydial EBs . Signaling by EGFR activates the MAP kinase pathway , which results in phosphorylation of ERK1/2 ( Fig . 5B; rEGF ) [17] . Like rEGF , C . pneumoniae EBs triggered rapid activation of ERK1/2 , which peaked at 30 min , in agreement with previous findings [6] . Furthermore , rM-Pmp21 induced ERK1/2 phosphorylation as well , while incubation with rM-Pmp21 and cetuximab , or with rOmcB , failed to activate the kinases ( Fig . 5B ) . These results strongly suggest that Pmp21 on the EB surface binds to EGFR , triggering its activation and inducing downstream signaling just as EGF does . To test whether the MAP kinase pathway facilitates invasion by C . pneumoniae , we blocked the MEK1/2 kinase , which phosphorylates ERK , by pre-incubating HEp-2 cells with the inhibitor UO126 . Indeed , inhibition of MEK1/2 activity reduced subsequent chlamydial infectivity by 33% ( Fig . 5E ) . This was entirely due to diminished internalization of chlamydial particles ( down to 63% ) , as binding of the bacteria to the host cell was not affected ( Fig . 5F ) [6] . Thus , both kinase inhibitors affect the internalization of EBs , implying that uptake of EBs is critically dependent on downstream signaling cascades . To further define the role of EGFR in C . pneumoniae infection , we ectopically expressed an EGFR-YFP fusion in EGFR-negative CHO-K1 cells ( Fig . 6A ) [15] . CHO cells are capable of expressing EGFR and activating downstream signaling cascades , as ERK phosphorylation has been shown in these cells [23] . YFP expressed on its own was detectable in the cytosol and accumulated in the nucleus ( Fig . 6B , YFP ) . In contrast , EGFR-YFP localized to the plasma membrane ( including filopodia ) and none was found in the nucleus ( Fig . 6B , middle panel; Fig . S5A ) . Expression of EGFR-YFP in CHO-K1 cells increased their susceptibility to C . pneumoniae by 180% relative to YFP-expressing cells ( Fig . 6C ) . The increase in infection was associated with a 265% increase in adhesion and a 365% rise in internalization ( Fig . 6D ) . Hence the presence of human EGFR makes hamster cells more sensitive to invasion by C . pneumoniae . Furthermore , while preincubation of EGFR-expressing CHO-K1 cells with the EGFR kinase inhibitor AG1478 did not alter the number of EBs attached to the cells , it reduced the number of internalized EBs by 30% ( Fig . S5C ) . As anticipated , blocking the EGFR ligand binding site in EGFR-positive CHO cells with cetuximab reduced the number of associated EBs by 25% and internalized EBs by 55% ( Fig . S5C ) . Since the anti-EGFR antibody cetuximab , which blocks the EGF-binding site , also blocks EB binding ( see Fig . 4 ) , EGF and Pmp21 might recognize overlapping binding sites . The EGF-binding pocket of EGFR is formed by four subdomains , two L ( ligand-binding ) and two CR ( cysteine-rich ) regions [24] , [16] . To analyze the role of this domain in infection by C . pneumoniae , CHO cells were transfected with a truncated version of EGFR ( EGFRΔBD2 ) that lacks L2 . EGFRΔBD2 and wild-type EGFR were expressed at comparable levels ( Fig . 6A ) , and EGFRΔBD2 was detected in the cytoplasm , the nucleus and on the plasma membrane ( Fig . 6B , right panel; Fig . S5A ) . Importantly , similar amounts of both wild-type EGFR and EGFRΔBD2 were detected on the surface of transfected CHO cells ( Fig . S5A , B ) . In CHO cells expressing EGFRΔBD2 , levels of adhesion and internalization of C . pneumoniae EBs were almost identical to those in YFP-expressing controls ( Fig . 6C , D ) . Moreover , preincubation of these CHO cells with either AG1478 or cetuximab did not affect either EB association or EB internalization levels ( Fig . S5C ) . These data prove that a functional EGFR is needed for successful infection by C . pneumoniae . Finally we asked whether domain L2 of EGFR is essential for its interaction with Pmp21 in Y2H experiments . Deletion of L2 markedly weakened EGFR's interaction with its ligand EGF , in agreement with published data [11] , However , M-Pmp21 was now completely unable to support growth on selective media when co-expressed with EGFRΔBD2 ( Fig . 6E ) . Thus , Pmp21 also binds EGFR , at least in part , via the L2 domain . EGFR activation leads to recruitment of the adaptor protein Grb2 and the ubiquitin ligase c-Cbl [25] . Grb2 binds activated EGFR at phosphotyrosines 1068/1086 , and induces ERK1/2 signaling via Ras and Raf , rather than the MAP kinase pathway . It also recruits c-Cbl , which is involved in receptor endocytosis . We tested whether EB binding to EGFR also results in recruitment of Grb2 and c-Cbl , using an analogous affinity approach to that used to detect the interaction of Pmp21 with EGFR ( Fig . 2C ) . Grb2 and c-Cbl were both significantly enriched in affinity eluates compared to the input controls . PDGFRβ which is implicated in the C . trachomatis infection was absent from the eluate ( Fig . 7A ) . The recruitment of Grb2 and c-Cbl by EBs was corroborated by microscopy . HEp-2 cells incubated with bacteria for 60 min revealed specific colocalization of bacterial DNA with endogenous EGFR and endogenous c-Cbl or Grb2 in 53% and 70% respectively , as shown in Fig . 7B . Recruitment of Grb2 and c-Cbl to the invading EB was also documented in CHO-K1 cells transfected with EGFR-mCherry and either c-Cbl-YFP or Grb2-YFP . EGFR and Grb-2 or c-Cbl formed ring- or patch-like structures surrounding or otherwise associated with the bacteria ( Fig . 7C ) . We also asked whether blockage of the receptor-adaptor protein interaction would negatively affect chlamydial infection . Transfection of a point-mutated EGFR construct ( Y1068F , Y1086F ) known to interfere with Grb2 binding into CHO-K1 cells reduced EB internalization by 72% and the subsequent C . pneumoniae infection by 82% . Similarly , the Y1045F mutation in EGFR , which is known to affect the interaction of EGFR with c-Cbl , resulted in reduction of internalized EBs by 56% and in a reduction in infection by 64% ( Fig . 7D+E ) . These results underline the importance of EGFR and its adaptor proteins for successful infection by C . pneumoniae .
Chlamydiae are obligate intracellular pathogens , and invasion of eukaryotic host cells is essential for their survival . Generally , initial association with target cells occurs via the chlamydial adhesin OmcB , which interacts with heparan sulfate glycosaminoglycans [26] . Three members of the large , heterogeneous Pmp family have recently been characterized as adhesins that mediate attachment of C . pneumoniae to epithelial cells , and are important for subsequent infection [8] . Here we show that C . pneumoniae Pmp21 also acts as an invasin ( Fig . 1 ) . We show that Pmp21 binds to EGFR ( Fig . 2 ) and activates EGFR ( Fig . 5 ) and that the interaction is required for internalization of infectious EBs ( Figs . 3 , 4 ) . Interestingly Pmp21 is the first pathogen-derived EGFR ligand shown to interact directly with EGFR . While EGFR activation has been associated with exposure to a number of bacterial and viral pathogens including influenza and HCMV viruses , a direct role for EGFR as a pathogen receptor has remained controversial until now [27] , [28] , [29] . In our pull-down experiments using biotinylated Pmp21 , we identified EGFR in three electrophoretically distinguishable forms ( all larger than 170 kDa ) ( Fig . 2A ) . The fact that only peptides from EGFR but not from the other three members of the ErbB family of receptors were identified by MS suggests that Pmp21 interacts with EGFR homodimers . Different biochemical and cell biological approaches have been used here to show the specificity of the EGFR-Pmp21 interaction . Binding of Pmp21 to EGFR was also verified by Y2H assays ( Fig . 2E ) , which show an interaction level similar to that found for EGFR with its natural ligand EGF [11] . The Y2H data also strongly argue that the interaction of Pmp21 with EGFR is direct and is not mediated by one of the receptor's natural ligands . The complete loss-of-function phenotype of the EGFRΔBD2 , which lacks the second EGF ligand-binding domain L2 , further indicates that EGF and Pmp21 may well use ( at least partially ) overlapping binding pockets . EGFR recognizes a specific motif of three disulfide bonds formed by six conserved Cys residues [30] , but there is no indication that Pmp21 can form such a typical EGF-like fold . The rate of adhesion and also of internalization of Pmp21-beads seems to be significantly lower than that found for invasin-beads ( Fig . 1A+B ) suggesting a lower affinity of the M-Pmp21 ligand to the EGF receptor . One may speculate that the Pmp21-EGFR interaction stabilizes the EB-host cell contact and is thus likely to be relevant for the activation of the bacterial Type III system required for secretion of early effector proteins like Tarp . The Pmp family in C . pneumoniae has 21 members , and it is intriguing to speculate that Pmp6 and Pmp20 [8] , and possibly other family members , might also act as invasins by binding and activating EGFR . Pmp proteins show little overall similarity , but all have multiple repeats of the tetrapeptide motifs GGA ( I , L , V ) and FxxN , and these might be relevant for the recognition and/or activation of EGFR . Thus Chlamydiae may optimize their chances of reaching their intracellular niche by using multiple adhesins and a ubiquitously expressed cellular receptor . Importantly , our findings suggest a direct dependence between the levels of EGFR on the cell surface and susceptibility to infection by C . pneumoniae . Depletion of the receptor in HEp-2 cells by specific siRNA , addition of EGF or blocking of the ligand-binding pocket with an anti-EGFR antibody significantly reduced both EB attachment and infection ( Fig . 4 ) . Conversely , expression of EGFR in normally receptor-deficient cells increased EB attachment and internalization , and susceptibility to infection ( Fig . 3D ) . However , the requirement for EGFR in C . pneumoniae entry is not absolute , which suggests that additional unidentified uptake mechanisms operate . Interestingly , depletion of EGFR in HeLa cells did not reduce infection by another chlamydial species , pointing to differences in receptor usage between chlamydial species [31] . This receptor specificity is supported by data showing that rPmp21 is unable to reduce a C . trachomatis infection ( Becker and Hegemann , unpubl . ) . Here we show that the PDGF receptor previously implicated in the attachment und uptake of a different chlamydial species shows no interaction with Pmp21 ( Figs . 2C , 2D ) . Thus although the Pmp proteins from different chlamydial species share certain sequence similarities they seem to use different receptors . Several other human cell surface proteins ( apolipoprotein E4 , mannose/mannose-6-phosphate receptor , PDI/estrogen receptor , FGFR ) have been implicated in adhesion of certain Chlamydia spp , but a direct interaction between any of these receptors and chlamydial EBs has yet to be shown [26] , [32] . Binding of C . pneumoniae EBs or recombinant Pmp21 to HEp-2 or transfected CHO cells results in rapid EGFR activation , which colocalized with attached and internalized EBs and clustered around Pmp21-coated beads ( Figs . 3 , 5C+D ) . EGFR activation is necessary for chlamydial EB entry , as incubation with the EGFR kinase inhibitor AG1478 reduced the number of internalized EBs . EGFR phosphorylation at Y1068 and Y1086 induces recruitment of the adapter protein Grb2 , which then allows binding of c-Cbl to EGFR Y1045 . This protein complex enables EGFR internalization via clathrin-dependent as well as -independent endocytosis [21] , [33] . Remarkably our biochemical and microscopical data show that both Grb2 and c-Cbl co-localized with wild-type EGFR and internalized Chlamydia ( Fig . 7A+B ) , and this interaction was critical for infection , as infectivity was reduced 5-fold when an Y1068/1086 EGFR mutant was expressed ( Fig . 7D ) , and this reduction is almost identical to the 5-fold reduction in rates of internalization previously measured for this EGFR mutant form [34] . Interestingly , the invading C . pneumoniae EBs did not colocalize with the transferrin receptor , a classical marker for the clathrin-derived endocytic system ( Fig . S3A+B ) , which is compatible with new data suggesting that entry of C . pneumoniae may not depend on clathrin but on lipid rafts , although the molecular details remain to be clarified [35] , [36] . Our data show that C . pneumoniae recruits the EGFR/Grb2/c-Cbl complex via Pmp21 and activates the ERK1/2 kinases , and thus confirm and extend data indicating that C . pneumoniae infection activates SHC , MEK1/2 , ERK and PI3K [6] . Activated PI3K can modulate actin dynamics [37] . Blocking MEK1/2 or PI3 kinase activity reduced EB internalization and infection but not EB binding , proving the relevance of this EGFR-mediated signaling pathway for chlamydial entry [6] ( this work ) . Finally the C . pneumoniae infection leads to FAK1 activation [6] , and it is conceivable that EGFR activation by Pmp21 induces phosphorylation of FAK , which also is involved in cytoskeleton regulation . Thus binding of Pmp21-coated beads or infectious EBs ( via Pmp21 ) to EGFR induces receptor activation , and subsequent endocytosis of the bacterial cell . The latter process probably requires not only downstream signaling cascades in the host cell , but is also modulated by the secretion of bacterial effector proteins like Tarp , which contributes to actin cytoskeleton reorganization at the EB entry site [38] . The accumulation of active EGFR around the inclusion containing endocytosed EBs 60 min post infection ( see Fig . 5C ) , points to a role for ( active ) EGFR beyond the entry process per se . It should be emphasized that our results do not exclude the involvement of other yet unidentified chlamydial adhesins/invasins and host cell proteins in the entry process . A complete understanding of the molecular interplay between pathogen and host is a prerequisite for the development of novel efficient strategies to prevent chlamydial diseases .
The EGFR kinase inhibitor AG1478 , the MEK1/2 inhibitor U0126 and monoclonal antibodies directed against phosphorylated EGFR ( Y1068 , rabbit ) and phosphorylated ERK ( p44/42 , mouse ) were obtained from Cell Signaling . The neutralizing antibody cetuximab ( Merck ) was kindly provided by Dr . B . Homey . Polyclonal antibodies against EGFR , c-Cbl , Grb2 , PDGFRβ and Integrin-β1 ( CD29 ) were purchased from Santa Cruz , the β-actin antibody and recombinant EGF from Sigma , the hTfR antibody and Wheat Germ Agglutinin-Alexa594 ( WGA ) from Invitrogen and DTSSP from Thermo Scientific . The anti-GFP antibody was purchased from GeneTex . The anti-invasin antibody was donated by Petra Dersch . The antibody against recombinant M-Pmp21 and Momp have been described elsewhere [8] , [39] . All siRNA's were obtained from Santa Cruz Biotechnology , EGFR ( sc-29301 ) , and Control NT-siRNA-A ( sc37007 ) . C . pneumoniae GiD was propagated in the cell lines HEp-2 ( ATCC: CCL-23 ) , HeLa229 ( ATCC: CCL-2 . 1 ) and CHO-K1 ( ATCC: CCL-61 ) [40] , [41] , [42] , [43] . HEp-2 and HeLa229 cells were cultured in DMEM medium , and CHO-K1 cells in Ham's F12-K nutrient mixture medium , each supplemented with 10% fetal calf serum ( FCS; Invitrogen ) . Chlamydial elementary bodies ( EBs ) were purified using a 30% gastrographin solution ( Schering ) and , where appropriate , incubated for 10 min at 100°C and chilled on ice to inactivate infectivity . Escherichia coli strain XL-1 Blue ( Stratagene ) was used for protein expression and plasmid amplification , and Saccharomyces cerevisiae for two-hybrid experiments . Cloning in yeast was carried out by in vivo homologous recombination . Recombinant proteins or purified chlamydial particles were biotinylated with NHS-SS-biotin and incubated with non-biotinylated human epithelial cells . Interacting proteins were crosslinked and isolated by passage over a NeutrAvidin column . For further details see Supplemental Experimental Procedures . EGFR/M-Pmp21 or PDGFR were immunoprecipitated from HEp-2 cells infected with C . pneumoniae EBs at 60 min post-infection using specific antibodies . For further details see Supplemental Experimental procedures . Expression of YFP or mCherry-tagged EGFR variants was carried out in EGFR-deficient CHO-K1 cells , while depletion of EGFR by specific siRNA was performed by transfection of HeLa229 cells . Expression levels of EGFR were monitored by immunoblotting or fluorescence microscopy . For further details see Supplemental Experimental procedures . Internalization of C . pneumoniae EBs into epithelial cell lines expressing endogenous EGFR or YFP-tagged EGFR variants was analyzed in the presence of various EGFR inhibitors , and the ratio of external to internalized bacteria was quantified by fluorescence microscopy . For further details see Supplemental Experimental procedures . Adhesion assays with fluorescent protein-coated latex beads were performed with a five-fold excess of beads over cells as described previously [8] . Beads attached to epithelial cells were either counted by optical microscopy , or analyzed by flow cytometry in a FACSAria ( BD Biosciences ) after washing ( with PBS ) and dissociation ( with Cell Dissociation solution; Sigma ) . For internalization studies adhesion assays were carried out for 1 h at 4°C or 4 h at 37°C . Cells were washed twice with PBS and fixed with 3% formaldehyde for 20 min . External beads were stained ( without permeabilization ) with primary antibodies directed against the protein coupled to them . Internalization efficiency was determined by subtracting the numbers of external beads from the total numbers found associated with samples of 103 cells . | The obligate intracellular bacterial pathogen Chlamydia pneumoniae is an important cause of human and animal diseases and can infect various cell types . The molecular mechanisms of chlamydial adhesion to and invasion of human cells are not well defined . Recently we identified Pmp21 and other members of the family of polymorphic membrane proteins ( Pmp ) as the first chlamydial adhesins binding to proteinaceous host cell-surface structures . Here we show that recombinant Pmp21 functions as an invasin protein . Using a biochemical approach we identified the human epidermal growth factor receptor ( EGFR ) — an ubiquitously expressed cell surface-localized receptor tyrosine kinase — as the cellular receptor for Pmp21 , making Pmp21 the first pathogen-derived EGFR ligand . The EGF receptor is recruited to adherent and internalized EBs . Depletion of EGFR from the human cell surface significantly reduced chlamydia adhesion and internalization . Likewise , ectopic expression of EGFR in receptor-negative cells increased chlamydia adhesion , internalization and subsequent infectivity . Binding of Pmp21 to EGFR initiates receptor activation and downstream signaling , both of which we found to be equally important for bacteria entry . In conclusion , we show that the Pmp21 adhesin binds and activates EGFR , which initiates signaling cascades , finally leading to chlamydia/receptor internalization . | [
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] | 2013 | The Chlamydia pneumoniae Invasin Protein Pmp21 Recruits the EGF Receptor for Host Cell Entry |
Smc5/6 , a member of the conserved SMC family of complexes , is essential for growth in most organisms . Its exact functions in a mitotic cell cycle are controversial , as chronic Smc5/6 loss-of-function alleles produce varying phenotypes . To circumvent this issue , we acutely depleted Smc5/6 in budding yeast and determined the first cell cycle consequences of Smc5/6 removal . We found a striking primary defect in replication of the ribosomal DNA ( rDNA ) array . Each rDNA repeat contains a programmed replication fork barrier ( RFB ) established by the Fob1 protein . Fob1 removal improves rDNA replication in Smc5/6 depleted cells , implicating Smc5/6 in the management of programmed fork pausing . A similar improvement is achieved by removing the DNA helicase Mph1 whose recombinogenic activity can be inhibited by Smc5/6 under DNA damage conditions . DNA 2D gel analyses further show that Smc5/6 loss increases recombination structures at RFB regions; moreover , mph1∆ and fob1∆ similarly reduce this accumulation . These findings point to an important mitotic role for Smc5/6 in restraining recombination events when protein barriers in rDNA stall replication forks . As rDNA maintenance influences multiple essential cellular processes , Smc5/6 likely links rDNA stability to overall mitotic growth .
The conserved Smc5/6 complex ( or Smc5/6 ) is required during normal growth and for coping with genotoxins [1–4] . Due to the essential nature of the complex , studies thus far have examined partial loss of function mutants of the complex in various organisms . As its chronically sick alleles give varied phenotypes , a coherent view of Smc5/6 function during growth has yet to be established . In budding yeast , studies using temperature sensitive alleles suggest that Smc5/6 is required during S phase , as shifting to non-permissive temperatures during S , but not G2-M phase , leads to defects [5] . However , two views about the S phase functions of Smc5/6 have been proposed based on distinct mutant phenotypes . One smc6 allele ( smc6-56 ) impairs replication of longer chromosomes while another ( smc6-9 ) only diminishes the duplication of chromosome XII ( Chr XII ) , which harbors the entire ribosomal DNA ( rDNA ) array [5–7] . The former defect was interpreted as reflecting Smc5/6 roles in replication fork rotation [6] , while the mechanism for the latter defect was unclear [5 , 7] . More recently , another study proposed that Smc5/6 is essential in G2 , but not S phase , as fusion of Smc5/6 with a G2-cyclin , but not S-cyclin , cassette sustained cell growth [8] . A number of factors likely contribute to the diverse phenotypes observed for this collection of chronic alleles . For example , cells containing chronically altered alleles can accumulate different levels or types of stress over generations , engendering a phenotype compounded from primary defects and various secondary changes . Indeed , smc5/6 alleles show alterations in diverse processes , including cell cycle checkpoint responses , cohesin function , repair pathway usage , and centromeric regulation [9–12] . As such , it is a challenge to deconvolute chronic mutant phenotypes and derive primary roles for Smc5/6 . The lack of a cohesive understanding of primary Smc5/6 defects in normal cell growth hinders advances in the field and is an important issue to address . An effective way to overcome the drawbacks of chronic allele usage is to induce acute and potent Smc5/6 depletion , which enables identification of the immediate consequences of Smc5/6 loss . However , robust loss of Smc5/6 within one cell cycle is difficult to achieve . For example , the budding yeast Smc5/6 subunits appear to be stable and even low levels of the complex can be tolerated for multiple cell divisions [13] . Previous strategies using conditional promoters or DHFR-degron systems reduced Smc5/6 protein levels and cell growth , but failed to cause the null phenotype of lethality [14 , 15] . To improve the robustness of Smc5/6 depletion , we turned to an auxin-inducible degron ( AID ) system [16] . We found that while targeting a single subunit of the Smc5/6 complex with AID did not cause lethality , a null phenotype could be achieved by combining AID fusions of two subunits . Thus , we used this Smc5/6 ‘double degron’ system to examine the immediate consequences of robust Smc5/6 degradation in the first cell cycle after loss . Our findings using this system demonstrate that the primary effect of Smc5/6 loss is defective rDNA replication in yeast . We present further genetic and DNA analysis data to derive a mechanism by which Smc5/6 promotes rDNA replication . Our data suggest that Smc5/6 is involved in managing programmed replication pausing at rDNA and restrains Mph1-mediated recombinogenic events to enable proper duplication of this at-risk locus .
To circumvent the limitations of smc5/6 hypomorphs used in previous studies , we employed an inducible , plant hormone-based , AID degron system to achieve acute depletion of Smc5/6 complex subunits . This system exploits the ability of a diffusible plant hormone auxin ( IAA ) to bridge the interaction between a plant adapter protein ( TIR1 ) -bound endogenous ubiquitin ligase complex ( SCF ) and a TIR1-binding cassette ( AID ) fused to a target protein [16] . IAA addition recruits AID-fusion proteins to the TIR1-SCF ubiquitin ligase complex , which polyubiquitinates them for proteasome-mediated degradation ( S1A Fig ) . Neither TIR1 nor functional concentrations of IAA are toxic to cells or interfere with other cellular processes , and rapidly inducible degradation has been reported for many yeast proteins using this system [17] . Each of the eight subunits of the Smc5/6 complex ( Smc5 , Smc6 , Mms21 , and Nse1 , 3–6 ) was tagged with a C-terminal AID at its endogenous locus . Without IAA or TIR1 , these strains gave rise to wild-type sized colonies , except for the Nse1-AID allele , which showed slow growth and was thus excluded from further analyses ( Fig 1A ) . IAA addition elicited a slow growth phenotype in strains containing AID-tagged Smc5/6 alleles only if TIR1 was also present ( Fig 1A ) . Immunoblotting for targeted proteins confirmed significantly reduced levels within 90 minutes after IAA addition ( Fig 1B ) . These results indicate that single Smc5/6 AID degron alleles cause growth defects . As Smc5/6 null alleles are lethal [2] , the slow growth seen with single AID degrons of Smc5/6 subunits suggested that cells tolerate low levels of Smc5/6 . Similar conclusions were reached in previous studies titrating cellular levels of Smc5/6 , or another SMC complex , cohesin [14 , 15 , 18] . To enhance the robustness of Smc5/6 depletion , we constructed combined degrons based on the following rationale . We observed that loss of one subunit of the yeast Smc5/6 complex generally did not affect the levels of the other obligate members of the complex ( S1B–S1D Fig ) . Thus , reducing the level of a second subunit should further decrease the probability of forming intact complexes . Indeed , we found that combining AID degrons of Nse5 and Smc6 proved highly effective for eliminating colony formation upon IAA treatment ( Fig 1C ) . Thus , Nse5-AID Smc6-AID strains containing TIR1 , referred to as “Nse5-Smc6 double degron” or “double degron” for simplicity , were used to investigate the primary defects caused by Smc5/6 complex depletion . To evaluate whether the Nse5-Smc6 double degron strain suffers chronic defects prior to induced protein degradation , we compared it to three frequently used hypomorphic alleles: smc6-56 , smc6–P4 , and mms21-11 [2 , 6 , 19] . These three mutants grow more slowly than wild-type cells at permissive temperatures and are lethal at 37°C [2 , 6 , 19] . Even at permissive temperatures , they showed higher levels of dNTPs , a condition associated with increased DNA stress and altered replication profiles ( S2A Fig ) [20 , 21] . In contrast , cells harboring the Nse5-Smc6 double degron exhibited dNTP levels similar to wild-type or strains containing TIR1 alone , in both G1 and asynchronous cultures ( S2B Fig ) . These data suggest that Nse5-Smc6 double degron cells lack chronic genome stress prior to induced degradation with IAA . The double degron strain’s normal growth and wild-type dNTP levels under un-induced conditions offer a more optimal baseline for detecting primary defects after Smc5/6 depletion . We also examined the extent of Nse5 and Smc6 protein loss upon IAA addition and effects on bulk DNA replication . To this end , G1-arrested double degron strains were treated with IAA for 90 min to induce protein degradation , and then released into cycling with IAA ( Fig 1D , +IAA ) . As a control , the same strains were examined in parallel without IAA ( Fig 1D , –IAA ) . Protein levels were monitored at four time points after release from G1 ( Fig 1E ) . Relative to controls , double degron cells in IAA lost ~95% of Nse5 and 70% of Smc6 proteins in the first cell cycle ( 20–80 min ) ( Fig 1E ) . It is reasonable to infer that levels of the intact complex are likely lower than 5% of those in wild-type cells , as depletion of each subunit independently affects the complex . Next , we assessed bulk genome replication by FACS analysis . Throughout the time course , the double degron cells showed similar cell cycle progression in the presence or absence of IAA ( Fig 1D ) . On a molecular level , Clb2 kinetics , an indicator of cell cycle progression [22] , were also comparable ( Fig 1E ) . IAA-treated double degron cells also showed no increased phosphorylation of the Rad53 checkpoint kinase or gross differences in levels of γH2A , a marker for DNA replication or breaks ( Fig 1E ) [23 , 24] . Our data suggest that there are no detectable changes in cell cycle progression , DNA break marker increase , or checkpoint activity in the first cell cycle upon Smc5/6 subunit removal . Moreover , cells appear to undergo normal bulk genome replication even with robust Smc5/6 depletion . To achieve more sensitive detection of chromosome synthesis in the first cell cycle of Smc5/6 loss than that afforded by FACS , we used BrdU incorporation coupled to pulsed field gel electrophoresis ( PFGE ) . PFGE can separate replicated chromosomes , which enter the gel , from the branched forms still undergoing replication , which remain trapped in the wells [25] . BrdU labels the newly synthesized DNA in each replicated chromosome and can be detected by immunoblotting [26] . To apply these techniques , we used a similar procedure as described above and monitored cells for 180 min after G1 release ( Fig 2A ) . To capture new DNA synthesis , we added BrdU immediately after G1 release . We also used nocodazole to prevent additional rounds of cycling in order to focus on effects of Smc5/6 depletion in the first cell cycle ( Fig 2A ) . Nse5-Smc6 double degron cells were first compared to cells containing the TIR1 adaptor but without degron alleles . As before ( Fig 1D ) , FACS data showed that both strains were synchronized in G1 , progressed through S phase , and achieved G2/M arrest ( Fig 2B ) . For control cells , BrdU signals for all chromosomes increased from 20’ to 40’ and peaked at 60’ ( Fig 2C ) . This is consistent with the FACS profile , which shows bulk replication having largely completed by 60’ ( late S phase ) , with cells remaining in G2/M for subsequent time points . Strikingly , both BrdU blotting and DNA staining of PFGE gels showed little to no replicated Chr XII signal in Nse5-Smc6 double degron cells throughout the duration of the time course , despite wild-type-like cell cycle progression ( Fig 2C; S3A Fig ) . Quantification of BrdU signals for other chromosomes found no significant differences between IAA-treated double degron and control cells ( S3B Fig ) . To verify that the lack of fully replicated Chr XII signal was due to the loss of AID-targeted proteins , we repeated the assay with double degron strains in the presence or absence of IAA . Identical first cell cycle progression was seen for both conditions ( Fig 2D ) . Once again , only IAA-treated degron cells showed low Chr XII signal , as detected by both BrdU incorporation and DNA staining ( Fig 2E; S3C Fig ) . Based on these results , we concluded that acute Smc5/6 depletion leads to a Chr XII-specific replication defect in the first cell cycle . Chr XII is unique among yeast chromosomes in that it harbors the entire rDNA array . This large array ( ~1 . 4 Mb ) represents 10% of the yeast genome and half of Chr XII , and is intrinsically difficult to replicate . Uniquely , each of the 100–200 rDNA repeats in the array contains a programmed replication fork barrier , or RFB , located between the 5S and 35S rRNA genes ( Fig 3A ) [27] . When bound by Fob1 , the RFB sequence can block replication fork progression in the direction of 35S rRNA transcription [28] . This mechanism helps avert collisions between the replication and transcription machineries , but also requires careful management to enable replication completion and avoid repeat instability [29] . Knowing that rDNA harbors these specific sites of replication blockade , we asked whether their removal could ameliorate the observed Chr XII replication defect of Smc5/6-depleted cells . To this end , we removed Fob1 in the Nse5-Smc6 double degron strains , and repeated the BrdU and PFGE tests described above ( Fig 2A ) . Both degron and degron fob1∆ strains showed identical first cell cycle FACS profiles ( Fig 3B ) . Importantly , the fully replicated Chr XII BrdU signal was stronger in degron fob1∆ strains than in degron strains at 120’ and 180’ after release from G1 ( Fig 3C ) . This finding was further confirmed by Southern blotting with an rDNA-specific probe ( Fig 3D ) . Quantification showed ~3-fold increased Chr XII signals in degron fob1∆ cells over degron alone ( Fig 3D ) . As expected , these fob1 effects were specific to Chr XII , as signals for other chromosomes such as Chr III were similar for the two strains ( Fig 3D ) . To determine if the observed fob1∆ effect on Chr XII replication in double degron cells reflects an improvement of rDNA replication per se , we directly examined the rDNA array , which can be released from Chr XII by the restriction enzyme XhoI . The rDNA array is flanked by XhoI recognition sites but contains no internal ones , so XhoI cleavage releases the entire array from its chromosomal context [30] . The array’s large size enables its resolution from other smaller chromosome fragments by PFGE and can be subsequently detected by hybridization to an rDNA-specific probe . As shown in Fig 3E , ~90% of the rDNA signal failed to enter the gel in degron cells even by 180 min , consistent with our data for intact Chr XII ( Fig 3D ) . This confirmed that the rDNA array itself suffers from incomplete replication when Smc5/6 is depleted . The rDNA of degron fob1Δ cells entered the gel from 60 to 180 minutes , and in-gel levels of rDNA were 4–6 fold greater than those of degron cells ( Fig 3E ) . The ability of fob1Δ to improve replication of the rDNA array more than that of Chr XII ( Fig 3D and 3E ) confirms that rDNA is responsible for the beneficial effect exerted by fob1Δ on Chr XII replication in double degron cells . We also asked whether replication fork blockade by Fob1-RFB , outside the context of rDNA , were sufficient to create a requirement for Smc5/6 . It is known that two RFB sites inserted on Chr III can pause replication forks upon Fob1 overexpression , and that such pauses are resolved by the recruitment of the Rrm3 helicase [31] . We found that Smc5/6 loss in this system impaired Chr XII replication as expected , but did not affect Chr III replication ( S4 Fig ) . Thus , additional properties specific to the rDNA locus not recapitulated by these RFB sites contribute to the importance of Smc5/6 for rDNA replication ( see Discussion ) . We then investigated which other protein factors might play a role in Smc5/6-dependent effects at rDNA . Previous studies showed that Smc5/6 inhibits the pro-recombinogenic activity of the Mph1 DNA helicase at stalled forks under DNA damage conditions [19 , 32–34] . Although Mph1 has not been implicated in rDNA and Fob1-RFB-mediated replication pausing , replication forks stalled by Fob1-RFB , like those stalled by template lesions , require management to ensure replication completion . Thus , we tested whether Smc5/6 inhibition of Mph1 may also be relevant at endogenous rDNA fork blockage sites . To this end , we deleted MPH1 in Nse5-Smc6 double degron cells . FACS profiles of both degron and degron mph1Δ showed identical first cell cycles ( Fig 4A ) . Using the procedure described above , we found that mph1Δ increased levels of fully replicated Chr XII by about three-fold in degron cells ( Fig 4B ) . This effect was similar to fob1Δ , although significant suppression was seen by 60’ for mph1Δ , but not fob1Δ . Furthermore , when examining the rDNA array within Chr XII by XhoI digestion , we found that its duplication in degron mph1Δ cells increased to levels similar to that of Chr XII ( Fig 4C ) . We note an overall trend of weaker suppression of rDNA replication than Chr XII as a whole by mph1Δ , while fob1Δ had the opposite trend . This would be consistent with a role for Mph1 , but not Fob1 , at Chr XII loci outside rDNA . After observing fob1 and mph1 suppression , we tested their genetic interactions . If Smc5/6 is required to limit Mph1 activity at Fob1-mediated fork pausing sites , we would expect combined fob1Δ mph1Δ to confer no additive effects on the rDNA replication phenotype of degron cells . PFGE and Southern blotting for the rDNA array showed that fob1Δ mph1Δ improved rDNA gel entry at 60 , 120 , and 180 min in Nse5-Smc6 double degron cells , with only a small proportion of rDNA signal remaining in the wells ( Fig 5A and 5B ) . Quantification of several experiments shows that this improvement of rDNA replication by fob1Δ mph1Δ was not significantly greater than that shown by fob1Δ or mph1Δ single mutants ( Fig 5C ) . In addition , the observed suppression reached a level similar to those of wild-type strains and double degron cells without IAA treatment ( Fig 5C; S5 Fig ) . These data suggest that Mph1 and Fob1 function in the same pathways . We note that stronger effects for fob1Δ than mph1Δ at earlier time points may reflect additional roles played by Smc5/6 at rDNA beyond Mph1 regulation . Our data so far support a premise that fork stalling by Fob1-RFBs in rDNA necessitates the presence of Smc5/6 to inhibit the pro-recombinogenic Mph1 activity; as such , removing Fob1 or Mph1 bypasses the need for Smc5/6 . Such a potential role for Smc5/6 would mitigate recombination at RFBs and favor fork merging , an outcome less likely to cause rDNA repeat instability [29] . To test the above idea , we used 2D gel analyses to examine recombination structures formed at regions around RFB sites . An rDNA repeat fragment released by BglII restriction digest contains the RFB , rDNA replication origin ( rARS ) , 5S rRNA gene , and part of the 35S rRNA gene ( Fig 6A ) . As shown in previous studies , examining this fragment by 2D gel enables one to monitor levels of stalled forks at RFBs , regular replication forks ( Y-shaped DNA ) , and recombination intermediates ( X-shaped DNA or X-mols ) [35] ( Fig 6A ) . Using the same experimental schemes as for the PFGE experiments ( Fig 2A; S6A Fig ) , we first compared Nse5-Smc6 double degron cells and control cells with TIR1 alone . We examined samples from one S-phase time point ( 60 min ) and one G2-phase time point ( 120 min ) since double degron cells show reduced rDNA replication at both time points ( Fig 2B–2E ) . Nse5-Smc6 double degron and control cells differed significantly in their X-mol or recombination intermediate levels at both time points ( Fig 6B , arrows ) . Quantification of several experiments showed a ~1 . 5-fold increase at 60 min and 3-fold increase at 120 min for degron cells over controls ( Fig 6C ) . A ~3-fold increase of RFB signals in degron cells at 120 min was also seen ( Fig 6B; S6B Fig ) . These data suggest that the rDNA replication defect caused by Smc5/6 loss is associated with increased recombination and prolonged fork pausing . We went on to ask whether increased recombination at RFB regions upon Smc5/6 loss is mediated by fork pausing and Mph1 . 2D gel analyses found lower levels of recombination intermediates in both fob1Δ double degron and mph1Δ double degron strains relative to degron strains at 60 min and 120 min after G1 release; this reduction resulted in levels comparable to those of control cells ( Fig 6B and 6C ) . As expected , fob1Δ also eliminated RFB-dependent fork pausing , while mph1Δ did not exert such an effect ( Fig 6B ) . Moreover , the reduction in recombination intermediate levels for fob1Δ mph1Δ double degron cells was similar to that of fob1Δ or mph1Δ single mutants ( Fig 6B and 6C ) , a finding consistent with our PFGE findings for rDNA array replication ( Fig 5C ) . Together , our data suggest that both Mph1 activity and Fob1-mediated fork pausing contribute to recombination structure accumulation in the absence of Smc5/6 . The above data do not exclude additional mechanisms by which Smc5/6 could influence rDNA metabolism . It has been shown that the SUMO ligase activity of the Smc5/6 complex subunit Mms21 affects nucleolar function but is not essential for growth [2 , 36 , 37] . We thus tested whether Mms21 SUMO ligase function is directly linked to rDNA replication . We found that a SUMO ligase mutant of Mms21 did not affect rDNA replication at early time points after release from G1 ( S7 Fig ) . This is different from our data regarding Smc5/6 loss , but consistent with previous findings that sumoylation is not required for Mph1 regulation [19 , 33] . At a later time point , the mms21 SUMO E3 mutant showed moderately reduced rDNA gel-entry , suggesting a late role for sumoylation . This observation corroborates a proposed role for the Mms21 SUMO ligase in dissolving recombination intermediates that block replication completion [38 , 39] .
Despite being required for viability in multiple organisms , the role ( s ) played by Smc5/6 during mitotic growth remain poorly understood [1–3 , 40] . The varied phenotypes of chronic smc5/6 mutants have complicated the delineation of specific Smc5/6 functions . Acute Smc5/6 depletion offers a strategy for bypassing the obscuring secondary effects of chronic Smc5/6 loss . An inducible protein degradation system enabled us to investigate the effects of Smc5/6 loss on DNA replication and cell cycle progression within the first cell cycle after depletion ( Fig 1C–1E ) . This system developed for analysis of the immediate effects of robust Smc5/6 loss in yeast can stimulate the development of similar approaches in other organisms . Combining our acute Smc5/6 depletion system with chromosomal PFGE analyses , we show that Smc5/6 complex removal causes a striking Chr XII-specific replication defect ( Fig 2B–2E ) . This is not associated with detectable changes in cell cycle progression or markers of DNA damage and checkpoint activation , suggesting that we have isolated a primary defect of Smc5/6 loss ( Fig 1D and 1E ) . We further show that the observed replication defect is largely localized to the rDNA array on Chr XII ( Fig 3D and 3E ) . Our findings are in line with previous reports for smc6-9 , but not with the chromosome size-based model derived from studies of smc6-56 [5 , 6] . Considering that smc6-56 cells experience chronic genome stress based on their altered growth and dNTP levels , adaptive responses in these cells may contribute to their overall phenotype ( S2A Fig ) . Alternatively smc6-56 , but not smc6-9 , may alter the Smc5/6 function in dealing with longer chromosomes . Our finding that the Smc5/6-dependent Chr XII replication defect begins in S phase ( Fig 2E ) suggests that Smc5/6 plays a role in this cell cycle phase . This conclusion corroborates chromatin immunoprecipitation data localizing Smc5/6 to replication forks [41 , 42] . It can also be reconciled with the ability of Smc5/6 expressed from a G2-cyclin module to sustain viability , since our data suggests that even low level expression ( in S phase ) may be functionally adequate ( Fig 1A and 1B ) . Taking into consideration these findings , we conclude that Smc5/6 is required beginning in S phase , particularly for rDNA replication , through to post-replicative G2 events . Our phenotypic assessment of acute Smc5/6 loss clarifies the essential function of Smc5/6 in mitotic cells and enables more reliable interpretation of smc5/6 defects . After redirecting focus towards an essential role of Smc5/6 in replicating at-risk rDNA loci , we provided genetic , PFGE , and 2D gel data to derive a mechanism by which Smc5/6 promotes rDNA replication . We show that Smc5/6 loss leads to increased levels of recombination intermediates at programmed rDNA fork pausing sites ( Fig 6B and 6C ) . Importantly , removing the rDNA fork blocking protein Fob1 or the DNA helicase Mph1 in Smc5/6 degron cells reduces these intermediates and rDNA replication defects ( Figs 3D , 3E , 4B , 4C , 6B and 6C ) . As fob1Δ and mph1Δ show similar and non-additive effects , the simplest interpretation is that upon fork stalling at RFBs in rDNA , Mph1-mediated recombination is a major contributor to defective rDNA replication when Smc5/6 is absent ( Figs 5B , 5C , 6B and 6C ) . On the basis of these data , we propose a model in which Smc5/6 helps to manage replication forks paused at RFBs by inhibiting Mph1-mediated recombination ( Fig 6D ) . As such , Smc5/6 is an important factor that influences the fates of stalled forks at this locus . When Smc5/6 is present , Fob1 at RFBs can prevent transcription-replication conflicts , and fork merging is favored . When Smc5/6 is absent , paused forks are vulnerable to recombination . The recombination intermediates thus generated are especially toxic , because Smc5/6 SUMOylation function is required for their dissolution [38 , 39] . This is consistent with our observation that mms21 SUMO ligase mutants affect rDNA replication at a later time point ( S7 Fig ) . This model provides a straightforward explanation for fob1 and mph1 suppression , as the former reduces the number of forks that require Smc5/6 protection from Mph1 activity , while the latter reduces the potential for paused forks to undergo recombination . Both genetic changes decrease the Smc5/6 requirement at rDNA . As the fob1 and mph1 suppression patterns are not entirely identical , they must also play unique , yet-to-be determined roles in mediating Smc5/6 effects on rDNA replication . Our data also reveal a previously unappreciated function of Mph1 at the rDNA locus . Although recombination at RFBs is toxic when Smc5/6 is lost , such repair could be useful for adjusting rDNA repeat numbers . Whether and how cells enable restricted use of Mph1 for this purpose will be interesting to investigate in the future . Mph1 and its homologs have been suggested to promote recombination at stalled replication forks via their ability to regress forks , which entails the annealing of two nascent strands accompanied by re-annealing of their template strands [32] . Replication fork regression can provide a mechanism for replication restart , but also generate DNA structures prone to cleavage or recombination [32] . As such , fork regression is negatively regulated by multiple mechanisms; Smc5/6 participates in one such mechanism by directly binding to and preventing Mph1 oligomerization at fork junctions [32 , 33] . Though fork regression is mostly investigated in DNA damage conditions , our findings suggest that it can also occur in situations of endogenous fork pausing . In conjunction with previous findings of the Smc5/6-Mph1 relationship under DNA damage conditions , our data helps to establish the concept that a key Smc5/6 function is regulation of Mph1 in multiple conditions . Overall , our results suggest that Smc5/6 plays a primary role in managing programmed fork pausing at rDNA by inhibiting pro-recombinogenic Mph1 activity . We find this role to be rDNA-specific , as other chromosomes harboring protein barriers [43 , 44] replicate proficiently upon Smc5/6 depletion and artificially introduced protein barriers on Chr III did not affect its replication in double degron cells [31] ( S4 Fig ) . We suggest that multiple features unique to rDNA may help explain why Smc5/6 is particularly indispensable at this locus . First , the presence of many RFB sites in rDNA can generate much higher levels of replication fork pausing than any other locus . Second , some proteins that facilitate replication are known to be excluded from the nucleolus [45] . As such , Smc5/6 , known to be enriched at rDNA [7] , could be particularly important for managing stalled forks at this locus . When forks are paused outside rDNA , other pathways may compensate for the lack of Smc5/6 activity , so they do not manifest defects as robustly upon Smc5/6 loss . Third , rDNA has a unique chromatin environment and architecture connected to its activities in transcription , ribosome assembly , and mitotic exit . This high level of DNA , RNA , and protein transactions may generate a specific demand for Smc5/6 function . Finally , rDNA replication continues at the end of S and into G2 phase when other chromosomes have completed their replication [29] . This could further generate a demand for Smc5/6 . In-depth examination of which and how these unique rDNA features influence its duplication and pose a critical requirement for Smc5/6 will provide additional insight into how this highly conserved repetitive sequence sustains stability across evolution . In human cells , Smc5/6 also promotes replication and has been shown to localize to a subset of stalled replication forks [46 , 47] . Thus , our work also stimulates the examination of a similar role for human Smc5/6 in managing fork pausing in rDNA and other repetitive or highly organized regions where alternative mechanisms of fork management are ineffective . It is worth noting that disrupting rDNA replication has far-reaching consequences beyond this locus , through its strong influences on mitotic exit , nuclear organization , transcription , and translation [48 , 49] . Indeed , yeast Smc5/6 mutants show nucleolar fragmentation and stress [2 , 37] . As such , the importance of Smc5/6 function to rDNA replication may help explain the divergent phenotypes underlying its two human syndromes , which feature pleiotropic developmental defects across multiple lineages , including the musculoskeletal , endocrine and immune systems [4 , 50] .
Yeast strains are derivatives of W1588-4C , a RAD5 variant of W303 ( MATa ade2-1 can1-100 ura3-1 his3-11 , 15 leu2-3 , 112 trp1-1 rad5-535 ) [2] . At least two strains per genotype were examined in each experiment , and only strain is listed for each genotype in S1 Table . Standard methods were used for yeast strain construction . To tag subunits of the Smc5/6 complex , PCR products containing AID and FLAG tag sequences were generated with flanking homologous sequences to the insertion site . Standard PCR integration methods were used to generate fusion constructs , which were then fully sequenced to confirm the correct tagging . Two main protocols were used for cell cycle studies , as shown in Figs 1D and 2A . In Fig 1D , asynchronous cultures were arrested in G1 phase by adding 5μg/ml α-factor ( ThermoFisher ) to media for 90 min . 1 mM IAA ( Sigma ) was added for 90 min to degrade Nse5-AID and Smc6-AID proteins . Subsequently , cells were released into fresh YPD containing 1 mM IAA to sustain Nse5 and Smc6 depletion in degron cells . Samples were collected at several time points after release as indicated . Note that the same procedure was applied to cells without degron alleles to ensure parallel treatment . A similar experimental procedure shown in Fig 2A has two alterations . One is BrdU ( Sigma ) addition after cells were released from G1 arrest to monitor new DNA synthesis . Another is nocodazole ( US Biologicals ) addition to cultures 45 min after G1 release to prevent first cell cycle exit . Standard FACS analyses were performed as described previously [51] . 2D gel analyses were performed as previously described [52] . DNA was extracted and digested by BglII and separated by agarose gel electrophoresis in two dimensions . DNA was transferred onto Hybond-XL membranes ( GE Healthcare ) and analyzed by Southern blot using probes hybridizing specifically to rDNA . Primers used for probe amplification are available upon request . For quantification , the signals of 1N DNA were obtained from shorter exposures , while those of DNA intermediates came from longer exposures to ensure both types of signals fell within linear range of detection on the PhosphorImager . PFGE was performed as previously described [53] . In brief , cells harvested from the indicated time points were embedded in agarose plugs , spheroplasted , and deproteinized . Plugs were loaded into 0 . 5X TBE gels and run on a Bio-Rad CHEF-DR III Pulsed Field Electrophoresis System for 12 hours to achieve chromosome separation . Gels were stained by ethidium bromide and Sytox ( Molecular Probes ) and then transferred onto Hybond-N+ membranes ( GE Healthcare ) using standard capillary transfer technique . Membranes were probed with anti-BrdU antibody ( BD ) and α-mouse secondary antibody ( GE Healthcare ) . Membranes were scanned with Fujifilm LAS-3000 luminescent image analyzer , which has a linear dynamic range of 104 to achieve reliable quantification . The percentage of gel entry for each chromosome was calculated by dividing the chromosomal band signal by the sum of chromosomal band signal and well signal , after background subtraction . The positions of each chromosome were derived from [54] . Southern blotting of Chr XII , Chr III , and rDNA were performed using specific probes hybridizing to each region , and primers used for probe amplification are available upon request . To detect protein levels , standard TCA protein extraction methods were used [55] . Protein samples were resolved on 3–8% or 4–20% gradient gels ( Life Technologies and Bio-Rad ) and transferred onto 0 . 2 um nitrocellulose membranes ( G5678144 , GE ) . Antibodies used were: α-Myc ( 9E10 , Bio X Cell ) , α-HA ( F-7 , Santa Cruz ) , α-Flag M2 ( Sigma ) , α-V5 ( Life Technologies ) , α-Clb2 ( y-180 , Santa Cruz ) , α-Pds1 ( gift of E . Schiebel ) , α-Rad53 ( yC-19 , Santa Cruz ) , α-H2A pS129 ( Abcam ) , and PAP ( P1291 , Sigma ) . dNTP quantification was performed as previously described [56] . | Smc5/6 belongs to the SMC ( Structural Maintenance of Chromosomes ) family of protein complexes , all of which are highly conserved and critical for genome maintenance . To address the roles of Smc5/6 during growth , we rapidly depleted its subunits in yeast and found the main acute effect to be defective ribosomal DNA ( rDNA ) duplication . The rDNA contains hundreds of sites that can pause replication forks; these must be carefully managed for cells to finish replication . We found that reducing fork pausing improved rDNA replication in cells without Smc5/6 . Further analysis suggested that Smc5/6 prevents the DNA helicase Mph1 from turning paused forks into recombination structures , which cannot be processed without Smc5/6 . Our findings thus revealed a key role for Smc5/6 in managing endogenous replication fork pausing . As rDNA and its associated nucleolar structure are critical for overall genome maintenance and other cellular processes , rDNA regulation by Smc5/6 would be expected to have multilayered effects on cell physiology and growth . | [
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] | 2018 | Acute Smc5/6 depletion reveals its primary role in rDNA replication by restraining recombination at fork pausing sites |
In this paper , we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research . Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid ( NAA ) on successful rooting and also to optimize the two variables for maximum result . Observation-based modelling , as well as traditional approach , could identify NAA as a critical factor in rooting of the plantlets under the experimental conditions employed . Symbolic regression analysis using the software deployed here optimised the treatments studied and was successful in identifying the complex non-linear interaction among the variables , with minimalistic preliminary data . The presence of charcoal in the culture medium has a significant impact on root generation by reducing basal callus mass formation . Such an approach is advantageous for establishing in vitro culture protocols as these models will have significant potential for saving time and expenditure in plant tissue culture laboratories , and it further reduces the need for specialised background .
Relatively more straightforward and efficient empirical modeling techniques based on input-output models are gaining popularity to conventional statistical methods across various disciplines [1] . This surge is due to its relative ease of use and understanding . Genetic programming ( GP ) is an approach which uses the concept of biological evolution to handle a problem with many fluctuating variables . Computational optimisation techniques have recently debuted in plant tissue culture research as studied in neural networks models [2] . Symbolic regression was one of the earliest applications of GP and continues to be widely considered [3] . A broad array of scientific fields like Biology , Chemistry , Environmental Science , Neurology and Psychology reports the use of symbolic regression [4–9] . However , plant tissue culture data has not yet been analysed using symbolic regression . The data generated from plant tissue culture experiments includes continuous , count , binomial or multinomial and predominantly the information is validated using analysis of variance method ( ANOVA ) [2 , 10] . ANOVA is adequate for normally distributed continuous data; but without prior manipulation , it is erroneous to analyze count , binomial or multinomial data [11] . Neuro-fuzzy logic is the standard practice by which computational modeling is achieved in plant tissue culture [2 , 12] . In this context , genetic algorithm based symbolic regression remains unevaluated . Unlike conventional regression analysis which optimises parameters for a pre-defined model , symbolic regression avoids imposing any apriori assumptions . In generalised linear model ( GLM ) regression , the dependent variable is represented as linear combination of the given set of basic functions and optimise the coefficients to fit the data . However , symbolic regression searches for both a set of basic functions and coefficients . The added value of symbolic regression , compared to GLM , lies in its ability to quickly and accurately find an optimal set of basic functions [13 , 14] . The algorithm infers the model from the data by combining variables and mathematical operators and generates an empirical formula which is a mathematical equation that predicts observed results derived from conducted experiments . GP combines previous equations and forms new ones . Thus it produces models with interpretable structure , relating to input and output variables from a data set without pre-processing and identifying critical parameters and hence shed insight into the underlying processes involved in a given system [15] . Symbolic regression can recognise and model complex non-linear relationships between the inputs and outputs of biological processes even in the presence of disturbances and potential for parallel processing . The preliminary data generated from experiments during rooting of in vitro regenerated plantlets in Wrightia tinctoria was employed to study the utility of symbolic regression to analyze plant tissue culture data . The effect of two variables - NAA and charcoal on root proliferation was considered . The datasets were subjected to usual statistical analysis as well as observation based modeling via symbolic regression . Moreover , we aimed to optimise the process by examining the influencing factors . We propound the use of symbolic regression-based model prediction as an addendum to data analysis method for plant tissue culture experiments .
The genetic variability was kept minimum by using a single field grown ortet , thus minimising statistical errors [16] . Nodal regions derived from the fresh flushes of growth from the ortet , two weeks after lopping one major branch served as the explants [17] . The nodal explants were conditioned over a period of 4 months ( subculture/four weeks ) on MS medium ( 1962 ) [18] , pH 5 . 8 and 2 μM each of BAP and NAA for shoot multiplication . For rooting experiments individual shoots were transferred on MS medium containing 2 μM BAP with NAA ( 2 , 4 and 6 μM , respectively ) and charcoal ( 0 . 01 , 0 . 03 , 0 . 05 , 0 . 07 , 0 . 09 and 0 . 11% , respectively ) in 250 ml culture flasks in 50 ml of sterilized medium ( pH 5 . 8 ) . The cultures were maintained at 25±2°C in a culture room with 40 μmolm−2 s−1 irradiances and a photoperiod of 8 hrs with 55±5% of relative humidity . The plant tissue culture database , containing 21 conditions , followed a factorial design for two variables- concentration of NAA ( 2 , 4 and 6 μM ) and charcoal ( 0 , 0 . 01 , 0 . 03 , 0 . 05 , 0 . 07 , 0 . 09 and 0 . 11% ) in the medium . Each treatment consisted of 5-7 explants in a culture flask with three replicates . The subculture was done at the end of four weeks and five parameters were recorded to analyze the effects of the variables on rooting such as basal callus diameter ( mm ) ( BC ) , the percentage of shoots rooted ( R ) , the length of the longest root ( cm ) ( RL ) , the number of roots ( NR ) and the number of lateral roots ( NLR ) ( S1 ) . All experiments were conducted using Randomised Block Design ( RBD ) . Continuous data were analysed using multiple linear regression in R and posthoc comparisons of pairs performed by Tukey's test ( p>0 . 05 ) . Count data were analysed using Poisson regression model . Pearson's Chi-squared test for count data was employed to access statistical significance of the variables . Each of the observed parameters is modeled as a function of NAA and charcoal concentrations using symbolic regression and GLM for comparison . To obtain a global optimum , we have also modelled the combination ( R+RL+NR+NRL-BC ) by taking rooting factors together after normalisation by employing both GLM and symbolic regression . The optimum model for each case was generated by genetic programming based symbolic regression using the software package Eureqa ( Version 0 . 98 beta ) with 50% of the data randomly selected as training data , and 3-fold cross-validated with randomly selected 25% of the remaining data [19–21] . Corresponding to each symbolic regression model of the data partition , we have also obtained generalised linear model by including x , y , xy , 1/x , 1/y , sin ( x ) , cos ( x ) , sin ( y ) , cos ( y ) , xy sin ( x ) , xy cos ( x ) , xy sin ( y ) , xy cos ( y ) into the set of basic functions and cross-validated similarly . The remaining 25% the data was used for testing and reporting error [19–21] . The Target expression used to generate the regression model was the minimal equation z = f ( x , y ) where ‘x' corresponds to NAA concentration and ‘y' corresponds to charcoal concentrations , and ‘z' represents each of the five observed parameters and their combination . The models were based on the primary and trigonometric building blocks , with the R2 goodness of fit as the error metric [22 , 23] . Root Mean Squared Error ( RMSE ) was calculated for the test data sets . Sensitivity represents the relative impact of the variable on the parameter studied within this model and was calculated by the local method using the partial derivatives [24] . Given a model equation of the form z = f ( x , y ) , the influence metrics of x on z was; Sensitivity=|∂z∂x|¯ . σ ( x ) σ ( z ) , evaluatedatallinputdatapoints; The percentage positive was calculated as percentage of data points where σ ( x ) σ ( z ) >0 and percentage negative was calculated as percentage of data points where σ ( x ) σ ( z ) <0; where ∂z∂x was the partial derivative of z with respect to x , σ ( x ) was the standard deviation of x in the input data , σ ( z ) was the standard deviation of z , |x| denoted the absolute value of x , and x¯ denoted the mean of x [25] . The ‘fmin' function in MATLAB ( R2012b ) was used to obtain the maximum value of each of these functions .
The average values obtained for the five growth parameters observed during the study were given as the basal callus diameter ( Table 1 ) , the percentage of shoots rooted ( Table 2 ) , the length of longest roots ( Table 3 ) and the number of roots and the number of lateral roots ( Table 4 ) . The miniscule alphabets within a column indicated the significant influence of charcoal and majuscule alphabets in the row represented the significant interaction of NAA . The shoots inoculated on MS medium with 0% charcoal ( control ) showed maximum basal callus formation ( Fig 1 ) . The shoots inoculated on MS medium supplemented with 4μM NAA and 0 . 07% charcoal showed the maximum percentage of rooting ( Fig 2 ) . Multiple linear regression demonstrated a significant effect of NAA and its interaction with charcoal on basal callus ( p>0 . 001 ) , the percentage of shoots rooted ( p>0 . 05 ) and root length ( p>0 . 01 ) ( Tables 1–3 ) . The individualistic effect of NAA for the number of roots and lateral roots were found to be significant at p>0 . 05 and p>0 . 001 respectively ( Table 4 ) . The interaction of NAA and charcoal was not significant for the same parameters studied . Mathematical functions were successfully developed using symbolic regression to understand the correlation between the two variables for each of the parameters considered and is contrasted with those obtained by traditional regression models ( Table 5 ) . To analyze the effect of each of the variables on the parameter studied; variable sensitivity measures were calculated along with its percentage impact . Its sensitivity denoted the relative impact within this model that a variable has on the target variable . The individualistic effect of the two input variables on the output parameter was pointed out as percentage positive or negative of that input variable ( Table 6 ) . For the parameter basal calli diameter , the percentage positive value for variable ‘y' was zero . In other words , there was zero percent chance of basal calli mass increasing with increasing concentration of charcoal; or that basal calli mass decrease with increasing concentration of charcoal ( Fig 3 ) . The model predicted that increase in charcoal concentration had a consequent increase in root length and root number in 50% of all the trials while the same promoted rooting percentage and lateral root number in 75% of the trials . Root number and root length decreased with increasing concentration of NAA in 100% of the trials . Rooting percentage and lateral root numbers increased with increasing NAA concentration in 50% of all the trials . The function obtained and the 3D plots thus generated could be used to predict the combinations of input variables giving optimum results . The best response for rooting percentage was predicted at 3 . 7 μM NAA and 0 . 08% charcoal ( Fig 4 ) . The root length showed a non-linear pattern , and the highest value for its function was estimated with 2 . 8 μM NAA and 0 . 05% charcoal ( Fig 5 ) . The maximum root number was determined for 1 . 7 μM NAA and 0 . 06% charcoal ( Fig 6 ) . The maximum value of the function generated for lateral root number was with 6 . 3 μM NAA and 0 . 08% charcoal ( Fig 7 ) . The global optimum modelled upon the combination ( R+RL+NR+NRL-BC ) indicated the results as 2 . 44 μM NAA and 0 . 03% charcoal ( Fig 8 ) . The conclusion obtained by traditional statistics suggested that charcoal had a positive and stimulatory effect in rooting of shoots by reducing basal callus ( Table 1 ) . Percentage of shoots rooted and root length showed a significant impact with the combination of NAA and charcoal ( Tables 2 and 3 ) . In the present study , NAA has a significant effect on rooting as shown by the number of roots and lateral roots ( Table 4 ) . Similar results were reported in Acacia leucophloea and Cinnamomum verum [26 , 27] . With traditional statistics , we were not able to estimate the combination/s of both variables in producing the best results or able to identify the relative impact of a particular variable on the output parameter . Modeling of plant tissue culture data is practised using regression analysis where first an initial function is approximated and the data fitted to that function to obtain the optimum parameters [11 , 28 , 29] . In this procedure even when one gets the optimum parameter values , the model prediction was limited by the probable wrong selection of the model function . In contrast , symbolic regression procedures work simultaneously on model specification problem and the problem of fitting coefficients [30] . Thus it provides both optimum model function as well as the optimum variable values in the model . The simple relations derived from GP were more accessible to analyze the relationships between the input and output variables [31] . Observation-based predictive models using GP identified that the individualistic effect of charcoal was significant in all the output parameters . A previous investigation suggested basal callus mass formation as one of the primary constraints in the culture of this tree species [32] . In the present study , charcoal has a positive and stimulatory effect in rooting by reducing basal callus formation in shoots . For each of the functions , generated values can be obtained by increasing /decreasing the variables by a unit . After randomly testing the higher and lower limit of the additives with experiments , the magnitude of the observed parameters can be presumed at any concentration of the additives within this range using the models generated . It can be extended to analyze synergistic interactions between two parameters by testing whether increasing both variables by a unit , gives a higher or a lower value than the sum of the values obtained by increasing each individually by a unit . The basic requirement for any empirical model includes interpretability , robustness and reliability [33] . Symbolic regression gave comparably lesser RMSE values in comparison to multiple linear regression , thus adding validity to its use . In plant tissue culture obtaining an optimum model is crucial when one needs to find the optimum experimental parameters for large-scale production . The procedure adopted in the work can also be extended to similar experiments as it is general and computationally efficient . The analysis predicted the optimum concentration of medium for micropropagation of the selected tree species from the model plots derived from the preliminary experimental data . The study indicated that these models would have significant potential for saving time and expenditure in plant tissue culture laboratories for the commercial establishment of in vitro protocols in tree species . | Trials to find out the best combination of factors that contribute to the desired response takes up the chunk of time and resources in any plant tissue culture laboratory . The output of such experiments is analysed statistically to come to a conclusion . However , without prior statistical modifications , the results could be misleading . Recent reports from several labs point out the use of artificial neural networks to circumvent this . We have chosen to use a computational process that can predict the best combination of factors for the desired response after randomly testing the higher and lower limit of the factors with experiments . The magnitude of the desired response can be presumed at any concentration within this range using the models generated by symbolic regression . The procedure provides both optimum model function as well as the optimum variable values in the model . The variable sensitivity and percentage response add depth to the information thus obtained . The study indicated that these models would have significant potential for saving time and expenditure in plant tissue culture laboratories for the commercial establishment of in vitro protocols . | [
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] | 2018 | Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach |
In multispecies microbial communities , the exchange of signals such as acyl-homoserine lactones ( AHL ) enables communication within and between species of Gram-negative bacteria . This process , commonly known as quorum sensing , aids in the regulation of genes crucial for the survival of species within heterogeneous populations of microbes . Although signal exchange was studied extensively in well-mixed environments , less is known about the consequences of crosstalk in spatially distributed mixtures of species . Here , signaling dynamics were measured in a spatially distributed system containing multiple strains utilizing homologous signaling systems . Crosstalk between strains containing the lux , las and rhl AHL-receptor circuits was quantified . In a distributed population of microbes , the impact of community composition on spatio-temporal dynamics was characterized and compared to simulation results using a modified reaction-diffusion model . After introducing a single term to account for crosstalk between each pair of signals , the model was able to reproduce the activation patterns observed in experiments . We quantified the robustness of signal propagation in the presence of interacting signals , finding that signaling dynamics are largely robust to interference . The ability of several wild isolates to participate in AHL-mediated signaling was investigated , revealing distinct signatures of crosstalk for each species . Our results present a route to characterize crosstalk between species and predict systems-level signaling dynamics in multispecies communities .
Microbes communicate with each other in order to coordinate behavior and gene expression through a process known as quorum sensing . Several Gram-negative bacteria use acyl-homoserine lactones ( AHLs ) as a signal to communicate [1–6] . These signaling systems typically consist of a synthase , such as luxI , which produces a variant of AHL , and a receptor , such as luxR , which binds to AHLs . The receptor enacts global changes in gene expression in response to high concentrations of AHLs . Over 150 quorum sensing systems have been characterized [7 , 8] , with most species containing one or a few signaling pathways . Each system typically produces one dominant version of AHL [7 , 8] , and 56 different AHLs have been identified to date [7] . Variant versions of AHL involve changes in the length of the carbon chain extending from the lactone ring and chemical modifications of this carbon chain such as the addition of carbonyl groups [2 , 7] . Variation in the chemical structure of AHLs impacts both affinity for the receptor and the regulatory response [6–8] . Several examples of crosstalk between signaling microbes , in which signal produced from one species binds to the receptor of a second species , have been reported [1 , 6 , 9–14] . For example , Chromobacterium violaceum , a pathogenic Gram-negative bacteria that produces a shorter chained AHL , activates gene expression in Vibrio harveyi , a Gram-negative marine bacteria that produces a longer chained AHL [9] . Each such pairing of AHL and receptor inhibits or promotes the activation of gene expression to a variable degree . Multispecies communities collectively produce complex mixtures of signals and the activation of gene expression within the community is influenced by crosstalk between different AHL variants . Quantifying AHL-mediated crosstalk will help us build a predictive understanding of the signaling dynamics within heterogeneous microbial populations , potentially enabling us to control the activation of gene expression in natural and synthetic microbial communities [1 , 2 , 5 , 9 , 15–19] . Here we develop and implement a signaling assay using sender , receiver , and interactor strains to measure signaling dynamics in populations containing multiple AHLs , see Fig 1A . This crosstalk assay assesses the robustness of signal exchange within mixed microbial communities . Here we use a sender strain producing the AHL 3-oxo-C6 HSL and multiple interactor strains producing signals including 3-oxo-C12 HSL made by the synthase LasI and C4-HSL made by the synthase RhlI . Our work expands the scope of prior studies that focused on signal exchange in well-mixed environments , where diffusion of AHLs plays a minimal role [12 , 17 , 18 , 20–23] . Initial work done by Canton et al . [20] quantitatively explored the ability of multiple AHLs to bind to one receptor in a plate-reader . Wu et al . [23] used a microfluidic and flow-cytometry approach to measure the interactions between the lux and las circuits . Other studies such as McClean et al . [11] observed the response of the signaling system in Chromoacterium violaceum to purified variants of AHL . Although diffusion plays a role here , there was no significant quantitative work done to capture the spatial effects . In another study Dilanji et al . [24] looked at influences of a diffusive AHL wave front produced by an exogenously added chemical on agar plates . As an alternate to adding AHLs exogenously , we added a sender E . coli colony to the middle of the plate capable of synthesizing AHL molecules , as shown in Fig 1B and similar to previous experiments [25] . Our assay incorporates an interactor strain to determine the robustness of AHL signaling to crosstalk from several AHL signals , including signals produced by wild bacterial isolates . The exchange of multiple AHLs has been reported [14 , 26] , but these studies minimized crosstalk by using AHLs that will weakly interact with each other . Consequently , AHL crosstalk in natural contexts , where multiple signals are exchanged in spatially structured communities , is not well understood . Our study experimentally measured the effects of a neighboring interactor strain on the time scale of signaling between sender and receiver strains . The interactor strain produces a non-cognate AHL signal that influences the ability of a receiver strain to respond to the cognate AHL signal emitted from the sender strain . The consequences of such crosstalk were examined over length scales much larger than individual cells . A mathematical model was derived and compared to experimental results to predict complex AHL-receptor interactions occurring in microbial populations in nature .
Our experimental setup was based on a LuxI/LuxR sender-receiver type plate assay [14 , 20 , 24 , 25 , 27 , 28] with the addition of an interacting strain . In this setup , the sender strain produces the AHL 3-oxo-C6 HSL [21] , which then binds to the receptor protein LuxR and activates gene expression in the senders when there is a sufficient amount of AHLs present . Activated gene expression in the senders results in elevated GFP production , see Fig 1A . The receivers have the capability of producing the LuxR receptor protein , and in the presence of a sufficient amount of AHLs , they will activate gene expression of RFP . The interactors constitutively produce a non-cognate AHL corresponding to the lasI or rhlI synthase genes . The plasmids encoding these constructs are shown in Figure A in S1 File . Coculture of the receiver and the sender resulted in a threefold increase in fluorescence and the introduction of the interactors produced an equivalent or reduced level of fluorescence , see Figure B in S1 File . Receiver cells containing a LuxR regulated fluorescent reporter were distributed on a 20 mm diameter LB agar pad containing a sender strain colony in the middle of the plate , see Fig 1B . The sender contains a plasmid expressing the LuxRI circuit , see Figure A in S1 File . The activation times of the gene expression in the receivers were measured with respect to the distance from the sender colony . By adding an interacting strain producing an additional AHL into the lawn of receiver cells , the shift in the activation profile quantifies crosstalk between the interactor strain and the sender/receiver system . As shown in Fig 1C , in the plate assay receiver cells adjacent to the senders express RFP around 5 . 25 hours and the receiver cells located at larger distances activated RFP after 6–8 hours . To quantify this propagation of activation within the plate , RFP fluorescent images were used to calculate the time it takes to activate RFP expression in the receivers at multiple distances from the sender colony . Activation of RFP is defined as when at least 10% of the pixels belonging to cells have a pixel intensity greater than a threshold value . Activated cells displayed a clear increase in fluorescence intensity ( Figure B in S1 File ) and the measured activation times were not sensitive to small changes in the threshold intensity used in image analysis , see Figure C in S1 File . As mentioned previously [9] , there are two main types of AHL crosstalk mechanisms between microbial species as shown in Fig 1D . In the context of crosstalk , the senders will produce the cognate AHL for the receivers while the interactors will produce a non-cognate signal variant , which binds to the LuxR receptor . For excitatory crosstalk , the interacting species are promoting the activation of the receivers while for inhibitory crosstalk , the interacting species are repressing the activation . The introduction of an interactor species to the sender-receiver setup should shift the activation times of the receivers . As an initial positive control shown in Fig 2 , sender cells containing luxI were added to the lawn of receivers to verify a decrease in activation time , by 0 . 75 hr , for an interactor strain producing the cognate signal . In a negative control where the interactor strain is the wild type E . coli host strain not producing any AHL signal , the activation time is unchanged . This result indicates that the space taken up by interactor cells did not affect the response of the receiver cells ( see Figure D in S1 File ) . We tested crosstalk from non-cognate signals by introducing an E . coli strain containing the synthase genes rhlI or lasI , producing C4-HSL and 3-oxo-C12-HSL as their principle products , respectively [9 , 23] . We chose these two AHL circuits based on the evidence of both excitatory and inhibitory crosstalk with the LuxI/R system from previous studies [17 , 20 , 23 , 29] . As shown in Fig 2 , when the E . coli LasI strain was introduced as the interactor strain , the receivers activated RFP earlier compared to the no crosstalk control . When the E . coli RhlI strain was introduced as the interactor strain , the receivers activated RFP later . These initial tests confirmed that both inhibitory and excitatory crosstalk could be observed in our assay . As seen in Figure E in the S1 File , the introduction of these interactor strains does not influence the growth of the sender or receiver strains , supporting the conclusion that the observed effect was due to the crosstalk among the AHLs and receptor . The delay in the activation of the receiver strain as a function of the number of interactor cells was measured by varying the amount of interactor strain loaded onto the plate . The amount of interactor strain added to the plate is captured as the interactor to receiver ratio , which is defined as the ratio of the number of interactors cells loaded on the plate assay to the number of receiver cells loaded on the plate assay . The number of receivers was always kept constant at 108 cells . As shown in Fig 3 , the shift in the activation time was proportional to the amount of interacting cells . The activation curves are shown for the cases of excitatory crosstalk ( Fig 3A ) , with LasI as the interactor strain , and inhibitory crosstalk ( Fig 3B ) , with RhlI as the interactor strain . These experiments quantify how the propagation of the activation front depends on the community composition , both in terms of the types of signals produced and the relative amount of each interactor strain in the environment . In this section , we build a mathematical model to explore the correlations of the micro level binding of a signal to a receptor , and the macro level spatiotemporal patterns of gene expression in a system incorporating quorum sensing crosstalk . In previous work done by [21 , 22 , 28 , 30–32] , the authors have implemented a logistic growth Eq ( 1 ) and a reaction-diffusion model ( 2 ) to simulate signal production from growing cells . The logistic growth equation considers the transient behavior of the cell density ni , which is growing at a rate of μ per cell . As the media has a finite amount of resources , the total cell density ( senders + receivers + interactors ) , nT , will approach a saturated cell density of s . Initially the senders produce the AHLs at a basal rate of ρb per cell . The sender AHLs , with a concentration of cs , diffuse away from the cells with a diffusion coefficient of Dc and are degraded by the media at a rate of da . ρ accounts for an increase in AHL production in the presence of signal . For the senders , the activity ( A ) defines how this activated rate of signal production , due to changes in production of the synthase protein , depends on the concentrations of multiple AHLs . In the presence of an interacting AHL , the transcriptional activity will be modulated due to the binding of AHLs to the LuxR receptors . Each signal has a variable influence on the activity , both in the ability to bind to a receptor and the downstream influence of such binding on the expression of quorum sensing controlled genes . The ability of an AHL to bind to the LuxR receptor depends on the binding energy and the local concentration of each AHL . In simulations , we considered the probability of an AHL to bind to a receptor and introduced a weight to account for the downstream influence of each AHL variant on gene expression . Therefore , the activity takes the form of , A=g ( ∑i=0jP ( ci ) wi ) , ( 3 ) where , g is the number of receptors per cell , i is the index to describe the type of AHL , ci is the concentration of the ith AHL , P ( ci ) is the probability of an AHL binding to the receptor , wi is the weight parameter and j is the total number of interacting AHLs . The probability of binding accounts for differences in the binding affinity of each signal variant to the receptor , as well as the competition for multiple signals to bind to the same receptor . The number of receptors ( g ) changes from a basal level of 100 to 600 , as gene expression levels increase due to signal accumulation . To model this smooth transition we used a Hill’s function , see Table A in S1 File . It is only physical to have zero or positive levels of transcriptional activity , therefore the weights should also be greater than or equal to zero . The weight parameter ( wi ) relates the number of AHL-receptor complexes to the extent of gene regulation , with large positive weights indicating that complexes formed by that AHL lead to strong upregulation of quorum sensing regulated genes while weights close to zero lead to inhibition of these genes . The weight is determined by the affinity of the bound receptor for the promoter region of quorum sensing regulated genes , the efficiencies of transcription and translation of quorum sensing regulated genes , and the rate of dissociation for the AHL-receptor complex . A deterministic Boltzmann weight approach was applied to calculate the receptor binding probabilities from AHL concentrations and receptor binding energies [33] , see the mathematical model in Text A in the S1 File and Figures F-H in the S1 File . Parameter values given in Table A in the S1 File were measured in control experiments or obtained from previous experimental studies [19 , 31 , 34–36] . Since the interactor strain did not produce any receptors , signal production was constitutive and did not incorporate positive feedback from AHL level . The activity ( A ) for the interactors was zero and signal production occurred at a basal level , ∂cint∂t=Dc∇2cint+nintρ−dacint . ( 4 ) The constitutive production rate was assumed to have the same value as the maximum production rate of the senders . These equations were solved using the finite difference method . The model predictions were obtained considering the transient behavior of the AHL concentrations . In simulations , we considered two concentric circles; the inner circle has a radius of 1 mm while the outer circle has a radius of 10 mm . The cell densities are governed by Eq ( 1 ) , the dynamics of the signals of the senders are governed by Eqs ( 2 ) and ( 3 ) , and the interactors by Eq ( 4 ) . The initial conditions for simulations were chosen to mirror experimental conditions . As in experiments , initially 107 sender cells were added to the inner circle . The inner circle was assumed to have an initial AHL concentration of 70 nM . The outer circle has a variable mixture of receivers and interactors distributed evenly in space . In all cases , there were 108 receivers cells . The initial concentration of the interactor AHL in the outer circle was 70 nM [31] . In simulations , the amount of interactor strain was adjusted , as specified by the interactor to receiver ratio . Based on the diffusive gradients of signals created by the senders and the interacting species , the activity of the receivers was calculated using , AR=g ( P ( cS ) ws+P ( cint ) wint ) , ( 5 ) where , P ( cS ) is the probability that the AHLs from the sender will bind to the receptor , P ( cint ) is the probability that the AHLs from the interactor will bind to the receptor , ws is the weight associated with the sender AHL and wint is the weight associated with the interactor AHL . The activity of the receivers modulates the production of the fluorescent gene reporter ( RFP ) , as the reporter gene is transcribed by a promoter regulated by signal bound receptor . The level of activity of the receivers acts as an indicator of changes in gene expression resulting from the crosstalk . Therefore , we define a threshold activity level for the activation of gene expression and used Eq ( 5 ) to track whether the activity level of the receivers exceeded this threshold . In simulations , the threshold activity was taken to be half of the maximum activity level when there is no crosstalk . To obtain signal weights of the sender AHL and the two interacting AHLs , simulation results were fit using the experimental data from Fig 3 . The weight parameter ( w1 ) for the signal 3-oxo-C6-HSL binding to the LuxR receptor was fit using the experimental data for no crosstalk . Non-linear least square fitting method was used for this purpose , see Figure Ia in S1 File . Additional weight terms are needed to account for each interacting AHL . To identify the weight parameters of the interacting AHLs , experimental plots shown in Fig 3 were fit for the case of 0 . 9 ratio of interactor to receiver using the non-linear least square fitting ( Figure I in S1 File ) . Calculated weights for signals produced by LasI and RhlI interactor strains are shown in Table 1 . Using these weights , activation curves for the ratio of interactor to receiver of 0 . 2 and 0 . 5 were simulated , as shown in Fig 4A and 4B , revealing a scaling similar to experimental data shown in Fig 3 . The validated model of crosstalk has enabled the exploration of the robustness of signal propagation . Signal propagation in the presence of variable levels of crosstalk was simulated for both the LasI and RhlI interactor strains . Fig 4C shows the predicted delay in the activation of the receiver strain at distances of 0 , 2 , 5 , 7 and 10 mm for ratio of interactor to receiver values between 0 and 1 . Data points show experimental measurements of the activation of the RFP response at those distances and crosstalk levels , revealing a good agreement with model predictions . Using the model , we predicted the sensitivity of signaling dynamics to changes in model parameters , including cell growth rate ( Figure J in S1 File ) , signal production rate ( Figure K in S1 File ) , diffusion coefficient ( Figure L in S1 File ) , and the signal degradation rate ( Figure M in S1 File ) . Activation times strongly depended on the diffusion coefficient , signal production rate , and signal degradation rate , as together these parameters set the concentration profile of the signal . Growth rate did not affect the activation times , likely because cells were loaded onto the plate at a density near saturation , so few divisions took place during the experiment . Additionally , we observe that the crosstalk is highly correlated to the binding energy and the weight parameter , see Figure N and Figure O in S1 File . The activity of the receiver strain for variable concentrations of the signals made by the sender strain and the interactor strain is also plotted in Figure P in the S1 File . The model predicts that the activity of the senders are unaffected by signal exchange with the interactors , see Figure Q in the S1 File . The assay also enables measurements of crosstalk with wild isolates . As an initial test , crosstalk with wild type Pseudomonas aeruginosa was measured , as shown in Fig 5A . The presence of Pseudomonas aeruginosa at 0 . 9 ratio of interactor to receiver delays activation by several hours . Because the las and rhl genes are derived from Pseudomonas aeruginosa , the weight parameters extracted from the E . coli interactor strains were used to predict the expected delay in activation as a result of crosstalk with these two systems . We simulated interactions with a hypothetical interactor strain containing both the las and rhl . Here the activity for the receivers will be , AR=g ( P ( cLux ) wLux+P ( cLas ) wLas+P ( cRhl ) wRhl ) . ( 6 ) In Fig 5A , we observed that the trend of the simulated activation curve is similar to the experimental results , showing delayed activation and a shallower activation curve across the plate . The predicted delay was shorter than the experimentally measured delay by approximately 2 hours . In the S1 File , additional simulations are performed to determine if a delay in growth of the sender strain or the influence of the sender strain AHL on AHL production in P . aeruginosa might contribute to the additional delay in activation . A reduction in the sender strain growth rate , when cocultured with P . aeruginosa , was confirmed in growth measurements , see Figure R in S1 File . Fig 5A shows the prediction of the model that incorporates growth influences . Although QS activation was delayed , growth interactions alone were not sufficient to reproduce the 2 hour delay in activation , see Text B , Figure S and Figure T in S1 File . Next , the crosstalk potential of four additional wild isolates was tested . The four were added to the plate assay at 0 . 9 ratio of interactor to receiver . 16S rRNA sequencing identified the wild type species as Aeromonas hydrophila , Aeromoans veronii , Pantoea agglomerans , and Pantoea vagans . The ability of these strains to produce AHL has been reported previously [37–42] . In Fig 5B , we observed that when A . veronii or P . vagans were added to the lawn of receiver cells , the activation of the RFP in the receivers was earlier as compared to no interactor strain . A . hydrophila and P . agglomerans both delayed activation . The extent of crosstalk was different for each species , suggesting that the activation of genetic expression in diverse communities is likely influenced by crosstalk of variable strength from multiple species .
Our results give new insights into signaling within mixed communities of bacteria . Adapting an approach used in previous studies [24 , 25 , 28 , 43–45] , we created a sender-receiver type plate assay to quantify the activation of gene expression due to AHL-mediated signaling in the presence of multiple signal producing strains . The assay measured the robustness of specific signaling networks to interference by a strain producing a non-cognate signaling molecule . When comparing the spatial reaction-diffusion based assay to well-mixed systems , we found that the spatial assay is able to differentiate an interactor strain that produces a non-cognate signal from a strain that produces a signal destroying enzyme , see Figure U in S1 File . Although crosstalk between quorum sensing networks has been previously reported [9 , 11 , 12 , 23 , 46 , 47] , our titration of the interacting strain revealed the sensitivity of signal-mediated gene expression in a spatially distributed network to interference . As shown in Fig 4C , at distances 2 mm or less , the fold change is less than 10% even for crosstalk ratios of 1 . At distances of 10 mm , the activation time has changed by approximately 10% at only 20% ratio of interactor to receiver . These numbers suggest that quorum sensing based genetic activity is largely robust to interference and that any abundant species should activate its quorum sensing network in a typical system . Our results are specific to LasI and RhlI interference with LuxRI QS system , and it is yet unclear if some AHL systems would have evolved differing levels of robustness or sensitivity to particular non-cognate signals . The model demonstrates the robustness of the AHL network to interference is in part due to differences in the binding energies of cognate and non-cognate signals . Figure N in the S1 File shows that as the binding energy of the non-cognate signal weakens , crosstalk from the non-cognate signal has little effect on gene regulation . The ratio of interactors to receiving cells also influences robustness . As shown in Figure V in the S1 File , when interactor cells greatly outnumber the receiver cells , robustness is lost and the expression of LuxR/I regulated genes is delayed by several hours . A third factor affecting robustness is the interaction weight for the non-cognate signal , as shown in Figure O in the S1 File . Future work should further characterize the range of interaction weights present in real systems . A better understanding of the robustness of signal exchange in mixed populations would be beneficial to the implementation of quorum sensing gene circuits in synthetic microbial communities [2 , 15 , 18–20 , 44] . Our experimental measurements aided in the development of a detailed model to predict AHL-based signaling dynamics in mixed populations . The model accounts for crosstalk between strains using a single parameter called the weight that we calculate from experiments for a given receptor for each combination of signals . This weight accounts for the downstream regulatory consequences of a receptor binding to the AHLs , and would be related to fundamental processes such as receptor dimerization , interactions between the receptor , DNA , and RNA polymerase , and the transcription and translation of the AHL-regulated genes . We found that a model using a single weight value was in good agreement with experimental activation dynamics covering over 1 cm of space with variable amounts of interference . This close agreement between the model and experiments suggests that the model can be implemented to examine quorum sensing crosstalk in more complex and realistic contexts , such as in the presence of more than two strains , when cells are heterogeneously distributed in space , or even when transport dynamics are spatially dependent [21 , 25 , 30] . Since the experiments in these contexts would be challenging , our assay and model provide a straightforward path towards predicting signaling dynamics in complex conditions . In addition to predicting the dynamics in complex conditions , as mentioned above , the model has enabled an exploration of how robustness to interference might emerge by adjusting the parameters that regulate the response to signal exchange . Robustness can be achieved if the receptor has evolved to bind the non-cognate signal much more weakly than the cognate signal . The difference in the receptor binding energies between the non-cognate and cognate signals needed for robustness is influenced by the number of interactor cells and the influence of the non-cognate signal on gene expression , as captured in the weight term . Some receptors may have evolved a sufficient amount of binding discrepancy based on interactor strains and non-cognate signals typically encountered . Our analysis of signal interference with wild species revealed a wide variety of crosstalk patterns within natural populations . We found both excitatory and inhibitory crosstalk within our isolates and a variable extent of crosstalk with the luxRI quorum sensing system . Previous measurements have also shown that non-cognate AHLs can interact with receptors such as LuxR to varying degrees [9 , 23] . Because we use the wild isolate directly instead of purified signal , cell free supernatant , or synthetic producer strains , we capture both direct and indirect signaling interactions with the interacting strain . Examples of indirect interactions include modulation of growth rate and gene regulatory pathways , and the evolving spatial distributions of the interactor . We attempted in the case of the interaction with Pseudomonas aeruginosa to specify the source of these indirect signaling interactions by independently accounting for the influence of each AHL signal produced by the interactor strain and growth influences of the interactor strain on the receiver cells . Although the model predicted an increased delay in activation due to growth effects , as shown in Fig 5A , there are still additional currently unknown interactions that further delay activation . We speculate that the QscR receptor residing in Pseudomonas aeruginosa might be absorbing the sender AHLs and contributing to this delay [48] , although other non-AHL based regulatory interactions between species likely contribute to signaling dynamics . Future efforts should attempt to disentangle the direct and indirect interactions that influence signal transduction to improve our ability to predict signaling dynamics in real populations . In addition , for some ecological niches , growth dynamics and cell movement can affect the AHL gradients in unexpected ways and these factors should be incorporated to any future work to understand signaling dynamics in complex environments [49 , 50] . Using the assay to broadly sample interactions between known AHL signal-receptor system , such as luxRI , and wild signal producers should yield new insights into patterns of crosstalk within real environments and their consequences in ecosystem level regulation of quorum sensing .
In Table 2 , we have represented the details of the bacterial strains used in this study . The host strain used for the sender , receiver and interactors are Escherichia coli NEB 5-alpha . The major QS signals are 3-oxo-C6 HSL for the sender strain , 3-oxo-C12 HSL for the LasI interactor strain , and C4-HSL for the RhlI interactor strain , see Figure A in S1 File for further details . The plasmids were either obtained from Addgene [21] or constructed using Gibson assembly ( New England Biolabs ) . The bacterial strains were inoculated from frozen stocks in a 12 ml Falcon tube with 5 mL of LB broth with appropriate antibiotics . The inoculum was grown in a shaker at 220 RPM at 37°C for 16 hours . Cells were resuspended in fresh media to remove signal in the supernatant . Late log phase cultures were used such that quorum sensing of the sender strain was activated before measurement in the plate assay . The plate assay was setup as described in Silva et al [35] . The interactor strain was mixed with 100 μl of the receivers in a 1 . 5 mL centrifuge tube and spread onto the top of 2 . 5% LB agar plates using sterile 4 mm glass beads . Figure W in the S1 File shows that the spatial distribution of cells remained mixed during the assay . A Nikon eclipse TI fluorescent microscope was used for image acquisition . Experiments were done at 37°C using a temperature controlled chamber . Samples were imaged at a magnification of 20x . To record the RFP activation in the receiver cells , RFP images were taken every 15 minutes for 16 hours at 30 different distances from the sender colony . Activation times were calculated at each position . Exposure times were 1s for RFP and 500 ms for GFP . No significant photobleaching was observed . Each image taken was saved in . tiff format and analyzed using a custom Matlab code . A low threshold was applied to the RFP images to identify the location of the receiver cells within each image . An upper threshold was used to identify the receivers that had activated RFP . For each time point and position , the fraction of cellular pixels above the RFP activation threshold was calculated . If the fraction of activated pixels exceeded 10% , that position was included as part of the activated region , see Figure C in S1 File . To obtain growth curves , overnight cultures were diluted 1 to 1000 in LB media and selective plating was performed to measure cell density over time . To obtain growth curves from mixtures of strains , each strain had a unique resistance marker and was plated on the appropriate selection plate . Tecan Infinite m200 Pro plate reader was used to measure growth rates and fluorescence activation in well-mixed conditions . Cells were grown to late log phase , diluted 1000 fold in pure LB media , and cultured for an additional 3 hours . After three hours of growth , 200 μl of these early log-phase cells were loaded into a flat bottom 96-well plate . The plate was inserted into the plate reader set to 37°C and the optical density and fluorescent intensity were measured every 15 minutes for 16 hours . Optical density measurements were carried out at a wavelength of 600 nm . For GFP measurements , a wavelength of 485 nm was used for excitation and a wavelength of 515 nm was used for emission . For RFP fluorescence measurements , a wavelength of 590 nm was used for excitation and a wavelength of 650 nm was used for emission . | In nature , bacteria are commonly found in spatially heterogeneous mixtures . Within these environments , multiple species communicate using chemical signals , and crosstalk often governs the activities of microbial populations , including interactions with the host system , forming biofilms , and bioluminescence . Understanding such bacterial interactions is essential to control and prevent these population-level behaviors regulated by signal exchange . Additionally , quantifying bacterial crosstalk will help improve the robustness of synthetic cellular networks that utilize signal exchange . Although cellular signaling is understood in well-mixed systems with one signal , we lack a detailed understanding of signaling in spatially distributed cellular networks or networks with multiple signals . We created an experimental system to observe and quantify microbial crosstalk between three bacterial languages . A mathematical model was implemented to predict the consequences of the exchange of multiple signals within cellular networks and good agreement between the experimental results and theoretical predictions was observed . In the mathematical model , a single parameter was sufficient to account for crosstalk between bacterial species . These experimental and theoretical tools enable us to better understand and predict how signaling influences the behavior of both natural and synthetic microbial communities . | [
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] | 2017 | Quantifying the strength of quorum sensing crosstalk within microbial communities |
The function of Toll pathway defense against bacterial infection has been well established in shrimp , however how this pathway responds to viral infection is still largely unknown . In this study , we report the Toll4-Dorsal-AMPs cascade restricts the white spot syndrome virus ( WSSV ) infection of shrimp . A total of nine Tolls from Litopenaeus vannamei namely Toll1-9 are identified , and RNAi screening in vivo reveals the Toll4 is important for shrimp to oppose WSSV infection . Knockdown of Toll4 results in elevated viral loads and renders shrimp more susceptible to WSSV . Furthermore , Toll4 could be a one of upstream pattern recognition receptor ( PRR ) to detect WSSV , and thereby leading to nuclear translocation and phosphorylation of Dorsal , the known NF-κB transcription factor of the canonical Toll pathway . More importantly , silencing of Toll4 and Dorsal contributes to impaired expression of a specific set of antimicrobial peptides ( AMPs ) such as anti-LPS-factor ( ALF ) and lysozyme ( LYZ ) family , which exert potent anti-WSSV activity . Two AMPs of ALF1 and LYZ1 as representatives are demonstrated to have the ability to interact with several WSSV structural proteins to inhibit viral infection . Taken together , we therefore identify that the Toll4-Dorsal pathway mediates strong resistance to WSSV infection by inducing some specific AMPs .
Multicellular organisms have evolved for the ability to protect themselves from a wide variety of pathogens such as viruses . In invertebrates including shrimps that lacking immunoglobulin-based adaptive immune system , this protection is thus provided through the action of an innate immune system . The innate immune response is generally initiated via the detection of pathogen-associated molecular patterns ( PAMPs ) , some evolutionarily conserved structures or motifs shared by broad classes of invading organisms , by a wide diversity of host pattern recognition receptors ( PRRs ) [1] . One important class of PRRs is the Toll receptor superfamily , comprising invertebrate Tolls and vertebrate Toll-like receptors ( TLRs ) , and is now considered to be the primary sensor of pathogens in all metazoans [2] . Mammalian TLRs play universal and pivotal roles in host defenses mainly via the innate immune system , but also the immunoglobulin-based adaptive immune system that are devoid in invertebrate [3] . In human , the function of TLR signaling pathway is clearly clarified , and the ten TLRs can directly recognize and bind to a number of diverse molecular structures , including lipids ( e . g . , TLR4: LPS via MD2; TLR1/2/6: lipoproteins ) , proteins ( e . g . , TLR5: flagellin; TLR2 and TLR4: HMGB1 ) , and nucleic acids ( e . g . , TLR3: dsRNA; TLR7/8: ssRNA; and TLR9: unmethylated CpG motifs in bacterial , viral , and fungal DNA ) [4] . It is generally accepted that TLRs require ligand‐induced dimerization or crosslinking to initiate intracellular signaling event [4] . Ligand binding is likely to lead to a conformational rearrangement of the cytoplasmic TIR domains , thereby creating a docking site to which TIR domain containing adaptors MyD88/TIRAP/TRIF/TRAM can be recruited [2 , 4] . The stimulation of TLRs then results in activation of the NF-κB ( nuclear factor κB ) transcription factors that drive the transcription of proinflammatory cytokines , or/ and trigger IRF ( interferon regulatory factor ) transcription factors that induce transcription of type I interferon cytokines , both of which ultimately confer immune response against infection [2 , 4] . Drosophila genome encodes nine Toll receptor genes ( Toll1 to Toll9 ) , but these Tolls are different and elusive in the context of function , ligand sensing and intracellular signaling compared to those of mammals . Drosophila Toll1 ( or simply , Toll ) generally binds an endogenous cytokine Spätzle ( Spz ) rather than microbe motif [5] . In contrast to that mammalian TLRs function mainly in immunity , Drosophila Toll1 functions not only in developmental processes [6 , 7] , but also in innate immunity response to bacterial , fungal , and viral infections [8–10] . Drosophila Toll7 can directly interact with vesicular stomatitis virus ( VSV ) virion at the plasma membrane perhaps in a manner similar to mammalian TLRs , and induces antiviral autophagy independent of the canonical Toll signaling pathway ( MyD88/Tube/Pelle/Dorsal or Dif cascade ) [11] . Besides , Drosophila Toll2 ( 18-wheeler ) may play a minor role in the antibacterial response [12] , and Toll5 and Toll9 can trigger the production of the antifungal gene Drosomycin [13] . By now , only the Drosophila Toll1 is definitely identified as upstream receptor of Dorsal or Dif , two transcription factors homologous to Human NF-κB [14] . However , the function and intracellular signaling routes of the remaining Tolls in response to infection are not well characterized . Cultured shrimps frequently suffer from many DNA and RNA viruses , among which white spot syndrome virus ( WSSV ) has been considered as the most serious threat to shrimp aquaculture industries and caused serious economic losses every year [15 , 16] . WSSV is a large and enveloped dsDNA virus , which is highly pathogenic and especially virulent on penaeid shrimp [15 , 17] . There is growing interest in research of the interplay between WSSV and many aspects of shrimp host [18 , 19] , but the precise function of Toll receptors and Toll pathway related genes participating in viral infection remains to be determined . Until now , a large number of Tolls have been identified in multiple shrimps including Litopenaeus vannamei [20 , 21] , Fenneropenaeus chinensis [22] , Penaeus monodon [23–25] , Marsupenaeus japonicas [26] , Macrobrachium rosenbergi [27] , Cherax quadricarinatus [28] and Procambarus clarkii [29 , 30] . In L . vannamei , three Tolls of the Toll1 , Toll2 and Toll3 are up-regulated after WSSV challenge , whereas their functions during WSSV infection are not well characterized [21] . Some shrimp Tolls from C . quadricarinatus , P . clarkii and M . rosenbergii have been shown to induce some AMPs expression in response to WSSV infection , which indicate these AMPs could play an antiviral role [28 , 29 , 31] . Besides , WSSV infection can contribute to activation of many P . monodon Toll pathway genes , which suggest that the whole pathway play a crucial role in the immune response during WSSV infection [32] . In a recent study , the Toll3 from L . vannamei is demonstrated to have the ability to activate the expression of interferon regulatory factor ( IRF ) and its downstream Vago4/5 , suggesting it could play a critical role in host antiviral immunity independent of the canonical Toll signaling pathway [33] . Overall , some shrimp Tolls have been shown to participate in innate immune responses against viral infection , but their antiviral functions remain largely elusive and their underlying antiviral mechanism needs further investigation clearly . Herein , we clone and identify a total of nine Tolls ( Toll1-9 ) from L . vannamei , and RNAi screening demonstrates Toll4 as a critical antiviral factor against WSSV in vivo . Mechanismly , Toll4 senses WSSV infection and leads to activation of Dorsal to converge on the production of some specific AMPs such as ALFs and LYZs , which confer host defense against WSSV by targeting its structural proteins . These data provide evidence that a new identified Toll4 senses WSSV and initiates an antiviral response in shrimp .
In order to identify all candidate Toll genes from shrimp ( L . vannamei ) , we carried out protein homology search by local TBLASTN program against our transcriptome data from whole bodies of L . vannamei ( all tissues pooled ) [34] and other L . vannamei transcriptome data from NCBI [35–38] . A total of nine individual and putative Toll homologs from L . vannamei were obtained , of which the Toll1 , Toll2 and Toll3 were perfectly identical to previous reported LvToll1 , LvToll2 and LvToll3 , respectively [21] . We next cloned the full-length cDNA sequences of other Tolls by using rapid amplification cDNA ends ( RACE ) -PCR method , and we subsequently designated these Tolls from Toll1 to Toll9 according to the time order of their being cloned . Their sequences of Toll1-9 in FASTA format were available in Supplement Data S1 . Functional domain analysis indicated that each of the nine Tolls adopted a typical domain organization characteristic of Toll family gene including an N-terminal signal peptide , an extracellular domain , a single transmembrane region and an intracellular TIR domain in the C-terminal ( Fig 1 ) . These Tolls varied considerably in the number of extracellular LRRs from 7 ( Toll7 ) to 28 ( Toll5 ) ( Fig 1 ) , and the intracellular TIR domains were not conserved among each other except for the two pairs of Toll1/2 and Toll3/Toll8 bearing sequence identities of greater than or equal to 70% ( S1 Fig ) . The significant differences both in number of extracellular LRRs and sequence identities of intracellular TIR domains may suggest that these Tolls are able to respond to various extracellular stresses ( pathogens or ligands ) and exploit distinct intracellular signaling routes . Phylogenetic tree analysis showed that most of Tolls from invertebrates including the Tolls from L . vannamei Toll1 to Toll6 , and Toll8 , as well as the Tolls from insects and other species were clustered together . However , L . vannamei Toll7 and Toll9 and Drosophila Toll9 were clustered with vertebrates TLRs , which were separated with several groups such as TLR1 to TLR10 , TLR12 and TLR15 ( S2 Fig ) . Taken together , we cloned and identified nine Tolls from L . vannamei , among which six Tolls namely the Toll4 , Toll5 , Toll6 , Toll7 , Toll8 and Toll9 are firstly cloned and identified in L . vannamei . To determine whether any of the shrimp Tolls are involved in antiviral defense against WSSV , we generated double-stranded RNA ( dsRNA ) against each of the nine Tolls and silenced them in vivo via dsRNA mediated RNA interference ( RNAi ) . We firstly addressed the tissue distribution of shrimp nine Tolls transcripts by Semi-quantitative reverse transcription PCR ( Semi-qRT-PCR ) . The results showed that both Toll1 and Toll4 could be detected in all the examined tissues and were highly expressed in gill , hemocyte and intestine , whereas other Tolls were abundant in only a few specific tissues ( Fig 2A ) . According to the tissue distribution of each Tolls , the gill tissue was chosen to check the knockdown efficiencies for Toll4 , Toll6 , Toll7 and Toll8 ( Fig 2C ) , while hemocyte was as the target tissue to evaluate silencing efficiencies for Toll1 , Toll2 , Toll3 Toll5 and Toll9 ( Fig 2B ) . Efficient silencing for each Toll receptor was confirmed by quantitative reverse transcription PCR ( qRT-PCR ) ( Fig 2B and 2C ) . Next , we challenged RNAi-treated shrimps with WSSV and subsequently analyzed viral genome copies ( WSSV DNA ) by absolute quantitative PCR ( absolute q-PCR ) . We observed a greater number of each Toll except for Toll2 silenced shrimps exhibited higher quantities of viral titers in muscle when compared to control shrimps at 48 hours post infection ( hpi ) ( Fig 2D ) . Of note , shrimps with silencing of Toll4 had the highest WSSV titers , and the average of viral DNA burden was approximately 150 times higher than that of the control shrimps ( GFP dsRNA injected shrimps following WSSV infection ) ( Fig 2D ) . Therefore , we focused our special attention on the Toll4 mediated mechanism underlying resistance to WSSV in this study . To further confirm the above screening result , with in vivo RNAi again , a higher lethality was observed in the silencing of Toll4 shrimps followed by WSSV infection when compared to that of the dsRNA-GFP treated control group ( Fig 2E ) . To investigate whether the increased lethality rates of Toll4 silenced shrimps was due to decreased resistance or tolerance to WSSV , we analyzed the viral levels by absolute q-PCR in several tissues including hepatopancreas , gill and muscle at 48 hpi . We observed that shrimps with knockdown of Toll4 had elevated viral replication levels in all the three tissues than dsRNA-GFP treated control shrimps ( Fig 2F ) . Of note , dsRNA-GFP group had higher cumulative mortality rate and viral loads than the PBS group ( Fig 2E and 2F ) , which supported the conclusion of existence of nucleic acids induced antiviral immunity in shrimps [39–43] . The obviously lower viral levels observed in hepatopancreas than those of gill and muscle could be explained by that only the connective tissue and myoepithelial cells of the hepatopancreas sheath are infected by WSSV [44 , 45] . Collectively , these data strongly suggest Toll4 as a critical antiviral factor against WSSV in vivo . Because the Toll4 can confer protection against WSSV , we then explored whether the canonical Toll pathway components including MyD88 , Tube , Pelle and Dorsal are involved in antiviral responses . We injected shrimps with dsRNA of each component to depress their expression , which were confirmed by quantitative RT-PCR ( Fig 3A ) . We observed that knockdown of these critical Toll pathway components had notable impact on WSSV replication in vivo ( Fig 3B ) , suggesting that the canonical Toll pathway plays an important role against WSSV . In summary , these data show that Toll4 and the canonical Toll pathway components are crucial to oppose WSSV infection . Mammalian TLRs opposed viral infection mainly through inducing the type I interferon ( IFN ) expression [46] , whereas Drosophila Toll restricted viral infection via inspiring some specific AMPs production [10] . To address whether the involvement of Toll4 in regulating AMPs synthesis , we firstly studied the response of Toll4 to viral infection and measured the transcriptional changes of Toll4 after WSSV challenge in the two immune related tissues gill and hemocyte by quantitative RT-PCR . The results showed that Toll4 in gill was remarkably up-regulated from 8 hpi to 24 hpi compared to the control shrimps injected only PBS ( Fig 4A ) , and Toll4 in hemocytes maintained an elevated expression level in the whole period of infection ( Fig 4B ) . To test whether some known shrimp AMPs can respond to WSSV infection , we detected the transcriptional levels of fourteen shrimp AMPs consisting of four different AMP families of anti-lipopolysaccharide ( LPS ) factor ( ALF ) , lysozyme ( LYZ ) , penaeidin ( PEN ) and crustin ( CRU ) , by quantitative RT-PCR at 6 hpi in gill and hemocyte tissues ( Fig 4C and 4D ) . These sequences of fourteen shrimp AMPs in FASTA format were available in Supplement Data S2 . Vibrio parahaemolyticus is a pathogen of shrimp has been verified as an activator of the shrimp Dorsal-AMPs pathway [47] . Thus it was used here as a control to reveal the levels of AMPs expression in bacterial infection and in WSSV infection . PBS controls were used for baseline levels of AMPs expression . We found that , except for ALF4 , PEN2 and PEN3 with slight a up-regulation less than 2-fold in gills , both WSSV and V . parahaemolyticus infection induced considerably increased expression levels of the most of AMPs over 2-fold than those of control shrimps in the both tissues ( Fig 4C and 4D ) . Moreover , we used the RNAi to knockdown the expression of Toll4 in vivo once again , and the silencing efficiencies of Toll4 in gill and hemocyte at 24 h and 48 h post dsRNA injection were further confirmed by quantitative RT-PCR ( Fig 4E and 4F ) . After 48 h post dsRNA injection , we infected the RNAi treated shrimps by injection of WSSV , and probed the expression levels of the fourteen AMPs at 6 hpi . The results showed that all of these AMPs , except for CRU3 with a slight up-regulation , were dramatically down-regulated compared to the control in hemocytes ( Fig 4H ) . In the gills of WSSV infected shrimps , RNAi of Toll4 led to the down-regulated secretion of LYZ1 , LYZ2 , LYZ4 , ALF1 , ALF2 , ALF3 and CRU3 , almost of which belonged to LYZ and ALF families ( Fig 4G ) . These results suggest that Toll4 induced AMPs expression may vary in gill and hemocyte tissues under WSSV infection . Even so , most of the tested AMPs from LYZ and ALF families in both hemocytes and gills showed an identical down-regulation pattern in Toll4 silenced shrimps under WSSV infection . In summary , we conclude that WSSV infection in shrimp could activate Toll4 expression and perhaps through Toll4 to induce the production of a specific set of AMP genes including LYZ1 , LYZ2 , LYZ4 , ALF1 , ALF2 and ALF3 both in gill and hemocyte tissues . Since we observed that Toll4 responded to WSSV infection and ultimately led to the induction of a specific set of AMP genes , we explored whether Dorsal , the NF-κB transcription factor known to be downstream of the canonical Toll pathway [48] , is activated during viral infection . We firstly detected the tissue distribution of shrimp Dorsal , and found that Dorsal showed high expression levels in gill , hemocyte , stomach , intestine and epithelium , but low in hepatopancreas ( Fig 5A ) . We then investigated the effect of WSSV infection on Dorsal nuclear translocation by immunofluorescence staining in shrimp hemocytes . To gain more information on nuclear translocation of Dorsal upon WSSV infection , we firstly detected the dynamics of Dorsal translocation at several times , and we observed that shrimp hemocytes exhibited gradually and significantly increased nuclear translocation levels of Dorsal ( 53% , 88% and 94% ) compared to the control hemocytes treated with PBS ( Normal 0 h , 20% ) at 1 h , 3 h and 6 h after WSSV challenge , respectively ( Fig 5B and b ) . To further confirm the above results , we probed Dorsal translocation from the cytoplasm to the nucleus upon WSSV infection by using an L . vannamei Dorsal specific antibody prepared in our previous study [49] . In good agreement with the results of immunofluorescence staining , we were able to detect more nuclear import of Dorsal in shrimp hemocytes along with the times of WSSV infection from 0 h to 6 h , while at 6 hpi only a slight signal of Dorsal can be detected in the cytoplasm ( Fig 5C ) . In addition to nuclear translocation , the activation of Dorsal could be manifested by phosphorylation on some specific amino acids . We found that shrimp Dorsal contains a considerable conserved region , which displays a significant degree of sequence similarity to a comparison region of its mammalian counterparts . In this region , human p65 NF-κB factor contains a Ser276 , corresponding to Ser342 of shrimp Dorsal , which can be phosphorylated upon many stresses including viral infection [50] ( Fig 5D ) . We therefore hypothesize that the Ser342 of Dorsal could also be phosphorylated after WSSV infection , and we detected Dorsal phosphorylation by using the human p65 Ser276 phosphorylation antibody . The results showed that a strong phosphorylation signal of Dorsal was observed at 3 hpi and 6 hpi ( Fig 5E ) , correlating well with Dorsal enrichment in the nucleus at 3 hpi and 6 hpi , respectively . Taken together , WSSV infection did induce shrimp Dorsal translocated from the cytoplasm to the nucleus , and phosphorylation on Ser342 . Because Dorsal can be activated upon WSSV infection , we reason that this activation of Dorsal could lead to trigger the expression of some AMPs . To address this , we detected the expression levels of Toll4 dependent AMP genes of ALF1-4 and LYZ1-4 in vivo either when Dorsal activity was suppressed by NF-кB inhibitor or when Dorsal expression was silenced by RNAi , respectively . Firstly , we measured whether a NF-κB inhibitor QNZ ( EVP4593 ) can work for Dorsal activity suppression . We observed that the 2 ug/per shrimp was able to suppress Dorsal Ser342 phosphorylation efficiently in vivo and this quality was used in the following analysis ( Fig 6A ) . In order to confirm whether Dorsal regulated AMPs expression , we injected the shrimp with the NF-κB inhibitor QNZ prior to the WSSV challenge , and investigated Dorsal translocation , phosphorylation and AMPs expression . We analyzed the effect of QNZ on Dorsal nuclear translocation in hemocytes under WSSV infection by immunofluorescence staining and western blotting ( WB ) analysis . The results showed that QNZ significantly suppressed Dorsal translocated from the cytoplasm to the nucleus ( Fig 6B and b ) , which was consistent with the WB analysis of Dorsal cytoplasmic and nuclear localization ( Fig 6C ) . Expectedly , Dorsal Ser342 phosphorylation was efficiently suppressed by QNZ , although shrimps hemocytes were under WSSV challenge ( Fig 6D ) . Further , in the NF-κB inhibitor-injected shrimp the WSSV challenge failed to up-regulate Toll4 dependent AMPs expression both in hemocytes and gills , respectively ( Fig 6E and 6F ) . On the other hand , we carried out RNAi to knockdown Dorsal in vivo , WB and qRT-PCR analysis confirmed efficient silencing of its protein levels ( Fig 6G ) and mRNA levels ( Fig 6H ) , respectively . We observed that in the RNAi treated shrimps Toll4 dependent AMPs of ALF and LYZ families were marginally down-regulated after WSSV infection compared to GFP dsRNA treated control shrimps both in hemocytes and gills ( Fig 6I and 6J ) . These results persuasively confirm that Dorsal nuclear translocation and phosphorylation are functionally related to the increased expression of Toll4 dependent AMPs under WSSV infection in vivo . To further explore the potential mechanism by Dorsal to regulate expression of AMPs , ALF1 and LYZ1 as a representative was chosen and their 5' flanking regulatory regions ( Supplement Data S4 ) were obtained by Genome Walking method . By JASPER tool prediction , we found the presence of a putative NF-κB binding site in ALF1 and LYZ1 promoter , respectively ( S3A Fig ) . Luciferase reporter assays demonstrated that over-expression of L . vannamei Dorsal was able to activate the promoter activities of ALF1 and LYZ1 in a Dorsal-concentration dependent manner in Drosophila S2 cells ( S3B Fig ) . In addition , EMSA experiments indicated that Dorsal can interact with the putative NF-κB binding sites in ALF1 and LYZ1 promoters ( S3C Fig ) . Taken together , these evidences strongly suggest L . vannamei Dorsal is able to regulate the transcription of AMPs perhaps via interacting with the conserved NF-κB binding sites in their promoters such as ALF1 and LYZ1 . Because both Toll4 and Dorsal induced the same AMPs expression under WSSV infection , we tested whether WSSV mediated Dorsal activation is dependent on Toll4 . To assess this , RNAi was performed to evaluate the effects of Toll4 on Dorsal nuclear translocation and phosphorylation under WSSV infection in vivo . Efficient silencing of Toll4 mRNA was confirmed by quantitative RT-PCR ( Fig 7A ) . At 6 hours after WSSV infection , but not the negative control ( WSSV untreated , Fig 7B top panel ) , we were able to observe more nuclear imports of Dorsal in hemocytes of GFP dsRNA treated shrimps ( 96% ) than those of Toll4 silenced shrimps ( 57% ) ( Fig 7B and b , p < 0 . 01 ) . We confirmed these results by measuring Dorsal localization and phosphorylation after WSSV infection in hemocytes of GFP dsRNA and Toll4 dsRNA treated shrimps using WB analysis . A decreased percentage of nuclear translocation of Dorsal ( 46 . 18% ) was observed in Toll4 silenced shrimps under WSSV infection compared to that of dsRNA GFP treated shrimps ( 86 . 20% ) ( Fig 7C and c , p < 0 . 01 ) . Moreover , knockdown of Toll4 markedly reduced the WSSV induced phosphorylation of Dorsal when compared to GFP dsRNA treated shrimps ( Fig 7D and d , p < 0 . 01 ) . We next appraised the protein levels of shrimp Cactus , an inhibitor of shrimp Dorsal , by WB analysis using an L . vannamei Cactus specific antibody [49] . The results demonstrated that a very strong signal and a weak signal were detected in the negative control ( WSSV untreated ) and Toll4 dsRNA treated shrimps , respectively , but we failed to detect any signal of Cactus in the GFP dsRNA treated shrimps ( Fig 7E and e , p < 0 . 01 ) , which further confirmed that WSSV induced Dorsal activation was partially blocked in Toll4 silenced shrimps . To test whether Toll4 mediated Dorsal activation is pathogen specific , we tested its requirement to other shrimp pathogens including DNA viruses ( infectious hypodermal and hematopoietic necrosis virus , IHHNV , and shrimp hemocyte iridescent virus , SHIV ) , RNA virus ( yellow head virus , YHV ) and bacteria ( V . parahaemolyticus ) . The results showed that all the four types of pathogens could induce Dorsal activation with different degrees of nucleus translocations , but it seemed to be not relevant in the context of the silencing of Toll4 ( Fig 8 ) . The prominent nuclear translocation of Dorsal in hemocytes after V . parahaemolyticus challenge is in good concurrence with previous reports that G- bacterial infection can induce the activation of Dorsal and its translocation [47 , 51] . These analyses with different pathogens strongly suggest that Toll4 mediated Dorsal activation is WSSV specific , which indicate that Toll4 could play a vital role in recognizing WSSV infection . Taken together , our data suggest that Toll4 is a key factor for sensing WSSV and mediating downstream Dorsal activation , and thereby inducing some specific AMPs production . The induction of AMPs as a response to pathogenic infection is a crucial defense mechanism of innate immunity in invertebrates including insects and shrimps . Our results above have revealed that after WSSV infection Toll4 and Dorsal induced the same AMPs expression , raising the hypothesis of these AMPs playing antiviral role against WSSV . We silenced the eight AMPs including ALF1-4 and LYZ1-4 regulated by both Toll4 and Dorsal through dsRNA treatment . Silence efficiencies were confirmed by quantitative RT-PCR ( Fig 9A ) . To gain more information about the effects of AMPs on viral replication , a timeframe of 24 h , 48 h , 72 h , 96 h and 120 h after WSSV infection was selected to investigate the viral loads in gills of each AMP silenced shrimp . Compared with GFP dsRNA inoculated shrimps , shrimps in which ALF1 , ALF2 , ALF3 , ALF4 , LYZ1 , LYZ2 , LYZ3 or LYZ4 were silenced had higher viral burden ( Fig 9B , 9C , 9D , 9E and 9F ) at the whole timeframe . To further dissect the function of these AMPs during WSSV infection , a parallel experiment was performed to explore the survival phenotype of each AMP silenced shrimps followed by WSSV infection . Experimental shrimps were challenged with WSSV at 48 h post dsRNA injection , and the survival rate was recorded over a period of 168 h after the challenge . We observed that in each AMP-knockdown group except LYZ3-knockdown group , survival rate was lower than that of control group and shrimps were more susceptible to WSSV infection ( Fig 9G and 9H ) . Notably , the survival rate of LYZ3-knockdown group showed no significant difference , but had the trend of lower ( p = 0 . 3853 ) compared to that of control group ( Fig 9H ) . In summary , our data convincingly demonstrate that Toll4-Dorsal pathway regulated AMPs are involved in WSSV restriction in shrimp . Some AMPs have been reported to play a vital role in combating viral pathogens via directly acting on the viral virion [52 , 53] . To decipher the molecular basis underlying AMPs against WSSV , we performed an in vitro pull-down assay between AMPs and WSSV structural proteins in order to elucidate how these AMPs act on WSSV . In this study , we paid our attention to ALF1 and LYZ1 as a representative one of ALF and LYZ families , respectively . Several envelope and tegument proteins of WSSV such as VP24 , wsv134 ( WSSV189 ) and wsv339 ( WSSV395 ) have been proved to be the target of AMPs such as ALFs [54–56] . In addition , VP19 , VP28 , VP24 and VP26 are abundant in WSSV virion [15 , 57] , and they are always be targeted by other antiviral effectors such as Lectins [58–60] . So , four envelope proteins ( VP19 , VP28 , wsv134 and wsv321 ( VP16 ) ) and two tegument proteins ( VP24 and VP26 ) for the potential target of AMPs were used in the in vitro pull-down assay to explore the potential interaction between these structural proteins and ALF1 or LYZ1 . The six WSSV structural proteins with GST tag and the two AMPs ALF1 and LYZ1 with His tag were expressed and purified ( Fig 10A and 10B ) . In the His tagged ALF1 pull-down assay with six WSSV structural proteins ( GST tag ) , we observed that ALF1 precipitated VP19 , VP26 , VP28 , wsv134 and wsv321 by SDS–PAGE with coomassie blue staining ( Fig 10C , lanes 1 , 3 , 4 , 5 and 6 , respectively ) . However , His tagged ALF1 did not interact with GST tagged VP24 , which indicates that the interaction of between ALF1 and other four structural proteins is specific , but not related to the His and GST tags . We further confirmed this result by western blotting with GST tag antibody , which is in good agreement with that of coomassie blue staining ( Fig 10D ) . In the His tagged LYZ1 pull-down assay , we found that VP26 , VP28 , wsv134 and wsv321 were enriched ( Fig 10E , lanes 3 , 4 , 5 and 6 , respectively ) , and an identical result was observed by western blotting ( Fig 10F ) . To further identify the above results , six WSSV structural proteins with GST tag were used in a GST pull-down assay with purified His tagged ALF1 or LYZ1 followed by SDS-PAGE with coomassie blue staining and western blotting with His antibody , respectively . As shown in Fig 10G and 10H , VP19 , VP26 , VP28 , wsv134 and wsv321 interacted with ALF1 ( arrows ) in GST pull-down assay , which further confirmed the results of pull-down assay with His tagged ALF . In a similar manner , Fig 10I and 10J showed that VP26 , VP28 , wsv134 and wsv321 strongly interacted with LYZ1 . Thus , the results strongly suggest that ALF1 and LYZ1 were able to interact with WSSV structural proteins , to be specific , ALF1 interacted with VP19 , VP26 , VP28 , wsv134 and wsv321 , and LYZ1 interacted with VP26 , VP28 , wsv134 and wsv321 ( Fig 10K ) . To explore whether other members of ALF and LYZ family also exhibit the ability to interact with WSSV structural proteins , we additionally expressed and purified ALF3 and LYZ2 His-tag fused proteins ( S4A Fig ) . In the His pull-down assay , we observed that VP24 and VP26 was precipitated by ALF3 , and VP24 , VP26 , VP28 and wsv134 was precipitated by LYZ2 , respectively ( Figs S4B and 4C ) . The results demonstrated that it could be a general action by which Toll4-Dorsal cascade targeted ALFs and LYZs to interact with WSSV structural proteins . To further confirm the antiviral activities of ALF and LYZ family proteins , we performed a viral infection experiment by prior co-incubation of WSSV with recombinant ALF1 ( rALF1 ) and LYZ1 ( rLYZ1 ) . As shown in Fig 10L and 10M , the shrimps had reduced viral loads and showed more resistance against WSSV in the recombinant proteins co-incubation treated groups compared to TRX control group . Taken together , our results provide some substantial evidences to demonstrate that Toll-Dorsal pathway targeted AMPs exhibit antiviral activity by interacting with WSSV structural proteins .
Accumulating evidence indicates that shrimp Tolls participate in host defense against WSSV infection; however , the underlying mechanism of the Toll receptor mediated antiviral functions has been poorly understood . Herein , we have identified an antiviral role for a new Toll from L . vannamei , the Toll4 , in response to WSSV infection in vivo . Toll4 silenced shrimps demonstrate significantly elevated viral replication and mortality after WSSV challenge . Shrimps with knockdown of genes in some core components of the canonical Toll pathway such as MyD88 , Tube , Pelle and Dorsal have remarkably increased WSSV titers . Furthermore , Toll4 appears to be specific to sense WSSV infection to trigger Dorsal , which lead to induce a specific set of AMPs with the ability of interacting with viral structural proteins that confer resistance to viral infection . Our results have now demonstrated that the Toll4-Dorsal-AMPs cascade is involved in the control of WSSV infection in shrimp . Of note , a total of nine Tolls ( Toll1-9 ) have been identified from L . vannamei , but we can’t identify their corresponding orthologs to TLRs in mammals . As phylogenetic tree analysis shown , we find that nearly all of Tolls from invertebrates are clustered together , while vertebrates TLRs can separate into several groups such as TLR1 to TLR10 , TLR12 and TLR15 ( S2 Fig ) . Such results could be due to the following aspects: ( i ) Invertebrate Tolls display little conservation with vertebrate TLRs; ( ii ) The number of discovered Tolls from invertebrates is limited . As a result , we can’t draw a conclusion that Toll4 from L . vannamei is the ortholog of TLR4 in mammals . Therefore , the Tolls are named as Toll1 to Toll9 according to the time order of their being identified , like Drosophila Toll1 to Toll9 [61–65] . The Toll pathway is essential to establish an innate immune response to defend against a wide range of pathogens including virus . The importance of this pathway in the innate control of viral infections in insects is best demonstrated by that mutants in some core components such as Dif and Toll of Drosophila with increased susceptibility to infection [10] . The Drosophila Toll pathway has also been shown to play an universal antiviral role against multiply viruses by oral infection such as Drosophila C virus , Cricket paralysis virus , Flock house virus , and Nora virus [66] . In addition , several reports show that the Toll pathway has an antiviral role in innate immunity of mosquitoes [67–69] . Our results indicate that the increased lethality rates observed in the Toll4 silenced shrimps are associated with higher WSSV loads . Thus , the Toll4 is involved in resistance to WSSV and it is a major antiviral factor in shrimp . Moreover , several Tolls have been proved to confer antiviral immunity in other shrimps . For example , a Toll4 from P . clarkii [29] and a Toll from M . rosenbergii [31] are important for the innate immune responses against WSSV , although the exact antiviral mechanism is not elucidated . We also provide evidence indicating the key antiviral role of the canonical Toll pathway by that silencing of the core components such as MyD88 , Tube , Pelle and Dorsal results in increased WSSV titers . These data may suggest that the function of Toll pathway in the control of viral infections could be conserved through evolution . This is consistent with previous studies showing Toll pathway antiviral effect in other Arthropods including crayfish [28] , Drosophila [10 , 66] , mosquitoes [70] and honeybees [67] . In general , the canonical Toll pathway of Drosophila mediated immune response relies on the activation of the NF-κB transcription factors Dorsal or Dif , however whether it is true for shrimp is still largely unknown . Firstly , we demonstrate that Toll4 and Dorsal are involved in regulating the same AMPs after WSSV infection . In addition , our data show that detection of WSSV infection by Toll4 triggers transcriptional activity of Dorsal , but knockdown of Toll4 was not sufficient to restrain the activation of Dorsal in response to WSSV infection . This may be owing to inability to absolutely suppress Toll4 expression by RNAi method . But we cannot exclude the possibility that the activation of Dorsal in response to WSSV infection may integrate signals from other upstream receptors . In other words , there could be more than one upstream receptor , in addition to Toll4 , involved in the response to WSSV infection responsible for the activation of Dorsal . Of note , Dorsal has the ability to bind with the promoters of some WSSV genes such as the Immediate Early gene 1 ( IE1 ) and regulate their transcriptional expression in insect cells background or in virto [71] . Thus , it is thought that Dorsal is required for WSSV gene expression and genome replication [72] , more experiment evidence in vivo needs to support this . By RNAi method , we observe that shrimps with knockdown of Dorsal have elevated viral loads than those of the GFP control group , suggesting that Dorsal is important for host to limit viral replication . Besides , the report of WSSV encoding two MicroRNAs with the ability to suppress shrimp Dorsal also supports that Dorsal is a key restrict factor against viral infection [73] . However , knockdown of Dorsal results in lower viral loads than those of MyD88 , Tube and Pelle silenced groups , which may be explained by that Dorsal locates the lower levels at MyD88/Tube/Pelle/Dorsal cascade of the canonical Toll pathway . Some reports have showed that Tolls from Drosophila and shrimps inducing antiviral innate immunity are independent of activation of the transcription factor NF-κB ( Dorsal or Dif ) , as shown by the fact that Drosophila Toll7 activates antiviral autophagy not involvement of Dorsal or Dif [11] , as well as by the fact that shrimp Toll3 initiates a IRF-Vago dependent antiviral route [33] . Therefore , whether other Tolls are responsible for activation of Dorsal in response to WSSV infection , and how they confer resistance to WSSV infection deserve to be further studied . In addition , in the present study , we identified a total of nine Tolls , and silencing of each of Toll except for the Toll2 contributes to increased WSSV loads compared to the control shrimps . This observation is reminiscent of that the lack of immunoglobulin-based adaptive immune system and classical IFN mediated antiviral defense maybe require some invertebrates including shrimps and Drosophila to be more heavily dependent on the Tolls or other receptors for antiviral immunity . Identification of the target genes of the other Tolls after viral infection will be important to understand how they contribute to resistance to viruses . There are significant differences in the Toll and TLR receptors initiated activation by ligand recognition in invertebrate and vertebrate . In general , TLR receptors in mammals are able to detect microbial infection through directly binding to PAMP [2] , but Drosophila Toll1 instead interacts with the endogenous cytokine-like factor Spätzle , the product of a proteolytic cascade induced upon upstream recognition of fungal and bacterial PAMPs [68] . Notably , Toll7 in Drosophila can bind to vesicular stomatitis viruses at the plasma membrane and therefore has been considered as a specific and bona fide PRR for sensing this virus [11] . Interestingly , several Tolls from L . vananmei are able to detect some PAMPs directly , as shown by the fact that Toll1 and Toll3 can interact with CpG ODN 2395 in vitro [74] . Surprisingly , another study demonstrates that three Tolls from M . japonicas , two of them homologous to the above Toll1 and Toll3 from L . vananmei [74] , can directly bind to both PGN and LPS [51] . These data suggest that one type of shrimp Toll could recognize more than one PAMP . In the present study , we observed that Dorsal activation and translocation to the nucleus is dependent on Toll4 in response to WSSV infection , but not other tested pathogens , which indicates that Toll4 could also be a specific PRR to detect WSSV in a manner similar to Drosophila Toll7 . Unravelling how Toll4 senses WSSV in the future will be important to understand antiviral immunity in shrimp . The production of antimicrobial peptides ( AMPs ) is commonly considered to be an evolutionarily conserved mechanism of the innate immune response and has been extensively studied in vertebrates and other non-vertebrate organisms including shrimps . Some shrimp Tolls are able to resist bacterial infection via regulating a wide range of AMPs expression [28 , 29 , 75] , which inspires us to suppose that shrimp Tolls can also regulate some specific AMPs synthesis to oppose WSSV . In fact , our examinations of AMPs expression in WSSV-infected shrimp show an increase in expression of AMPs comparable to that found during a V . parahaemolyticus infection of shrimp . Moreover , the decreased expression of the same AMPs in Toll4 and Dorsal-silenced shrimp does show that Toll4-Dorsal pathway indeed devotes to induce AMPs transcription upon WSSV infection . By RNAi , we detect survival rates and viral titers in single AMPs silenced shrimps and find that each single AMP except for LYZ3 provides effective resistance to viral infection . Although evidence exists that some AMPs can respond to WSSV infection , it was not known how these shrimp AMPs affected viruses . Previous reports show that shrimp ALF can protect against WSSV infection via interfering with viral replication in vitro and in vivo in crayfish Pacifastacus leniusculus [76] and CqALF can disrupt WSSV envelope integrity that leads to the decrease of WSSV infectivity in the red claw crayfish Cherax quadricarinatus [77] . Furthermore , an ALF isoform 3 from P . monodon has performed its anti-WSSV action by binding to several viral structural proteins such as wsv131 ( WSSV186 ) , wsv134 ( WSSV189 ) and wsv339 ( WSSV395 ) [78] . On the other hand , lysozyme is a key effector of the innate immune system and kills bacteria by catalytic hydrolysis of cell wall peptidoglycan , but it also exhibits catalysis-independent antimicrobial properties . For example , human lysozyme has been shown to inhibit HIV-1 infection in vitro by preventing the adsorption and penetration of the virus [79 , 80] . HL9 , a nonapeptide fragment of human lysozyme , blocks HIV-1 viral entrance and replication by binding to the ectodomain of gp41 , the envelope glycoprotein of HIV-1 crucial to membrane fusion [80 , 81] . These data strongly suggest that ALF and LYZ family have effective antiviral activity , and it seems reasonable to hypothesize that shrimp ALF and LYZ family are able to interact with WSSV structural proteins . In this study , one anti-LPS-factor ALF1 and one Lysozyme LYZ1 are chosen to explore their antiviral actions . In agreement with this hypothesis , our results reveal that ALF1 interacts with VP19 , VP26 , VP28 , wsv134 and wsv321 , while LYZ1 interacts with VP26 , VP28 , wsv134 and wsv321 . Given their conserved sequences , the other AMPs , in addition to ALF1 and LYZ1 , could be able to interact with some specific WSSV structural proteins . Interestingly , currently , no mechanistic analysis on LYZ family genes responsible for antiviral role against WSSV has been performed except for a role in modulating the humoral response to this virus infection [82] . Thus , LYZ1 in this study is the first LYZ family gene identified with the capacity to interfere with replication of this important pathogen , which suggests that LYZ could be a new type of effectors for restricting WSSV infection . Of note , one type of AMP can interact with more than one WSSV structural protein , and vice versa . Likewise , WSSV structural proteins VP19 , VP26 and VP28 are shown to interact with each other to form a multiprotein complex [83] . So , it seems that AMPs interact with WSSV structural proteins as a manner of multiply layers or reticulation , which could be more effective to control virus . In addition to their important functions in maintaining the integrity of virion , WSSV structural proteins also play a key role in initiating viral infection [84] , as showed by fact that some structural proteins such as VP28 and VP26 are shown to being key factors essential for virus attachment and entry into host cells [84–87] . Therefore , based on our data together with previous reports , it is highly conceivable that the Toll4-Dorsal pathway regulated AMPs interacts with WSSV structural proteins to both disrupt WSSV integrity and interfere with viral invasion . Toll-Dorsal-AMPs pathway is quite clear in Drosophila . Gram-positive or fungal infection trigger the activation of Toll-Dorsal-AMPs pathway , which lead to the systemic production of Drosomycin and Metchnikowin [88] . Although the role of Toll-Dorsal-AMPs pathway against bacterial infection in some invertebrates such as Drosophila and shrimps has been reported , there is little information about its antiviral role . In our paper , we clone and identify a total of nine Tolls from L . vannamei , RNAi screens Toll4 as a key antiviral factor against WSSV infection . Considering the data obtained in the present study , we propose the following antiviral immune signaling pathway in L . vannamei ( Fig 11 ) : i . viral recognition and signal transduction: Toll4 recognizes WSSV infection to converge on Dorsal translocated from the cytoplasm to the nucleus and phosphorylation on Ser342; ii . AMP induction: the activation of Dorsal in the nucleus triggers a specific set of AMPs expression such as ALFs and LYZs; and iii . viral inactivation: Toll4-Dorsal drived AMPs can bind with the components of the viral surface , subsequently resulting in the WSSV inactivation . Uncovering the Toll4-Dorsal-AMPs cascade mediated antiviral program may provide novel strategies for limiting WSSV infection in shrimp aquaculture , and dissecting the pattern of Toll4 sensing WSSV in the further will provide additional insights into how the canonical Toll pathway responds to viral infection .
Shrimps ( L . vannamei , average weight 8 g each ) were purchased from the local shrimp farm in Zhuhai , Guangdong Province , China , and fed with a commercial diet in a recirculating water tank system filled with air-pumped sea water ( 2 . 5% salinity ) at 28°C . Before all experiment treatments , the shrimps ( 5% of total ) were detected and confirmed to be free of common pathogens including white spot syndrome virus ( WSSV ) , yellow head virus ( YHV ) , taura syndrome virus ( TSV ) , shrimp hemocyte iridescent virus ( SHIV , also known as CQIV ) , infectious hypodermal and hematopoietic necrosis virus ( IHHNV ) and Vibrio parahaemolyticus by PCR or RT-PCR methods according to standard operation procedures by Panichareon et al [89] and Qiu et al [90] . Because many genes from the shrimp canonical Toll-Dorsal pathway can be activated by Gram-negative ( G- ) bacteria [47 , 51] , V . parahaemolyticus thus was used here as a positive activator of the shrimp Toll-Dorsal pathway . The Gram-negative bacteria V . parahaemolyticus were cultured in Luria broth ( LB ) medium overnight at 37°C , and the bacteria were harvested by centrifugation ( 5000 g , 10 min ) and washed twice in phosphate buffer saline ( PBS ) to remove growth medium and finally resuspended in PBS to give 108 cells per ml . A final injection density of V . parahaemolyticus was adjusted to yield approximately 1 × 105 CFU/50 μl as a previous study [91] . WSSV was extracted from the WSSV-infected shrimp muscle tissue and stored at -80°C . Before injection , muscle tissue from WSSV infected shrimp was homogenized and prepared as WSSV inoculum with approximately 1 × 105 copies in 50 μl PBS following a published method [92] . The SHIV-infected shrimp samples of cephalothoraxes were homogenized in PPB-Tris buffer ( 376 . 07 mM NaCl , 6 . 32 mM K2SO4 , 6 . 4 mM MgSO4 , 14 . 41 mM CaCl2 , and 50 mM Tris-HCl , pH 7 . 0 ) and clarified at 9000 g for 10 min to gather the supernatant . The pellet was homogenized in PPB-Tris buffer again and clarified at 9000 g for 5 min , repeated three times and the supernatants were combined every time [93] . The supernatant was collected as the original stock , and diluted and adjusted to approximately 1 × 105 copies in 50 μl PBS before injection . Because gills were the main target of IHHNV , the viral stock was prepared from IHHNV-infected shrimps gill . The gills were homogenized in PBS buffer and centrifuged at 9000 g for 10 min at 4°C [94] . The supernatant was collected as the original stock , and diluted and adjusted to approximately 1 × 105 copies in 50 μl PBS before injection . The YHV stock was prepared from the gills of moribund shrimp showing clinical signs of yellow head disease . The gills of the infected shrimp were ground and further suspended in NTE buffer ( 0 . 02 M EDTA , 0 . 2 M NaCl , 0 . 2 M Tris-HCl , pH 6 . 5 ) . The solution was filtered using a 0 . 22-μm MILLEX HP Fillter Unit and centrifuged at 9000 g for 10 min at 4°C [95] . The supernatant was collected as the original stock , and diluted and adjusted to approximately 1 × 105 copies in 50 μl PBS before injection . In the pathogenic challenge or immunocytochemical staning experiments , each shrimp was received an intraperitoneal injection of 50 μl WSSV , SHIV , IHHNV , YHV or V . parahaemolyticus solution at the second abdominal segment by a 1-ml syringe . In order to obtain the cDNA sequence of all candidate Toll genes from shrimp , the amino acid ( aa ) sequences of the Tolls and TLRs from Drosophila and human ( DmToll1-9 and HsTLR1-10 ) were collected and used as query sequences for in silico searches of L . vannamei transcriptome data [34] using local TBLASTN alignment tool with E-value cutoff of 1e-5 . Nine assembled EST sequences were identified as having high homology to the Toll family genes . Gene-specific primers ( S1 Table ) were designed for 5′ and 3′ rapid amplification of cDNA ends ( RACE ) PCR to obtain the 5′ and 3′ end of L . vannamei Toll genes . In brief , total RNA was extracted from pooled tissues of L . vannamei gill , hemocyte and intestine followed by the protocol described in the RNeasy Mini Kit ( Qiagen ) . cDNA synthesis , 5′/3′-rapid amplification of cDNA ends ( 5′/3′-RACE ) PCR , and nested PCR were performed using a SMARTer RACE cDNA amplification kit ( Clontech , Japan ) in accordance with the manufacturer's instructions . The final PCR products were cloned into pMD-19T Cloning Vector ( TaKaRa , Japan ) and 12 positive clones were selected and sequenced . Then , we performed TBLASTN again by using the aa sequences of nine Tolls as query sequences to search against several RNA-Seq databases from NCBI and others [35–38] , but no new Toll was identified , suggesting there could be just a total of nine Tolls in shrimp . Protein domains of Tolls and TLRs were identified by using Simple Modular Architecture Research Tool ( SMART ) ( http://smart . embl . de/ ) . Shrimp TIR domains of nine Tolls were aligned by using Clustal X v2 . 0 program [96] and GeneDoc software where the identities among each other were labeled . The neighbor-joining ( NJ ) phylogenic tree was constructed based on the deduced amino acid sequences of Tolls and TLRs ( Supplement Data S3 ) by utilizing MEGA 5 . 0 software [97] . The polyclonal antibodies for L . vannamei Dorsal and Cactus were produced in guinea pigs and rabbits , respectively , by GL Biochem antibody manufacturing company ( China ) from our previous study [49] . Polyclonal rabbit anti-NF-κB p65 ( phospho S276 ) antibody ( ab194726 , Abcam ) was used to detect the phosphorylated shrimp Dorsal [51] . Rabbit anti-Histone H3 ( 4499s ) , Rabbit anti-Hsp90 ( ab13495 ) , and the secondary antibodies Goat Anti-Guinea pig IgG H&L ( Alexa Fluor 488 ) ( ab150185 ) , Goat anti-Guinea pig IgG H&L ( HRP ) ( ab6908 ) , anti-Mouse IgG H&L ( HRP ) ( ab6789 ) and anti-Rabbit IgG H&L ( HRP ) ( ab6721 ) , were purchased from Abcam ( USA ) . Mouse anti-Actin antibody was obtained from Merck ( MAB1501 ) . Mouse anti-6His antibody ( H1029 ) and Mouse anti-GST antibody ( SAB4200237 ) were gained from Sigma ( USA ) . In order to monitor the WSSV copies , absolute quantitative PCR ( qPCR ) was conducted by utilizing with the forward and reverse primers of wsv069 ( WSSV32678-F/WSSV32753-R ) , a WSSV single copy gene , and a TaqMan fluorogenic probe ( WSSV32706 ) followed by a published method [72] . The primers used here were shown in S1 Table . In brief , a 675-bp DNA amplicon of wsv069 with a region of 32678 to 32753 from WSSV genome ( AF332093 . 2 ) was obtained and subcloned into the pMD19-T plasmid . The plasmid pMD19-T containing the 675-bp DNA fragment was used as the internal standard , and serially diluted to 10-folds to generate a standard curve of absolute qPCR . Genomic DNA from shrimp muscle , hepatopancreases and/ or gill was extracted with Marine Animal Tissue Genomic DNA Extraction Kit ( TianGen Biochemical Technology ) . The extracted shrimp DNA and the internal standard plasmid were subjected to absolute qPCR . The PCR reaction mixture and cycling conditions were the same as previous research [72] . Each sample from one shrimp was made in three replicates by absolute qPCR . The WSSV genome copies were calculated and normalized to 0 . 1 μg of shrimp tissue DNA . Semi-quantitative reverse transcription PCR ( Semi-qRT-PCR ) was used to analysis the tissue distribution of nine Tolls in uninfected shrimp . Briefly , healthy shrimp tissues including hepatopancreases , gill , intestine , hemocyte , stomach , epithelium , heart and muscle were sampled . Three samples from each tissue were collected from 15 shrimps ( 5 shrimps pooled together ) . Total RNA was extracted from each tissue with RNeasy Mini Kit ( Qiagen ) , and reverse transcribed to cDNA with PrimeScript II 1st Strand cDNA Synthesis Kit ( Takara ) following the manufacturer's instructions . The cDNA fragments of nine Tolls were amplified using the gene specific primers ( S1 Table ) under the following conditions: 1 cycle of 94°C for 2 min , 28 cycles of 94°C for 30 s , 60°C for 30 s , 72°C for 30 s , followed by elongation at 72°C for 5 min . As an internal loading control , the shrimp EF1α ( GU136229 ) was amplified as the same PCR conditions . Quantitative reverse transcription PCR ( qRT-PCR ) was conducted to detect the mRNA levels of genes ( Tolls , Toll pathway components or AMPs ) under the pathogenic challenge experiments or RNAi in vivo . The method of tissues collection , total RNA extraction and cDNA synthesis was as described above . qRT-PCR analysis was performed in the LightCycler 480 System ( Roche , Germany ) with a volume of 10 μl comprised of 1 μl of 1:10 cDNA diluted with ddH2O , 5 μl of 2 × SYBR Green Master Mix ( Takara , Japan ) , and 250 nM of each primer ( S1 Table ) . The cycling programs were the following parameters: 95°C for 2 min to activate the polymerase , followed by 40 cycles of 95°C for 15 s , 62°C for 1 min , and 70°C for 1 s . Cycling ended at 95°C with 5°C/s calefactive velocity to create the melting curve . Expression level of each gene was calculated relative to internal control gene EF-1α by using the Livak ( 2-ΔΔCT ) method . The dsRNAs including Dorsal ( accession No . ACZ98167 ) , Tube ( KC346865 ) , Pelle ( KC346864 ) , MyD88 ( AFP49302 ) , nine Tolls ( Supplement data S1 ) , eight AMPs ( Supplement data S2 ) and the control GFP , were synthesized by T7 RiboMAX Express RNAi System kit ( Promega , USA ) followed by the user's manual . More detailed information about the primers for dsRNA synthesis was listed in S1 Table . The quality of dsRNA was checked after annealing via gel electrophoresis . The RNA interference ( RNAi ) assay was performed as we described else [91] . Briefly , each shrimp was received an intraperitoneal injection at the second abdominal segment of dsRNAs ( 20 μg ) for Dorsal , Tube , Pelle , Toll1 , Toll2 , Toll3 , Toll4 , Toll5 , Toll6 , Toll7 , Toll8 , Toll9 , ALF1 , ALF2 , ALF3 , ALF4 , LYZ1 , LYZ2 , LYZ3 , LYZ4 or GFP ( as a control ) . The gills and/ or hemocytes were collected from the shrimp 48 h after the dsRNA injection , and total RNA was extracted and assessed by qRT-PCR using the corresponding primers ( S1 Table ) to evaluate the efficacy of RNAi . To screen potential Toll with antiviral effects against WSSV , shrimps were divided into ten groups: one control group received GFP dsRNA injection and the other nine RNAi groups received each Toll dsRNA injection , respectively . At 48 h after the RNAi performance , each shrimp was challenged with 105 copies of WSSV particles by intraperitoneal injection , and 48 hours later again , muscle , hepatopancreas and/ or gill tissue from 12 shrimps was sampled to examine the virus copies by absolute qPCR . To further investigate whether the lethality rates of Toll4 silenced shrimps was associated with viral levels , hepatopancreas , gill and muscle was sampled at 48 hpi to examine the virus copies by absolute qPCR . To evaluate potential antiviral role of the canonical Toll pathway components including MyD88 , Tube , Pelle and Dorsal , shrimps with five groups were injected with each of the four components dsRNAs and the GFP dsRNA as control , respectively . Forty-eight hours later , each shrimp from the five groups was challenged with 105 copies of WSSV particles and the gill tissues from 8 shrimps were sampled at 48 hours post infection to examine the virus copies by absolute qPCR . To explore the antiviral function of AMPs , a similar manipulation of RNAi plus WSSV challenge were performed with differences that gills were collected at more sampling times , orderly at the 24 h , 48 h , 72 h , 96 h and 120 h post infection . To investigate the effects of Toll4 or Dorsal on the expression of AMPs in vivo after WSSV infection , AMPs expression in shrimps after receiving Toll4 dsRNA or Dorsal dsRNA plus WSSV challenge were analyzed . Hemocyte and/ or gill tissues from 9 shrimps were collected at 6 h post WSSV challenge , and the mRNA levels of fourteen AMPs were detected by qRT-PCR with specific primers ( S1 Table ) . Healthy shrimps were injected with gene specific dsRNAs including Toll4 , ALF1 , ALF2 , ALF3 , ALF4 , LYZ1 , LYZ2 , LYZ3 , LYZ4 or GFP dsRNA ( as a control ) and PBS , and 48 h later were challenged with 105 copies of WSSV particles in 50 μL PBS . Shrimps were kept in culture flasks for about 5–7 days following infection . The death of shrimp was recorded every 8 h and subjected to mortality or survival rate analysis . Hemocytes of normal shrimp and WSSV challenged shrimps were sampled with each sample collected and pooled from 5 shrimps . The nuclear and cytoplasmic fractions of hemocytes were extracted according to the protocol of NE-PER Nuclear and Cytoplasmic Extraction Reagents ( Thermo , USA ) , while the total proteins were collected by RIPA lysis buffer . Samples were boiled for 5 min , separated on SDS-PAGE gels followed by transfer to polyvinylidene difluoride ( PVDF ) membranes . After blocking in 5% bovine serum albumin ( BSA ) in TBS with 0 . 1% Tween-20 ( TBS-T ) for 1 h , membranes were incubated with anti-Dorsal , anti-NF-κB p65 ( phospho S276 ) , anti-Cactus , anti-HSP90 , anti-Histone H3 or anti-Actin for 16–18 h at 4°C . After washing in TBS-T , membranes were incubated for 1 h at RT with horseradish peroxidase ( HRP ) -labeled Goat secondary antibody to Guinea pig IgG ( H+L ) , Goat anti-Rabbit IgG ( H+L ) -HRP or Goat anti-Mouse IgG ( H+L ) -HRP . Both primary and secondary antibodies were incubated in TBS-T with 0 . 5% BSA . Membranes were developed with the enhanced chemiluminescent ( ECL ) blotting substrate ( Thermo Scientific ) and chemiluminescence was detected using the 5200 Chemiluminescence Imaging System ( Tanon ) . For relative densitometry of Dorsal , Dorsal-P or Cactus , the immunoblotted band volume was normalized to the corresponding internal protein volume in the lane , using the ImageJ software 1 . 6 . 0 ( National Institutes of Health , Bethesda , MD ) . Statistical analysis of densitometry data from three independent experiments was performed by using the Student’s t test . The L . vannamei AMPs ALF1 ( accession No . AVP74301 ) , ALF3 ( ABB22831 . 1 ) , LYZ1 ( ABD65298 ) and LYZ2 ( AY170126 . 2 ) without N-terminal signal peptide were cloned into pET-32a ( + ) plasmid ( Merck Millipore , Germany ) specific primers ( S1 Table ) , expressed in BL21 ( DE3 ) Escherichia coli strain , and purified with Ni-NTA agarose ( Qiagen , Germany ) according to user's manual . WSSV structural genes including VP19 ( accession No . NP_477936 . 1 ) [98] , VP24 ( NP_477524 . 1 ) [99] , VP26 ( NP_477833 . 1 ) [100] , VP28 ( NP_477943 . 1 ) [100] , wsv134 ( NP_477656 . 1 ) [101] and wsv321 ( NP_477843 . 1 ) [57] were cloned into pGEX-4T-1 plasmid ( GE Healthcare , USA ) with specific primers ( S1 Table ) , expressed in BL21 ( DE3 ) E . coli strain , and purified with Pierce GST agarose ( Thermo Scientific ) recommended by user's operation . For His pull-down assay , purified His-tagged ALF1 , ALF3 , LYZ1 or LYZ2 was incubated with Ni-NTA beads , to which purified WSSV structural protein was added and incubated at 4°C for overnight with slight rotation . The mixture ( beads and binding proteins ) was washed three times with wash buffer ( 20 mM Imidazole , 50 mM Tris-HCl , pH 8 . 0 ) , and then eluted in elution buffer ( 250 mM Imidazole , 50 mM Tris-HCl , pH 8 . 0 ) . Elute was run in 10% SDS-PAGE , followed by coomassie staining and western blotting with anti-GST antibody to probe interacting proteins in the complex . For GST pull-down assay , purified GST-tagged WSSV structural protein and purified His-tagged ALF1 or LYZ1 were incubated with glutathione beads at 4°C for overnight with slight rotation . The mixture was washed three times with PBS and the bound proteins were eluted in elution buffer ( 10 mM reduced glutathionem , 50 mM Tris-HCl , pH 8 . 0 ) and analyzed by SDS-PAGE as described above , followed by coomassie staining and western blotting with anti-His antibody . WSSV inoculum was incubated with His-tag fused proteins including ALF1 and LYZ1 or TRX ( as a control ) for 1 hour at room temperature before injection . Each shrimp was injected with mixture including 105 copies of WSSV particles together with 10 μg His-tag fused proteins in 50 μL PBS . Shrimps were kept in culture flasks for about 5–7 days following infection . The death of shrimp was recorded every 8 h and subjected to survival rate analysis . The gill tissues from 6 shrimps were sampled at 96 hours post infection to examine the viral copies by absolute qPCR as described above . Immunocytochemical staning was used to analysis shrimp Dorsal translocation in hemocyte recommended by a published method with a minor modification [51] . In short , hemocytes were collected by centrifugation ( 1000 g , 5 min ) at room temperature ( RT ) and deposited onto a glass slide , and then fixed immediately with 4% paraformaldehyde at RT for 5 min . The hemocytes on the glass slides was washed with PBS three times , followed by incubated with prepared Guinea pig anti-Dorsal antibody serum ( 1:2000 diluted in 5% BSA ) overnight at 4°C . The hemocytes were then washed with PBS and incubated with 5% BSA for 10 min; the Goat anti-Guinea pig IgG ( H+L ) Alexa Fluor 488 ( Abcam , 1:5000 diluted in 5% BSA ) was then added , and the samples were incubated for 1 h at RT in the dark . After being washed three times , the hemocytes were stained with Hochest ( Sigma , 1 μg/ ml in PBS ) for 10 min at RT and washed six times . Fluorescence was visualized on a confocal laser scanning microscope ( Leica TCS-SP5 , Germany ) . We calculated the colocalization percentage of Dorsal with nucleus stained with hochest , using the ImageJ according to the previously described methods [51 , 102] . In brief , we opened the picture and chosen Image-Color-Split channels , while closed the no need channels , and then clicked Plugins-colocalization analysis-colocalization threshold-OK . The colocalization percentage of Dorsal with nucleus stained with hochest should be the quotient of the shared area of Dorsal and nucleus divided by area of nucleus . The QNZ ( EVP4593 ) ( S4902 , Selleck ) was reported to be a high-affinity partial antagonist of NF-κB [103 , 104] . Firstly , this NF-κB inhibitor with 0 . 5 , 1 . 0 or 2 μg was injected into each shrimp ( ~8 g each ) to explore the suppression effect on Dorsal . Then , the 2 μg NF-κB inhibitor for each shrimp was determined and used in the following treatments . DMSO injection was used as a control . The hemocytes of NF-κB inhibitor injected shrimp were sampled for protein and RNA extraction , as well as Dorsal translocation assay , at 6 h after WSSV challenge . The nuclear location of Dorsal was addressed by immunofluorescence staining as described earlier , the phosphorylation level of Dorsal was analyzed by western blotting with anti-NF-κB p65 ( phospho S276 ) antibody , and the AMPs expressions were detected by qRT-PCR at 6 h after WSSV challenge . Besides , the gills were also collected for the AMPs expressions analysis by qRT-PCR . The 5' flanking regulatory regions of ALF1 and LYZ1 were cloned by Genome walking PCR amplification via GenomeWalker Universal Kit ( Clontech ) according to our previous paper [105] . Two pairs of primer AP1/ALF1-R1 and AP1/LYZ1-R1 were used to perform the first round of Genome walking PCR amplification , while AP2/ALF2-R2 and AP2/LYZ2-R2 were used for the second round PCR reaction . The PCR products were cloned to pMD-19T vector ( Takara ) and sequenced . Primers were listed in S1 Table . The expression plasmid containing the ORF of L . vannamei Dorsal ( FJ998202 ) was obtained from our previous study [106] . The 5' flanking regulatory regions of ALF1 or LYZ1 was gained by PCR amplification via the primer pairs of pGL3-ALF1-F/pGL3-ALF1-R or pGL3-LYZ1-F/pGL3-LYZ1-R , and subsequently cloned into pGL3-Basic ( Promega ) vector . Primers were listed in S1 Table . Dual luciferase reporter assay was performed as our previously described methods . In brief , Drosophila S2 Cells were plated into a 96-well plate ( TPP ) and transfections were performed on the next day . Plasmids were transfected using the Fugene HD Transfection Reagent ( Promega ) according to the user manual . S2 cells in each well of a 96-well plate were transfected with 50 ng reporter gene plasmids ( pGL3-ALF1-κB or pGL3-LYZ1-κB ) , 5 ng pRL-TK renilla luciferase plasmid ( as an internal control ) , 0 , 50 or 100 ng expression plasmids ( pAc5 . 1A-Dorsal ) . At 48 h post transfection , the activities of the firefly and renilla luciferases were measured according to user instruction . Each experiment was done at least three times , and for each assay , data were obtained from six repeated wells . After obtained the 5' flanking regulatory regions ( promoter ) of ALF1 and LYZ1 , the potential NF-κB binding motif sequences were predicted and analyzed by JASPAR database ( http://jaspardev . genereg . net/ ) . We found that both ALF1 and LYZ1 contained a putative NF-κB binding motif in their promoter regions . The wild-type probes namely ALF1-κB-bio-probe ( biotin labeled ) or ALF1-κB-unbio-probe ( un-biotin labeled ) were designed as the sequence containing the NF-κB binding motif sequence ( GGAAATGCAG ) . The mutant probe namely ALF1-κBm-bio-probe was designed as the sequence ( GGCCCTGCCG ) via replacing adenine with cytosine . Besides , the wild-type probes namely LYZ1-κB-bio-probe or LYZ1-κB-unbio-probe were designed as the sequences containing NF-κB binding motif sequence ( GGAAAGGCCA ) . The mutant probe namely LYZ1-κBm-bio-probe was designed as the sequence ( GGCCCGGCCC ) via substituting cytosine for adenine . All the probes were synthesized by Life Technology and sequences were listed in S1 Table . Drosophila S2 cells were transfected with pAc5 . 1A-Dorsal or pAc5 . 1A-GFP [106] . After 48 h , cells were collected and the nuclear proteins were extracted using NE-PER Nuclear and Cytoplasmic Extraction Reagents ( Thermo ) . EMSA was performed using a Light Shift Chemiluminescent EMSA kit ( Thermo ) according to our previous method [49] . In brief , the nuclear proteins ( 10 mg ) were incubated with 20 fmol probes ( wild-type ALF1-κB-bio-probe or LYZ1-κB-bio-probe ) for the binding reactions between probes and proteins , separated by 5% native PAGE , transferred to positively charged nylon membranes ( Roche ) , and cross-linked by UV light . Subsequently , the biotin-labeled DNA on the membrane was detected by chemiluminescence . In competition binding assays , the complexes of wild-type probes and proteins were challenged with the un-biotin labeled probes , at 10-fold , 50-fold or 100-fold molar excess over the labeled probes . All data were presented as means ± SD . Student t test was used to calculate the comparisons between groups of numerical data . For mortality or survival rates , data were subjected to statistical analysis using GraphPad Prism software to generate the Kaplan–Meier plot ( log-rank χ2 test ) . | The TLR pathway mediated antiviral immune response is well identified in mammals , yet , Toll pathway governing this protection in invertebrates remains unknown . In the present study , we uncover that a shrimp Toll4 from a total of nine Tolls in L . vannamei confers resistance to WSSV thought inducing the NF-κB transcription factor Dorsal to inspire the production of some antimicrobial peptides ( AMPs ) with antiviral activity . The anti-LPS-factor ( ALF ) and lysozyme ( LYZ ) family are identified as the Toll4-Dorsal pathway targeted genes with the ability to interact with viral structural proteins in response to WSSV infection . These results suggest that the Toll receptor induces the expression of AMPs with antiviral activity could be a general antiviral mechanism in invertebrates and Toll pathway established antiviral defense could be conserved during evolution . | [
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] | 2018 | RNAi screening identifies a new Toll from shrimp Litopenaeus vannamei that restricts WSSV infection through activating Dorsal to induce antimicrobial peptides |
Sleep and wakefulness are greatly influenced by various physiological and psychological factors , but the neuronal elements responsible for organizing sleep-wake behavior in response to these factors are largely unknown . In this study , we report that a subset of neurons in the lateral hypothalamic area ( LH ) expressing the neuropeptide neurotensin ( Nts ) is critical for orchestrating sleep-wake responses to acute psychological and physiological challenges or stressors . We show that selective activation of NtsLH neurons with chemogenetic or optogenetic methods elicits rapid transitions from non-rapid eye movement ( NREM ) sleep to wakefulness and produces sustained arousal , higher locomotor activity ( LMA ) , and hyperthermia , which are commonly observed after acute stress exposure . On the other hand , selective chemogenetic inhibition of NtsLH neurons attenuates the arousal , LMA , and body temperature ( Tb ) responses to a psychological stress ( a novel environment ) and augments the responses to a physiological stress ( fasting ) .
We first analyzed the distribution of Nts neurons in the LH by generating transgenic mice expressing green fluorescent protein ( GFP ) exclusively in Nts neurons ( Nts-Cre::L10-GFP; henceforth “Nts-GFP” mice; see Methods section ) . We found that Nts neurons are densely packed in the perifornical LH , intermingled with orexin and MCH neurons . We also observed another dense population of Nts neurons in the subthalamic nucleus of the basal ganglia located dorsolateral to the LH and a less-dense population in the dorsomedial hypothalamus ( DMH ) lying medial to the LH . However , this study specifically focuses on the Nts neurons in the LH ( NtsLH ) , and all brain injections were aimed at this population . As Nts neurons were found densely packed in the perifornical LH region , we examined whether these neurons co-express MCH or orexin by immunolabelling brain sections from Nts-GFP mice ( n = 6 ) for MCH and orexin . We found that none of the GFP+ neurons were labeled for MCH , whereas 3 . 7 ± 0 . 8% of GFP+ neurons were labeled for orexin ( Fig 1A and 1B ) , indicating that a small fraction of Nts neurons may express orexin . To determine whether this overlap between Nts and orexin expression in LH neurons is an artifact of developmental expression of Nts/Cre ( the Nts-GFP mouse might express GFP congenitally ) , we stereotaxically injected a Cre-dependent adeno-associated viral ( AAV ) vector containing mCherry ( AAV8-hSyn-DIO-hM3Dq-mCherry , henceforth “AAV-hM3Dq”; University of North Carolina Vector core , United States; see below ) into the LH ( anteroposterior: −1 . 7 mm , ventral: 5 . 1 mm , lateral: ±1 . 1 mm ) of adult ( 8 wk old ) Nts-Cre mice ( n = 4 ) . Six weeks after the injections , we perfused the mice and immunolabeled the brain sections for mCherry ( to label virally transfected , Cre-expressing Nts neurons ) and orexin ( Fig 1C ) . In these brain sections , we did not find any double-labeled neurons in the LH , indicating that NtsLH neurons are a distinct population from orexin neurons , but some of them may produce orexin during development . We next examined the projections of NtsLH neurons using conditional anterograde tracing to understand the neuronal targets through which NtsLH neurons may regulate sleep-wake and Tb . We unilaterally microinjected a Cre-dependent AAV coding for channelrhodospsin-2 ( ChR2 ) and the fluorescent tag mCherry ( AAV8-EF1α-DIO-ChR2-mCherry , henceforth “AAV-ChR2”; University of North Carolina Vector core , US ) [32 , 33] into the LH of Nts-Cre mice ( n = 6 ) . Six weeks after the injections , we perfused the mice and processed the brain sections for immunohistochemical labeling of mCherry to identify ChR2-expressing Nts neurons and their axon terminals . AAV-ChR2 injections in three out of six mice were restricted to the LH ( Fig 2A ) without spread to adjacent regions , including the subthalamic nucleus or DMH , and only these cases were used to identify NtsLH projections . A high density of Nts terminals were found in the VTA , ventrolateral periaqueductal gray ( vlPAG ) , parabrachial nucleus ( PB ) , locus coeruleus ( LC ) , retrorubral region , substantia innominata , diagonal band of Broca , ventral pallidum , nucleus accumbens , raphe pallidus ( RPa ) , parapyramidal region ( Ppy ) , and lateral preoptic area ( Fig 2B–2G ) . We observed a moderate density of Nts terminals in the dorsolateral septum and supramamillary nucleus . While all NtsLH projections were predominantly ipsilateral , we also observed some less-dense contralateral projections in many of the target regions . We used optogenetic tools to activate NtsLH neurons and investigated their role in sleep-wake control . We stereotaxically injected AAV-ChR2 bilaterally into the LH ( anteroposterior: −1 . 7 mm , ventral: 5 . 1 mm , lateral: ±1 . 1 mm; Fig 3A ) of Nts-Cre mice ( n = 7 ) and implanted them with bilateral optical fibers ( targeting 0 . 2 mm dorsal to the LH ) for illumination with blue laser light [33] , electrodes for recording electroencephalography ( EEG ) and electromyography ( EMG ) [34] and telemetry transmitters [35] for recording Tb and LMA . Injections of AAV-ChR2 into the LH of Nts-Cre mice resulted in robust expression of ChR2-mCherry in NtsLH neurons . In contrast , injections of the same AAV into the LH of WT littermates did not result in any expression of mCherry , indicating the Cre dependency of the AAV-ChR2 . The AAV injections in Nts-Cre mice were largely restricted to the LH and zona incerta , with little or no spread medially into the DMH ( Fig 3B ) . We first tested the response of NtsLH neurons to photo-illumination using ex vivo whole cell current clamp recordings . Illumination with blue laser light ( 473 nm at 1 , 5 , and 10 Hz ) evoked action potentials in ChR2-mCherry–expressing NtsLH neurons in a frequency-dependent manner ( Fig 3C ) . In vivo , 5-Hz stimulation for 2 h prior to perfusion caused robust cFos expression in mCherry-expressing NtsLH neurons , demonstrating that blue light illumination consistently drives activity in these neurons ( Fig 3D ) . To assess whether optogenetic activation of NtsLH neurons influences sleep-wake states , we applied blue laser light pulses ( 473 nm , 10 ms ) with different frequencies for 10 s specifically during NREM or REM sleep . We applied laser stimulations at frequencies of 1 , 5 , and 10 Hz ( 10 mW light power at the tip of the optical fibers ) after either 30 s of stable NREM sleep or 10 s of stable REM sleep during the light period ( from 10:00 AM to 5:00 PM ) . We applied 10 photostimulations of each frequency for each state . Photostimulations during NREM sleep in AAV-ChR2–injected Nts-Cre mice resulted in rapid transition to wake ( Fig 3E ) . On average , about 65% of 1-Hz stimulations and 93% of 5-Hz and 10-Hz stimulations during NREM sleep produced a rapid ( within 1–5 s of light pulse onset ) arousal response ( Fig 3F ) , characterized by EEG desynchronization , EMG activation , and behavioral wakefulness in Nts-Cre mice ( Fig 3E ) . In addition , the amount of wakefulness during the 30-s period immediately after cessation of photostimulations showed a frequency-dependent increase , indicating that higher stimulation frequencies produce more rapid and longer-lasting wake responses ( Fig 3G ) . In contrast , photostimulation during REM sleep had no effect on sleep-wake or EEG/EMG activity in Nts-Cre mice ( Fig 3H–3J ) . Mice remained undisturbed and REM sleep continued during the entire stimulation period ( Fig 3H and 3I ) . These findings demonstrate that activation of NtsLH neurons rapidly trigger NREM-wake but not REM-wake transitions . Although brief 10-s activations of NtsLH neurons are sufficient to evoke NREM-wake transitions , they are not sufficient to induce detectable changes in Tb or LMA . We therefore applied a continuous 5-Hz stimulation ( 10-ms pulse ) for 30 min during the light period ( at 10:00 AM ) in Nts-Cre mice injected with AAV-ChR2 . We observed a significant increase in Tb ( 1 . 35 ± 0 . 17°C increase ) during the 30-min stimulation period ( Fig 4A and 4B ) when compared with 30 min prior . Tb began to drop almost immediately upon cessation of the stimulation and returned to baseline within the next approximately 30 min ( Fig 4A ) . Because the photostimulations evoked immediate arousal in all mice ( except in one mouse that was in REM sleep when the photostimulations began ) , and the mice were awake during the entire stimulation period ( 98 . 78% ± 1 . 22% versus 18 . 89% ± 6 . 44% during the 30 min prior; P < 0 . 001 ) , we calculated the average Tb specifically during wake episodes ( Tbwake ) . We found that the Tbwake during the photostimulations was significantly higher ( 1 . 11 ± 0 . 3°C increase; P = 0 . 016 ) than Tbwake during the 30 min prior to stimulations ( Fig 4C ) , suggesting that the hyperthermia was not a mere coincidence of wake transitions . On the other hand , total LMA counts did not differ across these periods ( Fig 4D and 4E ) , although the EMG activity ( measured as integral EMG ) during the photostimulations was 151% higher than prestimulation values ( P = 0 . 004; one-way ANOVA; Fig 4F ) . The temporal correlation between the photostimulations and Tb on the one hand and the increase in Tb and EMG activity , even without substantial increase in LMA , on the other hand suggests that NtsLH neurons may participate in thermogenesis . While optogenetic activation is suitable for studying acute state transitions ( with millisecond-timescale precision ) , chemogenetic activation is better suited for studying long-term ( minutes to hours ) changes in sleep-wake behavior , LMA , and Tb . Therefore , we activated NtsLH neurons using chemogenetic tools and assessed changes in sleep-wake , LMA , and Tb . We stereotaxically injected either an AAV vector ( AAV-hM3Dq ) coding for the excitatory designer receptor exclusively activated by designer drugs ( DREADD ) [15 , 37] or a control vector ( AAV8-DIO-hsyn-mCherry; henceforth “AAV-mCherry” ) into the LH of Nts-Cre mice ( Fig 5A ) and implanted them with telemetry transmitters for simultaneous recordings of EEG , EMG , Tb , and LMA [15] . Similar to the optogenetic experiments , the AAV injections induced specific expression of the viral vector product in NtsLH neurons , which was primarily restricted to the LH ( Fig 5B ) . Bath application of 5 μM clozapine-n-oxide ( [CNO] the ligand for hM3Dq receptors ) depolarized hM3Dq-mCherry–expressing NtsLH neurons and increased their firing rate in ex vivo brain slices ( Fig 5C ) . Moreover , intraperitoneal ( i . p . ) injections of CNO ( 0 . 3 mg/kg ) 2 . 5 h prior to killing induced robust cFos expression in hM3Dq-mCherry–expressing NtsLH neurons ( 93 . 76% ± 1 . 52% of mCherry+ neurons expressed cFos after CNO in AAV-hM3Dq–injected Nts-Cre mice versus 16 . 78% ± 4 . 45% in AAV-mCherry–injected controls; Fig 5D ) . These data demonstrated that CNO effectively activated hM3Dq-expressing NtsLH neurons both ex vivo and in vivo . To assess sleep-wake , LMA , and Tb changes following chemogenetic activation of NtsLH neurons , we i . p . injected saline ( vehicle ) or CNO ( 0 . 3 mg/kg ) at 9:50 AM ( light period ) or 6:50 PM ( dark period ) and recorded EEG , EMG , LMA , and Tb for 12 h using the telemetry transmitters . Administration of CNO into Nts-Cre mice injected with the control vector AAV-mCherry ( negative controls ) did not produce any significant changes in sleep-wake , Tb , or LMA ( data can be found from the Open Science Framework , https://osf . io/nmrpq/ ) . Conversely , in the Nts-Cre mice expressing hM3Dq , CNO injections during the light period ( 9:50 AM ) resulted in prolonged and uninterrupted wake ( without any NREM or REM sleep ) for 4–6 h ( Fig 5E and S1 Table ) . The first NREM sleep and REM sleep were observed 275 . 00 ± 33 . 00 min and 312 . 66 ± 38 . 31 min , respectively , after CNO injections , significantly later than in the saline condition ( 40 . 74 ± 4 . 11 min and 68 . 29 ± 6 . 40 min , respectively; Fig 5F ) . Towards the end of the 12-h recording period , specifically hours 10–12 after CNO , we observed that REM sleep amounts , bout number , and mean duration were significantly higher than those after saline treatment , indicating a REM sleep rebound ( Fig 5E and S1 Table ) . Although the NREM sleep increase during this same period did not reach statistical significance , a trend was observed , along with a significant increase in the number of NREM sleep bouts , indicating rebound increase in NREM sleep as well ( Fig 5E and S1 Table ) . Finally , CNO injections at the dark onset ( 6:50 PM ) , when the circadian drive for wake is high , produced similar sleep-wake changes in Nts-Cre mice ( S1 Fig and S2 Table ) . The increase in wakefulness after CNO was accompanied by significant increases in Tb and LMA during both light ( Tb: 1 . 56 ± 0 . 18°C and LMA: 584 . 81% ± 98 . 92% increase ) and dark periods ( Tb: 0 . 85 ± 0 . 09°C; LMA: 269 . 18% ± 22 . 45% increase ) ( Fig 5G and 5H and S1 Fig ) . Hyperthermia and higher LMA after CNO during the dark period were particularly interesting as their levels were much higher than the usual circadian increase in LMA and Tb at that time of day . Moreover , LMA per unit time of wake was significantly elevated after CNO ( light period: 3 . 01 ± 0 . 50 versus 1 . 11 ± 0 . 13 counts per min after saline; P = 0 . 0084; dark period: 3 . 29 ± 0 . 61 versus 1 . 46 ± 0 . 19 counts per min after saline; P = 0 . 039 ) , indicating a hyperactive phenotype . These data suggest that NtsLH neurons directly increase LMA and Tb in addition to regulating sleep-wake behavior . Thus , chemoactivation of NtsLH neurons produced robust wake , hyperactivity , and hyperthermia , which are commonly observed after acute stress in rodents . While hyperthermia itself is considered a sensitive marker of stress [38] , we also counted the cFos+ neurons in the paraventricular nucleus ( PVH ) whose activation in response to stressors leads to corticosterone secretion ) [39 , 40] as an additional stress marker . CNO injections almost doubled the cFos+ neurons in the hM3Dq-injected Nts-Cre mice when compared with negative controls ( 197 . 13 ± 32 . 57 versus 103 . 67 ± 18 . 01; P < 0 . 05 , Mann–Whitney U test; S2 Fig ) , suggesting that activation of PVH occurs concurrently with the activation of NtsLH neurons . Next , we chemogenetically inhibited NtsLH neurons to determine whether these neurons are necessary for spontaneous wake and regulation of Tb under baseline conditions . We stereotaxically injected an AAV encoding inhibitory DREADDs ( AAV8-DIO-hsyn-hM4Di-mCherry; hereafter “AAV-hM4Di” ) [34] into the LH of Nts-Cre mice ( n = 7 ) ( Fig 6A ) . Similar to experiment 5 , we first confirmed the specific expression of hM4Di in NtsLH neurons by immunohistologically staining brain slices 4 wk after viral injections ( Fig 6B ) . Next , we assessed the response of hM4Di-transfected NtsLH neurons to CNO in ex vivo brain slices and found that bath application of CNO ( 10 μM ) caused complete inhibition of hM4Di-mCherry–expressing neurons ( Fig 6C ) . Similarly , in vivo application of CNO ( 1 . 5 mg/kg , i . p . ) in Nts-Cre mice 2 . 5 h before perfusions resulted in a complete absence of cFos in hM4Di-mCherry–expressing NtsLH neurons ( 2 . 86% ± 1 . 52% of mCherry+ neurons expressed cFos in AAV-hM4Di–injected Nts-Cre mice versus 16 . 78% ± 4 . 45% in AAV-mCherry–injected controls; Fig 6D ) . Administration of CNO ( 1 . 5 mg/kg , i . p . ) during the light or dark period did not cause any major alterations in sleep-wake states in Nts-Cre mice expressing hM4Di in NtsLH neurons ( Fig 6E and S3 Fig ) . Hourly percentages , bout numbers , and bout durations of wake , NREM and REM sleep , as well as latencies to NREM and REM sleep after CNO were not significantly different from those after saline injections ( Fig 6E and 6F , S3 Fig , S3 Table , and S4 Table ) . Similarly , total LMA and mean Tb after CNO were not significantly different from those after saline ( Fig 6G and 6H and S3 Fig ) . These data indicate that NtsLH neurons may not be critical for sleep-wake regulation or maintenance of Tb under baseline conditions . Although the inhibition of NtsLH neurons did not alter sleep-wake , LMA , and Tb during baseline conditions , activation of NtsLH neurons produced robust wake , hyperactivity , and hyperthermia as well as increased cFos expression in the PVH—all of which are usually observed in response to stress [31] . We therefore hypothesized that NtsLH neurons may specifically engage in stress-induced behavioral and physiological arousal . To test this hypothesis , we chemogenetically inhibited NtsLH neurons and assessed sleep-wake , LMA , and Tb in response to ( a ) the psychological stress induced by a novel environment and ( b ) the physiological/metabolic stress induced by fasting . To study the role of NtsLH neurons in the stress response induced by a novel environment , we injected mice expressing hM4Di in NtsLH neurons with CNO ( 1 . 5 mg/kg , i . p . ) or saline at 9:50 AM and immediately placed them in a new , clean cage with fresh bedding material ( Fig 7A ) . Mice placed in a new cage after saline injections ( saline+new cage ) showed a significant increase in wake , LMA , and Tb for approximately 4 h compared with saline injections in the home cage ( Fig 7B–7E and S5 Table ) . The first NREM sleep and REM sleep bouts in the new cage were observed after 218 . 74 ± 15 . 71 min and 264 . 03 ± 11 . 51 min , respectively ( Fig 7C ) . In contrast , when the mice were injected with CNO before placing them in a new cage ( CNO+new cage ) , sleep onset occurred faster with significantly decreased latencies for both NREM sleep ( 137 . 89 ± 13 . 65 min ) and REM sleep ( 161 . 60 ± 11 . 05 min ) ( Fig 7C ) . Consistently , the percentage of time spent in NREM and REM sleep in the third hour ( NREM: 41 . 57% ± 9 . 90% versus 0 . 00% ± 0 . 00% after saline; P < 0 . 0001; REM: 4 . 13% ± 1 . 83% versus 0 . 00% ± 0 . 00% after saline; P = 0 . 13 ) and the fourth hour ( NREM: 56 . 74% ± 5 . 29% versus 21 . 14% ± 10 . 22% after saline , P = 0 . 0007; REM: 6 . 01% ± 1 . 01% versus 1 . 09% ± 0 . 77% after saline , P = 0 . 034 ) after CNO+new cage were higher than in the saline+new cage condition ( Fig 7B ) . Similarly , the LMA and Tb during the period of 3–5 h after CNO+new cage were also substantially lower than after saline+new cage ( Fig 7D and 7E ) . These findings demonstrate that NtsLH neurons are necessary for modulating arousal , LMA , and hyperthermia in response to psychological stress induced by a novel environment . Based on the established role of the LH in feeding and energy homeostasis [41 , 42] , we then hypothesized that NtsLH neurons might contribute to the regulation of sleep-wake , LMA , and Tb in response to acute fasting ( or mealtime hunger ) —a form of physiologic/metabolic stress . Therefore , we tested whether NtsLH neurons contribute to these responses after metabolic stress by inhibiting hM4Di-expressing NtsLH neurons at dark onset by i . p . injections of CNO in the absence of food ( Fig 8 , S4 Fig , and S6 Table ) . Food was removed during the same time as i . p . injections and mice were not habituated to fasting or food restriction prior to these experiments . We recorded sleep-wake behavior , LMA and Tb during the 24-h fasting period following CNO . When fasted after saline injections ( saline+fasting ) , Nts-Cre mice ( expressing hM4Di in NtsLH neurons ) displayed an increase in wakefulness , with a corresponding decrease in NREM sleep for 3 h ( between 2 and 4 h after injections; S4 Fig ) . This wake increase was accompanied by an increase in LMA ( 55% higher than in the saline+fed condition ) . Following this period ( i . e . , 4 h after fasting ) , both Tb and LMA began to drop ( as expected in fasting mice ) , and we observed a significant increase in hypothermia ( <34°C ) and hypoactivity between 11 and 16 h after saline+fasting ( S4 Fig ) . Similarly , NREM sleep during the same period was higher than in the saline+fed condition ( S4 Fig ) . Interestingly , chemoinhibition of NtsLH neurons exaggerated the sleep-wake and Tb responses to fasting . When fasted after CNO injections ( CNO+fasting ) , Nts-Cre mice expressing hM4Di in NtsLH neurons displayed increased wakefulness for 6–7 h ( Fig 8B ) . While the amount of wakefulness during the first 3 h after CNO+fasting was comparable to that in the saline+fasting condition , it was significantly higher during the seventh hour after CNO+fasting ( 94 . 47 ± 5 . 41% versus 59 . 10 ± 14 . 19% after saline; P = 0 . 044 ) . Importantly , the latency to NREM sleep was significantly longer after CNO+fasting compared with saline+fasting ( Fig 8C ) . LMA levels remained elevated for 6–7 h after CNO+fasting compared with an increase lasting only 3 h after saline+fasting ( Fig 8D and S4 Fig ) . Similarly , Tb levels started falling 7 h after CNO+fasting ( compared with 5 h after saline ) , but fell more rapidly and with a greater magnitude between 13 and 15 h after CNO+fasting ( versus saline+fasting; Fig 8E ) ; this later response may not be a direct consequence of the inhibition of NtsLH neurons , because the plasma half-life of CNO is short ( <1 h ) and its behavioral effects generally last about 4 to 8 h [43–45] . In contrast , this deeper fall in Tb between 13 and 15 h is presumably a consequence of enhanced wake and hyperactivity during the first 6–7 h after CNO . These results indicate that NtsLH neurons are critical for the fine-tuning of sleep-wake behavior , LMA , and Tb in times of caloric scarcity . Naturally , when food is unavailable , mice must reduce their activity and levels of wakefulness to conserve energy and guard against metabolic stress . Our findings suggest that NtsLH neurons are critical for this response .
We show that brief optogenetic activation of NtsLH neurons produces immediate transitions to wake from NREM sleep , while sustained chemogenetic activation causes arousals lasting several hours , suggesting that NtsLH neurons play a crucial role in both initiation and maintenance of wakefulness . While rapid arousals from NREM sleep were consistently evoked by photoactivation of NtsLH neurons , arousals from REM sleep were never evoked . Because even high-frequency photostimulations failed to arouse mice from REM sleep , it is not likely that differences in arousal threshold between NREM sleep and REM sleep are responsible . On the other hand , it is possible that NtsLH neurons become unresponsive to external stimuli during REM sleep , which is an inherent property of hypothalamic thermosensitive neurons [51 , 52] . Considering the hyperthermia induced by NtsLH neuronal activation , these neurons may belong to a cold-sensitive neuronal population and may engage in cold defense behavior . Another possibility is that the arousal from REM sleep involves pathways and mechanisms different from those involved in the arousal from NREM sleep and that NtsLH neurons may be a part of the latter . Interestingly , the previously identified wake-promoting cell groups in the forebrain , such as GABAergic neurons in the LH [11] , bed nucleus of stria terminalis ( BNST ) [53] , and basal forebrain [54–56] , also did not elicit wakefulness from REM sleep . Considering the presence of sleep-wake alternation without REM sleep in the isolated forebrain [57 , 58] , it is probable that most wake-promoting neurons in the forebrain are wired for arousal from NREM sleep but not from REM sleep . Besides the rapid but relatively short arousals induced by optogenetic stimulation of NtsLH neurons , we show that arousals induced by chemogenetic activation of NtsLH neurons were long-lasting and accompanied by a hyperactivity and hyperthermia , suggesting that the NtsLH neurons may regulate LMA and Tb in addition to sleep-wake states . Although wake is associated with increased activity , the LMA count per unit time of wake was significantly higher after NtsLH activation than during baseline wake , suggesting mice were hyperactive . Wake and LMA do not always go hand in hand . For example , increased wake after systemic administration of certain pharmacological agents ( e . g . , modafinil ) or after activation of the wake-promoting cell groups in the brain ( e . g . , PB ) were not accompanied by increased LMA [59 , 60] . On the contrary , increased LMA after loss of MCH neurons in the LH was not accompanied by an increase in wake [15] . Thus , the increased LMA after NtsLH activation is not necessarily due to higher wake amounts . Similarly , subchronic ( 30-min ) photoactivation of NtsLH caused an increase in Tb that was neither related to state changes nor accompanied by an increase in LMA , even though chemoactivation increased Tb with a concurrent increase in LMA . While chemoactivation makes neurons more responsive to natural inputs , photoactivation causes rhythmic monotonous firing of neurons . Such differential activation of NtsLH neurons could have led to different downstream responses , contributing to the differential locomotor responses after optogenetic- versus chemoactivation . Nevertheless , these data clearly demonstrate that NtsLH activation may increase Tb independent of wake or hyperactivity and thereby suggest a direct role for NtsLH neurons in thermogenesis . Lack of arousal response after activation of NtsLH neurons during REM sleep indicates the potential cold-sensitive nature of these neurons [51 , 52] , further supporting this idea . Future studies are , however , necessary to test the cold sensitivity of NtsLH neurons and the thermoregulatory deficits induced by their loss . In contrast to chemogenetic activation , chemogenetic inhibition of NtsLH neurons had no effect on wake , LMA , or Tb , suggesting that these neurons may not be necessary for the regulation of spontaneous wakefulness or Tb control under baseline conditions . Because the increased wake , hyperthermia , and hyperactivity after chemogenetic activation of NtsLH neurons resembled a stress response , with concurrent increase in cFos in PVH neurons , we hypothesized that these neurons could be important for stress-induced arousals . We found that chemoinhibition of NtsLH neurons indeed attenuates wakefulness , LMA , and Tb responses to stress induced by a novel environment . Interestingly , NtsLH inhibition paradoxically amplified the sleep-wake , LMA , and Tb responses to metabolic stress induced by fasting . Clearly , the responses to stress should differ depending on the type , magnitude , and duration of the stress [61]—while it may be necessary to increase wake to explore a novel environment and search for potential threats and food sources , it is also necessary to decrease wakefulness and reduce energy expenditure during metabolic challenges , such as during prolonged absence of food . Thus , the observed changes in response to novelty stress and metabolic stress are not actually paradoxical but instead indicate that NtsLH neurons might integrate stress stimuli and generate the appropriate responses . It is also likely that NtsLH neurons are heterogenous ( in terms of co-expression of other neurotransmitter or molecular signatures ) [20] , and these different subsets of neurons may orchestrate responses to different stressors through their differential output pathways . Further studies are required to identify these subsets of NtsLH neurons selectively responding to different stressors . Similar to chemoinhibition of NtsLH neurons , attenuated arousal response to a novelty stress was observed in orexin neuron-ablated mice , and exaggerated arousal response to fasting was observed in MCH-knockout mice [62 , 63] . In addition , orexin neuron-ablated mice also did not exhibit increased arousal levels during fasting [63] . Both orexin and MCH neurons express receptors for Nts [22 , 64] , and both receive inputs from NtsLH neurons . Importantly , NtsLH neurons are the only cell population in the LH that expresses MCH receptors [65] . Nts has been shown to activate orexin neurons in vivo and ex vivo [26] . In contrast , activation of Nts terminals on orexin neurons may inhibit orexin neurons by releasing galanin [66] . Thus , it is possible that Nts neurons may excite or inhibit orexin neurons by releasing either Nts or galanin , respectively . Notably , Nts antagonists have no effect on sleep-wake states in orexin-ablated mice [26] . Thus , we propose that NtsLH neurons may act as a “master orchestrator” and modulate the activity of orexin and MCH neurons , depending upon the perceived stressors , and generate appropriate stress responses . Because we did not study the specific inputs to NtsLH neurons , it is unclear how stress signals reach NtsLH neurons . However , previous studies have shown that several major mediators of stress responses such as the medial prefrontal cortex , PVH , BNST , and amygdala heavily project to the LH [67–69] , and NtsLH neurons may receive these inputs . On the other hand , metabolic signals may directly target NtsLH because a subset of these neurons expresses leptin receptors and NtsLH neurons have been shown to be activated by leptin in brain slices [22 , 70] . In addition to local LH circuits linking Nts neurons with orexin and MCH neurons , we observed direct projections from Nts neurons to other brain structures regulating sleep-wake , Tb , and LMA . Based on our tracing data , we predict that NtsLH could activate the VTA , LC , and PB , which are known as potent wake-promoting cell groups [27 , 71–74] . Likewise , NtsLH neurons could inhibit the lateral preoptic area , which is sleep promoting [75 , 76] . Moreover , activity in NtsLH neurons could increase Tb by activating RPa/Ppy and ventromedial medulla neurons and promote nonshivering and shivering thermogenesis , respectively [77–80] . Finally , NtsLH projections to the VTA may mediate the LMA responses , as increased LMA after NtsLH neurons were blocked by dopamine antagonists , and intra-VTA administration of Nts-antagonist blocked the dopamine release from VTA neurons [81] . While Nts can be excitatory or inhibitory depending upon the receptor expression in the postsynaptic neurons , a subset of NtsLH neurons also express GABA [81] . Thus , the hyperthermia , hyperactivity , and wakefulness after NtsLH activation could be due to a complex integration of inhibitory and excitatory signals . Future studies are required to identify the specific neurotransmitter and pathways involved in each of these responses . Collectively , our results indicate that NtsLH neurons are capable of initiating and sustaining wakefulness and increasing Tb and LMA . While NtsLH neurons may not be necessary for spontaneous wakefulness or Tb maintenance under baseline conditions , they are necessary for modulating wake and hyperthermia after psychological or metabolic stressors . We show that NtsLH neurons reduce wake , LMA , and Tb in response to fasting , while they increase wake , LMA , and Tb in response to a novel environment . Moreover , NtsLH neurons may also be cold sensitive and potentially contribute to cold defense mechanisms , as they were unresponsive during REM sleep , and their activation induced strong hyperthermia . Considering the involvement of the LH in various physiological functions , including sleep-wake , feeding , and thermoregulation and the close-interrelationship between these functions , [1–3 , 20 , 82–85] , our results suggest that NtsLH neurons may play a crucial role in modulating sleep-wake states , LMA , and Tb in response to a variety of physiologic and metabolic demands .
All experiments were conducted in accordance with the National Institutes of Health guidelines for the Care and Use of Laboratory Animals and were approved by the institutional animal care and use committee of Beth Israel Deaconess Medical Center ( protocol #039–2016 ) . All efforts were made to minimize the number of animals used and their suffering . Prior to surgery , all mice were group-housed in a temperature ( 22 ± 1°C ) –and humidity ( 40%–60% ) –controlled animal room maintained on a 12:12-h light-dark cycle . All mice had ad libitum access to standard chow diet and water . After surgery , all animals were singly housed for 3–4 wk before the physiological data collection began . Male mice aged 8–12 wk and weighing between 20 and 24 g at the time of surgery were used for behavioral experiments , and 4-wk-old mice were used for ex vivo brain slice recordings . For this study , we used two transgenic mouse lines—mice expressing Cre recombinase under the Nts promoter ( Ntstm1 ( cre ) Mgmi/J mice; Jackson Laboratory , Stock No . 017525; “Nts-Cre mice” [22] ) and a GFP-reporter mouse line ( Rosa26-loxSTOPlox-L10-GFP; generated by Dr . Brad Lowell , BIDMC; “L10-GFP mice” [86] ) . Nts-Cre mice were crossed with L10-GFP mice to validate Cre expression in Nts neurons . For all experiments , we used heterozygous Nts-Cre mice on a mixed background . Genomic DNA from mice was extracted from tail biopsies and analyzed via polymerase chain reaction using a REDE Extract-N-Amp Tissue PCR Kit ( Sigma-Aldrich , US ) ( Nts-Cre: common forward , 5′-ATA GGC TGC TGA ACC AGG AA; WT reverse , 5′-CAA TCA CAA TCA CAG GTC AAG AA; Cre reverse , 5′-CCA AAA GAC GGC AAT ATG GT . Rosa26-loxSTOPlox-L10-GFP: WT forward , 5′-GAG GGG AGT GTT GCA ATA CC; mutant forward , 5′-TCT ACA AAT GTG GTA GAT CCA GGC; and common reverse , 5′-CAG ATG ACT ACC TAT CCT CCC ) . Adult male Nts-Cre mice were anesthetized with a ketamine/xylazine mixture ( 100 mg/kg ketamine and 10 mg/kg xylazine ) and were unilaterally microinjected with 60 nL of AAV-ChR2 ( University of North Carolina Vector core ) or AAV-hM3Dq ( University of North Carolina Vector core ) into the LH ( anteroposterior , −1 . 7 mm from bregma; lateral ±1 . 1 mm; dorsoventral , −5 . 1 mm from dura [36] ) . Six weeks after the injections , all mice were killed for histological processing . Adult male Nts-Cre mice were anesthetized with a ketamine/xylazine mixture ( 100 mg/kg ketamine and 10 mg/kg xylazine ) and were microinjected bilaterally with 60 nL AAV-ChR2 or AAV8-EF1α-DIO-mCherry ( AAV-mCherry; University of North Carolina Vector core ) into the LH [15] . All mice were then implanted with ( a ) optical fibers targeting 0 . 2 mm dorsal to the LH for blue light/laser stimulation , ( b ) electrodes for recording EEG and EMG-EEG signals by using ipsilateral stainless steel screws and EMG signals by a pair of stainless steel wires inserted into the neck extensor muscles , and ( c ) i . p . radio transmitter ( TA10TA-F20 , Data Science International , MN ) for measuring Tb and LMA [15 , 34] . Four weeks after surgery and AAV microinjections , mice were connected to the recording cables and habituated for 3 d , after which we performed baseline sleep-wake , LMA , and Tb recordings . The EEG/EMG signals were amplified ( AM systems , WA , US ) , digitized , and recorded using Vital recorder ( Kissei Comtec , Nagano , Japan ) [75] . Tb and LMA data were recorded using Dataquest ART 4 . 1 ( Data Sciences International , MN ) . We examined the effects of laser stimulation ( 10 s of stimulation at 1 , 5 , and 10 Hz ) after 30 s of stable NREM sleep ( 10 trials each stimulation frequency ) or 10 s of stable REM sleep ( 10 trials each stimulation frequency ) during the light period . For assessing changes in LMA and Tb , we applied 5-Hz laser stimulation for 30 min during the light period . Nts-Cre mice were anesthetized with ketamine/xylazine ( 100 mg/kg and 10 mg/kg , i . p . ) and injected with AAV8-hsyn-DIO-hM3Dq-mCherry , AV8-hsyn-DIO-hM4Di-mCherry , or AAV-mCherry bilaterally into the LH and were implanted with the telemetry transmitters ( TL11M2-F20-EET; Data Science International , St . Paul , MN ) that allow simultaneous recording of EEG , EMG , LMA , and Tb [15 , 87] . Four weeks after surgery , mice were habituated to the recording room conditions for 3 d . For the interventions , mice were injected with the ligand for hm3Dq and hM4Di , CNO ( 0 . 3 or 1 . 5 mg/kg; Sigma , St . Louis , MO ) , or the vehicle ( saline ) at 9:50 AM ( 10 min before ZT3 ) or 6:50 PM ( 10 min before dark onset ) , and postinjection recordings of sleep-wake , LMA , and Tb ( Dataquest ART 4 . 1 , Data Sciences International , US ) were performed for 24 h . The order of injections was counterbalanced and there were approximately 7 d between two CNO injections in the same mouse . The EEG , EMG data were divided into 12-s epochs and scored manually into one of the three sleep-wake states , wake , NREM sleep , or REM sleep , using SleepSign 3 ( Kissei Comtec , Nagano , Japan ) [15 , 34 , 75] . Percentages of time spent in each sleep-wake state and their mean number and bout durations in 1- and 3-h bins were calculated , along with the total LMA and mean Tb for these periods . Latency to NREM sleep and REM sleep were calculated as time taken to that stage from the time of i . p . injections . After the completion of physiological data collection , all mice were deeply anesthetized with 7% of chloral hydrate and were transcardially perfused with PBS ( 15 mL ) followed by 10% formalin ( 50 mL ) . Mouse brains were harvested immediately and incubated with 10% formalin overnight , followed by incubation in 20% sucrose in formalin solution at 4°C . Brains were cut into three series of 40-μm coronal sections on a freezing microtome and processed for immunohistochemistry , immunofluorescence , and/or in situ hybridization . For immunohistochemistry using diaminobenzidine ( DAB ) reactions , sections were incubated with the primary antibody for two nights ( for cFos labeling ) or overnight ( all others ) , followed by incubation in the appropriate biotin-SP-conjugated secondary antibody ( 1:1 , 000; Jackson ImmunoResearch , West Grove , PA ) for 2 h . Then , sections were incubated for 75 min in avidin-biotin-complex reagent ( 1:1 , 000; Vectastain ABC kit , Vector Lab , Burlingame ) , washed , and incubated in a 0 . 06% solution of DAB ( Sigma-Aldrich ) and 0 . 02% H2O2 for 2–5 min for staining in brown . CoCl2 ( 0 . 05% ) and 0 . 01% NiSO4 ( NH4 ) in PBS was added to the DAB solution for staining in black [15 , 34 , 87 , 88] . For immunofluorescence , sections were incubated in primary antibodies overnight . After washes in PBS , the sections were incubated in the appropriate fluorescent secondary antibodies ( 1:1 , 000; Alexa Fluor Dyes , Life Technologies , Carlsbad , CA ) for 2 h . The following antibodies were used: primary antibody for cFos ( 1:20 , 000; PC38; MilliporeSigma , Darmstadt , Germany ) , ds-Red ( 1:10 , 000; 632496; Clontech Laboratory , Mountain View , CA ) , Orexin-A ( 1: 5 , 000; SC-8070; Santa Cluz Biotechnology , Dallas , TX ) , and MCH ( 1: 5 , 000; gift from Dr . Eleftheria Maratos-Filer , Harvard University , Boston , MA ) [15 , 34 , 88 , 89] . Sections were mounted on Superfrost glass slides , dehydrated , cleared , and coverslipped using Permaslip ( Albose Scientific , MO ) in case of DAB staining or Vectashield ( Vector labs , CA ) in case of fluorescent labeling . All cell counting was performed by constructing a 500 × 500 μm box on the lateral hypothalamus . The dorsal border of the square was aligned with the dorsal edge of the third ventricle , while the lateral border was aligned with the lateral edge of the hypothalamus [89] . The Franklin and Paxinos mouse brain atlas [36] was used for determining anteroposterior coordinates . Abercrombie corrections were applied to all cell counts [90] . Under anesthesia , Nts-Cre mice ( 4 wk old ) were injected with AAV-ChR2 ( n = 3 ) , AAV-hM3Dq ( n = 4 ) , or AAV-hM4Di ( n = 3 ) into the LH and killed after 4 wk . Brains were removed and quickly transferred to ice-cold cutting solution consisting of 72 mM sucrose , 83 mM NaCl , 2 . 5 mM KCl , 1 mM NaH2PO4 , 26 mM NaHCO3 , 22 mM glucose , 5 mM MgCl2 , and 1 mM CaCl2 , carbogenated with 95% O2/5% CO2 , with a measured osmolarity of 310–320 mOsm/L . Brains were cut into 250-μm slices and the slices containing the LH were used for current-clamp recordings . mCherry ( ChR2/hM3Dq/hM4Di ) –expressing Nts neurons in the slices were visualized using an upright microscope ( SliceScope , Scientifica ) , and current-clamp recordings were performed using borosilicate glass microelectrodes ( 5–7 MΩ ) filled with internal solution [33 , 86 , 91] . After achieving stable baseline recordings for 5–10 mins , the response to photo-illumination or CNO ( depending upon the AAV injected ) was investigated . To test the response of ChR2-expressing NtsLH neurons to photo-illumination , 477 nm light was applied for 5–10 s at various frequencies ( 1–10 Hz ) , and the recordings were continued for 1–2 min . Data from 10 s before , during , and after the stimulus were compared . To test the CNO effects of hM3Dq- or hM4Di-expressing NtsLH neurons , artificial cerebrospinal fluid ( ACSF ) solution containing CNO ( 500 nM ) was perfused onto the slice preparation and recordings continued for 2–5 min , followed by ACSF perfusions to wash out the CNO . Data from 2 min just prior to bath application of CNO were considered as baseline; the response to CNO was measured during the last 1 min of CNO application . The resting membrane potentials before and during CNO were compared using paired t tests . Current ( 5–20 pA ) was applied via the patch pipette if mCherry+neurons did not fire action potentials ( for hM4Di inhibition experiments ) . Statistical analysis was performed using GraphPad Prism version 7 ( GraphPad Software , La Jolla , CA ) . For optogenetic experiments , data after photostimulations ( in Nts-Cre mice injected with AAV-ChR2 ) were compared with prestimulation data as well as after sham stimulations using a one-way ANOVA followed by Tukey multiple comparisons . In chemogenetic experiments , post-CNO data from the experimental group ( Nts-Cre mice injected with AAV-hM3Dq/AAV-hM4Di ) were compared with post-saline data from the same mice and post-CNO data from negative controls ( Nts-Cre mice injected with AAV-mCherry ) using a two-way repeated measures ANOVA , followed by Sidak post hoc test . All data are presented as the mean ± SEM unless otherwise noted . Differences were considered significant at P values less than 0 . 05 . | Adjusting sleep-wake behavior in response to environmental and physiological challenges may not only be of protective value , but can also be vital for the survival of the organism . For example , while it is crucial to increase wake to explore a novel environment to search for potential threats and food sources , it is also necessary to decrease wake and reduce energy expenditure during prolonged absence of food . In this study , we report that a subset of neurons in the lateral hypothalamic area ( LH ) expressing the neuropeptide neurotensin ( Nts ) is critical for orchestrating sleep-wake responses to such challenges . We show that brief activation of NtsLH neurons in mice evokes immediate arousals from sleep , while their sustained activation increases wake , locomotor activity , and body temperature for several hours . In contrast , when NtsLH neurons are inhibited , mice are neither able to sustain wake in a novel environment nor able to reduce wake during food deprivation . These data suggest that NtsLH neurons may be necessary for generating appropriate sleep-wake responses to a wide variety of environmental and physiological challenges . | [
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] | 2019 | Lateral hypothalamic neurotensin neurons promote arousal and hyperthermia |
Leishmaniasis , a human parasitic disease with manifestations ranging from cutaneous ulcerations to fatal visceral infection , is caused by several Leishmania species . These protozoan parasites replicate as extracellular , flagellated promastigotes in the gut of a sandfly vector and as amastigotes inside the parasitophorous vacuole of vertebrate host macrophages . Amastins are surface glycoproteins encoded by large gene families present in the genomes of several trypanosomatids and highly expressed in the intracellular amastigote stages of Trypanosoma cruzi and Leishmania spp . Here , we showed that the genome of L . braziliensis contains 52 amastin genes belonging to all four previously described amastin subfamilies and that the expression of members of all subfamilies is upregulated in L . braziliensis amastigotes . Although primary sequence alignments showed no homology to any known protein sequence , homology searches based on secondary structure predictions indicate that amastins are related to claudins , a group of proteins that are components of eukaryotic tight junction complexes . By knocking-down the expression of δ-amastins in L . braziliensis , their essential role during infection became evident . δ-amastin knockdown parasites showed impaired growth after in vitro infection of mouse macrophages and completely failed to produce infection when inoculated in BALB/c mice , an attenuated phenotype that was reverted by the re-expression of an RNAi-resistant amastin gene . Further highlighting their essential role in host-parasite interactions , electron microscopy analyses of macrophages infected with amastin knockdown parasites showed significant alterations in the tight contact that is normally observed between the surface of wild type amastigotes and the membrane of the parasitophorous vacuole .
More than 20 species of the genus Leishmania cause leishmaniasis , a human illness with a large spectrum of clinical manifestations that range from self-resolving skin lesions to life-threatening visceral diseases . Endemic in eighty-eight countries from tropical and subtropical areas of the world , Leishmaniasis has an estimated prevalence of 12 million cases with annual mortality rate of 60 , 000 people ( www . who . int/topics/leishmaniasis/en/ ) for which there is no vaccine or adequate treatment . Thus , studies of various Leishmania species including the complete genome sequences of Leishmania major , Leishmania infantum and Leishmania braziliensis [1 , 2] have been directed towards the identification of virulence factors used by the parasite to infect and survive within mammalian host cells as well as towards the development of new forms of treatment and disease prevention . Although comparative studies showed that L . major , L . braziliensis and L . infantum have very similar genomes regarding gene content and organization , the presence of specific sequences and pathways , such as retrotransposons and an active RNAi machinery found in L . braziliensis [3 , 4] , indicate a greater than expected diversity within this species . The differences regarding the presence of RNAi machinery also imply that different approaches must be used for functional genomic studies with these parasites . During their life cycle , all Leishmania parasites alternate between the alimentary tract of a sandfly vector , where they grow as extracellular , flagellated promastigotes , before differentiating into infective non-dividing metacyclic forms and the phagolysosome of vertebrate host mononuclear phagocytes , where they multiply as amastigotes [5] . Therefore , the study of Leishmania proteins involved with the receptor-mediated phagocytosis and intracellular survival in the phagolysosome is critical for our understanding of leishmaniasis and the complex interaction between this parasite and its mammalian hosts . One of the main characteristics of the genome of several members of the Trypanosomatid family is the presence of large numbers of repetitive sequences , especially multigene families encoding glycoproteins that are important components of the parasite surface directly involved in host-parasite interaction [6] . Among these multigene families are amastins , a group of surface glycoproteins containing 180–200 amino acids initially identified as a differentially expressed gene in T . cruzi amastigotes [7] as well as in L . major and L . infantum [8 , 9] . More recently , genome data from various trypanosomatids showed that amastins are present in other Leishmania species as well as in Crithidia ssp . and Leptomonas seymouri [10–12] , but no homologous sequences can be found outside the Trypanosomatid family . The predicted topologies of different amastin sequences in both T . cruzi and Leishmania spp . showed that all amastin proteins contain four hydrophobic transmembrane domains , interspersed with two serine and threonine rich extracellular domains and N- and C- terminal tails facing the cytosol . Comparative sequence analyses of different amastin genes have also indicated that , although the hydrophilic extracellular domain contains a conserved amastin signature ( C-[IVLYF]-[TS]-[LF]-[WF]-G-X-[KRQ]-X-[DENT]-C ) , its sequences display significantly higher variability compared to hydrophobic domain sequences [9 , 13] . Recent analyses of the evolution and diversification of amastin genes indicate that this family , which has undergone a major diversification after the genus Leishmania originated , can be grouped into four subfamilies , α , β , γ and δ-amastins , according to genomic position , structure and evolution [12] . Based on phylogenetic inferences and on the fact that the δ-amastin gene repertoire has been largely expanded in all Leishmania species , it has been suggested that the amastigote-specific function of amastins may possibly be limited to δ-amastins . It is noteworthy that δ-amastins are present in T . cruzi but absent in T . brucei and in other salivarian trypanosomes that do not have an intracellular stage , further suggesting that the expansion of δ-amastin genes in T . cruzi and Leishmania spp may be associated with the adaptation of amastigotes to the intracellular life stage [14] . With 45 copies identified in the genomes of L . major and L . infantum [12] , amastins constitute the largest gene family in the Leishmania genus whose members show regulated expression during the life cycle of the parasite . Leishmania amastins are among the most immunogenic of all the surface antigens in mice [15] and elicit a strong immune response in humans , particularly associated with visceral leishmaniasis [16] . With only 12 copies of δ-amastins , T . cruzi has a more limited amastin gene repertoire [7 , 17] . Besides δ-amastins , T . cruzi has two copies of β-amastins , which , surprisingly , have been found to be upregulated in the insect , epimastigote forms of the parasite [18] . Although it has been more than 20 years since their discovery , the function of amastins remains unknown . The fact that they are encoded by a family with a large number of copies in the genomes of different trypanosomatids that have an intracellular stage and are located on the surface of the parasites led us to infer that these proteins may interact with molecules from the host cell or act as membrane transporters . Since gene knockout is not an option , due to the large number of amastin genes , RNA interference ( RNAi ) knockdown constitutes the best strategy to address gene function in diploid pathogenic organisms that have no defined sexual cycle . First described in 1998 , RNAi has quickly proven to be an immensely useful tool for studying gene function in T . brucei [19] . However genome data has shown that the components of the RNAi machinery are absent in T . cruzi [20] , Leishmania major , L . donovani [21 , 22] as well as in other protozoan parasites such as Plasmodium falciparum [23] . Unexpectedly , in 2010 Lye and colleagues demonstrated that L . braziliensis and other species of the sub-genus Viannia have an active RNAi machinery [4] . Although these authors have clearly demonstrated that RNAi activity in L . braziliensis results in down-regulation of the expression of a reporter gene such as the green fluorescent protein ( GFP ) gene as well as endogenous genes , such as the paraflagellar rod protein , no other study aimed at investigating gene function in L . braziliensis using RNAi has been described . Here we present data on the sequence and structural organization of amastin genes in L . braziliensis and show that α , β , γ and δ-amastins are upregulated in L . braziliensis amastigotes and are localized to the parasite surface . We also show that RNAi knockdown of δ-amastin expression in L . braziliensis results in decreased survival and proliferation of intracellular parasites after either in vitro infection of mouse macrophages or in vivo infection of mice . These results , together with observations from ultrastructural analysis , which show significant alterations in the membrane contact between host macrophage and intracellular amastigotes , indicate that Leishmania amastins are virulence factors essential for parasite replication within the mammalian host cell .
To identify all protein coding genes present in L . braziliensis with sequence similarity to amastins , we screened the L . braziliensis genome database ( www . tritrypdb . org ) for sequences homologous to T . cruzi and L . infantum amastins . A total of 52 genes , located on nine different chromosomes and belonging to all four previously described amastin sub-families , namely α , β , γ and δ-amastins , were identified ( Fig 1 ) . Similar to the amastin gene family organization found in the genomes of T . cruzi and L . infantum [7 , 9] , only δ-amastins were found associated with tuzin gene orthologs . A phylogenetic tree based on amino acid sequences of all 52 L . braziliensis amastin sequences shows three major clusters with α and β-amastins clustered together and γ and δ-amastins showing highly divergent sequences ( Fig 2 ) . Fig 2 also shows that , similar to other Leishmania species , such as L . infantum , L . major and L . amazonensis , L . braziliensis has a diverse amastin gene repertoire and that its expansion derives from the expansion of the δ-amastin subfamily . Multiple sequence alignments of amastin-related sequences from L . braziliensis , L . infantum and T . cruzi demonstrated significant conservation both in sequence and in the predicted structures amongst the different family members . The four predicted transmembrane helices are highly conserved and show high sequence similarity amongst the amastin family members ( S1 Fig ) . Similar to T . cruzi δ-amastins [13] , the two predicted extracellular , hydrophylic domains , have increased sequence variability compared to the transmembrane domains . Also similar to T . cruzi and Leishamania spp amastins , a consensus sequence , previously identified in L . infantum amastins as the amastin signature [9] , is also found in all L . braziliensis sequences ( S1 Fig ) . Since primary sequence alignments showed no homology to any known protein sequence , we used the protein fold recognition server Phyre ( Protein Homology Recognition Engine ) [24] to generate a predicted structural model for amastins . Homology modeling indicated that α , β , γ and δ-amastins are related to a group of proteins that are components of tight junction complexes named claudins . Claudins constitute a protein family of 27 members in mammals , with a molecular mass ranging from 20 to 27 kDa [25] . Fig 3 shows that α , β , γ and δ-amastins have significant structural homology with the predicted 3D structures of claudin 15 . Similar to amastins , claudins bear four transmembrane domains , a short intracellular N-terminal sequence , a large first extracellular loop ( ~50 residues ) , a shorter second extracellular loop ( 16–33 residues ) and a cytoplasmic domain of variable length [26] . Members of the claudin protein family also display conserved residues in the first extracellular domain ( W-LW-C-C ) [27] that partially matches the amastin signature ( F/W-LW-C-C ) described by Rochette et al . ( 2005 ) [9] . Although amastin genes have been initially described as amastigote specific genes , we have recently demonstrated that , in contrast to δ-amastins transcripts , transcript levels of T . cruzi β-amastins are upregulated in epimastigotes , the stage found in the triatomine vector [18] . Here we determined steady state levels of amastin transcripts belonging to the four sub-families identified in the L . braziliensis genome using total RNA extracted from promastigotes and axenically growing amastigotes . Northern blots probed with radiolabelled fragments containing sequences corresponding to one member of α , β and γ-amastins ( LbrM . 28 . 1550 , LbrM . 30 . 0980 and LbrM . 24 . 1600 , respectively ) and two members of δ-amastins , ( LbrM . 20 . 1060 , LbrM . 08 . 0300 ) , showed that transcript levels of amastins belonging to all four subfamilies are upregulated in the amastigote stage compared to the promastigote stage ( Fig 4 ) . RNA quantification using rRNA as a loading control showed the level δ-amastin transcripts was increased by 19-fold in amastigotes compared to promastigotes , whereas α , β and γ-amastin transcripts were increased only 3 , 2 and 7-fold , respectively , when comparing amastigotes to promastigotes . As shown by Rochette et al . [9] transfection of L . infantum , L . major and L . infantum with vectors containing amastin sequences fused to green fluorescent proteins results in parasites with fluorescent signals in their plasma membranes . Similarly , T . cruzi epimastigotes and amastigotes transfected with GFP-fusion constructs of β and δ-amastins [18] , as well as immune-electron micrographs of amastigotes using an anti- δ-amastin peptide antibody [7] showed a surface localization of the protein . As indicated before , hydrophobicity profiling predicted four transmembrane helices for all L . braziliensis amastin homologs tested ( S1 Fig ) , thus suggesting that similar to T . cruzi amastins and other Leishmania amastin homologs , L . braziliensis amastins have a surface localization in the parasite . To verify this , we prepared GFP fusion constructs of three distinct amastin sequences in the expression vector pSPGFP [28] and transfected L . braziliensis promastigotes . As shown in Fig 5 , expression of GFP fusion proteins containing sequences of β , δ and γ-amastins resulted in parasites showing fluorescence signals in the plasma membrane but excluded from the flagellum . For reasons that are unknown , several attempts to obtain the expression of α-amastin fused to GFP have failed . The genome sequence of L . braziliensis predicts the existence of an active RNAi pathway [2] and this prediction has been experimentally validated [4] . To further investigate the role of amastin genes , we knocked-down their expression in L . braziliensis by transfecting promastigotes with the pIR1PHLEO vector containing sense and anti-sense coding sequences of the δ-amastin gene LbrM . 20 . 1060 ( Fig 6A ) . To confirm that amastin gene expression has been knocked down , we isolated RNA from two cloned transfected cell lines and probed northern blot with labelled LbrM . 20 . 1060 DNA fragment . As shown in Fig 6B , amastin transcripts were degraded in amastigotes derived from the two cloned cell lines that have been transfected with amastin dsRNA constructs , named RNAi1060-cl1 and RNAi1060-cl5 . The same northern blot showed a 19-fold increase in amastin mRNA levels in WT , untransfected amastigotes compared to promastigotes , as previously described . Northern blot analysis also showed that mRNA degradation occurs specifically with the amastin mRNA since high levels of full length GAPDH mRNAs were detected in all cell lines . High molecular weight bands observed in the RNA samples purified from RNAi knockdown mutants may correspond to intact or partially fragmented double stranded , stem-loop RNA , as previously detected by Lye et al . ( 2010 ) in L . braziliensis expressing stem-loop dsRNA with sequences from the GFP gene or from genes encoding enzymes for lipophosphoglycan synthesis [4] . To verify that the mRNA degradation is due to the presence of siRNAs in the transfected parasites , we purified small-molecular-weight RNAs from WT and from two Ama1060-RNAi clones , fractionated them on a 15% polyacrylamide gel and probed with labelled 60 oligonucleotides containing sequences spanning almost the entire coding region of amastin gene LbrM . 20 . 1060 . Fig 6C shows that siRNAs with about 26 nt are detected in the RNA population of promastigotes derived from one of the transfected L . braziliensis cell lines , whereas only weak signals were detected in amastigotes derived from the two transfected , Ama1060-RNAi clones . To confirm that the RNAi machinery has efficiently knocked down amastin protein expression in L . braziliensis , western blots with total protein extracts were incubated with antibodies made against the recombinant LbrM . 20 . 1060 protein . Fig 6D shows that whereas in WT parasites the expression of amastin was found exclusively in amastigotes , protein levels of amastins in the transfected cell lines are below the detection limit of the western blot . Knockdown of δ-amastin expression did not affect growth of promastigote mutants or differentiation into metacyclic forms in stationary phase promastigote cultures . To determine whether siRNA containing sequences derived from one amastin gene ( LbrM . 20 . 1060 ) affected the expression of additional members of the amastin gene familily , we performed a genome wide sequencing analysis of polyA+ RNA purified from amastigotes from WT L . braziliensis and from the one RNAi amastin knockdown cell line . As shown in S2 Fig and S1 Table , expression of dsRNAs derived from one specific δ-amastin gene ( LbrM . 20 . 1060 ) affected transcript levels of most members ( 60% ) of the δ-amastin subfamily but did not affect transcript levels of α , β and γ-amastins . In addition , RNAi knockdown of δ-amastin does not affect the expression of other L . braziliensis genes , such as gapdh . To verify whether RNAi-mediated depletion of δ-amastin mRNA affected parasite infection capacity , intraperitoneal macrophages were isolated from BALB/c mice and were incubated for 24 hours with either wild type ( WT ) L . braziliensis stationary phase promastigotes or promastigotes derived from two parasite cell lines with reduced expression of δ-amastins . After washing non-internalized parasites , the numbers of amastigotes per 100 cells were assessed 24 , 48 and 72 hours post-infection . As shown in Fig 7A , no significant differences in the numbers of intracellular amastigotes were observed 24 hours after the infection , indicating that the initial steps of the in vitro infection were not affected by knocking down δ-amastin expression . However , compared to WT parasites , the number of intracellular amastigotes showed an average 3 fold reduction in cells infected with the two cloned cell lines expressing amastin dsRNA at 48 and 72 hours post-infection , suggesting that δ-amastin expression is required for parasite intracellular multiplication . Next , we assessed the effect of knocking down δ-amastins in an in vivo model of infection by inoculating the footpad of BALB/c mice with equal numbers of stationary-phase promastigotes of ( 1 ) WT L . braziliensis , ( 2 ) a L . braziliensis cell line that has been transfected with the pIR1PHLEO vector containing the GFP gene or ( 3 ) the two cloned cell lines expressing δ-amastin dsRNA . By monitoring the infection through determination of parasite numbers in the infected animals , we verified that the expression of δ-amastins is essential for parasite survival in BALB/c mice . The results of three independent experiments clearly showed that whereas similar numbers of amastigotes were found in the tissues of mice infected with WT and with transfected , GFP-expressing L . braziliensis , no parasites could be recovered from tissues of mice inoculated with the two cloned , amastin knockdown cell lines two weeks after inoculation . Similar results were observed when parasite numbers were determined four and nine weeks post infection ( Fig 7B ) . To confirm that such strong , attenuated phenotype was not a consequence of altered expression of genomic sequences other than δ-amastins , we set out to rescue this phenotype by expressing an RNAi-resistant amastin construct in one of the amastin knock down cell lines . A plasmid construct carrying a synthetic δ-amastin sequence containing third base modifications in such a way that the mRNA sequence is divergent from the wild type LbrM . 20 . 1060 gene but the amino acid sequence remains exactly the same ( S3 Fig ) was transfected into the amastin cell line RNAi1060-cl5 . In addition , to verify that this synthetic gene was being expressed in the transfected cell lines that also express amastin dsRNA , an HA epitope tag was added in the region encoding the second hydrophilic , extracellular domain of the protein . As shown in Fig 8A , transfection of the RNAi1060-cl5 cell line with a plasmid containing the RNAi-resistant δ-amastin sequence resulted in parasite cell lines ( named RNAi1060-re-expressors , RNAi1060-R1 and RNAi1060-R2 ) expressing tagged amastin proteins that are recognized by anti-HA antibodies in both promastigotes and amastigotes . Fig 8B showed that these two cloned cell lines that are re-expressing δ-amastin LbrM . 20 . 1060 were able to survive in mouse macrophages almost as well as WT L . braziliensis . As described before , no differences in the number of intracellular amastigotes were detected in macrophages infected with WT and the two RNAi clones , RNAi1060-cl1 , RNAi1060-cl5 24 hours after infection . Twenty-four hours post-infection , the two re-expressor clones , RNAi1060-R1 and RNAi1060-R2 , also showed similar numbers of intracellular amastigotes compared to WT parasites . However , when intracellular amastigote numbers were determined 72 hours post-infection , a clear difference was observed between both re-expressor clones and the two RNAi clones: whereas almost no amastigotes were found in macrophages infected with RNAi1060-cl1 and RNAi1060-cl5 , the number of amastigotes in cells infected with the two re-expressor clones , RNAi1060-R1 and RNAi1060-R2 , reached 35% and 25% , respectively , of the numbers found in cells infected with WT parasites . Reversion of this attenuated phenotype is even more clearly observed during in vivo infection: whereas no parasites could be rescued from infected animals two weeks after inoculating the RNAi knockdown cell lines , infection with the two re-expressor clones resulted in parasite numbers corresponding to 61 e 42% of the numbers found in mice that were infected with WT L . braziliensis ( Fig 8C ) . To verify that the cells expressing the RNAi-resistant transgene continue to express δ-amastin dsRNA , we PCR amplified DNA purified from WT and the two RNAi clones , RNAi1060-cl1 , RNAi1060-cl5 , as well as from two cloned cell lines derived from the RNAi1060-cl5 that were transfected with the RNAi-resistant construct . PCR amplifications using a forward primer annealing in the PHLEO resistance marker present in the pIR1PHLEO-Ama1060 plasmid and a reverse primer annealing in the SSU ribosomal locus of the Leishmania genome showed that both re-expressor clones retained the pIR1PHLEO-Ama1060 plasmid construct , indicating that the these cells continue to express the amastin dsRNA ( S4 Fig ) . These results showed that it is possible to re-express δ-amastins in a parasite that had this gene knocked down by RNAi and , by doing so , the parasite regains the ability to infect and multiply in BALB/c mice . This observation adds support for a role of these surface proteins as a L . braziliensis virulence factor . Macrophages infected with WT L . braziliensis as well as with δ-amastin knockdown parasite cell lines were further examined using transmission electron microscopy ( TEM ) . As shown by Zauli et al . [29] , TEM of macrophages infected with L . braziliensis allowed the identification of amastigotes exhibiting their characteristic subpellicular microtubules , kDNA structure and a short flagellum inside tight parasitophorous vacuoles ( PV ) ( Fig 9A ) . Our TEM analyses also showed the existence of a tight interaction between the amastigote membrane of WT parasites and the parasitophorous vacuole ( PV ) membrane , with several points of close contact between the two membranes ( Fig 9A ) . In contrast , macrophages infected for 72 hours with the two amastin knockdown clones contain amastigotes showing not only morphological alterations such as larger vesicles and partial disorganization of the sub-pellicular microtubule , but also striking variations in their contact with the macrophage PV membrane ( Fig 9A and S5 Fig ) . Amastigote membranes from both RNAi knockdown clones present fewer regions of contact with the PV membrane than amastigote membranes from WT parasites . This difference observed in the interaction between the PV membrane and amastigotes of WT and RNAi knockdown parasites was quantified by measuring the total area of the PV and the area occupied by the parasite ( Fig 9B ) . It should be noted that , in contrast to the PV membranes from macrophages infected with WT parasites , PV nembranes from macrophages infected with both δ-amastin knockdown parasites present regions where the lipid bilayer appears to be disrupted . Finally , it is also noteworthy that in a few images , including images of macrophage infected with RNAi knockdown clones , we observed two amastigotes in one PV , indicating that these parasites are able to divide within the PV ( S5 Fig ) .
Expansion of families encoding surface proteins is one of the main characteristics revealed with the complete genome sequences of different trypanosomatid parasites . Several of these proteins appear to be involved with host parasite interactions and are likely to play a role in the parasite evasion of host immune responses . In the case of T . cruzi and Leishmania , gene families encoding surface proteins may be directly responsible for the ability of these parasites to invade and multiply within mammalian host cells . However , because they are encoded by multiple genes that are usually dispersed in the genome , knockout experiments to determine their functions may not be applied and , in the case of T . cruzi and most Leishmania species , the strategy of gene silencing has also been hampered by the absence of functional RNAi pathways in these parasites . Amastins constitute a group of glycoproteins encoded by a multigene family present in the genome of several trypanosomatids . In spite of being thoroughly investigated in T . cruzi [7 , 18] , L . major , L . infantum [9] and L . amazonensis [10] , a clear role for amastin genes has not been yet defined . In agreement with their proposed role related to parasite intracellular survival , comparative genomic studies have shown an increased amastin gene repertoire in most Leishmania species [12] . We showed here that , similar to other Leishmania , L . braziliensis has 52 amastin genes , with a vast majority ( 42 genes ) belonging to the δ-amastin subfamily . It is noteworthy that L . tarentolae , a lizard parasite non-pathogenic to humans , which does not multiply intracellularly , has only 11 amastin genes and from those only 2 genes belong to the δ-amastin subfamily [30] . Since the increased amastin gene repertoire observed in Leishmania is due to the expansion of the δ-amastin subfamily , it is proposed that δ-amastins evolved novel functions related to intracellular survival of Leishmania spp within mammalian host macrophages [12] . Based on northern blot and RNA-seq data , most likely all δ-amastins are upregulated in L . braziliensis amastigotes . We also showed evidence indicating that all amastins are localized to the parasite surface . In an attempt to elucidate the role of δ-amastins , we took advantage of the fact that , different from L . infantum and other Old World Leishmania species , L . braziliensis has a functional RNAi pathway . Using a construct directing the expression of dsRNA containing sequences from one δ-amastin gene , we were able to evaluate the consequences on the intracellular survival of L . braziliensis amastigotes of knocking down expression of several genes of this amastin subfamily . The substantial decrease in intracellular parasite numbers after in vitro infection of mouse macrophages and the complete attenuated phenotype observed during in vivo infection with RNAi knockdown parasites revealed , as previously anticipated , an essential role of δ-amastins in a long-term infection of Leishmania . Recent studies with different T . cruzi strains have also suggested a correlation between expression of δ-amastins and parasite virulence [31] . Different from most T . cruzi strains , the G strain exhibit very low infectivity both in vitro and in vivo [18] . Such lower infection capacity was associated with low transcript levels of δ-amastins , which are highly expressed in amastigotes in all T . cruzi strains analyzed , except in the G strain [18] . After transfecting the G strain with a vector that directs the constitutive expression of δ-amastins , an accelerated differentiation of amastigote into trypomastigotes was observed during late stages of the in vitro infection of HeLa cells . Moreover , in contrast to the infection of susceptible mouse strains with wild type G strain parasites , where amastigotes nests were observed only after day 5 , amastigotes of G strain overexpressing δ-amastin were observed in mouse tissues at the third day after inoculation [31] . In contrast to T . brucei , in which RNAi has been used as a powerful tool for functional genomic studies , especially after the development of a system for tetracycline-regulated expression of dsRNAs [21] neither T . cruzi [20] nor Old World L . major and L . donovani [22] has functional RNAi machinery . The characterization of the complete RNAi pathway in L . braziliensis , including the identification of endogenous small interfering RNAs , or siRNAs and the proteins required for this pathway [4 , 32] has allowed studies involving gene knockdown in this parasite and , most importantly , knocking down the expression of multigene families . RNA-seq analysis has shown that , besides reducing transcript levels of the target amastin gene , expression of dsRNA containing δ-amastin sequences affects other δ-amastin genes , while expression of amastin genes belonging to other subfamilies was not affected . This observation is highly relevant , since it allowed us to infer that the phenotypic differences observed in the knockdown parasites indeed resulted from specific reduced expression of δ-amastins . This prediction was confirmed by experiments testing the phenotype of parasites in which δ-amastin expression was knocked-down by RNAi and subsequently restored by transfecting a clone expressing amastin dsRNA with an RNAi-resistant amastin gene . The fact that we exclusively knocked-down δ-amastins also means that we need to test the function of members of other amastin subfamilies , which is currently underway . The reduced numbers of intracellular amastigotes observed 48 and 72 hours after in vitro infection of mouse macrophages with parasites where δ-amastin expression has been knocked-down compared to WT parasites provided the first direct evidence of a role of δ-amastins related to parasite proliferation inside the macrophage PV . The fact that no significant differences were observed 24 hours post-infection also suggested that amastins do not play a significant role in the initial steps of parasite internalization , which occurs through receptor-mediated phagocytosis by the macrophages [33] . This observation also suggests that δ-amastins could be important for protection against the content of the PV and hence for survival but not directly for proliferation . Because , after in vivo infection of BALB/c mice , no living parasites were found in the footpads of these animals , it can be also speculated that , besides their role related to intracellular amastigote proliferation or survival in the PV , δ-amastins may also participate in mechanisms developed by the parasite to maintain the infection , such as evasion of host immune response . It should be noted that the role played by δ-amastin in L . braziliensis may be different from its role in T . cruzi , where overexpression of this gene did not affect intracellular multiplication of the G strain , but affects parasite differentiation from intracellular amastigote to infective trypomastigotes [31] . It is also noteworthy that , different from Leishmania spp , T . cruzi amastigotes multiply in the cytoplasm of a variety of mammalian cell types . Hence , since T . cruzi and L . braziliensis δ-amastins share 35% identity at the amino acid level , these proteins may have developed functions that are specific for the distinct intracellular niches these parasites occupy within their host cells [14] . Together with the phenotypic changes observed in the interaction of L . braziliensis with host macrophages , the similarity found between the 3D structures of amastins and mammalian claudins represents a beam of light shedded on the long lasting question regarding amastin gene function . In striking similarity to amastins , claudin proteins have four transmembrane domains , two extracellular hydrophilic domains with two highly conserved cysteine residues in their first hydrophilic domain . Amastins also present structural similarities with the adenylate cyclase ( AC ) family of proteins described in African trypanosomes [34] , but in contrast to amastins , all members of the AC family localize to the flagellum of the parasite . As components of the apical intercellular seal in polarized epithelial cells formed by specific complexes named "tight junctions" [35] , claudins form structures that not only serve as barriers , but also function to control paracellular channels , which are selectively permeable to ions and small charged molecules [27] . The close interaction between the L . braziliensis membrane and the PV membrane , also observed in macrophages from infected mouse ear tissues described by Zauli and colleagues [29] suggests a role of amastins in mediating this interaction . Images of macrophages infected with parasites with reduced δ-amastin expression showed not only that this interaction was drastically affected , but also that the structure of PV membrane bilayer became disorganized in the regions where there is no contact with the parasite membrane . Whether these changes in the interaction between the Leishmania and the PV membrane is the cause of the drastic reduction of parasite load observed in macrophages infected with amastin knockdown cell lines remains to be investigated . Although we have no direct evidence for the existence of an interaction between amastins and claudins , or with any other protein present in the PV membrane , it is reasonable to speculate that amastins may participate in the formation of a complex with proteins present in the macrophage PV membrane and that the formation of this complex may be essential for the survival of Leishmania inside the PV .
All animal work was conducted according to national and international guidelines . Non infected and infected animals were kept with appropriate conditions of technical management in cages that were properly identified and sealed , preventing any contact with healthy animals . All animals were euthanized by cervical dislocation without sedation/anesthesia , before removal of footpad tissues . Sequence analyses were performed using the L . braziliensis MHOM/BR/75/M2904 genome database ( ww . tritrypdb . orgw ) to identify all amastin genes . Amastin sequences from T . cruzi [18] and L . infantum [9] retrieved from tritrypdb ( www . tritrypdb . org ) were used as queries in Blastp analyses ( www . ncbi . nlm . nih . gov/blast/Blast . cgi ) . Multiple alignment of L . braziliensis amastin polypeptide sequences was done using the MUSCLE software ( http://www . drive5 . com/muscle/ ) . Phylogenetic trees were constructed using a neighbor-joining algorithm with 1000 bootstrappings by MEGA 6 software [36] . PHYRE database was used to generate a predicted structural model . The protein sequence of amastins were obtained from the and submitted to Protein Homology/analogy Recognition Engine ( PHYRE version 2 ) [24] . Based on homology sequence in PHYRE server , the three-dimensional structure of amastin was predicted . Homology modeling was performed using UCFS Chimera [37] . Promastigote cultures of Leishmania braziliensis M2904 strain ( MHOM/BR/75/M2904 ) , kindly provided by Prof . Maria Norma Melo from the Parasitology Department at UFMG , were maintained by weekly passages in freshly prepared Schneider’s Insect Medium ( Sigma-Aldrich Cat . No . S9895 ) supplemented with 10% heat-inactivated fetal bovine serum . To obtain axenic amastigotes , promastigotes cultures were incubated at 34°C in 100% FBS , 5%CO2 for 72 hours , as previously described [38] . Plasmid constructs used to exogenously express in L . braziliensis short-hairpin RNAs targeting amastin mRNAs were derived from pIR1PHLEO plasmid [4] , which was kindly provided by S . Beverley ( Washington University ) . The vector pIR1PHLEO-Ama1060 was generated after inserted PCR products containing the coding region of amastin LbrM . 20 . 1060 in the “antisense” followed by the “sense” orientation , with 200 pb fragment corresponding to the 5´UTR region between the coding sequences , to produce a stem-loop , into the XbaI site . After verifying the construct by restriction mapping and sequencing , 100 μg of the pIR1PHLEO-Ama1060 plasmid were digested with SwaI and the linear SSU-targeting fragment purified for transfection . Stable transfections of L . braziliensis promastigotes were performed using a BioRad gene pulser . Parasites grown to mid-log phase were pelleted at 3000xg , washed once with cytomix electroporation buffer ( 120 mM KCl , 0 . 15 mM CaCl2 , 10mM K2HPO4 , 25 mM HEPES pH7 . 6 , 2 mM EDTA and 5 mM MgCl2 ) and resuspended in cytomix buffer at a final concentration of 2x108 cells/mL . Five hundred µL of cell suspension was mixed with 100μg of DNA in a 0 . 4 cm gap cuvette and submitted to two pulses of 1400 V ( 3 . 75 kV cm-1 ) , 25 mF with a 10 s interval between pulses . Following electroporation promastigotes were grown in drug-free media overnight , and then plated on semisolid media [22] containing 2 mg/mL phleomycin ( Sigma ) and incubated at 34°C to generate clonal cell lines expressing the PHLEO marker . After colonies emerged ( approximately , 2 weeks ) they were recovered and grown to stationary phase in 1 mL Schneider’s Insect Medium and passaged thereafter in media containing 0 . 1 mg/mL phleomycin . For cellular localization of GFP fusion proteins , sequences corresponding to the entire coding regions of three distinct amastin genes ( LbrM . 20 . 1060 , LbrM . 24 . 1600 and LbrM . 30 . 0980 ) were PCR amplified from L . braziliensis genomic DNA using forward and reverse primers carrying XbaI restriction sites ( see S1 Table ) . After digesting the amplicons with XbaI , they were inserted into the XbaI site of pSP72RαneoαGFP vector [28] in frame with the GFP coding region . A total of 100 μg of each plasmid construction was used to transfect L . braziliensis promastigotes as described above . Twenty four hours post-transfection , parasites were fixed with 2% paraformaldehyde for 30 min at 4°C and washed twice in phosphate-buffered saline ( PBS 1X ) at pH 7 . 4 . After DNA staining with 1 μg/mL of 4’ , 6-diamidino-2-phenylindole ( DAPI , Life Technologies ) , coverslips were mounted with ProLong Gold antifade reagent ( Life Technologies ) . Images were acquired with a 100 x objective in the fluorescence microscope Nikon Eclipse Ti Tecnai G2-12 SpiritBiotwin FEI ( 120kV ) at the Image Acquisition and Processing Center of ICB-UFMG . Plasmids containing RNAi-resistant amastin sequences were constructed to rescue the RNAi phenotype , using DNA sequences generated synthetically by GenScript ( http://www . genscript . com ) . Sequence encoding the amastin polypeptide LbrM . 20 . 1060 was assembled with the third base modified in such a way that the amino acid was not altered as shown in the alignment of the WT LbrM . 20 . 1060 amino acid sequence and the synthetic RNAi-resistant sequence ( S2 Fig ) . In addition , an HA epitope tag was added to the position corresponding to the second hydrophilic region ( amino acid position 131 and 149 ) and HindIII and BamHI restriction sites were added to the 5’ and 3’ ends , respectively to facilitate cloning into the pSP72RαneoαGFP plasmid . After replacing the GFP sequence by the synthetic gene into pSP72RαneoαGFP plasmid , transfection of promastigote cultures of WT parasites and from the RNAi1060-cl5 cell line with 100 μg of the circular plasmid was done by electroporation as described above . Following transfection , promastigotes were grown in drug-free medium over-night and plated on semi-solid media [21] containing 40 μg/mL of G418 ( GIBCO ) to select G418 resistant clones . Total protein extracts were obtained by pelleting 106 exponentially growing L . braziliensis promastigotes or in vitro derived amastigotes . Cell pellets were ressuspended in 100 μl of PBS and boiled before loading onto gel 12% SDS-PAGE . For western blotting , SDS-PAGE gels were transferred to membranes ( Millipore ) and the membranes were incubated with anti-HA antibodies ( 100 ng/mL , Sigma ) or a polyclonal antibody raised against the recombinant amastin protein Ama::his1060 expressed in E . coli ( immunized mouse sera diluted 1:100 ) for 1 hour at room temperature . The recombinant , his-tagged amastin was obtained by cloning in the pET-21 ( + ) vector ( Novagen ) sequences corresponding to amino acids 30 to 78 obtained by PCR amplification of the amastin gene LbrM . 20 . 1060 . After primary antibody incubation , membranes were incubated with anti-mouse IgG ( Sigma ) peroxidase-conjugated secondary antibodies for 1 h at room temperature and revealed with the chemiluminescent substrate using the ECL kit ( GE HealthCare ) . Total RNA from L . braziliensis promastigotes and amastigotes were isolated using the Trizol reagent ( Invitrogen ) . Northern blot analysis of total RNA separated on 1 . 2% agarose-formaldehyde gels was carried out as previously described [7] . All probes used in these studies were prepared by PCR amplification using specific primers for each amastin gene . To quantify the steady-state levels of the different L . braziliensis amastin transcripts in promastigotes and axenic amastigotes , we normalized the signals for each probe by hybridizing the same blots with a fragment corresponding to the 5S rRNA . Enrichment of small ( < 200 nt ) RNA fraction was carried out using the mirVanaTM miRNA Isolation Kit ( Ambion ) according the manufacturer’s procedure and resolved on a 15% Urea-polyacrylamide gel as previously described [17] . DNA oligonucleotide probes ( corresponding to the forward strand of each element ) were end-labelled with T4 polynucleotide kinase ( NEB ) and [γ-32P]-ATP and purified on a P6 column ( Bio-Rad ) . Hybridizations were carried out in ExpressHyb solution ( Clontech ) overnight at 30°C . The membranes were exposed to X-ray films ( Kodak ) or revealed using the STORM840 PhosphoImager ( GE HealthCare ) . To quantify the steady-state levels of the different L . braziliensis small RNAs present in promastigotes and axenic amastigotes , we normalized the signals after hybridizing the same blots with a probe corresponding to the glutamate-tRNA sequence . Total RNA was extracted from amastigotes obtained from WT cultures and cultures derived from the dsRNA expressing cell line RNAi1060-cl1 . Two independent cDNA preparations for each RNA sample were carried out using with TruSeq RNA Library Prep Kit v2 . Illumina next-generation sequencing was done using HiSeq ( at BGI Hong Kong Tech Solutions ) and MiSeq ( at UFMG ) following published methods . The quality of all RNA-Seq data was assessed using FastQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) . Illumina adaptors were trimmed using Trimmomatic software ( www . usadellab . org/cms/ ? page=trimmomatic ) . No additional processing of the primary data was required . After quality checking , paired end sequences were mapped to the L . braziliensis MHOM/BR/75/M290 genome V7 . 0 ( www . tritryp . org ) using TopHat version 2 . 0 . 11 [39] , allowing up to 2 mismatches , 3 nucleotides of gap length and only unique hit per read . Results were piped through Samtools version 0 . 1 . 19 to sort by gene name , and stored in indexed BAM format . HTseq-count version 0 . 6 . 1 was used to count mapped read numbers for each gene . Statistical and differential expression analyses of the sample groups were undertaken by DESeq2 package [40] in the R environment , using linear models and empirical Bayes methods . Before quantile normalization and group-wise comparisons , the data were filtered to remove loci whose mean values were below the 10% quantile for all samples . Read depths were calculated by the mean of normalized counts of mapped reads . Sequence data was deposited at Sequence Read Archive ( SRA ) of the NCBI under SRA accession number SRP065143 . Primary BALB/c peritoneal macrophages ( 5×105 adherent cells/well ) were cultured in RPMI 1640 medium supplemented with 20% FBS , 2 mM L-glutamine , 200 U/mL penicillin G , and 100 μg/mL streptomycin sulfate , at pH 7 . 4 , in 24-well culture plates with round glass coverslips within . To each well were added 5 x 106 promastigotes in the stationary phase ( five days ) of L . braziliensis WT and mutant cells at a ratio of 10 parasites per macrophage in RPMI with 10% FBS . After 24 hours of infection , cells were washed with RPMI to remove parasites that were not internalized and maintained at different periods at 37°C . The slides were stained with 1 mg/mL of 49 , 6-diamidino-2-phenylindole ( DAPI , Molecular Probes/Life Technologies ) for 5 minutes and evaluated the degree of infection under a fluorescence microscope by counting the parasites in 100 infected cells . The experiments were performed in triplicate and the results were analyzed for significant differences using One Way ANOVA and Bonferroni’s Multiple Comparison . Stationary phase promastigotes harvested on day 5 of culture were resuspended in PBS and injected subcutaneously in the hind footpad of 7-week old female BALB/c mice ( 4 mice per group ) at 1 x107 cells/mouse . One and 2 weeks post-infection , animals were sacrificed and their infected footpads were harvested for parasite quantification using the limiting-dilution assay . Cultures were examined under light microscopy for the presence of promastigotes 15 days after incubation at 25°C . Results were expressed as the negative logarithm titer of parasites corresponding to the last dilution where parasites were observed [41] . The statistical analysis of the in vitro and in vivo infectivity experiments was performed using the GraphPad Prism software ( version 5 . 0 for Windows ) . Both in vitro and in vivo infection studies were carried out in strict accordance with the Brazilian laws regarding animal use ( LEI N°11 . 794 , DE 8 DE OUTUBRO DE 2008 ) , all protocols being approved by the Committee on the Ethics of Animal Experiments of UFMG . Statistical analyzes were performed as indicated in the previous session . Electron microscopy analyses of macrophages infected by L . braziliensis were fixed in 5% glutaraldehyde in 0 . 1 M cacodylate buffer pH 7 . 2 and processed following standard protocols , including post-fixation in osmium tetroxide followed by block counterstaining with uranyl acetate and embedding in Epon resin [42] . Ultrathin sections were counterstaining with lead citrate and analyzed in the Transmission Electron Microscope Tecnai G2-12—SpiritBiotwin FEI—120 kV located at the Center of Microscopy at the Universidade Federal de Minas Gerais , Belo Horizonte , Brazil . The ratios between the areas of macrophage vacuoles and the areas of the amastigotes inside the vacuole were determined by measuring the areas of twenty vacuoles and their respective parasites from each experimental group using the Image J software . Kruskal-Wallis test followed by Dunn’s Multiple Comparison was performed to compare the three groups . | Leishmaniasis is a parasitic disease caused by more than 20 species of the genus Leishmania that affects about 12 million people throughout the world and for which there is not an effective vaccine . Depending on the Leishmania species , clinical manifestation of the disease varies from self-resolving skin lesions to life-threatening visceralizing diseases . In addition to the toxicity of currently available drugs , their long treatment course , and limited efficacy , a major concern is the development of drug resistant parasite and more virulent variants . Together with the urgent need to develop new drugs that are more effective against this parasite as well as a vaccine to prevent new infections , it is also imperative to develop a better understanding of the factors that determine Leishmania virulence . Here , we describe the characterization of a gene family encoding surface proteins preferentially expressed in the mammalian stage of Leishmania that may be directly involved with the close interaction that is established between the intracellular parasite and host cell membranes . By inhibiting amastin gene expression in L . braziliensis in a mouse model of infection , we showed that these proteins are essential for intracellular parasite survival . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Amastin Knockdown in Leishmania braziliensis Affects Parasite-Macrophage Interaction and Results in Impaired Viability of Intracellular Amastigotes |
Complex networks of interacting residues and microdomains in the structures of biomolecular systems underlie the reliable propagation of information from an input signal , such as the concentration of a ligand , to sites that generate the appropriate output signal , such as enzymatic activity . This information transduction often carries the signal across relatively large distances at the molecular scale in a form of allostery that is essential for the physiological functions performed by biomolecules . While allosteric behaviors have been documented from experiments and computation , the mechanism of this form of allostery proved difficult to identify at the molecular level . Here , we introduce a novel analysis framework , called N-body Information Theory ( NbIT ) analysis , which is based on information theory and uses measures of configurational entropy in a biomolecular system to identify microdomains and individual residues that act as ( i ) -channels for long-distance information sharing between functional sites , and ( ii ) -coordinators that organize dynamics within functional sites . Application of the new method to molecular dynamics ( MD ) trajectories of the occluded state of the bacterial leucine transporter LeuT identifies a channel of allosteric coupling between the functionally important intracellular gate and the substrate binding sites known to modulate it . NbIT analysis is shown also to differentiate residues involved primarily in stabilizing the functional sites , from those that contribute to allosteric couplings between sites . NbIT analysis of MD data thus reveals rigorous mechanistic elements of allostery underlying the dynamics of biomolecular systems .
The propagation of information over long distances at the molecular and cellular scale is essential for the expedient and efficient regulation of cell function . For example , biomolecular systems involved in cell signaling can detect an input signal , such as the concentration of a ligand , ion , or biomolecule , and transmit that signal through molecular interaction networks to specialized sites such as ligand release sites in transporters , or catalytic sites in enzymes . The intramolecular propagation of information between distant parts of the biomolecules is now known as allostery , and was first discussed in the context of end-product inhibition by Monod , Changeux , and Jacob [1] . It is now well documented that such allosteric communication underlies function in a vast number of biomolecular systems , to the point that it is believed that nearly all proteins display some level of allosteric behavior [2] . The development of new experimental and computational techniques has recently made it possible to observe allosteric behavior with high resolution . The prototypical member of the family of neurotransmitter∶sodium symporters ( NSS ) , the bacterial transporter LeuT analyzed here with the new approach , has been particularly well studied , and the results from many experimental and computational investigations suggest that transport is driven by a complex allosteric mechanism spanning the entire length of the transporter . The transport cycle is believed to adhere to the stages of the canonical alternating access model [3] involving transitions between at least three distinct conformational states: an extracellular-open , outward-facing state [4] in which the symported ions and substrate are bound , followed by an occluded state [5] that shields the transported substrate from the extracellular environment from which it came , and an intracellular-open , inward-facing state [4] which can then release the substrate . From single molecule FRET ( smFRET ) experiments carried out on LeuT , a number of transport-related structural transitions were identified in the intracellular gate region that occludes the substrate from the cytoplasm [6] , and these were shown to be modulated by binding events at the extracellular end [7] , [8] . Crystallographic studies have also revealed that a second binding site in the extracellular vestibule ( termed S2 ) is the target of several transport inhibitors ( including many of the psycho-active drugs acting on the cognate NSS neurotransmitter transporters ) [9] , [10] , and biochemical and computational evidence suggests that the release of substrate is allosterically connected to the binding of a second substrate in this site [11]–[13] . These results bring to light the cross-talk between several allosterically coupled domains in the transport mechanism of NSS transporters , and suggest that modulation of these domains can both facilitate and hinder function . The schematics in Fig . 1 depict the transport cycle that takes into account the recently described allosteric roles of substrate in bound in the primary site , S1 , and in S2 . Still lacking , however , is a suitable quantitative formulation of the channels through which information can be communicated from one part of the molecule to another in the individual states of the transporter that constitute the transport cycle . Indeed , the specific process of allosteric signal propagation in a molecular system through intramolecular interactions has not yet been subjected to experimental measurements , although the allosteric effect can be observed experimentally from the apparent relation between distal parts of a macromolecule . To date , there are no experimental methods capable of specifically and definitively defining the role of the intramolecular interactions involved in propagating allostery . Most proposed mechanisms are descriptions of series of local rearrangements presumed ( but not demonstrated ) to be causally sequential – a specific , quantitative definition of the information flow does not exist . For example , a successful experimental method for determining residues that are coupled to ligand binding , the mutant cycle analysis [14] , while able to quantify thermodynamic coupling at a distance , still relies on these sequential descriptions to propose the underlying mechanism of propagation . For these reasons , theoretical and computational approaches to determine if and how distant domains are coupled within a single state have been proposed [15]–[17] , with the intention of using atomic-level insight which in unavailable experimentally to propose physical mechanisms . In developing the new analysis described herein , we reasoned that if the macro ( i . e . , whole molecule ) states of two domains are coupled ( e . g . , if the population of an open and a closed state of the intracellular gate , as well as the transitions between them , are coupled to the occupancy state of the substrate sites ) , their micro ( i . e . , component ) states would also exhibit coupling ( e . g . , the fluctuations within the closed state of the intracellular gate would be coupled to the fluctuations within the bound state of the substrate site ) . Because this needs to be demonstrated rigorously , we undertook the investigation of the information coupling between such molecular domains known to have functional significance in LeuT in a particular state . Investigating the mechanics of the protein in one such state of the transport cycle enables the identification of potential allosteric channels that may be used to propagate information in general . In previous computational approaches to solve this problem , the focus is on modeling single states of a protein as an interaction network obtained by assigning nodes to residues and parameterizing edges using either crystal structure contacts [17]–[19] , or pair-wise atomic fluctuation correlations from Molecular Dynamics [15] , [16] , [20] , [21] . The advantage of such networks is that the parameterization of edges in the interaction network is computationally reasonable ( only requiring structures and reasonable simulation time ) and appropriate network theoretical approaches exist , mostly based on graph theory , to achieve the identification of ( a ) -paths through the network that may propagate allosteric effects [22] , and ( b ) -community structures that may act as information hubs or subnetworks [15] , [23] . However , analysis of allosteric mechanisms with these methods must be considered incomplete , because only pair-wise correlation is considered , and not the other N-body correlated motions . This is a drawback , because correlated motions at the N-body level are both present in , and required for , a complex collective behavior such as allostery ( see illustrative example in “Supporting Discussion 1: Efficient Information Transmission” and Fig . S1 in File S1 ) . The new method we describe here identifies communication channels within allosteric biomolecular systems through information theory-based analysis of N-body collective dynamics determined from the configurational entropy of the system . We describe the new method , which we call N-body Information Theory ( NbIT ) analysis , through the application to a structurally defined state of LeuT , the occluded state ( 3GJD ) described above [24] , [25] . A mechanistic scheme for the substrate-modulated gating dynamics in such a LeuT state can be considered intuitively as an information theoretical communication process . In such a mechanistic scheme , the binding signal is detected by the substrate site ( s ) , which then acts as a transmitter that sends the information through an intramolecular channel spanning the transmembrane region , to the receiver . In the case of LeuT , the receiver is the intracellular gate that needs to open in order for the transported substrate to be eventually released to the cytoplasm . Based on this representation in the frame of information transmission through the intramolecular channel , the goal of identifying the allosteric mechanism connecting the two distally positioned functional sites , translates into an analysis that can identify the specific residues that compose the intramolecular communication channel by identifying patterns of multi-body information sharing . The new NbIT analysis method presented here utilizes a generalization of the concept of co-information ( also known as interaction information ) [26]–[29] , an information theoretical measure which enables a description of the contribution that a variable makes to the mutual information shared between two other variables . We extend co-information to describe the contribution of a variable to the more general measure of total correlation , in order to describe the contribution of a variable to information shared between any number of other variables . The advantage of this extension beyond the mutual information [30] , which describes the information shared by 2 variables , to the total correlation ( also known as multi-information ) [31]–[33] , is that the latter describes the total amount of information shared between a set of N variables through all possible n-body correlations ranging from 2 to N . This generalization of co-information is called coordination information , and it can identify residues that coordinate the N-body correlated motions present within a set of residues , such as functional sites , by playing the role of channel across many different transmitter-receiver combinations ( see Fig . 2 , right ) . We show that the use of coordination information reveals how global motions within functional domains are modulated allosterically by distant sites . In addition , by developing another information theoretical measure , the mutual coordination information , we are able to identify channels that propagate coordination information . This is illustrated specifically when NbIT is applied to the analysis of configurational entropies estimated from Molecular Dynamics ( MD ) simulations of LeuT starting from the occluded state crystal structure . Thus , the molecular level mechanism of information transduction that emerges from the analysis describes how several already known allosteric couplings are generated . Specifically , we examine the communication within the ligand-bound occluded state in which the intracellular gate is closed . Importantly , we show that within this state , we can identify the specific contribution to the allosteric mechanism of “functional residues” ( both previously known and newly revealed here ) . Moreover , we contrast the roles of such “functional residues” to those of other residues that contribute only to the stability of the functional sites , but not the allosteric coupling . The detailed illustration shows how NbIT analysis applied to a functionally distinct macrostate for which the configurational entropy can be estimated reveals the allosteric channels conducive to a key component of the functional mechanism . This example further suggests that when the same NbIT analysis is applied to an ensemble of states of a particular molecular system such as the LeuT , which can include several functionally distinct macrostates , the results should reveal the complement of allosteric channels conducive to the functional mechanism of that molecular system .
Two separate trajectories of the same LeuT structure were analyzed with the NbIT method . The LeuTPOPE/POPG trajectory is a simulation of the occluded LeuT structure [24] ( PDB ID 3GJD ) bound to the two sodium ions and leucine , but with the octyl-glucoside ( OG ) detergent molecule removed , which has been described previously [25] . The LeuTMNG-3 trajectory is for the same LeuT structure simulated in lauryl maltose-neopentyl glycol ( MNG-3 ) , a detergent known for its excellent stabilization of transmembrane proteins , including LeuT , in micellar environments [34] , [35] . Both simulations were run at in an NPT ensemble at 310 K temperature using the CHARMM27 force field with CMAP corrections for proteins [36] and CHARMM36 lipid force field [37] in NAMD 2 . 7 [38] using the Nose-Hoover Langevin piston algorithm and PME for electrostatic interactions . LeuTPOPE/POPG was run under semi-isotropic pressure coupling conditions and LeuTMNG-3 was run under isotropic pressure coupling conditions . For more details , see Supporting Methods in File S1 . The trajectories used for the analysis are from the production phase and only include the segment of the simulations after the Cα RMSD had converged . The total lengths of the equilibrated trajectories were 148 ns for LeuTPOPE/POPG and 146 ns for LeuTMNG-3 . Mechanistic and structure-function studies of LeuT as a prototypical NSS transporter have identified specific residues and structural microdomains that have significant roles in functional mechanisms . These include the binding sites for substrate and ions identified in the crystal structures [5] , [10] , [24] , as well as the intracellular gate and surrounding interaction network , which has been shown to be involved in the transport mechanism [6] . We used these findings to define functional residue clusters ( frc-s ) . Specifically , we defined the S1-frc to include the substrate , leucine , and residues L25 , G26 , V104 , Y108 , F253 , T254 , S256 , F259 , S355 , and I359 . The NA1-frc includes the bound ion , leucine , and residues A22 , N27 , T254 , and N286 of the Na1 binding site . The NA2-frc is composed of the second ion bound , and residues G20 , V23 , A351 , T354 , and S355 of the Na2 binding site . We defined the S2-frc as composed of L29 , R30 , Y107 , I111 , W114 , F253 , A319 , F320 , F324 , L400 , and D404 , and the intracellular gate region as an “intracellular network of interactions” , INI-frc , composed of R5 , I187 , S267 , Y268 , Q361 , and D369 . The locations of these sites in the LeuT structure are presented in Fig . 3 .
The analysis of pairwise mutual information for each of the functional residue clusters ( frc-s ) we defined ( see “Methods: Defining Functional Residue Clusters” ) in the crystallographically determined state , is summarized in Table 1 . The calculated values show that the component residues in each of the frc-s exhibit coupled motions within the LeuT state studied here , as indicated by the mutual information that is greater than zero . Note , however , that it is difficult to compare the strength of coupling between two different sets of frc-s , because mutual information cannot be easily normalized from differential entropies calculated from multivariate normal distributions ( see “Supporting Discussion 2: Normalizing Mutual Information” in File S1 for additional discussion ) . Therefore , we will not discuss further below the coupling strength between sites until we discuss other measures of information that can be normalized . A central mechanistic question regarding the functional dynamics of transporters is how the binding of substrate can trigger the conformational reorganization leading to the intracellular-open state from which the substrate is eventually released . Because studies have shown that just the binding of Na+ and substrate cause measurable dynamic effects at the intracellular end of the LeuT molecule , even in the absence of transport [7] , [8] , we sought to determine the information channel enabling this allosteric behavior . To this end , we performed co-information analysis as described in Methods to evaluate which residues played the role of channel in the information exchange between the substrate sites and the INI . As described , the co-information describes the information shared between all residues in a set . We calculated the 3-body co-information between each frc and a potential single residue channel using Equation 6 . 1 and then normalized as to the mutual information between the sites ( see “Co-information” in Methods ) to determine how much of the allosteric coupling could be attributed to that residue . In the interpretation of these results we considered that in a simple transmitter-channel-receiver system , the 3-body co-information can be understood intuitively as the intersect of the three entropies in a 3-body information Venn diagram ( see Fig . S2 in File S1 ) , and can determine how much of the mutual information between the receiver and transmitter can be explained by the information they both share with the channel . The calculated values are shown as a co-information versus co-information rank plot , which features a linear middle region with high , and low , co-information extremes ( see Fig . S4 in File S1 ) . Based on the plot , we defined residues to be potential channels if they were in the region of the high co-information extreme ( see “Supporting Methods: Identifying High Co-Information Residues” in File S1 ) . We note that the criterion of high co-information is not sufficient to differentiate between a true channel and a residue that has high mutual information with a true channel . However , the latter will display lower co-information than the former , and thus our most confident channel predictions are the residues with the highest co-information as described below ( for an illustrative example using a model system , see “Supporting Discussion: Analysis of the K1 , 4 Network” and Fig . S4 in File S1 ) . Applying co-information analysis reveals that S1 and the INI are coupled through a set of residues consisting largely of residues from TM6b , TM8 , and TM2 ( See Fig . 4 ) . Co-information analysis also reveals a channel between S2 and the INI , which is similarly composed of residues from TM6b and TM8 , in addition to residues from S1 in the unstructured region between TM6a and TM6b ( see Fig . S5 in File S1 ) . Not all the residues in a particular frc contribute equally to the allosteric communication . In order to identify which residues within the substrate sites and the INI are essential for allosteric communication we identified the residues within these sites that made large contributions to the mutual information . Such residues contribute by coupling the sites directly to the channel , and by distributing the information throughout the rest of their respective site . They were identified from the calculated values of their contribution to the mutual information , expressed as the percentage of the mutual information that could be explained by conditioning on that residue ( see Methods , “Calculation of Single Residue Contributions to Information Measures” ) . Calculated in this manner , the percentage of the mutual information describes how much of the information shared between the two sites is shared with that residue specifically . It is essential to note that the total sum of contribution from all residues does not necessarily sum to 100% . This occurs because just as the residues share information , they can also share their contribution to the mutual information , so the sum of the contribution will exceed 100% . This is also the case for other contribution measures , as described further below . Using Equation S . 1 in File S1 , we found that for the coupling between the S1-frc and the INI , it is residues I359 , F259 , F253 in the S1-frc that make the largest contributions ( 21 . 2% 18 . 8% , and 12 . 5% respectively ) , and in the INI the largest contribution is from residues Q361 , R5 , and Y268 ( 28 . 3 , 21 . 6% , and 21 . 3% respectively% ) . These very specific identifications underscore the validity of the calculated communication channel , as they are consistent with results from previous work in which mutations of I359 and F259 were shown to modulate transport efficacy [44] . Interestingly , we find that for the coupling between the S2-frc and the INI , residues R30 , F324 , and W114 make the largest contributions in S2 ( 20 . 1% , 12 . 9% , and 12 . 5% ) , and in the INI residues R5 , I187 , and Y268 make the largest contributions ( 27 . 1% , 23 . 3% , and 9 . 5% respectively ) . Because R30 is considered to form an extracellular gate with D404 , the significant role we find for it here in the coupling of S2 and the INI underscores the strong relationship between the extracellular and intracellular gates . These results are summarized in Table 2 and 3 . We hypothesized that that the proper fold and specific local function of a given frc , such as substrate binding , are maintained through short-distance allosteric couplings underlying collective behavior among the residues in the clusters . We probed this by calculating the total correlation ( TC ) for each frc to obtain a measure of the total amount of information shared by a set of size N through any type of correlation from 2 to N-body . We then calculated the contribution of a given residue in the frc to this TC ( see Methods , “Total Correlation and Coordination Information” ) . With this approach , we find that in the INI , the three largest contributors are Y268 ( 60 . 7% ) , S267 ( 59 . 0% ) and R5 ( 42 . 7% ) . This is consistent with their central location in the INI topology and with previous reports that mutation of the highly conserved Y268 and R5 to alanine has a strong effect on the structure and dynamics of the intracellular gate [6] , [7] . In the S1-frc , the largest contributions to the TC were calculated to come from T254 ( 40 . 3% ) , the leucine substrate ( 38 . 9% ) , and F253 ( 38 . 9% ) . The bound Leu is expected to contribute strongly , as seen here , because it interacts with all other residues in S1 . Furthermore , as mutation of F253 has been shown to greatly reduce binding in S1 [8] , [45] , it is possible that its role is not only to stabilize Leu binding through direct interaction , but also to stabilize the site as a whole by coordinating the rest of the S1 residues . In the other frc-s we also found a small number of specific high contributions . Thus , in the Na1 site the largest contributions to the total correlation are made by the Na1 sodium ion ( 61 . 7% ) , T254 ( 60 . 1% ) , and by leucine ( 58 . 4% ) . Interestingly , in the Na2 site , T354 and S355 contribute significantly more ( 70 . 9% and 66 . 4% , respectively ) than the Na+ ion ( 52 . 1% ) . Finally , in S2 , residues F320 , A319 , and R30 are found to make the largest contributions of 39 . 6% , 33 . 0% , and 31 . 1% , respectively . These results are summarized in Table 4 . Key findings from smFRET experiments investigating the allosteric modulation of intracellular gating in LeuT [7] were that conformational changes in the intracellular gates require collective motions resulting in large spatial displacements , and that these motions are modulated ( in some undetermined way ) by the state of the substrate binding sites , S1 and S2 [8] . In order to investigate the role of these substrate binding sites in the collective dynamics within the INI-frc , we calculated how much each of the two binding sites contributed to the total correlation of INI . This contribution , termed here coordination information ( CI ) , describes the amount of total correlation in a set of variables ( the “coordinated set” , here the INI-frc ) that is shared with a variable ( or multivariate distribution ) that is not included in the coordinated set ( “the coordinator” , here the S1 or S2 frc-s ) ( see Methods , “Total Correlation” and “Coordination Information” , and Fig . S6 in File S1 ) . When calculated in this manner , CI describes the contribution of a site to all possible n-body correlations within another site ( for an illustrative example using a model system , see “Supporting Discussion: Analysis of the K1 , 4 Network” in File S1 ) . Here we used as the descriptor the normalized coordination information ( NCI ) , in which the coordination information is normalized to the total correlation within the coordinated site . It should be noted that coordinators are not all coordination channels . Coordinators can be coupled to coordination channels , and thus perturbation to the coordinator leads to a perturbation in the coordinated set . As summarized in Table 5 , the NCI calculated for S1 and S2 show that they both coordinate the INI , with values of 19 . 1% for S1 , and 21 . 2% for S2 . The Na1 and Na2 sites coordinate the INI only weakly ( NCI = 9 . 0% and 6 . 9% , respectively ) , and their combined NCI in coordinating the INI is 11 . 1% . The coordination of INI by the combination of S1 , S2 , and the Na1 and Na2 frc-s is 27 . 1% , indicating that just under a third of all the correlated motions in the INI are related to these sites . The coordination exerted by INI on the binding sites was also calculated , because coordination information is not symmetric . We find that while S1 and S2 coordinate the INI strongly , the INI coordinates the two only moderately ( NCI = 12 . 0% and 7 . 4% , respectively ) . Interestingly , in the MD trajectory we analyzed , the coordination by INI of the Na1 ( NCI = 14 . 2% ) and Na2 ( NCI = 10 . 5% ) sites is stronger than in the opposite direction . These results , along with results for all comparisons of sites , are summarized in Table 5 . To estimate the importance of these coordination values for the allosteric mechanism , we performed control calculations of the normalized coordination information for S1 and S2 , with several other intracellular sites not known for their functional roles , including specific helices , loops , and interfaces between them . In all cases , S1 and S2 coordination of any of these control sites was half ( or much less ) that of the INI ( see “Supplementary Results: Coordination of Other Intracellular Domains” , Fig . S8 , and Table S1 in File S1 ) . Given the importance of the INI in the function of the transporter , we also determined which individual residues make the largest contributions to coordination of the INI . For each residue in the S1-frc and S2-frc residue we calculated the contribution of the residue to the particular frc coordination of the INI , as well as the contribution of INI residues to receiving that coordination , using Equation S . 3 in File S1 . Results summarized in Table 6 show that for coordination of the INI-frc by S1 , the top 3 coordinators are F259 ( contribution = 69 . 6% ) , S256 ( contribution = 34 . 9% ) , and I359 ( contribution = 34 . 6% ) , and the top 3 receivers are R5 ( contribution = 67 . 8% ) , I187 ( contribution = 63 . 8% ) , and S267 ( contribution = 59 . 9% ) . For coordination by S2 ( see Table 7 ) , the top 3 coordinators are R30 ( contribution = 54 . 7% ) , F253 ( contribution = 28 . 7% ) , and F324 ( contribution = 24 . 0% ) , and the top 3 receivers are R5 ( contribution = 80 . 8% ) , I187 ( contribution = 71 . 0% ) , and D369 ( contribution = 58 . 1% ) . This underscores the important role of INI residues R5 , I187 , and S267 in the coordination of the INI-frc by the known allosteric substrate sites . Because TM6b emerged as the major channel for communication between S1 and the INI , we investigated whether it was also the major channel for the CI between the substrate sites and the INI . We calculated the mutual coordination information ( MCI ) using Equation 13 . 1 , which described how much of the coordination information is shared between two coordinators that are coordinating the same set ( see Methods , Coordination Channel Analysis ) , and then normalized to the coordination information of the coordinator of interest ( NMCI ) . Using this analysis , we identified residues in the high NMCI region using the same criteria described for co-information . The results identify a coordination channel that is nearly identical to the channel revealed by the co-information analysis , with a significantly larger signal in TM6b than that calculated with co-information analysis ( see Fig . 4 ) . We are able to identify a similar coordination channel for S2 ( see Fig . S9 in File S1 ) . These results indicate that TM6b is the major channel for the coordination of the INI by S1 and S2 . Detergent micelles are a common environment used in experimental studies of membrane proteins e . g . , crystallography and biophysical experiments such as isothermal calorimetry and smFRET . Previous work has indicated that some detergents may affect measurements such as binding affinity and stoichiometry [24] , [46] , [47] . Here we investigated the same LeuT construct examined by simulations in membranes , in a micellar environment composed of MNG-3 detergent , which has been shown not to have the same detrimental effects as other detergents in several experimental measurements of LeuT [48] . Our findings agree , as the allosteric coupling measures calculated for LeuTMNG-3 are comparable to those we obtained for LeuTPOPE/POPG ( see Table S3 in File S1 for LeuTMNG-3 and Table 5 for LeuTPOPE/POPG ) , albeit with some noticeable changes to allosteric couplings involving only the Na+ sites . Despite these changes , the contribution of specific residues to the total correlation of their frc remains conserved , and so do the major contributors to the total correlation ( see Table S4 in File S1 for LeuTMNG-3 and Table 4 for LeuTPOPE/POPG ) . In addition , the major contributors to coordination between the substrate site frc-s and the INI are also preserved ( see Table S5 in File S1 for LeuTMNG-3 and Table 6 for LeuTPOPE/POPG ) , and together the results for LeuTMNG-3 indicate that the allosteric behavior seen in the membrane simulation is conserved in the micelle simulation . It is worth noting however , that in the LeuTMNG-3 the coordination channel between the S1 and the INI frc-s includes fewer residues than in LeuTPOPE/POPG , although they are still mainly from TM6b ( see Fig . S10 in File S1 for LeuTMNG-3 and Fig . 5 for LeuTPOPE/POPG ) , but so few residues are identified for coordination by S2 ( see Fig . S11 in File S1 for LeuTMNG-3 and Fig . S9 in File S1 for LeuTPOPE/POPG ) that a clear coordination channel is not resolvable between S2 and the INI in LeuTMNG-3 . In an additional analysis suggested in the review process , we compared these results to those obtained from an apo ( substrate-free ) state of LeuT , by analyzing a trajectory ( see “Supporting Methods: MD Simulations” in File S1 for details ) provided by Dr . Lei Shi ( data unpublished , personal communication ) . Again , we find TM6b to be the major channel for coordination of the INI by both S1 and S2 . | We developed the new information theory-based analysis framework presented here , NbIT analysis , for the study of allosteric mechanisms in biomolecular systems from Molecular Dynamics trajectories . The illustrative application of NbIT to the analysis of the occluded state in the bacterial transporter LeuT , produced a quantitative representation of the allosteric behavior , and identified intramolecular channels that enable the long-distance information transmission . Our findings , identifying the roles of specific residues in the communication of the allosteric information , were validated by the recognition of residues that have been previously shown to play functional roles in this very well studied system . In addition , we show that application of NbIT analysis leads to the discrimination of functional roles by differentiating between residues that are essential to the dynamics within functional sites ( e . g . , the substrate binding sites ) , and residues whose role is to communicate between such functional sites . These results demonstrate that the information theoretical analysis presented here is a powerful tool for quantifying complex allosteric behavior in biomolecular systems and for identifying the crucial components underlying those behaviors . | [
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] | 2014 | NbIT - A New Information Theory-Based Analysis of Allosteric Mechanisms Reveals Residues that Underlie Function in the Leucine Transporter LeuT |
Although CD4+ Foxp3+ T cells are largely described in the regulation of CD4+ T cell responses , their role in the suppression of CD8+ T cell priming is much less clear . Because the induction of CD8+ T cells during experimental infection with Trypanosoma cruzi is remarkably delayed and suboptimal , we raised the hypothesis that this protozoan parasite actively induces the regulation of CD8+ T cell priming . Using an in vivo assay that eliminated multiple variables associated with antigen processing and dendritic cell activation , we found that injection of bone marrow-derived dendritic cells exposed to T . cruzi induced regulatory CD4+ Foxp3+ T cells that suppressed the priming of transgenic CD8+ T cells by peptide-loaded BMDC . This newly described suppressive effect on CD8+ T cell priming was independent of IL-10 , but partially dependent on CTLA-4 and TGF-β . Accordingly , depletion of Foxp3+ cells in mice infected with T . cruzi enhanced the response of epitope-specific CD8+ T cells . Altogether , our data uncover a mechanism by which T . cruzi suppresses CD8+ T cell responses , an event related to the establishment of chronic infections .
Mouse models of self-curing infections with lymphocytic choriomeningitis virus ( LCMV ) and Listeria monocytogenes enable CD8+ T cells to be rapidly activated , proliferate and peak between 5 to 10 days post-infection . These lymphocytes differentiate into effector cells and participate in pathogen control and clearance [1–6] . Conversely , during experimental mouse infections with Mycobacterium tuberculosis , Salmonella spp , Toxoplasma gondii or Trypanosoma cruzi , the peak of the primary CD8+ T cell immune response occurs only later than 20 days following challenge , in association with host death or the establishment of chronic infections [7–13] . Understanding the mechanisms underlying the delayed onset of CD8+ T cell responses in these cases entail the development of interventions to restrain infection . However , such mechanisms remain ill defined . Trypanosoma cruzi is an intracellular protozoan that currently infects more than 10 million people in the Americas and may cause a chronic digestive and/or cardiac pathology known as Chagas disease . Murine models of infection revealed that CD8+ T cells are essential for T . cruzi control [10–12 , 14 , 15] . However , the primary response of specific CD8+ T cells after T . cruzi infection is significantly delayed and marked by a high frequency of proapoptotic cells [10 , 12 , 14–16] . On the other hand , coopting viruses as genetic vectors to induce faster and long-lasting CD8+ T cell responses against T . cruzi has been shown feasible in either prophylactic or therapeutic vaccination protocols [16–18] . Here , we tested the hypothesis that this contrasting control of the onset of CD8+ T cell immunity induced by T . cruzi infection as compared to genetic immunization with viral vectors occurs very early during the priming of CD8+ T cells by dendritic cells ( DC ) and involves active mechanisms of suppression . In order to precisely identify these mechanisms and eliminate other variables related to antigen uptake , processing and presentation , we employed a simple and controlled system in which we used in vitro generated bone marrow-derived dendritic cells ( BMDC ) stimulated with LPS and loaded with the ovalbumin MHC I-restricted epitope SIINFEKL ( BMDC-SIINFEKL ) to optimally prime cognate OTI transgenic CD8+ T cells in vivo . By using this system we normalized the provision of signals required for CD8+ T cell activation and were able to study the specific impact of T . cruzi in an otherwise maximized response . We observed that T . cruzi-exposed BMDC-SIINFEKL induced regulatory CD4+ Foxp3+ T cells that inhibited proliferation , differentiation and cytokine production of OTI CD8+ T cells . Furthermore , this newly described suppression was shown to be independent of IL-10 and partially mediated by CTLA-4 and TGF-β .
To investigate whether BMDC exposure to T . cruzi could affect their ability to prime specific CD8+ T cells , we set up an experimental model using the peptide SIINFEKL ( MHC I-restricted epitope from ovalbumin ) as antigen and cognate transgenic CD8+ T cells ( OTI cells ) as responder cells . Transgenic OTI cells harboring Vα2 Vβ5 TCR specific for SIINFEKL were transferred into naïve C57BL/6 mice . One day later , animals were transferred with BMDC previously stimulated with LPS and loaded or not with SIINFEKL peptide . Alternatively , BMDC were in vitro-exposed to T . cruzi 24 h before LPS stimulation and SIINFEKL peptide loading . Five days after transfer , the specific response of OTI cells was evaluated in the spleen , as depicted in Fig 1a . In comparison to baseline levels observed in control mice that received BMDC stimulated only with LPS ( Gr . 1 ) , the transfer of BMDC-SIINFEKL ( Gr . 2 ) induced the expansion of OTI CD8+ T cells , as measured by H-2Kb SIINFEKL tetramer staining ( Fig 1b and 1c ) and Vα2 and Vβ5 TCR staining ( S1a and S1b Fig ) . This increase was due to the proliferation of OTI cells , as demonstrated by BrdU incorporation in tetramer-positive CD8+ T cells ( Fig 1d and 1e ) . We also observed that Vα2+ Vβ5+ CD8+ cells differentiated into an effector phenotype upon stimulation with BMDC-SIINFEKL , as indicated by CD44 upregulation and CD62L downregulation ( Fig 1f and 1g ) . In remarkable contrast to the scenario described above , the response of OTI cells from mice injected with T . cruzi-exposed BMDC-SIINFEKL ( Gr . 3 ) was significantly impaired , as indicated by their numbers , proliferation and differentiation into an effector phenotype ( Fig 1b–1g ) . By intracellular staining ( ICS ) , we also observed that 5 days after BMDC transfer , the frequencies of splenic CD8+ T cells positive for IL-2 , TNF or IFN-γ upon ex-vivo restimulation with different concentrations of SIINFEKL peptide were significantly lower in mice injected with T . cruzi-exposed BMDC-SIINFEKL as compared to animals that received BMDC-SIINFEKL ( Fig 1h ) . Not only the quantity , but also the quality of the cytokine response was altered when BMDC-SIINFEKL were previously exposed to T . cruzi . Polyfunctional cells , defined as simultaneously positive for at least two parameters ( IL-2 and/or TNF and/or IFN-γ and/or the degranulation marker CD107a ) , were dominant among the total cytokine-producing CD8+ T cells from animals that received BMDC-SIINFEKL , whereas single-positive cells prevailed when animals were injected with T . cruzi-exposed BMDC-SIINFEKL ( Fig 1i ) . Production of IL-4 , IL-10 or IL-17 was below ICS detection limits in CD8+ T cells restimulated with SIINFEKL from animals of all groups . Elispot to detect IFN-γ producing cells after SIINFEKL restimulation confirmed an impaired cytokine response of OTI cells from animals injected with T . cruzi-exposed BMDC-SIINFEKL when compared to BMDC-SIINFEKL ( S1c and S1d Fig ) . A possible caveat in the experiments described above could be that H-2Kb-restricted CD8+ T cells specific for T . cruzi epitopes could compete with the transgenic OTI CD8+ T cells for priming by the same BMDC . However , on day 5 after T . cruzi-exposed BMDC-SIINFEKL transfer , no IFN-γ and/or TNF cytokine responses of splenic CD8+ T cells was observed upon ex vivo restimulation with the peptides VNHRFTLV and ANYKFTLV , which correspond to the two T . cruzi immunodominant H-2Kb-restricted epitopes ( S2 Fig ) . This observation ruled out the possibility that the lower response of OTI cells in mice from Gr . 3 was due to competition with dominating parasite-specific CD8+ T lymphocytes . In fact , the CD8+ T cell immune responses to the peptides VNHRFTLV and ANYKFTLV could be measured only later ( after 12 days ) , as we have previously described [12 , 15] ( S2 Fig ) . In addition , when BMDC were exposed to adenovirus type 5 expressing amastigote surface protein 2 from T . cruzi prior to LPS stimulation and SIINFEKL load , the response of OTI CD8+ T cells was not impaired ( S3 Fig ) . From these data , we concluded that exposure to T . cruzi significantly disturbs DC ability to induce cognate naïve OTI CD8+ T cells to proliferate , differentiate and produce cytokines in vivo . To investigate whether T . cruzi could interfere with the activation and antigen presentation function of DC , we generated BMDC and exposed them in vitro to T . cruzi . After 24 h of exposure to the parasite , approximately 60% of BMDC were infected and featured intracellular amastigotes detected by Giemsa-staining . BMDC were stained with 7AAD , annexin-V , and fluorescent probes to detect active forms of caspases 3 and 7 , and analyzed by flow cytometry . As shown in Fig 2a and 2b , T . cruzi-exposed BMDC and untreated cells were equally viable , whereas control cells treated with actinomycin D showed a significant degree of apoptosis . To evaluate the ability of BMDC to modulate expression of MHC and co-stimulatory molecules , BDMC were incubated with T . cruzi for 24 h in the absence or presence of LPS and then FACS-stained for CD40 , CD80 , CD86 , H-2Kb and I-Ab . As shown in Fig 2c , T . cruzi alone induced the upregulation of MHC and co-stimulatory molecules on BMDC . Furthermore , the upregulation achieved by LPS exposure was not inhibited by the parasite . BMDC gene expression of cytokines in response to T . cruzi was assessed by RT-PCR ( Fig 2d ) . Of note , presence of the parasite induced mRNA expression of cytokines important for the induction of CD8+ T cell responses , including IL-6 , IL-12 , TNF , IL-23 , and IL-27 , concomitantly with the immune suppressive cytokine IL-10 . When BMDC were exposed to LPS , gene expression of these cytokines was highly upregulated , regardless of T . cruzi pre-exposure , suggesting that cytokine levels induced in BMDC LPS SIINFEKL , even in presence of the parasite , could be enough to induce the priming of CD8+ T cells . However , LPS alone was more potent at triggering this upregulation than T . cruzi and LPS combined ( S4a Fig ) . To explain the suboptimal in vivo induction of OTI CD8+ T cells by T . cruzi-exposed BMDC-SIINFEKL , we reasoned that H-2Kb-SIINFEKL peptide complexes could be either not formed or removed from the surface of T . cruzi-exposed BMDC-SIINFEKL . To quantify the amount of cognate peptide provided , we surface-stained BMDC with the 25-D1 . 16 antibody , which recognizes the H2Kb-SIINFEKL complexes . As depicted in Fig 2e , BMDC-SIINFEKL efficiently displayed the complex H-2Kb-SIINFEKL regardless of T . cruzi pre-exposure . Additionally , BMDC-SIINFEKL previously exposed or not to T . cruzi were equally able to induce proliferation of naïve OTI CD8+ T cells in vitro , as measured by CFSE dilution after three days in co-culture ( Fig 2f ) . Accordingly , naïve OTI CD8+ T cells equally secreted IFN-γ in vitro after five days in co-culture with BMDC SIINFEKL or T . cruzi-exposed BMDC SIINFEKL ( S4b Fig ) . Altogether , these data indicate that BMDC exposed to T . cruzi are able to provide the three signals required for T cell activation: i ) MHCI-peptide complex , ii ) co-stimulatory molecules , and iii ) inflammatory cytokines . To better understand the mechanisms responsible for the disturbance of the CD8+ T cell response caused by the exposure of DC to T . cruzi , we evaluated the in vivo activation of OTI CD8+ T cells upon transfer of BMDC-SIINFEKL ( carrying the cognate peptide ) and T . cruzi-exposed BMDC ( not carrying the cognate peptide ) simultaneously into the same mice ( outlined in Fig 2g ) . Under these conditions , if T . cruzi impairs the antigen presentation function of BMDC loaded with SIINFEKL peptide , one would expect that separating the cells that carry the peptide ( transferred i . v . in the right retro-orbital sinus ) from the cells exposed to the parasite ( transferred i . v . in the left retro-orbital sinus ) could restore the stimulation of OTI CD8+ T cells to optimal levels . However , we observed that the proliferation ( Fig 2h ) , differentiation into effector phenotype ( CD44hi CD62Llow ) ( Fig 2i ) and cytokine response ( Fig 2j ) of OTI cells were significantly impaired in mice that received both , parasite-exposed BMDC and BMDC-SIINFEKL , compared to the animals that received only BMDC-SIINFEKL . It is important to note that , exactly as reported for BMDC , transfer of splenic DC exposed to T . cruzi also led to the in trans impairment of the response of OTI CD8+ T cells ( S5 Fig ) . These data support the idea that a weaker OTI cell response could not be explained merely by death or lack of antigen presentation function of the T . cruzi-exposed BMDC-SIINFEKL and also suggest that the parasite-exposed BMDC rather actively trigger the in trans suppression of OTI CD8+ T cells . A detailed phenotypic characterization of the OTI CD8+ T cells from mice transferred with BMDC-SIINFEKL or T . cruzi-exposed BMDC-SIINFEKL was performed to further elucidate the mechanisms underlying suboptimal CD8+ T cell response . OTI cells collected from mice of each group were stained with CD8 antibody and H-2Kb SIINFEKL tetramers along with mAbs to surface markers related to activation , exhaustion , and migration of T cells , as well as nuclear staining of transcription factors related to T cell polarization and markers of cell viability . The major differences observed in tetramer-positive OTI CD8+ T cells from mice injected with T . cruzi-BMDC-SIINFEKL in comparison to BMDC-SIINFEKL were: higher expression of surface CD95 and PDL-1 ( Fig 3a ) , lower staining of intracellular Bcl-2 and higher intracellular staining of active caspases 3 and 7 ( Fig 3b ) , and higher frequency of Annexin V+ 7AAD+ cells ( Fig 3c ) . For the other 36 markers analyzed the expression was , at most , slightly changed ( S6 Fig ) . The proapoptotic profile of OTI CD8+ T cells from animals that received T . cruzi-exposed BMDC-SIINFEKL resembles the augmented expression of CD95 by infection-induced CD8+ T cells specific for the immunodominant H-2Kb-restricted epitope VNHRFTLV from T . cruzi [16] . Therefore , we further evaluated the role of CD95 in the suboptimal priming of CD8+ T cells . To this end , we generated OTIlpr/lpr CD8+ T cells , which express mutated alleles that render CD95 non-functional . These OTIlpr/lpr CD8+ T cells were employed in our in vivo model , as depicted in Fig 3d and 3e . Mice were transferred with OTIlpr/lpr cells prior to immunization with BMDC-SIINFEKL or T . cruzi-exposed BMDC-SIINFEKL . The immune response of OTIlpr/lpr CD8+ T cells from mice injected with T . cruzi-exposed BMDC-SIINFEKL was significantly lower than the BMDC-SIINFEKL counterpart , as measured by their numbers ( Fig 3f ) , phenotype ( CD44hi CD62Llow ) ( Fig 3g ) and cytokine response ( Fig 3h ) . These data thus indicate that CD95 expression by OTI cells is not the only factor responsible for the impaired response observed in mice transferred with T . cruzi-exposed BMDC-SIINFEKL . To determine whether CD4+ T cells mediate the suppression of the CD8+ T cell immune response in our model , we adoptively transferred OTI cells into cd4-/- animals and subsequently injected them with BMDC-SIINFEKL or T . cruzi-exposed BMDC-SIINFEKL ( Fig 4a and 4b ) . In the absence of CD4+ T cells , the proliferation ( Fig 4c and 4d ) , phenotype ( CD44hi CD62Llow ) ( Fig 4e ) and cytokine response ( Fig 4f ) of OTI cells from animals that received BMDC-SIINFEKL ( Gr . 2 ) or T . cruzi-exposed BMDC-SIINFEKL ( Gr . 3 ) were comparable . Of note , Vα2+Vβ5+ CD8+ T cells from mice immunized with T . cruzi-exposed BMDC-SIINFEKL showed upregulated CD95 expression ( Fig 4g ) , which further supports the notion that the impaired response of OTI CD8+ T cells is not ( at least exclusively ) dependent on CD95 expression . To confirm these findings , we also transferred purified splenic CD4+ T cells isolated from mice injected with T . cruzi-exposed BMDC into mice carrying OTI cells and further injected them with BMDC-SIINFEKL ( S7a Fig ) . After 5 days , we assessed the proliferation ( S7b and S7c Fig ) , phenotype ( CD44hi CD62Llow ) ( S7d Fig ) and cytokine response ( S7e Fig ) of OTI cells in these mice . We found that suppression of OTI CD8+ T cell priming could be transferred to non-infected mice by the specific injection of T . cruzi-induced CD4+ T cells . To further investigate the involvement of CD4+ T cells in the suppression of CD8+ T cell priming , we transferred either untreated BMDC or T . cruzi-exposed BMDC into congenic mice expressing GFP under control of the Foxp3 promoter ( Foxp3-GFP CD45 . 1 mice ) . After 5 days , we sorted the splenic CD4+ Foxp3+ CD45 . 1+ congenic cells and transferred them ( 1 x 106 cells/mouse ) into C57BL/6 ( CD45 . 2+ ) mice that had received OTI CD8+ T cells on the day before . These animals were then injected with BMDC previously stimulated with LPS and loaded or not with SIINFEKL peptide , and the response of cognate OTI CD8+ T cells was assessed after 5 days , as outlined in Fig 5a . As indicated in Fig 5b , the frequencies of CD45 . 1+ and GFP+ cells found in the spleen of the recipient C57BL/6 mice 5 days after the adoptive transfer were similar between the group that received CD4+ Foxp3+ cells induced in the presence ( Gr . 3 ) or absence ( Gr . 4 ) of T . cruzi . Although both groups received equal numbers of Foxp3+ cells that exerted regulatory functions over OTI cell priming , the expansion ( Fig 5c ) , activation ( CD44hi CD62Llow ) ( Fig 5d ) and cytokine response ( Fig 5e ) of OTI CD8+ T cells was significantly more impaired upon transfer of CD4+ Foxp3+ cells induced in the presence of T . cruzi-exposed BMDC as compared to the transfer of CD4+ Foxp3+ cells isolated from mice previously injected with BMDC unexposed to the parasite . Additionally , when the function of the responding OTI CD8+ T cells after stimulation with SIINFEKL peptide was assessed by the expression of CD107a , IL-2 , TNF , or IFN-γ combined , we observed that although CD4+ Foxp3+ T cells induced in the absence of T . cruzi suppressed the magnitude of the cytokine response of CD8+ T cells , the remainder responding cells were still polyfunctional , whereas CD8+ T cells suppressed by CD4+ Foxp3+ T cells induced by T . cruzi-exposed BMDC were mostly positive for only one out of the four markers analyzed ( Fig 5f ) . We further characterized the CD4+ Foxp3+ T cells present in mice adoptively transferred with BMDC exposed or not to T . cruzi , as depicted in Fig 6a . Consistent with the data presented in Fig 5 , T . cruzi-exposed BMDC did not induce any augment in total numbers of splenic CD4+ Foxp3+ cells ( Fig 6b ) . Nonetheless , upon transfer of T . cruzi-exposed BMDC , the CD4+ Foxp3+ cells upregulated CTLA-4 ( Fig 6c ) and were induced to proliferate , as indicated by EdU incorporation ( Fig 6d ) and Ki67 staining ( Fig 6e ) . We also characterized these cells by neuropilin-1 ( Nrp-1 ) staining , a marker highly expressed in thymic-derived Treg , but downregulated in Treg induced in the periphery [19 , 20] . As shown in Fig 6f and 6g , the frequency and numbers of Nrp-1lo Ki67+ Treg increased in animals receiving T . cruzi-exposed BMDC . Altogether , these data suggest that T . cruzi is able to induce Treg in the periphery , and the enhanced suppressive function of Treg correlated with high levels of CTLA-4 expression . We further investigated possible mechanisms of CD8+ T cell regulation in our model . IL-10 , which was highly expressed by T . cruzi-exposed BMDC ( Fig 2 ) , is an immune regulatory cytokine described as an inhibitor of CD8+ T cell immune responses [21] . We evaluated the in vivo activation of OTI cells using il10-/- mice as recipients and BMDC donors ( S8a and S8b Fig ) . Similar to the observed in C57BL/6 WT mice , the response of OTI CD8+ T cells in il10-/- mice injected with T . cruzi-exposed BMDC-SIINFEKL was significantly lower when compared to the OTI cell response of mice injected with BMDC-SIINFEKL , as measured by TCR and H-2Kb SIINFEKL tetramer staining ( S8c and S8d Fig ) , effector phenotype ( CD44hi CD62Llow ) ( S8e Fig ) and cytokine response ( S8f Fig ) . We also addressed the involvement of CTLA-4 ( as suggested by the phenotype reported in Fig 6 ) and TGF-β in the suppression of OTI CD8+ T cell priming by T . cruzi-exposed BMDC loaded with SIINFEKL peptide . To this end , we treated the recipient mice with CTLA-4 and TGF-β blocking antibodies ( clones 9D9 and 1D11 . 16 . 8 , respectively ) as outlined in Fig 7a . We observed that the treatment with clone 1D11 . 16 . 8 to block TGF-β partially restored the ability of OTI CD8+ T cells to proliferate upon in vivo stimulation with T . cruzi-exposed BMDC-SIINFEKL , as measured by tetramer staining ( Fig 7b ) , whereas the treatment with 9D9 CTLA-4-blocking antibody rescued the ability of OTI CD8+ T cells to produce TNF and IFN-γ ( Fig 7c ) . Accordingly , 9D9 treatment partially recovered the polyfunctionality of OTI CD8+ T cells , as shown by the frequencies of cells simultaneously stained for TNF , IFN-γ , IL-2 and/or CD107a ( Fig 7d ) . Therefore , we concluded that the suppression of OTI CD8+ T cell priming by DC exposed to T . cruzi is independent of IL-10 , but may be partially mediated by CTLA-4 and TGF-β . To extend the findings described above to the context of T . cruzi infection , we challenged DEREG mice ( which express the diphtheria toxin receptor under control of Foxp3 promoter ) with the parasite . As control group , we infected their WT littermates with T . cruzi . All animals were i . p . treated with diphtheria toxin ( DT ) on the two consecutive days after infection , as outlined in Fig 8a . After 22 days , the response of CD8+ T cells specific to T . cruzi epitopes was assessed . The numbers of CD8+ T cells specific to the immunodominant H-2Kb-restricted epitope VNHRFTLV from T . cruzi were significantly increased in the group depleted of Foxp3+ cells early after infection , as measured by H-2Kb-VNHRFTLV pentamer staining ( Fig 8b and 8c ) , although the numbers of total CD8+ CD44hi CD62Llow cells was not significantly altered by early Foxp3+ cell depletion ( Fig 8d ) . Accordingly , the cytokine response of CD8+ T cells specific to the epitopes VNHRFTLV and ANYKFTLV from T . cruzi was higher in the group subjected to depletion of Foxp3+ cells , as measured by ICS , whereas the response to the subdominant H-2Kb-restricted epitope ANYDFTLV was equally low in both groups ( Fig 8e ) . However , the polyfunctionality of CD8+ T cells ex vivo-stimulated with VNHRFTLV peptide on day 22 post-infection was not enhanced in DT-treated DEREG mice ( Fig 8f ) , even though these mice were able to control T . cruzi infection better than their WT littermates , as measured by parasitemia in blood ( Fig 8g ) . Jointly , these findings thus suggest a role of Foxp3+ cells in the suboptimal priming of specific CD8+ T cells early after T . cruzi infection .
Dendritic cells initiate CD8+ T cell-mediated immune responses in different experimental models of infection with viruses , bacteria and protozoans [22–27] . Here , we aimed at clarifying whether exposure to T . cruzi would interfere with antigen presentation function of DC . We observed that T . cruzi exposure drastically impaired the ability of BMDC-SIINFEKL to prime OTI cells in vivo , but not in vitro . Our results greatly diverged from a recent study using a similar approach for OTI CD8+ T cell in vivo priming in the context of LCMV or Listeria monocytogenes infection , where presence of these pathogens did not inhibit and even improved the avidity of cognate OTI cells [28] . These discrepant observations are compatible with the fact that acute infections with these microorganisms elicit a rapid and efficient CD8+ T cell immune response that in most cases cures the host . In contrast , T . cruzi infection elicits a delayed and suboptimal immune response that is unable to protect susceptible mice from death and allows the establishment of a chronic infection in resistant mouse strains [15 , 16] . We observed that T . cruzi-exposed BMDC-SIINFEKL-induced suboptimal priming of OTI cells is unlikely to be mediated by its direct effects on CD8+ T cells , as it did not take place in cd4-/- mice . The observation that the priming of OTI CD8+ T cells deficient in CD95 was still impaired in the presence of T . cruzi-exposed BMDC-SIINFEKL also favors this assumption . Furthermore , the experiments using cd4-/- mice that allowed the observation of unaltered OTI cell priming by T . cruzi-exposed BMDC in comparison to parasite-unexposed BMDC are in agreement with our in vitro observations that ruled out several other possibilities impacting DC antigen presentation function , such as lack of MHCI-peptide complexes ( signal I ) , co-stimulatory molecules ( signal 2 ) , or inflammatory cytokines ( signal 3 ) . On the other hand , adoptive transfer of CD4+ Foxp3+ T cells from mice stimulated with T . cruzi-exposed BMDC was able to reproduce the suppression of OTI CD8+ T cell priming in vivo , in absence of infection . It is reasonable to speculate that host cells infected with T . cruzi may trigger Treg-mediated suppression of CD8+ T cell priming . Because in our experimental model for in vivo priming of OTI cells we transferred T . cruzi intracellular parasites within BMDC , and these BMDC most likely have not released parasites during the short term of our experimental set up ( and we were never able to find parasites in blood of mice from Gr . 3 ) , presumably the transferred BMDC were the major cell subset responsible for the induction of Treg , thus ensuing CD8+ T cell priming suppression . Nonetheless , we did not completely ruled out the possibility that host cells other than the transferred BMDC might also have contributed to this induction . Treg isolated from mice that received T . cruzi-exposed BMDC were significantly more suppressive than equal numbers of CD4+ Foxp3+ cells isolated from T . cruzi-unexposed animals , which correlated with higher levels of CTLA-4 expression . Moreover , proliferation and downregulation of Nrp-1 indicated that in mice injected with T . cruzi-exposed BMDC there was induction of Treg in the periphery . The precise mechanism of Treg induction in our model remains to be elucidated . In this regard , similarly to our observations , Poncini and colleagues recently reported on the induction of CD4+ Foxp3+ cells by DC upon T . cruzi infection in a galectin-1-dependent fashion [29] . The group suggested that this lectin confers tolerogenic properties to dendritic cells , and mice deficient in galectin-1 presented increased response of CD8+ T cells following T . cruzi infection [29] . Another open question regarding the induction of Treg by T . cruzi refers to their TCR specificity and affinity . Although we were unable to determine the specificity and affinity of the Treg TCR repertoire in mice injected with T . cruzi-exposed BMDC , their phenotype could suggest that induction was antigen-dependent . Favoring this idea , injection of recombinant T . cruzi amastigote antigen SSP4 was reported to induce Treg with enhanced suppressive function in BALB/c mice [30] . Although the induction of Treg by T . cruzi may be antigen-specific , the in trans suppression of OTI CD8+ T cell priming observed when T . cruzi-exposed BMDC and SIINFEKL-loaded BMDC were co-injected clearly demonstrated that Treg suppressive functions reported here are not antigen-specific . This is in line with other classic reports and recent findings suggesting that , in comparison to effector T cells , Treg are largely unresponsive to TCR stimulation , but highly sensitive to cytokines mediating intercellular communication [31] . Most mechanisms of T cell regulation were described in CD4+ T cell-mediated responses , whereas their extension to CD8+ T cells is less clear . Recent studies have highlighted that TGF-β mediates the immunosuppression of CD8+ T cells by elevating miR-23a and downregulating Blimp-1 , or by upregulating Foxp1 [32–34] . When CD4-DNRII mice ( lacking TGF-βRII kinase domain in both CD4+ and CD8+ T cells ) were infected with T . cruzi , parasite-specific CD8+ T cells proliferated more , but remained functionally impaired [9] . Here , using the model of OTI CD8+ T cell priming by T . cruzi-exposed BMDC-SIINFEKL during treatment with antibodies to block TGF-β , we also observed augmented proliferation of OTI CD8+ T cells , although their capacity to produce effector cytokines persisted compromised . In addition , CTLA-4 has been suggested as an important mediator of CD8+ T cell regulation , both in mice and humans [35 , 36] . For instance , it has been proposed that memory CD8+ T cell quiescence relies on its active suppression by Treg in a CTLA-4-dependent way [35] . Here , we showed that T . cruzi-induced Treg significantly upregulated CTLA-4 , and antibodies blocking the inhibitory activity of CTLA-4 partially restored the magnitude and polyfunctionality of OTI CD8+ T cell cytokine response after adoptive transfer of T . cruzi-exposed BMDC-SIINFEKL . In line with this , administration of anti-CTLA-4 antibodies during infection with T . cruzi has been shown to improve CD8+ T cell-mediated immunity [37 , 38] . Ideally , however , the cell-intrinsic role of CTLA-4 specifically expressed by the subset of Treg induced by T . cruzi would confirm the actual contribution of this molecule to the suppression of CD8+ T cell priming . Collectively , our data indicate that CD4+ Foxp3+ Treg induced by T . cruzi are able to suppress the induction of CD8+ T cell responses . These findings are not in accordance with previous studies using antibodies to deplete CD25+ cells , which suggested that Treg did not impact the onset of the CD8+ T cell-mediated immunity during T . cruzi infection [39 , 40] . In line with these previous reports , we also observed the suppression of CD8+ T cell priming upon transfer of T . cruzi-exposed BMDC-SIINFEKL when we depleted CD25+ cells with PC61 antibody ( S9 Fig ) . Incomplete depletion of CD25+ Foxp3+ cells by antibodies , as well as the putative contribution of CD25- Foxp3+ cells may explain these observations . The increase in numbers and cytokine response of T . cruzi-specific CD8+ T cells upon Foxp3+ T cell depletion in vivo soon after infection and the enhanced protection of these mice extend our results from the model of OTI CD8+ T cell priming and sustains the role of Treg in the suppression of CD8+ T cells . These data are also in line with recent studies showing that Foxp3+ Treg effectively maintained CD8+ T cell exhaustion during chronic infection with LCMV and inhibited the activation of CD8+ T cells by DC [41] . In conclusion , the data presented here is consistent with a model in which T . cruzi- infected DC suppress rather than induce specific CD8+ T cell immunity . This immune evasion mechanism might be relevant to the establishment of a chronic phase of infection and relies on the induction of regulatory CD4+ Foxp3+ T cells that actively suppress CD8+ T cell priming and curtail their functionality .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( http://www . cobea . org . br/ ) . The protocol was approved by the Committee on the Ethics of Animal Experiments of the Institutional Animal Care and Use Committee at the Federal University of Sao Paulo ( Id # CEP 0426/09 ) . Female 8- to 12-week-old animals were used in all experiments . C57BL/6 mice were purchased from CEDEME ( Federal University of São Paulo ) . C57BL/6-Tg ( TcraTcrb ) 1100 Mjb/J mice with transgenic OTI CD8 T cells were purchased from Jackson Laboratories ( Bar Harbour , ME ) and bred at CEDEME . B6 . MRL-Faslpr/J and B6 . 129P2-Il10tm1Cgn/J mice originally purchased from The Jackson Laboratory were bred and provided by Dr . Gustavo Amarante Mendes ( University of São Paulo ) . B6 . MRL-Faslpr/J mice were crossed with C57BL/6-Tg ( TcraTcrb ) 1100 Mjb/J at CEDEME to generate animals with transgenic OTIlpr/lpr CD8 T cells . B6 . 129S2-Cd4tm1Mak/J mice were originally purchased from The Jackson Laboratory and provided by Dr . Alexandre Keller ( Federal University of São Paulo ) . Mice with a bicistronic insertion of the reporter gene encoding eGFP in the Foxp3 locus ( Foxp3-GFP ) were generated in the laboratory of Vijay Kuchroo ( Harvard Medical School ) [42] . These animals were crossed with Cby . SJL ( B6 ) -Ptprca/J ( CD45 . 1 ) mice from the Jackson Laboratory for use in the experiments . Mice expressing the simian receptor of diphtheria toxin and eGFP under control of Foxp3 promoter ( DEREG ) were purchased from the Jackson Laboratory . Animals were injected i . v . with 1×104 transgenic OTI CD8 T cells one day before the i . v . transfer of 5×105 BMDC . For in vivo cellular proliferation assays , mice were injected i . p . at the same day of BMDC transfer with 200μL 10 mg/mL BrdU diluted in PBS . BrdU treatment was repeated every 48 h until the end of the experiment . EdU ( 200μL 5 mg/mL ) was injected i . v . 4 h before FACS . For CD25+ cell depletion , mice were injected i . p . with 0 . 5 mL PC61 ascite fluid every 48 h during the experiment , with the first treatment 2 days prior to BMDC transfer . To inhibit CTLA-4 function in vivo , mice were treated i . p . every 48h with 1mg of 9D9 antibody ( BioXCell ) . To neutralize TGF-β function in vivo mice were treated with 2mg of 1D11 . 16 antibody ( BioXCell ) every 72h . Control groups were similarly treated with rat polyclonal IgG ( Sigma ) . Bloodstream trypomastigotes of the Y strain of T . cruzi were cultured in vitro in LLC-MK2 cells ( ATCC ) for in vitro incubation with BMDC , or obtained from mice infected 7 days earlier and injected s . c . in the base of the tail ( 1×104 parasites/animal ) for in vivo challenge . The mice were i . p . treated with 0 . 5 μg diphtheria toxin diluted in 200 μL PBS on the two consecutive days following challenge . Bone marrow cells were flushed from femurs and cultured in vitro in RPMI 1640 supplemented with 10 mM Hepes , 0 . 2% sodium bicarbonate , 59 mg/L of penicillin , 133 mg/L of streptomycin , 10% Hyclone fetal bovine serum , 2 mM L-glutamine , 1 mM sodium pyruvate , 55μM 2-mercaptoethanol and 20 ng/mL GM-CSF ( R&D Systems ) at a concentration of 2×105 cells/mL . After 4 days in culture , half of the volume was replaced by fresh medium . At day 6 , the cells were exposed for additional 24 h to tissue culture trypomastigotes of Trypanosoma cruzi at the ratio of 3 parasites/cell or left unexposed . At day 7 , BMDC were stimulated with 1 μg/mL LPS ( Sigma ) for 6 h , washed , incubated with 2μM SIINFEKL peptide for 1 h , washed , and transferred into mice . Control BMDC were stimulated with LPS only . For the RT-PCR and the in vitro antigen presentation Elispot assay BMDC were exposed to T . cruzi for 24 h or 48 h , respectively , or unexposed . Where stated , BMDC were stimulated with LPS 1 μg/mL for the last 6 h in culture . In order to assess the viability of BMDC and lymphocytes , cells were prepared and stained according to manufacturer’s instructions using PE Annexin V Apoptosis Detection Kit I ( BD ) , Vybrant FAM Caspase-3 and -7 Assay Kit ( Molecular Probes ) , and FITC Hamster anti-mouse Bcl-2 sets ( BD ) . As a positive control for apoptosis induction , cells were incubated with actinomycin D 5 μg/mL for 5 h . Synthetic peptides SIINFEKL , VNHRFTLV and ANYKFTLV were purchased from Genscript ( Piscataway , New Jersey ) . The biotinylated tetramer H-2Kb-SIINFEKL was purchased from ProImmune Inc . ( Oxford , UK ) . Tetramer staining was performed before other FACS staining per manufacturer’s instructions . The intracellular cytokine staining of spleen cells was performed after 6 h of ex vivo restimulation with SIINFEKL 10 μM as described earlier [16] . CTLA-4 staining was performed following ICS procedures . The intranuclear staining of transcription factors was performed following the manufacturer’s instructions using Foxp3 Fixation/Permeabilization Concentrate and Diluent and Permeabilization Buffer ( eBioscience ) . BrdU staining was performed with BD FITC BrdU Flow Kit according to the manufacturer’s instructions . EdU staining was performed with Click it Plus EdU Alexa 488 kit ( Molecular Probes ) . Samples were acquired immediately after staining in a BD FACSCanto II flow cytometer and analyzed in FlowJo 8 . 7 ( Tree Star ) . For flow cytometry stainings the following antibodies were used: CD11c APCCy7 ( HL3 , BD ) , CD40 APC ( 2/23 , BD ) , CD80 PerCP ( 16-10A1 , BD ) , CD86 PECy7 ( GL1 , BD ) , H-2Kb FITC ( AF6-88 . 5 , BD ) , I-Ab PE ( AF6-120 . 1 , BD ) , CD8 PerCP or PB or FITC ( 53–6 . 7 , BD ) , CD4 PECy7 ( RM4-5 , BD ) , Vβ5 . 1 , 5 . 2 FITC ( MR9-4 , BD ) , Vα2 APCCy7 ( B20 . 1 , BD ) , CD44 PE ( IM7 , BD ) , CD62L APC ( MEL-14 , BD ) , IL-2 PerCP ( JES6-5H4 ) , TNF PE ( MP6-XT22 , BD ) , IFN-γ APC ( XMG1 . 2 , BD ) , IL-4 PE ( 11B11 , BD ) , IL-10 PE ( JES5-16E3 , BD ) , IL-17 PE ( TC11-18H10 , BD ) , CD11a FITC ( 2D7 , BD ) , CD49d FITC ( R1-2 , BD ) , CD38 PE ( 90 , BD ) , CD27 FITC ( LG3A10 , BD ) , CD69 PerCP ( H1 . 2F3 , BD ) , KLRG-1 FITC ( 2F1 , eBioscience ) , CD25 FITC or PE ( 7D4 or PC61 , BD ) , CD122 FITC ( TM-β1 , BD ) , CD43 PECy7 ( 1B11 , BioLegend ) , CD45 FITC ( 30-F11 , BD ) , CD70 PE ( FR70 , BioLegend ) , CD71 FITC ( C2 , BD ) , CD134 PE ( OX-86 , BioLegend ) , CD127 PE ( SB/199 , BD ) , CTLA-4 PE ( UC10-4B9 , eBioscience ) , CD272 PE ( 8F4 , eBioscience ) , CD279 FITC ( J43 , eBioscience ) , CD274 PE ( MIH5 , BD ) , Tim-3 Alexa Fluor 647 ( B8 . 2C12 , BioLegend ) , CD223 PE ( C9B7W , BD ) , CD262 PE ( MD5-1 , BioLegend ) , CD254 PE ( IK22/5 , BioLegend ) , CD95 PECy7 ( Jo2 , BD ) , CD178 PE ( MFL3 , BD ) , CD195 FITC ( C34-3448 , BD ) , CD197 Alexa Fluor 647 ( 4B12 , BD ) , CD199 Alexa Fluor 647 ( 9B1 , BioLegend ) , CD183 PerCP/Cy5 . 5 ( CXCR3-173 , BioLegend ) , CD184 PE ( 2B11/CXCR4 , BD ) , CXCR7 PE ( 8F11-M16 , BioLegend ) , β7 PerCP ( FIB27 , BioLegend ) , T-bet PE ( 4B10 , BD ) , Eomes FITC ( Dan11mag , eBioscience ) , GATA-3 PE ( L50-823 , BD ) , RORγ-t PE ( Q31-378 , BD ) , FOXP3 PE ( R16-715 , BD ) , H-2KbSIINFEKL PE ( 25-D1 . 16 , eBioscience ) , Ki67 e450 ( SolA15 , eBioscience ) . To stain biotinylated H-2Kb-SIINFEKL tetramer was used streptavidin APC or PE ( BD ) . As isotype controls were used IgG1kappa FITC or PE or PECy7 or APC , IgG2a kappa FITC or PE or APC , IgG2b kappa FITC or PE , IgG2 kappa FITC , and IgG1 lambda PE , all from BD . The transcription of cytokine mRNA in BMDC after T . cruzi exposure was assessed by RT-PCR . To this end , 10×106 BMDC from each condition were used . Total RNA was extracted with Trizol ( Invitrogen ) and purified with Quick RNA Miniprep columns ( Zymo Research ) according to the manufacturer instructions . RNA was quantified in Nanodrop and cDNA was synthesized with SuperScript III kit ( Invitrogen ) , following manufacturer instructions . The absence of genomic DNA was confirmed by using controls to which no reverse transcriptase was added . The resulting cDNA was amplified in a StepOne Plus equipment ( Applied Biosystems ) with SYBR Green ( Thermo Scientific ) using specific primers . Relative expression of target genes was normalized using gapdh as endogenous control and calculated by the Δ Δ Ct method . Primer sequences are given in S1 Table . To perform in vitro antigen presentation assays , spleen cells from OTI naïve animals were harvested and the CD8+ T cell population was isolated through negative selection with CD8+ T cell MACS beads isolation kit ( Miltenyi ) according to the manufacturer specification . The isolated lymphocytes were co-cultured for 5 days with BMDC previously exposed or not to T . cruzi , stimulated with LPS and loaded with SIINFEKL peptide . The ratio of 1 BMDC to 5 CD8+ T cells was used ( with 1 x 105 T cells/well ) and the number of IFN-γ secreting cells was determined by Elispot as described elsewhere [12 , 15] . Groups were compared using Two-Way ANOVA followed by Tukey’s HSD test ( http://faculty . vassar . edu/lowry/VassarStats . html ) . Differences were considered significant at a P value of <0 . 05 . | CD8+ T lymphocytes mediate immunity to intracellular pathogens by killing infected cells . However , some pathogens are able to evade the response of CD8+ T cells and , thus , establish chronic infections . This is the case of Trypanosoma cruzi , the protozoan parasite that causes Chagas disease . Here , we investigated the basis of the suboptimal response of CD8+ T cells during T . cruzi infection . We observed that cells incubated with the parasite and then adoptively transferred into mice are able to convert an optimal in vivo response of transgenic CD8+ T cells specific to an unrelated epitope into suboptimal . The mechanism of this disturbance relies on the induction of regulatory CD4+ Foxp3+ T cells that interfere with the priming of CD8+ T cells by dendritic cells . These findings illustrate the involvement of regulatory T cells in the regulation of CD8+ T cell priming and contribute to understand how T . cruzi evades host immunity to establish a chronic infection . | [
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] | 2016 | A Human Trypanosome Suppresses CD8+ T Cell Priming by Dendritic Cells through the Induction of Immune Regulatory CD4+ Foxp3+ T Cells |
The immunologic findings that most consistently correlate with resistance in human schistosomiasis are high levels of IgE and low levels of IgG4 . We have genotyped gene and promoter polymorphisms of cytokines associated with regulation of these isotypes in a cohort of men occupationally exposed to Schistosoma mansoni in western Kenya and evaluated their patterns with respect to resistance and susceptibility to reinfection after treatment and cure with praziquantel ( PZQ ) . In this cohort , polymorphisms in IL-4 ( −590T high IgE ) , IL-13 ( −1055T high producer ) and IFN-γ ( +874A high producer ) demonstrated several correlations with resistance to reinfection . Resistance to reinfection was significantly correlated with the heterozygous IL-4 −590 genotype C/T ( OR 3 . 5 , [CI 1 . 2 , 10 . 2] ) compared to T/T . Among men with a homozygous IL-13 genotype CC/TT , having a T allele at the IFN-γ +874 position increased the odds of resistance relative to individuals with the IFN-γ +874 A/A genotype ( OR = 17 . 5 [CI 3 . 0 , 101 . 5] ) . Among men with homozygous A/A IFN-γ genotype , the heterozygous IL-13 genotype C/T was associated with resistance relative to the homozygous C/C or T/T genotypes ( OR = 22 . 5 [CI 3 . 5 , 144 . 4] ) . No increases in odds of resistance were found in relation to the IL-13 genotype among those with a T allele in the IFN-γ gene or in relation to the IFN-γ genotype among those with a heterozygous IL-13 genotype . Calculation of the attributable proportion of resistance showed a significant synergistic interaction between IL-13 −1055 C/T and IL-4 −590 C/T . The identified polymorphisms do not by themselves confer resistance or susceptibility , but we propose that these genotypes allow the resistant phenotype to be developed and expressed upon suitable immune exposure . Based on the literature , these polymorphisms contribute to the regulation of their respective cytokines , likely leading to downstream differences in the production and interrelationships of critical defense mechanisms .
There have been many studies of resistance to schistosome infections in humans following treatment and reinfection . Such studies involve documentation of cases of schistosomiasis , their treatment and cure , and examinations at a later date to see if reinfections occurred [1] , [2] , [3] , [4] . Of all the immunologic findings associated with these investigations , the most consistent observation is that resistance ( usually defined as lower levels of infection ) correlates with high IgE and low IgG4 antibodies against schistosome antigens [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] . Other studies have reported that production of IFN-γ and IL-5 to schistosome antigens are also correlated with resistance [19] , [20] , [21] , [22] . A genetic region ( SM1 ) has been identified that shows a strong positive correlation to the level of infection in humans [23] . SM1 maps to chromosome 5 in the 5q31–q33 region that contains several genes associated with immune responses [24] , [25] . These genes code for proteins that are associated with the regulation of Th2-type responses such as IL-3 , IL-4 , IL-5 , IL-9 , and IL-13 and IgE . Polymorphisms in these cytokines that lead to an increase or decrease in cytokine levels could influence the antibody isotypes and cellular interactions that in turn may contribute to resistance or susceptibility of individuals to reinfection with schistosomiasis . There have been contradictory reports on the effect the IL-4 −590 C/T ( rs 2243250 ) polymorphism has on IgE levels in different settings . One group found that infants with a IL-4 −590 C allele had a higher risk of elevated IgE in their cord blood [26] . However , it was also reported that total IgE levels were significantly elevated in children with severe malaria carrying the −590T allele [27] . It is well known that IL-4 plays an important role in IgE class switching [28] and reports that the −590 C/T polymorphism are associated with differing amounts of IgE made it a candidate for this study . Kouriba , et al . reported that IL-13 −1055C ( rs 1800925 ) and −591A ( rs 2069743 ) were associated with the upper 10% infection levels in individuals infected with S . haematobium ( susceptibility ) [29] . Van der Pouw Kraan et al . described an NF-AT binding site at IL-13 -1055 that showed increased binding of nuclear proteins with the T allele [30] . Furthermore , it was reported that transcription of the IL-13 -1055T allele was enhanced in Th2 polarized CD4+ cells , but not in nonpolarized ( i . e . , Th0 ) CD4+ T cells [31] . Thus , the IL-13-1055 polymorphism , and possibly −591 , could be important in resistance to S . mansoni . The R130Q SNP ( G/A ) ( rs 20541 ) in IL-13 causes a replacement of arginine with glutamine in α helix D , a region involved in IL-13 interactions with IL-13 receptors [32] . The glutamine variant has been associated with increased levels of total serum IgE [33] and atopy [34] . It has also been shown that soluble IL-13Rα2 ( an IL-13 decoy receptor ) neutralizes the arginine variant more effectively than the glutamine variant [32] . This may suggest a possible feedback mechanism that could account for increased total serum IgE in the glutamine variant . While most studies have found that Th2 responses are associated with resistance to reinfection with schistosomiasis , other studies found that IFN-γ production correlates to resistance [19] , [20] , [21] . Pravica et al . showed that the +874T ( rs 2430561 ) allele corresponds with high production of IFN-γ and that the A/T site coincides with a putative NF-kappa B binding site [35] . These studies prompted us to include the IFN-γ +874 polymorphism in our study . IL-10 has been shown to influence IgE and IgG4 production [36] . Differential production of IL-10 has been shown to be associated with different allelic variants of the IL-10 promoter ( −1082 A/G [rs 1800896] , −819 T/C [rs 1800871] , and −592 A/C [rs 1800872] ) . The different haplotypes that have been associated with production of IL-10 are as follows: “high” IL-10 producer haplotype ( GCC/GCC ) , “intermediate” producer haplotypes ( GCC/ACC , GCC/ATA ) , and “low” producer haplotypes ( ATA/ATA , ACC/ATA , ACC/ACC ) [37] . Indeed , the ATA/ATA haplotype has been associated with increased eosinophil counts and circulating IgE in adult asthma when compared to the other possible haplotypes [38] . However , the group that first described these polymorphisms conclude that high IL-10 production was dependant on having a G in the −1082 position independent of the −819 and −592 polymorphisms [39] . Furthermore , Ruess et al . has shown that −1082A binds PU . 1 , which can inhibit gene transcription . Based on these and other studies , we examined possible relationships between single nucleotide polymorphisms in the IL-4 ( −590 C/T ) , IL-13[ ( −1055 C/T ) , ( −591A/G ) , ( R130Q G/A ) ] , IL-10 ( −1082 A/G; −819 C/T; −592 C/A ) , and IFN-γ ( +874A/T ) genes or promoters in relation to resistance and susceptibility to schistosomiasis of a cohort of occupationally exposed adult men many of whom we have studied longitudinally for as long as 12 years [40] , [41] .
The field site for this study was the western Kenyan city of Kisumu on the shores of Lake Victoria . S . mansoni-infected Biomphalaria sudanica snails have been identified ( data unpublished ) in the area around the exposure site . The study participants were all car washers occupationally exposed to S . mansoni as they stood in Lake Victoria , using its waters to wash cars driven into the shallow areas of the lake . This study was performed on a total of 87 car washers ( all adult men ) . However , it was not possible to use all data from every individual in every analysis; some epidemiologic data regarding infections , cures or reinfections were insufficient or incomplete . This investigation was approved by the Institutional Review Boards of the University of Georgia and the Centers for Diseases Control and Prevention , the Scientific Steering Committee of the Kenya Medical Research Institute ( KEMRI ) , and the National Ethics Review Committee of Kenya . After obtaining written informed consent and enrolling the participants , we examined their stools for S . mansoni eggs and for other helminth ova by the modified Kato-Katz method ( Vestergaard-Frandsen , Denmark ) . In most instances , this involved 2 slides per stool specimen from 3 stool specimens over a one week period . The participants who were positive for S . mansoni were treated with 40 mg/kg praziquantel ( PZQ ) ; men positive for other soil-transmitted helminth eggs were treated with 400 mg albendazole . Individuals' stools were checked 6 weeks after treatment and the men were retreated if still egg positive . Upon becoming egg negative , they were then followed by stool examination every 4 weeks to determine their time to reinfection as a means of determining their relative resistance to reinfection ( see below ) . White blood cell-containing buffy coats were separated using the Ficoll-hypaque technique from blood obtained for immunological assays . The buffy coats were stored at −20°C and DNA was isolated at KEMRI/CGHR laboratories using the Wizard® Genomic DNA Purification Kit from Promega . DNA from 300 µl of buffy coat for each car washer was isolated per the manufacturer's instructions . Dried DNA pellets were then transported to Athens , Georgia , USA for genotyping . PCR reactions of IL-13 and IL-4 SNPs were performed on a PTC-200 DNA Engine from MJ Research . PCR of the IL-13 −1055 C/T was conducted in a 50 µl reaction containing 100 ng DNA , 5 µl of 1× Qiagen PCR buffer , 0 . 5 µM of each dNTP , 0 . 4 µM of each primer , 1 mM MgCl2 , and 2 . 5 U of Taq polymerase ( Qiagen ) . The following sequences were used: forward primer , 5′-ATGCCTTGTGAGGAGGGTCAC; reverse primer , 5′-CCAGTCTCTGCAGGATCAACC [42] . Initial denaturation was performed at 95°C for 3 min followed by 30 cycles of PCR with the following conditions: 95°C for 30 sec , 62°C for 30 sec for annealing , 72°C for 1 min , and a final 72°C for 3 min . PCR of the IL-13 −591 A/G was conducted in a 50 µl reaction containing 100 ng DNA , 5 µl of 1× Qiagen PCR buffer , 0 . 5 µM of each dNTP , 0 . 4 µM of each primer , 3 mM MgCl2 , and 2 . 5 U of Taq polymerase ( Qiagen ) . Initial denaturation was performed at 94°C for 5 min followed by 34 cycles of PCR with the following conditions: 94°C for 1 min , 61°C for 45 sec for annealing , 72°C for 45 sec , and a final 72°C for 3 min . The following sequences were used: forward primer , 5′-CCAGCCTGGCCCAGTTAAGAGTTT; reverse primer , 5′-CTAATTCCTCCTTGGCCCCACT [29] . PCR of the IL-13 +130 G/A was conducted in a 50 µl reaction containing 100 ng DNA , 5 µl of 1× Qiagen PCR buffer , 0 . 5 µM of each dNTP , 0 . 4 µM of each primer , 1 mM MgCl2 , and 2 . 5 U of Taq polymerase ( Qiagen ) . Initial denaturation was performed at 94°C for 5 min followed by 34 cycles of PCR with the following conditions: 94°C for 1 min , 60°C for 45 sec for annealing , 72°C for 45 sec , and a final 72°C for 3 min . The following sequences were used: forward primer , 5′-TGGCGTTCTACTCACGTGCT; reverse primer , 5′-CAGCACAGGCTGAGGTCTAA [43] . PCR of the IL-4 −590 C/T was conducted in a 50 µl reaction containing 100 ng DNA , 5 µl of 1× Qiagen PCR buffer , 0 . 5 µM of each dNTP , 0 . 4 µM of each primer , 1 mM MgCl2 , and 2 . 5 U of Taq polymerase ( Qiagen ) . Initial denaturation was performed at 95°C for 5 min followed by 31 cycles of PCR with the following conditions: 94°C for 30 sec , 59°C for 30 sec for annealing , 72°C for 30 sec , and a final 72°C for 3 min . The following sequences were used: forward primer , 5′-ACTAGGCCTCACCTGATACG; reverse primer , 5′-GTTGTAATGCAGTCCTCCTG [44] . Purified PCR products of the IL-13 −1055 C/T and IL-4 −590 C/T PCR reactions were sequenced using the reverse primers for each and the forward primers for IL-13 +130 G/A and IL-13 −591 A/G on an ABI 3100 by the Office of Research Services at The University of Georgia . The polymorphisms IL-10 −1082 , IL-10 −819 , IL-10 −592 , and IFN-γ +874A/T were genotyped using sequence-specific primers ( SSP ) in The Cytokine Genotyping Tray ( One Lambda; Canoga Park , CA ) as per the manufacturer's instructions . Resistance is based on the number of cars washed from the time of a successful cure until the next reinfection . For all participants , the number of cars washed between each cure and reinfection over the entire duration of the study was plotted and the patterns examined . Two dominant patterns emerged , with participants either becoming reinfected after washing approximately the same number of cars between each cure and reinfection or participants washing progressively more cars between successive cure-reinfection intervals . The majority of those in the former group ( classified as “susceptible” ) became reinfected after washing approximately 250–300 cars regardless of how many times they were cured and reinfected . Those men who demonstrated a pattern of increasing numbers of cars washed before subsequent reinfections were classified as “developing resistance” during this study . All men classified as “developing resistance” eventually washed at least 450 cars before reinfection after being followed for at least 3 cure-to-reinfection intervals . Some men ( classified as “initially resistant” ) washed at least 450 cars after the initial cure and continued to wash a high number of cars before each reinfection . For analysis purposes , men classified as “developing resistance” and “initially resistant” were grouped into a single “resistant” category because frequencies of genotypes did not differ significantly between these two groups , and conceptually these groups indicate either the existence of established resistance or the ability to develop resistance . Resistance data are based on a mean follow-up time of 7 . 5 years ( range 0 . 9–12 . 2 yrs ) months and a mean of 7 cure-to-reinfection intervals . Men were excluded from the analysis when the number of cars washed between each cure and reinfection could not be classified into a particular pattern ( 3 men ) or they had insufficient follow up data ( 16 men ) for accurate classification . Odds ratios ( ORs ) and corresponding 95% confidence intervals ( CIs ) for the association between resistance and each genotype were calculated using univariate analyses and in a multivariate logistic regression model containing variables for all three genotypes . To test for interactions between genotypes , categorical interaction terms with 4 levels were created for each possible two-way interaction between the 3 dichotomized genotypes . The combination of alleles with the lowest frequency of resistant subjects was considered the referent category in each set of terms . Separate logistic regression models were run for each series of two-way interaction terms . Inclusion of a term for the third genotype did not appreciably change the results of any of the interaction models and was thus ultimately not included in any of the reported analyses of interactions . Interactions were assessed on both a multiplicative and additive scale . Multiplicative interactions are indicative of the need to estimate stratum-specific effects for the combination of two genotypes , rather than a single estimate for each genotype , in order to improve the fit of the model to the data [45] . Multiplicative interactions were assessed by testing the significance of an interaction term between two genotypes in the logistic regression models by the Wald test . As many researchers believe that departure from additivity is a better indicator of biologic interaction than departure from multiplicativity [45] , [46] , we also calculated the attributable proportion ( AP ) of resistance due to interactions between two genotypes . A positive AP is indicative of synergy between the two genotypes , while a negative AP indicates antagonism [47] . APs and corresponding 95% CIs for each of the two-way interactions between genotypes were calculated based on the output from the logistic regression models using the code provided by Andersson et al [48] based on the methodology described by Hosmer and Lemeshow [49] . All analyses were performed with SAS version 9 . 1 .
The distribution of genotypes for each polymorphism and frequency of resistance in each genotype are given in Table 1 . A higher percentage of car washers with a T allele ( T/T or T/A ) at IFN-γ +874 are resistant ( T/A 65 . 2% and T/T 77 . 8% ) than men that are A/A homozygous ( 38 . 9% ) . For subsequent analyses , men with T/A and T/T at the IFN-γ +874 position were grouped as the occurrence of resistance for each was significantly higher when compared to homozygous individuals ( A/A ) and they did not differ from each other . Among men heterozygous ( C/T ) at the IL-13 −1055 position , 76 . 0% were resistant to reinfection; whereas only 41 . 9% of C/C and only 42 . 9% of T/T individuals were resistant to reinfection ( Table 1 ) . Both homozygous genotypes for IL-13 −1055 were grouped for the univariate analysis as the occurrences of resistance in either of those genotypes were substantially lower than in the heterozygous group and they did not differ from each other . The frequency of homozygous C/C at IL-4 −590 was not high enough in our cohort of men to allow analysis . However , men heterozygous ( C/T ) at IL-4 −590 represent a higher percentage of resistance ( 70 . 8% ) than men that are T/T homozygous ( 40 . 9% ) . No overt differences are seen in the proportions of the different genotypes of IL-10 ( −1082; −819; −592 ) promoter SNPs in relationship to resistance or susceptibility to reinfection , and as expected due to linkage disequilibrium , SNPs at IL-10 −819 and IL-10 −592 segregate together . Resistance was significantly associated with the T allele at IFN-γ +874 ( TT and TA ) when compared to the homozygous A/A individuals ( OR 3 . 5 [CI 1 . 3 , 9 . 4] ) ( Table 2 ) . Car washers heterozygous ( C/T ) at the IL-13 −1055 position also showed a significant correlation with resistance to reinfection when compared to homozygous ( C/C and T/T ) car washers ( OR 4 . 4 [CI 1 . 4 , 13 . 4] ) ( Table 2 ) , and heterozygousity ( C/T ) at the IL-4 −590 position correlated significantly with resistance when compared to homozygous ( T/T ) car washers ( OR 3 . 5 [CI 1 . 2 , 10 . 2] ) ( Table 2 ) . These associations remained significant when all three genotypes were included in a multivariate analysis , indicating an independent association between resistance and each of the 3 genotypes ( Table 2 ) . For IL-13 −591 , there was no significant difference in resistance between the A/A and A/G genotypes ( Table 1 , p = 0 . 2399 ) . Having an A allele at the IL-13 +130 position was associated with a modest increase in resistance relative to the homozygous G/G genotype ( OR = 2 . 4 [0 . 9 , 6 . 7] ) . However , when IL-13 +130 and IL-13 −1055 were evaluated simultaneously in a logistic regression model , the association between resistance and IL-13 +130 disappeared ( OR = 1 . 4 [0 . 4 , 4 . 5] ) , while IL-13 −1055 remained significantly associated with resistance . This suggests that the observed relationship between resistance and IL-13 +130 was spurious and likely due to the close association between IL-13 +130 and IL-13 −1055 . Therefore , we only included the IL-13 −1055 polymorphism in further analyses . We found a relationship of borderline significance with IL-10 −819 or −592 ( any C versus homozygous G/G or A/A ) : ( OR = 3 . 1 [0 . 9 , 10 . 2] , p = 0 . 0577 ) and resistance to reinfection . These two ( −819 and −592 ) polymorphisms are in tight linkage disequilibrium; hence the results are interchangeable between the two . The OR remained virtually unchanged in a model controlling for the other three significant genotypes , indicating that this possible low-grade association between resistance and the IL-10 polymorphism was not confounded by its association with another genotype . There were no significant additive or multiplicative interactions between IL-10 −819 or −592 and any of the IL-4 , IL-13 or IFN-γ polymorphisms . No significant associations were found with IL-10 −1082 with resistance or susceptibility ( data not shown ) . Combination of the IL-13 −1055 and IFN-γ +874 genotypes that were independently associated with resistance did not increase the odds of being resistant over having either one of these genotypes alone . However , although all combinations of IL-13 and IFN-γ genotypes had comparable odds of resistance when compared to the reference group ( Table 3 ) , effect modification was seen between the IL-13 and IFN-γ genotypes . Among men with a homozygous IL-13 genotype , having a T allele at the IFN-γ +874 position increased the odds of resistance relative to those with the A/A genotype ( OR = 17 . 5 [CI 3 . 0 , 101 . 5] ) ( Table 3 ) . There was no association between IFN-γ genotype and resistance among men with the heterozygous C/T IL-13 genotype . Likewise , among men with homozygous A/A IFN-γ genotype , the heterozygous IL-13 genotype was associated with resistance relative to the homozygous C/C or T/T genotypes ( OR = 22 . 5 [CI 3 . 5 , 144 . 4] ) ( Table 3 ) , while no association between IL-13 and resistance was seen among men with a T allele in the IFN-γ gene . Men with a combination of the heterozygous C/T alleles for IL-13 −1055 and IL-4 −590 genes had more than a 20-fold increased odds of resistance relative to those with homozygous alleles at both genes ( OR = 20 . 1 [CI 2 . 3 , 176 . 0] ) ( Table 3 ) . Men who were heterozygous for only one of the IL-13 or IL-4 genes were not more likely to be resistant compared to men with both homozygous alleles ( Table 3 ) . A significant additive interaction between IL-13 and IL-4 genotypes was detected , with 90% of the resistance among those with heterozygous alleles at both genes attributable to the interaction between the two genes ( AP = 0 . 90 [0 . 64 , 1 . 15] ) . In contrast , no significant additive or multiplicative interactions were detected between IL-4 −590 and IFN-γ +874 ( Table 3 ) .
The car washers in this study are part of a longitudinal study that dates back 12 years [40] . Their exposure ( water contact ) to infection with S . mansoni is documented by the number of cars washed for payment purposes , providing us with a unique , reliable means to quantify an individual's exposure in an active transmission site over time . This longitudinal field setting allows us to account for water contact as a variable and pose some interesting immunologic and genetic questions with regard to what influences may play a role in determining resistance to reinfection after treatment . We found significant correlates between resistance to reinfection with S . mansoni and the heterozygous ( C/T ) IL-13 −1055 genotype , any T allele in the IFN-γ +874 genotype , and the heterozygous ( C/T ) in the IL-4 −590 genotype by univariate analysis . Furthermore , these associations remained significant when all three genotypes were included in a multivariate analysis , indicating independent associations between resistance and each of the 3 genotypes . However , our data suggest that having a combination of the IL-13 C/T and an IFN-γ T allele at +874 does not provide increased odds of being resistant over having just one of these genotypes . Instead , both IL-13 C/T and any IFN-γ T allele demonstrated very high odds of being resistant when compared to the reference group . Our data did , however , show a significant synergistic effect between the IL-13 −1055 C/T and IL-4 −590 C/T genotypes . Thus , the proportion of resistant men seen with a combination of these two cytokine genotypes was much greater than that seen with the sum of the separate effects of IL-13 −1055 C/T and IL-4 −590 C/T on resistance . We interpret these data to indicate that individuals heterozygous at the IL-13 −1055 and IL-4 −590 position are more likely to require fewer reinfections and treatments to become resistant to reinfection than individuals who are homozygous at either position . Due to our small sample size , our interaction analysis resulted in wide confidence intervals and we were unable to evaluate 3-way interactions between groups . Also , because car washing is an exclusively male profession at this study site , the associations in this study apply to men and cannot necessarily be generalized to women . However , these analyses provide interesting findings in a situation that provides exceptional exposure data that are not generally available in studies of resistance to reinfection . Clearly our findings need to be investigated further in larger cohorts . However , the current data provide initial insights into the potential genetic foundation of propensities of people to develop resistance to reinfection by schistosomes , and they offer a basis for further molecular studies of how these polymorphisms might work at the transcriptional and gene product level . | Approximately 200 million people have schistosomiasis in parts of Africa , South America , the Middle East , the Caribbean and Asia . Several studies of multiple treatments and reinfections indicate that some people develop resistance to reinfection . Of all the immunologic findings associated with such studies , the most consistent observation is that resistance ( usually defined as lower levels of infection upon reinfection ) correlates with high IgE and low IgG4 antibodies against schistosome antigens . Our studies test whether single nucleotide polymorphisms residing in the gene or promoter regions of cytokines pivotal in controlling production of these antibody isotypes are different amongst those that develop resistance to reinfection as opposed to those that do not . Through genotyping of these polymorphisms in a cohort of occupationally exposed car washers , we found that men with certain genotypic patterns of polymorphisms in IL-4 , IFN-γ , and IL-13 were significantly more likely to be resistant to reinfection than those with different patterns . These data provide initial insights into the potential genetic foundation of propensities of people to develop resistance to reinfection by schistosomes , and offer a basis for further molecular studies of how these polymorphisms might work at the transcriptional and gene product level in cells stimulated by schistosome antigens . | [
"Abstract",
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] | 2009 | Association of the Gene Polymorphisms IFN-γ +874, IL-13 −1055 and IL-4 −590 with Patterns of Reinfection with Schistosoma mansoni |
The ubiquitin proteasome system regulates meiotic recombination in yeast through its association with the synaptonemal complex , a ‘zipper’-like structure that holds homologous chromosome pairs in synapsis during meiotic prophase I . In mammals , the proteasome activator subunit PA200 targets acetylated histones for degradation during somatic DNA double strand break repair and during histone replacement during spermiogenesis . We investigated the role of the testis-specific proteasomal subunit α4s ( PSMA8 ) during spermatogenesis , and found that PSMA8 was localized to and dependent on the central region of the synaptonemal complex . Accordingly , synapsis-deficient mice show delocalization of PSMA8 . Moreover , though Psma8-deficient mice are proficient in meiotic homologous recombination , there are alterations in the proteostasis of several key meiotic players that , in addition to the known substrate acetylated histones , have been shown by a proteomic approach to interact with PSMA8 , such as SYCP3 , SYCP1 , CDK1 and TRIP13 . These alterations lead to an accumulation of spermatocytes in metaphase I and II which either enter massively into apoptosis or give rise to a low number of aberrant round spermatids that apoptose before histone replacement takes place .
Intracellular protein content is controlled through the balance between the rates of their synthesis and degradation . In eukaryotic cells , the bulk of the degradation is carried out by the ubiquitin-proteasome system ( UPS ) . The proteasome is a multi-subunit complex that eliminates proteins , typically labeled with ubiquitin , by ATP-driven proteolysis [1] . Proteasome complexes comprise a cylindrical catalytic core particle ( CP , 20S ) and different regulatory particles ( RPs , 19S ) that regulate the access to the CP by capping it at either end [2] . The CP is composed of seven α-type subunits and seven β-type subunits arranged as a cylinder of four rings ( α1–7 , β1–7 , β1–7 , α1–7 ) [1 , 3] . RPs are composed of 20 subunits and their association with the CP is ATP-dependent . There are four additional activators , the 11S regulator PA28α/β/γ and the ubiquitous PA200 ( Psme4 ) regulator that stimulates protein degradation independently of ubiquitin [4] and plays a main role in acetylation-dependent degradation of somatic core histones during DNA repair and spermiogenesis [5 , 6] . Hybrid proteasomes enclosing a RP at one end and an activator at the other end are also possible [7] . In addition , there are paralogs for three β-genes that are expressed only in the immunological system , which constitutes the immunoproteasome [8] , and one β5t gene expressed exclusively in the thymus , which constitutes the thymoproteasome [9] . Finally , there is a meiotic paralog of the α4 subunit ( Psma7 ) , named α4s ( Psma8 ) [10] , which might provide substrate specificity and heterogeneity to the α4s-cotaning proteasome . The proteolytic activity of the proteasome is regulated by the rate of protein ubiquitylation , but also by its association with E3 ubiquitin ligases and deubiquitinating enzymes that edit their potential substrates [11 , 12] . The classical targets of the UPS are misfolded or damaged proteins and/or short-lived regulatory proteins , whose concentration is regulated by fine-tuning of their synthesis and degradation kinetics [13 , 14] . Typical examples of the latter proteins are cyclins [15 , 16] . More recently , it has been hypothesized but not proven that the ZMM complex ( also known as the synapsis initiation complex ) involved in meiotic homologous recombination is similarly regulated in the mouse [17 , 18] . Meiosis is a fundamental process in sexually reproducing species that ensures the production of genetic diversity and the generation of haploid gametes from diploid progenitors [19] . This reduction in genome content is achieved by the physical connections between homologs by chiasmata [20] , which are mediated by the repair of self-induced DNA double-strand breaks ( DSBs ) as crossing-overs ( COs ) . Meiotic recombination takes place on proteinaceous core structures or axial elements ( AEs ) that scaffold the chromosomal DNA content and physically connect ( synapse ) homologs through the assembly of the synaptonemal complex ( SC ) during prophase I [21] . The UPS regulates meiotic recombination in yeast and mouse via its physical association to AEs [17 , 22] . Given the unknown function that the α4s-containing proteasome plays during spermatogenesis , we explored its function in the mouse . In this study , we show that PSMA8 is localized to and dependent on the central element of the SC , and promotes the assembly of the proteasome activator PA200 . Accordingly , synapsis-deficient mice show delocalization of PSMA8 . Also , Psma8-deficient mice are proficient in meiotic homologous recombination , but show alterations in the proteostasis of several key meiotic players including acetylated histones , SYCP3 , SYCP1 , CDK1 and TRIP13 , which in turn leads to an aberrant meiotic exit , accumulation of apoptotic spermatocytes in metaphase I and II , and finally early spermatid arrest long before histone replacement takes place .
Psma8 mRNA expression in mouse tissues is almost exclusively restricted to the testis ( GTEx database [23] and previous studies [10] ) . To elucidate the cell type in which PSMA8 is expressed , we examined by western blotting testis extracts at various postnatal ages during the first wave of spermatogenesis , which progresses more synchronously than in adult mice . PSMA8 expression ( using a specific antibody against the PSMA8 C-terminus [10] , see Fig 1A ) was first detected at P12 and increased from P14 to P20 . We also used a PSMA8-R2 antibody raised against the entire recombinant PSMA8 protein , which detected the expression of both PSMA7 ( already apparent at P8 , before meiosis has started ) and PSMA8 ( Fig 1A and S1 Fig ) . Analysis of testis cell lines ( including spermatogonium GC1-spg , Leydig cell TM3 , and Sertoli cell TM4 lines ) , revealed the expression of PSMA7 but not PSMA8 ( Fig 1A ) . These results indicate that its expression is restricted to cells undergoing meiosis . To explore the subcellular localization of PSMA8 , we employed the R2 antibody ( PSMA7/8 ) since the PSMA8 C-terminus antibody did not produce any specific labeling . Double immunolabeling of PSMA8 with the AE protein SYCP3 or with SYCP1 , the transverse filament protein essential for synapsis ( Fig 1B and S2 Fig ) , revealed PSMA7/8 presence at the central region of the synaptonemal complex ( super resolution imaging , Fig 1B ) . We validated this localization by in vivo electroporating [24] an expression plasmid encoding GFP-PSMA8 in the testis ( Fig 1C ) . These results agree with the recent localization of the proteasome to the chromosome axes [17] . To investigate the possible dependence of PSMA8 localization on synapsis , we analyzed synaptic mutants with mild ( Rec8-/- [25] ) and severe ( Six6os1-/- [24] ) phenotypes . Mutants for the meiotic cohesin REC8 show pseudo-synapsis between sister chromatids [25] , and PSMA8 was detected at these atypical synapsed-like regions ( Fig 1D ) . In mice lacking the novel central element protein SIX6OS1 , in which AEs are physically separated and unsynapsed at pachynema [24] , PSMA8 signal was not restricted to their AEs and showed a broader and more disperse labeling ( Fig 1D ) . These results indicate that PSMA8 localization to the SC central region is consequently dependent on the assembly of the SC . To study the role of PSMA8 , we generated a targeted mutation in exon 1-intron 1 of the murine Psma8 gene by CRISPR/Cas9 genome editing ( S3A and S3B Fig ) . Homozygous mutant testes showed no PSMA8 protein expression by western blotting when analyzed using two independent polyclonal antibodies ( S3C Fig ) . Immunofluorescence analysis of PSMA8 expression ( R2 antibody , S3D Fig ) revealed a weaker signal in the SC of the mutant spermatocytes than in WT spermatocytes ( 51% less; 4 . 22±1 . 9 WT vs 2 . 05±1 . 7 KO ) , likely representing PSMA7 detected by the R2 antibody ( also observed in the western blot; S3C Fig ) . These results indicate that the generated mutation is a null allele of the Psma8 gene ( herein termed Psma8-/- ) . Mice lacking PSMA8 did not display any somatic abnormalities; however , male but not female mice were sterile ( S1 Table ) . Indeed , Psma8 mutation resulted in a reduction of the testis weight ( 63 . 09% decrease; N = 6 ) and the absence of spermatozoa in the epididymides ( Fig 2A and 2B ) . Histological analysis of adult Psma8-/- testes revealed the presence of apparently normal numbers of spermatogonia , spermatocytes , Sertoli cells and Leydig cells ( Fig 2B ) . Mouse seminiferous tubules can be classified from epithelial stage I to XII by determining the groups of associated germ cell types in histological sections . Following these criteria , we found that spermatogenesis in the mutant testes proceeded normally up to diplotene in epithelial stage XI . However , the proportion of tubules at stage XII was more than 2-fold increased in the mutant sections ( 12 . 5% in mutants versus 5 . 4% in WT , S2A Table ) . Given that spermatocytes in meiotic divisions were seen to occur at epithelial stage XII , we used p-ser10-H3 ( pH3 ) staining to analyze the number of metaphase I and II cells present in these tubules , finding an increase in the mutant ( Fig 2C and S2B Table ) . Quantitative analysis of seminiferous tubules in squashed preparations confirmed the increase in the number of metaphase I and metaphase II cells as compared with WT testes ( 77% and 89% respectively , Fig 2D and S2C Table ) . Moreover , a large proportion of these metaphases were positive for Caspase-3 and TUNEL indicating apoptosis ( Figs 2D , 3A and 3B and S2C Table ) . Together with the accumulation of apoptotic meiotic divisions , other apoptotic cells could be also observed that , from their size and molecular markers of the acrosome and chromatoid body , were round spermatids ( Fig 3C and S4 Fig ) . Indeed , seminiferous tubules in PSMA8-deficient testes sometimes contained a few surviving round spermatids . However , these round spermatids were unable to form a proper acrosome but did accumulate some PAS positive material . Apoptotic round spermatids were also seen and no elongating spermatids were observed ( Fig 2B ) . We corroborated that round spermatids were arrested at early stages by immunolabeling for H2AL2 . H2AL2 is a transition histone essential for the first replacement of histones by TNP1 and TNP2 before protamine incorporation [26] . H2AL2 was absent from mutant spermatids ( S5A Fig ) . We also used FACs analysis of whole cells from seminiferous tubules to verify this analysis . The results obtained confirmed the presence of a small haploid compartment in Psma8-/- testes ( Fig 3D and S5B Fig ) . We conclude from these results that PSMA8 deficiency causes the accumulation of spermatocytes in metaphase I and II which either enter massively into apoptosis or give rise to a low number of aberrant round spermatids that finally apoptose long before histone replacement takes place . Metaphase I accumulation can occur either because of a failure to enter anaphase or because of some event taking place during prophase ( SC formation , DBSs repair or chromosome recombination ) that aberrantly triggers a checkpoint-mediated delay . To test this , we first analyzed the assembly/disassembly of the SC by monitoring the distribution of SYCP1 , as co-labeling of SYCP3 and SYCP1 highlights regions of synapsis in spermatocytes . We did not observe any differences in this process from zygonema to diakinesis ( S6 Fig ) . We next studied the kinetics of DSB repair during meiosis . Meiotic DSBs are generated by the nuclease SPO11 and are then resected to form ssDNA ends that invade into the homologous chromosome . DSBs are marked by the presence of phosphorylated H2AX ( γ-H2AX ) [27] . The distribution of γ-H2AX in mutant spermatocytes was similar to that found in WT cells at prophase I ( S7A Fig and S3 Table ) . We also did not observe any differences in the distribution of RAD51 , a recombinase that promotes homologous strand invasion [28] , ( S7B Fig and S3 Table ) . Because defective DNA repair ultimately abrogates CO formation [29] and because of the involvement of ubiquitylation / sumoylation in CO designation [30] , we analyzed the distribution of MLH1 foci [31] , a mismatch repair protein ( marker of crossover sites ) that functions in the resolution of joint molecules at the end of crossover formation [32] . We found a similar value between the KO ( 24 . 9±0 . 9 foci ) and the WT ( 24 . 3±1 . 1 foci; S7C Fig and S3 Table ) . These results indicate that the repair of meiotic DSBs and synapsis/desynapsis proceed normally during prophase I in the absence of PSMA8 , and is not responsible for the observed metaphase I accumulation . We also analyzed the morphology of the metaphase I / II cells by staining for tubulin ( spindle ) and SYCP3 . The results showed an aberrant morphology , the presence of multipolar spindles ( Fig 3E ) , and also a striking aberrant labeling of SYCP3 at the centromeres of metaphase II chromosomes ( SYCP3 labeling is barely visible in metaphase II sister kinetochores in WT cells , Fig 3F ) . Finally , the arrested round spermatids showed the presence of multiple patches of heterochromatin after DAPI staining ( Fig 3C and S4 Fig , chromocenter fragmentation ) , suggesting abnormal chromosome segregation or cytokinesis . During spermiogenesis , most of the histones are replaced by basic transition proteins , and ultimately by protamines , facilitating chromatin compaction . Hyperacetylation of core histones during this process , and especially the acetylation of H4K16 , is assumed to play a pivotal role in the initiation of histone displacement and chromatin ultracondensation [33 , 34] . The proteasome activator subunit PA200 targets acetylated histones for degradation during histone replacement [5] . The core subunit PSMA8 co-immunoprecipitated PA200 ( S4 Table ) . Given the stoichiometric relationship between the CP and RP , we analyzed the expression of PA200 by immunofluorescence in the absence of PSMA8 . Whilst PA200 decorated the AEs of WT spermatocytes , we failed to observe any signal in the AEs of mutants ( Fig 3G and S8 Fig ) . In addition , we were not able to detect PA200 by mass spectrometry analysis of PSMA7/8 immunoprecipitation of Psma8-deficient testis extracts ( see section Purification of PSMA8-interacting proteins , S4 Table ) . These results indicate that PSMA8 is necessary or promotes the assembly of PA200 to the CP . Thus , within the limits of detection , the deficiency of Psma8 leads to a drastic decrease of PA200 . To understand the acetylated-dependent degradation of histones by the proteasome [5] , we measured the acetylation status of three core histones , H2AK5ac , H3ac and H4ac ( pan-H4ac and H4K16ac ) in chromosome spreads by double immunolabeling for SYCP3 and the corresponding acetylated histone ( Fig 4A–4D and S9–S12 Figs ) . This procedure enables a more precise staging of the spermatocytes and is a more efficient mean to quantitate signals than peroxidase immunostaining of testis sections [5] . The loss of PSMA8 led to the accumulation of H2AK5ac , H3ac , H4ac and H4K16ac , albeit to different degrees . Results showed that the levels of H2AK5ac , H3ac , H4ac and H4K16ac were moderately higher in Psma8-/- cells , with a relative increase at late prophase I ( Fig 4A–4D and S9–S12 Figs ) . We failed to detect staining for H2AK5ac and H3ac in spermatocytes in late diakinesis and round/arrested spermatids . In contrast , pan-H4ac and H4K16ac also labeled metaphase I chromosomes , interkinesis nuclei and round/arrested spermatids , with greater intensity in mutant than in WT cells ( Fig 4C and 4D and S11 and S12 Figs ) . The accumulation of acetylated histones during prophase I and particularly of H4ac and H4K16ac in the arrested round spermatids suggests that the PSMA8-containing proteasomes are involved in the acetylation-dependent degradation of histones . We next investigated the biochemical activity of testis extracts lacking PSMA8-containing proteasomes by measuring chymotrypsin-like activity ( corresponding to the catalytic subunit β1 ) , caspase-like activity ( corresponding to β5 ) and trypsin-like activity ( β3 ) by a standard fluorogenic assay [35] in the presence and absence of SDS ( activated proteasome ) . Results showed that proteasomal activity in Psma8-deficient testis extracts was not noticeably different from that in WT extracts . Indeed , the trypsin-like activity was the only proteolytic function with a modest reduction in the KO ( Fig 4E ) . Overall , these results show that the general proteasome activity of the Psma8-deficient testis is not radically changed , which is likely due to the presence of PSMA7-dependent CPs ( see dataset 1 in [36] ) . To ascertain the degree of activity in vivo , we first investigated the steady-state levels of protein ubiquitylation in testis during mouse meiosis . Using immunofluorescence , we analyzed spermatocytes obtained from spreads and squashed preparations with ubiquitin antibodies ( Fig 4F and S13 Fig ) . The results showed a slight decrease of chromatin bound ubiquitylated proteins but an increase in the soluble fraction of ubiquitylated proteins during prophase I ( Fig 4F and S13 Fig ) . These results are partially in agreement with the observed increase in the ubiquitylation state of cultured spermatocytes treated with the proteasome inhibitor MG132 ( 18 ) , and suggest a specific function of the PSMA8-containing proteasomes in the controlled degradation of ubiquitylated proteins during spermatogenesis . The composition of the CP and its RPs has previously been established by mass-spectrometric analysis of crude preparation of proteasomes from whole testes [37] . To better understand the molecular mechanism underlying the mutant phenotype , we purified PSMA7/8-interacting proteins by single-step affinity chromatography ( see Material and methods for a detailed description ) . Most of the canonical subunits of the CP and RP were present within the more than 596 proteins of the PSMA8 proteome ( S5 Table , using a conservative cut-off , see methods ) . In agreement with previous results , among the two activators of the testis-specific proteasome detected ( PA200 and Pa28γ ) [5] , PA200 was the most abundant . In contrast to previous observations , we were unable to detect Pa28α and Pa28β or the inducible catalytic subunits of the immunoproteasome ( β1i , β2i and β5i ) [5] , suggesting a very low abundance or absence . We could not detect PA200 as an interacting protein of PSMA7/8 in testis extracts from Psma8-deficient testes ( S4 Table ) . Among the novel proteasome-interacting proteins ( PIPs ) detected were chaperones including CCT6b and CCT2 , ubiquitin ligases ( TRIP12 , NEDD4 , TRIM36 and RAD18 ) , and novel ubiquitin specific proteases ( USPs ) such as USP9X , USP34 , USP5 and USP47 ( S6 Table ) . We studied the proteins enriched in the immunoprecipitation through functional ( gene ontology , GO ) and pathway analysis ( KEGG ) . The top GO and KEGG results were related to the proteasome and to ribonucleoproteins . Pathway analysis showed links to spermatogenesis , cell cycle , and meiosis ( see S1 Text ) , in accordance with the observed mutant phenotype . Interestingly , we identified meiotic proteins a priori unrelated to the UPS such as DAZL ( deleted in azoospermia ) , SPAG1 ( Sperm-associated antigen 1 ) , SPATA5/20 ( Spermatogenesis-associated protein 5/20 ) , the tudor domain proteins TDRD1/6/9 , MAEL ( repressor of transposable elements ) , and RNF17 . These PIPs could represent proteins captured during ubiquitin-dependent targeted degradation [38] and/or proteins interacting via ubiquitin-independent proteasomal degradation , as has been shown for the related subunit α4/PSMA7 [39] . Altogether , the list of novel PIPs included novel potential readers , erasers and writers of the ubiquitin code [40] of the testis-specific proteasome , reflecting its complexity . Among these PIPs , we focused our attention on the following candidates for their role in chromosome segregation and synapsis: SYCP1 , TRIP13 , TEX30 , PIWIL1 , PIWIL2 and CDK1 ( S6 Table ) . Among the possible interactors , we first evaluated the transverse filament protein SYCP1 . Because Sycp1 mutant mice are infertile but otherwise healthy [41] , we analyzed the interaction of SYCP1 with PSMA8 and its localization in mutant meiosis . We co-transfected Sycp1 with Psma8 in HEK293T cells and we detected co-immunoprecipitation between SYCP1 and PSMA8 ( Fig 5A ) . Despite the observation that SYCP1 is properly loaded to the SC and removed from desynapsed regions ( S6 Fig ) , we observed an abnormal accumulation of SYCP1 in Psma8-deficient metaphase I cells , ( Fig 5B ) . These results suggest defective degradation of SYCP1 with very likely detrimental functional consequences in the exit of meiosis . We next extended the validation analysis of the remaining candidate interactors by co-immunoprecipitation with PSMA8 , making use of the same heterologous system of HEK293T cells . These included TEX30 , PIWIL1 , PIWIL2 , CDK1 and TRIP13 . All protein-protein interaction assays carried out were negative ( S14A Fig ) with the exceptions of the cyclin dependent kinase CDK1 and the AAA-ATPase TRIP13 ( AAA-ATPases associated with diverse cellular activities; see Figs 6A and 7A ) . Because of the relevance of CDK1 in metaphase transition , we first determined the expression levels of CDK1 by immunofluorescence . The results showed that more CDK1 but not the related kinase CDK2 [42] could be detected in the centromeres of metaphase I chromosome from mutant cells ( Fig 6B and S15A Fig; KO 0 . 31±0 . 2 vs 0 . 19±0 . 1 WT; an increase of ~ 40% ) . To determine whether the increased level of CDK1 corresponded to its active or inactive phosphorylated form , we used an antibody against CDK1-Tyr15-p ( inactive form , Fig 6C ) . The results showed no differences in the labeling at the centromeres of the metaphase I chromosomes , and therefore a decrease in phospho-CDK1/total CDK1 ratio in mutant cells . Given that CDK1 must be complexed with cyclin B1 to be active , we reasoned that if higher levels of active CDK1 are present , cyclin B1 would be similarly increased . Results showed an increase of cyclin B1 at the centromeres of metaphase I chromosomes ( Fig 6D ) . This result was congruent with the increased amount of CDK1 and CyclinB1 observed by western blot and in squashed seminiferous tubules ( Fig 6E and S15B and S15C Fig ) . Overall , these findings suggest that loss of PSMA8 causes an increase of CDK1 / CyclinB1 which would cooperate in the accumulation of metaphase I / metaphase II that ultimately results in apoptotic metaphase plates . We also analyzed the distribution of TRIP13 , a pleiotropic ATPase that participates in meiotic DNA repair and chromosome synapsis through HORMAD interaction and somatic spindle assembly checkpoint ( SAC ) proficiency through MAD2 interaction [43–46] . We first performed immunofluorescence analysis of TRIP13 in Psma8-deficient and WT spermatocytes . Results using two independent antibodies showed robust labeling of the telomeres from zygonema ( two dots ) to pachynema ( fused to a single dot ) in WT cells , which declined from diplonema to diakinesis . The staining pattern was similar but enhanced in mutant spermatocytes ( Fig 7B ) . However , the staining pattern of TRIP13 at metaphase I differed between WT and mutant cells . Specifically , it was detected at the kinetochores of Psma8-/- spermatocytes but was absent in WT cells ( Fig 7B ) . This labeling pattern at the metaphase I kinetochores resembles TRIP13 staining in somatic cells [47] . These results thus suggest that TRIP13 accumulates in the absence of a functional PSMA8-containing proteasome . We next analyzed several downstream effectors of TRIP13 , HORMAD1 , HORMAD2 , and the mitotic checkpoint protein MAD2 [48–50] . No differences were observed in the HORMAD1/2 labeling pattern between WT and mutant cells ( S16 Fig ) . It has been shown in C . elegans that in the absence of TRIP13 , MAD2 recruitment to kinetochores is delayed and that in addition to its role in checkpoint silencing , TRIP13 also contributes to spindle checkpoint activation [50] . It could thus be argued that an excess of TRIP13 would increase MAD2 loading to kinetochores thereby delaying mitotic exit . We confirmed this prediction and found that MAD2 expression at the kinetochores was enhanced in Psma8-/- spermatocytes ( Fig 7C ) , further validating a functional consequence of TRIP13 accumulation at the kinetochores . In order to validate the substrate specificity of the PSMA8-containing proteasome in protein degradation , we analyzed the expression levels of the separase inhibitor securin ( PTTG1 ) , a well-known substrate of the somatic proteasome . Immunofluorescence analysis showed similar levels of PTTG1 in Psma8-/- and WT spermatocytes ( S17 Fig ) . This result suggests that PSMA8-containing proteasomes are not involved in the degradation of classical ubiquitylated substrates degraded by the somatic proteasome . To investigate the molecular basis of PSMA8 localization in the SC , and considering the alteration of SYCP3 and SYCP1 in Psma8-/- spermatocytes ( Fig 3F and Fig 5B ) , we used a candidate gene approach to identify additional putative interactors of PSMA8 . We co-transfected Psma8 with cDNAs encoding each of the known central element proteins ( SIX6OS1 , SYCE1 , SYCE2 , SYCE3 , and TEX12 ) , and the AE protein SYCP3 . As positive controls , we exploited the well-known interaction between SYCE2 and TEX12 [51] ( S14C Fig ) . Surprisingly , we detected specific co-immunoprecipitation of PSMA8 with SIX6OS1 and SYCE3 ( Fig 8A and S14B Fig ) . We were unable to immunoprecipitate transfected SYCP3 ( using several tags or antibodies against SYCP3 ) , likely due to the highly complex structures of transfected SYCP3 , which prevented to perform co-immunoprecipitation experiments . Because SYCP3 forms filamentous structures in the cytoplasm of transfected cells , termed polycomplexes [52] , co-expression of an interacting protein with SYCP3 may lead to its recruitment to polycomplexes [24] , an indication of protein interaction . Indeed , we obtained self assembled higher structures when Psma8 was co-transfected with Sycp3 ( Fig 8B ) . This SYCP3-dependent cytological interaction was not observed when Psma7 was co-transfected ( Fig 8B ) , further validating the specificity of the interaction given the extensive protein similarity between both PSMA8 and PSMA7 ( 92% ) . To validate this interaction in vivo , we performed a detailed analysis of SYCP3 in mouse mutant squashed spermatocytes , a procedure in which no solubilization or protein extraction is performed . We observed SYCP3 aggregates/polycomplexes in the Psma8-deficient spermatocytes during prophase I and metaphase I / II ( Fig 8C and 8D and S7 Table ) . SYCP3 accumulated in metaphase II chromosomes as abnormal SYCP3 labeling at the centromeres between sister kinetochores and as aggregates in the cytosol ( Fig 3F and Fig 8D ) . Global accumulation of SYCP3 was also observed by western blot of whole testis under high denaturing conditions ( Fig 8E ) [53] . Interestingly , it has been previously shown that cultured spermatocytes chemically treated with the proteasome inhibitor MG132 form SYCP3 aggregates [17] . Overall , our results suggest that SYCP3 is targeted for degradation by the PSMA8-containing proteasome and that in the absence of PSMA8 its accumulation could mediate , at least in part , the arrest and apoptosis of spermatocytes .
The testis-specific proteasome is one of the three tissue-specific proteasomes identified in mammals ( together with the immunoproteasome and the thymoproteasome ) ; however , little is known about its biochemical and physiological function . The groundbreaking work of Xiao-Bo Qiu and colleagues showing the acetyl-histone preference of the PA200 subunit of the proteasome [5] has provided novel insights into the proteasome-dependent degradation of non-ubiquitylated proteins and led to the designation of spermatoproteasome to the PA200-containing proteasome . However , following the criteria employed for the designation of the thymoproteasome , which were devised based on the restricted expression of its β5t subunit in the thymus [9] ( GTEx portal ) , we suggest that the term spermatoproteasome be restricted exclusively to the PSMA8-containing proteasome instead of the widely expressed PA200 subunit [5] . We have shown that genetic depletion of Psma8 causes the delocalization and the drastic decrease ( loss of detection ) of the proteasome activator PA200 in spermatocytes . Accordingly , Psma8-deficient spermatocytes accumulate acetylated histones . PSMA8 deficiency is comparatively more severe than that of the PA200 single mutant ( subfertile ) and of the PA200 and PA28γ double mutant , which do not show an arrest in spermatogenesis despite being infertile in vivo but not in vitro ( spermatozoa are not motile but can fertilize in vitro [54] ) . From a genetic analysis perspective , this result would suggest that PSMA8 has additional functions that are independent of the activators PA200 and PA28γ . Our proteomic analysis , together with other data [10] , supports this notion and indicates that PSMA8-containing proteasomes can be associated with other regulators such as the 19S subunit , expanding its targets . Beyond its role in initiation of histone replacement [34] , H4K16ac is involved in the three waves of H2AX phosphorylation during prophase I [55] . We have shown that Psma8 deficiency causes the accumulation of H4ac and H4K16ac during prophase I . However , we did not observe defects in this process in the form of a different staining pattern for γ-H2AX ( leptonema and zygonema ) , including the expansion of γ-H2AX staining to the chromatin of the sex body ( in pachynema ) . However , the observed premature accumulation of H4K16ac at early round spermatid might cause a defect in histone removal later on in spermiogenesis if the Psma8-/- mutants spermatids would not have entered apoptosis before this event . We have shown that spermatoproteasome deficiency causes severe defects in protein turnover of key meiotic players that affect metaphase I/II exit , but not the complex process of meiotic recombination that occurs during prophase I ( CO ) . By using a candidate approach of PIPs , we have identified CDK1 and TRIP13 as likely crucial proteins that have an abnormal expression pattern during meiotic metaphase in mutant mice . Given the key roles of these proteins in all aspects of mitotic/meiotic division ( including SAC activation ) , the accumulation of aberrant metaphase I/II spermatocytes in Psma8-deficient mice is to be expected . The role of CDK1 in the metaphase-anaphase transition is complex and is multifaceted . CDK1 inhibits and activates APC/C by promoting the SAC and also by a SAC-independent mechanism [56] . The balance between these opposing functions determines cyclin B1 destruction and separase activation , giving rise to cohesin cleavage and anaphase onset [57] . Based on the normal expression levels of PTTG1 in Psma8-/- metaphase I cells , it can be argued that there is no precocious APC activation in Psma8-deficient cells ( S17 Fig ) . Given that CDK1 activation of the SAC is dominant over the activation of APCCdc20 [58] in oocytes , we suggest that the former effect is acting on Psma8-deficient spermatocytes . The question how CDK1 promotes the SAC is still unresolved in oocytes and even less is known about this in spermatocytes Another group of proteins found to be deregulated in spermatoproteasome-deficient mice are the SC structural proteins SYCP1 and SYCP3 . The precise effect of the accumulated SYCP1 in the cytoplasm of Psma8-/- spermatocytes cannot be experimentally analyzed . However , the coiled-coil structure and self-assemblance abilities of SYCP1 strongly suggest a functionally detrimental consequence . Similarly , the presence of SYCP3 aggregates during pachynema and metaphase I mutant spermatocytes and its persistence at metaphase II centromeres , where SYCP3 is barely visible in WT cells , also suggest a detrimental effect on these cells causing their entrance into apoptosis . We have also shown that PSMA8 is delocalized in the severe synapsis Six6os1 mutant , which is consistent with the observed co-immunoprecipitation of PSMA8 with SYCP1 , SIX6OS1 and SYCE3 . All the synapsis-less mutants of CE proteins failed to load properly or lacked SYCP1 and the remaining CE proteins [24 , 59–61] . Thus , we would predict delocalization of the spermatoproteasome from the SC in the remaining mouse mutants of the CE proteins . Overall , our results support the idea of a physical anchorage or recruitment of the spermatoproteasome to the SC especially through SYCP3 , possibly facilitated or mediated by SYCP1 , SIX6OS1 and SYCE3 as their most relevant structural partners . Supporting this notion , the Zip1 transverse filament protein of the yeast SC participates in the recruitment of the proteasome to the SC [22] , suggesting an evolutionary conservation of the mechanism . Yeast mutated for a nonessential subunit of the proteasome ( pre9 ) showed abnormal meiotic recombination , pairing and synapsis [22] . Similar but milder defects were also observed in spermatocytes cultured with a proteasome inhibitor [17] . It has been proposed that the UPS regulates the proteostatic turnover of the ZMM which is required for efficient synapsis and CO [17] , through the RNF212 ( E3 sumo ligase ) -Hei10 ( E3 ubiquitin ligase ) pathway [31] . Given this , the lack of a meiotic recombination phenotype ( DSBs are generated and repaired and COs are generated normally ) in our Psma8-deficient mouse is surprising . It can be argued that PSMA7-containing proteasomes are still present and at the early stages of meiosis are compensating for the loss of function of Psma8 . Another possible but not mutually exclusive explanation is that the main targets of the PSMA8-containing proteasome are proteins from mid-prophase I onwards . The spermatoproteasome through its complex interactome would serve as a hub for the fine tuning of several fundamental key molecules of the spermatogenic process such as those analyzed during the present work ( SYCP1 , SYCP3 , TRIP13 , CDK1 and acetyl-histones ) . Our data suggest that deregulation of proteostasis of key meiotic proteins promoting cell division leads to the presence of multipolar spindles and aberrant meiotic exit . Thus , we favor an explanation in which the joint contribution of several pathways is responsible for the observed infertility . In relation to human disease , protein degradation was one of the top cellular functions found in an unbiased differential proteomic profiling of spermatozoa proteins from infertile men with a varicocele [62] . More specifically , PSMA8 is among the top 7 in this list of proteins that are differentially expressed , suggesting a causal role in the severity of the disease . From an organismal perspective , Psma8 transcription is mainly restricted to the human testis and to some tumors like Burkit lymphoma and melanoma ( TCGC database ) . Altogether , and considering the PSMA8 dependency of the mouse male germline , we suggest that the spermatoproteasome may be an effective target for male contraception and for the treatment of some human malignancies .
Testes were freed from the abdominal cavity and 10 μl of DNA solution ( 50 μg ) mixed with 1μl of 10×FastGreen ( Sigma Aldrich F7258 ) was injected into the rete testis with a DNA embryo microinjection tip . After a period of 1 h following the injection , testes were held between electrodes and four electric pulses were applied ( 35 V for 50 ms each pulse ) using a CUY21 BEX electroporator . Psma8-sgRNAs G71 5’- GGGCATACT CCACTTGGAAA -3’ G84 5’-ACCGCGGTAAGCTGCTCCCC-3’ targeting exon 1 and intron 1 were predicted at crispr . mit . edu . Psma8-sgRNAs were produced by cloning annealed complementary oligos at the BbsI site of pX330 ( #42230 , Addgene ) , generating PCR products containing a T7 promoter sequence that were purified ( NZYtech ) , and then in vitro transcribed with the MEGAshortscrip T7 Transcription Kit ( Life Technologies ) . The plasmid pST1374-NLS-flag-linker-Cas9 ( #44758; Addgene ) was used for generating Cas9 mRNA . After linearization with AgeI , it was transcribed and capped with the mMESSAGE mMACHINE T7 Transcription Kit ( AM1345; Life Technologies ) . RNAs were purified using the RNeasy Mini Kit ( Qiagen ) . RNAs ( 100 ng/μl Cas9 and 50ng/μl each guide RNA ) were microinjected into B6/CBA F2 zygotes ( hybrids between strains C57BL/6J and CBA/J ) [63] at the Transgenic Facility of the University of Salamanca . Edited founders were identified by PCR amplification ( Taq polymerase , NZYtech ) with primers flanking exons 1 and intron 1 ( Primer F 5`-CTTCTCGGTATGACAGGGCAATC-3’ and R 5’- ACTCTACCTCCACTGCCAAC CTG-3’ ) and either direct sequenced or subcloned into pBlueScript ( Stratagene ) followed by Sanger sequencing . The predicted best null mutation was selected by PCR sequencing of the targeted region of Psma8 ( S3B Fig ) . The selected mutant allele was 166 bp long versus 222bp of the wild-type . The founder was crossed with wild-type C57BL/6J to eliminate possible unwanted off-targets . Psma8+/- heterozygous mice were re-sequenced and crossed to give rise to Psma8-/- homozygous . Genotyping was performed by analysis of the PCR products of genomic DNA with primers F and R . Mouse mutants for Rec8 and Six6os1 have been previously developed [24 , 25] . For histological analysis of adult testes , mice were perfused and their testes were processed into serial paraffin sections and stained with hematoxylin-eosin or were fixed in Bouin´s fixative and stained with Periodic acid–Schiff ( PAS ) and hematoxylin . Slides were visualized at room temperature using a microscope ( Axioplan 2; Carl Zeiss , Inc . ) with 63 × objectives with an aperture of 1 . 4 ( Carl Zeiss , Inc . ) . Images were taken with a digital camera ( ORCA-ER; Hamamatsu ) and processed with OPENLAB 4 . 0 . 3 and Photoshop ( Adobe ) . Quantification of fluorescence signals was performed using Image J software . Squashed preparations were visualized with a Delta vision microscopy station . Stimulated emission depletion ( STED ) microscopy ( SP8 , Leica ) was used to generate the super-resolution images . Secondary antibodies for STED imaging were conjugated to Alexa 555 and 488 ( Invitrogen ) . Slides were mounted in Prolong Antifade Gold without DAPI . Testes were detunicated and processed for spreading using a conventional "dry-down" technique or squashing [64] . Antibody against the C-term of PSMA8 was a gift from Dr . Murata ( Univ of Tokyo , Japan ) and has been previously described [10] . Rabbit polyclonal antibodies against PSMA8 were developed by Proteintech ( R1 and R2 ) against a fusion protein of poly-His with full length PSMA8 ( pET vector ) of mouse origin ( see S1 Fig for validation ) and was used to validate the immunofluorescence and western results . The primary antibodies used for immunofluorescence were rabbit αSYCP1 IgG ab15090 ( 1:200 ) ( Abcam ) , rabbit anti-γH2AX ( ser139 ) IgG #07–164 ( 1:200 ) ( Millipore ) , ACA or purified human α-centromere proteins IgG 15–235 ( 1:5 , Antibodies Incorporated ) , mouse αMLH1 51-1327GR ( 1:5 , BD Biosciences ) , mouse αSYCP3 IgG sc-74569 ( 1:100 ) , rabbit αRAD51 PC130 ( 1:50 , Calbiochem ) , Mouse αCDK1 sc-54 ( 1:20 IF; 1:1000 wb , Santa Cruz ) , rabbit αCDK1 Tyr15p #4539 ( 1:10 , Cell Signaling ) , rabbit αCDK2 sc-6248 ( 1:20 , Santa Cruz ) , rabbit αPTTG1 serum K783 ( 1:20 IF , 1:1000 wb ) , rabbit αTRIP13 19602-1-AP ( 1:20 , Proteintech ) , rabbit αH2AL2 ( 1:100 , from Dr . Saadi Khochbin ) , rabbit αPA200 ( 1:20 , Bethyl A303-880A ) , rabbit α-Caspase3 #9661 ( 1:30 , Cell Signaling ) , rabbit αH2AK5ac ab45152 ( 1:20 , Abcam ) , Rabbit αH4K16ac #07–329 ( 1:50 Millipore ) , Rabbit αH3ac ( K9 and K14 ) #06–599 ( 1:20 , Millipore ) , Rabbit αH4ac ( K5 , K8 , K12 and K16 ) #06–598 ( 1:20 , Millipore ) , Mouse αUbiquitin 11023 ( 1:20 IF , 1:1000 wb , QED Bioscience ) , Rabbit αHORMAD1 and αHORMAD2 and chicken anti SYCP1 ( 1:50 , from Dr . Attila Toth; [65] ) , Rabbit anti p-ser10-H3 06–570 ( 1:100 , Millipore ) , Mouse anti α-tubulin T9026 ( 1:100 , Sigma ) , Rabbit αCyclin B1 ab72 ( 1:20 , Abcam ) , Rabbit αMAD2 ( 1:30 provided by Dr . Stemmann ) , Peanut agglutinin lectin L7381 ( 15μg/ml , Sigma ) , SMC6 ab18039 ( 1:50 , Abcam ) , Human αVASA 560189 ( 1:100 , BD ) , Rabbit αINCENP 1186 ( 1:50 , provided by Dr . Earnshaw ) . TUNEL staining of chromosome spreads was performed with the in situ cell death detection kit ( Roche ) . Psma8+/+ and Psma8−/− testicular cells preparation and measurement of their DNA content were performed by a standard procedure [66] . Briefly , the testes were detunicated and the seminiferous tubules were kept in 5 ml of ice-cold separation medium ( DMEM supplemented with 10% FCS , 0 . 1 mM NEAA , 1 . 5 mM sodium pyruvate , 4 mM L-glutamine and 75 μg/ml ampicillin ) . They were treated with 0 . 1 mg/ml collagenase at 37°C for 10 min under mild shaking . The sedimented seminiferous tubules were washed twice with separation medium and treated for 2 min at 37°C with 2 . 5 μg/ml trypsin and 1 U/ml DNAse I in separation medium and transferred to ice . Afterwards , single cells were extracted from the seminiferous cords with a Pasteur pipette and filtered through a 40 μm nylon mesh . The cell suspension ( 2 × 106 cells/ml ) was diluted 1:1 with a solution containing 0 . 05 mg/ml propidium iodide and 0 . 1 mg/ml RNAse for 15 min . Finally , the cells were analyzed through flow cytometry in a cytometer FACSCalibur and the BD Cell-Quest software . The cell cycle distribution was analyzed with the Kaluza Analysis software ( Beckman Coulter ) . The 26S proteasome assay was carried out in a total volume of 250 μl in 96 well plates with 2 mM ATP in 26S buffer using 100 μg of protein supernatants from whole extracts of mouse testis . Fluorescently labeled substrates employed were: succinyl-Leu-Leu-Val-Tyr-7-amino-4-methylcoumarin ( Suc-LLVY-AMC ) , Z-Ala-Arg-Arg-AMC ( Z-ARR-AMC , Bachem ) , and Z-Leu-Leu-Glu-AMC ( Z-LLE-AMC ) for the detection of the chymotrypsin- ( β5 catalytic subunit ) , trypsin- ( β2 catalytic subunit ) and caspase- ( β1 catalytic ) like activity measurements respectively . The final substrate concentration in each assay was 100 μM . The HEK293T , GC1-spg , Leydig TM3 , and Sertoli TM4 cell lines were directly purchased at the ATCC and cultured in standard cell media . HEK293T cell line was transfected with Lipofectamine ( Invitrogen ) or Jetpei ( PolyPlus ) . Cell lines were tested for mycoplasma contamination ( Mycoplasma PCR ELISA , Sigma ) . Full-length cDNAs encoding PSMA8 , PSMA7 , CDK1 , SYCP1 and SIX6OS1 , SYCP3 , SYCE2 , TEX12 , TEX30 , PIWIL1 and PIWIL2 were RT-PCR amplified from murine testis RNA . Full-length cDNAs were cloned into the EcoRV pcDNA3-2XFlag or SmaI pEGFP-C1 expression vectors under the CMV promoter . In frame cloning was verified by Sanger sequencing . 200 μg of antibody R1 and R2 were bound to 100 μl of sepharose beads slurry ( GE Healthcare ) . Testis extracts were prepared in 50mM Tris HCl ( pH8 ) , 500mM NaCl , 1mM EDTA 1% tritonX-100 . 20 mg of proteins extracts were incubated o/n with the Sepharose beads . Protein-bound beads were packed into columns and washed in extracting buffer for three times . Protein were eluted in 100 mM glycine pH3 . The whole immunoprecipitation of PSMA8 was performed in a buffer lacking ATP and glycerol to increase the stringency of the interactors and regulators/activators subunits . HEK293T cells were transiently transfected and whole cell extracts were prepared and cleared with protein G Sepharose beads ( GE Healthcare ) for 1 h . The antibody was added for 2 h and immunocomplexes were isolated by adsorption to protein G-Sepharose beads o/n . After washing , the proteins were eluted from the beads with 2xSDS gel-loading buffer 100mM Tris-Hcl ( pH 7 ) , 4% SDS , 0 . 2% bromophenol blue , 200mM β-mercaptoethanol and 20% glycerol , and loaded onto reducing polyacrylamide SDS gels . The proteins were detected by western blotting with the indicated antibodies . Immunoprecipitations were performed using mouse αFlag IgG ( 5μg; F1804 , Sigma-Aldrich ) , mouse αGFP IgG ( 4 μg; CSB-MA000051M0m , Cusabio ) , rabbit αMyc Tag IgG ( 4μg; #06–549 , Millipore ) , mouse αHA . 11 IgG MMS- ( 5μL , aprox . 10μg/1mg prot; 101R , Covance ) , ChromPure mouse IgG ( 5μg/1mg prot; 015-000-003 ) , ChomPure rabbit IgG ( 5μg/1mg prot . ; 011-000-003 , Jackson ImmunoResearch ) , ChomPure goat IgG ( 5μg/1mg prot . ; 005-000-003 , Jackson ImmunoResearch ) . Primary antibodies used for western blotting were rabbit αFlag IgG ( 1:2000; F7425 Sigma-Aldrich ) , goat αGFP IgG ( sc-5385 , Santa Cruz ) ( 1:3000 ) , rabbit αHA IgG ( H6908 , Sigma-Aldrich ) ( 1:1 . 000 ) , mouse αMyc obtained from hybridoma cell myc-1-9E10 . 2 ATCC ( 1:5 ) . Secondary horseradish peroxidase-conjugated α-mouse ( 715-035-150 , Jackson ImmunoResearch ) , α-rabbit ( 711-035-152 , Jackson ImmunoResearch ) , or α-goat ( 705-035-147 , Jackson ImmunoResearch ) antibodies were used at 1:5000 dilution . Antibodies were detected by using Immobilon Western Chemiluminescent HRP Substrate from Millipore . Protein extracts for the analysis of SYCP3 , CDK1 and CyclinB1 were extracted in Tris-HCl 250mM , SDS10% , Glycerol 50% ( denaturing buffer ) . Raw MS data were analized using MaxQuant ( v . 1 . 5 . 7 . 4 ) and Perseus ( v . 1 . 5 . 6 . 0 ) programmes 71 . Searches were generated versus the Mus musculus proteome ( UP000000589 , May 2017 release ) and Maxquant contaminants . All FDRs were of 1% . Variable modifications taken into account were oxidation of M , acetylation of the N-term and ubiquitylation remnants di-Gly and LRGG , while fixed modifications included considered only carbamidomethylation of C . The maximum number of modifications allowed per peptide was 5 . For the case of the protein group of CDK1 to 3 , experimental results showed that the protein detected was CDK1 . For the PSMA8 antibodies R1 and R2 , ratios of their respective iBAQ intensity versus the correspondent iBAQ intensity in the control sample were calculated . Proteins with ratio higher or equal to 5 and two or more unique peptides for at least one RP antibody were selected for ulterior analysis . Additionally , in order to avoid filtering rare proteins , those with at least one unique peptide and one peptide for both Rabbit antibodies ( R1 and R2 ) and none for anti-IgG were also selected for further analysis . GO and KEGG over-representation tests were performed using the R package clusterProfiler [67] using standard parameters except for a FDR cutoff of 0 . 01 . KEGG pathways where some key genes ( TRIP13 , CDK1 , SYCP1 , DDX4 , SYCP3 , SYCE3 , SIX6OS1 ) operate and the role of the co-immunoprecipitated proteins were studied using the R package pathview [68] . In order to compare counts between genotypes at different stages , we used the Welch´s t-test ( unequal variances t-test ) , which was appropriate as the count data were not highly skewed ( i . e . , were reasonably approximated by a normal distribution ) and in most cases showed unequal variance . We applied a two-sided test in all the cases . Asterisks denote statistical significance: *p-value <0 . 01 , **p-value <0 . 001 and ***p-value<0 . 0001 . Mice were housed in a temperature-controlled facility ( specific pathogen free , spf ) using individually ventilated cages , standard diet and a 12 h light/dark cycle , according to EU laws at the “Servicio de Experimentación Animal , SEA” . Mouse protocols were approved by the Ethics Committee for Animal Experimentation of the University of Salamanca ( USAL ) . We made every effort to minimize suffering and to improve animal welfare . Blinded experiments were not possible since the phenotype was obvious between wild type and Psma8-deficient mouse for all of the experimental procedures used . No randomization methods were applied since the animals were not divided in groups/treatments . The minimum size used for each analysis was two animals/genotype . | Proteins within the cells that are unnecessary or damaged are degraded by a large protein complex named the proteasome . The proteins to be degraded are marked by a small protein called ubiquitin . The addition of a small modification ( acetyl group ) to some proteins also promotes their degradation by the proteasome . Proteasomal degradation of proteins is an essential mechanism for many developmental programs including gametogenesis , a process whereby a diploid cell produces a haploid cell or gamete ( sperm or egg ) . The mechanism by which this genome reduction occurs is called meiosis . Here , we report the study of a protein , named PSMA8 that is specific for the testis proteasome in vertebrates . Using the mouse as a model , we show that loss of PSMA8 leads to infertility in males . By co-immunoprecipitation-coupled mass spectroscopy we identified a large list of novel PSMA8 interacting proteins . We focused our functional analysis on several key meiotic proteins which were accumulated such as SYCP3 , SYCP1 , CDK1 and TRIP13 in addition to the known substrate of the spermatoproteasome , the acetylated histones . We suggest that the altered accumulation of these important proteins causes a disequilibrium of the meiotic division that produces apoptotic spermatocytes in metaphase I and II and also early spermatids that die soon after reaching this stage . | [
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] | 2019 | The PSMA8 subunit of the spermatoproteasome is essential for proper meiotic exit and mouse fertility |
Inflammasome is an intracellular protein complex that serves as cytosolic pattern recognition receptor ( PRR ) to engage with pathogens and to process cytokines of the interleukin-1 ( IL-1 ) family into bioactive molecules . It has been established that interleukin-1β ( IL-1β ) is important to host defense against Histoplasma capsulatum infection . However , the detailed mechanism of how H . capsulatum induces inflammasome activation leading to IL-1β production has not been studied . Here , we showed in dendritic cells ( DCs ) that H . capsulatum triggers caspase-1 activation and IL-1β production through NLRP3 inflammasome . By reciprocal blocking of Dectin-1 or Dectin-2 in single receptor-deficient DCs and cells from Clec4n-/- , Clec7a-/- , and Clec7a-/-Clec4n-/- mice , we discovered that while Dectin-2 operates as a primary receptor , Dectin-1 serves as a secondary one for NLRP3 inflammasome . In addition , both receptors trigger Syk-JNK signal pathway to activate signal 1 ( pro-IL-1β synthesis ) and signal 2 ( activation of caspase-1 ) . Results of pulmonary infection with H . capsulatum showed that CD103+ DCs are one of the major producers of IL-1β and Dectin-2 and Dectin-1 double deficiency abolishes their IL-1β response to the fungus . While K+ efflux and cathepsin B ( but not ROS ) function as signal 2 , viable but not heat-killed H . capsulatum triggers profound lysosomal rupture leading to cathepsin B release . Interestingly , cathepsin B release is regulated by ERK/JNK downstream of Dectin-2 and Dectin-1 . Our study demonstrates for the first time the unique roles of Dectin-2 and Dectin-1 in triggering Syk-JNK to activate signal 1 and 2 for H . capsulatum-induced NLRP3 inflammasome activation .
Inflammasome is a large intracellular multimeric protein platform which is activated upon infection or stress [1] . The function of inflammasome is to drive the maturation of proinflammatory cytokines of the IL-1 family , most importantly IL-1β and IL-18 and induction of inflammatory cell death [2] . Among all identified inflammasome complexes , NLRP3 inflammasome is well-characterized . It is generally accepted that NLRP3-driven processing and secretion of IL-1β and IL-18 in macrophage and DC require two signals [3] . Signal 1 is induced by engagement of pathogen-associated molecular patterns ( PAMPs ) with pattern recognition receptors ( PRRs ) leading to gene transcription and synthesis of NLRP3 , inactive pro-IL-1β and pro-IL-18 [4] . Signal 2 induces the assembly of inflammasome complex and activates caspase-1 to facilitate pro-IL-1β and pro-IL-18 cleavage into their mature forms , and is induced by intracellular events including reactive oxygen species ( ROS ) production , potassium ( K+ ) efflux , cathepsin B release , calcium influx and mitochondrial destabilization [5–9] . There are multiple PAMPs on a single fungal pathogen . It is of interest to determine the complex interaction between a fungus and the host cell and how the interaction triggers either signal 1 or 2 or both for inflammasome activation . Histoplasma capsulatum is a dimorphic fungal pathogen . The microconidia and mycelial fragments of H . capsulatum spread in the air and infect humans through inhalation [10 , 11] . H . capsulatum stimulates mouse dendritic cell ( DC ) to secrete pro-inflammatory cytokines such as IL-1β , IL-18 , TNF and IL-6 [12] . Human DC phagocytoses H . capsulatum yeasts through fibronectin receptor VLA-5 and kills the organism via phagolysosomal fusion [13 , 14] . A recent study showed that CD103+ conventional DC in the lungs produces IFN-I to restrict the growth of H . capsulatum during pulmonary infection [15] . These studies point to a crucial role of DC in secreting cytokines and killing H . capsulatum during early phase of infection [13–15] . There is still much to be learned about the detailed mechanisms of cytokine production by DC through interaction with H . capsulatum . The fungal pathogens Candida albicans , Aspergillus fumigatus , Cryptococcus neoformans , Microsporum canis and Malassezia spp . induce inflammasome activation [16–21] . In a systemic C . albicans infection model , NLRP3 or caspase-1 deficiency leads to increased fungal burdens and higher mortality [16] . In protection against mucosal candidiasis , NLRC4 functions at the level of mucosal stroma and NLRP3 at both the hematopoietic and stromal compartments [21] . AIM2 and NLRP3 are both required for mice that are treated with immunosuppressive agents to confine A . fumigatus in inflammatory foci after intranasal inoculation with conidia [18] . In pulmonary infection with acapsular form of C . neoformans , NLRP3 inflammasome activation results in immune cell infiltration and effective fungal clearance [17] . While elevated IL-1β in the lungs of mice infected with H . capsulatum plays a critical role in host defense against H . capsulatum [22] , it has never been determined which mechanisms are involved in inflammasome activation and IL-1β production . Hyphal forms of C . albicans recognized by either TLR-2 or Dectin-1 on bone marrow-derived macrophage triggers the synthesis of pro-IL-1β through Syk kinase [23] . In addition to triggering pro-IL-1β accumulation , Syk signaling is also involved in ROS production and caspase-1 activity in bone marrow-derived dendritic cells ( BMDCs ) stimulated by C . albicans [16] . Interestingly , in contrast to stimulation by hyphal forms , stimulation of BMDC by yeast form of C . albicans for IL-1β production is mediated by Dectin-2 through MAPKs signaling [24 , 25] . Both CR3 and Dectin-1 are involved in macrophage TNF and IL-6 response to H . capsulatum through activation of Syk-JNK-AP-1 pathway [26] . In studying vaccine immunity , Wang et al . reported that Dectin-1 and Dectin-2 fusion proteins separately bind to H . capsulatum and that CARD9 signaling is important for development of Th17 cells and adaptive immunity against H . capsulatum [27] . In this study , we demonstrated that H . capsulatum induced NLRP3 inflammasome for IL-1β production in BMDCs . Dectin-2 was the primary receptor that mediated both signal 1 and 2 for NLRP3 inflammasome . Results of reciprocal blocking of Dectin-1 or Dectin-2 in Dectin-2- and Dectin-1-deficient cells and that of using single- and double-deficient cells showed that Dectin-1 played a role as a secondary receptor . Both Dectin-2 and Dectin-1 activated the Syk/JNK signaling pathway but the role of Dectin-1 was less prominent than Dectin-2 . Pulmonary infection results showed that CD103+ DCs are one of the major sources of IL-1β , and Dectin-2 and Dectin-1 together mediated the IL-1β response of CD103+ DC to H . capsulatum infection . Both K+ efflux and cathepsin B release but not ROS functioned as signal 2 for H . capsulatum-induced NLRP3 inflammasome . While Dectin-2 did not affect K+ efflux , signals from Dectin-1 and Dectin-2 cooperatively regulated cathepsin B release . Our work revealed the roles of Dectin-2 and Dectin-1 in inducing signals for activation of NLRP3 inflammasome during fungal infection .
Inflammasome activation is comprised of two signals: the first involves an increase in the transcription and expression of certain inflammasome components , and the second is comprised of the assembly of inflammasome protein components resulting in pro-caspase-1 activation to caspase-1 . Inflammasome is reported to be associated with and acts as a defense mechanism against fungal infections [28] . Here , we sought to investigate whether H . capsulatum triggers inflammasome activation in DC . BMDCs were stimulated with viable H . capsulatum yeast cells at different yeast-to-cell ( MOI ) ratios . IL-1β and caspase-1 p20 were detected in culture supernatants at a MOI as low as 1 and their levels increased with increasing MOI ( Fig 1A and 1B ) . IL-1β production was detectable at as early as 6 h , peaked at 12 h and maintained at the peak level until 24 h after stimulation ( Fig 1C ) . Treatment with caspase-1 inhibitor ( Z-YVAD-FMK ) significantly reduced IL-1β production in a dose-dependent manner ( Fig 1D ) . NLRP3 deficiency completely abolished IL-1β production in both BMDCs and MHCII+CD11c+ splenic DCs while it did not affect the production of TNF ( Fig 1E and 1F and S1 Fig ) . Western blotting analysis showed that H . capsulatum caused increased expression of pro-IL-1β and NLRP3 . While the absence of NLRP3 did not affect pro-IL-1β and pro-caspase-1 p45 , the secretion of IL-1β p17 and caspase-1 p20 was completely abrogated in Nlrp3-/- cells ( Fig 1G ) . A protein-crosslinking experiment showed that H . capsulatum induced robust NLRP3-dependent ASC oligomerization , which reflected inflammasome assembly ( Fig 1H ) . Nlrp3-/- mice had greater fungal burden ( Fig 1I ) and poorer survival ( Fig 1J ) compared to wild type mice after intravenous infection with H . capsulatum . It appears that higher fungal burden caused by NLRP3 deficiency may be one of the contributing factors to poor survival . In addition , splenic DCs from Nlrp3-/- infected-mice produced lower IL-1β compared with cells from infected wild type mice ( Fig 1K ) . These results together show that H . capsulatum induces IL-1β response through activation of both signal 1 and signal 2 of an NLRP3-dependent inflammasome in DCs and that NLRP3 is important to protection against histoplasmosis . We then used blocking antibodies to explore surface receptor ( s ) that is involved in inflammasome activation by H . capsulatum . Results show that BMDCs treated with anti-Dectin-2 , but not anti-CR3 , -Dectin-1 or -TLR-2 blocking antibody significantly reduced H . capsulatum-induced pro-IL-1β p35 , IL-1β p17 and caspase-1 p20 expressions ( Fig 2A and 2B ) and Dectin-2 blocking antibody dose-dependently reduced pro-IL-1β p35 , IL-1β p17 and caspase-1 p20 production ( Fig 2C and 2D ) . Thus , it appears that Dectin-2 is involved in pro-IL-1β synthesis , IL-1β maturation and caspase-1 activation in response to H . capsulatum . Results in Fig 2E and 2F show that Dectin-2 deficiency ( Clec4n-/- cells ) exhibited reduction of IL-1β secretion , which is comparable with wild type BMDCs treated with anti-Dectin-2 antibody . Sorted MHCII+CD11c+ splenic DCs from Clec4n-/- mice also produced lower IL-1β compared with their wild type counterparts ( Fig 2G ) . Since Dectin-2 signal is transduced through Fc receptor γ chain ( FcRγ ) , we studied inflammasome activation in FcRγ-/- BMDCs . We found that FcRγ deficiency , like Dectin-2 deficiency , significantly reduced IL-1β and caspase-1 p20 ( S2 Fig ) . These results together indicate that through interaction with Dectin-2 , H . capsulatum triggers both signal 1 and signal 2 for inflammasome activation in BMDC , demonstrating the importance of Dectin-2 in H . capsulatum-induced inflammasome activation in DC . It has been reported that Dectin-2 coupling to Syk leads to downstream activation of MAPKs in response to C . albicans [25] . Whether Syk-MAPK signaling pathway is involved in H . capsulatum-induced inflammasome activation is still unclear . We analyzed Syk , JNK , ERK and p38 phosphorylation in BMDCs after stimulation with H . capsulatum . Western blotting showed that phosphorylation of Syk , JNK , ERK and p38 occurred as early as 10 min after H . capsulatum stimulation ( Fig 3A ) . The role of Syk in H . capsulatum-induced MAPKs and inflammasome activation was validated in Syk-deficient fetal liver-derived dendritic cells ( FLDCs ) . Results showed that phosphorylation of JNK , ERK and p38 was largely diminished in Syk-deficient cells upon H . capsulatum stimulation ( Fig 3B ) , which demonstrates that MAPKs activation is downstream of Syk . While Syk-deficiency completely abolished FLDCs IL-1β secretion upon H . capsulatum stimulation , it did not affect IL-1β response to LPS plus ATP stimulation showing the specificity of this pathway ( Fig 3C ) . Western blotting analysis also showed that in the absence of Syk , pro-IL-1β expression was abolished , which is congruent with the absence of mature IL-1β p17 production and indicates that Syk was required for signal 1 priming ( Fig 3D ) . In addition , pro-caspase-1 p45 was partially reduced but caspase-1 p20 was completely absent in Syk deficiency , suggesting that Syk is likely necessary for optimal signal 2 activation ( Fig 3D and S3 Fig ) . Pharmacological inhibition of either ERK or JNK dramatically reduced the production of pro-IL-1β p35 , IL-1β p17 and caspase-1 p20 upon H . capsulatum stimulation ( Fig 3E and 3F ) . Interestingly , inhibition of p38 increased pro-IL-1β expression that led to increase IL-1β secretion ( Fig 3E and 3F ) . Again , these inhibitors greatly diminished signal 1 by reducing pro-IL-1β production , but also affected signal 2 by reducing the conversion of pro-caspase-1 to active caspase-1 . Together , these data demonstrate that H . capsulatum-induced inflammasome activation is through Syk and its downstream ERK and JNK activation . Our data in Figs 2 and 3 demonstrated that while Syk deficiency completely abolished IL-1β production , Dectin-2 deficiency reduced IL-1β production by only ~50% . It is reported that blocking Dectin-2 in Dectin-1-deficient BMDC leads to further reduction in TNF and IL-10 production in response to C . albicans compared to Dectin-1 deficiency alone [25] . Flow cytometric analysis results showed that Dectin-1 and Dectin-2 single deficiency did not affect the expression of the other receptor nor the uptake of H . capsulatum ( S4 and S5 Figs ) . We used blocking antibodies and receptor single- and double-deficient BMDCs to study whether Dectin-1 works together with Dectin-2 to transduce downstream signaling for inflammasome activation . Western blotting analysis and ELISA results showed that blocking Dectin-1 and Dectin-2 in Dectin-2-deficient and Dectin-1-deficient , respectively , reduced not only the secretion of mature IL-1β p17 ( S6A and S6B Fig ) , but also the synthesis of pro-IL-1β p35 , the active form of caspase-1 p20 , and ASC oligomerization , compared to wild type cells and cells with either receptor deficiency alone ( S6B Fig and Fig 4A ) . Dectin-1 and Dectin-2 double deficiency completely abolished IL-1β production ( Fig 4B and 4C ) and pro-IL-1β p35 , caspase-1 p20 ( Fig 4C ) . It appears that , in the presence of Dectin-2 , Dectin-1 does not function to respond to H . capsulatum for inflammasome activation . Yet in the absence of functional Dectin-2 , recognition of H . capsulatum by Dectin-1 is involved in triggering both signal 1 and signal 2 . Interestingly , while Dectin-2 ( but not Dectin-1 ) deficiency significantly reduced p-Syk and p-JNK , Dectin-1 and Dectin-2 double deficiency and blockade of Dectin-2 in Dectin-1-deficient cells further diminished Syk and JNK phosphorylation ( Fig 4D and S7 , S8A and S8B Figs ) . Blockade of Dectin-1 in Dectin-2-deficient cells also reduced Syk and JNK phosphorylation ( S8A Fig ) , although it did not reach statistical significance ( S8B Fig ) . Thus , Dectin-2 activates both Syk and JNK to transduce signals for inflammasome activation upon interaction with H . capsulatum . Dectin-1 is also involved in inflammasome activation , although less prominently than Dectin-2 , in triggering Syk and JNK signaling pathway . We found that CD103+ DCs , Siglec-F+F4/80+ alveolar macrophages and Ly6G+CD11b+ neutrophils were three major cell populations in the lungs before and after intratracheal H . capsulatum infection ( S9 Fig and Fig 5A ) . While infection did not change the percentages of CD103+ DCs ( 13–14% ) , CD11b+ DCs ( 2–3% ) , alveolar macrophages ( 11–16% ) , and neutrophils ( 10–12% ) in the lungs ( Fig 5A ) , there was a 10-fold induction of Il1b mRNA in CD103+ DCs , 2-fold induction in CD11b+ DCs , 1 . 5-fold induction in alveolar macrophages and 2-fold induction in neutrophils after infection ( Fig 5B ) . These data showed that CD103+ DCs are one of the major producers of IL-1β in the lungs upon pulmonary H . capsulatum infection . Interestingly , the levels of Il1b mRNA in CD103+ DCs from infected Dectin-2-deficient mice were lower than cells from infected wild type mice although the difference did not reach statistical significance ( Fig 5C ) . Dectin-1 and Dectin-2 double deficiency significantly reduced Il1b mRNA expression in all four cell populations ( Fig 5C ) . Together , these data demonstrate that CD103+ DC being one of the major sources of IL-1β in pulmonary H . capsulatum infection , both Dectin-2 and Dectin-1 play an important role in DC response to H . capsulatum . Next , we analyzed the mechanisms that are required for inflammasome activation by H . capsulatum and addressed the downstream signaling events triggered by Dectin-2 . Results showed that blocking cathepsin B activity or K+ release , but not inhibition of ROS , reduced the production of IL-1β in BMDC response to H . capsulatum ( Fig 6A , 6B and 6C ) . We used DQ ovalbumin fluorescence imaging to investigate whether H . capsulatum induces lysosomal rupture in BMDCs . Without H . capsulatum stimulation , DQ ovalbumin ingested by BMDCs was located in endosome/lysosome ( Fig 6D upper panel ) . After stimulation with H . capsulatum , endosome/lysosome became enlarged and leaky ( Fig 6D lower panel ) . Cathepsin B activity assay also showed that activated cathepsin B was released to culture supernatants after stimulation ( Fig 6E ) . Inhibiting cathepsin B activity or phagosome acidification reduced the release of activated cathepsin B ( Fig 6E ) . In addition , inhibition of cathepsin B activity reduced the secretion of caspase-1 p20 and IL-1β p17 , but not the level of pro-IL-1β , confirming the role of cathepsin B as an upstream regulator of signal 2 during inflammasome activation ( Fig 6F ) . Together , these data suggest that uptake of H . capsulatum induces dendritic cell lysosomal rupture and cathepsin B release that trigger inflammasome activation . Cathepsin B activity was quantified in cells with Dectin-1 , Dectin-2 single or double deficiency . Data showed that Dectin-1-deficient , Dectin-2-deficient , and double-deficient cells retained activated cathepsin B in the cytosol and double-deficient cells retained significantly more than single-deficient cells ( Fig 7A left panel ) . Single- and double-deficient cells reduced the release of activated cathepsin B to culture supernatants although the differences between single- and double-deficient cells were not significant ( Fig 7A right panel ) . Additionally , pharmacological inhibition of either ERK or JNK reduced the release of active cathepsin B ( Fig 7B ) . Interestingly , Dectin-2 deficiency , did not make a difference in the kinetics of the drop of intracellular K+ levels compared to wild type cells ( S10 Fig ) . These data indicate that , while Dectin-2 does not play a role in K+ efflux , Dectin-1 and Dectin-2 singly and cooperatively regulate activated cathepsin B release . It has been reported that both heat-killed and UV-inactivated C . albicans , unlike their viable counterparts , fail to induce IL-1β secretion from LPS-primed BMDMs [29] . A separate study showed that heat inactivation and proteinase K digestion of Schistosoma egg antigen ( SEA ) abolish its ability to induce signal 2 [30] . We found that viable H . capsulatum induced higher levels of IL-1β than their heat-killed counterparts while the viability of the organism did not affect the levels of TNF production ( Fig 8A ) . Western blot analysis also showed that live organism induced higher levels of caspase-1 p20 and IL-1β p17 ( Fig 8B ) than killed microorganism . It is worth noting that the viability of H . capsulatum did not affect the levels of pro-IL-1β p35 or NLRP3 ( Fig 8B ) . These data showed that the viability of H . capsulatum affects signal 2 , but not signal 1 . Acridine orange was used to analyze lysosome swelling and fluorescent cathepsin B peptide substrate and activity assay to study activated cathepsin B release . Results showed that stimulation with viable but not heat-killed H . capsulatum led to lysosome swelling ( Fig 8C ) and cathepsin B release from cytosol ( Fig 8D and 8E ) . Therefore , it appears that viable H . capsulatum efficiently triggers signal 2 by inducing lysosomal rupture and greater cathepsin B release .
Pathogens interacting with PRRs is the first step in inflammasome activation [28] . Microsporum canis , Malassezia spp . and C . albicans recognized by C-type lectin receptors transduce signals to induce NLRP3 inflammasome activation in macrophages and DCs [19 , 20 , 23] . M . canis interacting with Dectin-1 induces pro-IL-1β transcription in THP-1 cells [20] . Malassezia spp . triggers IL-1β production in human monocyte-derived DCs through Dectin-1 [19] . TLR-2 and Dectin-1 are both involved in induction of pro-IL-1β transcription by hyphal forms of C . albicans in mouse BMDM [23] . However , Dectin-2 is solely responsible for cytokine induction in mouse BMDC including that of IL-1β by yeast form of C . albicans [24] . Dectin-2 deficiency affects , but does not completely abolish , cytokine induction by hyphal form of C . albicans [24] . Ritter et al . showed in BMDC that Schistosoma egg antigen ( SEA ) engagement with Dectin-2 provides signal 2 after triggering signal 1 with Pam3Cys for NLRP3 inflammasome [30] . Thus , it appears that the interaction of fungal pathogens with receptors to initiate inflammasome activation is complex . It varies according to cell types , the type of pathogens , the different morphological forms and the triggering of signal 1 or signal 2 . Data in this study show that Dectin-2 plays a role as a primary receptor that mediates both signal 1 and 2 for NLRP3 inflammasome in H . capsulatum-stimulated BMDC . In the presence of Dectin-2 , Dectin-1 does not respond to H . capsulatum . In the absence of Dectin-2 , however , recognition of H . capsulatum by Dectin-1 does take place , although less prominently , and it can trigger both signal 1 and 2 . Triggering Dectin-2 downstream signaling by agonistic anti-Dectin-2 antibody results in phosphorylation of Syk , ERK , p38 and JNK [25] . Stimulation of Dectin-2 by C . albicans yeasts activates Syk downstream CARD9-dependent MAPKs signaling and cytokine production in BMDC [24 , 25] . The hyphal form of C . albicans activates PLC-γ2 through Dectin-2 , and it is critical for ROS production in BMDM [31] . Our finding showing that H . capsulatum stimulation activates Dectin-2 downstream Syk , ERK , JNK and p38 signaling is consistent with that is reported for C . albicans [24 , 25] . We further demonstrated that Syk deficiency or pharmacological inhibition of ERK and JNK phosphorylation inhibits pro-IL-1β , caspase-1 activation and IL-1β production . It is worth noting that inhibiting p38 significantly increases pro-IL-1β expression , suggesting that p38 negatively regulates Il1b gene transcription . In a study of malaria hemozoin stimulation of macrophages , Shio et al . showed that both ERK and PI3K are involved in activation of NLRP3 inflammasome through Syk and Lyn kinases , whereas p38 plays no role [32] . Thus , MAPK molecules triggered by different stimuli may have distinct functions even on a specific pathway like that results in IL-1β production . ERK , JNK and p38 are activated upon Dectin-2 engagement with H . capsulatum . While Syk-JNK/ERK signaling positively regulates NLRP3 inflammasome , Syk-p38 serves as a negative regulator . Growing evidence shows that Dectin-2 collaborates with Dectin-1 to induce cytokine response upon fungal stimulation [25 , 33–35] . Blocking Dectin-2 in Dectin-1-deficient BMDC stimulated with C . albicans nearly abrogates TNF and IL-10 production compared to wild type and Dectin-1-deficient cells [25] . Blocking both Dectin-1 and Dectin-2 completely abolishes the expression of Il1b mRNA in human primary monocyte-derived DC responding to C . albicans compared to blocking each receptor separately [35] . Dectin-1 and Dectin-2 double-deficient BMDC fails to secrete IL-1β in response to the fungal pathogen Trichophyton rubrum [34] . These studies indicate that Dectin-1 and Dectin-2 together mediate cytokine production in response to fungal pathogens . However , the relation between the signaling pathway ( s ) downstream of Dectin-1 and Dectin-2 has not been fully investigated . Results of our study show a collaborative relationship between Dectin-2 and Dectin-1 in activation of Syk-MAPKs pathway for NLRP3 inflammasome activation . Dectin-2 dominates as a receptor to transduce downstream Syk-JNK signaling in triggering NLRP3 inflammasome even in the presence of Dectin-1 . When Dectin-2 is absent , Dectin-1-mediated recognition takes over , although responding less prominently , and activates the same signaling pathway . When both Dectin-2 and Dectin-1 are absent , Syk-JNK signaling becomes almost null . Thus , it appears that while Dectin-2 is the primary receptor that recognizes H . capsulatum , Dectin-1 takes its place in its absence for triggering Syk-JNK signaling for NLRP3 inflammasome in BMDC response to H . capsulatum . Coady et al . showed in pulmonary H . capsulatum infection that IL-1R-/- mice survive intranasal infection with 1 . 8 × 104 of H . capsulatum [36] . However , Deepe et al . reported that while IL-1R-/- mice survive infection with low dose of H . capsulatum ( 1 × 104 and 2 × 105 ) , high dose of H . capsulatum ( 2 × 106 ) causes IL-1R-/- mice to die [22] . These results together demonstrate that IL-1β is protective when mice are challenged with high but not low dose of H . capsulatum . We observed that when mice were infected with lower dose ( 2 × 106 ) of H . capsulatum ( S11 Fig ) , there was no difference in survival between Nlrp3-/- and wild type mice . Infection with high dose of the fungus either intratracheally or intravenously , Nlrp3-/- mice had significantly less survival than wild type mice ( Fig 1J and S12 Fig ) . Our in vitro data show that stimulation of cells with higher MOI of fungus dose-dependently elicits greater inflammasome response . It appears that high dose of H . capsulatum triggers greater NLRP3 inflammasome response and higher IL-1β production that are protective against lethal H . capsulatum challenge . It is reported that CD103+ conventional DCs in the lungs produce IFN-I through TLR7/9 upon H . capsulatum infection [15] . CD103+ DC IL-2 response to A . fumigatus is mediated by Dectin-1 and the downstream Ca++-calmodulin-dependent NFAT signaling pathway [37] . We provide evidence to show that CD103+ DCs are one of the major producers of IL-1β in the lungs in H . capsulatum infection . Infection induces upregulation of pro-IL-1β in CD103+ and CD11b+ DCs as well as in neutrophils . The induction was much higher in CD103+ DC than in CD11b+ DCs and neutrophils , showing that CD103+ DCs are more active in producing IL-1β than other two cell types in response to infection . In addition , Dectin-2 singly and in collaboration with Dectin-1 are involved in CD103+ DC IL-1β response . Interestingly , while Dectin-2 single deficiency does not affect IL-1β response in CD11b+ DCs , alveolar macrophages and neutrophils , Dectin-2 and Dectin-1 double deficiency almost completely abolish IL-1β production in all cell types . It appears that both Dectin-2 and Dectin-1 are important in host IL-1β response to H . capsulatum pulmonary infection . The assembly and activation of canonical NLRP3 inflammasome can be triggered by K+ efflux , Ca++ uptake , reactive oxygen species and lysosomal protein cathepsin B [5–8 , 38] . Both ROS production and K+ efflux but not cathepsin B are known to drive NLRP3 inflammasome activation as signal 2 in BMDC response to C . albicans yeasts and A . fumigatus conidia [16 , 18] . Cathepsin B activity is required , however , for NLRP3 inflammasome activation in BMDM response to hyphal form of C . albicans [29] . It appears that stimulation of different types of cells by different fungal pathogens employs different pathways to activate signal 2 . A number of studies indicate that viable microorganism is required to induce signal 2 [18 , 29 , 30] . In this study , we show that K+ efflux and cathepsin B , but not ROS production ( even it was produced in S13 Fig ) , function as signal 2 for H . capsulatum-induced NLRP3 inflammasome in BMDC . Our data also show that , although viable and heat-killed H . capsulatum induce comparable levels of pro-IL-1β , viable H . capsulatum stimulation leads to production of higher levels of activated caspase-1 , more lysosomal swelling and increased cathepsin B release than heat-killed organism . It has been previously shown that human monocyte-derived DC is capable of killing intracellular H . capsulatum by pronounced phagolysosomal fusion [14] and that phagosomal acidification is an early event preceding lysosomal rupture [6] . We speculate that viable H . capsulatum-induced lysosomal rupture leading to cathepsin B release takes place after phagolysosomal fusion . The relationship between receptor ( s ) and its downstream signaling that mediates signal 2 activation has not been very well-established . Recognition of C . albicans yeast by Dectin-1 induces ROS production in BMDMs [39] . Hyphal form of C . albicans triggers Dectin-2 downstream PLCγ-2 signaling that activates ROS production [31] . C . albicans yeast has also been shown to activate Syk to induce ROS production leading to inflammasome activation in BMDC [16] . Compared to ROS , less is known about the receptor ( s ) and signaling pathway ( s ) that lead to cathepsin B release . Shio et al . reported that malarial hemozoin activates Syk-mediated cathepsin B activation in THP-1 cells [32] . In an arthritis mouse model , intra-articular injection of zymosan induces cathepsin release in a Dectin-1- and NOD2-dependent manner [40] . Interestingly , cathepsin B release is Syk-dependent in B cell receptor-mediated apoptosis [41] . In this study we provide the first direct evidence that C-type lectins , Dectin-2 and Dectin-1 , collaboratively regulate cathepsin B release in BMDC . Based on our findings demonstrated in this study , we propose a working model as depicted in Fig 9 of the roles of Dectin-2 and Dectin-1 and their downstream signals in H . capsulatum-induced NLRP3 inflammasome activation in BMDC . Dectin-2 serves as a primary receptor and Dectin-1 as a secondary one in response to H . capsulatum . Dectin-2 and Dectin-1 downstream signals Syk-JNK trigger both signal 1 to induce pro- IL-1β expression and signal 2 to regulate cathepsin B release for NLRP3 inflammasome activation and IL-1β release . While ROS is not involved , K+ efflux also functions as signal 2 but is independent of receptor regulation . Our data are the first to provide insight into the roles of Dectin-2 and Dectin-1 in signaling events for NLRP3 inflammasome activation by H . capsulatum .
All animal experiments were undertaken in accordance with the Guidebook for the Care and Use of Laboratory Animals , 3rd Ed . , 2007 , published by The Chinese-Taipei Society of Laboratory Animal Sciences , approved by the Institutional Animal Care and Use Committee ( IACUC , Permit number: 20140533 ) of National Taiwan University , College of Medicine . C57BL/6 wild type mice ( originally from the Jackson Laboratory , Bar Harbor , Maine , USA ) , Nlrp3-/- [42] , Clec7a-/- ( from Dr . Gordon Brown , University of Cape Town , Cape Town , South Africa ) , Clec4n-/- [43] , Clec7a-/-Clec4n-/- , Syk+/- and FcRγ-/- ( originally from Dr . Clifford Lowell , University of California , San Francisco , CA , USA ) were bred and maintained in the Laboratory Animal Center , National Taiwan University , College of Medicine . Clec7a-/-Clec4n-/- mice were obtained from crossing Clec7a-/- and Clec4n-/- mice . All mice were housed in sterilized cages with sterilized bedding and filter cage tops and were fed with sterilized food and water . Mice at 8–12 weeks of age were used in all experiments . H . capsulatum strain 505 was cultured at 37°C on brain heart infusion agar [37 mg/ml; Becton Dickinson Biosciences ( BD ) ] supplemented with 1 mg/ml cysteine ( Sigma ) , 20 mg/ml dextrose ( BD ) and 20% heat-inactivated certified FBS ( Biological Industries ) . Fresh yeast cell suspensions were prepared in RPMI-1640 medium ( Invitrogen ) for each experiment . Heat-killed yeast cells were prepared by heating at 65°C water bath for 2 h . Bone marrow cells were flushed from mouse femurs and tibias . Cells were seeded in 24-well culture plate after RBC lysis , cultured in RPMI 1640 complete medium containing 10% heat-inactivated fetal bovine serum ( FBS ) and 15 ng/ml of GM-CSF . Culture medium was replenished on days 3 and 6 . Non-adherent cells were harvested on day 7 . About 75–80% of the cells were CD11c+ as determined by FACS analysis . Cells were seeded in 96-well plate at 2 × 105 cells /well or in 12-well plate at 2 × 106 cells / well in RPMI 1640 complete medium containing 10% heat-inactivated FBS . Cells were used for experiments 18–24 h later . To obtain Syk-/- cells , Syk-/- embryos were separated from Syk+/+ and Syk+/- embryos after crossing Syk+/- mice by their exhibition of severe petechiae and confirmed by genotyping [44] . Single-cell suspensions from fetal liver tissues were cultured in RPMI 1640 complete medium containing GM-CSF for 7 days . Over 80% of the nonadherent cells were CD11C+ which were identified as fetal liver-derived dendritic cells ( FLDCs ) . Splenocytes were collected from wild type , Nlrp3-/- and Clec4n-/- mice and stained with phycoerythrin ( PE ) -conjugated anti-MHC class II and allophycocyanin ( APC ) -conjugated anti-CD11c ( eBioscience ) antibodies . MHCII+CD11c+ cells were sorted by FACSAria II cell sorter ( BD ) . Sorted MHCII+CD11c+ cells were seeded in 96-well plate in RPMI-1640 complete medium containing 10% heat-inactivated FBS and used in experiments immediately . Mice were intravenously infected with 1 × 107 of H . capsulatum yeast cells and monitored for 18 days for survival . To determine fungal burdens , spleens were collected on day 11 and spleen homogenates were plated on glucose-peptone agar plates . Mycelial colonies were counted 10 days later . Blocking antibodies anti-Dectin-1 ( 2A11 ) , anti-Dectin-2 ( D2 . 11E4 ) and anti-CR3 ( 5C6 ) were purchased from Serotec and anti-TLR-2 ( 6C2 ) , IgG2a ( eBR2a ) and IgG2b ( eB149/10H5 ) were from eBioscience . PE/Cy7-conjugated anti-CD45 ( 30-F11 ) , APC-conjugated anti-F4/80 ( BM8 ) , Alexa 488-conjugated anti-CD103 ( 2E7 ) and Alexa 647-conjugated anti-Ly6G ( 1A8 ) were obtained from BioLegend . APC-conjugated anti-CD11c ( N418 ) and PE-conjugated anti-CD11b ( M1/70 ) were from eBioscience and PE-conjugated anti-Siglec-F ( E50-2440 ) from Pharmingen . Z-YVAD-FMK ( Caspase-1 inhibitor ) was obtained from BioVision . U1026 ( ERK inhibitor ) , SP600125 ( JNK inhibitor ) , SB203580 ( p38 inhibitor ) , N-acetyl-L-cysteine ( ROS scavenger ) , Apocynin ( NADPH-oxidase inhibitor ) , Ca-074 methyl ester ( cathepsin B inhibitor ) , bafilomycin A1 ( phagosomal acidification inhibitor ) and glibenclamide ( K+ channel inhibitor ) were obtained from Sigma-Aldrich . BMDCs ( 2 × 105/well ) or splenic DCs ( 1 × 105/well ) were seeded in 96-well plates and cultured overnight before treatment with indicated reagents or blocking antibodies . Live or heat-killed H . capsulatum ( yeast:cell ratio of 10:1 or 20:1 ) were added 60 min later . Culture supernatants were collected at different time points and stored at -80°C . ELISA kits ( eBioscience ) were used to quantify IL-1β and TNF in the culture supernatants with 7 . 8125 pg/ml as the lowest limit of detection . BMDCs ( 2 × 106 ) were treated with or without blocking antibody for 60 min before the addition of live or heat-killed H . capsulatum . For inflammasome analysis , cells were cultured in medium containing 0 . 1% heat-inactivated FBS and for signaling molecule analysis , in medium containing 10% FBS . Cells were detached from the wells and lysed with PhosphoSafe lysis buffer ( MERCK ) at different time points . Harvested cell-free supernatants were concentrated by 10-fold with Vivaspin 500 ( GE Healthcare ) . Cell lysates were subjected to electrophoresis at 10% ( for cell lysates ) or 12 . 5% ( for supernatants ) SDS polyacrylamide gel and transferred to a 0 . 45 ( for cell lysates ) or 0 . 22 ( for supernatants ) μm PVDF membrane . The membrane was blocked with 5% non-fat milk and left in buffer containing anti-IL-1β p17 ( R&D system ) , anti-Caspase-1 p20 ( Adipogen ) , anti-NLRP3 ( Adipogen ) , anti-ASC ( Adipogen ) , anti-p-Syk ( Abcam ) , anti-p-ERK ( Cell Signaling ) , anti-p-JNK ( Cell Signaling ) , anti-p-p38 ( Cell Signaling ) or anti-β-actin ( GeneTex ) antibody at 4°C overnight . The membrane was washed with TBST before addition of HRP-conjugated anti-goat IgG ( 1:3000 ) , anti-rabbit ( 1:20 , 000 ) or anti-rat IgG ( 1:20 , 000 ) . ECL reagent ( PerkinElmer Life Science , Merck Millipore and GE Healthcare ) was used for detection . BMDCs were seeded in 12-well plates ( 2 × 106 cells/well ) and treated with live H . capsulatum or LPS ( 100 ng/ml , 6 h ) plus ATP ( 5 mM , 30 minutes ) for 18 h . Cells were centrifuged at 4500 rpm for 5 min . Cell pellets were resuspended in cold 0 . 3 ml buffer A ( 20 mM HEPES-KOH , pH 7 . 5 , 10 mM KCl , 1 . 5 mM MgCl2 , 1 mM EDTA , 1 mM EGTA , 320 mM sucrose ) and protease inhibitor cocktail . Cell lysate was obtained after passing the suspension through 29-G syringe for 15 times . Cell lysates were spun at 4500 rpm for 8 min to remove contaminating nuclei , unlysed cells and H . capsulatum . Supernatants were diluted with 1 volume of CHAPS buffer ( 20 mM HEPES-KOH , pH 7 . 5 , 5 mM MgCl2 , 0 . 5 mM EGTA and 0 . 1% CHAPS ) to lyse residual organelles before centrifugation at 8000 rpm for 8 min . After centrifugation , supernatants were discarded and the pellets were treated with disuccinimidyl suberate ( DSS , 2 mM , Sigma-Aldrich ) for 30 min at room temperature . The cross-linked pellets were re-suspended in 40 μl SDS sample buffer and proteins were separated on 12 . 5% SDS polyacrylamide gel followed by immunoblotting with anti-ASC antibody ( Adipogen ) as previously described [45] The detailed methodology has been previous described [46] . BMDCs ( 2 × 106 ) from wild type , Clec7a-/- and Clec4n-/- mice were seeded in 12-well plates . The plates were cooled on ice for 20 min before the addition of FITC-labeled H . capsulatum at MOI of 20 . After 60 min cooling on ice , the plates were moved to 37°C CO2 incubator for 60 min . Cells were collected and quenched FITC-labeled yeast by trypan blue for 5 min . After washing with dPBS , cells were fixed with 1% paraformaldehyde . The phagocytosis rate was determined by flow cytometry . Wild type , Clec4n-/- and Clec7a-/-Clec4n-/- mice were intratracheally infected with 2 . 5 × 105 H . capsulatum . Lungs were collected on day 7 after infection and digested with 0 . 15 mg/ml of Liberase TM ( Roche ) at 37°C for 30 min on a rotatory agitator . Cell pellets were resuspended in 45% Percoll ( GE Healthcare ) and overlaid on 81% Percoll . After centrifugation , cells at interface ( 45/81% Percoll ) were harvested and stained with antibodies before sorting by FACSAria II cell sorter ( BD ) . CD45+ cells were sub-gated to sort out different cell populations: CD103+ DCs ( CD11c+CD103+CD11b- ) , CD11b+ DCs ( CD11c+CD103-CD11b+ ) , alveolar macrophages ( Siglec-F+F4/80+ ) , neutrophil ( CD11b+Ly6G+ ) . Splenic DCs and lung cells RNA was extracted by using Quick-RNA™ MiniPrep ( Zymo Research ) . cDNA was synthesized in reaction mixture including RNA , 5× first-strand buffer , DTT ( 0 . 1M ) , dNTP ( 10mM ) , random primers ( 50 μM ) , SuperScript III Reverse Transcriptase ( Invitrogen ) and RNaseOUT™ recombinant ribonuclease inhibitor ( Invitrogen ) in DEPC-H2O . Quantitative real-time PCR was performed by using ABI7900 Real-time PCR detection system in total volume of 10 μl per reaction , containing 2 μl of cDNA with 0 . 2 μM forward and reverse primers in 8 μl Fast SYBR Green Master Mix ( Applied Biosystems ) . Il1b mRNA was normalized against Actb mRNA . Primers for Il1b are 5’-GAA CTC AAC TGT GAA ATG CCA CC-3’ ( forward ) and 5’-CCA CAG CCA CAA TGA GTG ATA CT-3’ ( reverse ) and for Actb 5’-TGT ATG AAG GCT TTG GTC TCC CT-3’ ( forward ) and 5’-AGG TGT GCA CTT TTA TTG GTC TCA A-3’ ( reverse ) . BMDCs ( 1 × 105 ) were seeded in 96-well plate and treated with 10 μM of DQ ovalbumin ( Sigma-Aldrich ) for 1 h before stimulation with or without H . capsulatum at MOI of 1 . Cells were collected 1 h later and cytospun on microscope slides . The cytospun cells were fixed in 4% paraformaldehyde , blocked with 5% heat-inactivated FBS and stained with APC-conjugated anti-CD11c antibody ( eBioscience ) . Hoechst 33258 was used to stain cell nuclei . The slides were viewed under Zeiss LSM 510 META Confocal Microscope . BMDCs ( 2 × 106 ) were cultured in 12-well plate with phenol red-free RPMI complete medium ( Invitrogen ) and stimulated with or without H . capsulatum at MOI of 20 . Cells and culture supernatants were collected separately 18 h after stimulation . Cell-free supernatants were concentrated 10-fold by Vivaspin 500 ( GE Healthcare ) . Cells were lysed with cathepsin B lysis buffer ( Abcam , ab65300 ) . Both cell lysates and concentrated supernatants were loaded unto 96-well plate ( solid black , Corning ) before addition of CB reaction buffer . CB substrate Ac-RR-AFC ( final concentration of 200 μM ) was added and incubated at 37°C for 1 h . Samples were read in SpectraMax M5 ( Molecular Devices ) at 400 nm excitation and 505 nm emission wavelengths . BMDCs ( 2 × 105 ) were seeded in 96-well plate ( black with clear flat-bottom tissue culture plate , Corning ) and were loaded with 2 μM potassium-binding benzofuran isophthalate-AM ( PBFI/AM , Molecular Probes , Thermo Fisher Scientific ) in phenol red-free RPMI medium ( Invitrogen ) in the presence of 0 . 05% ( w/w ) Pluronic F-127 ( Sigma-Aldrich ) at 25°C in the dark for 60 min . After one wash , cells were stimulated with H . capsulatum at MOI of 1 . Fluorescence intensity of PBFI ( excitation wavelength 340 nm , emission wavelength 500 nm ) was recorded every min by SpectraMax M5 while the culture was maintained in 37°C . Stimulation with ATP ( 5 mM ) was used as a positive control . BMDCs ( 2 × 105 ) in phenol red-free RPMI complete medium were seeded in 96-well plate . Acridine orange was added to medium 1 h before and Magic Red® ( ImmunoChemistry Technologies ) added 1 h after addition of live or heat-killed H . capsulatum stimulation . Hoechst 33258 was added 3 h later . Cells were washed and cell suspension was placed onto microscope slide for viewing under confocal microscope . Acridine orange bound to nuclear and cytosolic DNA and RNA emits green fluorescence ( excitation wavelength 480 nm , emission wavelength 550 nm ) . Due to its cationic nature , acridine orange emits red fluorescence ( excitation wavelength 550 nm , emission wavelength 620 nm ) in acidic compartments such as lysosome . Active cathepsin B reacts with Magic Red® substrates and generates a strong red fluorescence ( excitation wavelength 592 nm , emission wavelength 628 nm ) . BMDCs ( 1 . 2 × 106 ) were incubated in phenol red-free HBSS containing 10 μM CM-H2DCFDA ( Thermo Fisher Scientific ) for 30 min at 37°C . After replenishment with fresh phenol red-free RPMI 1640 complete medium , cells were stimulated with H . capsulatum ( MOI = 20 ) . Oxidative DCF was analyzed by flow cytometry . Differences between treatment groups were analyzed with the student 2-tailed t test ( comparing two group ) and one-way ( comparing multiple groups with one control ) or two-way ( comparing multiple groups with two control groups ) ANOVA followed by Tukey or Sidak post-hoc test . Log-rank test was used to analyze % survival . All statistics analyses were calculated by Prism 6 software . The level of statistical significance was defined as p<0 . 05 . All results were expressed as mean ± standard deviation of the mean . | Histoplasma capsulatum is a dimorphic fungal pathogen . The microconidia and hyphal elements are breathed in and transform to become yeasts in the lungs . Histoplasmosis occurs worldwide and endemic in mid-western United States . The infection is primarily in the lungs that can become disseminated and cause fatal disease when left untreated . It was reported that IL-1β is important to host defense against H . capsulatum infection , but the detailed mechanism of how myeloid cells respond to this fungal pathogen and which receptor ( s ) is involved to induce IL-1β production is largely unknown . We demonstrate in this study that H . capsulatum-induced caspase-1 activation leading to IL-1β production is NLRP3-dependent . NLRP3-deficienct mice succumb to otherwise sublethal H . capsulatum infection . Although the role of Dectin-1 in fungus-induced NLRP3 inflammasome is well-established , we found that Dectin-2 serves as a primary receptor and Dectin-1 plays a secondary role in inducing Syk-JNK signaling to mediate NLRP3 inflammasome in response to H . capsulatum . In addition , while both K+ efflux and cathepsin B function as signal 2 , the viability H . capsulatum affects the amounts of cathepsin B release . Our study is the first to reveal the roles of Dectin-2 and Dectin-1 and the downstream signaling events in fungal pathogen-induced NLRP3 inflammasome . | [
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] | 2017 | Dectin-2 is a primary receptor for NLRP3 inflammasome activation in dendritic cell response to Histoplasma capsulatum |
Reprogramming of a gene’s expression pattern by acquisition and loss of sequences recognized by specific regulatory RNA binding proteins may be a major mechanism in the evolution of biological regulatory programs . We identified that RNA targets of Puf3 orthologs have been conserved over 100–500 million years of evolution in five eukaryotic lineages . Focusing on Puf proteins and their targets across 80 fungi , we constructed a parsimonious model for their evolutionary history . This model entails extensive and coordinated changes in the Puf targets as well as changes in the number of Puf genes and alterations of RNA binding specificity including that: 1 ) Binding of Puf3 to more than 200 RNAs whose protein products are predominantly involved in the production and organization of mitochondrial complexes predates the origin of budding yeasts and filamentous fungi and was maintained for 500 million years , throughout the evolution of budding yeast . 2 ) In filamentous fungi , remarkably , more than 150 of the ancestral Puf3 targets were gained by Puf4 , with one lineage maintaining both Puf3 and Puf4 as regulators and a sister lineage losing Puf3 as a regulator of these RNAs . The decrease in gene expression of these mRNAs upon deletion of Puf4 in filamentous fungi ( N . crassa ) in contrast to the increase upon Puf3 deletion in budding yeast ( S . cerevisiae ) suggests that the output of the RNA regulatory network is different with Puf4 in filamentous fungi than with Puf3 in budding yeast . 3 ) The coregulated Puf4 target set in filamentous fungi expanded to include mitochondrial genes involved in the tricarboxylic acid ( TCA ) cycle and other nuclear-encoded RNAs with mitochondrial function not bound by Puf3 in budding yeast , observations that provide additional evidence for substantial rewiring of post-transcriptional regulation . 4 ) Puf3 also expanded and diversified its targets in filamentous fungi , gaining interactions with the mRNAs encoding the mitochondrial electron transport chain ( ETC ) complex I as well as hundreds of other mRNAs with nonmitochondrial functions . The many concerted and conserved changes in the RNA targets of Puf proteins strongly support an extensive role of RNA binding proteins in coordinating gene expression , as originally proposed by Keene . Rewiring of Puf-coordinated mRNA targets and transcriptional control of the same genes occurred at different points in evolution , suggesting that there have been distinct adaptations via RNA binding proteins and transcription factors . The changes in Puf targets and in the Puf proteins indicate an integral involvement of RNA binding proteins and their RNA targets in the adaptation , reprogramming , and function of gene expression .
The phenotypic diversity of life on earth results not only from differences in the proteins encoded by each genome but , perhaps even more , from differences in the programs that specify where , when , under what conditions , and at what levels these proteins are expressed . A grand challenge in biology is to understand these gene expression programs . Uncovering the similarities in and differences between gene expression programs in related organisms can help reveal fundamental properties of these programs , how they have evolved , how they may be wired and rewired , and ultimately how they can be engineered . The seminal step in gene expression and the focus of much current effort is the initiation of transcription through transcription factors that bind in proximity to genes and regulate the timing and magnitude of RNA synthesis ( see [1–7] for reviews ) . Each transcription factor regulates a set of genes , numbering a few to thousands , specified by short DNA sequences that are in proximity to those genes and are recognized by that transcription factor . One major mechanism for diversification of gene expression programs is the loss or gain of regulation by individual transcription factors , due to mutations that , respectively , disrupt or create the proximal recognition sequences ( see [8–13] for reviews ) . The binding specificity , regulation , and targets of a transcription factor tend to be conserved over a short evolutionary timescale , but each of these properties has changed over evolution , allowing the regulatory roles of orthologous transcription factors to diverge and diversify . Evolutionary changes in regulation at the next level of gene expression are virtually unexplored . After transcription , each messenger RNA ( mRNA ) undergoes a functional odyssey and can be regulated at steps that include splicing , transport , localization , translation , and decay [14] . RNA binding proteins function in each step , and each mRNA interacts with many RNA binding proteins over its lifetime [15–22] . Each RNA binding protein can recognize a few to thousands of mRNAs , and the target sets of each individual RNA binding protein often share functional themes , encoding proteins involved in a particular biological process or localized to the same part of the cell [15 , 23–37] . These effects can be described in terms of a model originally referred to as the “RNA operon” model in which RNA binding proteins bind to and coordinate the regulation of mRNAs encoding functionally or cytotopically related proteins [18 , 20 , 21] . We set out to trace the evolutionary history of an RNA binding protein and how its interactions with targets change over evolution . Identifying this natural history is a step toward understanding the critical differences between organisms , how evolution has progressed , why these differences have arisen , and how gene expression programs are “wired . ” We chose to investigate the Puf ( Pumilio–Fem-3-binding factor ) family of RNA binding proteins , taking particular advantage of the relatively well-understood relationship between Puf protein sequences and the specific RNA sequences they recognize ( Fig 1 ) . Puf proteins are found in most , if not all , eukaryotes [38–40] and have been implicated in regulating the decay , translation , and localization of distinct sets of functionally related RNA targets [38 , 41–43] . For example , in Saccharomyces cerevisiae Puf3 binds and regulates hundreds of distinct RNAs transcribed from the nuclear genome that , almost without exception , encode for proteins localized to the mitochondrion [25] . Puf3 promotes localization of its target mRNAs to the periphery of mitochondria [44–46] and can repress the expression of these mRNAs by promoting their decay [25 , 47 , 48] . Puf3 recognizes a specific sequence element usually found in the 3' untranslated region ( 3' UTR ) of its targets ( Fig 1 ) [25] . S . cerevisiae Puf3 and its orthologs in Drosophila melanogaster ( Pumilio ) and Homo sapiens ( Pum1 and Pum2 ) recognize nearly identical RNA sequence motifs , but they bind to distinct sets of mRNAs that encode proteins with distinct functional themes [24 , 25 , 27 , 29] . Fewer than 20% of the targets of the Puf3 orthologs in humans and flies are themselves orthologs [24 , 29] , and the functional themes of their mRNA targets in flies and humans starkly contrast with those for yeast Puf3 [24 , 27 , 29] . Thus , the mRNA targets of Puf3 orthologs have diverged since humans , flies , and yeast shared ancestors . Nevertheless , bioinformatics studies have suggested that Puf targets are conserved over short timescales , underscoring the importance of these distinct interactions [60–65] . We first systematically investigated the conservation and divergence of the RNA targets that are likely to be recognized by orthologs of S . cerevisiae Puf3 in diverse eukaryotes . We then focused in detail on the larger family of Puf RNA binding proteins and their RNA targets in fungi , as the many sequenced fungal genomes provide the power to identify major and minor evolutionary changes in the repertoires of Puf proteins , their binding specificities , and their RNA targets . The numerous and often concerted changes in this single family of proteins and their RNA targets provide strong corroborative evidence for the role of coordinated protein binding to sets of related mRNAs in organizing gene expression [18 , 20 , 21] . The observed extensive evolutionary changes suggest that changes in RNA binding proteins and their interacting mRNAs are an important source of biological diversification and specialization; studies of these changes across evolutionary time may provide a powerful complement to traditional deep investigations of specific model organisms .
We searched for orthologs of S . cerevisiae Puf3 in 99 diverse eukaryotes ( S1 Text , S1 Fig , S1 Table , Materials and Methods ) and used the identified orthologs to determine the conservation of features important for RNA binding specificity . Puf3 is a canonical Puf protein containing eight Puf repeats [39 , 40 , 66 , 67] that together fold to form a characteristic crescent shape with an RNA binding interface on the inner side ( Fig 1 ) [54 , 55 , 59 , 68–73] . Three amino acid residues within each Puf repeat typically contact an RNA base directly and are important determinants of RNA binding specificity ( Fig 1 legend and references [49 , 54 , 55 , 59 , 68–73] ) . The observations that Puf3 orthologs have a distinctly conserved pocket around the bound RNA and that the residues that determine RNA binding specificity are especially conserved suggest that orthologs of Puf3 recognize the same RNA sequence motifs ( S2 Text , S2 Fig ) . This inference is consistent with experimental results from Puf3 orthologs in diverse eukaryotes [24 , 25 , 27 , 29 , 50 , 51] . We used this insight to infer , by analysis of RNA sequences , the extent to which the RNA targets of Puf3 are conserved . The diversity of the fungal kingdom is a result of more than one billion years of evolution [77] , and the many available sequenced genomes and their relatively low complexity render fungi accessible and powerful for evolutionary studies . Here we synthesize the sequence data with biochemical and functional data to build a model of the evolution of Puf proteins and their targets in fungi . The rewiring of gene expression programs plays a major role in evolution and adaption of new species . Considerable effort has been dedicated to analyzing evolutionary changes in transcription factors and in their targets ( see [8–13] for reviews ) , but far less is known about rewiring at the level of RNA and its binding proteins . We surveyed the evolutionary changes in one family of RNA binding proteins and their cognate recognition elements , broadly across eukaryotes and more deeply within fungi ( Figs 2 and 8 ) . Our evidence points to the existence of mRNA targets of Puf proteins that have been maintained for hundreds of millions of years ( Figs 2 and 8 ) . Overlaid on this conservation are numerous and remarkable changes in the number of Puf proteins , their specificity , their regulatory output , and their targets . The substantial changes in Puf proteins and targets over evolution followed by long periods of high conservation together underscore the importance of these protein–RNA interactions for organismal adaptation and fitness . Puf proteins represent only ~1% of all RNA binding proteins [15] , but similar rewiring of interactions between RNA binding proteins and their targets has likely been a pervasive adaptive strategy throughout evolution . The highly conserved binding specificity of Pufs suggests that the conserved interactions between each protein and its many mRNA targets place a large constraint on binding specificity . A change in binding specificity thus marks a period of innovation in the gene regulatory program . In the time following Puf4 duplication in Saccharomycotina , the binding specificity of the paralogs ( Puf4 and Puf5 ) became restricted with respect to the ancestral specificity and diverged with respect to each other ( Figs 5B and 8 #3 ) . Analogous binding and catalytic promiscuity has been proposed to have been present in ancestral enzymes that later duplicated and specialized [98–103] . Our phylogenetic studies and evolutionary model suggest specificity changes , potential physical origins ( S15 Text ) , and support the idea that aspects of the evolution of RNA binding proteins and their targets proceeded via early promiscuous binding proteins that later underwent gene duplication and subdivision of the ancestral RNA recognition . The observations that the conserved RNA targets of each Puf protein share functional themes and that a set of functionally-related RNA targets can switch in concert from specific interactions with one RNA-binding protein to another , provide strong support for the notion that RNA binding proteins play an important biological role in organizing and coordinating aspects of gene expression [18 , 20 , 21] . Concerted evolutionary changes in mRNAs encoding mitochondrial organization and biogenesis proteins involved hundreds of RNA sequences , placing the same set of orthologous genes in distinct fungal lineages under the regulation of Puf3 , Puf4 , or both proteins . The evolutionary history of changes in their post-transcriptional regulation , suggested by this analysis , provides strong evidence for the fitness advantage of coordinating the regulation of distinct sets of genes and may harbor clues to the selective pressures that led to changes in the regulatory program . Whereas essentially all of the inferred RNA targets of Puf3 in Saccharomycotina are transcribed from nuclear genes encoding proteins with mitochondrial functions , not every ortholog of each gene we identified as encoding a Puf3 target in the Saccharomycotina contains a recognizable Puf3 binding site . It is possible that the fitness advantage ( or disadvantage ) conferred by Puf3 regulation of each of the individual genes in this set is often small enough to allow for considerable genetic drift within the lineage . The evolutionary plasticity that this would allow might help account for the distinct but overlapping functional and cytotopic themes shared by the targets of a given Puf protein in distinct species and lineages . Although Saccharomycotina Puf3 is essentially monogamous in its relationship to RNAs with mitochondrial functions and has served as a “poster child” for RNA binding protein-based coordination of gene expression , the targets of other Puf proteins are functionally and cytotopically more promiscuous . For example , Saccharomycotina Puf4 binds RNAs encoding histone and nucleolar proteins , while Pezizomycotina Puf4 binds RNAs encoding histone and mitochondrial proteins . The RNA targets of Leotiomyceta Puf4 also encompass a broader array of cellular functions relative to the Saccharomycotina Puf3 targets , including targets with roles in energy metabolism ( through the ETC and TCA cycle ) and the proteasome . We do not know whether these multiple themes arise because RNA binding proteins help coordinate and integrate cell status and signals between different systems or whether they represent multiple uses of the same protein for independent functions [104–106] . It is also possible that limitations in our understanding of and ability to identify biological function could account for our inability to map mRNA targets to function in a 1:1 fashion . Evolutionary changes in regulatory RNA–protein interactions are likely to have many similarities to the changes observed in the evolution of transcriptional control ( S12 Table ) . By comparing the changes in transcriptional regulation ( as reflected by gain or loss of specific promoter elements ) and post-transcriptional regulation ( as reflected by gain or loss of Puf-protein recognition elements in the corresponding transcripts ) in sets of functionally related genes that share features of both transcriptional regulation and putative Puf-protein regulation , we found that the timing and likely the consequences of evolutionary changes at these two levels of regulation of a common set of genes can be distinct ( S13 Text ) . RNA–protein interactions can thus provide an additional and independently evolvable infrastructure by which global gene expression networks can be orchestrated and reconfigured to generate phenotypic diversity . By using systematic investigation of evolutionary changes in gene expression programs to enrich the pictures of these programs acquired from years of detailed studies of “representative” model organisms , we found compelling evidence for dramatic changes in the gene expression program at the level of RNA–RNA binding protein interactions during fungal evolution . Mapping evolutionary changes in post-transcriptional regulation can provide new insights into the makeup , logic , and malleability of gene expression programs , and may contribute to our ability to engineer new phenotypes by rewriting or de novo design of post-transcriptional programs .
Protein sequence files and SQL tables containing ortholog information were downloaded from InParanoid [107] ( version 7 . 0 , http://inparanoid . sbc . su . se/ ) . Genome sequences for each species were downloaded in July 2010 from the sources listed in S3 Table . We used a two-step BLASTP search to identify putative Puf proteins in each species . A custom BLAST database was created for each species' protein sequences using makeblastdb ( part of the blast+ package from NCBI ) . In the first step , the sequences of the Pum domains of S . cerevisiae Pufs 1–6 ( Puf1:557–913 , Puf2:511–872 , Puf3:513–871 , Puf4:539–888 , Puf5:188–596 , Puf6:133–483 ) and the complete protein sequence of S . cerevisiae Nop9 were used as a query to search for similar protein sequences in each species using blastp ( NCBI BLAST version 2 . 2 . 23 [108–110] ) , using an E-value cutoff of 10−5 . Sequences identified in the first step were then used to search for additional Puf proteins in a second step , also with an E-value cutoff of 10−5 . In the second step only the parts of the protein sequence identified in the first step as having significant similarity to S . cerevisiae Pum domains were used . If more than one of the query sequences from the first step was similar to a searched sequence , the similar sequence of longest length was kept . Results from the first round yielded near-complete coverage of known Pufs from Caenorhabditis elegans , A . thaliana , and O . sativa ( 12/12 , 24/26 , and 17/19 , respectively ) [38 , 111–113] . The second round yielded one more known Puf from A . thaliana and two from O . sativa . Additionally , putative Pufs in these organisms were found in both rounds ( one from the first round , two from the second ) . Two of the three additional putative Pufs contained one or more Puf repeats according to the SMART annotation tool [114 , 115] , suggesting these hits are real Puf proteins . As our next step was to classify Puf proteins , we aimed for high coverage at the expense of a small fraction of false positives . We classified Puf proteins as orthologs to each of the S . cerevisiae Puf proteins or to N . crassa Puf8 , a previously uncharacterized Puf that we identified and named . We chose S . cerevisiae because of our focus on fungi in this work , and the results suggest that S . cerevisiae Pufs well represent the diversity of Puf proteins found across eukaryotes , with the exception of N . crassa Puf8 , which our initial phylogenetic analysis suggested was deleted in an ancestor of S . cerevisiae . More than 90% of the eukaryotic Pufs and 98% of the fungal Pufs were classified as orthologs to S . cerevisiae Pufs or N . crassa Puf8 . We classified Puf proteins based on a combination of information: reciprocal best BLAST hits , the pattern of amino acids predicted to contact RNA bases within each Puf repeat , and phylogenetic analysis . For reciprocal best BLAST , we checked each Puf against S . cerevisiae and N . crassa Pufs . A protein was tentatively assigned as an ortholog if it was a reciprocal best BLAST hit to at least one S . cerevisiae or N . crassa Puf protein , and the reciprocal best BLAST hit did not disagree between the S . cerevisiae Puf and its N . crassa ortholog . A Puf protein was also tentatively assigned as an ortholog to S . cerevisiae Puf1/Puf2 , Puf3 , Puf4/Puf5 , or N . crassa Puf8 based on predicted RNA-contacting amino acids . RNA-contacting amino acids are highly conserved but are different in distantly related Pufs . The S . cerevisiae Puf1 and Puf2 have similar RNA-contacting amino acids , and those in S . cerevisiae Puf4 and Puf5 are identical to each other so this type of classification cannot distinguish between these two proteins . Outside of these two pairs , the RNA-contacting amino acids are sufficiently different to allow this classification . We performed this classification manually and note any differences between the protein and its tentatively assigned ortholog with respect to these amino acids in S1 and S2 Tables . Puf proteins were assigned a final ortholog if the BLAST-based classification or the RNA contact classification identified a tentative ortholog and so long as the assignment from the two classification methods did not disagree . Any Pufs not assignable by these criteria were subject to a phylogenetic analysis . Protein sequences for S . cerevisiae Pufs , N . crassa Pufs , and the unassigned Pufs were aligned as a group using MUSCLE [116 , 117] in Geneious ( using default settings ) . Columns with more than 50% gaps were stripped , and a maximum likelihood tree was built using PhyML [118 , 119] implemented through Geneious ( WAG substitution model , 8 substitution rate categories , best of NNI [Nearest Neighbor Interchange] and SPR [Subtree Pruning and Regrafting] search ) . Many of the remaining Pufs were classified based on this tree ( S1 and S2 Tables ) . In some cases , we referred back to the pattern of RNA-contacting amino acids to inform our decision ( see notes in column “unknownGroup_ML tree” in S1 and S2 Tables ) The relationship of a group of Puf proteins from worms , including C . elegans Fbf-1 and Fbf-2 , remained ambiguous . This relationship was resolved by considering which Pufs were likely present in the ancestor of these species . These worm Pufs tend to have eight predicted Puf repeats and are closest to Puf3 and Puf4 among S . cerevisiae Pufs . We inferred that the Puf4 gene was deleted in an ancestor to metazoans and the choanoflagellate Monosiga brevicollis and therefore could not be orthologous to these worm Pufs . In contrast , Puf3 is inferred to be present in the ancestor of these worms , and we had already identified other Puf3 orthologs in these species . We assigned the worm Pufs as orthologs to Puf3 under a model that Puf3 underwent several duplications ( duplication of Puf3 and duplication of duplicates ) along the worm lineage with subsequent divergence of many of the duplicates . For S2 and S5 Figs , protein sequences were aligned using MUSCLE [116 , 117] , as implemented through the program Geneious and using default settings . For calculating percent identity of residues , all columns containing gaps in S . cerevisiae Puf3 were removed . Percent identity was calculated as the percent of residues matching the most abundant residue within each column of the alignment . Puf repeats were defined using the SMART annotation tool [114 , 115] . The S . cerevisiae Puf3 repeats are residues 538–573 , 574–609 , 610–645 , 646–681 , 682–717 , 718–752 , 760–795 , and 809–844 . The multiple sequence alignments and calculated percent identities are presented in S2 Dataset . Protein sequences were mapped back to the respective genome to identify coding sequence boundaries using standalone BLAT v34 [120] ( with parameters–q = prot–t = dnax ) . BLAT output was processed to identify for each query the hit with the smallest discrepancy ( defined as the smallest difference between query and match lengths ) . We assessed overall performance by calculating the average percent discrepancy and average coverage for the best hits . The median across all InParanoid species for average coverage was 99 . 8% , and the average discrepancy was 0 . 2% . Eighty of the InParanoid species had proteins mapping back to the genome with an average coverage >99% and a discrepancy <1% . G . gallus had the lowest average coverage ( 90 . 6% ) , and G . lamblia had the highest average discrepancy ( 12 . 5% ) . All 80 fungi had an average coverage of >99% and a discrepancy of <1% ( median: 99 . 9% coverage , 0 . 1% discrepancy ) . The 500 nucleotides downstream ( 3' on the coding strand ) of each best BLAT hit were extracted as the 3' UTR . We used a custom Perl script analogous to Fastcompare [63 , 74 , 75] to search for the Puf3 motif in orthologous sequence sets of two species , yielding a 2 x 2 contingency table of the number of sets that have a motif match in both species , in only one of the species , or in neither of the species . We searched 3' UTRs of orthologs identified by InParanoid in 99 eukaryote species [107] . The significance of ortholog sets that both have motif matches was computed by the hypergeometric test . To control for sequence similarity expected between closely related species , we repeated the search using permutations of the Puf3 motif ( e . g . , UA[ACU]AUAGU ) and used the hypergeometric p-value as a score to rank the Puf3 motif against all of its permutations ( n = 1119 ) . We report a p-value if the overlap between two species for the Puf3 motif is significant after correcting the hypergeometric p-value for multiple hypothesis testing ( p < 0 . 05 after Bonferroni correction ) and if the Puf3 motif is ranked in the top 1% ( i . e . , empirical p < 0 . 01 for comparison against all permutations ) . Phylogenetic trees were inferred using methods similar to those used previously [121–123] . To identify proteins whose sequence has preserved the underlying phylogenetic signal , we searched for proteins that contained an ortholog to a human protein in at least 90 of the 99 species investigated herein , and that within each species contained at most two orthologs to a human protein ( 1:1 or 2:1 orthologs ) ; we identified a total of 53 sets of proteins meeting this criteria , and within each set , most species only had one ortholog for each human protein used ( 1:1 orthologs ) . Each set of orthologs was multiply aligned using standalone MUSCLE [116 , 117] ( version 3 . 8 . 31 with default settings ) . The alignments were concatenated , and during the concatenation process , we kept only the first ortholog encountered for each species and added a sequence of gaps where an ortholog was not found . Columns containing more than 5% gaps were removed , yielding a final alignment with 27 , 239 columns . A tree was inferred by maximum likelihood using standalone PhyML [118 , 119] ( version 20120412 , parameters -d aa -b 1 -m WAG -o tlr -s SPR—n_starts 10 -v e -c 8 ) . For the phylogeny displayed , the descendants of a node were collapsed if a branch length from the ancestor node to one of the descendant nodes ( i . e . , the internode distance ) was greater than 0 . 65 . The branches that were collapsed largely reflect uncertainty in the relationship of species diverging earliest within eukaryotes and uncertainty about the root of the tree . The final phylogeny displayed generally agrees with the literature consensus , and points of disagreement did not affect our conclusions . For example , N . vectensis , T . adhaerens , Capitella sp . I , H . robusta , and L . gigantea are proposed to be basal metazoan species in the literature consensus , and the worms ( nematode , trematode ) are proposed to be grouped with the insects to the exclusion of vertebrates . The final phylogeny with species names can be found in S20 Fig . The multiple sequence alignment and a newick-formatted tree can be found in S3 Dataset . For fungi we identified 20 sets of proteins that across all species were 1:1 ortholog to an S . cerevisiae protein . We allowed A . macrogynus to have multiple orthologs to each S . cerevisiae protein because its genome contains many duplicated genes . Each set of orthologs was multiply aligned using standalone MUSCLE [116 , 117] ( version 3 . 8 . 31 with default settings ) . The alignments were concatenated , and during the concatenation process , we kept only the first A . macrogynus sequence encountered . Columns containing gaps were removed , yielding a final alignment with 4 , 251 columns . An initial maximum likelihood tree was inferred using standalone PhyML [118 , 119] ( version 20120412 , parameters -d aa -b 100 -m WAG -o tlr -s SPR—n_starts 10 -v e ) . The initial fungi phylogeny placed A . oligospora ( a species within Orbiliomycetes ) and T . melanosporum ( a species within Pezizomycetes ) together . We suspected that this was a long-branch artifact , as it disagreed with previous studies that used a higher sampling of species within Orbiliomycetes and Pezizomycetes [88 , 92–94] . The previous studies placed Orbiliomycetes and Pezizomycetes as separate lineages that diverged the earliest within Pezizomycotina . Nevertheless , one study [88] disagreed with others [92–94] in terms of which lineage is most basal ( i . e . , earliest diverging ) . We chose to constrain the topology to place Orbiliomycetes ( A . oligospora ) as the most basal lineage followed by Pezizomycetes ( T . melanosporum ) then the rest of Pezizomycotina . This order is consistent with two of the three studies that inferred phylogenies using multiple gene sequences [92 , 94] and the study using the "ultrastructure" character of different species [93] . The alternative topologies ( the one from the literature and our unconstrained topology ) lead to models in which an additional loss event is required to account for the Puf3 pattern and thus would alter details of our models but not the overall conclusions drawn ( S21 Fig ) . We constrained the tree topology and optimized the branch lengths and rate parameters using PhyML ( with parameter -o lr ) . The resulting tree was rooted between the species within Chyridiomycota ( A . macrogynus , B . dendrobatidis , S . punctatus ) and all other fungi , but this root should be viewed as a hypothesis . The final phylogeny used for fungi contains discrepancies with previously published trees , but the discrepancies occur at parts of the tree where the literature itself is inconsistent . As the alternative topologies would not affect our conclusions , we did not attempt to resolve these discrepancies . The multiple sequence alignment and a newick-formatted tree can be found in S3 Dataset . Protein and genome sequence data were retrieved from the sources listed in S4 Table . We used InParanoid v4 . 1 [107 , 124–126] ( default settings with no outgroup species ) to identify orthologs of S . cerevisiae or N . crassa proteins in each of the other fungi . Tables containing orthologs can be found in S4 Dataset . N . crassa strains were obtained from the Fungal Genetics Stock Center [127] . Strains were the wild-type N . crassa 74-OR23-1VA ( FGSC #2489 ) [128] and knockout strains of the gene NCU06199 . 2 ( PUF1 , FGSC #13194 ) , NCU06511 . 2 ( PUF3 , FGSC #13380 ) , NCU01774 . 2 ( part of PUF4 removes N-terminus of protein , FGSC #14089 ) , NCU01775 . 2 ( part of PUF4 removes Pumilio domain , FGSC #14547 ) , NCU01760 . 2 ( PUF8 , FGSC #15499 ) , or NCU06199 . 2 ( PUF1 , FGSC #13194 [129] . The Puf4 gene was originally annotated as two separate genes , so the Neurospora knockout collection had a separate knockout strain for each of the original annotated genes . One strain has a deletion of the sequence encoding the 5' portion of the mRNA including the predicted natural start codon . The other deletion strain is missing the sequence encoding the 3' end of the mRNA , including the sequence that encodes the Pumilio RNA binding domain and the natural translation stop codon . The knockout strains were homokaryons and of mating-type A . Strains were preserved long-term by resuspending conidia in sterile 7% milk , mixing with an equal volume of 50% glycerol , and storing at −80°C . Agar "race" tubes were prepared in 25 mL pipets ( Falcon 352575 ) . Pipets were filled with 13 mL of autoclaved medium containing 1X Vogel's Medium , 1 . 5% agar ( BD Difco 214530 ) , and 2% of a carbon source ( sucrose , glucose , maltose , or glycerol ) . Medium was allowed to solidify on a flat surface . Each N . crassa strain was streaked onto 3 mL agar slants made with Vogel's Medium with 2% sucrose and grown for 7–10 days at room temperature with constant exposure to indoor light . Conidia were obtained by adding 1 mL of water to each slant , vortexing , and extracting the liquid . Resuspended conidia ( 20 μL ) were used to inoculate a race tube through the hole made at the top of the pipet using a heated needle . Tubes were incubated at 37°C in the dark for 24 h to allow the strains to reach a maximal growth rate , and then measurements were taken twice daily until mycelium growth neared the end of the tube . Growth rates were calculated as a weighted average of the rates obtained between every two measurements , where the weight is the fraction of time elapsed between two given measurements . The calculated rates from this approach displayed lower variability than those calculated from linear regression . Measurements were obtained from two replicates for sucrose and maltose conditions , four for glycerol , and five for glucose . Statistical significance was assessed by the two-sided t test . Conidia were extracted in water from N . crassa strains streaked onto 3 mL agar slants made with Vogel's Medium with 2% sucrose and grown for 7–10 days at room temperature with constant exposure to indoor light . An estimate of conidia concentration was made by taking a sample , diluting 1:40 into water , and measuring the optical density at 530 nm . An OD530 of 0 . 25 was found to correspond to approximately 108 conidia/mL in the undiluted sample . Conidia were added to a final concentration of 106 conidia per mL into 25 mL of Vogel's Medium with 2% glucose as the carbon source . Cultures were shaken at 200 rpm in a 30°C incubator with lights on . After 8 h ~100% of cells exhibited hyphal growth with most having germ tube lengths between 50 and 400 μm . At this point , mycelia were collected by vacuum filtration . Material was scraped from the filter and placed into tubes containing 0 . 5 mL of buffer AE ( 50 mM sodium acetate , 10 mM EDTA ) , 33 . 3 μL of 25% SDS , and 0 . 5 mL of acid phenol:chloroform pH 4 . 5 ( Ambion AM9720 ) then inverted to mix and flash frozen in liquid nitrogen . RNA was isolated by hot acid phenol/chloroform extraction . Samples were placed at 65°C in a thermomixer shaking at 1 , 400 rpm for 10 min , vortexed for 10 s , then placed back in the thermomixer for another 5 min . Samples were cooled on ice for 5 min , then spun at 12 , 000 rpm in a microcentrifuge for 15 min . The aqueous phase was extracted and placed into a 2 mL phase-lock gel tube . Two more extractions with acid phenol:chlorofom were performed , followed by an extraction with chloroform . RNA was precipitated by adding one-tenth volume of 3 M sodium acetate , mixing , and then adding 1 volume of isopropanol . Samples were mixed and placed at −20°C for at least an hour . Samples were spun for 20 min at top speed in a microcentrifuge . The RNA pellet was washed with ice-cold 75% ethanol then air dried for 10 min before being resuspended in 100 μL of water . RNA yields were ~250 μg , and 15 μg of RNA were used in each reverse transcription reaction ( see "Sample processing for microarrays" ) . RNA was reverse transcribed in the presence of 5- ( 3-Aminoallyl ) -dUTP . Sample RNA ( filled with water to 13 . 8 μL ) was mixed with 1 μL of control RNA ( Ambion AM1780 ) and 2 μL of N9 and dT20VN primers ( each at 2 . 5 μg/μL ) . This mixture was heated to 70°C for 2 min then cooled to 4°C . Six microliters of 5x 1st Strand Buffer ( Invitrogen 18080–085 ) , 1 . 2 μL of 25x dNTP/aminoallyl-dUTP mix ( Ambion AM8439 ) , 3 μL of 0 . 1 M DTT , 1 μL SuperaseIn ( Ambion AM2696 ) , and 2 μL of Superscript III ( Invitrogen 18080–085 ) was added as a 13 . 2 μL master mix , and reverse transcription performed at 42°C for 2 h . RNA was then hydrolyzed by addition of 15 μL of 1 M NaOH and heating to 70°C for 15 min . The sample was neutralized by addition of 15 μL of 1 M HCl and 10 μL of sodium acetate pH 5 . 2 . cDNA was purified using the Qiagen MinElute kit and eluted from the column with 20 μL of 10 mM sodium phosphate , pH 8 . 5 . Experimental sample cDNA was labeled with Cy5 dye while cDNA made from the wild-type strain was labeled with Cy3 dye ( GE Healthcare Life Science RPN5661 ) . A tube of NHS-monoester Cy dye was resuspended in 60 μL of DMSO , and 20 μL was used for each sample to be labeled . Coupling was performed at room temperature in the dark for 1–2 h . The labeling reaction was quenched by addition of 9 μL of 3 M hydroxylamine and incubation for 15 min . Labeled cDNA was purified using the Qiagen MinElute kit . Labeled cDNA from experimental and wild-type samples were mixed ( 27 μL total ) along with 6 μL of 20X SSC ( 1X SSC is 150 mM NaCl , 15 mM sodium citrate at pH 7 . 0 ) , 2 μL of Qiagen buffer EB ( 10 mM Tris-HCl , pH 8 . 5 ) , 3 μL of 10 μg/μL polyA RNA ( Sigma P4303 ) , 1 μL of 1 M Hepes-NaOH , pH 7 . 0 , and 1 μL of 10% SDS . This 40 μL probe mixture was heated at 95°C for 2 min then centrifuged for 5 min . N . crassa microarrays were obtained from the Fungal Genetics Stock Center [127] and were printed on aminosilane-coated glass as part of the Neurospora functional genomics project [130] . S . cerevisiae microarrays were obtained from the Stanford Functional Genomics Facility and were printed on epoxysilane-coated glass . Arrays were postprocessed on the day of hybridization . Arrays were rehydrated by placing slides face down over 50 mL of 0 . 5X SSC in a humidity chamber ( Sigma H6644 ) for 30 min and were then snap dried on a 70–80°C inverted heat block for 5 s . For N . crassa arrays , the DNA was crosslinked to the slide using 600 millijoules of UV energy in a Stratalinker . The aminosilane surface was blocked by incubation for 35 min in a solution of 5X SSC , 1% SDS , and 1% w/v of Blocking Reagent ( Roche 11096176001 ) at 60°C . Arrays were washed twice in water for 2 minutes at room temperature then dried by centrifugation . For S . cerevisiae arrays , the epoxysilane surface of the slides was blocked by incubation in a solution of 1 M Tris-HCl , pH 9 . 0 , 100 mM ethanolamine , and 0 . 1% SDS for 20 min at 50°C . Arrays were washed twice in high-quality water for 1 min then dried by centrifugation . Probe mixture containing labeled cDNA was hybridized to postprocessed microarrays using the MAUI hybridization system ( BioMicro ) at 65°C for ~16 h . The MAUI mixer was removed from the microarray while submerged in a warm solution of 2X SSC and 0 . 01% SDS . The array was then placed in a 2X SSC solution at room temperature until all arrays were ready for washing . Arrays were washed with 2X SSC and 0 . 05% SDS at 65°C for 5 min with agitation , then at room temperature with agitation in 2X SSC for 1 minute , another 2X SSC wash for 2 min , 1X SSC for 2 min , and 0 . 2X SSC for 2 min . The arrays were dried by centrifugation in a low-ozone environment . Microarrays were scanned using an AxonScanner 4000B and GenePix 6 . 0 software ( Molecular Devices ) . PMT levels were set to maximize signal in each channel and only saturate a few spots . Spots were located using the GenePix software with some manual adjustment and flagging . Spots were then auto-flagged as bad if they met any of the following criteria: greater than 10% of the spot pixels were saturated in either channel , the spot contained 12 pixels or less , the R2 for the fit between Cy5 ( red ) and Cy3 ( green ) pixel intensities was less than 0 . 6 , or if in either channel the signal intensity minus the local background was less than three times the standard deviation of the local background . Array data were exported in a GenePix Results file ( . gpr ) and further processed and analyzed within the R statistical environment . After data were loaded into R , a spot was filtered if signal intensity was not 2-fold over background in both channels for N . crassa gene expression experiments or 1 . 5-fold over background for S . cerevisiae affinity purifications . For features passing flagging and filtering , we calculated the ratio between the experiment and reference channels as log2 ( red signal − red background ) / ( green signal − green background ) ) . Log2 ratio data for each experiment were mean centered and then replicate spots on the array were averaged . The microarray data can be found in S9 and S10 Tables and also have been submitted to the Gene Expression Omnibus ( GEO ) under the accession number GSE50997 . HIS4 was amplified by PCR from the S . cerevisiae strain BY4741 and used to replace his4-539 by homologous recombination in the 5Δpufs strain [47] ( named yRP1253 or yWO24 , genotype is MATα , his4-539 , leu2-3 , 112 , lys2 , trp1-1 , ura3-52 , cup1::LEU2/PM , puf1::Neor , puf2::TRP1 , puf3::Neor , puf4::LYS2 , puf5::URA3 ) . Transformation was performed using the lithium acetate method . Transformants were selected by growth on SD − His ( synthetic defined without histidine ) plates , yielding 5Δpufs his4-539::HIS4 ( named GHY001 ) . HIS3 in this strain was then replaced with HPH , which confers resistance to hygromycin B . HPH with flanking HIS3 homologous arms was produced by fusion PCR [131] . Transformants were selected on YPD plates containing 500 μg/mL hygromycin B . Correct integration was tested by restreaking colonies on another YPD + hygromycin plate ( + control ) and a SD − His plate ( −control ) . This transformation produced 5Δpufs his4-539::HIS4 his3::HPH ( named GHY002 ) . The N . crassa PUF3 coding sequence ( NCU06511 ) was made by gene synthesis ( GenScript ) and placed into pUC57 . The TAP-tag [132] , which includes two copies of the IgG binding domain of Staphylococcus aureus protein A , was added in-frame to the 3' end of the N . crassa PUF3 gene by fusion PCR . The PCR product , which included XbaI and SmaI restriction enzyme sites on its ends , was digested and ligated into the yeast expression vector p413ADH ( ATCC 87669 ) [133] at the XbaI and SmaI sites . The ligation reaction mixture was used to transform Escherichia coli , and p413ADH plasmid containing PUF3-TAP was isolated . This plasmid was then used to transform GHY002 . Transformants were selected on SD − His plates , and protein expression was verified by western blot . Affinity purifications were performed in parallel and in triplicate . GHY002 not expressing TAP tag protein ( used as a "mock" ) , GHY002 expressing N . crassa Puf3-TAP protein , and S . cerevisiae Puf3-TAP [132] ( derivative of BY4741 , Thermo Scientific YSC1177 ) were grown as 250 mL cultures in SD − His ( +His for GHY002 alone ) media to midlog phase ( OD600 of 0 . 6–0 . 9 ) . Cells were collected by centrifugation at 5 , 000 xg , and cell pellets were chilled on ice . The cell pellet was washed twice in 5 mL of ice-cold buffer A ( 50 mM Hepes-KOH pH 8 . 0 , 140 mM KCl , 1 . 8 mM MgCl2 , 0 . 1% NP-40 alternative , and 0 . 2 mg/mL heparin ) . The cell pellet was resuspended in 0 . 5 mL of buffer B ( buffer A plus 1 μg/mL pepstatin and leupeptin , 2 . 5 μg/mL aprotinin , 1 mM PMSF , 0 . 5 mM DTT , and 100 units/mL Murine RNase Inhibitor ( NEB M0314 ) ) . Cells were lysed using 0 . 65 mL of glass beads ( Biospec 11079105 ) and a Beadbeater ( Biospec ) in four 1 min cycles with 1 min on ice between cycles . Beads were removed by centrifugation at 1 , 000 xg , and the lysate was cleared by centrifugation at 8 , 000 xg for 5 min at 4°C . The supernatant was extracted , and the total protein concentration was adjusted to 15 mg/mL by dilution with buffer B . Magnetic beads were prepared for use in Protein A purification . Rabbit IgG ( Calbiochem 401590 ) was made free of detectable RNase activity by spin column purification ( Sartorius VS-ARAMAXIK ) then biotinylated ( Pierce 21329 ) and bound to Dynabeads MyOne Streptavidin C1 magnetic beads ( Invitrogen 65002 ) . Biotinylated-IgG ( 100 μg ) and 250 μL of magnetic beads were used for each affinity purification . Lysate ( 1 mL at 15 mg/mL ) was added to beads after its buffer was removed . Lysate and beads were mixed for 2 h at 4°C . Depleted supernatant ( 100 μL ) was saved for reference RNA . The beads were washed 1x with 1 . 5 mL of buffer B for 15 min and 3x with 1 . 5 mL of buffer C ( buffer B plus 10% glycerol ) for 15 min at 4°C then resuspended in 300 μL of buffer C and flash frozen in liquid nitrogen . RNA was isolated from affinity purification samples and the depleted supernatants , 35 μL of 10% SDS ( 1% final ) and 7 μL of 0 . 5 M EDTA ( 10mM final ) was added to each sample , and samples were adjusted to 350 μL total with water . RNA was purified by successive extractions with hot acid phenol:chloroform , phenol:chloroform , and chloroform alone . For the depleted supernatants , RNA from 100 μL of the final aqueous phase was then purified using Qiagen RNeasy columns by adding one-tenth volume sodium acetate , mixing with 5 volumes of Qiagen buffer PB , loading onto the column , washing with buffer PE , and eluting off the column with 50 μL of warm water . For the affinity purification samples , RNA from the aqueous phase was isopropanol precipitated , washed with 75% ethanol , dried , and resuspended in 20 μL of water . Two-thirds of the affinity purification sample RNA ( ~0 . 5–2 μg ) was used for reverse transcription; 12 μL ( ~8–10 μg ) of depleted supernatant ( "reference" ) RNA was used ( see "Sample processing for microarrays" ) . We used the programs FIRE [82] and REFINE [15 , 61] to search for enriched sequence patterns in sets of 3' UTRs . For each search , 3' UTRs for a given set of transcripts were compared to a background set of 3' UTRs ( e . g . , 3’ UTRs of orthologs to S . cerevisiae proteins ) . FIRE ( v1 . 1 ) was run with parameters ( —exptype = discrete—dodna = 0—seqlen_rna = 500—nodups = 1—kungapped = 6—gap = 0–4 ) . REFINE ( v0 . 1 ) , which uses dust and MEME ( v4 . 7 . 0 ) [134–137] , was run with defaults except the parameter for the minimum number of significant k-mer sites for target sequences to be kept was set to 1 ( CT = 1 ) . The background mononucleotide frequencies used in MEME were calculated from the complete set of 3' UTRs sequences used as input . For the IP data , the S . cerevisiae 3' UTRs of targets of S . cerevisiae Puf3 or N . crassa Puf3 were searched using both REFINE and FIRE . Both programs returned motifs that resembled the canonical Puf3 recognition element; the results from FIRE are displayed in Fig 4A as its motifs had lower p-values than REFINE based on the hypergeometric test . For other searches , only REFINE was used . Files containing position frequencies ( PFM ) for each motif can be found in S5 Dataset . The statistical significance of motifs returned by REFINE was compared to results generated by shuffling the target assignment of input sequences and running the motif search . One hundred permutations were performed for each real motif search , and the hypergeometric p-value of the top motif from each permutation was compared to the motifs found from real data . A motif was considered significant if its p-value was lower than all p-values found from permutations ( i . e . , p < 0 . 01 ) . A summary of the permutation results can be found in S11 Table . GO term searches were performed using "GOstats" [138] within Bioconductor [139] in R and using annotations from the S . cerevisiae database in "org . Sc . sgd . db" . For each comparison we used set S . cerevisiae genes that had orthologs in the respective species as a background ( i . e . , used only genes with data ) . p-Values from GO term searches were Bonferroni corrected for multiple hypothesis testing by multiplying the p-value by 7 , 097 , which represents the number of GO terms within molecular function , biological process , and cellular component . The results of all GO term searches can be found in S6 Dataset . p-Values in these files have not been Bonferroni corrected . We identified a series of motifs that represent each Puf protein’s RNA binding specificity using the following three steps: identifying the top informative UGUA-based 10mers ( i . e . , 10mers that discriminate putative targets from nontargets ) , identifying a cutoff for informative 10mers to keep , and clustering these 10mers into groups . To identify informative UGUA-based 10mers , we first selected an in-group and out-group . For the Saccharomyces Pufs , the in-group consisted of experimentally identified S . cerevisiae Puf targets and its orthologs in post-WGD species ( S . cerevisiae-RM11-1 , S . paradoxus , S . mikatae , S . kudriavzevii , S . bayanus , S . castellii , C . glabrata ) . For Pezizomycotina Puf4 , the in-group consisted of orthologs of conserved Saccharomycotina Puf3 targets in all Pezizomycotina species except A . oligospora and T . melanosporum . The out-group consisted of orthologs that were not in the in-group . We searched the 3' UTRs with every possible UGUA-based 10mer ( UGUANNNNNN ) and tallied how many 3' UTRs in the in-group did or did not have a match and how many in the out-group did or did not have a match . From the resulting 2 x 2 contingency table , we calculated the mutual information for each 10mer , which is a measure of how much information the presence or absence of a 10mer match contributes to the classification of the two groups . Mutual Information=∑i , jpi , j×log2pi , jpi × pj ( 1 ) pi , j represents the joint probability while pi and pj represent the marginal probabilities . The 10mer with highest mutual information was kept . We repeated the search to identify 250 total 10mers , and after each round we masked 3' UTRs that had already been accounted for by a kept 10mer . To identify a cutoff for informative 10mers , the 10mers were ranked based on which round of the search they were found , and we calculated the local slope ( centered window of 11 points ) of the false positive rate ( FPR ) against the true positive rate ( TPR ) as in a ROC ( Receiver Operator Characteristic ) curve . The cutoff used was the first point before which the local slope drops below one ( FPR > TPR ) . For the Pezizomycotina Puf4 search , the local slope took a sharp decline around the 100th 10mer but did not fall below one until the 137th 10mer , so we used a more stringent cutoff to include only the top 100 10mers . We then identified subsets of informative 10mers that shared a common sequence pattern . To group 10mers based on similarity to each other , we made a network where a node represents a 10mer and an edge represents 10mers that have a Hamming distance of one ( one substitution between two 10mers ) . This network was visualized in Cytoscape 2 . 7 . 0 [140] and organized using the yFiles Organic layout . From the network display , we manually placed the 10mers into groups if a sequence pattern was shared with nearby nodes ( S11 Fig ) . Although this was a manual procedure , we clearly identified the unifying pattern within each cluster of 10mers ( S11 Fig ) . For comparing motifs between Puf proteins , we represented each Puf protein’s specificity as a sequence logo [141] based on the number of matches to each 10mer in that group . We only used matches to in-group sequences and matches found as part of the iterative search . For calculating conservation scores , we derived a regular expression to represent each group . A nucleotide was included at a position in the regular expression if it was found in more than 10% of the sequences . The regular expressions are TGTA[ACT]ATA ( Puf3 ) , TGTA[ACT]A[ACT]TA or TGTA[ACT][ACT]ATA ( Saccharomycotina Puf4 ) , TGTA[AT][CT][AT][AT]TA or TGTA[CGT]TATA ( Saccharomycotina Puf5 ) , and TGTA[ACT]A . TA or TGTA[ACT] . ATA or TGTA[CT]AACA or TGTA[ACT] . [AT] . TA ( Pezizomycotina Puf4 ) . In a regular expression , a period ( . ) permits any nucleotide to be present at that position , and a position within brackets ( e . g . , [AT] ) permits the nucleotides indicated . For the Puf4 regular expressions , the group that required A at positions 6 and 7 ( TGTA[ACT]AATA ) was split into the groups that required A at only one of positions ( TGTA[ACT]A . TA or TGTA[ACT] . ATA ) . Files containing the results of each motif search can be found in S7 Dataset . To statistically assess the similarity between motifs , we used position frequency matrices from the six positions downstream of UGUA as input to MotifComparison ( p-BLiC , 100 shuffles , no shift permitted between matrices ) in the MotifSuite [84] . MotifComparison calculated the p-value of the Bayesian Likelihood 2-Component ( BLiC ) score [142] , and we considered two motifs to be similar if p < 0 . 05 . Motifs are displayed as sequence logos [141] , and images were made using a version of the seqLogo package in R that was modified to accommodate U in the logos . We calculated a conservation score to represent the prevalence of 3' UTRs that have a match to a given motif within a set of orthologs ( e . g . , an S . cerevisiae protein and its orthologs in Saccharomycotina species ) . For each set of orthologs , we first assigned each species a presence ( 1 ) call if the 3' UTR had a motif match or an absence ( 0 ) call if it did not . Species without an ortholog were removed from the tree . If a species had more than one ortholog , we searched the 3' UTRs of all of them and assigned a presence call if any had a motif match . The conservation score ( CS ) for the ancestor of A and B is defined as follows: CSA , B=PA×BLA+PB×BLB ( 2 ) The conservation score is the weighted sum of branch lengths over which matches to the motif ( i . e . , putative binding sites ) are inferred to be present . This score helps to control for the uneven sampling of species within each phylogeny . The weight is the proportion ( P ) of the branch length ( BL ) over which a motif match is present . BLA and BLB represent the sum of branch lengths from the ancestor to the respective descendant ( s ) . The proportion ( P ) is defined as follows: PA={1 , if descendant is leaf and motif match present0 , if descendant is leaf and motif match absentCSA1 , A2BLA1+BLA2 , if descendant is internal node ( 3 ) A1 and A2 represent the descendants of A . We assumed no change in state along each branch from descendant to ancestor if the descendant is an extant species ( terminal node or leaf ) . If the immediate descendant is an internal node , we assumed that the proportion of the branch length spent with a motif match present between an ancestor and the immediate descendant is the same proportion that the descendants of that descendant spent with a motif match present . The conservation score was calculated recursively upwards from the terminal nodes . To estimate FDRs , we calculated conservation scores ( CS ) using 100 permuted motifs ( pm ) and calculated the FDR for a given ortholog set A and real motif ( rm ) as follows: FDR ( A , rm ) =1100Σpm=1100Σi=1n{1 , if CS ( i , pm ) ≥CS ( A , rm ) 0 , if CS ( i , pm ) <CS ( A , rm ) Σi=1n{1 , if CS ( i , rm ) ≥CS ( A , rm ) 0 , if CS ( i , rm ) <CS ( A , rm ) ( 4 ) where i represents a single ortholog set and n represents the total number of ortholog sets . The numerator represents the average number of ortholog sets with a conservation score from a permuted motif that are greater than or equal to the conservation score for ortholog set A from the real motif . The denominator represents the number of ortholog sets with a conservation score from the real motif that are greater than or equal to the conservation score for ortholog set A from the real motif . We permuted the position of the motifs , thereby maintaining the redundant information when a regular expression has more than one motif . For example , a regular expression with two motifs TGTAC and TGTAT could result in a permutation with CATGT and TATGT , where for instance the G in position 2 of both motifs is now moved to position 5 in the permuted motifs . Files containing calculated conservation scores and FDRs are in S8 Dataset . Our definition of conserved targets has the potential to identify novel targets of Puf proteins . In S4 Text , we provide strong support for the identification of over 100 novel targets for Puf3 , Puf4 , and Puf5 in S . cerevisiae . Multiple genes often encode each core histone , and as our conservation analysis only reports one number per species , we wanted to test whether Puf4 and Puf5 sites were enriched in the ortholog set of each histone while accounting for the number of 3' UTRs we searched . We collected the orthologs of each S . cerevisiae histone protein and collapsed these into a set based on the histone type ( Hta1 and Hta2 for H2A , Htb1 and Htb2 for H2B , Hht1 and Hht2 for H3 , Hhf1 , and Hhf2 for H4 , Htz1 for H2A . Z ) . For each type of histone , we extracted and searched the 3' UTRs for a match to the appropriate Puf motif ( Saccharomycotina Puf4 , Saccharomycotina Puf5 , or Pezizomycotina Puf4 ) . A species was assigned a presence ( 1 ) or absence ( 0 ) call if at least one of the 3' UTRs for a given histone type had a motif match . For each lineage and histone type tested , we calculated a conservation score ( See "Calculating conservation scores and identifying significantly conserved ortholog sets" ) . For a null distribution , we calculated conservation scores from searches using permuted versions of the Puf motif . Comparison of conservation scores from the permuted motifs to the conservation score using the real motif yielded an empirical p-value . We did not test a histone type and lineage if the maximum conservation score possible could not yield a significant p-value ( i . e . , if the test is statistically underpowered ) . Custom Perl or R scripts were written for data processing , statistical testing , and data visualization as needed . We used Bioperl [143] for some input and output operations and for traversing phylogenetic trees . We used the R functions fisher . test ( ) to calculate a p-value for Fisher's exact test and also report an odds-ratio , t . test ( ) to calculate its p-value , phyper ( ) to calculate a p-value for the hypergeometric test , fisher . exact ( ) in the exact 2 x 2 package [144] to calculate confidence intervals for the odds-ratio , and binom . test ( ) to calculate a p-value for the binomial test . Hypergeometric test p-values reported herein were calculated from the one-tailed test . Fisher's exact test is a two-tailed version of the hypergeometric test , and for reference can be calculated as follows: p= ( a+bb ) ( c+dc ) ( na+c ) = ( a+b ) ! ( c+d ) ! ( a+c ) ! ( b+d ) ! a ! b ! c ! d ! n ! ( 5 ) where a , b , c , and d represent the cells in a 2 x 2 contingency table , n is the sum of all cells , and ( a+bb ) represents a binomial coefficient . A binomial coefficient ( nk ) can be expressed as n ! k ! ( n−k ) ! . | We set out to trace the evolutionary history of an RNA binding protein and how its interactions with targets change over evolution . Identifying this natural history is a step toward understanding the critical differences between organisms and how gene expression programs are rewired during evolution . Using bioinformatics and experimental approaches , we broadly surveyed the evolution of binding targets of a particular family of RNA binding proteins—the Puf proteins , whose protein sequences and target RNA sequences are relatively well-characterized—across 99 eukaryotic species . We found five groups of species in which targets have been conserved for at least 100 million years and then took advantage of genome sequences from a large number of fungal species to deeply investigate the conservation and changes in Puf proteins and their RNA targets . Our analyses identified multiple and extensive reconfigurations during the natural history of fungi and suggest that RNA binding proteins and their RNA targets are profoundly involved in evolutionary reprogramming of gene expression and help define distinct programs unique to each organism . Continuing to uncover the natural history of RNA binding proteins and their interactions will provide a unique window into the gene expression programs of present day species and point to new ways to engineer gene expression programs . | [
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] | [] | 2015 | Evolutionary Conservation and Diversification of Puf RNA Binding Proteins and Their mRNA Targets |
The ability of Staphylococcus aureus and other pathogens to consume glucose is critical during infection . However , glucose consumption increases the cellular demand for manganese sensitizing S . aureus to host-imposed manganese starvation . The current investigations were undertaken to elucidate how S . aureus copes with the need to consume glucose when metal-limited by the host . A critical component of host defense is production of the manganese binding protein calprotectin . S . aureus has two variants of phosphoglycerate mutase , one of which is manganese-dependent , GpmI , and another that is manganese-independent , GpmA . Leveraging the ability to impose metal starvation in culture utilizing calprotectin revealed that the loss of GpmA , but not GpmI , sensitized S . aureus to manganese starvation . Metabolite feeding experiments revealed that the growth defect of GpmA when manganese-starved was due to a defect in glycolysis and not gluconeogenesis . Loss of GpmA reduces the ability of S . aureus to cause invasive disease in wild type mice . However , GpmA was dispensable in calprotectin-deficient mice , which have defects in manganese sequestration , indicating that this isozyme contributes to the ability of S . aureus to overcome manganese limitation during infection . Cumulatively , these observations suggest that expressing a metal-independent variant enables S . aureus to consume glucose while mitigating the negative impact that glycolysis has on the cellular demand for manganese . S . aureus is not the only bacterium that expresses manganese-dependent and -independent variants of phosphoglycerate mutase . Similar results were also observed in culture with Salmonella enterica serovar Typhimurium mutants lacking the metal-independent isozyme . These similar observations in both Gram-positive and Gram-negative pathogens suggest that expression of metal-independent glycolytic isozymes is a common strategy employed by bacteria to survive in metal-limited environments , such as the host .
The preferred carbon source for many pathogens is glucose and disruption of glycolysis reduces the ability of many invaders to cause infection [1–10] . The primacy of glucose as an energy source is emphasized by catabolite repression , which prevents bacteria from utilizing other carbon sources when glucose is present [1–3 , 11–13] . The advantage that sugar consumption provides is highlighted by the increased sensitivity of individuals with diabetes , especially those with hyperglycemia , to infections by Staphylococcus aureus , Streptococcus pneumoniae , Mycobacterium tuberculosis , Escherichia coli , Klebsiella pneumoniae and Candida albicans [14–17] . At the same time , host defenses reduce the ability of pathogens to consume glycolytic substrates by limiting metal availability or via other mechanisms [18 , 19] . The spread of antibiotic-resistant isolates has led the Centers for Disease Control and Prevention ( CDC ) and the World Health Organization ( WHO ) to call for the development of novel therapeutics to treat S . aureus and other pathogens [20 , 21] . Understanding how pathogens generate energy and preserve the activity of critical metabolic pathways despite the concerted efforts of the immune system has the potential to identify new opportunities for therapeutic intervention . Transition metals such as iron ( Fe ) , manganese ( Mn ) and zinc ( Zn ) are essential for life , as they play an important role in facilitating the structure and function of proteins [22] . The host takes advantage of this essentiality by restricting the availability of Fe , Mn and Zn during infection , a defense known as nutritional immunity [23–28] . The prototypic example of Mn and Zn restriction is the staphylococcal abscess , which is virtually devoid of these metals [25 , 29] . A key mediator of the host Mn-withholding response is the immune effector calprotectin ( CP ) . A heterodimer of S100A8 and S100A9 , CP possesses two transition metal binding sites that chelate Mn and Zn with nanomolar and femtomolar affinities , respectively [25 , 27 , 30–35] . Although CP binds other metals , including Fe ( II ) and nickel ( Ni ) [36 , 37] , the primary metals withheld from S . aureus are Mn and Zn . CP comprises ~50% of the total protein in the neutrophil cytoplasm and CP concentrations can exceed 1 mg/ml at sites of infection [38 , 39] . Mice lacking CP have defects in Mn sequestration and are more sensitive to a range of bacterial and fungal pathogens , including S . aureus , Acinetobacter baumannii , K . pneumoniae and Aspergillus fumigatus [25–27 , 40 , 41] . During infection , nutritional immunity inactivates Mn-dependent bacterial processes such as the Mn-dependent superoxide dismutase possessed by S . aureus . This in turn renders S . aureus more sensitive to the oxidative burst of immune cells . While glycolysis is canonically believed to be a magnesium-dependent process [42] , many bacteria possess Mn-dependent variants of glycolytic enzymes , including phosphoglycerate mutase , enolase and pyruvate kinase [43–50] . In fact , in a number of pathogens , including S . aureus and S . pneumoniae , the consumption of sugars is dependent on Mn availability [18 , 51] . At the same time , glycolysis is critically important for the ability of S . aureus to cause infection and mutations that reduce the activity of this pathway frequently result in virulence defects and sensitize the bacterium to other host defenses such as NO· produced by immune cells [4–8] . While the best characterized mechanism utilized by bacteria to resist nutritional immunity is the use of high-affinity metal transporters , S . aureus and other pathogens also possess transporter-independent adaptations that critically contribute to the ability of the bacteria to overcome nutritional immunity [18 , 29 , 52–55] . However , mechanisms that would enable them to maximize the consumption of sugars via glycolysis when metal-starved have not been described . Despite increasing the cellular demand for Mn and sensitizing S . aureus to Mn starvation , we recently observed that S . aureus prefers to consume glucose even when Mn-starved by CP [18 , 55] . This paradoxical use of glycolysis despite the associated increase in cellular demand for Mn likely occurs in part as the fermentation of sugars enables the bacterium to generate energy when exposed to NO· produced by activated immune cells [8] . At the same time , it highlights a challenge faced by S . aureus and other pathogens , where adapting to one host defense sensitizes the bacterium to another aspect of the immune response [18 , 55] . Many bacteria , including S . aureus , possess multiple copies of glycolytic enzymes . While the presence of multiple isozymes is frequently associated with glycolytic and gluconeogenic flux , we wondered if another reason for maintaining multiple isozymes might exist . Specifically , we hypothesized that multiple copies of glycolytic enzymes may help S . aureus and other pathogens mitigate the stress of consuming sugars when metal-starved . The current investigations revealed that expression of a second metal-independent variant of phosphoglycerate mutase enables S . aureus to maintain the ability to consume glucose when Mn-starved and critically contributes to resisting nutritional immunity during infection . Similar results were also observed with Salmonella enterica , suggesting that expression of metal-independent isozymes is a common strategy employed by bacteria to survive in metal-limited environments .
As an initial step to identify how S . aureus promotes retention of glycolysis during infection , the repertoire of glycolytic enzymes possessed by S . aureus was assessed . This analysis revealed that S . aureus possess two copies of glyceraldehyde-3-phosphate dehydrogenase , aldolase and phosphoglycerate mutase . Notably , one of the phosphoglycerate mutase isozymes , GpmI , is predicted to be Mn-dependent , while the other , GpmA , is Mn-independent , utilizing 2 , 3-bisphosphoglycerate as a catalytic cofactor [56 , 57] . GpmI is encoded by the glycolytic operon that contains gapR , gapA , pgk , tpiA and eno , whereas gpmA is not part of an operon and is expressed independently of other glycolytic enzymes ( Fig 1A ) . The fact that gpmI is in a locus with many other glycolytic enzymes , while gpmA is in a separate location , suggests that GpmI is the primary phosphoglycerate mutase and that GpmA is the secondary enzyme . One possibility is that GpmI and GpmA have directional preferences , with GpmI being essential for glycolysis and GpmA being essential for gluconeogenesis , as has been suggested for the two isozymes of glyceraldehyde-3-phosphate dehydrogenase encoded by S . aureus [58] . However , it is also possible that the metal-independent variant promotes retention of glycolytic flux when metal-starved by the host . As an initial step in evaluating this latter idea , expression of gpmA and gpmI in wild type bacteria exposed to CP was assessed . While expression of gpmI did not change , gpmA levels increased ~40-fold in response to CP ( Fig 1B ) . Expression of gpmA was also induced in a S . aureus mutant lacking the Mn transporters MntABC and MntH ( ΔmntCΔmntH ) to a level comparable to that caused by CP ( Fig 1C ) . In contrast , expression of gpmI did not change in the ΔmntCΔmntH mutant ( Fig 1D ) . Cumulatively , these observations suggest that GpmI is the primary phosphoglycerate mutase used by S . aureus and that GpmA may be important when the bacteria experience Mn limitation . To elucidate the respective contributions of the two staphylococcal phosphoglycerate mutases to resisting metal starvation , wild type as well as ΔgpmA and ΔgpmI mutants were evaluated for their ability to grow in the presence of CP . Loss of the metal-dependent isozyme , GpmI , in S . aureus Newman did not alter the sensitivity of S . aureus to CP . Conversely , loss of the metal-independent isozyme profoundly sensitized S . aureus to CP ( Fig 2A , S1A Fig ) . Expression of GpmA from a plasmid reversed the increased sensitivity of ΔgpmA to CP ( Fig 2B , S1B Fig ) . Increased sensitivity to CP was also observed upon loss of GpmA in the community-acquired MRSA strain USA300 JE2 ( Fig 2C , S1C Fig ) , which was also reversed by expression of GpmA from a plasmid ( Fig 2D , S1D Fig ) . Together , these results demonstrate that loss of GpmA makes S . aureus more sensitive to CP-imposed metal starvation . To determine if Mn or Zn restriction was responsible for the increased sensitivity of ΔgpmA to CP , we leveraged CP binding site mutants with altered metal-binding properties [27 , 32] . Similar to WT CP , when ΔgpmA was grown in the presence of the ΔS2 mutant , which can bind either Mn or Zn , it was more sensitive to CP treatment than wild type bacteria or ΔgpmI ( Fig 2E ) . However , in the presence of the ΔS1 mutant , which cannot bind Mn , the increased sensitivity of ΔgpmA was abrogated . These observations suggest that loss of GpmA impairs the ability of the bacteria to cope with CP-induced Mn limitation . To further test this idea , wild type S . aureus , ΔgpmA and ΔgpmI were grown in medium depleted of Mn and Zn ( NRPMI ) . Similar to what was observed in the presence of CP , the ΔgpmA mutant had a severe growth defect compared to wild type bacteria and the ΔgpmI mutant ( Fig 2F , S1E Fig ) . The growth defect of the ΔgpmA mutant in this medium was reversed by the addition of Mn but not Zn . Collectively , these results demonstrate that GpmA is crucial for growth when S . aureus is Mn-starved . The majority of enzymes in the glycolytic pathway , including phosphoglycerate mutase , can be used for flux in both directions [59 , 60] . To determine if the GpmA-dependent growth defect is associated with decreased glycolytic or gluconeogenic activity , wild type S . aureus , ΔgpmA and ΔgpmI were grown in the presence of CP in a defined medium supplemented with either glucose or Casamino acids as the sole energy source . In the presence of glucose , the ΔgpmA mutant was more sensitive to CP than wild type bacteria or ΔgpmI ( Fig 3A , S2A Fig ) . In contrast , there was no difference between any of the strains when Casamino acids were provided ( Fig 3B , S2B Fig ) . Supplementation of the glucose-containing medium with sodium pyruvate , which bypasses the phosphoglycerate mutase step , also enabled the ΔgpmA mutant to grow as well as wild type S . aureus or the ΔgpmI mutant ( Fig 3C , S2C Fig ) . Together , these observations indicate that loss of GpmA reduces the ability of S . aureus to consume glucose when Mn-starved . To evaluate if this apparent defect in glycolysis extended to other substrates dependent on glycolysis for consumption , growth of the ΔgpmA and ΔgpmI mutants in defined medium supplemented with glycerol was assessed . In the presence of glycerol , which enters glycolysis upstream of phosphoglycerate mutase , as the sole carbon source the ΔgpmA mutant was more sensitive to CP than wild type bacteria or ΔgpmI ( Fig 3D , S2D Fig ) . Combined , these results reveal that GpmA plays an important role in retaining the ability to consume glycolytic substrates when Mn-starved . The expression of Mn transporters is critical for the ability of S . aureus to resist host-imposed Mn starvation [29] . To confirm that the enhanced sensitivity of the ΔgpmA mutant was not due to an unanticipated impact on Mn transporter activity , ΔmntCΔmntHΔgpmA and ΔmntCΔmntHΔgpmI strains were assessed for CP sensitivity . Loss of GpmA , but not GpmI , in the ΔmntCΔmntH background further increased the sensitivity of the transporter double mutant , suggesting that GpmA and the Mn transporters function independently to promote resistance to Mn starvation ( Fig 4A and 4B , S4A–S4C Fig ) . Expression of GpmA from a plasmid reverted the CP sensitivity of ΔmntCΔmntHΔgpmA back to that of the ΔmntCΔmntH mutant ( Fig 4C ) . Cumulatively , these results suggest that loss of GpmA does not sensitize the bacteria to Mn starvation by reducing Mn transport but rather by a Mn transporter-independent mechanism . Culture-based experiments suggest that the Mn-independent activity of GpmA enhances the ability of S . aureus to maintain glycolytic flux when Mn-limited . To evaluate the contribution of the two phosphoglycerate mutases to S . aureus pathogenesis , wild type ( C57BL/6 ) mice were retro-orbitally infected with wild type S . aureus , ΔgpmA , or ΔgpmI and the infection was allowed to proceed for 4 days . During the course of the infection mice infected with ΔgpmA lost significantly less weight than mice infected with wild type S . aureus or ΔgpmI ( Fig 5A ) . Interestingly , mice infected with ΔgpmI lost slightly , but significantly , less weight than mice infected with wild type bacteria . Consistent with the weight loss , the ΔgpmA mutant had significantly decreased bacterial burdens in the liver , heart , and kidneys when compared to wild type bacteria ( Fig 5B and 5C ) indicating that GpmA plays an important role in establishing systemic disease . While mice infected with ΔgpmI did not show a statistically significant decrease in bacterial burdens in any of the organs , bacterial burdens in the kidneys were slightly lower than in mice infected with wild type bacteria ( Fig 5C ) . To evaluate if the importance of GpmA during infection is driven by host restriction of Mn availability , CP-deficient ( C57BL/6 S100A9-/- ) mice , which do not remove Mn from liver abscesses [25 , 29] , were infected with wild type bacteria , ΔgpmA or ΔgpmI . Relative to wild type mice , the CP-deficient mice infected with ΔgpmA had increased bacterial burdens , indicating that the importance of GpmA during infection is driven by host-imposed Mn limitation . Moreover , in CP-deficient mice there was no difference in bacterial burdens between wild type bacteria , ΔgpmA or ΔgpmI ( Fig 5B ) , suggesting either phosphoglycerate mutase isozyme is sufficient when Mn is available . Cumulatively , these results indicate that GpmA contributes to staphylococcal infection by promoting retention of glycolytic activity when Mn-starved by the host . The expression of metal-dependent and -independent variants of phosphoglycerate mutase is not unique to S . aureus , and many pathogenic bacteria including E . coli , Shigella flexneri , S . enterica , Pseudomonas aeruginosa , Listeria monocytogenes and others possess both forms [57] . However , the molecular rationale for retaining two phosphoglycerate mutase isozymes in these organisms remains unknown [57] . In light of our observation with S . aureus , we wondered if metal-independent variants of phosphoglycerate mutase broadly promote retention of glycolytic potential when experiencing Mn starvation . To test this idea , the ability of wild type S . enterica serovar Typhimurium and a ΔgpmA mutant to grow in rich medium in the presence of CP was assessed . Similar to S . aureus , the Salmonella ΔgpmA mutant was more sensitive to CP treatment than wild type bacteria ( Fig 6A , S4A Fig ) . Use of the CP variants with altered metal-binding properties revealed that loss of GpmA impaired the ability of Salmonella to cope with CP-induced Mn limitation ( Fig 6B ) . Cumulatively , these observations establish that expression of a Mn-independent variant of phosphoglycerate mutase promotes the growth of Salmonella when metal-starved . To test if the importance of GpmA to Salmonella growth when metal-starved is attributable to its ability to promote consumption of glycolytic substrates , bacteria were grown in the presence of CP and provided with either glucose or Casamino acids as a carbon source ( Fig 6C and 6D , S4B and S4C Fig ) . As in rich medium , loss of GpmA increased the sensitivity of Salmonella to CP when glucose was provided as the sole carbon source . However , the enhanced sensitivity of the Salmonella ΔgpmA mutant was ablated in the presence of Casamino acids , which do not require the glycolytic pathway for consumption . Additionally , providing sodium pyruvate as a carbon source reversed the increased sensitivity to CP , whereas glycerol did not ( Fig 6E and 6F , S4D and S4E Fig ) . Combined , these results suggest that the expression of metal-independent versions of phosphoglycerate mutase are a common mechanism employed by pathogenic bacteria to resist Mn limitation .
The consumption of sugars via glycolysis and metals are important for pathogens during infection [4 , 5 , 7–10 , 24 , 61–66] . Sugars are the preferred carbon source for S . aureus and other pathogens and increased availability of glucose renders individuals more sensitive to infection [14–17] . At the same time , the host restricts the availability of metals , inactivating Mn-dependent bacterial processes , including glycolysis [18 , 55] . While expression of high-affinity metal transporters enhances the ability of S . aureus and other pathogens to maintain glycolytic flux and are critical for infection , they are insufficient to ensure that the pathogen obtains sufficient Mn to activate Mn-dependent superoxide dismutases [27 , 53] . The current work provides yet another example of a critical Mn-dependent enzyme , GpmI , which is inactivated by nutritional immunity . At the same time , host defenses force S . aureus to ferment sugars via the glycolytic pathway , increasing the cellular demand for this metal [5 , 7 , 8] . The current work identified a critical role for a priori redundant metal-independent variants of phosphoglycerate mutases in both S . aureus and Salmonella in enabling glucose consumption and resisting nutritional immunity . The Mn-independent variant of phosphoglycerate mutase in S . aureus , which is encoded outside of the canonical glycolytic operon , is the primary isozyme used by the bacteria when Mn-starved and is critical for infection due to the host metal-withholding response . The conserved function of the metal-independent variant of phosphoglycerate mutase in both S . aureus and Salmonella suggests that this approach is likely a common strategy for preserving the ability to consume glucose while minimizing the cellular demand for Mn . S . aureus and Salmonella are not the only pathogens that experience metal limitation during infection nor are they the only pathogens to require glucose consumption for disease [1 , 5–10 , 28 , 65 , 66] . It therefore seems likely that other successful pathogens have adaptations that allow them to consume glucose when metal-limited . Notably , in addition to S . aureus and Salmonella , many other bacterial pathogens , including S . flexneri , P . aeruginosa , and L . monocytogenes possess metal-dependent and -independent variants of phosphoglycerate mutase [57] . As CP is one of the most abundant proteins at sites of infection [38 , 39] , it seems likely that all of these pathogens experience Mn limitation during infection . Combined with the current observations , this suggest that using metal-independent variants of phosphoglycerate mutase to preserve glycolytic function in the host when Mn-starved may be a conserved strategy . Intriguingly , a number of bacterial pathogens , including S . pneumoniae , Enterococcus faecalis , Haemophilus influenzae , Neisseria meningiditis , and M . tuberculosis possess only a metal-independent version of phosphoglycerate mutase [56 , 57] . Similar to other pathogens , all of these organisms would be expected to encounter Mn-limited environments within the host . This raises the possibility that possessing only the metal-independent variant of phosphoglycerate mutase represents further adaptation to life in a Mn-poor environment . At the same time , the majority of bacteria , archaea , protozoa and fungi , including pathogens , possesses both metal-dependent and -independent versions of phosphoglycerate mutase . However , others , such as Helicobacter pylori , possesses only the metal-dependent variant but also encounter CP during infection [67] . The biochemical differences between the metal-dependent and independent-variants of phosphoglycerate mutase extend beyond their cofactor [47 , 56 , 68–70] . Notably , in S . aureus the metal-dependent variant is in the primary glycolytic operon . Together , these observations suggest that the metal-dependent variants of phosphoglycerate mutase provide some biological advantage . Phosphoglycerate mutase is not the only enzyme in the glycolytic pathway that contains both metal-dependent and -independent versions . Both metal-dependent and -independent variants of fructose bisphosphate aldolase exist , and many pathogenic bacteria , including S . aureus , Salmonella , Borrelia burgdorferi , S . pneumoniae , and K . pneumoniae contain both enzymes . The metal-dependent variant is classically thought to utilize Zn as a cofactor [71–73] . Similar to GpmA , which is upregulated in response to CP treatment , the metal-independent staphylococcal aldolase is also upregulated in the presence of CP [74] . As aldolase is the only staphylococcal enzyme in glycolysis predicted to use Zn , this could explain why CP-imposed Mn limitation but not Zn limitation inactivates glycolysis in S . aureus [18 , 55] . More broadly , it suggests that the metal-independent fructose bisphosphate aldolase may contribute to retaining glycolytic function when metal-starved by the host . In both Salmonella and B . burgdorferi , the Zn-independent aldolase has been suggested to provide a mechanism for the bacteria to consume glucose when experiencing nitrosative stress [19] . In the case of B . burgdorferi , nitrosative stress was suggested to damage the Zn-dependent isozyme [19] . At the same time , in S . aureus nitrosative stress has been observed to lead to a transcriptional response similar to that seen when Zn-starved [74] . Regardless of the stress that the metal-independent aldolase responds to , these observations suggest that metal-independent isozymes are likely to be generally important to the ability of bacteria to maintain glycolytic flux during infection . The presence of two isozymes with differing metal utilization is not limited to glycolysis or S . aureus and Salmonella . In Bacillus subtilis , Fe limitation leads to induction of the Fe-sparing response , which includes increased expression of flavodoxin that can replace the iron-containing electron transfer protein ferredoxin [75] . Similarly , E . coli contains two ribonucleotide reductases , a primary Fe-dependent enzyme , and a Mn-dependent enzyme that is crucial for survival when bacteria experience superoxide stress or Fe limitation [76 , 77] . Similarly , in response to Zn limitation , B . subtilis will induce expression of an alternative Zn-independent folate biosynthesis enzyme , GTP cyclohydrolase-IB ( GCYH-IB ) . Induction of GCYH-IB prevents Zn starvation from inducing a folate auxotrophy [78] . B . subtilis also possesses L31* and L33* , Zn-independent ribosomal proteins , which promote survival when Zn-starved [79 , 80] . Combined with the current observations , this suggests that replacing metal-dependent enzymes with metal-independent variants is a common strategy for surviving Fe , Zn and Mn limitation . Notably , the switch between utilizing the metal-dependent and -independent isozyme is frequently driven by a metal-sensing regulator [75 , 76 , 78–80] . In S . aureus , loss of the Fe-sensing regulator represses the expression of gpmA , but it is not predicted to be regulated by the Mn-sensing regulator MntR [81] . While the mechanisms that control expression of GpmA are unknown , this suggests that metal-independent regulatory circuits also play an important role in coordinating a response to metal starvation . This idea is supported by the observation that ArlRS , which contributes to staphylococcal growth in Mn-poor environments , appears not to sense Mn directly but rather the impact that Mn starvation has on glycolytic flux ( Parraga et . al . In Press ) . The current investigations add metal-dependent and -independent phosphoglycerate mutases to the growing list of enzymes that carry out apparently redundant biochemical reactions , but whose distinct reaction mechanisms enable microbes to cause infection or survive in other stressful environments [5 , 6 , 19 , 75 , 76 , 78–80] . Many of these false redundancies have been identified by studying how microbes respond to metal limitation [75–80] . However , the importance of other pseudo-redundant enzymes has been revealed by investigating how pathogens cope with other stresses experienced during infection , such as the contribution of a second staphylococcal lactic acid dehydrogenase to growth in the presence of nitric oxide and infection [5 , 6 , 19] . While diverse stresses have been examined , a common denominator in all of these studies is that they have pushed the microbes outside of their ideal environments . As the importance of physiology to pathogenesis continues to be revealed , the current and prior studies reveal the importance of considering the environment encountered within the host when evaluating the contribution of enzymes to metabolism and infection .
All experiments involving animals were reviewed and approved by the Institutional Animal Care and Use Committee of the University of Illinois Urbana-Champaign ( IACUC license number 15059 ) and performed according to NIH guidelines , the Animal Welfare Act , and US Federal law . Bacteria were routinely grown on tryptic soy agar ( TSA ) plates . For routine overnight cultures , bacteria were grown in 5 ml of tryptic soy broth ( TSB ) or in Chelex-treated RPMI plus 1% Casamino acids ( NRPMI ) supplemented with 1 mM MgCl2 , 100 μM CaCl2 and 1 μM FeCl2 [29] in 15 ml conical tubes at 37°C on a roller drum . As needed , 10 μg/ml of chloramaphenicol was added for plasmid maintenance . S . aureus strain Newman and its derivatives were used for all of the experiments , unless otherwise indicated . For experiments using USA300 JE2 and derivatives ( USA300 JE2 gpmA:erm and USA300 JE2 gpmI:erm ) , strains were obtained from the Nebraska library [82] . gpmA:erm , gpmI:erm , ΔmntCΔmntH gpmA:erm and ΔmntCΔmntH gpmI:erm were generated by transducing the gpmA:erm and gpmI:erm alleles via Φ85 phage from USA300 JE2 gpmA:erm and USA300 JE2 gpmI:erm . As needed 500 μM MnCl2 was added to agar plates to facilitate recovery of mutants . All constructs were confirmed to be hemolytic by growth on TSA blood agar plates . To generate constructs for complementation studies , the gpmA coding sequence was amplified with the indicated primers ( Table 1 ) and cloned into the pOS1 vector under the control of the Plgt promoter [83] . S . enterica serovar Typhimurium strain 14028 was used for all Salmonella experiments . The deletion of gpmA by inserting a chloramphenicol cassette was carried out using lambda red-mediated recombination as described previously using the indicated primers ( Table 1 ) [84 , 85] . The insertion of the cassette was checked by PCR analysis and the construct was moved into a clean background by P22 transduction . CP growth assays were performed as described previously [27 , 32] . Briefly , overnight cultures ( grown 16–18 h at 37°C on a roller drum ) were used directly and diluted 1:100 into 96-well round-bottom plates containing 100 μl of growth medium ( 38% TSB and 62% calprotectin buffer ( 20 mM Tris pH 7 . 5 , 100 mM NaCl , 3 mM CaCl2 , 10 mM β-mercaptoethanol ) ) in presence of varying concentrations of CP . Unless otherwise indicated , the bacteria were grown overnight in NRPMI supplemented with 1 mM MgCl2 , 100 μM CaCl2 and 1 μM FeCl2 and directly inoculated 1:100 into the assay medium . For all assays , the bacteria were incubated with orbital shaking ( 180 RPM ) at 37°C and growth was measured by assessing optical density ( OD600 ) every 2 hours . Prior to measuring optical density , the 96-well plates were vortexed . For experiments utilizing defined medium , the bacteria were precultured overnight in TSB . The defined medium consisted of 1 . 3 g/L NaCl , 2 . 6 g/L NH4Cl , 5 . 2 g/L KH2PO4 , 18 . 2 g/L Na2HPO4 , 0 . 593 μg/L biotin , 0 . 593 mg/L nicotinic acid , 0 . 593 mg/L pyridoxine-HCl , 0 . 593 mg/L thiamine-HCl , 0 . 296 mg/L riboflavin , 1 . 778 g/L calcium pantothenate , 0 . 104 g/L phenylalanine , 0 . 078 g/l isoleucine , 0 . 13 g/l tyrosine , 0 . 053 g/L cysteine , 0 . 26 g/L glutamic acid 0 . 026 g/L lysine , 0 . 182 g/L methionine , 0 . 078 g/L histidine , 0 . 026 g/L tryptophan , 0 . 234 g/L leucine , 0 . 234 g/L aspartic acid , 0 . 182 g/L arginine , 0 . 078 g/L serine , 0 . 15 g/L alanine , 0 . 078 g/L threonine , 0 . 130 g/L glycine , 0 . 208 g/L valine and 0 . 026 g/L proline . The defined medium was supplemented with 6 mM MgSO4 , 1 μM FeCl2 , 1 μM MnCl2 and 1 μM ZnSO4 . Casamino acids ( 6 . 5% ) , glucose ( 1 . 3% ) , glycerol ( 1 . 3% ) , glucose ( 1 . 3% ) + sodium pyruvate ( 0 . 22% ) or sodium pyruvate ( 1 . 3% ) only were provided as carbon sources as indicated . Calprotectin was purified as previously described [27 , 32] . For growth assays in NRPMI , bacteria were grown overnight in NRPMI supplemented with 1 mM MgCl2 , 100 μM CaCl2 and 1 μM FeCl2 and directly inoculated 1:100 into the assay medium . The assay medium consisted of NRPMI and the bacteria were grown in the presence and absence of 1 μM MnCl2 or 1 μM ZnSO4 . For CP growth assays with Salmonella , 5 mM β-mercaptoethanol was used . To assess the expression of gpmA and gpmI , S . aureus was grown as for CP growth assays in complex medium in the presence and absence of 240 μg/ml of CP , with the exception that they were precultured overnight in TSB . Bacteria were harvested during log phase growth ( OD600 = ~0 . 1 ) , when the samples were collected , an equal volume of ice-cold 1:1 acetone-ethanol was then added to the cultures , and they were frozen at -80°C until RNA extraction . RNA was extracted and cDNA was generated , as previously described [86–88] . Gene expression was assessed by quantitative reverse transcription-PCR ( qRT-PCR ) using the indicated primers ( Table 1 , [29] ) and 16S was used as a normalizing control . Mouse infections were performed as described previously [25 , 27] . Briefly , 9-week old wild type or CP-deficient ( S100A9-/- ) mice were infected retro-orbitally with approximately 5 x 106 CFU in 100 μl of sterile phosphate-buffered saline . Following injection , the infection was allowed to proceed for 96 h before the mice were sacrificed . Livers , hearts and kidneys were removed , the organs were homogenized , and bacterial burden was determined by plating serial dilutions . | Pathogens , such as Staphylococcus aureus and Salmonella species , must be able to consume glucose in order to cause infection . However , glycolysis can increase the need for manganese and sensitize invaders to the manganese-withholding defense of the host , known as nutritional immunity . How pathogens manage these conflicting pressures is currently unknown . The current investigations revealed that a second metal-independent variant of phosphoglycerate mutase possessed by both S . aureus and Salmonella enables them to grow and consume glycolytic substrates in the presence of the manganese-binding immune effector calprotectin . Infection experiments revealed that the manganese-independent isozyme critically contributes to the ability of S . aureus to overcome manganese starvation during infection . Together , these results suggest that using metal-independent isozymes to enable the consumption of sugars within the host or other metal-limited environments is a common strategy employed by diverse bacteria . | [
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] | 2019 | Metal-independent variants of phosphoglycerate mutase promote resistance to nutritional immunity and retention of glycolysis during infection |
Deeply sampled community genomic ( metagenomic ) datasets enable comprehensive analysis of heterogeneity in natural microbial populations . In this study , we used sequence data obtained from the dominant member of a low-diversity natural chemoautotrophic microbial community to determine how coexisting closely related individuals differ from each other in terms of gene sequence and gene content , and to uncover evidence of evolutionary processes that occur over short timescales . DNA sequence obtained from an acid mine drainage biofilm was reconstructed , taking into account the effects of strain variation , to generate a nearly complete genome tiling path for a Leptospirillum group II species closely related to L . ferriphilum ( sampling depth ∼20× ) . The population is dominated by one sequence type , yet we detected evidence for relatively abundant variants ( >99 . 5% sequence identity to the dominant type ) at multiple loci , and a few rare variants . Blocks of other Leptospirillum group II types ( ∼94% sequence identity ) have recombined into one or more variants . Variant blocks of both types are more numerous near the origin of replication . Heterogeneity in genetic potential within the population arises from localized variation in gene content , typically focused in integrated plasmid/phage-like regions . Some laterally transferred gene blocks encode physiologically important genes , including quorum-sensing genes of the LuxIR system . Overall , results suggest inter- and intrapopulation genetic exchange involving distinct parental genome types and implicate gain and loss of phage and plasmid genes in recent evolution of this Leptospirillum group II population . Population genetic analyses of single nucleotide polymorphisms indicate variation between closely related strains is not maintained by positive selection , suggesting that these regions do not represent adaptive differences between strains . Thus , the most likely explanation for the observed patterns of polymorphism is divergence of ancestral strains due to geographic isolation , followed by mixing and subsequent recombination .
A characteristic of natural populations is that they are comprised of individuals that are , in the majority of cases , not genomically identical to each other . Heterogeneity present at any time reflects the outcome of the interplay between processes that create variation ( e . g . , mutation and lateral gene transfer ) and those that remove it ( e . g . , selection and genetic drift ) . In addition , genetic variation can be introduced into a population via migration and subsequent recombination . Variation between individuals appears both as divergence at the single nucleotide level and the presence of hypervariable gene “islands” within a more stable set of genes shared by multiple isolates [1–4] . The potential adaptive value of this variation is an important and controversial question in microbial ecology [5 , 6] . Through analyses of natural populations that explicitly consider genetic variability , it is possible to evaluate the basis for potential metabolic differences and infer aspects of the evolutionary processes that occur over relatively short timescales within natural communities . Initial deductions of the processes that shape microbial lineages were based on genomic studies of single isolated strains . More recently , genomic comparisons amongst multiple isolated strains of the same species have been possible [2 , 7–13] . These studies defined the concept of a pangenome for a species , reflecting the observation that the gene content of a population exceeds that of any single individual member [14] . Comparative genome analyses revealed extensive gene transfer via mechanisms that include insertion of phage or plasmid DNA . However , there has been only limited analysis of the extent of heterogeneity in gene content within coexisting cells that comprise natural populations [15–17] . Such studies benefit from relatively deep genomic sampling so that orthologous regions can be compared . Despite the rapid increase in number of metagenomic studies of natural communities [18–22] and environments [16 , 17 , 23–27] over the past few years , few datasets provide the amount of sequence required for near-complete genome reconstruction and analysis of population-level heterogeneity . Relatively low-diversity microbial communities are especially tractable for this type of analysis , especially those dominated by one organism type with a tightly defined population structure . Acid mine drainage ( AMD ) biofilms have proven particularly well suited to community genomic analyses because the majority of the biofilm community consists of four to six organism types , one of which is typically dominant . This organism can be relatively deeply genomically sampled by shotgun sequencing [21] . Tyson et al . [21] reported extensive reconstruction of genome fragments from the five species in one AMD biofilm using approximately 76 Mb of genomic sequence data . Most of the composite genome for the dominant bacterium , a member of Leptospirillum group II that is closely related to Leptospirillum ferriphilum , was sampled to a depth of approximately 12× . Binning of assembled fragments was achieved primarily based on GC content ( to separate out fragments deriving from coexisting archaea ) and sequence coverage ( sampling depth , to separate out fragments from a Leptospirillum group III population ) . Initial studies using this dataset revealed evidence for transposon proliferation [15] , genetic exchange between relatively closely related archaeal organisms via homologous recombination [21 , 28] , and gain and loss of genes from proviral DNA [15] . Simultaneous analysis of DNA sequences derived from natural communities presents a number of challenges , the first of which is to correctly assign genome fragments to the appropriate organism . The most robust approach for binning is genome reconstruction [18 , 21] , but this task is complicated by fragmentation of assemblies due to genomic heterogeneity [29] , as confirmed at a limited number of loci in a metagenomic dataset of complex environmental systems [16] . In the current study , we resolved much of this fragmentation via manual curation of an assembly of an expanded genomic dataset ( 130 Mb ) to capture the form and distribution of genetic variation genome-wide in a natural population . The coexistence of multiple closely related strains is a commonly observed phenomenon in microbial communities , and is apparent from studies of cultured isolates [2 , 11 , 30] , marker gene surveys [31–33] , and metagenomic data [3 , 15–17] . Despite the existence of a robust theoretical apparatus in population genetics to determine the strength and direction of selection using polymorphism and divergence data ( reviewed in [34] ) , these approaches have not been previously applied to the question of whether genome-wide nucleotide divergence within microbial populations reveals adaptive differences between closely related , coexisting strains . In this paper , we address the question of whether adaptive differences between closely related , coexisting strains of Leptospirillum group II can be detected using population genetic analysis of single nucleotide polymorphisms ( SNPs ) . We attempt to distinguish in situ differentiation and selection from migration and recombination as the origin of the observed patterns of nucleotide variation . Through comprehensive analysis of both variation in gene sequence and gene content , we uncover details of the recent natural history of Leptospirillum group II .
Leptospirillum group II scaffolds , their functional annotations , and alignments to the earlier assembly [21] are reported in Table S1 . The near-complete genome , spread over 79 contigs , representing 2 . 72 Mb of sequence , as well as the 2 , 588 ORFs with manually curated start positions and functional annotations , is deposited at the National Center for Biotechnology Information ( NCBI ) , along with the trace files for the 54 Mb of new sequence ( accession number AADL00000000 ) . The total number of assembled reads is 42 , 701 , indicating that each read likely derived from a single cell ( there were ∼108 cells in the sequenced sample [21] ) . Metabolic analyses and ecological profiles of the Leptospirillum group II species will be reported separately ( D . S . A . Goltsman , V . J . Denef , S . W . Singer , N . C . VerBerkmoes , M . Lefsrud , et al . , unpublished data ) . The core genome of the 5-way CG Leptospirillum group II population described here is largely syntenous with that of the UBA Leptospirillum group II population reported previously [18] . ( “5-way CG” and “UBA” indicate different sites within the Richmond Mine from which samples were collected for genomic sequencing . The dominant Leptospirillum group II species derived from each of these assemblies were labeled to indicate their geographic origin . ) Where lack of sequence information precluded ordering of scaffolds , scaffolds were ordered based on the UBA assembly . These junctions are evident in Table S1 as points where scaffold numbers change . Locations with uncertain ordering in the UBA assembly are circled on the outer ring of Figure 1 . Although it is possible that some of these junctions correspond to Leptospirillum group II genome rearrangements , only one genomic rearrangement , probably associated with virus insertion , was observed within the contiguous sequences . Although extensive reconstruction of a core genome following the dominant path during assembly was possible , variation in gene content , sequence composition , and recombination events contribute to population-level heterogeneity . We mapped the sources of variation for a 700-kb fragment ( ∼1/4 of the Leptospirillum group II genome ) that includes the origin of replication ( Figure 2 ) . Although the majority of sequencing reads originating from the Leptospirillum group II population could be coassembled , there were numerous small contigs held in by mate-paired reads that did not coassemble due to localized high sequence variation , often due to sequence insertions or deletions . These indels can be identified by mate-paired reads separated by distances greater than expected based on the clone size and reads that transition to sequence that is highly divergent relative to the composite ( Figure 3 ) . Multiple variant paths , with shorter or longer inserts , are also evident in many of these regions . Most of these strain variant paths consist of one or a few reads ( Figure 2 ) , and hence , full paths of most low-abundance variants could not be established . Some contigs , however , link at both ends to the core genome at locations consistent with insertions or deletion of sequence blocks that preserve synteny of flanking genomic regions . No clear correlation was observed between SNP density and the density of strain variant paths ( Figure 2 ) . A common cause of genome assembly fragmentation is the insertion of transposases into the genomes of some individuals and not others . The genome path for the manually curated assembly ( Table S1 ) was reconstructed across many of these insertion points . Inserted blocks predominantly encode hypothetical proteins ( e . g . , Figure 3A ) , as well as integrases , transposases , or proteins involved in plasmid maintenance , replication , and/or transmission . For one anomalously highly variable region , six different strain paths could be reconstructed . Interestingly , three of these variants encode LuxR or LuxI , though only one variant has a potentially functional LuxIR system ( Figure 4 ) . We identified and classified every polymorphic site in the assembly . Different Phred quality score cutoffs were used to examine the effect of sequencing error on SNP identification ( Table S2 ) , because SNPs appearing in only one read ( “singletons” ) are more likely to result from sequencing error than are replicated SNPs , yet the number of true singletons is important for subsequent population genetic analysis . Raising cutoff scores reduced the number of singleton polymorphic sites identified , particularly indels . Notably , the ratio of nonsynonymous to synonymous SNPs remained relatively constant for quality cutoffs of 25 and higher ( range 0 . 84–0 . 88 ) , as did the overall SNP density ( range 0 . 06%–0 . 09% ) , suggesting that sequencing error affected these site classes in an unbiased fashion . The proportion of all SNPs found in two or more reads ( replicated ) ranged from 32% for a cutoff score of 20 to 47% for a cutoff score of 50 . We used a quality cutoff of 25 , corresponding to a per-base error probability of 3 . 2 × 10−3 , based on an analysis of the effect of increasing quality scores on the outcome of population genetic tests ( Table S4 and Text S1 ) . For this quality cutoff , the overall SNP density was 0 . 09% , and 38% of SNPs were replicated . We mapped the density of replicated and nonreplicated SNPs around the entire genome using sliding windows of length 1 kb and displacement 50 bp ( Figure 1 ) . The minor allele frequency was calculated for each polymorphic site in the assembly , and is defined as the fractional abundance of the less common allele . The density of SNPs is higher in the approximately one quarter of the genome flanking the origin of replication ( “ori_rep” , located in contig 11389 ) . The distribution of SNPs is skewed slightly to the right of the origin of replication . The SNP density is not noticeably higher near the putative origin of transfer ( “ori_T , ” in a large integrated plasmid containing conjugal transfer proteins ) located in contigs 8831–9933 . The distributions of replicated and nonreplicated SNP density are not noticeably different . Regions with both high SNP density and high minor allele frequency are indicative of the presence of blocks of variant sequence . We grouped closely related SNP patterns into strains genome-wide with Strainer [26] ( Figure 5 ) . Substrains were defined based on replicated , linked SNPs ( light-blue highlights in Figure 1 ) . The strains are separated from each other by regions with lower SNP density , making long-range reconstruction of substrains impossible . Nearly all the substrains are closely related ( >99 . 5% nucleotide identity ) to the dominant 5-way CG type . Although the similar coverage depth of some of these substrains suggests their linkage ( Figure S1 ) , we do not know how many distinct lineages containing these substrains occur in the present population . We identified a number of locations where a subset of individuals with a replicated SNP pattern transitioned into another replicated pattern within or between mate-paired reads , indicating homologous recombination ( Figure 6 ) . Recombination events between very closely related sequence types are difficult to identify , so the best examples involve events between the dominant strain type and relatively SNP-rich variants ( e . g . , Figure 6A ) . Recombination events also involve a distinct sequence type with approximately 94% sequence identity to the composite sequence . Some reads were approximately 100% identical to the UBA Leptospirillum group II sequence type ( Figures 6 and 7 ) . In the entire genome , only nine loci showed direct evidence of recombination between CG and UBA types , three of which are covered at high-read depths ( Table S3 ) . Two of these are CG-UBA recombinants ( contigs 11364 and 11389 ) , whereas one is a recombinant between CG-type reads and a distinct , divergent Leptospirillum group II genome type not previously observed ( contig 11277 , genes 147–149 ) . All three deeply sampled recombinant regions are indicated with purple highlights in Figure 1 . One of the deeply sampled CG-UBA recombinant regions begins very close to the origin of replication in contig 11389 ( Figure 6 ) and continues for 25 kb . This block is divided into two pieces by apparent dual phage insertion events at the end of contig 11389 and the center of contig 11386 . These phages are not present in the corresponding region of the UBA genome , suggesting that the insertion events either occurred subsequent to the recombination event or that one of the phages mediated the initial recombination event . In the first half of the block ( shown in Figure 7A ) , most cells in the 5-way CG population share 100% nucleotide identity to the UBA sequence type , whereas a subset contain a distinct sequence . Following a small region containing divergent phage-type proteins , all cells in the population contain an additional block of 100% UBA sequence encoding cas genes ( Figure 7C ) . These cas genes are associated with clustered , regularly interspaced short palindromic repeat ( CRISPR ) regions , which have recently been shown to confer viral resistance [35] . The three deeply sampled recombinant regions between divergent Leptospirillum group II and CG-type reads were also analyzed with the McDonald-Kreitman ( MK ) test for selection . The MK test uses a 2 × 2 table to test the independence of the ratio of nonsynonymous to synonymous polymorphisms and the ratio of nonsynonymous to synonymous fixed differences [36] . The UBA-CG recombinant region near the origin of replication in contig 11389 ( Figure 7 ) did not show significant evidence for positive or negative selection ( dN/dS = 0 . 05 , pN/pS = 0 . 13; Fisher exact test , p = 0 . 27 ) . ( pN equals the fraction of polymorphic nonsynonymous sites , pS equals the fraction of polymorphic synonymous sites , dN equals the fraction of sites with nonsynonymous fixed differences , and dS equals the fraction of sites with synonymous fixed differences . ) The UBA-CG recombinant region in contig 11364 also did not show significant evidence for selection ( dN/dS = 0 . 18 , pN/pS undefined due to the lack of synonymous polymorphisms; Fisher exact test , p = 0 . 33 ) . The region in contig 11277 , which is a deeply sampled recombinant region between CG-type and unknown additional Leptospirillum group II variant reads ( Figure 6 ) , showed a statistically significant excess of polymorphic nonsynonymous sites relative to fixed nonsynonymous sites ( dN/dS = 0 . 19 , pN/pS = 0 . 90; Fisher exact test , p = 0 . 008 ) . Fourteen Phrap contigs ( 1 . 8 Mbp ) , totaling 66% of the 5-way CG Leptospirillum group II composite genome , had substrains of sufficient depth ( average read depth 3 . 3 ) for analysis of polymorphism and divergence in comparison with the dominant strain group . These contigs are primarily located near the origin of replication ( Figure 1 ) and overlap the region mapped manually in Table S3 . A total of 50 separate substrains were defined , with average length 9 . 3 kbp and total length 464 kbp . The dominant strain in these regions had an average read depth of 9 . Examples of contigs with low and absent strain variation are shown in Figure S2 . We estimated θ , the product of two times the population size and mutation rate , for these contigs using methods designed specifically to work with variable-coverage metagenomic data [37] . The per-site θ under a model of stable population size was 0 . 049 , whereas estimates of θ using a model of exponential population growth were highly variable between contigs . The likelihood of an exponentially growing population was not significantly different from that of a constant-sized population for any individual contig ( likelihood ratio test ) . The genome-wide per-site θ calculated using Watterson′s infinite sites model and a finite sites modification [38] was 2 × 10−4 . Overall , the average density of polymorphisms is low in both the dominant strain and all substrains . Within regions of the dominant strain overlapping variant blocks , pN/pS = 0 . 43 . Within all substrains , pN/pS = 0 . 33 . dN between the dominant strain and all substrains was 0 . 05% , whereas for synonymous sites , dS = 0 . 26% , giving dN/dS = 0 . 18 . The average SNP density within the dominant strain was calculated separately for regions overlapping defined substrains and those between substrains ( “interstrain regions , ” Figure 5 ) . The mean SNP density in the dominant strain in areas overlapping substrains is 0 . 014% , and in interstrain regions is 0 . 020% . The average polymorphism density within substrains is 0 . 021% . Polymorphism densities within the dominant strain and in interstrain regions are not significantly different ( t-test , p = 0 . 15 ) . Additionally , polymorphism densities within the dominant strain and within substrain blocks are not significantly different ( t-test , p = 0 . 12 ) . We calculated Tajima′s D [39] , an estimator of whether observed polymorphism frequencies deviate from the neutral expectation ( see Materials and Methods ) , for all main strains , substrains , and interstrain regions . No individual values of D were significantly different from 0 . The distribution of D is suggestive , however . Within main strains , 43 out of 51 values of D were negative ( binomial probability 9 × 10−8 , probability of success 0 . 5 ) and in interstrain regions 38 of 51 values of D were negative ( binomial probability 1 × 10−4 , probability of success 0 . 5 ) . These results suggest an excess of rare mutations in most of the genome relative to the number of segregating sites . Within substrains , there was a trend towards positive values of D ( D > 0 for 16 of 31 strains with defined D ) , but this was not significant ( binomial probability 0 . 14 ) . The numbers of nonsynonymous and synonymous substitutions within and between strains for a quality score cutoff of 25 are shown in Table 1 . The contingency table for the MK test yielded χ2 = 20 . 932 ( p < 0 . 0001 ) , indicating that the ratios of nonsynonymous to synonymous mutations within ( 1 . 12 ) and between populations ( 0 . 52 ) are significantly divergent . The relative number of fixed nonsynonymous substitutions is smaller than the relative number of polymorphic nonsynonymous substitutions , suggesting negative ( purifying ) selection . Contingency tables for individual substrains with fewer than a total of ten substitutions were tested using the Fisher exact test rather than the χ2 test . After a sequential Bonferroni correction for multiple tests , no strain had a statistically significant difference at p < 0 . 05 between polymorphisms and fixed differences . The Poisson random field refinement of the MK test ( MKPRF ) test [40] also did not find evidence that selection maintains substrains . Because this test uses segregating sites to calculate maximum likelihood estimates of shared parameters , it cannot be applied to substrains with very few segregating substitutions . Only 11 strains could be analyzed with the MKPRF test , and none of these showed statistically significant evidence for selection . We also examined the effect of Phred quality score cutoffs and read depth on the outcome of the MK test ( Table S4 and Text S1 ) . The conclusion that purifying selection is acting on strain variant regions is robust to the effect of sequence error , read depth , and polymorphism frequency . Overall , the results indicate that genome-wide evidence of positive selection is not being masked by the presence of invalid single-copy SNPs caused by sequencing error .
There are two possible sources of new intrapopulation sequence variation: mutation and/or immigration of new variant types , followed by recombination with members of the preexisting population . The variation may have adaptive value , may be deleterious , or may be neutral . These alternative hypotheses predict distinct patterns of variation within the Leptospirillum group II population . Recombination has been widely documented in natural microbial populations [18 , 21 , 28 , 41–44] . We observed the signatures of recombination events among two or more closely related 5-way CG Leptospirillum group II strains , as well as recombination between the 5-way CG population and more distantly related Leptospirillum group II genome types ( Figure 6 ) . Distinct strains defined by shared SNP patterns and frequencies were apparent even when the recombination breakpoints were not directly observable ( Figure 1 ) . We were not able to manually determine recombination block length and frequency ( as we did in a previous study with metagenomic data , [28] ) because extremely low sequence divergence between strains prevented confident identification of transition points . The similarity between the read depths of the observed strains ( Figure S1 ) raises the question of how many distinct haplotypes are present in the population . It is tempting to conclude that because strains in different regions of the genome are present at similar frequencies , they must therefore belong to a single haplotype present at that frequency in the population . Short read lengths and low overall SNP density prevent us from directly linking distinct strains within the assembly , however . It will be necessary to examine whether the frequencies of these strains remain correlated in multiple samples across space and time to determine the number of distinct haplotypes present in the population . Notably , the value of the per-site population mutation parameter θ in the 5-way Leptospirillum group II population is smaller than most observations of θ obtained from multi locus sequence typing ( MLST ) data from bacterial pathogens [45 , 46] . Per-site MLST estimates of θ averaged over multiple loci ranged from a low of 5 × 10−3 for Neisseria gonorrhoeae to 0 . 1 for Haemophilus influenzae , probably reflecting the more diverse populations from which alleles for MLST were drawn and the limited sampling of individual loci compared to our genome-wide estimate . The value of θ that we observe using an identical formula ( 2 × 10−4 ) is indicative of the overall low levels of variation in the Leptospirillum group II population , perhaps suggesting a relatively recent population expansion or purifying selection . We also calculated an estimate of θ using a model for metagenomic data that explicitly accounts for sequencing error in estimation , and tested whether the data fit a model of constant population size or exponential growth [37]; the fit of these models was not significantly different . Contig-based estimates of θ were consistent for a model of constant population size ( 0 . 05 ± 0 . 02 ) . The only other available estimate of θ for microbial metagenomic data is for a population from an activated sludge bioreactor [37] , where θ* was estimated to be 0 . 015 under a model of population growth . The activated sludge population was nearly clonal and had undergone recent population expansion prior to sampling . The slightly higher value of θ we observe in Leptospirillum group II using this method is indicative of a larger amount of standing variation than was present in the sludge population . Given the clear evidence that multiple closely related strains coexist in the Leptospirillum group II population , the key question is whether this variation results in differential fitness between strains . If strains are maintained because each is maximally fit in a different ecological niche , we expect to see evidence of positive or balancing selection in variant regions . This expectation underlies the ecotype or periodic selection concept , which posits that distinct sequence clusters in microbial populations correspond to ecologically separate units [47] . The “Stable Ecotype Model” [5] suggests that diversity within separate clusters is periodically purged by selection , and distinct clusters persist due to the low frequency of intercluster recombination . Permanent divergence only arises if a mutation allows an individual of one ecotype to colonize a new niche [5] . This model suggests that selection maintains coexisting clusters , and that within-cluster diversity is low . Under this scenario , we expect to see an excess of fixed nonsynonymous differences between strains , as these amino acid changes would indicate niche-specific adaptations . Alternatively , if sequence variation is not maintained by positive selection , we expect to find evidence for neutral or negative selection in these regions . We tested these alternative hypotheses for the coexistence of closely related strains using the MK test for polymorphism and divergence [36] . This test posits that under a model of neutral evolution , the ratio of nonsynonymous ( replacement ) to synonymous substitutions within a population is the same as the ratio of replacement to synonymous fixed differences between populations . An excess of replacement fixed differences indicates positive selection , whereas a dearth indicates negative selection . Our results from MK tests on regions of strain variation indicate strongly that positive selection on amino acid substitutions does not separate coexisting strains . We observe instead a significant deficiency of fixed replacements relative to polymorphic replacements , indicating that negative ( purifying ) selection is acting to remove deleterious nonsynonymous mutations differentiating closely related strains . This result is robust to the effect of sequencing error in SNP estimation and the use of low-frequency ( nonreplicated ) polymorphisms ( Tables S2 and S4 , and Text S1 ) . These additional analyses clearly demonstrate that an excess of low-frequency polymorphisms due to sequencing error is not masking evidence of genome-wide positive selection . Recent theoretical results indicate that an abundance of slightly deleterious polymorphisms can substantially mask the presence of adaptive evolution [48] . However , in that case , one would expect the average allele frequency of nonsynonymous polymorphisms to be significantly lower than that of synonymous polymorphisms [48] , which we do not observe ( average synonymous site frequency in dominant strain , 0 . 14; average nonsynonymous site frequency , 0 . 16 ) . Our results therefore indicate that this population does not fit the predictions of the stable ecotype model , and suggests that strains of 5-way CG Leptospirillum group II detected by sequence variation may occupy a single niche within the biofilm environment . These results are also consistent with the strong purifying selection acting on most genes in the coexisting archaeal genus Ferroplasma , despite a far more extensive mosaic genome structure than that of Leptospirillum group II [15] . Our results also rule out an alternative to the stable ecotype model , in which high recombination rates prevent periodic selective sweeps from purging diversity within strains . In this scenario , strain variant regions contain beneficial mutations decoupled from the remainder of the genome . This hypothesis—that recombination speeds adaptation by spreading beneficial variants throughout the population—again posits that these variant regions are under positive selection . Thus , it predicts a detectable signature of excess fixed amino acid replacements in recombinant regions and distinct patterns of polymorphism frequency between regions containing adaptive variants and the remainder of the genome . Results from MK tests rule out positive selection on strain variant regions . The observed patterns of polymorphism density also do not match the second prediction of the high recombination model . We do not observe a significant difference in polymorphism density between reads within substrain blocks , reads within the main strain , and reads in interstrain regions , as would be expected if the action of selection on these regions was decoupled due to high recombination rates . Although the substrains within the 5-way CG population do not appear to be under selection themselves , it is plausible that that linkage between them is absent due to selective sweeps eliminating variation in intersubstrain regions . This hypothesis predicts a lower polymorphism density in intersubstrain regions than within substrains , since a selective sweep would carry hitchhiking neutral variants to fixation . Again , the observation that polymorphism density within substrain and interstrain regions is not significantly different is strong evidence against this hypothesis . The interblock selective sweep scenario therefore requires a higher mutation rate in intersubstrain regions , which would have allowed polymorphisms to accumulate after a selective sweep to the same levels observed in adjacent substrain regions . Such extreme spatial variation in genomic mutation rate is unlikely , and hence , we can also rule out the “interstrain” sweep model . An alternative explanation for the observed patterns of polymorphism and divergence is that a few sites within each substrain have experienced positive or balancing selection , while all other neutral polymorphisms in the substrain are hitchhiking to higher frequency along with the selected sites . Such a signal might not be detectable using MK polymorphism-divergence tests . This scenario requires that recombination has not yet separated the adaptive site from linked neutral sites . If the mutation occurred in the distant past , the recombination rate would have to have been extremely low relative to the mutation rate to preserve the well-defined substrain blocks observed here . Because there is evidence for significant recombination , this scenario seems unlikely . If the positively selected mutation occurred recently , it would be in the process of sweeping to fixation along with linked neutral polymorphisms . Under this selective sweep scenario , we expect low levels of polymorphism in the region associated with the adaptive mutation relative to the intersubstrain region ( Figure 5 ) . This would manifest as a lower polymorphism density in substrain sequences relative to interstrain sequences , which was not observed . Of the three deeply sampled recombinant regions between CG-type and other Leptospirillum group II types ( Figure 1 ) , two do not show significant evidence for either positive or negative selection . The third shows strong evidence for purifying selection , consistent with our results for recombinant 5-way CG strains . These regions do not , therefore , appear to be an exception to the general pattern of purifying selection in recombinant blocks . The most likely explanation for the existence of blocks of closely related variant sequence is geographic separation of two or more ancestral strains , followed by subsequent mixing and recombination . The mosaic genome structure of Ferroplasma is also consistent with this model [15] . The high read depth and connectivity of the dominant strain compared to the smaller fragments of alternate sequence type ( Figure 5 ) suggest that a small number of individuals from one or more strains migrated into a preexisting population . Genetic exchange likely occurred between the immigrants and the existing strain , as shown by the incorporation of 1- to 10-kb fragments , but was insufficient to erase the genomic signature of the existing strain . It is possible that this strain was better adapted to its environment than immigrant strains , and retained a selective advantage , which explains its higher frequency in the present population . For a migration/recombination model to be plausible , however , we need to determine whether the observed divergence between the dominant strain and immigrant substrain fragments is consistent with a geographic separation scenario , given the geologic history of the Richmond Mine . Assuming that the observed divergence between strains is typical of ancestral haplotypes , and a mutation rate of 10−9 per site per generation [49] , the ancestral strains had to be separated for at least 8 × 105 generations to accumulate the observed divergence . This translates into a separation time of at least 1 , 400 y , given a minimum generation time of 15 h [50] . The Richmond ore body was exposed to weathering for 780 , 000 y prior to the onset of mining operations around 150 y ago [51] . A physical separation of the strains of order 103 y is therefore possible . Assuming that the two strains were reunited 150 years ago ( at most about 100 , 000 generations ) , the observed level of variation would be maintained , provided that the effective population size ( Ne ) is sufficiently large that the original variation would not all drift to fixation in this time ( Ne at least the same order of magnitude as the number of generations ) . If we postulate that the effective population size of Leptospirillum group II is at least of this order of magnitude ( plausible , given 108 cells per cm3 in the biofilm ) , it is certainly possible that the two strains could have comingled as the result of increased connectivity within the mountain due to mining . In summary , the combination of assumptions required to fit alternative explanations to the data makes the simpler explanation of geographic separation and subsequent remixing more likely . Substrains are distributed unevenly around the genome in a mosaic of 1–10-kb fragments ( Figure 1 ) . Both SNP density and minor allele frequency appear to be higher on both sides of the origin of replication ( Figure 1 ) rather than the origin of transfer , suggesting the preferential incorporation of novel genetic material in this region . A similar pattern was observed in an environmental population of Ferroplasma acidarmanus , fer1 and fer2 , in which 77% of interpopulation recombination events clustered within 500 genes of the origin of replication [28] . The mechanism responsible for this symmetry is not known , but would seem to indicate a higher probability of recombination events associated with newly initiated replication forks proceeding bidirectionally from the origin of replication . It is well known that recombinational intermediates can be converted into functional replication forks ( reviewed in [52] ) . Replication forks resulting from recombination of introduced fragments could interact with replication forks initiated at oriC , resulting in the duplication of a chromosome containing the introduced fragment , but this process is not well understood [52] . Another example of genomic symmetry across the replication axis involves large- and small-scale inversions in closely related bacterial species [53 , 54] . The reason for this is thought to be selection against disruption of polarized motifs clustered symmetrically near the terminus of replication and important for chromosome replication and segregation [55] . Perhaps recombination events closer to the terminus of replication are counterselected due to interference with these motifs , resulting in an apparent clustering of events around the origin . A breakdown in synteny between related strains further from the origin is evoked as an explanation for symmetry around the origin of replication in Ferroplasma [28] , but the lack of major genome rearrangement events observed in our assembly makes it less likely as an explanation for the 5-way CG Leptospirillum group II population . The presence of a large integrated plasmid within the Leptospirillum group II population suggests conjugation as a possible mechanism of genetic transfer between individuals . This plasmid region contains genes coding for proteins in the tra and trb loci , which are known to be involved in formation of a bridge between two cells through which chromosomal DNA transfer can occur . In Escherichia coli Hfr strains , transfer is initiated at the oriT site and proceeds in the 5′ to 3′ direction . Marker genes closer to the oriT site are transferred more frequently due to the increased probability of breakage of conjugation bridges with time . Therefore , if conjugation were the primary mechanism of transfer , we would expect to see more incorporation of novel fragments in 5-way CG Leptospirillum group II close to , and on one side of , the putative oriT ( sequence and polarity not known ) . Despite uncertainty about how the block containing the origin of replication is linked to the block containing the oriT , we can evaluate both of these considerations to some extent . The incidence of recombined substrain blocks is higher clockwise from the integrated plasmid region than counterclockwise from it , except immediately adjacent to the oriT . Selection against recombined regions carrying variants of conserved proteins ( e . g . , a large region encoding ribosomal proteins ) may have obscured this pattern to some extent . The short recombinant fragments observed both in this study and in Ferroplasma [28] are strikingly similar to those observed among strains of E . coli . Milkman and colleagues [56–58] showed that E . coli chromosomes typically contain distinct sequence blocks ( size range around 1 kb ) embedded in larger regions that have overall high similarity to other strains . A subsequent statistical comparison of six whole E coli genome sequences also supports a mosaic structure , albeit with shorter fragment size ( 50% < 1 kb , 80% < 2 kb [59] ) . These segments are an order of magnitude less than the average length observed in individual conjugation ( 48–105 kb ) or transduction ( 10–32 kb ) events [57] . The degree of fragmentation of donor DNA was determined by differences in donor–recipient restriction-modification ( RM ) systems [57 , 60] . The clustered pattern of small segments that we observe is consistent with the idea that large pieces of DNA are transferred by conjugation or transduction between closely related strains , fragmented , and then incorporated as a series of small segments [56] . The fragmentation likely occurred through the action of restriction enzymes . It is interesting that most genes annotated as part of RM pathways in Leptospirillum group II are found in strain variant regions , suggesting that restriction specificity varies among closely related organisms ( Table S1 ) . A similar observation was made for an environmental Ferroplasma population [15] . Thus , the diversity of RM systems within the 5-way CG population could explain the generation of small DNA fragments from DNA introduced from closely related strains . In addition to variation in sequence composition , variation in gene content could lead to an increase in fitness , be neutral , or be deleterious . As shown from between-strain genomic comparisons of isolates [2 , 7–13] and population analyses [15] , a high fraction of individuals in a population contain unique regions due to phage , plasmid , or transposon insertion . Community genomics identifies the laterally transferred regions that are present population-wide in addition to those present at low frequency at the time and conditions of sampling , and also provides information about the degree of gene content variation in these regions . Our data suggest that the majority of the genes on strain variant paths are present in low copy number in the population . Most observed alternate genome paths are short ( a single read length , or about 1 kb; Figure 2 ) but incomplete . This could be in part because the paths are undersampled and in part because the insertions are on the scale of a single gene . The shallow coverage , but high density , of these variants suggests that there is a large pool of low-frequency genotypes from which the observed variants are drawn . Given that 118 alternate paths ( an average length of 1 kb spanned by approximately two reads per variant ) are observed in a 500-kb segment , and that there are on the order of 42 , 000 reads in the main genome path , the observed frequency of variant reads is approximately 3% . The variants thus span approximately 20% of this 500-kb region . If the genome of every cell in the biofilm was 20% different from every other cell , and all were present at the same frequency , we would expect the frequency of unique reads to be 20% . Therefore , a few types are present at high frequency , while the majority of variants are present at very low frequencies . The minimum number of genome types occurs when each of these low-frequency types only occurs in one cell ( combinatorial variation would greatly increase the number of possible genome types ) . Based on the observed frequency of variant reads , low-frequency variants comprise approximately 15% of the total population . Based on earlier estimates of approximately 108 Leptospirillum group II cells in the sample used for sequencing [21] , there are a minimum of approximately 107 genome types present . The true frequency is probably somewhat higher as not all possible strain paths were counted in the assembly process . This finding of a very heterogeneous genome pool suggests that the population has not been strongly shaped by recent pervasive selective sweeps unless the genotypic variants are generated at a rate that is high compared to the frequency of such sweeps . The high estimated number of coexisting genome types is fairly consistent with estimates based on genotyping of isolates from a complex system [11] . Importantly , however , the majority of the cells in the population share the majority of their genomes , which is what makes reconstruction of a composite genome possible . Based on their low frequency , the functional significance of these highly variable regions is unclear . Tracking of strain population structure over time may help to resolve the extent to which different genotypes represent distinct adaptations . It is notable that many of the strain variant regions present at higher abundance in the population ( reconstructed alternative genome paths listed near the end of Table S1 ) encode genes typical of plasmids ( e . g . , trwB , traA , traB , and mobD ) or viruses ( e . g . , integrases ) . Of those integrated elements present at high frequency , some encode genes of potential functional significance . A case in point involves a region where most individuals carry one or more genes of the LuxIR system , involved in quorum sensing [61] , whereas only a minor part of the population encodes the complete pathway ( Figure 4 ) . High-frequency variants , which are often associated with marked variation in gene content ( such as the LuxIR region ) , could be the result of multiple processes: ( 1 ) a mobile element encoding multiple genes inserts into one genome , and that type increases in frequency; ( 2 ) the variable loss of genes from mobile elements results in different individuals with differences in gene content; ( 3 ) a mobile element integrates into different individuals at the same site , localized by some genomic feature ( e . g . , tRNAs ) ; and ( 4 ) a mobile element integrates at a single site in one individual , followed by an increase in frequency of that genome type , followed by recombination of related but distinct elements in or near the insertion site . For example , closely related viruses derived from a diverse viral population could use the initial virus insertion as a locus for homologous recombination . This could result in a high diversity of genome types at the altered locus . High-frequency variants also result from recombination between related individuals followed by an increase in frequency of individuals carrying the recombinant block . A notable example of functionally significant genes on a recombinant block is the block of UBA genes containing a CRISPR system , including CRISPR-associated ( cas ) genes ( Figure 7 ) . Recent experimental work with Streptococcus thermophilus demonstrated that CRISPRs confer resistance to viral infection [35] . The CRISPR locus contains spacers approximately 30 bp long that match predominantly viral sequence , separated by repeats of similar length and adjacent cas genes inferred to be the protein machinery of the acquired resistance system ( recently reviewed in [62] ) . Results of the current study suggest that a block containing CRISPR genes recombined from the UBA population into the 5-way CG population in a single event ( Figure 7A ) and subsequently increased in frequency . The region of this UBA-type block containing the cas genes has risen to 100% frequency in the population ( genes 28–36 in Figure 7C ) , whereas the first half of the UBA block co-occurs with cells containing sequence of the 5-way type ( genes 1–26 in Figure 7C ) . A region containing phage/transposon insertions separates the two halves of the recombination block . The absence of any fixed differences between the cas locus in the UBA genome and the UBA-type integrated cas locus in the 5-way CG population suggests that this was either a very recent event and/or that strong purifying selection is acting to eliminate variation . Evidence from previous work indicating very rapid diversification of the spacer complement supports the first explanation but does not rule out the second [63] ) . The observation that the genome type carrying the CRISPR locus has apparently rapidly risen to high frequency in the population suggests that viral resistance conferred a selective advantage to the recipient types . Deep sequencing of the 5-way CG Leptospirillum group II population has given us both an unprecedented catalog of within-population variation and the opportunity to test hypotheses relating to the origin and maintenance of this variation . This opens a route for incorporating population genomic evidence into reconstruction of the natural history of the Iron Mountain microbial ecosystem . Very few metagenomic studies of microbial communities have applied the tools of population genetics to understand the origin and maintenance of sequence-level variation ( e . g . , [15 , 37] ) . In the present study , we translate community genomic data into a form suitable for one such test [36] by separation and comparison of genomic regions varying in their SNP composition . We show that , contrary to many models of microbial population structure , extensive strain variation in Leptospirillum group II is not maintained by positive selection for adaptive variants . Instead , we detected strong evidence for purifying selection in strain variant regions . The data support a model of geographic isolation , mixing , and extensive recombination , a scenario consistent with the geological history of Iron Mountain . We believe that the application of population genetics to metagenomic data , still a nascent field , has the potential for many more such insights into the processes governing microbial population structure . In addition to variation at the nucleotide level , our data present a snapshot of ongoing genetic exchange in a natural population , including recombination and the rapid uptake and loss of plasmid and phage-derived genes . Most gene-content variants are present at low frequency , consistent with a very large number of coexisting genotypes . This suggests a dynamic process involving the continual generation and loss of gene-content variants . Very few variant regions have reached high frequency in the population , but two examples documented here involve regions of potential functional importance ( LuxIR , involved in quorum sensing , and CRISPR , involved in viral resistance ) . Despite the high number of variants , the genome structure of the majority of population members is sufficiently similar to allow for assembly of composite genome paths . The functional significance of this extensive gene-content variation remains unclear . To provide an answer to this question , it will be important to examine the expression levels of gene variants across space and time , as well as examine signatures of selection in genes present in regions with high gene-content variation ( such as the LuxIR region discussed above ) . Due to the effect of strain variation on downstream postgenomic approaches such as proteomics [64] , a thorough documentation of the population-level diversity in community genomic datasets is of great importance . Current technologies and methods have progressed to strain-level resolution , allowing the discrimination of closely related protein variants [18] . Databases that include population-level strain variant sequences will allow us to track the presence and activity of these variants over time and as a function of geochemical conditions . These types of studies will allow us to determine the functional importance of variation within natural populations .
Genomic sequence was obtained from DNA extracted from a pink biofilm growing on a pH 0 . 83 , 42 °C acid mine drainage stream and sampled from the 5-way location within the Richmond Mine , California , in March 2002 . Library construction , sequencing , and the draft assembly methods used to analyze the first 76 Mb of data were reported previously [21] . We obtained an additional 54 Mb of sequence from a second 3-kb library constructed from the same sample and reassembled the data for the analyses reported here . The 130 Mb of DNA sequence ( GenBank accession number AADL00000000 ) was assembled using phredPhrap ( P . Green , http://www . phrap . org ) with parameters chosen to maximize assembly extent ( e . g . , to allow coassembly of sequences with some SNPs ) while minimizing assembly errors . Parameters used were “-minmatch 50 -minscore 50 -penalty −15 -revise_greedy . ” Typically , all reads deriving from the Leptospirillum group II population were incorporated at a single genomic locus , except in the presence of strain-specific gene insertions or deletions . However , as noted previously , the dataset contains a very small number of reads with very high sequence identity ( ∼100% ) to another Leptospirillum group II type ( reconstructed from a sample from the UBA location in the Richmond Mine [18] ) . Reads from this organism ( distantly related strain or closely related species ) share approximately 94% average nucleotide-level sequence identity , and were brought into assemblies occasionally . All contigs were manually curated using Consed [65] to eliminate assembly errors and to resolve regions of strain heterogeneity . The set of contigs assigned to Leptospirillum group II , initially based on GC content and depth , was augmented with new contigs that could be confidently linked into the assembly through mate pairs anchored into unique sequence . In a subset of cases , alternative paths resulting from insertion or deletion of genes or sequence divergence result in fragmentation of assemblies . However , strain-specific fragments can be linked into the main genome path via mate pairs from one or both ends of the strain-specific contig . The outcome of reassembly , new data , and manual analysis is much larger genome fragments than achieved via the JAZZ assembly [21] and some differences in gene order . The assembly order is shown in Table S1 . ORF positions were determined and refined based on previously reported gene calls [21] as well as the manually curated annotation of the closely related Leptospirillum group II organism assembled from the UBA community genomic data [18] . Functional annotation relied upon on the manually curated annotation of Leptospirillum group II from the UBA genomic dataset ( D . S . Aliaga Goltsman , V . J . Denef , S . W . Singer , N . C . VerBerkmoes , M . Lefsrud , unpublished data ) . Contigs generated in the assembly are a composite sequence , and thus do not show SNPs present at a subset of loci . For analysis of sequence variation , the final Phrap contigs and aligned reads were imported from Consed into the program Strainer [66] . Substrains consisting of linked polymorphisms were defined for all large Phrap contigs . A substrain was defined heuristically to consist of two or more linked polymorphisms with Phred scores greater than 20 . These were considered to be separate subpopulations from the dominant strain due to the improbability of multiple linked polymorphisms arising simultaneously due to mutation alone . For strain grouping analyses , the composite sequence was corrected to reflect the dominant polymorphism pattern so long as these changes retained SNP grouping defined by reads and their mate pairs . Sequences were grouped based on the presence of SNPs in more than one read ( considering only high-quality base calls , with Phred scores > 20 ) . Additionally , custom Perl scripts were developed to identify and classify every polymorphic site in the assembly as synonymous , nonsynonymous , intergenic , or indel . SNPs were classified as replicated or nonreplicated . These counts are summarized in Table S2 and were used to generate Figure 1 for a Phred quality score cutoff of 25 . The effect of different quality score cutoffs on the distribution of these classes was also examined ( Table S2 and Text S1 ) . In a region spanning approximately one quarter of the genome , indel SNPs were examined in detail for their effect on frameshifts and gene splits ( Figure 2 and Table S3 ) . The location of divergent UBA-type sequences was also mapped for this region . For each substrain consisting of at least four reads , we counted the synonymous and nonsynonymous ( replacement ) substitutions both within the substrain and between the substrain and the dominant composite sequence ( Figure 5 ) . A custom Perl pipeline was developed to extract aligned reads and a consensus sequence for every gene in the Phrap assembly . Bases with Phrap quality scores less than 25 were masked . This threshold approach treats all bases with higher quality scores as “true” and does not take into account the error probabilities associated with these scores , resulting in a somewhat inflated estimate of the true number of SNPs in the population [37] . Because substitutions at roughly two-thirds of all sites will result in nonsynonymous substitutions , we would expect a bias towards nonsynonymous SNPs due to sequencing errors . To address this question , we repeated the analysis with different levels of sequence quality score cutoffs ( Table S4 ) . Additionally , we examined the effect of read depth on all analyses ( see Table S4 and Text S1 ) . Due to common sequencing errors at read ends and the presence of inserted genes , reads diverging from the consensus by more than 2% were eliminated from the analysis . This cutoff also eliminated reads derived from the UBA-type Leptospirillum group II . The two short , deeply sampled regions containing multiple UBA-type reads were analyzed separately in comparison with syntenous CG-type reads . Additional custom Perl scripts were used to calculate synonymous and nonsynonymous polymorphisms within each strain and substrain . Substitutions occurring immediately adjacent to masked bases were not counted due to the high probability of alignment or sequencing errors in these regions . The consensus sequence determined by Phrap was used to determine synonymous and nonsynonymous fixed differences between each substrain and the main strain . The number of synonymous and nonsynonymous sites in each strain was calculated using the program codeml in the PAML package [67] . The numbers of polymorphisms and fixed substitutions were used to test the hypothesis that substrains are maintained in the population through selection for adaptively significant variants . Two polymorphism-divergence tests were used: the MK test [36] and the MKPRF test [40] . For the purpose of these tests , each substrain is considered to be a distinct population ( note that this differs from the terminology used elsewhere in this paper ) . The rationale behind the MK test is that in the absence of selection , the number of synonymous and nonsynonymous fixed differences between two populations , and the number of synonymous and nonsynonymous polymorphisms within each population , depend only on the mutation rate and time since divergence . The ratio of nonsynonymous to synonymous polymorphisms within populations should therefore be the same as the ratio of nonsynonymous to synonymous fixed differences between populations . Positive selection for nonsynonymous mutations will produce an excess of nonsynonymous fixed substitutions relative to nonsynonymous polymorphisms . The MKPRF test refines this idea , using a population genetic model to explain the distribution of within-population variation via maximum likelihood estimation of mutation rate , divergence time , and a selection parameter . Polymorphism-divergence data were combined for all genes within a strain for the MK and MKPRF tests , whereas individual genes were also analyzed using the MKPRF test . The MKPRF tests were carried out using an online implementation ( Computational Biology Service Unit , Cornell University ) . In addition to the MK test , we also calculated several summary statistics for each strain and substrain as well as the entire assembly . Because of variable coverage depth across the assembly , it was necessary to weight these parameters by the fraction of total sites at a given depth [68] . The parameter θ = 2Neu , an estimator of the product of population size and mutation rate for a neutrally evolving population , was calculated with Watterson′s infinite sites estimator [69] and a finite sites modification [38] . Estimates for each coverage class were combined to yield a single value . The program “piim” [37] , which corrects for sequencing error in the estimation of population genetic parameters from metagenomic data , was also used to calculate θ for the entire assembly . Pairwise heterozygosity ( π ) was calculated as described for a variable coverage assembly of Drosophila simulans [68] using custom scripts . Under a neutral model of evolution for a Wright-Fisher population , π and θ are expected to be equal . Tajima′s D measures the discrepancy between these statistics: DT = ( π – θ ) /C , where C is a normalizing constant calculated from the data [39] . The statistical significance of D depends on the sample size , but roughly , for sample sizes greater than 7 , D is considered significant at the 95% level if it is smaller than −2 or larger than 2 [39] . DT for strains , substrains , and interstrain regions was calculated with BioPerl population genetics modules [70] . | Communities of microbes in nature consist of a large number of distinct individuals . The variation in DNA sequence between these individuals contains a record of the evolutionary processes that have shaped each community . In most environments , however , the high number of distinct species makes obtaining information about the nature of this variation difficult or impossible . We obtained large amounts of sequence data for a natural community in an acid mine drainage system consisting of only a few species . This enabled us to reconstruct the genome of the dominant bacterium ( Leptospirillum group II ) and obtain detailed information about sequence variation between individuals , including differences in both gene content and gene sequence . Our analysis shows extensive recombination between closely related populations , as well as fewer instances of recombination between more distantly related individuals . Additionally , viruses and plasmids account for high variability in gene content between individuals . We conclude that sequence-level variation in this population is maintained through neutral processes ( migration , recombination , and genetic drift ) rather than natural selection . This suggests that closely related strains of the Leptospirillum group II population may not be ecologically distinct . | [
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"genomics"
] | 2008 | Population Genomic Analysis of Strain Variation in Leptospirillum Group II Bacteria Involved in Acid Mine Drainage Formation |
Human Long interspersed element-1 ( L1 ) retrotransposons contain an internal RNA polymerase II promoter within their 5′ untranslated region ( UTR ) and encode two proteins , ( ORF1p and ORF2p ) required for their mobilization ( i . e . , retrotransposition ) . The evolutionary success of L1 relies on the continuous retrotransposition of full-length L1 mRNAs . Previous studies identified functional splice donor ( SD ) , splice acceptor ( SA ) , and polyadenylation sequences in L1 mRNA and provided evidence that a small number of spliced L1 mRNAs retrotransposed in the human genome . Here , we demonstrate that the retrotransposition of intra-5′UTR or 5′UTR/ORF1 spliced L1 mRNAs leads to the generation of spliced integrated retrotransposed elements ( SpIREs ) . We identified a new intra-5′UTR SpIRE that is ten times more abundant than previously identified SpIREs . Functional analyses demonstrated that both intra-5′UTR and 5′UTR/ORF1 SpIREs lack Cis-acting transcription factor binding sites and exhibit reduced promoter activity . The 5′UTR/ORF1 SpIREs also produce nonfunctional ORF1p variants . Finally , we demonstrate that sequence changes within the L1 5′UTR over evolutionary time , which permitted L1 to evade the repressive effects of a host protein , can lead to the generation of new L1 splicing events , which , upon retrotransposition , generates a new SpIRE subfamily . We conclude that splicing inhibits L1 retrotransposition , SpIREs generally represent evolutionary “dead-ends” in the L1 retrotransposition process , mutations within the L1 5′UTR alter L1 splicing dynamics , and that retrotransposition of the resultant spliced transcripts can generate interindividual genomic variation .
Long interspersed element-1 ( L1 ) is a non-long terminal repeat ( non-LTR ) retrotransposon that comprises approximately 17% of human genomic DNA [1] . Over 99 . 9% of human L1s cannot retrotranspose due to 5′ truncations , internal DNA rearrangements , or point mutations that inactivate the L1-encoded proteins [1–4] . However , the average diploid genome harbors approximately 80–100 full-length retrotransposition-competent L1s ( RC-L1s ) [5] , including a small number of expressed [6–8] , highly active ( i . e . , “hot” ) L1s [5 , 9–11] that can retrotranspose efficiently in cultured cells or cancers . RC-L1 retrotransposition affects both intra- and interindividual human genetic variation ( reviewed in [12 , 13] ) and , on occasion , can lead to disease-producing mutations [14–16] . Human RC-L1s are approximately six kilobases ( kb ) in length [17 , 18] . They contain a 5′ untranslated region ( UTR ) that harbors both sense [19] and antisense [20] RNA polymerase II promoters ( Fig 1A ) as well as an antisense open reading frame ( ORF0 ) [21] , which encodes a protein that may mildly enhance L1 retrotransposition efficiency . The 5′UTR is followed by two open reading frames ( ORF1 and ORF2 ) that are separated by a 63–base pair ( bp ) inter-ORF spacer that contains two in-frame stop codons [18 , 22] ( Fig 1A ) . L1s end with a 3′UTR , which contains a conserved polypurine motif , a “weak” RNA polymerase II polyadenylation signal , and a variable length polyadenosine ( poly ( A ) ) tract ( Fig 1A ) [17 , 23–25] . ORF1 encodes an approximately 40-kilodalton ( kDa ) protein ( ORF1p ) [26] that contains an amino-terminal coiled-coil ( CC ) domain required for ORF1p trimerization [27–29] , a centrally located noncanonical RNA recognition motif ( RRM ) domain [29 , 30] , and a carboxyl-terminal domain ( CTD ) harboring conserved basic amino acid residues [29–32] ( Fig 1A ) . The RRM and CTD are critical for ORF1p nucleic acid binding [32–36]; the nucleic acid chaperone activity is postulated to play a role in L1 integration [30 , 36 , 37] . ORF2 encodes an approximately 150-kDa protein ( ORF2p ) [38–40] that contains conserved apurinic/apyrimidinic-like endonuclease ( EN ) [41 , 42] and reverse transcriptase ( RT ) domains [31 , 43 , 44] as well as a conserved cysteine-rich ( C ) domain [31 , 45] ( Fig 1A ) . Biochemical activities contained within both ORF1p and ORF2p are required for canonical EN-dependent L1 retrotransposition in cultured human cells [31 , 41] . A round of human RC-L1 retrotransposition begins with the internal sense-strand promoter initiating transcription at or near the first nucleotide of the 5′UTR [13 , 19 , 46 , 47] . The resultant bicistronic L1 mRNA is exported to the cytoplasm , where it undergoes translation [22 , 46 , 48 , 49] . Following translation , ORF1p and ORF2p preferentially bind to their encoding mRNA in cis to form a ribonucleoprotein particle ( RNP ) [33 , 35 , 50–53] . The 3′ poly ( A ) tail of L1 mRNA is a critical Cis-acting determinant for recruitment of nascent ORF2p to L1 RNA [51 , 54] . Components of the L1 RNP gain access to the nucleus by a mechanism that does not require nuclear envelope breakdown [55 , 127] . L1 integration likely occurs by target-site primed reverse transcription ( TPRT ) [41 , 53 , 56 , 57] . During TPRT , the L1 EN makes a single-strand endonucleolytic nick at a thymidine-rich sequence ( e . g . , 5′-TTTT/A-3′ , 5′-TTTC/A-3′ , etc . ) present on the “bottom” strand of a target site in genomic DNA to liberate a 3′ hydroxyl ( 3′-OH ) group [41 , 57 , 58] . Microhomology-based annealing between the L1 poly ( A ) tail and thymidine residues at the L1 EN cleavage site in genomic DNA enhances the ability of the L1 ORF2p RT to use the resultant 3′-OH group as a primer to initiate reverse transcription of L1 mRNA [53 , 59] . How TPRT is completed requires elucidation . However , as demonstrated for the related R2 non-LTR retrotransposon from Bombyx mori [60] , it is possible that enzymatic activities associated with L1 ORF2p participate in both second-strand ( i . e . , “top” strand ) genomic DNA cleavage and second-strand L1 cDNA synthesis . Although retrotransposition assays and biochemical studies revealed the L1-encoded proteins preferentially retrotranspose their encoding mRNA in cis [50 , 53 , 61 , 62] , L1 ORF1p and/or ORF2p can act in trans ( Trans-complementation ) to retrotranspose RNAs encoded by nonautonomous short interspersed elements ( SINEs; e . g . , Alu RNA [63 , 64] and SINE-R/VNTR/Alu [SVA] RNA [65–67] ) . Additionally , the L1-encoded protein ( s ) can act in trans to retrotranspose noncoding RNAs [12 , 68–73] and cellular mRNAs , with the latter process leading to the formation of processed pseudogenes [50 , 62 , 73–77] . The evolutionary success of L1 requires the faithful retrotransposition of full-length L1 mRNAs . Previous studies have revealed the presence of functional splice donor ( SD ) , splice acceptor ( SA ) , and premature polyadenylation signals in primary full-length RC-L1 transcripts [24 , 78–81] . Paradoxically , the use of these sites during posttranscriptional RNA processing leads to the production of truncated and/or internally deleted L1 mRNAs [24 , 78–81] , which could adversely affect L1 retrotransposition . Thus , it is somewhat surprising that Cis-acting sequences that could negatively affect L1 retrotransposition have not been removed by negative selection during L1 evolution . Here , we address how the retrotransposition of spliced L1 mRNAs leads to the generation of spliced integrated retrotransposed elements ( SpIREs ) . We describe two classes of SpIREs: those that splice within the 5′UTR ( intra-5′UTR SpIREs ) and those that splice from within the 5′UTR into the ORF1 sequence ( 5′UTR/ORF1 SpIREs ) . Additionally , we suggest a mechanism for why some apparently deleterious Cis-acting splice sites within L1 mRNA are conserved throughout L1 evolution . Finally , we provide experimental evidence revealing that L1 splicing dynamics are altered by structural changes within the 5′UTR that allow L1s to evade host repression and that retrotransposition of the resultant spliced variants can lead to the generation of new classes of SpIREs . Thus , these data provide a snapshot of how an “arms race” between L1 and host repressive factors may affect the evolutionary trajectory of L1 5′UTRs . In sum , we conclude that SpIREs are deficient for retrotransposition and likely represent evolutionary “dead-ends” in the L1 retrotransposition process .
Using fosmid-based discovery methods , we previously identified a polymorphic L1 ( fosmid accession #AC225317 ) in the human population that contains a 524-nucleotide deletion within its 5′UTR [10] . Upon closer inspection , we determined that this deletion likely resulted from the retrotransposition of a spliced L1 RNA that used a previously identified SD ( G98U99 ) [78] and an unreported SA ( A620G621 ) within the L1 5′UTR ( numbering based on L1 . 3 , accession #L19088; [9 , 82] ) ( Fig 1A and 1B ) . The structure of this element resembled previous L1s characterized by Belancio and colleagues , supporting the hypothesis that spliced L1 transcripts can complete retrotransposition in the human genome [78 , 79] . We named these L1s SpIREs to distinguish them from bona fide full-length genomic L1s . The three SpIREs investigated here all use the same SD ( G98U99 ) but use different SA sequences that reside within either the L1 5′UTR ( SA: A620G621 or SA: A788G789 ) or L1 ORF1 ( SA: A974G975 ) ( Fig 1B , 1C and 1D ) . We used the BLAST-like alignment tool ( BLAT ) ( https://genome . ucsc . edu ) [83] ( in which transposable element—derived DNAs are not masked ) to search the human genome reference ( HGR , GRCh38/hg38 ) for SpIRE G98U99/A620G621 sequences ( referred to as SpIRE97/622 ) . The HGR contains an annotated record of L1s that have accumulated over evolutionary time ( i . e . , millions of years ) ; thus , searching the genome should reveal how SpIREs contribute to the genomic L1 repertoire . We used a 100-nucleotide in silico probe that spans the intra-5′UTR splice junction present in SpIRE97/622 ( nucleotides 47–97 and 622–672 of L1 . 3 ) to query the HGR . We identified 116 SpIRE97/622 sequences , which span the youngest L1PA1 subfamily ( also known as L1Hs , members of which are currently amplifying in the human population ) through the older L1PA6 subfamily ( which amplified approximately 27 million years ago [MYA] ) , but none in older ( L1PA7–L1PA17 , L1PB , and L1MA ) L1 subfamilies ( S1A Fig ) [84 , 85] . Thus , 116 out of 6 , 609 ( about 1 . 8% ) of previously annotated full-length L1s in the L1PA1–L1PA6 subfamilies are actually SpIRE97/622 sequences ( S1 Fig; S1 Data; S1 Table ) . Almost half of the SpIRE97/622 sequences we identified belong to the L1PA3 subfamily ( 53 sequences , comprising about 3 . 4% of previously annotated full-length L1s in that subfamily ) ( S1A Fig; S1 Data; S1 Table ) . The L1PA1 subfamily harbors six SpIRE97/622 ( comprising about 2 . 0% of previously annotated full-length L1s in that subfamily ) and the L1PA6 subfamily contains only one SpIRE97/622 ( comprising 0 . 1% of previously annotated full-length L1s in that subfamily ) ( S1 Fig; S1 Data; S1 Table ) . Seven SpIRE97/622 sequences could not be unambiguously assigned to a specific L1 subfamily and are classified as either L1PA2–L1PA3 or L1PA4–L1PA6 sequences ( S1 Fig; S1 Data; S1 Table ) [86] . Given the above data , we used BLAT to search the HGR for additional L1s containing G98U99/A788G789 and G98U99/A974G975 splicing events identified by Belancio and colleagues ( referred to as SpIRE97/790 and SpIRE97/976 , respectively ) [78 , 79] . These searches confirmed the presence of four previously identified SpIRE97/790 sequences in the L1PA1–L1PA2 subfamilies ( S1A Fig; S1 Data; S1 Table ) [78] . We also discovered an additional SpIRE97/976 sequence in addition to the ten previously identified SpIRE97/976 sequences ( S1A Fig; S1 Data; S1 Table ) [78 , 79] . In total , these three classes of SpIREs comprise a small but notable ( 131/6 , 609 or about 2% ) percentage of previously annotated full-length L1s from the L1PA1–L1PA6 subfamilies . The SpIRE97/622 sequences discovered here represent the majority ( 116/131 or about 89% ) of identified SpIREs . We next characterized the 131 SpIRE97/622 , SpIRE97/790 , and SpIRE97/976 sequences . We first examined the post-integration ( i . e . , filled ) site of each SpIRE in the HGR sequence . We then used the genomic sequences flanking each SpIRE to reconstruct a putative pre-integration ( i . e . , empty ) site . Many of the SpIRE sequences , especially those from older L1 subfamilies , have degenerate poly ( A ) tails at their 3′ ends , which , in some cases , made it difficult to reconstruct the putative pre-integration site to bp resolution ( S1 Data; S1 Table ) . These analyses revealed that SpIREs generally are flanked by target site duplications that ranged in size from about 6–25 bp , end in a 3′ poly ( A ) tract , and integrated into an L1 EN consensus cleavage site ( 5′-TTTT/A-3′ and variants of that sequence ) ( S1 Data; S1 Table ) . Consistent with previous studies , approximately 39% ( 51/131 ) of the SpIREs are present within the introns of RefSeq ( https://www . ncbi . nlm . nih . gov/refseq/ ) [87] annotated genes [69 , 88 , 89] , and the majority ( 32/51 or about 63% ) of these SpIREs are present in the opposite transcriptional orientation of the annotated gene ( S1 Table ) [90 , 91] . Other structural features of the SpIREs are shown in S1 Data and S1 Table . In sum , our analyses strongly suggest that SpIREs represent a subset of genomic L1 insertions and retrotranspose by the canonical process of L1 EN-dependent TPRT . The formation of SpIRE97/622 results in the deletion of five known transcription factor binding sites within the L1 5′UTR [47 , 92–97] ( Fig 1A ) ; thus , we hypothesized the SpIRE97/622 5′UTR would have reduced promoter activity . To test this hypothesis , we subcloned the wild-type ( WT ) L1 . 3 [9 , 82] or SpIRE97/622 5′UTR sequences upstream of a promoter-less firefly ( Photinus pyralis ) luciferase gene ( vector pGL4 . 11 ) , creating pPLWTLUC and pPL97/622LUC , respectively ( Fig 2A ) . We then characterized the promoter activity of these 5′UTRs using functional assays . We first conducted northern blot analyses using polyadenylated mRNAs isolated from untransfected HeLa-JVM cells and HeLa-JVM cells transfected with the luciferase expression vectors ( Fig 2 ) . An RNA probe complementary to ribonucleotides 7–99 of the L1 5′UTR ( Fig 2A; purple line ) detected a strong signal at the expected size of about 2 . 7 kb in mRNAs derived from HeLa-JVM cells transfected with pPLWTLUC , but not in mRNAs derived from HeLa-JVM cells transfected with pPL97/622LUC or pGL4 . 11 or from untransfected HeLa-JVM cells ( Fig 2B , first panel ) . Similar results were obtained using riboprobes complementary to either ribonucleotides 103–336 of the L1 5′UTR ( Fig 2A , red line; Fig 2B , second panel ) or the 3′ end of luciferase ( Fig 2A , blue line; Fig 2B , third panel ) . These data are consistent with previously published findings [47] , which demonstrated that L1 transcription begins at or near the first nucleotide of the L1 5′UTR . Control experiments verified the integrity and quality of the mRNAs ( Fig 2B , actin probe ) . We were able to detect a faint band representing the predicted approximately 2 . 2 kb mRNA from HeLa-JVM cells transfected with pPL97/622LUC upon the prolonged exposure of the northern blots using probes complementary to ribonucleotides 7–99 of the L1 5′UTR , but not using a probe complementary to ribonucleotides 103–336 of the L1 5′UTR ( S2A Fig; purple arrow ) . The absence of the predicted approximately 2 . 2-kb band in HeLa-JVM cells transfected with pPL97/622LUC using a probe complementary to the 3′ end of the luciferase gene is likely due to the limits of detection in our assay ( S2A Fig ) . The origin of the approximately 2-kb transcript remains unclear ( Fig 2B , S2A Fig , orange arrow ) ; however , it could be representative of transcript initiation downstream of the canonical transcriptional start site within the 5′UTR [47 , 98] . These data suggest that the SpIRE97/622 5′UTR retains weak promoter activity . Because the splicing events that gave rise to SpIRE97/790 and SpIRE97/976 led to larger deletions of the 5′UTR when compared to SpIRE97/622 , we reasoned that they would lead to a similar , if not a greater , reduction in transcriptional activity; thus , they were not tested in this assay . To corroborate the northern blot analyses , we conducted dual luciferase expression assays on whole cell lysates ( WCLs ) derived from HeLa-JVM cells co-transfected with firefly luciferase-based vectors ( pPLWTLUC , pPL97/622LUC , or pGL4 . 11 ) and a constitutively expressed Renilla ( Renilla reniformis ) luciferase internal control plasmid ( pRL-TK; Methods ) . Consistent with the northern blot data , HeLa-JVM cells transfected with pPLWTLUC exhibited an approximately 267-fold increase of normalized firefly luciferase activity when compared to cells transfected with the promoter-less pGL4 . 11 vector ( Fig 2C; S2 Table ) . By comparison , HeLa-JVM cells transfected with pPL97/622LUC exhibited only about a 7-fold increase of normalized firefly luciferase activity when compared to cells transfected with the promoter-less pGL4 . 11 vector ( Fig 2C; S2 Table ) . Together , the above data suggest that the splicing event leading to the generation of SpIRE97/622 severely compromises its promoter activity . Given that splicing reduces L1 promoter activity , we examined why the G98U99 SD may be conserved in the L1 5′UTR . Previous studies revealed that a RUNX3 binding site within the 5′UTR is important for maximal L1 promoter activity [96] . Interestingly , the SD site used to generate the three classes of SpIREs reported here is contained within the core sequence of a RUNX3 binding site that is conserved from the L1PA1–L1PA10 subfamilies ( Fig 1A; SD: G98U99; S1B Fig ) [84] . Thus , we hypothesized that this SD is retained to maintain an active RUNX3 transcription factor binding site . To test this hypothesis , we mutated the SD sequence within the WT 5′UTR ( U99C , creating pPLSDmLUC ) [99] and tested if this mutation affects 5′UTR promoter activity . Northern blot analyses using the previously described riboprobes detected a signal at about 2 . 7 kb in mRNAs derived from HeLa-JVM cells transfected with pPLSDmLUC . However , there is markedly less of this mRNA when compared to cells transfected with pPLWTLUC ( Fig 2B; about 18% of pPLWTLUC ) . In contrast , mutating the SA site within the WT 5′UTR ( A620C , creating pPLSAmLUC ) did not drastically affect L1 promoter activity ( Fig 2B ) . Thus , our data are consistent with previous findings [96] and suggest that the retention of the complete RUNX3 site containing the G98U99 SD is critical for L1 promoter activity . We next sought to identify spliced L1 mRNAs that might have given rise to SpIREs . To this end , we conducted end-point reverse transcription PCR ( RT-PCR ) experiments using poly ( A ) mRNAs isolated from HeLa-JVM cells transfected with a series of L1/firefly luciferase expression vectors ( S2B Fig; Methods ) . The REV-LUC oligonucleotide ( S2B Fig , purple line ) was used to initiate L1/firefly luciferase first-strand cDNA synthesis; the cDNA products then were PCR amplified using FWD-5′UTR ( S2B Fig , red line ) and REV-LUC ( S2B Fig , purple line ) oligonucleotide primers . The resultant cDNAs were separated on an agarose gel , cloned , and characterized using Sanger DNA sequencing . Control experiments conducted in the absence of RT revealed that the characterized PCR products were derived from the amplification of cDNAs ( S2C Fig ) . We detected the predicted full-length L1/firefly luciferase cDNA products from HeLa-JVM cells transfected with pPLWTLUC , pPLSDmLUC , and pPLSAmLUC ( Fig 2D , yellow “*” in lanes 1 , 3 , and 4 ) as well as the shorter predicted L1/firefly luciferase cDNA product from HeLa-JVM cells transfected with pPL97/622LUC ( Fig 2D , yellow “#” in lane 2 ) . In agreement with our northern blot experiments ( Fig 2B ) , we did not detect cDNAs consistent with SpIRE97/622 splicing in pPLWTLUC transfected HeLa-JVM cells ( Fig 2D ) . However , we did detect an L1/firefly luciferase cDNA that corresponds to the SpIRE97/790 splicing event from cells transfected with pPLWTLUC and pPLSAmLUC ( Fig 2D , yellow “+” , lanes 1 and 4; Fig 1C ) [78] . Importantly , this product was not detected in HeLa-JVM cells transfected with either pGL4 . 11 or pPLSDmLUC or untransfected HeLa-JVM cells . We next tested whether intra-5′UTR splicing affects L1 mRNA translation . L1 sequences were cloned into an episomal pCEP4 expression vector that contains a hygromycin B resistance gene and a cytomegalovirus ( CMV ) early promoter , which augments L1 expression . HeLa-JVM cells were transfected with a WT L1 ( pJM101/L1 . 3 ) , an L1 that contains a 5′UTR deletion ( pJM102/L1 . 3 ) , or an L1 containing the SpIRE97/622 deletion ( pPL97/622/L1 . 3 ) ( Fig 3A ) [9 , 50] . Western blot analyses were conducted using WCLs that were derived from hygromycin-resistant HeLa-JVM cells transfected with the above constructs 9 days post-transfection . An ORF1p polyclonal antibody ( α-N-ORF1p; directed against amino acids +31 to +49 in L1 . 3 [100] [UniProtKB accession #Q9UN81] ) detected an approximately 40-kDa product in cells transfected with pJM101/L1 . 3 , pJM102/L1 . 3 , and pPL97/622/L1 . 3 but not in cells transfected with the pCEP/GFP control ( Fig 3B ) . HeLa-JVM cells transfected with pPL97/622/L1 . 3 exhibited a slight reduction in the steady-state level of ORF1p when compared to HeLa-JVM cells transfected with pJM101/L1 . 3 or pJM102/L1 . 3 ( Fig 3B ) . Because a CMV promoter augmented L1 transcription , it is unlikely that this reduction is due to reduced L1 expression . It is possible that the slight reduction in ORF1p is due to an alteration of the L1 5′UTR RNA secondary structure and/or minor changes in the stability of pPL97/622/L1 . 3 mRNA when compared to pJM101/L1 . 3 and pJM102/L1 . 3 mRNAs . The splicing event yielding SpIRE97/976 results in an amino-terminal ORF1 deletion of 66 nucleotides , including the canonical ORF1p methionine start codon ( Fig 3C , black AUG , 40 kDa ) . We hypothesized that ORF1p synthesis might initiate from two methionine codons ( AUG ) that are located in weak Kozak consensus sequences either 102 or 270 ribonucleotides downstream from the canonical AUG start codon ( Fig 3C ) [101] . If the downstream methionine codons are used for translation initiation , we expect to detect amino terminal truncated ORF1 proteins of about 33 kDa and 27 kDa , respectively . Western blot analyses were conducted as above using WCLs derived from hygromycin-resistant HeLa-JVM cells transfected with pJM101/L1 . 3 , an L1 containing the SpIRE97/976 deletion ( pPL97/976/L1 . 3 ) , or pCEP/GFP control vectors ( Fig 3A ) [22] . As predicted , the α-N-ORF1p and α-C-ORF1p antibodies detected an approximately 40-kDa protein in WCLs derived from HeLa-JVM cells transfected with pJM101/L1 . 3 but did not detect a protein in WCLs derived from HeLa-JVM cells transfected with the pCEP/GFP control ( Fig 3D , left and right panels ) . The α-N-ORF1p antibody detected an approximately 33-kDa protein in WCLs derived from HeLa-JVM cells transfected with pPL97/976/L1 . 3 ( Fig 3D , left panel ) , whereas the α-C-ORF1p antibody detected approximately 33-kDa and approximately 27-kDa proteins in the same extracts and an unknown cross-reacting protein at about 25 kDa ( Fig 3D , right panel ) . Similar results were obtained when RNP extracts were used in western blot experiments , although western blots performed with the α-C-ORF1p antibody did not detect the cross-reacting approximately 25-kDa protein ( S3A Fig ) . To confirm that the approximately 33-kDa and 27-kDa products were ORF1p derived , we introduced a T7-gene10 epitope tag to the 3′ end of ORF1 , creating pPL97/976/L1 . 3-T7 . Western blots using a α-T7 antibody recapitulated our previous results and , similar to RNP preparations , did not identify the cross-reacting approximately 25-kDa protein ( S3B Fig ) . Thus , the 5′UTR/ORF1 splicing event leads to the generation of an mRNA that , if translated , results in the synthesis of amino-terminal truncated derivatives of ORF1p . Our data indicate that SpIRE97/622 contains a defective promoter and , if transcribed , SpIRE97/622 mRNA is translated at slightly lower levels than WT L1 mRNA . Thus , we hypothesized that an intra-5′UTR spliced L1 mRNA would be capable of undergoing an initial round of L1 retrotransposition . However , the resultant full-length retrotransposition events would contain a defective promoter , which may compromise subsequent retrotransposition . To test the above hypothesis , we examined whether RNAs derived from a cohort of L1 expression constructs could retrotranspose using a cultured cell retrotransposition assay [31] . The 3′UTR of each construct contains a retrotransposition indicator cassette ( mneoI ) . The mneoI cassette consists of an antisense copy of a neomycin phosphotransferase gene whose coding sequence is interrupted by an intron that resides in the same transcriptional orientation as the L1 [31 , 102] . This arrangement ensures that the expression of a functional neomycin phosphotransferase gene will only be activated upon L1 retrotransposition , thereby conferring cellular resistance to the drug G418 [31 , 102] . Retrotransposition efficiency then can be quantified by counting the resultant numbers of G418-resistant foci [31 , 61] . Consistent with previous reports ( e . g . , [31 , 41] ) , mRNAs derived from RC-L1s that contain both CMV and 5′UTR ( Fig 4A , pJM101/L1 . 3 , black bar; S3 Table ) , CMV only ( Fig 4A , pJM102/L1 . 3 , black bar; S3 Table ) , or 5′UTR only ( Fig 4A , pJM101/L1 . 3ΔCMV , gray bar; S3 Table ) promoters could efficiently retrotranspose . By comparison , the pPL97/622/L1 . 3 expression construct produced mRNAs that could undergo efficient retrotransposition when a CMV promoter augmented L1 expression ( Fig 4A , black bar , about 70% the activity of pJM101/L1 . 3; S3 Table ) , but not when L1 expression was driven from the 5′UTR harboring the intra-5′UTR splicing event ( Fig 4A , pPL97/622/L1 . 3ΔCMV gray bar , about 7% the activity of pJM101/L1 . 3; S3 Table ) . Consistent with this observation , control experiments revealed that an L1 lacking promoter sequences ( Fig 4A , pJM102/L1 . 3ΔCMV; S3 Table ) [50] was unable to retrotranspose . Additional controls demonstrated that an L1 containing a missense mutation ( pJM105/L1 . 3; D702A ) that disrupts ORF2p RT activity [50] severely reduced L1 retrotransposition efficiency ( Fig 4A; S3 Table ) . Thus , the data suggest that the SpIRE97/622 intra-5′UTR splicing event severely compromises L1 5′UTR promoter activity as well as subsequent rounds of L1 retrotransposition . The retrotransposition of an mRNA derived from a 5′UTR/ORF1 splicing event would generate a SpIRE ( e . g . , SpIRE97/976 ) that contains a defective promoter and , if transcribed and translated , would produce amino-terminal truncated versions of ORF1p . If the truncated version ( s ) of ORF1p were nonfunctional , we reasoned that the 5′UTR/ORF1 splicing event would lead to an L1 mRNA that is compromised for an initial round of retrotransposition in cis . Indeed , RNAs derived from pPL97/976/L1 . 3 could not retrotranspose despite expression being driven by CMV ( Fig 4B; S4 Table ) . We next hypothesized that a source of WT ORF1p would be required to act in trans to promote the retrotransposition of L1 mRNAs containing a 5′UTR/ORF1 splicing event . To test this hypothesis , we co-transfected pPL97/976/L1 . 3 ( whose expression is augmented by a CMV promoter ) with a series of “driver” L1 expression plasmids that lack the mneoI retrotransposition indicator cassette [22 , 50] . The co-transfection of pPL97/976/L1 . 3 with “driver” plasmids that express WT ORF1p , pJBM561 ( a monocistronic ORF1p expression vector ) , pJM101/L1 . 3NN , or pJM105/L1 . 3NN , resulted in low levels of pPL97/976/L1 . 3 RNA retrotransposition in trans ( Fig 4C; columns 1 , 2 , and 3 , respectively; S5 Table ) . By comparison , the co-transfection of pPL97/976/L1 . 3 with “driver” plasmids that do not express ORF1p ( pORF2/L1 . 3NN [a monocistronic ORF2p expression vector] or pCEP4 ) did not support retrotransposition in trans ( Fig 4C; columns 4 and 5 , respectively; S5 Table ) [22] . Thus , the expression of ORF1p , but not ORF2p , can promote low levels of retrotransposition of mRNAs derived from pPL97/976/L1 . 3 in trans . RT-PCR experiments using L1/firefly luciferase expression vectors uncovered evidence of SpIRE97/790 splicing events ( Fig 2D ) . Intriguingly , SpIRE97/790 sequences are only present in the L1PA1 and L1PA2 subfamilies ( S1A Fig; S1 Data; S1 Table ) [84] . Indeed , the analysis of 1 , 000 genomes data [103] revealed that the L1PA1 SpIRE97/790-3 sequence ( S1 Data; S1 Table ) is polymorphic with respect to presence in the human population ( about 41% homozygous “filled”; 35% heterozygous; 24% homozygous “empty” ) , whereas L1PA2 SpIRE97/790 sequences appear to be fixed with respect to presence in humans . Additionally , we identified four non-reference L1PA1 SpIRE97/790 sequences in data from the 1000 Genomes Project ( S1 Data; S1 Table ) . Thus , SpIRE97/790 sequences may represent an evolutionarily younger SpIRE subfamily than the SpIRE97/622 and SpIRE97/976 sequences , which are predominantly found in older L1 subfamilies ( S1 Data; S1 Table ) . Recently , an elegant study from the Haussler laboratory demonstrated that the Krüppel-associated Box-containing Zinc-Finger Protein 93 ( ZNF93 ) could bind within L1PA3 and L1PA4 5′UTRs to repress their expression [104] . Intriguingly , a 129-bp deletion that eliminates the ZNF93 binding site within the L1PA2 and L1PA1 5′UTRs allowed them to evade ZNF93-mediated repression [104] . This 129-bp sequence resides between a putative branch site and the SA sequence used to generate the spliced L1 RNA that gave rise to SpIRE97/790 sequences ( Fig 5A ) . Thus , we hypothesized this 129-bp deletion may have altered L1 5′UTR splicing dynamics by relocating the SpIRE97/790 SA ( A916G917 in L1PA3 ) to a favorable splicing context in L1PA2 and L1PA1 subfamily members ( Fig 5A ) . To test the above hypothesis , we generated L1/firefly luciferase expression vectors containing the 5′UTR of a “hot” L1 ( L1RP [accession #AF148856] ) [106] or a version of the L1RP 5′UTR that includes the 129-bp L1PA4 sequence containing the ZNF93 binding site [104] upstream of a promoter-less firefly luciferase gene ( pGL4 . 11 ) , creating pJBMWTLUC and pJBMWT129PA4LUC , respectively ( Fig 5B , top panel ) . We also created a control vector that has a “scrambled” version of the 129-bp L1PA4 sequence ( pJBMWT129SCRLUC ) [104] ( Fig 5B , top panel ) . Dual luciferase assays using WCLs derived from HeLa-JVM cells co-transfected with pJBMWTLUC , pJBMWT129PA4LUC , pJBMWT129SCRLUC , or pGL4 . 11 and a constitutively expressed Renilla luciferase internal control plasmid ( pRL-TK; Methods ) revealed that pJBMWTLUC and pJBMWT129PA4LUC exhibited an increase ( about 345- or about 320-fold , respectively ) of normalized firefly luciferase activity , when compared to the promoter-less pGL4 . 11 vector ( Fig 5B , bottom panel; S6 Table ) . By comparison , pJBMWT129SCRLUC exhibited a significant , though less pronounced , increase ( about 88-fold ) of normalized firefly luciferase activity ( Fig 5B , bottom panel; S6 Table ) . Thus , in general agreement with previous studies [104] , the 129-bp L1PA4 insert does not negatively affect L1RP5′UTR transcriptional activity . As an additional control , we confirmed that the 129-bp L1PA4 sequence did not significantly affect L1 activity using an EGFP-based retrotransposition assay ( Fig 5C; S7 Table ) [104] . To test whether the presence or absence of the 129-bp L1PA4 sequence affects intra-L1 5′UTR splicing , we used a slightly modified version of the end-point RT-PCR strategy depicted in Fig 2D . In agreement with experiments performed with pPLWTLUC ( Fig 2D ) , we detected the predicted full-length L1RP 5′UTR cDNAs as well as SpIRE97/790 spliced cDNAs in cells transfected with pJBMWTLUC ( Fig 5D , yellow “*” and yellow “+ , ” respectively , lane 3 ) . By comparison , HeLa-JVM cells transfected with pJBMWT129PA4LUC yielded the predicted full-length 5′UTR L1 cDNA ( Fig 5C , yellow “**” lane 4 ) , but did not yield cDNAs corresponding to the SpIRE97/790 splicing event . Instead , we detected a new spliced cDNA that used the same G98U99 SD and a new SA that resides within the 129-bp L1PA4 sequence ( A851G852 ) , which is not present in the WT L1RP sequence ( Fig 5A and 5D , lane 4 , yellow “@” ) . Finally , we detected the predicted full-length L1RP 5′UTR cDNAs from cells transfected with pJBMWT129SCRLUC , as well as a biologically irrelevant product that utilized the same G98U99 SD and an SA that resides within the 129-bp L1PA4 scrambled sequence ( Fig 5D , lane 5 , yellow “***” and yellow “$ , ” respectively ) . Thus , our data demonstrate that the loss of the 129-bp sequence from L1PA3 resulted in a new splicing pattern that led to the emergence of SpIRE97/790 sequences ( Fig 5A and 5D ) . Finally , we examined whether the new cDNA detected from cells transfected with pJBMWT129PA4LUC corresponds to a SpIRE . Indeed , a BLAT search of the human genome using an in silico probe that spans the intra-5′UTR splice junction present in this putative SpIRE ( nucleotides 47–97 and 853–903 of pJBMWT129PA4LUC ) yielded nine additional SpIRE97/853 sequences ( S1 Data; S1 Table ) . These additional SpIREs retain L1 structural hallmarks ( S1 Data; S1 Table ) , indicating that canonical EN-dependent TPRT led to their generation .
The evolutionary success of L1 requires the continued retrotransposition of full-length L1 RNAs . Thus , it was surprising when Belancio and colleagues identified a small number of L1 retrotransposition events in the HGR that apparently were derived from spliced L1 RNAs [78 , 79] . Here , we confirmed and extended those findings and report a novel group of retrotransposed L1s that are derived from an L1 RNA containing an intra-5′UTR splicing event ( SpIRE97/622; Fig 1 ) . SpIRE97/622 is 10 times more prevalent than previously identified SpIREs and comprises about 1 . 8% of the annotated full-length L1 retrotransposition events accumulated during the past approximately 27 million years ( MY ) ( S1 Fig ) . Numerous studies have demonstrated that L1 ORF1p and L1 ORF2p exhibit Cis-preference and preferentially bind to their encoding mRNA to promote its retrotransposition ( Fig 6A ) [33 , 35 , 38 , 50–53] . Using a cell culture based retrotransposition assay in HeLa cells , we demonstrated that L1 mRNAs that contain intra-5′UTR splicing events can produce ORF1p and ORF2p and undergo an initial round of retrotransposition in cis ( Fig 6B ) . However , the resultant SpIREs lack Cis-acting sequences required for efficient L1 transcription ( Fig 2 ) [47 , 94 , 96] and , as a result , are compromised for subsequent rounds of retrotransposition ( Figs 4A and 6B ) . In contrast to intra-5′UTR splicing events , L1 mRNAs containing 5′UTR/ORF1 splicing events produce nonfunctional , amino-terminal truncated versions of ORF1p ( Fig 3C; S3A and S3B Fig ) . As a result , these mRNAs are retrotransposition defective in cis and must rely on exogenous sources of ORF1p to promote their retrotransposition by Trans-complementation ( Figs 4B and 4C and 6C ) . Notably , these experiments also provide genetic evidence that ORF2p can be translated from the 5′UTR/ORF1 spliced L1 mRNAs . In the rare cases in which Trans-complementation occurs , the resultant 5′UTR/ORF1 SpIRE will lack Cis-acting sequences required for efficient L1 transcription and , if transcribed , would produce nonfunctional versions of ORF1p . The loss of Cis-acting sequences and the requirement for Trans-complementation make it highly unlikely that the resultant 5′UTR/ORF1 SpIREs could undergo subsequent rounds of retrotransposition ( Fig 6C ) . The above data strongly indicate that SpIREs represent evolutionary “dead ends” in the L1 amplification process . It is possible that a small number of SpIREs could give rise to new L1 retrotransposition events . For example , the insertion of a SpIRE97/622 downstream of a cellular promoter could , in principle , enhance its expression and subsequent retrotransposition . However , any resultant retrotransposition event would contain a defective promoter and ultimately be compromised for subsequent rounds of retrotransposition . Thus , we conclude that splicing negatively affects L1 retrotransposition . The SpIREs examined in this study each use a common SD site ( G98U99 ) but different SA sites ( A620G621 , A788G789 , A851G852 , or A974G975 ) [78 , 79] . These findings raise the following question: if splicing adversely affects L1 retrotransposition , why are these splice sites retained in L1 RNA ? The G98U99 SD site is about 46 MY old , is conserved in the L1PA1–L1PA10 subfamilies ( S1B Fig ) [84] , and resides within a core binding site for the RUNX3 transcription factor [96] . Indeed , previous studies indicated that mutating U99 in the L1 5′UTR impairs RUNX3 binding and decreases 5′UTR transcriptional activity [96] . Consistent with these findings , we found that mutating the SD sequence leads to an approximately 5-fold reduction in L1 steady-state RNA levels ( Fig 2B ) . Together , these data strongly suggest that the benefit conferred by the RUNX3 transcription factor binding site at the DNA level outweighs the cost of harboring the SD site ( G98U99 ) in L1 RNA . Despite the evolutionary conservation of the G98U99 SD , northern blotting experiments revealed that the vast majority of L1 5′UTRs are not subject to splicing ( Fig 2B ) . SpIREs are therefore most likely formed when L1 RNAs containing rare splicing events undergo retrotransposition . The reason ( s ) G98U99 is not efficiently utilized as a functional SD site requires elucidation . However , it is possible that the G98U99 sequence is sequestered into a secondary structure within L1 RNA that restricts its access to U1 small nuclear RNA ( snRNA ) ( reviewed in [107 , 108] ) . Alternatively , a cellular protein ( s ) might bind at or near the SD site , thereby blocking its ability to interact with U1 snRNA . Either scenario provides a plausible mechanism for how L1 maintains a functional SD sequence in its mRNA and could , in part , explain why SpIREs only represent about 2% of annotated full-length L1 retrotransposition events that occurred during the past approximately 27 MY . SA sites within the 5′UTR might also reside in functional transcription factor binding sites or functionally conserved regions of ORF1p . For example , the A788G789 SA is about 70 MY old and is conserved through the L1PA15B subfamily ( S1B Fig ) , suggesting that it may reside in a conserved Cis-acting motif . The ORF1 A974G975 SA resides at codon positions two and three of lysine 22 , and any nucleotide change at codon position two would result in an amino acid substitution in ORF1p that may adversely affect its function . Thus , it is possible that some functional splice sites are embedded in sequences that are critical for 5′UTR and/or ORF1p function . Our data reveal how host-factor—driven L1 5′UTR evolution can alter L1 splicing dynamics . We demonstrated that structural changes in the 5′UTR can lead to collateral intra-5′UTR splicing changes , which have resulted in the generation of new SpIRE97/790 sequences ( Fig 5A and 5D ) . In addition to yielding insights into the evolution of human L1 5′UTR sequences , these experiments demonstrate the utility of our luciferase-based reporter constructs to prospectively detect ancestral L1 splicing events that led to the generation of an older SpIRE ( SpIRE97/853 ) subfamily ( Fig 5A and 5D; S1 Data; S1 Table ) . Although the SpIRE97/622 sequence is the most abundant SpIRE in the HGR , only SpIRE97/790 sequences were detected in our RT-PCR experiments . These data , as well as the finding that five of eight SpIRE97/790 sequences are polymorphic with respect to presence/absence in the human population , suggest that SpIRE97/790 sequences are currently amplifying in modern human genomes . It is noteworthy that the splicing events detected from engineered L1 mRNAs in transfected HeLa cells recapitulate many splicing events that led to SpIRE formation in the human genome ( Figs 2D and 5D , and [78 , 79] ) . It has recently been shown that a small number of distinct genomic L1 loci are expressed in a cell type—specific manner [6–8] . Moreover , L1 splicing and/or premature polyadenylation patterns vary among human tissues and cell types [79 , 80 , 109 , 110] , host proteins involved in splicing and polyadenylation associate with L1 RNPs [100 , 111–113] , and overexpression of the Epstein-Barr Virus SM protein alters L1 splicing and premature polyadenylation patterns [79] . Thus , it is tempting to speculate that L1 posttranscriptional processing may suppress expression and/or retrotransposition of full-length L1s in a developmental or cell type—specific manner . In sum , our data strongly indicate that L1 mRNA splicing is detrimental to L1 retrotransposition and further strengthen the hypothesis that ORF1p and ORF2p predominantly retrotranspose their encoding full-length L1 RNAs to new genomic locations in cis . In addition , we demonstrated that despite harboring evolutionarily conserved functional SD and SA sites within their 5′UTR , the vast majority of L1 transcripts apparently evade splicing . Finally , we provide experimental evidence revealing that changes within the L1 5′UTR , which are driven by the escape from host-factor repression , lead to collateral changes in L1 splicing profiles . Together , these data provide insights into the evolutionary dynamics of the L1 5′UTR and raise the intriguing possibility that host factors that promote L1 splicing or alter L1 splicing profiles may represent a mechanism by which the cell can disrupt full-length L1 RNA to prevent unabated L1 retrotransposition .
All plasmids were propagated in DH5α Escherichia coli ( genotype: F- φ80lacZΔM15 Δ ( lacZYA-argF ) U169 recA1 endA1 hsdR17 ( rk- , mk+ ) phoA supE44 λ- thi-1 gyrA96 relA1 ) ( Invitrogen , Carlsbad , CA ) . Competent cells were generated as described previously [114] . Plasmids were prepared using the Plasmid Midi Kit ( Qiagen , Germany ) according to the protocol provided by the manufacturer . HeLa-JVM cells ( obtained from Dr . Maxine Singer and originally cited in reference [31] ) were cultured in high glucose Dulbecco’s Modified Eagle Medium ( DMEM ) lacking pyruvate ( Invitrogen ) . DMEM was supplemented with 10% fetal bovine calf serum ( FBS ) and 1X penicillin/streptomycin/glutamine to create DMEM-complete medium , as described previously [31] . HeLa-JVM cells were grown in a humidified tissue culture incubator ( Thermo Scientific , Waltham , MA ) at 37°C in the presence of 7% CO2 . BLAT [83] was used to screen build 38 ( GRCh38/hg38 ) of the UCSC genome browser ( https://genome . ucsc . edu ) using 100 bp in silico probes that spanned ( 50 bases upstream and downstream ) the splice junctions of SpIRE97/622 , SpIRE97/790 , and SpIRE97/976 . The in silico probes were designed using the L1 . 3 sequence ( accession #L19088 [9 , 82] ) as a template . A 100-bp in silico probe that spanned ( 50 bases upstream and downstream ) the splice junction of SpIRE97/853 was designed using the pJBMWT129PA4LUC sequence . Putative SpIREs shared >95% sequence identity with the in silico probes . Putative SpIREs were downloaded from the UCSC genome browser and manually curated with the aid of repeat masker ( http://repeatmasker . org ) . Each sequence was inspected to ensure it contained a splicing event and represented a bona fide SpIRE . For four events that were prematurely 3′ truncated , we analyzed 4 kb of genomic DNA flanking the 3′ end of the SpIRE to determine if it shared >95% sequence identity with L1 . 3 using the Serial Cloner alignment tool ( http://serialbasics . free . fr/Serial_Cloner . html ) . We were unable to identify any L1 sequence in the flanking DNA; thus , we cannot determine the reason for the apparent 3′ truncation in these four SpIREs . Structural hallmarks of L1 integration events that occur by canonical TPRT ( e . g . , the presence of target site duplications , the presence of untemplated nucleotides at the 5′ genomic DNA/L1 junction [47 , 69 , 89 , 115] , a 3′ poly ( A ) tract , and putative L1-mediated sequence transductions ) [23 , 116 , 117] were determined manually by analyzing sequences flanking the 5′ and 3′ ends of each SpIRE [69 , 116 , 118] . The L1 “empty site” for all SpIREs is inferred; the 3′ TSD was considered the ancestral “empty site” and any nucleotide differences between the 5′ and 3′ TSD are annotated in the 5′ TSD only . Sequences are named based on the splicing event contained within the SpIRE ( SpIRE97/622 , SpIRE97/790 , SpIRE97/976 , or SpIRE97/853 ) and a corresponding number for easy referral between S1 Data and S1 Table ( for example; SpIRE97/622-1 is the first of the analyzed 116 SpIRE97/622 sequences ) . Khan et al . 2006 provided full-length L1 subfamily consensus sequences of L1PA1 ( L1Hs ) through L1PA16 and assembled an alignment of the respective 5′UTRs [84] . We manually inspected these alignments to determine the oldest L1 subfamily that contained the 5′UTR SD/SA sequences utilized in generating the reported SpIREs . We next determined the conservation of the ORF1 SA sequence ( A974G975 ) by aligning full-length L1 consensus sequences provided in Khan et al . 2006 using the ClustalW alignment function [84 , 119] from the MegAlign ( http://www . dnastar . com/t-megalign . aspx ) software . As with the 5′UTR , we manually inspected the resulting alignment to determine the oldest L1 subfamily that contained the ORF1 SA sequence ( A974G975 ) . To identify putative splicing branch point sequences , we utilized the L1 . 3 5′UTR ( accession #L19088 ) sequence and the pJBMWT129PA4LUC 5′UTR sequence and submitted them for analysis using the Human Splicing Finder v3 . 0 online prediction program ( http://www . umd . be/HSF3/HSF . html ) [105] . The resultant analyses identify potential SD , SA , and branch point sequences and assign consensus value scores for each motif [105] . Motif scores greater than 80 represent “strong” splice sites; sequences with scores less than 80 represent “weaker” splice sites . The 5′UTR sequence of each L1 was uploaded and analyzed by the general “Analyze a Sequence” function . We then selected predicted branch points that might pair with the known SA: A788G789 ( L1 . 3 ) and A851G852 ( pJBMWT129PA4LUC ) based on their proximity to the SA sequence [120] . We identified a putative branch point ( A763C764C765T766C767A768C769 ) with a score of 95 . 75 that could pair with the SA: A788G789 in the L1 . 3 5′UTR . We also identified a putative branch point ( T795C796C797A798G799A800G801 ) with a score of 75 . 73 that could pair with the SA: A851G852 in the pJBMWT129PA4LUC 5′UTR ( Fig 5A ) . We performed in silico genotyping of four SpIRE97/790 loci using reads from the 1000 Genomes Project [103 , 121] . Read pairs anchored within 600 bp of each locus were extracted from each of 2 , 453 samples from the 1000 Genomes Project . Extracted read pairs were aligned to reconstructed reference ( insertion ) and alternative ( empty site ) sequences and the most likely genotype for each sample was determined based on the number and mapping quality of read pairs aligned to each allele [121] . Read pairs that aligned entirely within the L1 sequence as well as read pairs that show equivalent alignments to both the reference and alternative sequences were ignored in the analysis . We utilized an anchored read pair mapping approach to identify additional non-reference SpIRE97/790 insertions in the 1000 Genomes Project samples . We searched alignment files from 2 , 453 samples for read pairs in which one read is aligned across the splice junction in one of the four SpIRE97/790 sequences represented in the genome reference sequence and the other read is uniquely aligned elsewhere in the genome . We then intersected the resulting anchored locations with a recently published map of non-reference L1 insertions discovered in the same samples [122] , identifying four insertions supported by multiple SpIRE-associated read pairs . To further characterize these loci , we extracted insertion-supporting read pairs for each locus and performed a de novo read assembly using the CAP3 assembler [123] . This analysis results in a collection of short contigs for each locus , which extend into the flanking edges of each inserted L1 element . The resulting contigs were filtered for repeat content , aligned to the genome reference , and annotated for characteristics indicative of bona fide SpIRE97/790 insertions ( S1 Data and S1 Table ) . The following L1 constructs contain a derivative of an RC-L1 ( L1 . 3 , accession #L19088 [9 , 82] ) cloned into the pCEP4 plasmid backbone ( Life Technologies ) , unless indicated otherwise . Cloning strategies used to create these constructs are available upon request . pJM101/L1 . 3 contains a full-length version of L1 . 3 in the pCEP4 backbone . The 3′UTR of L1 . 3 contains the mneoI retrotransposition indicator cassette [9 , 31 , 82] . pJM101/L1 . 3ΔCMV is identical to pJM101/L1 . 3 , but the CMV promoter was deleted from the pCEP4 plasmid [9 , 31 , 82] . pJM101/L1 . 3NN is a derivative of pJM101/L1 . 3 that lacks the mneoI retrotransposition indicator cassette [50] . pDK101/L1 . 3 is a derivative of pJM101/L1 . 3 that expresses a version of ORF1p that contains a T7 gene10 epitope tag on its carboxyl-terminus [35] . pJM105/L1 . 3 is identical to pJM101/L1 . 3 , but contains a D702A missense mutation in the ORF2p RT active site [50] . pJM105NN is a derivative of pJM105/L1 . 3 that lacks the mneoI retrotransposition indicator cassette [50] . pJM102/L1 . 3 is a derivative of pJM101/L1 . 3 that lacks the L1 5′UTR [58] . pJM102/L1 . 3ΔCMV is identical to pJM102/L1 . 3 , but the CMV promoter was deleted from the pCEP4 plasmid [50] . pPL97/622/L1 . 3 is a derivative of pJM101/L1 . 3 that contains a 524 intra-5′UTR deletion ( L1 . 3 nucleotides 98–621 ) present in SpIRE97/622 [10] . pPL97/622/L1 . 3ΔCMV is identical to pPL97-622/L1 . 3 , but the CMV promoter was deleted from the pCEP4 plasmid . pPL97/976/L1 . 3 is a derivative of pJM101/L1 . 3 that contains an 878-bp 5′UTR/ORF1 deletion ( L1 . 3 nucleotides 98–975 ) present in SpIRE97/976 . pPL97/976/L1 . 3-T7 is a derivative of pPL97-976/L1 . 3 that expresses a version of ORF1p that contains a T7 gene10 epitope tag on its carboxyl-terminus . pORF2/L1 . 3NN is a monocistronic L1 ORF2 expression plasmid that lacks the mneoI retrotransposition indicator cassette [22] . pJBM561 is a monocistronic L1 ORF1 expression plasmid that lacks the mneoI retrotransposition indicator cassette . pCEP/GFP is a pCEP4-based plasmid that expresses a humanized Renilla green fluorescent protein ( hrGFP ) from phrGFP-C ( Stratagene ) . A CMV promoter drives the expression of the hrGFP gene [22] . The following L1 constructs contain a derivative of an RC-L1 ( L1RP , accession #AF148856 . 1 [124] ) cloned into the pCEP4 plasmid backbone ( Life Technologies ) lacking the CMV promoter . pL1RP-EGFP contains a full-length version of L1RP element . The 3′UTR contains the EGFP retrotransposition indicator cassette [124] . pL1RP ( JM111 ) -EGFP: a derivative of pL1RP-EGFP that contains two missense mutations in ORF1 that abolish retrotransposition [31 , 124] . L1Hs+129L1PA4: a derivative of pL1RP-EGFP that carries a 129-bp sequence element from the L1PA4 5′UTR that is not present in L1Hs [104] . L1Hs+129scrambleL1PA4: a derivative of pL1RP-EGFP that carries a scrambled version of the 129-bp sequence element from the L1PA4 5′UTR that is not present in L1Hs [104] . The following plasmids are based on the pGL4 . 11 promoter-less firefly luciferase expression vector ( Promega , Madison , WI ) . Oligonucleotides and cloning strategies used to create these constructs are available upon request . pPLWTLUC is a derivative of pGL4 . 11 that contains the WT L1 . 3 5′UTR upstream of the firefly luciferase reporter gene . pPL97/622LUC is a derivative of pGL4 . 11 that contains the pPL97-622/L1 . 3 5′UTR deletion derivative upstream of the firefly luciferase reporter gene . pPLSDmLUC is a derivative of pPLWTLUC that contains a U99C SD mutation in the L1 . 3 5′UTR upstream of the firefly luciferase reporter gene . pPLSAmLUC is a derivative of pPLWTLUC that contains an A620C SA mutation in the L1 . 3 5′UTR upstream of the firefly luciferase reporter gene . pRL-TK is an expression plasmid where the HSV-TK promoter drives Renilla luciferase transcription ( Promega ) . pJBMWTLUC is a derivative of pGL4 . 11 that contains the L1RP 5′UTR from the plasmid pL1RP-EGFP [124] and was cloned upstream of the firefly luciferase reporter gene . pJBMWT129PA4LUC is a derivative of pGL4 . 11 that contains the 5′UTR from L1Hs+129L1PA4 [104] and was cloned upstream of the firefly luciferase reporter gene . pJBMWT129SCRLUC: the 5′UTR from L1Hs+129scrambleL1PA4 [104] that was cloned upstream of the firefly luciferase reporter gene . RNA isolation was performed as previously described with minor modifications [100] . Briefly , 8×106 HeLa-JVM cells were seeded into a T-175 Falcon tissue culture flask ( BD Biosciences , San Jose , CA ) . On the following day , transfections were conducted using the FuGene HD transfection reagent ( Promega , Madison , WI ) . The transfection reactions contained 1 mL of Opti-MEM ( Life Technologies ) , 120 μl of the FuGene HD transfection reagent , and 20 μg of plasmid DNA per flask . The tissue culture medium was changed 24 hours post-transfection . The cells were collected 48 hours post-transfection . Briefly , cells were washed in ice-cold 1X phosphate buffered saline ( PBS ) ( Life Technologies ) . The cells then were scraped from the tissue culture flasks , transferred to a 15-mL conical tube ( BD Biosciences ) , and centrifuged at 3 , 000 × g for 5 minutes at 4°C . Cell pellets were frozen at −20°C overnight . The frozen pellets were thawed and total RNA was prepared using the TRIzol reagent following the protocol provided by the manufacturer ( Life Technologies ) . Poly ( A ) RNAs then were isolated from the total RNAs using a Oligotex mRNA Midi Kit ( Qiagen ) , suspended in UltraPure DNase/RNase-Free distilled water ( Thermo Fisher Scientific , Waltham , MA ) , and quantified using a NanoDrop 1000 spectrophotometer ( Thermo Fisher Scientific ) . For the RT-PCR experiments in Fig 5D , total RNA was collected using an RNeasy kit ( Qiagen ) , and polyadenylated RNA was isolated from total RNA using Dynabeads Oligo ( dT ) 25 ( Ambion ) . Northern blot experiments were performed as previously described [100] . Briefly , Northern blot experiments were conducted using the NorthernMax-Gly Kit ( Thermo Fisher Scientific ) following the protocol provided by the manufacturer . Briefly , aliquots of poly ( A ) RNAs ( 2 μg ) were incubated for 30 minutes at 50°C in Glyoxal Load Dye ( containing DMSO and ethidium bromide ) and then were separated on a 1 . 2% agarose gel . The RNAs were transferred by capillary action to a Hybond-N nylon membrane ( GE Healthcare , Marlborough , MA ) for 4 hours and cross-linked to the membrane using the Optimum Crosslink setting of a Stratalinker ( Stratagene , La Jolla , CA ) . Membranes were then baked at 80°C for 15 minutes . Membranes were prehybridized for approximately 4 hours at 68°C in NorthernMax Prehybridization/Hybridization Buffer ( Thermo Fisher Scientific ) and then were incubated overnight at 68°C with a strand-specific RNA probe ( final concentration of probe , 3×106 cpm/ml ) . The following day , the membranes were washed once with low stringency wash solution ( 2x saline sodium citrate ( SSC ) , 0 . 1% sodium dodecyl sulfate [SDS] ) and then twice with high stringency wash solution ( 0 . 1x SSC , 0 . 1% SDS ) . The washed membranes were placed in a film cassette ( Thermo Fisher Scientific , Autoradiography Cassette FBCA 57 ) and exposed to Amersham Hyperfilm ECL ( GE Healthcare ) overnight at −80°C . Films were developed using a JP-33 X-Ray Processor ( JPI America Inc . , New York , NY ) . Northern blot probes were prepared as previously described [100] . Strand-specific αP32-UTP radiolabeled riboprobes were generated using the MAXIscript T3 system ( Thermo Fisher Scientific ) . Briefly , oligonucleotide primers were used to PCR amplify portions of the L1 . 3 5′UTR [100] ( L1 . 3 nucleotides 7–99 or L1 . 3 nucleotides 103–336 ) or the 3′ end of the luciferase gene ( see below ) . The resultant PCR products were separated on a 1% agarose gel and were purified using QIAquick gel extraction ( Qiagen ) . The labeling reaction was carried out at 37°C using the following reaction conditions: 500 ng of gel purified DNA template , 2 μL of transcription buffer supplied by the manufacturer , 1 μL each of unlabeled 10 mM ATP , CTP , and GTP , 5 μL of αP32-UTP ( 10 mCi/mL ) , and 2 μL of T3 RNA polymerase . The reaction components then were mixed and brought to a total volume of 20 μL using nuclease-free water in a 1 . 5-mL Eppendorf tube , which was incubated at 37°C for 10 minutes in a heating block . Unincorporated nucleotides were subsequently depleted using the Ambion NucAway Spin Columns ( Thermo Fisher Scientific ) following the protocol provided by the manufacturer . To generate a control β-actin riboprobe , the pTRI-β-actin-125-Human Antisense Control Template ( Applied Biosystems ) was used in T3 labeling reactions . Biological triplicates of each northern blot exhibited similar results . Oligonucleotide sequences were used to generate northern blot probes . A T3 RNA polymerase promoter sequence was included on the antisense ( AS ) primer used to generate the antisense riboprobe ( underlined below ) : L1 . 3 5′UTR 7–99 Sense: 5′-GGAGCCAAGATGGCCGAATAGGAACAGCT-3′ L1 . 3 5′UTR 7–99 AS: 5′-AATTAACCCTCAAAGGGACCTCAGATGGAAATGCAG-3′ L1 . 3 5′UTR 103–336 Sense: 5′-GGGTTCATCTCACTAGGGAGTG-3′ L1 . 3 5′UTR 103–336 AS: 5′-AATTAACCCTCACTAAAGGGTATAGTCTCGTGGTGCGCCG-3′ Luciferase 3′ FFLuc Sense: 5′-GGCAAGATCGCCGTGAATTCTCAC-3′ Luciferase 3′ FFLuc AS: 5′-AATTAACCCTCACTAAAGGGCCTGGCGCTGGCGCAAGCAGC-3′ Northern blot bands were quantified using the ImageJ software ( https://imagej . nih . gov/ij/download . html ) [125] . The intensity of the bands in the pPLWTLUC and pPLSDmLUC lanes were determined and normalized to the actin loading control . Three independent northern blots were subject to quantification . We then computed that average intensity of the bands and calculated a standard deviation . As reported in the text ( Fig 2B ) , the steady-state level of pPLSDmLUC mRNA is about 18% the level of pPLWTLUC mRNA with a standard deviation of ±3 . 1% . Luciferase assays were performed using the Dual-Luciferase Reporter Assay System ( Promega , Madison , WI ) following the manufacturers protocol . Briefly , 2×104 HeLa cells were plated into each well of a 6-well plate ( BD Biosciences ) . Approximately 24 hours later , each well was transfected using a transfection mixture of 100 μl Opti-MEM ( Life Technologies ) , 3 μl of FuGENE6 transfection reagent ( Promega ) , and 1 μg plasmid DNA ( 0 . 5 μg of a firefly luciferase test plasmid and 0 . 5 μg of an internal control Renilla luciferase expression ) . Each transfection was performed as a technical duplicate ( i . e . , in two wells of a 6-well tissue culture plate ) . Approximately 24 hours post-transfection , the transfected cells were washed once with ice-cold 1X PBS and the cells in each well were subjected to lysis for 15 minutes at room temperature using 500 μl of the 1X Passive Lysis Buffer supplied by the manufacturer . Following homogenization of the lysate by manual pipetting , 60 μl of the lysate from each well of the 6-well tissue culture plate was distributed equally in 3 wells of a 96-well white opaque , optically transparent top plate ( BD Biosciences ) , allowing six luminescence readings for each transfection condition ( six technical replicates—3 readings per well of a 6-well plate ) . The 96-well plate then was subject to luciferase detection assays using a GloMax-Multi Detection System ( Promega ) following the manufacturer’s protocol . Luminescence readings from the six technical replicates were averaged to give a single normalized luminescence reading ( NLR ) . This assay then was performed in biological triplicate , yielding three independent NLRs . The resultant data were subsequently analyzed using a Student one-tailed t test . Error bars indicate the standard deviation . Luminescence readings from lysis buffer alone and from lysates derived from untransfected cells were included used as negative controls . Poly ( A ) selected mRNA from transfected HeLa-JVM cells in a T-175 tissue culture flask was collected as previously described for northern blots . The resultant mRNAs were subjected to targeted RT-PCR using SuperScript III One-Step RT-PCR System , with Platinum Taq DNA Polymerase ( Thermo Fisher Scientific ) , following the manufacturer’s protocol . The REVLUC primer was used to synthesize first-strand cDNA . The FWD5′UTR and REVLUC primers then were used to amplify the resultant cDNAs ( see sequences below ) . For RT-PCR experiments in Fig 5D , cDNA was synthesized from polyadenylated RNA with a SuperScript First-Strand Synthesis System for RT-PCR ( Invitrogen ) using the REVLUC primer . The resultant cDNA was then subjected to PCR using the FWD5′UTR and REVLUC primers and Platinum Taq DNA polymerase ( Invitrogen ) ( 30 cycles; annealing temp: 54°C; 1-minute extension ) . The RT-PCR products were separated on a 2 . 0% agarose gel , excised from the gel using QIAquick gel extraction ( Qiagen ) , and cloned using the TOPO TA Cloning Kit ( Thermo Fisher Scientific ) . Sanger DNA sequencing performed at the University of Michigan DNA Sequencing Core verified the cDNA sequences in the resultant plasmids . Biological triplicates of this experiment yielded similar results . The following oligonucleotide sequences were used in the RT-PCR experiments: FWD5′UTR: 5′-GGAACAGCTCCGGTCTACAGCTCCC-3′ REVLUC′ 5′-CCCTTCTTAATGTTTTTGGCATCTTCC-3′ The plating and transfection of HeLa-JVM cells in T-175 tissue culture flasks was performed as detailed above in the mRNA isolation section except that HeLa-JVM cells were subjected to selection in DMEM-complete medium supplemented with 200 μg/ml of hygromycin B ( Thermo Fisher Scientific ) 48 hours post-transfection and the selection medium was changed every other day for 7 days . The hygromycin resistant HeLa-JVM cells were harvested 9 days post-transfection as described in the mRNA isolation section . The cell pellets were frozen at −80°C overnight . The following day , pellets were lysed for 15 minutes on ice by incubation in 0 . 5 mL of lysis buffer: 10% glycerol , 20 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 0 . 1% NP-40 ( IGPAL ) ( Sigma-Aldrich , St . Louis , MO ) , and 1X Complete Mini EDTA-free Protease Inhibitor Cocktail ( Roche Applied Science , Germany ) . The resultant protein lysates then were centrifuged at 15 , 000 × g for 30 minutes to clear the lysate . The resultant supernatant ( approximately 0 . 4 mL ) was designated as the WCL . Alternatively , the supernatant fraction was subject to RNP collection , as previously described [35] . Briefly , 200 μL of the WCL was layered onto a sucrose solution cushion ( 6 mL of 17% sucrose , bottom layer , followed by 4 mL of 8 . 5% sucrose , top layer , overlaid by 200 μL of the WCL ) and ultracentrifuged at 178 , 000 × g for 2 hours at 4°C . Following ultracentrifugation , the supernatant was aspirated and the resultant RNP pellet was suspended in 100 μL of water supplemented with 1X Complete Mini EDTA-free Protease Inhibitor Cocktail ( Roche Applied Science ) . Bradford assays ( Bio-Rad Laboratories , Hercules , CA ) were used to determine protein concentrations . WCLs generally yielded 15–19 μg/μL of protein . RNP preparations yielded 6–10 μg/μL of protein . The protein samples were stored at −80°C . Western blot experiments were performed as previously described , with minor modifications [100] . Briefly , protein samples were collected as described above and then were incubated with a 2X solution of NuPAGE reducing buffer ( containing 1 . 75%–3 . 25% lithium dodecyl sulfate and 50 mM dithiothreitol [DTT] ) ( ThermoFisher Scientific ) . An aliquot ( 20 μg ) of the reduced proteins were incubated at 100°C for 10 minutes and then were separated by electrophoresis on 10% precast mini-PROTEAN TGX gels ( Bio-Rad Laboratories , Hercules , CA ) run at 200 V for 1 hour in 1X Tris/Glycine/SDS ( 25 mM Tris-HCL , 192 mM glycine , 0 . 1% SDS , pH 8 . 3 ) buffer ( Bio-Rad Laboratories ) . Transfer was performed using the Trans-Blot Turbo Mini PVDF Transfer Packs ( BioRad Laboratories ) with the Trans-Blot Turbo Transfer System ( BioRad Laboratories ) at 25 V for 7 minutes . The resultant membranes then were cut at the 75-kDa marker using the Precision Plus Protein Kaleidoscope marker ( Bio-Rad Laboratories ) as a guide . The membranes then were incubated at room temperature in blocking solution ( containing 1X PBS and 5% dry low-fat milk ) ( Kroger , Cincinnati , OH ) . The eIF3 antibody ( Santa Cruz Biotechnology Inc . [SC-28858] ) was used at a 1:1 , 000 dilution to probe membranes for eIF3 at 110 kDa as a loading control . The α-N-ORF1p [100] antibody ( directed against ORF1p amino acids 31–49; EQSWMENDFDELREEGFRR ) , α-C-ORF1p ( directed against ORF1p amino acids 319–338; EALNMERNNRYQPLQNHAKM ) , and anti-T7 ( Merck Millipore 69048 T7•Tag Antibody HRP Conjugate ) antibodies were used at 1:10 , 000 , 1:2 , 000 , and 1:5 , 000 dilutions , respectively , to probe membranes for ORF1p . Antibody hybridizations were carried out overnight at 4°C in blocking solution . The blots were washed three times with 1X PBS , 0 . 1% Tween-20 ( Sigma Aldrich ) and then were incubated with a 1:5 , 000 dilution of secondary Amersham ECL HRP Conjugated Donkey anti-rabbit IgG Antibodies ( GE Healthcare Life Sciences ) for 60 minutes at room temperature blocking solution . The membranes were washed three times with 1X PBS , 0 . 1% Tween-20 ( Sigma Aldrich ) . The signals then were visualized using the SuperSignal West Pico Chemiluminescent Substrate reagent ( ThermoFisher Scientific ) according to the protocol provided by the manufacturer . The membranes were exposed to Amersham Hyperfilm ECL ( GE Healtchare ) for a time that spanned 5 seconds to 5 minutes and were developed using a JP-33 X-Ray Processor ( JPI America Inc . ) . | Long interspersed element-1 ( L1 ) sequences comprise about 17% of the human genome reference sequence . The average human genome contains about 100 active L1s that mobilize throughout the genome by a “copy and paste” process termed retrotransposition . Active L1s encode two proteins ( ORF1p and ORF2p ) . ORF1p and ORF2p preferentially bind to their encoding RNA , forming a ribonucleoprotein particle ( RNP ) . During retrotransposition , the L1 RNP translocates to the nucleus , where the ORF2p endonuclease makes a single-strand nick in target site DNA that exposes a 3′ hydroxyl group in genomic DNA . The 3′ hydroxyl group then is used as a primer by the ORF2p reverse transcriptase to copy the L1 RNA into cDNA , leading to the integration of an L1 copy at a new genomic location . The evolutionary success of L1 requires the faithful retrotransposition of full-length L1 mRNAs; thus , it was surprising to find that a small number of L1 retrotransposition events are derived from spliced L1 mRNAs . By using genetic , biochemical , and computational approaches , we demonstrate that spliced L1 mRNAs can undergo an initial round of retrotransposition , leading to the generation of spliced integrated retrotransposed elements ( SpIREs ) . SpIREs represent about 2% of previously annotated full-length primate-specific L1s in the human genome reference sequence . However , because splicing leads to intra-L1 deletions that remove critical sequences required for L1 expression , SpIREs generally cannot undergo subsequent rounds of retrotransposition and can be considered “dead on arrival” insertions . Our data further highlight how genetic conflict between L1 and its host has influenced L1 expression , L1 retrotransposition , and L1 splicing dynamics over evolutionary time . | [
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] | 2018 | Spliced integrated retrotransposed element (SpIRE) formation in the human genome |
Oral microbiota contribute to health and disease , and their disruption may influence the course of oral diseases . Here , we used pyrosequencing to characterize the oral bacteriome and mycobiome of 12 HIV-infected patients and matched 12 uninfected controls . The number of bacterial and fungal genera in individuals ranged between 8–14 and 1–9 , among uninfected and HIV-infected participants , respectively . The core oral bacteriome ( COB ) comprised 14 genera , of which 13 were common between the two groups . In contrast , the core oral mycobiome ( COM ) differed between HIV-infected and uninfected individuals , with Candida being the predominant fungus in both groups . Among Candida species , C . albicans was the most common ( 58% in uninfected and 83% in HIV-infected participants ) . Furthermore , 15 and 12 bacteria-fungi pairs were correlated significantly within uninfected and HIV-infected groups , respectively . Increase in Candida colonization was associated with a concomitant decrease in the abundance of Pichia , suggesting antagonism . We found that Pichia spent medium ( PSM ) inhibited growth of Candida , Aspergillus and Fusarium . Moreover , Pichia cells and PSM inhibited Candida biofilms ( P = . 002 and . 02 , respectively , compared to untreated controls ) . The mechanism by which Pichia inhibited Candida involved nutrient limitation , and modulation of growth and virulence factors . Finally , in an experimental murine model of oral candidiasis , we demonstrated that mice treated with PSM exhibited significantly lower infection score ( P = . 011 ) and fungal burden ( P = . 04 ) compared to untreated mice . Moreover , tongues of PSM-treated mice had few hyphae and intact epithelium , while vehicle- and nystatin-treated mice exhibited extensive fungal invasion of tissue with epithelial disruption . These results showed that PSM was efficacious against oral candidiasis in vitro and in vivo . The inhibitory activity of PSM was associated with secretory protein/s . Our findings provide the first evidence of interaction among members of the oral mycobiota , and identifies a potential novel antifungal .
Organisms residing in the oral cavity ( oral microbiota ) contribute to health and disease , and influence diseases like oral candidiasis , the most common oral complication of HIV-infection [1] , [2] . Pathogenesis of oral candidiasis is linked to variables like changes in the CD4+ cell count and antiretroviral therapy ( ART ) in HIV-1-infected patients [3] . Although the introduction of ART has reduced mortality and morbidity as well as the incidence of opportunistic infections among HIV-infected patients , oral candidiasis remains a significant disease , even in the era of ART . In this regard , recent studies indicate that the decline of oral candidiasis among ART-experienced HIV-infected patients is transient in some HIV-infected individuals [4] . In addition , preliminary results reported by Thompson et al . [5] showed that symptomatic oral Candida infection occurred in one-third of patients with advanced AIDS ( n = 122 ) , even in the setting of ART . More recently , Patel et al . [6] reported symptomatic oral candidiasis in 27% ( 59/215 ) HIV-infected patients . Therefore , even in the era of ART , oral candidiasis remains a significant problem . Characterization of the microbiota ( bacteriome and mycobiome ) in health and disease is expected to expedite the discovery , testing and validation of novel drugs [7] . Most studies that characterized the human microbiome in health and disease have focused on the bacteriome , in both oral and non-oral body sites [8]–[12] . Recently , Iliev et . al . [13] showed that while no significant differences in major phyla of commensal bacteria was observed in the intestinal microflora between wild-type and mice lacking Dectin-1 , members of the mycobiome interacted with the intestinal immune system to influence inflammatory bowel disease ( IBD ) highlighting the role of the fungal community in disease . Earlier , we characterized the oral mycobiome in healthy individuals using high-throughput multitag pyrosequencing ( MTPS ) , and reported that humans are colonized with up to 85 fungal genera [14] . Although these studies demonstrated the complexity of the human oral microbiome , the specific contribution of the mycobiome to oral diseases was not investigated . Previous studies have shown that alteration in the bacterial population has direct impact on the development of Candida infections [for reviews , see 15] . However , the interactions between members of the oral microbiota and Candida in HIV disease setting have not been investigated . In the current study , we identified the core oral mycobiome ( COM ) and bacteriome ( COB ) [defined as those organisms present in ≥20% of the subjects] in HIV-infected and uninfected individuals , and demonstrated that the COM undergoes a change in HIV disease . Furthermore , we noted that a decrease in abundance of the yeast Pichia coincided with an increase in Candida colonization , suggesting an antagonistic relation between these two fungi . We also found that nutrient competition as well as Candida growth and modulation of its virulence factors by Pichia is a mechanism underlying this interaction . In addition , treatment with Pichia Spent Medium ( PSM ) was efficacious against oral candidiasis when tested in an experimental murine model . Our results provide the first evidence of interaction among members of the oral mycobiome community , particularly between Pichia and pathogenic fungi . These findings could lead to the development of novel antifungals to prevent and treat fungal infections including mucosal Candida infections , thereby impacting the management of oral candidiasis and other fungal infections . They also highlight the need to characterize the mycobiome at different body sites which may lead to novel discoveries of how fungi can be exploited to control health and disease .
Written informed consent was obtained from all study participants . Study participants were recruited according to protocol ( #20070413 ) approved by the Human Subjects Institutional Review Board ( IRB ) at University Hospitals Case Medical Center . Oral rinse samples were obtained from 12 HIV-infected and 12 uninfected individuals ( matched for age , sex , and ethnicity ) . Inclusion criteria for these participants were: >18 years of age and no clinical signs of oral mucosal disease including oral candidiasis , while the exclusion criteria were: recent use of antimicrobial or antifungal agents ( within a month ) , use of topical or systemic steroids , pregnancy , and insulin-dependent diabetes mellitus . Oral wash samples were collected using a standardized Standard Operating Procedure developed by the Oral HIV/AIDS Research Alliance ( OHARA ) as described earlier [14] . All animal experimentation was performed in strict accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol for animal infection was approved by the Institutional Animal Care and Use Committee ( IACUC ) at Case Western Reserve University School of Medicine , Cleveland , Ohio ( protocol approval number 2013-0020 ) . All procedures were performed under general anesthesia and all efforts were made to minimize animal suffering . Oral rinse samples were processed individually using the Fast DNA Spin Kit following manufacturer's instructions ( BIO 101; Vista , CA ) . Each extraction tube was agitated three times using a Fast Prep FP120 instrument at a speed setting of 5 for 30 s . Tubes were cooled on ice between agitations . Fungi and bacteria present in these samples were identified with ITS-based and 16S probes , respectively . The ITS1 region from DNA sample extracts was amplified in triplicate using primers with high specificity for ascomycete fungi ( fluorescently labeled forward primer ITS1F ( CTTGGTCATTTAGAGGAAGTAA ) and unlabeled reverse primer ITS2 ( GCTGCGTTCTTCATCGATGC ) . The ITS primers were selected in this study to detect the presence of various fungi since these primers are able to detect consensus sequences present in a broad range of fungi [16] , [17] . For bacterial identification , extracted DNA was amplified by PCR using routinely employed universal primers [fluorescently labeled forward primer 27F ( 5′-6FAM- AGAGTTTGATCCTGGCTCAG-3′ ) and unlabeled reverse primer 355R5′ ( 5′- GCTGCCTCCCGTAGGAGT-3′ ) ] [18] , which amplify the first two hyper-variable regions of 16S rRNA [19] and are commonly used for microbiome analysis [10] , [20] . Microbiome analysis was performed using multitag 454 pyrosequencing ( MTPS ) technique , which was used for characterization of nucleic acids [21] ( for details , please see Method S1 ) . Candida albicans [strains SC5312 , 10341 , GDH2346 ) , Pichia ( MRL81 ) ] , Penicillium ( MRL22345 ) and Cladosporium ( MRL1458 ) strains tested in this study were obtained from the OHARA Repository at Case and the culture collection of the Center for Medical Mycology . Fungal strains were maintained on Sabouraud dextrose agar ( SDA , [yeast extract , peptone , and dextrose at 1∶2∶1] ) ( Difco Laboratories , Detroit , MI ) medium . Since species-level identification of Pichia based upon morphological or physiological features alone is usually not possible , we used a molecular approach ( based on sequence analysis of the internal transcribed spacer and D1/D2 ribosomal DNA regions ) [22] to confirm the identity of Pichia MRL81 strain . Our analysis revealed that this strain was P . farinosa . All strains were kept at −80°C for long-term storage . To determine whether inhibition of pathogenic fungi by Pichia spent medium ( PSM ) is due to nutrient competition between Pichia and Candida , we assessed Candida growth when mixed with Pichia at different ratios . P . farinosa and a GFP-tagged C . albicans strain ( generous gift from Dr . B . Cormack ) [23] were mixed together at equivalent cell densities ( 103 , 104 , or 105 cells each ) in yeast nitrogen base ( YNB ) medium supplemented with glucose ( 0 . 9% w/v ) and uridine ( 100 µg/mL ) . These mixed cell suspensions were incubated at 37°C and allowed to grow ( as “mixed cultures” ) . Candida or Pichia cells alone were grown in the same growth medium ( “mono cultures” ) serving as controls . At specific time intervals ( 24 , 48 , 72 h ) , 1-mL aliquots were withdrawn from the mixed- and mono-cultures , centrifuged to collect the cells in pellet , and re-suspended in 250 µL phosphate buffered saline . Fifty µL of the resuspended cell mixture was placed on a glass slide and observed under phase-contrast and fluorescence microscope . The remaining cell suspension ( 200 µL ) was transferred to a 96-well microtiter plate , and fluorescence was measured using a spectrofluorimeter ( excitation and emission wavelength of 485 and 435 nm , respectively ) . The effect of Pichia supernatant on Candida growth , germination , adherence , biofilm formation , and its biochemical properties was determined as described below . To evaluate the effect of Pichia supernatant on the growth of various fungi , Pichia spent ( “conditioned” ) medium ( PSM ) was obtained by centrifuging 100-mL culture of Pichia grown in Sabouraud dextrose broth ( SDB ) for 48 h , and filter sterilizing it . Next , fungal cells/conidia ( 1×105 cells/mL ) were incubated with PSM at 35°C and growth was followed for 48 h . Aliquots were collected at 2 h intervals and fungal growth was measured spectrophotometrically at 600 nm . Effect of Pichia spent media ( PSM ) on Candida germination was determined using C . albicans strain SC5314 , as described previously [24] . Candida cells were grown planktonically in the absence or presence of PSM . Germination rate was compared with that of cells grown in SDB media containing fetal bovine serum ( FBS ) ( Hyclone , Thermo Fisher Scientific , Rockford , IL ) , a known inducer of germination . Briefly , C . albicans cells were grown and the cell density was adjusted to 1×107 cells/mL in Hanks balanced salt solution ( HBSS ) ( Mediatech ) . In separate 1 . 5-mL tubes , 50 mL of these cells were diluted to a density of 5×105 cells/mL in HBSS ( blank control ) , FBS , or PSM , and incubated on a rocker at 37°C for up to 4 h . At 15-min intervals , 10-µL samples from each media type were microscopically examined using a hemacytometer . Total cell count and germination ( defined as a germ-tube length greater than or equal to the blastospore diameter ) was determined from an average of 4 observations . One hundred to 200 cells were counted per observation . The assays were discontinued when cells clumped together , due to germination , which made it difficult to count individual cells . The effect of Pichia or Penicillium ( used as a control ) cells or supernatant on Candida adherence ( using strain C . albicans SC5314 , a clinical isolate used conventionally in Candida adhesion and germination assays ) was determined as described earlier [24] , [25] . Briefly , standardized suspensions of 50 to 200 cells/mL were added onto silicone elastomer disks for 90 min . Disks were then washed in phosphate-buffered saline ( PBS ) to remove non-adherent cells and placed in wells of 12-well tissue culture plates ( Becton Dickinson , Franklin Lakes , NJ ) . Two milliliters of warm ( 55°C ) liquefied SDA was added per well to completely cover the SE disks and allowed to solidify . Plates were incubated overnight ( 37°C ) , and the number of colonies adhering per disk was counted using a dissecting microscope . The effect of oral fungi ( Pichia , Cladosporium or Penicillium , the latter fungi were used as controls since Cladosporium was present only in the uninfected subjects like Pichia , while Penicillium was present in both infected and uninfected subjects to the same abundance ) or their supernatant on the ability of Candida to form biofilms was evaluated using metabolic activity assay and confocal microscopy , as described earlier [26]–[28] . Briefly , Candida cells were incubated in the presence or absence of Pichia cells or spent medium ( supernatant , PSM ) at different relative ratios ( 1∶3 , 1∶1 , 3∶1 ) , and allowed to form biofilms for 48 h on silicone elastomer catheter discs . The amount of biofilm formed was assayed colorimetrically using the XTT ( 2 , 3-bis ( 2-methoxy-4-nitro-5-sulfophenyl ) -5-[ ( phenylamino ) carbonyl]-2H-tetrazolium hydroxide , Sigma-Aldrich ) metabolic activity assay in which XTT is converted by metabolically active cells to a red formazan product [26] . In addition , the effect of fungal supernatants on the morphology and architecture of the formed biofilms was evaluated using confocal scanning laser microscopy ( CSLM ) [26] . Briefly , biofilms were stained with the fluorescently labeled polysaccharide-indicating lectin Concanavalin Alexa Fluor 488 conjugate ( CON-A , 25 µg/mL; Invitrogen ) and metabolic activity indicator dye FUN1™ ( 10 µM; Invitrogen ) . After staining , discs containing biofilms were flipped and placed on a 35-mm-diameter glass-bottom petri dish ( MatTek Corp . , Ashland , Mass . ) . Stained biofilms were observed with a Zeiss LSM510 confocal scanning laser microscope equipped with argon and HeNe lasers and mounted on a Zeiss Axiovert100 M microscope ( Carl Zeis , Inc . ) . The objective used was a water immersion C-apochromat lens ( 40×; numerical aperture , 1 . 2 ) . Wild-type C57BL/6 mice ( purchased from Charles River Laboratories , Wilmington , MA ) were immunosuppressed with 4 mg of cortisone acetate ( Sigma Chemical Co . , St . Louis , Mo . ) administered subcutaneously on the day before and 1 and 3 days after challenge with Candida cells . Mice were given tetracycline hydrochloride ( Sigma Chemical Co . , St . Louis , Mo . ) in their drinking water ( 0 . 5 mg/ml ) , starting the day before infection . On the day of inoculation , mice were anesthetized and light scratches made on the dorsum of the tongue followed by the introduction of C . albicans GDH ( 108 blastospores ) . The scratches were superficial , limited to the outermost stratum corneum , and did not cause trauma or bleeding . Mice were divided into groups ( n = 4 ) ; treated with Pichia supernatant , 100 µl in the oral cavity twice a day , a “mock” vehicle control , and untreated control . Topical nystatin ( widely used clinically to treat oral candidiasis [29] ) was used as a comparator . Treatment began on day 4 post inoculation , mice were sacrificed on day 7 and the tongues harvested for enumeration of tissue fungal burden or histopathology with Periodic acid-Schiff stain . Additionally , tongues were visually assessed daily beginning day 1 post infections to assess severity of the infection using a previously described scoring system [30]: A score of 0 indicates the appearance of a normal tongue , with intact light reflection and no visible signs of infection , a score of 1 denotes isolated patches of fungus , a score of 2 when confluent patches of fungus are observed throughout the oral cavity , and a score of 3 indicates the presence of wide-spread fungal plaques and erosive mucosal lesions . The histology slides were assessed by an independent pathologist who was blinded to the study arms . The animal studies were repeated on different days to ensure reproducibility . Since the antifungal activity of PSM was secretory in nature , we determined whether this activity was due to a protein , carbohydrate or small molecule ( metabolite ) . We exposed PSM to proteinase K ( which digests most proteins ) , NaOH ( which denatures carbohydrates ) [31] , or acetonitrile extraction ( that isolates metabolites ) [32] . We also determined the effect of heat on PSM activity by exposing it to 90°C temperature in a water bath for 10 min . The ability of these differently treated PSM to inhibit Candida biofilms was evaluated as above . Microbiome data were analyzed using the QIIME and R platforms [33] , [34] . Wilcoxon–Mann–Whitney rank sum test was used for comparison between the two groups , with P-value of <0 . 05 considered as a significant difference . For comparison of several groups , Kruskal–Wallis one-way analysis of variance on ranks was used , and pairwise multiple comparison procedures ( Dunns method ) post-hoc test was used for multiple pairwise comparisons . Univariate analyses was used to compare the prevalence of specific fungi and bacteria between the two groups using a Pearson chi-squared test or two-sample t-test ( assuming unequal variances ) . For correlation analysis , microbiome abundance data was divided into independent data matrices ( disease and no disease ) and correlation analyses was conducted using the “psych” package ( corr . test function ) in R statistical platform ( pairwise Spearman's correlation and two-tailed probability of t for each correlation ) [34] , [35] . The function “Circle . corr” was used to graphically illustrate the correlation coefficients and significant correlations ( P< . 05 ) in circle graph , where red and blue circle indicate positive and negative correlations , respectively . Numerical variables ( e . g . metabolic activity , dry biomass , thickness ) were all assessed using paired or unpaired t-test , or ANOVA as appropriate . Comparison of clinical scores were performed using box-plots and non-parametric independent samples Kruskal-Wallis test , while mean fungal burden ( log CFU/g ) were compared using ANOVA . All statistical analyses were performed using R [34] or SPSS ( ver . 13 ) statistical software packages .
A total of 24 individuals were enrolled in the study , with 12 HIV-infected patients and 12 uninfected individuals ( 11 males and one female in both study groups , Table 1 ) . The mean age was 38 . 7 and 38 . 8 years in HIV-infected ( age range: 22–56 ) and uninfected ( age range: 22–59 ) groups , respectively . Among the 12 HIV-infected patients , eight had initiated antiretroviral therapy . In both study groups , self-reported ethnicities were: six African-Americans , two Hispanics , and four Caucasians . While all samples were analyzed for fungal microbiota , one of the samples did not provide robust signals for the bacterial microbiome , and hence was excluded from the analysis . In addition , the corresponding matched uninfected control sample was also excluded . As a result , there were 12 uninfected-HIV-infected sample pairs for mycobiome analysis but only 11 sample pairs for bacteriome analysis . Our results showed that the number of bacterial genera in the oral microbiota of study participants ranged between 8–14 per person among HIV-infected and uninfected individuals . Among HIV-infected patients , Prevotella , Streptococcus and Rothia were the most common genera; while in controls the most abundant bacteria were Prevotella , Streptococcus and Fusobacterium ( Fig . 1A , and Table S1 ) . The core oral bacteriome ( COB ) consisted of 14 genera in both HIV-infected and uninfected individuals , of which 13 ( Actinomyces , Granulicatella , Fusobacterium , Leptotrichia , Rothia , Neisseria , Haemophilus , Pasteurella , Porphyromonas , Prevotella , Gemella , Streptococcus , and Veillonella ) were common to both groups ( Fig . 1B ) . We found that Capnocytophaga was present only in HIV-infected patients while Aggregatibacter was present in uninfected individuals only ( Fig . 1B ) . These results suggest that the COB of HIV-infected patients was similar to that of uninfected individuals with minimal difference . Our results showed that the number of fungal genera present in oral wash samples ranged between 1–9 per person among uninfected and HIV-infected individuals ( Fig . 2A and Table S2 ) . Among HIV-infected patients , Candida , Epicoccum , and Alternaria were the most common genera ( present in 92% , 33% , and 25% , respectively ) , while in uninfected participants , the most abundant fungi were Candida , Pichia , and Fusarium ( 58% , 33% , and 33% , respectively; Fig . 2A ) . The COM of HIV-infected and uninfected individuals consisted of five genera ( Fig . 2B ) ; of these , Candida and Penicillium were common between the two groups , while differing in the remaining genera demonstrating that the COM of HIV-infected patients differs from that of age- and sex-matched uninfected controls . Among the Candida species detected , C . albicans was the most common ( 58% in uninfected and 83% in HIV-infected patients ) , followed by C . dubliniensis ( 17% in both groups ) . Interestingly , C . intermedia and C . sake were present only in uninfected ( n = 1 ) and HIV-infected ( n = 1 ) groups , respectively . Next , we determined how the individual members of the oral bacteriome and mycobiome are correlated within their respective communities , and also across the two communities . We grouped the microbiome abundance data into independent mycobiome and bacteriome data matrices and conducted correlation analysis using R statistical computing software . We found 15 bacteria-fungi pairs that were correlated significantly in samples from non-infected study participants ( Fig . 3 A , Table 2 ) . Among these significant correlation pairs , two pairs ( Rothia-Cladosporium and Granulicatella-Cryptococcus ) were negatively correlated ( coefficient −0 . 61 and −0 . 65 , respectively ) . The remaining 13 pairs of significantly correlated pairs exhibited positive correlation with coefficients ranging from 0 . 64 ( Aggregatibacter-Lactarius ) to 0 . 86 ( Capnocytophaga-Cladosporium ) . In comparison , there were 12 statistically significant bacteria-fungi pairs in HIV-infected patients , with 11 positive ( coefficient of 0 . 64 for 8 pairs , 0 . 74 for 2 pairs , Fig . 3 B , Table 2 ) and one with negative correlation ( Campylobacter-Candida , coefficient −0 . 67 ) . We also evaluated the correlation between different members of the mycobiome in the uninfected and HIV-infected groups . Our analyses revealed that in the uninfected group , 23 fungal-fungal interactions were statistically significant ( P≤0 . 035 , Fig . 3C , Table 3 ) , while in the HIV-infected group , 6 fungus-fungus pairs were significantly correlated ( P-value of ≤0 . 03 , Fig . 3D ) , which included Candida-Epicoccum , Candida-Trichosporon , Epicoccum-Trichosporon , Penicillium-Corynespora , Penicillium-Fusarium , and Alternaria-Serpula . Having defined the core mycobiome , next we investigated whether members of the core oral mycobiome are associated with Candida , the most common oral fungal pathogen of HIV-infection [36] . Our sequencing data indicated the presence of 3 Pichia species , including P . guillermondii , P . burtonii , and P . jadinii in the tested samples . We found that decrease in Pichia abundance coincided with an increase in Candida colonization ( Fig . 4A ) , suggesting antagonism between Pichia and Candida . Furthermore , we found that among the 4 uninfected subjects where Pichia was present , 24 fungal genera were absent , including Aspergillus and Cryptococcus ( Table 4 ) . In addition , in these 4 uninfected individuals , 9 fungal genera ( including Fusarium ) were present as co-colonizers ( Table 4 ) . Analysis of the abundance profile of Fusarium in all uninfected individuals ( n = 12 ) , revealed that its abundance was 3-fold lower when Pichia was present ( 0 . 016% , n = 4 ) compared to where Pichia was absent ( 0 . 048% , n = 8 ) . Taken together , these results show that Pichia interacts with other fungi , with an antagonistic interaction with known pathogens including Candida , Cryptococcus , Aspergillus and Fusarium . Next , we investigated the ability of Pichia to inhibit growth of C . albicans , by allowing blastospores to grow in the presence or absence of Pichia spent medium ( PSM ) . As mentioned above , our results revealed three Pichia species ( P . guillermondii , P . burtonii , and P . jadinii ) in the tested samples . These Pichia spp , along with P . farinosa , are common biocontrol agents used against plant pathogens [37]–[40] . Moreover , literature search showed that P . farinosa exhibits more biocontrol activity than P . guilliermondii [41] . Since P . farinosa and P . guilliermondii are very closely related to each other based on their whole genome [38] and mitochondrial DNA [42] sequences , we selected P . farinosa to investigate the interactions between this species and pathogenic fungi including Candida . As shown in Figure 4B , PSM completely inhibited Candida growth , demonstrating a direct inhibitory effect of Pichia against Candida . We also assessed the effect of PSM on growth of Aspergillus and Fusarium by determining their dry weight . As shown in Figure 4C and D , Aspergillus and Fusarium were unable to exhibit growth in presence of PSM . These studies demonstrated that PSM exhibits broad-spectrum activity against pathogenic fungi . Next , we initiated studies to gain insight into the underlying mechanism for the allowing Pichia to inhibit Candida . Since Pichia is commonly used as a post-harvest biocontrol agent against plant pathogens [43]–[47] , and several studies have suggested that this inhibitory activity involves nutrient competition , biofilm formation , germination , metabolites , secretory proteins [48]–[57] , we explored whether Pichia-mediated inhibition of Candida involves these mechanisms . To determine whether inhibition of Candida by Pichia is due to nutrient competition , we assessed Candida growth when mixed with Pichia at different ratios . We found that control Candida or Pichia cells exhibited robust , confluent growth when incubated by themselves ( Fig . 5A–C , G ) . In contrast , mixing of Candida and Pichia ( at 103 cells each ) resulted in noticeably reduced cell density ( Fig . 5D–F ) . Quantitative measurement of fluorescence signal confirmed reduced Candida growth in the presence of Pichia cells , with up to 58% decrease in fluorescence ( Fig . 5H ) . Similar trends were observed when Candida and Pichia cells were mixed at 104 or 105 cells each ( data not shown ) . These results suggested that Pichia outcompetes/overgrows Candida . Our results showed that when both Candida and Pichia were present together , their abundance ratio was close to 1∶1 . Therefore , to determine whether a threshold number of Pichia cells was necessary to inhibit Candida biofilms , we used mixtures of Pichia and Candida combined at different ratios ( 1∶1 , 1∶3 , 3∶1 ) . Co-incubation of C . albicans with Pichia cells at different ratios resulted in significant inhibition of biofilm formation at all ratios tested ( Fig . 6A , P< . 05 ) . Moreover , there was no significant difference in the extent of biofilm inhibition between the different cells densities of Pichia examined . Next , to determine whether the biofilm-inhibitory activity of P . farinosa was mediated by secretory factor/s , we determined the effect of Pichia spent medium ( PSM ) on C . albicans biofilms using a metabolic activity ( XTT ) assay described earlier [26] . Spent medium from Penicillium or Cladosporium ( both prepared similarly as PSM ) was used as a control since Penicillium had the same abundance in HIV-infected and uninfected individuals ( present in 25% of samples in each group ) , while Cladosporium was present only in uninfected participants . Our results showed that when exposed to PSM , metabolic activity of Candida biofilms was significantly reduced ( 39% compared to untreated control , P = . 02 , Fig . 6B ) . Moreover , exposure to equivalently prepared spent media from Penicillium or Cladosporium did not have a significant effect on Candida biofilms ( metabolic activity = 84% and 71% , respectively , P≥ . 12 for both comparisons , Fig . 6B ) . Next , we used confocal laser scanning microscopy ( CLSM ) to determine the effect of PSM on C . albicans biofilm architecture . While untreated and Penicillium-treated Candida formed robust biofilms ( Fig . 6C–D , respectively ) , exposure to PSM resulted in biofilm disruption , with sparse yeast cells and no extracellular matrix or hyphae observed ( Fig . 6E ) . Moreover , thickness of Candida biofilms exposed to PSM was significantly reduced compared to that of controls ( Fig . 6F , P< . 05 ) . Finally , to determine whether the activity of PSM is dose-dependent , we compared the ability of 50% and 100% PSM to inhibit Candida biofilms . Metabolic activity and thickness of Candida biofilms exposed to 100% PSM was significantly lower than those exposed to 50% PSM ( Fig . 7A , B; P = . 007 and . 006 , respectively ) . Taken together , these results demonstrate that the Candida-inhibitory activity of Pichia is specific and dose-dependent . Since adhesion and germination are key steps in mature Candida biofilm formation [58]–[60] and are known Candida virulence factors , we examined whether P . farinosa spent medium affects these processes . Our data showed that while untreated Candida formed robust hyphae ( Fig . 7C ) , exposure to PSM resulted in stunted Candida germ tubes ( Fig . 7D ) , indicating that a secreted component of Pichia inhibits Candida germination . We also found that the number of Candida colony forming units ( CFUs ) adhering to silicone elastomer catheter substrate when treated with Pichia supernatant was significantly lower than untreated Candida cells ( Fig . 7E , P< . 003 ) . These results showed that Pichia inhibits the ability of Candida to germinate and adhere to catheter substrate . To determine whether the active ingredient of PSM is a metabolite or a protein , we evaluated Candida growth in presence or absence of metabolites extracted [32] from PSM . Extracted Pichia metabolites had no effect on Candida growth ( Fig . 8A ) . Next , we evaluated the influence of proteinase- , alkali- , or heat ( 90°C for 10 min ) -treated PSM on C . albicans biofilms . Our data showed that proteinase-K treatment abrogated the ability of PSM to inhibit biofilms , while alkali- and heat-treatment did not have any effect , as determined by analysis of biofilm thickness ( Fig . 8B ) and architecture ( Fig . 8C–G ) . These studies indicate that the active component in PSM is proteinaceous , heat-stable , and non-glycosylated . To determine whether the in vitro activity of PSM against Candida is also exhibited in vivo , we evaluated the efficacy of PSM in an experimental murine model of oral candidiasis . We found that at the end of treatment ( Day 7 ) , clinical score of PSM-treated mice was significant reduced compared to untreated , vehicle-treated , or nystatin-treated mice ( Fig . 9A , P = . 002 by Kruskal-Wallis test ) . The fungal burden of tongue from PSM-treated mice was also significantly reduced compared to untreated , vehicle-treated , or nystatin-treated controls ( P≤ . 029 for all comparisons , Fig . 9B ) . Additionally , histological examination showed extensive tissue invasion by fungal hyphae and destruction of the epithelium in untreated or vehicle-treated controls or nystatin-treated mice ( Fig . 9C–E ) . In contrast , tongue epithelium in PSM-treated mice revealed only superficial hyphal invasion and intact tissue structures ( Fig . 9F ) . These results demonstrated that PSM was efficacious in treating oral candidiasis in vivo when tested in an experimental oral model of candidiasis .
In the current study , we identified the core oral mycobiome and core oral bacteriome in HIV-infected and uninfected individuals , and demonstrated that the oral mycobiome in HIV infection differs from non-diseased controls . In contrast to previous studies that characterized the bacterial component of the oral microbiome [61]–[63] , our study defines both the bacterial and fungal components of the core oral microbiome in the same sample in HIV setting . Our findings revealed that the oral bacteriome of HIV-infected individuals was similar to that of uninfected individuals , indicating the presence of a shared bacteriome in these individuals . The core oral bacteriome of uninfected individuals in our study is in agreement with results reported earlier by Zaura et al . [64] who showed that 15 bacterial genera were present in the core oral bacteriome of healthy individuals . Correlation analyses of the relationship between bacteriome and mycobiome revealed that 15 and 12 bacteria-fungi pairs were correlated significantly in samples from uninfected and HIV-infected patients , respectively . It is possible that these correlations indicate mutually dependent relationships within the oral microbiome , in which bacteria may be assisting or scavenging their fungal neighbors . Alternatively , fungal members of the microbiome may impact bacterial growth and drug susceptibility . Such interactions could influence the course and extent of oral diseases in the HIV setting . In this regard , interactions between bacteria and Candida have been investigated previously by Hogan and Kolter [65] , who showed that P . aeruginosa kills hyphal form of C . albicans via biofilm formation . Furthermore , Candida-bacterial interactions are also associated with diseases like ventilator-associated pneumonia [66] , [67] and bloodstream infections [68] . Our analyses revealed no correlation between Candida and bacteria in uninfected individuals , while in HIV-infected patients Candida and Campylobacter were negatively correlated . This correlation is in agreement with the findings of Navazesh et al . [69] , who showed that antiretroviral therapy increased the risk for recovering bacteria ( including Campylobacter species ) with a concomitant decrease in the recovery rate of Candida , in HIV-infected women . Moreover , Workman et al . [70] reported that proteins secreted by Campylobacter inhibit the growth of C . albicans . The clinical relevance of this correlation remains to be investigated . In the current study , we also defined the oral mycobiome of HIV-infected and uninfected study participants , and showed this fungal community to comprise up to nine different genera in both groups . To our knowledge , the only other study to have used a sequencing-based approach to identify oral fungi in HIV setting was that performed by Aas et al . [12] , who analyzed sub-gingival plaque of HIV-infected patients , and reported the presence of only two fungal species ( Saccharomyces cerevisiae and C . albicans in 4 and 2 patients , respectively ) . The difference in fungal profile between these investigators and our study may be due to differences in sample types ( oral wash vs . sub-gingival plaque ) , detection probe ( pan-fungal ITS probe vs . 18S rDNA ) , and sequencing technique ( real-time pyrosequencing vs . rDNA sequencing ) . Our study showed that the core oral mycobiome of HIV-infected patients was different from that of uninfected individuals . In a recent study , Iliev et al . [13] characterized the gut microbiome in wild type mice and isogenic Clec7a−/− mice that lack Dectin-1 ( the innate immune receptor ) and exhibited increased levels of chemically induced colitis . These investigators reported that there were no significant differences in bacteriome between wild type and Clec7a−/− mice , while the mycobiome profile differed between these isogenic mice , with an increase in opportunistic pathogenic fungi ( Candida and Trichosporon ) during colitis in Clec7a−/− mice , and a decrease in the nonpathogenic fungi ( e . g . Saccharomyces ) . Thus , severe colitis in Dectin-1 knockout mice was associated with alterations in the gut mycobiome ( but not the bacteriome ) . This study is similar to our findings , showing that HIV disease is associated with changes in the oral mycobiome , and highlight the importance of characterizing the mycobiome and its role in disease . Our analyses showed that increase in Candida colonization was associated with a concomitant decrease in the abundance of Pichia , suggesting an antagonistic relation between these two fungi . In addition , these analyses also suggested that Pichia exhibits antagonistic interaction with other known pathogens including Cryptococcus , Aspergillus and Fusarium . These interactions were confirmed using growth assays that demonstrated broad-spectrum inhibitory activity of Pichia . The anti-Candida activity of Pichia was validated in vivo using an experimental murine model of oral candidiasis . The biocontrol activity of Pichia against fungal plant pathogens has been shown to involve multiple mechanisms , including biofilm formation and germination [48]–[53] . The results of the current study are in agreement with these findings , where we showed that the anti-Candida activity of Pichia is mediated by inhibition of Candida growth and virulence factors like germination , adherence , and biofilm formation . It is well known that interfering with virulence factors decreases the ability of Candida to cause infection including oral candidiasis [71]–[79] . Therefore , Pichia , by inhibiting Candida virulence factors may limit the ability of this pathogenic fungus to cause infection . Such involvement of virulence factors is a common theme in interactions among microorganisms in the context of human infection [65] . Pichia biocontrol activity has also been attributed to nutrient limitation [39] , [41] , [51] , [80] . Our results showed that Pichia outcompetes Candida when the two fungi are mixed together and allowed to grow . The possible reason for this phenomenon could be due to the ability of Pichia to consume nutrients more efficiently than Candida . Alternatively , it is possible that Pichia secretes factor/s that attenuate the ability of Candida to grow . Data in support of the latter possibility can be derived from our findings that Pichia spent medium inhibits pathogenic fungi including Candida , Aspergillus , Fusarium , and Cryptococcus . Furthermore , our results also demonstrate that the inhibitory activity of PSM is proteinaceous in nature , and not a metabolite . Earlier studies have shown that biocontrol activity of Pichia against fungal plant pathogens is mediated by secretory metabolite or proteins [48] , [53] , [81] , [82] . In conclusion , we identified the core bacteriome and mycobiome in HIV setting , and identified HIV-specific changes in the mycobiome . We also identified a critical antagonistic interaction between Pichia and fungal pathogens including Candida . This interaction was demonstrated using in vitro and in vivo models . We also defined the mechanisms underlying the antagonistic interaction between Pichia and Candida . Our findings show for the first time that normal fungal community interacts with Candida in the oral cavity . Detailed investigations are warranted to purify and characterize the secretory factor/s mediating such interactions , and their mechanism/s of action at the molecular level . Our findings have wide implications regarding the discovery of novel antifungal agents that new therapeutic approaches for the management of fungal infections . | Oral microbiota contribute to health and disease , and their disruption may influence the course of oral diseases like oral candidiasis . Here we identify the core oral mycobiome ( COM ) and core oral bacteriome ( COB ) in HIV-infected and uninfected individuals , and demonstrate that the COM differs between these two groups . Decrease in abundance of Pichia ( a resident oral fungus ) in uninfected individuals coincided with increase in abundance of Candida , suggesting an antagonistic relationship . In vitro testing showed that Pichia spent medium ( PSM ) inhibits growth of pathogenic fungi; these findings were validated in an experimental mouse modal of oral candidiasis . The mechanism by which Pichia antagonizes Candida involves nutrient competition and secretory factor/s that inhibit the latter's ability to adhere , germinate , and form biofilms . This study is the first to characterize the mycobiome and the bacteriome in the oral cavity of HIV infected patients , and provides the first evidence that a fungus present in the same host microenvironment antagonizes Candida and identifies potential novel antifungal approach . | [
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] | 2014 | Oral Mycobiome Analysis of HIV-Infected Patients: Identification of Pichia as an Antagonist of Opportunistic Fungi |
Mosquito-borne chikungunya virus ( CHIKV ) is a positive-sense , single-stranded RNA virus from the genus Alphavirus , family Togaviridae , which causes fever , rash and severe persistent polyarthralgia in humans . Since there are currently no FDA licensed vaccines or antiviral therapies for CHIKV , the development of vaccine candidates is of critical importance . Historically , live-attenuated vaccines ( LAVs ) for protection against arthropod-borne viruses have been created by blind cell culture passage leading to attenuation of disease , while maintaining immunogenicity . Attenuation may occur via multiple mechanisms . However , all examined arbovirus LAVs have in common the acquisition of positively charged amino acid substitutions in cell-surface attachment proteins that render virus infection partially dependent upon heparan sulfate ( HS ) , a ubiquitously expressed sulfated polysaccharide , and appear to attenuate by retarding dissemination of virus particles in vivo . We previously reported that , like other wild-type Old World alphaviruses , CHIKV strain , La Réunion , ( CHIKV-LR ) , does not depend upon HS for infectivity . To deliberately identify CHIKV attachment protein mutations that could be combined with other attenuating processes in a LAV candidate , we passaged CHIKV-LR on evolutionarily divergent cell-types . A panel of single amino acid substitutions was identified in the E2 glycoprotein of passaged virus populations that were predicted to increase electrostatic potential . Each of these substitutions was made in the CHIKV-LR cDNA clone and comparisons of the mutant viruses revealed surface exposure of the mutated residue on the spike and sensitivity to competition with the HS analog , heparin , to be primary correlates of attenuation in vivo . Furthermore , we have identified a mutation at E2 position 79 as a promising candidate for inclusion in a CHIKV LAV .
In the last few years , considerable attention has been focused upon mosquito-borne chikungunya virus ( CHIKV ) ; once a relatively obscure member of the Alphavirus genus in the Togaviridae family of enveloped , positive-sense RNA viruses [1]–[3] . In 2005 , an East African clade CHIKV strain emerged on the Indian Ocean island of La Réunion that was maintained in a human-mosquito-human transmission cycle and caused a massive outbreak of CHIK fever [4] . Spread and/or re-emergence of CHIKV in Indian Ocean areas , Asia , southern Europe , and most recently in the Caribbean , in the following years has resulted in an estimated four to six million cases of CHIK fever with painful , often chronic , arthritides and an ongoing worldwide public health problem [3] , [5] . The future occurrence of autochthonous cases in the mainland Americas seems inevitable with frequent travel-associated virus introduction , and the likelihood of a resulting outbreak is predicted to be high [6] , [7] . Thus , the need to develop therapeutics and vaccine candidates for protection against this virus is ever more urgent . A cell culture-adapted LAV ( 181/25 ) is available as an investigational drug to at-risk researchers [8] , [9] . However , insufficient attenuation and consequent reactogenicity problems have precluded its licensure for general use [8] . New strategies are required to develop CHIKV LAVs combining a more refined balance between attenuation and immunogenicity [10] . Like all members of the genus , CHIKV has an infectious , single-stranded RNA genome of ∼11 kb with a m7G 5′ cap structure and a 3′ polyadenylated tail ( reviewed by [11] ) . Within this genome are encoded four non-structural ( nsP1-4 ) and three structural ( C , 6K/E1 and pE2 ) proteins from two open reading frames in the genomic and subgenomic RNAs , respectively [12] , [13] . CHIKV virions have icosahedral symmetry , with a glycoprotein shell enclosing the viral membrane and nucleocapsid [14] . pE2 and E1 glycoproteins insert into the cell's endoplasmic reticulum as they are synthesized , forming heterodimers that trimerize into the viral ‘spikes’ and envelop nucleocapsids , budding as virus particles from the cell's plasma membrane [11] . pE2 is cleaved by host furin into E2 and a released E3 fragment during egress to produce the mature , fusion-competent virions in preparation for the next round of infection . CHIKV isolates bind to cell surface receptors via E2 and fuse with cell membranes by clathrin-independent , Eps15-dependent , endocytosis via E1 [15] . The relationship between alphaviruses and their receptors is a complex one with many questions still unanswered and the identity of the receptor ( s ) utilized by wild-type isolates is still elusive with the exception of C-type lections , DC-SIGN and L-SIGN [16] . For many years , receptor identification was clouded by the use of strains that had adapted to growth in cultured cells . Fifteen years ago , we demonstrated that in vitro passage of the prototypic Old World alphavirus , Sindbis ( SINV ) , in different laboratories had resulted in the accumulation of positively charged mutations in the E2 glycoprotein , which dramatically improved the virus-cell surface receptor interaction in vitro [17] , [18] . In the converse experiment , Griffin and coworkers showed that in vivo passage in immune-deficient mice of a laboratory SINV strain could select for acquisition of negative charge and reduced heparan sulfate ( HS ) -dependence in vitro [19] , [20] . Amino acid substitutions that increased net positive charge in certain E2 regions could dramatically increase per particle infectivity in cultured cells , dependent upon ionic interaction with negatively charged , cell-surface HS chains [17] , [19] , [21] . The following observations are of profound importance to the current study and to the biology of Old World alphaviruses in general: i ) the substitution for positively charged residues in E2 that confer enhanced , HS-dependent infectivity in vitro is a common phenomenon amongst cell culture-passaged strains of SINV [17]–[19] , [21] , Ross River ( RRV; [22] ) and Semliki Forest ( SFV; [23] ) viruses; ii ) these mutations can be selected within only a few serial passages in vitro [17]; and iii ) viruses whose in vitro infectivity is enhanced by artificial HS attachment/entry are typically attenuated/avirulent in vivo from the natural infection route , at least in part due to reduced level and/or duration of viremia [20] , [24] . We demonstrated recently that a wild-type CHIKV strain ( LR2006 OPY1 ) , isolated during the La Réunion outbreak and sequence-stabilized in cDNA clone form ( CHIKV-LR; [25] , [26] ) , exhibited no significant dependence upon HSPGs or other glycosaminoglycans ( GAGs ) for infectivity in vitro [27] . In contrast , the 181/25 CHIKV LAV candidate , derived by 18 serial passages of wild-type CHIKV ( strain 15561 ) in MRC-5 fibroblasts to achieve attenuation [9] , was highly dependent upon ionic interaction with HS for infectivity [27] . We predicted that this was due to an amino acid substitution at E2 position 82 [27] , which was subsequently shown by Weaver and coworkers to attenuate both CHIKV-15561 and CHIKV-LR in vivo [28] . Here , we have exploited existing knowledge to deliberately select for and identify a set of E2 mutations that confer HS-dependence for infectivity by serial passage of wild-type CHIKV-LR on different cell-types in vitro . Single amino acid mutations that became predominant in the virus population within only five to ten passages through mammalian or mosquito cells were predicted by computational modeling to alter the electrostatic profile of the E2 glycoprotein and increase net positive charge in two exposed regions . By individual introduction of these mutations into CHIKV-LR , we have identified a panel of E2 mutations that confer reduced virulence in a murine model of musculoskeletal disease ( MSD ) and associated these with particular aspects of dependence upon HS for attachment/infectivity in vitro . In particular , we identified a novel mutation at E2 position 79 that increased attenuation over the E2-82R mutation present in the 181/25 LAV but did not diminish immunogenicity or protective efficacy . The positions of the attenuating mutations are clustered within two regions in the three-dimensional structure of the alphavirus trimer-heterodimer with the most attenuating mutated residues prominently exposed to the exterior of the spike . Furthermore , mutations conferring the greatest attenuation were associated with very small plaque size and sensitivity to competition with the HS analog , heparin . We propose this approach as an informed means to create mutant viruses and utilize in vitro HS interaction phenotypes and structural modeling to identify promising candidates for inclusion in CHIKV and other arboviral LAVs .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All animal procedures were performed according to a protocol approved by the Institutional Animal Care and Use Committee of the University of Pittsburgh ( Protocol 1001073 ) . Pregnant and 21 d old CD-1 ( Charles River Laboratories ) , and 8 wk old STAT129 ( Taconic Laboratories ) mice were housed under specific pathogen free conditions and all experiments were conducted at ABSL-3 . BHK-21 , CHOK1 , pgsA745 , pgsD677 , and RAW264 . 7 cells were cultured as previously described [27] , [29] . MC3T3-E1 osteoblasts were maintained in alpha minimum essential medium ( AMEM ) supplemented with 10% fetal bovine serum ( FBS ) , 1 mM sodium pyruvate and 0 . 05 g/mL L-glutamine . CHIKV-LR virus stock was serially passaged 10 times in triplicate series on CHOK1 , pgsA745 , or C6/36 cells , with a 1∶100 dilution of progeny virions between passages . At P5 and P10 supernatant from infected cells was placed in Tri Reagent-LS ( MRC ) containing 5 µg of tRNA carrier , and total RNA was extracted as per manufacture instructions . To sequence mutations in E2 , cDNA was generated using RT-PCR ( Roche ) with a specific primer in the E1 gene , immediately downstream of the E2 gene 3′ terminus ( GCAGCCTCTTGGTATGTGGC ) , and the entire pE2 gene was PCR amplified ( S-CTAATGAAGGAGCCCGTACA; AS-GCAGCCTCTTGGTATGTGGC ) using Deep Vent polymerase ( NEB ) . The PCR fragment was either directly sequenced ( Retrogen ) or cloned into pCR-Blunt ( Invitrogen ) and sequenced . pE2 gene mutations were introduced into the cDNA clone of CHIKV-LR using the Quick Change II XL mutagenesis kit ( Stratagene ) . CHIKV-LR reporter viruses were created by inserting a cleavable in-frame fusion between capsid and E3 using Quick Change II XL mutagenesis to insert a PCR fragment at the capsid/E3 junction that encodes the first five amino acids of E3 fused in-frame with firefly luciferase ( fLuc ) followed by the 2A-like protease of Thosea asigna virus . Stocks of CHIKV-LR , E2 mutant viruses , and reporter viruses were generated from cDNA clones as previously described [27] . Briefly , cDNA was linearized and in vitro transcribed ( mMessage mMachine , Ambion ) to generate infectious , capped viral RNA genomes . Viral particles were harvested from supernatant of BHK cells 18–24 h post-electroporation with these RNAs . For all virus stocks , supernatant was clarified by centrifugation and single-use aliquots were stored at −80°C . Virus stocks ( 200 µL ) were treated with 40 U of RNase ONE ( Promega ) for 1 h at 37°C to remove any contaminating RNA that was not encapsulated in the virion and added to Tri Reagent-LS ( MRC ) along with 5 µg of carrier tRNA , before RNA was extracted per manufacture instructions . Equal total RNA concentration was used for reverse transcription ( RT ) with a primer complementary to sequence in the nsP2 gene and tagged with T7 to reduce background ( 5′-CGTAATACGACTCACTATAAGTACGTTGACGTGCTCTGACGTT-3′ ) . Equal volumes of cDNA were used for real-time ( q ) PCR using SYBR green ( Fermentas ) to detect nsP2 on the positive strand ( nsP2-TCfGTGTTAACGTGCTTCAGAGGGT; T7-GCGTAATACGACTCACTATA ) . A standard curve for viral genome number by qRT-PCR of RNA in vitro-transcribed from the CHIKV-LR cDNA clone . qRT-PCR results were analyzed using CFX Manager software ( BioRad ) . The models of the E1/E2 trimer of the 05-115 CHIKV strain and mutants , including the E2 N-terminal tail that is missing in the CHIKV template trimer structure ( pdb: 2XFB ) [14] , were constructed by using Modeller version 9v8 [30] . The presence of the N-terminal tail was found to have significant influence on the electrostatic potential in the areas of interest in the current studies . Charge of individual atoms and their radius parameters based on an amber force field [31] were generated by pdb2pqr program [32] . Electrostatic potential was generated by Adaptive Poisson-Boltzmann Solver ( APBS ) package [33] . A linearized Poisson-Boltzmann equation was applied with dielectric constant 2 . 0 for protein and 78 . 0 for solvent . Electrostatic potential on solvent accessible surface in the range from -5 kT to 5 kT and solvent radius 1 . 4 A was visualized with the PyMol Molecular Graphics System ( PyMol Molecular Graphics System , Schrodinger , LLC ) . 1 d old CD-1 mice were inoculated subcutaneously in the ventral thorax , 21 d CD-1 and 8 wk old STAT129 mice were inoculated subcutaneously in the hind footpad with either 10 µl of 105 genomes or 103 plaque forming units ( PFU ) of CHIKV viruses diluted in Optimem ( Invitrogen ) . Mice inoculated with equal genome equivalents ( GE ) were inoculated with 105 genomes ( equivalent to ∼103 PFU of CHIKV-LR ) . Mice were weighed and monitored daily for clinical signs of disease and mice showing severe signs of disease were monitored twice a day . The width and height of the metatarsal region of the rear footpad of CD-1 and STAT129 mice were measured daily with a caliper . Changes in footpad swelling were expressed as fold change in area ( width×height ) compared to pre-inoculation area . AST and percent mortality were calculated . Surviving mice were bled and challenged with 103 PFU of CHIKV-LR 21 d post primary infection . At 48 h post-infection ( p . i . ) , groups of three mice were euthanized with isofluorane and then exsanguinated by cardiac puncture to collect blood . The serum was separated from the blood using Microtainer tubes ( Becton-Dixon ) . Mice were then perfused with PBS-1% DBS virus diluent ( VD ) . Tissues collected were homogenized in VD by mechanical disruption . Virus titer was assessed in supernatants from homogenized tissue by standard plaque assay on BHK cells and titers were expressed as PFU/g , mL or draining lymph node ( DLN ) . Serum cytokine concentrations were measured using a mouse cytokine 20-plex kit ( Invitrogen ) per manufacture instructions and analyzed using the BioRad Bioplex 200 . Serum was diluted at a final concentration of 1∶20 in VD containing the CHIKV-LR reporter virus expressing fLuc . Serum and virus were incubated together at room temperature for 30 min before being used to infect a 96-well plate of BHK cells for 1 h at 37°C . Cells were washed twice with VD before culture media was added and the cells were then incubated for 16 h before cell lysates were harvested using passive lysis buffer ( Promega ) . fLuc substrate ( Promega ) was added and relative light units ( RLUs ) were determined by microplate luminometer ( Orion ) . Virus was diluted in either RPMI1640 ( has a basal salt level of 103 mM NaCl ) or RPMI1640 containing different concentrations of NaCl and used to infect MC3T3-E1 cells for 1 h at 37°C before overlaying with immunodiffusion-grade agarose . For the footpad swelling , the area of under the curve was determined ( GraphPad PRISM software ) to assess the differences in swelling during the entire course of infection and then Student's t test was used to determine significance . Student's t test was used for all other experiments .
As a proof of concept , we began these studies by passaging CHIKV on cells from two evolutionarily divergent organisms , Chinese hamster ovary ( CHO ) K1 fibroblasts and C6/36 Aedes albopictus mosquito cells . A population of wild-type strain CHIKV-LR particles with maximal genome sequence homogeneity was generated by transfection of in vitro-transcribed , full-length , infectious viral RNA genomes into BHK-21 fibroblasts . This virus population was then subjected to a positive selection pressure for rapid growth on CHOK1 or C6/36 cells by performing ten sequential passages in triplicate parallel series . Infectious virion yields in supernatants , harvested after each amplification , declined slightly on both cell-types in the first two passages ( data not shown ) but then gradually increased to surpass passage 1 ( P1 ) yields by ∼10 or 1 , 000-fold on mammalian or mosquito cells , respectively ( Fig . 1 ) . These data gave an initial indication of adaptation to cell culture within five to 10 passages . Importantly , infectious virion yields were not significantly enhanced by passage on CHOK1-derivative pgsA745 cells , deficient in the synthesis of GAG chains , suggestive that GAG-dependent changes underlay cell culture adaptation . The sequence encoding the entire pE2 protein ( E3 and E2 ) was analyzed from virus populations at P5 and P10 to identify mutations that might have accumulated during cell culture passage ( Table 1 ) . Each population of RT-PCR products was sequenced to reveal mutations present in the majority of packaged genomes . In addition , several individually cloned RT-PCR products from each P10 population were sequenced to determine whether amino acid substitutions were occurring alone or together , and to identify mutations occurring at lower frequency in the population . Passage on C6/36 mosquito cells selected strongly for an E to K substitution at E2 position 79 , as this variant was the majority ( or consensus ) sequence for progeny virus genomes by P5 in three parallel passage series , and was retained through P10 . In two of five individually cloned pE2 sequences from one passage series , we also identified a deletion of the codon for negatively charged E2-166E in mosquito cell-passaged virus populations . This mutation arose independently of E2-79K , which was found in the other three sequenced clones for this passage series , and in all sequenced clones for the two other passage series . Surprisingly , no other nucleotide mutations were detectable at this depth . Passage on CHOK1 fibroblasts selected strongly for an S to R substitution at E2-159 in the majority sequence in each of the three parallel passage series within five passages , and was retained as the dominant mutation in two passage series through P10 . Interestingly , however , by P10 in the third passage series the dominant mutation in the population , and in four of six individual clones , was the E2-79 E to K substitution also selected on C6/36 cells . Three additional pE2 mutations were revealed in individual virion sequences . An E2-55 G to R substitution occurred at two out of five frequency in the second passage series on CHOK1 cells , while the combination of E2-99 H to Y/E2-168 E to K was present in two out of six of the population of the third passage series . After serial passage on GAG-deficient CHO cells , a dominant E2-264 V to A substitution was detected in one of three passage series at P5 and two of three at P10 . In total , seven amino acid changes were identified in the E2 proteins of the cell culture-passaged virus populations , four of which substituted a neutrally , or negatively , charged residue with a positively charged one ( G to R at E2-55; E to K at E2-79; S to R at E2-159 and E to K at E2-168 ) , while a fifth deleted a negatively charged amino acid ( E2-Δ166E ) . Neither the V to A substitution at E2-264 nor the H to Y substitution at E2-99 would be anticipated to alter the net charge . Based upon the X-ray crystallographic E1/E2 heterotrimeric structure of the CHIKV clinical isolate 05-115 [14] , [34] , we generated a 3D structural model of the trimeric envelope glycoprotein heterodimer ( Fig . 2A ) and used Adaptive Poisson-Boltzmann Solver ( APBS; [33] ) to determine the electrostatic potentials for the trimer-heterodimers of the wild-type CHIKV-LR strain and the identified mutations ( Fig . 2B–D ) . The cell culture passage-selected E2 mutations mapped to two regions previously defined by Voss et al . [14] . Residues E2-55 and E2-79 mapped to the “wing” portion of Domain A in the region of insertion strands i3 , i5 and i6 into Ig-like domains ( Fig . 2C ) . The E2-82R mutation selected during 18 serial passages of the 15561 wild-type CHIKV strain on MRC-5 fibroblasts to produce the 181/25 CHIKV LAV candidate [35] , and previously shown to attenuate both 15661 and CHIKV-LR [28] , also mapped within this region . When viewed from above the three-fold axis of symmetry ( Fig . 2B ) , the E2-55 residue resides in a cleft , overhung by other portions of Domain A , but facing outward from the spike interior adjacent to the β ribbon connector , whereas the E2-79 and E2-82 residues lie toward the apical surface of the protein , facing the solvent-exposed interior of the E1/E2 heterotrimer , with E2-79 more exposed than E2-82 . The E2-159R , E2-Δ166E and E2-168K mutations mapped to the acid-sensitive region in arch 1 of the β ribbon connector between Domains A and B ( Fig . 2D ) . Interestingly , an E to K substitution at E2-166 was previously selected during passage of CHIKV-06 . 049 on human epithelial carcinoma HeLa cells [36] . The E2-159 , E2-166 and E2-168 residues lie within the β ribbon connector facing outward from the heterotrimeric spike interior essentially on the opposite side of the E1/E2 heterodimer from E2-79 and E2-82 residues . E2-166 and E2-168 residues are close to areas of contact between the β connector and E1 , although residue E2-166 is located more towards the heterotrimer exterior than E2-168 which is deeper within an invagination of the spike between the β connector and E1 . The E2-159 residue lies higher and more towards the cleft in Domain A , where E2-55 is located . For ease of viewing , regions affected by the changes in electrostatic potential created by each mutation are shown magnified on only one of the E2 molecules in the heterotrimer ( Fig . 2E & F ) . Those amino acid mutations that were predicted to increase net positive charge also produced localized increases in the computer-predicted positive electrostatic potential on the surface of the E2 protein . In contrast , the E2-Δ166E deletion mutation appeared to affect a broader area and , at the resolution of the model , alter contact regions between the β connector and E1 . The prominent exposure of the positive-charge shift conferred by the highly attenuating E2-79K mutation in comparison with the similarly located E2-55R and E2-82R is particularly apparent in the top view of the spike three-fold axis of symmetry ( Figs . 2H–J ) . Each amino acid substitution or deletion discussed above , including E2-82R and E2-166K , was introduced separately into the CHIKV-LR cDNA clone . Stocks of CHIKV-LR and the E2 mutant viruses were generated by transfection of in vitro-transcribed , capped genomes , and not passaged further . Reasoning that the positive electrostatic potential increases in E2 would result in a dependency upon cell surface HS for infectivity , we compared particle infectivity on CHOK1 cells versus derivatives that lack the ability to synthesize either all GAG chains ( pgsA745 ) or just HS ( pgsD677; [37] ) . Like other Old World alphaviruses , the infectivity of wild-type CHIKV-LR did not depend upon the presence of these sulfated glycans ( Supplemental Fig . S1; [27] , [38] ) . In contrast , the infectivities of LR-55R , LR-79K , LR-82R , LR-159R , LR-166K , LR-Δ166E and LR-168K for CHO cells all exhibited significant dependence upon GAGs , and this phenotype was almost completely conferred by the absence of HS alone . Neither E2-99Y , nor E2-264A mutation , exhibited significant dependence upon GAGs for infectivity ( data not shown ) . In our experience the usefulness of the pgsD677 and pgsA745 CHO cells is limited to determining whether or not viral infectivity is affected by the absence of HS or GAGs but does not accurately determine relative degrees of dependency . However , these data indicate that predicted increase in exposed positive charge on E2 in the trimer-heterodimeric spike correlates with a significant dependence upon HS for infection of CHO cells . Most of the mutations increased per-particle infectivity , with the notable exceptions of E2-Δ166E and E2-159R ( data not shown ) , which reduced infectivity unless HS was present , indicating that the latter mutations may compromise attachment/entry via another receptor ( s ) pathway used by CHIKV-LR . Focusing upon those mutations that increase electrostatic potential on the E2 surface positive charge compared to wild-type CHIKV-LR ( Fig . 2 ) , and conferred the ability to bind HS ( Supplemental Fig . S1 ) , the virulence of each mutant relative to wild-type CHIKV-LR was assessed in a murine model of MSD with edema/inflammation , by measuring hind-limb swelling across the metatarsal region after subcutaneous inoculation of virus into the footpad , as described previously [27] , [39] , [40] . Two waves of limb swelling were consistently observed in mice infected with CHIKV-LR , the first peaking 1–2 d p . i . , and the second 6–7 d p . i . , with complete resolution by 12–14 d p . i . ( Fig . 3A ) . Varying degrees and patterns of attenuation compared to the wild-type virus were observed for the mutant viruses , which we grouped into three categories for clarity . In the first category ( purple ) , little or no attenuation was observed for LR-Δ166E infection ( Fig . 3B ) compared to wild-type CHIKV-LR , although the onset of clinical signs was delayed by ∼24 h and the duration of disease was longer for some animals . In the second category ( blue ) , partial attenuation was observed for several virus mutants ( Fig . 3C–E ) . LR-168K ( Fig . 3C ) was ∼24 h delayed and attenuated at 1–2 d p . i . but produced wild-type levels of swelling in some cases in the second phase . Interestingly , some animals infected with LR-168K also exhibited a more prolonged swelling than we observed for the wild-type virus infection . LR-55R ( Fig . 3D ) and LR-159R ( Fig . 3E ) were not delayed but demonstrated significantly reduced swelling compared with CHIKV-LR in both waves . In the third category ( green ) , three virus mutants were highly attenuated , causing little or no hind-limb swelling ( Fig . 3F–H ) . LR-166K ( Fig . 3F ) caused a transient , mild swelling only in the second wave , whereas no evidence of swelling was detectable for LR-79K ( Fig . 3G ) or LR-82R ( Fig . 3H ) . This categorization and color scheme is used for subsequent figures to demonstrate prominent associations of genotype and phenotype . A number of chemokines and cytokines have been associated with the acute phase of disease in humans , including MIG ( CXCL9 ) , MCP-1 ( CCL2 ) and IP-10 ( CXCL10 ) [41]–[45] . Furthermore , in a murine model similar to the one used here , MCP-1 has been shown to play an important role in pathogenesis of CHIKV-induced MSD [39] , [41] . Interestingly , comparison of inflammatory responses to CHIKV-LR and the E2 mutant viruses revealed that higher induction of two cytokines ( IL-12 p35/p40 and IL-5 ) and three chemokines ( MCP-1 , MIG and IP-10 ) correlated well with the severity of MSD ( Fig . 4 ) . On the other hand , IL-1α , IFN-γ and IL-2 were significantly induced in all virus-infected animals at 48 h p . i . versus mock-infected counterparts but no association between disease severity and these cytokine levels was observed ( Fig . 4 and Supplemental Table S1 ) . No significant elevation of other measured cytokines was observed over mock-infected animals for any of the viruses during this acute phase of infection ( Supplemental Table S1 ) . These data not only indicate that the levels of certain inflammatory molecules provide early biomarkers of MSD severity or attenuation in mice , but further validate the murine model of human infection . In an effort to identify particular in vitro phenotypes that could be used to identify promising mutations for a LAV , we compared multiple characteristics of the dependence of infectivity of each mutant upon HS . By calculating plaques per GE ( specific infectivity ) , we estimated that under plaque assay conditions , ∼1% of wild-type CHIKV-LR virus particles initiated infection on BHK-21 fibroblasts , 0 . 1% on Vero cells , 0 . 05% on CHOK1 cells and 0 . 01% on MC3T3-E1 osteoblasts ( Fig . 5A ) . These susceptibility differences may be due to reduced attachment , entry and/or to subsequent steps in propagation . The LR-Δ166E mutant exhibited minimal changes in specific infectivity on the four cell-types compared to wild-type CHIKV-LR . Otherwise , with a few exceptions ( E2-55R on Vero and MC3T3-E1 , and E2-159R on CHOK1 and MC3T3-E1 ) , the cell culture passage-derived E2 mutations tended to increase the infectivity of CHIKV particles by up to 70-fold ( Fig . 5B ) and thus provided an advantage to virions in culture conditions . However , these data also indicated that the virus-host receptor interactions differ between cell-types , as the hierarchies of cell susceptibility and virion infectivity were not consistently maintained . To further explore in vitro phenotypes we focused upon the MC3T3-E1 osteoblasts as a cell-type with relevance to CHIKV-induced MSD in vivo [46] and known to secrete extracellular matrix components including HSPGs [47] . Virus inocula were prepared in media with a range of salt concentrations to disrupt potential ionic interactions between the virus particles and cell-surfaces during the infection period . As expected from our prior studies , the infectivities of wild-type SINV ( SINV-TR339 ) and CHIKV-LR particles exhibited no significant dependence upon ionic interaction , whereas an E2-70K mutation in SINV-TR339 ( SINV-K70 ) makes this virus highly sensitive to ionic interactions [17] , [21] , [27] . Interestingly , although the electrostatic model of the E2 protein predicted that the deletion of residue E2-166E would increase net-positive charge , the infectivity of LR-Δ166E virus for MC3T3-E1 osteoblasts was insensitive to even the highest salt concentration ( 350 mM; Fig . 5C ) , and indistinguishable from wild-type CHIKV-LR by this assay . The infectivity of the other viruses with positively charged E2 amino acid mutations was significantly disrupted at 200–250 mM salt concentration ( Fig . 5C ) , indicating that the vast majority of virions now relied upon an ionic interaction for infectivity . When we examined the ability of a high concentration of soluble heparin ( 200 µg/mL ) to block infection of MC3T3-E1 osteoblasts ( Fig . 6A ) , we were surprised to discover that only LR-79K , LR-82R and LR-166K were effectively competed versus BSA-treated controls and this treatment reduced infectivity by >90% . However , heparin is a uniformly highly sulfated GAG , lacking the diversity of HS chain charge distributions and therefore does not necessarily block all HS-ligand interactions . It should also be noted that , because the soluble heparin treatment unexpectedly increased the infectivity of wild-type CHIKV-LR on MC3T3-E1 cells by ∼15% , all of the mutations except for E2-Δ166E significantly increased sensitivity to heparin blocking even if only by a small fraction . Compared to previous observations for other alphaviruses that ionic interactions were almost completely HS-mediated and competed by soluble heparin [17] , [19] , [20] , [29] , [48] , these findings suggest a more complicated interaction for these CHIKV mutants . While performing the above experiments , it was noted that plaque sizes on MC3T3-E1 osteoblasts were highly variable between mutants ( Fig . 6B ) . When quantified and compared with CHIKV-LR ( Fig . 6C ) , it was revealed that the plaques formed by LR-166K , LR-82R and LR-79K were extremely small , LR-168K plaques were of intermediate size , while the plaque sizes of LR-Δ166E , LR-55R and LR-159R did not differ significantly from wild-type virus . The heparin blocking assay measures only the ability of the virus to initiate infection in the presence of varying amounts of heparin . As an extension to this assay , we determined whether or not the addition of heparin to the overlay impacted virus plaque size phenotypes as a more sensitive measurement of HS-dependence . As expected the plaque size of the wild-type virus was not affected ( Fig . 6D ) . The three viruses exhibiting the smallest plaque size were also unaffected by added heparin , presumably because cell-surface or extracellular matrix HS already inhibits the spread of these highly sensitive mutants . However , heparin reduced the plaque sizes of LR-55R and LR-168K to a size comparable with LR-79K , LR-82R and LR-166K , and reduced the plaque size of LR-159R and LR-Δ166E , significantly . The reduced plaque size of LR-Δ166E under heparin overlay along with the reduced infectivity on HS-deficient CHO cells ( Supplemental Fig . S1 ) suggested that this virus has a slight dependence on HS depending upon culture conditions , which fit with the prediction of an alteration of the positive electrostatic potential . We examined the infectivity of each virus for MC3T3-E1 cells digested with microbial heparinases , ( Hep ) I , II and III , which digest cell-surface HS chains ( recently reviewed in [49] ) . Hep II acts with little specificity , cleaving both HS chains and heparin regardless of sulfation . In contrast , Hep I cleaves primarily heparin and highly sulfated HS regions , while Hep III primarily cleaves less-sulfated regions of HS chains . Thus , the HS structures remaining on the cell surface after digestion differ between heparinases . Successful digestion of HS was confirmed by the greatly reduced infectivity of SINV-K70 . As expected , viruses with little infectivity dependence upon HS ( SINV-TR339 , CHIKV-LR and LR-Δ166E ) were unaffected by the digestion of HS chains with Hep I , II or III , even when high concentrations of enzyme were used ( Fig . 7A–C ) . With the exception of LR-55R , heparin blocking ( Fig . 6A ) correlated with >90% reduction in infectivity on MC3T3-E1 cells digested with Hep II . However , the infectivities of these viruses were only reduced ∼50% by digestion with Hep I or III suggesting that they were able to utilize residual chains for infection regardless of their sulfation level , unlike SINV-K70 . The infectivity of LR-55R was reproducibly reduced by only ∼50% following digestion with Hep I , II or III . Overall , LR-79K infectivity appeared to be the most sensitive to the digestion of HS , followed by LR-166K . To better understand the reasons for attenuation of the HS-binding E2 mutants in vivo , especially LR-79K and LR-82R , we measured viral load in various tissues at 48 h p . i . ( Fig . 8 ) . This time-point was chosen to represent the first peak of swelling , and the previously observed peak of CHIKV-LR replication in this model [27] . Based upon the studies above , we proposed that two of the mutant viruses were sufficiently attenuated for inclusion in LAV vaccine formulations: LR-82R and LR-79K . To stringently determine the degree and stability of their attenuated phenotypes , we infected more susceptible animals . We have previously shown that mice deficient in STAT1-dependent type I IFN signaling pathways had exacerbated MSD , whereas CHIKV-181/25 remained partially attenuated in these animals [27] . STAT1-deficient mice infected with LR-82R exhibited exacerbated hind limb swelling compared to 129/Sv control animals , similar to that caused by wild-type CHIKV-LR ( Supplemental Fig . S2 ) . However , LR-79K infection of the STAT1-deficient mice caused only mild MSD compared to CHIKV-LR and LR-82R , from which we infer that LR-79K is substantially more attenuated for ability to cause MSD than LR-82R or even the 181/25 LAV . Both LR-82R and LR-79K infections were lethal to the STAT1-deficient mice , however , supporting our prior contention that MSD and fatality are not closely linked . Finally , we immunized CD-1 mice with LR-82R or LR-79K , and challenged with CHIKV-LR three weeks p . i . , to determine whether or not these attenuated E2 mutants could efficiently elicit a protective adaptive immune response . All of the immunized mice were completely protected from development of MSD upon CHIKV-LR challenge , whereas mock-immunized mice developed mild MSD ( Fig . 9A ) . Only the second wave of swelling was observed in these control animals most likely due to the ongoing age-dependent attenuation of CHIK-LR-induced MSD in this model . Protection from disease was coincident with the presence of neutralizing antibodies prior to challenge ( Fig . 9B ) and , although we have not shown directly that this confers the immunity , antibody responses have been shown to protect [50] , [51] . Interestingly , all of the E2 mutants elicited levels of neutralizing antibody not dissimilar to the wild-type infection by 21 d p . i . ( Fig . 9B and data not shown ) , despite there being highly significant differences in dissemination , replication and disease .
The gold standards for a successful LAV against mosquito-borne virus infection are the 17D strains of yellow fever virus ( YFV ) , currently used for routine immunization where YFV is endemic and in regional mass vaccination campaigns at the first sign of an outbreak [52] . The 17D vaccines immunize 99% of vaccinees with only one inoculum dose , provide immune memory for decades , and very rarely cause vaccine-associated adverse events . The original 17D virus was fortuitously selected by extensive blind cell culture-passage in the 1930's [53] . Although , the molecular mechanisms underlying the consistent immunogenicity and stable attenuation of 17D are largely unknown and cannot yet be intentionally duplicated for viruses or even for YFV , increased HS interaction has been identified as significantly contributing to the attenuation of 17D [54] . Alphavirus LAV candidates have been generated for VEEV ( TC83; [55] ) and CHIKV ( 181/25; [9] ) by serial passage in cell culture but both have encountered problems during clinical trial for reasons of inadequate immunogenicity in some vaccinees and residual virulence in others . Yet , both of these vaccines are attenuated , at least in part due to more efficient interaction with HS than their wild-type counterparts [27] , [48] . Attaining the ideal balance between attenuation of disease and immunogenicity is extremely challenging , and to do so by design will require knowledge of virus biology and the optimization of rational attenuation strategies that can be combined to produce a safe and effective vaccine . Thus , one characteristic of most , if not all , arbovirus LAV is increased HS interaction over the wild-type virus , yet these mutations have previously been selected at random as a component of extensive blind passages without knowledge of their contribution to attenuation of the vaccine . Our current studies represent the first attempt to evaluate systematically selected mutations that confer interaction with HS and attenuate CHIKV in vivo . We began by assuming that a rational method for identification of a panel of E2 glycoprotein mutants highly enriched in HS binding mutations would be serial passage of CHIKV virus on two evolutionarily divergent cell types . Within an organism , HS biosynthesis is primarily regulated by domain distribution and degree of sulfation ( i . e . , the distribution of N-substituents and the levels of 2-O- and 6-O-sulfation ) resulting in different composition of HS species from different cellular or tissue sources [56] , [57] . Furthermore , invertebrate HS has a number of unique properties , including unusually low O-sulfation , yielding different domain structures from vertebrate HS [58] , [59] . This diversity most likely results in the duality of both non-specific ionic interactions occurring between HS and positively charged ligands versus highly specific interactions of particular ligands with certain HS species [60]–[63] . Specificity for particular HS structures has been documented for several virus-HS interactions [38] , [64] . Therefore , it was possible that mutations affecting the charge balance of E2 selected on different cell types might exhibit different types of HS interaction . While the particular contribution of HS binding to alphavirus infection is not fully characterized beyond increases in virus association with cells [17] , [21] , [24] , it is possible that structural differences in HS chains , their attachment to core proteins or their associations with other cell surface factors may be involved in the selective advantage provided by a particular virus mutation in vitro . Comparing CHO and C6/36 cell passage series , we obtained one common mutation altering charge ( E2-79 E-K ) , three CHO-only mutations ( E255 G-R , E2-159 S-R and E2-168 E-K ) and one C6/36-only mutation ( deletion of E2-166E ) . Neither the V to A substitution at E2-264 , nor the H to Y substitution at E2-99 , would be anticipated to have considerable impact upon electrostatic potential; therefore , these were not considered . Interestingly , the E2-159R selected in these studies was previously selected during passage of CHIKV on Vero primate kidney fibroblast cells [65] , the E2-166K was selected on HeLa human adenocarcinoma cells [36] , and the E2-82R in the vaccine strain was selected on MRC-5 human fetal fibroblast cells [9] , although none of these studies deliberately selected for increased infectivity or HS interaction . Therefore , common mutations can be selected between evolutionarily divergent hosts ( E2-79 E-K in hamster and mosquito , and E2-159 S-R in monkey and mouse ) while it is possible that others are unique to particular cells or species . While not considered in our studies , it is possible that the number of different mutations selected would be increased by using cells from different tissue types reflecting the diversity of tissue specific HS structure described above . To determine whether or not the mutations identified were unique to cell culture-adapted virus populations , we examined these positions in an alignment of 200 pE2 sequences available in GenBank and believed to be from isolates minimally or unpassaged before sequencing ( Gardner et al . , unpublished observations ) . Only two of the mutations selected in our study were present in any of these isolates: four isolates had E2-55R ( ADV31296 , AEE60792 , AEE60797 and BAH97931 ) and 10 isolates had the E2-264A ( AEX25348 , AEX25344 , AEX25346 , AEE60795 , AEE60793 , AEE60796 , AEE60794 , AEE60792 , CCA61129 and CCA61128 ) . Whether or not these residues are present in naturally circulating viruses or represent cell culture adaptive mutations in the minimally passaged strains remains to be determined . With the exception of LR-Δ166E , all of the E2 mutants were attenuated for the ability to cause MSD in mice . Although all of the viruses were clearly able to replicate at the site of inoculation in the footpad , in general their replication in MST was reduced . The virus mutants fell into three groups of disease severity with LR-Δ166E disease indistinguishable from CHIKV-LR; LR-168K , LR-55R and LR-159R eliciting a biphasic MSD similar to CHIKV-LR but with reduced disease; and LR-166K , LR-82R and LR-79K eliciting either a minor monophasic MSD ( LR-166K ) or no detectable MSD ( LR-82R and LR-79K ) . As suggested by studies with other alphaviruses [17] , [20] , [48] , the dependence upon HS for infectivity prevents efficient spread of CHIKV in vivo . In the current studies , this led to decreased levels of inflammatory chemokines such as MCP-1 , MIG and IP-10 that are associated with CHIKV-induced disease . Further , the most attenuated viruses elicited the lowest levels of these factors . It was not surprising that the levels of IP-10 and MCP-1 correlated with attenuation , as these cytokines are chemoattractants for monocytes/macrophages which have been shown to be important for disease mice [39] , [41] and humans , especially during the acute phase of disease [42]–[44] , [66] , and can be associated with higher CHIKV loads [44] . MIG and IL-12 are involved in T cell recruitment and differentiation respectively and have been shown in some human cohorts to be elevated during infection [42]–[45] , [66] , and CD4+ T cells have been shown to contribute to hind limb swelling in mice [67] . IL-1α , IFN-γ and IL-2 were increased in the serum of mutant viruses similarly to wild-type CHIKV-LR and several of these cytokines were also shown to be upregulated during human infections [41] , [43]–[45] , [66] . It remains to be determined if elevation of these cytokines is associated with the attenuated phenotype and/or the induction of the protective immune response . Taken together , the cytokine profiles in mice correlated with virulence and for many of the cytokines are similar to profiles in humans following CHIKV-LR infection . We attempted to associate in vitro measurements of HS infection dependence with virulence , examining virus specific infectivity , infection efficiency in the absence of HS or all GAGs , plaque size , resistance of infectivity to competition with increasing salt concentrations , sensitivity to competition with heparin , and infection sensitivity to digestion of cell surfaces with heparinases . Each of these assays has previously been used to compare HS infectivity dependence of alphaviruses ( e . g . , [17] , [19] , [21] , [22] , [48] , [68] ) . All viruses attenuated in comparison with CHIKV-LR did exhibit increased specific infectivity on at least one of the four cell types tested and this was associated with similar infectivity diminution in cells genetically deficient in GAG synthesis . LR-Δ166E dependence on HS was significantly less than the other mutants but much greater than CHIKV-LR indicating that at least partial dependence upon HS for infectivity is not invariably associated with attenuation . Interestingly , in contrast with the SINV-K70 mutant whose infectivity appeared to depend solely upon HS among GAGs , all CHIKV mutants exhibited a minor dependence of infectivity upon GAGs other than HS evidenced by slightly increased infectivity on HS-deficient pgsD-677 cells versus GAG-deficient pgsA745 cells . This suggests the possibility of differences in attachment/entry mechanisms of SINV versus CHIKV . Disruption of infection with salt did not distinguish between attenuated mutants with each exhibiting moderate decrease at 200 mM and ∼90% decrease in infectivity at 250 mM and above . Interestingly , LR-Δ166E , although sensitive to genetic deficiency in HS and all GAGs , was insensitive to all salt concentrations used , similar to CHIKV-LR . Furthermore , digestion of HS chains with Hep I , II or III did not distinguish the three groups of attenuation phenotypes . Likely reflecting the different substrate specificities of the three enzymes , differential sensitivity to the three heparinases can distinguish between virulence phenotypes with other alphaviruses with genetic differences in HS binding domains [17] , [29] . In these studies , CHIKV-LR was insensitive to all three heparinases , while LR-166K , LR-168K , LR-55R , LR-159R and LR-82R were partially sensitive to Hep I , highly sensitive to Hep II and partially sensitive to Hep III . Notably , LR-79K was significantly more sensitive to Hep I and Hep III than the other E2 mutants . LR-Δ166E , again , exhibited an intermediate dependence phenotype with all three heparinases . SINV-K70 , in comparison , was highly sensitive to all three heparinases , again suggesting differences in GAG interactions between SINV and CHIKV mutants . In contrast with these assays , blocking of infectivity by reaction of virus particles with soluble heparin clearly distinguished LR-166K , LR-82R and LR-79K from the other viruses . Small plaque size and lack of change in size with addition of heparin to the overlay provided a similar distinction . Comparison of location in an electrostatic map of the CHIKV heterotrimeric spike complex revealed that mutations that were more attenuating to disease in mice appeared to create additional positive charge that was more exposed to the exterior of the spike ( e . g . , E2-79K , E2-82R and E2-166K as opposed to E2-55R , E2-Δ166E or E2-159R ) . The lack of any attenuating phenotype with E2- E2-Δ166E was surprising , especially since the electrostatic model of this mutant suggested that the deletion should lead to increased electrostatic potential of E2 similar to that of the other E2 amino acid substitution mutants listed in Table 1 which demonstrated various degrees of attenuation . Clearly , the interpretation of the electrostatic potential maps of particular E2 mutant proteins will depend upon confirmation with in vitro assessment of GAG dependency . Our data for the E2-166K mutation , which we tested because of its identical location to the selected E2-Δ166E mutation , indicate that it confers efficient attachment to HS . This mutation was selected by passage in a context where the authors concluded that the mutation increased virus resistance to the OAS3 antiviral protein , possibly by conferring a rapid entry phenotype [36] . Many of the HS-binding E2 mutants for SINV and VEEV were originally selected and identified as rapid entry mutants and only later shown to confer efficient HS binding [17] , [24] , [69]–[72] . The relationship of HS binding , rapid entry and antiviral resistance is unclear . However , if the LR-166K virus effectively antagonized the antiviral response as suggested by Henrik Gad et al . [36] , one would expect the virus to be more virulent in vivo instead of more attenuated compared to CHIKV-LR and thus this phenotype may be an in vitro artifact or a localized phenomenon . Overall , our data suggest that positively charged amino acid substitutions in CHIKV E2 that result in small plaques and efficient competition with heparin will be highly attenuated in the adult mouse model of MSD . The LR-166K , LR-82R and LR-79K viruses were very similar in terms of the primary in vitro correlates of attenuation: plaque size and heparin sensitivity . In addition , LR-79K was dramatically more attenuated for MSD than LR-82R in the severely immunocompromised STAT1-deficient mice . Testing in this model will refine choices between mutants with similar in vitro characteristics and sensitivity to all three heparinases may indicate the highest degree of in vivo attenuation within this group . Furthermore , combinations of such mutations can be tested to improve immunogenicity and/or stabilize attenuation . One question raised by these studies is whether or not this paradigm for deliberate mutation selection will be applicable to other alphaviruses and/or other arboviruses . We have detected differences between SINV and CHIKV mutants in dependency upon HS versus other GAGs for infectivity and it is unclear how this phenotype correlates with attenuation in vivo especially when evaluating the results of assays that are not specific to a particular GAG . It is likely that the specific combination of in vitro assays most correlated with attenuation must be determined for each virus type . In summation , these studies demonstrate that very limited passage of CHIKV is sufficient to generate HS binding mutations that are highly attenuating but retain immunogenicity , and would make satisfactory candidates for testing in a LAV . Furthermore , using this approach , we have identified E2-79K as a single site mutation that is highly attenuating for MSD and stable even in a severely immunocompromised model of disease . It is possible that attenuating mutations present in other areas of vaccine genomes ( e . g . , nonstructural or non-translated region mutations in the YFV 17D vaccine or the VEEV TC83 vaccine [73] ) would require further passage to accrue . Therefore , rational LAV creation may benefit from multiple selection strategies with an initial screen identifying mutants meeting the criteria we have outlined for HS binding-mediated attenuation followed by additional passage on the same cell type or others or introduction of mutations identified with other alphaviruses that could be reasonably transferred to CHIKV . | With the adaptation of chikungunya virus ( CHIKV ) to transmission by the Aedes albopictus mosquito , a pandemic has occurred resulting in four to six million human infections , and the virus continues to become endemic in new regions , most recently in the Caribbean . CHIKV can cause debilitating polyarthralgia , lasting for weeks to years , and there are currently no licensed vaccines or antiviral therapies available . While an investigational live-attenuated vaccine ( LAV ) exists , problems with reactogenicity have precluded its licensure . The purpose of the current study was to: i ) devise an in vitro passage procedure that reliably generates a panel of CHIKV envelope glycoprotein mutations for screening as vaccine candidates; ii ) determine the position of the mutations in the three-dimensional structure of the alphavirus spike complex and their effect on electrostatic potential; iii ) determine the attenuation characteristics of each mutation in a murine model of CHIKV musculoskeletal disease; and iv ) to identify in vitro assays examining the dependency of infection upon HS that correlate with attenuation and localization in the glycoprotein spike . This approach provides a paradigm for the rational design of future LAVs for CHIKV and other mosquito-borne viruses , by deliberately selecting and combining attenuating processes . | [
"Abstract",
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] | 2014 | Deliberate Attenuation of Chikungunya Virus by Adaptation to Heparan Sulfate-Dependent Infectivity: A Model for Rational Arboviral Vaccine Design |
Given the option , humans and other animals elect to distribute their time between work and leisure , rather than choosing all of one and none of the other . Traditional accounts of partial allocation have characterised behavior on a macroscopic timescale , reporting and studying the mean times spent in work or leisure . However , averaging over the more microscopic processes that govern choices is known to pose tricky theoretical problems , and also eschews any possibility of direct contact with the neural computations involved . We develop a microscopic framework , formalized as a semi-Markov decision process with possibly stochastic choices , in which subjects approximately maximise their expected returns by making momentary commitments to one or other activity . We show macroscopic utilities that arise from microscopic ones , and demonstrate how facets such as imperfect substitutability can arise in a more straightforward microscopic manner .
When suitably free , humans and other animals divide their limited time between work , i . e . , performing employer-defined tasks remunerated by rewards such as money or food , and leisure , i . e . , activities pursued for themselves that appear to confer intrinsic benefit . The division of time provides insights into these quantities and their interaction , and has been addressed by both microeconomics and behavioral psychology . Microeconomic labor supply theorists [1] have adopted a normative perspective , formulating what a rational agent should do . Accounts from behavioral psychology have been descriptive , detailing how subjects allocate their time , for example , proportionally to the relative payoffs from work and leisure [2]–[8] . Common to these approaches is the coarse , macroscopic timescale at which behavior is characterised , focusing on average times spent in work and leisure . By contrast , microscopic analyses characterise the fine temporal topography of work and leisure choices , and so offer a foundation for examining , rather than averaging away , rich psychological and neural processes . Tying microscopic and macroscopic choices together is known to be difficult in general [9] , because the former involves a much more elaborate state space than the latter . Here , we build an approximately optimal stochastic control theoretic model of decision-making at a microscopic level . We show how averaging over the microscopic choices yields a characterizable superset of traditional macroscopic theories , and casts the assumptions necessary for the latter to capture partial allocation in a different light . We make the novel prediction that partial allocation requires neither stochastic choices ( as generally assumed by accounts from behavioral psychology ) nor the marginal utility of leisure to depend on the amount of work performed . We use a simplification of a particularly stark labor task as a paradigm example to show how macroscopic and microscopic theories of the partial allocation of time between work and leisure can be tied . We therefore do not attempt to model actual data from this task; a qualitative account is available in [10] .
We consider a Cumulative Handling Time task [11] , [12] in which subjects must accumulate work up to a total time-period called the price ( see Table 1 for a list of symbols and their meanings ) to gain a reward . The price and the objective strength of the reward are defined by the experimenter . Note that the price is an experimenter determined time-period , hence we shall use “long” and “short” to denote its duration . Subjects are free to distribute leisure bouts in between work bouts ( Fig . S1A ) . The CHT controls both the ( average ) minimum inter-reward interval and the amount of work required to earn a reward . This makes the CHT a generalisation of common schedules of reinforcement such as Fixed Ratio , or Variable Interval , which control one but not the other . Reward and leisure are both assumed to enjoy a subjective worth . We call these microscopic utilities to distinguish them from the macroscopic utilities used by traditional theories . The microscopic utility of the former is called the ( subjective ) reward intensity ( , in arbitrary units ) ; the ratio of this to the price is called the payoff ( or in economic nomenclature , wage rate ) . For simplicity , we consider the objective price , recognising that its subjective value may differ . We explore different functional forms for the presumed microscopic utility of leisure . This paradigm was originally developed in the context of rats pressing down an unweighted lever to gain non-satiating , brain stimulation reward ( BSR ) , or alternatively choosing leisure in the form of resting , grooming , exploring , etc . However , as noted above , we do not model data , but rather consider an abstracted version of the task in order to concentrate on the relationship between microscopic and macroscopic descriptions . The key macroscopic statistic is the Time Allocation ( ) : the proportion of trial time that the subject spends working [2] . Fig . S1B shows example TAs for a typical subject . As expected , the TA increases with reward intensity and decreases with price . A microscopic analysis , as shown by ethograms in ( Fig . S1C ) , considers the detailed temporal topography of choice , recording when and for how long each act of work or leisure occurred . Note that at intermediate payoffs , when partial allocation is most noticeable , subjects consume almost all leisure immediately after getting a reward , and then work continuously for each entire price [13] . Labor supply theory and generalized matching average over the temporal topography shown in Fig . S1C ) . By contrast , we follow [10] , [18] , [19] in formulating a so-called micro Semi-Markov Decision Process ( SMDP ) [20] , [21] ( Fig . 3A ) with actions , states , and utilities , for which policies ( i . e . , the stochastic choices of actions at states ) are quantified by the average reward per unit time accrued over the long run . We formulated the general normative , microscopic theoretical framework in [10] . Here we delineate a simplified model pertinent to the partial allocation problem . By integrating the microscopic choices from our model , we can compare it with macroscopic descriptions such as the mountain model . We saw that linear generates partial allocation with stochasticity . It therefore generates smooth ( non-step function ) macroscopic time allocation curves as a function of both reward intensity and price . Consequently , 3-dimensional relationships can be derived that are qualitatively similar to those specified by the mountain model ( when expressed in terms of subjective reward intensity , compare Fig . 6A with Fig . 5 ) . However , when is non-linear , more complicated structures arise . If the price is increased while holding the reward intensity fixed , the reward rate ( Eq . ( 2 ) ) decreases hyperbolically and eventually asymptotes ( Fig . 7A ) . Consequently , unlike the mean , the mode of the gamma-like distribution does not substantially increase with the price ( see Figs . 3C and 7B ) . Since the mode determines the duration of the majority of leisure bouts , these do not increase substantially . If the subject continues to work for the entire price duration ( Fig . 7C ) , then , surprisingly from the macroscopic perspective of the generalized matching model , the total work time and thus the TA will increase , rather than decrease with the price ( Figs . 6B and 7A , lower panel ) . This prediction is readily amenable to experimental test . Since for linear , leisure durations are governed by substantially changing means and not modes , TAs are in general smaller than for strictly concave , implying that higher payoffs are necessary to capture the entire TA range .
We studied the problem of partial time allocation – when reward intensities and prices are not extreme , both animals and humans divide their time between work and leisure . Traditional theories such as the microeconomic theory of labor supply , or accounts from behavioral psychology based on the generalised matching law , have characterised behavior at a macroscopic level , studying average times spent in work or leisure . While labor supply approaches have studied choices within periods of time , these have been limited to maximising utility within these time windows [32]–and thus , still average times within these windows . We proposed a normative , microscopic approach using the reinforcement learning framework of Semi-Markov Decision Processes . Although we applied it to the labor-leisure tradeoff , this is actually a more general theoretical framework for temporally relevant decision-making . By integrating the microscopic choices of our model over time , we were able to account for the nature of macroscopic partial allocation . We showed how assumptions about microscopic and macroscopic quantities relate . In labor supply theory , the marginal utility of leisure may ( although not necessarily ) depend on the amount of work ( or rewards ) consumed , and ( unlike in the behavioral data ) choices are classically deterministic . We considered a stochastic policy of the same form as emerges for standard random utility models , but directed at microscopic , rather than macroscopic , choices . Macroscopic random utility theory considers stochasticity to be due to unobservable noise , which is added to the representation of utility . The subject chooses the combination of cumulative work and leisure times that maximizes this net utility ( including the noise term ) . If the noise is assumed to be Gumbel distributed ( i . e . drawn from an extreme value distribution of type I ) , then the probability of choosing the optimal combination is a softmax . The softmax function that we employ is over microscopic durations , and arises from an ( equivalently arbitrary ) assumption that subjects have a taste for entropic policies . Randomness is thus directly built into the fabric of our model , rather than being an afterthought . It generates partial allocation even when the marginal microscopic utility of leisure is independent of work . Previous exercises attempting to link macroscopic static and dynamic frameworks have not been generally successful [9] . Optimal choice in a dynamic context generally depends on the microscopic state , whose evolution is invisible at a macroscopic level . This allows the macroscopic average choice obtained after integrating out such states ( i . e . , the average choice under the stationary distribution ) to appear counterintuitive , possibly even violating rationality constraints . In our case , the key feature of the microscopic state is implicit in the non-memorylessness of the policies allowed in an SMDP – e . g . , that the hazard function governing the probability a leisure bout will end a certain time after it begun is not independent of time . An example of the problems comes from observing that time allocation to working under conventional macroscopic labor supply accounts generally increases with reward and decreases with price . Something similar is true of the macroscopic , mountain-like , consequence of generalized matching . We showed in our framework that , although this can be true , it is nevertheless the case that for certain non-linearities , the time allocated to working can increase rather than decrease as the price increases , yielding complicated 3-dimensional relationships and non-monotonic contours that elude the mountain model . We thus derived a transparent link between microscopic and macroscopic frameworks . Whereas animals have been previously shown consistently to work more when work-requirements are greater ( one idea is that this arises from sunk costs [33] , [34] ) , the apparent anomaly discussed here only occurs at longer prices and is due to the form of the microscopic utility of leisure . This is an obvious candidate for empirical investigation [35] . Non-linear benefit of leisure functions can also lead to partial allocation for deterministic choices . This applies even for functions that differ from those common in labor supply theory in virtue of satisfying independence between the microscopic utilities of working and engaging in leisure . Of course , the marginal microscopic utility of leisure might depend on work or rewards – for instance due to fatigue or satiation . However , carefully eliminating such dependencies ( by , e . g . , allowing subjects sufficient rest inbetween trials , and using non-satiating rewards like BSR ) may provide an avenue to quantify aspects of the microscopic utility of leisure empirically . This should help reveal why and how subjects partially allocate their time . It would then be natural to extend the study to considerations of effort , fatigue and cognitive computational costs [36]–[40] ( e . g . from holding down weighted levers or performing cognitively demanding tasks ) and the effects of manipulating motivational state [12] , [41] , [42] . It is by taking advantage of the greater precision available from the detailed topography of work and leisure that we may hope to gain insight into these most important details . Although previous work has described aspects of this topography [37] , [43] , our precise control theoretic formalization could offer enrichment . The utilities considered in macroscopic labor supply theory are ordinal , whereas the microscopic utilities used in our framework are cardinal and , by analogy with quantities investigated in discrete choice paradigms [22]–[24] , open for direct neural investigation . One of the key goals of our work is to provide a formal framework within which this can happen . Finally , our work provides a foundation for studying critical psychological processes and neural computations at an appropriate timescale . Real-time or quasi-real-time recording methods in routine use in neuroscience such as electrophysiology , large-scale imaging , or fast-scan cyclic voltammetry allow us to correlate the activity of neural populations or concentrations of neuromodulators with the execution of behaviors . Likewise , fast causal manipulations via such methods as optogenetics allow the circuits governing these behaviors to be probed in a highly selective manner . There is an evident mismatch between the microscopic timescale over which these methods operate and the macroscopic timescales over which ( a ) behavior has often been characterised; and ( b ) the quantities such as costs and benefits which underpin the pertinence of the behavior have been defined . Our normative microscopic account may therefore provide an illuminating framework within which to build explanations that span multiple levels .
See Micro-SMDP methods in Text S1 . | Dividing limited time between work and leisure when both are attractive is a common everyday decision . Rather than doing one exclusively , humans and other animals distribute their time between both . Traditional explanations of this phenomenon have studied the macroscopic average times spent in both . By contrast , we develop a microscopic framework in which we can model the real-time decisions that underpin these averages . In the framework , subjects' choices are approximately optimal , according to a natural , microscopic , utility function . We show that the assumptions of previous theories are not necessary for partial allocation to be optimal , and show possibilities and limits to the integration of macroscopic and microscopic views . Our approach opens new vistas onto the real-time processes underlying cost-benefit decision-making . | [
"Abstract",
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] | 2014 | Some Work and Some Play: Microscopic and Macroscopic Approaches to Labor and Leisure |
It has been proposed that interactions between mammalian chromosomes , or transchromosomal interactions ( also known as kissing chromosomes ) , regulate gene expression and cell fate determination . Here we aimed to identify novel transchromosomal interactions in immune cells by high-resolution genome-wide chromosome conformation capture . Although we readily identified stable interactions in cis , and also between centromeres and telomeres on different chromosomes , surprisingly we identified no gene regulatory transchromosomal interactions in either mouse or human cells , including previously described interactions . We suggest that advances in the chromosome conformation capture technique and the unbiased nature of this approach allow more reliable capture of interactions between chromosomes than previous methods . Overall our findings suggest that stable transchromosomal interactions that regulate gene expression are not present in mammalian immune cells and that lineage identity is governed by cis , not trans chromosomal interactions .
Each chromosome contains just one DNA molecule . Recent technological advances have allowed characterisation of the elaborate three-dimensional structures that form from this DNA [1] . These structures include topologically associated domains , which partition the chromosome , and elegant DNA loops that link gene promoters to distant enhancers . In addition to these intrachromosomal structures formed within the same DNA molecule , there are transchromosomal interactions formed between different chromosomes . Relative to intrachromosomal interactions , the frequency , nature and function of transchromosomal interactions are poorly understood [2] . In contrast to the multitude of intrachromosomal interactions known to regulate gene expression , only a handful of transchromosomal interactions have been described . For example , transchromosomal interactions were reported to be crucial for the appropriate expression of a single olfactory gene amongst the ~1300 within the genome [3 , 4] and for X chromosome inactivation [5–7] . Interestingly , a large number of the reported transchromosomal interactions have been characterised in cells of the immune system . For example , in both mouse and human T cells the insulin like growth factor 2 ( Igf2 ) locus was reported to interact with a number of loci on different chromosomes [8 , 9] . Also in T cells , a regulatory region on mouse chromosome 11 ( the T helper 2 locus control region; LCR ) was suggested to interact with loci encoding the cytokine interferon gamma ( Ifng ) on chromosome 10 [10] and interleukin 17 ( IL-17 ) on chromosome 1 [11] . Perturbation of these interactions was associated with altered expression of Ifng and IL-17 , respectively . In mouse B cell progenitors , the interaction between the immunoglobulin heavy chain ( Igh ) locus on chromosome 12 and the immunoglobulin light chain ( Igk ) locus on chromosome 6 was important for the rearrangement of the heavy chain locus [12] . These transchromosomal interactions were all identified by either chromatin conformation capture , in which crosslinking , dilution of a ligation reaction and PCR are used to deduce the relative physical proximity of two loci in three-dimensions , or DNA FISH in which microscopy and labelled probes are used to locate loci within individual nuclei , or both . These techniques are targeted approaches . Here we aimed to use an unbiased , genome-wide approach to identify novel gene regulatory transchromosomal interactions in three distinct mouse and human immune cell populations . Unexpectedly , we found very few interactions between chromosomes , and none were gene regulatory or conserved . Overall , our findings question the existence of stable , gene-regulatory transchromosomal interactions underlying immune cell identity .
To elucidate novel transchromosomal interactions , we generated in situ HiC libraries from both mouse and human B cells and CD4+ and CD8+ T cells of the immune system ( S1A and S1B Fig ) . The resulting ~200 million paired-end reads were then mapped to the appropriate genome , filtered for artefacts , such as dangling ends and self-circling reads , and counted into 50kb bins with the diffHic software package [13] . DNA-DNA interactions were detected by comparing the interaction intensity in each bin to those surrounding it to determine significant interactions relative to background [14] . Using this pipeline we detected hundreds of interactions between chromosomes in each cell population ( S1 Table ) . Furthermore , our data and publicly available promoter capture HiC data [15] validated numerous previously reported interactions within chromosomes ( Fig 1A , S1C–S1E Fig ) . These include lineage specific interactions [16–18] and others seen in multiple cell lineages [19 , 20] . Consistent with previous literature [21] , transchromosomal interactions are enriched in gene-rich , centrally located chromosomes ( Fig 1B , S2A Fig ) . However , closer examination of these interactions reveals that a high percentage ( 74–90% in mouse and 82–94% in human ) contain regions recommended to be removed , or ‘blacklisted’ , from analyses due to their high or low mappability , repeated nature , location within telomeres or centromeres , among others [22 , 23] . After application of blacklisting the majority of transchromosomal interactions are removed ( Fig 1B and 1C , S2 Table ) . This is in stark contrast to intrachromosomal interactions , of which less than 3% contain blacklisted regions ( Fig 1C ) . The majority of transchromosomal interactions remaining after blacklisting linked regions close to telomeres ( Fig 1D and 1E , S2B Fig ) or centromeres ( Fig 1E , S2C Fig ) . Thus it appears that the majority of the transchromosomal interactions detected in mammalian immune cells may be a consequence of telomeric and centromeric clustering [24] . Additional experiments would be required to characterize the true specificity and possible functionality of these interactions . Importantly , the detection of these interactions confirms that in situ HiC is able to detect interactions between chromosomes . To determine if any of the detected transchromosomal interactions , whether associated with telomeres or centromeres or not , have a gene regulatory function , we examined the relationship between lineage-specific transchromsomal interactions ( those found in only one of the cell populations ) ( S2 Table ) and expression of gene associated with these interactions [25] . In the mouse , we found that the 15 lineage-specific transchromosomal interactions ( 3 B cell , 8 CD8+ T cell and 4 CD4+ T cell ) overlap only 3 genes ( Cct4 , Lars2 , Hjurp ) expressed ( >5 RPKM ) in any of the three lineages and none of these was expressed specifically , or differentially , in the lineage exhibiting the lineage-specific transchromosomal interaction . Similarly , in humans , we found that none of the 38 lineage-specific transchromosomal interactions ( 18 B cell , 5 CD8+ T cell and 15 CD4+ T cell ) ( S2 Table ) associated with any protein-coding genes differentially expressed ( >5 RPKM ) in the lineage exhibiting the lineage-specific transchromosomal interaction . This suggests that none of the detected lineage-specific transchromosomal interactions perform a gene regulatory function in mouse or human B or T cells . It has been suggested that if transchromosomal interactions were functionally important they would be evolutionarily conserved [2] . Therefore , we examined the handful of genes and genomic regions associated with all transchromosomal interactions in mouse and human B and T cells . We found that none of the lineage-specific transchromosomal interactions link orthologous regions in mouse and human . As we were able to detect transchromosomal interactions , but none of a gene regulatory nature , we examined regions previously reported to be involved in regulatory interactions between chromosomes . We examined our CD4+ T cell data for interactions between the previously mentioned LCR region on mouse chromosome 11 and loci encoding the cytokine interferon gamma ( Ifng ) on chromosome 10 [10] and interleukin 17 ( IL-17 ) on chromosome 1 [11] . Curiously , no interactions were detected between the LCR and Ifng or IL17 loci in mouse CD4+ T cells ( Fig 2A–2D ) . Intrachromosomal interactions at the loci exhibited three-dimensional structure as expected ( Fig 2E and 2F ) , indicating that the in situ HiC data was of sufficient quality . Similarly , in human CD4+ T cells we found no interactions between the LCR and Ifng or IL17 loci ( Fig 2G–2J ) . Again , intrachromosomal interactions at the loci were as expected ( Fig 2K and 2L ) . These analyses were repeated with raw data ( no artefact removal step during data processing ) to ensure that reads potentially indicating interactions had not been filtered out . No interactions between the LCR and Ifng or IL17 loci in either mouse or human were detected in the raw , unfiltered data ( S3A–S3D Fig ) . To determine if the depth of sequencing of our in situ HiC had inhibited detection of the previously reported transchromosomal interactions , we examined publicly available promoter capture HiC data from human CD4+ T cells [15] . The LCR-Ifng or IL17 interactions were also undetectable in this extremely high-resolution data ( S3E and S3F Fig ) . We then attempted to detect another previously reported transchromosomal interaction suggested to occur between the immunoglobulin heavy ( Igh ) and light chain ( Igk ) loci in mouse B cell progenitors [12] . Our transchromosomal interaction detection pipeline was applied to in situ HiC libraries generated from two B cell progenitors: pro-B cells and immature B cells . Curiously again , using our unbiased , genome-wide approach , we found no interactions between Igh on chromosome 12 and Igk on chromosome 6 in either B cell progenitor population ( S3G and S3H Fig ) . Intrachromosomal interactions at both loci were as expected ( S3G and S3H Fig ) . In summary , using an unbiased , genome-wide approach we detect neither novel , nor previously reported , gene-regulatory transchromosomal interactions in three dominant mouse and human immune cell populations .
For many years DNA Fluorescent In situ Hybridisation ( FISH ) [26] and chromatin conformation capture ( 3C ) [27] were the dominant technologies used to examine chromosomal interactions , whether in cis or trans . However , incongruous results from FISH versus 3C within cell types , or in fact from the same technique between studies , has been a persistent issue when examining transchromosomal interactions . For example , two studies reporting transchromosomal interactions between Ifg2 and loci on other chromosomes in mouse T cells found no common interactions [8 , 9] , while studies of interactions in human T cells found contradictory evidence of interaction [28–30] . To address this vexed issue , we used the in situ HiC technique to search for transchromosomal interactions across two species and three distinct cell populations . With this unbiased , genome-wide approach , we were unable to detect any conserved , gene regulatory transchromosomal interactions . While our findings are clear and suggest gene regulatory transchromosomal interactions do not function in the mammalian immune system , it is not possible to be totally conclusive about a negative finding . For example , we cannot rule out gene regulatory interactions that are weak , transient , present in highly repetitive regions or in regions without MboI restriction sites . Furthermore , because we used only male-derived DNA we could not examine interactions reported to occur between X chromosomes during X chromosome inactivation [31] . Although we were unable to detect gene regulatory transchromosomal interactions , we do detect large numbers of interactions between sub-centromeric and sub-telomeric regions in all cell populations . In addition to demonstrating that in situ HiC is able to detect physiologically relevant transchromosomal interactions , these interactions may provide a genomic window into three-dimensional nuclear architecture . For example , changes in interactions in particular centromere associated clusters detected by in situ HiC might betray changes in nuclear architecture , such as relocating nucleoli , around which some centromeres are known cluster [32] . These kinds of analyses may also provide insight into previously observed transchromosomal interactions thought to be a consequence of nuclear reorganization [30] . Physiologically relevant transchromosomal interactions that are transient and/or weak may not be detectable by in situ HiC . However , this does not explain the absence of the interactions between LCR and Ifng or IL17 loci in T cells , or the immunoglobulin loci in B cell progenitors , as these interactions are reported to occur in 40–50% of cells [10 , 12] and the interactions are reported to be as strong as intrachromosomal interactions [10] . Differences between results presented here and those previously reported are likely due to differences in methodology . Previous studies relied on targeted amplification-dependent chromatin capture techniques and/or DNA FISH . It is increasingly clear that even with the appropriate controls [27] , a minute amplification bias in a targeting probe combined with the large number of amplification steps required for 3C-based approaches can lead to false positives [2] . Furthermore , it has been suggested that up to half of the ligation events in chromatin capture techniques that rely on dilution of the ligation reaction to deduce proximity , such as 3C or ‘dilution’ HiC , link regions of DNA that were not truly associated in the intact nucleus [33] . Although DNA-FISH does not exhibit amplification bias , it does suffer from the resolution limitations of light microscopy ( 250–500nm ) . Thus it may be that the Igh and Igk loci in B cell progenitors , or other FISH-demonstrated interactions , frequently lie within hundreds of nanometres of each other , but are nevertheless not sufficiently proximate to be regulatory or chemically crosslinked and thus detected by in situ HiC . In summary , the unbiased , genome-wide in situ HiC approach found no evidence for the existence of conserved , lineage-specific , gene regulatory transchromosomal interactions in mammalian immune cells , bringing into question the existence of stable , gene-regulatory transchromosomal interactions underlying immune cell identity .
Animal experiments were approved by The Walter and Eliza Hall Institute’s animal research ethics committee ( No . 2016 . 003 and 2018 . 004 ) and performed under the Australian code for the care and use of animals for scientific purposes . Approval for sourcing of human material and experimentation was obtained from The Walter and Eliza Hall Institute’s human research ethics committee ( HREC No . 88 . 03 ) . Results were analysed without blinding of grouping . Anonymized human samples were obtained from a volunteer blood donor registry ( http://www . blooddonorregistry . org/home/ ) , which requires donors give consent to their donation being used for research purposes , thus no specific consent was required , or acquired , for the work . All animal experiments were performed using C57B/6 male mice at age 6–8 w . Mice were maintained at The Walter and Eliza Hall Institute Animal Facility under specific pathogen–free conditions . Males were randomly chosen from the relevant pool . Murine CD4+ T cells ( TCRβ+ CD4+ CD8- CD44- CD62L+ ) , CD8+ T cells ( TCRβ+ CD4- CD8+ CD44- CD62L+ ) , immature B cells ( TCRβ- CD19+ B220+ IgM+ IgD- ) and B cells ( TCRβ- CD19+ B220+ IgM+ IgD+ ) were obtained from mechanically homogenized spleens . Pro-B cells were expanded from B220+ cells from bone marrow on an OP9 cell layer for 7 days in MEM+Glutamax ( Gibco ) supplemented with 10mM HEPES , 1mM Sodium Pyruvate , 1x non-essential amino acids ( Sigma ) and 50μM β-mercaptoethanol ( Sigma ) . At day 7 the IgM- fraction was isolated using immunomagnetic depletion , following manufacturer’s instructions . Cryopreserved human peripheral blood mononuclear cells were thawed and stained with antibodies against human αβ TCR , CD4 , CD45RA , CD25 , CD14 , CD16 , HLA-DR , and CD19 . CD4+ T cells ( CD14− CD16− TCRαβ+ CD4+ CD45RA+ CD25− ) , CD8+ T cells ( CD14− CD16− TCRαβ+ CD4- CD45RA+ CD25- ) , and B cells ( TCRαβ- HLA-DR+ CD19+ ) and isolated by flow cytometric sorting . Flow cytometric analyses were performed on BD FACSCanto with sorting on the BD Aria or Influx ( BD Bioscience ) . Antibodies were purchased from BD Bioscience or eBioscience ( S3 Table ) . HiC was performed as previously published [14] . Primary immune cell libraries for both human and mouse were generated in biological duplicate . Libraries were sequenced on an Illumina NextSeq 500 to produce 75bp paired-end reads . Between 160 million and 375 million valid read pairs were generated per sample ( S4 Table ) . Hi-C sequencing data for mouse pro-B cells and immature B cells was obtained from gene expression omnibus accession number GSE99163 . RNA was isolated using the miRNeasy Micro Kit ( QIAGEN ) following manufacturer’s instructions . All samples were acquired from two male human donors . Each donor provided one sample per biological condition , giving each condition two replicates . RNA libraries were prepared using an Illumina's TruSeq Total Stranded RNA kit with Ribo-zero Gold ( Illumina ) according to the manufacturer’s instructions . The rRNA-depleted RNA was purified , and reverse transcribed using SuperScript II reverse transcriptase ( Invitrogen ) . Total RNA-Seq libraries were sequenced on the Illumina NextSeq 500 generating 80 base pair paired end reads . The reads were aligned to the human genome ( GRCh38/hg38 ) using the Rsubread aligner [34] . The number of fragments overlapping Ensembl genes were summarized using featureCounts [35] . Differential expression analyses were undertaken using the edgeR [36] and limma [37] software packages . Any gene which did not achieve a count per million mapped reads ( CPM ) of 0 . 1 in at least 2 samples was deemed to be unexpressed and subsequently filtered from the analysis . Compositional differences between libraries were normalized using the trimmed mean of log expression ratios ( TMM ) [38] method . Counts were transformed to log2-CPM with associated precision weights using voom [39] . Differential expression was assessed using linear models and robust empirical Bayes moderated t-statistics [40] . P-values were adjusted to control the false discovery rate ( FDR ) below 5% using the Benjamini and Hochberg method . To increase precision , the linear model incorporated a correction for a donor batch effect . Promoter capture Hi-C sequencing data for human naive CD4+ T cells was obtained from EGA ( https://www . ebi . ac . uk/ega ) accession number EGAS00001001911 . The read processing and alignment was with the same methods as the Hi-C data except , as the restriction enzyme HindIII was used in the assay , the reads were split with a ligation signature of AAGCTAGCTT . Plaid plots were constructed using the plotPlaid function from the diffHic package . The range of colour intensities in each plot was scaled according to the library size of the sample , to facilitate comparisons between plots from different samples . Heatmaps of the loops between chromosomes where generated using the R package gplots with the function heatmap . 2 . Circos plots were generated with the R package RCircos [44] . | It is a widely held belief that , in the darkness of the nucleus , strands of DNA that make up different chromosomes frequently meet to ‘kiss’ . These kisses , or transchromosomal interactions , are thought to be important for the expression of genes and thus cell development . Here , we aimed to identify novel transchromosomal interactions in mouse and human immune cells by high-resolution genome-wide chromosome conformation capture methods . Although we readily identified stable interactions within chromosomes and also between centromeres and telomeres on different chromosomes , surprisingly we identified no gene regulatory transchromosomal interactions in either mouse or human cells , including those previously described . Overall our findings suggest that stable transchromosomal interactions that regulate gene expression are not present in mammalian immune cells and that chromosomes are doing far less kissing than was previously believed . | [
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] | 2018 | Genome-wide analysis reveals no evidence of trans chromosomal regulation of mammalian immune development |
Staphylococcus aureus asymptomatically colonises the anterior nares , but the host and bacterial factors that facilitate colonisation remain incompletely understood . The S . aureus surface protein ClfB has been shown to mediate adherence to squamous epithelial cells in vitro and to promote nasal colonisation in both mice and humans . Here , we demonstrate that the squamous epithelial cell envelope protein loricrin represents the major target ligand for ClfB during S . aureus nasal colonisation . In vitro adherence assays indicated that bacteria expressing ClfB bound loricrin most likely by the “dock , lock and latch” mechanism . Using surface plasmon resonance we showed that ClfB bound cytokeratin 10 ( K10 ) , a structural protein of squamous epithelial cells , and loricrin with similar affinities that were in the low µM range . Loricrin is composed of three separate regions comprising GS-rich omega loops . Each loop was expressed separately and found to bind ClfB , However region 2 bound with highest affinity . To investigate if the specific interaction between ClfB and loricrin was sufficient to facilitate S . aureus nasal colonisation , we compared the ability of ClfB+ S . aureus to colonise the nares of wild-type and loricrin-deficient ( Lor−/− ) mice . In the absence of loricrin , S . aureus nasal colonisation was significantly impaired . Furthermore a ClfB− mutant colonised wild-type mice less efficiently than the parental ClfB+ strain whereas a similar lower level of colonisation was observed with both the parental strain and the ClfB− mutant in the Lor−/− mice . The ability of ClfB to support nasal colonisation by binding loricrin in vivo was confirmed by the ability of Lactococcus lactis expressing ClfB to be retained in the nares of WT mice but not in the Lor−/− mice . By combining in vitro biochemical analysis with animal model studies we have identified the squamous epithelial cell envelope protein loricrin as the target ligand for ClfB during nasal colonisation by S . aureus .
Staphylococcus aureus is a commensal of humans that permanently colonises the anterior nares of about 20% of the population with the remainder being colonised transiently [1] . The bacterium is an opportunistic pathogen that can cause a variety of disorders ranging in severity from superficial skin lesions to more serious invasive and life-threatening infections such as endocarditis and septicaemia . Nasal carriage is an established risk factor for S . aureus infections both in the hospital and in the community with individuals often being infected with the strain that they carry [2]–[4] . Nasal carriage can be transiently eradicated by topical administration of the antibiotic mupirocin but this is compromised by the development of resistance [5] . Alternative strategies for reducing nasal carriage are required which will involve a detailed understanding of the molecular basis of interactions between the host and the bacterium that underlie the process . Host factors that determine nasal colonisation are incompletely understood . Polymorphisms in the genes encoding the glucocorticoid receptor , C-reactive proteins , interleukin-4 and complement inhibitor proteins have been associated with persistent nasal carriage [6]–[8] . In addition , reduced expression of antimicrobial peptides in nasal secretions is associated with nasal carriage [9] . The normal flora can also influence the ability of S . aureus to colonise the nares [10]–[11] . A fundamental feature that likely dictates the interaction between S . aureus and the host during nasal colonisation is adhesion of bacteria to nasal epithelial surfaces , a process which depends upon specific interactions between adhesins on the bacterial cell surface and their target ligands in the epithelium . The primary habitat of S . aureus in colonised individuals is the moist squamous epithelium in the anterior nares [12]–[13] . The outer part of this epithelial surface , known as the stratum corneum , consists of layers of dead keratinized cells called squames . Keratinocytes in the basal layer are continuously dividing . As cells migrate toward the stratum corneum they differentiate into squames , a process which involves expression of proteins that will eventually form the cornified envelope ( CE ) that replaces the cytoplasmic membrane in these cells . The CE is composed of proteins such as loricrin , involucrin and small proline-rich proteins that are extensively cross-linked , in addition to ceramides that are attached both covalently and non-covalently [14]–[16] . The extensive cross-linking in addition to conformational properties makes the CE a highly resilient structure that plays an important role in barrier function [17] . Loricrin is the most abundant protein of the CE forming about 80% of the protein mass [18] . Cytokeratins 1 and 10 are present on the interior of squames and are exposed on their surface [19] . Despite the importance of the CE in barrier function and the fact that loricrin is highly abundant in the CE of squames , it is surprisingly non-essential . A loricrin knock-out mouse has been generated and while these mice demonstrate a delay in the formation of the skin barrier in embryonic development , by days 4–5 after birth the skin phenotype disappears . Loricrin-deficient ( Lor−/− ) mice breed normally and appear phenotypically indistinct from wild-type litter mates [20] . The absence of a more severe phenotype in these mice is due to the existence of a compensatory loricrin back-up system . Increased expression of small proline rich proteins has been observed in the CE of Lor−/− mice . Interestingly expression of other CE components such as involucrin , filaggrin and Cytokeratin 10 ( K10 ) were similar in Lor−/− and wild-type mice [21] . Two S . aureus surface proteins , clumping factor B ( ClfB ) and iron regulated surface determinant A ( IsdA ) , have been strongly implicated in nasal colonisation . By comparing wild-type strains with isogenic mutants lacking the proteins , both ClfB and IsdA were shown to promote adhesion to squames in vitro [19] , [22] in addition to promoting colonisation of the nares of rodents [22]–[23] and in the case of ClfB , humans [24] . S . aureus surface protein G ( SasG ) and the serine-aspartate repeat proteins SdrC and SdrD also promote bacterial adhesion to squames in vitro [25] but their roles , if any , in vivo have not yet been tested . ClfB is a member of a family of proteins that are structurally related to clumping factor A ( ClfA ) , the archetypal fibrinogen ( Fg ) binding protein of S . aureus . It is attached covalently to peptidoglycan in the cell wall by sortase . The C-terminal cell wall anchorage domain comprises an LPXTG sortase cleavage motif , a hydrophobic membrane-spanning region followed by a stretch of positively charged residues ( Figure S1A ) [26] . The N-terminal 542 residues comprise the ligand-binding A domain followed by a flexible stalk formed by repeats of the dipeptide serine-aspartate . The A domain is composed of three separately folded subdomains N1 , N2 and N3 , the last two of which are the minimum required for binding to ligands Fg and K10 [27]–[29] . The binding site for ClfB in Fg is a single repeat ( number 5 ) in the αC region of the α-chain [30] . In addition the protein binds to the C-terminus of K10 which is composed of quasi repeats of the amino acid sequence Y[GS]nY [19] . This type of sequence can form omega loops where the Y residues bind to each other by hydrophobic interactions resulting in the GS sequences protruding as loops forming rosette-like structures [31]–[32] . One such omega loop sequence ( YGGGSSGGGSSSGGGY ) was shown to bind to recombinant ClfB A domain with a KD in the low micromolar range [33] . Recently the X-ray crystal structures of both the apo form of ClfB N2N3 and the protein in complex with peptides mimicking the binding domains in Fg and K10 were solved [28]–[29] . These studies demonstrated that the two seemingly disparate proteins contain related peptides that can bind in a hydrophobic trench located between the separately folded N2 and N3 domains . In each study , similar consensus motifs ( GSSGXG , [28] , GSSGG/S/TGXXG , [29] ) were defined for Fg and K10 with residues in the trench forming bonds with each of the peptide residues [28] . These studies confirmed earlier predictions that ClfB bound its ligands by the “dock , lock and latch” mechanism first defined for the Fg binding proteins SdrG and ClfA [34]–[35] . After the peptide inserts into the ligand-binding trench a C-terminal extension of domain N3 undergoes a conformational change , covers the inserted peptide and binds residues in N2 by β-strand complementation which locks the peptide in place . Loricrin is the major structural protein of the CE of squames [16] , [36]–[37] . Human loricrin comprises sequences capable of forming GS-rich omega loops similar to those in K10 . Located between the loop domains and also at the N- and C-termini are stretches rich in glutamate and cysteine residues that form covalent links to other proteins in the CE by transglutamination and disulfide bond formation [36] , [38] . Polymorphisms can occur in the loricrin gene in humans that result in loss of four residues within loop region 2 [39] . The GS-rich omega loop composition of loricrin and its abundance in the CE of squames suggested to us that it could serve as an important ligand for ClfB . Previous studies have identified K10 as a ligand for ClfB in vitro . It has been assumed that K10 is the element on squames to which ClfB binds and that this interaction is an important determinant of nasal colonisation . However , this remains only an association and has not been proven unambiguously . Herein we identify a novel interaction between S . aureus ClfB and loricrin that is critically required for S . aureus nasal colonisation . S . aureus was shown to adhere to immobilized human and murine loricrin in a ClfB-dependent fashion . The affinity of recombinant ClfB for human and murine loricrin was comparable to K10 and Fg by surface plasmon resonance . Wild-type and Lor−/− mice were inoculated intra-nasally with ClfB-expressing bacteria to investigate the role of loricrin in vivo and we demonstrate that a specific interaction between ClfB and loricrin occurs during the colonisation process . We conclude that loricrin is the major ligand for ClfB in the nares of mice .
ClfB is known to bind to the C-terminal “tail” region of human and murine K10 which is composed of quasi repeats of Y[GS]nY , sequences which can form omega loops [31] , [33] . Loricrin , the major component of the CE of squames , is almost entirely composed of similar structures . In order to investigate if ClfB binds loricrin and to dissect the binding domains within the protein , DNA encoding human and murine loricrin was synthesized and cloned into the expression vector pGEX , so that N-terminally GST-tagged proteins could be expressed and purified . Initially a streptomycin resistant ( Smr ) mutant of S . aureus Newman and ClfB-deficient mutant Newman Smr ΔclfB ( hereafter referred to as Newman and Newman ΔclfB ) were tested for adhesion to immobilized recombinant human and murine loricrin ( GST-human loricrin1–315 , ( Hlor ) ; GST-murine loricrin1–480 , ( MLor ) ) and to human and murine K10 peptides ( GST-human K10544–563 , ( HK10 ) ; GST-murine K10454–570 , ( MK10 ) ) . S . aureus Newman adhered avidly to all ligands ( Figure 1 ) . S . aureus Newman ΔclfB did not adhere detectably to HLor and MLor and exhibited significantly reduced adherence to HK10 and MK10 ( Figure 1 ) . This is in agreement with a previous observation that Newman appears to have a second , albeit less potent , K10 adhesin [33] . Complementation of Newman ΔclfB with pCU1:clfB restored adherence to HLor ( Figure S2 ) . These data show that loricrin is a ligand for ClfB . To establish if ClfB alone can promote bacterial adhesion to immobilized loricrin , adhesion assays were performed using L . lactis MG1363 carrying a plasmid that expressed ClfB ( pKS80:clfB ) [40] and compared to L . lactis carrying the empty vector . L . lactis ( pKS80:clfB ) adhered strongly to each of the proteins indicating that ClfB alone is sufficient for promoting bacterial adhesion to the omega loop-containing ligands ( Figure 1 ) . In order to demonstrate a direct interaction between ClfB and loricrin and to measure the affinity of binding , surface plasmon resonance ( SPR ) was employed . HLor was captured on the surface of a sensor chip that had been coated with anti-GST IgG . Previous studies have indicated that the minimum Fg and K10 binding region of ClfB comprises the N2 and N3 subdomains [27]–[28] ( Figure S1B ) . Recombinant ClfB N2N3 ( rClfB201–542 ) was expressed with an N-terminal hexahistidine tag . Increasing concentrations of rClfB201–542 were passed over the surface of the HLor-coated chip . rClfB201–542 bound to HLor in a concentration-dependent manner indicating that the loricrin binding site is located within the ClfB N2N3 subdomains ( Figure 2A ) . From analysis of the equilibrium binding data the dissociation constant ( KD ) of the interaction between rClfB201–542 and loricrin was determined to be 4 . 33±1 . 1 µM . Similar experiments were carried out with MLor ( Figure 2B ) which had a slightly lower affinity for rClfB201–542 ( KD = 15 . 66±3 . 4 µM ) . The affinity of rClfB201–542 for GST-tagged proteins corresponding to high affinity binding sites in human ( Figure 2C ) and murine ( Figure 2D ) K10 was determined by the same method . rClfB201–542 bound HK10 and MK10 with a similar affinity as it did to HLor and MLor , respectively ( Table 1 ) . The affinity of rClfB201–542 for a GST-tagged protein corresponding to repeat domain 5 of the αC region of Fg ( GST-Fgα316–367 ) was also measured . The KD measured for GST-Fgα316–367 was 5 . 25 µM , in the same low µM range as the other ligands . This indicates that ClfB has a similar affinity for loricrin , K10 and the αC-region of Fg . SPR was also used to identify binding sites within human loricrin . Loricrin is composed of three Gly-Ser-rich regions capable of forming omega loops ( Figure S3 ) [37] . Regions 2 and 3 are separated by short stretches rich in glutamine , where transglutamination reactions occur [36] . The N- and C-termini of the protein also comprise short Glu-rich stretches . Region 1 contains the largest omega loop region , which is interrupted by a single lysine ( K88 ) . In order to determine if individual loop regions could bind ClfB , DNA was synthesized corresponding to the individual regions along with flanking sequences ( Figure S3A ) . As with full length loricrin and the K10 sequences , DNA was cloned into a pGEX vector allowing expression and purification of GST-tagged proteins . Region 1 was divided at K88 in order to make two separate constructs designated 1A and 1B . Loop region 1A began at residue S1 and terminated at residue K88 . Loop region 1B began at K88 and terminated at S159 . Loop region 2 began at residue G152 and terminated at S230 . One variant of the second loop region ( loop region 2v ) , which corresponds to an allelic variant of the lor gene ( a 12 base pair deletion ) [39] that results in a loop that is 4 residues shorter was also expressed ( G152-S226 ) . Loop region 3 spanned amino acids S216-K315 . The affinity of ClfB N2N3201–542 for loop regions 1A , 1B , 2 , 2v and 3 was measured by SPR . Recombinant ClfB N2N3201–542 bound all GST-tagged loop regions and the KD for each interaction was measured ( Table 1 ) . ClfB bound loop region 2v and 2 with the highest affinity ( KD = 2 . 21±1 . 1 µM and 3 . 31±0 . 81 µM respectively ) . Loop region 1B had the lowest affinity for ClfB ( 34 . 48±2 . 70 µM ) . These data indicate that loricrin contains more than one binding site for ClfB and that the highest affinity binding site is present within loop region 2 . ClfB binds to K10 and Fg peptide ligands using the “dock , lock and latch” mechanism [28]–[29] . In order to determine whether ClfB binds loricrin by this mechanism , adhesion assays were performed using L . lactis NZ9800 carrying a plasmid which expresses a variant of ClfB ( Q235A ) that cannot bind Fg or K10 [30] , [41] . Glu235 makes direct contact with Fg and K10 in the ClfB binding trench and this interaction is crucial for binding to occur by the “dock , lock and latch” mechanism [28] . L . lactis expressing ClfBQ235A ( L . lactis pNZ8037:clfBQ235A ) did not adhere detectably to HLor or HK10 in comparison to L . lactis expressing wild-type ClfB ( L . lactis pNZ8037:clfB ) ( Figure 3A ) . When induced with the same concentration of nisin , the expression levels of ClfB and ClfBQ235A on the surface of L . lactis were equal ( data not shown ) . This suggests that ClfB may bind HLor using the “dock , lock and latch” mechanism . Recombinant HLor loop region L2v and HK10 were used in inhibition studies in order to provide further evidence that ClfB uses the “dock , lock and latch” mechanism to bind loricrin . Pre-incubation of S . aureus with HK10 almost completely abolished its ability to bind immobilised loricrin ( Figure 3B ) . Similarly , pre-incubation of recombinant ClfB with HK10 ( or L2v ) inhibited its ability to bind immobilised loricrin ( Figure 3C ) . These data indicate that HLor and HK10 bind to the same or overlapping sites in ClfB , providing strong evidence that loricrin is also bound using the “dock , lock and latch” mechanism . ClfB has previously been shown to facilitate adherence of S . aureus to squames [19] , [22] . In order to determine whether loricrin plays a major role in ClfB-mediated S . aureus adherence to squames , S . aureus was pre-incubated with recombinant loricrin loop region L2v and was then tested for adherence to nasal squamous epithelial cells . Pre-incubation of S . aureus with L2v caused a significant ( p = 0 . 0072 ) reduction in adherence to squames compared to S . aureus pre-incubated with GST alone ( Figure 4A ) . This illustrates that an interaction with loricrin is necessary for efficient S . aureus adherence to squames . Consistent with previously published studies [19] , [22] , adherence of S . aureus ΔclfB to squames was also significantly reduced ( p = 0 . 002 ) . However , there was no further reduction in adherence when S . aureus ΔclfB was pre-incubated with L2v compared to GST alone , indicating ClfB is the only S . aureus factor binding to loricrin on squames ( Figure 4A ) . IsdA has been shown to play a role in S . aureus adherence to squames under iron-limited conditions , and is also known to facilitate adherence to recombinant human loricrin in vitro [22] , [42] . To establish if an interaction between IsdA and loricrin occurs during adherence to squames , squamous cell adherence assays were repeated using S . aureus grown under iron-limited conditions . Consistent with our previous results ( Figure 4A ) , we saw a similar significant reduction in adherence to squames by S . aureus ΔclfB and by wild-type S . aureus pre-incubated with L2v , with no further reduction in adherence when S . aureus ΔclfB was pre-incubated with L2v compared to GST alone ( Figure S4 ) . These data confirm that even under conditions in which IsdA is maximally expressed L2v is only inhibiting the interaction between ClfB and squames . To confirm that IsdA plays a role in S . aureus adherence to human squames we studied a ΔisdA mutant of Newman , with bacteria grown in iron-limited conditions . There was a significant reduction ( p = 0 . 0045 ) in adherence of S . aureus ΔisdA to squames when compared to a wild-type strain ( Figure 4B ) . Pre-incubation of S . aureus ΔisdA with L2v significantly ( p = 0 . 0224 ) impaired adherence to squames compared to pre-incubation with GST alone , most likely by inhibiting the interaction between ClfB and its ligand ( s ) . A double mutant ( S . aureus Newman ΔclfBΔisdA ) had a similar impaired ability to adhere to squames , to that observed when S . aureus ΔisdA was pre-incubated with L2v ( p = 0 . 0322 ) . Pre-incubation with L2v did not cause any further reduction in adherence of S . aureus ΔclfBΔisdA ( Figure 4B ) . Taken together , these results confirm that IsdA contributes significantly to adherence of S . aureus to human squames but that this does not involve an interaction between IsdA and loricrin loop region L2v . Having identified loricrin as a ligand for ClfB in vitro , the importance of loricrin in S . aureus nasal colonisation was then investigated . Nasal colonisation was established in specific pathogen-free wild-type FVB ( WT ) mice and loricrin-deficient mice ( Lor−/− ) on the same background . To establish colonisation , mice were inoculated intra-nasally with S . aureus Newman Smr ( 2×108 CFU ) ( hereafter called Newman ) . Mice were administered streptomycin in their drinking water 24 hours prior to inoculation and for the duration of the experiment in order to reduce interference from the commensal bacterial flora . At specific time points after inoculation , nasal tissue was excised and homogenized and the number of CFU per nose enumerated . On day 1 , WT and Lor−/− mice had similar levels of Newman in their noses . WT mice remained stably colonised with Newman over a period of 10 days with the number of bacteria actually increasing slightly during this period . This suggested that the bacteria had adhered to the nasal epithelium and were able to proliferate . In contrast , there was a significant reduction in the levels of Newman present in the noses of Lor−/− mice compared to WT mice on day 3 ( p = 0 . 0355 ) and day 10 ( p = 0 . 0343 ) indicating that Lor−/− mice were unable to retain S . aureus in their noses ( Figure 5 , Figure S5A ) . By day 21 , Newman was completely cleared from the noses of Lor−/− mice , while low numbers of bacteria were still detectable in the WT mice . These results suggest that the absence of loricrin does not impact initial attachment of S . aureus to the nasal tissue , but the protein appears to be essential for the maintenance of the bacterium in the nose and for sustained colonisation up to a period of 21 days . No bacteria were detectable in the blood of either WT or Lor−/− mice ( data not shown ) and similar low levels of bacteria were detectable in the lungs of WT and Lor−/− at 3 days ( median = 1 CFU ) and 10 days ( median<10 CFU ) post inoculation , indicating that minimal systemic dissemination of the bacteria occurred . To confirm the role played by loricrin in mediating nasal colonisation by S . aureus , we investigated the ability of recombinant loricrin to inhibit colonisation in WT mice . Mice were inoculated intra-nasally with S . aureus Newman in combination with recombinant L2v or GST on day 1 and day 2 post inoculation . Nasal colonisation with Newman was significantly decreased on day 3 in the presence of recombinant loricrin but not GST ( Figure S5B ) . To investigate if the loricrin defect also affected invasive infection , groups of WT and Lor−/− mice were challenged by intra-peritoneal injection with S . aureus Newman ( 5×108 CFU ) . On day 2-post infection there were no significant differences in the levels of systemic bacterial infection between the WT and Lor−/− mice ( Table 2 ) . These data suggest that the interaction that is occurring between S . aureus and loricrin is specific to the nasal passage . In order to investigate any variability in expression of loricrin and K10 that occurred during nasal colonisation with S . aureus , loricrin and keratin expression in the noses of FVB and Lor−/− mice was compared in the absence of S . aureus nasal colonisation and on day 10 post colonisation . Nasal tissue was excised from WT and Lor−/− mice . Proteins were solubilised from nasal tissue homogenates , separated by SDS-PAGE and Western immunoblotting performed , probing with loricrin-specific antibodies . A ∼56 kDa band corresponding to loricrin was seen in the nasal tissue from WT but not Lor−/− mice ( Figure 6 ) . Consistent with previously published data [20] , there was no detectable difference in K10 expression in Lor−/− mice when compared to WT mice , either in the absence or presence of S . aureus nasal colonisation ( Figure 6 ) . Similarly the levels of loricrin expression did not vary significantly in colonised or non-colonised animals . We did observe some variation in the levels of loricrin in the nasal tissue of individual mice , however this variation did not correlate with the variability seen in S . aureus colonisation levels in these mice ( data not shown ) . To investigate further the importance of ClfB in mediating the interaction between S . aureus and loricrin during colonisation , we developed a novel model of nasal colonisation using L . lactis expressing ClfB . Groups of WT and Lor−/− mice were inoculated intra-nasally with 2×1011 CFU L . lactis ( pKS80:clfB ) or L . lactis ( pKS80 ) as a control . After 24 hours , significant levels of ClfB-expressing L . lactis could be recovered from the noses of WT mice ( Figure 7A ) , while there was a ∼80% reduction in the levels of ClfB-expressing L . lactis colonizing the noses of Lor−/− mice . The majority of mice did not retain the control strain L . lactis ( pKS80 ) in their noses ( >5 CFU ) . These results demonstrate that the interaction between ClfB and loricrin is sufficient to facilitate nasal colonisation . L . lactis was not detected in the lungs of either mouse strain ( data not shown ) . To confirm the importance of the interaction between ClfB and loricrin in S . aureus nasal colonisation , groups of WT and Lor−/− mice were inoculated intra-nasally with Newman or Newman ΔclfB . After 10 days , the nasal bacterial burden was quantified . There was a significant reduction ( p = 0 . 015 ) in colonisation of WT mice by Newman ΔclfB compared to the parental strain ( Figure 7B ) , confirming the role played by ClfB in nasal colonisation . In contrast , there was no significant difference between colonisation with the parental Newman strain and Newman ΔclfB in the Lor−/− mice . By day 10 both Newman and Newman ΔclfB were almost completely cleared from the nares of Lor−/− mice . To confirm the importance of the ClfB-loricrin interaction we generated a clfB mutant of strain SH1000 . Similar results were obtained when we performed colonisation experiments using this strain ( Figure S6 ) . From these data we can conclude that loricrin is the primary ligand for ClfB in vivo and is required for the maintenance of S . aureus during nasal colonisation .
It is well established that nasal carriage of S . aureus represents a significant risk factor for subsequent infection with this organism [2]–[4] . Current strategies for decolonising carriers rely on the use of topical treatment with the antibiotic mupirocin to which S . aureus is becoming increasingly resistant [5] , [43] . The development of new therapeutic options for controlling nasal colonisation by this organism requires a deeper appreciation of the molecular interactions that occur between the bacterium and the host at the nasal epithelial surface . In this study , we demonstrate for the first time that nasal colonisation with S . aureus is critically dependent upon an interaction between the squamous epithelial cell cornified envelope protein loricrin and the S . aureus surface protein ClfB . Previous studies have identified an important role for ClfB in S . aureus nasal colonisation [23]–[24] , and have demonstrated that ClfB can promote adhesion to squames in vitro [19] , [33] . Given that the dominant CE protein loricrin is composed of GS-rich omega loops [14]–[16] , we predicted that this protein might be an important target for ClfB binding in vivo during S . aureus nasal colonisation . Using bacterial adherence assays we have demonstrated that ClfB promotes adherence of S . aureus to immobilized loricrin . S . aureus Newman grown to exponential phase in TSB adhered strongly to loricrin whereas bacteria lacking ClfB did not adhere . Consistent with previous findings we demonstrated that ClfB also promotes adherence of S . aureus to cytokeratin 10 . Furthermore , ClfB promoted adherence of L . lactis to immobilized loricrin . Taken together these results indicate that S . aureus adhesion to loricrin is dependent on the expression of ClfB . We used SPR to demonstrate a direct interaction between recombinant ClfB and loricrin and to measure the affinity of binding . The loricrin binding site is located in the N2N3 subdomains of ClfB . ClfB bound to human loricrin with a KD of 4 . 33±1 . 10 µM , which is similar to the affinities for the ClfB-K10 and ClfB-Fg interactions ( 7 . 89±2 . 10 and 5 . 52±1 . 5 µM respectively ) . The KD determined here for rClfB201–542 binding to GST-HK10 by SPR ( 7 . 89±2 . 10 µM ) is similar to the KD previously determined for rClfB binding to His-tagged rMK10454–570 and synthetic HK10 peptides in solution ( isothermal titration calorimetry , 1 . 4 µM , intrinsic tryptophan fluorescence , 1 . 7 and 5 . 4 µM , respectively [33] ) . By subdividing the human loricrin molecule into three major loop regions we demonstrated that binding sites for ClfB exist throughout the protein . However the highest affinity ClfB binding site was localised to loop region 2 . Previous studies on the human loricrin gene have shown that the major loop region designated loop region 2 contains a polymorphism , and can undergo a 12 bp deletion , resulting in a loop region that is 4 amino acids shorter [38]–[39] . We synthesized two size variants of loop region 2 in order to investigate whether this particular polymorphism had an effect on the binding ability of ClfB . The results from SPR analysis showed that the affinities of ClfB for both loop 2 variants are similar . Nevertheless , it is possible that other sequence variants of loricrin may have an effect on the ability of ClfB to bind . The ability of ClfB to recognise murine K10 and loricrin was also tested . ClfB promoted bacterial adherence to MK10 and loricrin in a similar way to the human proteins and rClfB bound to MK10 and loricrin similarly to HK10 and loricrin , albeit with a slightly reduced affinity . There are size and sequence differences between human and murine loricrins , but they have similar amino acid composition and omega loop region organization [37] ( Figure S3 ) . In addition , it has been shown using fluorescence spectrometry and circular dichroism that the structures of recombinant human loricrin and murine loricrin are indistinguishable in solution [36] . We can therefore assume with confidence that the ClfB-loricrin interactions that were characterised in vitro would have in vivo relevance in our murine nasal colonisation model . Previous studies demonstrated that ClfB containing amino-acid substitution Q235A is defective in K10- and Fg-binding by the “dock , lock and latch” mechanism [28] , [30] , [41] . Residue Q235 is located in the ligand binding trench and makes direct contact with the K10 and the Fg peptide [28] . L . lactis expressing ClfBQ235A was unable to adhere to loricrin . Furthermore , pre-incubation of S . aureus cells or recombinant ClfB with HK10 inhibited binding to loricrin , indicating that both ligands recognise similar sites in ClfB . This is consistent with ClfB binding to loricrin by the “dock , lock and latch” mechanism . This is supported by the similarities between the sequences recognised by ClfB in K10 and Fg ( GSSGXG motif ) [28] and the GS-rich regions in loricrin . Solving the crystal structure of ClfB in complex with peptides corresponding to binding sites within loricrin will provide further insight into the mechanism of loricrin binding by ClfB . It is clear that nasal colonisation with S . aureus is a multifaceted process that involves the interaction of several bacterial surface molecules with different host ligands [25] . However , we have shown that a specific interaction between ClfB and loricrin is critically important for the adherence of S . aureus to human squames . In agreement with previous studies , adherence to squames was not completely abolished in the absence of ClfB which is consistent with this being a multifactorial process . We did not observe any reduction in adherence to squames after pre-incubating S . aureus ΔclfB with L2v , indicating that the loop region of loricrin is not bound detectably by other staphylococcal surface proteins under these conditions . Furthermore the in vivo studies demonstrate that in the absence of loricrin , S . aureus nasal colonisation is reduced by approximately 80% confirming the absolute requirement of loricrin for this process . Interestingly however , the absence of loricrin during systemic infection does not appear to impact upon bacterial dissemination . Both WT and Lor−/− mice were initially colonised with S . aureus to the same extent suggesting that the loricrin-ClfB interaction is not required for initial attachment of bacteria to the nasal tissue . However over time the Lor−/− mice were unable to retain S . aureus in their noses compared to the WT animals . A low level of S . aureus colonisation was achieved in the Lor−/− mice , presumably due to the ability of ClfB and/or other S . aureus factors to bind alternative receptors [19] , [33] , [42] . Translating our in vitro observations to the in vivo situation has been difficult . S . aureus is a human commensal and does not normally colonise rodents , so a major challenge for the field is to establish robust and sustained levels of nasal colonisation with S . aureus in rodents . Nasal colonisation of mice is particularly attractive due to the availability of transgenic and knock-out animals which facilitate in-depth investigations into the interaction of the commensal organism with the host . The availability of loricrin-deficient animals has afforded us a unique opportunity to characterise for the first time in vivo , a specific interaction between a host and a bacterial factor that facilitates the process of nasal colonisation . S . aureus nasal colonisation in mice may be influenced by factors such as mouse strain , bacterial strain and bacterial load [23] , [44] . Using an inbred strain of mouse , we achieved a stable level ( ≤103 CFU per nose ) of nasal colonisation for a period of 21 days that were comparable to those previously observed in outbred mouse strains [44] . The specificity of the interaction between ClfB and loricrin in vivo was established using a novel murine nasal colonisation model in which mice were inoculated with the surrogate host L . lactis expressing ClfB . These studies demonstrated that expression of ClfB alone is sufficient to promote nasal colonisation , without any dependence on other staphylococcal factors . The low numbers of bacteria recovered from the noses of these mice and the short duration of colonisation is likely due to the fact that L . lactis is an avirulent and nutritionally fastidious organism [45] that grows optimally at 28–30°C . We observed an 80% decrease in the levels of L . lactis ClfB+ colonisation in Lor−/− mice compared to WT mice confirming that loricrin represents the major binding target for ClfB in the nares . The residual binding of L . lactis ClfB+ likely reflects a minimal interaction of ClfB with other CE proteins such as K10 . A similar model using a K10-deficient mouse [46] would be required to establish definitively the relative contribution of these two CE proteins to colonisation . Although redundancy occurs in S . aureus surface-expressed factors that promote binding to host squames , our data indicate that the interaction between ClfB and loricrin is crucial for S . aureus nasal colonisation . Consistent with previous studies we demonstrated that a ClfB-deficient mutant of S . aureus was significantly impaired in its ability to colonise WT mice compared to the parental S . aureus strain . In contrast , a similar low level of colonisation was achieved when Lor−/− mice were inoculated with either the clfB mutant or the parental strain confirming the specificity of the interaction between ClfB and loricrin in vivo during S . aureus nasal colonisation . It has been assumed that the interaction between ClfB and K10 is crucial for nasal colonisation by S . aureus [19] , [33] . However , our data demonstrates that loricrin is recognised by ClfB in vivo and suggests that K10 may not serve as its major ligand in the nose . IsdA is the only other cell-wall anchored surface protein with a proven role in S . aureus nasal colonisation [22] . In vitro studies have shown that IsdA promotes bacterial adhesion to components of the CE such as loricrin , involucrin and K10 [42] . Consistent with this we demonstrated reduced adherence to human squames by S . aureus ΔisdA . However , our data implies that IsdA does not interact with loricrin loop region 2v on human squames . Pre-incubation of S . aureus ΔisdA but not S . aureus ΔclfBΔisdA with L2v resulted in a reduction in adhesion to squames suggesting that in the absence of IsdA there are other ligands to which loricrin can bind and subsequently inhibit squame binding , whereas in the absence of ClfB this is not the case . A mutant of S . aureus lacking wall teichoic acid ( WTA ) was also shown to be defective in its ability to colonise the nares of cotton rats after only one day suggesting that WTA-mediated attachment may also be important for the initial stages of colonisation [47] . In contrast an IsdA-deficient mutant of S . aureus had reduced colonisation ability over an extended time course [22] similar to the results obtained for a ClfB-deficient mutant in this study and by others [23] . This data suggests that different molecules expressed by S . aureus may have distinct roles to play in facilitating nasal colonisation . Further studies , that are beyond the scope of this paper , are required to determine the nature of their relative contributions to S . aureus nasal colonisation in vivo . The primary finding from our study is that loricrin is the major binding partner for ClfB during S . aureus nasal colonisation . The bacterial adherence data revealed that loricrin is a ligand for S . aureus and that this interaction is facilitated specifically by ClfB . SPR analysis confirmed that ClfB binds the omega loop regions of loricrin and provides new information on the relative affinities that ClfB has for its ligands . Similar to previous rodent studies , our mouse models have proven useful in the characterization of factors involved in nasal colonisation . Our in vivo findings have confirmed that ClfB is one of the primary bacterial adhesins involved in nasal colonisation and have provided further in vivo evidence that ClfB is a crucial promoter and mediator of S . aureus carriage in the nose . Furthermore , through the use of a gene-deficient mouse model we have defined the mechanism by which ClfB interacts with the host , through binding to the CE protein loricrin . We can conclude therefore that loricrin is a major determinant of S . aureus nasal colonisation and represents the most important target for ClfB in the nose .
Experiments on mice were conducted under Irish Department of Health guidelines with ethical approval from the Trinity College Dublin ethics committee . Ethical approval for the use of human squames was obtained from the TCD Faculty of Health Sciences ethics committee . Female FVB mice were obtained from Harlan UK . Loricrin-deficient FVB mice have been previously described [20] and were obtained from Dr . Dennis Roop , University of Colorado Anschutz Medical Centre , Colorado , USA and were bred in-house at Trinity College , Dublin . E . coli strains XL1-Blue ( Stratagene ) and DNA cytosine methyltransferase mutant DC10B [48] were used as hosts for cloning . They were grown with shaking in L broth or on L agar at 37°C . S . aureus Newman strains were grown to exponential or stationary phase with shaking in tryptic soy ( TS ) broth or on TS agar at 37°C . RPMI 1640 medium ( Sigma ) was used to grow bacteria under iron-limitation . S . aureus SH1000 strains were grown on TS agar at 37°C . L . lactis MG1363 ( pKS80 ) [40] , L . lactis MG1363 ( pKS80:clfB ) [40] , L . lactis NZ9800 ( pNZ8037 ) [49] , L . lactis NZ9800 ( pNZ8037:clfB ) [41] and L . lactis NZ9800 ( pNZ8037:clfBQ235A ) [41] were grown statically in brain heart infusion ( BHI ) broth or agar at 28°C . Nisin ( 150 ng/ml , Sigma ) was added to L . lactis NZ9800 cultures to induce expression of ClfB . Antibiotics were added to the media as required: ampicillin ( 100 µg/ml ) , streptomycin ( 500 µg/ml ) , chloramphenicol ( 10 µg/ml ) and erythromycin ( 10 µg/ml ) . A streptomycin-resistant mutant of S . aureus strain Newman ( Newman Smr ) and S . aureus strain SH1000 was isolated by growth overnight in TSB , and then plating onto TSA plates containing streptomycin ( 500 µg/ml ) . Mutations in the rpsL gene often result in high level streptomycin resistance [50]–[51] . The rpsL gene was amplified by PCR from Newman and Newman Smr DNA . DNA sequencing revealed a single nucleotide substitution that resulted in a single site amino acid substitution ( K55T ) in the S12 protein of 30S ribosomal subunit . Newman Smr was phenotypically identical to its parent strain in terms of growth rate , expression of ClfB ( Figure S4 ) and haemolysis on sheep blood agar ( data not shown ) [52]–[53] . S . aureus Newman Smr ΔclfB and S . aureus SH1000 Smr ΔclfB were constructed by allelic exchange using pIMAY [48] . Briefly , primers were designed to amplify 500 bp of DNA located upstream and downstream of clfB ( Table S1 ) to delete the entire gene leaving only the start and stop codons . Genomic DNA from Newman or SH1000 was used as template and the resulting PCR products were denatured , allowed to reanneal via the complementary sequences in primers B and C and then amplified using primers A and D , resulting in a 1000 bp fragment consisting of linked sequences upstream and downstream of the clfB gene ( ΔclfB cassette ) . The amplimer was cloned into pIMAY between EcoRI and SalI restriction sites . The plasmid was transformed into E . coli DC10B [48] and then transformed into electrocompetent Newman Smr or SH1000 Smr . Deletion of the clfB gene was achieved by allelic exchange [48] . The resulting ClfB-deficient strains ( Newman ΔclfB , SH1000 ΔclfB ) were phenotypically identical to their parent strains in terms of growth rate and haemolysis on sheep blood agar [52]–[53] . Lack of expression of ClfB in S . aureus Newman was verified by Western immunoblotting ( Figure S7 ) . S . aureus Newman ΔclfB was complemented with pCU1 carrying the full length clfB gene [26] . Plasmid pCU1:clfB was transformed into E . coli DC10B [48] and then transferred to Newman ΔclfB . IsdA mutants of S . aureus Newman and S . aureus Newman ΔclfB were constructed using the same method , with primers designed to amplify 500 bp of DNA located upstream and downstream of isdA ( Table S1 ) . Cell wall-associated proteins were prepared as previously described [54] . Exponential phase cultures were harvested , washed in phosphate-buffered saline and resuspended to OD600 of 10 in lysis buffer ( 50 mM Tris-HCl , 20 mM MgCl2 , pH 7 . 5 ) supplemented with 30% ( w/v ) raffinose and complete protease inhibitors ( 40 µl/ml , Roche ) . Cell-wall proteins were solubilised by incubation with lysostaphin ( 200 µg/ml; AMBI , New York ) for 10 min at 37°C . Protoplasts were removed by centrifugation at 12 , 000× g for 10 min and the supernatant containing solubilised cell-wall proteins was aspirated . Solubilised cell wall proteins or purified recombinant proteins were boiled for 5 min in Laemmli final sample buffer ( Sigma ) , separated on polyacrylamide gels and transferred onto polyvinylidene difluoride membranes ( Roche ) . Filters were blocked in 10% ( w/v ) skimmed milk proteins before being probed with antibody . Reactive bands were visualised using the LumiGLO reagent and peroxide detection system ( Cell Signalling Technology ) . The following antibodies were used: rabbit anti-murine loricrin IgG ( 1∶1000; Covance ) , rabbit anti-murine K10 IgG raised against recombinant full length murine cytokeratin 101–570 ( 1∶500; Bioresources Unit , Trinity College Dublin ) , rabbit anti-ClfB A region IgG ( 1∶1000; described previously [26] ) and HRP-conjugated rabbit anti-His antibodies ( 1∶500 , Roche ) were used as primary antibodies . HRP-conjugated goat anti-rabbit IgG ( 1∶3000; Dako ) and HRP-conjugated protein A ( 1∶500; Sigma ) were used as secondary labelling reagents . Plasmid pCU1:clfB [26] was used as template for PCR amplification of DNA encoding amino acids 201–542 of ClfB . Primers incorporating BamHI and HindIII restriction sites , respectively , were used ( Table S1 ) . The PCR product was cloned into pCR-blunt-II-TOPO ( Invitrogen ) and subcloned into pQE30 ( Qiagen ) which had been cut with BamHI and HindIII . rClfB201–542 was expressed and purified from E . coli XL-1 Blue by Ni2+ affinity chromatography . The protein was analysed by SDS-PAGE and Western immunoblotting . DNA encoding full length human and murine loricrin , a human K10 peptide , a murine K10 peptide and human loricrin subdomains ( 1A , 1B , 2 , 2v , 3 ) was codon optimised for E . coli and synthesised commercially ( Genscript Corporation ) . DNA was subcloned between the BamHI and EcoRI sites of the expression vector pGEX-4T ( GE Lifesciences ) . DNA encoding human loricrin subdomain 2 ( Table S1 ) was amplified by PCR using plasmid pET11a carrying the full length cDNA clone encoding human loricrin [36] as template and the PCR product was cloned into pGEX-4T2 between BamHI and EcoRI sites . GST-tagged proteins were purified on a GSTrap FF purification column ( GE Healthcare ) according to the manufacturer's instructions ( Figure S3C–E ) . Microtiter plates ( Nunc ) were coated with recombinant protein ( 1 µM ) in carbonate buffer and incubated overnight at 4°C . Wells were blocked with 5% ( w/v ) bovine serum albumin ( BSA ) for 2 h at 37°C . The plates were washed three times with PBS . A bacterial cell suspension ( OD600 = 1 . 0 in PBS ) was added ( 100 µl per well ) , and the plates were incubated for 2 h at 37°C , washed three times with PBS , and bound cells were fixed with formaldehyde ( 25% v/v ) for 20 min and stained with crystal violet ( 0 . 5% v/v , 100 µl per well ) for 1 min followed by PBS and acetic acid ( 5% v/v ) washes . The absorbance was measured at 570 nm in an ELISA plate reader . S . aureus ( 1×108 colony-forming units ) was pre-incubated with recombinant GST , HK10 or Loricrin L2v ( 2 µM ) in PBS at room temperature for 30 min . The bacteria were added to loricrin-coated microtiter wells for 90 min at room temperature . Adherent cells were stained with crystal violet , and the absorbance was measured at 570 nm as described above . For inhibition studies with recombinant ClfB , loricrin-coated microtitre plates were prepared as above and were blocked with 5% skimmed milk proteins in PBS at 37°C for 2 h . Recombinant ClfB N23201–542 ( 3 µM ) was pre-incubated with recombinant GST , HK10 or Loricrin L2v ( 14 µM ) in PBS at room temperature for 1 h . The protein mixture was added to loricrin-coated microtitre wells and was incubated for 1 h at 37°C . Any unbound protein was removed by washing with PBS , and plates were incubated with HRP-conjugated rabbit anti-His antibodies diluted 1∶500 in 1% skimmed milk/PBS for 1 h at room temperature with shaking . 100 µl of a chromogenic substrate solution ( 1 mg/ml tetramethylbenzidine and 0 . 006% H202 in 0 . 05 M phosphate citrate buffer pH 5 . 0 ) was added , and plates were developed for 10 min in the dark . The reaction was stopped by the addition of 2 M H2S04 ( 50 µl/well ) , and plates were read at 450 nm . Surface plasmon resonance ( SPR ) was performed using the BIAcore ×100 system ( GE Healthcare ) . Goat anti-GST IgG ( 30 µg/ml; GE Healthcare ) was diluted in 10 mM sodium acetate buffer ( pH 5 . 0 ) and immobilized on CM5 sensor chips using amine coupling as described by the manufacturer . Recombinant GST-tagged protein ( 10–30 µg/ml ) in PBS was passed over the anti-GST surface of one flow cell while recombinant GST ( 10–30 µg/ml ) was passed over the other flow cell to provide a reference surface . Increasing concentrations of rClfB201–542 in PBS were passed in succession over the surface of the chip without regeneration [55]–[56] . All sensorgram data were subtracted from the corresponding data from the reference flow cell . The response generated from injection of buffer over the chip was also subtracted . Data was analysed using the BIAevaluation software version 3 . 0 . A plot of the level of binding ( response units ) at equilibrium against concentration of rClfB201–542 was used to determine the KD . The data shown is representative of 3 individual experiments each performed on 2 individual CM5 sensor chips . Nasal desquamated epithelial cells were harvested from the anterior nares of healthy human volunteers and were prepared using a previously described protocol [19] . S . aureus was grown to exponential phase , harvested , washed and adjusted to 1×108 cells/ml . 150 µl of bacterial cells were incubated with recombinant GST ( 30 µM ) , recombinant loricrin L2v-GST ( 30 µM ) or an equivalent volume of PBS at room temperature for 30 minutes . 100 µl bacterial cells were then mixed with 100 µl nasal cells for 1 h at 37°C . Nasal cells were then collected , washed , stained and enumerated as previously described [19] . Murine nasal tissue was excised from euthanized WT and Lor−/− mice and was homogenised in 500 µl PBS . Homogenised nasal tissue was diluted 2-fold in final sample buffer ( Laemmli , Sigma ) , and heated to 95°C for 10 min . The total protein concentration of each nose homogenate was measured using a bicinchoninic acid assay ( BCA assay ) and was normalised to 500 µg/ml . Samples were then analysed by Western Immunoblotting using rabbit anti-murine loricrin polyclonal IgG followed by HRP-conjugated goat anti-rabbit IgG . Bound antibody was removed by incubating at 50°C in stripping buffer ( 2% ( w/v ) sodium dodecyl sulfate , 100 mM β-mercatoethanol , 50 mM Tris-HCl , pH 6 . 8 ) and then re-probed with rabbit anti-murine K10 IgG followed by HRP-conjugated protein A . Gels were Coommassie-stained to confirm that equal protein concentrations were loaded for each sample ( data not shown ) . Specific pathogen-free female FVB wildtype and FVB loricrin-deficient ( Lor−/− ) mice were housed in groups of 5 animals . Mice ( 8–12 weeks ) were given sterile distilled water containing 500 µg/ml streptomycin or 10 µg/ml erythromycin ( for L . lactis colonisation ) 24 hours prior to nasal inoculation and for the duration of the experiment . The S . aureus inocula were prepared by growing cultures for 18 h on TSA , washing cells in PBS and resuspending cells in PBS containing 5% ( w/v ) BSA and 20% ( v/v ) DMSO before being frozen in small aliquots at −80°C . A single sample was thawed and cells were washed in PBS prior to inoculation . L . lactis MG1363 ( pKS80 ) and L . lactis MG1363 ( pKS80:clfB ) were grown for 18 h in BHI containing erythromycin ( 10 µg/ml ) and cells were washed in PBS prior to inoculation . Mice were inoculated intra-nasally with 2×108 CFU of S . aureus or 2×1011 CFU L . lactis ( 10 µl per nostril ) . At specific time points post inoculation mice were euthanized . The area surrounding the nose was wiped with 70% ethanol and the nose was excised and homogenised in 500 µl PBS . Lungs were also excised and homogenised in 5 ml PBS . The nose and lung homogenates were plated onto 5% horse blood agar ( HBA ) plates to obtain a total count of the nasal flora , and on TSA containing 500 µg/ml streptomycin or BHI containing erythromycin ( 10 µg/ml ) to obtain the number of S . aureus and L . lactis CFU respectively , per nose and lungs . For in vivo blocking studies S . aureus was pre-incubated with recombinant GST or recombinant L2v-GST ( 30 µM ) for 30 mins at room temperature before intra-nasal administration . On days 1 and 2 post inoculation , mice were administered 10 µl of recombinant L2v or recombinant GST intra-nasally . Nasal tissue was then harvested on day 3 to assess bacterial burden as described above . Statistical analysis was performed using Prism Graphpad 5 software . Adherence and binding was analysed using an unpaired t test . Statistical analysis on nasal colonisation data was performed using a Mann-Whitney test or the Krustal-Wallis test . Pairwise comparisons for multiple groups were made using Dunns Multiple Comparisons test . | Staphylococcus aureus is an important human commensal , present permanently in the noses of about 20% of the population and representing a significant risk factor for infection . The host and bacterial factors that facilitate nasal colonisation remain to be fully characterised . S . aureus adheres to the squamous epithelial cells found in the nose . Proteins expressed on the surface of S . aureus , including clumping factor B ( ClfB ) , are responsible for this interaction . We demonstrate that loricrin , a major component of the squamous epithelial cell envelope , represents the primary ligand for ClfB and that the interaction between ClfB and loricrin is required for efficient nasal colonisation by S . aureus . Using purified proteins we have demonstrated that ClfB binds loricrin and propose a mechanism by which this binding occurs . We have established a murine model of S . aureus nasal colonisation and have demonstrated reduced colonisation in loricrin-deficient mice compared to wild-type mice which is dependent upon ClfB . Using Lactococcus lactis as a surrogate host expressing ClfB , we could show that the interaction between ClfB and loricrin in the nares is sufficient to support nasal colonisation . Cumulatively , these data show that the ClfB-loricrin interaction is crucial for nasal colonisation by S . aureus . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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"Methods"
] | [
"biology",
"microbiology",
"host-pathogen",
"interaction"
] | 2012 | Nasal Colonisation by Staphylococcus aureus Depends upon Clumping Factor B Binding to the Squamous Epithelial Cell Envelope Protein Loricrin |
Viruses encode suppressors of cell death to block intrinsic and extrinsic host-initiated death pathways that reduce viral yield as well as control the termination of infection . Cytomegalovirus ( CMV ) infection terminates by a caspase-independent cell fragmentation process after an extended period of continuous virus production . The viral mitochondria-localized inhibitor of apoptosis ( vMIA; a product of the UL37x1 gene ) controls this fragmentation process . UL37x1 mutant virus-infected cells fragment three to four days earlier than cells infected with wt virus . Here , we demonstrate that infected cell death is dependent on serine proteases . We identify mitochondrial serine protease HtrA2/Omi as the initiator of this caspase-independent death pathway . Infected fibroblasts develop susceptibility to death as levels of mitochondria-resident HtrA2/Omi protease increase . Cell death is suppressed by the serine protease inhibitor TLCK as well as by the HtrA2-specific inhibitor UCF-101 . Experimental overexpression of HtrA2/Omi , but not a catalytic site mutant of the enzyme , sensitizes infected cells to death that can be blocked by vMIA or protease inhibitors . Uninfected cells are completely resistant to HtrA2/Omi induced death . Thus , in addition to suppression of apoptosis and autophagy , vMIA naturally controls a novel serine protease-dependent CMV-infected cell-specific programmed cell death ( cmvPCD ) pathway that terminates the CMV replication cycle .
Cell death is central to viral infection , as an evolutionarily-conserved means to eliminate intracellular pathogens and as a way that lytic viruses mediate release of progeny . Human cytomegalovirus ( CMV ) , the major infectious cause of birth defects as well as an important cause of opportunistic disease worldwide [1] , remains cell-associated during productive replication . Release of progeny virus depends upon the exocytic pathway [1] and continues until cells die via a poorly understood fragmentation process . CMV is well-armed to modulate cell-intrinsic as well as extrinsic innate and adaptive host clearance pathways [1] . The product of the UL37x1 gene , vMIA , a potent suppressor of apoptosis [2]–[4] , also controls the timing of infected cell death [5]–[7] . Premature death in vMIA-mutant virus-infected cells reduces the period of progeny release by three to four days [5]–[7] without affecting cell-to-cell spread [5] . All vMIA-mutant viruses exhibit this premature death phenotype , but the involvement of caspases and the impact on viral yield varies with CMV strain . AD169varATCC strain ( AD-BAC ) depends upon vMIA to a greater extent [6] , [7] than TownevarATCC ( Towne-BAC ) , although vMIA prolongs the period of viral replication and release in both strains [5] . Importantly , vMIA from either strain retains the capacity to block caspase-dependent apoptosis [5] . The caspase-independent death pathway that is blocked by vMIA is not known . Other cell death suppressors are encoded by CMV [1] , but , aside from vMIA , only UL38 has been implicated in control of infected fibroblast death to prolong replication [8] , [9] . Studies to date reveal a complexity of infected cell death and a need for a more complete understanding of events that naturally terminate CMV infection . Major pathways associated with death ( apoptosis , necrosis , and autophagy ) are triggered by specific host cell and immune system initiators and exhibit characteristic molecular events and cell morphological changes [10]–[12] . Proteases in the caspase , calpain , lysosomal cathepsin , and proteasomal serine protease classes are central to the execution of various death pathways . The fact that the premature death induced by vMIA-mutant CMV is resistant to inhibitors of caspases , cathepsins , and calpains [5] suggests a novel programmed pathway distinct from characterized death pathways [11] , [13] . For those viral strains that have been characterized , infected cell death initiates approximately 7 to 10 days after infection of fibroblasts . In contrast , the premature death that occurs in vMIA mutant virus infection initiates 3 to 4 days postinfection [5]–[7] . The 12–24 h timing of individual cell fragmentation , association with cytopathic effect ( CPE ) , and nominal impact of vMIA [5] all suggest that the final stages of productive replication terminate with a CMV infected cell-specific programmed cell death ( cmvPCD ) . Many DNA viruses encode antiapoptotic functions that sustain replication in the face of cell-intrinsic defenses [14]–[16] . vMIA equips CMV to counteract intrinsic host clearance pathways leading to cell death [5]–[7] . As an outer mitochondrial membrane protein , vMIA sits in a central position analogous to antiapoptotic Bcl-2 family members Bcl-2 and Bcl-xL [2] , and prevents the formation of a mitochondrial permeability transition pore , release of cytochrome c and proapoptotic factors into the cytoplasm , and activation of executioner caspases . Unlike antiapoptotic Bcl-2 family members [2] , [5] , [17] , vMIA lacks Bcl-2 homology domains but depends on an antiapoptotic domain that mediates interaction with GADD45 family members [18] , [19] . vMIA also recruits BAX to mitochondria [20] , [21] and disrupts mitochondrial networks [22] . This disruption normally accompanies BAX oligomerization at the outer mitochondrial membrane [23] , [24] , although vMIA mutants that fail to bind BAX continue to disrupt networks [25] . The vMIA-dependent recruitment of BAX does not lead to the formation of a transition pore complex or release of proapoptotic mediators [20] , [21] . The contribution of BAX oligomerization or mitochondrial network disruption to cell death suppression remains to be investigated . Although both of these events are signs of apoptosis [23] , [24] , [26] , [27] neither mitochondrial network disruption [28] nor BAX oligomerization [29] , [30] are sufficient to induce apoptosis . These alterations are also associated with vMIA-mediated suppression of cell death during viral infection where the pathway ( s ) of death are not fully understood . Consequences of mitochondrial release of proapoptotic mediators have been extensively studied [10] , [31]–[33] . Cytochrome c controls apoptosome formation and downstream executioner caspase activation . Endonuclease G and apoptosis-inducing factor ( AIF ) promote nuclear events . Mitochondrial release of Smac/DIABLO and HtrA2/Omi overcomes the activity of inhibitor of apoptosis proteins ( IAPs ) . The HtrA2/Omi proenzyme is processed within the mitochondria , removing a mitochondrial targeting sequence ( amino terminal 33 amino acids ) and a transmembrane domain [34]–[36] . Mature , active HtrA2/Omi resides in the intermembrane space , and is released into the cytoplasm through the transition pore complex at the same time as cytochrome c . Release of the serine protease HtrA2/Omi from mitochondria can result in two downstream effects: ( 1 ) cleavage of IAPs and an ultimate increase in caspase-dependent death and ( 2 ) trigger IAP-independent , caspase-independent death [37] . This latter pathway is also induced by extramitochondrial overexpression of HtrA2/Omi in the cytoplasm [35] , [37]–[40] . The role of this serine protease as an inducer of cell death [38] , [39] in mammalians seems opposite the role of the founding member of this protein family as a pro-survival serine protease in eubacteria [41]–[43] . Here , we demonstrate the central role of HtrA2/Omi executing a serine protease-dependent pathway that is controlled by vMIA during infection .
We evaluated cmvPCD during wild type ( wt ) virus ( Towne-BAC , a GFP-expressing virus [44] ) infection by scoring morphological changes in cells during replication ( Fig . 1 , supplemental Fig . S1 ) . Termination of infection was associated with the accumulation of GFP-positive cell debris that remained associated with the monolayer ( Fig . 1A ) . Cell fragmentation and death was first observed at 120 h postinfection in a small percentage of GFP+ foci ( Fig . 1A , Fig . S1 ) . GFP+ dead cell debris was observed only in foci ( Fig . S1 ) . Almost all ( >90% ) foci showed evidence of fragmentation by 240 h postinfection ( Fig . 1C ) . Thus , cmvPCD occurred very late in infection , after maturation and release of progeny virus had peaked . GFP+ debris was observed much earlier during infection with vMIA null mutant virus , ΔUL37x1 ( Fig . 1C ) , although the fragmentation process appeared similar to wt virus ( Fig . 1B , Fig . S1 ) . As previously reported [5] , a majority ( 70% ) of mutant virus foci contained debris by 120 h due to the single GFP+ cells that started to fragment between 72 and 96 h postinfection prior to the formation of foci ( Fig . S1 ) . There was a gradual increase in foci containing fragmented cells between 120 and 192 h postinfection such that , by 192 h , >90% of foci contained fragmented cells ( Fig . 1C ) . This was consistent with our previous report showing both viruses spread with equivalent efficiency but that ΔUL37x1 induced premature caspase-independent death [5] . Intact GFP+ ΔUL37x1-infected cells appeared to fragment before virus spread to form foci ( Fig . 1C , Fig . S1 ) [5] . To determine whether intact ΔUL37x1-infected cells released progeny virus before fragmenting , we evaluated the formation of immediate early nuclear antigen positive ( IE+ ) foci by immunofluorescence . Infected cells became IE+ earlier than they became GFP+ ( Fig . 2 ) . IE+ foci surrounding single GFP+ cells were detected at 72 h postinfection ( Fig . 2A–E ) , when a majority ( >50% ) of GFP+ cells were still intact ( Fig . 2A , 2D ) . Whether intact or fragmented , >99% of ΔUL37x1-infected cells produced foci by 120 h postinfection [5] . Thus , most virus spread occurs before cells fragmented ( Fig . 2A , 2D ) , suggesting that , like wt , mutant virus is released before infected cells die ( Fig . 1 , Fig . S1 ) . To confirm that the death of mutant virus-infected cells was dependent on late phase events , the DNA synthesis inhibitor phosphonoformate ( PFA ) or the DNA encapsidation inhibitor 2-bromo-5 , 6-dichlorobenzimidazole ( BDCRB ) was added at the time of CMV infection and cell fate was scored by staining for IE+ cells at 96 h postinfection ( Fig . 2B , 2F ) . PFA inhibits late gene expression , including GFP , while BDCRB blocks virion maturation but allows late gene expression to proceed [45] , [46] . Untreated control or BCDRB-treated infected cultures appeared similar , whereas PFA-treated cultures contained single IE+ cells that remained GFP- . Only about 30% of GFP+ cells or debris in untreated ΔUL37x1-infected cells were IE+ at 96 h postinfection ( Fig . 2F ) . The remaining 70% were no longer IE+ , suggesting that IE expression was lost as cells fragmented . BDCRB-treated cells exhibited a pattern similar to untreated cells , suggesting that fragmentation was triggered by events prior to DNA encapsidation . All infected cells in PFA-treated cultures remained IE+ ( Fig . 2F ) and did not fragment , suggesting that initiation of cell death was dependent on events that followed viral DNA synthesis . Thus , initiation of death was dependent on cellular changes associated with viral DNA replication and/or late phase gene expression . We evaluated cell morphology [11] associated with death in ΔUL37x1 and wt virus-infected cells ( Fig . 3 ) . Late in CMV infection , inclusions form within enlarged cells coincident with replication and maturation processes that take place in the nucleus as well as in the cytoplasm . At 72 h postinfection , ΔUL37x1 and wt virus-infected cells exhibited similar nuclear and cytoplasmic inclusions [1] , [47] , [48] as well as enlarged cell CPE ( Fig . 3A , 3F and Fig . S2 ) [5] . Stain for total nuclear DNA revealed a diffuse pattern ( Fig . 3F , Fig . S2 ) that became distorted ( Fig . 3G ) and progressed through shrinkage and collapse and loss of nuclei ( Fig . 3H–J ) as cells fragmented ( Fig . 3C–E ) . A similar process accompanied fragmentation in mutant or wt infected cells . The fragmentation process produced cell debris ( Fig . 3E ) lacking signs of DNA ( Fig . 3J ) . Loss of nuclei scored by DNA stain or IE antigen ( Fig . 2 ) was similar . Cell debris remained GFP+ and unstained by ethidium homodimer ( Fig . S3 ) , suggesting a non-necrotic death . Fragmentation was not synchronous in infected cultures , such that only 10% of infected cells exhibited intermediate fragmentation patterns ( e . g . Fig . 3B–D ) at any time ( Fig . 3K and Fig . S1 ) . The same types of morphological changes that started at 72 h in ΔUL37x1-infected cultures started at 120 h postinfection in Towne-BAC-infected cells ( Fig . S1 and S4 ) . The fragmentation of GFP+ cells ( Fig . 1 ) , loss of IE+ cells ( Fig . 2 ) and loss of DNA+ nuclei ( Fig . 3 ) were all characteristic of cmvPCD in wt and premature death in mutant virus-infected cells . Previous work showing that caspases , cathepsins , or calpains were not involved in ΔUL37x1-initiated premature death [5] , lead us to evaluate the contribution of cellular serine proteases to this process . We started by assessing the impact of a broad-spectrum inhibitor , TLCK [49]–[62] , because this inhibitor does not affect the viral maturational serine protease at concentrations that are sufficient to block cellular serine proteases [63] . Addition of TLCK ( 11 , 33 , or 100 µM ) to infected cultures at 30 h lead to a concentration-dependent reduction in cell fragmentation at 72 h postinfection ( Fig . 4A ) . These concentrations of inhibitor did not reduce virus yields ( Fig . 4B ) . Thus , TLCK inhibited premature cell death without any impact on virus replication . When TLCK was added at 30 h and fragmented cells were counted at 72 , 96 , and 120 h postinfection ( Fig . 4C ) , cell death was reduced approximately twofold , suggesting that serine proteases play a central role in the timing of fragmentation . Despite experiment-to-experiment variability in the levels and rate of fragmentation death observed between 72 and 120 h postinfection , TLCK consistently inhibited this process ( Fig . 4A and C ) and increased the proportion of live , intact cells while absolute numbers of GFP+ cells or debris remained the same ( Fig . 4D ) . This result implicated serine proteases early in the premature death induced by mutant virus . Previously , we reported that the pan-caspase inhibitor zVAD . fmk had no effect on ΔUL37x1-induced premature death [5] . To determine whether caspases influenced death levels when serine proteases were inhibited , we applied zVAD . fmk alone as well as in combination with TLCK . The caspase inhibitor did not influence the serine protease-dependent process ( Fig . 4E ) . In contrast , zVAD . fmk showed the expected [5] , [6] , [64] inhibition of apoptosis induced in CMV strain AD169varATCC infected cells ( Fig . 4F ) . Thus , these data imply that cmvPCD is controlled by serine proteases that work independent of caspases . To determine the timing of serine protease activity in controlling premature death , TLCK was added to ΔUL37x1-infected cells at 30 , 54 , or 78 h . Addition of TLCK at each of these times was found to dramatically reduce the level of death at 96 h postinfection ( Fig . 5A ) . These results suggest serine proteases act within 24 h of fragmentation ( Fig . 4D ) and demonstrated the importance of these proteases late in infection . Taken together with data on timing of the death stimulus ( Figs . 2 and 3 , and [5] ) , serine proteases active late in CMV infection may either trigger or play an intermediary role in the cell death pathway . To determine the timing of serine protease activity in wt virus-induced cmvPCD , TLCK was added at 30 , 54 , and 78 h ( Fig . 5B ) . Addition of TLCK at each of these times effectively reduced cell fragmentation at 144 h postinfection , suggesting that proteases active after the 78 h time period played a critical role during wt virus infection as well ( Fig . 5B ) . These data demonstrate a common serine protease cell death pathway terminates mutant or wt virus infection , and demonstrate that the premature death in mutant virus infected cells follows a similar pathway as cmvPCD . Differences in timing show the importance of vMIA control in the timing of cmvPCD . One mitochondrial serine protease , HtrA2/Omi , has been implicated in cell death pathways and exhibits sensitivity to TLCK [40] , [65]–[67] . The specific HtrA2/Omi inhibitor UCF-101 [68] was added to ΔUL37x1- or Towne-BAC-infected cultures ( Fig . 5C–E ) at a concentration ( 10 µM ) anticipated to minimize a previously recognized impact on other cellular targets [69] . UCF-101 reduced death when added to ΔUL37x1 or Towne-BAC-infected cultures at 30 or 54 h , implicating HtrA2/Omi as a mediator of cmvPCD ( Fig . 5C–D ) . Although UCF-101 added at 54 h reduced death of ΔUL37x1-infected cells at 96 h postinfection , addition at 78 h was ineffective ( Fig . 5C ) . Towne-BAC-associated death at 144 h was reduced by UCF-101 added as late as 102 h , but not when added at 126 h ( Fig . 5D ) . UCF-101 treatment did not reduce viral yields ( Fig . 5E ) . Most importantly , these data suggest that events over the 24 to 48 h preceding fragmentation of cells are influenced by HtrA2/Omi , regardless of whether considering the premature cmvPCD in mutant virus infected cells or cmvPCD in wt infection . The differences between UCF-101 and TLCK addition at 78 h ( Fig . 5A ) may be due to the effectiveness of these inhibitors on HtrA2/Omi or to additional serine proteases that contribute to cmvPCD . Overall , these data demonstrate that UCF-101 specifically reduces infected cell death and implicate the serine protease HtrA2/Omi in the pathway . Further , these data implicate HtrA2/Omi as a target of vMIA modulation . To determine the impact of mutant or wt virus infection on HtrA2/Omi expression levels and subcellular localization as well as to investigate any impact of vMIA on HtrA2 expression , immunoblot analysis was carried out on Towne-BAC infected cells ( Fig . 6 ) . Levels of mature 36 kDa HtrA2/Omi levels were similar to uninfected cells at 24 h , but increased by 48 h and continued to accumulate over the course of infection ( Fig . 6A–B ) . Comparisons of mutant and wt infected cells showed similar accumulation of HtrA2 by 48 h ( Fig . 6B , Fig . S5 ) . Premature cmvPCD initiated in mutant virus-infected cells prevented comparisons by immunoblot later in infection; however , immunofluorescence analyses at 96 h postinfection confirmed the dramatically increased HtrA2/Omi levels in mutant or wt virus-infected cells ( Fig . 6C–H and Fig . S6 ) . HtrA2/Omi colocalized with the mitochondrial membrane potential marker MitoTracker Red ( Fig . 6C , 6F and Fig . S6 ) at late times of infection with either virus . These data indicate that HtrA2/Omi levels increase within mitochondria before the initiation of cmvPCD . vMIA does not alter expression pattern or mitochondrial localization of this protease but nevertheless prevents death . Mitochondria in wt CMV infected cells followed the expected [22] reticular to punctate transition associated with disruption of mitochondrial networks ( Fig . 6F , 6L , and Fig . S6 ) and mutant virus-infected cells retained a reticular pattern ( Fig . 6C , 6I and Fig . S6 ) when stained for HtrA2/Omi , cytochrome c , mitochondrial HSP ( mtHSP70 ) , or MitoTracker Red . MitoTracker Red staining indicated that mitochondria retained a similar membrane potential despite this difference in morphology due to vMIA ( Fig . S6 ) . When the kinetics of the reticular to punctate transition was evaluated in Towne-BAC-infected cells , almost all ( ≥90% ) of cells contained reticular mitochondria at 48 h , but transitioned to punctate by 96 h . In contrast , ΔUL37x1-infected cells retained a reticular morphology ( ≥80% ) throughout infection . These data suggest that a vMIA-dependent process disrupts reticular mitochondria beginning at 48 h postinfection and this change in mitochondrial organization may contribute to cell survival . Despite this striking difference in mitochondria , the organelles of the secretory apparatus that form the viral assembly compartment at late times of infection [48] , [70] were similar in either virus infection ( Fig . S2 , Fig . S6 ) . Thus , disruption of mitochondrial networks by wt virus may contribute to control of HtrA2/Omi-dependent death and the failure of mutant virus to induce these changes may lead to premature HtrA2/Omi-dependent death . We sought to determine whether mitochondria released cytochrome c prior to premature cmvPCD . Cells that had not yet started to fragment all showed reticular cytochrome c staining ( Fig . 6 and Fig . S6 ) whereas diffuse staining was detected only as cells became highly fragmented ( Fig . S7 ) . These data suggest that release of cytochrome c follows the fragmentation that characterizes cmvPCD . To directly address the impact of HtrA2/Omi overexpression on the cell fate , full-length HtrA2/Omi as well as a catalytic site mutant ( HtrA2S306A ) [34] were transiently expressed in uninfected and virus-infected cells . Initially , expression levels and impact on uninfected cell viability were assessed ( Fig . 7A–H , Fig . S8 ) . HtrA2/Omi ( or mutant HtrA2/Omi ) overexpression did not induce death in uninfected HFs ( Fig . 7H ) or HeLa cells ( Fig . S8 ) , consistent with published characterization of full-length protease [37] . Immunofluorescence patterns revealed the expected mitochondrial localization at 48 h post transfection ( Fig . 7A–F ) , and immunoblot analyses using HeLa cells indicated equivalent expression levels of the wt and mutant protease ( Fig . 7G ) . To determine the impact of HtrA2/Omi overexpression on infected cells , Towne-BAC or ΔUL37x1 were cotransfected with HtrA2/Omi or HtrA2S306A expression plasmids ( Fig . 7I ) and assessed for spread to form foci [71] . By 10 days posttransfection , wt and mutant BACmids had produced comparable numbers of plaques , as expected [5] . Cotransfection of HtrA2/Omi expression plasmid reduced the plaquing efficiency >10-fold compared to vector control or HtrA2S306A mutant ( Fig . 7I ) . These data show that overexpression of catalytically active HtrA2/Omi prevents plaque formation independent of vMIA expression . To determine whether the reduction in plaguing efficiency following overexpression of HtrA2/Omi was due to cell death induction , the fate of individual cells was monitored ( Fig . S9 ) . When Towne-BAC was cotransfected with HtrA2/Omi or HtrA2S306A , individual GFP+ cells were observed at 48 h , although even at this time the levels could be lower in cells receiving the protease active form ( Fig . 7J–K ) . HtrA2/Omi/GFP+ cells began to fragment by 72 h posttransfection ( Fig . 7J ) and were lost from cultures by 168 h ( Fig . S9 ) . HtrA2/Omi overexpression-induced death required the active protease , based on the failure of HtrA2S306A to induce death ( Fig . 7J ) as well as on the ability of the inhibitor UCF-101 to block HtrA2/Omi overexpression-induced death ( Fig . 7K ) . The numbers of GFP+ cells ( Fig . 7J ) or plaques ( Fig . 7I ) that formed following cotransfection of Towne-BAC with HtrA2S306A could not be distinguished from transfection of Towne-BAC with vector . These data show that overexpression of catalytically active HtrA2/Omi induces infected cell death that is independent of vMIA expression . The sensitivity of virus-infected cells and lack of impact on uninfected HFs ( Fig . 7H ) supports the specific role of HtrA2/Omi in a novel cell death pathway in CMV-infected cells . A role of vMIA in HtrA2-induced death was investigated using the cotransfection assay carried out using lower doses of expression plasmids as well as using vMIA-expressing cells . Cotransfection of HtrA2/Omi expression plasmid at a 25 or 30-fold lower level revealed a differential impact on these viruses ( Fig . 8A ) , where Towne-BAC exhibited a greater resistance to HtrA2/Omi-induced death . These conditions were also employed to demonstrate that vMIA overexpression overcame HtrA2/Omi-induced death ( Fig . 8A ) . To address the role of vMIA further , HFs as well as HFs stably transduced with retroviruses expressing Myc-tagged vMIA or mutant protein [18] vMIAmut ( Fig . 8B–C ) were infected . As expected [5] , vMIA-HFs suppressed the premature cmvPCD when assessed at 96 and 120 h postinfection , whereas vMIAmut-HF or nontransduced control HFs did not ( Fig . 8B ) . These data suggest the intact antiapoptotic domain of vMIA is required to control premature cmvPCD . The experimental plating efficiency of ΔUL37x1 virus was the same on either cell line ( Fig . S10 and [5] ) . These results were consistent with a role for vMIA in controlling kinetics of cmvPCD and suggest that similar functional domains of vMIA are required in suppression of apoptosis or HtrA2-dependent cmvPCD . Immunoblot analyses were used to compare transduced vMIA ( or vMIAmut ) levels relative to native viral expression ( Fig . 8C ) . The lower levels of transduced gene expression likely contribute to the death suppression observed ( Fig . 8B ) . Rescue viruses derived from ΔUL37x1 confirmed that an intact UL37x1 locus is sufficient to completely control premature death , mitochondrial organization , and viral yield ( Fig . 8D , Fig . S10 ) . Overall , these data confirm the critical role of vMIA as a determinant of cmvPCD when induced by overexpression of HtrA2/Omi transfection or during the late phase of infection . Thus , ΔUL37x1 infection sensitizes to the prodeath impact of HtrA2/Omi , and vMIA controls HtrA2/Omi prodeath pathways during wt CMV infection . In order to determine whether the antiapoptotic activity of vMIA is preserved in cells where HtrA2/Omi is overexpressed , we performed experiments with HtrA2/Omi expression constructs in HeLa cells exposed to Fas-mediated apoptosis ( Fig . 9 ) [2] , [19] . Immunofluorescence analyses showed expected levels and localization of HtrA2/Omi and vMIA in transfected cells ( Fig . 9A–I ) . These analyses indicated that vMIA and HtrA2/Omi ( or HtrA2S306A ) colocalize with mitochondria under all conditions . Introduction of HtrA2/Omi or mutant expression constructs did not influence the antiapoptotic activity of vMIA ( Fig . 9J–L ) , consistent with previous work showing vMIA-dependent antiapoptotic function is active at late times of infection [5] , [6] . Together , these data suggest that HtrA2/Omi does not interfere with vMIA-mediated control of apoptosis . To directly visualize levels of serine proteases in infected cells , the fluorescent reagent sulforhodamine 101-leucine chloromethyl ketone ( SLCK ) was used to reveal the distribution and activity of serine proteases [72] in ΔUL37x1 or Towne-BAC infected live cell cultures ( Fig . 10 ) . By day 5 , foci with brightly stained GFP+ debris was observed in cultures infected with either virus ( Fig . 10E–F ) , although fragmentation was rare in wt virus-infected cultures at this time . The SLCK staining pattern was distinct in ΔUL37x1-infected cells and included bright SLCK+ debris ( Fig . 10A ) that was distinguishable from Towne-BAC infected cells by differences in the amount of staining as well as the size and distribution of debris ( Fig . 10A–B ) . By 8 days after infection , most ΔUL37x1-infected cells in each plaque were brightly fluorescent ( Fig . 10C ) whereas cells infected with wt virus ( Fig . 10D ) showed only SLCK+ debris . SLCK staining patterns did not appear to be mitochondrial at any time in either virus infection . These patterns were distinct from HtrA2/Omi ( Fig . 6C ) , suggesting that SLCK labeling detected serine proteases in addition to HtrA2/Omi . Addition of 0 . 1 or 1 mM TLCK reduced but did not eliminate SLCK binding to mutant virus-infected cells , consistent with the induction of serine proteases ( Fig . S11 ) . Overall , >50% of GFP+ cells in ΔUL37x1 plaques also labeled with SLCK . SLCK staining was reduced to ≤30% by addition of 100 µM TLCK . Thus , SLCK revealed a higher level of protease activation in CMV infected cells that were susceptible to premature cmvPCD . This data suggests that vMIA may control a broader serine protease-dependent death pathway by counteracting mitochondrial HtrA2/Omi during viral infection .
CMV replicates in the nucleus , matures in the cytoplasm and is released into the surrounding medium or adjacent cells over the course of a 7 to 10 day replication cycle [1] . Host cells are dramatically reprogrammed for production of progeny virus until death occurs via a process that begins late in CMV infection , associated with late gene expression that drives CPE and cell cycle dysregulation [73]–[76] . In an effort to define viral and cellular contributions to morphological and biochemical events that terminate CMV infection , we have discovered the key role of mitochondrial HtrA2/Omi and a novel cell death pathway . This cellular serine protease appears to be responsible for induction of cmvPCD following a pathway that is held in balance by the viral cell death suppressor , vMIA . vMIA resides in the mitochondrion where it is a potent suppressor of cytochrome c release , thereby preventing activation of executioner caspases during apoptosis [2]–[4] . In addition to suppression of apoptosis , vMIA carries out a distinct and nonoverlapping role suppressing death induced by HtrA2/Omi during the late phase of viral infection . This cmvPCD pathway is triggered only in the context of infection . Late phase infected cell events promote cell fragmentation together with collapse , shrinkage , and loss of nuclei in a pathway that is dependent on HtrA2/Omi protease activity and associated with the activation of cytoplasmic serine proteases that may act as executioners . HtrA2/Omi levels rise before induction of death , consistent with a central role of this protease in initiation of cmvPCD . Suppression of this death pathway , like suppression of apoptosis , is associated with global disruption of mitochondrial networks by vMIA . Unlike apoptosis , however , cmvPCD apparently does not require cytochrome c release from mitochondria to trigger downstream events . Further , HtrA2/Omi remains mitochondrial late during infection suggesting death may be initiated by the activity of the intramitochondrial protease , which raises an interesting question as to how transduction of the death signal occurs . Our data reveal a pathway that is triggered by high intramitochondrial HtrA2/Omi protease and controlled by vMIA . Although vMIA-mutant virus undergoes premature cmvPCD , the fragmentation process is similar to cmvPCD in wt virus-infected cells . The difference appears to be in timing of cell death . vMIA delays death for several days beyond the initiating trigger which is coincident with the late phase of replication . Although induction of HtrA2/Omi is independent of vMIA , the impact of induction appears to be the target of vMIA function at the mitochondria where both reside . Suppression of cmvPCD benefits the virus by extending the period of virus production by infected cells [5] , although cultured fibroblasts show only slight reduction in yield and cell-to-cell transmission in the absence of vMIA . A prolonged period of virus production increases the amount of virus released cell-free and potentially benefits transmission in natural settings . A delay in fragmentation would also delay phagocytosis and clearance of virus-infected cells [77] . HtrA2/Omi-dependent death may be viewed as an intrinsic host antiviral process analogous to apoptosis . vMIA control of HtrA2/Omi-mediated death is analogous to control of apoptosis , as both appear to be independent cell-intrinsic mechanisms of pathogen control . Importantly , vMIA appears to provide concurrent protection from both pathways . vMIA disruption of reticular networks and organization of mitochondria [22] is independent of HtrA2/Omi accumulation within mitochondria , but does correlate with cell death suppression activity . Thus , ΔUL37x1-mutant virus-infection preserves mitochondrial networks throughout infection , during HtrA2/Omi accumulation and initiation of premature cmvPCD . In contrast , wt virus infected cells support the same accumulation of HtrA2/Omi and a vMIA-driven disruption of mitochondrial networks but survive . The correlation between this vMIA-dependent disruption and cell death protection suggests that punctate mitochondria may be more resistant to the stress induced by late phase events . Reticular mitochondria are known to rapidly disseminate Ca++ or ATP signals; whereas , punctate mitochondria have slower responses to changes in intracellular mediators [78] . Additional experiments will be needed to understand the mechanism underlying resistance of punctate mitochondria to death , whether mediated via caspases or HtrA2/Omi . Emerging evidence suggests vMIA , viral strain differences , and cellular factors contribute to the control of mitochondria and death . Thus , AD-BAC kills cells earlier [6] and disrupts mitochondrial networks by 24 h postinfection [22] whereas Towne-BAC disruption occurs later , by 48 h postinfection and cells die later . vMIA associates with the outer mitochondrial membrane within 24 h [79] , [80] . AD-BAC ( or its parent AD169varATCC ) depends upon vMIA to suppress caspase-dependent apoptosis that develops by 48 to 72 h postinfection . Towne-BAC ( or its parent TownevarATCC ) depends on vMIA to suppress caspase-independent , HtrA2-dependent cell death that develops by 72 to 96 h postinfection . It remains to be seen whether vMIA suppresses both pathways in cells infected with strains like AD-BAC . Accumulation of HtrA2/Omi occurs in other viral strains ( McCormick , unpublished ) , underscoring the overall importance of the process described here . There are many potential factors contributing to qualitative or quantitative differences in the way characterized viral strains initiate and control death , with apoptosis apparently predominating in some settings and HtrA2-mediated death predominating in others . We focused here on dissecting the novel death pathway in Towne-BAC-infected cells , to characterize a novel HtrA2/Omi pathway that is independent of apoptosis . cmvPCD may be influenced by or even associated with a number of additional modulatory effects of this virus that impact late times of infection , including dysregulation of the cell cycle [73]–[76] , disruption of p53 activation [81] , DNA damage response [82] , [83] and unfolded protein response [84] that all remain incompletely understood . vMIA may reduce ATP levels during infection as it does in established cell lines . Although suggested to control late CPE in AD-BAC derivatives [85] vMIA has no impact on development of CPE in Towne-BAC derivatives . Any vMIA-specific reduction in ATP levels is likely highly coordinated with other viral processes contributing to late CPE . CMVs encode multiple factors that target mitochondria [86] , [87] , regulate expression of mitochondrial proteins [75] and even stimulate mitochondrial DNA synthesis [88] suggesting viral control of mitochondria functions is complex . The vMIA-specific impact on ATP levels as related to HtrA2/Omi remains unknown but may certainly be a feature of control . As an event that occurs very late in replication , cmvPCD is crucial to sustaining viral infection in individual cells . Our observations that either mutation of vMIA or premature overexpression of HtrA2/Omi levels dramatically alters the timing of death indicate that these two may balance each another in controlling cmvPCD . Previously , pharmacological inhibitor and overexpression studies have implicated HtrA2/Omi as a regulator of death [35] , [37] , [40] , [89] , [90] . Genetic studies have suggested this protease functions primarily to ensure normal mitochondrial homeostasis [39] , [91] , [92] , perhaps controlling protein quality and cellular stress responses [34] similar to the related bacterial protease HtrA [41]–[43] . The role of HtrA2/Omi in caspase-independent cell death has not previously been studied in detail , although the truncated , active form of HtrA2 drives death when released from mitochondria or expressed directly in the cytoplasm [35] , [37] , [40] , [90] , [93] . We have shown that the active form drives death specifically and only in CMV-infected cells , which we correlate with the fact that the enzyme remains mitochondrial throughout CMV infection . CMV infection is a unique setting that has unveiled a direct role for HtrA2/Omi in a caspase-independent cell death pathway analogous to apoptosis . vMIA controls the programmed death of infected cells after a week or more of replication , following a period of persistent virus production . CMV infects many cell types in addition to HFs , and given that the timing of replication varies with cell type , vMIA control of HtrA2/Omi-dependent death may be critical in other cell types or in natural infection of the human host . Given the many functions that CMV has evolved to manage the virus:host standoff , we speculate that viral control of cmvPCD represents a benefit to the virus , potentially allowing infected cells to avoid sending alarm signals . Other examples of viral proteins acting together to control the type of cell death that follows replication can be identified . Thus , the adenovirus death protein , ADP , functions in the presence of E1B-19k , the viral Bcl-2 protein , and both contribute to the type of death that terminates infection [94]–[96] . Caspase-dependent apoptosis is itself a cell-intrinsic pathogen clearance process , minimizing inflammation and pathology while alarming the immune system to initiate cellular responses [77] . CMV-encoded cell death suppressors provide a means of evading cell death directed by host cell intrinsic , innate , and adaptive responses [97] . The benefit of controlling the mechanism and timing of cell death includes persistence , as well as the interface of virus-infected cells with the host immune system . In the host , cmvPCD may provide for greater success in attaining a foothold without evoking clearance . The presence of vMIA-like functions in other cytomegaloviruses [5] , [17] as well as the broad distribution of mitochondrial cell death suppressors in other viruses suggests this novel serine protease pathway may occur in other biological settings .
The HtrA2 protease has MEROPS accession number S01 . 278 and the I . M . A . G . E . consortium clone obtained for these studies was identical in sequence to NCBI accession ID BC0000096 . The vMIA [2] used to complement and repair ΔUL37x1 was obtained from AD169varATCC genomic DNA; NCBI accession ID X17403 . The sequence of Towne-BAC was deposited to NCBI [8]; accession ID AY315197 . Human fibroblasts ( HFs ) , vMIA-HFs , vMIAmut-HFs , and HeLas were cultured as previously described [5] . Viruses derived from the BACmid clones Towne-BAC and ΔUL37x1 [8] were maintained as DNA clones in E . coli or on complementing vMIA-HFs prior to experiments . AD169varATCC was maintained as previously described [22] . The kanamycin selection cassette in ΔUL37x1 was replaced with UL37x1 sequence derived from AD169varATCC to generate RC2707 . Transfection of pON2707 [5] into HFs was followed by infection with ΔUL37x1 virus . Plaques that included cell death at a frequency similar to Towne-BAC virus [5] were isolated for further analysis . Sequencing of viral DNA from Towne-BAC and two independently derived isolates ( ΔUL37x1R1 , ΔUL37x1R2 ) confirmed replacement of the selection marker in ΔUL37x1 with UL37x1 nucleotide sequence identical to pON2707 and AD169varATCC while the control , Towne-BAC , was identical to the expected sequence [8] , [98] . Expression of vMIA was confirmed by immunoblot analysis . The HtrA2/Omi expression plasmid , pON601 , was derived by restriction of the I . M . A . G . E . cDNA ( HtraA2 clone #5344667 , ATCC , Manassas , VA ) with BsrGI , followed by removal of the single-stranded overhangs with Klenow DNA polymerase , and restriction with XhoI . The HtrA2/Omi encoding fragment was ligated to EcoRV and XhoI restricted pcDNA3 . 1+ ( Invitrogen , San Diego , CA ) . The HtrA2S306A expression plasmid , pON602 , was generated by PCR-site directed mutagenesis of the HtrA2 ORF to introduce the S306A mutation and a novel NaeI restriction enzyme site and utilized the mutagenic primer 5′-CTATTGATTTTGGAAACGCCGGCGGTCCCCTGGTTAAC-3′ . Both clones were sequenced to confirm expected results . The vMIA and GFP expression clones and retroviral constructs used in these experiments were reported previously [2] , [5] , [18] . Immunodetection employed mouse monoclonal antibodies to c-myc epitope ( 9E10; Santa Cruz Biotechnology , Santa Cruz , CA ) , HtrA2/Omi ( MAB1458; R&D Systems , Inc , Minneapolis , MN ) , cytochrome c ( Clone 7H8 . 2C12 , BD Pharmingen , San Jose , CA ) , β-actin ( AC-74 , Sigma , St . Louis , MO ) , golgin-97 ( CDF4; Molecular Probes , Eugene , OR ) , mitochondrial heat shock protein 70 ( mtHSP70 ) ( a gift from Susan Pierce , Northwestern University ) , viral nuclear antigens IE1p72 and IE2p86 ( MAB 810 , Chemicon , Temeculah , CA ) , ICP36 ( CH16 ) and pp28 ( CH19 ) ( both from Virusys Corporation , Randallstown , MD ) or rabbit polyclonal antiserum to native vMIA [2] and peroxidase-conjugated horse anti-mouse IgG or goat anti-rabbit IgG , Texas Red-conjugated horse anti-mouse IgG ( all from Vector , Burlingame , Calif . ) , or AlexaFluor 350-conjugated goat anti-mouse IgG ( Molecular Probes , Eugene , OR ) . Immunoblot analysis of total protein from infected cells and immunofluorescence assays followed previously described methods [22] . MitoTracker Red CMXRos ( Molecular Probes , Eugene , OR ) staining of mitochondria followed previously described methods [22] . To assess morphological changes in infected cells and nuclei , cells grown on coverslips and infected for varying periods of time were fixed with 3 . 7% formaldehyde , permeabilized with Triton X-100 ( EMD Biosciences , Darmstadt , Germany ) , stained with Hoechst 33258 ( AnaSpec , San Jose , CA ) , and processed for microscopic evaluation as previously described [22] . Some cultures were stained with ethidium homodimer 1 ( Molecular Probes , Eugene , OR ) , as previously described [5] to assess virus-induced cell death . Images from live cell cultures were obtained as previously described [5] or utilized Simple PCI software , a Retiga Exi digital camera , and a Leica DM IRB microscope . Imaging of cultures grown on coverslip employed an AxioCam MRc5 camera attached to a Zeiss Axio Imager . A1 and AxioVision Release 4 . 5 software . Replication inhibitors included phosphonoformate ( PFA Sigma , St . Louis , Mo ) dissolved in water and 2-bromo-5 , 6-dichlorobenzimidazole ( BDCRB from L . B . Townsend , University of Michigan ) dissolved in dimethyl sulfoxide ( DMSO ) ( Sigma , St . Louis , MO ) . Protease inhibitors included TLCK , N-alpha-p-tosyl-L-lysine chloromethyl ketone , ( Sigma , St . Louis , MO ) in water , and UCF-101 or zVAD . fmk ( both from Calbiochem , La Jolla , CA ) dissolved in DMSO . Inhibitors were added by replacing culture medium with medium containing inhibitor while control medium included the appropriate solvent ( DMSO ) or no addition . Morphology and presence of viral nuclear antigens IE1p72 and IE2p86 were assessed as described above and viral yield was determined from total virus recovered on day 7 from supernatant and sonicated cells [5] . DMSO does not impact CMV death or CMV replication levels at the concentrations used ( ≤0 . 1% ) [5] . Transfections of BACmid DNA have been described [5] . GFP-positive ( GFP+ ) cells and GFP-positive foci ( >2 GFP+ cell ) were evaluated by live cell microscopy 2–10 days post transfection . Viral presence was confirmed by immunodection of viral nuclear antigens IE1p72 and IE2p86 and some experiments utilized a plasmid encoding GFP [5] for detection of transfected cells . Reported results were obtained from multiple DNA preparations . Conditions for induced apoptosis of ADvarATCC infections and vMIA-dependent survival following transfection of HeLa have been described [2] , [64] . Cell numbers were determined following Hoechst stain of surviving cells ( HeLas ) or following immunodetection of viral nuclear antigens IE1p72 and IE2p86 ( ADvarATCC ) , comparing to untreated controls . To assess the impact of HtrA2/Omi on HFs , GFP expression plasmid was cotransfected with control DNA ( vector ) or HtrA2/Omi or HtrA2S306A expression plasmids . By 48 h cells had recovered and were confluent . Images obtained of GFP fluorescence at 24 h intervals between 48–120 h post transfection were evaluated for numbers of GFP+ cells from 12 microscopic fields per day . Mean % survival ( ±standard deviation ) was calculated from numbers of GFP+ cells compared to those at 48 h . Sulforhodamine 101-leucine chloromethyl ketone , SLCK , ( Immunochemistry Technologies , LLC , Bloomington , MN ) was suspended in DMSO ( Sigma , St . Louis , MO ) . For labeling , 2 . 5 µM SLCK was applied in the presence or absence of TLCK to live cultures for 30 minutes prior to fixation and imaging as described above . | Cellular suicide is an effective host defense mechanism to control viral infection . Host cells encode proteins that induce infected cell death while viruses encode proteins that prevent death and facilitate viral replication . Human cytomegalovirus encodes vMIA to suppress host-initiated death pathways . Cytomegalovirus infection is controlled by the evolutionarily ancient mitochondrial serine protease , HtrA2/Omi . HtrA2/Omi levels rise dramatically within mitochondria at late times during viral infection , eventually overcoming viral control of a cell death pathway that is dependent on this serine protease and independent of the well-studied apoptotic cell death pathway that conventionally depends upon a class of proteases called caspases . vMIA naturally counteracts HtrA2/Omi-dependent cell death and allows infected cells to survive and produce virus for several days . The natural inhibitory role of vMIA can be overwhelmed by overexpression of HtrA2/Omi in virus-infected cells , but uninfected cells are insensitive to HtrA2/Omi-induced death . The broad distribution of HtrA2/Omi within mammalian host species suggests this may represent an ancient antiviral response or a process of viral detente that establishes the timing of infection . Either way , the success of cytomegalovirus rests in the balance between cell death initiation and the viral cell death suppressor vMIA . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [
"infectious",
"diseases/viral",
"infections",
"virology/host",
"antiviral",
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] | 2008 | HtrA2/Omi Terminates Cytomegalovirus Infection and Is Controlled by the Viral Mitochondrial Inhibitor of Apoptosis (vMIA) |
Protein phosphatase 2A ( PP2A ) plays a major role in dephosphorylating the targets of the major mitotic kinase Cdk1 at mitotic exit , yet how it is regulated in mitotic progression is poorly understood . Here we show that mutations in either the catalytic or regulatory twins/B55 subunit of PP2A act as enhancers of gwlScant , a gain-of-function allele of the Greatwall kinase gene that leads to embryonic lethality in Drosophila when the maternal dosage of the mitotic kinase Polo is reduced . We also show that heterozygous mutant endos alleles suppress heterozygous gwlScant; many more embryos survive . Furthermore , heterozygous PP2A mutations make females heterozygous for the strong mutation polo11 partially sterile , even in the absence of gwlScant . Heterozygosity for an endos mutation suppresses this PP2A/polo11 sterility . Homozygous mutation or knockdown of endos leads to phenotypes suggestive of defects in maintaining the mitotic state . In accord with the genetic interactions shown by the gwlScant dominant mutant , the mitotic defects of Endos knockdown in cultured cells can be suppressed by knockdown of either the catalytic or the Twins/B55 regulatory subunits of PP2A but not by the other three regulatory B subunits of Drosophila PP2A . Greatwall phosphorylates Endos at a single site , Ser68 , and this is essential for Endos function . Together these interactions suggest that Greatwall and Endos act to promote the inactivation of PP2A-Twins/B55 in Drosophila . We discuss the involvement of Polo kinase in such a regulatory loop .
Greatwall ( Gwl ) is a highly conserved protein kinase that has been shown to have important mitotic functions in Drosophila , Xenopus and humans [1]–[10] . Loss-of-function alleles of the Drosophila gene Greatwall ( gwl ) lead to cell cycle delay at the G2-to-M transition and mitotic chromosomes show unusual states of condensation [1] . Depletion of the Gwl kinase from cultured Drosophila cells resulted in similar mitotic delays and a characteristic phenotype of conjoined chromatids scattered upon mitotic spindles that were elongated as if in anaphase B [2] . Xenopus Gwl is activated by MPF and is required for M-phase entry . Removal of Gwl from CSF Xenopus extracts leads to an unusual mitotic exit in which cyclins remain undegraded but Cyclin-dependent kinase 1 ( Cdk1 ) is inactivated by phosphorylation at Thr14 and Tyr15 [3] , [5] . Thus it seemed that Gwl could facilitate activation of Cdk1 via the phosphorylation-dependent activation of Cdc25 and inhibition of Myt1/Wee1 [5] . That Gwl might be regulating a protein phosphatase was suggested by the finding that addition of the phosphatase inhibitor okadaic acid re-enables Gwl-depleted interphase extracts to enter M phase [5] . This phosphatase proved to be PP2A , a heterotrimeric protein comprising a catalytic C subunit , a structural A subunit , and one of several regulatory B subunits , in this case the B55 regulatory subunit . Inhibition or depletion of PP2A from mitotic extracts rescued the inability of Gwl-depleted extracts to enter M phase [6] , [7] . PP2A-B55 has been shown to be a major protein phosphatase responsible for reversing Cdk1-mediated phosphorylation in both Drosophila [11] and Xenopus [12] . Two recent biochemical studies have identified two related substrates of Greatwall kinase , α-Endosulfine ( Ensa ) and Arpp19 , as inhibitors of PP2A in Xenopus egg extracts [13] , [14] . The first gwl allele to be identified in Drosophila was a gain-of-function allele given the name Scant [15] . When heterozygous with one mutant copy of polo ( and one copy of polo+ ) , the gene for the mitotic Polo kinase , Scant causes females to produce embryos that have greatly reduced viability . Because the Scant mutation causes no recessive phenotype , it could not be mapped precisely until recessive alleles of its gene were identified . This was achieved by inducing revertants of the polo-Scant sterility; three of these are simple recessive mutations in gwl [4] . Two of these gwl alleles showed mutant phenotypes in larval neuroblasts similar to those previously described [1] and one allele , a female-specific germline splicing mutant , showed only female sterility . The oocytes of females hemizygous for this allele , gwlSr18 , fail to arrest in metaphase of the first meiotic division and both homologues and sister chromatids separate on elongated meiotic spindles with little or no segregation . The Scant mutation results from a single amino acid change , K97M; this shows dramatically increased activity of the kinase towards artificial substrates . Surprisingly , the defect in embryos from polo Scant/+ + mothers is simple and single; centrosomes tend to detach from the nuclear envelope during migration during the syncytial divisions [4] , leading to aberrations in subsequent mitoses . Since Scant has no phenotype when there are two wild-type copies of polo , and polo itself is recessive , this observation implies that the level of functional Polo ( or one of its targets ) is reduced in the presence of Scant; and that in turn suggests that maintaining centrosome-nuclear envelope conjunction is the Polo function requiring the highest level of Polo activity , since everything else that Polo is known to promote still occurs normally in these embryos . The highly sensitized polo Scant/+ + genotype therefore allows us to probe this specific role of Polo protein . Scant revertants also included two polo+ duplications , consistent with the requirement for reduced polo function in order to see reduced fertility in the presence of gwlScant , and a third suppressor genotype that is a large deficiency elsewhere . We now show that this latter suppression is due to reduced dosage of endos , which encodes the Drosophila α-Endosulfine , previously shown to be required for oocytes to progress to metaphase of the first meiotic division and to regulate levels of Twine , the germline-specific Cdc25 phosphatase , and Polo kinase in those oocytes [16] . We also show that mutations in the catalytic and B55 ( twins in Drosophila ) regulatory subunits of PP2A enhance the polo Scant maternal effect suggesting antagonistic effects of Endos and PP2A . We find that down-regulation of endos in tissue-culture cells leads to abnormal mitotic cells having elevated cyclin B and elongated spindles on which chromosomes are highly scattered and have not undergone sister chromatid separation . This phenotype is suppressed by simultaneous depletion of the catalytic or structural subunits of PP2A and by its twins regulatory B subunit but not by depletion of its other regulatory B subunits . These genetic interactions in Drosophila are in accord with the recently published biochemical studies in Xenopus [13] , [14] . Indeed we find that Drosophila Greatwall kinase phosphorylates Endos at a single site and mutation of this residue perturbs Endos's mitotic function . Thus the major aspects of the Greatwall – Endos – PP2A regulatory circuit appear to be conserved in evolutionarily diverged metazoans .
To gain insight into Greatwall function , we first examined the cytology of the above third site suppressor in larval salivary gland chromosomes and found it to be a deficiency on 3L: Df ( 3L ) Sr5 , 70C7-15;70F3-7 . Identification of the suppressor was facilitated by recombining the gwlScant mutation and polo1 or polo11 onto the same chromosomes [4] . Tests with independent large deficiencies heterozygous with this polo1 gwlScant chromosome confirmed that the original suppression is due to haplo-insufficiency of a gene ( s ) in the 70C7-70D5 interval . Tiling this interval with small deficiencies mapped the major suppressor to the 70C7-15 interval within which lies the endos gene previously shown to encode a small phospho-protein , α-endosulfine or Endos . In a direct test of the ability of endos mutants to act as dominant suppressors , we found that endos/polo1 gwlScant females ( one copy of endos+; Figure 1 , Table 1 ) are reasonably fertile ( 11 adult progeny per female per day for endosEY01105 ) , whereas + +/polo1 gwlScant females ( two copies of endos+ ) are nearly sterile ( 0 . 6 adult progeny per female per day ) . We then asked what the consequence of increasing the gene dosage of endos+ is by testing the fertility of polo1 gwlScant/+ + females also heterozygous for an endos+ transgene ( see below ) ; three copies of the wild-type endos gene enhanced the polo1 gwlScant phenotype ( Figure 1 , Table 1 ) , i . e . , there were no adult progeny at all , or , indeed , any egg development . Several laboratories have shown that the Xenopus counterpart of Greatwall kinase acts by down-regulating PP2A activity ( see Introduction; [5]–[7] ) . These findings therefore led us to confirm our crude observations that PP2A mutants enhance the infertility of polo1 gwlScant . We found that each of two independent mutations in twins that encodes the B55 regulatory subunit , twsaar1 [17] and twsP [18] , were completely female sterile when heterozygous with polo1 gwlScant ( Figure 1 , Table 1 ) . Furthermore , mutation in the catalytic C subunit , microtubule star ( mtsE2202; [19] ) , also led to complete sterility when heterozygous in polo1 gwlScant/+ + females . Thus , in contrast to endos , mutations in the PP2A subunits enhance the Scant phenotype . In addition , endos/+ also slightly suppresses the sterility of PP2A/polo gwlScant females in an allele-specific manner: endosEY01105 twsaar1/polo1 gwlScant ( stronger endos allele , weaker tws allele , see Materials and Methods ) females produced a few progeny ( Table 1 ) whereas endosEY01103 twsP/polo1 gwlScant ( weaker endos allele , stronger tws allele ) produced eggs that died without any significant embryonic development . Together these genetic interactions suggest that endos and tws ( PP2A-B55 ) have opposing roles in regulating the dominant effect of gwlScant when Polo function is reduced . For suppression , here of the polo gwlScant sterility by endos , the prediction is that there will be no effect when the two components are tested separately , and indeed both the endos alleles are fully fertile trans-heterozygous either with polo1 or polo11 alone or with gwlScant alone ( Table 1 ) . In these genotypes a single mutant copy of either endosEY01103 or EY01105 has no effect upon Polo levels in contrast to the reduction in Polo levels seen in oocytes/ovaries from homozygous endos females ( [16] , Figure S1A ) . Enhancement , on the other hand , here of the polo gwlScant sterility by PP2A , offers the possibility of asking which component is the more important . Although no reduction in fertility was observed in polo1/PP2A ( tws or mts ) transheterozygotes , these PP2A mutations have reduced fertility when transheterozygous with the amorphic allele polo11 or with the dominant gwlScant . Moreover , the stronger twsP allele ( see Materials and Methods ) reduces fertility of heterozygous polo11 more than the weaker twsaar1 allele . As expected , reducing the dosage of endos+ has little effect on the already-fertile combinations of PP2A ( tws or mts ) with polo1 , but such reduction generally improves the fertility of PP2A/polo11 females; indeed , the endosEY01105 ( stronger allele ) twsaar1 ( weaker allele ) /polo11 combination has nearly normal fertility . Thus , even in the absence of the confounding gwlScant mutation , Endos and PP2A-BTwins can still be seen to have opposing roles . Together , these genetic interactions suggest that endos and tws ( PP2A-B55 ) have opposing roles in responding to the dominant effect of gwlScant . Since Gwl has been shown to down-regulate PP2A activity in Xenopus , this suggests that Gwl and Endos might also function together towards this end in Drosophila . The above findings led us to ask whether loss of endos function has the same consequences for mitotic progression that loss of gwl does . Because only maternal-effect endos phenotypes had been reported for female meiosis and in the rapid mitotic cycles of syncytial embryos [16] , we examined the zygotic mitotic phenotypes of endos mutants to determine whether they are similar to gwl . Flies hemizygous for endos67006 , a P-element insertion in the 5′ region of the endos gene [20] , had multiple defects typical of abnormalites in cell cycle progression: shrivelled wings , missing thoracic bristles , irregular abdominal bristles , disrupted tergites and legs , and male and female sterility . These mutant phenotypes were all reverted following precise excision of the P element responsible for the mutation , a procedure that also generated additional alleles ( endos79 , endos60 and endos1 ) from imprecise excisions ( Figure S1B ) . A transgenic rescue construct containing the endos gene , but not one carrying the divergent transcription unit ( CG6650 ) , rescued the mutant phenotypes and sterility of all alleles tested . The new endos alleles generated in this way were similar to the original P-element mutant with respect to their external phenotype and sterility , with the exception of endos79; here , males are fertile , suggesting that it is a weaker hypomorph . Western blotting , using an antibody raised against the full-length Endos protein , showed that there was no detectable protein present in larval neuroblasts isolated from the endos1 mutant ( Figure S1C ) in accord with its partial deletion of the ORF ( Figure S1B ) . Protein levels in endos60 were similar to the parental endos67006 whereas higher levels were found in endos79 . Larval brains from homozygous endos1 null larvae were approximately half the wild-type size , suggesting cell cycle defects [21] . Squashed preparations of larval neuroblasts revealed an almost doubling of the mitotic index and an increase in the proportion of metaphase∶anaphase figures ( 4∶1 in wild-type compared to 11∶1 in hemizygous endos1; Table S1 ) . The few anaphases present showed a high incidence of anaphase bridging; this was never seen in wild-type cells and was proportional to the strength of the mutant allele examined ( Figure 2A ) . Some mitotic cells were very difficult to score , because they did not appear to be fully in mitosis judged by the low level of condensation of their chromosomes; nevertheless , they were positive for phospho-histone H3 , an indicator of mitotic activity of the Aurora B kinase ( Figure 2A ) . Taken together these phenotypes are very similar to those of recessive alleles of gwl [1] , [4] . This is in accord with the two proteins acting as positive mediators of the same pathway and the ability of reduced dosage of endos to suppress the dominant phenotype of polo1 gwlScant . Defective mitoses accrue in mitotic mutants of Drosophila as the maternally provided wild-type proteins are gradually depleted during development . The kinetics of this depletion depends upon the particular protein and also upon the nature of the mutant allele . It is therefore not uncommon for mutant phenotypes to be complicated by secondary defects that follow from the primary one . Therefore , as an alternative way to examine the mitotic effect of endos , we examined cultured DMEL cells following depletion of Endos by RNAi . A 72 h treatment of cells with dsRNA targeting the endos gene eliminated >90% of the Endos protein and led to a decreased proportion of cells in G1 ( from 53 . 0% in GFP depleted control cells to 38 . 7% in Endos depleted cells; Figure S2 ) . Immunostaining of fixed preparations also revealed an increase in the mitotic index . This was associated with a substantial reduction of the proportion of mitotic cells in telophase and cytokinesis ( 39% in control cells vs . 15% in endos RNAi cells; Table S2 ) . Time lapse imaging of cells expressing GFP-Cid ( Drosophila CENP-A; [22] ) and β-tubulin-mRFP revealed that , following depletion of Endos , prometaphase was greatly prolonged in comparison with control cells ( Figure 2B , Table S3 ) . The centromeres of chromosomes did not congress fully and became scattered as the spindle eventually began to elongate in what appeared to be an attempted anaphase . This led in some cases to unequally distributed chromosomes ( Figure 2B , Video S1 and Video S2 ) . This scatter was also observed in immunostained fixed preparations in which a high proportion ( 84% ) of mitotic cells following endos RNAi treatment cells showed mis-aligned and scattered chromosomes frequently on elongated spindles ( Figure 2C , Table S2 ) . Whereas in control cells depleted with dsRNA targeting the GFP gene ( GFP-RNAi ) , the Drosophila Shugoshin , MeiS332 ( reviewed in [23] ) , is lost from centromeres at the metaphase-anaphase transition , in endos RNAi-treated cells it was still present at the centromeric regions of many of the scattered chromosomes ( Figure 2C ) . Furthermore , whereas control cells with elongated spindles had undergone Cyclin B destruction at the metaphase-anaphase transition , endos RNAi-treated cells with elongated spindles and scattered chromosomes still had high levels of Cyclin B ( Figure 2C ) . The presence of at least some conjoined centromeres and presence of Cyclin B suggests a prolonged checkpoint response that markedly delays APC/C-dependent processes at the metaphase – anaphase transition . Such phenotypes are extremely similar to the phenotype we reported for the depletion of Greatwall kinase in cultured cells ( [2] , Figure S4 in [4] ) . Thus this assay is also in accord with Greatwall and Endos participating in the same pathway . Further support for this conclusion comes from the similar patterns of sub-cellular localisation of Endos ( Figure S3A , Figure S3B ) and Greatwall ( Figure S7 in [4] ) . The endos mutations used in the genetic studies are homozygous viable but have delayed eclosion relative to their balancer siblings . We found that this eclosion delay is rescued in endos +/endos tws flies . However , the rescued females are still completely sterile , so for phenes other than developmental timing the suppression is weak . Moreover , there was little effect of these endos mutations , even as homozygotes , upon the lethal phase of tws mutants . Notwithstanding the multiple developmental roles of PP2A and the pleiotropy of its phenotypes , there are also considerable difficulties in making comparisons of mutant mitotic phenotypes in pupae because of the potential for differential perdurance of maternal protein during the first week of development . Thus we turned to cell culture to examine the consequences of co-depleting either Greatwall or various PP2A subunits upon phenotypes characteristic of endos depletion . We chose to quantitate the dispersion of chromosomes on prometaphase figures because this phenotype is the most characteristic of the endos knockdown ( Figure 3 ) . gwl knockdown gives a similar , but less dramatic , phenotype . Upon the double knockdown of endos and gwl there is only a slight increase in the severity of the phenotype ( Figure 3C ) suggesting that Endos and Greatwall are acting in the same pathway to control chromosome dispersion on the spindle . In contrast , the endos depletion phenotype was suppressed by co-depletion of the B subunit of PP2A ( B55 ) encoded by twins but not by depletion of any of Drosophila's other three B subunits , Widerborst , B′ or B″ . Depletion of either the catalytic ( mts ) or structural ( PP2A 29B ) subunits also suppressed the endos knockdown phenotype ( Figure 3C ) . Cells depleted of either mts or PP2A 29B in addition to endos were not completely normal in distribution of microtubules; they had many long microtubules that were not captured by the spindle ( Figure S4B ) . Nevertheless , the rescue of spindle morphology in doubly-depleted cells was striking . Another phenotype of Endos depletion , telophase cells with chromosome bridges , was also suppressed by simultaneously depleting the Twins B subunit of PP2A ( Figure S4C , Figure S4D ) . That Endos knockdown is suppressed by knockdown of components of the PP2A-Twins heterotrimer is in accord with the genetic interactions of the gain-of-function gwlScant allele with mts and tws mutants suggesting that Endos functions by opposing PP2A function . Because the level of Polo has been reported to be greatly diminished in Drosophila oocytes from endos homozygous mutant females [16] , we assayed whether Polo levels are affected after depletion of Endos in cell culture . In the conditions used for our assay neither single depletion of Endos or Greatwall or combined depletions of Endos and PP2A subunits lead to any major effect on Polo levels ( Figure S4A ) . The above findings led us to ask whether Greatwall and Endos might be working together because Endos is a substrate of Greatwall . To this end we immunoprecipitated Greatwall protein from extracts of Drosophila cell lines stably expressing Myc-tagged wild-type Greatwall ( Gwl wt ) , Scant mutant Greatwall ( Gwl act: K97M ) , or kinase dead Greatwall ( Gwl KD: K87R ) to determine whether these enzymes were able to phosphorylate Endos expressed as a GST-fusion protein in bacterial cells and released by thrombin protease after affinity purification ( Figure 4A , Figure S5A ) . The wild-type and activated ( K97M ) forms of Greatwall were able to phosphorylate wild-type Endos but not a variant of Endos in which Serine 68 was mutated to an Alanine residue; this single mutation totally abolished Greatwall kinase-mediated phosphorylation of Endos ( Figure 4A ) . Mass spectrometric analysis also identified Serine 68 as the single site phosphorylated in vitro by Greatwall in the Endos protein . It lies in a region strongly conserved with the analogous Greatwall kinase phosphorylation site in Xenopus Ensa [13] , [24] . Several lines of evidence suggest that Endos is phosphorylated at Serine 68 in vivo . Firstly , we were able to detect this modification in cell extracts using an antibody directed specifically against an Endos peptide phosphorylated at Serine 68 ( [13] , Figure 4B ) . Endos phosphorylated at Serine 68 ( P-Ser68 Endos ) could be weakly detected in extracts of asynchronous cells and more strongly after okadaic acid treatment ( Figure S5B , Figure S5C ) . This treatment also resulted in an increase in total Endos levels suggesting that the phospho-form might show increased stability ( Figure S5B , Figure S5C ) . Expression of the hyperactive Greatwall mutant ( Gwl act: K97M ) gave no elevation of P-Ser68 Endos above that seen following okadaic acid treatment ( Figure 4B ) . Expression of a kinase-dead mutant form of Greatwall , however , resulted in a reduced level of P-Ser68 Endos , suggesting that it competes with endogenous Greatwall in phosphorylating this site ( Figure 4B ) . Treatment of cells with varying concentrations of okadaic acid followed by electrophoresis of the cell extracts on conventional SDS-containing gels or SDS gels also containing Phos-tag ( Figure S5B , Figure S5C ) revealed that Endos is subject to at least three different phospho-modifications . The sensitivity of one of these sites to low concentrations of okadaic acid suggests that it is normally dephosphorylated by PP2A but P-Ser68 and at least one other phosphorylation are sensitive to two other protein phosphatases . Together this suggests that Endos is phosphorylated in vivo at Serine 68 and at least two other amino acid residues . The kinases responsible for these phosphorylations are quite likely to be PKA and CDK or kinases related to them since these enzymes are known to phosphorylate Endos in Xenopus [13] . To determine whether Serine 68 is essential for the function of Endos in cultured cells , we mutated this residue to Alanine ( S68A ) . We used dsRNA directed against the 3′ non-coding sequence of endogenous endos to deplete its protein from cultured cells leading to the characteristic endos depletion phenotype described above ( Figure 4C , Figure 4D ) . Complete rescue of the phenotype was achieved by transfecting these cells with a construct expressing HA-tagged wild-type Endos protein from the Actin 5C promoter . In contrast , the S68A Endos construct was completely unable to rescue the phenotype . We then asked whether substitution of an acidic amino acid for Serine 68 mimics the effects of phosphorylation but substitution by an Aspartic acid residue ( S68D ) also failed to rescue depletion of the wild-type protein ( Figure 4C , Figure 4D ) . The inability of the S68D mutant to rescue the phenotype might reflect either the degree to which the acidic amino acid can mimic a phosphate residue at this site or the possibility that the ability to cycle between phosphorylated and dephosphorylated states is required at different mitotic stages ( see also below ) . Nevertheless , together these experiments strongly suggest that Greatwall-mediated phosphorylation of Endos on Serine 68 is required for its function .
We identify endos mutations as heterozygous suppressors of the dominant mutant phenotype of polo1 gwlScant . This suggests that Greatwall and Endos promote the same mitotic pathway . In accord with this we find that the consequences of loss of gwl and of endos function in mitosis are very similar . We found that larval neuroblasts from homozygous endos mutants show poorly condensed chromosomes and anaphase bridging , a phenotype very similar to recessive gwl mutants . In cultured Drosophila cells , depletion of endos interferes with proper mitotic exit and allows cells to accumulate that have elongated spindles but have not undertaken chromatid separation or Cyclin B destruction . This is similar to the removal of Gwl from CSF Xenopus extracts; there , an unusual mitotic exit occurs in which cyclins remained undegraded but Cyclin-dependent kinase 1 ( Cdk1 ) is inactivated by phosphorylation at Thr14 and Tyr15 [3] , [5] . Three lines of genetic evidence indicate that Greatwall and Endos are required to down-regulate the function of B55/Twins-bound PP2A . Lowering the dosage of either the catalytic C subunit or the B55/Twins regulatory subunit of PP2A enhances the maternal dominant effect of polo1 gwlScant and this is suppressed by lowering the dosage of endos . Secondly , opposing roles for Endos and PP2A in regulating Polo kinase function are seen in the absence of the gwlScant mutation; the low fertility of twins/polo trans-heterozygous females is also dramatically suppressed by one mutant copy of endos . Thirdly , the Endos depletion phenotype in cultured cells is suppressed by simultaneous depletion of either the catalytic C subunit , the structural A subunit , or the B55/Twins regulatory subunit of PP2A but notably not by co-depletion of the three other regulatory B subunits . Together these interactions suggest that Greatwall activates Endos leading to the inhibition of PP2A-B55/Twins . This is in accord with recent studies in Xenopus showing that inhibition or depletion of PP2A-B55 from mitotic extracts rescues the inability of Gwl-depleted extracts to enter M phase [6] , [7] and also with two recent biochemical studies that show that the Xenopus counterpart of Gwl kinase can phosphophorylate two related members of the cAMP-regulated phosphoprotein family , Ensa ( the Endos counterpart ) or Arpp19 , to make these molecules highly effective inhibitors of PP2A [13] , [14] . Endos is the unique cAMP-regulated phosphoprotein family member in Drosophila [24] . Indeed , such is the degree of conservation that Drosophila Gwl kinase phosphorylates Endos only at Serine 68 , a site essential for Endos function; this is the exact counterpart of the Serine 67 site in Xenopus . Studies in Drosophila , Xenopus and human cells [11] , [12] , [25] indicate that PP2A is a major protein phosphatase acting to dephosphorylate Cdk1 substrates . Thus gwl or endos reduced-function mutants should have increased activity of PP2A and therefore accumulate dephosphorylated Cdk1 substrates . Failure of Cdk1 substrates to become maximally phosphorylated in spite of high levels of Cyclin B accumulation would account for the prolonged prometaphase-like state and the eventual development of elongated spindles without having appeared to activate the anaphase-promoting complex in these mutants ( Figure 5 ) . This leads to a model in which Greatwall kinase is active in mitosis in order to convert Endos into an inhibitor of PP2A-Twins/B55 , which is then inactived upon mitotic exit to permit the dephosphorylation of Cdk1 substrates by this phosphatase . The above simple model is , however , confounded by genetic interactions suggesting that the gain-of-function mutation gwlScant negatively regulates the function of the mitotic kinase Polo or one of its downstream targets . Such evidence comes largely from the search for suppressors of polo11 gwlScant that identified mutations in two broad categories [4]: 1 ) those that decrease the effect of Gwl or its downstream targets as exemplified by endos mutations and reversion of gwlScant to recessive mutant alleles; 2 ) those that increase the activity of Polo kinase such as the polo+ duplications we obtained . Moreover , the degree of sterility ( adult progeny per female ) and frequency of embryonic centrosome loss co-vary with strength of polo allele [4] . polo1 , a hypomorphic allele with sufficient residual Polo function to be homozygous viable , is slightly fertile heterozygous with Scant and its embryos are only moderately defective , whereas polo11 , a lethal amorphic mutation , is completely sterile heterozygous with Scant and its embryos are much more defective . Furthermore , over-expressing Map205 ( a known binding partner of Polo which sequesters the kinase on microtubules ) in ovaries of polo11/+ mothers mimics Scant regarding the centrosome detachment phenotype , and more defective nuclei are seen when the transgene carries a mutation preventing Polo release [26] . Together our results suggest that the specific defect in Scant polo-derived embryos , detachment of centrosomes from the nuclear envelope , is a consequence of the reduction of the level of functional Polo below a critical threshold . Indeed this is the only phenotype we have been able to attribute to the Scant allele of gwl and its sensitivity to the gene dosage of polo suggests that this function requires the highest level of Polo kinase activity in comparison to all of Polo's other roles . It is important to note that centrosome detachment is an interphase phenotype . It occurs after the centrosomes have separated , which in wild type is during telophase in anticipation of the next round of mitosis in the rapidly alternating S and M phases of the syncytial Drosophila embryo . In the normal mitotic cycle , Greatwall kinase would not be active at this stage . Thus the functional complex of PP2A and its B55/Twins regulatory subunit seems to be required to positively regulate Polo activity or a process controlled by Polo between the exit from one mitotic cycle and entry into the next . This accounts for our finding that mutations in the PP2A subunit genes , mts and twins , enhance sterility when transheterozygous with polo11 , and that this sterility is in turn relieved by heterozygous endos mutations . Although it is possible that PP2A removes an inhibitory phosphorylation from Polo , this seems unlikely because no such phosphorylation has been identified to date . Thus we favour the alternative that PP2A acts to stimulate a process promoted by Polo and a dephosphorylated partner . Indeed it is known that Polo interacts with phosphorylated partners after mitotic entry and with dephosphorylated partners from late anaphase onwards ( reviewed in [27] ) . We depict this model in the context of the oscillating Cdk1+ ( prophase and prometaphase ) vs . Cdk1- ( post anaphase ) states in Figure 5A . In prophase and prometaphase Greatwall , activated by Cdk1 , inhibits PP2A via Endos . This sustains the Cdk1 phosphorylation that enables Polo to bind phosphorylated partners ( e . g . , protein X in Figure 5A ) for its early mitotic functions . Once Cdk1 activity levels have fallen at anaphase , Polo instead binds a set of proteins dephosphorylated at their Cdk1 sites by PP2A-B55/Twins ( e . g . , protein Y in Figure 5A ) . At least one such protein of the Y-type is required to maintain attachment of the centrosome to the nuclear envelope although the specific functions of Polo in maintaining the attachment of the centrosome to the nuclear envelope remain to be uncovered . We suggest that , in the syncytial embryo , the kinase-activating Scant mutation of Greatwall leads to inappropriate inactivity of PP2A in interphase reducing the levels of functional complexes between Polo and dephosphorylated partners ( Figure 5B ) . In this model , the gain-of-function GreatwallScant kinase tips the balance by transiently reducing interphase PP2A levels to a point that prevents sufficient dephosphorylation of Polo's interphase partner ( s ) . We postulate that one such partner might be required for a threshold activity of Polo needed to maintain centrosome attachment . Supporting evidence for the combined roles of PP2A and Polo in a common process comes from the accompanying study [28] . In this analysis , Archambault's lab has systematically placed chromosomes carrying either the polo11 allele or gwlScant against chromosome deficiencies from the DrosDel deficiency core kit . This led to the independent identification of twins as an enhancer of polo11 and of gwlScant; as in our study , embryos derived from twins/polo11 or twins/gwlScant trans-heterozygous mothers exhibit lethality . Interestingly , their analysis of the mutant phenotype of both polo11 or twins-derived embryos indicated that both genes were indeed required to maintain the attachment of centrosomes to the nuclear envelope . Our studies support a major mitotic role of the Greatwall kinase in phosphorylating Endos to activate it as an inhibitor of PP2A that is bound to its B55/Twins regulatory subunit and thus promote the mitotic state . This model can accommodate a differential effect upon Polo kinase regulation depending upon the phase of mitosis and so seems likely to be a conserved central tenet of mitotic regulation . However , there are other anomalies that suggest that this may not be the complete story . Several pieces of evidence point to PP2A not being the only protein phosphatase able to reverse Cdk1-mediated phosphorylation . A debate about the relative importance of PP1 and PP2A in this function has been ongoing for years and indeed PP2A in association with other regulatory B subunits has other mitotic functions [29] , [30] . We were unable to demonstrate a substantial inhibitory effect of phospho-Endos upon the ability of PP2A to dephosphorylate histone H1 phosphorylated by Cdk1 ( data not shown ) . Gharbi-Ayachi and colleagues and Mochida and colleagues , using respectively either c-Mos or a phosphopeptide derived from Cdc20 , were able to demonstrate such an inhibitory activity with Xenopus phospho-Endos [13] , [14] although the use of other substrates for this type of assay was apparently less effective ( Hunt , personal communication ) . The involvement of other phosphatases would certainly add complexity to this simple model . From a genetic perspective , it is also noteworthy that we observed only weak zygotic interactions between endos and twins mutants . For example , adding either tws allele to either endos allele ( as endos+/endos tws ) rescues the eclosion delay and the mild cuticular phenotype but not the female sterility; since the delays are only 1 or 2 days in the first place , this rescue is mild , though it is encouraging that the apparently stronger endosEYO1105 is rescued just as well as the apparently weaker endosEYO1103 allele . Although none of these mutations is amorphic , they nevertheless each reduce their products enough so that even as heterozygotes they give strong interactions in the polo1 Scant test . The above considerations , together with the finding that endos null mutants ( e . g . , endos1 ) are viable ( but sterile ) , suggests that Endos function may have particular importance in the germline and in the rapid cycles of the early embryo . In this context , it is important to distinguish between the effects we describe here of endos on Polo function and previously reported effects of endos on Polo levels in the female germline [16] . The present study examines embryos derived from heterozygous females that have at least 50% of the wild-type levels of Endos and in which levels of Polo are not significantly affected . However , this reduction in Endos is sufficient to rescue a common function of polo and PP2A-twins in the nuclear cycles of the syncytial embryo . In this context , the function of wild-type Endos levels would be to down-regulate Polo activity . On the other hand , the study of Von Stetina and colleagues [16] examines female meiosis in heteroallelic or hemizygous endos combinations where levels of Endos are reduced by more than 95% . This leads to substantial reductions in Polo kinase levels that are likely to contribute significantly to the meiotic phenotypes observed . In this context , the requirement for Endos appears to be to facilitate the post-transcriptional regulation of the expression of the polo ( and cdc25twine ) genes . This might have resonance in the recently demonstrated functions of budding yeast proteins Rim15 , Igo1 and Igo2 , ( counterparts of Greatwall , Endos/Ensa and Arpp19 ) in translational control in quiescent cells [31] . The observations that Polo protein is reduced in oocytes/ovaries of endos homozygotes ( [16] , Figure S1 ) but not in ovaries of heterozygotes ( Figure S1 ) and apparently not in their embryos ( endos/polo and endos/gwlScant females are fertile ) or in cultured cells after Endos depletion suggest that female meiosis and early embryogenesis have different regulation . Indeed , this could reflect the absence of centrosomes in female meiosis . Finally , it is unlikely that Endos is the only substrate of Greatwall kinase . This may help to provide an explanation for the observation that , although down-regulation of gwl or endos function leads to similar defects in mitosis , the phenotypes of their mutants in female meiosis are quite different . endos mutants show prolonged prophase and failure to progress to metaphase I , whereas a germline-specific gwl mutant exhibits failure to maintain arrest at metaphase I and premature progression through all of the meiotic stages . Taken together , these anomalies are reminders that our understanding of the regulation of protein phosphatases in cell cycle progression is still rudimentary .
Fly stocks used were described previously [4] , [20] or obtained from the Bloomington Drosophila Stock Center . The endos alleles 79 , 60 , and 1 resulted from remobilisation of the P element in P{lacW}endosSO67006 , referred to as endos67006 in this paper . The endos alleles EY01103 and EY01105 are also associated with P-element insertions ( FlyBase ) ; both strongly reduce the level of protein detected by an anti-Endos antiserum in Western blots ( Figure S1 ) though traces remain . Female fertility was tested by taking single newly-eclosed females , adding Oregon R males , and scoring daily for the onset of egg laying; that day = day 0 . Parents were transferred to fresh food every three days for a total of 15 days egg laying then discarded; progeny were counted ( each vial separately ) until eclosion was complete . Six females per genotype were tested , three each on two types of medium; the results presented here are all from the richer medium – the other medium gave identical rankings . Relative strengths of the endos P insertion alleles EY01103 and EY01105 and the tws alleles aar1 and P are based on phenotypic observations , namely: EY01105 has longer eclosion ( adult emergence from the pupal case ) delay than EY01103 , so it seems to be the stronger of the two , even though the levels of residual protein in ovary extracts are indistinguishable ( Figure S1A ) . Moreover , EY01103 homozygous males are fertile , so it is a hypomorphic mutation ( see text re endos79 ) . Males homozygous for the EY01105-bearing chromosome are sterile , but this sterility has not been mapped so may be due to an additional , extraneous male sterile mutation . aar1/P pupae die later than P/P so P seems to be the stronger . These rankings are consistent with other phenotypic comparisons , see Results . Gateway vector pAGW from Invitrogen encoding GFP downstream of the actin 5C promoter and greatwall Gateway entry clones [4] were used as plasmid templates to generate dsRNA directed against GFP and Greatwall respectively . The endos entry clones were generated according to the instructions for the Gateway system ( primers are listed separately , Table S4 ) . endos ( CG6513 ) cDNA was obtained from DRGC clone LD18034 ( http://dgrc . cgb . indiana . edu/ ) . PCR products for the endos ORF flanked with att recombination sites were generated from LD18034 and ligated into the pDONR221 entry vector . Entry clones of endos , pDONR221-endos-stop and pDONR221-thrombin-endos-stop were generated that either did or did not contain a cleavage site for thrombin . The endos S68A and S68D mutants were made in the entry clones with the quick-change Site-Directed Mutagenesis Kit ( Stratagene , see primers list in Table S4 ) . GST-endos and GST-endos S68A constructs were obtained by LR recombination of pDONR221-thrombin-endos-stop and pDONR221-thrombin-endos S68A-stop entry clones into pDEST15 Gateway destination vector . pActin-HA-endos , pActin HA-endos S68A and HA-endos S68D constructs were obtained by LR recombination of pDONR221-endos-stop , pDONR221-endos S68A-stop and pDONR221-endos S68D-stop entry clones into the pAHW Gateway destination vector . The CG6513 ( endos ) rescue construct was generated by PCR amplification from fly genomic DNA with primers flanked with NotI restriction sites and cloned into the pCasPer4 vector using its NotI restriction site ( primers are listed in Table S4 ) . The CG6650 ( divergent transcript ) rescue construct was generated by PCR amplification from fly genomic DNA with primers flanked with NotI restriction sites and cloned into the pCasPer4 vector using its NotI restriction site ( primers are listed in Table S4 ) . The pCasPer4-endos-EGFP construct was generated from the CG6513 rescue construct by inserting an EGFP cassette in frame just before the stop codon of endos . A BglII restriction site was created by site-directed mutagenesis in place of the stop codon of endos in the CG6513 rescue construct so that the EGFP cassette flanked by BglII restriction sites could be inserted there ( primers are listed in Table S4 ) . The following antibodies were used: rabbit anti-phospho-Histone H3 ( Upstate ) , immunofluorescence ( IF ) 1/4000 for cells , 1/500 for larval brains; chicken anti-Dplp [32] , IF 1/1000; mouse anti-α-tubulin ( DM1A , affinity purified , Sigma ) , IF 1/1000 , Western blot ( WB ) 1/10000; rabbit anti-GFP ( Molecular Probes ) , IF 1/600; rabbit anti-actin ( A2066 , Sigma ) , WB 1/2000; mouse anti-γ-tubulin ( GTU88 , Sigma ) ; chicken anti-Cid [33] ( 10811 ) , IF 1/2000; rabbit anti-Cyclin B ( Rb271 ) [34] , IF 1/200; anti-Mei-S332 [35] , IF 1/10000; rabbit anti-Endos ( 7648 ) , WB 1/3000 . The antibody specific to Endos phosphorylated at Serine 68 was a generous gift from T . Hunt and S . Mochida [13] . This antibody was initially raised against the sequence surrounding the phosphorylated Ser67 in the Xenopus counterpart of Endos , Ensa : QKYFDSpGDYN . This sequence is close to the sequence surrounding the Ser68 in Drosophila Endos : QKFFDSpGDYQ . The secondary antibodies used were conjugated with Alexa 488 , Alexa 568 or Alexa 697 ( Molecular Probes , 1/800 ) and peroxidase ( Jackson Immunochemicals , 1/10000 ) . DMEL cells were grown at 25°C in Express Five SFM Drosophila media ( Invitrogen ) supplemented with L-glutamine ( 2 mM , Gibco ) and penicillin-streptomycin ( 50000units/L-50000 µg/L , Gibco ) . DMEL cells stably expressing Myc-Gwl wt , Myc-Gwl K87R ( kinase dead form ) and Myc-Gwl K97M ( hyperactive form ) under the actin promoter were described in [4] . DMEL cells stably expressing GFP-Cid and β-tubulin-mRFP were the generous gift of Luisa Capalbo ( University of Cambridge , Department of Genetics ) . Okadaic acid ( Potassium salt , Calbiochem ) treatments were performed by adding the drug at the indicated final concentration for 2 h prior to harvesting the cells . dsRNAs against endos ( coding region ) , greatwall and GFP ( as control ) were each made from plasmid DNA . dsRNAs against the 3′UTR of endos and the PP2A subunit genes microtubule star , 29B , twins , widerborst , B′ and B″ were all made from genomic DNA generated from DMEL cells . A list of primer pairs is given in the primers list ( Table S4 ) . 1 . 4×106 cells per well were plated in 6-well plates one day before transfection with 25 µg of dsRNA . For co-depletions , 25 µg of each dsRNA was used and single dsRNAs were supplemented with 25 µg of control dsRNA . dsRNAs in 10 µl of H2O were incubated with 20 µl of Transfast ( Promega ) and 970 µl of medium ( 960 µl for co-depletions ) for 15 min before transfection . The dsRNA solution ( 1 ml mix ) was then incubated on the cells for 1 h prior to the addition of 3 ml of medium . Cells were harvested after 3 days . Brains from 3rd instar larvae were dissected in PBS , fixed 20 min in PBS containing 10% formaldehyde , permeabilised in PBST ( PBS-0 . 1% Tween 20 ) for 2 min , and preincubated in PBST containing 1% BSA . Overnight incubations with the primary antibody were followed by 2×20 min washes before incubation for 2 h with secondary antibodies . Brains were washed again before mounting on slides in Vectashield containing DAPI ( Vector Laboratories ) . DMEL cells were harvested and plated on 13 mm diameter glass coverslips coated with concanavalin-A in a 24-well plate at 3×105 cells per well for 1–2 h before fixation . Cells were then pre-extracted for 5 s in 0 . 1% NP40 in BRB80 buffer ( 80 mM K-Pipes pH 6 . 8 , 1 mM MgCl2 , 1 mM Na-EDTA pH 8 ) and immediately fixed in BRB80-4% formaldehyde for 20 min . They were then permeabilised in BRB80-0 . 1% Triton-X100 for 10 min and washed 3×5 min with PBS . Antibodies were diluted in PBS containing 0 . 1% Tween 20-3% BSA and incubated for 1 h at room temperature or overnight at 4°C for primary antibodies and for 1 h at room temperature for secondary antibodies . Samples were washed after each incubation in PBS-0 . 1% Tween 20 . Finally , cells were rinsed in water and mounted on slides in Vectashield containing DAPI ( Vector Laboratories ) . Images were acquired with a Zeiss Axiovert 200 M microscope using a 100× objective , 1 . 4 NA , and a Coolsnap HQ2 camera controlled by Metamorph software ( Universal Imaging ) . Figures shown are projections of optical sections acquired at 0 . 2 µm z steps . Some image stacks were deconvolved using 10 iterations of the blind deconvolution algorithm in the AutoquantX software ( Media Cybernetics ) . All images were imported into Adobe Photoshop for contrast manipulation and figure assembly . For analysis , mitotic cells were detected by their phospho-Histone H3 staining . The phenotype of dispersed chromosomes in prometaphase was quantified in cells whose DAPI-stained chromosomes are not aligned on the equatorial plate . Cells exhibiting at least one chromosome ( disregarding the small 4th chromosome ) near the spindle poles ( visualized with Dplp as a centrosomal marker ) were scored as defective . This phenotype is given relative to the number of cells in prometaphase . Telophases with lagging chromosomes/chromosome bridges were quantified in cells whose two daughter nuclei had reformed . Cells exhibiting chromosomes dispersed between the two re-forming nuclei were scored as defective . This phenotype is relative to the number of cells in telophase . Flies expressing Endos-EGFP were held in egg-collection chambers and embryos were collected after 1 h at 20°C . Embryos were transferred to a sieve , dechorionated in 50% bleach for 1 to 2 min and washed with water . They were transferred to a drop of Voltalef oil on a membrane maintained in a metal frame and a 22×40 mm coverslip was placed on top . DMEL cells stably expressing GFP-Cid and β-tubulin-mRFP were depleted for Endos for three days or left untreated ( control ) . Time lapse recordings were carried out using a Zeiss Axiovert 200 fluorescence microscope equipped with the Perkin Elmer UltraVIEW RS confocal scanner and Volocity software . Images of embryos were acquired with a 63× objective , NA 1 . 4 , at a z-distance of 1 µm between planes using 2× 2 binning , every 30 s . Images of DMEL cells were acquired with a 100× objective , NA 1 . 4 , using 10 z slices and ca . 1 µm between planes with 2× 2 binning . Images were taken every 2 min for control cells and every 5 min for cells after Endos depletion . 1 ml of cells depleted for GFP or Endos for 3 days were pelleted at 1000 rpm for 5 min and resuspended with 200 µl PBS . 2 ml of cold 70% ethanol was added dropwise to the resuspended cells while vortexing . Cells were stored at −20°C until analysis . Before analysis , 10 ml of PBS was added to the cells and they were pelleted at 1800 rpm for 10 min . The supernatant was carefully removed and the cells were resuspended in 0 . 5 ml PBS containing 100 µg/ml propidium iodide ( Sigma ) and 100 µg/ml RNAse A ( from bovine pancreas , Sigma ) and incubated for 30 min at 37°C . The DNA content of the cells was analysed using a Beckton Dickinson FACScan and LSR , which required 30 , 000 cells for each sample . Results were analysed with Summit software from DaKoCytomation . Protein extracts from tissue-culture cells were prepared by resuspending pellets of cells in SDS-PAGE sample buffer ( 100 µl of sample buffer 2× per 1×106 cells ) and boiling for 5 min . Extracts equivalent to 6×105 cells were processed for Western blot analysis . Brains from third instar larvae were dissected in PBS and kept at −80°C . Tissues were pestle homogenized in 1D lysis buffer ( 50 mM Tris pH 8 , 150 mM NaCl and 1% NP40; 5 µl of buffer per brain ) , incubated on ice for 20 min and centrifuged at 13 , 000 rpm for 10 min . Soluble protein fractions were processed for Western blot analysis; variable amounts of samples were used to obtain equal loading: Oregon R , endos67006 , CG6513 , CG6650 , endos79 , each equivalent to 1 brain; endos60 , equivalent to 1½ brains; endos1 , equivalent to 2 brains . Ovaries of adult flies were dissected in 0 . 7% NaCl and kept on dry ice until preparation of the extract . Tissues were pestle homogenized in 1D lysis buffer ( 5 µl of buffer per pair of ovaries ) , incubated on ice for 20 min and centrifuged at 13 , 000 rpm for 10 min . The protein concentration of soluble protein fractions was quantified and 25 µg of proteins were processed for Western blot analysis . Cell , larval brain extracts or ovary extracts were loaded onto SDS-PAGE ( ProGel Tris Glycine 8–16% , Anamed ) and transferred onto nitrocellulose membranes ( Hybond ECL , Amersham Biosciences ) . Membranes were blocked with TBS-0 . 2% Tween 20-3% BSA for 30 min at room temperature . Incubation with primary or secondary antibodies diluted into TBS-0 . 2% Tween 20-3% BSA were performed at 4°C overnight or 1 h at room temperature respectively . Peroxidase activity was detected with the Amersham ECL plus Western blotting detection system ( GE healthcare ) . Phos-tag containing gels were prepared by adding 25 µM of Phos-tag reagent ( Wako ) and 50 µM of MnCl2 to the mix used to prepare a 12% Tris Glycine SDS-PAGE . Gels were incubated 10 min in running buffer containing 1 mM EDTA and then 10 more minutes in running buffer only to wash out the EDTA before transfer onto nitrocellulose membranes . Transfers were performed using the Iblot Dry Blotting Transfer system from Invitrogen ( 9 min transfer ) . Transient transfections were performed to assess the effect of over-expression of wild-type or the S68A and S68D forms of endos in cells previously treated for 24 h with dsRNA against GFP or the 3′UTR of endogenous endos . 2 ml of the 4 ml of culture medium was removed before transfection . 3 µg of HA-tagged endos constructs was diluted in 100 µl of H2O . 15 µl of Fugene-HD ( Roche ) was mixed with the DNA and incubated at room temperature for 15 min . 115 µl of the mix was then added to the cells . 2 days after transfection ( and 3 days after depletion ) , the cells were harvested and processed for immunofluorescence or Western blot analysis . GST-tagged Endos and GST-tagged Endos ( S68A ) proteins were produced in BL21 Star pLysS bacteria ( Invitrogen ) after IPTG induction . Soluble proteins were purified with glutathione-Sepharose-coupled beads ( GE Healthcare ) and Endos and Endos ( S68A ) were dissociated from beads by cleavage with Thrombin protease ( Amersham Biosciences ) for 2 h at room temperature according to the manufacturer's instructions . Immunoprecipitation of Myc-tagged Greatwall kinases and phosphorylation assays were performed as described previously [4] . Briefly , Myc-tagged Greatwall wild-type , K87R and K97M were immunoprecipitated from DMEL cell lines stably expressing the kinases . A solution of PBS containing thrombin protease at the same dilution as the one in which Endos substrates are kept was used as the negative control . Kinases and substrates were incubated for 30 min at 30°C with γ-32P-ATP . The reaction products were resolved by SDS-PAGE and stained with Coomassie Blue ( Bio-Safe Coomassie G-250 stain , BIO-RAD ) ; 32P-labeled proteins were visualized by autoradiography . Peptide mixtures were analyzed by liquid chromatography coupled to Orbitrap Velos mass spectrometry ( Hybrid-2D-Linear Quadrupole Ion Trap – Orbitrap Analyzer Mass Spectrometer , Thermo Electron Corp . ) . Prior to the analysis , gel slices were subjected to a standard “in-gel digestion” procedure during which proteins were reduced with 100 mM DTT ( for 30 min at 56°C ) , alkylated with iodoacetamide ( 45 min in a darkroom at room temperature ) and digested overnight with trypsin ( sequencing Grade Modified Trypsin , Promega ) . The resulting peptides were eluted from the gel with 0 . 1% TFA , 2% ACN . The peptide mixture was applied to an RP-18 precolumn ( nanoACQUITY Symmetry C18 , Waters ) using water containing 0 . 1% TFA as mobile phase and then transferred to a nano-HPLC RP-18 column ( nanoACQUITY BEH C18 , Waters ) using an acetonitrile gradient ( 0%–60% AcN in 30 min ) in the presence of 0 . 05% formic acid with a flowrate of 150 nl/min . The column outlet was directly coupled to the ion source of an LTQ-Orbitrap-Velos-MS working in the regime of data-dependent MS to MS/MS switch . A blank run to ensure lack of cross contamination from previous samples preceded each analysis . Acquired raw data were processed by Mascot Distiller followed by Mascot Search ( Matrix Science , locally installed http://proteom . pl/mascot ) against FlyBase . Search parameters for precursor and product ion's mass tolerance were respectively ±20 ppm and ±0 . 4 Da , without allowance for missed Trypsin cleavage sites , fixed modifications of Cysteine through carbamidomethylation or variable modifications through Lysine carbamidomethylation , Methionine oxidation , Serine , Threonine and Tyrosine phosphorylation . Proteins of interest were then subsequently submitted for an Error Tolerant search with enzyme specificity changed into semiTrypsin and allowance for one missed semiTrypsin cleavage site . Peptides with Mascot Score above the expectation cut-off ( for FlyBase and the above Mascot search parameters , the threshold was set at 21 ) were considered to be significant . Presence of phosphorylation was confirmed on the basis of visual inspection of the spectra . | Progression through mitosis requires the addition of phosphate groups onto specific proteins by enzymes collectively known as mitotic protein kinases . At the end of mitosis , these phosphates are removed by protein phosphatases . Whereas we know quite a lot about the mitotic protein kinases , we know much less about the phosphatases . Here we used the fruit fly Drosophila as a model organism to identify a pathway regulating a phosphatase required for mitotic exit . Using mutations in genes for this pathway in the fly and by depleting levels of corresponding proteins from cultured cells , we established the relationships between the gene products . This has revealed that Greatwall mitotic kinase works in concert with the protein Endos to antagonise Protein Phosphatase 2A ( PP2A ) . Specifically , Greatwall and Endos affect the activity of a particular form of PP2A that is associated with only one of the four different regulatory subunits found in Drosophila . We found that phosphorylation of Endos at a defined position by Greatwall kinase is required for its function . Together this provides genetic evidence that the Greatwall mitotic kinase inhibits the PP2A phosphatase required for mitotic exit thus complementing biochemical experiments using frog eggs and indicating the universality of this mechanism . | [
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] | 2011 | Suppression of Scant Identifies Endos as a Substrate of Greatwall Kinase and a Negative Regulator of Protein Phosphatase 2A in Mitosis |
Humans frequently need to allocate resources across multiple time-steps . Economic theory proposes that subjects do so according to a stable set of intertemporal preferences , but the computational demands of such decisions encourage the use of formally less competent heuristics . Few empirical studies have examined dynamic resource allocation decisions systematically . Here we conducted an experiment involving the dynamic consumption over approximately 15 minutes of a limited budget of relief from moderately painful stimuli . We had previously elicited the participants’ time preferences for the same painful stimuli in one-off choices , allowing us to assess self-consistency . Participants exhibited three characteristic behaviors: saving relief until the end , spreading relief across time , and early spending , of which the last was markedly less prominent . The likelihood that behavior was heuristic rather than normative is suggested by the weak correspondence between one-off and dynamic choices . We show that the consumption choices are consistent with a combination of simple heuristics involving early-spending , spreading or saving of relief until the end , with subjects predominantly exhibiting the last two .
Humans are often required to allocate limited resources across time , for example having to choose whether to go to an expensive restaurant today or put the money towards a future holiday . Economic theory assumes that they do so in a manner which maximizes an intertemporal preference function . This function describes how a decision-maker values events as a function of both their future timing and magnitude [1] and is typically partitioned into two independent sub-functions , an instantaneous utility function , describing the effect of magnitude , and a temporal discount function , describing the effect of delay , with discounted utility of multiple outcomes being summed across time periods [1 , 2] . Temporal discount functions are conventionally estimated by eliciting choices between one-off outcomes of varying magnitude at varying delays ( the instantaneous utility function is often assumed to take some plausible prior form ) . It is widely observed that people prefer to receive one-off rewards as soon as possible , consistent with the value of rewards decaying with delay , referred to as positive temporal discounting [for reviews see 3 , 4] . However under some circumstances people display an opposite tendency , namely a deferral of reward into the future . In a well-known example , [5] participants were asked to state how much money they would be willing to pay now to receive a kiss from a movie star at varying points in time . The maximum willingness-to-pay occurred when the kiss was scheduled to occur three days in the future , implying a growth in value with delay ( over the short term in this example ) , which is called negative time preference or negative discounting [6 , 7] . Negative time preference is also prominent in choices between aversive outcomes , where many people prefer to receive pain ( or hypothetical illness ) immediately rather than after a delay [5 , 8 , 9] . An explanation is that the anticipation of future events in itself provides additional present-time utility , termed savoring for positive outcomes and dread for negative ones [5 , 10] . According to an assumption of additive discounted utility , an individual’s preferred allocation of rewards over several time periods ought to be predictable from their discount and utility functions derived from choices between the same one-off rewards [2] . In reality the assumption of additive utility is violated . For instance eating a meal reduces the utility of food for some time afterwards . Similar violations occur prospectively too . For example although , as noted , people overwhelmingly prefer sooner one-off rewards to delayed rewards of equivalent magnitude , when the same rewards are framed as sequences people tend to prefer sequences which improve over time—behavior which cannot be reconciled with a single discount function whilst also preserving additive utility [11–15] . The conventional economic model also assumes that humans have the necessary cognitive capacity to optimize their discounted utility . However , when deciding how to allocate reward over several time steps , the number of possible allocation plans grows exponentially as outcomes further into the future are considered , generating decision-problems of considerable complexity [16] . In response to this people apparently adopt simplifying strategies . For instance , transfers into retirement savings plans cluster around the minimum and maximum allowable contributions , as well as around multiples of five dollars , suggesting that investors choose these as convenient ‘rules-of-thumb’ [17] . Such strategies are examples of ‘heuristics’ , which are generic , though possibly only partly competent , solutions to classes of problems [18–20] . Notably the use of heuristics can generate behavior that differs from the predictions of conventional economic models of intertemporal choice , in particular leading to on-going choices in a dynamic context that are not consistent with preferences that the decision-maker might exhibit in simpler , e . g . one-shot , contexts [16 , 21] . In addition the form of temporal discount function interacts with the ability to execute one’s best-laid plans . A decision-maker with an exponential discount function ( and an increasing concave utility function over outcome magnitude ) has time-consistent preferences—i . e . will make the same decision between options with different temporal profiles no matter how close or far in time these are [2] . Such a decision-maker would naturally adhere to her plans , however frequently they were re-evaluated . By contrast if the discount function is positive but hyperbolic , as frequently observed [22–24] and/or approximated [25–28] in humans and other animals , then the decision-maker would be expected to exhibit dynamically inconsistent behavior: by seeking immediate reward , they would tend to undo previous long-sighted plans [2 , 23 , 29 , 30] , however see [31–33] for an alternative account] . Temporally inconsistent preferences theoretically compound the complexity of planning resource allocations in real-time , since they necessitate a dynamic model of the behavior of future selves [2 , 30] . All the difficulties and violations of the conventional economic model of intertemporal choice described above make it unlikely that individuals exhibit fully optimal intertemporal allocations . However very few studies have directly examined resource allocation decisions in real-time , tested the extent to which these are consistent with discount functions derived from one-off choices , or indeed found a parsimonious description that accounts well for actual choices . Thus we designed a task that involved allocating a limited budget in real-time in which we could examine the various forms of inconsistency and explore possible heuristics in a rather open-ended manner . Specifically the task involved choosing how to consume relief from painful stimuli over an extended period of time . In a separate experiment , performed on the same day , [fully described elsewhere [9]] , participants made binary choices between different numbers of , and delays to , painful shock stimuli , which were identical to those used for the consumption-savings experiment . We were therefore able to compare the consistency of observed behavior in one-off and dynamic choices . The existing binary choice study illustrated a range of intertemporal choice behavior; some participants displayed positive discounting , others negative discounting and others discounting very little or not at all . These three patterns would be expected to give rise in the dynamic task to spending relief early , saving relief for the end and spreading relief evenly over time , respectively ( the latter assumes a concave utility function for relief ) . We therefore tested the prediction that if one-off and dynamic choices are consistent , then individuals who positively discounted one-off pains would tend to spend their relief early , those who displayed greater negative discounting ( dread ) for one-off pains would be more likely to save their relief ( to mitigate future punishment ) , and those who did not discount pain at all would be more likely to spread their relief across time . More specifically we also compared behavior with the optimal predictions of an anticipation-discounting model fitted to the one-off choices . In the light of our findings , we went on to explore more heuristic descriptions of the behaviour that we elicited . The particular anticipation-discounting functions that we observed can generate non-adherence to past plans of a different form to that entailed by hyperbolic discounting [see 5 and S1 Fig . ] . In our case subjects who dread one-off pains are motivated to save more in the present than they would desire to use in the future . Since we did not elicit participants’ plans prior to the experiment , we could not directly test for this . However , to explore the theoretical implications of the model in more detail we simulated optimal consumption choices under various parameterizations of the utility and anticipation-discounting functions , allowing for the possibility that subjects might have different degrees of insight into their future tendencies , being either inaccurate ( naïve ) or accurate ( sophisticated ) [34] . We observed prominent tendencies to spread relief across time and to save relief . However higher dread of pain in one-off choices showed no significant correlation with the latter tendency . We found that while some participants displayed behavior consistent with the optimal paths predicted from their dread-discounting functions , several participants exhibited consumption profiles which were not self-consistent . Overall observed consumption behavior was parsimoniously described by post-hoc models which assumed that participants combine a set of heuristics to ‘save-now-spend-later’ , ‘spread-spending’ , and , to a much lesser extent , ‘spend-now-suffer-later’ .
The task required participants to perform a series of 60 trials over 14 minutes wherein they were scheduled to receive a number of identical , moderately painful , cutaneous electric shock stimuli on each trial . At the outset , each participant was endowed with a fixed budget of computerized pain relief , an amount insufficient to relieve all shocks in the session . On each trial they were allowed to choose how much relief they wished to use , up to a maximum allowable “dose” . The scenario was embedded within a hypothetical health-related context , and pain relief was described in units of milligrams . Fig . 1 illustrates the experimental protocol . On each trial , subjects received a number of shocks drawn from a Poisson distribution . Without pain relief , the mean of this distribution was 14 shocks; for every 1mg of relief the subjects spent on a trial , the mean decreased by 0 . 1 shocks . Subjects were allowed to spend a maximum of 120mg of relief on a trial; this reduced the mean number of shocks to 2 , a level termed the ‘baseline pain’ . Subjects had to spend within a total budget of 2400mg . Before making their choice , participants were informed of the total relief capital remaining , the number of trials remaining and the mean remaining relief per trial . In a separate experiment , performed on the same day , [fully described elsewhere 33] , participants made binary choices between different numbers of , and delays to , painful shock stimuli ( which were identical to those used for the consumption-savings experiment ) . The unit of time in both experiments was a single trial , of equivalent length in both experiments , and resulted in delays of the order of zero to 15 minutes in both experiments To illustrate the effects of changes to the instantaneous utility and anticipation-discounting functions , we used dynamic programming to simulate optimal behavior on a reduced version of the task lasting 10 time periods ( with a budget of 400mg ) . Effects of the instantaneous utility function . Within the standard economic model , the instantaneous utility function can affect the optimal consumption path , even for a decision-maker who treats the same outcome as equally valuable regardless of its timing ( Fig . 2 ) . Let the utility for consuming an amount , c , at time , t , given current capital , st be given by U ( ct , st ) ( The only effect of st on the instantaneous utility function is to constrain consumption to be less than current capital , such that ct≤st; as a result we abbreviate U ( ct , st ) to simply U ( ct ) , however the above constraint is still implied ) . For the special case of linear utility , where U ( ct ) = ct , provided there is no discounting or interest rate , all possible consumption paths in which total consumption corresponds to spending the entire capital are equally valued . As a result , at each time step , and each state of capital , all possible consumption levels have equal value . The result is that consumption in the first time period , c1 could be selected at random from a uniform distribution , in which case , the expected consumption level , c1 , is close to 60 units . Single-period consumption , ct , then continues in this manner until the capital is entirely consumed ( Fig . 2 , left panels ) . With a concave utility function , here illustrated with U ( c t ) = c t k , where 0 < k < 1 , low levels of consumption are relatively more valuable than would be the case under a linear function . Here , the optimal path is to spread consumption evenly across time ( Fig . 2 , right panels ) . Anticipation-discounting functions . In the existing binary choice study , we estimated an anticipation-discounting function , here termed Δ ( d ) for each participant , determining how the value of pain depends on its delay , d . The anticipation term is computed as the forward-looking sum of exponentially discounted value , with a per-period rate , γc ( C for consumption ) the contribution of which is determined by the parameter α ( Fig . 3 ) : Δ ( d ) = γ C d + α [ ∑ τ = 0 d − 1 γ C d − τ γ A τ ] The full model , as shown here , allows for the possibility that prospective anticipation is itself discounted by an additional factor , γA ( A for consumption ) , representing the extent to which future anticipation is taken into account . Fig . 3 illustrates typical forms for an anticipation-discounting function for a positively-valenced outcome , where γA = 1 Where anticipation dominates ( Fig . 3A ) , the overall value is an increasing function of delay; where discounting dominates ( Fig . 3B ) , the overall value is a decreasing function of delay . Fig . 4 plots predicted consumption paths under four possible parameterizations of the anticipation-discounting function ( Fig . 4A ) , under both full naivety or full sophistication , for a concave utility function: U ( c t ) = c t 0 . 75 . Complete sophistication entails that the agent at t = 1 knows that a future decision-maker , for example at t = 11 will apply the same degree of discounting to periods t = 11 , 12 , 13… and so on as the agent currently applies to periods t = 1 , 2 , 3… and so on . Naivety by contrast would entail that the agent at t = 1 assumes that the decision-maker at t = 11 will apply the same discount factors to periods t = 11 , 12 , 13… and so on as the agent currently applies to those time periods . Given dynamic inconsistency in the discounting function , a naïve agent would be expected to change their plans at each time step . It can be appreciated from Fig . 4B that , where discounting dominates ( first column ) , optimal consumption is decreasing . With no discounting ( second column ) , optimal consumption is even over time , owing to concave utility . Where anticipation dominates ( third column ) , the predicted consumption path is increasing . Where anticipation is itself discounted ( γA < 1; fourth column ) non-monotonic consumption profiles result . Where there is a degree of savoring ( α>0 ) , the consumption paths for naïve and sophisticated consumers diverge , albeit subtly in some cases . The underlying dynamic inconsistency is illustrated in Fig . 4C , which plots consumption plans made at the first three time periods for fully naïve agents . Rather than consumption itself , these plots depict the naive plans for future consumption from the current time-period onwards . Where discounting dominates ( left column ) , inconsistency similar to that implied by hyperbolic discounting results: consumption at the next period turns out to be greater than planned . Where savoring dominates ( right hand two panels ) , the naïve decision-maker consumes less than planned . A sophisticated agent takes these future discrepancies into account and adjusts their plan accordingly . To summarize , normative considerations justify at least three obvious qualitative classes of relief spending—increasing , decreasing and flat ( or spreading ) . They also make strong predictions about the relationship between single- and multi-period decisions , and potentially the effect of degrees of game-theoretic sophistication . Our experiment was designed to test for these classes , but , motivated by the complexity of planning , also to provide insights into possible heuristics . We tested 35 participants , of whom 5 had to be excluded from analysis ( see Methods for details ) . The experimental data ( S1 Dataset ) consisted of the number of units of relief consumed on each trial by each participant . Fig . 5A plots the median consumption of relief on each trial at the group level ( N = 30 , bars indicate the interquartile ranges ) . Across subjects , the profile of consumption is increasing over time , showing the tendency for relief to be saved for towards the end of the experimental session . Robust linear regression on all choices made by all subjects ( N = 1980 ) , using iteratively reweighted least squares with a bi-square weighting function , demonstrated a significantly positive effect of time on relief consumption ( β = 0 . 47 , p<0 . 001 ) . However , the group-level presentation of the data conceals the complexity of subject-specific choices . To examine this we calculated the proportion of participants choosing a particular level of consumption on each trial . To reduce the computational complexity of the subsequent modeling analysis ( necessary when fitting more complex models using dynamic programming ) , relief consumption was rounded to the nearest 10mg , creating 13 possible spending choices on each trial ( 0 to 12 ) . We refer to each rounded centigram simply as a ‘unit’ of relief . The observed distribution of rounded relief-consumption at the group level is displayed in Fig . 5B . Darker bars indicate a higher proportion of participants choosing a given consumption level on each trial . There were very few choices to consume close to the maximum quota of relief early in the experimental session . Rather , higher intensities corresponding to spending close to zero relief in the first 40 trials , and above-average consumption across the final 20 trials , demonstrated that participants tended to conserve relief for the final portion of the session , which would be consistent with savoring . Since there was a budget of 240 rounded units of relief , to be allocated across 60 trials , even spreading of relief would entail spending 4 units per trial . Notably , high intensities corresponding to spending close to 4 units of relief indicate that participants also demonstrated a tendency to spread relief across time , which would be consistent with participants having concave utility for relief . There is also a weak tendency to sample the maximum allowable quota of relief throughout the experimental run . An additional interesting feature is that participants were more likely to consume close to the mean relief remaining early in the experiment , tending to switch to consuming zero relief during the middle of the experiment . S2A Fig . plots raw consumption choices ( in mg ) as a series of histograms over time , illustrating that multiples of 10mg are over-represented . This suggests that participants used strategies to reduce the dimensionality of the task , rather than performing optimization at the native resolution . When rounded consumption ( in units ) is also plotted in this manner ( S2B Fig . ) , choices to consume zero relief or 4 units of relief are prominent . Raw data for the 30 participants included in the analysis are displayed in S3 , S4 and S5 Figs . At the individual level , participants appeared to display one or more of the above three tendencies , though strikingly , no participant systematically consumed close to the maximum available relief at the outset of the experiment . To illustrate this , consumption profiles from six sample participants are displayed in Fig . 5C , overlaid with the mean relief remaining per trial ( dashed lines ) , termed ρt . This quantity ( displayed to participants on-screen before each choice ) is given by the total remaining relief on that trial , st , divided by the number of trials remaining: ρ t = s t / [ 60 – ( t − 1 ) ] 2 ) For any trial during the experiment , consuming exactly ρt units of relief on every remaining trial would entail even consumption of relief over the remainder of the experiment . Although the subjects exhibited the same qualitative patterns of behavior as expected from the normative accounts ( Fig . 4 ) , this does not mean that each subject’s own choices were consistent with their own one-off preferences . To compare one-off and dynamic behavior , we first derived summary measures of behavior on both tasks . In the one-off choice task , the frequency of choosing sooner pain indicates the extent of negative time preference , and is a correlate of dread . As described previously , [9] , one-off choices between delayed pains were elicited under two descriptive frames , a ‘pain’ frame , in which outcomes were described as an increase in the expected number of shocks above the baseline level of pain , and a ‘relief’ frame , in which the same outcomes were described as a decrease in the expected number of shocks from a maximum level of pain . The latter description corresponds to that used in the relief consumption experiment . Nevertheless we examined the relationships between dynamic relief consumption behavior and sooner choice frequency on both frames . The signed slope of the dynamic consumption path ( fitted with least-squares linear regression ) , is a measure of the overall tendency to conserve relief , while the absolute magnitude of the slope is a measure of the deviation , in either direction , from even spreading of relief . Contrary to a normative account , we observed no significant positive relationship between the tendency to dread ( on either frame ) and the slope of the consumption path ( Fig . 6A; p>0 . 25 , N = 30 ) , although there was a trend in this direction for the relief frame choices ( Pearson r = 0 . 2 ) . Neither was there a significant relationship between dread and the tendency to spread relief over time ( Fig . 6B; p>0 . 25 , N = 30 ) . For those participants for whom estimates of anticipation-discounting functions were available from one-off choices ( N = 23; see Methods ) , we compared observed relief consumption with the predicted consumption profiles for both naïve and fully sophisticated agents with this anticipation-discounting function , assuming a concave instantaneous utility function for relief , U ( c ) = c0 . 75 . We considered the policy to be a softmax function of the underlying values , setting the inverse temperature parameter to an arbitrary value for all participants ( β = 10 ) , whilst fixing the anticipation-discounting parameters to those previously derived from one-off choices . Sample results for four participants are plotted in Fig . 7 . It can readily be seen that the observed consumption profiles ( blue circles ) in some instances diverge markedly from the predictions ( sophisticated predictions , red circles; naïve predictions , green circles ) . It is possible that variability in the utility function and softmax temperature parameters could account for some of the observed differences between dynamic and one-off choice settings , whilst preserving the basic intertemporal preferences . To explore this we implemented a model in which the softmax inverse temperature , β , and the exponent governing the utility function , k , were fitted freely , whilst holding the previously-derived anticipation-discounting parameters constant . To fit the model we used constrained non-linear optimization to find subject-specific parameters , which maximized the log-likelihood of the observed consumption paths for each participant , given their remaining capital on each trial . The observed group level distribution of consumption in the same 23 participants is displayed in Fig . 8A , for comparison with the distribution predicted by the model . The latter , formed by taking the mean across the likelihood distributions for individual participants , is shown in Fig . 8Bi ( pain frame preferences ) and 8Bii ( relief frame preferences ) . Although the optimal preferences predict saving of relief at the group level , they underestimate the tendency to spread relief over time , even allowing for concave utility , and the fitted policies are relatively imprecise . To estimate the proportion of variance in the observed data accounted for by the models , we found the mean consumption level for each participant across each 10 trials of the experiment , before calculating the same measure by simulating 10000 consumption paths resulting from the maximum likelihood parameterization of the model . As shown in Figs . 8Ci ( pain frame preferences ) and 8Cii ( relief frame preferences ) , there was a significant positive relationship between predicted and observed consumption paths ( robust regression , pain frame: β1 = 0 . 22 , p<0 . 001; relief frame: β1 = 0 . 44 , p<0 . 001 ) . However least squares fits indicated that the model accounted for only a relatively small proportion of the observed variance ( pain frame R2 = 0 . 03 , relief frame R2 = 0 . 07 ) . Given that consumption behavior showed only weak correspondence with the predictions of anticipation-discounting as derived from one-off choices , we tested alternative generative accounts . This analysis was performed post hoc , and we focused on characterizing simple computations that might feasibly have produced the observed consumption choices . To do so we assumed that participants implemented the three main behavioral tendencies , namely spending , spreading and saving relief , as heuristics . The first model , which we termed the Direct Action Heuristic model , proposed that participants implemented the three observed behavioral tendencies directly , with choices between them governed by propensities . The three are termed spend-now-suffer-later ( with propensity Mspend ) , spread-spending ( with propensity Mspread ) , and save-now-spend-later ( with propensity Msave ) . The extent to which observed relief consumption , ct , fell below the mean relief remaining on each trial , ρt , is given by: d t = ρ t − c t 3 ) Positive dt entails using less than the mean relief remaining per trial , while |dt| indicates the extent of deviation from spreading . Formally , the three heuristics were defined as ( see Methods for details ) : M spend ( s t , c t , t ) = c t 4 ) M spread ( s t , c t , t ) = − | d t | 5 ) M save ( s t , c t , t ) ={ d t if ρ t <12 0 otherwise 6 ) Mspend formalizes a spend-now-suffer-later heuristic , by assuming linear utility for relief consumption , and thus a propensity to consume the maximum allowable relief . Mspread formalizes a spread-spending heuristic , by penalizing deviations from the mean relief remaining , and so generates a propensity to spread relief over time . Msave formalizes a save-now-spend-later heuristic , by assigning higher value to consuming less relief , provided that the mean remaining relief per trial is less than the maximum possible consumption level . Msave therefore generates a propensity to consume as little relief as possible until there is sufficient remaining relief to reduce pain to the baseline level for the remainder of the experiment , at which point the remaining heuristics encourage spending this quantity . The three action propensities were implemented as separate policies , each with a unique softmax inverse temperature parameter; the final probability of consuming each level of relief was assumed to arise from a weighted average across these policies with weight for a policy determined by its inverse variance ( see Methods ) . As previously , to fit the model we used constrained non-linear optimization to find subject-specific parameters , which maximized the log-likelihood of the observed consumption paths for each participant ( N = 30 ) , given their remaining capital on each trial . The group-level distribution of observed consumption choices is reproduced in Fig . 9A , for comparison with the model fits . The distribution predicted by the Direct Action heuristic model is displayed in the left-hand panel of Fig . 9B . The model provided a parsimonious summary of observed consumption choices , albeit not convincingly capturing the observation that some participants were more likely to consume close to the mean relief remaining per trial ( ρt ) near the start of the experimental run , before switching to conserve relief . The above pattern might have several different explanations . One simplification in the model is that the explicit relative weightings of the heuristics are assumed to be constant . However , participants may have adopted the spread-spending heuristic at the outset , before learning the extent that they were able to tolerate pain as the experiment progressed then switching to save-now-spend-later ( see S1 Text ) . Similarly they may have consumed the mean relief at the outset as a default option , until they learned to trust the experimental setup . A further possibility is that participants , rather than using a save-now-spend-later heuristic directly as defined above , may have sought to maximize the mean relief remaining per trial ( ρt ) over the near future: since saving relief would have more immediate effect on ρt later in the experiment compared with at the start , the propensity to save would be expected to increase as the experiment continued . The data do not admit a direct distinction between the above hypotheses . However in order to illustrate one of the possibilities we fit a modified version of the above model in which the save-now-spend-later heuristic described above is replaced with a heuristic to maximize ρt over a limited future horizon , which we term an income maximization heuristic ( and eponymous model ) . Thus Msave in this model was replaced by an action-value function , which described the value of consuming an amount , ct , at the current capital level , st , and time period , t , given knowledge of the future policy for action , π ( see Methods ) . In other words this model assumed that participants were in part attempting to maximize the expected mean relief remaining per trial , akin to maximizing their expected income . To account for limited computational resources , we incorporate a probability , 1–γ , that the decision-maker terminates their search at every level deeper into the tree ( the γ parameter is mathematically equivalent to an exponential discount rate ) . We fitted this part of the model using dynamic programming . The remaining two action propensities , Mspend and Mspread were implemented in the same manner as the Direct Action model , and policies were combined using the same weighting method . The distribution of consumption at the group level predicted by the Income Maximization model is shown in the right-hand panel of Fig . 9B . It can be seen that this model accounts for the tendency to save relief being higher during the middle part of the experiment . As expected , the Income Maximization model produced an improvement in Bayesian Information Criterion ( BIC ) [63 , see Methods] , of 78 at the group level over the Direct Action model . The BIC favors models with higher likelihood estimates and penalizes increasing model complexity , where lower values of BIC indicate a more favorable model fit . ( Notably the Income Maximization model was optimized post hoc to account for a particular feature of the observed data , and therefore our primary goal was not to compare the two heuristic models ) . The maximum-likelihood model fits of the Income Maximization model for the six participants whose data is displayed in Fig . 5 are shown in S7 Fig . The proportion of variance explained by the models at the ten-trial resolution is shown in Fig . 9C . Least squares fits indicate R2 = 0 . 56 for the Direct Action heuristic model and R2 = 0 . 80 for the Income Maximization heuristic model . To illustrate the contribution of each of the three heuristics , the policy weightings of the two heuristic models are displayed in Fig . 10 . The Income Maximization model results in a larger relative weight being placed on saving during the middle half of the experiment ( Fig . 10A ) . Also throughout the experiment saving ( save-now-spend-later and income maximization ) and spread-spending receive considerably higher weightings ( Figs . 10A and 10B ) than spend-now-suffer-later . The maximum likelihood parameters for the Income Maximization model are listed in S1 Table . Finally we implemented a model in which participants could combine optimal choices according to anticipation-discounting function with the above heuristics , attributing deviations from optimality to the use of heuristics . Here , the heuristics can be viewed as attractions towards spending salient quantities of relief , and/or embodying additional valuation processes which play a role in the dynamic task over-and-above anticipatory utility , such as adaptation . In this model participants ( N = 23 ) were assumed to perform dynamic utility maximization , according to their previously-derived anticipation-discounting functions , whilst also being biased towards spending either zero , the mean remaining or the maximum relief . Biases were implemented by augmenting the values of consuming these quantities ( with Gaussian blur either side , see Methods ) . The extent of each bias was governed by a weighting parameter , giving rise to three parameters ωmin , ωmean and ωmax . The softmax inverse temperature , β , and the exponent governing the utility function , k , were also freely fitted . We used intertemporal preferences from the relief frame here , since these showed closer correspondence with the observed data . Our aim here was to illustrate formally that deviations from optimality can be parsimoniously described by postulating the use of heuristics . The results are displayed in S7 Fig . , showing that the model captures a substantial proportion of the observed variance ( R2 = 0 . 83 ) . This model produced an improvement in BIC of 430 over the Income Maximization heuristic model , for the subset of 23 participants for whom anticipation-discounting functions were available , suggesting that the addition of utility optimization improved the fit quality over heuristics alone . However both the set of intertemporal preferences and the heuristics for this model were chosen post hoc , making it potentially susceptible to over-fitting .
Decision-makers routinely plan the allocation of limited resources over time . Economic theory proposes that they should do so in a self-consistent manner [1] . That is , allocation choices made sequentially ought to be predictable from choices between equivalent one-off delayed outcomes . We tested this by observing the real-time consumption of a limited budget relief from a series of 60 painful stimuli in the laboratory , over the course of approximately 15 minutes , in a group of participants whose intertemporal preferences for one-off future pains of the same nature had been elicited previously . We also sought to provide parsimonious descriptions of the observed behavior in this complex dynamic task . Tendencies to consume the minimum allowable relief early on , thus saving for the end , and to consume close to the mean remaining relief were prominent , with several participants alternating between these two tendencies . Consistent with retirement-savings decisions , [17] choices to spend multiples of 10mg of relief were over-represented in the data . No participant systematically consumed close to the maximum available relief at the outset of the experiment , as conventional temporal discounting would predict . Whilst two out of the thirty participants analyzed did generate declining profiles of relief , these two participants also showed trial-to-trial variability in consumption , suggesting that they may have chosen consumption levels largely at random ( with the decline resulting from exhausting the budget ) . We observed no significant correlation between a preference for sooner pain in one-off choices and the tendency to save relief in the dynamic task , although there was a trend towards a positive relationship . When anticipation-discounting functions derived from one-off choices were used to generate optimal consumption paths , whilst freely fitting the utility function and the degree of choice randomness , there was a weak but statistically significant positive relationship between the observed and predicted paths . We conceptualized deviations from optimality in terms of heuristics , rule-of-thumb strategies designed to ease computational demands . We generated putative heuristics post hoc , in light of the three observed behavioral tendencies , finding that consumption behavior was well-described by a combination of three corresponding simple rules , namely save-now-spend-later , spread-spending and spend-now-suffer-later , implemented as direct action propensities . However this Direct Action Heuristic model failed to capture an interesting dynamical feature of the data , namely the tendency of several participants to commence saving relief during the middle of the experiment . A possible explanation for this phenomenon posits that rather than directly implementing a save-now-spend-later heuristic , participants attempted to maximize their mean remaining relief ( income ) over the near future . This Income Maximization Heuristic model outperformed its Direct Action counterpart and accounted for a substantial proportion of the observed variance . Finally we showed that superimposing the heuristics on dynamic utility maximization improved model fits over the heuristic models alone . At an empirical level the three heuristics serve as parsimonious descriptions of the observed behavior . At a computational level we envision the heuristics as resulting from attractions towards spending salient quantities of relief , hence their usefulness as simplifying strategies , but also as approximating , through their dynamics , more fundamental valuation processes . It is important to note here that the three heuristics can generate behavior indistinguishable from what is optimal under several possible utility functions . For this reason , based on the current data we cannot draw firm conclusions regarding the fundamental valuation processes; however , we outline below a broad framework for categorizing the possible underlying psychological phenomena in terms of relative ( reference-dependent ) and absolute valuation processes ( Table 1 ) . The psychological processes motivating the choice of heuristics might be classified as both relative ( reference-dependent ) and absolute valuation mechanisms . Relative valuation mechanisms include adaptation to current consumption levels , sensitization to repeated punishment and loss aversion . Absolute valuation processes include anticipatory utility , temporal discounting and risk aversion . Relative valuation processes involve comparison of outcomes against an assumed baseline , or reference-point [35 , 36] . Relative valuation might generate a preference for improvement over time , if consumption levels are compared with those that precede them , leading people to choose deliberate privation in order to increase the hedonic impact of subsequent consumption [10 , 13 , 37] . This would be consistent with existing findings showing that , due to psychological adaptation to the current pain level , a moderate intensity pain can appear more severe when following a low intensity pain than when following a high intensity pain [38] . The opposite effect may also occur , namely sensitization to repeated high level pain , leading participants to occasionally consume the maximum relief as ‘respite’ . A further possibility is that decreases in consumption from one time period to the next are valued as more negative than equivalent increases are valued positively , i . e . loss aversion [39–41] . Loss aversion would be expected to further penalize deviations from either even spreading or saving , for the reason that any increases in consumption above even spreading inevitably lead to future decreases [14] . Notably loss aversion itself may represent the heuristic assumption that losses predict further decline , which if unchecked carries the risk of eventual ruin . Absolute valuation processes might also explain spreading and saving of relief . Firstly a preference for spreading rewards or punishments evenly could arise out of a desire to avoid being left with little or no reward , or high levels of punishment , in some time periods . As demonstrated here through simulation , this desire can be formalized as a non-linear utility function for both reward and punishment , i . e . decreasing marginal ( concave ) utility for reward and increasing marginal ( convex ) disutility for punishment . [for a description of how a non-linear instantaneous utility function can affect inferred discounting see 42] . Secondly , saving behavior might result from either anticipatory utility [5 , 9 , 10] , or uncertainty regarding future resources [43] . An interesting direction for future work will be to attempt to prime these mechanisms individually within a more constrained task . The plurality of possible mechanisms contributing to dynamic behavior might in part explain the low correlation between the anticipatory utility of pain in one-off choices and the tendency to save relief [13]; in particular , relative valuation processes might be expected to play a greater role in the dynamic task , where transitions between outcomes are more salient . Notably , this kind of context-dependent engagement of valuation mechanisms lies outside the conventional economic model of intertemporal preferences , in which the effect of delay is encapsulated by a unitary discount function ( if the parameters of the discount function are entirely context-dependent , the model ceases to make useful predictions ) . Since we developed the heuristic models after observing the data , they require independent validation in related experimental contexts to establish their generalizability . For example , presenting participants with on-screen details of mean relief remaining may have primed a spread-spending heuristic out of a desire to conform to the demands of the experiment . However , in support of the heuristic models proposed here , existing studies show that similar heuristics appear evident in other settings . The widespread use of such strategies suggests common underlying valuation processes . In particular , preferences for spreading rewards evenly across time and for improvement over time are evident in choices between predetermined sequences of outcomes , including wages [15] , health [11 , 12] and other desirable or undesirable events such as dining at a favorite restaurant or scheduling a visit from a troublesome relative [14] . Loewenstein and Prelec [14] propose a model for classifying these preferences , which resembles the Direct Action heuristic model used here , albeit not in the context of whole sequences of choices over time , as here . An interesting direction for future work is to determine how choices made in advance between pre-determined sequences differ from choices made sequentially . If people have time-inconsistent preferences , choosing in advance may offer an opportunity for pre-commitment [44–46] . For example , Read and colleagues provide evidence that sequential choice promotes the selection of options that yield small immediate rewards ( ‘vices’ ) , while choosing the sequence in advance encourages the selection of long-term rewarding options ( ‘virtues’ ) , a pattern consistent with hyperbolic discounting [47] . As demonstrated here ( as well as in existing studies ) , the anticipation-discounting functions described previously for one-off choices predict a novel form of inconsistent choice , distinct from that of hyperbolic discounting , which entails the perpetual deferral of consumption ( S1 Fig . ) [5] . It is unclear whether such behavior is manifest in real-time , or indeed influences the kind of consumption choices demonstrated here . Finally , an advantage of this study is its face validity as a naturalistic scenario . The prominent tendencies to either save relief or to spread relief across time here may have implications for dynamic health-related decision-making in the field . In the UK , personal budgets for healthcare have recently been piloted , potentially giving an individual control over a component of their health spending [48] . Our results suggest that individuals differ considerably in their preferred budget allocations over time . From a policy perspective , such individual differences will be interesting to examine as more data on the use of personal health budgets emerge [49] . Applied measures of choice over time have tended to focus exclusively on one-off choice paradigms [50–53] , and the modelling of dynamic decision-making tasks suggests a novel and quantitatively rich behavioral predictor . In summary we examined how people allocate resources for mitigation of punishment , showing that behavior is not clearly consistent with conventional economic models of intertemporal preference , but is consistent with a simple set of heuristics that encapsulates saving in the present to spend in the future , spreading consumption out evenly over time and ( less prominently ) spending in the present at the expense of the future . We note that similar behavior is seen in choices between predetermined outcome sequences .
The research received approval from the National Health Service National Research Ethics Service , Central London Research Ethics Committee 3 ( Ethics number 08/H0716/6 , Amendment AM1 ) . All participants gave informed consent before taking part in the study . Participants . Thirty-five participants ( 18 females ) took part in the study , with full informed consent . Participants were recruited via an advertisement on the website of the University College London Psychology Subject Pool . The experiments were carried out at the Wellcome Trust Centre for Neuroimaging , University College London . Participants were initially briefed that they would be making choices about how to allocate relief from different numbers of moderately painful electric shocks . Throughout the experiment the participant sat in front of a computer monitor; where trials were presented on-screen , and decisions were indicated using keys on the keyboard . Two participants were excluded prior to coding and analysis of data because at the end of the experimental run they stated that they did not find the painful stimulation aversive , creating a dataset of 33 participants ( S1 Dataset ) . Three were excluded from the analysis , since they performed a pilot version of the task in which they did not receive on-screen information regarding the mean relief remaining per trial . The remaining 30 participants all also took part in the binary intertemporal choice experiment , which they performed first , on the same day as the relief consumption task ( published previously ) . Anticipation-discounting parameters were estimable in 23 participants from these thirty . The remaining 7 participants always choose sooner pain on the binary choice experiment , precluding reliable model fitting [See 33] . Procedure and design: dynamic task . Participants made choices over an experimental session consisting of 60 trials in which by default they were due to receive painful shocks on each trial . Participants were briefed with on-screen instructions that embedded the task in a naturalistic health-related scenario ( see S1 Text ) . At the start of the session participants were endowed with a fixed budget of computerized pain relief , described in units of milligrams , 2400mg in total . The budget was not sufficient to relieve all the shocks in the session , and participants were informed of this fact , and therefore the possibility that they might expend all their relief before the end of the session . Before each trial , participants were informed of the total number of trials remaining , the number of units of relief remaining and the calculated mean relief remaining per trial in mg . They were then given the opportunity to indicate how much relief they wished to consume on that trial , by moving a pointer along a visual scale using the keyboard . There followed a painful shock stimulus , the severity of which was determined by the amount of relief consumed . The painful shocks occurred within a five second stimulus train , where the intensity of each discrete shock , which consisted of a single 200μs square-wave pulse , did not vary . The duration of the stimulus was fixed therefore an increasing number of shocks was equivalent to an increasing shock rate . At each sampled time interval during the stimulus train the probability of receiving a shock was sampled from a uniform distribution . By default the outcome on each trial was a shock train with the maximum rate of 2 . 8 shocks/s ( 14 shocks within 5 seconds ) . Consuming 10mg of relief reduced the expected number of shocks in the immediately following stimulus train by one . Participants were informed that the pain relief was probabilistic , chosen so as to achieve a more naturalistic context . The maximum allowable consumption of relief on each trial was 120mg , sufficient to reduce the expected shock rate to 2 shocks/5s ( 0 . 4 shocks/s ) , which was referred to as the “Baseline Pain” . Prior to entering into the session , participants were given three samples of the maximum ( default ) and minimum ( baseline ) shock rates which they could expect to experience with using no relief or using maximum relief respectively . The choice phase was limited to 6 seconds , and each trial lasted 14 seconds in total , the experimental session therefore lasted 14 minutes . Before the experiment , participants underwent a standardized procedure , to control for individual variability in pain perception , so that the maximum shock rate used during the experiment corresponded to an approximately equivalent subjective level of pain for each participant . We aimed to set a target current level ( the stimulator then adjusted the voltage to achieve this target current ) such that participants rated the five second stimulus at the maximum shock rate ( 2 . 8 shocks/s ) as moderately severe pain . To achieve this we used an expected shock rate of 2 . 8 shocks/s , whilst varying the target current amplitude . Participants provided a pain rating for each stimulus train on a continuous visual analogue scale ( VAS ) from 0 ( not painful ) to 10 ( intolerable pain ) . Voltage level was increased in small increments until the participant gave the stimulus a VAS rating of 6 out of 10 . The staircase procedure was then repeated , giving participants opportunity to adapt to initial anxiety about the shocks . This procedure determined a single voltage level corresponding to moderately severe pain for each participant . At the end of the experimental session we also verified that increasing the mean shock rate within the range used for the experiment corresponded to monotonic increases in VAS pain ratings , by asking participants to rate stimulus trains of constant voltage , equal to that used during the choice phase , whilst shock rate was increased in increments of 2 shocks/5s , starting from the baseline mean rate of 2 shocks/5s up to the maximum rate of 14 shocks/5s . This was followed by a symmetrical decreasing staircase in which shock rate was decreased by the same increment . 2 out of the 35 participants rated the maximum shock rate as below 4/10 ( which corresponded to “mild pain” on the visual analog rating scale ) at the end of experiment , suggesting that significant adaptation had occurred over the course of the experiment . These 2 participants were therefore excluded from the analysis . Procedure and design: one-off choices . The procedure for estimating temporal value functions from one-off binary intertemporal choices has been described elsewhere [9] . In brief , the experiment proceeded according to a trial-based design in which the unit of time was a single trial and participants’ choices determined outcomes on future trials . The painful shocks were delivered within a five second stimulus train , identical to that used in the dynamic choice setting . Prior to making their choices participants received samples of stimulus trains at different shock rates , so that they were familiar with the outcomes . On each trial the default outcome was a shock train with mean 2 shocks/5s ( 0 . 4 shocks/s ) , identical to the “Baseline Pain” in the dynamic setting . Participants made two sets of 95 choices between two options for outcomes with higher expected shock rates , up to a maximum of 14 shocks/5s ( i . e . 2 . 8 shocks/s , identical to the maximum rate in the dynamic context ) , delivered at between 4 to 51 trials in the future . There was an equal number of choices in which the delayed outcome had a higher expected shock rate as choices in which the sooner outcome had a higher expected shock rate . Each trial lasted an average of approximately 10 seconds in total , equivalent to the duration of a single trial in the dynamic setting . All choices were genuine , with shock delivered reliably according to subjects’ choices . Participants were briefed with instructions that embedded the task in a health-related scenario , similar to that used for the dynamic choice setting . Intertemporal choice data was collected in two blocks , the order of which was counterbalanced: a block in which outcomes were framed as an increase in shock rate , referred to as the ‘pain’ frame and an otherwise identical experimental block in which outcomes were framed as a decrease in shock rate from the maximum rate , referred to as the ‘relief’ frame . The same participants performed these static intertemporal choices , prior to the dynamic choice experiment , on the same day . Responses were analyzed by fitting a series of alternative temporal value functions to participants’ choices using maximum-likelihood estimation . The best-fitting class of model was an exponential-sum dread model of the form described below . Data processing . To reduce the computational complexity of the modeling and simulation analysis ( necessary when fitting more complex models using dynamic programming ) , relief consumption was rounded to the nearest 10mg , creating 13 possible spending choices on each trial ( 0 to 12 ) . This procedure produced occasional rounding errors such that the cumulative total rounded consumption exceeded the budget constraint . These errors were corrected by disallowing rounded consumption to exceed the remaining total relief , resulting in fictitious observations on the final trial for some participants . These discrepancies from the true observed consumption profiles were small by comparison to predominant patterns of consumption . Simulating consumption paths . To simulate consumption paths predicted by the dread-discounting functions derived from one-off choices we implemented a dynamic program [54–56] over all possible states of capital at each time point . A deterministic transition function , T ( ct , st ) described how actions in the current state mapped to subsequent states , such that: s t + 1 = s t − c t7 ) Where st , denotes capital at time t , and ct consumption at t . Borrowing is not allowed , therefore st ≥0 and ct≤st . Consuming a quantity of relief , ct , was associated with utility U ( ct ) at the current state , where U ( ct ) is the utility function for relief . Since ct ≤st , the function U also depends on current capital st . The overall value of consuming relief , ct , when situated at time , t , with a state of capital , st , termed a Q-value , was then described recursively as a function of the resulting relief utility at the current state , U ( ct , st ) , followed by the expected utility of relief at all future states , given a future action policy , π , and a discount function , Δ ( d ) , giving rise to: Q π ( s t , c t ) = U ( c t , s t ) + E [ ∑ d − 1 T − t Δ ( d ) ⋅ U ( c t + d , s t + d ) c t + d ~ π ] 8 ) The action policy , π , dictates the probability of consuming an amount of relief , ct , given that the agent is currently situated at t and has capital , st , here represented by a softmax policy for action selection , such that: p ( c t | s t , t ) = π ( s t , t ) 9 ) Where: π : s t , t → e β Q π ( s t , c t , t ) ∑ c t e β Q π ( s t , c t , t ) 10 ) Higher values of the inverse temperature parameter , β , lead to behavior becoming more deterministic for choosing the option with higher utility . Δ ( d ) was represented by the anticipation-discounting function derived from one-choices , which assumed the following form: Δ ( d ) = γ C d + α [ ∑ τ = 0 d − 1 γ C d − τ γ A τ ] 11 ) Parameters from the Exponential Dread model ( γD— framing; both Pain and Relief frames separately ) , namely α , γP and γD were carried forward to generate resulting optimal consumption paths on the dynamic experiment . To do so , discounting of relief consumption was set to be equivalent to discounting of pain ( γC = γP ) and discounting of dread was set to be equivalent to the discounting of savoring ( γA = γD ) . A linear utility function for pain and relief was assumed . Optimal policies were implemented using a high value of the softmax inverse temperature , β = 10000 . The value function Qπ ( st , ct ) expresses the notion that the value of consuming an amount , c , at the current capital level and time period depends on the immediate utility of consuming c plus the expected value of ( discounted ) future consumption , given accurate knowledge of one’s likely future policy for action . This model therefore entails complete sophistication . In other words the model assumes for example that the agent at t = knows that a future decision-maker at t = 11 will apply the same degree of discounting to periods t = 11 , 12 , 13… and so on as the agent currently applies to periods t = 1 , 2 , 3… and so on . Naivety by contrast would entail for example that the agent at t = 1 assumes that the decision-maker at t = 11 will apply the same discount factors to periods t = 11 , 12 , 13… and so on as the agent currently applies to those time periods . Given dynamic inconsistency in the discounting function , a naïve agent would be expected to change their plans at each time step . To explore the predicted future plans resulting from different forms of anticipation-discounting and utility functions , under naivety as well as sophistication , we also simulated the behavior of an agent facing a discrete-time dynamic intertemporal reward allocation task lasting 10 time periods . The agent was endowed with a budget of 100 units of reward at the start of the task , and was allowed to consume any proportion of the total remaining reward ( capital ) at each time period . There was no experimenter-determined interest rate on assets not yet consumed . To simulate naïve behavior , the form of the discount function was made dependent on the absolute timing of the outcomes as well as their delay , such that the decision-maker at each time step believed that future decision-makers would apply the same preferences as those currently held for those time-periods . To achieve this , the dynamic program was iterated once for each trial of the simulation , with the following recursive value function: ) Q n a i v e π ( s t , c t , t , i ) = Δ ( t − i ) ⋅ U ( c t , s t ) + E [ ∑ τ = t + 1 T Δ ( τ − i ) ⋅ U ( c t + d , s t + d ) c t + d ~ π ]12 ) Where each iteration is represented by i , which ranges between 1 and T . Naive consumption plans ( as shown in Fig . 4C ) at trial i were sampled from a policy based on Q n a i v e π ( s t , c t , i ) over the remaining trials t = i , i+1 , i+2… T . Naïve consumption paths themselves were simulated by sampling from Q n a i v e π ( s t , c t , t ) over all trials t = 1 , 2 , 3… T . Model fitting procedures . For each of the models , we assumed a standard probabilistic model of action selection in the form of a softmax function . Model fitting followed a maximum likelihood framework , using the softmax policy to generate the probability of observing each possible ( rounded ) level of relief consumption , given a particular set of model parameters . For each model we sought parameters which maximized the log likelihood of ( minimized the negative log likelihood ) of the observed consumption choices of each participant . To do so , simplex optimization was performed using the Matlab ( Mathworks , MA , USA ) fminsearch optimization tool ( Nelder-Mead search algorithm [57] ) with the addition of bound constraints by transformation . For each subject 10 iterations of the optimization were performed , and the maximum likelihood estimate across all iterations was selected . On each iteration the optimizer was called within a random multi-started overlay ( RMsearch ) , with 100 starting points selected from a uniform distribution between the parameter bounds , in order to reduce convergence on local minima . To find the best-fitting values of the softmax temperature parameter , β , and the exponent of the utility function , k , assuming that participants behaved so as to maximize the previously-derived anticipation-discounting functions , we implemented the value function shown in Equation 8 , assuming full sophistication , whilst optimizing over β and k . The value function of the Direct Action heuristic model is described in the main text . The Direct Action model had only three parameters: the inverse temperatures of each softmax function , respectively termed , βspend , βspread and βsave . The value function for the Income Maximization model was identical to the Direct Action model , with the exception that the propensity to spend-now-save-later , Msave , was replaced with an action-value function , the maximization of which maximizes the mean relief remaining , ρt , over the immediate future , given knowledge of the future policy for action , π: Q ρ − m a x π ( s t , c t , t , γ ) = ρ t ( s t , t ) + E [ ∑ d = 1 T − t γ d ρ t + d ( s t + d , t + d ) c t + d ~ π ]13 ) Where: ρ t ( s t , t ) = s t / [ 60 – ( t − 1 ) ] 14 ) The Income Maximization model thus had four parameters: βspend , βsread , βmaximize and γ , where the latter governs the probability , 1-γ , that the decision-maker terminates their search at every level deeper into the tree . For both heuristic models , the softmax temperature of each policy was bounded between 0 and 10 . The parameter , γ , governing the search depth of the Income Maximization model , was bounded between 0 and 1 . For these models , the resulting three policies were combined by a weighted average , in which the weight given to each policy was proportional to the inverse variance of the resulting distribution of consumption choices , such that: ω i = [ 1 v a r ( π i ) ] / ∑ j = 1 3 [ 1 v a r ( π j ) ]15 ) Where ωi is the weighting on policy , πi . This procedure served as a useful heuristic for combining policy estimates . To combine heuristics with utility maximization , we assumed that the value of consuming each possible quantity of relief at each time point was governed by a weighted sum of the value function in Equation 8 , here termed Q o p t π ( s t , c t ) and a bias towards consuming either zero , the mean remaining or the maximum relief . Each bias assumed that the propensity to spend each possible quantity of relief was proportional to a Gaussian probability density function with a mean centered on the quantity of interest , and standard deviation equal to two units of relief , such that: M m i n ~ N ( 0 , 2 ) 16 ) M m e a n ( ρ t ) ~ N ( ρ t , 2 ) 17 ) M m a x ( s t ) ~ { N ( 12 , 2 ) i f s t ≥ 12 N ( s t , 2 ) o t h e r w i s e 18 ) The final value function , Q o p t − h e u r π ( s t , c t ) , was then a weighted sum of the optimal values and the biases: Q o p t − h e u r π ( s t , c t ) = Q o p t π ( s t , c t ) + ω m i n M m i n + ω m e a n M m e a n ( ρ t ) + ω m a x M m a x ( s t ) 19 ) Fixed effects model comparison was performed at the group level by summation of log likelihoods across participants . Model comparison used the Bayesian Information Criterion ( BIC ) [58] , where B I C = − 2 L + k ln ( n ) 20 ) and L is the maximized group level log likelihood , k is the number of free parameters in the model and n the number of independent observations . The BIC favors models with higher likelihood estimates and penalizes increasing model complexity . Lower values of BIC indicate a more favorable model fit . | People often have to trade-off their present wellbeing against their future wellbeing , for example whether to go to an expensive restaurant today or put the money towards a future holiday . Many studies have examined how people make such trade-offs . However , the majority have done so by analyzing choices between one-off future outcomes . By contrast , real-world choices are often made sequentially , with today’s choices influencing the possibilities available tomorrow . This generates decision problems of near limitless complexity . To explore how people approach such decisions in a naturalistic ( health-related ) setting , we describe participants’ use of a limited budget of relief from moderately painful stimuli over a period of approximately 15 minutes . Participants showed a range of different behaviors , with the majority either conserving relief for the future , or preferring to spread relief evenly over time . Notably no participant consistently consumed the maximum allowable relief at the outset . We show that sequential decision-making behavior cannot easily be predicted from the results of simple one-off choices made at the beginning of the task . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Anticipation and Choice Heuristics in the Dynamic Consumption of Pain Relief |
Leishmania parasites infect macrophages , cells that play an important role in organismal iron homeostasis . By expressing ferroportin , a membrane protein specialized in iron export , macrophages release iron stored intracellularly into the circulation . Iron is essential for the intracellular replication of Leishmania , but how the parasites compete with the iron export function of their host cell is unknown . Here , we show that infection with Leishmania amazonensis inhibits ferroportin expression in macrophages . In a TLR4-dependent manner , infected macrophages upregulated transcription of hepcidin , a peptide hormone that triggers ferroportin degradation . Parasite replication was inhibited in hepcidin-deficient macrophages and in wild type macrophages overexpressing mutant ferroportin that is resistant to hepcidin-induced degradation . Conversely , intracellular growth was enhanced by exogenously added hepcidin , or by expression of dominant-negative ferroportin . Importantly , dominant-negative ferroportin and macrophages from flatiron mice , a mouse model for human type IV hereditary hemochromatosis , restored the infectivity of mutant parasite strains defective in iron acquisition . Thus , inhibition of ferroportin expression is a specific strategy used by L . amazonensis to inhibit iron export and promote their own intracellular growth .
Intracellular parasites must obtain essential nutrients from their host cells . Iron is a vital nutritional resource for mammalian hosts and also for many pathogens , acting as an essential cofactor of proteins and enzymes involved in metabolic pathways . Under physiological conditions , iron is normally found in the insoluble , oxidized Fe3+ form associated with carrier proteins such as transferrin and ferritin . After endocytosis mediated by transferrin receptors , Fe3+ is reduced to the soluble Fe2+ form and translocated to the cytosol . In the cytosol , iron may be utilized by the host , stored as complex with ferritin , or exported out of the cells . Thus , the pool of available iron within mammalian cells is determined by a carefully orchestrated balance between uptake through plasma membrane and endosomal receptors/transporters and cellular export . The only known mammalian iron exporter is ferroportin ( Fpn1 ) , a protein with an essential role in iron homeostasis [1] . Fpn1 is expressed on the surface of cells specialized in storage or transport of iron , including enterocytes , hepatocytes and macrophages . Transcription of Fpn1 is increased by several factors , including iron . Further , Fpn1 mRNA has a 5′iron-responsive element ( IRE ) that limits its translation when iron availability is low and enhances it under high iron conditions . Fpn1 levels on the cell surface are regulated by hepcidin , a peptide hormone that triggers Fpn1 internalization and lysosomal degradation [2] . Modulation of Fpn1 expression allows macrophages to function as mediators of iron homeostasis in vivo , responding to systemic and localized signals by retaining or exporting iron [3] , [4] . Missense mutations in Fpn1 are autosomal dominant in humans and some mutations lead to hereditary hemochromatosis type IV ( form A ) , a human disease characterized by high ferritin levels , low transferrin saturation and iron accumulation inside macrophages [5] . Other Fpn1 mutations lead to constitutive iron export due to the inability to respond to hepcidin . The protozoan parasite Leishmania , which causes serious infections in millions of people throughout the world , replicates mainly in macrophages . Until recently very little was known about mechanisms of iron acquisition and utilization by Leishmania , and how changes in host cell iron homeostasis affect these intracellular parasites . Recent studies improved this scenario , by identifying three L . amazonensis membrane proteins that mediate iron uptake and are required for parasite virulence: the Fe3+ reductase LFR1 [6] , the Fe2+ transporter LIT1 [7] and the heme transporter LHR1 [8] . In L . donovani , the ATP-binding cassette membrane protein LABCG5 was also implicated in the salvage of heme generated by degradation of hemoglobin in parasite lysosomes [9] . These studies showed that the iron-poor environment of macrophage phagolysosomes induces Leishmania to express molecules that promote uptake of iron and heme , factors essential for intracellular survival and replication . However , it is still not understood how the parasites compete with the highly developed pathways of iron export and storage of macrophages to gain access to iron . In this study we examined how iron efflux and modulation of host cell iron content affect the intracellular growth of L . amazonensis . Our results revealed a parasite strategy for inhibition of iron export that is required for the establishment of productive infections in macrophages .
We investigated the impact of infection with L . amazonensis on the expression of Fpn1 in mouse bone marrow-derived macrophages ( BMDM ) . Low levels of Fpn1 transcripts were detected in wild type and TLR4−/− BMDM with and without L . amazonensis infection ( Figure 1A , B ) . In agreement with previous studies showing Fpn1 upregulation in response to iron loading [10] , [11] , treatment with Fe-nitrilotriacetate ( Fe-NTA ) for 16 h markedly increased Fpn1 transcripts ( Figure 1A , B ) . Following infection with L . amazonensis , Fpn1 mRNA levels were reduced in Fe-NTA-treated wild type BMDM ( Figure 1A ) . In contrast , although TLR4−/− BMDM showed higher amounts of Fpn1 mRNA ( particularly 24 h after Fe-NTA treatment ) , the transcript levels were very similar between infected and non-infected cells ( Figure 1B ) . At the protein level , immunoblots detected low amounts of Fpn1 in untreated BMDM and elevated levels 48 h after Fe-NTA treatment , in non-infected BMDM derived from either wild type or TLR4−/− mice ( Figure 1C ) . Similar to what was observed with the mRNA , infection with L . amazonensis reduced the amounts of Fpn1 protein in Fe-NTA-treated wild type , but not TLR4−/− BMDM . Fpn1 levels detected in BMDM not treated with Fe-NTA were low , but quantification and ratio determinations in relation to actin showed a similar trend under these conditions as well - Fpn1was reduced in infected wild type , but not in infected TLR4−/− BMDM ( Figure 1C ) . Similar experiments performed with BMDM from TLR2−/− mice showed results undistinguishable from wild type ( data not shown ) . These findings suggest that the signalling pathway involved in Fpn1 downregulation by L . amazonensis is TLR4-dependent . The peptide hormone hepcidin is a potent regulator of iron export in macrophages , inducing Fpn1 internalization and degradation [2] , [12] . We found that L . amazonensis infection upregulates hepcidin ( Hamp ) transcripts in BMDM ( Figure 2A ) , an effect not observed with heat-killed parasites ( data not shown ) . The ability of L . amazonensis to induce hepcidin expression was TLR4-dependent , consistent with what was observed for Fpn1 downregulation ( Figure 1C ) . Importantly , upregulation of Hamp transcripts was also observed in vivo , in the liver and in footpad tissue associated with cutaneous lesions of mice infected for 9 weeks with L . amazonensis ( Figure 2B , C ) ( see also supporting information S1 ) . These results strongly suggest that macrophages infected with L . amazonensis express hepcidin . The inhibition in Fpn1 surface expression induced by L . amazonensis infection was expected to increase the intracellular iron content of macrophages . To test this hypothesis , we used a ferrozine assay to quantify iron in BMDM infected or not with L . amazonensis amastigotes . The results showed a significant increase in the total iron content of wild type BMDM at 24 and 48 h after infection . This effect was not seen in TLR4−/− BMDM ( Table 1 ) . At the same time points , infected wild type BMDM showed upregulation of the iron storage protein ferritin , at both the mRNA and protein levels ( Figure 3A , B ) . This increase in intracellular iron content is likely to be a consequence of the parasite-induced inhibition of Fpn1 expression , which is expected to result in decreased iron export . We proceeded to investigate the impact of Fpn1-mediated iron export on the intracellular replication of L . amazonensis . In untreated BMDM the parasites increased in number over time , doubling in density by 48 h after infection . When BMDM were pre-treated with 1 µg/ml hepcidin for 4 h before infection , the intracellular growth rate of the parasites was enhanced ( Figure 4A , B ) . These results suggest that hepcidin-mediated Fpn1 downregulation and block in iron export is beneficial for the establishment of L . amazonensis intracellular infections . After pre-treating BMDM with Fe-NTA , a condition that markedly stimulates Fpn1 expression ( Figure 1 ) , the opposite effect was observed: L . amazonensis intracellular replication was inhibited . This inhibitory effect of iron loading was apparently not due to iron toxicity , since Fe-NTA-treated BMDM remained >80% viable up to 48 h ( Alamar Blue viability assay , results not shown ) . Furthermore , the inhibition in parasite replication was fully reversed by a subsequent exposure of the Fe-NTA-treated BMDM to hepcidin ( Figure 4A , B ) . Microscopic examination showed that Fe-NTA-loaded macrophages contained small parasitophorous vacuoles ( PV ) with only one or two amastigotes . In contrast , after hepcidin treatment PVs were markedly expanded and housed numerous replicating parasites ( Figure 4B ) . These findings suggest that even when macrophages are loaded with an iron source such as Fe-NTA , continuous iron efflux through Fpn1 on the plasma membrane has a negative impact on the ability of L . amazonensis to replicate in macrophages . Confirming the importance of hepcidin production by macrophages for the establishment of intracellular infections , the growth rate of L . amazonensis was reduced in BMDM prepared from Hamp−/− mice , when compared to their wild type counterparts ( Figure 4C ) . Our results suggested that hepcidin production during L . amazonensis infection reduces the amount of Fpn1 present on the surface of macrophages , a condition that promotes intracellular parasite replication . In order to directly visualize this process , we transfected BMDM with three different GFP-tagged constructs: Wild Type Fpn1 , Fpn1 ( H32R ) and Fpn1 ( N144H ) . Fpn1 ( H32R ) has a dominant-negative effect , severely reducing the amount of Fpn1 that reaches the cell surface [13] . On the other hand , the N144H mutation renders Fpn1 resistant to hepcidin-mediated internalization and degradation , increasing levels at the cell surface [14] . As expected , we detected Fpn1-GFP on the plasma membrane of BMDM transfected with the wild type and hepcidin-resistant N144H constructs , and on intracellular compartments of BMDM expressing the dominant-negative H32R mutation ( Figure 5 , Control ) . Hepcidin treatment triggered removal of wild type Fpn1-GFP from the plasma membrane of macrophages , while the hepcidin-resistant Fpn1 ( N144H ) -GFP version remained on the cell surface ( Figure 5 , +hepcidin ) . Confirming that L . amazonensis infection triggers removal of Fpn1 from the cell surface of BDMM , after 48 h of infection the level of wild type Fpn1-GFP associated with the plasma membrane was strongly reduced , with small amounts detected in intracellular compartments . In contrast , downregulation of surface-associated Fpn1 was not detectable in infected BMDM overexpressing the hepcidin-resistant Fpn1 ( N144H ) -GFP ( Figure 5 , +L . amazonensis ) . These results further support the hypothesis that L . amazonensis downregulates Fpn1 expression by triggering endogenous hepcidin production by macrophages . The hepcidin-resistant version of Fpn1-GFP remained associated with the BMDM plasma membrane up to 72 h after infection ( data not shown ) . Intracellular replication assays showed that L . amazonensis growth in BMDM overexpressing wild type Fpn1-GFP was inhibited , in agreement with the increased levels of surface-associated Fpn1 and lower levels of cellular iron . In contrast , expression of the dominant-negative Fpn1 ( H32R ) -GFP construct that interferes with Fpn1 surface targeting stimulated parasite intracellular replication . Reinforcing the conclusion that intracellular growth of L . amazonensis depends on hepcidin-mediated removal of Fpn1 from the macrophage surface , parasite replication over a period of 48 h was strongly inhibited in BMDM expressing the hepcidin-resistant Fpn1 ( N144H ) -GFP ( Figure 6 ) . We hypothesized that the enhanced replication of wild type L . amazonensis in BMDM expressing dominant-negative Fpn1 ( H32R ) was due to an increased availability of intracellular iron to the parasites . To test this hypothesis , we examined the effect of dominant-negative Fpn1 on the intracellular fate of L . amazonensis null mutant strains deficient in iron uptake . Strains lacking the LIT1 ferrous iron transporter ( Δlit1 ) and the LFR1 ferric iron reductase ( Δlfr1 ) are incapable of growing inside macrophages , but their replication can be fully rescued by loading the endosomal compartment of host macrophages with an abundant iron source , cationic ferritin [6] . Strikingly , we found that expression of dominant-negative Fpn1 restored intracellular growth rates of Δlit1 and Δlfr1 null strains to levels comparable to wild type L . amazonensis ( Figure 7A , B ) . A similar effect was observed in BMDM from flatiron mice [13] , which carry the dominant-negative H32R mutation in one Fpn1 allele , and have elevated levels of ferritin ( Figure 8A ) and iron ( Figure 8B ) . The Δlit1 and Δlfr1 null parasite strains did not grow intracellularly in control C3H BMDM , but were able to replicate in ffe/+ BMDM ( Figure 8C , D ) . Collectively , these results show that Fpn1 removal from the cell surface facilitates L . amazonensis access to iron while replicating inside macrophage PVs , and compensates for specific defects in the parasite's iron acquisition pathway .
Genes involved in iron acquisition play a critical role in pathogen virulence , as extensively demonstrated in bacteria [15] and more recently in the protozoan parasite Leishmania amazonensis [6] , [7] . In mammals Leishmania is an obligate intracellular parasite of macrophages , replicating within PVs with properties of phagolysosomes . Utilization of the macrophage as host cell has important implications for how Leishmania gains access to iron . Macrophages from the reticuloendothelial system play a fundamental role in iron recycling in vivo , through the process of erythrophagocytosis . Heme released from phagocytosed senescent red blood cells is translocated to the macrophage cytosol , where iron is extracted through the activity of heme oxygenase . Cytosolic iron is then utilized by the cell , stored as a complex with ferritin , or exported by Fpn1 [1] . In this study we showed that L . amazonensis directly interferes with the iron export function of macrophages , by inhibiting cell surface expression of Fpn1 . This Leishmania-driven process is associated with increased total macrophage iron content and stimulation of parasite intracellular replication ( Figure 9 ) . Consistent with an inhibitory effect on iron export , infection by L . amazonensis enhanced expression of ferritin , the cytosolic iron storage protein . At the protein level , this observation is consistent with the presence of a 5′ IRE in the ferritin mRNA [16] . However , the total amount of ferritin mRNA was also elevated after L . amazonensis infection . This indicates that signaling events regulating transcription , possibly associated with macrophage inflammatory responses , are also triggered during infection and participate in the cellular response to elevated iron pools . Indeed , we found that the ability of Leishmania to inhibit Fpn1 expression depends on TLR4 , a major pattern-recognition receptor of macrophages that is commonly associated with pro-inflammatory responses . Different species of Leishmania were described to elicit distinct TLR4-mediated responses in macrophages , although the exact identity of the molecule ( s ) serving as TLR4 ligand ( s ) remain unknown [17] . While TLR4 plays a protective role in infections with L . major [18] , it appears to have an anti-inflammatory effect in macrophages infected with L . mexicana , a species closely related to the L . amazonensis used in our study [19] . We found that TLR4-dependent downregulation of Fpn1 requires live parasites , suggesting that the molecule ( s ) involved may be actively secreted , or may only come in contact with the TLR4 receptors in an intracellular compartment generated during cell invasion . Several lines of evidence indicate that L . amazonensis-induced Fpn1 downregulation is mediated , at least in part , by production of the peptide hormone hepcidin by infected macrophages . First , there was a marked increase in hepcidin transcripts in infected macrophages . Second , Fpn1 carrying the N144H mutation that renders it resistant to hepcidin-mediated internalization and degradation [13] remained on the plasma membrane of infected macrophages , while wild type Fpn1 was removed . Third , the effects of L . amazonensis infection on both hepcidin and Fpn1 expression in macrophages were TLR4-dependent . Hepcidin is produced in the liver during inflammation and other stimuli in a paracrine manner [20] , but it can also be produced locally by macrophages in an autocrine fashion in response to infections , as previously shown for Pseudomonas aeruginosa [21] and Mycobacterium tuberculosis [22] . Similar to our findings with L . amazonensis , hepcidin production and decrease cell surface-associated Fpn1 triggered by infection with Pseudomonas aeruginosa require TLR4 [21] . Earlier studies demonstrated that Fpn1 containing the H32R mutation has a dominant-negative effect , preventing the traffic of Fpn1 to the cell surface [13] . We found that Fpn1 carrying this mutation enhances L . amazonensis intracellular replication when expressed in macrophages . This was observed after expression of Fpn1 ( H32R ) -GFP , and also in macrophages from flatiron mice , which are heterozygous for this mutation and represent a model for the autosomal-dominant human type IV hereditary hemochromatosis type A [13] . Notably , macrophages expressing Fpn1 ( H32R ) -GFP or isolated from flatiron mice rescued the intracellular growth defect of LFR1 and LIT1 null mutants deficient in iron uptake [6] , [7] , directly supporting the conclusion that Fpn1 removal from the cell surface facilitates parasite access to iron . Our findings indicate that blocking Fpn1 surface expression increases iron availability in the cytosol , which is associated with L . amazonensis intracellular replication . This result suggests the existence of a crosstalk between the cytosolic iron pool and the iron content of L . amazonensis PVs , a process that is still poorly understood ( Figure 9 ) . Interestingly , we found that increasing ferritin-iron cytosolic stores by treating macrophages with an exogenous iron source is not sufficient to promote parasite replication . Hepcidin-mediated downregulation of Fpn1 appears to be required to facilitate amastigote access to iron , suggesting that Fpn1 internalization promotes formation of a cytosolic iron pool that can be more readily mobilized by the parasites . Evidence for such a mechanism was recently obtained in breast cancer cells , where hepcidin-mediated internalization of Fpn1 was associated with an increased labile iron pool [23] . Another intriguing possibility is that Fpn1 may be delivered to the membrane of the lysosome-like PV after hepcidin-mediated internalization , where it may remain transiently active and transport cytosolic iron into the lumen of the parasite-containing vacuole . Fpn1-mediated iron depletion inhibits the intracellular growth of several bacterial pathogens , such as Chlamydia psittaci , Chlamydia trachomatis , Legionella pneumophila [24] . Elevated Fpn1 expression was also identified as a host defense mechanism against infections with Salmonella typhimurium and Mycobacterium tuberculosis [25] . To our knowledge , the present study represents the first demonstration that a protozoan pathogen blocks Fpn1 expression as a strategy to facilitate their intracellular replication . This strategy may complement additional mechanisms that facilitate parasite access to iron , such as the elevated expression of transferrin receptors observed in macrophages infected with Leishmania donovani [26] . Our study demonstrates the important role played by cytosolic iron pools in L . amazonensis infections , and opens the way for future investigations of how iron enters the PV , and whether differential access to iron in cutaneous or visceral sites influences the clinical disease caused by different Leishmania species .
All animal work was conducted according to the National Institutes of Health guidelines for the housing and care of laboratory animals and performed under protocol # R-11-73 approved by the University of Maryland College Park Institutional Animal Care and Use Committee on January 3 , 2013 . The University of Maryland at College Park is a fully AAALAC-accredited institution . Wild type Leishmania amazonensis ( IFLA/BR/67/PH8 strain , expressing or not RFP ) or knockout strains ( Δlfr1 and Δlit1; Flannery et al . , 2011; Huynh et al . , 2006 ) promastigotes were maintained in vitro at 26°C in M199 ( Invitrogen ) , 40 mm HEPES , pH 7 . 4 , 20% heat-inactivated FBS , 5% penicillin/streptomycin , 0 . 1% hemin ( from a 25 mg/ml stock in 50% triethanolamine ) , 10 mm adenine , and 5 mm l-glutamine . To obtain axenic amastigotes , stationary phase cultures rich in metacyclic promastigotes were incubated at 32°C in acidified media , as previously described [6] . Heat-killed ( 65°C incubation for 1 h ) axenic amastigotes were washed , resuspended in BMDM medium and added to BMDM at a MOI = 5 . BMDM obtained from WT or TLR4−/− C57Bl/6 mice ( The Jackson Laboratory ) , C3H mice or Flatiron ( ffe/+ heterozygous ) mice in the C3H genetic background ( provided by D . Ward and J . Kaplan , U . Utah ) or Hamp−/− C57BL/6 mice ( provided by S . Vaulont , Cochin Institite - [27] ) were prepared as described [28] and cultured in RPMI 1640 containing 10% heat-inactivated FBS , 100 units/ml penicillin and 100 µg/ml streptomycin and 50 ng/ml human Recombinant M-CSF ( Peprotech ) . Mature , adherent BMDM were detached from dishes with PBS/1 mM EDTA , viability was determined with a trypan-blue exclusion test , cells were seeded into tissue culture-treated 6 well plates at a density of 2×106 cells/well , and incubated overnight at 37°C in a 5% CO2 incubator prior to use in experiments . BMDM were plated on glass coverslips in RPMI 10% FBS . After 24 h , BMDM were incubated with freshly prepared Fe-nitrilotriacetate ( Fe-NTA , 1∶4 solution of FeCl3 200 µM and NTA 400 µM ) for 16 h , and washed before infection . For infection , axenic amastigotes were added to BMDM for 1 h at 34°C at a MOI = 5 , washed in PBS to remove non-internalized parasites , reincubated for 1 , 24 , 48 or 72 h at 34°C , fixed with 4% PFA , stained with DAPI and anti-L . amazonensis polyclonal mouse antibodies , and mounted ( Prolong Antifade; Molecular Probes ) . Intracellular parasites were quantified microscopically , as previously described [29] . To assess the effect of hepcidin on inhibition of ferroportin expression and parasite growth , BMDM were treated for 4 or 18 h with 1 µg/ml hepcidin ( kindly provided by J . Kaplan and D . M . Ward , University of Utah ) [29] . Fpn1-GFP constructs were kindly provided by J . Kaplan and D . M . Ward , University of Utah ) [13] . BMDM were transfected with wild type ferroportin ( Fpn1 -GFP ) , hepcidin-resistant ferroportin ( Fpn1 ( N144H ) -GFP ) and dominant-negative Ferroportin ( Fpn1 ( H32R ) -GFP ) . 4 µg of each plasmid was used to transfect 1×106 BMDM using nucleofactor technology ( Amaxa ) according to the manufacturer's instructions . As controls , BMDMs were mock-transfected with the plasmid elution buffer . 1×106 BMDM plated in 96 well plates were treated with Fe-NTA ( 150 , 200 , 400 , 800 µM ) or 0 . 1% saponin for 24 h , washed in PBS , and incubated in medium containing 10% Alamar Blue Cell Viability Reagent ( Invitrogen ) at 37°C for 4 h protected from light . The fluorescence signal generated by the fluorimetric redox indicator was detected using a SpectraMAX M5 microplate fluorimeter at 560 nm excitation and 590 nm emission , and analyzed using software SoftMAX R _ PRO ( Molecular Devices ) . For live imaging , transfected BMDM were plated in 35 mm glass-bottom dishes ( Mattek ) , infected or not with axenic amastigotes for 1 h , washed and reincubated for 48 h . Dishes were then transferred to an environmental chamber at 34°C with 5% CO2 and humidity control ( Pathology Devices ) . Confocal stacks were acquired using a spinning disk UltraVIEW VoX system ( PerkinElmer ) attached to an Eclipse Ti inverted microscope ( Nikon ) with a 60×1 . 4 N . A . objective and equipped with a C9100-50 camera ( Hamamatsu ) , or on a Leica TCS SP5 X Supercontinuum confocal microscope using a 63× NA 1 . 2 objective . Acquisition was performed using Volocity Suite ( PerkinElmer ) , or Leica application suite software package ( Leica ) . C57BL/6 BMDMs plated at 2×106 in 6 well plates were loaded or not with Fe-NTA , and infected or not with L . amazonensis axenic amastigotes for 1 h . BMDM were washed in PBS and reincubated for 1 , 24 or 72 h before lysis and RNA extraction ( Nucleospin RNA II kit , Macherey-Nagel ) according to the manufacturer's instructions . For in vivo analysis of hepcidin mRNA levels , five week-old female C57BL/6 mice were inoculated with 1×106 purified metacyclics of wild type L . amazonensis ( Pinto-da-Silva et al . , 2005 ) in the left hind footpad in a volume of 50 µl PBS . After 9 weeks , liver or footpad from 3 infected mice were recovered and vigorously homogenized using micro tissue grinder ( Radnoti LLC ) . Non-infected mice were used as experiment controls . The samples were kept on ice for subsequent RNA extraction . cDNA synthesis was carried out using 1 µg of total RNA and Superscript II Reverse Transcriptase ( Invitrogen ) according to the manufacturer's protocol . To quantify transcript levels , 1 µl of cDNA was amplified using the following primers: mouse FPN1-Forward 5′- CTA CCATTAGAAGGATTGACCAGCT-3′ , mouse FPN1-Reverse 5′- ACTGGAGAACCAAATGTCATAATCTG-3′; mouse HAMP- Forward 5′-TGTCTCCTGCTTCTCCTCCT-3′ , mouse HAMP-Reverse5′- CTCTGTAGTCTGTCTCATCTGTTG-3′; mouse FTH1-Forward 5′- GCGAGGTGGCCGAATCT-3′ , mouse FTH1-Reverse 5′- CAGCCCGCTCTCCCAGT-3′; mouse HPRT1-Forward 5′- TCAGTCAACGGGGGAACATAAA-3′ , mouse HPRT1-Reverse 5′- GGGGCTGTACTGCTTAACCAG-3′ , mouse β-III-Tubulin-Forward 5′-CGCACGACATCTAGGACTGA-3′ , mouse β-III-Tubulin-Reverse 5′- TGAGGCCTCCTCTCACAAGT-3′ Real-time PCR was performed using a BioRad iQ icycler Detection system ( BioRad Laboratories ) using SYBR green fluorophore ( BioRad Laboratories ) according to the manufacturer's instructions . Fluorescence was detected at each annealing step , and the cycle threshold ( Ct ) was calculated by determining the point at which the fluorescence exceeded a threshold limit . All reactions were performed in triplicate , and negative controls ( no template cDNA ) were included in each experiment . Data were normalized to the level of HPRT1 ( mouse BMDM ) or tubulin ( mouse liver and footpad ) expression in each sample . BMDM were washed twice with cold PBS , scraped from the dish , collected by centrifugation ( 335 g ) for 10 min at 4°C , lysed in 30 µl RIPA buffer ( 50 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 1% Nonidet P-40 , 0 . 50% sodium deoxycholate , 0 . 10% SDS ) with protease inhibitors ( Roche ) , and centrifuged at 14000 g for 15 min at 4°C to remove insoluble material . Lysates were assayed for protein content ( Pierce BSA Protein Assay kit ) , 60 µg per sample were mixed with SDS sample buffer at room temperature for 30 min , separated by SDS-PAGE ( without sample boiling ) and transferred to a PVDF membrane ( Millipore ) . Membranes were incubated overnight with 1∶1000 rabbit anti-Fpn1 antibody ( Alpha Diagnostics ) , 1∶800 anti L-ferritin ( Abcam ) or 1∶5000 anti-actin ( Sigma ) in blocking buffer ( PBS 3% milk , 0 . 1% Tween ) , followed by HRP-coupled anti-rabbit IgG or anti mouse IgG secondary antibodies ( Amersham Biosciences ) . Blots were developed using Immun-Star HRP Luminol Enhancer and peroxidase buffer ( Bio-Rad ) and detected with a Fuji LAS-3000 Imaging System and Image Reader LAS-3000 software . Digital quantifications of chemiluminescence were performed using Image J software . A ferrozine-based colorimetric assay was performed as previously described [30] . Briefly , BMDMs were infected or not with L . amazonensis axenic amastigotes for 1 h and reincubated for 2 , 24 or 48 h . At different time points , the dishes were washed twice with ice-cold PBS and stored at −20°C . Cells were lysed with 200 µl of 50 mM NaOH for 2 h on a shaker in a humidified atmosphere and aliquots were used for iron and protein determinations . For iron quantification , 100 µl of lysates were mixed with 100 µl 10 mM HCl , 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 ) . After for 2 h at 60°C in a fume hood , samples were cooled to room temperature and 30 µl of the iron-detection reagent ( 6 . 5 mM ferrozine , 6 . 5 mM neocuproine , 2 . 5 M ammonium acetate , and 1 M ascorbic acid ) was added to each sample . After 30 min , 280 µl of each sample was transferred into a 96-well plate and the absorbance measured at 550 nm in a microplate reader . The iron content in each sample was calculated based on a standard curve ( 0–300 µM FeCl3 ) prepared under the same conditions , and normalized to protein levels . Protein levels were determined by a modified Lowry assay ( Pierce ) per manufacturer's instructions . | Infection with the protozoan parasite Leishmania causes significant human disease in many parts of the world , particularly in the Middle East , India and South America . The parasite is transmitted by sand flies , which are difficult to control and are becoming increasingly common in urban areas . With domestic dogs serving as reservoirs of the disease and global travel increasing the population of infected human patients , the overall burden of leishmaniasis is on the rise . In mammals these parasites replicate inside macrophages , and therefore need strategies to survive within a cell that is specialized in killing pathogens . Earlier work demonstrated that iron is one of the essential nutrients that Leishmania must acquire from host cells to survive . Acquiring iron is particularly challenging inside macrophages , which play an important role in host iron homeostasis and export iron extracellularly through the membrane transporter ferroportin . We found that Leishmania amazonesis induces their host macrophages to produce hepcidin , a peptide that triggers internalization and degradation of ferroportin . This strategy increases the macrophage intracellular iron pool , and stimulates Leishmania replication . These results suggest that defects in iron homeostasis , which occur frequently in the human population , can have an important role in susceptibility to Leishmania infections . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"biology"
] | 2014 | Leishmania-Mediated Inhibition of Iron Export Promotes Parasite Replication in Macrophages |
Disrupting either the DNA annealing factor RAD52 or the A-family DNA polymerase POLQ can cause synthetic lethality with defects in BRCA1 and BRCA2 , which are tumor suppressors important for homology-directed repair of DNA double-strand breaks ( DSBs ) , and protection of stalled replication forks . A likely mechanism of this synthetic lethality is that RAD52 and/or POLQ are important for backup pathways for DSB repair and/or replication stress responses . The features of DSB repair events that require RAD52 vs . POLQ , and whether combined disruption of these factors causes distinct effects on genome maintenance , have been unclear . Using human U2OS cells , we generated a cell line with POLQ mutations upstream of the polymerase domain , a RAD52 knockout cell line , and a line with combined disruption of both genes . We also examined RAD52 and POLQ using RNA-interference . We find that combined disruption of RAD52 and POLQ causes at least additive hypersensitivity to cisplatin , and a synthetic reduction in replication fork restart velocity . We also examined the influence of RAD52 and POLQ on several DSB repair events . We find that RAD52 is particularly important for repair using ≥ 50 nt repeat sequences that flank the DSB , and that also involve removal of non-homologous sequences flanking the repeats . In contrast , POLQ is important for repair events using 6 nt ( but not ≥ 18 nt ) of flanking repeats that are at the edge of the break , as well as oligonucleotide microhomology-templated ( i . e . , 12–20 nt ) repair events requiring nascent DNA synthesis . Finally , these factors show key distinctions with BRCA2 , regarding effects on DSB repair events and response to stalled replication forks . These findings indicate that RAD52 and POLQ have distinct roles in genome maintenance , including for specific features of DSB repair events , such that combined disruption of these factors may be effective for genotoxin sensitization and/or synthetic lethal strategies .
Exploiting synthetic lethal relationships in cancer cells has emerged as a promising therapeutic approach [1 , 2] . As a key example , cells deficient in BRCA1 or BRCA2 are hypersensitive to inhibitors of Poly-ADP-ribose Polymerase ( PARP ) [1 , 2] . Both BRCA1 and BRCA2 are important for homology-directed repair ( HDR ) of chromosomal breaks , which involves RAD51-mediated invasion of a homologous sequence to template nascent DNA synthesis [3] . In addition , BRCA1 and BRCA2 are important for protection of stalled replication forks by blocking recruitment of the MRE11 nuclease to reversed forks [4–6] . PARP inhibitors appear toxic to cells deficient in BRCA1 and BRCA2 , by causing DNA lesions that require HDR for repair , and/or replication defects that require protection from degradation via BRCA1 and BRCA2 [6] . However , since PARP inhibitors are effective in only a fraction of cancer patients [7] , it is important to develop additional targets for this synthetic lethality approach . In particular , deficiencies in BRCA1 or BRCA2 are synthetic lethal with disruption of either RAD52 or POLQ [8–11] , which have distinct biochemical activities . RAD52 forms multimeric ring structures and has a strong affinity for ssDNA [12] . Moreover , RAD52 is capable of facilitating the displacement of the ssDNA binding protein replication protein A ( RPA ) to anneal complementary strands of ssDNA [13 , 14] . RAD52 also interacts with dsDNA , although with a weaker affinity than with ssDNA [15] . Consistent with a role in promoting stable DNA annealing , RAD52 appears to protect dsDNA from force-induced strand separation [16] . POLQ is an A-family DNA polymerase that has also been shown to anneal complementary ssDNA [17] . The polymerase domain of POLQ has a unique structure that consists of three insertion loops , which are not conserved among other A-family DNA polymerases [18] . This distinct polymerase domain structure allows for the interaction , annealing , and extension of short ssDNA primers [17 , 19] . In addition to its C-terminal polymerase domain , POLQ also has an N-terminal helicase domain [20 , 21] . To develop RAD52 and POLQ as therapeutic targets for synthetic lethal approaches , it is important to understand their role in genome maintenance . As one possibility , disruption of POLQ or RAD52 may cause similar effects as PARP inhibitors , e . g . , cause defects at replication forks that require BRCA1 and BRCA2 [6] . Although , an additional potential mechanism of such synthetic lethality is that these factors mediate alternative DSB repair pathways to HDR . In particular , one class of repair pathways involves annealing of homologous repeat sequences that flank the break . These pathways are referred to as Single Strand Annealing ( SSA ) and Alternative end-joining ( Alt-EJ ) , which generally are distinguished by the use of long vs . short repeat sequences ( the latter referred to as microhomology ) , and the involvement of RAD52 vs . POLQ , respectively [22–28] . However , a limitation of these terms is that the precise parameters that define the mechanism of these events remain poorly understood . Such parameters include repeat length , and influence of a non-homologous intervening sequence . Thus , we refer to these events collectively as repeat-mediated repair ( RMR ) to avoid a presumption of mechanism . In this study , we have sought to define whether RAD52 and POLQ have distinct vs . redundant functions in chromosomal break repair or response to replication stress . Specifically , we have examined the influence of these factors on several distinct features of DSB repair , as well as in response to genotoxic agents and replication stress . To test whether these factors have distinct ( i . e . , non-epistatic ) roles in these aspects of genome maintenance , we have also compared cells with combined deficiency in POLQ and RAD52 vs . cells with disruption of the individual factors . Finally , we posited that RAD52 and POLQ have distinct roles in genome maintenance vs . BRCA2 , due to their synthetic lethality with BRCA2 loss . Thus , we have also compared the influence of these factors vs . BRCA2 on DSB repair events and in response to replication stress .
We have sought to examine the relative roles of RAD52 and POLQ in cellular response to genotoxic stress , including distinct DSB repair events . For this , we developed cell lines with disruptions of these genes ( both single and double mutants ) using the RNA-guided nuclease Cas9 . For our parental cell line , we used human osteosarcoma U2OS cells [29 , 30] , which retain intact cell cycle checkpoints [31 , 32] . Notably , these cells rely on the ALT-pathway of telomere maintenance , which could possibly influence repair mechanisms [33] . Our parental cell line was also stably transfected with pFRT/lacZeo ( i . e . , U2OS Flp-In T-Rex ) [29 , 30] , which is used to integrate the reporter assays described below . We used single guide RNAs ( sgRNAs ) and Cas9 to generate cell lines deficient in POLQ and RAD52 . To generate a POLQ-deficient cell line , we used two sgRNAs targeting exon 16 ( Fig 1A ) . We targeted this region of POLQ to disrupt expression of the C-terminal polymerase domain , and thereby cause loss of POLQ-mediated primer extension [20] . We screened for clones with deletion mutations by PCR , and identified a clone with three mutations in exon 16 ( POLQ exon 16 mutant , POLQe16m ) : 1 ) one allele with deletion of the segment between the two DSBs , causing mutation of I862 to a termination codon ( I862X ) , 2 ) a second allele with an inversion of this segment causing mutation of I862 to V , and encoding another 8 amino acids followed by a termination codon ( I862V8X ) , and 3 ) a third allele with a single nucleotide insertion at the 3' DSB site causing an S1152 to K mutation , and encoding 2 amino acids followed by a termination codon ( S1152K2X ) ( Fig 1A ) . These mutant alleles disrupt the coding sequence for POLQ upstream of the C-terminal polymerase domain ( Fig 1A ) . We also used Cas9 to generate a RAD52 knockout ( RAD52KO ) cell line , and a RAD52KOPOLQe16m cell line from the POLQe16m cell line , both of which were identified using RAD52 immunoblotting ( Fig 1B ) . Using these cell lines , we first examined cell cycle profiles using BrdU and propidium iodide labeling , and found that RAD52KO and RAD52KOPOLQe16m cells , but not POLQe16m cells , showed a modest , but statistically significant increase in G1 cells compared to the parental cell line ( Fig 1C ) . To examine the response to genotoxic stress , we exposed cells to DNA damaging agents and measured clonogenic survival based on colony formation . In addition to testing the cell lines described above , we also examined POLQ using RNA-interference ( RNAi ) . Specifically , we treated parental and RAD52KO cells with siRNAs targeting POLQ ( siPOLQ ) , or a non-targeting siRNA ( siCTRL ) . We confirmed that siPOLQ treatment causes depletion of the POLQ mRNA in both the parental and RAD52KO cells ( Fig 1D ) . Beginning with the crosslinking agent cisplatin , we examined the effect of two doses of cisplatin on clonogenic survival . At the higher dose , we found that both the RAD52KO and POLQe16m cell lines were hypersensitive , compared to the parental cell line ( Fig 1E ) . Furthermore , the RAD52KOPOLQe16m cells were hypersensitive compared to both the parental cells and the single mutants , at both doses ( Fig 1E ) . Notably , the fold-effect on clonogenic survival for the RAD52KOPOLQe16m cells was at least additive , compared to the effects of the single mutants ( Fig 1E ) . Similarly , we found that siPOLQ treatment caused hypersensitivity to cisplatin at both doses , in both the parental and RAD52KO cells ( Fig 1F ) . Finally , the RAD52KO cells treated with siPOLQ showed at least additive hypersensitivity to cisplatin , as compared to the effects of siPOLQ treatment in the parental cell line , and the RAD52KO cells vs . the parental cells ( Fig 1F ) . Thus , disruption of RAD52 and POLQ appear to cause hypersensitivity to cisplatin , which is at least additive with combined disruption of these factors . We also examined clonogenic survival in response to ionizing radiation ( IR ) , and the PARP inhibitor Olaparib . Using two doses of IR , we found that the single mutant cell lines either showed no hypersensitivity , or showed a modest hypersensitivity ( Fig 1E , < 2-fold ) . Similarly , siPOLQ treatment did not caused an obvious effect on IR response in either the parental or RAD52KO cells ( Fig 1F ) . In contrast , the RAD52KOPOLQe16m cells showed significant hypersensitivity to both doses of IR ( Fig 1E ) . These findings indicate that RAD52 and POLQ have modest effects on resistance to IR . Although , the results from the RAD52KOPOLQe16m cell line indicate that combined genetic disruption of these factors can cause IR hypersensitivity . Using two doses of Olaparib , the RAD52KO and RAD52KOPOLQe16m cells were both hypersensitive compared to the parental cell line at both doses ( Fig 1E ) . The RAD52KO and RAD52KOPOLQe16m cells were not statistically different from each other ( Fig 1E ) . The POLQe16m and siPOLQ-treated parental cells showed a modest hypersensitivity to Olaparib ( ≤ 2 . 1-fold ) , and siPOLQ-treatment in the RAD52KO cell line caused hypersensitivity to Olaparib at both doses ( Fig 1F ) . Thus , RAD52 and POLQ appear important for resistance to Olaparib , although RAD52 appears to have a greater effect . We then sought to examine the influence of RAD52 and POLQ on distinct DSB repair events . Both RAD52 and POLQ have been implicated in DSB repair that uses homologous repeat sequences that flank a DSB to bridge the break [23 , 34] . These events often cause a deletion between the repeat , along with one copy of the repeat , such that we refer to all of these events as repeat-mediated repair ( RMR ) . The parameters of RMR events that are mediated by RAD52 vs . POLQ have remained unclear . Thus , we sought to establish a reporter assay platform to examine two variable features of RMR events: repeat length and non-homologous tail removal . For this , we generated a set of reporter assays in which an expression cassette for green fluorescent protein ( GFP ) was disrupted by a non-homologous insert sequence ( Fig 2A ) . We then added a homologous repeat of varying lengths ( 200–6 nt ) , by expanding the size of the 3' GFP sequence ( S1A Fig ) . Each reporter was integrated in the U2OS cell lines , using the FRT/Flp system [35] ( S1B and S1C Fig ) . In these reporter assays , the RMR events are induced by expression of Cas9 and various sgRNAs . We tested these reporters in the parental U2OS cells with different combinations of DSBs . To begin with , we targeted a DSB at the 5' edge of the non-homologous insert , such that an RMR event that uses the flanking homology would restore the GFP expression cassette ( Fig 2B ) . In the parental cells , we found that inducing this DSB in the reporters with repeat lengths of 200–72 nt caused similar frequencies of GFP+ cells ( Fig 2B ) . However , with a repeat length of 50 nt , the frequency of GFP+ events was reduced approximately 2-fold compared to the longer repeats , and with repeat lengths of 23–6 nt , induction of GFP+ cells was nearly abolished ( Fig 2B ) . We then considered that the inability to detect RMR events at the shorter repeats was due to the presence of the non-homologous insert . So , we examined these reporters using two DSBs to excise the insert: the first sgRNA targets the edge of the 5' GFP sequence as described above , and the second sgRNA targets the edge of the 3' GFP sequence , which is distinct for each reporter ( 5' & 3' edge; Fig 2C ) . With this approach , we were able to readily detect GFP+ events at each of the shorter repeat lengths ( 50–6 nt , Fig 2C ) . Notably , as with the 5' edge DSB alone , the 3' edge DSB alone was insufficient to significantly induce GFP+ cells for repeat lengths of 23–6 nt ( Fig 2C ) . Thus , both DSBs are required to significantly induce these repair events . The restoration of the GFP coding sequence was confirmed for each of the reporters by sorting cells to enrich for GFP+ cells , followed by PCR amplification and sequencing analysis ( S2A Fig ) . We then analyzed the influence of RAD52 and POLQ on this series of RMR events . Since the RAD52KO , POLQe16m , and RAD52KOPOLQe16m cell lines were generated using the U2OS Flp-In T-REx cell line , we were able to integrate each reporter into these lines using the FRT/Flp system . At least two independent integrants of each reporter for each cell line were analyzed . A technical limitation of DSB reporter assay experiments is that different cell lines and experimental replicates can show variations in transfection efficiency , although this issue is partially mitigated by normalizing each experiment to transfection frequency using a parallel well with a GFP expression vector . To address this technical limitation via another method , we used transient complementation , which enables examination of the same cell line with parallel transfections of the complementation vector vs . empty vector ( EV ) . The complementation vector was included in the transient transfection with the sgRNA/Cas9 plasmid ( s ) . However , a drawback of this approach is that complementation vectors do not readily mimic endogenous levels of the respective protein . Indeed , for the POLQ complementation vector , while we confirmed expression using the Flag-immunotag ( Fig 3A ) , we were unable to identify an antibody that is sensitive to detect endogenous POLQ . Thus , we were unable to compare endogenous POLQ levels vs . expression from the complementation vector . Furthermore , while we used a relatively low concentration of the RAD52 complementation vector , we found that these experimental conditions caused a marked increase in RAD52 protein levels ( Fig 3B ) . Accordingly , in addition to using complementation analysis , we also independently assessed the influence of RAD52 and POLQ on these RMR events using RNAi , by treating cells with siRNAs targeting these factors ( siRAD52 and siPOLQ , respectively ) . As with complementation experiments , RNAi enables comparisons of the same cell line with parallel transfections ( i . e . , the targeting siRNA vs . siCTRL ) . As mentioned above , we confirmed that siPOLQ treatment causes depletion of the POLQ mRNA in these cell lines ( Fig 1D ) . We also confirmed that siRAD52 causes a reduction in RAD52 protein ( Fig 3B ) . Beginning with POLQ , we found several repair events were reduced in POLQe16m cells compared to the parental line ( S3A Fig ) . However , POLQ expression in the POLQe16m cells promoted only one RMR event: the RMR with the 6 nt repeat , which was induced using two DSBs to excise the non-homologous insert ( 5' & 3' edge; Fig 3A ) . Similarly , siPOLQ caused a significant decrease in the 6 nt repeat RMR event , but not any of the others ( i . e . , RMR events with ≥ 18 nt repeats ) ( Fig 3A ) . For RAD52 , beginning with the RMR events with repeat lengths of 200–50 nt and using the 5' edge DSB , the RAD52KO cell line exhibited lower frequencies vs . the parent cell line for each of these events ( S3B Fig ) . Regarding complementation , we found that RAD52 expression in the RAD52KO cells significantly promoted these events for repeat lengths of 101 , 72 , and 50 nt , but not for repeats of 200 and 140 nt ( Fig 3B ) . We found similar results with the RAD52KOPOLQe16m cell line , although in this case RAD52 expression promoted each of these repair events ( i . e . , 200–50 nt and using the 5' edge DSB , Fig 3C , S3C Fig ) . Similarly , siRAD52 treatment caused a significant reduction in each of these repair events ( 200–50 nt and using the 5' edge DSB , Fig 3B ) . We then examined the influence of RAD52 on the events with shorter repeats ( 50–6 nt ) by inducing two DSBs to excise the non-homologous insert ( i . e . , 5' & 3' edge ) . The RAD52KO and RAD52KOPOLQe16m cells showed reduced frequencies of several of these events , compared to the parental line ( S3B and S3C Fig ) . However , we found that RAD52 expression in RAD52KO and RAD52KOPOLQe16m cells showed only a modest increase in events for the 50 nt repeat ( 1 . 2-fold ) , and did not promote events involving 23 , 18 , or 6 nt ( Fig 3B and 3C ) . Similarly , again using the two DSBs to excise the insert , siRAD52 treatment caused a modest reduction in the event with the 50 nt repeat , but did not affect the frequencies of the events with the shorter repeat lengths ( Fig 3B ) . Altogether , considering effects of both complementation and RNAi , these findings indicate that RAD52 is important for RMR events using ≥ 50 nt repeats , whereas POLQ promotes RMR events using 6 nt repeats , but not ≥ 18 nt . Regarding the double-mutant cell line , as mentioned above , the findings were similar for the RAD52KO and RAD52KOPOLQe16m cell lines with this panel of reporters and complementation analysis ( Fig 3C ) . We also found that POLQ complementation in the RAD52KOPOLQe16m cell line showed the same results as with the POLQe16m cell line ( i . e . , promoted only the RMR event using the 6 nt repeat; Fig 3C ) . Thus , the analysis with the RAD52KOPOLQe16m cell line indicates that combined disruption of RAD52 and POLQ does not appear to generate a synthetic defect in RMR events , but rather shows a combination of two independent defects found in the single mutant cell lines . In the above analysis , for RMR events with a 50 nt repeat , we found that RAD52 is more important for such events when induced by one DSB at the 5' edge , compared to when the non-homologous sequence was excised with the 5' & 3' edge DSBs ( Fig 3B ) . The distinction between these events is that the former requires the removal of the non-homologous sequence upstream of the 3' GFP segment . Accordingly , we sought to also examine RMR events requiring removal of non-homologous sequences from both sides of the DSB . To test this , we used an sgRNA to induce a DSB approximately in the middle of the non-homologous insert ( mid-ins; 0 . 3 kb from both 5' GFP and 3' GFP ) . Using this mid-ins DSB , the repeat lengths are increased by 1 nt , compared to the above analysis ( Fig 4A ) , since the 5' DSB cleaves upstream of this single nucleotide of homology between the repeats . In the parental cell line , we found that the frequency of RMR events restoring GFP was highest for the 201 nt repeat , and decreased with the length of the repeat ( Fig 4A ) . Indeed , such repair using the 51 nt repeat was largely undetectable . Therefore , inducing a DSB with non-homologous sequences on both sides of the repeats causes a greater requirement for a longer repeat to induce RMR events . We then analyzed the influence of RAD52 on RMR events using the mid-ins DSB , and found that RAD52 complementation promoted these events for each of the repeat lengths ( i . e . , 201 , 141 , 102 , and 73 nt repeats , Fig 4B , S3D Fig ) . We found similar results for RAD52 complementation in the RAD52KOPOLQe16m cell line , and RNAi depletion of RAD52 ( siRAD52 treatment ) , whereas POLQ complementation did not promote these events ( Fig 4B ) . Notably , overexpression of RAD52 in the parental cells also promoted RMR events induced by the mid-ins DSB with the 201 , 141 , 102 nt repeats , but not any of the other RMR events ( S4A Fig ) . This finding indicates that the level of RAD52 is a limiting factor for RMR events with repeats flanked by non-homologous sequences . In summary , these results with the mid-ins DSB , combined with the above finding ( Fig 3B ) that RAD52 has a greater effect on 50 nt RMR events that are induced by the 5' DSB vs . excision of the non-homologous sequence ( i . e . the 5' & 3' edge DSBs ) , indicate that RAD52 is particularly important for RMR events that involve removal of a non-homologous sequence . Since POLQ appears important only for the RMR event using the 6 nt repeat , we considered that POLQ might also be important for other repair events . In particular , we considered that POLQ might be important for DSB repair events that require nascent DNA synthesis . We based this hypothesis on previous studies showing that POLQ mediates annealing of oligonucleotides using short complementary ssDNA to template nascent DNA synthesis [19 , 24] . To examine events that require nascent DNA synthesis , we modified our chromosomal RMR reporter system by deleting 7 nt from the 5' edge of the 3' GFP segment ( Δ7 reporter; Fig 5A ) . Repair using an oligonucleotide with microhomology as a template that contains the missing 7 nt would restore GFP expression ( i . e . , oligonucleotide microhomology-templated repair ) . We used oligonucleotides that contained the missing 7 nt , which are flanked by equal lengths of homology to both the 5' and 3' GFP sequences , using several different lengths: 12 , 14 , 16 , 18 , or 20 nt ( referred to as 12-7-12 , 14-7-14 , 16-7-16 , 18-7-18 , and 20-7-20 , respectively , Fig 5A ) . The oligonucleotides also contain phosphorothioate linkages at the two terminal bases at both ends to promote stability [36] . These oligonucleotides were co-transfected with the sgRNA/Cas9 plasmids to induce DSBs at the edge of the 5' GFP and 3' GFP segments ( i . e . , the 5' & 3' edge DSBs , as described above ) . Using the parental U2OS cells , we found that each of the oligonucleotides induced GFP+ cells , which increased in frequency with the length of the flanking sequence homology ( Fig 5A ) . To confirm the restoration of GFP in the Δ7 reporter with each of the oligonucleotides , the cells were sorted to enrich for GFP+ cells , and examined by PCR and sequencing ( S2B Fig ) . We also confirmed that both the 5' and 3' DSBs are required to induce these oligonucleotide microhomology-templated events ( S4B Fig ) . We then analyzed the role of RAD52 and POLQ on the Δ7 reporter assay using each of the oligonucleotide templates . We performed both complementation and RNAi analysis , although since the 12-7-12 oligonucleotide events were near background levels , we found it difficult to examine effects of RNAi in potentially reducing these events ( S4C Fig ) . In any case , both types of analysis were feasible for the rest of the oligonucleotides ( 14-7-14 and longer ) . From both complementation and RNAi analysis , we found that RAD52 was dispensable for such repair with each of the oligonucleotides ( Fig 5B and 5C and S4B and S4D Fig ) . In contrast , we found that POLQ expression in the POLQe16m cells significantly promoted the induction of GFP+ cells using all of the oligonucleotides ( Fig 5B and S4B and S4D Fig ) . POLQ expression had a similar effect on RAD52KOPOLQe16m cells , and the fold-effects were magnified ( Fig 5B and S4D Fig ) . Importantly , and consistent with the complementation analysis , siPOLQ treatment caused a reduction in each of these events ( Fig 5C , i . e . , with the 14-7-14 oligonucleotide and longer ) . To provide a contrast for these assays , we also examined end joining ( EJ ) events that do not require annealing of a homologous repeat or nascent DNA synthesis . Specifically , we used EJ7ins ( S5A Fig ) , in which the non-homologous insert is flanked by the first two bases ( GG ) and the final base ( C ) of the GGC codon for Glycine 67 for GFP . Following DSBs to excise the non-homologous insert , EJ without indels between the distal DSBs would restore the GGC codon . Thus , restoration of GFP+ does not involve any nascent DNA synthesis nor annealing of microhomology . This assay is a variant of EJ7-GFP [30]; the only difference is the size of the non-homologous insert . We also performed experiments with an oligonucleotide that is homologous to the EJ junction that could possibly bridge the DSB ends during repair . Specifically , we used an oligonucleotide with 14 nt of homology to each side of the EJ junction , but with no bases in between ( i . e . , 14-0-14 , S5A Fig ) . We included this experiment to provide a contrast to the Δ7 reporter assays , which use oligonucleotides to template nascent DNA synthesis . We found that including the 14-0-14 oligonucleotide did not promote the EJ event measured by EJ7ins , compared to a control oligonucleotide ( luciferase/LUC ) , or to transfections without any oligonucleotide ( S5A Fig ) . We then examined the influence of POLQ and RAD52 on these EJ events . We found that siPOLQ and siRAD52 treatments did not cause a decrease in the frequency of such EJ events , with or without the 14-0-14 bridging oligonucleotide ( Fig 6A ) . From analysis of the mutant cell lines , expression of POLQ from the complementation vector caused a modest increase in these EJ events , irrespective of whether an oligonucleotide was included ( Fig 6A , S5B Fig ) . Notably , these effects of the POLQ complementation vector on EJ were less than for the oligonucleotide microhomology-templated repair events ( Figs 5B and 6A , in the POLQe16m cells , EJ7ins promoted ≤1 . 36-fold , whereas the Δ7 reporter promoted between 1 . 6 to 2 . 6-fold , depending on the oligonucleotide ) . Furthermore , as mentioned above , the oligonucleotide microhomology-templated events ( Fig 5C ) , but not the EJ events ( Fig 6A ) , were reduced by siPOLQ treatment . Altogether these findings indicate that POLQ promotes oligonucleotide microhomology-templated repair to a greater degree than EJ without use of microhomology . For another contrast to the above DSB repair events , we also examined HDR , using the DR-GFP reporter , which measures use of a homologous sequence as a template for gene conversion [37] . For these experiments , we used Cas9 and an sgRNA to induce the DSB in DR-GFP [38] . We found that neither RAD52 nor POLQ complementation vectors caused an increase in HDR in the respective mutant cell lines ( Fig 6B , S5D Fig ) . Similarly , siRAD52 treatment did not cause a decrease in HDR , although siPOLQ caused a modest decrease in HDR ( Fig 6B , 1 . 3-fold ) . These findings indicate that RAD52 and POLQ do not have a substantial role in HDR , as measured using the DR-GFP reporter . To provide a contrast with RAD52 and POLQ , we also examined the influence of BRCA2 on several DSB repair events . BRCA2 is important for RAD51 recruitment to DNA damage and HDR [39] . We first sought to confirm that BRCA2 is important for HDR using the DR-GFP reporter [37] , using siRNAs targeting BRCA2 ( siBRCA2 , depletion of BRCA2 validated by immunoblotting , Fig 6C ) . As expected , we found that siBRCA2 treatment caused a marked decrease in HDR ( Fig 6D ) . We then examined the RMR reporter assays , and found that siBRCA2 treatment caused a decrease in nearly all of the RMR events ( Fig 6D ) . Accordingly , BRCA2 appears to promote RMR events irrespective of the repeat length or DSB induced ( Fig 6D ) , which is distinct from the results with RAD52 and POLQ . Finally , siBRCA2 treatment did not have a substantial effect on EJ ( EJ7-ins reporter ) , nor the oligonucleotide microhomology-templated events ( the Δ7 reporter , Fig 6D ) . In summary , BRCA2 promotes several RMR events , but to a much lesser degree than its requirement for HDR . Disruption of BRCA1 and BRCA2 causes defects not only in HDR , but also the cellular response to replication stress [6] . Thus , we next examined whether disruption of RAD52 and POLQ may also affect replication stress responses , using DNA fiber analysis [40] . We first examined how the disruption of RAD52 and/or POLQ would affect the rate of replication fork progression in unstressed cells . Specifically , we pulse labeled cells with the thymidine analog CldU , followed by a pulse label with the thymidine analog IdU for equal amounts of time ( 40 min ) . Antibodies against each analog that are conjugated to different fluorophores allowed for the visualization of the fibers . We measured the lengths of the labels for individual fibers to calculate the IdU/CldU ratio , and thereby measure the rate of fork progression , which we refer to as replication fork velocity ( Fig 7A ) . We found that the POLQe16m cells showed a modest but significant increase in replication fork velocity , whereas disruption of RAD52 had no effect ( Fig 7A ) . In contrast , the RAD52KOPOLQe16m cells showed a significant reduction in replication fork velocity ( Fig 7A ) . Similarly , siPOLQ treatment caused a reduction in replication fork velocity in the RAD52KO cells , but not parental cells ( Fig 7A ) . As described above , depletion of POLQ mRNA via siPOLQ was confirmed in both parental and RAD52KO cells ( Fig 1D ) . We also examined the fraction of stalled replication forks ( i . e . , CldU-labeled fibers only ) . We found that POLQe16m and RAD52KOPOLQe16m cells , but not RAD52KO cells nor siPOLQ treated cells , showed a modest decrease in the frequency of stalled replication forks ( S6A Fig ) . We next examined the influence of RAD52 and POLQ on the restart of replication forks after replication stress . In this analysis , cells were pulse labeled with CldU , and then treated with hydroxyurea ( HU ) , which causes a depletion of dNTPs , thereby causing replication fork stalling [40] . Following release from HU , cells were pulse labeled with IdU , and the DNA fibers were analyzed for the IdU/CldU ratio to measure the rate of replication restart , which we refer to as replication restart velocity ( Fig 7B ) . We also quantified the frequency of stalled replication forks ( S6A Fig ) . We found that replication fork restart velocity was not distinct between the POLQe16m cell line and the parental cells line , but was higher in the RAD52KO vs . parental ( Fig 7B ) . Strikingly , the RAD52KOPOLQe16m cell line showed a marked decrease in replication fork restart velocity , compared to the parental cell line ( Fig 7B ) . Similarly , siPOLQ treatment in the RAD52KO cell line caused a marked decrease in replication fork restart velocity , whereas siPOLQ treatment only caused a modest decrease in the parental cell line ( Fig 7B ) . Apart from fork velocity , we did not observe major effects on the percentage of stalled replication forks , apart from a modest increase with the RAD52KOPOLQe16m cell line ( S6B Fig ) . We also examined replication fork protection during stalling , which has been shown to require BRCA2 , among other factors [5 , 41] . In this analysis , cells are pulse labeled with CldU , followed by IdU , and then treated with HU for 5 hr [5 , 41] . To begin with , we examined cells treated with siRNAs targeting BRCA2 , and consistent with prior studies , found that depletion of BRCA2 ( confirmed by immunoblotting ) causes a reduction in the IdU/CldU ratio , reflecting fork degradation [5 , 41] ( Fig 7C ) . In contrast , BRCA2 depletion did not cause an obvious effect on the IdU/CldU ratio when the HU treatment was positioned between the two labels ( S6B Fig ) . Regarding the influence of RAD52 and POLQ on fork protection during stalling , we found that the POLQe16m and RAD52KO cell lines were not distinct from the parental cell line ( Fig 7C ) . The RAD52KOPOLQe16m cells showed a modest decrease in the IdU/CldU ratio in these experiments ( P = 0 . 045 , Fig 7C ) , similar to the findings without replication stress ( see Fig 7A ) . Taken together , these finding indicate that the combined disruption of RAD52 and POLQ causes a significant decrease in the velocity of replication fork progression , particularly during restart of stalled replication forks , but does not have an obvious effect on protection of stalled replication forks from degradation .
As RAD52 and POLQ are each synthetic lethal targets for cells deficient in BRCA1 and BRCA2 [8–10] , we have sought to test whether RAD52 and POLQ have distinct vs . redundant functions in chromosomal break repair , sensitivity to genotoxins , and/or response to replication stress . Beginning with genotoxin sensitivity , we found that disruption of RAD52 and POLQ each caused hypersensitivity to cisplatin , and combined disruption of these factors caused an at least additive hypersensitization . Accordingly , RAD52 and POLQ appear to have non-epistatic roles in cisplatin resistance . We also found that RAD52 and POLQ have different effects on DSB repair , using a series of novel assays for RMR and oligonucleotide microhomology-templated repair events . The DSB reporter analysis involved multiple approaches to examine RAD52 and POLQ , i . e , both complementation analysis in mutant cell lines , and RNAi . We suggest that identifying DSB repair phenotypes that are relatively consistent between these approaches , and that reveal patterns among multiple reporter contexts , has provided insight into the influence of RAD52 and POLQ on such DSB repair events . Beginning with RAD52 , we found that this factor is important for RMR events using ≥ 50 nt , and when repeat sequences also require removal of non-homologous sequence flanking at least one of the repeats . The influence of RAD52 on events with this range of repeat length is consistent with biochemical properties of RAD52 . In particular , single-molecule studies have shown that multimeric rings of RAD52 interact with ssDNA by optimally binding ~30 nt around the protein ring [12 , 16 , 42–44] . Regarding removal of non-homologous sequences flanking a region of homology , other studies also support a role of RAD52 in such events . For example , our laboratory recently reported that an RMR event in mouse cells requiring removal of several kb of non-homologous sequence was particularly dependent on RAD52 [45] , and another recent study showed that HDR events requiring removal of a non-homologous sequences were also promoted by RAD52 [46] . Thus , we suggest that RAD52 may have a specific role in synapsis of ≥ 50 nt of homology that is embedded within a non-homologous sequence , and thereby stabilize this intermediate to facilitate cleavage of the non-homologous sequence to complete repair . For POLQ , we found that this factor was important for RMR events using 6 nt , but not ≥ 18 nt , as well as DSB repair events requiring nascent DNA synthesis from oligonucleotide templates with 12–20 nt of microhomology . These findings are consistent with studies of POLQ-dependent extension of oligonucleotide substrates that are annealed via a very short ( e . g . , 4 nt ) sequence [17 , 19] . This activity of POLQ is consistent with the structure of its C-terminal polymerase domain , which contains additional insertions loops that are not found in other A-family DNA polymerases [18] . Within these unique insertions loops , multiple residues facilitate specific interactions with the primer strand , which appear to enable extension of minimally annealed DNA substrates [17 , 18 , 47] . Notably , combined loss of POLQ and RAD52 did not reveal any synthetic defects in DSB repair events ( e . g . , repair events promoted by POLQ were the same in the POLQe16m cells as the POLQe16mRAD52KO cells ) , which altogether indicate that these factors have distinct roles in such repair . We also found that RMR events involving 18–23 nt of homology were unaffected by RAD52 and POLQ . Notably , events with ≤ 23 nt of homology are nearly undetectable if the repeat is flanked by a non-homologous sequence . Accordingly , the mechanisms that mediate such RMR events with ≤ 23 nt of homology may be insufficient to facilitate cleavage of a non-homologous tail . Alternatively , ≤ 23 nt of homology may not be sufficient to compete with shorter lengths of homology that are closer to the DSB end . In any case , other factors besides RAD52 and POLQ appear to be sufficient to mediate RMR events involving 18–23 nt of homology . Indeed , beyond these particular repair events , we suggest that other factors are likely involved in RMR events of diverse repeat lengths , since each RMR event we examined remains readily detectable in cells deficient in RAD52 and/or POLQ . The factors apart from RAD52 and POLQ that mediate RMR events remain unclear . Although , we found that BRCA2 mediates several RMR events at multiple repeat lengths ( i . e . , 201 nt– 6 nt ) , which is distinct from our findings with RAD52 and POLQ . However , the influence of BRCA2 on these RMR events was markedly lower than its influence on HDR . Furthermore , in other studies , BRCA2 has been shown to suppress RMR events , likely due to competition with HDR [26 , 48] . However , BRCA2-mediated HDR may not be a substantial competitive pathway for the RMR events measured here . Namely , the DSBs in these assays are not readily repaired by HDR , which requires a repair template with homology on both sides of the DSB . Nevertheless , our findings support the notion that BRCA2 has a distinct role in DSB repair vs . POLQ and RAD52 , since BRCA2 is required for HDR , whereas POLQ and RAD52 do not appear to have substantial roles in HDR . Consistent with BRCA2 having distinct roles in genome maintenance vs . POLQ and RAD52 , these factors differentially affect the response to replication stress . As in other studies [5 , 41] , we found that depletion of BRCA2 caused a defect in protecting stalled replication forks from degradation , but did not cause obvious effects on the restart of stalled forks . In contrast , disruption of POLQ and RAD52 , either alone or in combination , caused no major effects on protection of stalled replication forks , using the same experimental conditions that reveal a role for BRCA2 . We also found that disruption of RAD52 or POLQ individually did not obviously cause defects in the frequency of restart of stalled replication forks . These findings are consistent with other studies of RAD52 , in which this factor appears dispensable for restart of stalled replication forks , but rather appears important for restart of collapsed forks ( i . e . , following long-term HU treatment ) [49] . Although , a recent report found that combined treatment of a small molecule that targets RAD52 , along with a CDC7 inhibitor , caused an increase in the frequency of stalled replication forks after HU treatment [50] . Furthermore , our findings with POLQ are distinct from a report that cells depleted of POLQ via RNAi show an increase in the frequency of collapsed forks following recovery from HU [9] . Nevertheless , we found that combined disruption of POLQ and RAD52 caused a marked decrease in replication fork restart velocity , as indicated by a substantial reduction in the length of the labeled DNA fiber after release from HU . The cause of this effect on fork restart velocity could be due to several mechanisms . For example , RAD52 could promote an annealing intermediate important for stabilizing the stalled fork , and/or re-establishing the replisome [49] . Indeed , a recent report found that RAD52 is important to suppress excessive ssDNA formation at stalled forks [50] . Similarly , POLQ could stabilize the stalled fork via its primer extension activity [51] . RAD52 or POLQ could also recruit other factors important for these processes [47 , 52] . Alternatively , loss of one of these factors could cause accumulation of an intermediate that requires the other factor for resolution to enable rapid fork restart . Along these lines , disruptions of POLQ and/or RAD52 may affect other aspects of DNA replication that may not have been revealed in our analysis , such as suppressing fork discontinuities [50 , 53] , which could contribute to the reduced fork velocity that we observed . In summary , these findings indicate that RAD52 and POLQ have distinct roles in genome maintenance , including DSB repair and replication fork restart velocity . Since these factors are emerging therapeutic targets [8–11] , these findings indicate that combined disruption of these factors may be an effective approach for genotoxin sensitization and/or synthetic lethality strategies .
All sgRNAs , primers , and oligonucleotide template sequences are found in S1 Table . The parental cell line in this study is the human osteosarcoma U2OS Flp-In T-REx cell line , which is stably transfected with pFRT/lacZeo [29 , 30] . Cells were cultured as previously described [54] , and using the Lonza MycoAlert PLUS Mycoplasma Detection Kit , cell lines tested negative for mycoplasma contamination . To generate plasmids for inducing DSBs , sgRNA sequences were cloned into the px330 vector ( Addgene #42230 ) that expresses an sgRNA and Cas9 [55] . To generate the mutant cell lines , these sgRNA/Cas9 plasmids were co-transfected ( 400 ng of each sgRNA vector ) with the dsRED expression plasmid ( 120 ng ) and 3 . 6 μl of Lipofectamine 2000 . After 3 days , the cells were sorted ( using an Aria 3 or Aria SORP , Becton Dickinson ) to enrich for dsRED-positive transfected cells followed by low-density plating . To generate the POLQe16m cell line , two sgRNAs were used to target exon 16 of POLQ , and clones were screened by PCR amplification and sequencing . For the RAD52KO cell line , two sgRNAs were used to target exon 3 and exon 9 of RAD52 [49] . The RAD52KOPOLQe16m cell line , was generated in the POLQe16m cell line using the RAD52 exon 3 sgRNA and an sgRNA that targets RAD52 exon 4 . The RAD52KO and RAD52KOPOLQe16m cell lines were identified by screening individual clones using RAD52 immunoblot analysis . The RMR200 reporter plasmid was generated by inserting two gBLOCK fragments ( IDT ) into the pcDNA5-FRT-EJ7-GFP vector [30]: 1 ) a non-homologous sequence derived from the puromycin-resistance gene [54] to generate the EJ7-ins reporter , and 2 ) the 3' GFP fragment , which contains 200 nt of homology to the 5' GFP sequence . This RMR200 reporter plasmid was used to generate the variants with the different 3' repeat sequences . These RMR reporter plasmids ( 100 ng ) were integrated into the U2OS Flp-In T-REx cells by co-transfection with the PGK-Flp vector ( 400 ng ) [35] , using Lipofectamine 2000 ( Thermofisher ) as described below for the DSB reporter assays . Integrated clones were selected using hygromycin ( 0 . 2 μg/μl ) , and subsequently screened with PCR analysis to confirm integration ( S1B , S1C Fig ) . To integrate the DR-GFP reporter into the parental U2OS Flp-In T-Rex cell lines , and the various mutant cell lines , 10 μg of XhoI linearized Pim-DRGFP plasmid [54] was electroporated into each cell line ( 0 . 8 ml volume ) , followed by selection of stably transfected cells in 0 . 8 μg/ml puromycin , which were pooled together for analysis . The RNAi experiments to examine HDR used the previously described U2OS DR-GFP reporter cell line [54] . Cell cycle analysis was performed as previously described [45] . Briefly , the cells were pulse labeled with BrdU ( BD Pharmingen , 51-2420KC ) for 30 min at 37°C . The cells were then fixed with 70% ethanol , and stained with FITC-conjugated anti-BrdU ( BD Pharmingen , 51-33284X ) , followed by and propidium iodide ( Sigma , P4170 ) supplemented with RNase ( Sigma , R4642 ) for 30 min at 37°C . Each sample was analyzed by flow cytometry using a CyAn-ADP ( Dako ) . Cells with integrated reporter cassettes were seeded at 0 . 5 x 105 cells per well ( 24 well plate ) . The following day , the cells were transfected with 200 ng of each sgRNA/Cas9 vector and 1 . 8 μl of Lipofectamine 2000 with 0 . 5 ml of antibiotic-free media . To normalize the frequency of repair events between experiments , parallel transfections with GFP expression vector ( 200 ng , pCAGGS-NZE-GFP [54] ) were included . In the RAD52 complementation experiments , the reactions were performed as describe above with the addition of 25 ng of empty vector ( pCMV6-XL5 ) or RAD52 expression vector ( Origene RC238113 ) . For the POLQ complementation , 100 ng of empty vector ( pCAGGS-BSKX ) [56] or POLQ expression vector [57] was added to the reactions . Similar transfections with equivalent concentrations of expression vectors were used to generate samples for immunoblotting analysis . For RAD52 and POLQ complementation in the double mutant cell line , additional empty vector ( pCAGGS-BSKX ) was included to ensure an equivalent amount of total plasmid in each transfection . For the Δ7 reporter , transfections were scaled 2-fold onto a 12 well dish , and transfections were performed as described above with the addition of 10 nM ( final concentration ) of the indicated oligonucleotide to the reaction . Each oligonucleotide contained phosphorothioate linkages on the first two and last two terminal bases ( IDT ) . In the experiments with siRNA , 5 pmol of either non-targeting siRNA ( siCTRL; Dharmacon , D-001810-01-20 ) or a pool of four siRNAs targeting RAD52 , POLQ , or BRCA2 ( Dharmacon siGENOME siRNAs , sequences from manufacturer in S1 Table ) was included in the respective Lipofectamine 2000 transfections . In addition , for POLQ siRNA experiments , the day before the above transfections with Lipofectamine 2000 , cells were first treated with 5 pmol of either siCTRL or the four siRNAs targeting POLQ , using Lipofectamine RNAiMAX ( Thermofisher ) . For immunoblotting analysis to confirm BRCA2 and RAD52 depletion , an equivalent concentration of cells and siRNA as for the reporter assays was used for a transfection with Lipofectamine RNAiMAX . For each reporter assay , three days after transfection , the percentage of GFP+ cells were determined by flow cytometry using a CyAn-ADP ( Dako ) , as previously described [54] . The repair value for each sgRNA ( s ) /CAS9 transfection was first normalized to transfection efficiency using the parallel transfection with a GFP expression vector . For comparisons vs . EV or siCTRL , each repair value normalized to transfection efficiency was divided by the mean repair value for the parallel control transfections ( i . e . , siCTRL and/or EV ) . To confirm the sequence of GFP+ products for each reporter , transfected parental cells were sorted ( Aria III or Aria SORP , Becton Dickinson ) to enrich for cells expressing GFP , which were analyzed by PCR-amplification and sequencing ( S2 Fig ) . Clonogenic survival was assessed by plating 103 cells on 6 well plates in media containing cisplatin ( 1 . 0 or 3 . 0 μM , Pfizer ) or Olaparib ( 0 . 75 or 1 . 5 μM , Selleckchem ) , or were untreated ( equivalent volume of DMSO added as a control ) . For ionizing radiation , each cell line was exposed to 1 . 5 or 3 Gy ( Gammacell 3000 ) , or left untreated , prior to plating . Cells were cultured for 9 days , and stained with crystal violet ( Sigma ) . Colonies of approximately 50 or more cells were quantified under a 10x objective , and fraction survival was calculated relative to the number of colonies on the untreated control wells that were plated in parallel . For experiments with siRNA depletion , 105 cells were plated on a 12 well plate with either control siRNA ( siCTRL ) or a pool of four POLQ siRNAs ( 40 pmol; Dharmacon , sequences in S1 Table ) , using Lipofectamine RNAiMAX . Two days after transfection , cells were treated with genotoxins to test clonogenic survival , as described above . To test for depletion of POLQ mRNA , cells were transfected on a 6 well dish with 20 pmol of siCTRL or pool of four POLQ siRNAs ( see above ) using Lipofectamine RNAiMAX ( 2 ml total volume ) . On the following day , cells were transfected with the respective siRNA ( 20 pmol ) and two plasmids ( 400 ng of pgk-PURO and 1200 ng EV ) [54] , using Lipofectamine 2000 ( 2 ml total volume ) , as for the reporter assays . The day after transfection , cells were treated with puromycin ( 2 μg/ml concentration ) for one day to enrich for transfected cells , and then RNA was isolated using the RNeasy Plus Minikit ( Qiagen 74134 ) . The RNA was treated with M-MLV Reverse Transcriptase ( Promega M170A ) to generate cDNA , which was amplified in an Applied Biosystems 7900HT Fast Real Time PCR system using SYBR-green , with the primer sequences shown in S1 Table . Immunoblotting analysis was performed by lysing the cells using NETN buffer ( 20 mM Tris pH 8 , 100 mM NaCl , 1 mM EDTA , 0 . 5% IGEPAL , 1 . 25 mM DTT , and protease inhibitors , Roche ) followed by several freeze-thaw cycles . The blots were probed with antibodies against: RAD52 1:500 ( Santa Cruz Biotechnology , sc365341 ) , FLAG 1:1000 ( Sigma , A8592 ) , BRCA2 1:1000 ( Millipore , OP95-10006 ) , or ACTIN 1:3000 ( Sigma , A2066 ) ; and with the HRP-conjugated secondary antibodies rabbit anti-mouse 1:3000 ( Abcam , ab205719 ) or goat anti-rabbit 1:3000 ( Abcam , ab205718 ) . ECL Western Blotting Substrate ( Thermo Fisher Scientific , 32106 ) was used to detect HRP signal on film . For DNA fiber analysis , cells were plated at 105 cells/well on a 6 well plate . The following day , the cells were pulse labeled with CldU ( 50 μM , Sigma C6891 ) for 40 min followed by IdU ( 250 μM , Sigma I7125 ) for 40 min . When testing replication stress recovery , the cells were pulse labeled with CldU for 30 min , hydroxyurea ( 2 mM ) for 2 hr , then IdU for 30 min . In the fork protection assay the cells were pulse labeled with CldU for 30 min , IdU for 30 min , then hydroxyurea ( 4 mM ) for 5 hr . In the experiments with siRNA , 105 cells were plated on a 12 well plate with either control siRNA ( siCTRL ) , a pool of four BRCA2 or POLQ siRNAs ( 40 pmol; Dharmacon , sequences in S1 Table ) , using Lipofectamine RNAiMAX , and the following day the cells were seeded on a 6 well plate . The next day ( two days after transfection ) cells were treated with the nucleotides and HU as above . DNA was isolated from cells using the FiberPrep DNA extraction kit ( Genomic Vision , EXT-001 ) . These DNA preparations were combed onto vinylsilane coated coverslips ( Genomic Vision , COV-002-RUO ) using the FiberComb Molecular Combing System ( Genomic Vision , MCS-001 ) . After combing , the coverslips were dehydrated , and then denatured using 0 . 5 M NaOH and 1 M NaCl . The coverslips were blocked with 5% BSA in PBS , followed by treatment with a rat antibody to detect the CldU signal and a mouse antibody to detect the IdU signal ( 1:50; Abcam ab6326 and BD Biosciences 347580 , respectively ) , and then with goat anti-rat Alexa Fluor 488 and goat anti-mouse Alexa Fluor 555 ( colored green and violet , respectively , by the image capture software to clearly distinguish the signals ) ( 1:50; Thermo Fisher Scientific , A110060 and A28180 , respectively ) . The coverslips were mounted using ProLong Gold Antifade ( Thermo Fisher Scientific ) , and the slides were imaged using a Zeiss Observer II with a 40x oil immersion objective , and fiber lengths were quantified using Image J [58] . | We have examined the role of two factors , RAD52 and POLQ , in genome maintenance pathways . While these factors are biochemically distinct , they are both synthetic lethal with loss of the BRCA1 and BRCA2 tumor suppressor genes , and hence are emerging therapeutic targets . Furthermore , RAD52 and POLQ have been implicated in chromosomal break repair events that use flanking repeats to restore the chromosome . We identified distinct features of chromosomal break repair events that are mediated by RAD52 vs . POLQ . Additionally , we have found that combined disruption of RAD52 and POLQ causes at least additive hypersensitivity to cisplatin and a synthetic reduction in replication fork restart velocity . These findings indicate that POLQ and RAD52 have distinct roles in genome maintenance , such that combined disruption of these factors could be a potential therapeutic strategy . | [
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